max_stars_repo_path
stringlengths
3
269
max_stars_repo_name
stringlengths
4
119
max_stars_count
int64
0
191k
id
stringlengths
1
7
content
stringlengths
6
1.05M
score
float64
0.23
5.13
int_score
int64
0
5
pkgs/ops-pkg/src/genie/libs/ops/dot1x/ios/tests/test_dot1x.py
jbronikowski/genielibs
94
9900
# Python import unittest from copy import deepcopy from unittest.mock import Mock # ATS from pyats.topology import Device # Genie from genie.libs.ops.dot1x.ios.dot1x import Dot1X from genie.libs.ops.dot1x.ios.tests.dot1x_output import Dot1xOutput # Parser from genie.libs.parser.ios.show_dot1x import ShowDot1xAllDetail, \ ShowDot1xAllStatistics, \ ShowDot1xAllSummary, \ ShowDot1xAllCount class test_dot1x(unittest.TestCase): def setUp(self): self.device = Device(name='aDevice') self.device.os = 'ios' self.device.custom['abstraction'] = {'order':['os']} self.device.mapping={} self.device.mapping['cli']='cli' # Give the device as a connection type # This is done in order to call the parser on the output provided self.device.connectionmgr.connections['cli'] = self.device def test_complete_output(self): self.maxDiff = None dot1x = Dot1X(device=self.device) # Get outputs dot1x.maker.outputs[ShowDot1xAllDetail] = \ {'': Dot1xOutput.ShowDot1xAllDetail} dot1x.maker.outputs[ShowDot1xAllStatistics] = \ {'': Dot1xOutput.ShowDot1xAllStatistics} dot1x.maker.outputs[ShowDot1xAllSummary] = \ {'': Dot1xOutput.ShowDot1xAllSummary} dot1x.maker.outputs[ShowDot1xAllCount] = \ {'': Dot1xOutput.ShowDot1xAllCount} # Learn the feature dot1x.learn() # Verify Ops was created successfully self.assertEqual(dot1x.info, Dot1xOutput.Dot1x_info) # Check Selected Attributes self.assertEqual(dot1x.info['version'], 3) # info - mdot1x default self.assertEqual(dot1x.info['interfaces']['GigabitEthernet1/0/9']\ ['max_start'], 3) def test_empty_output(self): self.maxDiff = None dot1x = Dot1X(device=self.device) dot1x.maker.outputs[ShowDot1xAllDetail] = \ {'': {}} dot1x.maker.outputs[ShowDot1xAllStatistics] = \ {'': {}} dot1x.maker.outputs[ShowDot1xAllSummary] = \ {'': {}} dot1x.maker.outputs[ShowDot1xAllCount] = \ {'': {}} # Learn the feature dot1x.learn() # Check no attribute not found with self.assertRaises(AttributeError): dot1x.info['version'] def test_incomplete_output(self): self.maxDiff = None dot1x = Dot1X(device=self.device) # Get outputs dot1x.maker.outputs[ShowDot1xAllDetail] = \ {'': Dot1xOutput.ShowDot1xAllDetail} dot1x.maker.outputs[ShowDot1xAllStatistics] = \ {'': Dot1xOutput.ShowDot1xAllStatistics} dot1x.maker.outputs[ShowDot1xAllSummary] = \ {'': Dot1xOutput.ShowDot1xAllSummary} dot1x.maker.outputs[ShowDot1xAllCount] = \ {'': {}} # Learn the feature dot1x.learn() # Delete missing specific attribute values expect_dict = deepcopy(Dot1xOutput.Dot1x_info) del(expect_dict['sessions']) # Verify Ops was created successfully self.assertEqual(dot1x.info, expect_dict) if __name__ == '__main__': unittest.main()
2.40625
2
script/analysis/check_transformation_matrices.py
lanl/nubhlight
16
9901
# ====================================================================== # copyright 2020. Triad National Security, LLC. All rights # reserved. This program was produced under U.S. Government contract # 89233218CNA000001 for Los Alamos National Laboratory (LANL), which # is operated by Triad National Security, LLC for the U.S. Department # of Energy/National Nuclear Security Administration. All rights in # the program are reserved by Triad National Security, LLC, and the # U.S. Department of Energy/National Nuclear Security # Administration. The Government is granted for itself and others # acting on its behalf a nonexclusive, paid-up, irrevocable worldwide # license in this material to reproduce, prepare derivative works, # distribute copies to the public, perform publicly and display # publicly, and to permit others to do so. # ====================================================================== # Authors: <NAME> (<EMAIL>) # Purpose: # Provides a check of whether a coordinate transformation of the metric # from code coordinates to Kerr-Schild coordinates produces correct # metric, consistent with the closed form (as in e.g. Eq.(3) # McKinney & Gammie 2004, https://arxiv.org/abs/astro-ph/0404512) # # Functions: # - print_matrix # - check_transformation_matrices # from math import * import numpy as np def print_matrix(matrix,fmt="%19.11e",tostdout=True) -> str: """Pretty-prints a matrix to a string (optinally, to stdout) Parameters ---------- matrix : numpy.array([N,M]) matrix to print fmt : str C-style format of each element (default: "%19.11e") tostdout : bool output to stdout (default: true) Returns ------- str formatted output string """ N = matrix.shape[0] M = matrix.shape[1] s = "[" for i in range(N): s+= "[" for j in range(M): s+= (fmt % matrix[i,j]) if j < M - 1: s += ", " s+= "]" if i < N - 1: s += ",\n " s+="]" if tostdout: print(s) return s def check_transformation_matrices(geom, a, ir, jth, verbose=True, tol=1e-12) -> bool: """Transforms the metric to spherical KS and compares with analytic formula Test 1: covariant metric, gcov, at A = {ir, jth} 1.1 sample gcov and Lambda_h2bl_cov at A 1.2 transform gcov to gks using transofmration matrices 1.3 compare to expected values at {r,th} at A Parameters ---------- geom : dictionary nubhlight geom object a : Float dimensionless Kerr spin parameter ir : Integer index of sample point in radial direction jth : Integer index of sample point in angular theta-direction verbose : bool output steps to stdout tol : Float tolerance to relative error (wrt det g) Returns ------- bool True if all checks passed Examples -------- import hdf5_to_dict as io hdr = io.load_hdr("dump_00000010.h5") geom = io.load_geom(hdr,recalc=True) check_transformation_matrices(geom, -1, 64) """ # sample gcov and h2bl at point A gcov_A = geom['gcov'][ir,jth] h2bl_A = geom['Lambda_h2bl_cov'][ir,jth] # sample r and theta, compute BL metric-related quantities r = geom['r'][ir,jth,0]; r2 = r*r a2 = a*a th= geom['th'][ir,jth,0] sth2= sin(th)**2 Delta= r2 - 2*r + a2 Sigma= r2 + a2*cos(th)**2 A = (r2 + a2)**2 - a2*Delta*sin(th)**2 if verbose: print ("r = %19.11e" % r) print ("theta = %19.11e" % th) print ("a = %19.11e" % a) print ("Delta = %19.11e" % Delta) print ("Sigma = %19.11e" % Sigma) print ("A = %19.11e" % A) # output metric print ("gcov_A = ") print_matrix (gcov_A) print ("") # output transformation matrix print ("h2bl_A = ") print_matrix (h2bl_A) print ("") # compute BL metric at A gks_A = np.zeros([4,4]) for i in range(4): for j in range(4): for k in range(4): for l in range(4): gks_A[i,j] = gks_A[i,j] + h2bl_A[k,i]*h2bl_A[l,j]*gcov_A[k,l] if verbose: print ("gks_A = ") print_matrix (gks_A) print("") # expected values at {r, th} g_tt = -1. + 2.*r/Sigma g_rr = 1. + 2.*r/Sigma g_ff = sth2*(Sigma + a2*g_rr*sth2) g_thth = Sigma g_tr = 2*r/Sigma g_tf = -2*a*r*sth2/Sigma g_rf = -a*g_rr*sth2 det_g = -Sigma**2*sth2 if verbose: print ("Expected:") print (" g_tt = %19.11e" % g_tt ) print (" g_rr = %19.11e" % g_rr ) print (" g_thth = %19.11e" % g_thth) print (" g_ff = %19.11e" % g_ff ) print (" g_tr = %19.11e" % g_tr ) print (" g_rf = %19.11e" % g_rf ) print (" g_tf = %19.11e" % g_tf ) print ("") # check gks_A gks_expected = np.array( [[ g_tt, g_tr, 0.0, g_tf], [ g_tr, g_rr, 0.0, g_rf], [ 0.0, 0.0, g_thth, 0.0], [ g_tf, g_rf, 0.0, g_ff]] ) passed = True for i in range(4): for j in range(4): if abs(gks_A[i,j] - gks_expected[i,j])/abs(det_g) > tol: passed = False if verbose: print (f"WARNING: Significant mismatch in gks_A[{i},{j}]:") print (" -- expected: %19.11e" % gks_expected[i,j]) print (" -- actual: %19.11e" % gks_A[i,j]) return passed
2.21875
2
holobot/discord/sdk/models/channel.py
rexor12/holobot
1
9902
<gh_stars>1-10 from dataclasses import dataclass @dataclass class Channel: id: str
1.53125
2
pynet/models/braingengan.py
claireguichon/pynet
8
9903
<reponame>claireguichon/pynet # -*- coding: utf-8 -*- ########################################################################## # NSAp - Copyright (C) CEA, 2020 # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html # for details. ########################################################################## """ 3D MRI Brain Generation with Generative Adversarial Networks (BGGAN) with Variational Auto Encoder (VAE). """ # Imports import logging import collections import numpy as np import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as func from pynet.utils import Networks # Global parameters logger = logging.getLogger("pynet") @Networks.register class BGDiscriminator(nn.Module): """ This is the discriminator part of the BGGAN. """ def __init__(self, in_shape, in_channels=1, out_channels=1, start_filts=64, with_logit=True): """ Init class. Parameters ---------- in_shape: uplet the input tensor data shape (X, Y, Z). in_channels: int, default 1 number of channels in the input tensor. out_channels: int, default 1 number of channels in the output tensor. start_filts: int, default 64 number of convolutional filters for the first conv. with_logit: bool, default True apply the logit function to the result. """ super(BGDiscriminator, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.start_filts = start_filts self.with_logit = with_logit self.in_shape = in_shape self.shapes = _downsample_shape( self.in_shape, nb_iterations=4, scale_factor=2) self.conv1 = nn.Conv3d( self.in_channels, self.start_filts, kernel_size=4, stride=2, padding=1) self.conv2 = nn.Conv3d( self.start_filts, self.start_filts * 2, kernel_size=4, stride=2, padding=1) self.bn2 = nn.BatchNorm3d(self.start_filts * 2) self.conv3 = nn.Conv3d( self.start_filts * 2, self.start_filts * 4, kernel_size=4, stride=2, padding=1) self.bn3 = nn.BatchNorm3d(self.start_filts * 4) self.conv4 = nn.Conv3d( self.start_filts * 4, self.start_filts * 8, kernel_size=4, stride=2, padding=1) self.bn4 = nn.BatchNorm3d(self.start_filts * 8) self.conv5 = nn.Conv3d( self.start_filts * 8, self.out_channels, kernel_size=self.shapes[-1], stride=1, padding=0) def forward(self, x): logger.debug("BGGAN Discriminator...") self.debug("input", x) h1 = func.leaky_relu(self.conv1(x), negative_slope=0.2) self.debug("conv1", h1) h2 = func.leaky_relu(self.bn2(self.conv2(h1)), negative_slope=0.2) self.debug("conv2", h2) h3 = func.leaky_relu(self.bn3(self.conv3(h2)), negative_slope=0.2) self.debug("conv3", h3) h4 = func.leaky_relu(self.bn4(self.conv4(h3)), negative_slope=0.2) self.debug("conv4", h4) h5 = self.conv5(h4) self.debug("conv5", h5) if self.with_logit: output = torch.sigmoid(h5.view(h5.size(0), -1)) self.debug("output", output) else: output = h5 logger.debug("Done.") return output def debug(self, name, tensor): logger.debug(" {3}: {0} - {1} - {2}".format( tensor.shape, tensor.get_device(), tensor.dtype, name)) @Networks.register class BGEncoder(nn.Module): """ This is the encoder part of the BGGAN. """ def __init__(self, in_shape, in_channels=1, start_filts=64, latent_dim=1000): """ Init class. Parameters ---------- in_shape: uplet the input tensor data shape (X, Y, Z). in_channels: int, default 1 number of channels in the input tensor. start_filts: int, default 64 number of convolutional filters for the first conv. latent_dim: int, default 1000 the latent variable sizes. """ super(BGEncoder, self).__init__() self.in_channels = in_channels self.start_filts = start_filts self.latent_dim = latent_dim self.in_shape = in_shape self.shapes = _downsample_shape( self.in_shape, nb_iterations=4, scale_factor=2) self.dense_features = np.prod(self.shapes[-1]) logger.debug("BGGAN Encoder shapes: {0}".format(self.shapes)) self.conv1 = nn.Conv3d( self.in_channels, self.start_filts, kernel_size=4, stride=2, padding=1) self.conv2 = nn.Conv3d( self.start_filts, self.start_filts * 2, kernel_size=4, stride=2, padding=1) self.bn2 = nn.BatchNorm3d(self.start_filts * 2) self.conv3 = nn.Conv3d( self.start_filts * 2, self.start_filts * 4, kernel_size=4, stride=2, padding=1) self.bn3 = nn.BatchNorm3d(self.start_filts * 4) self.conv4 = nn.Conv3d( self.start_filts * 4, self.start_filts * 8, kernel_size=4, stride=2, padding=1) self.bn4 = nn.BatchNorm3d(self.start_filts * 8) self.mean = nn.Sequential( nn.Linear(self.start_filts * 8 * self.dense_features, 2048), nn.BatchNorm1d(2048), nn.ReLU(), nn.Linear(2048, self.latent_dim)) self.logvar = nn.Sequential( nn.Linear(self.start_filts * 8 * self.dense_features, 2048), nn.BatchNorm1d(2048), nn.ReLU(), nn.Linear(2048, self.latent_dim)) def forward(self, x): logger.debug("BGGAN Encoder...") batch_size = x.size(0) logger.debug(" batch_size: {0}".format(batch_size)) self.debug("input", x) h1 = func.leaky_relu(self.conv1(x), negative_slope=0.2) self.debug("conv1", h1) h2 = func.leaky_relu(self.bn2(self.conv2(h1)), negative_slope=0.2) self.debug("conv2", h2) h3 = func.leaky_relu(self.bn3(self.conv3(h2)), negative_slope=0.2) self.debug("conv3", h3) h4 = func.leaky_relu(self.bn4(self.conv4(h3)), negative_slope=0.2) self.debug("conv4", h4) mean = self.mean(h4.view(batch_size, -1)) self.debug("mean", mean) logvar = self.logvar(h4.view(batch_size, -1)) self.debug("logvar", logvar) std = logvar.mul(0.5).exp_() reparametrized_noise = Variable( torch.randn((batch_size, self.latent_dim))).to(x.device) reparametrized_noise = mean + std * reparametrized_noise self.debug("reparametrization", reparametrized_noise) logger.debug("Done.") return mean, logvar, reparametrized_noise def debug(self, name, tensor): logger.debug(" {3}: {0} - {1} - {2}".format( tensor.shape, tensor.get_device(), tensor.dtype, name)) @Networks.register class BGCodeDiscriminator(nn.Module): """ This is the code discriminator part of the BGGAN. """ def __init__(self, out_channels=1, code_size=1000, n_units=4096): """ Init class. Parameters ---------- out_channels: int, default 1 number of channels in the output tensor. code_size: int, default 1000 the code sier. n_units: int, default 4096 the number of hidden units. """ super(BGCodeDiscriminator, self).__init__() self.out_channels = out_channels self.code_size = code_size self.n_units = n_units self.layer1 = nn.Sequential( nn.Linear(self.code_size, self.n_units), nn.BatchNorm1d(self.n_units), nn.LeakyReLU(0.2, inplace=True)) self.layer2 = nn.Sequential( nn.Linear(self.n_units, self.n_units), nn.BatchNorm1d(self.n_units), nn.LeakyReLU(0.2, inplace=True)) self.layer3 = nn.Linear(self.n_units, self.out_channels) def forward(self, x): logger.debug("BGGAN Code Discriminator...") self.debug("input", x) h1 = self.layer1(x) self.debug("layer1", h1) h2 = self.layer2(h1) self.debug("layer2", h2) output = self.layer3(h2) self.debug("layer3", output) logger.debug("Done.") return output def debug(self, name, tensor): logger.debug(" {3}: {0} - {1} - {2}".format( tensor.shape, tensor.get_device(), tensor.dtype, name)) @Networks.register class BGGenerator(nn.Module): """ This is the generator part of the BGGAN. """ def __init__(self, in_shape, out_channels=1, start_filts=64, latent_dim=1000, mode="trilinear", with_code=False): """ Init class. Parameters ---------- in_shape: uplet the input tensor data shape (X, Y, Z). out_channels: int, default 1 number of channels in the output tensor. start_filts: int, default 64 number of convolutional filters for the first conv. latent_dim: int, default 1000 the latent variable sizes. mode: str, default 'trilinear' the interpolation mode. with_code: bool, default False change the architecture if code discriminator is used. """ super(BGGenerator, self).__init__() self.out_channels = out_channels self.start_filts = start_filts self.latent_dim = latent_dim self.in_shape = in_shape self.mode = mode self.with_code = with_code self.shapes = _downsample_shape( self.in_shape, nb_iterations=4, scale_factor=2) self.dense_features = np.prod(self.shapes[-1]) logger.debug("BGGAN Generator shapes: {0}".format(self.shapes)) if self.with_code: self.tp_conv1 = nn.ConvTranspose3d( self.latent_dim, self.start_filts * 8, kernel_size=4, stride=1, padding=0, bias=False) else: self.fc = nn.Linear( self.latent_dim, self.start_filts * 8 * self.dense_features) self.bn1 = nn.BatchNorm3d(self.start_filts * 8) self.tp_conv2 = nn.Conv3d( self.start_filts * 8, self.start_filts * 4, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm3d(self.start_filts * 4) self.tp_conv3 = nn.Conv3d( self.start_filts * 4, self.start_filts * 2, kernel_size=3, stride=1, padding=1, bias=False) self.bn3 = nn.BatchNorm3d(self.start_filts * 2) self.tp_conv4 = nn.Conv3d( self.start_filts * 2, self.start_filts, kernel_size=3, stride=1, padding=1, bias=False) self.bn4 = nn.BatchNorm3d(self.start_filts) self.tp_conv5 = nn.Conv3d( self.start_filts, self.out_channels, kernel_size=3, stride=1, padding=1, bias=False) def forward(self, noise): logger.debug("BGGAN Generator...") self.debug("input", noise) if self.with_code: noise = noise.view(-1, self.latent_dim, 1, 1, 1) self.debug("view", noise) h = self.tp_conv1(noise) self.debug("tp_conv1", h) else: noise = noise.view(-1, self.latent_dim) self.debug("view", noise) h = self.fc(noise) self.debug("dense", h) h = h.view(-1, self.start_filts * 8, *self.shapes[-1]) self.debug("view", h) h = func.relu(self.bn1(h)) h = nn.functional.interpolate( h, size=self.shapes[-2], mode=self.mode, align_corners=False) h = self.tp_conv2(h) h = func.relu(self.bn2(h)) self.debug("tp_conv2", h) h = nn.functional.interpolate( h, size=self.shapes[-3], mode=self.mode, align_corners=False) h = self.tp_conv3(h) h = func.relu(self.bn3(h)) self.debug("tp_conv3", h) h = nn.functional.interpolate( h, size=self.shapes[-4], mode=self.mode, align_corners=False) h = self.tp_conv4(h) h = func.relu(self.bn4(h)) self.debug("tp_conv4", h) h = nn.functional.interpolate( h, size=self.shapes[-5], mode=self.mode, align_corners=False) h = self.tp_conv5(h) self.debug("tp_conv5", h) h = torch.tanh(h) self.debug("output", h) logger.debug("Done.") return h def debug(self, name, tensor): logger.debug(" {3}: {0} - {1} - {2}".format( tensor.shape, tensor.get_device(), tensor.dtype, name)) def _downsample_shape(shape, nb_iterations=1, scale_factor=2): shape = np.asarray(shape) all_shapes = [shape.astype(int).tolist()] for idx in range(nb_iterations): shape = np.floor(shape / scale_factor) all_shapes.append(shape.astype(int).tolist()) return all_shapes
2.234375
2
research/object_detection/core/freezable_batch_norm_test.py
baranshad/models
3
9904
<filename>research/object_detection/core/freezable_batch_norm_test.py # 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 object_detection.core.freezable_batch_norm.""" import numpy as np import tensorflow as tf from object_detection.core import freezable_batch_norm class FreezableBatchNormTest(tf.test.TestCase): """Tests for FreezableBatchNorm operations.""" def _build_model(self, training=None): model = tf.keras.models.Sequential() norm = freezable_batch_norm.FreezableBatchNorm(training=training, input_shape=(10,), momentum=0.8) model.add(norm) return model, norm def _train_freezable_batch_norm(self, training_mean, training_var): model, _ = self._build_model() model.compile(loss='mse', optimizer='sgd') # centered on training_mean, variance training_var train_data = np.random.normal( loc=training_mean, scale=training_var, size=(1000, 10)) model.fit(train_data, train_data, epochs=4, verbose=0) return model.weights def _test_batchnorm_layer( self, norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var): out_tensor = norm(tf.convert_to_tensor(test_data, dtype=tf.float32), training=training_arg) out = tf.keras.backend.eval(out_tensor) out -= tf.keras.backend.eval(norm.beta) out /= tf.keras.backend.eval(norm.gamma) if not should_be_training: out *= training_var out += (training_mean - testing_mean) out /= testing_var np.testing.assert_allclose(out.mean(), 0.0, atol=1.5e-1) np.testing.assert_allclose(out.std(), 1.0, atol=1.5e-1) def test_batchnorm_freezing_training_none(self): with self.test_session(): training_mean = 5.0 training_var = 10.0 testing_mean = -10.0 testing_var = 5.0 # Initially train the batch norm, and save the weights trained_weights = self._train_freezable_batch_norm(training_mean, training_var) # Load the batch norm weights, freezing training to True. # Apply the batch norm layer to testing data and ensure it is normalized # according to the batch statistics. model, norm = self._build_model(training=True) for trained_weight, blank_weight in zip(trained_weights, model.weights): weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight)) tf.keras.backend.eval(weight_copy) # centered on testing_mean, variance testing_var test_data = np.random.normal( loc=testing_mean, scale=testing_var, size=(1000, 10)) # Test with training=True passed to the call method: training_arg = True should_be_training = True self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) # Test with training=False passed to the call method: training_arg = False should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) # Test the layer in various Keras learning phase scopes: training_arg = None should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) tf.keras.backend.set_learning_phase(True) should_be_training = True self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) tf.keras.backend.set_learning_phase(False) should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) def test_batchnorm_freezing_training_false(self): with self.test_session(): training_mean = 5.0 training_var = 10.0 testing_mean = -10.0 testing_var = 5.0 # Initially train the batch norm, and save the weights trained_weights = self._train_freezable_batch_norm(training_mean, training_var) # Load the batch norm back up, freezing training to False. # Apply the batch norm layer to testing data and ensure it is normalized # according to the training data's statistics. model, norm = self._build_model(training=False) for trained_weight, blank_weight in zip(trained_weights, model.weights): weight_copy = blank_weight.assign(tf.keras.backend.eval(trained_weight)) tf.keras.backend.eval(weight_copy) # centered on testing_mean, variance testing_var test_data = np.random.normal( loc=testing_mean, scale=testing_var, size=(1000, 10)) # Make sure that the layer is never training # Test with training=True passed to the call method: training_arg = True should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) # Test with training=False passed to the call method: training_arg = False should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) # Test the layer in various Keras learning phase scopes: training_arg = None should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) tf.keras.backend.set_learning_phase(True) should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) tf.keras.backend.set_learning_phase(False) should_be_training = False self._test_batchnorm_layer(norm, should_be_training, test_data, testing_mean, testing_var, training_arg, training_mean, training_var) if __name__ == '__main__': tf.test.main()
2.359375
2
offspect/gui/VWidgets/message.py
translationalneurosurgery/tool-offspect
1
9905
<reponame>translationalneurosurgery/tool-offspect # from PyQt5.QtWidgets import QMessageBox # def raise_error(message: str = "DEFAULT:Error Description:More Information"): # box = QMessageBox() # kind, msg, info = message.split(":") # box.setIcon(QMessageBox.Critical) # box.setWindowTitle(kind + " Error") # box.setText(msg) # box.setInformativeText(info) # box.exec_()
1.960938
2
Widen/LC759_Employee_Free_Time.py
crazywiden/Leetcode_daily_submit
0
9906
<filename>Widen/LC759_Employee_Free_Time.py """ 759. Employee Free Time We are given a list schedule of employees, which represents the working time for each employee. Each employee has a list of non-overlapping Intervals, and these intervals are in sorted order. Return the list of finite intervals representing common, positive-length free time for all employees, also in sorted order. (Even though we are representing Intervals in the form [x, y], the objects inside are Intervals, not lists or arrays. For example, schedule[0][0].start = 1, schedule[0][0].end = 2, and schedule[0][0][0] is not defined). Also, we wouldn't include intervals like [5, 5] in our answer, as they have zero length. """ # Line Swap method # if we met a start, cnt += 1 # if we met an end, cnt -= 1 # time complexity -- O(NlogN), need sort all intervals # Runtime: 96 ms, faster than 87.95% of Python3 online submissions for Employee Free Time. # Memory Usage: 14.7 MB, less than 25.00% of Python3 online submissions for Employee Free Time. """ # Definition for an Interval. class Interval: def __init__(self, start: int = None, end: int = None): self.start = start self.end = end """ class Solution: def employeeFreeTime(self, schedule: '[[Interval]]') -> '[Interval]': START, END = 0, 1 all_interval = [] for person in schedule: for interval in person: all_interval.append((interval.start, START)) all_interval.append((interval.end, END)) all_interval = sorted(all_interval, key=lambda x: x[0]) prev = None cnt = 0 res = [] for i in range(len(all_interval)): if cnt == 0 and prev is not None: if prev != all_interval[i][0]: res.append(Interval(prev, all_interval[i][0])) if all_interval[i][1] == START: cnt += 1 else: cnt -= 1 prev = all_interval[i][0] return res # priority queue # if the current end is less than the smallest start # then means there is a free time # use priority queue to maintain the smallest start # also only stort one of jobs of each person in the queue to save memory # time complexity -- O(NlogC), C is the number of employee """ # Definition for an Interval. class Interval: def __init__(self, start: int = None, end: int = None): self.start = start self.end = end """ import heapq class Solution: def employeeFreeTime(self, schedule: '[[Interval]]') -> '[Interval]': res = [] job_start_q = [(emp[0].start, emp_id, 0) for emp_id, emp in enumerate(schedule)] heapq.heapify(job_start_q) largest_end = min(interval.start for emp in schedule for interval in emp) while job_start_q: start, emp_id, job_id = heapq.heappop(job_start_q) if largest_end < start: res.append(Interval(largest_end, start)) largest_end = max(largest_end, schedule[emp_id][job_id].end) if job_id + 1 < len(schedule[emp_id]): heapq.heappush(job_start_q, (schedule[emp_id][job_id+1].start, emp_id, job_id+1)) return res
3.609375
4
storitch/config.py
thomaserlang/storitch
0
9907
<reponame>thomaserlang/storitch<filename>storitch/config.py import os, yaml config = { 'debug': False, 'port': 5000, 'store_path': '/var/storitch', 'pool_size': 5, 'logging': { 'level': 'warning', 'path': None, 'max_size': 100 * 1000 * 1000,# ~ 95 mb 'num_backups': 10, }, 'image_exts': [ '.jpg', '.jpeg', '.png', '.tiff', '.tif', '.gif', '.bmp', '.bmp2', '.bmp3', '.dcm', '.dicom', '.webp', ], } def load(path=None): default_paths = [ '~/storitch.yaml', './storitch.yaml', '../storitch.yaml', '/etc/storitch/storitch.yaml', '/etc/storitch.yaml', ] if not path: path = os.environ.get('STORITCH_CONFIG', None) if not path: for p in default_paths: p = os.path.expanduser(p) if os.path.isfile(p): path = p break if not path: raise Exception('No config file specified.') if not os.path.isfile(path): raise Exception('Config: "{}" could not be found.'.format(path)) with open(path) as f: data = yaml.load(f, Loader=yaml.FullLoader) for key in data: if key in config: if isinstance(config[key], dict): config[key].update(data[key]) else: config[key] = data[key]
2.1875
2
keras/lstm-securitai/model/pipeline_invoke_python.py
PipelineAI/models
44
9908
import io import os import numpy as np import pandas import json import logging #<== Optional. Log to console, file, kafka from pipeline_monitor import prometheus_monitor as monitor #<== Optional. Monitor runtime metrics from pipeline_logger import log import tensorflow as tf from tensorflow.contrib import predictor from keras.models import Sequential, load_model from keras.preprocessing import sequence from keras.preprocessing.text import Tokenizer from collections import OrderedDict _logger = logging.getLogger('pipeline-logger') _logger.setLevel(logging.INFO) _logger_stream_handler = logging.StreamHandler() _logger_stream_handler.setLevel(logging.INFO) _logger.addHandler(_logger_stream_handler) __all__ = ['invoke'] #<== Optional. Being a good Python citizen. _labels = { #<== Optional. Used for metrics/labels 'name': 'injection', 'tag': 'v1', 'type': 'tensorflow', 'runtime': 'python', 'chip': 'cpu', } def _initialize_upon_import(): #<== Optional. Called once upon server startup ''' Initialize / Restore Model Object. ''' model = load_model('securitai-lstm-model.h5') model.load_weights('securitai-lstm-weights.h5') model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy']) return model # This is called unconditionally at *module import time*... _model = _initialize_upon_import() #@log(labels=_labels, logger=_logger) #<== Optional. Sample and compare predictions def invoke(request): #<== Required. Called on every prediction '''Where the magic happens...''' with monitor(labels=_labels, name="transform_request"): #<== Optional. Expose fine-grained metrics transformed_request = _transform_request(request) #<== Optional. Transform input (json) into TensorFlow (tensor) with monitor(labels=_labels, name="invoke"): #<== Optional. Calls _model.predict() response = _model.predict(transformed_request) with monitor(labels=_labels, name="transform_response"): #<== Optional. Transform TensorFlow (tensor) into output (json) transformed_response = _transform_response(response) return transformed_response #<== Required. Returns the predicted value(s) def _transform_request(request): request_str = request.decode('utf-8') # tokenize the csv request and create json X = pandas.read_csv(io.StringIO(request_str), engine='python', quotechar='|', header=None).values[:,0] for index, item in enumerate(X): reqJson = json.loads(item, object_pairs_hook=OrderedDict) del reqJson['http']['timestamp'] del reqJson['http']['headers'] del reqJson['http']['source'] del reqJson['http']['route'] del reqJson['http']['responsePayload'] X[index] = json.dumps(reqJson, separators=(',', ':')) tokenizer = Tokenizer(filters='\t\n', char_level=True) tokenizer.fit_on_texts(X) # this used to be [log_entry] seq = tokenizer.texts_to_sequences([request_str]) max_log_length = 1024 log_entry_processed = sequence.pad_sequences(seq, maxlen=max_log_length) return log_entry_processed def _transform_response(response): return response[0] if __name__ == '__main__': with open('./pipeline_test_request.csv', 'rb') as fb: request_bytes = fb.read() response_bytes = invoke(request_bytes) print(response_bytes)
2.265625
2
src/act/common/aCTReport.py
ATLASControlTower/aCT
0
9909
<reponame>ATLASControlTower/aCT import argparse import importlib import os import re import signal import subprocess import sys import time import logging from act.common import aCTLogger from act.common.aCTConfig import aCTConfigAPP from act.arc import aCTDBArc class aCTReport: '''Print summary info on jobs in DB. Use --web to print html that is automatically refreshed. Add filenames to query more than one aCT DB''' def __init__(self, args): self.output = "" self.outfile = args.web self.actconfs = args.conffiles or [''] # empty string for default behaviour self.logger=aCTLogger.aCTLogger("aCTReport") self.actlog=self.logger() self.actlog.logger.setLevel(logging.INFO) self.criticallogger = aCTLogger.aCTLogger('aCTCritical', arclog=False) self.criticallog = self.criticallogger() if self.outfile: self.log('<META HTTP-EQUIV="refresh" CONTENT="60"><pre>') self.log(time.asctime() + '\n') self.db=aCTDBArc.aCTDBArc(self.actlog) def log(self, message=''): self.output += message + '\n' def AppReport(self): appconf = aCTConfigAPP() apps = appconf.getList(["modules", "app"]) for app in apps: try: ap = importlib.import_module(f'{app}.aCTReport').report self.log(ap(self.actconfs)) except ModuleNotFoundError as e: self.actlog.info(f'No report in module {app}') except AttributeError: self.actlog.info(f'aCTReport.report() not found in {app}') except Exception as e: self.actlog.error(f'Exception running {app}.aCTReport.report: {e}') def ProcessReport(self): if self.actconfs != ['']: return # don't print processes for combined report actprocscmd = 'ps ax -ww -o pid,etime,args' try: out = subprocess.run(actprocscmd.split(), check=True, encoding='utf-8', stdout=subprocess.PIPE).stdout except subprocess.CalledProcessError as e: self.log('Error: could not run ps command: %s' % e.stderr) return # Group processes by cluster cluster_procs = {} longprocesses = [] for line in out.split('\n'): reg = re.match(r'\s*(\d*)\s*(.*) .*python.* .*(aCT\w*)\.py\s?(\S*)', line) if reg: pid, runningtime, process, cluster = reg.groups() # ignore Main and this process if process in ['aCTReport', 'aCTMain', 'aCTHeartbeatWatchdog']: continue if cluster == '': cluster = '(no cluster defined)' elif not re.match(r'\d\d:\d\d$', runningtime): # Check for overrunning processes longprocesses.append((process, pid, cluster, runningtime)) if cluster in cluster_procs: cluster_procs[cluster].append(process) else: cluster_procs[cluster] = [process] for proc in longprocesses: self.log('WARNING: %s (pid %s) for %s running for more than one hour (%s), this process will be killed' % proc) # Kill process and log a critical message to send email # Too many emails, disable #self.criticallog.critical('Killing process %s (pid %s) for %s running for more than one hour (%s)' % proc) try: os.kill(int(proc[1]), signal.SIGKILL) except OSError: pass self.log() self.log('Active processes per cluster:') for cluster in sorted(cluster_procs): procs = cluster_procs[cluster] procs.sort() self.log(f'{cluster:>38.38}: {" ".join(procs)}') self.log() def ArcJobReport(self): rep={} rtot={} states = ["Undefined", "Accepted", "Preparing", "Submitting", "Queuing", "Running", "Finishing", "Finished", "Hold", "Killed", "Failed", "Deleted", "Other"] for conf in self.actconfs: if conf: os.environ['ACTCONFIGARC'] = conf db=aCTDBArc.aCTDBArc(self.actlog) c=db.db.conn.cursor() c.execute("select jobid,state from arcjobs") rows=c.fetchall() for r in rows: reg=re.search('.+//([^:]+)',str(r[0])) cl="" try: cl=reg.group(1) except: cl='WaitingSubmission' jid=str(r[1]) if jid == 'None': jid="Other" try: rep[cl][jid]+=1 except: try: rep[cl][jid]=1 except: rep[cl]={} rep[cl][jid]=1 try: rtot[jid]+=1 except: rtot[jid]=1 if sum(rtot.values()) == 0: return self.log(f"All ARC jobs: {sum(rtot.values())}") self.log(f"{'':39} {' '.join([f'{s:>9}' for s in states])}") for k in sorted(rep, key=lambda x: x.split('.')[-1]): log=f"{k:>38.38}:" for s in states: try: log += f'{rep[k][s]:>10}' except KeyError: log += f'{"-":>10}' self.log(log) log = f"{'Totals':>38}:" for s in states: try: log += f'{rtot[s]:>10}' except: log += f'{"-":>10}' self.log(log+'\n\n') def CondorJobReport(self): rep = {} rtot = {} condorjobstatemap = ['Undefined', # used before real state is known 'Idle', 'Running', 'Removed', 'Completed', 'Held', 'Transferring', 'Suspended'] for conf in self.actconfs: if conf: os.environ['ACTCONFIGARC'] = conf db=aCTDBArc.aCTDBArc(self.actlog) c = db.db.conn.cursor() c.execute("select cluster, JobStatus from condorjobs") rows = c.fetchall() for r in rows: cl = str(r[0]) if not cl: cl = 'WaitingSubmission' jid = r[1] try: rep[cl][jid]+=1 except: try: rep[cl][jid]=1 except: rep[cl]={} rep[cl][jid]=1 try: rtot[jid]+=1 except: rtot[jid]=1 if sum(rtot.values()) == 0: return self.log(f"All Condor jobs: {sum(rtot.values())}") self.log(f"{'':39} {' '.join([f'{s:>9}' for s in condorjobstatemap])}") for k in sorted(rep, key=lambda x: x.split('.')[-1]): log=f"{k:>38.38}:" for s in range(8): try: log += f'{rep[k][s]:>10}' except KeyError: log += f'{"-":>10}' self.log(log) log = f"{'Totals':>38}:" for s in range(8): try: log += f'{rtot[s]:>10}' except: log += f'{"-":>10}' self.log(log+'\n\n') def StuckReport(self): # Query for lost jobs older than lostlimit lostlimit = 86400 select = "(arcstate='submitted' or arcstate='running') and " \ + self.db.timeStampLessThan("tarcstate", lostlimit) + \ " order by tarcstate" columns = ['cluster'] jobs = self.db.getArcJobsInfo(select, columns) if jobs: self.log('Found %d jobs not updated in over %d seconds:\n' % (len(jobs), lostlimit)) clustercount = {} for job in jobs: try: host = re.search('.+//([^:]+)', job['cluster']).group(1) except: host = None if host in clustercount: clustercount[host] += 1 else: clustercount[host] = 1 for cluster, count in clustercount.items(): self.log(f'{count} {cluster}') self.log() def end(self): if self.outfile: self.log('</pre>') def main(): parser = argparse.ArgumentParser(description='Report table of aCT jobs.') parser.add_argument('conffiles', nargs='*', help='list of configuration files') parser.add_argument('--web', help='Output suitable for web page') parser.add_argument('--harvester', action='store_true', help='Dummy arg for backwards compatibility') args = parser.parse_args(sys.argv[1:]) acts = aCTReport(args) acts.AppReport() acts.ArcJobReport() acts.CondorJobReport() acts.StuckReport() acts.ProcessReport() acts.end() if acts.outfile is None: sys.stdout.write(acts.output) else: f=open(acts.outfile,"w") f.write(acts.output) f.close() if __name__ == '__main__': main()
2.203125
2
unittests/test_apiv2_user.py
mtcolman/django-DefectDojo
249
9910
<reponame>mtcolman/django-DefectDojo from rest_framework.test import APITestCase, APIClient from django.urls import reverse from rest_framework.authtoken.models import Token class UserTest(APITestCase): """ Test the User APIv2 endpoint. """ fixtures = ['dojo_testdata.json'] def setUp(self): token = Token.objects.get(user__username='admin') self.client = APIClient() self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key) def test_user_list(self): r = self.client.get(reverse('user-list')) self.assertEqual(r.status_code, 200, r.content[:1000]) user_list = r.json()['results'] self.assertTrue(len(user_list) >= 1, r.content[:1000]) for user in user_list: for item in ['username', 'first_name', 'last_name', 'email']: self.assertIn(item, user, r.content[:1000]) for item in ['password']: self.assertNotIn(item, user, r.content[:1000]) def test_user_add(self): # simple user without password r = self.client.post(reverse('user-list'), { "username": "api-user-1" }, format='json') self.assertEqual(r.status_code, 201, r.content[:1000]) # user with good password password = '<PASSWORD>!@#$' r = self.client.post(reverse('user-list'), { "username": "api-user-2", "password": password }, format='json') self.assertEqual(r.status_code, 201, r.content[:1000]) # test password by fetching API key r = self.client.post(reverse('api-token-auth'), { "username": "api-user-2", "password": password }, format='json') self.assertEqual(r.status_code, 200, r.content[:1000]) # user with weak password r = self.client.post(reverse('user-list'), { "username": "api-user-3", "password": "<PASSWORD>" }, format='json') self.assertEqual(r.status_code, 400, r.content[:1000]) self.assertIn('The password must contain at least 1 digit, 0-9.', r.content.decode("utf-8")) def test_user_change_password(self): # some user r = self.client.post(reverse('user-list'), { "username": "api-user-4" }, format='json') self.assertEqual(r.status_code, 201, r.content[:1000]) user_id = r.json()['id'] r = self.client.put("{}{}/".format(reverse('user-list'), user_id), { "username": "api-user-4", "first_name": "first" }, format='json',) self.assertEqual(r.status_code, 200, r.content[:1000]) r = self.client.patch("{}{}/".format(reverse('user-list'), user_id), { "last_name": "last" }, format='json') self.assertEqual(r.status_code, 200, r.content[:1000]) r = self.client.put("{}{}/".format(reverse('user-list'), user_id), { "username": "api-user-4", "password": "<PASSWORD>!@#$" }, format='json') self.assertEqual(r.status_code, 400, r.content[:1000]) self.assertIn("Update of password though API is not allowed", r.content.decode("utf-8")) r = self.client.patch("{}{}/".format(reverse('user-list'), user_id), { "password": "<PASSWORD>!@#$" }, format='json') self.assertEqual(r.status_code, 400, r.content[:1000]) self.assertIn("Update of password though API is not allowed", r.content.decode("utf-8"))
2.71875
3
src/init.py
ankit-kushwaha-51/RESTful_API
0
9911
<reponame>ankit-kushwaha-51/RESTful_API from flask import Flask from src.models import db from . import config def create_app(): flask_app = Flask(__name__) flask_app.config['SQLALCHEMY_DATABASE_URI'] = config.DATABASE_CONNECTION_URI flask_app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False flask_app.app_context().push() db.init_app(flask_app) db.create_all() return flask_app
2.28125
2
tests/helpers/examples/order/tasks.py
nicoddemus/dependencies
0
9912
<filename>tests/helpers/examples/order/tasks.py<gh_stars>0 from dependencies import Injector from dependencies import this from dependencies.contrib.celery import shared_task from examples.order.commands import ProcessOrder @shared_task class ProcessOrderTask(Injector): name = "process_order" run = ProcessOrder bind = True retry = this.task.retry
1.648438
2
001 - 050/ex032.py
SocrammBR/Desafios-Python-CursoEmVideo
0
9913
ano = int(input('Digite o ano: ')) if (ano%4) == 0: print ('Ele é bissexto') else: print ('Ele não é bissexto')
3.734375
4
rn/__init__.py
vikneswaran20/rn
0
9914
<reponame>vikneswaran20/rn __version__ = '0.0.1' __license__ = 'BSD'
0.8125
1
flexmeasures/cli/data_edit.py
FlexMeasures/flexmeasures
12
9915
from datetime import timedelta from typing import Union, List, Optional import click import pandas as pd from flask import current_app as app from flask.cli import with_appcontext from flexmeasures import Sensor from flexmeasures.data import db from flexmeasures.data.schemas.generic_assets import GenericAssetIdField from flexmeasures.data.schemas.sensors import SensorIdField from flexmeasures.data.models.generic_assets import GenericAsset from flexmeasures.data.models.time_series import TimedBelief from flexmeasures.data.utils import save_to_db @click.group("edit") def fm_edit_data(): """FlexMeasures: Edit data.""" @fm_edit_data.command("attribute") @with_appcontext @click.option( "--asset-id", "assets", required=False, multiple=True, type=GenericAssetIdField(), help="Add/edit attribute to this asset. Follow up with the asset's ID.", ) @click.option( "--sensor-id", "sensors", required=False, multiple=True, type=SensorIdField(), help="Add/edit attribute to this sensor. Follow up with the sensor's ID.", ) @click.option( "--attribute", "attribute_key", required=True, help="Add/edit this attribute. Follow up with the name of the attribute.", ) @click.option( "--float", "attribute_float_value", required=False, type=float, help="Set the attribute to this float value.", ) @click.option( "--bool", "attribute_bool_value", required=False, type=bool, help="Set the attribute to this bool value.", ) @click.option( "--str", "attribute_str_value", required=False, type=str, help="Set the attribute to this string value.", ) @click.option( "--int", "attribute_int_value", required=False, type=int, help="Set the attribute to this integer value.", ) @click.option( "--null", "attribute_null_value", required=False, is_flag=True, default=False, help="Set the attribute to a null value.", ) def edit_attribute( attribute_key: str, assets: List[GenericAsset], sensors: List[Sensor], attribute_null_value: bool, attribute_float_value: Optional[float] = None, attribute_bool_value: Optional[bool] = None, attribute_str_value: Optional[str] = None, attribute_int_value: Optional[int] = None, ): """Edit (or add) an asset attribute or sensor attribute.""" if not assets and not sensors: raise ValueError("Missing flag: pass at least one --asset-id or --sensor-id.") # Parse attribute value attribute_value = parse_attribute_value( attribute_float_value=attribute_float_value, attribute_bool_value=attribute_bool_value, attribute_str_value=attribute_str_value, attribute_int_value=attribute_int_value, attribute_null_value=attribute_null_value, ) # Set attribute for asset in assets: asset.attributes[attribute_key] = attribute_value db.session.add(asset) for sensor in sensors: sensor.attributes[attribute_key] = attribute_value db.session.add(sensor) db.session.commit() print("Successfully edited/added attribute.") @fm_edit_data.command("resample-data") @with_appcontext @click.option( "--sensor-id", "sensor_ids", multiple=True, required=True, help="Resample data for this sensor. Follow up with the sensor's ID. This argument can be given multiple times.", ) @click.option( "--event-resolution", "event_resolution_in_minutes", type=int, required=True, help="New event resolution as an integer number of minutes.", ) @click.option( "--from", "start_str", required=False, help="Resample only data from this datetime onwards. Follow up with a timezone-aware datetime in ISO 6801 format.", ) @click.option( "--until", "end_str", required=False, help="Resample only data until this datetime. Follow up with a timezone-aware datetime in ISO 6801 format.", ) @click.option( "--skip-integrity-check", is_flag=True, help="Whether to skip checking the resampled time series data for each sensor." " By default, an excerpt and the mean value of the original" " and resampled data will be shown for manual approval.", ) def resample_sensor_data( sensor_ids: List[int], event_resolution_in_minutes: int, start_str: Optional[str] = None, end_str: Optional[str] = None, skip_integrity_check: bool = False, ): """Assign a new event resolution to an existing sensor and resample its data accordingly.""" event_resolution = timedelta(minutes=event_resolution_in_minutes) event_starts_after = pd.Timestamp(start_str) # note that "" or None becomes NaT event_ends_before = pd.Timestamp(end_str) for sensor_id in sensor_ids: sensor = Sensor.query.get(sensor_id) if sensor.event_resolution == event_resolution: print(f"{sensor} already has the desired event resolution.") continue df_original = sensor.search_beliefs( most_recent_beliefs_only=False, event_starts_after=event_starts_after, event_ends_before=event_ends_before, ).sort_values("event_start") df_resampled = df_original.resample_events(event_resolution).sort_values( "event_start" ) if not skip_integrity_check: message = "" if sensor.event_resolution < event_resolution: message += f"Downsampling {sensor} to {event_resolution} will result in a loss of data. " click.confirm( message + f"Data before:\n{df_original}\nData after:\n{df_resampled}\nMean before: {df_original['event_value'].mean()}\nMean after: {df_resampled['event_value'].mean()}\nContinue?", abort=True, ) # Update sensor sensor.event_resolution = event_resolution db.session.add(sensor) # Update sensor data query = TimedBelief.query.filter(TimedBelief.sensor == sensor) if not pd.isnull(event_starts_after): query = query.filter(TimedBelief.event_start >= event_starts_after) if not pd.isnull(event_ends_before): query = query.filter( TimedBelief.event_start + sensor.event_resolution <= event_ends_before ) query.delete() save_to_db(df_resampled, bulk_save_objects=True) db.session.commit() print("Successfully resampled sensor data.") app.cli.add_command(fm_edit_data) def parse_attribute_value( attribute_null_value: bool, attribute_float_value: Optional[float] = None, attribute_bool_value: Optional[bool] = None, attribute_str_value: Optional[str] = None, attribute_int_value: Optional[int] = None, ) -> Union[float, int, bool, str, None]: """Parse attribute value.""" if not single_true( [attribute_null_value] + [ v is not None for v in [ attribute_float_value, attribute_bool_value, attribute_str_value, attribute_int_value, ] ] ): raise ValueError("Cannot set multiple values simultaneously.") if attribute_null_value: return None elif attribute_float_value is not None: return float(attribute_float_value) elif attribute_bool_value is not None: return bool(attribute_bool_value) elif attribute_int_value is not None: return int(attribute_int_value) return attribute_str_value def single_true(iterable) -> bool: i = iter(iterable) return any(i) and not any(i)
2.28125
2
semana2/mail_settings.py
ArseniumGX/bluemer-modulo2
0
9916
mail_settings = { "MAIL_SERVER": 'smtp.gmail.com', "MAIL_PORT": 465, "MAIL_USE_TLS": False, "MAIL_USE_SSL": True, "MAIL_USERNAME": '<EMAIL>', "MAIL_PASSWORD": '<PASSWORD>' }
1.210938
1
frame/base/parser.py
dingjingmaster/blog_spider
0
9917
#!/usr/bin/env python3.6 # -*- encoding=utf8 -*- import pyquery """ 需求字段: 標題、發表日期、分類、標籤、內容、圖片 需要的字段信息 1. 网站根URL 2. 解析器名字 3. 解析器类型 1. PARSER_PASSAGE_URL 文章URL 2. PARSER_PASSAGE_TITLE 文章标题 3. PARSER_PASSAGE_DATE 发表日期 4. PARSER_PASSAGE_CATEGORY 文章分類 5. PARSER_PASSAGE_TAG 文章標籤 6. PARSER_PASSAGE_CONTENT 文章内容 7. PARSER_PASSAGE_IMGURL 文章中的图片 URL """ class Parser(object): def __init__ (self): self._webURL = '' self._parserName = 'base_parser' def _parser_passage_url (self, doc: str) -> (bool, str): return def _parser_passage_title (self, doc: str) -> (bool, str): return def _parser_passage_date (self, doc: str) -> (bool, str): return def _parser_passage_category (self, doc: str) -> (bool, str): return def _parser_passage_tag (self, doc: str) -> (bool, str): return def _parser_passage_content (self, doc: str) -> (bool, str): return def _parser_passage_img_url (self, doc: str) -> (bool, str, bytes): return def get_parser_name (self): return self._parserName @staticmethod def _parser (doc: str, rule: str): return pyquery.PyQuery(doc).find(rule) def parse (self, doc: str, rule='', parse_type=-1): if self.PARSER_PASSAGE_URL == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_url(doc) elif self.PARSER_PASSAGE_TITLE == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_title(doc) elif self.PARSER_PASSAGE_DATE == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_date(doc) elif self.PARSER_PASSAGE_CATEGORY == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_category(doc) elif self.PARSER_PASSAGE_TAG == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_tag(doc) elif self.PARSER_PASSAGE_CONTENT == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_content(doc) elif self.PARSER_PASSAGE_IMGURL == parse_type: if doc == '' or doc == None: return (False, '') return self._parser_passage_img_url(doc) else: if doc == '' or doc == None: return (False, '') return Parser._parser(doc, rule) PARSER_PASSAGE_URL = 1 PARSER_PASSAGE_TITLE = 2 PARSER_PASSAGE_DATE = 3 PARSER_PASSAGE_CATEGORY = 4 PARSER_PASSAGE_TAG = 5 PARSER_PASSAGE_CONTENT = 6 PARSER_PASSAGE_IMGURL = 7
2.796875
3
tools/mkblocks.py
Commodore-Bench/u5remastered
14
9918
<filename>tools/mkblocks.py #!/usr/bin/env python3 # ---------------------------------------------------------------------------- # Copyright 2019 Drunella # # 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 sys import glob import subprocess import argparse import hashlib import traceback import pprint def readblockmap_info(filename): directory = dict() with open(filename) as f: result = [line.split() for line in f] for l in result: directory[l[0]] = l[1:] return directory def readdisks_info(filename): disks = [] with open(filename) as f: result = [line.split() for line in f] #pprint.pprint(result) return result def readdisks_getdiskinfo(disks, diskname): for d in disks: if d[0] == diskname: return d return [] def map_initialize(): global bank_data, map_data map_data = bytearray([0xff] * 0x800) def crtmap_appendentry(filename, block, name, address): with open(filename, "at") as f: content = "{0} f {1} addr 0x{2:04x}\n".format(block, name, address) return f.write(content) def load_file(filename): with open(filename, "rb") as f: return f.read() def write_prg(dirname, lowhigh, data): if lowhigh == 0: # low a = bytearray(2) a[0] = 0 a[1] = 0x80 elif lowhigh == 1: # high a = bytearray(2) a[0] = 0 a[1] = 0xA0 else: raise Exception("lowhigh can only be 0 or 1") with open(dirname, "wb") as f: #f.write(a) f.write(data) def blockmap_appendentry(diskid, line, bank, highaddress): global map_data base = diskid * 256 + line * 2 map_data[base] = bank map_data[base+1] = highaddress #print("blockmap_appendentry: " + str(base) + ": " + str(bank) + " " + str(highaddress)) def calculate_address(lowhigh): if lowhigh == 0: # low a = 0x80 elif lowhigh == 1: # high a = 0xA0 else: raise Exception("lowhigh can only be 0 or 1") return a def main(argv): global bank_data, map_data p = argparse.ArgumentParser() p.add_argument("-v", dest="verbose", action="store_true", help="Verbose output.") p.add_argument("-o", dest="disks", action="store", required=True, help="disk configuration file.") p.add_argument("-f", dest="files", action="store", required=True, help="files directory.") p.add_argument("-m", dest="crtfile", action="store", required=True, help="crt.map file") p.add_argument("-d", dest="destination", action="store", required=True, help="destination directory.") p.add_argument("-b", dest="blockmap", action="store", required=True, help="blockmap file.") #p.add_argument("-f", dest="fileoutput", action="store", required=True, help="output data content file.") args = p.parse_args() #temp_path = os.path.join(args.build, "temp") #os.makedirs(temp_path, exist_ok=True) files_path = args.files #os.path.join(args.build, "files") os.makedirs(files_path, exist_ok=True) destination_path = args.destination #os.path.join(args.build, "obj") os.makedirs(destination_path, exist_ok=True) disks = readdisks_info(args.disks) blockmap = readblockmap_info(args.blockmap) map_initialize() if os.path.exists(args.crtfile): os.remove(args.crtfile) # add blocks file for d in ("britannia", "towne", "dwelling", "castle", "keep", "dungeon", "underworld"): diskinfo = readdisks_getdiskinfo(disks, d) starttrack = int(diskinfo[2], 0) height = int(diskinfo[4], 0) - int(diskinfo[2], 0) + 1 diskid = int(diskinfo[1], 0) - 0x41 startbank = int(blockmap[d][0], 0) lowhigh = int(blockmap[d][1], 0) block_data = load_file(os.path.join(files_path, d + ".data")) # build map and blocks map_data[diskid*256+255] = starttrack for b in range(0, height, 2): # double line or single line #factor = 2 #if b+1 >= height: # factor = 1 # make data bank_data = bytearray([0xff] * 0x2000) baseaddress = calculate_address(lowhigh) if b+1 >= height: # one line s = b * 256*16 l = 0x1000 bank_data[0:l] = block_data[s:s+l] blockmap_appendentry(diskid, b, startbank, baseaddress) else: # two lines s = b * 256*16 l = 0x2000 bank_data[0:l] = block_data[s:s+l] blockmap_appendentry(diskid, b, startbank, baseaddress) blockmap_appendentry(diskid, b+1, startbank, baseaddress+0x10) # write data and map filename = "{0}_{1:02d}.aprg".format(d, b) write_prg(os.path.join(destination_path, filename), lowhigh, bank_data) crtmap_appendentry(args.crtfile, startbank, filename, baseaddress * 0x100) # increase values startbank += 1 # write block map blockmap_bank = int(blockmap["blockmap"][0], 0) blockmap_lowhigh = int(blockmap["blockmap"][1], 0) blockmap_address = calculate_address(blockmap_lowhigh) * 256 #blockmap_appendentry(0, b, startbank, baseaddress) blockmapname = os.path.join(destination_path, "blockmap.aprg") write_prg(blockmapname, blockmap_lowhigh, map_data) crtmap_appendentry(args.crtfile, blockmap_bank, "blockmap.aprg", blockmap_address) return 0 if __name__ == '__main__': try: retval = main(sys.argv) sys.exit(retval) except Exception as e: print(e) traceback.print_exc() sys.exit(1)
2.421875
2
src/apetest/decode.py
boxingbeetle/apetest
6
9919
<gh_stars>1-10 # SPDX-License-Identifier: BSD-3-Clause """ Text decode functions. These functions can be used to get Unicode strings from a series of bytes. """ from codecs import ( BOM_UTF8, BOM_UTF16_BE, BOM_UTF16_LE, BOM_UTF32_BE, BOM_UTF32_LE, CodecInfo, lookup as lookup_codec, ) from collections import OrderedDict from typing import Dict, Iterable, Optional, Tuple from apetest.typing import LoggerT def encoding_from_bom(data: bytes) -> Optional[str]: """ Look for a byte-order-marker at the start of the given C{bytes}. If found, return the encoding matching that BOM, otherwise return C{None}. """ if data.startswith(BOM_UTF8): return "utf-8" elif data.startswith(BOM_UTF16_LE) or data.startswith(BOM_UTF16_BE): return "utf-16" elif data.startswith(BOM_UTF32_LE) or data.startswith(BOM_UTF32_BE): return "utf-32" else: return None def standard_codec_name(name: str) -> str: """ Map a codec name to the preferred standardized version. The preferred names were taken from this list published by IANA: U{http://www.iana.org/assignments/character-sets/character-sets.xhtml} @param name: Text encoding name, in lower case. """ if name.startswith("iso8859"): return "iso-8859" + name[7:] return { "ascii": "us-ascii", "euc_jp": "euc-jp", "euc_kr": "euc-kr", "iso2022_jp": "iso-2022-jp", "iso2022_jp_2": "iso-2022-jp-2", "iso2022_kr": "iso-2022-kr", }.get(name, name) def try_decode(data: bytes, encodings: Iterable[str]) -> Tuple[str, str]: """ Attempt to decode text using the given encodings in order. @param data: Encoded version of the text. @param encodings: Names of the encodings to try. Must all be lower case. @return: C{(text, encoding)} The decoded string and the encoding used to decode it. The returned encoding is name the preferred name, which could differ from the name used in the C{encodings} argument. @raise ValueError: If the text could not be decoded. """ # Build sequence of codecs to try. codecs: Dict[str, CodecInfo] = OrderedDict() for encoding in encodings: try: codec = lookup_codec(encoding) except LookupError: pass else: codecs[standard_codec_name(codec.name)] = codec # Apply decoders to the document. for name, codec in codecs.items(): try: text, consumed = codec.decode(data, "strict") except UnicodeDecodeError: continue if consumed == len(data): return text, name raise ValueError("Unable to determine document encoding") def decode_and_report( data: bytes, encoding_options: Iterable[Tuple[Optional[str], str]], logger: LoggerT, ) -> Tuple[str, str]: """ Attempt to decode text using several encoding options in order. @param data: Encoded version of the text. @param encoding_options: C{(encoding | None, source)*} Each option is a pair of encoding name and a description of where this encoding suggestion originated. If the encoding name is C{None}, the option is skipped. @param logger: Non-fatal problems are logged here. Such problems include an unknown or differing encodings among the options. @return: C{(text, encoding)} The decoded string and the encoding used to decode it. @raise ValueError: If the text could not be decoded. """ # Filter and remember encoding options. options = [ (encoding, source) for encoding, source in encoding_options if encoding is not None ] encodings = [encoding for encoding, source in options] # Always try to decode as UTF-8, since that is the most common encoding # these days, plus it's a superset of ASCII so it also works for old or # simple documents. encodings.append("utf-8") text, used_encoding = try_decode(data, encodings) # Report differences between suggested encodings and the one we # settled on. for encoding, source in options: try: codec = lookup_codec(encoding) except LookupError: logger.warning( '%s specifies encoding "%s", which is unknown to Python', source, encoding, ) continue std_name = standard_codec_name(codec.name) if std_name != used_encoding: logger.warning( '%s specifies encoding "%s", ' 'while actual encoding seems to be "%s"', source, encoding, used_encoding, ) elif std_name != encoding: logger.info( '%s specifies encoding "%s", ' 'which is not the standard name "%s"', source, encoding, used_encoding, ) return text, used_encoding
3.171875
3
utils.py
jiangycTarheel/Compositional-Auxseq
8
9920
import os import json import gzip from copy import deepcopy, copy import numpy as np import csv import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader, RandomSampler from transformers.tokenization_utils import trim_batch class LabelSmoothingLoss(nn.Module): def __init__(self, label_smooth, tgt_vocab_size, ignore_index=-100): assert 0. < label_smooth <= 1. self.ignore_index = ignore_index super(LabelSmoothingLoss, self).__init__() smoothing_value = label_smooth / (tgt_vocab_size - 2) one_hot = torch.full((tgt_vocab_size,), smoothing_value) one_hot[self.ignore_index] = 0 self.register_buffer('one_hot', one_hot.unsqueeze(0).unsqueeze(0)) self.confidence = 1.0 - label_smooth self.lossfct = torch.nn.KLDivLoss(reduction='none') def forward(self, pred, target): """ Args: pred: [bsz, seq_len, vocab_size] target: [bsz, seq_len] Returns: """ model_prob = self.one_hot.repeat(target.size(0), target.size(1), 1) # [bsz, seq_len, vocab_size] model_prob.scatter_(2, target.unsqueeze(2), self.confidence) model_prob.masked_fill_((target == self.ignore_index).unsqueeze(2), 0) pred_prob = F.log_softmax(pred, dim=2) #return F.kl_div(pred_prob, model_prob, reduction='mean') loss = self.lossfct(pred_prob, model_prob) loss = torch.sum(loss, dim=2).masked_fill_((target == self.ignore_index), 0) avg_loss = torch.sum(loss) / torch.sum((target != self.ignore_index).to(torch.float)) return avg_loss # Special symbols SOS_token = "<SOS>" # start of sentence EOS_token = "<EOS>" # end of sentence PAD_token = SOS_token # padding symbol INPUT_TOKENS_SCAN = ['jump', 'opposite', 'right', 'twice', 'and', 'turn', 'thrice', 'run', 'after', 'around', 'left', 'walk', 'look'] OUTPUT_TOKENS_SCAN = ['I_TURN_RIGHT', 'I_JUMP', 'I_TURN_LEFT', 'I_RUN', 'I_WALK', 'I_LOOK'] # ACTION_TO_TEXT = {'I_TURN_RIGHT': 'right', 'I_JUMP': 'jump', 'I_TURN_LEFT': 'left', 'I_RUN': 'run', 'I_WALK': 'walk', 'I_LOOK': 'look'} class Lang: # Class for converting strings/words to numerical indices, and vice versa. # Should use separate class for input language (English) and output language (actions) # def __init__(self, symbols, io_type): # symbols : list of all possible symbols n = len(symbols) self.symbols = [_s.strip('\n') for _s in symbols] self.io_type = io_type if SOS_token not in self.symbols: assert EOS_token not in self.symbols self.index2symbol = {n: SOS_token, n+1: EOS_token} self.symbol2index = {SOS_token: n, EOS_token: n + 1} self.sos_id, self.eos_id = n, n + 1 else: self.index2symbol = {} self.symbol2index = {} self.sos_id, self.eos_id = 0, 1 self.pad_token_id = self.sos_id for idx,s in enumerate(self.symbols): self.index2symbol[idx] = s self.symbol2index[s] = idx self.n_symbols = len(self.index2symbol) def variableFromSymbols(self, mylist, add_eos=True): # Convert a list of symbols to a tensor of indices (adding a EOS token at end) # # Input # mylist : list of m symbols # add_eos : true/false, if true add the EOS symbol at end # # Output # output : [m or m+1 LongTensor] indices of each symbol (plus EOS if appropriate) mylist = copy(mylist) if add_eos: mylist.append(EOS_token) indices = [self.symbol2index[s] for s in mylist] output = torch.LongTensor(indices) #if USE_CUDA: output = output.cuda() return output def symbolsFromVector(self, v): # Convert indices to symbols, breaking where we get a EOS token # # Input # v : list of m indices # # Output # mylist : list of m or m-1 symbols (excluding EOS) mylist = [] for x in v: s = self.index2symbol[x] if s == EOS_token: break mylist.append(s) return mylist def encode_scan_file(self, data, max_length): encoded_data = [] for dp in data: input, output = dp[0], dp[1] if self.io_type == 'input': raw = input else: assert self.io_type == 'output' raw = output encoded = self.variableFromSymbols(raw.split(' ')) encoded_data.append(encoded) return encoded_data def encode_scan_file_2_seg(self, data, max_length, cutoffs): encoded_data_1, encoded_data_2 = [], [] for _id, dp in enumerate(data): input, output, cutoff = dp[0], dp[1], cutoffs[_id] assert self.io_type == 'output' raw = output encoded_1 = self.variableFromSymbols(raw.split(' ')[:cutoff]) encoded_2 = self.variableFromSymbols(raw.split(' ')[cutoff:]) encoded_data_1.append(encoded_1) encoded_data_2.append(encoded_2) return encoded_data_1, encoded_data_2 def encode_cfq_file(self, data, max_length): encoded_data = [] for dp in data: input, output = dp['query_ids'], dp['sparql_ids'] if self.io_type == 'input': raw = input else: assert self.io_type == 'output' raw = output + [self.eos_id] encoded = torch.LongTensor(raw).cuda() encoded_data.append(encoded) return encoded_data def encode_cogs_file(self, data, max_length): encoded_data = [] for dp in data: input, output = dp['src'], dp['trg'] if self.io_type == 'input': raw = input else: assert self.io_type == 'output' raw = output encoded = self.variableFromSymbols(raw.split(' ')) encoded_data.append(encoded) return encoded_data def decode(self, ids): out = self.symbolsFromVector(ids.cpu().numpy()) if out == []: return out if out[0] in ['<SOS>', '<SOS_2>']: out = out[1:] return out def calculate_accuracy(preds, gts): assert len(preds) == len(gts) match = 0 for pred, gt in zip(preds, gts): if pred == gt: match += 1 return match / len(preds) def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt", max_examples=None): examples = [] if data_path[-3:] == '.gz': print('Data file is gzipped') f = gzip.open(data_path, "rt") else: print('Data file is plain text') print(data_path) f = open(data_path, "r", encoding='utf-8') for i, text in enumerate(f.readlines()): tokenized = tokenizer.batch_encode_plus( [text + ' </s>'], max_length=max_length, pad_to_max_length=pad_to_max_length, return_tensors=return_tensors ) if max_examples and i >= max_examples: break examples.append(tokenized) f.close() return examples # def encode_file_iterator(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt", max_examples=None): # ''' # This provides a low-memory usage way of iterating thru all of the source/target lines for processing by JIT loader. # ''' # if data_path[-3:] == '.gz': # print('Data file is gzipped') # f = gzip.open(data_path, "rt") # else: # print('Data file is plain text') # f = open(data_path, "r", encoding='utf-8') # # for i, text in enumerate(f): # # tokenized = tokenizer.batch_encode_plus( [text + ' </s>'], max_length=max_length, # pad_to_max_length=pad_to_max_length, return_tensors=return_tensors ) # # yield tokenized # # if max_examples and i >= max_examples: # break # # f.close() # def convert_scan_actions_to_text(actions): # return ' '.join([ACTION_TO_TEXT[_action] for _action in actions.split(' ')]) # def encode_scan_file(tokenizer, data, io_type, max_length, pad_to_max_length=True, return_tensors="pt", max_examples=None): # examples = [] # # a = tokenizer.batch_encode_plus( ['right jump left run walk look' + ' <s> </s>'], max_length=max_length, # # pad_to_max_length=pad_to_max_length, return_tensors=return_tensors ) # # print(a) # # exit() # for dp in data: # input, output = dp[0], dp[1] # if io_type == 'input': # raw = input # else: # assert io_type == 'output' # raw = convert_scan_actions_to_text(output) # # tokenized = tokenizer.batch_encode_plus( [raw + ' </s>'], max_length=max_length, # pad_to_max_length=pad_to_max_length, return_tensors=return_tensors ) # # if max_examples and i >= max_examples: # break # examples.append(tokenized) # # return examples def load_scan_file(mytype, split): # Load SCAN dataset from file # # Input # mytype : type of SCAN experiment # split : 'train' or 'test' # # Output # commands : list of input/output strings (as tuples) assert mytype in ['simple', 'addprim_jump', 'length', 'addprim_turn_left', 'all', 'template_around_right', 'viz', 'examine', 'template_jump_around_right', 'template_right', 'template_around_right', 'mcd1', 'mcd2', 'mcd3', 'mcd1.1', 'mcd1.2', 'debug', 'attn_vis'] assert split in ['train', 'test', 'val'] if split == 'val' and mytype not in ['mcd1', 'mcd2', 'mcd3', 'mcd1.1', 'mcd1.2']: split = 'test' fn = 'data/scan/tasks_' + split + '_' + mytype + '.txt' fid = open(fn, 'r') lines = fid.readlines() fid.close() lines = [l.strip() for l in lines] lines = [l.lstrip('IN: ') for l in lines] commands = [l.split(' OUT: ') for l in lines] return commands class CompositionDataset(Dataset): def __init__( self, src_lang, trg_lang, data_dir, type_path, sub_task, max_source_length=20, max_target_length=20, tokenized=False, ): super().__init__() self.max_source_length = max_source_length self.max_target_length = max_target_length self.tokenized = tokenized self.src_lang = src_lang self.trg_lang = trg_lang def __len__(self): if self.tokenized: return len(self.dataset) else: return len(self.source) def __getitem__(self, index): if self.tokenized: dp = self.dataset[index] source_ids, src_mask, target_ids = dp[0], dp[1], dp[2] source_ids = source_ids[:self.max_source_length] #src_mask = src_mask[:self.max_source_length] target_ids = target_ids[:self.max_target_length] else: source_ids = self.source[index] target_ids = self.target[index] return {"source_ids": source_ids, "target_ids": target_ids} @staticmethod def trim_seq2seq_batch(batch, src_pad_token_id, trg_pad_token_id, trim_y=True): if trim_y: y = trim_batch(batch["target_ids"], trg_pad_token_id) else: y = batch["target_ids"] source_ids, source_mask = trim_batch(batch["source_ids"], src_pad_token_id, attention_mask=batch["source_mask"]) return source_ids, source_mask, y def pad_to_max_len(self, ids, max_len, pad_token_id): ids_length = ids.size(0) if ids_length == max_len: return ids pad_tokens = torch.tensor([pad_token_id] * (max_len - ids_length)) # if ids.type() == 'torch.cuda.FloatTensor': # print(ids) # exit() padded_ids = torch.cat([ids, pad_tokens.cuda()]) return padded_ids def create_mask(self, ids, max_len): ids_length = ids.size(0) mask = torch.tensor([1] * ids_length + [0] * (max_len - ids_length)).cuda() return mask def collate_fn(self, batch): max_src_len = max(map(len, [x["source_ids"] for x in batch])) max_trg_len = max(map(len, [x["target_ids"] for x in batch])) src_mask = torch.stack([self.create_mask(x["source_ids"], max_src_len) for x in batch]) src_ids = torch.stack([self.pad_to_max_len(x["source_ids"], max_src_len, self.src_lang.pad_token_id) for x in batch]) #masks = torch.stack([x["source_mask"] for x in batch]) trg_ids = torch.stack([self.pad_to_max_len(x["target_ids"], max_trg_len, self.trg_lang.pad_token_id) for x in batch]) y = trim_batch(trg_ids, self.trg_lang.pad_token_id) src_ids, src_mask = trim_batch(src_ids, self.src_lang.pad_token_id, attention_mask=src_mask) return {"source_ids": src_ids, "source_mask": src_mask, "target_ids": y} class ScanDataset(CompositionDataset): def __init__( self, src_lang, trg_lang, data_dir="./data/scan/", type_path="train", sub_task="addprim_jump", max_source_length=20, max_target_length=20, tokenized=False, ): super().__init__(src_lang, trg_lang, data_dir, type_path, sub_task, max_source_length, max_target_length, tokenized) scan_data = load_scan_file(sub_task, type_path) print(len(scan_data)) all_scan_dict = self.convert_to_dict(load_scan_file('all', 'train')) self.action_count_labels, self.action_group_labels, self.action_type_labels = self.construct_count_label(scan_data, all_scan_dict) if not tokenized: self.source = self.src_lang.encode_scan_file(scan_data, max_source_length) self.target = self.trg_lang.encode_scan_file(scan_data, max_target_length) else: self.dataset = torch.load(os.path.join(data_dir, type_path)) def construct_count_label(self, raw_data, all_data_dict): all_count_labels = [] count_label_scheme = "v1" group_label_scheme = "v2" type_label_scheme = "v2" all_action_group_labels, all_action_type_labels = [], [] # Group 1: single prim (jump), Group 2: prim + direction (jump left), Group 3: prim opposite, Group 4: prim around #no_skip_id = np.random.randint(0, len(raw_data), int(len(raw_data)*0.05)) #no_skip_id = np.random.choice(range(len(raw_data)), int(len(raw_data)*0.07), replace=False) # no_skip_id = np.random.choice(range(len(raw_data)), 10, replace=False) skip_cnt, sup_cnt = 0, 0 for _id, dp in enumerate(raw_data): input_text, output_text = dp[0], dp[1] input_tok, output_tok = input_text.split(' '), output_text.split(' ') count_labels, group_labels, type_labels = [], [], [] first_part_output_text, second_part_output_text = '', '' if 'and' in input_tok: first_part_input_tok = input_tok[:input_tok.index('and')] second_part_input_tok = input_tok[input_tok.index('and')+1:] first_part_output_text = all_data_dict[' '.join(first_part_input_tok)] second_part_output_text = all_data_dict[' '.join(second_part_input_tok)] elif 'after' in input_tok: second_part_input_tok = input_tok[:input_tok.index('after')] first_part_input_tok = input_tok[input_tok.index('after') + 1:] first_part_output_text = all_data_dict[' '.join(first_part_input_tok)] second_part_output_text = all_data_dict[' '.join(second_part_input_tok)] else: first_part_input_tok, second_part_input_tok = input_tok, [] first_part_output_text = output_text first_part_output_tok, second_part_output_tok = first_part_output_text.split(' '), second_part_output_text.split(' ') if second_part_output_text == '': second_part_output_tok = [] assert len(first_part_output_tok) + len(second_part_output_tok) == len(output_tok), \ (len(first_part_output_tok), len(second_part_output_tok), len(output_tok), first_part_output_text, second_part_output_text, output_text) ### 1. Build the action count labels ### if count_label_scheme == 'v1': ### For the first part output if 'twice' in first_part_input_tok: if 'after' in input_tok: count_labels += ([4] * int(len(first_part_output_tok) / 2) + [3] * int(len(first_part_output_tok) / 2)) else: count_labels += ([1] * int(len(first_part_output_tok) / 2) + [0] * int(len(first_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in first_part_input_tok: if 'after' in input_tok: count_labels += ([5] * int(len(first_part_output_tok) / 3) + [4] * int(len(first_part_output_tok) / 3) + \ [3] * int(len(first_part_output_tok) / 3)) else: count_labels += ([2] * int(len(first_part_output_tok) / 3) + [1] * int(len(first_part_output_tok) / 3) + \ [0] * int(len(first_part_output_tok) / 3)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok) / 3) - 1)) * 3 else: if 'after' in input_tok: count_labels += ([3] * len(first_part_output_tok)) else: count_labels += ([0] * len(first_part_output_tok)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok)) - 1)) ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: if 'after' in input_tok: count_labels += ([1] * int(len(second_part_output_tok) / 2) + [0] * int(len(second_part_output_tok) / 2)) else: count_labels += ([4] * int(len(second_part_output_tok) / 2) + [3] * int(len(second_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in second_part_input_tok: if 'after' in input_tok: count_labels += ([2] * int(len(second_part_output_tok) / 3) + [1] * int(len(second_part_output_tok) / 3) + \ [0] * int(len(second_part_output_tok) / 3)) else: count_labels += ([5] * int(len(second_part_output_tok) / 3) + [4] * int(len(second_part_output_tok) / 3) + \ [3] * int(len(second_part_output_tok) / 3)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok) / 3) - 1)) * 3 else: if 'after' in input_tok: count_labels += ([0] * len(second_part_output_tok)) else: count_labels += ([3] * len(second_part_output_tok)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok)) - 1)) elif count_label_scheme == 'v2': ### For the first part output if 'twice' in first_part_input_tok: count_labels += ([1] * int(len(first_part_output_tok) / 2) + [0] * int( len(first_part_output_tok) / 2)) elif 'thrice' in first_part_input_tok: count_labels += ([2] * int(len(first_part_output_tok) / 3) + [1] * int( len(first_part_output_tok) / 3) + \ [0] * int(len(first_part_output_tok) / 3)) else: count_labels += ([0] * len(first_part_output_tok)) ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: count_labels += ([1] * int(len(second_part_output_tok) / 2) + [0] * int( len(second_part_output_tok) / 2)) elif 'thrice' in second_part_input_tok: count_labels += ([2] * int(len(second_part_output_tok) / 3) + [1] * int( len(second_part_output_tok) / 3) + [0] * int(len(second_part_output_tok) / 3)) else: count_labels += ([0] * len(second_part_output_tok)) elif count_label_scheme == 'v3': ### For the first part output if 'thrice' in first_part_input_tok and 'thrice' in second_part_input_tok: start_count = 5 elif ('thrice' in first_part_input_tok and 'twice' in second_part_input_tok) or \ ('twice' in first_part_input_tok and 'thrice' in second_part_input_tok): start_count = 4 elif ('twice' in first_part_input_tok and 'twice' in second_part_input_tok) or \ ('thrice' in first_part_input_tok) or ('thrice' in second_part_input_tok): start_count = 3 elif 'twice' in first_part_input_tok or 'twice' in second_part_input_tok: start_count = 2 else: start_count = 1 if 'twice' in first_part_input_tok: if 'after' in input_tok: count_labels += ([start_count] * int(len(first_part_output_tok) / 2) + [start_count-1] * int(len(first_part_output_tok) / 2)) else: count_labels += ([1] * int(len(first_part_output_tok) / 2) + [0] * int(len(first_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in first_part_input_tok: if 'after' in input_tok: count_labels += ([start_count] * int(len(first_part_output_tok) / 3) + [start_count-1] * int(len(first_part_output_tok) / 3) + \ [start_count-2] * int(len(first_part_output_tok) / 3)) else: count_labels += ([2] * int(len(first_part_output_tok) / 3) + [1] * int(len(first_part_output_tok) / 3) + \ [0] * int(len(first_part_output_tok) / 3)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok) / 3) - 1)) * 3 else: if 'after' in input_tok: count_labels += ([start_count] * len(first_part_output_tok)) else: count_labels += ([0] * len(first_part_output_tok)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok)) - 1)) ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: if 'after' in input_tok: count_labels += ([1] * int(len(second_part_output_tok) / 2) + [0] * int(len(second_part_output_tok) / 2)) else: count_labels += ([start_count] * int(len(second_part_output_tok) / 2) + [start_count-1] * int(len(second_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in second_part_input_tok: if 'after' in input_tok: count_labels += ([2] * int(len(second_part_output_tok) / 3) + [1] * int(len(second_part_output_tok) / 3) + \ [0] * int(len(second_part_output_tok) / 3)) else: count_labels += ([start_count] * int(len(second_part_output_tok) / 3) + [start_count-1] * int(len(second_part_output_tok) / 3) + \ [start_count-2] * int(len(second_part_output_tok) / 3)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok) / 3) - 1)) * 3 else: if 'after' in input_tok: count_labels += ([0] * len(second_part_output_tok)) else: count_labels += ([start_count] * len(second_part_output_tok)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok)) - 1)) elif count_label_scheme == 'v3.1': ### For the first part output if 'thrice' in first_part_input_tok and 'thrice' in second_part_input_tok: start_count = 5 elif ('thrice' in first_part_input_tok and 'twice' in second_part_input_tok) or \ ('twice' in first_part_input_tok and 'thrice' in second_part_input_tok): start_count = 4 elif ('twice' in first_part_input_tok and 'twice' in second_part_input_tok) or \ ('thrice' in first_part_input_tok) or ('thrice' in second_part_input_tok): start_count = 3 elif 'twice' in first_part_input_tok or 'twice' in second_part_input_tok: start_count = 2 else: start_count = 1 if 'twice' in first_part_input_tok: count_labels += ([start_count] * int(len(first_part_output_tok) / 2) + [start_count - 1] * int( len(first_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(first_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in first_part_input_tok: count_labels += ([start_count] * int(len(first_part_output_tok) / 3) + [start_count - 1] * int( len(first_part_output_tok) / 3) + \ [start_count - 2] * int(len(first_part_output_tok) / 3)) else: count_labels += ([start_count] * len(first_part_output_tok)) ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: count_labels += ([1] * int(len(second_part_output_tok) / 2) + [0] * int( len(second_part_output_tok) / 2)) # count_labels += ([1] + [0] * (int(len(second_part_output_tok) / 2) - 1)) * 2 elif 'thrice' in second_part_input_tok: count_labels += ([2] * int(len(second_part_output_tok) / 3) + [1] * int( len(second_part_output_tok) / 3) + \ [0] * int(len(second_part_output_tok) / 3)) else: count_labels += ([0] * len(second_part_output_tok)) else: ### For the first part output if 'twice' in first_part_input_tok: if 'after' in input_tok: new_count_labels = list(range(int(len(first_part_output_tok) / 2)))[::-1] * 2 else: new_count_labels = list(range(int(len(first_part_output_tok) / 2)))[::-1] * 2 elif 'thrice' in first_part_input_tok: if 'after' in input_tok: new_count_labels = list(range(int(len(first_part_output_tok) / 3)))[::-1] * 3 else: new_count_labels = list(range(int(len(first_part_output_tok) / 3)))[::-1] * 3 else: if 'after' in input_tok: new_count_labels = list(range(len(first_part_output_tok)))[::-1] else: new_count_labels = list(range(len(first_part_output_tok)))[::-1] count_labels += new_count_labels ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: if 'after' in input_tok: new_count_labels = list(range(int(len(second_part_output_tok) / 2)))[::-1] * 2 new_count_labels = [_c + 8 for _c in new_count_labels] else: new_count_labels = list(range(int(len(second_part_output_tok) / 2)))[::-1] * 2 new_count_labels = [_c + 8 for _c in new_count_labels] elif 'thrice' in second_part_input_tok: if 'after' in input_tok: new_count_labels = list(range(int(len(second_part_output_tok) / 3)))[::-1] * 3 new_count_labels = [_c + 8 for _c in new_count_labels] else: new_count_labels = list(range(int(len(second_part_output_tok) / 3)))[::-1] * 3 new_count_labels = [_c + 8 for _c in new_count_labels] else: if 'after' in input_tok: new_count_labels = list(range(len(second_part_output_tok)))[::-1] new_count_labels = [_c + 8 for _c in new_count_labels] else: new_count_labels = list(range(len(second_part_output_tok)))[::-1] new_count_labels = [_c + 8 for _c in new_count_labels] count_labels += new_count_labels # count_labels = [] # count_labels += list(range(len(first_part_output_tok)))[::-1] # count_labels += list(range(len(second_part_output_tok)))[::-1] assert len(count_labels) == len(output_tok), (len(count_labels), len(output_tok), input_text, first_part_input_tok, count_labels, output_tok, first_part_output_text, first_part_output_tok, second_part_output_text, second_part_output_tok) count_labels.append(-1) # For the EOS token # count_labels.append(7) # For the EOS token ### 2. Build the action group labels ### if group_label_scheme == 'v1': ## As used in exp 9.0-9.4 if 'around' in first_part_input_tok: if 'after' in input_tok: group_labels += ([4] * len(first_part_output_tok)) else: group_labels += ([0] * len(first_part_output_tok)) elif 'opposite' in first_part_input_tok: if 'after' in input_tok: group_labels += ([5] * len(first_part_output_tok)) else: group_labels += ([1] * len(first_part_output_tok)) elif 'left' in first_part_input_tok or 'right' in first_part_input_tok: if 'after' in input_tok: group_labels += ([6] * len(first_part_output_tok)) else: group_labels += ([2] * len(first_part_output_tok)) else: if 'after' in input_tok: group_labels += ([7] * len(first_part_output_tok)) else: group_labels += ([3] * len(first_part_output_tok)) if 'around' in second_part_input_tok: if 'after' in input_tok: group_labels += ([0] * len(second_part_output_tok)) else: group_labels += ([4] * len(second_part_output_tok)) elif 'opposite' in second_part_input_tok: if 'after' in input_tok: group_labels += ([1] * len(second_part_output_tok)) else: group_labels += ([5] * len(second_part_output_tok)) elif 'left' in second_part_input_tok or 'right' in second_part_input_tok: if 'after' in input_tok: group_labels += ([2] * len(second_part_output_tok)) else: group_labels += ([6] * len(second_part_output_tok)) else: if 'after' in input_tok: group_labels += ([3] * len(second_part_output_tok)) else: group_labels += ([7] * len(second_part_output_tok)) else: ### For the first part output if 'twice' in first_part_input_tok: if 'after' in input_tok: new_group_labels = list(range(int(len(first_part_output_tok) / 2)))[::-1] * 2 new_group_labels = [_c + 8 for _c in new_group_labels] else: new_group_labels = list(range(int(len(first_part_output_tok) / 2)))[::-1] * 2 elif 'thrice' in first_part_input_tok: if 'after' in input_tok: new_group_labels = list(range(int(len(first_part_output_tok) / 3)))[::-1] * 3 new_group_labels = [_c + 8 for _c in new_group_labels] else: new_group_labels = list(range(int(len(first_part_output_tok) / 3)))[::-1] * 3 else: if 'after' in input_tok: new_group_labels = list(range(len(first_part_output_tok)))[::-1] new_group_labels = [_c + 8 for _c in new_group_labels] else: new_group_labels = list(range(len(first_part_output_tok)))[::-1] group_labels += new_group_labels ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: if 'after' in input_tok: new_group_labels = list(range(int(len(second_part_output_tok) / 2)))[::-1] * 2 else: new_group_labels = list(range(int(len(second_part_output_tok) / 2)))[::-1] * 2 new_group_labels = [_c + 8 for _c in new_group_labels] elif 'thrice' in second_part_input_tok: if 'after' in input_tok: new_group_labels = list(range(int(len(second_part_output_tok) / 3)))[::-1] * 3 else: new_group_labels = list(range(int(len(second_part_output_tok) / 3)))[::-1] * 3 new_group_labels = [_c + 8 for _c in new_group_labels] else: if 'after' in input_tok: new_group_labels = list(range(len(second_part_output_tok)))[::-1] else: new_group_labels = list(range(len(second_part_output_tok)))[::-1] new_group_labels = [_c + 8 for _c in new_group_labels] group_labels += new_group_labels assert len(group_labels) == len(output_tok) group_labels.append(-1) # For the EOS token # group_labels.append(17) # For the EOS token ### 3. Build the action type labels ### ### For the first part output if type_label_scheme == 'v1': if 'around' in first_part_input_tok: new_type_labels = [3] * len(first_part_output_tok) elif 'opposite' in first_part_input_tok: new_type_labels = [2] * len(first_part_output_tok) elif 'left' in first_part_input_tok or 'right' in first_part_input_tok: new_type_labels = [1] * len(first_part_output_tok) else: new_type_labels = [0] * len(first_part_output_tok) # if 'after' in input_tok: # new_type_labels = [_c + 4 for _c in new_type_labels] type_labels += new_type_labels ### For the second part output if len(second_part_output_tok) > 0: if 'around' in second_part_input_tok: new_type_labels = [3] * len(second_part_output_tok) elif 'opposite' in second_part_input_tok: new_type_labels = [2] * len(second_part_output_tok) elif 'left' in second_part_input_tok or 'right' in second_part_input_tok: new_type_labels = [1] * len(second_part_output_tok) else: new_type_labels = [0] * len(second_part_output_tok) # if 'after' not in input_tok: # new_type_labels = [_c + 4 for _c in new_type_labels] type_labels += new_type_labels elif type_label_scheme == 'v2': if 'twice' in first_part_input_tok: type_labels += ([1] * int(len(first_part_output_tok) / 2) + [0] * int( len(first_part_output_tok) / 2)) elif 'thrice' in first_part_input_tok: type_labels += ([2] * int(len(first_part_output_tok) / 3) + [1] * int( len(first_part_output_tok) / 3) + \ [0] * int(len(first_part_output_tok) / 3)) else: type_labels += ([0] * len(first_part_output_tok)) ### For the second part output if len(second_part_output_tok) > 0: if 'twice' in second_part_input_tok: type_labels += ([1] * int(len(second_part_output_tok) / 2) + [0] * int( len(second_part_output_tok) / 2)) elif 'thrice' in second_part_input_tok: type_labels += ([2] * int(len(second_part_output_tok) / 3) + [1] * int( len(second_part_output_tok) / 3) + [0] * int(len(second_part_output_tok) / 3)) else: type_labels += ([0] * len(second_part_output_tok)) assert len(type_labels) == len(output_tok) type_labels.append(-1) # For the EOS token # group_labels.append(17) # For the EOS token # if _id not in no_skip_id: # count_labels = [-1] * len(count_labels) # group_labels = [-1] * len(group_labels) # skip_cnt += 1 # else: # sup_cnt += 1 all_action_type_labels.append(torch.tensor(type_labels).cuda()) all_count_labels.append(torch.tensor(count_labels).cuda()) all_action_group_labels.append(torch.tensor(group_labels).cuda()) print(skip_cnt, sup_cnt) return all_count_labels, all_action_group_labels, all_action_type_labels def convert_to_dict(self, raw_data): dict_data = {} for dp in raw_data: input, output = dp[0], dp[1] assert input not in dict_data dict_data[input] = output return dict_data def __getitem__(self, index): if self.tokenized: dp = self.dataset[index] source_ids, src_mask, target_ids = dp[0], dp[1], dp[2] source_ids = source_ids[:self.max_source_length] #src_mask = src_mask[:self.max_source_length] target_ids = target_ids[:self.max_target_length] else: source_ids = self.source[index] target_ids = self.target[index] count_labels = self.action_count_labels[index] group_labels = self.action_group_labels[index] type_labels = self.action_type_labels[index] return {"source_ids": source_ids, "target_ids": target_ids, "action_count_labels": count_labels, "action_group_labels": group_labels, "action_type_labels": type_labels} @staticmethod def trim_seq2seq_batch(batch, src_pad_token_id, trg_pad_token_id, trim_y=True): if trim_y: y = trim_batch(batch["target_ids"], trg_pad_token_id) else: y = batch["target_ids"] source_ids, source_mask = trim_batch(batch["source_ids"], src_pad_token_id, attention_mask=batch["source_mask"]) return source_ids, source_mask, y def collate_fn(self, batch): max_src_len = max(map(len, [x["source_ids"] for x in batch])) max_trg_len = max(map(len, [x["target_ids"] for x in batch])) src_mask = torch.stack([self.create_mask(x["source_ids"], max_src_len) for x in batch]) trg_mask = torch.stack([self.create_mask(x["target_ids"], max_trg_len) for x in batch]) src_ids = torch.stack([self.pad_to_max_len(x["source_ids"], max_src_len, self.src_lang.pad_token_id) for x in batch]) #masks = torch.stack([x["source_mask"] for x in batch]) trg_ids = torch.stack([self.pad_to_max_len(x["target_ids"], max_trg_len, self.trg_lang.pad_token_id) for x in batch]) action_count_labels = torch.stack([self.pad_to_max_len(x["action_count_labels"], max_trg_len, -1) for x in batch]) action_group_labels = torch.stack([self.pad_to_max_len(x["action_group_labels"], max_trg_len, -1) for x in batch]) action_type_labels = torch.stack( [self.pad_to_max_len(x["action_type_labels"], max_trg_len, -1) for x in batch]) y = trim_batch(trg_ids, self.trg_lang.pad_token_id) #action_count_labels = trim_batch(action_count_labels, -1) # _src_ids, src_mask = trim_batch(src_ids, self.src_lang.pad_token_id, attention_mask=src_mask) # print(_src_ids.size(), src_ids.size()) return {"source_ids": src_ids, "source_mask": src_mask, "target_ids": y, "target_mask": trg_mask, "action_count_labels": action_count_labels, "action_group_labels": action_group_labels, "action_type_labels": action_type_labels}
2.328125
2
bridger/serializers/fields/related.py
intellineers/django-bridger
2
9921
from typing import Dict from rest_framework import serializers from rest_framework.fields import empty from rest_framework.relations import ManyRelatedField from rest_framework.request import Request from .mixins import BridgerSerializerFieldMixin from .types import BridgerType, ReturnContentType class BridgerManyRelatedField(ManyRelatedField): def __init__(self, *args, **kwargs): required = kwargs.get("required", True) if not required: kwargs["allow_null"] = True super().__init__(*args, **kwargs) def run_validation(self, data=empty): # If the data is send through form data, we need to convert the data into a proper list of ids if data not in [None, empty] and len(data) == 1 and isinstance(data[0], str) and "," in data[0]: data = data[0].split(",") # If the data is a list of an empty string we need to convert it (FORM DATA) if data not in [None, empty] and len(data) == 1 and isinstance(data[0], str) and data[0] == "": data = [] # If the data is a list and contains the string null, then we need to convert it (FORM DATA) if data == ["null"]: data = [] # If the data is None and null is an allowed value, data needs to be set to an empty list if data is None and self.allow_null: data = [] return super().run_validation(data) def get_representation(self, request: Request, field_name: str) -> Dict: representation = self.child_relation.get_representation(request, field_name) representation["multiple"] = True return representation class PrimaryKeyRelatedField(BridgerSerializerFieldMixin, serializers.PrimaryKeyRelatedField): MANY_RELATION_KWARGS = ( "read_only", "write_only", "required", "default", "initial", "source", "label", "help_text", "style", "error_messages", "allow_empty", "html_cutoff", "html_cutoff_text", "allow_null", ) def __init__(self, *args, **kwargs): self.field_type = kwargs.pop("field_type", BridgerType.SELECT.value) super().__init__(*args, **kwargs) def __new__(cls, *args, **kwargs): kwargs["style"] = {"base_template": "input.html"} return super().__new__(cls, *args, **kwargs) @classmethod def many_init(cls, *args, **kwargs): list_kwargs = {"child_relation": cls(*args, **kwargs)} for key in kwargs: if key in cls.MANY_RELATION_KWARGS: list_kwargs[key] = kwargs[key] return BridgerManyRelatedField(**list_kwargs) def run_validation(self, data=empty): if isinstance(data, str) and data == "null": data = None if data is empty: parent_model_id = self.parent.context["view"].kwargs.get(f"{self.field_name}_id") if parent_model_id: data = parent_model_id return super().run_validation(data) class ListSerializer(serializers.ListSerializer): """ A Wrapper around the normal DRF ListSerializer which also return the child representation """ def get_representation(self, request: Request, field_name: str) -> Dict: representation = self.child.get_representation(request, field_name) representation["multiple"] = True representation["related_key"] = self.source return representation
2.3125
2
python/graphscope/experimental/nx/tests/algorithms/forward/operators/test_product.py
wenyuanyu/GraphScope
2
9922
<filename>python/graphscope/experimental/nx/tests/algorithms/forward/operators/test_product.py import networkx.algorithms.operators.tests.test_product import pytest from graphscope.experimental.nx.utils.compat import import_as_graphscope_nx import_as_graphscope_nx(networkx.algorithms.operators.tests.test_product, decorators=pytest.mark.usefixtures("graphscope_session")) def test_tensor_product_combinations(): # basic smoke test, more realistic tests would be useful P5 = nx.path_graph(5) K3 = nx.complete_graph(3) G = nx.tensor_product(P5, K3) assert nx.number_of_nodes(G) == 5 * 3 G = nx.tensor_product(nx.DiGraph(P5), nx.DiGraph(K3)) assert nx.number_of_nodes(G) == 5 * 3 @pytest.mark.skip(reason="not support multigraph") def test_cartesian_product_multigraph(): pass def test_lexicographic_product_combinations(): P5 = nx.path_graph(5) K3 = nx.complete_graph(3) G = nx.lexicographic_product(P5, K3) assert nx.number_of_nodes(G) == 5 * 3 def test_strong_product_combinations(): P5 = nx.path_graph(5) K3 = nx.complete_graph(3) G = nx.strong_product(P5, K3) assert nx.number_of_nodes(G) == 5 * 3 @pytest.mark.skip(reason="not support multigraph") def test_graph_power_raises(): pass
2.375
2
Footy/UnsupportedBantzStrings.py
schleising/banter-bot
0
9923
<reponame>schleising/banter-bot # {team} -> Name of team # {name} -> Name of person who supports team teamMatchStarted: list[str] = [ "{team} are shit", "{team} cunts", "Dirty {team}", "Dirty {team}, dirty {name}", ] drawing: list[str] = [ "{team} level, this is a shit match", "Boring old {team}", "Happy with how it's going, {name}?", "Yawn...", "{team} wankers", "How can you support this rubbish, {name}?", "You get the feeling that {team} don't really want this", "No passion from {team}, {name}", "If a game of football is like making love to a beautiful woman, this {team} game is a £10 hand job from a swivel-eyed misfit", "This {team} match is like a game of chess. But with more players and only one piece", ] teamLeadByOne: list[str] = [ "{team} cheats, the ref's a cunt", "That was never a goal for {team}", "{team} don't deserve that", "Bollocks", "That should go to VAR", "Bit fortunuate for {team}", "Can't imagine {team} will keep this lead", "Lucky goal for {team}", "{team} got lucky there", "{team} aren't good enough to stay ahead", "Offside!", ] teamExtendingLead: list[str] = [ "There's no way {team} deserve this lead", "Have {team} paid the ref?", "This is bullshit", "The ref's a cunt, {name}'s a cunt", "The ref's a cunt, {team} are cunts, {name}'s a cunt", "Something has gone seriously wrong with this country", "When I voted for Brexit, I didn't vote for this", "At least Boris remains in charge, we've still got that", "<NAME> would be turning in his grave", "Liberal elite bullshit", "That was so offside", "VAR!", "Is the linesman OK?", "If only {name}'s wife was as dirty as this game", ] teamLosingLead: list[str] = [ "Lazy old {team}, lazy old {name}", "{team} are throwing it away", "{team} are rubbish", "{team} fucking it up again", "We really are being treated to some world class flouncing from {team} today", "Brace yourself, {name}. This is going to hurt", "This is brown trouser time for {team}", "I hope {name} brought a spare pair of underpants", "I see {team} are playing their B Team. B for Bullshit", ] teamDeficitOfOne: list[str] = [ "This is more like it from {team}", "Oh dear...", "{team} wankers", "How are you feeling, {name}?", "Bit disappointing, {name}?", "Not looking good for {team}, {name}", "You must be furious, {name}", "{team} have just got no heart", "This is what happens when you try to buy success", "All that money spent, {name}, and for what?", ] teamExtendingDeficit: list[str] = [ "Starting to feel a bit sorry for {team}", "Never mind, {name}, there's always the next game", "Poor {team}", "Whoops...", "Oh dear, everything OK, {name}?", "Hey {name}, where'd you get your keeper?\nPOUNDSHOP !! POUNDSHOP !!", "{team} can't raise themselves for this game, typical", "A team like {team} have such a proud history, but what we see today is just embarrassing", "{team} clearly not up for it today", "{team} are letting you down, {name}", "Watching {team} is like watching a bunch of cavemen: Neanderthal", ] teamLosingDeficit: list[str] = [ "Too little too late for {team}", "{team} won't come back from here", "The ref's a cunt", "This is awful", "What a mess", "Well this is an unmitigated shit show", ] teamWon: list[str] = [ "That was a shit game", "There's no way {team} deserved that", "Fuck you, {name} !!", "This will go down in history...\nAs the most tedious game I have ever had the misfortune to witness", ] teamLost: list[str] = [ "Justice done, {team} lost", "Job done for {team}?", "Job done, {name}?", "{name} !!?", "Probably the best {team} could hope for", "Everything OK, {name}?", "{team} continue to disappoint", "Well if the football thing doesn't work out for {team}, they can always consider a career on the stage", "{team} set the bar low", "{team} fail to meet their already low expectations", ] teamDrew: list [str] = [ "Another uninspiring result for {team}", "Thanks for nothing, {team}", "Well that's 90 minutes we won't get back, thanks {team}", "Another draw for {team}", "Boring old {team}", "You should be happy with that result, {name}", "If I could pick one highlight from this {team} game it would be when it finally ended.", "I think {name} will be happy with {team}'s performance today.", ]
2.875
3
bench/fibrec.py
codr7/alisp
8
9924
from bench import bench print(bench(100, ''' def fib(n): return n if n < 2 else fib(n-1) + fib(n-2) ''', ''' fib(20) '''))
2.890625
3
nlpir/native/classifier.py
NLPIR-team/nlpir-python
18
9925
# coding=utf-8 from nlpir.native.nlpir_base import NLPIRBase from ctypes import c_bool, c_char_p, c_int, POINTER, Structure, c_float class StDoc(Structure): __fields__ = [ ("sTitle", c_char_p), ("sContent", c_char_p), ("sAuthor", c_char_p), ("sBoard", c_char_p), ("sDatatype", c_char_p) ] class Classifier(NLPIRBase): @property def dll_name(self): return "LJClassifier" @NLPIRBase.byte_str_transform def init_lib(self, data_path: str, encode: int, license_code: str) -> int: """ Call **classifier_init** :param data_path: :param encode: :param license_code: :return: 1 success 0 fail """ return self.get_func("classifier_init", [c_char_p, c_char_p, c_int, c_char_p], c_bool)( "rulelist.xml", data_path, encode, license_code) @NLPIRBase.byte_str_transform def exit_lib(self) -> bool: """ Call **classifier_exit** :return: exit success or not """ return self.get_func("classifier_exit", None, None)() @NLPIRBase.byte_str_transform def get_last_error_msg(self) -> str: return self.get_func("classifier_GetLastErrorMsg", None, c_char_p)() @NLPIRBase.byte_str_transform def exec_1(self, data: StDoc, out_type: int = 0): """ Call **classifier_exec1** 对输入的文章结构进行分类 :param data: 文章结构 :param out_type: 输出是否包括置信度, 0 没有置信度 1 有置信度 :return: 主题类别串 各类之间用\t隔开,类名按照置信度从高到低排序 举例:“要闻 敏感 诉讼”, “要闻 1.00 敏感 0.95 诉讼 0.82” """ return self.get_func("classifier_exec1", [POINTER(StDoc), c_int], c_char_p)(data, out_type) @NLPIRBase.byte_str_transform def exec(self, title: str, content: str, out_type: int): """ Call **classifier_exec** 对输入的文章进行分类 :param title: 文章标题 :param content: 文章内容 :param out_type: 输出知否包括置信度,同 :func:`exec_1` :return: 同 :func:`exec_1` """ return self.get_func("classifier_exec", [c_char_p, c_char_p, c_int], c_char_p)(title, content, out_type) @NLPIRBase.byte_str_transform def exec_file(self, filename: str, out_type: int) -> str: """ Call **classifier_execFile** :param filename: 文件名 :param out_type: 输出是否包括置信度, 0 没有置信度 1 有置信度 :return: 主题类别串 各类之间用\t隔开,类名按照置信度从高到低排序 举例:“要闻 敏感 诉讼”, “要闻 1.00 敏感 0.95 诉讼 0.82” """ return self.get_func("classifier_execFile", [c_char_p, c_int], c_char_p)(filename, out_type) @NLPIRBase.byte_str_transform def detail(self, class_name: str): """ Call **classifier_detail** 对于当前文档,输入类名,取得结果明细 :param class_name: 结果类名 :return: 结果明细 例如: :: RULE3: SUBRULE1: 内幕 1 SUBRULE2: 股市 1 基金 3 股票 8 SUBRULE3: 书摘 2 """ return self.get_func("classifier_detail", [c_char_p], c_char_p)(class_name) @NLPIRBase.byte_str_transform def set_sim_thresh(self, sim: float): """ Call **classifier_setsimthresh** 设置阈值 :param sim: 阈值 :return: """ return self.get_func("classifier_setsimthresh", [c_float])(sim)
2.3125
2
tests/param/get_param_type_spec_test.py
nickgaya/bravado-core
122
9926
# -*- coding: utf-8 -*- import pytest from mock import Mock from bravado_core.exception import SwaggerMappingError from bravado_core.operation import Operation from bravado_core.param import get_param_type_spec from bravado_core.param import Param from bravado_core.spec import Spec @pytest.fixture def body_param_spec(): return { 'name': 'body', 'in': 'body', 'description': 'pet id', 'required': True, 'schema': { 'type': 'string', }, } def test_location_is_body(empty_swagger_spec, body_param_spec): param = Param(empty_swagger_spec, Mock(spec=Operation), body_param_spec) assert body_param_spec['schema'] == get_param_type_spec(param) def test_location_is_not_body(empty_swagger_spec): for location in ('path', 'query', 'header', 'formData',): param_spec = { 'name': 'petId', 'in': location, 'description': 'ID of pet that needs to be updated', 'required': True, 'type': 'string', } param = Param(empty_swagger_spec, Mock(spec=Operation), param_spec) assert param_spec == get_param_type_spec(param) def test_location_invalid(empty_swagger_spec, body_param_spec): body_param_spec['in'] = 'foo' param = Param(empty_swagger_spec, Mock(spec=Operation), body_param_spec) with pytest.raises(SwaggerMappingError) as excinfo: get_param_type_spec(param) assert 'location foo' in str(excinfo.value) def test_ref(minimal_swagger_dict, body_param_spec): minimal_swagger_dict['parameters'] = { 'PetIdParam': body_param_spec, } param_ref_spec = {'$ref': '#/parameters/PetIdParam'} swagger_spec = Spec(minimal_swagger_dict) param = Param(swagger_spec, Mock(spec=Operation), param_ref_spec) assert {'type': 'string'} == get_param_type_spec(param)
2.078125
2
functions/markdown-to-html/markdown2html.py
truls/faas-profiler
0
9927
# Copyright (c) 2019 Princeton University # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from markdown import markdown import base64 import json import base64 def main(params): try: md = json.loads(base64.decodebytes(params["__ow_body"].encode("utf-8")))["markdown"].encode("utf-8") md_text = base64.decodebytes(md).decode("utf-8") except KeyError: return {'Error' : 'Possibly lacking markdown parameter in request.'} test_id = params["__ow_query"].split("&")[0] html = markdown(md_text) return {"result": "ok", "html_response": html, "testid": test_id}
2.84375
3
Python Files/Dataset_Formating/Audio_splicing.py
brennanMosher/Music-Genre-Recognition-using-a-Machine-Learning-Appraoch
1
9928
from pydub import AudioSegment import os import math from pathlib import Path ''' Splice wav files into multiple segments. ''' LENGTH = 3 # Set splice length in seconds def splice(audioPath, outputPath): # try: # os.mkdir('Spliced Spectrogram training') # Need to figure out where to put this # except OSError: # print("Creation of the directory failed") audio = AudioSegment.from_wav(audioPath) count = math.ceil(audio.duration_seconds/LENGTH) # Do we want the last part of audio? t1 = 0 t2 = LENGTH*1000 for i in range(count): newAudio = audio[t1:t2] newPath = outputPath+Path(audioPath).stem+'_splice'+str(i)+'.wav' newAudio.export(newPath, format="wav") t1 = t2 t2 = t2 + LENGTH*1000
3.453125
3
manpages.py
mba811/dash-manpages-zh
1
9929
<reponame>mba811/dash-manpages-zh #!/usr/bin/env python # -*- coding: utf-8 -*- """ @author: <NAME> @contact: @date: 2014/06/23 """ import os import sqlite3 import urllib2 import shutil import tarfile import hashlib import codecs from mako.template import Template from pyquery import PyQuery currentPath = os.path.join(os.path.dirname(os.path.realpath(__file__))) name = "manpages" baseName = "manpages-zh" output = baseName + ".docset" appName = "dash-" + baseName tarFileName = baseName + ".tgz" feedName = baseName + ".xml" version = "1.5.0" docsetPath = os.path.join(currentPath, output, "Contents", "Resources", "Documents") # Step 2: Copy the HTML Documentation fin = codecs.open(os.path.join(docsetPath, "index.html"), "r", "utf-8") content = fin.read() fin.close() jQuery = PyQuery(content) jQuery.find("body").empty() fileNames = [] itemTemplate = Template("<a href='html/${fileName}'>${name}</a><br />\n") for fileName in os.listdir(os.path.join(docsetPath, "html")): fileNames.append({ "name": fileName.split(".")[0], "fileName": fileName }) jQuery.find("body").append(itemTemplate.render(name = fileName.split(".")[0], fileName = fileName)) fin = codecs.open(os.path.join(docsetPath, "index.html"), "w", "utf-8") newContent = jQuery.html() fin.write(newContent) fin.close() # Step 3: create the Info.plist file infoTemplate = Template('''<?xml version="1.0" encoding="UTF-8"?> <plist version="1.0"> <dict> <key>CFBundleIdentifier</key> <string>${name}</string> <key>CFBundleName</key> <string>${name}</string> <key>DocSetPlatformFamily</key> <string>${name}</string> <key>dashIndexFilePath</key> <string>index.html</string> <key>dashIndexFilePath</key> <string>index.html</string> <key>isDashDocset</key><true/> <key>isJavaScriptEnabled</key><true/> </dict> </plist>''') infoPlistFile = os.path.join(currentPath, output, "Contents", "Info.plist") fin = open(infoPlistFile, "w") fin.write(infoTemplate.render(name = name)) fin.close() # Step 4: Create the SQLite Index dbFile = os.path.join(currentPath, output, "Contents", "Resources", "docSet.dsidx") if os.path.exists(dbFile): os.remove(dbFile) db = sqlite3.connect(dbFile) cursor = db.cursor() try: cursor.execute("DROP TABLE searchIndex;") except Exception: pass cursor.execute('CREATE TABLE searchIndex(id INTEGER PRIMARY KEY, name TEXT, type TEXT, path TEXT);') cursor.execute('CREATE UNIQUE INDEX anchor ON searchIndex (name, type, path);') insertTemplate = Template("INSERT OR IGNORE INTO searchIndex(name, type, path) VALUES ('${name}', '${type}', '${path}');") # Step 5: Populate the SQLite Index for result in fileNames: sql = insertTemplate.render(name = result["name"], type = "Builtin", path = "html/" + result["fileName"]) print sql cursor.execute(sql) db.commit() db.close() # Step 6: copy icon shutil.copyfile(os.path.join(currentPath, "icon.png"), os.path.join(currentPath, output, "icon.png")) shutil.copyfile(os.path.join(currentPath, "[email protected]"), os.path.join(currentPath, output, "[email protected]")) # Step 7: 打包 if not os.path.exists(os.path.join(currentPath, "dist")): os.makedirs(os.path.join(currentPath, "dist")) tarFile = tarfile.open(os.path.join(currentPath, "dist", tarFileName), "w:gz") for root, dirNames, fileNames in os.walk(output): for fileName in fileNames: fullPath = os.path.join(root, fileName) tarFile.add(fullPath) tarFile.close() # Step 8: 更新feed url feedTemplate = Template('''<entry> <version>${version}</version> <sha1>${sha1Value}</sha1> <url>https://raw.githubusercontent.com/magicsky/${appName}/master/dist/${tarFileName}</url> </entry>''') fout = open(os.path.join(currentPath, "dist", tarFileName), "rb") sha1Value = hashlib.sha1(fout.read()).hexdigest() fout.close() fin = open(os.path.join(currentPath, feedName), "w") fin.write(feedTemplate.render(sha1Value = sha1Value, appName = appName, tarFileName = tarFileName, version = version)) fin.close()
2.15625
2
docs/conf.py
alexweav/nisystemlink-clients-python
0
9930
<reponame>alexweav/nisystemlink-clients-python import os import sys sys.path.insert(0, os.path.abspath("..")) # -------------------------------------------------------------------------------------- project = "nisystemlink" copyright = "2020, National Instruments" author = "National Instruments" # The short X.Y version version = "0.1" # The full version, including alpha/beta/rc tags release = "0.1.3" # -------------------------------------------------------------------------------------- extensions = [ "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx_autodoc_typehints", "docs.cleanup", ] master_doc = "index" html_theme = "sphinx_rtd_theme" html_extra_path = [ "../LICENSE", ] nitpicky = True nitpick_ignore = [ ("py:class", "datetime.datetime"), ("py:class", "datetime.timedelta"), ("py:class", "pathlib.Path"), ("py:data", "typing.Any"), ("py:data", "typing.Awaitable"), ("py:data", "typing.Dict"), ("py:data", "typing.Iterable"), ("py:data", "typing.List"), ("py:data", "typing.Optional"), ("py:data", "typing.Sequence"), ("py:data", "typing.Tuple"), ("py:data", "typing.Union"), ] autodoc_default_options = { "inherited-members": True, "special-members": "__init__", "no-private-members": True, } # Don't let napoleon force methods to be included in the docs; use autodoc flags and our # own docs.cleanup module for that. napoleon_include_init_with_doc = False napoleon_include_private_with_doc = False napoleon_include_special_with_doc = False
1.359375
1
multinet/api/views/common.py
multinet-app/multinet-api
0
9931
from typing import Dict, List from arango.cursor import Cursor from django.http.response import Http404 from django.shortcuts import get_object_or_404 from rest_framework.pagination import LimitOffsetPagination from rest_framework.request import Request from rest_framework_extensions.mixins import NestedViewSetMixin from multinet.api.models import Workspace, WorkspaceRole from multinet.api.utils.arango import ArangoQuery class MultinetPagination(LimitOffsetPagination): default_limit = 100 class ArangoPagination(LimitOffsetPagination): """Override the LimitOffsetPagination class to allow for use with arango cursors.""" def _set_pre_query_params(self, request): self.limit = self.get_limit(request) if self.limit is None: return None self.offset = self.get_offset(request) self.request = request def _set_post_query_params(self): if self.count > self.limit and self.template is not None: self.display_page_controls = True def paginate_queryset(self, query: ArangoQuery, request: Request) -> List[Dict]: self._set_pre_query_params(request) paginated_query = query.paginate(self.limit, self.offset) cur: Cursor = paginated_query.execute(full_count=True) self.count = cur.statistics()['fullCount'] self._set_post_query_params() return list(cur) class WorkspaceChildMixin(NestedViewSetMixin): def get_queryset(self): """ Get the queryset for workspace child enpoints. Check that the requeting user has appropriate permissions for the associated workspace. """ child_objects = super().get_queryset() # prevent warning for schema generation incompatibility if getattr(self, 'swagger_fake_view', False): return child_objects.none() parent_query_dict = self.get_parents_query_dict() workspace = get_object_or_404( Workspace.objects.select_related('owner'), name=parent_query_dict['workspace__name'] ) # No user or user permission required for public workspaces if workspace.public: return child_objects # Private workspace request_user = self.request.user if not request_user.is_authenticated: # anonymous user raise Http404 workspace_role = WorkspaceRole.objects.filter( workspace=workspace, user=request_user ).first() # If the user is at least a reader or the owner, grant access if workspace_role is not None or workspace.owner == request_user: return child_objects # Read access denied raise Http404
1.976563
2
scripts/tests/generate_host_files.py
NDevTK/cel
0
9932
# Copyright 2018 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import argparse import logging import os import sys def ParseArgs(): parser = argparse.ArgumentParser( description='Host file generator for CELab E2E tests') all_tokens = ['project_id', 'storage_bucket', 'storage_prefix'] template_help = 'The full path to the *.host.textpb template file to use. ' template_help += 'Must contain the following tokens: %s' % all_tokens parser.add_argument( '--template', metavar='<host_file>', required=True, help=template_help) parser.add_argument( '--projects', metavar='<projectA;projectB;...>', dest="projects", required=True, help='The values to replace "<project_id>" with.') parser.add_argument( '--storage_bucket', metavar='<token>', dest="storage_bucket", required=True, help='The value to replace "<storage_bucket>" with.') parser.add_argument( '--storage_prefix', metavar='<token>', dest="storage_prefix", required=True, help='The value to replace "<storage_prefix>" with.') parser.add_argument( '--destination_dir', metavar='<path>', dest='destination', required=True, action='store', help='Where to collect extra logs on test failures') return parser.parse_args() def ConfigureLogging(args): logfmt = '%(asctime)s %(filename)s:%(lineno)s: [%(levelname)s] %(message)s' datefmt = '%Y/%m/%d %H:%M:%S' logging.basicConfig(level=logging.INFO, format=logfmt, datefmt=datefmt) if __name__ == '__main__': args = ParseArgs() ConfigureLogging(args) logging.info("Arguments: %s" % args) if not os.path.exists(args.template): raise ValueError('Template host file not found: %s' % args.template) if not os.path.exists(args.destination): raise ValueError('Destination directory not found: %s' % args.destination) # Generate all the host files based off the arguments passed. with open(args.template, 'r') as f: template = f.read() for project_id in args.projects.split(';'): filename = "%s.host.textpb" % project_id destination = os.path.join(args.destination, filename) with open(destination, 'w') as f: logging.info("Generating %s" % destination) content = template.replace("<project_id>", project_id) content = content.replace("<storage_bucket>", args.storage_bucket) content = content.replace("<storage_prefix>", args.storage_prefix) f.write(content) sys.exit(0)
2.4375
2
users/models.py
moshthepitt/probsc
0
9933
from django.conf import settings from django.db import models from django.utils.translation import ugettext_lazy as _ from django.utils.encoding import python_2_unicode_compatible from django.urls import reverse from django_extensions.db.models import TimeStampedModel from mptt.models import MPTTModel, TreeForeignKey from .managers import UserProfileManager, DepartmentManager, PositionManager User = settings.AUTH_USER_MODEL class Department(MPTTModel, TimeStampedModel): """ Departments in an organisation """ name = models.CharField(_("Name"), max_length=255) description = models.TextField(_("Description"), blank=True, default="") parent = TreeForeignKey('self', verbose_name=_("Parent"), null=True, blank=True, related_name='children', db_index=True, on_delete=models.PROTECT, help_text=_("The parent department")) customer = models.ForeignKey( 'customers.Customer', verbose_name=_("Customer"), on_delete=models.PROTECT) manager = models.ForeignKey( User, verbose_name=_("Manager"), on_delete=models.PROTECT, blank=True, null=True) active = models.BooleanField(_("Active"), default=True) objects = DepartmentManager() class Meta: verbose_name = _("Department") verbose_name_plural = _("Departments") ordering = ['name'] def get_absolute_url(self): return "#" def get_edit_url(self): return reverse('users:departments_edit', args=[self.pk]) def get_delete_url(self): return reverse('users:departments_delete', args=[self.pk]) def get_list_url(self): return reverse('users:departments_list') def __str__(self): return self.name class Position(MPTTModel, TimeStampedModel): """ Job positions in an organisation """ name = models.CharField(_("Name"), max_length=255) description = models.TextField(_("Description"), blank=True, default="") department = models.ForeignKey( Department, verbose_name=_("Department"), on_delete=models.PROTECT) parent = TreeForeignKey('self', verbose_name=_("Reports To"), null=True, blank=True, related_name='children', db_index=True, on_delete=models.PROTECT, help_text=_("The parent Job Position")) supervisor = models.ForeignKey( User, verbose_name=_("Supervisor"), on_delete=models.PROTECT, blank=True, null=True) customer = models.ForeignKey( 'customers.Customer', verbose_name=_("Customer"), on_delete=models.PROTECT) active = models.BooleanField(_("Active"), default=True) objects = PositionManager() class Meta: verbose_name = _("Job Positions") verbose_name_plural = _("Job Positions") ordering = ['name'] def get_absolute_url(self): return "#" def get_edit_url(self): return reverse('users:positions_edit', args=[self.pk]) def get_delete_url(self): return reverse('users:positions_delete', args=[self.pk]) def get_list_url(self): return reverse('users:positions_list') def __str__(self): return "{} - {}".format(self.department.name, self.name) @python_2_unicode_compatible class UserProfile(models.Model): """ Model used to store more information on users """ ADMIN = '1' MEMBER = '2' EDITOR = '3' MEMBER_ROLE_CHOICES = ( (ADMIN, _('Admin')), (EDITOR, _('Editor')), (MEMBER, _('Member')), ) created_on = models.DateTimeField(_("Created on"), auto_now_add=True) updated_on = models.DateTimeField(_("Updated on"), auto_now=True) user = models.OneToOneField(User, verbose_name=_("User")) position = models.ForeignKey(Position, verbose_name=_( "job Position"), on_delete=models.SET_NULL, blank=True, null=True, default=None) customer = models.ForeignKey('customers.Customer', verbose_name=_( "Customer"), on_delete=models.SET_NULL, blank=True, null=True, default=None) role = models.CharField( _("Role"), max_length=1, choices=MEMBER_ROLE_CHOICES, blank=False, default=MEMBER) active = models.BooleanField( _("Active"), default=True, help_text="Is the staff member actively " "employed?") objects = UserProfileManager() class Meta: verbose_name = _("Staff Member") verbose_name_plural = _("Staff Members") ordering = ['user__first_name', 'user__last_name', 'user__email'] def get_name(self): if self.user.get_full_name(): return self.user.get_full_name() if self.user.email: return self.user.email return self.user.username def get_initials(self): if self.user.first_name and self.user.last_name: return "{}{}".format(self.user.first_name[0], self.user.last_name[0]) if self.user.first_name: return self.user.first_name[0] if self.user.last_name: return self.user.last_name[0] return self.user.email[0] def is_admin(self): return self.role == self.ADMIN def is_editor(self): return self.role == self.EDITOR def can_edit(self): return self.role == self.EDITOR or self.role == self.ADMIN def get_subordinates(self): """ Returns a queryset of UserProfile objects which report to this userprofile """ if self.position: queryset = UserProfile.objects.active().exclude( id=self.id).filter( models.Q( position__supervisor=self.user) | models.Q( position__department__manager=self.user) | models.Q( position__parent=self.position)) else: queryset = UserProfile.objects.active().exclude( id=self.id).filter( models.Q( position__supervisor=self.user) | models.Q( position__department__manager=self.user)) # get job positions of subs subordinate_positions = Position.objects.filter( userprofile__in=queryset) # get any position that may report to these positions # list of position ids of Positions that report to # subordinate_positions reporting_jp_ids = [] for sub_p in subordinate_positions: reporting_jps = sub_p.get_descendants(include_self=False) if reporting_jps is not None: reporting_jp_ids = reporting_jp_ids + list( reporting_jps.values_list('id', flat=True)) reporting_jp_ids = list(set(reporting_jp_ids)) # get user profiles wiht positions that report to subordinate_positions reporting_profiles = UserProfile.objects.active().filter( position__id__in=reporting_jp_ids) queryset = queryset.union(reporting_profiles) # unions result in weird filtering so we create a new queryset queryset_ids = list(set([x.id for x in queryset])) if queryset_ids: queryset = UserProfile.objects.filter(id__in=queryset_ids) else: queryset = UserProfile.objects.none() return queryset def has_subordinates(self): return self.get_subordinates().exists() def get_department(self): if self.position is not None: return self.position.department.name return None def get_absolute_url(self): return "#" def get_edit_url(self): return reverse('users:userprofiles_edit', args=[self.pk]) def get_delete_url(self): return "#" def get_list_url(self): return reverse('users:userprofiles_list') def __str__(self): return _("{user}").format(user=self.get_name())
2.15625
2
azure-devops/azure/devops/released/build/build_client.py
imafidon2020/azure-devops-python-api
248
9934
<filename>azure-devops/azure/devops/released/build/build_client.py # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest import Serializer, Deserializer from ...client import Client from ...v5_1.build import models class BuildClient(Client): """Build :param str base_url: Service URL :param Authentication creds: Authenticated credentials. """ def __init__(self, base_url=None, creds=None): super(BuildClient, self).__init__(base_url, creds) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._deserialize = Deserializer(client_models) resource_area_identifier = '965220d5-5bb9-42cf-8d67-9b146df2a5a4' def create_artifact(self, artifact, project, build_id): """CreateArtifact. Associates an artifact with a build. :param :class:`<BuildArtifact> <azure.devops.v5_1.build.models.BuildArtifact>` artifact: The artifact. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: :class:`<BuildArtifact> <azure.devops.v5_1.build.models.BuildArtifact>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') content = self._serialize.body(artifact, 'BuildArtifact') response = self._send(http_method='POST', location_id='1db06c96-014e-44e1-ac91-90b2d4b3e984', version='5.1', route_values=route_values, content=content) return self._deserialize('BuildArtifact', response) def get_artifact(self, project, build_id, artifact_name): """GetArtifact. Gets a specific artifact for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param str artifact_name: The name of the artifact. :rtype: :class:`<BuildArtifact> <azure.devops.v5_1.build.models.BuildArtifact>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if artifact_name is not None: query_parameters['artifactName'] = self._serialize.query('artifact_name', artifact_name, 'str') response = self._send(http_method='GET', location_id='1db06c96-014e-44e1-ac91-90b2d4b3e984', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('BuildArtifact', response) def get_artifact_content_zip(self, project, build_id, artifact_name, **kwargs): """GetArtifactContentZip. Gets a specific artifact for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param str artifact_name: The name of the artifact. :rtype: object """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if artifact_name is not None: query_parameters['artifactName'] = self._serialize.query('artifact_name', artifact_name, 'str') response = self._send(http_method='GET', location_id='1db06c96-014e-44e1-ac91-90b2d4b3e984', version='5.1', route_values=route_values, query_parameters=query_parameters, accept_media_type='application/zip') if "callback" in kwargs: callback = kwargs["callback"] else: callback = None return self._client.stream_download(response, callback=callback) def get_artifacts(self, project, build_id): """GetArtifacts. Gets all artifacts for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: [BuildArtifact] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') response = self._send(http_method='GET', location_id='1db06c96-014e-44e1-ac91-90b2d4b3e984', version='5.1', route_values=route_values) return self._deserialize('[BuildArtifact]', self._unwrap_collection(response)) def get_file(self, project, build_id, artifact_name, file_id, file_name, **kwargs): """GetFile. Gets a file from the build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param str artifact_name: The name of the artifact. :param str file_id: The primary key for the file. :param str file_name: The name that the file will be set to. :rtype: object """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if artifact_name is not None: query_parameters['artifactName'] = self._serialize.query('artifact_name', artifact_name, 'str') if file_id is not None: query_parameters['fileId'] = self._serialize.query('file_id', file_id, 'str') if file_name is not None: query_parameters['fileName'] = self._serialize.query('file_name', file_name, 'str') response = self._send(http_method='GET', location_id='1db06c96-014e-44e1-ac91-90b2d4b3e984', version='5.1', route_values=route_values, query_parameters=query_parameters, accept_media_type='application/octet-stream') if "callback" in kwargs: callback = kwargs["callback"] else: callback = None return self._client.stream_download(response, callback=callback) def delete_build(self, project, build_id): """DeleteBuild. Deletes a build. :param str project: Project ID or project name :param int build_id: The ID of the build. """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') self._send(http_method='DELETE', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values) def get_build(self, project, build_id, property_filters=None): """GetBuild. Gets a build :param str project: Project ID or project name :param int build_id: :param str property_filters: :rtype: :class:`<Build> <azure.devops.v5_1.build.models.Build>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if property_filters is not None: query_parameters['propertyFilters'] = self._serialize.query('property_filters', property_filters, 'str') response = self._send(http_method='GET', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('Build', response) def get_builds(self, project, definitions=None, queues=None, build_number=None, min_time=None, max_time=None, requested_for=None, reason_filter=None, status_filter=None, result_filter=None, tag_filters=None, properties=None, top=None, continuation_token=None, max_builds_per_definition=None, deleted_filter=None, query_order=None, branch_name=None, build_ids=None, repository_id=None, repository_type=None): """GetBuilds. Gets a list of builds. :param str project: Project ID or project name :param [int] definitions: A comma-delimited list of definition IDs. If specified, filters to builds for these definitions. :param [int] queues: A comma-delimited list of queue IDs. If specified, filters to builds that ran against these queues. :param str build_number: If specified, filters to builds that match this build number. Append * to do a prefix search. :param datetime min_time: If specified, filters to builds that finished/started/queued after this date based on the queryOrder specified. :param datetime max_time: If specified, filters to builds that finished/started/queued before this date based on the queryOrder specified. :param str requested_for: If specified, filters to builds requested for the specified user. :param str reason_filter: If specified, filters to builds that match this reason. :param str status_filter: If specified, filters to builds that match this status. :param str result_filter: If specified, filters to builds that match this result. :param [str] tag_filters: A comma-delimited list of tags. If specified, filters to builds that have the specified tags. :param [str] properties: A comma-delimited list of properties to retrieve. :param int top: The maximum number of builds to return. :param str continuation_token: A continuation token, returned by a previous call to this method, that can be used to return the next set of builds. :param int max_builds_per_definition: The maximum number of builds to return per definition. :param str deleted_filter: Indicates whether to exclude, include, or only return deleted builds. :param str query_order: The order in which builds should be returned. :param str branch_name: If specified, filters to builds that built branches that built this branch. :param [int] build_ids: A comma-delimited list that specifies the IDs of builds to retrieve. :param str repository_id: If specified, filters to builds that built from this repository. :param str repository_type: If specified, filters to builds that built from repositories of this type. :rtype: :class:`<GetBuildsResponseValue>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') query_parameters = {} if definitions is not None: definitions = ",".join(map(str, definitions)) query_parameters['definitions'] = self._serialize.query('definitions', definitions, 'str') if queues is not None: queues = ",".join(map(str, queues)) query_parameters['queues'] = self._serialize.query('queues', queues, 'str') if build_number is not None: query_parameters['buildNumber'] = self._serialize.query('build_number', build_number, 'str') if min_time is not None: query_parameters['minTime'] = self._serialize.query('min_time', min_time, 'iso-8601') if max_time is not None: query_parameters['maxTime'] = self._serialize.query('max_time', max_time, 'iso-8601') if requested_for is not None: query_parameters['requestedFor'] = self._serialize.query('requested_for', requested_for, 'str') if reason_filter is not None: query_parameters['reasonFilter'] = self._serialize.query('reason_filter', reason_filter, 'str') if status_filter is not None: query_parameters['statusFilter'] = self._serialize.query('status_filter', status_filter, 'str') if result_filter is not None: query_parameters['resultFilter'] = self._serialize.query('result_filter', result_filter, 'str') if tag_filters is not None: tag_filters = ",".join(tag_filters) query_parameters['tagFilters'] = self._serialize.query('tag_filters', tag_filters, 'str') if properties is not None: properties = ",".join(properties) query_parameters['properties'] = self._serialize.query('properties', properties, 'str') if top is not None: query_parameters['$top'] = self._serialize.query('top', top, 'int') if continuation_token is not None: query_parameters['continuationToken'] = self._serialize.query('continuation_token', continuation_token, 'str') if max_builds_per_definition is not None: query_parameters['maxBuildsPerDefinition'] = self._serialize.query('max_builds_per_definition', max_builds_per_definition, 'int') if deleted_filter is not None: query_parameters['deletedFilter'] = self._serialize.query('deleted_filter', deleted_filter, 'str') if query_order is not None: query_parameters['queryOrder'] = self._serialize.query('query_order', query_order, 'str') if branch_name is not None: query_parameters['branchName'] = self._serialize.query('branch_name', branch_name, 'str') if build_ids is not None: build_ids = ",".join(map(str, build_ids)) query_parameters['buildIds'] = self._serialize.query('build_ids', build_ids, 'str') if repository_id is not None: query_parameters['repositoryId'] = self._serialize.query('repository_id', repository_id, 'str') if repository_type is not None: query_parameters['repositoryType'] = self._serialize.query('repository_type', repository_type, 'str') response = self._send(http_method='GET', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values, query_parameters=query_parameters) response_value = self._deserialize('[Build]', self._unwrap_collection(response)) continuation_token = self._get_continuation_token(response) return self.GetBuildsResponseValue(response_value, continuation_token) class GetBuildsResponseValue(object): def __init__(self, value, continuation_token): """ Response for the get_builds method :param value: :type value: :class:`<[Build]> <azure.devops.v5_1.build.models.[Build]>` :param continuation_token: The continuation token to be used to get the next page of results. :type continuation_token: str """ self.value = value self.continuation_token = continuation_token def queue_build(self, build, project, ignore_warnings=None, check_in_ticket=None, source_build_id=None): """QueueBuild. Queues a build :param :class:`<Build> <azure.devops.v5_1.build.models.Build>` build: :param str project: Project ID or project name :param bool ignore_warnings: :param str check_in_ticket: :param int source_build_id: :rtype: :class:`<Build> <azure.devops.v5_1.build.models.Build>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') query_parameters = {} if ignore_warnings is not None: query_parameters['ignoreWarnings'] = self._serialize.query('ignore_warnings', ignore_warnings, 'bool') if check_in_ticket is not None: query_parameters['checkInTicket'] = self._serialize.query('check_in_ticket', check_in_ticket, 'str') if source_build_id is not None: query_parameters['sourceBuildId'] = self._serialize.query('source_build_id', source_build_id, 'int') content = self._serialize.body(build, 'Build') response = self._send(http_method='POST', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values, query_parameters=query_parameters, content=content) return self._deserialize('Build', response) def update_build(self, build, project, build_id, retry=None): """UpdateBuild. Updates a build. :param :class:`<Build> <azure.devops.v5_1.build.models.Build>` build: The build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param bool retry: :rtype: :class:`<Build> <azure.devops.v5_1.build.models.Build>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if retry is not None: query_parameters['retry'] = self._serialize.query('retry', retry, 'bool') content = self._serialize.body(build, 'Build') response = self._send(http_method='PATCH', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values, query_parameters=query_parameters, content=content) return self._deserialize('Build', response) def update_builds(self, builds, project): """UpdateBuilds. Updates multiple builds. :param [Build] builds: The builds to update. :param str project: Project ID or project name :rtype: [Build] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') content = self._serialize.body(builds, '[Build]') response = self._send(http_method='PATCH', location_id='0cd358e1-9217-4d94-8269-1c1ee6f93dcf', version='5.1', route_values=route_values, content=content) return self._deserialize('[Build]', self._unwrap_collection(response)) def get_build_changes(self, project, build_id, continuation_token=None, top=None, include_source_change=None): """GetBuildChanges. Gets the changes associated with a build :param str project: Project ID or project name :param int build_id: :param str continuation_token: :param int top: The maximum number of changes to return :param bool include_source_change: :rtype: :class:`<GetBuildChangesResponseValue>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if continuation_token is not None: query_parameters['continuationToken'] = self._serialize.query('continuation_token', continuation_token, 'str') if top is not None: query_parameters['$top'] = self._serialize.query('top', top, 'int') if include_source_change is not None: query_parameters['includeSourceChange'] = self._serialize.query('include_source_change', include_source_change, 'bool') response = self._send(http_method='GET', location_id='54572c7b-bbd3-45d4-80dc-28be08941620', version='5.1', route_values=route_values, query_parameters=query_parameters) response_value = self._deserialize('[Change]', self._unwrap_collection(response)) continuation_token = self._get_continuation_token(response) return self.GetBuildChangesResponseValue(response_value, continuation_token) class GetBuildChangesResponseValue(object): def __init__(self, value, continuation_token): """ Response for the get_build_changes method :param value: :type value: :class:`<[Change]> <azure.devops.v5_1.build.models.[Change]>` :param continuation_token: The continuation token to be used to get the next page of results. :type continuation_token: str """ self.value = value self.continuation_token = continuation_token def get_build_controller(self, controller_id): """GetBuildController. Gets a controller :param int controller_id: :rtype: :class:`<BuildController> <azure.devops.v5_1.build.models.BuildController>` """ route_values = {} if controller_id is not None: route_values['controllerId'] = self._serialize.url('controller_id', controller_id, 'int') response = self._send(http_method='GET', location_id='fcac1932-2ee1-437f-9b6f-7f696be858f6', version='5.1', route_values=route_values) return self._deserialize('BuildController', response) def get_build_controllers(self, name=None): """GetBuildControllers. Gets controller, optionally filtered by name :param str name: :rtype: [BuildController] """ query_parameters = {} if name is not None: query_parameters['name'] = self._serialize.query('name', name, 'str') response = self._send(http_method='GET', location_id='fcac1932-2ee1-437f-9b6f-7f696be858f6', version='5.1', query_parameters=query_parameters) return self._deserialize('[BuildController]', self._unwrap_collection(response)) def create_definition(self, definition, project, definition_to_clone_id=None, definition_to_clone_revision=None): """CreateDefinition. Creates a new definition. :param :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` definition: The definition. :param str project: Project ID or project name :param int definition_to_clone_id: :param int definition_to_clone_revision: :rtype: :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') query_parameters = {} if definition_to_clone_id is not None: query_parameters['definitionToCloneId'] = self._serialize.query('definition_to_clone_id', definition_to_clone_id, 'int') if definition_to_clone_revision is not None: query_parameters['definitionToCloneRevision'] = self._serialize.query('definition_to_clone_revision', definition_to_clone_revision, 'int') content = self._serialize.body(definition, 'BuildDefinition') response = self._send(http_method='POST', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values, query_parameters=query_parameters, content=content) return self._deserialize('BuildDefinition', response) def delete_definition(self, project, definition_id): """DeleteDefinition. Deletes a definition and all associated builds. :param str project: Project ID or project name :param int definition_id: The ID of the definition. """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if definition_id is not None: route_values['definitionId'] = self._serialize.url('definition_id', definition_id, 'int') self._send(http_method='DELETE', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values) def get_definition(self, project, definition_id, revision=None, min_metrics_time=None, property_filters=None, include_latest_builds=None): """GetDefinition. Gets a definition, optionally at a specific revision. :param str project: Project ID or project name :param int definition_id: The ID of the definition. :param int revision: The revision number to retrieve. If this is not specified, the latest version will be returned. :param datetime min_metrics_time: If specified, indicates the date from which metrics should be included. :param [str] property_filters: A comma-delimited list of properties to include in the results. :param bool include_latest_builds: :rtype: :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if definition_id is not None: route_values['definitionId'] = self._serialize.url('definition_id', definition_id, 'int') query_parameters = {} if revision is not None: query_parameters['revision'] = self._serialize.query('revision', revision, 'int') if min_metrics_time is not None: query_parameters['minMetricsTime'] = self._serialize.query('min_metrics_time', min_metrics_time, 'iso-8601') if property_filters is not None: property_filters = ",".join(property_filters) query_parameters['propertyFilters'] = self._serialize.query('property_filters', property_filters, 'str') if include_latest_builds is not None: query_parameters['includeLatestBuilds'] = self._serialize.query('include_latest_builds', include_latest_builds, 'bool') response = self._send(http_method='GET', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('BuildDefinition', response) def get_definitions(self, project, name=None, repository_id=None, repository_type=None, query_order=None, top=None, continuation_token=None, min_metrics_time=None, definition_ids=None, path=None, built_after=None, not_built_after=None, include_all_properties=None, include_latest_builds=None, task_id_filter=None, process_type=None, yaml_filename=None): """GetDefinitions. Gets a list of definitions. :param str project: Project ID or project name :param str name: If specified, filters to definitions whose names match this pattern. :param str repository_id: A repository ID. If specified, filters to definitions that use this repository. :param str repository_type: If specified, filters to definitions that have a repository of this type. :param str query_order: Indicates the order in which definitions should be returned. :param int top: The maximum number of definitions to return. :param str continuation_token: A continuation token, returned by a previous call to this method, that can be used to return the next set of definitions. :param datetime min_metrics_time: If specified, indicates the date from which metrics should be included. :param [int] definition_ids: A comma-delimited list that specifies the IDs of definitions to retrieve. :param str path: If specified, filters to definitions under this folder. :param datetime built_after: If specified, filters to definitions that have builds after this date. :param datetime not_built_after: If specified, filters to definitions that do not have builds after this date. :param bool include_all_properties: Indicates whether the full definitions should be returned. By default, shallow representations of the definitions are returned. :param bool include_latest_builds: Indicates whether to return the latest and latest completed builds for this definition. :param str task_id_filter: If specified, filters to definitions that use the specified task. :param int process_type: If specified, filters to definitions with the given process type. :param str yaml_filename: If specified, filters to YAML definitions that match the given filename. :rtype: :class:`<GetDefinitionsResponseValue>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') query_parameters = {} if name is not None: query_parameters['name'] = self._serialize.query('name', name, 'str') if repository_id is not None: query_parameters['repositoryId'] = self._serialize.query('repository_id', repository_id, 'str') if repository_type is not None: query_parameters['repositoryType'] = self._serialize.query('repository_type', repository_type, 'str') if query_order is not None: query_parameters['queryOrder'] = self._serialize.query('query_order', query_order, 'str') if top is not None: query_parameters['$top'] = self._serialize.query('top', top, 'int') if continuation_token is not None: query_parameters['continuationToken'] = self._serialize.query('continuation_token', continuation_token, 'str') if min_metrics_time is not None: query_parameters['minMetricsTime'] = self._serialize.query('min_metrics_time', min_metrics_time, 'iso-8601') if definition_ids is not None: definition_ids = ",".join(map(str, definition_ids)) query_parameters['definitionIds'] = self._serialize.query('definition_ids', definition_ids, 'str') if path is not None: query_parameters['path'] = self._serialize.query('path', path, 'str') if built_after is not None: query_parameters['builtAfter'] = self._serialize.query('built_after', built_after, 'iso-8601') if not_built_after is not None: query_parameters['notBuiltAfter'] = self._serialize.query('not_built_after', not_built_after, 'iso-8601') if include_all_properties is not None: query_parameters['includeAllProperties'] = self._serialize.query('include_all_properties', include_all_properties, 'bool') if include_latest_builds is not None: query_parameters['includeLatestBuilds'] = self._serialize.query('include_latest_builds', include_latest_builds, 'bool') if task_id_filter is not None: query_parameters['taskIdFilter'] = self._serialize.query('task_id_filter', task_id_filter, 'str') if process_type is not None: query_parameters['processType'] = self._serialize.query('process_type', process_type, 'int') if yaml_filename is not None: query_parameters['yamlFilename'] = self._serialize.query('yaml_filename', yaml_filename, 'str') response = self._send(http_method='GET', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values, query_parameters=query_parameters) response_value = self._deserialize('[BuildDefinitionReference]', self._unwrap_collection(response)) continuation_token = self._get_continuation_token(response) return self.GetDefinitionsResponseValue(response_value, continuation_token) class GetDefinitionsResponseValue(object): def __init__(self, value, continuation_token): """ Response for the get_definitions method :param value: :type value: :class:`<[BuildDefinitionReference]> <azure.devops.v5_1.build.models.[BuildDefinitionReference]>` :param continuation_token: The continuation token to be used to get the next page of results. :type continuation_token: str """ self.value = value self.continuation_token = continuation_token def restore_definition(self, project, definition_id, deleted): """RestoreDefinition. Restores a deleted definition :param str project: Project ID or project name :param int definition_id: The identifier of the definition to restore. :param bool deleted: When false, restores a deleted definition. :rtype: :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if definition_id is not None: route_values['definitionId'] = self._serialize.url('definition_id', definition_id, 'int') query_parameters = {} if deleted is not None: query_parameters['deleted'] = self._serialize.query('deleted', deleted, 'bool') response = self._send(http_method='PATCH', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('BuildDefinition', response) def update_definition(self, definition, project, definition_id, secrets_source_definition_id=None, secrets_source_definition_revision=None): """UpdateDefinition. Updates an existing definition. :param :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` definition: The new version of the definition. :param str project: Project ID or project name :param int definition_id: The ID of the definition. :param int secrets_source_definition_id: :param int secrets_source_definition_revision: :rtype: :class:`<BuildDefinition> <azure.devops.v5_1.build.models.BuildDefinition>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if definition_id is not None: route_values['definitionId'] = self._serialize.url('definition_id', definition_id, 'int') query_parameters = {} if secrets_source_definition_id is not None: query_parameters['secretsSourceDefinitionId'] = self._serialize.query('secrets_source_definition_id', secrets_source_definition_id, 'int') if secrets_source_definition_revision is not None: query_parameters['secretsSourceDefinitionRevision'] = self._serialize.query('secrets_source_definition_revision', secrets_source_definition_revision, 'int') content = self._serialize.body(definition, 'BuildDefinition') response = self._send(http_method='PUT', location_id='dbeaf647-6167-421a-bda9-c9327b25e2e6', version='5.1', route_values=route_values, query_parameters=query_parameters, content=content) return self._deserialize('BuildDefinition', response) def get_build_log(self, project, build_id, log_id, start_line=None, end_line=None, **kwargs): """GetBuildLog. Gets an individual log file for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param int log_id: The ID of the log file. :param long start_line: The start line. :param long end_line: The end line. :rtype: object """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if log_id is not None: route_values['logId'] = self._serialize.url('log_id', log_id, 'int') query_parameters = {} if start_line is not None: query_parameters['startLine'] = self._serialize.query('start_line', start_line, 'long') if end_line is not None: query_parameters['endLine'] = self._serialize.query('end_line', end_line, 'long') response = self._send(http_method='GET', location_id='35a80daf-7f30-45fc-86e8-6b813d9c90df', version='5.1', route_values=route_values, query_parameters=query_parameters, accept_media_type='text/plain') if "callback" in kwargs: callback = kwargs["callback"] else: callback = None return self._client.stream_download(response, callback=callback) def get_build_log_lines(self, project, build_id, log_id, start_line=None, end_line=None): """GetBuildLogLines. Gets an individual log file for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param int log_id: The ID of the log file. :param long start_line: The start line. :param long end_line: The end line. :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if log_id is not None: route_values['logId'] = self._serialize.url('log_id', log_id, 'int') query_parameters = {} if start_line is not None: query_parameters['startLine'] = self._serialize.query('start_line', start_line, 'long') if end_line is not None: query_parameters['endLine'] = self._serialize.query('end_line', end_line, 'long') response = self._send(http_method='GET', location_id='35a80daf-7f30-45fc-86e8-6b813d9c90df', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('[str]', self._unwrap_collection(response)) def get_build_logs(self, project, build_id): """GetBuildLogs. Gets the logs for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: [BuildLog] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') response = self._send(http_method='GET', location_id='35a80daf-7f30-45fc-86e8-6b813d9c90df', version='5.1', route_values=route_values) return self._deserialize('[BuildLog]', self._unwrap_collection(response)) def get_build_logs_zip(self, project, build_id, **kwargs): """GetBuildLogsZip. Gets the logs for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: object """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') response = self._send(http_method='GET', location_id='35a80daf-7f30-45fc-86e8-6b813d9c90df', version='5.1', route_values=route_values, accept_media_type='application/zip') if "callback" in kwargs: callback = kwargs["callback"] else: callback = None return self._client.stream_download(response, callback=callback) def get_build_log_zip(self, project, build_id, log_id, start_line=None, end_line=None, **kwargs): """GetBuildLogZip. Gets an individual log file for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param int log_id: The ID of the log file. :param long start_line: The start line. :param long end_line: The end line. :rtype: object """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if log_id is not None: route_values['logId'] = self._serialize.url('log_id', log_id, 'int') query_parameters = {} if start_line is not None: query_parameters['startLine'] = self._serialize.query('start_line', start_line, 'long') if end_line is not None: query_parameters['endLine'] = self._serialize.query('end_line', end_line, 'long') response = self._send(http_method='GET', location_id='35a80daf-7f30-45fc-86e8-6b813d9c90df', version='5.1', route_values=route_values, query_parameters=query_parameters, accept_media_type='application/zip') if "callback" in kwargs: callback = kwargs["callback"] else: callback = None return self._client.stream_download(response, callback=callback) def get_build_option_definitions(self, project=None): """GetBuildOptionDefinitions. Gets all build definition options supported by the system. :param str project: Project ID or project name :rtype: [BuildOptionDefinition] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') response = self._send(http_method='GET', location_id='591cb5a4-2d46-4f3a-a697-5cd42b6bd332', version='5.1', route_values=route_values) return self._deserialize('[BuildOptionDefinition]', self._unwrap_collection(response)) def get_definition_revisions(self, project, definition_id): """GetDefinitionRevisions. Gets all revisions of a definition. :param str project: Project ID or project name :param int definition_id: The ID of the definition. :rtype: [BuildDefinitionRevision] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if definition_id is not None: route_values['definitionId'] = self._serialize.url('definition_id', definition_id, 'int') response = self._send(http_method='GET', location_id='7c116775-52e5-453e-8c5d-914d9762d8c4', version='5.1', route_values=route_values) return self._deserialize('[BuildDefinitionRevision]', self._unwrap_collection(response)) def get_build_settings(self, project=None): """GetBuildSettings. Gets the build settings. :param str project: Project ID or project name :rtype: :class:`<BuildSettings> <azure.devops.v5_1.build.models.BuildSettings>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') response = self._send(http_method='GET', location_id='aa8c1c9c-ef8b-474a-b8c4-785c7b191d0d', version='5.1', route_values=route_values) return self._deserialize('BuildSettings', response) def update_build_settings(self, settings, project=None): """UpdateBuildSettings. Updates the build settings. :param :class:`<BuildSettings> <azure.devops.v5_1.build.models.BuildSettings>` settings: The new settings. :param str project: Project ID or project name :rtype: :class:`<BuildSettings> <azure.devops.v5_1.build.models.BuildSettings>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') content = self._serialize.body(settings, 'BuildSettings') response = self._send(http_method='PATCH', location_id='aa8c1c9c-ef8b-474a-b8c4-785c7b191d0d', version='5.1', route_values=route_values, content=content) return self._deserialize('BuildSettings', response) def add_build_tag(self, project, build_id, tag): """AddBuildTag. Adds a tag to a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param str tag: The tag to add. :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if tag is not None: route_values['tag'] = self._serialize.url('tag', tag, 'str') response = self._send(http_method='PUT', location_id='6e6114b2-8161-44c8-8f6c-c5505782427f', version='5.1', route_values=route_values) return self._deserialize('[str]', self._unwrap_collection(response)) def add_build_tags(self, tags, project, build_id): """AddBuildTags. Adds tags to a build. :param [str] tags: The tags to add. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') content = self._serialize.body(tags, '[str]') response = self._send(http_method='POST', location_id='6e6114b2-8161-44c8-8f6c-c5505782427f', version='5.1', route_values=route_values, content=content) return self._deserialize('[str]', self._unwrap_collection(response)) def delete_build_tag(self, project, build_id, tag): """DeleteBuildTag. Removes a tag from a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param str tag: The tag to remove. :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if tag is not None: route_values['tag'] = self._serialize.url('tag', tag, 'str') response = self._send(http_method='DELETE', location_id='6e6114b2-8161-44c8-8f6c-c5505782427f', version='5.1', route_values=route_values) return self._deserialize('[str]', self._unwrap_collection(response)) def get_build_tags(self, project, build_id): """GetBuildTags. Gets the tags for a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') response = self._send(http_method='GET', location_id='6e6114b2-8161-44c8-8f6c-c5505782427f', version='5.1', route_values=route_values) return self._deserialize('[str]', self._unwrap_collection(response)) def get_tags(self, project): """GetTags. Gets a list of all build and definition tags in the project. :param str project: Project ID or project name :rtype: [str] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') response = self._send(http_method='GET', location_id='d84ac5c6-edc7-43d5-adc9-1b34be5dea09', version='5.1', route_values=route_values) return self._deserialize('[str]', self._unwrap_collection(response)) def delete_template(self, project, template_id): """DeleteTemplate. Deletes a build definition template. :param str project: Project ID or project name :param str template_id: The ID of the template. """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if template_id is not None: route_values['templateId'] = self._serialize.url('template_id', template_id, 'str') self._send(http_method='DELETE', location_id='e884571e-7f92-4d6a-9274-3f5649900835', version='5.1', route_values=route_values) def get_template(self, project, template_id): """GetTemplate. Gets a specific build definition template. :param str project: Project ID or project name :param str template_id: The ID of the requested template. :rtype: :class:`<BuildDefinitionTemplate> <azure.devops.v5_1.build.models.BuildDefinitionTemplate>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if template_id is not None: route_values['templateId'] = self._serialize.url('template_id', template_id, 'str') response = self._send(http_method='GET', location_id='e884571e-7f92-4d6a-9274-3f5649900835', version='5.1', route_values=route_values) return self._deserialize('BuildDefinitionTemplate', response) def get_templates(self, project): """GetTemplates. Gets all definition templates. :param str project: Project ID or project name :rtype: [BuildDefinitionTemplate] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') response = self._send(http_method='GET', location_id='e884571e-7f92-4d6a-9274-3f5649900835', version='5.1', route_values=route_values) return self._deserialize('[BuildDefinitionTemplate]', self._unwrap_collection(response)) def save_template(self, template, project, template_id): """SaveTemplate. Updates an existing build definition template. :param :class:`<BuildDefinitionTemplate> <azure.devops.v5_1.build.models.BuildDefinitionTemplate>` template: The new version of the template. :param str project: Project ID or project name :param str template_id: The ID of the template. :rtype: :class:`<BuildDefinitionTemplate> <azure.devops.v5_1.build.models.BuildDefinitionTemplate>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if template_id is not None: route_values['templateId'] = self._serialize.url('template_id', template_id, 'str') content = self._serialize.body(template, 'BuildDefinitionTemplate') response = self._send(http_method='PUT', location_id='e884571e-7f92-4d6a-9274-3f5649900835', version='5.1', route_values=route_values, content=content) return self._deserialize('BuildDefinitionTemplate', response) def get_build_timeline(self, project, build_id, timeline_id=None, change_id=None, plan_id=None): """GetBuildTimeline. Gets details for a build :param str project: Project ID or project name :param int build_id: :param str timeline_id: :param int change_id: :param str plan_id: :rtype: :class:`<Timeline> <azure.devops.v5_1.build.models.Timeline>` """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') if timeline_id is not None: route_values['timelineId'] = self._serialize.url('timeline_id', timeline_id, 'str') query_parameters = {} if change_id is not None: query_parameters['changeId'] = self._serialize.query('change_id', change_id, 'int') if plan_id is not None: query_parameters['planId'] = self._serialize.query('plan_id', plan_id, 'str') response = self._send(http_method='GET', location_id='8baac422-4c6e-4de5-8532-db96d92acffa', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('Timeline', response) def get_build_work_items_refs(self, project, build_id, top=None): """GetBuildWorkItemsRefs. Gets the work items associated with a build. :param str project: Project ID or project name :param int build_id: The ID of the build. :param int top: The maximum number of work items to return. :rtype: [ResourceRef] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if top is not None: query_parameters['$top'] = self._serialize.query('top', top, 'int') response = self._send(http_method='GET', location_id='5a21f5d2-5642-47e4-a0bd-1356e6731bee', version='5.1', route_values=route_values, query_parameters=query_parameters) return self._deserialize('[ResourceRef]', self._unwrap_collection(response)) def get_build_work_items_refs_from_commits(self, commit_ids, project, build_id, top=None): """GetBuildWorkItemsRefsFromCommits. Gets the work items associated with a build, filtered to specific commits. :param [str] commit_ids: A comma-delimited list of commit IDs. :param str project: Project ID or project name :param int build_id: The ID of the build. :param int top: The maximum number of work items to return, or the number of commits to consider if no commit IDs are specified. :rtype: [ResourceRef] """ route_values = {} if project is not None: route_values['project'] = self._serialize.url('project', project, 'str') if build_id is not None: route_values['buildId'] = self._serialize.url('build_id', build_id, 'int') query_parameters = {} if top is not None: query_parameters['$top'] = self._serialize.query('top', top, 'int') content = self._serialize.body(commit_ids, '[str]') response = self._send(http_method='POST', location_id='5a21f5d2-5642-47e4-a0bd-1356e6731bee', version='5.1', route_values=route_values, query_parameters=query_parameters, content=content) return self._deserialize('[ResourceRef]', self._unwrap_collection(response))
2.125
2
tests/test_train.py
hugobb/sgda
1
9935
import unittest from gamesopt.train import train, TrainConfig class TestOptimizer(unittest.TestCase): def test_sgda(self): config = TrainConfig(num_iter=2) train(config)
2.3125
2
loadCSVdata.py
christostsekouronas/academyposttestanalysis
0
9936
<filename>loadCSVdata.py import pandas as pd def loadTest(filepath): df = pd.read_csv(filepath) return df
2.421875
2
sdk/applicationinsights/azure-mgmt-applicationinsights/azure/mgmt/applicationinsights/v2015_05_01/models/_application_insights_management_client_enums.py
iscai-msft/azure-sdk-for-python
8
9937
# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from enum import Enum class ApplicationType(str, Enum): web = "web" other = "other" class FlowType(str, Enum): bluefield = "Bluefield" class RequestSource(str, Enum): rest = "rest" class PurgeState(str, Enum): pending = "pending" completed = "completed" class FavoriteType(str, Enum): shared = "shared" user = "user" class WebTestKind(str, Enum): ping = "ping" multistep = "multistep" class ItemScope(str, Enum): shared = "shared" user = "user" class ItemType(str, Enum): query = "query" function = "function" folder = "folder" recent = "recent" class SharedTypeKind(str, Enum): user = "user" shared = "shared" class FavoriteSourceType(str, Enum): retention = "retention" notebook = "notebook" sessions = "sessions" events = "events" userflows = "userflows" funnel = "funnel" impact = "impact" segmentation = "segmentation" class ItemScopePath(str, Enum): analytics_items = "analyticsItems" myanalytics_items = "myanalyticsItems" class ItemTypeParameter(str, Enum): none = "none" query = "query" function = "function" folder = "folder" recent = "recent" class CategoryType(str, Enum): workbook = "workbook" tsg = "TSG" performance = "performance" retention = "retention"
2.015625
2
setup.py
dhruvdcoder/allennlp-wandb
0
9938
<reponame>dhruvdcoder/allennlp-wandb from setuptools import setup, find_packages install_requires = [ "allennlp>=0.9.0", "wandb==0.8.15", ] setup( name='allennlp_wandb', version='0.0.1', description='Utilities to use allennlp with wandb', packages=find_packages( exclude=["*.tests", "*.tests.*", "tests.*", "tests"]), package_data={'allennlp_wandb': ['py.typed']}, install_requires=install_requires, zip_safe=False)
1.546875
2
setup.py
mark-mishyn/django-axes
0
9939
<gh_stars>0 #!/usr/bin/env python from setuptools import setup, find_packages setup( name="django-axes", description="Keep track of failed login attempts in Django-powered sites.", long_description="\n".join( [ open("README.rst", encoding="utf-8").read(), open("CHANGES.rst", encoding="utf-8").read(), ] ), keywords="authentication django pci security", author=", ".join( [ "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", ] ), author_email="<EMAIL>", maintainer="Jazzband", maintainer_email="<EMAIL>", url="https://github.com/jazzband/django-axes", project_urls={ "Documentation": "https://django-axes.readthedocs.io/", "Source": "https://github.com/jazzband/django-axes", "Tracker": "https://github.com/jazzband/django-axes/issues", }, license="MIT", package_dir={"axes": "axes"}, use_scm_version=True, setup_requires=["setuptools_scm"], python_requires="~=3.6", install_requires=["django>=1.11", "django-appconf>=1.0.3", "django-ipware>=2.0.2"], include_package_data=True, packages=find_packages(), classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Web Environment", "Environment :: Plugins", "Framework :: Django", "Framework :: Django :: 1.11", "Framework :: Django :: 2.2", "Framework :: Django :: 3.0", "Intended Audience :: Developers", "Intended Audience :: System Administrators", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Internet :: Log Analysis", "Topic :: Security", "Topic :: System :: Logging", ], zip_safe=False, )
1.296875
1
achievements/admin.py
peterkrauz/rpg-achievements-django
1
9940
<reponame>peterkrauz/rpg-achievements-django<filename>achievements/admin.py from django.contrib import admin from achievements import models admin.site.register(models.Achievement)
1.359375
1
src/modules/loss.py
ab3llini/BlindLess
1
9941
<gh_stars>1-10 from torch.nn import CrossEntropyLoss class GPT2Loss(CrossEntropyLoss): def __init__(self, pad_token_id): super(GPT2Loss, self).__init__(ignore_index=pad_token_id) def forward(self, output, labels): """ Loss function for gpt2 :param output: :param labels: :return: """ # Flatten the tensors (shift-align) # Remove last token from output output = output[..., :-1, :].contiguous().view(-1, output.size(-1)) # Remove the first token from labels e do not care for question labels = (labels[..., 1:].contiguous()).view(-1) # Compute the actual loss return super(GPT2Loss, self).forward(output, labels) class VisualGPT2Loss(GPT2Loss): def __init__(self, pad_token_id, extract=None): super(VisualGPT2Loss, self).__init__(pad_token_id=pad_token_id) if extract is not None: assert type(extract) == int, 'Extract value MUST be integer' self.extract = extract def forward(self, output, labels): if self.extract is not None: output = output[self.extract] # Compute the actual loss return super(VisualGPT2Loss, self).forward(output, labels[0]) class BERTLoss(CrossEntropyLoss): def __init__(self, pad_token_id): super(BERTLoss, self).__init__(ignore_index=pad_token_id) def forward(self, output, labels): """ Loss function for gpt2 :param output: :param labels: :return: """ # Flatten the tensors (shift-align) # Remove last token from output output = output[..., :-1, :].contiguous().view(-1, output.size(-1)) # Remove the first token from labels e do not care for question labels = (labels[..., 1:].contiguous()).view(-1) # Compute the actual loss return super(BERTLoss, self).forward(output, labels)
2.4375
2
analyze_tls.py
khushhallchandra/CN-project
0
9942
<gh_stars>0 import numpy as np import pandas as pd import matplotlib.pyplot as plt def main(filename): data = pd.read_csv(filename, header=None) means = data.mean(axis = 0) stds = data.std(axis = 0) return means[0], means[1], stds[0], stds[1] if __name__ == '__main__': files_http1 = ['./results/benchmark_size/http1_txt1.csv', './results/benchmark_size/http1_txt2.csv', './results/benchmark_size/http1_txt3.csv', './results/benchmark_size/http1_txt4.csv', './results/benchmark_size/http1_txt5.csv'] files_http1_tls = ['./results/benchmark_size/http1_tls_txt1.csv', './results/benchmark_size/http1_tls_txt2.csv', './results/benchmark_size/http1_tls_txt3.csv', './results/benchmark_size/http1_tls_txt4.csv', './results/benchmark_size/http1_tls_txt5.csv'] files_http2 = ['./results/benchmark_size/http2_txt1.csv', './results/benchmark_size/http2_txt2.csv', './results/benchmark_size/http2_txt3.csv', './results/benchmark_size/http2_txt4.csv', './results/benchmark_size/http2_txt5.csv'] files_http2_tls = ['./results/benchmark_size/http2_tls_txt1.csv', './results/benchmark_size/http2_tls_txt2.csv', './results/benchmark_size/http2_tls_txt3.csv', './results/benchmark_size/http2_tls_txt4.csv', './results/benchmark_size/http2_tls_txt5.csv'] time_tot_http2, time_contentTransfer_http2 = [], [] std_tot_http2, std_contentTransfer_http2 = [], [] time_tot_http1, time_contentTransfer_http1 = [], [] std_tot_http1, std_contentTransfer_http1 = [], [] time_tot_http2_tls, time_contentTransfer_http2_tls = [], [] std_tot_http2_tls, std_contentTransfer_http2_tls = [], [] time_tot_http1_tls, time_contentTransfer_http1_tls = [], [] std_tot_http1_tls, std_contentTransfer_http1_tls = [], [] for f in files_http2: t1, t2, std1, std2 = main(f) time_contentTransfer_http2.append(t1) time_tot_http2.append(t2) std_contentTransfer_http2.append(2*std1) std_tot_http2.append(2*std2) for f in files_http1: t1, t2, std1, std2 = main(f) time_contentTransfer_http1.append(t1) time_tot_http1.append(t2) std_contentTransfer_http1.append(2*std1) std_tot_http1.append(2*std2) for f in files_http2_tls: t1, t2, std1, std2 = main(f) time_contentTransfer_http2_tls.append(t1) time_tot_http2_tls.append(t2) std_contentTransfer_http2_tls.append(2*std1) std_tot_http2_tls.append(2*std2) for f in files_http1_tls: t1, t2, std1, std2 = main(f) time_contentTransfer_http1_tls.append(t1) time_tot_http1_tls.append(t2) std_contentTransfer_http1_tls.append(2*std1) std_tot_http1_tls.append(2*std2) x = [100, 1000, 10000, 100000, 1000000] time_tot_http2, time_contentTransfer_http2 = np.array(time_tot_http2), np.array(time_contentTransfer_http2) std_tot_http2, std_contentTransfer_http2 = np.array(std_tot_http2), np.array(std_contentTransfer_http2) time_tot_http1, time_contentTransfer_http1 = np.array(time_tot_http1), np.array(time_contentTransfer_http1) std_tot_http1, std_contentTransfer_http1 = np.array(std_tot_http1), np.array(std_contentTransfer_http1) time_tot_http2_tls, time_contentTransfer_http2_tls = np.array(time_tot_http2_tls), np.array(time_contentTransfer_http2_tls) std_tot_http2_tls, std_contentTransfer_http2_tls = np.array(std_tot_http2_tls), np.array(std_contentTransfer_http2_tls) time_tot_http1_tls, time_contentTransfer_http1_tls = np.array(time_tot_http1_tls), np.array(time_contentTransfer_http1_tls) std_tot_http1_tls, std_contentTransfer_http1_tls = np.array(std_tot_http1_tls), np.array(std_contentTransfer_http1_tls) fig, ax = plt.subplots() ax.grid() ax.plot(x, time_contentTransfer_http1, 'o-', color='r', label="HTTP1") ax.plot(x, time_contentTransfer_http1_tls, 'o-', color='g', label="HTTP1_with_tls") ax.plot(x, time_contentTransfer_http2, 'o-', color='b', label="SPDY") ax.plot(x, time_contentTransfer_http2_tls, 'o-', color='k', label="SPDY_with_tls") ax.fill_between(x, time_contentTransfer_http1 - std_contentTransfer_http1, time_contentTransfer_http1 + std_contentTransfer_http1, color='gray', alpha=0.3) ax.fill_between(x, time_contentTransfer_http2 - std_contentTransfer_http2, time_contentTransfer_http2 + std_contentTransfer_http2, color='gray', alpha=0.3) ax.fill_between(x, time_contentTransfer_http1_tls - std_contentTransfer_http1_tls, time_contentTransfer_http1_tls + std_contentTransfer_http1_tls, color='gray', alpha=0.3) ax.fill_between(x, time_contentTransfer_http2_tls - std_contentTransfer_http2_tls, time_contentTransfer_http2_tls + std_contentTransfer_http2_tls, color='gray', alpha=0.3) # ax.errorbar(x, time_contentTransfer_http2, yerr=std_contentTransfer_http2, fmt='-', color='r', label="HTTP2") # ax.errorbar(x, time_contentTransfer_quic, yerr=std_contentTransfer_quic, fmt='-', color='b', label="QUIC") ax.set_xlabel('Size of data (Length)') ax.set_ylabel('Time (in ms)') ax.legend() ax.set_xscale('log') ax.set_title('Comparison of Time Taken for Data Transfer with TLS ON/OFF') fig.savefig('results/plots/time_contentTransfer_tls.png', dpi=fig.dpi) fig, ax = plt.subplots() ax.grid() ax.plot(x, time_tot_http1, 'o-', color='r', label="HTTP1") ax.plot(x, time_tot_http1_tls, 'o-', color='g', label="HTTP1_with_tls") ax.plot(x, time_tot_http2, 'o-', color='b', label="SPDY") ax.plot(x, time_tot_http2_tls, 'o-', color='k', label="SPDY_with_tls") ax.fill_between(x, time_tot_http1 - std_tot_http1, time_tot_http1 + std_tot_http1, color='gray', alpha=0.3) ax.fill_between(x, time_tot_http2 - std_tot_http2, time_tot_http2 + std_tot_http2, color='gray', alpha=0.3) ax.fill_between(x, time_tot_http1_tls - std_tot_http1_tls, time_tot_http1_tls + std_tot_http1_tls, color='gray', alpha=0.3) ax.fill_between(x, time_tot_http2_tls - std_tot_http2_tls, time_tot_http2_tls + std_tot_http2_tls, color='gray', alpha=0.3) # ax.errorbar(x, time_tot_http2, yerr=std_tot_http2, fmt='-', color='r', label="HTTP2") # ax.errorbar(x, time_tot_quic, yerr=std_tot_quic, fmt='-', color='b', label="QUIC") ax.set_xlabel('Size of data (Length)') ax.set_ylabel('Time (in ms)') ax.legend() ax.set_xscale('log') ax.set_title('Comparison of Total Time with TLS ON/OFF') fig.savefig('results/plots/total_time_tls.png', dpi=fig.dpi)
2.453125
2
validation/utils/m1.py
PedrV/stfX
0
9943
<filename>validation/utils/m1.py import unittest import os from matplotlib import pyplot as plt from shapely import geometry, affinity X_COORDINATE = 0 Y_COORDINATE = 1 def extract_x_y(polygon: list) -> (list, list): """Extract the x and y coordinates as two separate lists""" x_list = [] y_list = [] for vertex in polygon: x_list.append(vertex[X_COORDINATE]) y_list.append(vertex[Y_COORDINATE]) return (x_list, y_list) def save_fig(dir: str): """Save the current plt figure in the given directory under the name: m1.png""" plt.savefig(dir + '/m1.png') plt.clf() def plot_polygons(hull: list, min_hull: list, perceived_poly: list, real_poly: list, dir: str = None): """Plot the given two polygons, in a single figure, with different colors""" h1_x, h1_y = extract_x_y(hull) h2_x, h2_y = extract_x_y(min_hull) p1_x, p1_y = extract_x_y(perceived_poly) p2_x, p2_y = extract_x_y(real_poly) # Figure settings fig = plt.figure() # fig.suptitle('Convex hull area (red) VS real representation area (blue)') plt.xlabel('x') plt.ylabel('y') # Plotting hulls plt.fill(h1_x, h1_y, color="#FF000020") plt.fill(h2_x, h2_y, color="#0000FF20") # Plotting polygons lines plt.plot(p1_x, p1_y, color="#FF000060") # Red perceived poly plt.plot(p2_x, p2_y, color="#0000FF60") # Blue real poly # Plotting polygons points for p in perceived_poly: plt.plot(p[X_COORDINATE], p[Y_COORDINATE], 'o', color="#FF0000A0") for p in real_poly: plt.plot(p[X_COORDINATE], p[Y_COORDINATE], 'x', color="#0000FFA0") # plt.show() if dir is not None: save_fig(dir) def surveyor_formula(polygon: list) -> float: """Find the area of the given polygon using the surveyor formula""" # Check if first and last points of polygon are equal parsed_poly = polygon[0:-1]\ if polygon[0] == polygon[len(polygon)-1]\ else polygon area = 0 for i in range(-1, len(parsed_poly)-1): area += parsed_poly[i][X_COORDINATE] * parsed_poly[i+1][Y_COORDINATE] -\ parsed_poly[i][Y_COORDINATE] * parsed_poly[i+1][X_COORDINATE] return abs(area / 2) def polygon_to_vertices_list(polygon: geometry.Polygon) -> list: """Extract the polygon vertices as a list""" return list(polygon.exterior.coords) def apply_transformations(initial_representation: list, events: list) -> float: """Apply the transformations in the events list to the initial representation""" scale = 1 rot_angle = 0 trans_vector = [0, 0] for item in events: for event in item["events"]: if event["type"] == "TRANSLATION": trans_vector[X_COORDINATE] += event["trigger"]["transformation"][X_COORDINATE] trans_vector[Y_COORDINATE] += event["trigger"]["transformation"][Y_COORDINATE] elif event["type"] == "ROTATION": rot_angle += event["trigger"]["transformation"] elif event["type"] == "UNIFORM_SCALE": scale *= event["trigger"]["transformation"] # Apply multiplication polygon = geometry.Polygon(initial_representation) s_polygon = affinity.scale(polygon, xfact=scale, yfact=scale, origin=(0, 0)) r_s_polygon = affinity.rotate(s_polygon, rot_angle, origin=(0, 0)) t_r_s_polygon = affinity.translate(r_s_polygon, xoff=trans_vector[0], yoff=trans_vector[1]) return polygon_to_vertices_list(t_r_s_polygon) def apply_m1(real_representation: list, perceived_representation: list, dir: str = None) -> float: """Apply the metric M1 and obtain its result, between 0 and 1""" joint_point_set = real_representation + perceived_representation # Getting necessary hulls real_convex_hull = geometry.MultiPoint(real_representation).convex_hull perceived_hull = geometry.MultiPoint(perceived_representation).convex_hull convex_hull = geometry.MultiPoint(joint_point_set).convex_hull # Getting vertices of hulls real_vertices = polygon_to_vertices_list(real_convex_hull) perceived_vertices = polygon_to_vertices_list(perceived_hull) joint_vertices = polygon_to_vertices_list(convex_hull) # Getting the min area real_area = surveyor_formula(real_vertices) perceived_area = surveyor_formula(perceived_vertices) if real_area <= perceived_area: min_area = real_area min_vertices = real_vertices else: min_area = perceived_area min_vertices = perceived_vertices plot_polygons(hull=joint_vertices, min_hull=min_vertices, perceived_poly=perceived_representation, real_poly=real_representation, dir=dir) return min_area / surveyor_formula(joint_vertices) class TestM1(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestM1, self).__init__(*args, **kwargs) self.representation = [ [1, 1], [1, -1], [-1, -1], [-1, 1], [1, 1] ] self.transformations = [{ "events": [ {"type": "TRANSLATION", "trigger": {"transformation": [5, 5]}}, {"type": "ROTATION", "trigger": {"transformation": 180}}, {"type": "UNIFORM_SCALE", "trigger": {"transformation": 1.25}} ] }, { "events": [ {"type": "TRANSLATION", "trigger": {"transformation": [5, 0]}}, {"type": "ROTATION", "trigger": {"transformation": -90}}, {"type": "UNIFORM_SCALE", "trigger": {"transformation": 1.6}} ] }] self.min_scale = [{ "events": [ {"type": "UNIFORM_SCALE", "trigger": {"transformation": 0.5}} ] }] def test_area(self): square = [ [1, 1], [1, -1], [-1, -1], [-1, 1] ] self.assertEqual(surveyor_formula(square), 4) self.assertEqual(surveyor_formula(self.representation), 4) def test_transformations(self): self.assertEqual(apply_transformations(self.representation, self.transformations), [ (8.0, 7.0), (12.0, 7.0), (12.0, 3.0), (8.0, 3.0), (8.0, 7.0), ]) def test_M1(self): self.assertEqual(apply_m1(self.representation, self.representation), 1) self.assertTrue(apply_m1(self.representation, apply_transformations(self.representation, self.transformations)) < 0.1) self.assertEqual(apply_m1([ (8.0, 7.0), (12.0, 7.0), (12.0, 3.0), (8.0, 3.0), (8.0, 7.0)], apply_transformations(self.representation, self.transformations)), 1) def test_mean_perceived(self): self.assertEqual(apply_m1(self.representation, apply_transformations(self.representation, self.min_scale)), 0.25) if __name__ == '__main__': unittest.main()
2.78125
3
movefiles.py
linhailan/JPG-PNG-to-MNIST-NN-Format
0
9944
<reponame>linhailan/JPG-PNG-to-MNIST-NN-Format import os from PIL import Image from array import * from random import shuffle import shutil def move_file(src_path, dst_path, file): print("from : ",src_path) print("to : ",dst_path) try: # cmd = 'chmod -R +x ' + src_path # os.popen(cmd) f_src = os.path.join(src_path, file) if not os.path.exists(dst_path): os.mkdir(dst_path) f_dst = os.path.join(dst_path, file) shutil.move(f_src, f_dst) except Exception as e: print("move file ERROR: ",e) # Load from and save to def loadfile(Names): FileList = [] for dirname in os.listdir(Names[0][0]): path = os.path.join(Names[0][0], dirname) print(path) i = 0 for filename in os.listdir(path): if i >= 50: break if filename.endswith(".jpg"): print(i,":",filename) src_path = os.path.join(Names[0][0],dirname) dst_path = os.path.join(Names[1][0],dirname) move_file(src_path,dst_path,filename) i += 1 Names = [['./training-images','train'], ['./test-images','test']] for name in Names: FileList = [] for dirname in os.listdir(name[0]): path = os.path.join(name[0],dirname) print(path,":",len(os.listdir(path)))
2.6875
3
spark_work.py
nszceta/spark-python-celery-demo
8
9945
<reponame>nszceta/spark-python-celery-demo import sys from pyspark import SparkContext import json print('spark got python path -> ' + str(sys.executable)) logfile = sys.argv[1] sc = SparkContext() logdata = sc.textFile(logfile).cache() a_count = logdata.filter(lambda s: 'a' in s).count() b_count = logdata.filter(lambda s: 'b' in s).count() print(json.dumps({'a': a_count, 'b': b_count}))
2.546875
3
nodes/List/GetTaskRenderListIndex.py
atticus-lv/RenderNode
17
9946
import bpy from bpy.props import * from ...nodes.BASE.node_base import RenderNodeBase class RenderNodeGetListIndex(RenderNodeBase): """A simple input node""" bl_idname = 'RenderNodeGetListIndex' bl_label = 'Get List Index' def init(self, context): self.create_output('RenderNodeSocketInt', "index", 'Index') def process(self,context,id,path): node = self.id_data.nodes.get(bpy.context.window_manager.rsn_active_list) if not node or node.bl_idname != 'RenderNodeTaskRenderListNode': return self.outputs[0].set_value(node.active_index) def register(): bpy.utils.register_class(RenderNodeGetListIndex) def unregister(): bpy.utils.unregister_class(RenderNodeGetListIndex)
2.21875
2
src/recognizeDigit.py
RsTaK/Sudoku
2
9947
from keras.models import load_model import cv2 import pickle import keras.backend as K import numpy as np from src.model_path import MODEL_PATH '''def predict(self, cell): model = load_model('./model/Model.h5') f = K.function([model.layers[0].input, K.learning_phase()],[model.layers[-1].output]) rescaled_cell = self.rescale(cell) result = [] for _ in range(10): result.append(f([rescaled_cell, 1])) result = np.array(result) prediction = result.mean(axis=0) uncertainty = result.var(axis=0) if uncertainty.argmax() > 3: new_prediction = 0 print(prediction.argmax(),uncertainty.argmax(),new_prediction) else: print(prediction.argmax(),uncertainty.argmax())''' class recognizeDigit: def __init__(self, cell): self._prediction = self.predict(cell) def predict(self, cell): model = load_model(MODEL_PATH) rescaled_cell = self.rescale(cell) pred = model.predict(rescaled_cell) return pred.argmax() def rescale(self, cell): resized_cell = cv2.resize(cell, (28, 28)) return resized_cell.reshape(1, resized_cell.shape[0], resized_cell.shape[1], 1) @property def prediction(self): return self._prediction
2.828125
3
plots.py
olihawkins/penguin-models
1
9948
# -*- coding: utf-8 -*- """A module for plotting penguins data for modelling with scikit-learn.""" # Imports --------------------------------------------------------------------- import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import pandas as pd # Constants ------------------------------------------------------------------- SPECIES_COLORS = { 'Adelie': '#4daf4a', 'Gentoo': '#ffb000', 'Chinstrap': '#0084f7' } X_AXIS = [30, 60] Y_AXIS = [12, 22] # Set style ------------------------------------------------------------------- # Load the style from a file plt.style.use('./style/eda.mplstyle') # Alternatively, load the style from the library in ~/.matplotlib/stylelib # plt.style.use(['eda']) # Functions ------------------------------------------------------------------- def get_contour_data(model, pipeline, n_points=1000): """Create the data used to show the boundary of the decision function.""" x0s = np.linspace(X_AXIS[0], X_AXIS[1], n_points) x1s = np.linspace(Y_AXIS[0], Y_AXIS[1], n_points) x0, x1 = np.meshgrid(x0s, x1s) X = np.c_[x0.ravel(), x1.ravel()] df_X = pd.DataFrame(X, columns=['bill_length_mm', 'bill_depth_mm']) X = pipeline.transform(df_X) y_pred = model.predict(X).reshape(x0.shape) y_decision = model.decision_function(X).reshape(x0.shape) return x0, x1, y_pred, y_decision def get_target_colors(target): """Create a dictionary of colors to use in binary classification plots.""" return { target : '#984ea3', 'Other': '#ff7f00' } # Plots ----------------------------------------------------------------------- def plot_example(): plt.style.reload_library() plt.style.use(['eda']) fig, ax = plt.subplots() ax.set_title('Some random words of the title') ax.scatter(np.random.normal(0,1,10), np.random.normal(0,1,10)) fig.savefig('plots/test.svg', format='svg') fig.savefig('plots/test.png', format='png') plt.close() def plot_target_by_features(df): """Plot the different target species.""" fig, ax = plt.subplots() ax.set_title( label='Palmer penguins by species and bill characteristics', loc='center') ax.get_xaxis().set_major_formatter( mpl.ticker.FormatStrFormatter('%.0f')) ax.set_xlim(X_AXIS[0], X_AXIS[1]) ax.set_xlabel('Bill length (mm)') ax.get_yaxis().set_major_formatter( mpl.ticker.FormatStrFormatter('%.0f')) ax.set_ylim(Y_AXIS[0], Y_AXIS[1]) ax.set_ylabel('Bill depth (mm)') grouped = df.groupby('species') for key, group in grouped: ax.scatter( group['bill_length_mm'], group['bill_depth_mm'], c=SPECIES_COLORS[key], s=40, label=key, alpha=0.55) ax.legend(loc='lower left', handletextpad=0.2) fig.savefig('plots/target-by-features.png', format='png') plt.close() def plot_model(df, model, pipeline, f_score, target, title, filename): """Plot the results of a binary classification model.""" fig, ax = plt.subplots() ax.set_title(title, loc='center') ax.get_xaxis().set_major_formatter( mpl.ticker.FormatStrFormatter('%.0f')) ax.set_xlim(X_AXIS[0], X_AXIS[1]) ax.set_xlabel('Bill length (mm)') ax.get_yaxis().set_major_formatter( mpl.ticker.FormatStrFormatter('%.0f')) ax.set_ylim(Y_AXIS[0], Y_AXIS[1]) ax.set_ylabel('Bill depth (mm)') # Plot the boundary of the decision function x0, x1, y_pred, y_decision = get_contour_data(model, pipeline) ax.contourf(x0, x1, y_pred, cmap=plt.cm.PuOr, alpha=0.2) # This plots the decision score, if needed # ax.contourf(x0, x1, y_decision, cmap=plt.cm.PuOr, alpha=0.1) df = df.copy() df['species'] = df['target'].apply(lambda t: target if t == 1 else 'Other') colors = get_target_colors(target) grouped = df.groupby('species') for key, group in grouped: ax.scatter( group['bill_length_mm'], group['bill_depth_mm'], c=colors[key], s=40, label=key, alpha=0.55) ax.legend(loc='lower left', handletextpad=0.2) bbox_style = { 'boxstyle': 'round', 'facecolor': '#ffffff', 'edgecolor': '#d4d4d4', 'alpha': 0.8 } ax.text(53, 12.415, '$F_1$ score: {0}'.format(f_score), bbox=bbox_style) fig.savefig('plots/{0}.png'.format(filename), format='png') plt.close()
3.15625
3
dojo/db_migrations/0147_rename_sslyze_parser.py
dant24/django-DefectDojo
249
9949
<filename>dojo/db_migrations/0147_rename_sslyze_parser.py from django.db import migrations def rename_sslyze_parser(apps, schema_editor): Test_Type_model = apps.get_model('dojo', 'Test_Type') try: test_type_sslyze = Test_Type_model.objects.get(name='SSLyze 3 Scan (JSON)') test_type_sslyze.name = 'SSLyze Scan (JSON)' test_type_sslyze.save() except Test_Type_model.DoesNotExist: # This happens when a new instance of DD is initialized pass class Migration(migrations.Migration): dependencies = [ ('dojo', '0146_lead_optional'), ] operations = [ migrations.RunPython(rename_sslyze_parser), ]
1.914063
2
server/forestgame/game/test_world.py
Nick-Pearson/forestgame
0
9950
import unittest from forestgame.game.world import World class WorldTest(unittest.TestCase): def test_world_inits_to_empty_data(self): world = World(None, "1", "0", 0, 0, [], []) self.assertEqual(0, world.get_size_x()) self.assertEqual(0, world.get_size_y()) self.assertEqual([], world.get_tile_data()) def test_world_with_tiles_inits__with_tiles_to_empty_data(self): world = World(None, "1", "0", 3, 3, [(1, 1, 0)], []) expected_tile_data = [ [1, 1, 1], [1, 0, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_from_zero_initialsies_from_forest(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 3) expected_tile_data = [ [1, 1, 1], [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_with_larger_x_y_pads_with_forest(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(2, 2) world.set_size(3, 3) expected_tile_data = [ [1, 1, 1], [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_with_larger_x_pads_with_forest(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(2, 3) world.set_size(3, 3) expected_tile_data = [ [1, 1, 1], [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_with_larger_y_pads_with_forest(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 2) world.set_size(3, 3) expected_tile_data = [ [1, 1, 1], [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_with_smaller_x_y_removes_data(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 3) world.set_size(2, 2) expected_tile_data = [ [1, 1], [1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(2, world.get_size_x()) self.assertEqual(2, world.get_size_y()) def test_set_size_with_smaller_x_removes_data(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 3) world.set_size(2, 3) expected_tile_data = [ [1, 1], [1, 1], [1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(2, world.get_size_x()) self.assertEqual(3, world.get_size_y()) def test_set_size_with_smaller_y_removes_data(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 3) world.set_size(3, 2) expected_tile_data = [ [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(2, world.get_size_y()) def test_set_size_with_same_x_y_does_nothing(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(3, 3) world.set_size(3, 3) expected_tile_data = [ [1, 1, 1], [1, 1, 1], [1, 1, 1], ] self.assertEqual(expected_tile_data, world.get_tile_data()) self.assertEqual(3, world.get_size_x()) self.assertEqual(3, world.get_size_y()) # set tile range checks def test_set_tile_changes_tile_data(self): world = World(None, "1", "0", 0, 0, [], []) world.set_size(5, 5) world.set_tile_at(2, 3, 0) self.assertEqual(0, world.get_tile_at(2, 3)) expected_tile_data = [ [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 0, 1, 1], [1, 1, 1, 1, 1] ] self.assertEqual(expected_tile_data, world.get_tile_data())
3.484375
3
invconv/xlsx.py
TechPowerAwaits/ax-toolkit
0
9951
# Copyright 2021 <NAME> <<EMAIL>> # SPDX-license-identifier: 0BSD import string from loguru import logger try: import cell_pos from exceptions import InvconvMissingHeaders import ftype import msg_handler except ModuleNotFoundError: import invconv.cell_pos as cell_pos from invconv.exceptions import InvconvMissingHeaders import invconv.ftype as ftype import invconv.msg_handler as msg_handler used = True try: from openpyxl import load_workbook except ModuleNotFoundError: used = False # load_workbook is used repeatedly with similar settings # every time. WB_SETTINGS = { "read_only": True, "keep_vba": False, "data_only": True, "keep_links": False, } class XlsxDataTuple(ftype.BasicFtypeDataClass): def __init__(self, filename, wsname, headers): self.filename = filename self.wsname = wsname self.headers = headers self.cur_row = None self.cur_col = None super().__init__( filename=self.filename, sectionname=self.wsname, headers=self.headers ) # Set relevant values and gets the number of operations # to be performed based on the dimensions. def set_oper_num(self, min_row, max_row, max_col): self.min_row = min_row self.min_col = 1 self.max_row = max_row self.max_col = max_col delta_col = self.max_col - self.min_col + 1 delta_row = self.max_row - self.min_row + 1 self.num_oper = delta_col * delta_row return self.num_oper def load_workbook(self): return load_workbook(self.filename, **WB_SETTINGS) def parser(self): if self.cur_row is None: self.cur_row = self.min_row if self.cur_col is None: self.cur_col = self.min_col if self.cur_col > self.max_col: self.cur_col = self.min_col self.cur_row += 1 if self.cur_row > self.max_row: self.cur_row = None self.cur_col = None return None col_letter = cell_pos.get_col_letter(self.cur_col) row_str = str(self.cur_row) wb = self.load_workbook() ws = wb[self.wsname] cell_val = ws[col_letter + row_str].value return_str = str(cell_val) if cell_val is None: return_str = "" if return_str == "#REF!": logger.warning( string.Template( 'Unknown reference found at $cell_pos in $id. Defaulting to "unknown".' ).substitute( cell_pos=col_letter + row_str, id=msg_handler.get_id((self.filename, self.wsname), "WS"), ) ) return_str = "unknown" self.cur_col += 1 wb.close() return return_str # Will store a file, worksheet tuple-like class # with additional data accessible. xlsx_data_list = ftype.FtypeDataList() # Contains just a list of file, worksheet tuples. xlsx_tuple_list = [] # xlsx files always start counting at 1. INVALID_ROW = 0 def start(input_files): # Gets the name of worksheets and # adds it to xlsx_tuple_list. get_worksheets(input_files) # Sometimes, openpyxl can't get # the proper dimensions of a worksheet, # so it handles that. It also deals with # headers in the worksheets and removes # blank cells from the size of the sheet. set_data() # Check if some file worksheet pairs don't # have a valid header. if not xlsx_data_list: raise InvconvMissingHeaders # Can't directly check for membership of # items from xlsx_tuple_list in xlsx_data_list, # for they are different types. for file_section in xlsx_tuple_list: found_file_section = False for data_file_section in xlsx_data_list: # The first element in if statement # has to be XlsxDataTuple, as it # contains a __eq__() function # that should work in this case. if data_file_section == file_section: found_file_section = True break if not found_file_section: logger.error( f"{msg_handler.get_id(file_section, 'ws')} contains no valid headers." ) msg_handler.does_continue() return xlsx_data_list def get_worksheets(input_files): for input_file in input_files: wb = load_workbook(input_file, **WB_SETTINGS) sheetname_list = wb.sheetnames for sheetname in sheetname_list: xlsx_tuple_list.append((input_file, sheetname)) wb.close() def set_data(): for filename, wsname in xlsx_tuple_list: wb = load_workbook(filename, **WB_SETTINGS) ws = wb[wsname] # max_col and max_row can be None. cur_max_col = ws.max_column cur_max_row = ws.max_row # Close workbook right away so # it won't remain open in case script # gets closed or crashes. wb.close() max_col = get_max_col(filename, wsname, cur_max_col) max_row = get_max_row(filename, wsname, cur_max_row) # Get the row where a header was found. header_row = get_header_row(filename, wsname, max_row) # check_header_row() ensures that a non-blank row # is after header row. If not, it might not # actually be a header row. if ( header_row == INVALID_ROW or header_row == max_row or not check_header_row(filename, wsname, max_col, header_row) ): continue # The first row after the header_row. min_row = header_row + 1 header_list = get_header_list(filename, wsname, max_col, header_row) if max_col > len(header_list): logger.info( string.Template( "Reducing max column length of $id from $cur_col to $new_col due to None in $cell_pos." ) ) max_col = len(header_list) DataTuple = XlsxDataTuple(filename, wsname, header_list) DataTuple.set_oper_num(min_row, max_row, max_col) xlsx_data_list.append(DataTuple) def get_max_col(filename, wsname, max_col): xlsx_id = msg_handler.get_id((filename, wsname), "WS") while (not isinstance(max_col, int)) or (max_col <= INVALID_ROW): logger.error(f"Max col for {xlsx_id} is {str(max_col)}.") msg_handler.does_continue() try: logger.info("User providing number of columns (starting at 1).") max_col = int( input("Please provide the number of columns (starting at 1) > ") ) except (ValueError, TypeError): logger.log("FAILURE", "Input could not be converted to int.") max_col = None if (isinstance(max_col, int)) and (max_col <= 0): logger.log("FAILURE", "Input is less than one.") return max_col def get_max_row(filename, wsname, max_row): xlsx_id = msg_handler.get_id((filename, wsname)) while (not isinstance(max_row, int)) or (max_row <= 0): logger.error(f"Max row for {xlsx_id} is {str(max_row)}.") msg_handler.does_continue() try: logger.info("User providing number of rows (starting at 1).") max_row = int(input("Please provide the number of rows (starting at 1) > ")) except (ValueError, TypeError): logger.log("FAILURE", "Input could not be converted to int.") max_row = None if (isinstance(max_row, int)) and (max_row <= 0): logger.log("FAILURE", "Input is less than one.") return max_row def get_header_row(filename, wsname, max_row): wb = load_workbook(filename, **WB_SETTINGS) ws = wb[wsname] # header_row starts at 1, # so a value of 0 indicates # it wasn't found. header_row = INVALID_ROW for row in cell_pos.row_iter(max_row): row_str = str(row) # A row with just a title would not fill up the entire max_column. # As a result, there would be None at either the first or second # position. cell1 = ws["A" + row_str].value cell2 = ws["B" + row_str].value if cell1 is not None and cell2 is not None: header_row = row break wb.close() return header_row def check_header_row(filename, wsname, max_col, header_row): wb = load_workbook(filename, **WB_SETTINGS) ws = wb[wsname] # Check the row after the header row # for content. post_header_row = header_row + 1 row_str = str(post_header_row) # List of items in row. row_list = [] for col in cell_pos.col_iter(max_col): col_letter = cell_pos.get_col_letter(col) row_list.append(str(ws[col_letter + row_str].value)) wb.close() # Ensure the row is not blank. if row_list.count("None") != len(row_list): return True return False def get_header_list(filename, wsname, max_col, header_row): wb = load_workbook(filename, **WB_SETTINGS) ws = wb[wsname] header_list = [] row_str = str(header_row) for col in cell_pos.col_iter(max_col): col_letter = cell_pos.get_col_letter(col) header_item = ws[col_letter + row_str].value # Assuming the header doesn't have blank # items between entries. Only at the end. if header_item is None: logger.warning( f"Blank header {col_letter+row_str} in {msg_handler.get_id((filename, wsname), 'WS')} will be ignored." ) break header_list.append(header_item) wb.close() return header_list if used: ftype.add("xlsx", start)
2.09375
2
solvers/generation_solver/img_interface.py
Anthony102899/Lego-ImageGenerator
1
9952
import os from tkinter import * import tkinter.filedialog as tkfd from PIL import Image import numpy as np import solvers.generation_solver.image_seperation as IS def layer_interface(img_num): layer_names = [] layer_nums = [] for k in range(img_num): master = Toplevel() master.title(f"Image number {k+1}") master.geometry("+300+200") # input image and layer img_label = Label(master, text="Image").grid(row=0) layer_label = Label(master, text="Layer").grid(row=1) entry_img = Entry(master, width=30) entry_layer = Entry(master, width=30) entry_img.grid(row=0, column=1) entry_layer.grid(row=1, column=1) if k == img_num - 1: Button(master, text='Done', command=master.quit).grid(row=2, column=2, sticky=W, pady=4) else: Button(master, text='Next', command=master.quit).grid(row=2, column=2, sticky=W, pady=4) img_path = "inputs/images/" img_path = os.path.join(os.path.dirname(__file__), img_path) path = tkfd.askopenfilename(initialdir = img_path, title = "Select file", filetypes = (("png files","*.png"),("all files","*.*"))) entry_img.insert('0', os.path.basename(path)) image = Image.open(path) img = PhotoImage(file=path) width, height = img.width(), img.height() if width > 250: scale_w = int(round(width / 250, 0)) scale_h = int(round(height / 250, 0)) img = img.subsample(scale_w, scale_h) if width < 250: scale_w = int(round(250 / width, 0)) scale_h = int(round(250 / height, 0)) img = img.zoom(scale_w, scale_h) Label(master, image=img).grid(row=2, column=1) mainloop() img_name = entry_img.get() img_layer = entry_layer.get() layer_names.append(img_name) layer_nums.append(img_layer) return layer_names, layer_nums def show_interface(): root = Tk() root.geometry("+300+300") Label(root, text="Graph", font=("", 14, "bold", "underline"), fg='#696969').grid(row=0, sticky='w') entry_graph = Entry(root, width=15) entry_graph.grid(row=0, column=1) graph_path = "connectivity/" graph_path = os.path.join(os.path.dirname(__file__), graph_path) path = tkfd.askopenfilename(initialdir = graph_path, title = "Select file", filetypes = (("pkl files", "*.pkl"), ("all files","*.*"))) entry_graph.insert('0', os.path.basename(path)) # input No. image and button Label(root, text="Input image", font=("", 14, "bold", "underline"), fg='#696969').grid(row=1, sticky='w') entry_file = Entry(root, width=15) entry_file.grid(row=1, column=1) entry_path = "inputs/images/" entry_path = os.path.join(os.path.dirname(__file__), entry_path) input_path = tkfd.askopenfilename(initialdir=entry_path, title="Select input image", filetypes=(("png files", "*.png"), ("jpg files", "*.jpg"))) entry_file.insert('0', os.path.basename(input_path)) Button(root, text='Next', command=root.quit).grid(row=1, column=2, sticky='e', pady=4) # input background color Label(root, text="").grid(row=2, column=1) Label(root, text="Background color", font=("", 14, "bold", "underline"), fg='#696969').grid(row=3, sticky='w') Label(root, text="R", fg='#4f4f4f').grid(row=4, column=0) Label(root, text="G", fg='#4f4f4f').grid(row=4, column=1) Label(root, text="B", fg='#4f4f4f').grid(row=4, column=2) entry_r = Entry(root, width=15) entry_g = Entry(root, width=15) entry_b = Entry(root, width=15) entry_r.grid(row=5, column=0) entry_g.grid(row=5, column=1) entry_b.grid(row=5, column=2) # input rotation and scaling Label(root, text="").grid(row=6, column=1) Label(root, text="Rotation degree", font=("", 14, "bold", "underline"), fg='#696969').grid(row=7, sticky='w') entry_degree = Entry(root, width=15, textvariable=StringVar(root, value='0')) entry_degree.grid(row=7, column=1) Label(root, text="Scale", font=("", 14, "bold", "underline"), fg='#696969').grid(row=7, column=2) entry_scale = Entry(root, width=15, textvariable=StringVar(root, value='1')) entry_scale.grid(row=7, column=3) # input translation Label(root, text="").grid(row=8, column=1) Label(root, text="x translation", font=("", 14, "bold", "underline"), fg='#696969').grid(row=9, sticky='w') entry_x = Entry(root, width=15, textvariable=StringVar(root, value='0')) entry_x.grid(row=9, column=1) Label(root, text="y translation", font=("", 14, "bold", "underline"), fg='#696969').grid(row=9, column=2) entry_y = Entry(root, width=15, textvariable=StringVar(root, value='0')) entry_y.grid(row=9, column=3) Label(root, text="").grid(row=9, column=1) mainloop() img_path = input_path print(img_path) img_num = IS.seperate_color(img_path, "./cache/") r, g, b = entry_r.get(), entry_g.get(), entry_b.get() if len(r) == 0: r = 0 if len(g) == 0: g = 0 if len(b) == 0: b = 0 if r == 0 and g == 0 and b == 0: rgb = [] else: rgb = np.array((int(r), int(g), int(b))) layer_names, layer_nums = layer_interface(img_num) return entry_graph.get(), img_num, layer_names, layer_nums, rgb, int(entry_degree.get()), float(entry_scale.get()), int(entry_x.get()), int(entry_y.get()) if __name__ == '__main__': print(show_interface())
2.984375
3
Constellations/get_brightest_stars.py
PatD123/Polar-Constellation
0
9953
<filename>Constellations/get_brightest_stars.py<gh_stars>0 from bs4 import BeautifulSoup as soup from urllib.request import urlopen as uReq import re, json # Getting the page URL = "https://www.astronomytrek.com/star-constellations-brightest-stars/" uClient = uReq(url=URL) page_html = uClient.read() page_soup = soup(page_html, "html.parser") # Opening a file to write in stars_file = open("brightest_stars.txt", 'w') # def find_space(star): for i in range(0, len(star)): if star[i] == " " and star[i + 1] == "(": return i brightest_uncleaned = page_soup.find_all("tr") for html in brightest_uncleaned: col_4 = html.contents[4].contents[0] col_5 = html.contents[5].string if col_5 is not None: idx = find_space(col_5) col_5 = col_5[0:idx] if col_5 == "Brightest Star": continue stars_file.write(col_5 + "\n") else: idx = find_space(col_4) col_4 = col_4[0:idx] stars_file.write(col_4 + "\n") stars_file.close()
3.1875
3
TwoPointers/Leetcode11.py
Rylie-W/LeetRecord
0
9954
class Solution: def maxArea(self, height) -> int: left=0 right=len(height)-1 res=min(height[left],height[right])*(right-left) while right>left: res=max(res,(right-left)*min(height[right],height[left])) if height[left]<height[right]: left+=1 else: right-=1 return res if __name__ == '__main__': sol=Solution() # height = [1, 1] height=[1,3,2,5,25,24,5] print(sol.maxArea(height))
3.375
3
tensorflow_text/python/ops/bert_tokenizer_test.py
hashim361/text
1
9955
# coding=utf-8 # Copyright 2020 TF.Text 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. # encoding=utf-8 r"""Tests for BertTokenizer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_map_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.platform import test from tensorflow_text.python.ops import bert_tokenizer def _utf8(x): return x.encode('utf-8') # TODO(thuang513): It appears there isn't a Ragged version of substr; consider # checking this into core TF. def _ragged_substr(text_input, begin, end): text_input_flat = None if ragged_tensor.is_ragged(text_input): text_input_flat = text_input.flat_values else: text_input_flat = text_input def _ragged_tile(x): input_text, indices = x multiple = math_ops.reduce_sum(indices.row_lengths()) return array_ops.tile([input_text], [multiple]) broadcasted_text = ragged_map_ops.map_fn( _ragged_tile, (text_input_flat, begin), dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.string, ragged_rank=1), infer_shape=False, ) size = math_ops.sub( array_ops.squeeze(end.flat_values), array_ops.squeeze(begin.flat_values)) new_tokens = string_ops.substr_v2(broadcasted_text, array_ops.squeeze(begin.flat_values), size) return begin.with_flat_values(new_tokens.flat_values) _VOCAB = [ b'[unused1]', b'[unused23]', b"'", b'##%', b'##af', b'##book', b'##c', b'##fr', b'##hey', b'##is', b'##o', b'##ost', b'##s', b'##tri', b'##y', b'$', b'%', b'&', b'(', b')', b'*', b'-', b'.', b'20', b':', b'?', b'[CLS]', b'[SEP]', _utf8(u'國'), _utf8(u'暐'), _utf8(u'瀚'), _utf8(u'韓'), _utf8(u'食'), _utf8(u'黃'), _utf8(u'🤔'), _utf8(u'🤣'), b'^', b'a', b'ago', b'among', b'an', b'and', b'are', b'aren', b'awesome', b'between', b'candy', b'china', b'companies', b'company', b'crushed', b'dug', b'earnings', b'engaged', b'even', b'few', b'forecast', b'getting', b'had', b'han', b'has', b'hers', b'high', b'hit', b'hs', b'hurting', b'in', b'indie', b'is', b'isn', b'ka', b'ku', b'major', b'maker', b'moth', b'nearly', b'new', b'now', b'president', b'record', b'regulators', b'reported', b'rift', b'rust', b'sales', b'shares', b'slightly', b'sprint', b'states', b'stock', b't', b'taste', b'tension', b'that', b'the', b'this', b'today', b'told', b'topped', b'trade', b'trump', b'united', b'up', b'weeks', b'what', b'why', b'with', b'year', b'yo', b'yu', _utf8(u'\u7231'), _utf8(u'\u4e0a'), _utf8(u'\u4e00'), _utf8(u'\u4e2a'), _utf8(u'\u4e0d'), _utf8(u'\u56de'), _utf8(u'\u5bb6'), _utf8(u'\u7684'), _utf8(u'\u4eba'), ] def _create_table(vocab, num_oov=1): init = lookup_ops.KeyValueTensorInitializer( vocab, math_ops.range( array_ops.size(vocab, out_type=dtypes.int64), dtype=dtypes.int64), key_dtype=dtypes.string, value_dtype=dtypes.int64) return lookup_ops.StaticVocabularyTableV1( init, num_oov, lookup_key_dtype=dtypes.string) class BertTokenizerTest(test_util.TensorFlowTestCase, parameterized.TestCase): def test_bert_tokenizer_outputs(self): text_inputs = constant_op.constant([_utf8('Test')]) vocab = _VOCAB table = _create_table(vocab, 2) self.evaluate(table.initializer) tokenizer = bert_tokenizer.BertTokenizer( table, token_out_type=dtypes.int32) results = tokenizer.tokenize(text_inputs) self.assertAllEqual(results.dtype, dtypes.int32) @parameterized.parameters([ dict( text_inputs=[ _utf8(u'taste the rustisc indiefrost'), _utf8(u'Han Kuo-yu (韓國食)🤔'), _utf8(u'Añade la información del formulario y tus preguntas'), ], expected_tokens=[[b'taste', b'the', b'rustisc', b'indiefrost'], [ b'Han', b'Kuo', b'-', b'yu', b'(', b'\xe9\x9f\x93', b'\xe5\x9c\x8b', b'\xe9\xa3\x9f', b')', b'\xf0\x9f\xa4\x94' ], [ b'A\xc3\xb1ade', b'la', b'informaci\xc3\xb3n', b'del', b'formulario', b'y', b'tus', b'preguntas' ]], ), dict( text_inputs=[ _utf8(u'UNwant\u00E9d,running'), _utf8(u'Añade la información del formulario y tus preguntas'), ], expected_tokens=[[b'unwanted', b',', b'running'], [ b'anade', b'la', b'informacion', b'del', b'formulario', b'y', b'tus', b'preguntas' ]], lower_case=True, ), dict( text_inputs=[ _utf8(u'Añade la información del formulario y tus preguntas') ], expected_tokens=[[ b'An\xcc\x83ade', b'la', b'informacio\xcc\x81n', b'del', b'formulario', b'y', b'tus', b'preguntas' ]], normalization_form='NFD', ), # Test CJK are tokenized by unicode characters dict( text_inputs=[ _utf8(u'香港では4日'), _utf8(u'영어독해 자만심 왜 문제일까'), _utf8(u'據港媒《東網》報導') ], expected_tokens=[ [_utf8(u'香'), _utf8(u'港'), _utf8(u'では4'), _utf8(u'日')], [ _utf8(u'영어독해'), _utf8(u'자만심'), _utf8(u'왜'), _utf8(u'문제일까'), ], [ _utf8(u'據'), _utf8(u'港'), _utf8(u'媒'), _utf8(u'《'), _utf8(u'東'), _utf8(u'網'), _utf8(u'》'), _utf8(u'報'), _utf8(u'導') ], ], normalization_form=None, ), # Test Katakana followed by Hiragana. dict( text_inputs=[_utf8(u'のテキストとして')], expected_tokens=[ [_utf8(u'のテキストとして')], ], normalization_form=None, ), ]) @test_util.run_in_graph_and_eager_modes def test_basic_tokenize(self, text_inputs, expected_tokens, lower_case=False, normalization_form='NFC'): text_inputs = ragged_factory_ops.constant(text_inputs) tokenizer = bert_tokenizer.BasicTokenizer( lower_case=lower_case, normalization_form=normalization_form) tokens = tokenizer.tokenize(text_inputs) self.assertAllEqual(tokens, expected_tokens) @parameterized.parameters([ dict( text_inputs=[ b'taste the rustisc indiefrost', _utf8(u'Han Kuo-yu (韓國食)🤔'), _utf8(u'dugtrio had an awesome 🤣 dugbook'), b'yo^what$is*up?', b'mothaf*&%ka', ], expected=[[[b'taste'], [b'the'], [b'rust', b'##is', b'##c'], [b'indie', b'##fr', b'##ost']], [[b'han'], [b'ku', b'##o'], [b'-'], [b'yu'], [b'('], [_utf8(u'韓')], [_utf8(u'國')], [_utf8(u'食')], [b')'], [_utf8(u'🤔')]], [[b'dug', b'##tri', b'##o'], [b'had'], [b'an'], [b'awesome'], [_utf8(u'🤣')], [b'dug', b'##book']], [[b'yo'], [b'^'], [b'what'], [b'$'], [b'is'], [b'*'], [b'up'], [b'?']], [[b'moth', b'##af'], [b'*'], [b'&'], [b'%'], [b'ka']]], expected_extracted=[[[b'taste'], [b'the'], [b'rust', b'is', b'c'], [b'indie', b'fr', b'ost']], [[b'Han'], [b'Ku', b'o'], [b'-'], [b'yu'], [b'('], [_utf8(u'韓')], [_utf8(u'國')], [_utf8(u'食')], [b')'], [_utf8(u'🤔')]], [[b'dug', b'tri', b'o'], [b'had'], [b'an'], [b'awesome'], [_utf8(u'🤣')], [b'dug', b'book']], [[b'yo'], [b'^'], [b'what'], [b'$'], [b'is'], [b'*'], [b'up'], [b'?']], [[b'moth', b'af'], [b'*'], [b'&'], [b'%'], [b'ka']]], lower_case=True, ), # Test when we are expecting multiple OOV vocab ids and tf.string just # maps out [UNK] token. dict( text_inputs=[ b'mothaf*&%ka cantfindme whodis', ], expected=[[[b'moth', b'##af'], [b'*'], [b'&'], [b'%'], [b'ka'], [b'[UNK]'], [b'[UNK]']]], expected_extracted=[[[b'moth', b'af'], [b'*'], [b'&'], [b'%'], [b'ka'], [b'cantfindme'], [b'whodis']]], lower_case=True, num_oov=2, ), dict( text_inputs=[ b'candy', ], expected=[[[b'candy']]], lower_case=True, num_oov=2, ), dict( text_inputs=[ _utf8(u'爱上一个不回家的人'), ], expected=[[[_utf8(u'爱')], [_utf8(u'上')], [_utf8(u'一')], [_utf8(u'个')], [_utf8(u'不')], [_utf8(u'回')], [_utf8(u'家')], [_utf8(u'的')], [_utf8(u'人')]]], lower_case=True, num_oov=2, ), # Test 'preserve_unused_token' option dict( text_inputs=[ b'taste the rustisc indiefrost [unused1]', _utf8(u'爱上一个不回家的人[unused23]'), ], expected=[[[b'taste'], [b'the'], [b'rust', b'##is', b'##c'], [b'indie', b'##fr', b'##ost'], [b'[unused1]']], [[_utf8(u'爱')], [_utf8(u'上')], [_utf8(u'一')], [_utf8(u'个')], [_utf8(u'不')], [_utf8(u'回')], [_utf8(u'家')], [_utf8(u'的')], [_utf8(u'人')], [b'[unused23]']]], preserve_unused_token=True, ), ]) @test_util.run_in_graph_and_eager_modes def test_bert_tokenizer(self, text_inputs, expected, vocab=None, expected_extracted=None, lower_case=True, num_oov=1, preserve_unused_token=False): text_inputs = constant_op.constant(text_inputs) if not vocab: vocab = _VOCAB table = _create_table(vocab, num_oov) self.evaluate(table.initializer) tokenizer = bert_tokenizer.BertTokenizer( table, token_out_type=dtypes.string, lower_case=lower_case, preserve_unused_token=preserve_unused_token) results = tokenizer.tokenize(text_inputs) self.assertAllEqual(results, expected) # Verify that the int ids are the same. expected_rt = ragged_factory_ops.constant(expected) expected_int = table.lookup(expected_rt.flat_values) expected_int_rt = ragged_tensor.RaggedTensor.from_nested_row_splits( expected_int, expected_rt.nested_row_splits) int_tokenizer = bert_tokenizer.BertTokenizer( vocab_lookup_table=table, token_out_type=dtypes.int64, lower_case=lower_case, preserve_unused_token=preserve_unused_token) results_int = int_tokenizer.tokenize(text_inputs) self.assertAllEqual(results_int, expected_int_rt) # Verify that the offsets can extract the expected tokens _, begin, end = tokenizer.tokenize_with_offsets(text_inputs) extracted_wordpieces = _ragged_substr(text_inputs, begin, end) if expected_extracted: self.assertAllEqual(extracted_wordpieces, expected_extracted) else: # The extracted won't have any wordpieces with '##' prefix. Strip them # out. stripped_prefix_flat = string_ops.regex_replace(expected_rt.flat_values, '##', '') stripped_prefix = expected_rt.with_flat_values(stripped_prefix_flat) self.assertAllEqual(extracted_wordpieces, stripped_prefix) if __name__ == '__main__': test.main()
2.40625
2
tests/index_test.py
DubeySandeep/pending-review-notification
0
9956
<filename>tests/index_test.py """Unit test for the index.py file.""" import unittest from datetime import datetime, timedelta, timezone import json from unittest.mock import patch, mock_open import requests_mock from src import index from src import github_services class ModuleIntegerationTest(unittest.TestCase): """Integeration test for the send notification feature.""" def setUp(self): self.orgName = 'orgName' self.repoName = 'repo' self.pull_response = [{ 'html_url': 'https://githuburl.pull/123', 'number': 123, 'title': 'PR title 1', 'user': { 'login': 'authorName', }, 'assignees': [{ 'login': 'reviewerName1', }, { 'login': 'reviewerName2', }] }, { 'html_url': 'https://githuburl.pull/234', 'number': 234, 'title': 'PR title 2', 'user': { 'login': 'authorName', }, 'assignees': [{ 'login': 'reviewerName1', }, { 'login': 'reviewerName2', }] }] def get_past_time(hours=0): return ( datetime.now(timezone.utc) - timedelta(hours=hours)).strftime( "%Y-%m-%dT%H:%M:%SZ") self.timeline1 = [{ 'event': 'created' }, { 'event': 'assigned', 'assignee': { 'login': 'reviewerName1' }, 'created_at': get_past_time(hours=22) },{ 'event': 'assigned', 'assignee': { 'login': 'reviewerName2' }, 'created_at': get_past_time(hours=56) }] self.timeline2 = [{ 'event': 'created' }, { 'event': 'assigned', 'assignee': { 'login': 'reviewerName1' }, 'created_at': get_past_time(hours=23) }, { 'event': 'assigned', 'assignee': { 'login': 'reviewerName2' }, 'created_at': get_past_time(hours=19) }] self.test_template = "{{ username }}\n{{ pr_list }}" def mock_all_get_requests(self, mock_request): param_page_1='?page=1&per_page=100' param_page_2='?page=2&per_page=100' mock_request.get( github_services.PULL_REQUESTS_URL_TEMPLATE.format( self.orgName, self.repoName) + param_page_1, text=json.dumps(self.pull_response)) mock_request.get( github_services.PULL_REQUESTS_URL_TEMPLATE.format( self.orgName, self.repoName) + param_page_2, text=json.dumps([])) mock_request.get( github_services.ISSUE_TIMELINE_URL_TEMPLATE.format( self.orgName, self.repoName, 123) + param_page_1, text=json.dumps(self.timeline1)) mock_request.get( github_services.ISSUE_TIMELINE_URL_TEMPLATE.format( self.orgName, self.repoName, 123) + param_page_2, text=json.dumps([])) mock_request.get( github_services.ISSUE_TIMELINE_URL_TEMPLATE.format( self.orgName, self.repoName, 234) + param_page_1, text=json.dumps(self.timeline2)) mock_request.get( github_services.ISSUE_TIMELINE_URL_TEMPLATE.format( self.orgName, self.repoName, 234) + param_page_2, text=json.dumps([])) def mock_post_discussion_request(self, mock_request): request = mock_request.post( github_services.CREATE_DISCUSSION_URL_TEMPLATE.format( self.orgName, 'teamName'), text=json.dumps({})) return request def test_executing_main_function_sends_notification(self): with requests_mock.Mocker() as mock_request: self.mock_all_get_requests(mock_request) request = self.mock_post_discussion_request(mock_request) file_data = mock_open(read_data=self.test_template) with patch("builtins.open", file_data): index.main([ '--team', 'teamName', '--repo', 'orgName/repo', '--max-wait-hours', '20', '--token', 'githubTokenForApiRequest' ]) self.assertTrue(request.called) self.assertEqual(request.call_count, 2) expected_messages = [ { 'title': '[@reviewerName1] Pending review on PRs', 'body': '@reviewerName1\n- [#123](https://githuburl.pull/123) ' '[Waiting from the last 22 hours]\n' '- [#234](https://githuburl.pull/234) ' '[Waiting from the last 23 hours]' }, { 'title': '[@reviewerName2] Pending review on PRs', 'body': '@reviewerName2\n- [#123](https://githuburl.pull/123) ' '[Waiting from the last 2 days, 8 hours]' }, ] self.assertEqual( request.request_history[0].json(), expected_messages[0]) self.assertEqual( request.request_history[1].json(), expected_messages[1])
2.890625
3
rqalpha/data/instrument_mixin.py
mysky528/rqalpha
3
9957
<gh_stars>1-10 # -*- coding: utf-8 -*- # # Copyright 2017 Ricequant, 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 six class InstrumentMixin(object): def __init__(self, instruments): self._instruments = {i.order_book_id: i for i in instruments} self._sym_id_map = {i.symbol: k for k, i in six.iteritems(self._instruments) # 过滤掉 CSI300, SSE50, CSI500, SSE180 if not i.order_book_id.endswith('INDX')} try: # FIXME # 沪深300 中证500 固定使用上证的 for o in ['000300.XSHG', '000905.XSHG']: self._sym_id_map[self._instruments[o].symbol] = o # 上证180 及 上证180指数 两个symbol都指向 000010.XSHG self._sym_id_map[self._instruments['SSE180.INDX'].symbol] = '000010.XSHG' except KeyError: pass def sector(self, code): return [v.order_book_id for v in self._instruments.values() if v.type == 'CS' and v.sector_code == code] def industry(self, code): return [v.order_book_id for v in self._instruments.values() if v.type == 'CS' and v.industry_code == code] def concept(self, *concepts): return [v.order_book_id for v in self._instruments.values() if v.type == 'CS' and any(c in v.concept_names.split('|') for c in concepts)] def all_instruments(self, types, dt=None): return [i for i in self._instruments.values() if ((dt is None or i.listed_date.date() <= dt.date() <= i.de_listed_date.date()) and (types is None or i.type in types))] def _instrument(self, sym_or_id): try: return self._instruments[sym_or_id] except KeyError: try: sym_or_id = self._sym_id_map[sym_or_id] return self._instruments[sym_or_id] except KeyError: return None def instruments(self, sym_or_ids): if isinstance(sym_or_ids, six.string_types): return self._instrument(sym_or_ids) return [i for i in [self._instrument(sid) for sid in sym_or_ids] if i is not None] def get_future_contracts(self, underlying, date): date = date.replace(hour=0, minute=0, second=0) futures = [v for o, v in six.iteritems(self._instruments) if v.type == 'Future' and v.underlying_symbol == underlying and not o.endswith('88') and not o.endswith('99')] if not futures: return [] return sorted(i.order_book_id for i in futures if i.listed_date <= date <= i.de_listed_date)
1.992188
2
Python 3/19.prac_no2.py
ByeonUi-Hyeok/practice
1
9958
import funcvote as vote votes = input("투표내용 >>>") # print(votes) # print(type(votes)) result = vote.str2int(votes) print(vote.countvotes(result)) result = vote.countvotes(result) vote.printvote(result) # 투표 초안
3.421875
3
tutorials/30-days-of-code/30-operators.py
PingHuskar/hackerrank
41
9959
<filename>tutorials/30-days-of-code/30-operators.py # Day 2: Operators # Start using arithmetic operators. # # https://www.hackerrank.com/challenges/30-operators/problem # #!/bin/python3 import sys if __name__ == "__main__": meal_cost = float(input().strip()) tip_percent = int(input().strip()) tax_percent = int(input().strip()) cost = meal_cost * (1 + tip_percent / 100 + tax_percent / 100) print("The total meal cost is {:.0f} dollars.".format(cost))
4.15625
4
articles/views.py
qwghlm/CommentIsMee
0
9960
from django.http import HttpResponse from django.template import RequestContext, loader from django.shortcuts import render, get_object_or_404, redirect from django.core.urlresolvers import reverse from django.core.cache import cache from articles.models import CIFArticle from .forms import CIFArticleForm def index(request): """ Handle requests to the homepage """ article = None # If a user has submitted a URL... if request.POST: form = CIFArticleForm(request.POST) if (form.is_valid()): try: article = form.save(commit=False) existing_articles = CIFArticle.objects.filter(url=article.url).count() if existing_articles: article = CIFArticle.objects.get(url=article.url) else: article.measure_ego() article.save() except ValueError, e: article = None form._errors["url"] = form.error_class([str(e)]) # If no URL submitted, just set up a blank form else: form = CIFArticleForm() # If an article is found or created due to a user submission, redirect there if article: return redirect(reverse("articles:detail", args=(article.id,))) # Else show the homepage & rendered form else: top_articles = cache.get('cim:top_articles') if top_articles is None: top_articles = CIFArticle.objects.filter(is_cif=1).order_by('-score')[:10] cache.set('cim:top_articles', top_articles, 60) latest_articles = cache.get('cim:latest_articles') if latest_articles is None: latest_articles = CIFArticle.objects.filter(is_cif=1).order_by('-id')[:5] cache.set('cim:latest_articles', latest_articles, 30) return render(request, 'articles/index.html', { 'form' : form , 'top_articles' : top_articles, 'latest_articles' : latest_articles }) def detail(request, article_id): """ Handle detail view for an article """ # Quite simple, set up article and form form = CIFArticleForm() article_key = 'cim:article:%s' % article_id article = cache.get(article_key) if article is None: article = get_object_or_404(CIFArticle, id=article_id) cache.set(article_key, article, 300) return render(request, 'articles/detail.html', { 'article' : article, 'form' : form })
2.359375
2
utils/runtime_mode.py
omiderfanmanesh/dengue-infections-prediction
0
9961
<gh_stars>0 # Copyright (c) 2021, <NAME>, All rights reserved. class RuntimeMode: TRAIN = 0 TUNING = 1 CROSS_VAL = 2 FEATURE_IMPORTANCE = 3
1.296875
1
pydoc_fork/__main__.py
matthewdeanmartin/pydoc_fork
0
9962
<gh_stars>0 # noinspection PyPep8 """pydoc_fork A fork of pydoc that is optimized for generating html documentation in a CI context Usage: pydoc_fork <package>... [options] pydoc_fork (-h | --help) pydoc_fork --version Options: -h --help Show this screen. -v --version Show version. --quiet No printing or logging. --verbose Crank up the logging. --config <config> pyproject.toml or other toml config. --document_internals respect underscore or __all__ private --prefer_docs_python_org link to python.org or generate own stdlib docs -o --output <folder> where to write files """ # TODO: implement this # pydoc_fork dot_notation <importable>... [--output=<folder>] [--document_internals] # pydoc_fork source_path <path>... [--output=<folder>] [--document_internals] import logging import sys import docopt from pydoc_fork import commands, settings from pydoc_fork.settings import load_config LOGGER = logging.getLogger(__name__) LOGGERS = [] __version__ = "3.0.0" def main() -> int: """Get the args object from command parameters""" arguments = docopt.docopt(__doc__, version=f"pydoc_fork {__version__}") config_path = arguments.get("<config>") if config_path: load_config(config_path) LOGGER.debug(f"Invoking with docopts: {str(arguments)}") output_folder = arguments["--output"] # TODO: add lists of packages package = arguments["<package>"] or [] # quiet = bool(arguments.get("--quiet", False)) if arguments.get("--document_internals"): settings.DOCUMENT_INTERNALS = arguments["--document_internals"] if arguments.get("--prefer_docs_python_org"): settings.PREFER_DOCS_PYTHON_ORG = arguments["--prefer_docs_python_org"] if arguments.get("--verbose"): # root logger, all modules for root in ("pydoc_fork", "__main__"): logger = logging.getLogger(root) logger.setLevel(logging.DEBUG) handler = logging.StreamHandler() handler.setLevel(logging.DEBUG) log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" formatter = logging.Formatter(log_format) handler.setFormatter(formatter) logger.addHandler(handler) LOGGERS.append(logger) commands.process_path_or_dot_name( package, output_folder=output_folder, ) # # TODO # print("Don't recognize that command.") # return -1 return 0 if __name__ == "__main__": sys.exit(main())
2.296875
2
backend/src/libs/strings.py
codeglitchz/attendance-system
37
9963
""" libs.strings By default, uses `en-gb.json` file inside the `strings` top-level folder. If language changes, set `libs.strings.default_locale` and run `libs.strings.refresh()`. """ import json default_locale = "en-us" cached_strings = {} def refresh(): global cached_strings with open(f"strings/{default_locale}.json") as f: cached_strings = json.load(f) def gettext(name): return cached_strings[name] refresh()
2.53125
3
nemo_cmd/deflate.py
SalishSeaCast/NEMO-Cmd
1
9964
# Copyright 2013-2021 The Salish Sea MEOPAR Contributors # and The University of British Columbia # 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. """NEMO-Cmd command plug-in for deflate sub-command. Deflate variables in netCDF files using Lempel-Ziv compression. """ import logging import math import multiprocessing from pathlib import Path import shlex import subprocess import time import attr import cliff.command logger = logging.getLogger(__name__) class Deflate(cliff.command.Command): """Deflate variables in netCDF files using Lempel-Ziv compression.""" def get_parser(self, prog_name): parser = super(Deflate, self).get_parser(prog_name) parser.description = """ Deflate variables in netCDF files using Lempel-Ziv compression. Converts files to netCDF-4 format. The deflated file replaces the original file. This command is effectively the same as running ncks -4 -L -O FILEPATH FILEPATH for each FILEPATH. """ parser.add_argument( "filepaths", nargs="+", type=Path, metavar="FILEPATH", help="Path/name of file to be deflated.", ) parser.add_argument( "-j", "--jobs", type=int, default=math.floor(multiprocessing.cpu_count() / 2), help=( "Maximum number of concurrent deflation processes allowed. " "Defaults to 1/2 the number of cores detected." ), ) return parser def take_action(self, parsed_args): """Execute the :command:`nemo deflate` sub-command. Deflate variables in netCDF files using Lempel-Ziv compression. Converts files to netCDF-4 format. The deflated file replaces the original file. This command is effectively the same as :command:`ncks -4 -L -O filename filename`. """ deflate(parsed_args.filepaths, parsed_args.jobs) @attr.s class DeflateJob(object): """netCDF file deflation job.""" #: Path/name of the netCDF file to deflate. filepath = attr.ib() #: Lempel-Ziv compression level to use. dfl_lvl = attr.ib(default=4) #: Deflation job subprocess object. process = attr.ib(default=None) #: Deflation job process PID. pid = attr.ib(default=None) #: Deflation job process return code. returncode = attr.ib(default=None) def start(self): """Start the deflation job in a subprocess. Cache the subprocess object and its process id as job attributes. """ cmd = "nccopy -s -4 -d{0.dfl_lvl} {0.filepath} {0.filepath}.nccopy.tmp".format( self ) self.process = subprocess.Popen( shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, ) self.pid = self.process.pid logger.debug("deflating {0.filepath} in process {0.pid}".format(self)) @property def done(self): """Return a boolean indicating whether or not the job has finished. Cache the subprocess return code as a job attribute. """ finished = False self.returncode = self.process.poll() if self.returncode is not None: if self.returncode == 0: Path("{0.filepath}.nccopy.tmp".format(self)).rename(self.filepath) finished = True logger.debug( "deflating {0.filepath} finished " "with return code {0.returncode}".format(self) ) return finished def deflate(filepaths, max_concurrent_jobs): """Deflate variables in each of the netCDF files in filepaths using Lempel-Ziv compression. Converts files to netCDF-4 format. The deflated file replaces the original file. :param sequence filepaths: Paths/names of files to be deflated. :param int max_concurrent_jobs: Maximum number of concurrent deflation processes allowed. """ logger.info( "Deflating in up to {} concurrent sub-processes".format( int(max_concurrent_jobs) ) ) jobs = [DeflateJob(fp) for fp in filepaths if fp.exists()] jobs_in_progress = _launch_initial_jobs(jobs, max_concurrent_jobs) while jobs or jobs_in_progress: time.sleep(1) _poll_and_launch(jobs, jobs_in_progress) def _launch_initial_jobs(jobs, max_concurrent_jobs): jobs_in_progress = {} for process in range(int(max_concurrent_jobs)): try: job = jobs.pop(0) except IndexError: break else: job.start() jobs_in_progress[job.pid] = job return jobs_in_progress def _poll_and_launch(jobs, jobs_in_progress): for running_job in jobs_in_progress.copy().values(): if running_job.done: result, _ = running_job.process.communicate() logger.error(result) if result else logger.info( "netCDF4 deflated {.filepath}".format(running_job) ) jobs_in_progress.pop(running_job.pid) try: job = jobs.pop(0) except IndexError: continue else: job.start() jobs_in_progress[job.pid] = job
2.09375
2
src/UQpy/distributions/collection/__init__.py
SURGroup/UncertaintyQuantification
0
9965
"""distributions module.""" from UQpy.distributions.collection.Beta import Beta from UQpy.distributions.collection.Binomial import Binomial from UQpy.distributions.collection.Cauchy import Cauchy from UQpy.distributions.collection.ChiSquare import ChiSquare from UQpy.distributions.collection.Exponential import Exponential from UQpy.distributions.collection.Gamma import Gamma from UQpy.distributions.collection.GeneralizedExtreme import GeneralizedExtreme from UQpy.distributions.collection.InverseGaussian import InverseGauss from UQpy.distributions.collection.Laplace import Laplace from UQpy.distributions.collection.Levy import Levy from UQpy.distributions.collection.Logistic import Logistic from UQpy.distributions.collection.Lognormal import Lognormal from UQpy.distributions.collection.Maxwell import Maxwell from UQpy.distributions.collection.Multinomial import Multinomial from UQpy.distributions.collection.MultivariateNormal import MultivariateNormal from UQpy.distributions.collection.Normal import Normal from UQpy.distributions.collection.Pareto import Pareto from UQpy.distributions.collection.Poisson import Poisson from UQpy.distributions.collection.Rayleigh import Rayleigh from UQpy.distributions.collection.TruncatedNormal import TruncatedNormal from UQpy.distributions.collection.Uniform import Uniform from UQpy.distributions.collection.JointIndependent import JointIndependent from UQpy.distributions.collection.JointCopula import JointCopula
1.148438
1
Learning Python/Exercise Files/Ch2/helloworld_my.py
RomanShevtsiv/linkedin-learning
0
9966
# # Example file for HelloWorld # def main(): print("Hello World") name = input("What is your name? ") print("Nice to meet you,", name) if __name__ == "__main__": main()
3.5625
4
RocketSimulation.py
pietrotrope/SolarSystemSimulation
0
9967
<filename>RocketSimulation.py<gh_stars>0 import sys import csv import json import math import pygame import numpy as np from pygame.locals import * import pandas as pd from data import * from agent import agentsList, Agent global screenSize screenSize = [1920, 1080] def load_parameters(path): package = [] file = open(path, 'r') j = json.load(file) for subgroup in j.values(): package.append([cast(x) for x in subgroup.values()]) env_variables = package.pop(4) file.close() return (package, env_variables) def cast(x): try: return float(x) except Exception: return str(x) class Environment: def __init__(self, vars): # Environmental Constants self.elev, self.t, self.g, self.M_air, self.R, self.gamma, self.P_zero = vars # noqa self.g_zero = self.g self.Re = 6356766 # Layer base altitudes self.hb = [0, 11000, 20000, 32000, 47000, 51000, 71000] # Layer base pressures self.Pb = [101325, 22632.1, 5474.89, 868.019, 110.906, 66.9389, 3.95642] # Layer base temperatures self.Tb = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65] # Layer lapse rates self.Lm = [-0.0065, 0.0, 0.001, 0.0028, 0.0, -0.0028, -0.002] def get_geopotential_altitude(self, z: float) -> float: return self.Re*z / (self.Re+z) def atmo_heterosphere_equ(self, z: float, a, b, c, d, e): z_km = z/1000 return math.exp(a * z_km**4 + b * z_km**3 + c * z_km**2 + d * z_km + e) # noqa def get_gravity(self, z: float) -> float: return self.g_zero * (self.Re / (self.Re + z))**2 def get_temp(self, z: float, h: float) -> float: if h <= 84852: for i in range(len(self.hb)-1): if self.hb[i] <= h <= self.hb[i+1]: return (self.Tb[i] + self.Lm[i]*(h-self.hb[i]), i) return (self.Tb[i+1] + self.Lm[i+1]*(h-self.hb[i+1]), i+1) elif 86000 < z <= 91000: return (186.87, 7) elif 91000 < z <= 110000: if 91000 < z <= 100000: layer = 8 elif 100000 < z <= 110000: layer = 9 return ( 263.1905 - 76.3232 * math.sqrt(1 - ((z - 91000) / -19942.9)**2), # noqa layer ) elif 110000 < z <= 120000: return (240 + 0.012 * (z - 110000), 10) elif 120000 < z <= 1000000: if 120000 < z <= 150000: layer = 11 elif 150000 < z <= 200000: layer = 12 elif 200000 < z <= 300000: layer = 13 elif 300000 < z <= 500000: layer = 14 elif 500000 < z <= 750000: layer = 15 elif 750000 < z <= 1000000: layer = 16 xi = (z - 120000) * (6356766 + 120000) / (6356766 + z) return (1000 - 640 * math.exp(-0.00001875 * xi), layer) def get_pressure(self, z: float, h: float, T: float, b: int) -> float: if b <= 6: if self.Lm[b] != 0: return self.Pb[b] * (self.Tb[b]/T)**(self.g_zero*self.M_air/(self.R*self.Lm[b])) # noqa else: return self.Pb[b] * math.exp(-self.g_zero * self.M_air * (h-self.hb[b]) / (self.R*self.Tb[b])) # noqa elif b == 7: return self.atmo_heterosphere_equ( z, 0.000000, 2.159582e-6, -4.836957e-4, -0.1425192, 13.47530) elif b == 8: return self.atmo_heterosphere_equ( z, 0.000000, 3.304895e-5, -0.009062730, 0.6516698, -11.03037) elif b == 9: return self.atmo_heterosphere_equ( z, 0.000000, 6.693926e-5, -0.01945388, 1.719080, -47.75030) elif b == 10: return self.atmo_heterosphere_equ( z, 0.000000, -6.539316e-5, 0.02485568, -3.223620, 135.9355) elif b == 11: return self.atmo_heterosphere_equ( z, 2.283506e-7, -1.343221e-4, 0.02999016, -3.055446, 113.5764) elif b == 12: return self.atmo_heterosphere_equ( z, 1.209434e-8, -9.692458e-6, 0.003002041, -0.4523015, 19.19151) elif b == 13: return self.atmo_heterosphere_equ( z, 8.113942e-10, -9.822568e-7, 4.687616e-4, -0.1231710, 3.067409) elif b == 14: return self.atmo_heterosphere_equ( z, 9.814674e-11, -1.654439e-7, 1.148115e-4, -0.05431334, -2.011365) elif b == 15: return self.atmo_heterosphere_equ( z, -7.835161e-11, 1.964589e-7, -1.657213e-4, 0.04305869, -14.77132) elif b == 16: return self.atmo_heterosphere_equ( z, 2.813255e-11, -1.120689e-7, 1.695568e-4, -0.1188941, 14.56718) def get_density(self, z: float, P: float, T: float, b) -> float: if b <= 6: return (P * self.M_air)/(self.R * T) elif b == 7: return self.atmo_heterosphere_equ( z, 0.000000, -3.322622E-06, 9.111460E-04, -0.2609971, 5.944694) elif b == 8: return self.atmo_heterosphere_equ( z, 0.000000, 2.873405e-05, -0.008492037, 0.6541179, -23.62010) elif b == 9: return self.atmo_heterosphere_equ( z, -1.240774e-05, 0.005162063, -0.8048342, 55.55996, -1443.338) elif b == 10: return self.atmo_heterosphere_equ( z, 0.00000, -8.854164e-05, 0.03373254, -4.390837, 176.5294) elif b == 11: return self.atmo_heterosphere_equ( z, 3.661771e-07, -2.154344e-04, 0.04809214, -4.884744, 172.3597) elif b == 12: return self.atmo_heterosphere_equ( z, 1.906032e-08, -1.527799E-05, 0.004724294, -0.6992340, 20.50921) elif b == 13: return self.atmo_heterosphere_equ( z, 1.199282e-09, -1.451051e-06, 6.910474e-04, -0.1736220, -5.321644) elif b == 14: return self.atmo_heterosphere_equ( z, 1.140564e-10, -2.130756e-07, 1.570762e-04, -0.07029296, -12.89844) elif b == 15: return self.atmo_heterosphere_equ( z, 8.105631e-12, -2.358417e-09, -2.635110e-06, -0.01562608, -20.02246) elif b == 16: return self.atmo_heterosphere_equ( z, -3.701195e-12, -8.608611e-09, 5.118829e-05, -0.06600998, -6.137674) def get_c(self, T: float) -> float: return math.sqrt((self.gamma * self.R * T) / self.M_air) def get_status(self, z: float): h = round(self.get_geopotential_altitude(z), 0) self.g = self.get_gravity(z) self.T, b = self.get_temp(z, h) self.P = self.get_pressure(z, h, self.T, b) self.Rho = self.get_density(z, self.P, self.T, b) self.c = self.get_c(self.T) class System: def __init__(self, params, env, burn_time: float): package = params print(package) # Environment self.env = env # Burn time self.num_steps = int(burn_time // self.env.t) self.burn_time = self.num_steps * self.env.t # Engine specs self.etype = package[0][0] package[0].pop(0) if self.etype == "Liquid": self.isp, self.thrust = package[0] elif self.etype == "Solid": self.isp, self.avg_thrust, path = package[0] # noqa with(open(path)) as f: csv_reader = csv.reader(f) self.thrust_curve = {} for row in csv_reader: self.thrust_curve.update({ float(row[0]): float(row[1]) }) f.close() # Fuel Specs if self.etype == "Liquid": self.OFratio, self.Reserve = package[1] elif self.etype == "Solid": self.OFratio = 0 self.Reserve = package[1][0] # Flow Rate if self.etype == "Liquid": self.w = (self.thrust/self.env.g_zero)/self.isp elif self.etype == "Solid": self.w = (self.avg_thrust/self.env.g_zero)/self.isp self.dF = self.w * (1 / (self.OFratio + 1)) self.dOx = (self.w - self.dF) # Fuel & Oxidizer self.F = (self.dF * self.burn_time)/(1 - self.Reserve/100) self.Ox = (self.dOx * self.burn_time)/(1 - self.Reserve/100) # Mass self.dry_mass = package[2][0] # Aerodynamics self.Cd, self.cross_section = package[3] # Output self.csvout = package[4][0] self.field_names = ["t", "thrust", "drag", "m", "v", "mach", "a", "altitude", "asl", "twr", "max_v", "max_mach", "max_acc", "min_acc", "max_g", "min_g"] with open(self.csvout, "w", newline="") as f: csv_writer = csv.writer(f) csv_writer.writerow(self.field_names) f.close() # Flight def launch(self): """Runs a simulation within the given parameters.""" # Variables setup self.t = 0 self.altitude = 0 self.asl = self.altitude + self.env.elev self.calc_mass() self.env.get_status(self.asl) self.calc_thrust() self.calc_twr() self.drag = 0 self.v = 0 self.max_v = 0 self.mach = 0 self.max_mach = 0 self.max_acc = 0 self.max_g = 0 self.min_acc = 0 self.min_g = 0 self.a = 0 self.j = 0 self.s = 0 # Used by matplotlib self.data = [[], [], [], [], [], [], [], [], [], [], []] # Accelaration phase for i in range(self.num_steps): # Output management self.add_data() # Environment-related self.update_env() # Thrust-related self.calc_thrust() # Accelaration/derivative-related self.calc_acc() self.calc_additional_derivatives() # Position-related self.set_altitude() # Velocity-related self.calc_velocity() # Force-related self.calc_drag() self.calc_twr() # Mass-related self.calc_propellant() self.calc_mass() # Time-related self.t += self.env.t if self.a > self.max_acc: self.max_acc = self.a self.max_g = self.max_acc/self.env.g if self.v > self.max_v: self.max_v = self.v self.max_mach = self.mach self.thrust = 0 # Deceleration phase while self.v > 0: # Output management self.add_data() # Environment-related self.update_env() # Accelaration/derivative-related self.calc_acc() self.calc_additional_derivatives() # Position-related self.set_altitude() # Velocity-related self.calc_velocity() # Force-related self.calc_drag() self.calc_twr() # Mass-related self.calc_mass() # Time-related self.t += self.env.t if self.a < self.min_acc: self.min_acc = self.a self.min_g = self.min_acc/self.env.g self.output("max_v", "max_mach", "max_acc", "min_acc", "max_g", "min_g") def suicide_burn(self): """Run a suicide burn simulation, will affct ascent simulation.""" self.Vt = math.sqrt((2 * self.m * self.env.g) / (self.env.Rho * self.cross_section * self.Cd)) # noqa # Mass def calc_mass(self): self.propellant_mass = (self.Ox + self.F) self.m = self.propellant_mass + self.dry_mass def calc_propellant(self): if self.etype == "Liquid": self.w = (self.thrust/self.env.g_zero)/self.isp elif self.etype == "Solid": self.w = (self.avg_thrust/self.env.g_zero)/self.isp self.dF = self.w * (1/(self.OFratio+1)) self.dOx = (self.w - self.dF) self.Ox -= self.dOx * self.env.t self.F -= self.dF * self.env.t # Position def set_altitude(self): self.altitude += self.v * self.env.t + (self.a * self.env.t**2)/2 # noqa self.asl = self.altitude + self.env.elev # Derivatives of position def calc_velocity(self): self.v += self.a * self.env.t self.mach = self.v/self.env.c def calc_acc(self): self.a = (self.thrust - (self.m * self.env.g + self.drag)) / self.m def calc_additional_derivatives(self): self.j = (self.a - self.data[4][-1]) / self.env.t self.s = (self.j - self.data[5][-1]) / self.env.t # Forces def calc_thrust(self): if self.etype == "Liquid": pass elif self.etype == "Solid": self.thrust = self.thrust_curve[round(self.t, 3)] def calc_drag(self): self.drag = 0.5 * (self.env.Rho * self.v**2 * self.Cd * self.cross_section) # noqa def calc_twr(self): self.twr = self.thrust / (self.m * self.env.g) # Environment def update_env(self): self.env.get_status(self.asl) # Ouput def output(self, *args): values = [] for field in self.field_names: value = str(round(eval(field, self.__dict__), 5)) values.append(value) with open(self.csvout, "a", newline="") as f: csv_writer = csv.writer(f) csv_writer.writerow(values) f.close() def add_data(self): self.data[0].append(self.t) self.data[1].append(self.altitude) self.data[2].append(self.v) self.data[3].append(self.env.c) self.data[4].append(self.a) self.data[5].append(self.j) self.data[6].append(self.s) self.data[7].append(self.drag) self.output("t", "thrust", "drag", "m", "v", "mach", "a", "altitude", "asl", "twr") def run_simulation(burn_time): params = load_parameters("RocketSimulationData/info.json") env = Environment(params[1]) s = System(params[0], env, burn_time) s.launch() def renderAgents(screen, res, ratio): screen.fill((0, 0, 0)) pygame.draw.rect(screen, (0, 0, 255), (0, 1080-108, 1920, 108)) pos = screenSize[1]-158 - res["altitude"]*ratio # print("altitude: "+str(res["altitude"])+", pos: "+str(pos)) pygame.draw.rect(screen, (255, 255, 255), (940, pos, 20, 50)) pygame.display.update() def simulateRocket(screen): run_simulation(150) df = pd.read_csv('RocketSimulationData/Flight.csv') result = df.to_dict("index") ratio = screenSize[1]/1000000 interestingPoint = None for res in result: # print("time: "+str(result[res]["t"])+" Altitude: "+str(result[res]["altitude"])) for event in pygame.event.get(): if event.type == QUIT: pygame.quit() sys.exit() renderAgents(screen, result[res], ratio) if result[res]["altitude"] < 800000: interestingPoint = result[res] pygame.display.update() return interestingPoint
2.625
3
sourcecode/GAN/FID/__init__.py
toufeeqahamedns/GeneratingHumanFaces
0
9968
""" Package has implementation for the FID score calculation """ from GAN.FID import fid_score from GAN.FID import inception
1.203125
1
flask_monitoringdashboard/test/core/profiler/util/test_stringhash.py
timgates42/Flask-MonitoringDashboard
3
9969
import unittest from flask_monitoringdashboard.core.profiler.util.stringHash import StringHash class TestStringHash(unittest.TestCase): def test_stringhash(self): string_hash = StringHash() self.assertEqual(string_hash.hash('abc'), 0) self.assertEqual(string_hash.hash('def'), 1) self.assertEqual(string_hash.hash('abc'), 0) def test_unhash(self): string_hash = StringHash() self.assertEqual(string_hash.unhash(string_hash.hash('abc')), 'abc') self.assertRaises(ValueError, string_hash.unhash, 'unknown')
3.03125
3
news/views.py
valch85/newssite
0
9970
from django.shortcuts import render, get_object_or_404 from .models import News # Create your views here. def index(request): latest_news_list = News.objects.order_by('-pub_date')[:10] context = {'latest_news_list': latest_news_list} return render(request, 'news/index.html', context) def detail(request, news_id): new = get_object_or_404(News, pk=news_id) return render(request, 'news/detail.html', {'new': new})
2
2
benchmark/generate_libs/jamplus.py
chadaustin/ibb
4
9971
#!/usr/bin/python import os.path import cppcodebase import random def CreateLibJamfile(lib_number, classes): os.chdir(cppcodebase.lib_name(lib_number)) handle = file("Jamfile.jam", "w") handle.write ("SubDir TOP lib_" + str(lib_number) + " ;\n\n") handle.write ("SubDirHdrs $(INCLUDES) ;\n\n") handle.write ("Library lib_" + str(lib_number) + " :\n") for i in xrange(classes): handle.write(' class_' + str(i) + '.cpp\n') handle.write (' ;\n') os.chdir('..') def CreateFullJamfile(libs): handle = file("Jamfile.jam", "w") handle.write ("SubDir TOP ;\n\n") for i in xrange(libs): handle.write('SubInclude TOP ' + cppcodebase.lib_name(i) + ' ;\n') handle.write('\nWorkspace GeneratedLibs :\n') for i in xrange(libs): handle.write('\t\t' + cppcodebase.lib_name(i) + '\n') handle.write(';\n') handle = file("Jamrules.jam", "w") handle.write ('INCLUDES = $(TOP) ;\n') def CreateCodebase(libs, classes, internal_includes, external_includes): cppcodebase.SetDir('jamplus') cppcodebase.CreateSetOfLibraries(libs, classes, internal_includes, external_includes, CreateLibJamfile) CreateFullJamfile(libs) os.chdir('..')
2.46875
2
inbm/dispatcher-agent/dispatcher/fota/fota.py
intel/intel-inb-manageability
5
9972
""" FOTA update tool which is called from the dispatcher during installation Copyright (C) 2017-2022 Intel Corporation SPDX-License-Identifier: Apache-2.0 """ import logging import os import platform from threading import Timer from typing import Any, Optional, Mapping from future.moves.urllib.parse import urlparse from inbm_common_lib.exceptions import UrlSecurityException from inbm_common_lib.utility import canonicalize_uri from inbm_common_lib.constants import REMOTE_SOURCE from .constants import * from .fota_error import FotaError from .manifest import parse_tool_options, parse_guid, parse_hold_reboot_flag from .os_factory import OsFactory, OsType from ..common import dispatcher_state from ..common.result_constants import * from ..constants import UMASK_OTA from ..dispatcher_callbacks import DispatcherCallbacks from ..dispatcher_exception import DispatcherException from ..downloader import download from ..packagemanager.local_repo import DirectoryRepo logger = logging.getLogger(__name__) class FOTA: """AKA FOTA Tool An instance of this class will be called from the dispatcher if the requested type of update is FOTA """ def __init__(self, parsed_manifest: Mapping[str, Optional[Any]], repo_type: str, dispatcher_callbacks: DispatcherCallbacks) -> None: """Base class constructor for variable assignment, to send telemetry info and create a new directory if no repo is present @param parsed_manifest: Parsed parameters from manifest @param repo_type: OTA source location -> local or remote @param dispatcher_callbacks: DispatcherCallbacks instance """ logger.debug(f"parsed_manifest: {parsed_manifest}") self._ota_element = parsed_manifest.get('resource') logger.debug(f"ota_element: {self._ota_element}") self._dispatcher_callbacks = dispatcher_callbacks self._uri: Optional[str] = parsed_manifest['uri'] self._repo_type = repo_type repo_path: Optional[str] """If repo_type=local, then use path and not URI""" if self._repo_type == REMOTE_SOURCE: if not self._uri: raise FotaError("missing URI.") else: self._pkg_filename = os.path.basename(urlparse(self._uri).path) repo_path = None else: if self._ota_element is None or 'path' not in self._ota_element: raise FotaError('attempting to use local repo for FOTA but no path specified') self._pkg_filename = os.path.basename(self._ota_element['path']) path = self._ota_element.get('path', None) logger.debug(f"path: {path}") if path is None: repo_path = None else: repo_path = os.path.dirname(path) logger.debug(f"repo_path: {repo_path}") self.__signature = parsed_manifest['signature'] self._hash_algorithm = parsed_manifest['hash_algorithm'] self._username = parsed_manifest['username'] self._password = parsed_manifest['password'] if self._dispatcher_callbacks is None: raise FotaError("dispatcher_callbacks not specified in FOTA constructor") self._dispatcher_callbacks.broker_core.telemetry("Firmware Update Tool launched") if repo_path: logger.debug("Using manifest specified repo path") self._repo = DirectoryRepo(repo_path) else: logger.debug("Using default repo path") self._repo = DirectoryRepo(CACHE) def install(self) -> Result: """checks current platform versions and then issues download and install. Performs clean() in failure conditions @return: (Result) containing status code and message """ logger.debug("") return_message: Result = Result() hold_reboot = False try: factory = OsFactory.get_factory( self._verify_os_supported(), self._ota_element, self._dispatcher_callbacks) bios_vendor, platform_product = factory.create_upgrade_checker().check() if self._repo_type.lower() == REMOTE_SOURCE: # need to perform this check here because some FOTA commands don't have a URI -- see constructor # (instead they have a path) if self._uri is None: raise FotaError( "internal error: _uri uninitialized in Fota.install with download requested in manifest") uri = canonicalize_uri(self._uri) download(dispatcher_callbacks=self._dispatcher_callbacks, uri=uri, repo=self._repo, umask=UMASK_OTA, username=self._username, password=self._password) else: logger.debug("Skipping FOTA upgradable check for local repo") if self._ota_element is None: raise FotaError("missing ota_element") tool_options = parse_tool_options(self._ota_element) logger.debug(f"tool_options: {tool_options}") guid = parse_guid(self._ota_element) logger.debug(f"guid: {guid}") hold_reboot = parse_hold_reboot_flag(self._ota_element) logger.debug(f"holdReboot: {hold_reboot}; pkg_filename: {self._pkg_filename}") factory.create_installer(self._repo, FOTA_CONF_PATH, FOTA_CONF_SCHEMA_LOC).\ install(guid=guid, tool_options=tool_options, pkg_filename=self._pkg_filename, signature=self.__signature, hash_algorithm=self._hash_algorithm, bios_vendor=bios_vendor, platform_product=platform_product) def trigger_reboot() -> None: """This method triggers a reboot.""" factory.create_rebooter().reboot() if not hold_reboot: logger.debug("") state = {'restart_reason': "fota"} dispatcher_state.write_dispatcher_state_to_state_file(state) time_to_trigger_reboot = Timer(0.1, trigger_reboot) time_to_trigger_reboot.start() return_message = COMMAND_SUCCESS else: status = 'Reboot on hold after Firmware update...' state = {'restart_reason': "pota"} dispatcher_state.write_dispatcher_state_to_state_file(state) logger.debug(status) self._dispatcher_callbacks.broker_core.telemetry(status) except (DispatcherException, FotaError, UrlSecurityException, ValueError, FileNotFoundError) as e: error = 'Firmware Update Aborted: ' + str(e) logger.error(error) self._dispatcher_callbacks.broker_core.telemetry(error) return_message = INSTALL_FAILURE self._repo.delete(self._pkg_filename) # In POTA, mender file needs to be deleted also. if hold_reboot: self._repo.delete_all() finally: if return_message == COMMAND_SUCCESS: status = 'Firmware update in process...' else: status = 'Firmware Update Aborted' dispatcher_state.clear_dispatcher_state() logger.debug('Firmware update status: ' + status) self._dispatcher_callbacks.broker_core.telemetry(status) return return_message @staticmethod def _verify_os_supported(): """checks if the current OS is supported. @return True if OS is supported; otherwise, false. @raise ValueError Unsupported OS """ logger.debug("") os_type = platform.system() logger.debug(f"os_type: {os_type}") if os_type in OsType.__members__: return os_type else: logger.error("Unsupported OS type.") raise ValueError('Unsupported OS type.') def check(self) -> None: """validate the manifest before FOTA""" logger.debug("") factory = OsFactory.get_factory( self._verify_os_supported(), self._ota_element, self._dispatcher_callbacks) factory.create_upgrade_checker().check()
1.734375
2
syncflux.py
nagylzs/syncflux
0
9973
<reponame>nagylzs/syncflux import copy import datetime import sys import os import time import argparse import traceback import pytz import syncthing from influxdb import InfluxDBClient import yaml from yaml2dataclass import Schema, SchemaPath from typing import Optional, Dict, Type, List from dataclasses import dataclass, asdict, field @dataclass class SyncthingConfiguration(Schema): name: str api_key: str host: str = 'localhost' port: int = field(default=8384) timeout: float = field(default=10.0) is_https: bool = field(default=False) ssl_cert_file: Optional[str] = field(default=None) tags: Optional[List[str]] = field(default_factory=lambda: []) def get_client_params(self): result = asdict(self) if "name" in result: del result["name"] if "tags" in result: del result["tags"] return result @dataclass class InfluxDbConfiguration(Schema): host: str port: int # Common ports: 443 ssl: bool verify_ssl: bool database: str username: str password: <PASSWORD> def get_client_params(self): result = asdict(self) if "tags" in result: del result["tags"] return result @dataclass class MeasurementConfiguration(Schema): devices: str folders: str @dataclass class AppConfiguration(Schema): syncthings: Dict[str, SyncthingConfiguration] influxes: Dict[str, InfluxDbConfiguration] measurements: MeasurementConfiguration @classmethod def _load_dict(cls, props_dict, dest_cls: Type[Schema], add_name: bool = False): result = {} for name, value in props_dict.items(): arguments = {} arguments.update(value) if add_name: arguments["name"] = name result[name] = dest_cls.scm_load_from_dict(arguments) return result @classmethod def scm_convert(cls, values: dict, path: SchemaPath): values["syncthings"] = cls._load_dict(values["syncthings"], SyncthingConfiguration, True) values["influxes"] = cls._load_dict(values["influxes"], InfluxDbConfiguration) return values def load_app_config(stream) -> AppConfiguration: """Load application configuration from a stream.""" obj = yaml.safe_load(stream) return AppConfiguration.scm_load_from_dict(obj) def error(message: str): sys.stderr.write("\nerror: " + message + "\n") sys.stderr.flush() raise SystemExit(-1) def info(*values): if not args.silent: print(*values) def main(): # Collect data points = [] for sync in config.syncthings.values(): info(" Connect syncthing %s" % sync.name) proto_tags = {"cfg_name": sync.name} if sync.tags: proto_tags.update(sync.tags) conn_args = sync.get_client_params() q_started = time.time() conn = syncthing.Syncthing(**conn_args) now = datetime.datetime.now(tz=pytz.UTC) sync_cfg = conn.system.config() # My own device id my_device = sync_cfg["defaults"]["folder"]["devices"][0] my_id = my_device["deviceID"] proto_tags["my_id"] = my_id # Collect device stats device_stats = conn.stats.device() # List all remote devices remote_devices = [] for device in sync_cfg["devices"]: device_id = device["deviceID"] if device_id == my_id: proto_tags["my_name"] = device["name"] else: stats = device_stats[device_id] last_seen = syncthing.parse_datetime(stats["lastSeen"]) last_seen_since = now - last_seen remote_devices.append({ "tags": { "id": device["deviceID"], # Device ID "name": device["name"], # Device Name }, "fields": { "last_seen_since_sec": last_seen_since.total_seconds(), # Number of seconds last seen } }) # Folders folders = [] for folder in sync_cfg["folders"]: # Get completion for my own device completion = conn.database.completion(my_id, folder["id"]) folders.append({ "tags": {"id": folder["id"], "label": folder["label"], "path": folder["path"]}, "fields": {"completion": completion}, }) q_elapsed = time.time() - q_started proto_fields = {"q_elapsed": q_elapsed} # Create data points for devices for device in remote_devices: tags = copy.copy(proto_tags) tags.update(device["tags"]) fields = copy.copy(proto_fields) fields.update(device["fields"]) point = dict(measurement=config.measurements.devices, tags=tags, fields=fields) points.append(point) # Create points for folders for folder in folders: tags = copy.copy(proto_tags) tags.update(folder["tags"]) fields = copy.copy(proto_fields) fields.update(folder["fields"]) point = dict(measurement=config.measurements.folders, tags=tags, fields=fields) points.append(point) if not points: return for influx_name, influx in config.influxes.items(): info(" Sending %d point(s) to influxdb %s" % (len(points), influx_name)) try: influx = config.influxes[influx_name] client = InfluxDBClient(**asdict(influx)) client.write_points(points) except: if args.halt_on_send_error: raise else: traceback.print_exc(file=sys.stderr) parser = argparse.ArgumentParser(description='Monitor your Syncthing instances with influxdb.') parser.add_argument('-c', "--config", dest="config", default=None, help="Configuration file for application. Default is syncflux.yml. " "See syncflux_example.yml for an example.") parser.add_argument("--config-dir", dest="config_dir", default=None, help="Configuration directory. All config files with .yml extension will be processed one by one.") parser.add_argument('-n', "--count", dest="count", default=1, type=int, help="Number of test runs. Default is one. Use -1 to run indefinitely.") parser.add_argument('-w', "--wait", dest="wait", default=60, type=float, help="Number of seconds between test runs.") parser.add_argument("-s", "--silent", dest='silent', action="store_true", default=False, help="Supress all messages except errors.") parser.add_argument("-v", "--verbose", dest='verbose', action="store_true", default=False, help="Be verbose." ) parser.add_argument("--halt-on-send-error", dest="halt_on_send_error", default=False, action="store_true", help="Halt when cannot send data to influxdb. The default is to ignore the error.") args = parser.parse_args() if args.silent and args.verbose: parser.error("Cannot use --silent and --verbose at the same time.") if args.config is None: args.config = "syncflux.yml" if (args.config is not None) and (args.config_dir is not None): parser.error("You must give either --config or --config-dir (exactly one of them)") if args.count == 0: parser.error("Test run count cannot be zero.") if args.wait <= 0: parser.error("Wait time must be positive.") if args.config: config_files = [args.config] else: config_files = [] for file_name in sorted(os.listdir(args.config_dir)): ext = os.path.splitext(file_name)[1] if ext.lower() == ".yml": fpath = os.path.join(args.config_dir, file_name) config_files.append(fpath) index = 0 while args.count < 0 or index < args.count: if args.count != 1: info("Pass #%d started" % (index + 1)) started = time.time() for config_file in config_files: if not os.path.isfile(config_file): parser.error("Cannot open %s" % config_file) config = load_app_config(open(config_file, "r")) main() elapsed = time.time() - started index += 1 last_one = (args.count > 0) and (index == args.count) if not last_one: remaining = args.wait - elapsed if remaining > 0: if not args.silent: info("Pass #%d elapsed %.2f sec, waiting %.2f sec for next." % (index, elapsed, remaining)) time.sleep(args.wait) else: info("Pass #%d elapsed %.2f sec" % (index, elapsed)) info("")
2.3125
2
tests/input/pdf/test_pdf.py
asweeney86/preview-generator
0
9974
<filename>tests/input/pdf/test_pdf.py<gh_stars>0 # -*- coding: utf-8 -*- import os import re import shutil import typing from PIL import Image from PyPDF2 import PdfFileReader import PyPDF2.utils import pytest from preview_generator.exception import UnavailablePreviewType from preview_generator.manager import PreviewManager from tests import test_utils CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) CACHE_DIR = "/tmp/preview-generator-tests/cache" PDF_FILE_PATH = os.path.join(CURRENT_DIR, "the_pdf.pdf") PDF_FILE_PATH__ENCRYPTED = os.path.join(CURRENT_DIR, "the_pdf.encrypted.pdf") PDF_FILE_PATH__A4 = os.path.join(CURRENT_DIR, "qpdfconvert.pdf") def setup_function(function: typing.Callable) -> None: shutil.rmtree(CACHE_DIR, ignore_errors=True) def test_to_jpeg() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_jpeg_preview(file_path=PDF_FILE_PATH) is True path_to_file = manager.get_jpeg_preview( file_path=PDF_FILE_PATH, height=512, width=321, force=True ) assert os.path.exists(path_to_file) is True assert os.path.getsize(path_to_file) > 0 assert re.match(test_utils.CACHE_FILE_PATH_PATTERN__JPEG, path_to_file) with Image.open(path_to_file) as jpeg: assert jpeg.height in range(453, 455) assert jpeg.width == 321 def test_to_jpeg__encrypted_pdf() -> None: with pytest.raises(PyPDF2.utils.PdfReadError): #  ensure file is encrpyted pdf = PdfFileReader(PDF_FILE_PATH__ENCRYPTED) pdf.getPage(0) manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_jpeg_preview(file_path=PDF_FILE_PATH) is True path_to_file = manager.get_jpeg_preview( file_path=PDF_FILE_PATH__ENCRYPTED, height=512, width=321, force=True ) assert os.path.exists(path_to_file) is True assert os.path.getsize(path_to_file) > 0 assert re.match(test_utils.CACHE_FILE_PATH_PATTERN__JPEG, path_to_file) with Image.open(path_to_file) as jpeg: assert jpeg.height in range(453, 455) assert jpeg.width == 321 def test_to_jpeg_no_size() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_jpeg_preview(file_path=PDF_FILE_PATH) is True path_to_file = manager.get_jpeg_preview(file_path=PDF_FILE_PATH, force=True) assert os.path.exists(path_to_file) is True assert os.path.getsize(path_to_file) > 0 assert re.match(test_utils.CACHE_FILE_PATH_PATTERN__JPEG, path_to_file) with Image.open(path_to_file) as jpeg: assert jpeg.height == 256 assert jpeg.width in range(180, 182) def test_to_text() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_text_preview(file_path=PDF_FILE_PATH) is False with pytest.raises(UnavailablePreviewType): manager.get_text_preview(file_path=PDF_FILE_PATH, force=True) def test_to_json() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_json_preview(file_path=PDF_FILE_PATH) is True manager.get_json_preview(file_path=PDF_FILE_PATH, force=True) # TODO - G.M - 2018-11-06 - To be completed def test_to_pdf() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_pdf_preview(file_path=PDF_FILE_PATH) is True manager.get_pdf_preview(file_path=PDF_FILE_PATH, force=True) # TODO - G.M - 2018-11-06 - To be completed def test_to_pdf_one_page() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_pdf_preview(file_path=PDF_FILE_PATH) is True path_0 = manager.get_pdf_preview(file_path=PDF_FILE_PATH, page=0, force=True) assert os.path.exists(path_0) is True assert os.path.getsize(path_0) > 1000 # verify if the size of the pdf refer to a normal content assert re.match(test_utils.CACHE_FILE_PATH_PATTERN_WITH_PAGE__PDF, path_0) pdf = PdfFileReader(open(path_0, "rb")) assert pdf.getNumPages() == 1 path_1 = manager.get_pdf_preview(file_path=PDF_FILE_PATH, page=1, force=True) assert os.path.exists(path_1) is True assert os.path.getsize(path_1) > 1000 # verify if the size of the pdf refer to a normal content assert re.match(test_utils.CACHE_FILE_PATH_PATTERN_WITH_PAGE__PDF, path_1) pdf = PdfFileReader(open(path_1, "rb")) assert pdf.getNumPages() == 1 def test_algorithm4() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) assert manager.has_jpeg_preview(file_path=PDF_FILE_PATH__A4) is True path_to_file = manager.get_jpeg_preview(file_path=PDF_FILE_PATH__A4, force=True) with Image.open(path_to_file) as jpeg: assert jpeg.height == 256 assert jpeg.width in range(180, 182) def test_get_nb_page() -> None: manager = PreviewManager(cache_folder_path=CACHE_DIR, create_folder=True) nb_page = manager.get_page_nb(file_path=PDF_FILE_PATH) assert nb_page == 2 nb_page = manager.get_page_nb(file_path=PDF_FILE_PATH__ENCRYPTED) assert nb_page == 2 nb_page = manager.get_page_nb(file_path=PDF_FILE_PATH__A4) assert nb_page == 2
2.390625
2
ipt/ipt_filter_contour_by_size.py
tpmp-inra/ipapi
1
9975
from ipso_phen.ipapi.base.ipt_abstract import IptBase from ipso_phen.ipapi.tools import regions import numpy as np import cv2 import logging logger = logging.getLogger(__name__) from ipso_phen.ipapi.base import ip_common as ipc class IptFilterContourBySize(IptBase): def build_params(self): self.add_enabled_checkbox() self.add_spin_box( name="min_threshold", desc="Lower bound limit", default_value=0, minimum=0, maximum=100000000, hint="Only contours bigger than lower limit bound will be kept", ) self.add_spin_box( name="max_threshold", desc="Upper bound limit", default_value=100000000, minimum=0, maximum=100000000, hint="Only contours smaller than lower limit bound will be kept", ) self.add_roi_selector() def process_wrapper(self, **kwargs): """ Filter contour by size: 'Keep or descard contours according to their size Real time: False Keyword Arguments (in parentheses, argument name): * Activate tool (enabled): Toggle whether or not tool is active * Lower bound limit (min_threshold): Only contours bigger than lower limit bound will be kept * Upper bound limit (max_threshold): Only contours smaller than lower limit bound will be kept * Name of ROI to be used (roi_names): Operation will only be applied inside of ROI * ROI selection mode (roi_selection_mode): """ wrapper = self.init_wrapper(**kwargs) if wrapper is None: return False res = False try: if self.get_value_of("enabled") == 1: mask = self.get_mask() if mask is None: logger.error(f"FAIL {self.name}: mask must be initialized") return lt, ut = self.get_value_of("min_threshold"), self.get_value_of( "max_threshold" ) # Get source contours contours = [ c for c in ipc.get_contours( mask=mask, retrieve_mode=cv2.RETR_LIST, method=cv2.CHAIN_APPROX_SIMPLE, ) if cv2.contourArea(c, True) < 0 ] contours.sort(key=lambda x: cv2.contourArea(x), reverse=True) colors = ipc.build_color_steps(step_count=len(contours)) dbg_img = np.dstack( (np.zeros_like(mask), np.zeros_like(mask), np.zeros_like(mask)) ) for clr, cnt in zip(colors, contours): cv2.drawContours(dbg_img, [cnt], 0, clr, -1) dbg_img = np.dstack( ( cv2.bitwise_and(dbg_img[:, :, 0], mask), cv2.bitwise_and(dbg_img[:, :, 1], mask), cv2.bitwise_and(dbg_img[:, :, 2], mask), ) ) wrapper.store_image( image=dbg_img, text="all_contours", ) fnt = (cv2.FONT_HERSHEY_SIMPLEX, 0.6) for cnt in contours: area_ = cv2.contourArea(cnt) x, y, w, h = cv2.boundingRect(cnt) x += w // 2 - 10 y += h // 2 if area_ > 0: cv2.putText( dbg_img, f"{area_}", (x, y), fnt[0], fnt[1], (255, 255, 255), 2, ) wrapper.store_image( image=dbg_img, text="all_contours_with_sizes", ) dbg_img = np.dstack( (np.zeros_like(mask), np.zeros_like(mask), np.zeros_like(mask)) ) out_mask = np.zeros_like(mask) # Discarded contours size_cnts = np.dstack( (np.zeros_like(mask), np.zeros_like(mask), np.zeros_like(mask)) ) for cnt in contours: area_ = cv2.contourArea(cnt) if area_ < lt: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_RED, -1) elif area_ > ut: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_BLUE, -1) else: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_WHITE, -1) wrapper.store_image(image=size_cnts, text="cnts_by_size") # Discarded contours size_cnts = np.dstack( (np.zeros_like(mask), np.zeros_like(mask), np.zeros_like(mask)) ) for cnt in sorted( contours, key=lambda x: cv2.contourArea(x), reverse=True ): area_ = cv2.contourArea(cnt) if area_ < lt: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_RED, -1) elif area_ > ut: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_BLUE, -1) else: cv2.drawContours(size_cnts, [cnt], 0, ipc.C_WHITE, -1) wrapper.store_image(image=size_cnts, text="cnts_by_size_reversed") for cnt in contours: area_ = cv2.contourArea(cnt) if not (lt < area_ < ut): cv2.drawContours(dbg_img, [cnt], 0, ipc.C_RED, -1) # Discarded contours borders for cnt in contours: area_ = cv2.contourArea(cnt) if not (lt < area_ < ut): cv2.drawContours(dbg_img, [cnt], 0, ipc.C_MAROON, 4) # Kept contours for cnt in contours: area_ = cv2.contourArea(cnt) if lt < area_ < ut: cv2.drawContours(out_mask, [cnt], 0, 255, -1) cv2.drawContours(dbg_img, [cnt], 0, ipc.C_GREEN, -1) else: cv2.drawContours(out_mask, [cnt], 0, 0, -1) cv2.drawContours(dbg_img, [cnt], 0, ipc.C_RED, -1) dbg_img = np.dstack( ( cv2.bitwise_and(dbg_img[:, :, 0], mask), cv2.bitwise_and(dbg_img[:, :, 1], mask), cv2.bitwise_and(dbg_img[:, :, 2], mask), ) ) # Discarded sizes for cnt in contours: area_ = cv2.contourArea(cnt) if not (lt < area_ < ut): x, y, w, h = cv2.boundingRect(cnt) x += w // 2 - 10 y += h // 2 cv2.putText( dbg_img, f"{area_}", (x, y), fnt[0], fnt[1], ipc.C_RED, thickness=2, ) # Kept sizes for cnt in contours: area_ = cv2.contourArea(cnt) if lt < area_ < ut: x, y, w, h = cv2.boundingRect(cnt) x += w // 2 - 10 y += h // 2 cv2.putText( dbg_img, f"{area_}", (x, y), fnt[0], fnt[1], ipc.C_LIME, thickness=2, ) out_mask = cv2.bitwise_and( out_mask, mask, ) # Apply ROIs if needed rois = self.get_ipt_roi( wrapper=wrapper, roi_names=self.get_value_of("roi_names").replace(" ", "").split(","), selection_mode=self.get_value_of("roi_selection_mode"), ) if rois: untouched_mask = regions.delete_rois(rois=rois, image=self.get_mask()) self.result = cv2.bitwise_or( untouched_mask, regions.keep_rois(rois=rois, image=out_mask) ) self.demo_image = cv2.bitwise_or( dbg_img, np.dstack((untouched_mask, untouched_mask, untouched_mask)), ) else: self.result = out_mask self.demo_image = dbg_img wrapper.store_image(image=self.result, text="filtered_contours") wrapper.store_image(image=self.demo_image, text="tagged_contours") res = True else: wrapper.store_image(wrapper.current_image, "current_image") res = True except Exception as e: res = False logger.exception(f"Filter contour by size FAILED, exception: {repr(e)}") else: pass finally: return res @property def name(self): return "Filter contour by size" @property def package(self): return "TPMP" @property def real_time(self): return False @property def result_name(self): return "mask" @property def output_kind(self): return "mask" @property def use_case(self): return [ipc.ToolFamily.MASK_CLEANUP] @property def description(self): return """'Keep or descard contours according to their size"""
2.328125
2
tests/main.py
deeso/json-search-replace
1
9976
from wrapper_tests.upsert_test import * from wrapper_tests.upsertvaluedict_test import * import os import logging import sys import argparse import signal logging.getLogger().setLevel(logging.DEBUG) ch = logging.StreamHandler(sys.stdout) ch.setLevel(logging.DEBUG) formatter = logging.Formatter('[%(asctime)s - %(name)s] %(message)s') ch.setFormatter(formatter) logging.getLogger().addHandler(ch) parser = argparse.ArgumentParser( description='Unit testing for fiery snap.') parser.add_argument('-config', type=str, default=None, help='toml config for keys and such, see key.toml') if __name__ == '__main__': unittest.main() os.kill(os.getpid(), signal.SIGKILL)
2.265625
2
Sending_email/email.py
Satyam-Bhalla/Python-Scripts
8
9977
<reponame>Satyam-Bhalla/Python-Scripts import smtplib gmail_user = 'your email' gmail_password = '<PASSWORD>' sent_from = gmail_user to = ['reciever email'] #Create a list for all the recievers subject = 'OMG Super Important Message' body = 'Hey, what\'s up?\n- You' email_text = """\ From: %s To: %s Subject: %s %s """ % (sent_from, ", ".join(to), subject, body) try: server = smtplib.SMTP_SSL('smtp.gmail.com', 465) server.ehlo() server.login(gmail_user, gmail_password) server.sendmail(sent_from, to, email_text) server.close() print('Email sent!') except Exception as e: print(e)
2.6875
3
FlaskDaemon/load_test.py
caffeinate/test-pylot
0
9978
<filename>FlaskDaemon/load_test.py ''' Created on 11 Sep 2015 @author: si ''' import json import random import time from threading import Thread # import urllib import urllib2 from Queue import Queue import logging logger = logging.getLogger(__name__) API_URL = "http://127.0.0.1:5000/" class LoadTest(object): """ Create a single process with one thread per test user. """ def __init__(self, test_users_count, requests_per_user): """ @param test_users_count: int @param requests_per_user: int """ self.thread_table = [] self.test_users_count = test_users_count self.requests_per_user = requests_per_user self.stats = { 'return_codes' : {}, 'requests_made' : 0, 'total_seconds_waiting' : 0.0 } self.stats_q = Queue(0) def go(self): start_time = time.time() msg = "%s test users with %s requests each..." % \ (self.test_users_count, self.requests_per_user) self.logger(msg) for i in range(self.test_users_count): p = TestUser(i, self.requests_per_user, self.stats_q) p.start() self.thread_table.append(p) end_time = time.time() self.logger("time taken to create threads : %s" % (end_time-start_time,)) start_time = time.time() # wait for threads to complete while True: alive_count = len(self.thread_table) # could time.sleep(0.5) or just wait for all threads to finish for p in self.thread_table: if not p.is_alive(): alive_count -= 1 else: p.join() if alive_count == 0: break #print "alive:%s" % alive_count end_time = time.time() time_taken = end_time-start_time self.logger("finished. Time taken : %s" % time_taken) while not self.stats_q.empty(): user_stats = self.stats_q.get() for http_status, count in user_stats['return_codes'].iteritems(): if http_status not in self.stats['return_codes']: self.stats['return_codes'][http_status] = 0 self.stats['return_codes'][http_status] += count self.stats['requests_made'] += user_stats['requests_made'] self.stats['total_seconds_waiting'] += user_stats['total_seconds_waiting'] print self.stats # time_taken is real time not CPU req_per_sec = float(self.stats['requests_made'])/time_taken print "Requests per second: %s" % req_per_sec def logger(self, msg): logger.info(msg) print msg class TestUser(Thread): """ Act like a user. Bit over simplified at the moment. """ def __init__(self, user_id, requests_count, stats_queue): super(TestUser, self).__init__() self.remaining_request = requests_count self.base_url = API_URL self.stats_queue = stats_queue self.user_id = user_id def logger(self, msg): logger.info(msg) #print msg def run(self): """ @return: dictionary of stats to be collected by main process """ stats = { 'return_codes' : {}, 'requests_made': self.remaining_request, 'total_seconds_waiting' : 0.0, # waiting for requests } while self.remaining_request > 0: # sleep for average of half a second time.sleep(random.random()) start_time = time.time() # for POST #raw = {} #d = json.dumps(raw) #h = {'Content-type': 'application/json'} #req = urllib2.Request(self.base_url, data=d, headers=h) # for GET req = urllib2.Request(self.base_url) f = urllib2.urlopen(req) end_time = time.time() d = end_time-start_time stats['total_seconds_waiting'] += d http_status = f.getcode() if http_status not in stats['return_codes']: stats['return_codes'][http_status] = 0 stats['return_codes'][http_status] += 1 self.remaining_request -= 1 self.logger("Thread %s finished: %s" % (self.user_id, stats)) self.stats_queue.put(stats, False) if __name__ == '__main__': l = LoadTest(10,30) l.go()
2.609375
3
urls.py
jeylani99/Real-Estate
0
9979
<reponame>jeylani99/Real-Estate from django.contrib import admin from django.conf.urls import include,url from .import views urlpatterns = [ url(r'^$', views.IndexView.as_view(),name='index'), #homeapp_detail_view_url url(r'^(?P<pk>[0-9]+)/$',views.LocationView.as_view(),name='property'), #homeapp/detailview/moredetailview url(r'^([0-9]+)/(?P<pk>[0-9]+)/$',views.PropertyView.as_view(),name='propertyview'), ]
1.859375
2
main.py
ngh3053/auto_spacing_with_tensorflow
0
9980
<filename>main.py from utils import * from model import Model2 if __name__ == '__main__': train_data = DataLoader('../data/trainX.txt', '../data/trainY.txt') test_data = DataLoader('../data/testX.txt', '../data/testY.txt') train_data.set_batch(100) test_data.set_batch(100) char_dic = CharDic([train_data]) model = Model2(train_data=train_data, test_data=test_data, char_dic=char_dic, model_name='bilstm_crf_n3_e300_h2002') model.train() model.test()
2.234375
2
src/sardana/taurus/qt/qtgui/extra_macroexecutor/macrodescriptionviewer.py
marc2332/sardana
43
9981
#!/usr/bin/env python ############################################################################## ## # This file is part of Sardana ## # http://www.sardana-controls.org/ ## # Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## # Sardana is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. ## # Sardana is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. ## # You should have received a copy of the GNU Lesser General Public License # along with Sardana. If not, see <http://www.gnu.org/licenses/>. ## ############################################################################## """ macrodescriptionviewer.py: """ import taurus.core from taurus.external.qt import Qt from taurus.qt.qtgui.base import TaurusBaseWidget class TaurusMacroDescriptionViewer(Qt.QTextEdit, TaurusBaseWidget): __pyqtSignals__ = ("modelChanged(const QString &)",) def __init__(self, parent=None, designMode=False): name = "TaurusMacroDescriptionView" self.call__init__wo_kw(Qt.QTextEdit, parent) self.call__init__(TaurusBaseWidget, name) self.setReadOnly(True) self.setFont(Qt.QFont("Courier", 9)) def defineStyle(self): """ Defines the initial style for the widget """ self.updateStyle() def getModelClass(self): return taurus.core.taurusdevice.TaurusDevice def updateStyle(self): self.update() def onMacroNameChanged(self, macroName): """Can be connected to an event emitted after macro name was changed. As an argument receives macroName and ask BaseMacroServer object about already prepared and stored in MacroInfoObj object macro description""" macroServer = self.getModelObj() if macroServer is None or macroName is None or macroName == "": self.setText("") return self.setText(str(macroServer.getMacroInfoObj(macroName).doc)) def getFormatedToolTip(self, cache=True): """This method was overridden to get rid of the default tooltip of TaurusWidget""" return "" model = Qt.pyqtProperty("QString", TaurusBaseWidget.getModel, TaurusBaseWidget.setModel, TaurusBaseWidget.resetModel) useParentModel = Qt.pyqtProperty("bool", TaurusBaseWidget.getUseParentModel, TaurusBaseWidget.setUseParentModel, TaurusBaseWidget.resetUseParentModel) def test(): import sys from sardana.taurus.core.tango.sardana.macroserver import registerExtensions registerExtensions() app = Qt.QApplication(sys.argv) taurusMacroDescriptionView = TaurusMacroDescriptionViewer(designMode=1) if len(sys.argv) != 2: taurusMacroDescriptionView.setModel("macroserver/zreszela/1") else: taurusMacroDescriptionView.setModel(sys.argv[1]) taurusMacroDescriptionView.onMacroChanged("mv") taurusMacroDescriptionView.show() sys.exit(app.exec_()) if __name__ == "__main__": test()
1.710938
2
torch/metrics/accuracy_score.py
LilDataScientist/PyTorch-From-Scratch
0
9982
<gh_stars>0 import numpy as np def accuracy_score(y_true, y_pred): a = np.argmax(y_true, axis=1) b = np.argmax(y_pred, axis=1) return np.count_nonzero(a == b) / y_true.shape[0]
2.328125
2
asaas/financial_transactions.py
marlonjsilva/asaas_sdk_python
0
9983
from asaas.typing import SyncAsync from typing import Any, Optional, Dict class FinancialTransactions: def __init__(self, parent: Any) -> None: self.parent = parent def list( self, query: Optional[Dict[Any, Any]] = None, **kwars: Any ) -> SyncAsync[Any]: return self.parent.request( path="/financialTransactions", method="GET", query=query, auth=kwars.get("auth"), )
2.234375
2
datahub/core/serializers.py
uktrade/data-hub-api
6
9984
from functools import partial from uuid import UUID from dateutil.parser import parse as dateutil_parse from django.apps import apps from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from rest_framework import serializers from rest_framework.exceptions import ValidationError from rest_framework.fields import ReadOnlyField, UUIDField from datahub.core.constants import Country as CountryEnum from datahub.core.validate_utils import DataCombiner from datahub.core.validators import InRule, OperatorRule, RulesBasedValidator, ValidationRule from datahub.metadata.models import AdministrativeArea, Country MAX_LENGTH = settings.CHAR_FIELD_MAX_LENGTH class ConstantModelSerializer(serializers.Serializer): """Constant models serializer.""" id = serializers.ReadOnlyField() name = serializers.ReadOnlyField() disabled_on = serializers.ReadOnlyField() class PermittedFieldsModelSerializer(serializers.ModelSerializer): """Lets you get permitted fields only. Needs 'permissions' attribute on Meta class in following format: permissions = { 'app_name.permission': 'field' } If user doesn't have required permission, corresponding field will be filtered out. Note: The current implementation does not allow access to the field if request.user is None. """ def get_fields(self): """Gets filtered dictionary of fields based on permissions.""" assert hasattr(self.Meta, 'permissions'), ( 'Class {serializer_class} missing "Meta.permissions" attribute'.format( serializer_class=self.__class__.__name__, ) ) fields = super().get_fields() request = self.context.get('request', None) if request: permissions = self.Meta.permissions for permission, field in permissions.items(): if not request.user or not request.user.has_perm(permission): del fields[field] return fields class NestedRelatedField(serializers.RelatedField): """DRF serialiser field for foreign keys and many-to-many fields. Serialises as a dict with 'id' plus other specified keys. """ default_error_messages = { 'required': 'This field is required.', 'missing_pk': 'pk not provided.', 'does_not_exist': 'Invalid pk "{pk_value}" - object does not exist.', 'incorrect_type': 'Incorrect type. Expected object, received {' 'data_type}.', } def __init__(self, model, extra_fields=('name',), **kwargs): """Initialises the related field. :param model: Model of the related field. :param extra_fields: List of extra fields to include in the representation. Can contain field names as strings or as tuples of (field name, DRF field). E.g. ['field1', ('field2', CharField())] :param kwargs: Keyword arguments to pass to RelatedField.__init__() """ super().__init__(**kwargs) model_class = (apps.get_model(model) if isinstance(model, str) else model) self.pk_field = UUIDField() self._fields = [ field if isinstance(field, tuple) else (field, ReadOnlyField()) for field in extra_fields ] self._model = model_class def get_queryset(self): """Returns the queryset corresponding to the model.""" return self._model.objects.all() def to_internal_value(self, data): """Converts a user-provided value to a model instance.""" try: if isinstance(data, (str, UUID)): id_repr = data else: id_repr = data['id'] data = self.pk_field.to_internal_value(id_repr) return self.get_queryset().get(pk=data) except ObjectDoesNotExist: self.fail('does_not_exist', pk_value=data) except KeyError: self.fail('missing_pk') except (TypeError, ValueError): self.fail('incorrect_type', data_type=type(data).__name__) def to_representation(self, value): """Converts a model instance to a dict representation.""" if not value: return value extra = { field_name: field.to_representation(getattr(value, field_name)) for field_name, field in self._fields } return { **extra, 'id': self.pk_field.to_representation(value.pk), } def get_choices(self, cutoff=None): """Returns choices for DRF UI. Standard implementation uses a dict, but that doesn't work as our representation isn't hashable. """ queryset = self.get_queryset() if queryset is None: return () if cutoff is not None: queryset = queryset[:cutoff] return _Choices( ( self.pk_field.to_representation(item.pk), self.display_value(item), ) for item in queryset ) RelaxedDateField = partial(serializers.DateField, input_formats=('iso-8601', '%Y/%m/%d')) class RelaxedDateTimeField(serializers.Field): """ Relaxed DateTime field. Front end uses free text field for data filters, that's why we need to accept date/datetime in various different formats. DRF DateTimeField doesn't offer that flexibility. """ default_error_messages = { 'invalid': 'Date is in incorrect format.', } def to_internal_value(self, data): """Parses data into datetime.""" try: data = dateutil_parse(data) except ValueError: self.fail('invalid', value=data) return data def to_representation(self, value): """Formats the datetime using a normal DateTimeField.""" repr_field = serializers.DateTimeField() return repr_field.to_representation(value) class RelaxedURLField(serializers.URLField): """URLField subclass that prepends http:// to input and output when a scheme is not present.""" def to_internal_value(self, data): """Converts a user-provided value to an internal value.""" return super().to_internal_value(self._fix_missing_url_scheme(data)) def to_representation(self, value): """Converts a stored value to the external representation.""" return super().to_representation(self._fix_missing_url_scheme(value)) @staticmethod def _fix_missing_url_scheme(value): if value and '://' not in value: return f'http://{value}' return value class _Choices: """Wrapper for choices to make them compatible with DRF.""" def __init__(self, choices): self._choices = choices def items(self): """Returns the choices.""" return self._choices class AddressSerializer(serializers.ModelSerializer): """ ModelSerializer that can be used to simulate nested address objects. E.g. Model: class MultiAddressModel(models.Model): primary_address_1 = models.CharField(max_length=MAX_LENGTH) primary_address_2 = models.CharField(max_length=MAX_LENGTH, blank=True) primary_address_town = models.CharField(max_length=MAX_LENGTH) primary_address_county = models.CharField(max_length=MAX_LENGTH, blank=True) primary_address_country = models.ForeignKey( Country, on_delete=models.PROTECT, related_name='+', ) primary_address_postcode = models.CharField(max_length=MAX_LENGTH, blank=True) secondary_address_1 = models.CharField(max_length=MAX_LENGTH, blank=True) secondary_address_2 = models.CharField(max_length=MAX_LENGTH, blank=True, null=True) secondary_address_town = models.CharField(max_length=MAX_LENGTH, blank=True) secondary_address_county = models.CharField(max_length=MAX_LENGTH, blank=True) secondary_address_country = models.ForeignKey( Country, null=True, on_delete=models.SET_NULL, related_name='+', ) secondary_address_postcode = models.CharField(max_length=MAX_LENGTH, blank=True) Serializer: class MultiAddressModelSerializer(serializers.ModelSerializer): primary_address = AddressSerializer( source_model=MultiAddressModel, address_source_prefix='primary_address', ) secondary_address = AddressSerializer( source_model=MultiAddressModel, address_source_prefix='secondary_address', required=False, allow_null=True, ) class Meta: model = MultiAddressModel fields = ['primary_address', 'secondary_address'] Will produce the following API response: { 'primary_address': { 'line_1': '2', 'line_2': '', 'town': 'London', 'county': '', 'postcode': '', 'country': { 'id': '80756b9a-5d95-e211-a939-e4115bead28a', 'name': 'United Kingdom', }, }, 'secondary_address': { 'line_1': '1', 'line_2': '', 'town': 'Muckamore', 'county': '', 'postcode': '', 'country': { 'id': '736a9ab2-5d95-e211-a939-e4115bead28a', 'name': 'Ireland', }, }, }, Please note: 1. None values for CharFields will be converted to '' 2. If all address field values are blank the nested object in the response will return None E.g. Fiven the following fields' values: secondary_address_1='' secondary_address_2='' secondary_address_town='' secondary_address_county='' secondary_address_postcode='' secondary_address_country_id=None The equivalent API response body will be: 'secondary_address': None The same applies for changing the data. 3. If AddressSerializer has required=False, the validation is triggered only if at least one of the fields is passed in. """ line_1 = serializers.CharField( max_length=MAX_LENGTH, allow_blank=True, required=False, default='', source='{source_prefix}_1', ) line_2 = serializers.CharField( max_length=MAX_LENGTH, allow_blank=True, required=False, default='', source='{source_prefix}_2', ) town = serializers.CharField( max_length=MAX_LENGTH, allow_blank=True, required=False, default='', source='{source_prefix}_town', ) county = serializers.CharField( max_length=MAX_LENGTH, allow_blank=True, required=False, default='', source='{source_prefix}_county', ) postcode = serializers.CharField( max_length=MAX_LENGTH, allow_blank=True, required=False, default='', source='{source_prefix}_postcode', ) area = NestedRelatedField( AdministrativeArea, allow_null=True, required=False, source='{source_prefix}_area', ) country = NestedRelatedField( Country, allow_null=True, required=False, source='{source_prefix}_country', ) REQUIRED_FIELDS = ( 'line_1', 'town', 'country', ) def __init__( self, source_model, *args, address_source_prefix='address', area_can_be_required=False, postcode_can_be_required=False, **kwargs, ): """ Initialises the serializer. It populates all necessary parts (e.g. Meta model, source, fields' source). """ # Define a custom Meta so that the Meta model can be specified as an argument class MultiAddressMeta(self.Meta): model = source_model self.Meta = MultiAddressMeta kwargs.setdefault('source', '*') super().__init__(*args, **kwargs) # populate fields' source for field in self.fields.values(): field.source = field.source.format(source_prefix=address_source_prefix) field.source_attrs = field.source.split('.') self.area_can_be_required = area_can_be_required self.postcode_can_be_required = postcode_can_be_required self.address_source_prefix = address_source_prefix def add_area_validator(self, validators): """ Mark area as required for US and Canadian companies. """ validators.append( RulesBasedValidator( ValidationRule( 'required', OperatorRule(f'{self.address_source_prefix}_area', bool), when=InRule( f'{self.address_source_prefix}_country', ( CountryEnum.united_states.value.id, CountryEnum.canada.value.id, ), ), ), ), ) def add_postcode_validator(self, validators): """ Mark postcode as required for US and Canadian companies. """ validators.append( RulesBasedValidator( ValidationRule( 'required', OperatorRule(f'{self.address_source_prefix}_postcode', bool), when=InRule( f'{self.address_source_prefix}_country', ( CountryEnum.united_states.value.id, CountryEnum.canada.value.id, ), ), ), ), ) def get_validators(self): """ Append ValidationRule for area/postcode depending on feature flag/context Only mark area/postcode required if country is US/Canada & called from context where area is safe to require, and if feature flag enabled. Currently the only context where area is safe to require is CompanySerializer """ validators = super().get_validators() if self.area_can_be_required: self.add_area_validator(validators) if self.postcode_can_be_required: self.add_postcode_validator(validators) return validators def run_validation(self, data=serializers.empty): """ Converts None to dict with default values so that those values can be used to reset the fields on the model. """ if data or not self.allow_null: normalised_data = data else: normalised_data = { field_name: None if (field.default == serializers.empty) else field.default for field_name, field in self.fields.items() } return super().run_validation(data=normalised_data) def to_representation(self, value): """ It returns None if none of the address values is set. E.g. { 'address': None } instead of { 'address': { 'line_1': '', 'line_2': '', 'town': '', 'county': '', 'postcode': '', 'country': None } } """ address_dict = super().to_representation(value) if not any(address_dict.values()): return None # for each address field, replace None with default if possible for field_name, value in address_dict.items(): field_default = self.fields[field_name].default if value is None and field_default is not serializers.empty: address_dict[field_name] = field_default return address_dict def should_validate(self, data_combiner): """ Returns true if the data should be validated. """ if self.required: return True return any( data_combiner.get_value(field.source) for field in self.fields.values() ) def validate(self, attrs): """ Validates the data if necessary. This is needed because some addresses only need to be validated if they are passed in. """ validated_data = super().validate(attrs) data_combiner = DataCombiner(self.parent.instance, validated_data) if self.should_validate(data_combiner): errors = {} for field_name in self.REQUIRED_FIELDS: field = self.fields[field_name] value = data_combiner.get_value(field.source) if not value: errors[field_name] = self.error_messages['required'] if errors: raise ValidationError(errors) return validated_data class Meta: """Meta options.""" model = None fields = ( 'line_1', 'line_2', 'town', 'county', 'postcode', 'area', 'country', )
2.125
2
samples/s07-rigid-objects/main.py
nomadsinteractive/ark
5
9985
<reponame>nomadsinteractive/ark<filename>samples/s07-rigid-objects/main.py import math import random from ark import dear_imgui, ApplicationFacade, Arena, Event, Integer, Collider, RenderObject, Size, Camera, Vec3, Numeric class Application: def __init__(self, application: ApplicationFacade): self._down_x = 0 self._down_y = 0 self._application = application self._light_position = Vec3(100, 500, 0) self._resource_loader = self._application.create_resource_loader('main.xml') self._arena = self._resource_loader.load(Arena, 'main', c=self._application.camera, lp=self._light_position) self._application.arena = self._arena self._arena.add_event_listener(self.on_event) self._imgui = self._arena.resource_loader.refs.imgui self._world_box2d = self._resource_loader.load(Collider, 'b2World') self._world_bullet = self._resource_loader.load(Collider, 'btWorld') self._l001 = self._resource_loader.layers.l001 self._l003 = self._resource_loader.layers.l003 self._shape_id = Integer(0) self._collider_id = Integer(0) self._body_size = Numeric(50) self._body_ttl = Numeric(5) self._rigid_body_ground_b2 = self._world_box2d.create_body(Collider.BODY_TYPE_STATIC, Collider.BODY_SHAPE_BOX, (4.8, 0), Size(6.0, 1.0)) self._rigid_body_ground_bt = self._world_bullet.create_body(Collider.BODY_TYPE_STATIC, Collider.BODY_SHAPE_BOX, (480, 0, 0), Size(600, 100, 600)) self._shapes = [Collider.BODY_SHAPE_BALL, Collider.BODY_SHAPE_BOX, 3] @property def imgui(self): return self._imgui def on_event(self, event): action = event.action if action == Event.ACTION_DOWN: (self._down_x, self._down_y) = event.xy elif action == Event.ACTION_UP: if abs(event.x - self._down_x) + abs(event.y - self._down_y) < 10: self.on_click(event) return True return False def on_click(self, event: Event): shape_id = self._shape_id.val collider_id = self._collider_id.val render_object = [self.make_object_box2d, self.make_object_bullet][collider_id](shape_id, event) self._defer_dispose(render_object) def make_object_box2d(self, shape_id: int, event: Event) -> RenderObject: xy = (event.x / 100, event.y / 100) s = self._body_size / 100 shape = self._shapes[shape_id] rigid_body = self._world_box2d.create_body(Collider.BODY_TYPE_DYNAMIC, shape, xy, Size(s, s)) render_object = RenderObject(random.randrange(1, 100), None, Size(self._body_size, self._body_size), None) rigid_body.bind(render_object) self._l003.add_render_object(render_object) return render_object def make_object_bullet(self, shape_id: int, event: Event) -> RenderObject: xy = event.xy shape = self._shapes[shape_id] s = self._body_size.val s1 = s / [2, 100, 50][shape_id] rigid_body = self._world_bullet.create_body(Collider.BODY_TYPE_DYNAMIC, shape, xy, Size(s, s, s)) render_object = RenderObject(self._shape_id.val + 1, None, Size(s1, s1, s1)) rigid_body.bind(render_object) self._l001.add_render_object(render_object) return render_object def create_toolbox(self): builder = dear_imgui.RendererBuilder(self._imgui) builder.begin('RigidBodies') builder.text('Which collider engine shall we use?') builder.radio_button('Box2D', self._collider_id, 0) builder.same_line() builder.radio_button('Bullet3', self._collider_id, 1) builder.separator() builder.text('Click somewhere to create a RigidBody typed below:') builder.radio_button('Ball', self._shape_id, 0) builder.same_line() builder.radio_button('Box', self._shape_id, 1) builder.same_line() builder.radio_button('Duck', self._shape_id, 2) builder.slider_float('RigidBody size', self._body_size, 10, 100, '%.1f') builder.slider_float('RigidBody TTL', self._body_ttl, 5, 50, '%.1f') builder.slider_float3('Light Position', self._light_position, 0, 1000, '%.1f') builder.end() self._imgui.add_renderer(builder.build()) @staticmethod def _make_camera() -> Camera: e = 500 camera = Camera() camera.perspective(math.radians(45), 16 / 9, 0.1, 2000) camera.look_at(Vec3(0, 0, e), Vec3(0, 0, e - 100), Vec3(0, 1, 0)) return camera def _defer_dispose(self, render_object: RenderObject): self._application.post(lambda: render_object.dispose(), self._body_ttl.val) def main(app: Application): app.create_toolbox() if __name__ == '__main__': main(Application(_application))
2.125
2
jamf/models/computer_extension_attribute.py
jensenbox/python-jamf
1
9986
<reponame>jensenbox/python-jamf # coding: utf-8 """ Jamf Pro API ## Overview This is a sample Jamf Pro server which allows for usage without any authentication. The Jamf Pro environment which supports the Try it Out functionality does not run the current beta version of Jamf Pro, thus any newly added endpoints will result in an error and should be used soley for documentation purposes. # noqa: E501 The version of the OpenAPI document: 10.25.0 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from jamf.configuration import Configuration class ComputerExtensionAttribute(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_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. """ openapi_types = { 'definition_id': 'str', 'name': 'str', 'description': 'str', 'enabled': 'bool', 'multi_value': 'bool', 'values': 'list[str]', 'data_type': 'str', 'options': 'list[str]', 'input_type': 'str' } attribute_map = { 'definition_id': 'definitionId', 'name': 'name', 'description': 'description', 'enabled': 'enabled', 'multi_value': 'multiValue', 'values': 'values', 'data_type': 'dataType', 'options': 'options', 'input_type': 'inputType' } def __init__(self, definition_id=None, name=None, description=None, enabled=None, multi_value=None, values=None, data_type=None, options=None, input_type=None, local_vars_configuration=None): # noqa: E501 """ComputerExtensionAttribute - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._definition_id = None self._name = None self._description = None self._enabled = None self._multi_value = None self._values = None self._data_type = None self._options = None self._input_type = None self.discriminator = None if definition_id is not None: self.definition_id = definition_id if name is not None: self.name = name self.description = description if enabled is not None: self.enabled = enabled if multi_value is not None: self.multi_value = multi_value self.values = values self.data_type = data_type self.options = options self.input_type = input_type @property def definition_id(self): """Gets the definition_id of this ComputerExtensionAttribute. # noqa: E501 An identifier of extension attribute definition. # noqa: E501 :return: The definition_id of this ComputerExtensionAttribute. # noqa: E501 :rtype: str """ return self._definition_id @definition_id.setter def definition_id(self, definition_id): """Sets the definition_id of this ComputerExtensionAttribute. An identifier of extension attribute definition. # noqa: E501 :param definition_id: The definition_id of this ComputerExtensionAttribute. # noqa: E501 :type definition_id: str """ self._definition_id = definition_id @property def name(self): """Gets the name of this ComputerExtensionAttribute. # noqa: E501 A human-readable name by which attribute can be referred to. # noqa: E501 :return: The name of this ComputerExtensionAttribute. # noqa: E501 :rtype: str """ return self._name @name.setter def name(self, name): """Sets the name of this ComputerExtensionAttribute. A human-readable name by which attribute can be referred to. # noqa: E501 :param name: The name of this ComputerExtensionAttribute. # noqa: E501 :type name: str """ self._name = name @property def description(self): """Gets the description of this ComputerExtensionAttribute. # noqa: E501 An additional explanation of exact attribute meaning, possible values, etc. # noqa: E501 :return: The description of this ComputerExtensionAttribute. # noqa: E501 :rtype: str """ return self._description @description.setter def description(self, description): """Sets the description of this ComputerExtensionAttribute. An additional explanation of exact attribute meaning, possible values, etc. # noqa: E501 :param description: The description of this ComputerExtensionAttribute. # noqa: E501 :type description: str """ self._description = description @property def enabled(self): """Gets the enabled of this ComputerExtensionAttribute. # noqa: E501 :return: The enabled of this ComputerExtensionAttribute. # noqa: E501 :rtype: bool """ return self._enabled @enabled.setter def enabled(self, enabled): """Sets the enabled of this ComputerExtensionAttribute. :param enabled: The enabled of this ComputerExtensionAttribute. # noqa: E501 :type enabled: bool """ self._enabled = enabled @property def multi_value(self): """Gets the multi_value of this ComputerExtensionAttribute. # noqa: E501 :return: The multi_value of this ComputerExtensionAttribute. # noqa: E501 :rtype: bool """ return self._multi_value @multi_value.setter def multi_value(self, multi_value): """Sets the multi_value of this ComputerExtensionAttribute. :param multi_value: The multi_value of this ComputerExtensionAttribute. # noqa: E501 :type multi_value: bool """ self._multi_value = multi_value @property def values(self): """Gets the values of this ComputerExtensionAttribute. # noqa: E501 A value of extension attribute, in some rare cases there may be multiple values present, hence the array. # noqa: E501 :return: The values of this ComputerExtensionAttribute. # noqa: E501 :rtype: list[str] """ return self._values @values.setter def values(self, values): """Sets the values of this ComputerExtensionAttribute. A value of extension attribute, in some rare cases there may be multiple values present, hence the array. # noqa: E501 :param values: The values of this ComputerExtensionAttribute. # noqa: E501 :type values: list[str] """ self._values = values @property def data_type(self): """Gets the data_type of this ComputerExtensionAttribute. # noqa: E501 A data type of extension attribute. # noqa: E501 :return: The data_type of this ComputerExtensionAttribute. # noqa: E501 :rtype: str """ return self._data_type @data_type.setter def data_type(self, data_type): """Sets the data_type of this ComputerExtensionAttribute. A data type of extension attribute. # noqa: E501 :param data_type: The data_type of this ComputerExtensionAttribute. # noqa: E501 :type data_type: str """ allowed_values = [None,"STRING", "INTEGER", "DATE_TIME"] # noqa: E501 if self.local_vars_configuration.client_side_validation and data_type not in allowed_values: # noqa: E501 raise ValueError( "Invalid value for `data_type` ({0}), must be one of {1}" # noqa: E501 .format(data_type, allowed_values) ) self._data_type = data_type @property def options(self): """Gets the options of this ComputerExtensionAttribute. # noqa: E501 A closed list of possible values (applies to `popup` input type). # noqa: E501 :return: The options of this ComputerExtensionAttribute. # noqa: E501 :rtype: list[str] """ return self._options @options.setter def options(self, options): """Sets the options of this ComputerExtensionAttribute. A closed list of possible values (applies to `popup` input type). # noqa: E501 :param options: The options of this ComputerExtensionAttribute. # noqa: E501 :type options: list[str] """ self._options = options @property def input_type(self): """Gets the input_type of this ComputerExtensionAttribute. # noqa: E501 The input method. `text` is most common and means simply free text, `popup` i a closed list of values from which one or many can be selected and `script` value is calculated and can never be set directly. # noqa: E501 :return: The input_type of this ComputerExtensionAttribute. # noqa: E501 :rtype: str """ return self._input_type @input_type.setter def input_type(self, input_type): """Sets the input_type of this ComputerExtensionAttribute. The input method. `text` is most common and means simply free text, `popup` i a closed list of values from which one or many can be selected and `script` value is calculated and can never be set directly. # noqa: E501 :param input_type: The input_type of this ComputerExtensionAttribute. # noqa: E501 :type input_type: str """ allowed_values = [None,"TEXT", "POPUP", "SCRIPT", "LDAP"] # noqa: E501 if self.local_vars_configuration.client_side_validation and input_type not in allowed_values: # noqa: E501 raise ValueError( "Invalid value for `input_type` ({0}), must be one of {1}" # noqa: E501 .format(input_type, allowed_values) ) self._input_type = input_type def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_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 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, ComputerExtensionAttribute): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, ComputerExtensionAttribute): return True return self.to_dict() != other.to_dict()
1.742188
2
SSOKeyGen/ssokeygendialog.py
chrcoe/sso-keygen
0
9987
<reponame>chrcoe/sso-keygen<filename>SSOKeyGen/ssokeygendialog.py<gh_stars>0 # -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'ssokeygendialog.ui' # # Created: Sun Feb 1 12:33:36 2015 # by: PyQt5 UI code generator 5.4 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Dialog(object): def setupUi(self, Dialog): Dialog.setObjectName("Dialog") Dialog.resize(400, 300) self.buttonBox = QtWidgets.QDialogButtonBox(Dialog) self.buttonBox.setGeometry(QtCore.QRect(30, 240, 341, 32)) self.buttonBox.setOrientation(QtCore.Qt.Horizontal) self.buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Cancel|QtWidgets.QDialogButtonBox.Ok) self.buttonBox.setObjectName("buttonBox") self.testLabel = QtWidgets.QLabel(Dialog) self.testLabel.setGeometry(QtCore.QRect(50, 40, 181, 31)) self.testLabel.setObjectName("testLabel") self.retranslateUi(Dialog) self.buttonBox.accepted.connect(Dialog.accept) self.buttonBox.rejected.connect(Dialog.reject) QtCore.QMetaObject.connectSlotsByName(Dialog) def retranslateUi(self, Dialog): _translate = QtCore.QCoreApplication.translate Dialog.setWindowTitle(_translate("Dialog", "Dialog Test")) self.testLabel.setText(_translate("Dialog", "TextLabel"))
1.617188
2
util/n_download_util.py
TwrFyr/n-hen.py
0
9988
<reponame>TwrFyr/n-hen.py import urllib.request import os from typing import List from util.n_util import NUser from util.n_util import get_n_entry import time import threading from util.array_util import slice_array delay: float = 2.5 class ProgressWrapper: """The progress wrapper keeps track of the progress of a operation by wrapping a current number and a total number. It also wraps an optional function, which uses the current values and has to have the form 'func(current, total)'.""" def __init__(self, start, total, update): self.current = start self.total = total self.update_callback = update def update(self): if self.update_callback is not None: self.update_callback(self.current, self.total) def download_images(lock, file_url_list: List[str], path: str, progress=None): for file_url in file_url_list: filename = os.path.join(path, file_url.split('/')[-1]) print('writing {} to {}'.format(file_url, filename)) urllib.request.urlretrieve(file_url, filename) if progress is not None: with lock: progress.current += 1 progress.update() def save_files_to_dir(file_url_list: List[str], path: str, update=None, thread_count: int = 1) -> None: """Saves all files represented by a list of url resources to the folder specified. The files are being named after the last part of the url. The number of threads can be increased to use more threads for the downloading of the images.""" # pretend to be normal user # opener=urllib.request.build_opener() # opener.addheaders=[('User-Agent','Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1941.0 Safari/537.36')] # urllib.request.install_opener(opener) progress = ProgressWrapper(0, len(file_url_list), update) progress.update() if thread_count < 1 or thread_count > 16: print(f'invalid thread count: {thread_count} not in [1, 16]') return else: lock = threading.Lock() threads = [] for i in range(thread_count): slices = slice_array(file_url_list, thread_count) t = threading.Thread(target=download_images, kwargs=dict(lock=lock, file_url_list=slices[i], path=path, progress=progress), daemon=True) threads.append(t) t.start() for t in threads: t.join() def download_all_favorites(n_user: NUser, base_dir: str, update_entry=None, update_page=None, thread_count=1) -> None: """Downloads all entries favorited by `n_user` using the number of `thread_count` threads.""" print('downloading {}\'s {} favorites...'.format(n_user.username, n_user.fav_count)) current_entry = 1 total_entries = n_user.fav_count for min_entry in n_user.favorite_list: if update_entry is not None: update_entry(current_entry=min_entry, current=current_entry, total=total_entries) # get entry data print('downloading entry with id {}'.format(min_entry.n_id)) entry = get_n_entry(min_entry.n_id) if entry is None: print('no connection possible, skipping...') current_entry += 1 continue # check directory is valid if not os.path.exists(base_dir): print('base directory does not exist, aborting...') break save_dir = os.path.join(base_dir, entry.digits) if os.path.exists(save_dir): print('entry already exists, skipping...') current_entry += 1 continue else: os.mkdir(save_dir) # download images save_files_to_dir(entry.image_url_list, save_dir, update=update_page, thread_count=thread_count) print('waiting for {} seconds...'.format(delay)) time.sleep(delay) current_entry += 1 if update_entry is not None: update_entry(current_entry=None, current=current_entry, total=total_entries) print('download finished')
3.109375
3
stable-baselines/tests/test_deterministic.py
princeton-vl/PackIt
49
9989
import pytest from stable_baselines import A2C, ACER, ACKTR, DeepQ, DDPG, PPO1, PPO2, TRPO from stable_baselines.ddpg import AdaptiveParamNoiseSpec from stable_baselines.common.identity_env import IdentityEnv, IdentityEnvBox from stable_baselines.common.vec_env import DummyVecEnv PARAM_NOISE_DDPG = AdaptiveParamNoiseSpec(initial_stddev=float(0.2), desired_action_stddev=float(0.2)) # Hyperparameters for learning identity for each RL model LEARN_FUNC_DICT = { 'a2c': lambda e: A2C(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acer': lambda e: ACER(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acktr': lambda e: ACKTR(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'deepq': lambda e: DeepQ(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ddpg': lambda e: DDPG(policy="MlpPolicy", env=e, param_noise=PARAM_NOISE_DDPG).learn(total_timesteps=1000), 'ppo1': lambda e: PPO1(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ppo2': lambda e: PPO2(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'trpo': lambda e: TRPO(policy="MlpPolicy", env=e).learn(total_timesteps=1000), } @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'acer', 'acktr', 'deepq', 'ppo1', 'ppo2', 'trpo']) def test_identity(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnv(10)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape # Free memory del model, env @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'ddpg', 'ppo1', 'ppo2', 'trpo']) def test_identity_continuous(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnvBox(eps=0.5)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape
2.03125
2
tests/models/tensorflow/convert_to_tensorflow_serving.py
filipecosta90/dlbench
14
9990
import tensorflow as tf from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants export_dir = './reference/00000002' graph_pb = './creditcardfraud.pb' builder = tf.saved_model.builder.SavedModelBuilder(export_dir) with tf.gfile.GFile(graph_pb, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sigs = {} with tf.Session(graph=tf.Graph()) as sess: # name="" is important to ensure we don't get spurious prefixing tf.import_graph_def(graph_def, name="") g = tf.get_default_graph() inp1 = g.get_tensor_by_name("transaction:0") inp2 = g.get_tensor_by_name("reference:0") out = g.get_tensor_by_name("output:0") sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \ tf.saved_model.signature_def_utils.predict_signature_def( {"transaction": inp1, "reference": inp2}, {"output": out}) builder.add_meta_graph_and_variables(sess, [tag_constants.SERVING], signature_def_map=sigs) builder.save()
2.34375
2
pycuda/characterize.py
grlee77/pycuda
0
9991
<gh_stars>0 from __future__ import division from __future__ import absolute_import from pycuda.tools import context_dependent_memoize import numpy as np def platform_bits(): return tuple.__itemsize__ * 8 def has_stack(): from pycuda.driver import Context return Context.get_device().compute_capability() >= (2, 0) def has_double_support(): from pycuda.driver import Context return Context.get_device().compute_capability() >= (1, 3) @context_dependent_memoize def sizeof(type_name, preamble=""): from pycuda.compiler import SourceModule mod = SourceModule(""" %s extern "C" __global__ void write_size(size_t *output) { *output = sizeof(%s); } """ % (preamble, type_name), no_extern_c=True) import pycuda.gpuarray as gpuarray output = gpuarray.empty((), dtype=np.uintp) mod.get_function("write_size")(output, block=(1, 1, 1), grid=(1, 1)) return int(output.get())
2.125
2
bopt/transforms.py
georgedeath/bomean
2
9992
import torch from scipy.stats import median_absolute_deviation class Transform_Base(object): """ Base class for transformations based on some data. """ def __init__(self, Ytr): self.Ytr = Ytr # Transform the mean def scale_mean(self, mu): return mu # Reverse the transformation to the mean def unscale_mean(self, mu): return mu # Reverse the transformation to the variance def unscale_var(self, var): return var class Transform_Standardize(Transform_Base): """ Standardize the data """ def __init__(self, Ytr): super().__init__(Ytr) self.Ytr_mean = Ytr.mean() self.Ytr_std = Ytr.std() self.Ytr_var = Ytr.var() def scale_mean(self, mu): return (mu - self.Ytr_mean) / self.Ytr_std def unscale_mean(self, mu): return mu * self.Ytr_std + self.Ytr_mean def unscale_var(self, var): return var * self.Ytr_var class Transform_StandardizeRobustly(Transform_Base): """ Robustly standardize the data by estimating its scale """ def __init__(self, Ytr): super().__init__(Ytr) self.Ytr_median = Ytr.median() Ytr_numpy = Ytr.numpy().ravel() self.Ytr_scale = torch.tensor(median_absolute_deviation(Ytr_numpy)) self.Ytr_scaleSQR = self.Ytr_scale**2 def scale_mean(self, mu): return (mu - self.Ytr_median) / self.Ytr_scale def unscale_mean(self, mu): return mu * self.Ytr_scale + self.Ytr_median def unscale_var(self, var): return var * self.Ytr_scaleSQR
2.65625
3
pygs/graphserver/compiler/dedupe.py
abyrd/graphserver
2
9993
# eliminate duplicate service periods from a GTFS database from graphserver.ext.gtfs.gtfsdb import GTFSDatabase import sys from optparse import OptionParser def main(): usage = """usage: python dedupe.py <graphdb_filename>""" parser = OptionParser(usage=usage) (options, args) = parser.parse_args() if len(args) != 1: parser.print_help() exit(-1) graphdb_filename = args[0] gtfsdb = GTFSDatabase( graphdb_filename ) query = """ SELECT count(*), monday, tuesday, wednesday, thursday, friday, saturday, sunday, start_date, end_date FROM calendar GROUP BY monday, tuesday, wednesday, thursday, friday, saturday, sunday, start_date, end_date""" duped_periods = gtfsdb.execute( query ) equivilants = [] for count, m,t,w,th,f,s,su,start_date,end_date in duped_periods: # no need to check for dupes if there's only one if count==1: continue #print count, m, t, w, th, f, s, su, start_date, end_date # get service_ids for this dow/start_date/end_date combination service_ids = [x[0] for x in list( gtfsdb.execute( "SELECT service_id FROM calendar where monday=? and tuesday=? and wednesday=? and thursday=? and friday=? and saturday=? and sunday=? and start_date=? and end_date=?", (m,t,w,th,f,s,su,start_date,end_date) ) ) ] # group by service periods with the same set of exceptions exception_set_grouper = {} for service_id in service_ids: exception_set = list(gtfsdb.execute( "SELECT date, exception_type FROM calendar_dates WHERE service_id=?", (service_id,) ) ) exception_set.sort() exception_set = tuple(exception_set) exception_set_grouper[exception_set] = exception_set_grouper.get(exception_set,[]) exception_set_grouper[exception_set].append( service_id ) # extend list of equivilants for i, exception_set_group in enumerate( exception_set_grouper.values() ): equivilants.append( ("%d%d%d%d%d%d%d-%s-%s-%d"%(m,t,w,th,f,s,su,start_date,end_date,i), exception_set_group) ) for new_name, old_names in equivilants: for old_name in old_names: print old_name, new_name c = gtfsdb.conn.cursor() c.execute( "UPDATE calendar SET service_id=? WHERE service_id=?", (new_name, old_name) ) c.execute( "UPDATE calendar_dates SET service_id=? WHERE service_id=?", (new_name, old_name) ) c.execute( "UPDATE trips SET service_id=? WHERE service_id=?", (new_name, old_name) ) gtfsdb.conn.commit() c.close() if __name__=='__main__':
2.90625
3
10/01/03/2.py
pylangstudy/201707
0
9994
class MyClass: def __repr__(self): return self.__class__.__name__ + '()' print(MyClass().__repr__())
3.015625
3
tests/tasks/core/test_core.py
andykawabata/prefect
2
9995
import pytest from prefect.core import Edge, Flow, Parameter, Task from prefect.tasks.core import collections from prefect.tasks.core.constants import Constant from prefect.tasks.core.function import FunctionTask class IdentityTask(Task): def run(self, x): return x class TestConstant: def test_constant_task_returns_its_value(self): x = Constant("x") assert x.run() == "x" y = Constant(100) assert y.run() == 100 def test_automatic_create_constant_task(self): with Flow(name="test") as flow: t = Task() t.set_dependencies(upstream_tasks=[4]) assert len(flow.tasks) == 2 assert any(isinstance(t, Constant) for t in flow.tasks) class TestFunctionTask: def test_function_task_requires_callable(self): with pytest.raises(TypeError): FunctionTask(fn=1) def test_function_task_takes_name_from_callable(self): def my_fn(): pass f = FunctionTask(fn=my_fn) assert f.name == "my_fn" def test_function_task_takes_name_from_arg_if_provided(self): def my_fn(): pass f = FunctionTask(fn=my_fn, name="test") assert f.name == "test" def test_function_task_docstring(self): def my_fn(): """An example docstring.""" pass # Original docstring available on class assert "FunctionTask" in FunctionTask.__doc__ # Wrapped function is docstring on instance f = FunctionTask(fn=my_fn) assert f.__doc__ == my_fn.__doc__ # Except when no docstring on wrapped function f = FunctionTask(fn=lambda x: x + 1) assert "FunctionTask" in f.__doc__ def test_function_task_sets__wrapped__(self): def my_fn(): """An example function""" pass t = FunctionTask(fn=my_fn) assert t.__wrapped__ == my_fn assert not hasattr(FunctionTask, "__wrapped__") class TestCollections: def test_list_returns_a_list(self): l = collections.List() with Flow(name="test") as f: l.bind(1, 2) assert f.run().result[l].result == [1, 2] def test_list_binds_varargs(self): t1 = Task() t2 = Task() l = collections.List() with Flow(name="test") as f: l.bind(t1, t2) assert set([t1, t2, l]) == f.tasks assert Edge(t1, l, key="arg_1") in f.edges assert Edge(t2, l, key="arg_2") in f.edges def test_tuple_returns_a_tuple(self): l = collections.Tuple() with Flow(name="test") as f: l.bind(1, 2) assert f.run().result[l].result == (1, 2) def test_tuple_binds_varargs(self): t1 = Task() t2 = Task() l = collections.Tuple() with Flow(name="test") as f: l.bind(t1, t2) assert set([t1, t2, l]) == f.tasks assert Edge(t1, l, key="arg_1") in f.edges assert Edge(t2, l, key="arg_2") in f.edges def test_set_returns_a_set(self): l = collections.Set() with Flow(name="test") as f: l.bind(1, 2) assert f.run().result[l].result == set([1, 2]) def test_set_binds_varargs(self): t1 = Task() t2 = Task() l = collections.Set() with Flow(name="test") as f: l.bind(t1, t2) assert set([t1, t2, l]) == f.tasks assert Edge(t1, l, key="arg_1") in f.edges assert Edge(t2, l, key="arg_2") in f.edges def test_dict_returns_a_dict(self): l = collections.Dict() with Flow(name="test") as f: l.bind(keys=["a", "b"], values=[1, 2]) assert f.run().result[l].result == dict(a=1, b=2) def test_dict_handles_non_string_keys(self): l = collections.Dict() with Flow(name="test") as f: l.bind(keys=[None, 55], values=[1, 2]) assert f.run().result[l].result == {None: 1, 55: 2} def test_dict_raises_for_differing_length_key_value_pairs(self): l = collections.Dict() with Flow(name="test") as f: l.bind(keys=["a"], values=[1, 2]) state = f.run() assert state.result[l].is_failed() assert isinstance(state.result[l].result, ValueError) def test_list_automatically_applied_to_callargs(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() with Flow(name="test") as f: identity.bind(x=[x, y]) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.List) for t in f.tasks) == 1 assert state.result[identity].result == [1, 2] def test_list_automatically_applied_to_callargs_imperative(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() f = Flow(name="test") f.add_task(identity) identity.bind(x=[x, y], flow=f) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.List) for t in f.tasks) == 1 assert state.result[identity].result == [1, 2] def test_tuple_automatically_applied_to_callargs(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() with Flow(name="test") as f: identity.bind(x=(x, y)) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.Tuple) for t in f.tasks) == 1 assert state.result[identity].result == (1, 2) def test_tuple_automatically_applied_to_callargs_imperative(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() f = Flow(name="test") f.add_task(identity) identity.bind(x=(x, y), flow=f) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.Tuple) for t in f.tasks) == 1 assert state.result[identity].result == (1, 2) def test_set_automatically_applied_to_callargs(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() with Flow(name="test") as f: identity.bind(x=set([x, y])) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.Set) for t in f.tasks) == 1 assert state.result[identity].result == set([1, 2]) def test_set_automatically_applied_to_callargs_imperative(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() f = Flow(name="test") f.add_task(identity) identity.bind(x=set([x, y]), flow=f) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 4 assert sum(isinstance(t, collections.Set) for t in f.tasks) == 1 assert state.result[identity].result == set([1, 2]) def test_dict_automatically_applied_to_callargs(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() with Flow(name="test") as f: identity.bind(x=dict(a=x, b=y)) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 5 # 2 params, identity, Dict, List of dict values assert sum(isinstance(t, collections.Dict) for t in f.tasks) == 1 assert state.result[identity].result == dict(a=1, b=2) def test_dict_automatically_applied_to_callargs_imperative(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() f = Flow(name="test") f.add_task(identity) identity.bind(x=dict(a=x, b=y), flow=f) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 5 # 2 params, identity, Dict, List of dict values assert sum(isinstance(t, collections.Dict) for t in f.tasks) == 1 assert state.result[identity].result == dict(a=1, b=2) def test_nested_collection_automatically_applied_to_callargs(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() with Flow(name="test") as f: identity.bind(x=dict(a=[x, dict(y=y)], b=(y, set([x])))) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 10 assert state.result[identity].result == dict(a=[1, dict(y=2)], b=(2, set([1]))) def test_nested_collection_automatically_applied_to_callargs_imperative(self): x = Parameter("x") y = Parameter("y") identity = IdentityTask() f = Flow(name="test") f.add_task(identity) identity.bind(x=dict(a=[x, dict(y=y)], b=(y, set([x]))), flow=f) state = f.run(parameters=dict(x=1, y=2)) assert len(f.tasks) == 10 assert state.result[identity].result == dict(a=[1, dict(y=2)], b=(2, set([1]))) def test_list_maintains_sort_order_for_more_than_10_items(self): # https://github.com/PrefectHQ/prefect/issues/2451 l = collections.List() with Flow(name="test") as f: l.bind(*list(range(15))) assert f.run().result[l].result == list(range(15)) def test_tuple_maintains_sort_order_for_more_than_10_items(self): # https://github.com/PrefectHQ/prefect/issues/2451 t = collections.Tuple() with Flow(name="test") as f: t.bind(*list(range(15))) assert f.run().result[t].result == tuple(range(15))
2.21875
2
Tree/node.py
philipwerner/python_data_structures
0
9996
"""Node class module for Binary Tree.""" class Node(object): """The Node class.""" def __init__(self, value): """Initialization of node object.""" self.value = value self.left = None self.right = None def __str__(self): """Return a string representation of the node object.""" return f'{self.value}' def __repr__(self): """Return a representation of the node object.""" return f'<Node | Value: {self.value} | Left: {self.left} | Right: {self.right}>'
3.9375
4
qutip/operators.py
pschindler/qutip
1
9997
# This file is part of QuTiP: Quantum Toolbox in Python. # # Copyright (c) 2011 and later, <NAME> and <NAME>. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names # of its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A # PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ############################################################################### """ This module contains functions for generating Qobj representation of a variety of commonly occuring quantum operators. """ __all__ = ['jmat', 'spin_Jx', 'spin_Jy', 'spin_Jz', 'spin_Jm', 'spin_Jp', 'spin_J_set', 'sigmap', 'sigmam', 'sigmax', 'sigmay', 'sigmaz', 'destroy', 'create', 'qeye', 'identity', 'position', 'momentum', 'num', 'squeeze', 'squeezing', 'displace', 'commutator', 'qutrit_ops', 'qdiags', 'phase', 'qzero', 'enr_destroy', 'enr_identity', 'charge', 'tunneling'] import numbers import numpy as np import scipy import scipy.sparse as sp from qutip.qobj import Qobj from qutip.fastsparse import fast_csr_matrix, fast_identity from qutip.dimensions import flatten # # Spin operators # def jmat(j, *args): """Higher-order spin operators: Parameters ---------- j : float Spin of operator args : str Which operator to return 'x','y','z','+','-'. If no args given, then output is ['x','y','z'] Returns ------- jmat : qobj / ndarray ``qobj`` for requested spin operator(s). Examples -------- >>> jmat(1) # doctest: +SKIP [ Quantum object: dims = [[3], [3]], \ shape = [3, 3], type = oper, isHerm = True Qobj data = [[ 0. 0.70710678 0. ] [ 0.70710678 0. 0.70710678] [ 0. 0.70710678 0. ]] Quantum object: dims = [[3], [3]], \ shape = [3, 3], type = oper, isHerm = True Qobj data = [[ 0.+0.j 0.-0.70710678j 0.+0.j ] [ 0.+0.70710678j 0.+0.j 0.-0.70710678j] [ 0.+0.j 0.+0.70710678j 0.+0.j ]] Quantum object: dims = [[3], [3]], \ shape = [3, 3], type = oper, isHerm = True Qobj data = [[ 1. 0. 0.] [ 0. 0. 0.] [ 0. 0. -1.]]] Notes ----- If no 'args' input, then returns array of ['x','y','z'] operators. """ if (np.fix(2 * j) != 2 * j) or (j < 0): raise TypeError('j must be a non-negative integer or half-integer') if not args: return jmat(j, 'x'), jmat(j, 'y'), jmat(j, 'z') if args[0] == '+': A = _jplus(j) elif args[0] == '-': A = _jplus(j).getH() elif args[0] == 'x': A = 0.5 * (_jplus(j) + _jplus(j).getH()) elif args[0] == 'y': A = -0.5 * 1j * (_jplus(j) - _jplus(j).getH()) elif args[0] == 'z': A = _jz(j) else: raise TypeError('Invalid type') return Qobj(A) def _jplus(j): """ Internal functions for generating the data representing the J-plus operator. """ m = np.arange(j, -j - 1, -1, dtype=complex) data = (np.sqrt(j * (j + 1.0) - (m + 1.0) * m))[1:] N = m.shape[0] ind = np.arange(1, N, dtype=np.int32) ptr = np.array(list(range(N-1))+[N-1]*2, dtype=np.int32) ptr[-1] = N-1 return fast_csr_matrix((data,ind,ptr), shape=(N,N)) def _jz(j): """ Internal functions for generating the data representing the J-z operator. """ N = int(2*j+1) data = np.array([j-k for k in range(N) if (j-k)!=0], dtype=complex) # Even shaped matrix if (N % 2 == 0): ind = np.arange(N, dtype=np.int32) ptr = np.arange(N+1,dtype=np.int32) ptr[-1] = N # Odd shaped matrix else: j = int(j) ind = np.array(list(range(j))+list(range(j+1,N)), dtype=np.int32) ptr = np.array(list(range(j+1))+list(range(j,N)), dtype=np.int32) ptr[-1] = N-1 return fast_csr_matrix((data,ind,ptr), shape=(N,N)) # # Spin j operators: # def spin_Jx(j): """Spin-j x operator Parameters ---------- j : float Spin of operator Returns ------- op : Qobj ``qobj`` representation of the operator. """ return jmat(j, 'x') def spin_Jy(j): """Spin-j y operator Parameters ---------- j : float Spin of operator Returns ------- op : Qobj ``qobj`` representation of the operator. """ return jmat(j, 'y') def spin_Jz(j): """Spin-j z operator Parameters ---------- j : float Spin of operator Returns ------- op : Qobj ``qobj`` representation of the operator. """ return jmat(j, 'z') def spin_Jm(j): """Spin-j annihilation operator Parameters ---------- j : float Spin of operator Returns ------- op : Qobj ``qobj`` representation of the operator. """ return jmat(j, '-') def spin_Jp(j): """Spin-j creation operator Parameters ---------- j : float Spin of operator Returns ------- op : Qobj ``qobj`` representation of the operator. """ return jmat(j, '+') def spin_J_set(j): """Set of spin-j operators (x, y, z) Parameters ---------- j : float Spin of operators Returns ------- list : list of Qobj list of ``qobj`` representating of the spin operator. """ return jmat(j) # # Pauli spin 1/2 operators: # def sigmap(): """Creation operator for Pauli spins. Examples -------- >>> sigmap() # doctest: +SKIP Quantum object: dims = [[2], [2]], \ shape = [2, 2], type = oper, isHerm = False Qobj data = [[ 0. 1.] [ 0. 0.]] """ return jmat(1 / 2., '+') def sigmam(): """Annihilation operator for Pauli spins. Examples -------- >>> sigmam() # doctest: +SKIP Quantum object: dims = [[2], [2]], \ shape = [2, 2], type = oper, isHerm = False Qobj data = [[ 0. 0.] [ 1. 0.]] """ return jmat(1 / 2., '-') def sigmax(): """Pauli spin 1/2 sigma-x operator Examples -------- >>> sigmax() # doctest: +SKIP Quantum object: dims = [[2], [2]], \ shape = [2, 2], type = oper, isHerm = False Qobj data = [[ 0. 1.] [ 1. 0.]] """ return 2.0 * jmat(1.0 / 2, 'x') def sigmay(): """Pauli spin 1/2 sigma-y operator. Examples -------- >>> sigmay() # doctest: +SKIP Quantum object: dims = [[2], [2]], \ shape = [2, 2], type = oper, isHerm = True Qobj data = [[ 0.+0.j 0.-1.j] [ 0.+1.j 0.+0.j]] """ return 2.0 * jmat(1.0 / 2, 'y') def sigmaz(): """Pauli spin 1/2 sigma-z operator. Examples -------- >>> sigmaz() # doctest: +SKIP Quantum object: dims = [[2], [2]], \ shape = [2, 2], type = oper, isHerm = True Qobj data = [[ 1. 0.] [ 0. -1.]] """ return 2.0 * jmat(1.0 / 2, 'z') # # DESTROY returns annihilation operator for N dimensional Hilbert space # out = destroy(N), N is integer value & N>0 # def destroy(N, offset=0): '''Destruction (lowering) operator. Parameters ---------- N : int Dimension of Hilbert space. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper : qobj Qobj for lowering operator. Examples -------- >>> destroy(4) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isHerm = False Qobj data = [[ 0.00000000+0.j 1.00000000+0.j 0.00000000+0.j 0.00000000+0.j] [ 0.00000000+0.j 0.00000000+0.j 1.41421356+0.j 0.00000000+0.j] [ 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j 1.73205081+0.j] [ 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j]] ''' if not isinstance(N, (int, np.integer)): # raise error if N not integer raise ValueError("Hilbert space dimension must be integer value") data = np.sqrt(np.arange(offset+1, N+offset, dtype=complex)) ind = np.arange(1,N, dtype=np.int32) ptr = np.arange(N+1, dtype=np.int32) ptr[-1] = N-1 return Qobj(fast_csr_matrix((data,ind,ptr),shape=(N,N)), isherm=False) # # create returns creation operator for N dimensional Hilbert space # out = create(N), N is integer value & N>0 # def create(N, offset=0): '''Creation (raising) operator. Parameters ---------- N : int Dimension of Hilbert space. Returns ------- oper : qobj Qobj for raising operator. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Examples -------- >>> create(4) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isHerm = False Qobj data = [[ 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j] [ 1.00000000+0.j 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j] [ 0.00000000+0.j 1.41421356+0.j 0.00000000+0.j 0.00000000+0.j] [ 0.00000000+0.j 0.00000000+0.j 1.73205081+0.j 0.00000000+0.j]] ''' if not isinstance(N, (int, np.integer)): # raise error if N not integer raise ValueError("Hilbert space dimension must be integer value") qo = destroy(N, offset=offset) # create operator using destroy function return qo.dag() def _implicit_tensor_dimensions(dimensions): """ Total flattened size and operator dimensions for operator creation routines that automatically perform tensor products. Parameters ---------- dimensions : (int) or (list of int) or (list of list of int) First dimension of an operator which can create an implicit tensor product. If the type is `int`, it is promoted first to `[dimensions]`. From there, it should be one of the two-elements `dims` parameter of a `qutip.Qobj` representing an `oper` or `super`, with possible tensor products. Returns ------- size : int Dimension of backing matrix required to represent operator. dimensions : list Dimension list in the form required by ``Qobj`` creation. """ if not isinstance(dimensions, list): dimensions = [dimensions] flat = flatten(dimensions) if not all(isinstance(x, numbers.Integral) and x >= 0 for x in flat): raise ValueError("All dimensions must be integers >= 0") return np.prod(flat), [dimensions, dimensions] def qzero(dimensions): """ Zero operator. Parameters ---------- dimensions : (int) or (list of int) or (list of list of int) Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the ``dims`` property of the new Qobj are set to this list. This can produce either `oper` or `super` depending on the passed `dimensions`. Returns ------- qzero : qobj Zero operator Qobj. """ size, dimensions = _implicit_tensor_dimensions(dimensions) # A sparse matrix with no data is equal to a zero matrix. return Qobj(fast_csr_matrix(shape=(size, size), dtype=complex), dims=dimensions, isherm=True) # # QEYE returns identity operator for a Hilbert space with dimensions dims. # a = qeye(N), N is integer or list of integers & all elements >= 0 # def qeye(dimensions): """ Identity operator. Parameters ---------- dimensions : (int) or (list of int) or (list of list of int) Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the ``dims`` property of the new Qobj are set to this list. This can produce either `oper` or `super` depending on the passed `dimensions`. Returns ------- oper : qobj Identity operator Qobj. Examples -------- >>> qeye(3) # doctest: +SKIP Quantum object: dims = [[3], [3]], shape = (3, 3), type = oper, \ isherm = True Qobj data = [[ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.]] >>> qeye([2,2]) # doctest: +SKIP Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, \ isherm = True Qobj data = [[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]] """ size, dimensions = _implicit_tensor_dimensions(dimensions) return Qobj(fast_identity(size), dims=dimensions, isherm=True, isunitary=True) def identity(dims): """Identity operator. Alternative name to :func:`qeye`. Parameters ---------- dimensions : (int) or (list of int) or (list of list of int) Dimension of Hilbert space. If provided as a list of ints, then the dimension is the product over this list, but the ``dims`` property of the new Qobj are set to this list. This can produce either `oper` or `super` depending on the passed `dimensions`. Returns ------- oper : qobj Identity operator Qobj. """ return qeye(dims) def position(N, offset=0): """ Position operator x=1/sqrt(2)*(a+a.dag()) Parameters ---------- N : int Number of Fock states in Hilbert space. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper : qobj Position operator as Qobj. """ a = destroy(N, offset=offset) return 1.0 / np.sqrt(2.0) * (a + a.dag()) def momentum(N, offset=0): """ Momentum operator p=-1j/sqrt(2)*(a-a.dag()) Parameters ---------- N : int Number of Fock states in Hilbert space. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper : qobj Momentum operator as Qobj. """ a = destroy(N, offset=offset) return -1j / np.sqrt(2.0) * (a - a.dag()) def num(N, offset=0): """Quantum object for number operator. Parameters ---------- N : int The dimension of the Hilbert space. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper: qobj Qobj for number operator. Examples -------- >>> num(4) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isHerm = True Qobj data = [[0 0 0 0] [0 1 0 0] [0 0 2 0] [0 0 0 3]] """ if offset == 0: data = np.arange(1,N, dtype=complex) ind = np.arange(1,N, dtype=np.int32) ptr = np.array([0]+list(range(0,N)), dtype=np.int32) ptr[-1] = N-1 else: data = np.arange(offset, offset + N, dtype=complex) ind = np.arange(N, dtype=np.int32) ptr = np.arange(N+1,dtype=np.int32) ptr[-1] = N return Qobj(fast_csr_matrix((data,ind,ptr), shape=(N,N)), isherm=True) def squeeze(N, z, offset=0): """Single-mode Squeezing operator. Parameters ---------- N : int Dimension of hilbert space. z : float/complex Squeezing parameter. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper : :class:`qutip.qobj.Qobj` Squeezing operator. Examples -------- >>> squeeze(4, 0.25) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isHerm = False Qobj data = [[ 0.98441565+0.j 0.00000000+0.j 0.17585742+0.j 0.00000000+0.j] [ 0.00000000+0.j 0.95349007+0.j 0.00000000+0.j 0.30142443+0.j] [-0.17585742+0.j 0.00000000+0.j 0.98441565+0.j 0.00000000+0.j] [ 0.00000000+0.j -0.30142443+0.j 0.00000000+0.j 0.95349007+0.j]] """ a = destroy(N, offset=offset) op = (1 / 2.0) * np.conj(z) * (a ** 2) - (1 / 2.0) * z * (a.dag()) ** 2 return op.expm() def squeezing(a1, a2, z): """Generalized squeezing operator. .. math:: S(z) = \\exp\\left(\\frac{1}{2}\\left(z^*a_1a_2 - za_1^\\dagger a_2^\\dagger\\right)\\right) Parameters ---------- a1 : :class:`qutip.qobj.Qobj` Operator 1. a2 : :class:`qutip.qobj.Qobj` Operator 2. z : float/complex Squeezing parameter. Returns ------- oper : :class:`qutip.qobj.Qobj` Squeezing operator. """ b = 0.5 * (np.conj(z) * (a1 * a2) - z * (a1.dag() * a2.dag())) return b.expm() def displace(N, alpha, offset=0): """Single-mode displacement operator. Parameters ---------- N : int Dimension of Hilbert space. alpha : float/complex Displacement amplitude. offset : int (default 0) The lowest number state that is included in the finite number state representation of the operator. Returns ------- oper : qobj Displacement operator. Examples --------- >>> displace(4,0.25) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isHerm = False Qobj data = [[ 0.96923323+0.j -0.24230859+0.j 0.04282883+0.j -0.00626025+0.j] [ 0.24230859+0.j 0.90866411+0.j -0.33183303+0.j 0.07418172+0.j] [ 0.04282883+0.j 0.33183303+0.j 0.84809499+0.j -0.41083747+0.j] [ 0.00626025+0.j 0.07418172+0.j 0.41083747+0.j 0.90866411+0.j]] """ a = destroy(N, offset=offset) D = (alpha * a.dag() - np.conj(alpha) * a).expm() return D def commutator(A, B, kind="normal"): """ Return the commutator of kind `kind` (normal, anti) of the two operators A and B. """ if kind == 'normal': return A * B - B * A elif kind == 'anti': return A * B + B * A else: raise TypeError("Unknown commutator kind '%s'" % kind) def qutrit_ops(): """ Operators for a three level system (qutrit). Returns ------- opers: array `array` of qutrit operators. """ from qutip.states import qutrit_basis one, two, three = qutrit_basis() sig11 = one * one.dag() sig22 = two * two.dag() sig33 = three * three.dag() sig12 = one * two.dag() sig23 = two * three.dag() sig31 = three * one.dag() return np.array([sig11, sig22, sig33, sig12, sig23, sig31], dtype=object) def qdiags(diagonals, offsets, dims=None, shape=None): """ Constructs an operator from an array of diagonals. Parameters ---------- diagonals : sequence of array_like Array of elements to place along the selected diagonals. offsets : sequence of ints Sequence for diagonals to be set: - k=0 main diagonal - k>0 kth upper diagonal - k<0 kth lower diagonal dims : list, optional Dimensions for operator shape : list, tuple, optional Shape of operator. If omitted, a square operator large enough to contain the diagonals is generated. See Also -------- scipy.sparse.diags : for usage information. Notes ----- This function requires SciPy 0.11+. Examples -------- >>> qdiags(sqrt(range(1, 4)), 1) # doctest: +SKIP Quantum object: dims = [[4], [4]], \ shape = [4, 4], type = oper, isherm = False Qobj data = [[ 0. 1. 0. 0. ] [ 0. 0. 1.41421356 0. ] [ 0. 0. 0. 1.73205081] [ 0. 0. 0. 0. ]] """ data = sp.diags(diagonals, offsets, shape, format='csr', dtype=complex) if not dims: dims = [[], []] if not shape: shape = [] return Qobj(data, dims, list(shape)) def phase(N, phi0=0): """ Single-mode Pegg-Barnett phase operator. Parameters ---------- N : int Number of basis states in Hilbert space. phi0 : float Reference phase. Returns ------- oper : qobj Phase operator with respect to reference phase. Notes ----- The Pegg-Barnett phase operator is Hermitian on a truncated Hilbert space. """ phim = phi0 + (2.0 * np.pi * np.arange(N)) / N # discrete phase angles n = np.arange(N).reshape((N, 1)) states = np.array([np.sqrt(kk) / np.sqrt(N) * np.exp(1.0j * n * kk) for kk in phim]) ops = np.array([np.outer(st, st.conj()) for st in states]) return Qobj(np.sum(ops, axis=0)) def enr_destroy(dims, excitations): """ Generate annilation operators for modes in a excitation-number-restricted state space. For example, consider a system consisting of 4 modes, each with 5 states. The total hilbert space size is 5**4 = 625. If we are only interested in states that contain up to 2 excitations, we only need to include states such as (0, 0, 0, 0) (0, 0, 0, 1) (0, 0, 0, 2) (0, 0, 1, 0) (0, 0, 1, 1) (0, 0, 2, 0) ... This function creates annihilation operators for the 4 modes that act within this state space: a1, a2, a3, a4 = enr_destroy([5, 5, 5, 5], excitations=2) From this point onwards, the annihiltion operators a1, ..., a4 can be used to setup a Hamiltonian, collapse operators and expectation-value operators, etc., following the usual pattern. Parameters ---------- dims : list A list of the dimensions of each subsystem of a composite quantum system. excitations : integer The maximum number of excitations that are to be included in the state space. Returns ------- a_ops : list of qobj A list of annihilation operators for each mode in the composite quantum system described by dims. """ from qutip.states import enr_state_dictionaries nstates, state2idx, idx2state = enr_state_dictionaries(dims, excitations) a_ops = [sp.lil_matrix((nstates, nstates), dtype=np.complex) for _ in range(len(dims))] for n1, state1 in idx2state.items(): for n2, state2 in idx2state.items(): for idx, a in enumerate(a_ops): s1 = [s for idx2, s in enumerate(state1) if idx != idx2] s2 = [s for idx2, s in enumerate(state2) if idx != idx2] if (state1[idx] == state2[idx] - 1) and (s1 == s2): a_ops[idx][n1, n2] = np.sqrt(state2[idx]) return [Qobj(a, dims=[dims, dims]) for a in a_ops] def enr_identity(dims, excitations): """ Generate the identity operator for the excitation-number restricted state space defined by the `dims` and `exciations` arguments. See the docstring for enr_fock for a more detailed description of these arguments. Parameters ---------- dims : list A list of the dimensions of each subsystem of a composite quantum system. excitations : integer The maximum number of excitations that are to be included in the state space. state : list of integers The state in the number basis representation. Returns ------- op : Qobj A Qobj instance that represent the identity operator in the exication-number-restricted state space defined by `dims` and `exciations`. """ from qutip.states import enr_state_dictionaries nstates, _, _ = enr_state_dictionaries(dims, excitations) data = sp.eye(nstates, nstates, dtype=np.complex) return Qobj(data, dims=[dims, dims]) def charge(Nmax, Nmin=None, frac = 1): """ Generate the diagonal charge operator over charge states from Nmin to Nmax. Parameters ---------- Nmax : int Maximum charge state to consider. Nmin : int (default = -Nmax) Lowest charge state to consider. frac : float (default = 1) Specify fractional charge if needed. Returns ------- C : Qobj Charge operator over [Nmin,Nmax]. Notes ----- .. versionadded:: 3.2 """ if Nmin is None: Nmin = -Nmax diag = np.arange(Nmin, Nmax+1, dtype=float) if frac != 1: diag *= frac C = sp.diags(diag, 0, format='csr', dtype=complex) return Qobj(C, isherm=True) def tunneling(N, m=1): """ Tunneling operator with elements of the form :math:`\\sum |N><N+m| + |N+m><N|`. Parameters ---------- N : int Number of basis states in Hilbert space. m : int (default = 1) Number of excitations in tunneling event. Returns ------- T : Qobj Tunneling operator. Notes ----- .. versionadded:: 3.2 """ diags = [np.ones(N-m,dtype=int),np.ones(N-m,dtype=int)] T = sp.diags(diags,[m,-m],format='csr', dtype=complex) return Qobj(T, isherm=True) # Break circular dependencies by a trailing import. # Note that we use a relative import here to deal with that # qutip.tensor is the *function* tensor, not the module. from qutip.tensor import tensor
1.515625
2
tests/test_deepsv.py
lsantuari/deepsv
0
9998
<filename>tests/test_deepsv.py #!/usr/bin/env python # -*- coding: utf-8 -*- import pytest from deepsv import deepsv from unittest.mock import patch """Tests for the deepsv module. """ def test_something(): assert True def test_adding_numbers(): assert deepsv.add_numbers(1, 1) == 2 assert deepsv.add_numbers(1, 2) != 2 def test_with_error(): with pytest.raises(ValueError): # Do something that raises a ValueError raise ValueError # Fixture example @pytest.fixture def an_object(): return {} def test_deepsv(an_object): assert an_object == {} def side_effect_function(mock): print('This part of the code runs when patched') return 'Some text that I want to test with' def test_word_count_of_book_base(): book = 'https://www.gutenberg.org/files/59560/59560-0.txt' wc = deepsv.word_count(book) assert wc == 30577 @patch('deepsv.deepsv.download_text', side_effect=side_effect_function) def test_word_count_of_book(mock): # book = 'https://www.gutenberg.org/files/59560/59560-0.txt' wc = deepsv.word_count(mock.text) assert wc == 8 def test_count_single_base(): sequence = 'TTAGGACCA' assert deepsv.count_single_base('A', sequence) == 3 assert deepsv.count_single_base('C', sequence) == 2 assert deepsv.count_single_base('G', sequence) == 2 assert deepsv.count_single_base('T', sequence) == 2 def side_effect_get_sequence(): return 'GTACGTCAG' @patch('deepsv.deepsv.get_sequence', return_value='GTACGTCAG') def test_count_bases(sequence): seq_dict = {'A': 2, 'C': 2, 'G': 3, 'T': 2} assert deepsv.count_bases(sequence) == seq_dict
3.3125
3
tcex/bin/dep.py
phuerta-tc/tcex
0
9999
<filename>tcex/bin/dep.py #!/usr/bin/env python """TcEx Dependencies Command""" # standard library import os import platform import shutil import subprocess # nosec import sys from distutils.version import StrictVersion # pylint: disable=no-name-in-module from pathlib import Path from typing import List from urllib.parse import quote # third-party import typer # first-party from tcex.app_config.models.tcex_json_model import LibVersionModel from tcex.bin.bin_abc import BinABC class Dep(BinABC): """Install dependencies for App.""" def __init__( self, branch: str, no_cache_dir: bool, proxy_host: str, proxy_port: int, proxy_user: str, proxy_pass: str, ) -> None: """Initialize Class properties.""" super().__init__() self.branch = branch self.no_cache_dir = no_cache_dir self.proxy_host = proxy_host self.proxy_port = proxy_port self.proxy_user = proxy_user self.proxy_pass = proxy_pass # properties self.latest_version = None self.lib_directory = ( f'lib_{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}' ) self.proxy_enabled = False self.proxy_env = {} self.requirements_fqfn = Path('requirements.txt') self.static_lib_dir = 'lib_latest' # update tcex.json self.tj.update.multiple() def _build_command(self, python_executable: Path, lib_dir: Path) -> str: """Build the pip command for installing dependencies. Args: python_executable: The fully qualified path of the Python executable. lib_dir: The fully qualified path of the lib directory. Returns: list: The Python pip command with all required args. """ exe_command = [ str(python_executable), '-m', 'pip', 'install', '-r', str(self.requirements_fqfn), '--ignore-installed', '--quiet', '--target', lib_dir.name, ] if self.no_cache_dir: exe_command.append('--no-cache-dir') if self.proxy_enabled: # trust the pypi hosts to avoid ssl errors trusted_hosts = ['pypi.org', 'pypi.python.org', 'files.pythonhosted.org'] for host in trusted_hosts: exe_command.append('--trusted-host') exe_command.append(host) return exe_command def _create_lib_latest(self) -> None: """Create the lib_latest symlink for App Builder.""" if platform.system() == 'Windows': shutil.copytree(f'lib_{self.latest_version}', self.static_lib_dir) else: if os.path.islink(self.static_lib_dir): os.unlink(self.static_lib_dir) elif os.path.isfile(self.static_lib_dir): os.rmdir(self.static_lib_dir) os.symlink(f'lib_{self.latest_version}', self.static_lib_dir) @staticmethod def _remove_previous(fqpn: Path) -> None: """Remove previous lib directory recursively.""" if os.access(fqpn, os.W_OK): shutil.rmtree(fqpn) def configure_proxy(self) -> None: """Configure proxy settings using environment variables.""" if os.getenv('HTTP_PROXY') or os.getenv('HTTPS_PROXY'): # don't change proxy settings if the OS already has them configured. return if self.proxy_host is not None and self.proxy_port is not None: # proxy url without auth proxy_url = f'{self.proxy_host}:{self.proxy_port}' if self.proxy_user is not None and self.proxy_pass is not None: proxy_user = quote(self.proxy_user, safe='~') proxy_pass = quote(self.proxy_pass, safe='~') # proxy url with auth proxy_url = f'{proxy_user}:{proxy_pass}@{proxy_url}' # update proxy properties self.proxy_enabled = True self.proxy_env = { 'HTTP_PROXY': f'http://{proxy_url}', 'HTTPS_PROXY': f'http://{proxy_url}', } # display proxy setting self.print_setting('Using Proxy Server', f'{self.proxy_host}:{self.proxy_port}') def create_temp_requirements(self) -> None: """Create a temporary requirements.txt. This allows testing again a git branch instead of pulling from pypi. """ # Replace tcex version with develop branch of tcex with self.requirements_fqfn.open() as fh: current_requirements = fh.read().strip().split('\n') self.requirements_fqfn = Path(f'temp-{self.requirements_fqfn}') with self.requirements_fqfn.open(mode='w') as fh: requirements = [] for line in current_requirements: if not line: continue if line.startswith('tcex'): line = ( 'git+https://github.com/ThreatConnect-Inc/tcex.git@' f'{self.branch}#egg=tcex' ) requirements.append(line) fh.write('\n'.join(requirements)) # display branch setting self.print_setting('Using Branch', self.branch) def install_deps(self) -> None: """Install Required Libraries using pip.""" # check for requirements.txt if not self.requirements_fqfn.is_file(): self.handle_error('A requirements.txt file is required to install modules.') # install all requested lib directories for lib_version in self.lib_versions: # remove lib directory from previous runs self._remove_previous(lib_version.lib_dir) if ( not lib_version.python_executable.is_file() and not lib_version.python_executable.is_symlink() ): # display error typer.secho( f'The Python executable ({lib_version.python_executable}) could not be found. ' 'Skipping building lib directory for this Python version.', fg=typer.colors.YELLOW, ) continue # display lib dir setting self.print_setting('Lib Dir', f'{lib_version.lib_dir.name}') # build the sub process command exe_command = self._build_command(lib_version.python_executable, lib_version.lib_dir) # display command setting self.print_setting('Running', f'''{' '.join(exe_command)}''', fg_color='GREEN') # recommended -> https://pip.pypa.io/en/latest/user_guide/#using-pip-from-your-program p = subprocess.Popen( # pylint: disable=consider-using-with exe_command, shell=False, # nosec stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=self.proxy_env, ) _, err = p.communicate() # pylint: disable=unused-variable if p.returncode != 0: # display error err = err.decode('utf-8') failure_display = typer.style( f'Failure: {err}', fg=typer.colors.WHITE, bg=typer.colors.RED ) typer.echo(f'{failure_display}') sys.exit(1) # TODO: [low] can this be updated to use version from model? # version comparison try: python_version = lib_version.lib_dir.name.split('_', 1)[1] except IndexError: python_version = None self.handle_error('Could not determine version from lib string.') # TODO: [low] investigate using sematic_version package # track the latest Python version if self.latest_version is None or StrictVersion(python_version) > StrictVersion( self.latest_version ): self.latest_version = python_version if self.branch != 'master': # remove temp requirements.txt file self.requirements_fqfn.unlink() # create lib_latest directory self._create_lib_latest() @property def lib_versions(self) -> List[LibVersionModel]: """Return the lib_version data required to build lib directories.""" if self.tj.model.lib_versions: self.print_setting('Python Version', 'using version(s) defined in tcex.json') # return the python versions defined in the tcex.json file return self.tj.model.lib_versions # return the current python version return [ LibVersionModel(**{'python_executable': sys.executable, 'lib_dir': self.lib_directory}) ]
2.09375
2