fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c310a363cd43bc3cf0bf2612db8eb1c88950cce

23,180

py

Python

models/nets/cpm_hand.py

Aniket1998Agrawal/robotic-palm

3062db8da9a32a9606aa19ef0ee632013cd40b30

[ "Apache-2.0" ]

828

2017-08-28T07:42:53.000Z

2022-03-24T07:24:22.000Z

models/nets/cpm_hand.py

abcx3261/convolutional-pose-machines-tensorflow

b9a30fbb5a2f1d15faf8f553201203a431cb34cb

[ "Apache-2.0" ]

81

2017-08-27T13:46:54.000Z

2022-01-20T11:31:44.000Z

models/nets/cpm_hand.py

abcx3261/convolutional-pose-machines-tensorflow

b9a30fbb5a2f1d15faf8f553201203a431cb34cb

[ "Apache-2.0" ]

312

2017-08-29T08:13:02.000Z

2022-01-16T12:27:21.000Z

import tensorflow as tf import pickle from models.nets.CPM import CPM class CPM_Model(CPM): def __init__(self, input_size, heatmap_size, stages, joints, img_type='RGB', is_training=True): self.stages = stages self.stage_heatmap = [] self.stage_loss = [0 for _ in range(stages)] self.total_loss = 0 self.input_image = None self.center_map = None self.gt_heatmap = None self.init_lr = 0 self.merged_summary = None self.joints = joints self.batch_size = 0 self.inference_type = 'Train' if img_type == 'RGB': self.input_images = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 3), name='input_placeholder') elif img_type == 'GRAY': self.input_images = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 1), name='input_placeholder') self.cmap_placeholder = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 1), name='cmap_placeholder') self.gt_hmap_placeholder = tf.placeholder(dtype=tf.float32, shape=(None, heatmap_size, heatmap_size, joints + 1), name='gt_hmap_placeholder') self._build_model() def _build_model(self): with tf.variable_scope('pooled_center_map'): self.center_map = tf.layers.average_pooling2d(inputs=self.cmap_placeholder, pool_size=[9, 9], strides=[8, 8], padding='same', name='center_map') with tf.variable_scope('sub_stages'): sub_conv1 = tf.layers.conv2d(inputs=self.input_images, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv1') sub_conv2 = tf.layers.conv2d(inputs=sub_conv1, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv2') sub_pool1 = tf.layers.max_pooling2d(inputs=sub_conv2, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool1') sub_conv3 = tf.layers.conv2d(inputs=sub_pool1, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv3') sub_conv4 = tf.layers.conv2d(inputs=sub_conv3, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv4') sub_pool2 = tf.layers.max_pooling2d(inputs=sub_conv4, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool2') sub_conv5 = tf.layers.conv2d(inputs=sub_pool2, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv5') sub_conv6 = tf.layers.conv2d(inputs=sub_conv5, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv6') sub_conv7 = tf.layers.conv2d(inputs=sub_conv6, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv7') sub_conv8 = tf.layers.conv2d(inputs=sub_conv7, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv8') sub_pool3 = tf.layers.max_pooling2d(inputs=sub_conv8, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool3') sub_conv9 = tf.layers.conv2d(inputs=sub_pool3, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv9') sub_conv10 = tf.layers.conv2d(inputs=sub_conv9, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv10') sub_conv11 = tf.layers.conv2d(inputs=sub_conv10, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv11') sub_conv12 = tf.layers.conv2d(inputs=sub_conv11, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv12') sub_conv13 = tf.layers.conv2d(inputs=sub_conv12, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv13') sub_conv14 = tf.layers.conv2d(inputs=sub_conv13, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv14') self.sub_stage_img_feature = tf.layers.conv2d(inputs=sub_conv14, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_stage_img_feature') with tf.variable_scope('stage_1'): conv1 = tf.layers.conv2d(inputs=self.sub_stage_img_feature, filters=512, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='conv1') self.stage_heatmap.append(tf.layers.conv2d(inputs=conv1, filters=self.joints+1, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='stage_heatmap')) for stage in range(2, self.stages + 1): self._middle_conv(stage) def _middle_conv(self, stage): with tf.variable_scope('stage_' + str(stage)): self.current_featuremap = tf.concat([self.stage_heatmap[stage - 2], self.sub_stage_img_feature, # self.center_map], ], axis=3) mid_conv1 = tf.layers.conv2d(inputs=self.current_featuremap, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv1') mid_conv2 = tf.layers.conv2d(inputs=mid_conv1, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv2') mid_conv3 = tf.layers.conv2d(inputs=mid_conv2, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv3') mid_conv4 = tf.layers.conv2d(inputs=mid_conv3, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv4') mid_conv5 = tf.layers.conv2d(inputs=mid_conv4, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv5') mid_conv6 = tf.layers.conv2d(inputs=mid_conv5, filters=128, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv6') self.current_heatmap = tf.layers.conv2d(inputs=mid_conv6, filters=self.joints+1, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv7') self.stage_heatmap.append(self.current_heatmap) def build_loss(self, lr, lr_decay_rate, lr_decay_step, optimizer='Adam'): self.total_loss = 0 self.total_loss_eval = 0 self.init_lr = lr self.lr_decay_rate = lr_decay_rate self.lr_decay_step = lr_decay_step self.optimizer = optimizer self.batch_size = tf.cast(tf.shape(self.input_images)[0], dtype=tf.float32) for stage in range(self.stages): with tf.variable_scope('stage' + str(stage + 1) + '_loss'): self.stage_loss[stage] = tf.nn.l2_loss(self.stage_heatmap[stage] - self.gt_hmap_placeholder, name='l2_loss') / self.batch_size tf.summary.scalar('stage' + str(stage + 1) + '_loss', self.stage_loss[stage]) with tf.variable_scope('total_loss'): for stage in range(self.stages): self.total_loss += self.stage_loss[stage] tf.summary.scalar('total loss train', self.total_loss) with tf.variable_scope('total_loss_eval'): for stage in range(self.stages): self.total_loss_eval += self.stage_loss[stage] tf.summary.scalar('total loss eval', self.total_loss) with tf.variable_scope('train'): self.global_step = tf.contrib.framework.get_or_create_global_step() self.cur_lr = tf.train.exponential_decay(self.init_lr, global_step=self.global_step, decay_rate=self.lr_decay_rate, decay_steps=self.lr_decay_step) tf.summary.scalar('global learning rate', self.cur_lr) self.train_op = tf.contrib.layers.optimize_loss(loss=self.total_loss, global_step=self.global_step, learning_rate=self.cur_lr, optimizer=self.optimizer) def load_weights_from_file(self, weight_file_path, sess, finetune=True): # weight_file_object = open(weight_file_path, 'rb') weights = pickle.load(open(weight_file_path, 'rb'))#, encoding='latin1') with tf.variable_scope('', reuse=True): ## Pre stage conv # conv1 for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer) + '/bias') loaded_kernel = weights['conv1_' + str(layer)] loaded_bias = weights['conv1_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) # conv2 for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 2) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 2) + '/bias') loaded_kernel = weights['conv2_' + str(layer)] loaded_bias = weights['conv2_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) # conv3 for layer in range(1, 5): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 4) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 4) + '/bias') loaded_kernel = weights['conv3_' + str(layer)] loaded_bias = weights['conv3_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) # conv4 for layer in range(1, 5): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 8) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 8) + '/bias') loaded_kernel = weights['conv4_' + str(layer)] loaded_bias = weights['conv4_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) # conv5 for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 12) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 12) + '/bias') loaded_kernel = weights['conv5_' + str(layer)] loaded_bias = weights['conv5_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) # conv5_3_CPM conv_kernel = tf.get_variable('sub_stages/sub_stage_img_feature/kernel') conv_bias = tf.get_variable('sub_stages/sub_stage_img_feature/bias') loaded_kernel = weights['conv5_3_CPM'] loaded_bias = weights['conv5_3_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) ## stage 1 conv_kernel = tf.get_variable('stage_1/conv1/kernel') conv_bias = tf.get_variable('stage_1/conv1/bias') loaded_kernel = weights['conv6_1_CPM'] loaded_bias = weights['conv6_1_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) if finetune != True: conv_kernel = tf.get_variable('stage_1/stage_heatmap/kernel') conv_bias = tf.get_variable('stage_1/stage_heatmap/bias') loaded_kernel = weights['conv6_2_CPM'] loaded_bias = weights['conv6_2_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) ## Stage 2 and behind for stage in range(2, self.stages + 1): for layer in range(1, 8): conv_kernel = tf.get_variable('stage_' + str(stage) + '/mid_conv' + str(layer) + '/kernel') conv_bias = tf.get_variable('stage_' + str(stage) + '/mid_conv' + str(layer) + '/bias') loaded_kernel = weights['Mconv' + str(layer) + '_stage' + str(stage)] loaded_bias = weights['Mconv' + str(layer) + '_stage' + str(stage) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias))

56.674817

116

0.40302

import tensorflow as tf import pickle from models.nets.CPM import CPM class CPM_Model(CPM): def __init__(self, input_size, heatmap_size, stages, joints, img_type='RGB', is_training=True): self.stages = stages self.stage_heatmap = [] self.stage_loss = [0 for _ in range(stages)] self.total_loss = 0 self.input_image = None self.center_map = None self.gt_heatmap = None self.init_lr = 0 self.merged_summary = None self.joints = joints self.batch_size = 0 self.inference_type = 'Train' if img_type == 'RGB': self.input_images = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 3), name='input_placeholder') elif img_type == 'GRAY': self.input_images = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 1), name='input_placeholder') self.cmap_placeholder = tf.placeholder(dtype=tf.float32, shape=(None, input_size, input_size, 1), name='cmap_placeholder') self.gt_hmap_placeholder = tf.placeholder(dtype=tf.float32, shape=(None, heatmap_size, heatmap_size, joints + 1), name='gt_hmap_placeholder') self._build_model() def _build_model(self): with tf.variable_scope('pooled_center_map'): self.center_map = tf.layers.average_pooling2d(inputs=self.cmap_placeholder, pool_size=[9, 9], strides=[8, 8], padding='same', name='center_map') with tf.variable_scope('sub_stages'): sub_conv1 = tf.layers.conv2d(inputs=self.input_images, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv1') sub_conv2 = tf.layers.conv2d(inputs=sub_conv1, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv2') sub_pool1 = tf.layers.max_pooling2d(inputs=sub_conv2, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool1') sub_conv3 = tf.layers.conv2d(inputs=sub_pool1, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv3') sub_conv4 = tf.layers.conv2d(inputs=sub_conv3, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv4') sub_pool2 = tf.layers.max_pooling2d(inputs=sub_conv4, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool2') sub_conv5 = tf.layers.conv2d(inputs=sub_pool2, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv5') sub_conv6 = tf.layers.conv2d(inputs=sub_conv5, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv6') sub_conv7 = tf.layers.conv2d(inputs=sub_conv6, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv7') sub_conv8 = tf.layers.conv2d(inputs=sub_conv7, filters=256, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv8') sub_pool3 = tf.layers.max_pooling2d(inputs=sub_conv8, pool_size=[2, 2], strides=2, padding='valid', name='sub_pool3') sub_conv9 = tf.layers.conv2d(inputs=sub_pool3, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv9') sub_conv10 = tf.layers.conv2d(inputs=sub_conv9, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv10') sub_conv11 = tf.layers.conv2d(inputs=sub_conv10, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv11') sub_conv12 = tf.layers.conv2d(inputs=sub_conv11, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv12') sub_conv13 = tf.layers.conv2d(inputs=sub_conv12, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv13') sub_conv14 = tf.layers.conv2d(inputs=sub_conv13, filters=512, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_conv14') self.sub_stage_img_feature = tf.layers.conv2d(inputs=sub_conv14, filters=128, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='sub_stage_img_feature') with tf.variable_scope('stage_1'): conv1 = tf.layers.conv2d(inputs=self.sub_stage_img_feature, filters=512, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='conv1') self.stage_heatmap.append(tf.layers.conv2d(inputs=conv1, filters=self.joints+1, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='stage_heatmap')) for stage in range(2, self.stages + 1): self._middle_conv(stage) def _middle_conv(self, stage): with tf.variable_scope('stage_' + str(stage)): self.current_featuremap = tf.concat([self.stage_heatmap[stage - 2], self.sub_stage_img_feature, ], axis=3) mid_conv1 = tf.layers.conv2d(inputs=self.current_featuremap, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv1') mid_conv2 = tf.layers.conv2d(inputs=mid_conv1, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv2') mid_conv3 = tf.layers.conv2d(inputs=mid_conv2, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv3') mid_conv4 = tf.layers.conv2d(inputs=mid_conv3, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv4') mid_conv5 = tf.layers.conv2d(inputs=mid_conv4, filters=128, kernel_size=[7, 7], strides=[1, 1], padding='same', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv5') mid_conv6 = tf.layers.conv2d(inputs=mid_conv5, filters=128, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv6') self.current_heatmap = tf.layers.conv2d(inputs=mid_conv6, filters=self.joints+1, kernel_size=[1, 1], strides=[1, 1], padding='valid', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='mid_conv7') self.stage_heatmap.append(self.current_heatmap) def build_loss(self, lr, lr_decay_rate, lr_decay_step, optimizer='Adam'): self.total_loss = 0 self.total_loss_eval = 0 self.init_lr = lr self.lr_decay_rate = lr_decay_rate self.lr_decay_step = lr_decay_step self.optimizer = optimizer self.batch_size = tf.cast(tf.shape(self.input_images)[0], dtype=tf.float32) for stage in range(self.stages): with tf.variable_scope('stage' + str(stage + 1) + '_loss'): self.stage_loss[stage] = tf.nn.l2_loss(self.stage_heatmap[stage] - self.gt_hmap_placeholder, name='l2_loss') / self.batch_size tf.summary.scalar('stage' + str(stage + 1) + '_loss', self.stage_loss[stage]) with tf.variable_scope('total_loss'): for stage in range(self.stages): self.total_loss += self.stage_loss[stage] tf.summary.scalar('total loss train', self.total_loss) with tf.variable_scope('total_loss_eval'): for stage in range(self.stages): self.total_loss_eval += self.stage_loss[stage] tf.summary.scalar('total loss eval', self.total_loss) with tf.variable_scope('train'): self.global_step = tf.contrib.framework.get_or_create_global_step() self.cur_lr = tf.train.exponential_decay(self.init_lr, global_step=self.global_step, decay_rate=self.lr_decay_rate, decay_steps=self.lr_decay_step) tf.summary.scalar('global learning rate', self.cur_lr) self.train_op = tf.contrib.layers.optimize_loss(loss=self.total_loss, global_step=self.global_step, learning_rate=self.cur_lr, optimizer=self.optimizer) def load_weights_from_file(self, weight_file_path, sess, finetune=True): weights = pickle.load(open(weight_file_path, 'rb')) with tf.variable_scope('', reuse=True): for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer) + '/bias') loaded_kernel = weights['conv1_' + str(layer)] loaded_bias = weights['conv1_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 2) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 2) + '/bias') loaded_kernel = weights['conv2_' + str(layer)] loaded_bias = weights['conv2_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) for layer in range(1, 5): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 4) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 4) + '/bias') loaded_kernel = weights['conv3_' + str(layer)] loaded_bias = weights['conv3_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) for layer in range(1, 5): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 8) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 8) + '/bias') loaded_kernel = weights['conv4_' + str(layer)] loaded_bias = weights['conv4_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) for layer in range(1, 3): conv_kernel = tf.get_variable('sub_stages/sub_conv' + str(layer + 12) + '/kernel') conv_bias = tf.get_variable('sub_stages/sub_conv' + str(layer + 12) + '/bias') loaded_kernel = weights['conv5_' + str(layer)] loaded_bias = weights['conv5_' + str(layer) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) conv_kernel = tf.get_variable('sub_stages/sub_stage_img_feature/kernel') conv_bias = tf.get_variable('sub_stages/sub_stage_img_feature/bias') loaded_kernel = weights['conv5_3_CPM'] loaded_bias = weights['conv5_3_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) conv_kernel = tf.get_variable('stage_1/conv1/kernel') conv_bias = tf.get_variable('stage_1/conv1/bias') loaded_kernel = weights['conv6_1_CPM'] loaded_bias = weights['conv6_1_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) if finetune != True: conv_kernel = tf.get_variable('stage_1/stage_heatmap/kernel') conv_bias = tf.get_variable('stage_1/stage_heatmap/bias') loaded_kernel = weights['conv6_2_CPM'] loaded_bias = weights['conv6_2_CPM_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias)) stage in range(2, self.stages + 1): for layer in range(1, 8): conv_kernel = tf.get_variable('stage_' + str(stage) + '/mid_conv' + str(layer) + '/kernel') conv_bias = tf.get_variable('stage_' + str(stage) + '/mid_conv' + str(layer) + '/bias') loaded_kernel = weights['Mconv' + str(layer) + '_stage' + str(stage)] loaded_bias = weights['Mconv' + str(layer) + '_stage' + str(stage) + '_b'] sess.run(tf.assign(conv_kernel, loaded_kernel)) sess.run(tf.assign(conv_bias, loaded_bias))

true

1c310aa9cac9b207948edbf0c276725f744ffb7e

672

py

Python

Day 09/1.py

Xerisu/Advent-of-Code

bd068a90b26b04a2345f62cb2054566fbbfce631

[ "MIT" ]

1

2021-12-02T13:58:00.000Z

Day 09/1.py

Xerisu/Advent-of-Code

bd068a90b26b04a2345f62cb2054566fbbfce631

[ "MIT" ]

null

Day 09/1.py

Xerisu/Advent-of-Code

bd068a90b26b04a2345f62cb2054566fbbfce631

[ "MIT" ]

null

input_file = open("./cave.txt","r") floor = input_file.readlines() input_file.close() floor = ["9" + elem.strip() + "9" for elem in floor] floor = ["9" * len(floor[0])] + floor + ["9" * len(floor[0])] floor = [[int(x) for x in row] for row in floor] #floor[y][x] low_points = [] sum_low_points = 0 for y in range(1, len(floor) - 1): for x in range(1, len(floor[0]) - 1): if floor[y][x] < floor[y+1][x] and floor[y][x] < floor[y-1][x] and floor[y][x] < floor[y][x+1] and floor[y][x] < floor[y][x-1]: sum_low_points += floor[y][x] + 1 point = (y , x) low_points.append(point) print(sum_low_points) print(low_points)

24

135

0.568452

input_file = open("./cave.txt","r") floor = input_file.readlines() input_file.close() floor = ["9" + elem.strip() + "9" for elem in floor] floor = ["9" * len(floor[0])] + floor + ["9" * len(floor[0])] floor = [[int(x) for x in row] for row in floor] low_points = [] sum_low_points = 0 for y in range(1, len(floor) - 1): for x in range(1, len(floor[0]) - 1): if floor[y][x] < floor[y+1][x] and floor[y][x] < floor[y-1][x] and floor[y][x] < floor[y][x+1] and floor[y][x] < floor[y][x-1]: sum_low_points += floor[y][x] + 1 point = (y , x) low_points.append(point) print(sum_low_points) print(low_points)

true

1c310b4c33b1a3f0e7e4d9388796098395a17ff7

3,888

py

Python

Python/whatsView/apps/views.py

min9288/Multicampus

2aaac730b35e530f8f91cb1ba41c08ee18d59142

[ "MIT" ]

2

2022-01-18T09:27:42.000Z

2022-03-29T14:59:00.000Z

Python/whatsView/apps/views.py

min9288/Multicampus

2aaac730b35e530f8f91cb1ba41c08ee18d59142

[ "MIT" ]

null

Python/whatsView/apps/views.py

min9288/Multicampus

2aaac730b35e530f8f91cb1ba41c08ee18d59142

[ "MIT" ]

null

import json, os, sys, urllib.request, requests, re from django.shortcuts import render, redirect from django.conf import settings from django.views.generic import FormView from requests import request from bs4 import BeautifulSoup def index(request): return render(request, 'common/main.html') def make_naver_search_api_url(search_text, start_num, disp_num): base_url = 'https://openapi.naver.com/v1/search/blog.json' param_query = "?query=" + urllib.parse.quote(search_text) param_start = "&start=" + str(start_num) param_disp = "&display=" + str(disp_num) return base_url + param_query + param_start + param_disp def get_request_url(request): searchValue = request.GET.get('searchValue',"") API_URL = make_naver_search_api_url(searchValue, 1, 10) config_secret_debug = json.loads(open(settings.SECRET_DEBUG_FILE).read()) client_id = config_secret_debug['NAVER']['CLIENT_ID'] client_secret = config_secret_debug['NAVER']['CLIENT_SECRET'] request = urllib.request.Request(API_URL) request.add_header("X-Naver-Client-Id", client_id) request.add_header("X-Naver-Client-Secret", client_secret) response = urllib.request.urlopen(request) rescode = response.getcode() if (rescode == 200): response_body = response.read() result = json.loads(response_body.decode('utf-8')) items = result.get('items') context = { 'items': items } return render(request, 'searchView/info.html', {'info_items': context[0]}) else: print("---error---") return None def youtube(request): url = 'https://www.googleapis.com/youtube/v3/search' params = { 'key': 'AIzaSyAWvSovFGym1Wj9116pOIGF4Fcx4wigK3Y', 'part': 'snippet', 'type': 'video', 'maxResults': '10', 'regionCode': "KR", 'q': request.GET.get('searchValue',""), } response = requests.get(url, params) response_dict = response.json() context = { 'youtube_items': response_dict['items'] } return render(request, 'searchView/video.html', {'video_items': context['youtube_items']}) def all(request): if request.method == 'GET': # naver search value # search_text = request.POST.get('searchValue', "") search_text = request.GET.get('searchValue') API_URL = make_naver_search_api_url(search_text, 1, 40) config_secret_debug = json.loads(open(settings.SECRET_DEBUG_FILE).read()) client_id = config_secret_debug['NAVER']['CLIENT_ID'] client_secret = config_secret_debug['NAVER']['CLIENT_SECRET'] naver_request = urllib.request.Request(API_URL) naver_request.add_header("X-Naver-Client-Id", client_id) naver_request.add_header("X-Naver-Client-Secret", client_secret) response = urllib.request.urlopen(naver_request) rescode = response.getcode() if (rescode == 200): # youtube search value url = 'https://www.googleapis.com/youtube/v3/search' params = { 'key': '유튜브 키', 'part': 'snippet', 'type': 'video', 'maxResults': '12', 'regionCode': "KR", 'q': search_text, } response1 = requests.get(url, params) response_dict = response1.json() response_body = response.read() naver_result = json.loads(response_body.decode('utf-8')) naver_items = naver_result.get('items') context = { 'items': naver_items, 'youtube_items': response_dict['items'] } return render(request, 'searchView/all.html', {'info_items':context['items'], 'video_items':context['youtube_items']}) else: print("---error---") return None

34.714286

130

0.623971

import json, os, sys, urllib.request, requests, re from django.shortcuts import render, redirect from django.conf import settings from django.views.generic import FormView from requests import request from bs4 import BeautifulSoup def index(request): return render(request, 'common/main.html') def make_naver_search_api_url(search_text, start_num, disp_num): base_url = 'https://openapi.naver.com/v1/search/blog.json' param_query = "?query=" + urllib.parse.quote(search_text) param_start = "&start=" + str(start_num) param_disp = "&display=" + str(disp_num) return base_url + param_query + param_start + param_disp def get_request_url(request): searchValue = request.GET.get('searchValue',"") API_URL = make_naver_search_api_url(searchValue, 1, 10) config_secret_debug = json.loads(open(settings.SECRET_DEBUG_FILE).read()) client_id = config_secret_debug['NAVER']['CLIENT_ID'] client_secret = config_secret_debug['NAVER']['CLIENT_SECRET'] request = urllib.request.Request(API_URL) request.add_header("X-Naver-Client-Id", client_id) request.add_header("X-Naver-Client-Secret", client_secret) response = urllib.request.urlopen(request) rescode = response.getcode() if (rescode == 200): response_body = response.read() result = json.loads(response_body.decode('utf-8')) items = result.get('items') context = { 'items': items } return render(request, 'searchView/info.html', {'info_items': context[0]}) else: print("---error---") return None def youtube(request): url = 'https://www.googleapis.com/youtube/v3/search' params = { 'key': 'AIzaSyAWvSovFGym1Wj9116pOIGF4Fcx4wigK3Y', 'part': 'snippet', 'type': 'video', 'maxResults': '10', 'regionCode': "KR", 'q': request.GET.get('searchValue',""), } response = requests.get(url, params) response_dict = response.json() context = { 'youtube_items': response_dict['items'] } return render(request, 'searchView/video.html', {'video_items': context['youtube_items']}) def all(request): if request.method == 'GET': search_text = request.GET.get('searchValue') API_URL = make_naver_search_api_url(search_text, 1, 40) config_secret_debug = json.loads(open(settings.SECRET_DEBUG_FILE).read()) client_id = config_secret_debug['NAVER']['CLIENT_ID'] client_secret = config_secret_debug['NAVER']['CLIENT_SECRET'] naver_request = urllib.request.Request(API_URL) naver_request.add_header("X-Naver-Client-Id", client_id) naver_request.add_header("X-Naver-Client-Secret", client_secret) response = urllib.request.urlopen(naver_request) rescode = response.getcode() if (rescode == 200): url = 'https://www.googleapis.com/youtube/v3/search' params = { 'key': '유튜브 키', 'part': 'snippet', 'type': 'video', 'maxResults': '12', 'regionCode': "KR", 'q': search_text, } response1 = requests.get(url, params) response_dict = response1.json() response_body = response.read() naver_result = json.loads(response_body.decode('utf-8')) naver_items = naver_result.get('items') context = { 'items': naver_items, 'youtube_items': response_dict['items'] } return render(request, 'searchView/all.html', {'info_items':context['items'], 'video_items':context['youtube_items']}) else: print("---error---") return None

true

1c310c172c06261161c1ed4dfea6d45ac54fb34c

655

py

Python

var/spack/repos/builtin/packages/lndir/package.py

carlabguillen/spack

7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

9

2018-04-18T07:51:40.000Z

2021-09-10T03:56:57.000Z

var/spack/repos/builtin/packages/lndir/package.py

carlabguillen/spack

7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

907

2018-04-18T11:17:57.000Z

2022-03-31T13:20:25.000Z

var/spack/repos/builtin/packages/lndir/package.py

carlabguillen/spack

7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

29

2018-11-05T16:14:23.000Z

2022-02-03T16:07:09.000Z

# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class Lndir(AutotoolsPackage, XorgPackage): """lndir - create a shadow directory of symbolic links to another directory tree.""" homepage = "http://cgit.freedesktop.org/xorg/util/lndir" xorg_mirror_path = "util/lndir-1.0.3.tar.gz" version('1.0.3', sha256='95b2d26fb3cbe702f828146c7a4c7c48001d2da52b062580227b7b68180be902') depends_on('xproto@7.0.17:', type='build') depends_on('pkgconfig', type='build')

32.75

95

0.737405

from spack import * class Lndir(AutotoolsPackage, XorgPackage): homepage = "http://cgit.freedesktop.org/xorg/util/lndir" xorg_mirror_path = "util/lndir-1.0.3.tar.gz" version('1.0.3', sha256='95b2d26fb3cbe702f828146c7a4c7c48001d2da52b062580227b7b68180be902') depends_on('xproto@7.0.17:', type='build') depends_on('pkgconfig', type='build')

true

1c310c178edaf20ebe5d2e344ace27beb2ba5739

30,327

py

Python

tests/sagemaker/test_deployment.py

devlibx/mlflowx

291c51161ec26450b1e79c8e4a32af960da79591

[ "Apache-2.0" ]

1

2022-03-15T00:19:10.000Z

tests/sagemaker/test_deployment.py

dengzhihai/mlflow

1ce3b5eadf6543878a62b070fd06735d471d75d5

[ "Apache-2.0" ]

null

tests/sagemaker/test_deployment.py

dengzhihai/mlflow

1ce3b5eadf6543878a62b070fd06735d471d75d5

[ "Apache-2.0" ]

null

import os import pytest import time from collections import namedtuple from unittest import mock import boto3 import botocore import numpy as np from click.testing import CliRunner from sklearn.linear_model import LogisticRegression import mlflow import mlflow.pyfunc import mlflow.sklearn import mlflow.sagemaker as mfs import mlflow.sagemaker.cli as mfscli from mlflow.exceptions import MlflowException from mlflow.models import Model from mlflow.protos.databricks_pb2 import ( ErrorCode, RESOURCE_DOES_NOT_EXIST, INVALID_PARAMETER_VALUE, INTERNAL_ERROR, ) from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository from mlflow.tracking.artifact_utils import _download_artifact_from_uri from tests.helper_functions import set_boto_credentials # pylint: disable=unused-import from tests.sagemaker.mock import mock_sagemaker, Endpoint, EndpointOperation TrainedModel = namedtuple("TrainedModel", ["model_path", "run_id", "model_uri"]) @pytest.fixture def pretrained_model(): model_path = "model" with mlflow.start_run(): X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1) y = np.array([0, 0, 1, 1, 1, 0]) lr = LogisticRegression(solver="lbfgs") lr.fit(X, y) mlflow.sklearn.log_model(lr, model_path) run_id = mlflow.active_run().info.run_id model_uri = "runs:/" + run_id + "/" + model_path return TrainedModel(model_path, run_id, model_uri) @pytest.fixture def sagemaker_client(): return boto3.client("sagemaker", region_name="us-west-2") def get_sagemaker_backend(region_name): return mock_sagemaker.backends[region_name] def mock_sagemaker_aws_services(fn): from functools import wraps from moto import mock_s3, mock_ecr, mock_sts, mock_iam @mock_ecr @mock_iam @mock_s3 @mock_sagemaker @mock_sts @wraps(fn) def mock_wrapper(*args, **kwargs): # Create an ECR repository for the `mlflow-pyfunc` SageMaker docker image ecr_client = boto3.client("ecr", region_name="us-west-2") ecr_client.create_repository(repositoryName=mfs.DEFAULT_IMAGE_NAME) # Create the moto IAM role role_policy = """ { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "*", "Resource": "*" } ] } """ iam_client = boto3.client("iam", region_name="us-west-2") iam_client.create_role(RoleName="moto", AssumeRolePolicyDocument=role_policy) # Create IAM role to be asssumed (could be in another AWS account) iam_client.create_role(RoleName="assumed_role", AssumeRolePolicyDocument=role_policy) return fn(*args, **kwargs) return mock_wrapper @mock_sagemaker_aws_services def test_assume_role_and_get_credentials(): assumed_role_credentials = mfs._assume_role_and_get_credentials( assume_role_arn="arn:aws:iam::123456789012:role/assumed_role" ) assert "aws_access_key_id" in assumed_role_credentials.keys() assert "aws_secret_access_key" in assumed_role_credentials.keys() assert "aws_session_token" in assumed_role_credentials.keys() assert len(assumed_role_credentials["aws_session_token"]) == 356 assert assumed_role_credentials["aws_session_token"].startswith("FQoGZXIvYXdzE") assert len(assumed_role_credentials["aws_access_key_id"]) == 20 assert assumed_role_credentials["aws_access_key_id"].startswith("ASIA") assert len(assumed_role_credentials["aws_secret_access_key"]) == 40 @pytest.mark.large @mock_sagemaker_aws_services def test_deployment_with_non_existent_assume_role_arn_raises_exception(pretrained_model): match = ( r"An error occurred $NoSuchEntity$ when calling the GetRole " r"operation: Role non-existent-role-arn not found" ) with pytest.raises(botocore.exceptions.ClientError, match=match): mfs.deploy( app_name="bad_assume_role_arn", model_uri=pretrained_model.model_uri, assume_role_arn="arn:aws:iam::123456789012:role/non-existent-role-arn", ) @pytest.mark.large @mock_sagemaker_aws_services def test_deployment_with_assume_role_arn(pretrained_model, sagemaker_client): app_name = "deploy_with_assume_role_arn" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, assume_role_arn="arn:aws:iam::123456789012:role/assumed_role", ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] @pytest.mark.large def test_deployment_with_unsupported_flavor_raises_exception(pretrained_model): unsupported_flavor = "this is not a valid flavor" match = "The specified flavor: `this is not a valid flavor` is not supported for deployment" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy( app_name="bad_flavor", model_uri=pretrained_model.model_uri, flavor=unsupported_flavor ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large def test_deployment_with_missing_flavor_raises_exception(pretrained_model): missing_flavor = "mleap" match = "The specified model does not contain the specified deployment flavor" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy( app_name="missing-flavor", model_uri=pretrained_model.model_uri, flavor=missing_flavor ) assert exc.value.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST) @pytest.mark.large def test_deployment_of_model_with_no_supported_flavors_raises_exception(pretrained_model): logged_model_path = _download_artifact_from_uri(pretrained_model.model_uri) model_config_path = os.path.join(logged_model_path, "MLmodel") model_config = Model.load(model_config_path) del model_config.flavors[mlflow.pyfunc.FLAVOR_NAME] model_config.save(path=model_config_path) match = "The specified model does not contain any of the supported flavors for deployment" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy(app_name="missing-flavor", model_uri=logged_model_path, flavor=None) assert exc.value.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST) @pytest.mark.large def test_validate_deployment_flavor_validates_python_function_flavor_successfully(pretrained_model): model_config_path = os.path.join( _download_artifact_from_uri(pretrained_model.model_uri), "MLmodel" ) model_config = Model.load(model_config_path) mfs._validate_deployment_flavor(model_config=model_config, flavor=mlflow.pyfunc.FLAVOR_NAME) @pytest.mark.large def test_get_preferred_deployment_flavor_obtains_valid_flavor_from_model(pretrained_model): model_config_path = os.path.join( _download_artifact_from_uri(pretrained_model.model_uri), "MLmodel" ) model_config = Model.load(model_config_path) selected_flavor = mfs._get_preferred_deployment_flavor(model_config=model_config) assert selected_flavor in mfs.SUPPORTED_DEPLOYMENT_FLAVORS assert selected_flavor in model_config.flavors @pytest.mark.large def test_attempting_to_deploy_in_asynchronous_mode_without_archiving_throws_exception( pretrained_model, ): with pytest.raises(MlflowException, match="Resources must be archived") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, archive=False, synchronous=False, ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local( pretrained_model, sagemaker_client ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local( pretrained_model, sagemaker_client ): app_name = "test-app" result = CliRunner(env={"LC_ALL": "en_US.UTF-8", "LANG": "en_US.UTF-8"}).invoke( mfscli.commands, [ "deploy", "-a", app_name, "-m", pretrained_model.model_uri, "--mode", mfs.DEPLOYMENT_MODE_CREATE, ], ) assert result.exit_code == 0 region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3( pretrained_model, sagemaker_client ): local_model_path = _download_artifact_from_uri(pretrained_model.model_uri) artifact_path = "model" region_name = sagemaker_client.meta.region_name default_bucket = mfs._get_default_s3_bucket(region_name) s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket)) s3_artifact_repo.log_artifacts(local_model_path, artifact_path=artifact_path) model_s3_uri = "s3://{bucket_name}/{artifact_path}".format( bucket_name=default_bucket, artifact_path=pretrained_model.model_path ) app_name = "test-app" mfs.deploy(app_name=app_name, model_uri=model_s3_uri, mode=mfs.DEPLOYMENT_MODE_CREATE) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] s3_client = boto3.client("s3", region_name=region_name) object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3( pretrained_model, sagemaker_client ): local_model_path = _download_artifact_from_uri(pretrained_model.model_uri) artifact_path = "model" region_name = sagemaker_client.meta.region_name default_bucket = mfs._get_default_s3_bucket(region_name) s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket)) s3_artifact_repo.log_artifacts(local_model_path, artifact_path=artifact_path) model_s3_uri = "s3://{bucket_name}/{artifact_path}".format( bucket_name=default_bucket, artifact_path=pretrained_model.model_path ) app_name = "test-app" result = CliRunner(env={"LC_ALL": "en_US.UTF-8", "LANG": "en_US.UTF-8"}).invoke( mfscli.commands, ["deploy", "-a", app_name, "-m", model_s3_uri, "--mode", mfs.DEPLOYMENT_MODE_CREATE], ) assert result.exit_code == 0 region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploying_application_with_preexisting_name_in_create_mode_throws_exception( pretrained_model, ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) with pytest.raises( MlflowException, match="an application with the same name already exists" ) as exc: mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_synchronous_mode_waits_for_endpoint_creation_to_complete_before_returning( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_creation_latency ) app_name = "test-app" deployment_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=True, ) deployment_end_time = time.time() assert (deployment_end_time - deployment_start_time) >= endpoint_creation_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_IN_SERVICE @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_create_in_asynchronous_mode_returns_before_endpoint_creation_completes( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_creation_latency ) app_name = "test-app" deployment_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=False, archive=True, ) deployment_end_time = time.time() assert (deployment_end_time - deployment_start_time) < endpoint_creation_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_CREATING @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_replace_in_asynchronous_mode_returns_before_endpoint_creation_completes( pretrained_model, sagemaker_client ): endpoint_update_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_update_latency ) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=True, ) update_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, synchronous=False, archive=True, ) update_end_time = time.time() assert (update_end_time - update_start_time) < endpoint_update_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_UPDATING @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_create_mode_throws_exception_after_endpoint_creation_fails( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_creation_latency) boto_caller = botocore.client.BaseClient._make_api_call def fail_endpoint_creations(self, operation_name, operation_kwargs): """ Processes all boto3 client operations according to the following rules: - If the operation is an endpoint creation, create the endpoint and set its status to ``Endpoint.STATUS_FAILED``. - Else, execute the client operation as normal """ result = boto_caller(self, operation_name, operation_kwargs) if operation_name == "CreateEndpoint": endpoint_name = operation_kwargs["EndpointName"] sagemaker_backend.set_endpoint_latest_operation( endpoint_name=endpoint_name, operation=EndpointOperation.create_unsuccessful( latency_seconds=endpoint_creation_latency ), ) return result with mock.patch( "botocore.client.BaseClient._make_api_call", new=fail_endpoint_creations ), pytest.raises(MlflowException, match="deployment operation failed") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, ) assert exc.value.error_code == ErrorCode.Name(INTERNAL_ERROR) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_add_mode_adds_new_model_to_existing_endpoint(pretrained_model, sagemaker_client): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) models_added = 1 for _ in range(11): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD, archive=True, synchronous=False, ) models_added += 1 endpoint_response = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name = endpoint_response["EndpointConfigName"] endpoint_config_response = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name ) production_variants = endpoint_config_response["ProductionVariants"] assert len(production_variants) == models_added @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_model_removes_preexisting_models_from_endpoint( pretrained_model, sagemaker_client ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD ) for _ in range(11): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD, archive=True, synchronous=False, ) endpoint_response_before_replacement = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name_before_replacement = endpoint_response_before_replacement[ "EndpointConfigName" ] endpoint_config_response_before_replacement = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name_before_replacement ) production_variants_before_replacement = endpoint_config_response_before_replacement[ "ProductionVariants" ] deployed_models_before_replacement = [ variant["ModelName"] for variant in production_variants_before_replacement ] mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=True, synchronous=False, ) endpoint_response_after_replacement = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name_after_replacement = endpoint_response_after_replacement[ "EndpointConfigName" ] endpoint_config_response_after_replacement = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name_after_replacement ) production_variants_after_replacement = endpoint_config_response_after_replacement[ "ProductionVariants" ] deployed_models_after_replacement = [ variant["ModelName"] for variant in production_variants_after_replacement ] assert len(deployed_models_after_replacement) == 1 assert all( [ model_name not in deployed_models_after_replacement for model_name in deployed_models_before_replacement ] ) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_throws_exception_after_endpoint_update_fails( pretrained_model, sagemaker_client ): endpoint_update_latency = 5 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_update_latency) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) boto_caller = botocore.client.BaseClient._make_api_call def fail_endpoint_updates(self, operation_name, operation_kwargs): """ Processes all boto3 client operations according to the following rules: - If the operation is an endpoint update, update the endpoint and set its status to ``Endpoint.STATUS_FAILED``. - Else, execute the client operation as normal """ result = boto_caller(self, operation_name, operation_kwargs) if operation_name == "UpdateEndpoint": endpoint_name = operation_kwargs["EndpointName"] sagemaker_backend.set_endpoint_latest_operation( endpoint_name=endpoint_name, operation=EndpointOperation.update_unsuccessful( latency_seconds=endpoint_update_latency ), ) return result with mock.patch( "botocore.client.BaseClient._make_api_call", new=fail_endpoint_updates ), pytest.raises(MlflowException, match="deployment operation failed") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, ) assert exc.value.error_code == ErrorCode.Name(INTERNAL_ERROR) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_waits_for_endpoint_update_completion_before_deleting_resources( pretrained_model, sagemaker_client ): endpoint_update_latency = 10 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_update_latency) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) endpoint_config_name_before_replacement = sagemaker_client.describe_endpoint( EndpointName=app_name )["EndpointConfigName"] boto_caller = botocore.client.BaseClient._make_api_call update_start_time = time.time() def validate_deletes(self, operation_name, operation_kwargs): """ Processes all boto3 client operations according to the following rules: - If the operation deletes an S3 or SageMaker resource, ensure that the deletion was initiated after the completion of the endpoint update - Else, execute the client operation as normal """ result = boto_caller(self, operation_name, operation_kwargs) if "Delete" in operation_name: # Confirm that a successful endpoint update occurred prior to the invocation of this # delete operation endpoint_info = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_info["EndpointStatus"] == Endpoint.STATUS_IN_SERVICE assert endpoint_info["EndpointConfigName"] != endpoint_config_name_before_replacement assert time.time() - update_start_time >= endpoint_update_latency return result with mock.patch("botocore.client.BaseClient._make_api_call", new=validate_deletes): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=False, ) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_with_archiving_does_not_delete_resources( pretrained_model, sagemaker_client ): region_name = sagemaker_client.meta.region_name sagemaker_backend = get_sagemaker_backend(region_name) sagemaker_backend.set_endpoint_update_latency(5) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) object_names_before_replacement = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] endpoint_configs_before_replacement = [ config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] models_before_replacement = [ model["ModelName"] for model in sagemaker_client.list_models()["Models"] ] model_uri = "runs:/{run_id}/{artifact_path}".format( run_id=pretrained_model.run_id, artifact_path=pretrained_model.model_path ) sk_model = mlflow.sklearn.load_model(model_uri=model_uri) new_artifact_path = "model" with mlflow.start_run(): mlflow.sklearn.log_model(sk_model=sk_model, artifact_path=new_artifact_path) new_model_uri = "runs:/{run_id}/{artifact_path}".format( run_id=mlflow.active_run().info.run_id, artifact_path=new_artifact_path ) mfs.deploy( app_name=app_name, model_uri=new_model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=True, synchronous=True, ) object_names_after_replacement = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] endpoint_configs_after_replacement = [ config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] models_after_replacement = [ model["ModelName"] for model in sagemaker_client.list_models()["Models"] ] assert all( [ object_name in object_names_after_replacement for object_name in object_names_before_replacement ] ) assert all( [ endpoint_config in endpoint_configs_after_replacement for endpoint_config in endpoint_configs_before_replacement ] ) assert all([model in models_after_replacement for model in models_before_replacement])

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import os import pytest import time from collections import namedtuple from unittest import mock import boto3 import botocore import numpy as np from click.testing import CliRunner from sklearn.linear_model import LogisticRegression import mlflow import mlflow.pyfunc import mlflow.sklearn import mlflow.sagemaker as mfs import mlflow.sagemaker.cli as mfscli from mlflow.exceptions import MlflowException from mlflow.models import Model from mlflow.protos.databricks_pb2 import ( ErrorCode, RESOURCE_DOES_NOT_EXIST, INVALID_PARAMETER_VALUE, INTERNAL_ERROR, ) from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository from mlflow.tracking.artifact_utils import _download_artifact_from_uri from tests.helper_functions import set_boto_credentials from tests.sagemaker.mock import mock_sagemaker, Endpoint, EndpointOperation TrainedModel = namedtuple("TrainedModel", ["model_path", "run_id", "model_uri"]) @pytest.fixture def pretrained_model(): model_path = "model" with mlflow.start_run(): X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1) y = np.array([0, 0, 1, 1, 1, 0]) lr = LogisticRegression(solver="lbfgs") lr.fit(X, y) mlflow.sklearn.log_model(lr, model_path) run_id = mlflow.active_run().info.run_id model_uri = "runs:/" + run_id + "/" + model_path return TrainedModel(model_path, run_id, model_uri) @pytest.fixture def sagemaker_client(): return boto3.client("sagemaker", region_name="us-west-2") def get_sagemaker_backend(region_name): return mock_sagemaker.backends[region_name] def mock_sagemaker_aws_services(fn): from functools import wraps from moto import mock_s3, mock_ecr, mock_sts, mock_iam @mock_ecr @mock_iam @mock_s3 @mock_sagemaker @mock_sts @wraps(fn) def mock_wrapper(*args, **kwargs): ecr_client = boto3.client("ecr", region_name="us-west-2") ecr_client.create_repository(repositoryName=mfs.DEFAULT_IMAGE_NAME) role_policy = """ { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "*", "Resource": "*" } ] } """ iam_client = boto3.client("iam", region_name="us-west-2") iam_client.create_role(RoleName="moto", AssumeRolePolicyDocument=role_policy) iam_client.create_role(RoleName="assumed_role", AssumeRolePolicyDocument=role_policy) return fn(*args, **kwargs) return mock_wrapper @mock_sagemaker_aws_services def test_assume_role_and_get_credentials(): assumed_role_credentials = mfs._assume_role_and_get_credentials( assume_role_arn="arn:aws:iam::123456789012:role/assumed_role" ) assert "aws_access_key_id" in assumed_role_credentials.keys() assert "aws_secret_access_key" in assumed_role_credentials.keys() assert "aws_session_token" in assumed_role_credentials.keys() assert len(assumed_role_credentials["aws_session_token"]) == 356 assert assumed_role_credentials["aws_session_token"].startswith("FQoGZXIvYXdzE") assert len(assumed_role_credentials["aws_access_key_id"]) == 20 assert assumed_role_credentials["aws_access_key_id"].startswith("ASIA") assert len(assumed_role_credentials["aws_secret_access_key"]) == 40 @pytest.mark.large @mock_sagemaker_aws_services def test_deployment_with_non_existent_assume_role_arn_raises_exception(pretrained_model): match = ( r"An error occurred $NoSuchEntity$ when calling the GetRole " r"operation: Role non-existent-role-arn not found" ) with pytest.raises(botocore.exceptions.ClientError, match=match): mfs.deploy( app_name="bad_assume_role_arn", model_uri=pretrained_model.model_uri, assume_role_arn="arn:aws:iam::123456789012:role/non-existent-role-arn", ) @pytest.mark.large @mock_sagemaker_aws_services def test_deployment_with_assume_role_arn(pretrained_model, sagemaker_client): app_name = "deploy_with_assume_role_arn" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, assume_role_arn="arn:aws:iam::123456789012:role/assumed_role", ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] @pytest.mark.large def test_deployment_with_unsupported_flavor_raises_exception(pretrained_model): unsupported_flavor = "this is not a valid flavor" match = "The specified flavor: `this is not a valid flavor` is not supported for deployment" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy( app_name="bad_flavor", model_uri=pretrained_model.model_uri, flavor=unsupported_flavor ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large def test_deployment_with_missing_flavor_raises_exception(pretrained_model): missing_flavor = "mleap" match = "The specified model does not contain the specified deployment flavor" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy( app_name="missing-flavor", model_uri=pretrained_model.model_uri, flavor=missing_flavor ) assert exc.value.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST) @pytest.mark.large def test_deployment_of_model_with_no_supported_flavors_raises_exception(pretrained_model): logged_model_path = _download_artifact_from_uri(pretrained_model.model_uri) model_config_path = os.path.join(logged_model_path, "MLmodel") model_config = Model.load(model_config_path) del model_config.flavors[mlflow.pyfunc.FLAVOR_NAME] model_config.save(path=model_config_path) match = "The specified model does not contain any of the supported flavors for deployment" with pytest.raises(MlflowException, match=match) as exc: mfs.deploy(app_name="missing-flavor", model_uri=logged_model_path, flavor=None) assert exc.value.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST) @pytest.mark.large def test_validate_deployment_flavor_validates_python_function_flavor_successfully(pretrained_model): model_config_path = os.path.join( _download_artifact_from_uri(pretrained_model.model_uri), "MLmodel" ) model_config = Model.load(model_config_path) mfs._validate_deployment_flavor(model_config=model_config, flavor=mlflow.pyfunc.FLAVOR_NAME) @pytest.mark.large def test_get_preferred_deployment_flavor_obtains_valid_flavor_from_model(pretrained_model): model_config_path = os.path.join( _download_artifact_from_uri(pretrained_model.model_uri), "MLmodel" ) model_config = Model.load(model_config_path) selected_flavor = mfs._get_preferred_deployment_flavor(model_config=model_config) assert selected_flavor in mfs.SUPPORTED_DEPLOYMENT_FLAVORS assert selected_flavor in model_config.flavors @pytest.mark.large def test_attempting_to_deploy_in_asynchronous_mode_without_archiving_throws_exception( pretrained_model, ): with pytest.raises(MlflowException, match="Resources must be archived") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, archive=False, synchronous=False, ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local( pretrained_model, sagemaker_client ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local( pretrained_model, sagemaker_client ): app_name = "test-app" result = CliRunner(env={"LC_ALL": "en_US.UTF-8", "LANG": "en_US.UTF-8"}).invoke( mfscli.commands, [ "deploy", "-a", app_name, "-m", pretrained_model.model_uri, "--mode", mfs.DEPLOYMENT_MODE_CREATE, ], ) assert result.exit_code == 0 region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3( pretrained_model, sagemaker_client ): local_model_path = _download_artifact_from_uri(pretrained_model.model_uri) artifact_path = "model" region_name = sagemaker_client.meta.region_name default_bucket = mfs._get_default_s3_bucket(region_name) s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket)) s3_artifact_repo.log_artifacts(local_model_path, artifact_path=artifact_path) model_s3_uri = "s3://{bucket_name}/{artifact_path}".format( bucket_name=default_bucket, artifact_path=pretrained_model.model_path ) app_name = "test-app" mfs.deploy(app_name=app_name, model_uri=model_s3_uri, mode=mfs.DEPLOYMENT_MODE_CREATE) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] s3_client = boto3.client("s3", region_name=region_name) object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3( pretrained_model, sagemaker_client ): local_model_path = _download_artifact_from_uri(pretrained_model.model_uri) artifact_path = "model" region_name = sagemaker_client.meta.region_name default_bucket = mfs._get_default_s3_bucket(region_name) s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket)) s3_artifact_repo.log_artifacts(local_model_path, artifact_path=artifact_path) model_s3_uri = "s3://{bucket_name}/{artifact_path}".format( bucket_name=default_bucket, artifact_path=pretrained_model.model_path ) app_name = "test-app" result = CliRunner(env={"LC_ALL": "en_US.UTF-8", "LANG": "en_US.UTF-8"}).invoke( mfscli.commands, ["deploy", "-a", app_name, "-m", model_s3_uri, "--mode", mfs.DEPLOYMENT_MODE_CREATE], ) assert result.exit_code == 0 region_name = sagemaker_client.meta.region_name s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_production_variants = endpoint_description["ProductionVariants"] assert len(endpoint_production_variants) == 1 model_name = endpoint_production_variants[0]["VariantName"] assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]] object_names = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] assert any([model_name in object_name for object_name in object_names]) assert any( [ app_name in config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] ) assert app_name in [ endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"] ] model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][ "Environment" ] assert model_environment == { "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function", "SERVING_ENVIRONMENT": "SageMaker", } @pytest.mark.large @mock_sagemaker_aws_services def test_deploying_application_with_preexisting_name_in_create_mode_throws_exception( pretrained_model, ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) with pytest.raises( MlflowException, match="an application with the same name already exists" ) as exc: mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_synchronous_mode_waits_for_endpoint_creation_to_complete_before_returning( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_creation_latency ) app_name = "test-app" deployment_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=True, ) deployment_end_time = time.time() assert (deployment_end_time - deployment_start_time) >= endpoint_creation_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_IN_SERVICE @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_create_in_asynchronous_mode_returns_before_endpoint_creation_completes( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_creation_latency ) app_name = "test-app" deployment_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=False, archive=True, ) deployment_end_time = time.time() assert (deployment_end_time - deployment_start_time) < endpoint_creation_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_CREATING @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_replace_in_asynchronous_mode_returns_before_endpoint_creation_completes( pretrained_model, sagemaker_client ): endpoint_update_latency = 10 get_sagemaker_backend(sagemaker_client.meta.region_name).set_endpoint_update_latency( endpoint_update_latency ) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, synchronous=True, ) update_start_time = time.time() mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, synchronous=False, archive=True, ) update_end_time = time.time() assert (update_end_time - update_start_time) < endpoint_update_latency endpoint_description = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_description["EndpointStatus"] == Endpoint.STATUS_UPDATING @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_create_mode_throws_exception_after_endpoint_creation_fails( pretrained_model, sagemaker_client ): endpoint_creation_latency = 10 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_creation_latency) boto_caller = botocore.client.BaseClient._make_api_call def fail_endpoint_creations(self, operation_name, operation_kwargs): result = boto_caller(self, operation_name, operation_kwargs) if operation_name == "CreateEndpoint": endpoint_name = operation_kwargs["EndpointName"] sagemaker_backend.set_endpoint_latest_operation( endpoint_name=endpoint_name, operation=EndpointOperation.create_unsuccessful( latency_seconds=endpoint_creation_latency ), ) return result with mock.patch( "botocore.client.BaseClient._make_api_call", new=fail_endpoint_creations ), pytest.raises(MlflowException, match="deployment operation failed") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE, ) assert exc.value.error_code == ErrorCode.Name(INTERNAL_ERROR) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_add_mode_adds_new_model_to_existing_endpoint(pretrained_model, sagemaker_client): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) models_added = 1 for _ in range(11): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD, archive=True, synchronous=False, ) models_added += 1 endpoint_response = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name = endpoint_response["EndpointConfigName"] endpoint_config_response = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name ) production_variants = endpoint_config_response["ProductionVariants"] assert len(production_variants) == models_added @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_model_removes_preexisting_models_from_endpoint( pretrained_model, sagemaker_client ): app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD ) for _ in range(11): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_ADD, archive=True, synchronous=False, ) endpoint_response_before_replacement = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name_before_replacement = endpoint_response_before_replacement[ "EndpointConfigName" ] endpoint_config_response_before_replacement = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name_before_replacement ) production_variants_before_replacement = endpoint_config_response_before_replacement[ "ProductionVariants" ] deployed_models_before_replacement = [ variant["ModelName"] for variant in production_variants_before_replacement ] mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=True, synchronous=False, ) endpoint_response_after_replacement = sagemaker_client.describe_endpoint(EndpointName=app_name) endpoint_config_name_after_replacement = endpoint_response_after_replacement[ "EndpointConfigName" ] endpoint_config_response_after_replacement = sagemaker_client.describe_endpoint_config( EndpointConfigName=endpoint_config_name_after_replacement ) production_variants_after_replacement = endpoint_config_response_after_replacement[ "ProductionVariants" ] deployed_models_after_replacement = [ variant["ModelName"] for variant in production_variants_after_replacement ] assert len(deployed_models_after_replacement) == 1 assert all( [ model_name not in deployed_models_after_replacement for model_name in deployed_models_before_replacement ] ) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_throws_exception_after_endpoint_update_fails( pretrained_model, sagemaker_client ): endpoint_update_latency = 5 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_update_latency) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) boto_caller = botocore.client.BaseClient._make_api_call def fail_endpoint_updates(self, operation_name, operation_kwargs): result = boto_caller(self, operation_name, operation_kwargs) if operation_name == "UpdateEndpoint": endpoint_name = operation_kwargs["EndpointName"] sagemaker_backend.set_endpoint_latest_operation( endpoint_name=endpoint_name, operation=EndpointOperation.update_unsuccessful( latency_seconds=endpoint_update_latency ), ) return result with mock.patch( "botocore.client.BaseClient._make_api_call", new=fail_endpoint_updates ), pytest.raises(MlflowException, match="deployment operation failed") as exc: mfs.deploy( app_name="test-app", model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, ) assert exc.value.error_code == ErrorCode.Name(INTERNAL_ERROR) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_waits_for_endpoint_update_completion_before_deleting_resources( pretrained_model, sagemaker_client ): endpoint_update_latency = 10 sagemaker_backend = get_sagemaker_backend(sagemaker_client.meta.region_name) sagemaker_backend.set_endpoint_update_latency(endpoint_update_latency) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) endpoint_config_name_before_replacement = sagemaker_client.describe_endpoint( EndpointName=app_name )["EndpointConfigName"] boto_caller = botocore.client.BaseClient._make_api_call update_start_time = time.time() def validate_deletes(self, operation_name, operation_kwargs): result = boto_caller(self, operation_name, operation_kwargs) if "Delete" in operation_name: endpoint_info = sagemaker_client.describe_endpoint(EndpointName=app_name) assert endpoint_info["EndpointStatus"] == Endpoint.STATUS_IN_SERVICE assert endpoint_info["EndpointConfigName"] != endpoint_config_name_before_replacement assert time.time() - update_start_time >= endpoint_update_latency return result with mock.patch("botocore.client.BaseClient._make_api_call", new=validate_deletes): mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=False, ) @pytest.mark.large @mock_sagemaker_aws_services def test_deploy_in_replace_mode_with_archiving_does_not_delete_resources( pretrained_model, sagemaker_client ): region_name = sagemaker_client.meta.region_name sagemaker_backend = get_sagemaker_backend(region_name) sagemaker_backend.set_endpoint_update_latency(5) app_name = "test-app" mfs.deploy( app_name=app_name, model_uri=pretrained_model.model_uri, mode=mfs.DEPLOYMENT_MODE_CREATE ) s3_client = boto3.client("s3", region_name=region_name) default_bucket = mfs._get_default_s3_bucket(region_name) object_names_before_replacement = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] endpoint_configs_before_replacement = [ config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] models_before_replacement = [ model["ModelName"] for model in sagemaker_client.list_models()["Models"] ] model_uri = "runs:/{run_id}/{artifact_path}".format( run_id=pretrained_model.run_id, artifact_path=pretrained_model.model_path ) sk_model = mlflow.sklearn.load_model(model_uri=model_uri) new_artifact_path = "model" with mlflow.start_run(): mlflow.sklearn.log_model(sk_model=sk_model, artifact_path=new_artifact_path) new_model_uri = "runs:/{run_id}/{artifact_path}".format( run_id=mlflow.active_run().info.run_id, artifact_path=new_artifact_path ) mfs.deploy( app_name=app_name, model_uri=new_model_uri, mode=mfs.DEPLOYMENT_MODE_REPLACE, archive=True, synchronous=True, ) object_names_after_replacement = [ entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"] ] endpoint_configs_after_replacement = [ config["EndpointConfigName"] for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"] ] models_after_replacement = [ model["ModelName"] for model in sagemaker_client.list_models()["Models"] ] assert all( [ object_name in object_names_after_replacement for object_name in object_names_before_replacement ] ) assert all( [ endpoint_config in endpoint_configs_after_replacement for endpoint_config in endpoint_configs_before_replacement ] ) assert all([model in models_after_replacement for model in models_before_replacement])

true

1c310c2fa7447febf5131a6eb41de0b79a189580

2,587

py

Python

tools/xf-batch.py

vanlink/xf-traffic-generator

32d10b1d19af413acbd498f9ffe8c399aa5b3b49

[ "Apache-2.0" ]

null

tools/xf-batch.py

vanlink/xf-traffic-generator

32d10b1d19af413acbd498f9ffe8c399aa5b3b49

[ "Apache-2.0" ]

null

tools/xf-batch.py

vanlink/xf-traffic-generator

32d10b1d19af413acbd498f9ffe8c399aa5b3b49

[ "Apache-2.0" ]

null

#!/usr/bin/python import os import sys import getopt import traceback import json import time import copy import pprint import requests from libtools import * UNIQUE = None TO_KILL = False TO_LWIP = False TO_GENERATOR = False TO_DISPATCH = False TO_STREAM = False TO_INTERFACE = False # ------------------------------ main ------------------------------ if __name__ != '__main__': sys.exit(0) try: kvs, leftargs = getopt.getopt(sys.argv[1:], "u:klgdsi", [ "unique=", "kill", "lwip", "generator", "dispatch", "stream", "interface", ] ) for k, v in kvs: if k in ("-u", "--unique"): UNIQUE = v elif k in ("-k", "--kill"): TO_KILL = True elif k in ("-l", "--lwip"): TO_LWIP = True elif k in ("-g", "--generator"): TO_GENERATOR = True elif k in ("-d", "--dispatch"): TO_DISPATCH = True elif k in ("-s", "--stream"): TO_STREAM = True elif k in ("-i", "--interface"): TO_INTERFACE = True except Exception as e: print("Invalid args.") sys.exit(-1) if TO_KILL: if not UNIQUE: print("No unique ID.") sys.exit(-1) pids = get_unique_pids(UNIQUE) if not pids: print("No unique found.") sys.exit(-1) for i in pids: os.system("kill -9 %s" % (i)) pids = get_unique_pids(UNIQUE) if pids: print("Kill fail %s." % (pids)) print("Unique ID [%s] killed." % (UNIQUE)) sys.exit(0) if TO_LWIP or TO_GENERATOR or TO_DISPATCH or TO_STREAM or TO_INTERFACE: if TO_LWIP: url = "get_stat_lwip" elif TO_GENERATOR: url = "get_stat_generator" elif TO_DISPATCH: url = "get_stat_dispatch" elif TO_STREAM: url = "get_stat_stream" elif TO_INTERFACE: url = "get_interface" lport = get_unique_lport(UNIQUE) if not lport: print("Unique ID [%s] local port not found." % (UNIQUE)) sys.exit(-1) cmd = 'curl http://127.0.0.1:%s/%s' % (lport, url) (ret, outstr, errstr) = run_cmd_wrapper(cmd, check_interval=0.1, timeout=3) obj = json.loads(outstr) s = json.dumps(NonzeroDict(obj), indent=2) print(s)

26.670103

79

0.478933

import os import sys import getopt import traceback import json import time import copy import pprint import requests from libtools import * UNIQUE = None TO_KILL = False TO_LWIP = False TO_GENERATOR = False TO_DISPATCH = False TO_STREAM = False TO_INTERFACE = False if __name__ != '__main__': sys.exit(0) try: kvs, leftargs = getopt.getopt(sys.argv[1:], "u:klgdsi", [ "unique=", "kill", "lwip", "generator", "dispatch", "stream", "interface", ] ) for k, v in kvs: if k in ("-u", "--unique"): UNIQUE = v elif k in ("-k", "--kill"): TO_KILL = True elif k in ("-l", "--lwip"): TO_LWIP = True elif k in ("-g", "--generator"): TO_GENERATOR = True elif k in ("-d", "--dispatch"): TO_DISPATCH = True elif k in ("-s", "--stream"): TO_STREAM = True elif k in ("-i", "--interface"): TO_INTERFACE = True except Exception as e: print("Invalid args.") sys.exit(-1) if TO_KILL: if not UNIQUE: print("No unique ID.") sys.exit(-1) pids = get_unique_pids(UNIQUE) if not pids: print("No unique found.") sys.exit(-1) for i in pids: os.system("kill -9 %s" % (i)) pids = get_unique_pids(UNIQUE) if pids: print("Kill fail %s." % (pids)) print("Unique ID [%s] killed." % (UNIQUE)) sys.exit(0) if TO_LWIP or TO_GENERATOR or TO_DISPATCH or TO_STREAM or TO_INTERFACE: if TO_LWIP: url = "get_stat_lwip" elif TO_GENERATOR: url = "get_stat_generator" elif TO_DISPATCH: url = "get_stat_dispatch" elif TO_STREAM: url = "get_stat_stream" elif TO_INTERFACE: url = "get_interface" lport = get_unique_lport(UNIQUE) if not lport: print("Unique ID [%s] local port not found." % (UNIQUE)) sys.exit(-1) cmd = 'curl http://127.0.0.1:%s/%s' % (lport, url) (ret, outstr, errstr) = run_cmd_wrapper(cmd, check_interval=0.1, timeout=3) obj = json.loads(outstr) s = json.dumps(NonzeroDict(obj), indent=2) print(s)

true

1c310c6664d26c85ae161d02b9015c70e433ed29

3,494

py

Python

coil_model_warmstart.py

havefun28/scenario_runner

d24e9563179b7a345705c53e7da877b42736acf2

[ "MIT" ]

1

2020-10-09T07:25:36.000Z

coil_model_warmstart.py

RuihanGao/scenario_runner

d24e9563179b7a345705c53e7da877b42736acf2

[ "MIT" ]

null

coil_model_warmstart.py

RuihanGao/scenario_runner

d24e9563179b7a345705c53e7da877b42736acf2

[ "MIT" ]

null

import os, sys sys.path.append('/home/ruihan/coiltraine/') import yaml import torch from network.models.coil_icra import CoILICRA from coilutils import AttributeDict # from attribute_dict import AttributeDict # # Sample from PyTorch docs: https://pytorch.org/tutorials/beginner/saving_loading_models.html#warmstarting-model-using-parameters-from-a-different-model # # save # torch.save(modelA.state_dict(), PATH) # # load # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # modelB = TheModelBClass(*args, **kwargs) # modelB.load_state_dict(torch.load(PATH, map_location = device), strict=False) # # Sample load a pretrained model # load part of the pre trained model # save # torch.save(pre_model.state_dict(), PATH) # # load # pretrained_dict = torch.load(PATH) # model = TheModelClass(*args, **kwargs) # model_dict = model.state_dict() # # 1. filter out unnecessary keys # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} # # 2. overwrite entries in the existing state dict # model_dict.update(pretrained_dict) # # 3. load the new state dict # model.load_state_dict(model_dict) torch.set_default_dtype(torch.float32) torch.set_default_tensor_type('torch.cuda.FloatTensor') # read yaml file yaml_filename = 'coil_configs.yaml' with open(yaml_filename, 'r') as f: # TODO: combine all know configuraitons into one file and load it into a dict yaml_file = yaml.load(f, Loader=yaml.FullLoader) yaml_cfg = AttributeDict(yaml_file) # # load checkpoint dict # checkpoint = torch.load(os.path.join('/home/ruihan/scenario_runner/models/CoIL/'+str(180000)+'.pth')) # # load model # model = CoILModel(yaml_cfg.MODEL_TYPE, yaml_cfg.MODEL_CONFIGURATION) # model.cuda() # checkpoint_iteration = checkpoint['iteration'] # print("Pretrained CoIL loaded ", checkpoint_iteration) # model.load_state_dict(checkpoint['state_dict']) # model.eval() # torch.save(model.state_dict(), '/home/ruihan/scenario_runner/models/CoIL/CoIL_180000.pth' ) print("load empty CoIlModel") modelB = CoILICRA(yaml_cfg.MODEL_CONFIGURATION) for param_tensor in modelB.state_dict(): print(param_tensor, "\t", modelB.state_dict()[param_tensor].size()) param_tensor = 'branches.branched_modules.0.layers.0.0.weight' print(param_tensor, "\t", modelB.state_dict()[param_tensor]) print("try to copy pretrained model to B") modelB.load_state_dict(torch.load('models/CoIL/CoIL_180000.pth')) print(param_tensor, "\t", modelB.state_dict()[param_tensor]) modelB.eval() # TODO: The structure is specified in coil_icra. # check which module you want to reuse and create your own. # then load the state_dict with `strict=False` class FC_coil_cut(nn.Module): """ copy the full-connectin network from coil, adpted for MLP controller """ def __init__(self, nx=106, ny=2, nh=53, p=0.2): """ original coil (512-256-3) input: latent_embeddings dim_z = 106 one hidden layer: 64 output: dim_u = 3 p: possibility for dropout """ super(FC_coil, self).__init__() self.layers = nn.Sequential( nn.Linear(nx, nh), nn.Dropout2d(p=p), nn.ReLU(), nn.Linear(nh, ny), nn.Dropout2d(p=p) ) self.sig = nn.Sigmoid() self.tanh = nn.Tanh() def forward(self, x): x = x.view(x.size(0), -1) x = self.layers(x) # throttle = self.sig(x[:, 0]).view(x.shape[0],-1) # steer = self.tanh(x[:, 1]).view(x.shape[0],-1) # brake = self.sig(x[:, 2]).view(x.shape[0],-1) # return torch.cat([throttle, steer, brake], dim=1) return self.sig(x)

31.763636

154

0.726388

import os, sys sys.path.append('/home/ruihan/coiltraine/') import yaml import torch from network.models.coil_icra import CoILICRA from coilutils import AttributeDict modelB.state_dict(): print(param_tensor, "\t", modelB.state_dict()[param_tensor].size()) param_tensor = 'branches.branched_modules.0.layers.0.0.weight' print(param_tensor, "\t", modelB.state_dict()[param_tensor]) print("try to copy pretrained model to B") modelB.load_state_dict(torch.load('models/CoIL/CoIL_180000.pth')) print(param_tensor, "\t", modelB.state_dict()[param_tensor]) modelB.eval() class FC_coil_cut(nn.Module): def __init__(self, nx=106, ny=2, nh=53, p=0.2): super(FC_coil, self).__init__() self.layers = nn.Sequential( nn.Linear(nx, nh), nn.Dropout2d(p=p), nn.ReLU(), nn.Linear(nh, ny), nn.Dropout2d(p=p) ) self.sig = nn.Sigmoid() self.tanh = nn.Tanh() def forward(self, x): x = x.view(x.size(0), -1) x = self.layers(x) return self.sig(x)

true

1c310cf5f4de81d0af84be7fff6b32545eb07092

408

py

Python

src/mpl_styles/__init__.py

icaros-usc/dqd-rl

83e3da62df37b45c4b8fc549c07f566797b5f685

[ "MIT" ]

6

2022-02-09T05:35:37.000Z

2022-03-12T11:54:59.000Z

src/mpl_styles/__init__.py

icaros-usc/dqd-rl

83e3da62df37b45c4b8fc549c07f566797b5f685

[ "MIT" ]

null

src/mpl_styles/__init__.py

icaros-usc/dqd-rl

83e3da62df37b45c4b8fc549c07f566797b5f685

[ "MIT" ]

null

"""Styles for Matplotlib.""" from matplotlib.colors import ListedColormap # Qualitative colormap that (should) be color-blind friendly. See # https://personal.sron.nl/~pault/ for more accessible color schemes. QUALITATIVE_COLORS = ( '#77AADD', '#EE8866', '#44BB99', '#FFAABB', '#99DDFF', '#BBCC33', '#EEDD88', '#AAAA00', ) QualitativeMap = ListedColormap(QUALITATIVE_COLORS)

24

69

0.676471

from matplotlib.colors import ListedColormap QUALITATIVE_COLORS = ( '#77AADD', '#EE8866', '#44BB99', '#FFAABB', '#99DDFF', '#BBCC33', '#EEDD88', '#AAAA00', ) QualitativeMap = ListedColormap(QUALITATIVE_COLORS)

true

1c310defa615980cda6cafd978363a331a7e0346

4,385

py

Python

dev-utils/_windows/msys/msys.py

gitdevmod/craft-blueprints-kde

81a866d2d606dabd57347fbac7cdab42979332dd

[ "BSD-2-Clause" ]

null

dev-utils/_windows/msys/msys.py

gitdevmod/craft-blueprints-kde

81a866d2d606dabd57347fbac7cdab42979332dd

[ "BSD-2-Clause" ]

null

dev-utils/_windows/msys/msys.py

gitdevmod/craft-blueprints-kde

81a866d2d606dabd57347fbac7cdab42979332dd

[ "BSD-2-Clause" ]

null

import io import info import shells from CraftOS.osutils import OsUtils from Package.MaybeVirtualPackageBase import * class subinfo(info.infoclass): def setTargets(self): #as updates are applied with msys and not by craft don't ever change the name of the target, its a bad idea... self.targets["base"] = f"https://github.com/msys2/msys2-installer/releases/download/2020-05-22/msys2-base-x86_64-20200522.tar.xz" self.targetInstSrc["base"] = "msys64" self.targetInstallPath["base"] = "msys" self.targetDigests["base"] = (['deec23a772774d874b557bcc5dfb2a8a115224fb6a919f19062af108b6bf4735'], CraftHash.HashAlgorithm.SHA256) self.defaultTarget = "base" def setDependencies(self): self.runtimeDependencies["virtual/bin-base"] = None self.runtimeDependencies["dev-utils/python3"] = None def msysInstallShim(self, installDir): return utils.createShim(os.path.join(installDir, "dev-utils", "bin", "msys.exe"), os.path.join(installDir, "dev-utils", "bin", "python3.exe"), args=[os.path.join(CraftStandardDirs.craftBin(), "shells.py")]) def updateMsys(self): msysDir = CraftCore.settings.get("Paths", "Msys", os.path.join(CraftStandardDirs.craftRoot(), "msys")) shell = shells.BashShell() useOverwrite = CraftCore.cache.checkCommandOutputFor(os.path.join(msysDir, "usr/bin", "pacman.exe"), "--overwrite", "-Sh") # force was replace by overwrite overwrite = "--overwrite='*'" if useOverwrite else "--force" def stopProcesses(): return OsUtils.killProcess("*", msysDir) def queryForUpdate(): out = io.BytesIO() if not shell.execute(".", "pacman", f"-Sy --noconfirm {overwrite}"): raise Exception() shell.execute(".", "pacman", "-Qu --noconfirm", stdout=out, stderr=subprocess.PIPE) out = out.getvalue() return out != b"" # start and restart msys before first use if not (shell.execute(".", "echo", "Init update") and stopProcesses() and shell.execute(".", "pacman-key", "--init") and shell.execute(".", "pacman-key", "--populate")): return False try: # max 10 tries for _ in range(10): if not queryForUpdate(): break # might return 1 on core updates... shell.execute(".", "pacman", f"-Su --noconfirm {overwrite} --ask 20") if not stopProcesses(): return False except Exception as e: CraftCore.log.error(e, exc_info=e) return False if not (shell.execute(".", "pacman", f"-S base-devel msys/binutils --noconfirm {overwrite} --needed") and stopProcesses()): return False return utils.system("autorebase.bat", cwd=msysDir) from Package.BinaryPackageBase import * class MsysPackage(BinaryPackageBase): def __init__(self): BinaryPackageBase.__init__(self) def postInstall(self): return self.subinfo.msysInstallShim(self.imageDir()) def postQmerge(self): return self.subinfo.updateMsys() class VirtualPackage(VirtualPackageBase): def __init__(self): VirtualPackageBase.__init__(self) def install(self): if not VirtualPackageBase.install(self): return False return self.subinfo.msysInstallShim(self.imageDir()) and self.subinfo.updateMsys() def qmerge(self): if self.package.isInstalled: return True return super().qmerge() class Package(MaybeVirtualPackageBase): def __init__(self): useExternalMsys = ("Paths", "Msys") not in CraftCore.settings self.skipCondition = useExternalMsys and not CraftPackageObject.get("dev-utils/msys").isInstalled MaybeVirtualPackageBase.__init__(self, condition=self.skipCondition, classA=MsysPackage, classB=VirtualPackage) if not useExternalMsys: # override the install method def install(): CraftCore.log.info(f"Using manually installed msys {CraftStandardDirs.msysDir()}") return self.baseClass.install(self) setattr(self, "install", install)

38.80531

140

0.620525

import io import info import shells from CraftOS.osutils import OsUtils from Package.MaybeVirtualPackageBase import * class subinfo(info.infoclass): def setTargets(self): self.targets["base"] = f"https://github.com/msys2/msys2-installer/releases/download/2020-05-22/msys2-base-x86_64-20200522.tar.xz" self.targetInstSrc["base"] = "msys64" self.targetInstallPath["base"] = "msys" self.targetDigests["base"] = (['deec23a772774d874b557bcc5dfb2a8a115224fb6a919f19062af108b6bf4735'], CraftHash.HashAlgorithm.SHA256) self.defaultTarget = "base" def setDependencies(self): self.runtimeDependencies["virtual/bin-base"] = None self.runtimeDependencies["dev-utils/python3"] = None def msysInstallShim(self, installDir): return utils.createShim(os.path.join(installDir, "dev-utils", "bin", "msys.exe"), os.path.join(installDir, "dev-utils", "bin", "python3.exe"), args=[os.path.join(CraftStandardDirs.craftBin(), "shells.py")]) def updateMsys(self): msysDir = CraftCore.settings.get("Paths", "Msys", os.path.join(CraftStandardDirs.craftRoot(), "msys")) shell = shells.BashShell() useOverwrite = CraftCore.cache.checkCommandOutputFor(os.path.join(msysDir, "usr/bin", "pacman.exe"), "--overwrite", "-Sh") # force was replace by overwrite overwrite = "--overwrite='*'" if useOverwrite else "--force" def stopProcesses(): return OsUtils.killProcess("*", msysDir) def queryForUpdate(): out = io.BytesIO() if not shell.execute(".", "pacman", f"-Sy --noconfirm {overwrite}"): raise Exception() shell.execute(".", "pacman", "-Qu --noconfirm", stdout=out, stderr=subprocess.PIPE) out = out.getvalue() return out != b"" # start and restart msys before first use if not (shell.execute(".", "echo", "Init update") and stopProcesses() and shell.execute(".", "pacman-key", "--init") and shell.execute(".", "pacman-key", "--populate")): return False try: # max 10 tries for _ in range(10): if not queryForUpdate(): break # might return 1 on core updates... shell.execute(".", "pacman", f"-Su --noconfirm {overwrite} --ask 20") if not stopProcesses(): return False except Exception as e: CraftCore.log.error(e, exc_info=e) return False if not (shell.execute(".", "pacman", f"-S base-devel msys/binutils --noconfirm {overwrite} --needed") and stopProcesses()): return False return utils.system("autorebase.bat", cwd=msysDir) from Package.BinaryPackageBase import * class MsysPackage(BinaryPackageBase): def __init__(self): BinaryPackageBase.__init__(self) def postInstall(self): return self.subinfo.msysInstallShim(self.imageDir()) def postQmerge(self): return self.subinfo.updateMsys() class VirtualPackage(VirtualPackageBase): def __init__(self): VirtualPackageBase.__init__(self) def install(self): if not VirtualPackageBase.install(self): return False return self.subinfo.msysInstallShim(self.imageDir()) and self.subinfo.updateMsys() def qmerge(self): if self.package.isInstalled: return True return super().qmerge() class Package(MaybeVirtualPackageBase): def __init__(self): useExternalMsys = ("Paths", "Msys") not in CraftCore.settings self.skipCondition = useExternalMsys and not CraftPackageObject.get("dev-utils/msys").isInstalled MaybeVirtualPackageBase.__init__(self, condition=self.skipCondition, classA=MsysPackage, classB=VirtualPackage) if not useExternalMsys: # override the install method def install(): CraftCore.log.info(f"Using manually installed msys {CraftStandardDirs.msysDir()}") return self.baseClass.install(self) setattr(self, "install", install)

true

1c310e8484badd79ac34991597b64d85aecd23a2

1,766

py

Python

python_ast/flake8_tests/test_ast_linter.py

jerryliu55/pyre-check

ca779758cda4468c95dc2cd84f97a896bb983e3c

[ "MIT" ]

3

2019-03-29T22:32:12.000Z

2019-04-16T08:54:57.000Z

python_ast/flake8_tests/test_ast_linter.py

jerryliu55/pyre-check

ca779758cda4468c95dc2cd84f97a896bb983e3c

[ "MIT" ]

null

python_ast/flake8_tests/test_ast_linter.py

jerryliu55/pyre-check

ca779758cda4468c95dc2cd84f97a896bb983e3c

[ "MIT" ]

null

#!/usr/bin/env python3 import ast import unittest from pathlib import Path from typing import List, NamedTuple from .ast_linter import AstChecker, Error class ExpectedError(NamedTuple): line: int message: str class AstVisitorBaseCase(unittest.TestCase): def load_checker(self, test_file) -> AstChecker: test_repository = "tools/pyre/python_ast/flake8_tests/mock_repository" test_file = Path(test_repository) / test_file source_code = test_file.read_text() tree = ast.parse(source_code) return AstChecker( # pyre-fixme[6]: Expected `Module` for 1st param but got `AST`. tree, source_code.split("\n"), test_repository, str(test_file), ) def assert_errors(self, actual: List[Error], expected: List[ExpectedError]) -> None: if len(expected) != len(actual): self.fail( f"Expected {len(expected)} errors, got {len(actual)}:\n" + "\n".join(str(error) for error in actual) ) for expected_error, actual_error in zip(expected, actual): self.assertEqual(expected_error.line, actual_error.line) self.assertEqual(expected_error.message, actual_error.message) def setUp(self) -> None: self.checker = self.load_checker("") def run_checker(self) -> List[Error]: return list(self.checker.run()) class AstVisitorTestCase(AstVisitorBaseCase): def setUp(self) -> None: self.checker = self.load_checker("a.py") def test_linter(self): errors = self.run_checker() self.assert_errors( errors, [ExpectedError(line=9, message="Assigning to expression of type `int`.")], )

30.982456

88

0.632503

import ast import unittest from pathlib import Path from typing import List, NamedTuple from .ast_linter import AstChecker, Error class ExpectedError(NamedTuple): line: int message: str class AstVisitorBaseCase(unittest.TestCase): def load_checker(self, test_file) -> AstChecker: test_repository = "tools/pyre/python_ast/flake8_tests/mock_repository" test_file = Path(test_repository) / test_file source_code = test_file.read_text() tree = ast.parse(source_code) return AstChecker( tree, source_code.split("\n"), test_repository, str(test_file), ) def assert_errors(self, actual: List[Error], expected: List[ExpectedError]) -> None: if len(expected) != len(actual): self.fail( f"Expected {len(expected)} errors, got {len(actual)}:\n" + "\n".join(str(error) for error in actual) ) for expected_error, actual_error in zip(expected, actual): self.assertEqual(expected_error.line, actual_error.line) self.assertEqual(expected_error.message, actual_error.message) def setUp(self) -> None: self.checker = self.load_checker("") def run_checker(self) -> List[Error]: return list(self.checker.run()) class AstVisitorTestCase(AstVisitorBaseCase): def setUp(self) -> None: self.checker = self.load_checker("a.py") def test_linter(self): errors = self.run_checker() self.assert_errors( errors, [ExpectedError(line=9, message="Assigning to expression of type `int`.")], )

true

1c310f7bbabe1c66ae36be1262be6c97762c5011

13,942

py

Python

mhs/common/mhs_common/messages/ebxml_request_envelope.py

nhsconnect/integration-adaptor-mhs

fa9006ad8b64b696040d48cd469d60c9fc803b3e

[ "Apache-2.0" ]

1

2020-05-20T12:26:46.000Z

mhs/common/mhs_common/messages/ebxml_request_envelope.py

nhsconnect/integration-adaptor-mhs

fa9006ad8b64b696040d48cd469d60c9fc803b3e

[ "Apache-2.0" ]

41

2020-05-18T12:49:29.000Z

2022-02-28T13:34:01.000Z

mhs/common/mhs_common/messages/ebxml_request_envelope.py

nhsconnect/integration-adaptor-mhs

fa9006ad8b64b696040d48cd469d60c9fc803b3e

[ "Apache-2.0" ]

6

2020-06-04T18:59:25.000Z

2021-12-16T16:42:32.000Z

"""This module defines the envelope used to wrap asynchronous request messages to be sent to a remote MHS.""" from __future__ import annotations import base64 import copy import email import email.message import email.policy from typing import Dict, Tuple, Union, List, Sequence, Generator from xml.etree.ElementTree import Element from builder import pystache_message_builder from defusedxml import ElementTree from comms.http_headers import HttpHeaders from utilities import integration_adaptors_logger as log, message_utilities from mhs_common.messages import ebxml_envelope logger = log.IntegrationAdaptorsLogger(__name__) EBXML_TEMPLATE = "ebxml_request" MESSAGE = "hl7_message" EBXML = "ebxml" DUPLICATE_ELIMINATION = "duplicate_elimination" ACK_REQUESTED = "ack_requested" ACK_SOAP_ACTOR = "ack_soap_actor" SYNC_REPLY = "sync_reply" ATTACHMENTS = 'attachments' EXTERNAL_ATTACHMENTS = 'external_attachments' ATTACHMENT_CONTENT_ID = 'content_id' ATTACHMENT_CONTENT_TYPE = 'content_type' ATTACHMENT_BASE64 = 'is_base64' ATTACHMENT_CONTENT_TRANSFER_ENCODING = 'content_transfer_encoding' ATTACHMENT_PAYLOAD = 'payload' ATTACHMENT_DESCRIPTION = 'description' EBXML_CONTENT_TYPE_VALUE = 'multipart/related; boundary="--=_MIME-Boundary"; type=text/xml; ' \ 'start=ebXMLHeader@spine.nhs.uk' class EbxmlRequestEnvelope(ebxml_envelope.EbxmlEnvelope): """An envelope that contains a request to be sent asynchronously to a remote MHS.""" def __init__(self, message_dictionary: Dict[str, Union[str, bool, List[Dict[str, Union[str, bool]]]]]): """Create a new EbxmlRequestEnvelope that populates the message with the provided dictionary. :param message_dictionary: The dictionary of values to use when populating the template. Example `message_dictionary`:: { 'from_party_id': 'TESTGEN-201324', 'to_party_id': 'YEA-0000806', 'cpa_id': 'S1001A1630', 'conversation_id': '79F49A34-9798-404C-AEC4-FD38DD81C138', 'service': 'urn:nhs:names:services:pdsquery', 'action': 'QUPA_IN000006UK02', 'duplicate_elimination': True, 'ack_requested': True, 'ack_soap_actor': 'urn:oasis:names:tc:ebxml-msg:actor:toPartyMSH', 'sync_reply': True, 'hl7_message': '<QUPA_IN000006UK02 xmlns="urn:hl7-org:v3"></QUPA_IN000006UK02>', 'attachments': [ # Optional, defaults to empty list if not set { 'content_type': 'text/plain', 'payload': 'Some text here', 'is_base64': False, 'description': 'Attachment description' }, { 'content_type': 'image/png', 'payload': 'base64-encoded content here', 'is_base64': True, 'description': 'Another attachment description' } ], 'external_attachments': [ # Optional, defaults to empty list if not set { 'document_id' : '6a7b4c68-8be8-46ba-8fbc-9b8313569380', 'message_id': '4dd554f1-2827-4b98-adf3-7cefab763fff', 'description': 'attachment description' } ] } """ super().__init__(EBXML_TEMPLATE, message_dictionary) def serialize(self, _message_dictionary=None) -> Tuple[str, Dict[str, str], str]: message_dictionary = copy.deepcopy(self.message_dictionary) self._set_headers_for_attachments(message_dictionary) message_id, http_headers, message = super().serialize(_message_dictionary=message_dictionary) http_headers[HttpHeaders.CONTENT_TYPE] = EBXML_CONTENT_TYPE_VALUE return message_id, http_headers, message @staticmethod def _set_headers_for_attachments(message_dictionary): """ Generate a content ID for each attachment and set the content transfer encoding based on whether the attachment is Base64-encoded or not. :param message_dictionary: message dictionary that has the attachments """ message_dictionary.setdefault(EXTERNAL_ATTACHMENTS, []) attachment: dict for attachment in message_dictionary.setdefault(ATTACHMENTS, []): attachment[ATTACHMENT_CONTENT_ID] = f'{message_utilities.get_uuid()}@spine.nhs.uk' try: attachment[ATTACHMENT_CONTENT_TRANSFER_ENCODING] = 'base64' if attachment.pop(ATTACHMENT_BASE64) \ else '8bit' except KeyError as e: logger.error('Failed to find {Key} when generating message from {TemplateFile} . {ErrorMessage}', fparams={ 'Key': f'{ATTACHMENTS}[].{ATTACHMENT_BASE64}', 'TemplateFile': EBXML_TEMPLATE, 'ErrorMessage': e }) raise pystache_message_builder.MessageGenerationError(f'Failed to find ' f'key:{ATTACHMENTS}[].{ATTACHMENT_BASE64} when ' f'generating message from template ' f'file:{EBXML_TEMPLATE}') from e @classmethod def from_string(cls, headers: Dict[str, str], message: str) -> EbxmlRequestEnvelope: """Parse the provided message string and create an instance of an EbxmlRequestEnvelope. :param headers A dictionary of headers received with the message. :param message: The message to be parsed. :return: An instance of an EbxmlAckEnvelope constructed from the message. """ msg = EbxmlRequestEnvelope._parse_mime_message(headers, message) ebxml_part, payload_part, attachments = EbxmlRequestEnvelope._extract_message_parts(msg) xml_tree: Element = ElementTree.fromstring(ebxml_part) extracted_values = super().parse_message(xml_tree) cls._extract_more_values_from_xml_tree(xml_tree, extracted_values) extracted_values[EBXML] = ebxml_part extracted_values[ATTACHMENTS] = attachments if payload_part: extracted_values[MESSAGE] = payload_part return EbxmlRequestEnvelope(extracted_values) @classmethod def _extract_more_values_from_xml_tree(cls, xml_tree: Element, extracted_values: Dict[str, Union[str, bool]]): """ Extract more values from XML tree (DuplicateElimination, SyncReply, AckRequested and SOAP actor). Some of the values extracted are booleans (ie if the element is present or not). :param xml_tree: XML tree to extract values from. :param extracted_values: Values extracted so far. The additional extracted values will be added to this dict. """ cls._add_flag(extracted_values, DUPLICATE_ELIMINATION, cls._extract_ebxml_value(xml_tree, "DuplicateElimination")) cls._add_flag(extracted_values, SYNC_REPLY, cls._extract_ebxml_value(xml_tree, "SyncReply")) cls._add_flag(extracted_values, ACK_REQUESTED, cls._extract_ebxml_value(xml_tree, "AckRequested")) cls._extract_attribute(xml_tree, "AckRequested", ebxml_envelope.SOAP_NAMESPACE, "actor", extracted_values, ACK_SOAP_ACTOR) @staticmethod def _parse_mime_message(headers: Dict[str, str], message: str) -> email.message.EmailMessage: """ Take the provided message string (and set of HTTP headers received with it) and parse it to obtain a Message object. :param headers: The HTTP headers received with the message. :param message: The message (as a string) to be parsed. :return: a Message that represents the message received. """ content_type_header = f'{HttpHeaders.CONTENT_TYPE}: {headers[HttpHeaders.CONTENT_TYPE]}\r\n\r\n' msg = email.message_from_string(content_type_header + message, policy=email.policy.HTTP) if msg.defects: logger.warning('Found defects in MIME message during parsing. {Defects}', fparams={'Defects': msg.defects}) return msg @staticmethod def _extract_message_parts(msg: email.message.EmailMessage) -> Tuple[str, str, List[Dict[str, Union[str, bool]]]]: """Extract the ebXML and payload parts of the message and return them as a tuple. :param msg: The message to extract parts from. :return: A tuple containing the ebXML and payload (if present, otherwise None) parts of the message provided. """ # EIS section 2.5.4 defines that the first MIME part must contain the ebML SOAP message and the message payload # (if present) must be the first additional attachment. if not msg.is_multipart(): logger.error('Non-multipart message received') raise ebxml_envelope.EbXmlParsingError("Non-multipart message received") message_parts: Sequence[email.message.EmailMessage] = tuple(msg.iter_parts()) EbxmlRequestEnvelope._report_any_defects_in_message_parts(message_parts) # ebXML part is the first part of the message ebxml_part = EbxmlRequestEnvelope._extract_ebxml_part(message_parts[0]) payload_part = None attachments = [] if len(message_parts) > 1: # HL7 payload part is the second part of the message payload_part = EbxmlRequestEnvelope._extract_hl7_payload_part(message_parts[1]) # Any additional attachments are from the third part of the message onwards attachments.extend(EbxmlRequestEnvelope._extract_additional_attachments_parts(message_parts[2:])) return ebxml_part, payload_part, attachments @staticmethod def _report_any_defects_in_message_parts(message_parts: Sequence[email.message.EmailMessage]): for i, part in enumerate(message_parts): if part.defects: logger.warning('Found defects in {PartIndex} of MIME message during parsing. {Defects}', fparams={'PartIndex': i, 'Defects': part.defects}) @staticmethod def _extract_ebxml_part(message_part: email.message.EmailMessage) -> str: ebxml_part, is_base64_ebxml_part = EbxmlRequestEnvelope._convert_message_part_to_str(message_part) if is_base64_ebxml_part: logger.error('Failed to decode ebXML header part of message as text') raise ebxml_envelope.EbXmlParsingError("Failed to decode ebXML header part of message as text") return ebxml_part @staticmethod def _extract_hl7_payload_part(message_part: email.message.EmailMessage) -> str: payload_part, is_base64_payload = EbxmlRequestEnvelope._convert_message_part_to_str(message_part) if is_base64_payload: logger.error('Failed to decode HL7 payload part of message as text') raise ebxml_envelope.EbXmlParsingError("Failed to decode HL7 payload part of message as text") return payload_part @staticmethod def _extract_additional_attachments_parts(message_parts: Sequence[email.message.EmailMessage]) \ -> Generator[Dict[Union[str, bool]]]: for attachment_message in message_parts: payload, is_base64 = EbxmlRequestEnvelope._convert_message_part_to_str(attachment_message) attachment = { ATTACHMENT_PAYLOAD: payload, ATTACHMENT_BASE64: is_base64, # The [1:-1] is to remove angle brackets (<>) that surround the content ID ATTACHMENT_CONTENT_ID: str(attachment_message['Content-Id'][1:-1]), ATTACHMENT_CONTENT_TYPE: attachment_message.get_content_type() } yield attachment @staticmethod def _convert_message_part_to_str(message_part: email.message.EmailMessage) -> Tuple[str, bool]: content: Union[str, bytes] = message_part.get_content() content_type = message_part.get_content_type() content_transfer_encoding = message_part['Content-Transfer-Encoding'] logger_dict = {'ContentType': content_type, 'ContentTransferEncoding': content_transfer_encoding} if isinstance(content, str): logger.info('Successfully decoded message part with {ContentType} {ContentTransferEncoding} as string', fparams=logger_dict) return content, False try: if content_type == 'application/xml': decoded_content = content.decode() logger.info('Successfully decoded message part with {ContentType} {ContentTransferEncoding} ' 'as a string', fparams=logger_dict) return decoded_content, False decoded_content = base64.b64encode(content).decode() logger.info('Successfully encoded binary message part with {ContentType} {ContentTransferEncoding} as ' 'a base64 string', fparams=logger_dict) return decoded_content, True except UnicodeDecodeError as e: logger.error('Failed to decode ebXML message part with {ContentType} {ContentTransferEncoding}.', fparams=logger_dict) raise ebxml_envelope.EbXmlParsingError(f'Failed to decode ebXML message part with ' f'Content-Type: {content_type} and ' f'Content-Transfer-Encoding: {content_transfer_encoding}') from e

49.091549

120

0.650624

from __future__ import annotations import base64 import copy import email import email.message import email.policy from typing import Dict, Tuple, Union, List, Sequence, Generator from xml.etree.ElementTree import Element from builder import pystache_message_builder from defusedxml import ElementTree from comms.http_headers import HttpHeaders from utilities import integration_adaptors_logger as log, message_utilities from mhs_common.messages import ebxml_envelope logger = log.IntegrationAdaptorsLogger(__name__) EBXML_TEMPLATE = "ebxml_request" MESSAGE = "hl7_message" EBXML = "ebxml" DUPLICATE_ELIMINATION = "duplicate_elimination" ACK_REQUESTED = "ack_requested" ACK_SOAP_ACTOR = "ack_soap_actor" SYNC_REPLY = "sync_reply" ATTACHMENTS = 'attachments' EXTERNAL_ATTACHMENTS = 'external_attachments' ATTACHMENT_CONTENT_ID = 'content_id' ATTACHMENT_CONTENT_TYPE = 'content_type' ATTACHMENT_BASE64 = 'is_base64' ATTACHMENT_CONTENT_TRANSFER_ENCODING = 'content_transfer_encoding' ATTACHMENT_PAYLOAD = 'payload' ATTACHMENT_DESCRIPTION = 'description' EBXML_CONTENT_TYPE_VALUE = 'multipart/related; boundary="--=_MIME-Boundary"; type=text/xml; ' \ 'start=ebXMLHeader@spine.nhs.uk' class EbxmlRequestEnvelope(ebxml_envelope.EbxmlEnvelope): def __init__(self, message_dictionary: Dict[str, Union[str, bool, List[Dict[str, Union[str, bool]]]]]): super().__init__(EBXML_TEMPLATE, message_dictionary) def serialize(self, _message_dictionary=None) -> Tuple[str, Dict[str, str], str]: message_dictionary = copy.deepcopy(self.message_dictionary) self._set_headers_for_attachments(message_dictionary) message_id, http_headers, message = super().serialize(_message_dictionary=message_dictionary) http_headers[HttpHeaders.CONTENT_TYPE] = EBXML_CONTENT_TYPE_VALUE return message_id, http_headers, message @staticmethod def _set_headers_for_attachments(message_dictionary): message_dictionary.setdefault(EXTERNAL_ATTACHMENTS, []) attachment: dict for attachment in message_dictionary.setdefault(ATTACHMENTS, []): attachment[ATTACHMENT_CONTENT_ID] = f'{message_utilities.get_uuid()}@spine.nhs.uk' try: attachment[ATTACHMENT_CONTENT_TRANSFER_ENCODING] = 'base64' if attachment.pop(ATTACHMENT_BASE64) \ else '8bit' except KeyError as e: logger.error('Failed to find {Key} when generating message from {TemplateFile} . {ErrorMessage}', fparams={ 'Key': f'{ATTACHMENTS}[].{ATTACHMENT_BASE64}', 'TemplateFile': EBXML_TEMPLATE, 'ErrorMessage': e }) raise pystache_message_builder.MessageGenerationError(f'Failed to find ' f'key:{ATTACHMENTS}[].{ATTACHMENT_BASE64} when ' f'generating message from template ' f'file:{EBXML_TEMPLATE}') from e @classmethod def from_string(cls, headers: Dict[str, str], message: str) -> EbxmlRequestEnvelope: msg = EbxmlRequestEnvelope._parse_mime_message(headers, message) ebxml_part, payload_part, attachments = EbxmlRequestEnvelope._extract_message_parts(msg) xml_tree: Element = ElementTree.fromstring(ebxml_part) extracted_values = super().parse_message(xml_tree) cls._extract_more_values_from_xml_tree(xml_tree, extracted_values) extracted_values[EBXML] = ebxml_part extracted_values[ATTACHMENTS] = attachments if payload_part: extracted_values[MESSAGE] = payload_part return EbxmlRequestEnvelope(extracted_values) @classmethod def _extract_more_values_from_xml_tree(cls, xml_tree: Element, extracted_values: Dict[str, Union[str, bool]]): cls._add_flag(extracted_values, DUPLICATE_ELIMINATION, cls._extract_ebxml_value(xml_tree, "DuplicateElimination")) cls._add_flag(extracted_values, SYNC_REPLY, cls._extract_ebxml_value(xml_tree, "SyncReply")) cls._add_flag(extracted_values, ACK_REQUESTED, cls._extract_ebxml_value(xml_tree, "AckRequested")) cls._extract_attribute(xml_tree, "AckRequested", ebxml_envelope.SOAP_NAMESPACE, "actor", extracted_values, ACK_SOAP_ACTOR) @staticmethod def _parse_mime_message(headers: Dict[str, str], message: str) -> email.message.EmailMessage: content_type_header = f'{HttpHeaders.CONTENT_TYPE}: {headers[HttpHeaders.CONTENT_TYPE]}\r\n\r\n' msg = email.message_from_string(content_type_header + message, policy=email.policy.HTTP) if msg.defects: logger.warning('Found defects in MIME message during parsing. {Defects}', fparams={'Defects': msg.defects}) return msg @staticmethod def _extract_message_parts(msg: email.message.EmailMessage) -> Tuple[str, str, List[Dict[str, Union[str, bool]]]]: if not msg.is_multipart(): logger.error('Non-multipart message received') raise ebxml_envelope.EbXmlParsingError("Non-multipart message received") message_parts: Sequence[email.message.EmailMessage] = tuple(msg.iter_parts()) EbxmlRequestEnvelope._report_any_defects_in_message_parts(message_parts) ebxml_part = EbxmlRequestEnvelope._extract_ebxml_part(message_parts[0]) payload_part = None attachments = [] if len(message_parts) > 1: payload_part = EbxmlRequestEnvelope._extract_hl7_payload_part(message_parts[1]) attachments.extend(EbxmlRequestEnvelope._extract_additional_attachments_parts(message_parts[2:])) return ebxml_part, payload_part, attachments @staticmethod def _report_any_defects_in_message_parts(message_parts: Sequence[email.message.EmailMessage]): for i, part in enumerate(message_parts): if part.defects: logger.warning('Found defects in {PartIndex} of MIME message during parsing. {Defects}', fparams={'PartIndex': i, 'Defects': part.defects}) @staticmethod def _extract_ebxml_part(message_part: email.message.EmailMessage) -> str: ebxml_part, is_base64_ebxml_part = EbxmlRequestEnvelope._convert_message_part_to_str(message_part) if is_base64_ebxml_part: logger.error('Failed to decode ebXML header part of message as text') raise ebxml_envelope.EbXmlParsingError("Failed to decode ebXML header part of message as text") return ebxml_part @staticmethod def _extract_hl7_payload_part(message_part: email.message.EmailMessage) -> str: payload_part, is_base64_payload = EbxmlRequestEnvelope._convert_message_part_to_str(message_part) if is_base64_payload: logger.error('Failed to decode HL7 payload part of message as text') raise ebxml_envelope.EbXmlParsingError("Failed to decode HL7 payload part of message as text") return payload_part @staticmethod def _extract_additional_attachments_parts(message_parts: Sequence[email.message.EmailMessage]) \ -> Generator[Dict[Union[str, bool]]]: for attachment_message in message_parts: payload, is_base64 = EbxmlRequestEnvelope._convert_message_part_to_str(attachment_message) attachment = { ATTACHMENT_PAYLOAD: payload, ATTACHMENT_BASE64: is_base64, ATTACHMENT_CONTENT_ID: str(attachment_message['Content-Id'][1:-1]), ATTACHMENT_CONTENT_TYPE: attachment_message.get_content_type() } yield attachment @staticmethod def _convert_message_part_to_str(message_part: email.message.EmailMessage) -> Tuple[str, bool]: content: Union[str, bytes] = message_part.get_content() content_type = message_part.get_content_type() content_transfer_encoding = message_part['Content-Transfer-Encoding'] logger_dict = {'ContentType': content_type, 'ContentTransferEncoding': content_transfer_encoding} if isinstance(content, str): logger.info('Successfully decoded message part with {ContentType} {ContentTransferEncoding} as string', fparams=logger_dict) return content, False try: if content_type == 'application/xml': decoded_content = content.decode() logger.info('Successfully decoded message part with {ContentType} {ContentTransferEncoding} ' 'as a string', fparams=logger_dict) return decoded_content, False decoded_content = base64.b64encode(content).decode() logger.info('Successfully encoded binary message part with {ContentType} {ContentTransferEncoding} as ' 'a base64 string', fparams=logger_dict) return decoded_content, True except UnicodeDecodeError as e: logger.error('Failed to decode ebXML message part with {ContentType} {ContentTransferEncoding}.', fparams=logger_dict) raise ebxml_envelope.EbXmlParsingError(f'Failed to decode ebXML message part with ' f'Content-Type: {content_type} and ' f'Content-Transfer-Encoding: {content_transfer_encoding}') from e

true

1c311011386e04a22ecac2c71ba793d750f6de17

7,872

py

Python

hcipy/field/field.py

dskleingeld/hcipy

85cacfb7a8058506afb288e3acdf3b6059ba2b50

[ "MIT" ]

1

2020-07-20T23:25:17.000Z

hcipy/field/field.py

dskleingeld/hcipy

85cacfb7a8058506afb288e3acdf3b6059ba2b50

[ "MIT" ]

null

hcipy/field/field.py

dskleingeld/hcipy

85cacfb7a8058506afb288e3acdf3b6059ba2b50

[ "MIT" ]

null

import numpy as np import string class Field(np.ndarray): '''The value of some physical quantity for each point in some coordinate system. Parameters ---------- arr : array_like An array of values or tensors for each point in the :class:`Grid`. grid : Grid The corresponding :class:`Grid` on which the values are set. Attributes ---------- grid : Grid The grid on which the values are defined. ''' def __new__(cls, arr, grid): obj = np.asarray(arr).view(cls) obj.grid = grid return obj def __array_finalize__(self, obj): if obj is None: return self.grid = getattr(obj, 'grid', None) @property def tensor_order(self): '''The order of the tensor of the field. ''' return self.ndim - 1 @property def tensor_shape(self): '''The shape of the tensor of the field. ''' return np.array(self.shape)[:-1] @property def is_scalar_field(self): '''True if this field is a scalar field (ie. a tensor order of 0), False otherwise. ''' return self.tensor_order == 0 @property def is_vector_field(self): '''True if this field is a vector field (ie. a tensor order of 1), False otherwise. ''' return self.tensor_order == 1 @property def is_valid_field(self): '''True if the field corresponds with its grid. ''' return self.shape[-1] == self.grid.size @property def shaped(self): '''The reshaped version of this field. Raises ------ ValueError If this field isn't separated, no reshaped version can be made. ''' if not self.grid.is_separated: raise ValueError('This field doesn\'t have a shape.') if self.tensor_order > 0: new_shape = np.concatenate([np.array(self.shape)[:-1], self.grid.shape]) return self.reshape(new_shape) return self.reshape(self.grid.shape) def at(self, p): '''The value of this field closest to point p. Parameters ---------- p : array_like The point at which the closest value should be returned. Returns ------- array_like The value, potentially tensor, closest to point p. ''' i = self.grid.closest_to(p) return self[...,i] def field_einsum(subscripts, *operands, **kwargs): '''Evaluates the Einstein summation convention on the operand fields. This function uses the same conventions as numpy.einsum(). The input subscript is multiplexed over each position in the grid. The grids of each of the input field operands don't have to match, but must have the same lengths. The subscripts must be written as you would for a single position in the grid. The function alters these subscripts to multiplex over the entire grid. .. caution:: Some subscripts may yield no exception, even though they would fail for a single point in the grid. The output in these cases can not be trusted. Parameters ---------- subscripts : str Specifies the subscripts for summation. operands : list of array_like or `Field` These are the arrays or fields for the operation. out : {ndarray, None}, optional If provided, the calculation is done into this array. dtype : {data-type, None}, optional If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal `casting` parameter to allow the conversions. Default is None. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the output. 'C' means it should be C contiguous. 'F' means it should be Fortran contiguous, 'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise. 'K' means it should be as close to the layout as the inputs as is possible, including arbitrarily permuted axes. Default is 'K'. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. Setting this to 'unsafe' is not recommended, as it can adversely affect accumulations. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. Default is 'safe'. optimize : {False, True, 'greedy', 'optimal'}, optional Controls if intermediate optimization should occur. No optimization will occur if False and True will default to the 'greedy' algorithm. Also accepts an explicit contraction list from the ``np.einsum_path`` function. See ``np.einsum_path`` for more details. Default is False. Returns ------- Field The calculated Field based on the Einstein summation convention. Raises ------ ValueError If all of the fields don't have the same grid size. If the number of operands is not equal to the number of subscripts specified. ''' is_field = [isinstance(o, Field) for o in operands] if not np.count_nonzero(is_field): return np.einsum(subscripts, *operands, **kwargs) field_sizes = [o.grid.size for i, o in enumerate(operands) if is_field[i]] if not np.allclose(field_sizes, field_sizes[0]): raise ValueError('All fields must be the same size for a field_einsum().') # Decompose the subscript into input and output splitted_string = subscripts.split('->') if len(splitted_string) == 2: ss_input, ss_output = splitted_string else: ss_input = splitted_string[0] ss_output = '' # split the input operands in separate strings ss = ss_input.split(',') if len(ss) != len(operands): raise ValueError('Number of operands is not equal to number of indexing operands.') # Find an indexing letter that can be used for field dimension. unused_index = [a for a in string.ascii_lowercase if a not in subscripts][0] # Add the field dimension to the input field operands. ss = [s + unused_index if is_field[i] else s for i,s in enumerate(ss)] # Recombine all operands into the final subscripts if len(splitted_string) == 2: subscripts_new = ','.join(ss) + '->' + ss_output + unused_index else: subscripts_new = ','.join(ss) res = np.einsum(subscripts_new, *operands, **kwargs) grid = operands[np.flatnonzero(np.array(is_field))[0]].grid if 'out' in kwargs: kwargs['out'] = Field(res, grid) return Field(res, grid) def field_dot(a, b, out=None): '''Perform a dot product of `a` and `b` multiplexed over the field dimension. Parameters ---------- a : Field or array_like Left argument of the dot product. b : Field or array_like Right argument of the dot product. out : Field or array_like If provided, the calculation is done into this array. Returns ------- Field The result of the dot product. ''' # Find out if a or b are vectors or higher dimensional tensors if hasattr(a, 'tensor_order'): amat = a.tensor_order > 1 elif np.isscalar(a): if out is None: return a * b else: return np.multiply(a, b, out) else: amat = a.ndim > 1 if hasattr(b, 'tensor_order'): bmat = b.tensor_order > 1 elif np.isscalar(b): if out is None: return a * b else: return np.multiply(a, b, out) else: bmat = b.ndim > 1 # Select correct multiplication behaviour. if amat and bmat: subscripts = '...ij,...jk->...ik' elif amat and not bmat: subscripts = '...i,...i->...' elif not amat and bmat: subscripts = '...i,...ij->...j' elif not amat and not bmat: subscripts = '...i,...i->...' # Perform calculation and return. if out is None: return field_einsum(subscripts, a, b) else: return field_einsum(subscripts, a, b, out=out) def field_trace(a, out=None): if out is None: return field_einsum('ii', a) else: return field_einsum('ii', a, out=out) def field_inv(a): if hasattr(a, 'grid'): if a.tensor_order != 2: raise ValueError("Only tensor fields of order 2 can be inverted.") res = np.rollaxis(np.linalg.inv(np.rollaxis(a, -1)), 0, 3) return Field(res, a.grid) else: return np.linalg.inv(a)

29.70566

85

0.696138

import numpy as np import string class Field(np.ndarray): def __new__(cls, arr, grid): obj = np.asarray(arr).view(cls) obj.grid = grid return obj def __array_finalize__(self, obj): if obj is None: return self.grid = getattr(obj, 'grid', None) @property def tensor_order(self): return self.ndim - 1 @property def tensor_shape(self): return np.array(self.shape)[:-1] @property def is_scalar_field(self): return self.tensor_order == 0 @property def is_vector_field(self): return self.tensor_order == 1 @property def is_valid_field(self): return self.shape[-1] == self.grid.size @property def shaped(self): if not self.grid.is_separated: raise ValueError('This field doesn\'t have a shape.') if self.tensor_order > 0: new_shape = np.concatenate([np.array(self.shape)[:-1], self.grid.shape]) return self.reshape(new_shape) return self.reshape(self.grid.shape) def at(self, p): i = self.grid.closest_to(p) return self[...,i] def field_einsum(subscripts, *operands, **kwargs): is_field = [isinstance(o, Field) for o in operands] if not np.count_nonzero(is_field): return np.einsum(subscripts, *operands, **kwargs) field_sizes = [o.grid.size for i, o in enumerate(operands) if is_field[i]] if not np.allclose(field_sizes, field_sizes[0]): raise ValueError('All fields must be the same size for a field_einsum().') # Decompose the subscript into input and output splitted_string = subscripts.split('->') if len(splitted_string) == 2: ss_input, ss_output = splitted_string else: ss_input = splitted_string[0] ss_output = '' # split the input operands in separate strings ss = ss_input.split(',') if len(ss) != len(operands): raise ValueError('Number of operands is not equal to number of indexing operands.') # Find an indexing letter that can be used for field dimension. unused_index = [a for a in string.ascii_lowercase if a not in subscripts][0] # Add the field dimension to the input field operands. ss = [s + unused_index if is_field[i] else s for i,s in enumerate(ss)] # Recombine all operands into the final subscripts if len(splitted_string) == 2: subscripts_new = ','.join(ss) + '->' + ss_output + unused_index else: subscripts_new = ','.join(ss) res = np.einsum(subscripts_new, *operands, **kwargs) grid = operands[np.flatnonzero(np.array(is_field))[0]].grid if 'out' in kwargs: kwargs['out'] = Field(res, grid) return Field(res, grid) def field_dot(a, b, out=None): # Find out if a or b are vectors or higher dimensional tensors if hasattr(a, 'tensor_order'): amat = a.tensor_order > 1 elif np.isscalar(a): if out is None: return a * b else: return np.multiply(a, b, out) else: amat = a.ndim > 1 if hasattr(b, 'tensor_order'): bmat = b.tensor_order > 1 elif np.isscalar(b): if out is None: return a * b else: return np.multiply(a, b, out) else: bmat = b.ndim > 1 # Select correct multiplication behaviour. if amat and bmat: subscripts = '...ij,...jk->...ik' elif amat and not bmat: subscripts = '...i,...i->...' elif not amat and bmat: subscripts = '...i,...ij->...j' elif not amat and not bmat: subscripts = '...i,...i->...' # Perform calculation and return. if out is None: return field_einsum(subscripts, a, b) else: return field_einsum(subscripts, a, b, out=out) def field_trace(a, out=None): if out is None: return field_einsum('ii', a) else: return field_einsum('ii', a, out=out) def field_inv(a): if hasattr(a, 'grid'): if a.tensor_order != 2: raise ValueError("Only tensor fields of order 2 can be inverted.") res = np.rollaxis(np.linalg.inv(np.rollaxis(a, -1)), 0, 3) return Field(res, a.grid) else: return np.linalg.inv(a)

true

1c31111aac8aff95f5f45202a6e11e841a5044da

6,289

py

Python

python-basic-template.py

realSnoopy/python-ffmpeg-multi-conversion

3e9986252fabe229273771e021ea55fd9b208bd4

[ "MIT" ]

null

python-basic-template.py

realSnoopy/python-ffmpeg-multi-conversion

3e9986252fabe229273771e021ea55fd9b208bd4

[ "MIT" ]

null

python-basic-template.py

realSnoopy/python-ffmpeg-multi-conversion

3e9986252fabe229273771e021ea55fd9b208bd4

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Beautiful is better than ugly. # Explicit is better than implicit. # Simple is better than complex. # Complex is better than complicated. # Flat is better than nested. # Python 3.5 and up # getestet Python 3.6.5 VERSION = 'ALPHA' def clear_console(): if os.name=='nt': os.system('cls') else: os.system('clear') def exit(error_msg=None): if error_msg: print('\n[ERROR]\n{}\n[EXIT]'.format(error_msg)) sys.exit(0) try: import os import sys import unicodedata import logging from pathlib import Path from platform import python_version from codecs import BOM_UTF8, BOM_UTF16, BOM_UTF16_BE, BOM_UTF16_LE, BOM_UTF32_BE, BOM_UTF32_LE except Exception as error: exit(error) def check_python(): try: assert(python_version() >= '3.8') except AssertionError: error = 'This script requires at least Python 3.5. Please update or use "python3" to invoke.\n' error += 'Python {} found.'.format(python_version()) exit(error) def get_files(path, ): file_list = [] try: assert(python_version() >= '3.6') except AssertionError: directory = str(directory) for entry in os.scandir(path): if entry.is_dir(follow_symlinks=False): tmp_files = get_files(entry, ) [file_list.append(Path(file)) for file in tmp_files] if entry.is_file(): file_list.append(Path(entry)) return file_list def get_files_filter(files, file_filter, ): file_list_filter = [] for file in files: if file.name in file_filter or file.suffix.lower() in [filter.lower() for filter in file_filter]: file_list_filter.append(Path(file)) return file_list_filter def get_size(file): return (Path(file).stat().st_size) def size_to_human(filesize, base='KB'): if base == 'KB': return '{:.2f} KB'.format(filesize/1024) elif base == 'MB': return '{:.2f} MB'.format(filesize/(1024*1024)) class GetWork: def __init__(self, path, file_filter=None): self._path = path self._file_filter = file_filter self._root = Path.cwd() self._outpath = Path(self._root) / '#-OUT-#' if not self._outpath.exists(): self._outpath.mkdir(parents=True) if not self._path: logging.info('Input-Path not found') exit() self._files = get_files(self._path, ) if self._file_filter: self._files_filtered = get_files_filter(self._files, self._file_filter) else: self._files_filtered = self._files @property def files(self): return self._files @property def files_filtered(self): return self._files_filtered @property def outpath(self): return self._outpath BOMS = ( (BOM_UTF8, 'UTF-8-SIG'), (BOM_UTF32_BE, 'UTF-32-BE'), (BOM_UTF32_LE, 'UTF-32-LE'), (BOM_UTF16_BE, 'UTF-16-BE'), (BOM_UTF16_LE, 'UTF-16-LE'), ) def check_bom(data): return [encoding for bom, encoding in BOMS if data.startswith(bom)] def get_content(file, read, ): def read_file(file, encoding, read, mode='r'): with open(file=file, encoding=encoding, mode=mode, errors='strict') as file_object: if read == 'read': file_content = file_object.read() elif read == 'lines': file_content = file_object.readlines() return file_content error_msg = '' with open(file, mode='rb') as file_object: encoding = check_bom(file_object.readline()) encoding = ''.join(encoding) if encoding != '': logging.info('encoding\t{}'.format(encoding)) else: logging.info('encoding\tNo BOM found') # mit erkannter BOM-Codierung auslesen, ohne Codierung auf UTF-8 ausweichen if encoding != '': try: file_content = read_file(file=file, encoding=encoding, read=read, ) except Exception as error: logging.debug('{}'.format(error)) encoding = '' if encoding == '': try: file_content = read_file(file=file, encoding='UTF-8', read=read) except Exception as error: logging.debug('{}'.format(error)) exit('Krasser Fehler') return file_content def write_to_file(path, content, mode='w'): path = Path(path) # pathlib path object # create required folders if needed if not path.parent.exists(): path.parent.mkdir(parents=True) with open(path, mode=mode, encoding='UTF-8', errors='strict') as output: if isinstance(content, (str)): content = content + '\n' output.writelines(content) else: content = [str(entry) + '\n' for entry in content] output.writelines(content) ### Logging-Stuff # CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET def create_basic_logger(file_handler): logger = logging.getLogger() logger.setLevel(logging.DEBUG) fh = logging.FileHandler(file_handler) fh.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch_formatter = logging.Formatter(fmt='{levelname}\t{message}', style='{', ) fh_formatter = logging.Formatter(fmt='{asctime}\t{levelname}\t{message}', style='{', datefmt='%H:%M:%S') ch.setFormatter(ch_formatter) fh.setFormatter(fh_formatter) logger.addHandler(ch) logger.addHandler(fh) return logger ####### # Ich bin das Alpha und das Omega, der Erste und der Letzte, der Anfang und das Ende. ####### clear_console() check_python() settings = { 'path' : Path.cwd() , 'file_filter' : ('.txt', ) , } root = GetWork(**settings) # create basic logger logging = create_basic_logger(file_handler = Path(root.outpath) / 'debug.log') if __name__ == '__main__': for file in root.files: pass for file in root.files_filtered: pass # basic infos for files logging.info('filename\t{}'.format(file.name)) logging.info('filesize\t{}'.format(size_to_human(get_size(file), base='KB'))) file_content = get_content(file, read='lines')

27.583333

109

0.618699

VERSION = 'ALPHA' def clear_console(): if os.name=='nt': os.system('cls') else: os.system('clear') def exit(error_msg=None): if error_msg: print('\n[ERROR]\n{}\n[EXIT]'.format(error_msg)) sys.exit(0) try: import os import sys import unicodedata import logging from pathlib import Path from platform import python_version from codecs import BOM_UTF8, BOM_UTF16, BOM_UTF16_BE, BOM_UTF16_LE, BOM_UTF32_BE, BOM_UTF32_LE except Exception as error: exit(error) def check_python(): try: assert(python_version() >= '3.8') except AssertionError: error = 'This script requires at least Python 3.5. Please update or use "python3" to invoke.\n' error += 'Python {} found.'.format(python_version()) exit(error) def get_files(path, ): file_list = [] try: assert(python_version() >= '3.6') except AssertionError: directory = str(directory) for entry in os.scandir(path): if entry.is_dir(follow_symlinks=False): tmp_files = get_files(entry, ) [file_list.append(Path(file)) for file in tmp_files] if entry.is_file(): file_list.append(Path(entry)) return file_list def get_files_filter(files, file_filter, ): file_list_filter = [] for file in files: if file.name in file_filter or file.suffix.lower() in [filter.lower() for filter in file_filter]: file_list_filter.append(Path(file)) return file_list_filter def get_size(file): return (Path(file).stat().st_size) def size_to_human(filesize, base='KB'): if base == 'KB': return '{:.2f} KB'.format(filesize/1024) elif base == 'MB': return '{:.2f} MB'.format(filesize/(1024*1024)) class GetWork: def __init__(self, path, file_filter=None): self._path = path self._file_filter = file_filter self._root = Path.cwd() self._outpath = Path(self._root) / '#-OUT-#' if not self._outpath.exists(): self._outpath.mkdir(parents=True) if not self._path: logging.info('Input-Path not found') exit() self._files = get_files(self._path, ) if self._file_filter: self._files_filtered = get_files_filter(self._files, self._file_filter) else: self._files_filtered = self._files @property def files(self): return self._files @property def files_filtered(self): return self._files_filtered @property def outpath(self): return self._outpath BOMS = ( (BOM_UTF8, 'UTF-8-SIG'), (BOM_UTF32_BE, 'UTF-32-BE'), (BOM_UTF32_LE, 'UTF-32-LE'), (BOM_UTF16_BE, 'UTF-16-BE'), (BOM_UTF16_LE, 'UTF-16-LE'), ) def check_bom(data): return [encoding for bom, encoding in BOMS if data.startswith(bom)] def get_content(file, read, ): def read_file(file, encoding, read, mode='r'): with open(file=file, encoding=encoding, mode=mode, errors='strict') as file_object: if read == 'read': file_content = file_object.read() elif read == 'lines': file_content = file_object.readlines() return file_content error_msg = '' with open(file, mode='rb') as file_object: encoding = check_bom(file_object.readline()) encoding = ''.join(encoding) if encoding != '': logging.info('encoding\t{}'.format(encoding)) else: logging.info('encoding\tNo BOM found') if encoding != '': try: file_content = read_file(file=file, encoding=encoding, read=read, ) except Exception as error: logging.debug('{}'.format(error)) encoding = '' if encoding == '': try: file_content = read_file(file=file, encoding='UTF-8', read=read) except Exception as error: logging.debug('{}'.format(error)) exit('Krasser Fehler') return file_content def write_to_file(path, content, mode='w'): path = Path(path) if not path.parent.exists(): path.parent.mkdir(parents=True) with open(path, mode=mode, encoding='UTF-8', errors='strict') as output: if isinstance(content, (str)): content = content + '\n' output.writelines(content) else: content = [str(entry) + '\n' for entry in content] output.writelines(content) _handler): logger = logging.getLogger() logger.setLevel(logging.DEBUG) fh = logging.FileHandler(file_handler) fh.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch_formatter = logging.Formatter(fmt='{levelname}\t{message}', style='{', ) fh_formatter = logging.Formatter(fmt='{asctime}\t{levelname}\t{message}', style='{', datefmt='%H:%M:%S') ch.setFormatter(ch_formatter) fh.setFormatter(fh_formatter) logger.addHandler(ch) logger.addHandler(fh) return logger gs = { 'path' : Path.cwd() , 'file_filter' : ('.txt', ) , } root = GetWork(**settings) logging = create_basic_logger(file_handler = Path(root.outpath) / 'debug.log') if __name__ == '__main__': for file in root.files: pass for file in root.files_filtered: pass logging.info('filename\t{}'.format(file.name)) logging.info('filesize\t{}'.format(size_to_human(get_size(file), base='KB'))) file_content = get_content(file, read='lines')

true

1c31124a28d1dee46f542f0014529787d8946185

222

py

Python

tests/test_iterator_TCP.py

jcarreira/cirrus-kv

a44099185e02859385997956333b364ae836fee5

[ "Apache-2.0" ]

8

2018-07-18T22:13:36.000Z

2021-08-24T12:28:42.000Z

tests/test_iterator_TCP.py

jcarreira/ddc

a44099185e02859385997956333b364ae836fee5

[ "Apache-2.0" ]

7

2016-11-22T11:07:14.000Z

2016-12-17T22:49:23.000Z

tests/test_iterator_TCP.py

jcarreira/ddc

a44099185e02859385997956333b364ae836fee5

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import sys import subprocess import time import test_runner # Set name of test to run testPath = "./tests/object_store/test_iterator" # Call script to run the test test_runner.runTestTCP(testPath)

18.5

47

0.788288

import sys import subprocess import time import test_runner testPath = "./tests/object_store/test_iterator" test_runner.runTestTCP(testPath)

true

1c3112ce7a1bbf5a04753c11f0da9a56ef16ce27

22,703

py

Python

python-profiles/STANDA/8MID12-1-AR.py

EPC-MSU/libximc

b0349721f57c8274b098a7b646d7ae67b8e70b9d

[ "BSD-2-Clause" ]

3

2020-12-08T14:41:48.000Z

2022-02-23T13:42:42.000Z

python-profiles/STANDA/8MID12-1-AR.py

EPC-MSU/libximc

b0349721f57c8274b098a7b646d7ae67b8e70b9d

[ "BSD-2-Clause" ]

4

2020-12-08T20:15:06.000Z

2021-12-08T14:15:24.000Z

python-profiles/STANDA/8MID12-1-AR.py

EPC-MSU/libximc

b0349721f57c8274b098a7b646d7ae67b8e70b9d

[ "BSD-2-Clause" ]

2

2020-11-02T02:17:35.000Z

2021-03-18T14:13:56.000Z

def set_profile_8MID12_1_AR(lib, id): worst_result = Result.Ok result = Result.Ok feedback_settings = feedback_settings_t() feedback_settings.IPS = 4000 class FeedbackType_: FEEDBACK_ENCODER_MEDIATED = 6 FEEDBACK_NONE = 5 FEEDBACK_EMF = 4 FEEDBACK_ENCODER = 1 feedback_settings.FeedbackType = FeedbackType_.FEEDBACK_NONE class FeedbackFlags_: FEEDBACK_ENC_TYPE_BITS = 192 FEEDBACK_ENC_TYPE_DIFFERENTIAL = 128 FEEDBACK_ENC_TYPE_SINGLE_ENDED = 64 FEEDBACK_ENC_REVERSE = 1 FEEDBACK_ENC_TYPE_AUTO = 0 feedback_settings.FeedbackFlags = FeedbackFlags_.FEEDBACK_ENC_TYPE_SINGLE_ENDED | FeedbackFlags_.FEEDBACK_ENC_TYPE_AUTO feedback_settings.CountsPerTurn = 4000 result = lib.set_feedback_settings(id, byref(feedback_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result home_settings = home_settings_t() home_settings.FastHome = 50 home_settings.uFastHome = 0 home_settings.SlowHome = 500 home_settings.uSlowHome = 0 home_settings.HomeDelta = 1020 home_settings.uHomeDelta = 0 class HomeFlags_: HOME_USE_FAST = 256 HOME_STOP_SECOND_BITS = 192 HOME_STOP_SECOND_LIM = 192 HOME_STOP_SECOND_SYN = 128 HOME_STOP_SECOND_REV = 64 HOME_STOP_FIRST_BITS = 48 HOME_STOP_FIRST_LIM = 48 HOME_STOP_FIRST_SYN = 32 HOME_STOP_FIRST_REV = 16 HOME_HALF_MV = 8 HOME_MV_SEC_EN = 4 HOME_DIR_SECOND = 2 HOME_DIR_FIRST = 1 home_settings.HomeFlags = HomeFlags_.HOME_USE_FAST | HomeFlags_.HOME_STOP_SECOND_REV | HomeFlags_.HOME_STOP_FIRST_BITS | HomeFlags_.HOME_DIR_SECOND result = lib.set_home_settings(id, byref(home_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result move_settings = move_settings_t() move_settings.Speed = 800 move_settings.uSpeed = 0 move_settings.Accel = 3200 move_settings.Decel = 4800 move_settings.AntiplaySpeed = 800 move_settings.uAntiplaySpeed = 0 class MoveFlags_: RPM_DIV_1000 = 1 result = lib.set_move_settings(id, byref(move_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_settings = engine_settings_t() engine_settings.NomVoltage = 50 engine_settings.NomCurrent = 200 engine_settings.NomSpeed = 1600 engine_settings.uNomSpeed = 0 class EngineFlags_: ENGINE_LIMIT_RPM = 128 ENGINE_LIMIT_CURR = 64 ENGINE_LIMIT_VOLT = 32 ENGINE_ACCEL_ON = 16 ENGINE_ANTIPLAY = 8 ENGINE_MAX_SPEED = 4 ENGINE_CURRENT_AS_RMS = 2 ENGINE_REVERSE = 1 engine_settings.EngineFlags = EngineFlags_.ENGINE_LIMIT_RPM | EngineFlags_.ENGINE_ACCEL_ON | EngineFlags_.ENGINE_REVERSE engine_settings.Antiplay = 326 class MicrostepMode_: MICROSTEP_MODE_FRAC_256 = 9 MICROSTEP_MODE_FRAC_128 = 8 MICROSTEP_MODE_FRAC_64 = 7 MICROSTEP_MODE_FRAC_32 = 6 MICROSTEP_MODE_FRAC_16 = 5 MICROSTEP_MODE_FRAC_8 = 4 MICROSTEP_MODE_FRAC_4 = 3 MICROSTEP_MODE_FRAC_2 = 2 MICROSTEP_MODE_FULL = 1 engine_settings.MicrostepMode = MicrostepMode_.MICROSTEP_MODE_FRAC_256 engine_settings.StepsPerRev = 20 result = lib.set_engine_settings(id, byref(engine_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result entype_settings = entype_settings_t() class EngineType_: ENGINE_TYPE_BRUSHLESS = 5 ENGINE_TYPE_TEST = 4 ENGINE_TYPE_STEP = 3 ENGINE_TYPE_2DC = 2 ENGINE_TYPE_DC = 1 ENGINE_TYPE_NONE = 0 entype_settings.EngineType = EngineType_.ENGINE_TYPE_STEP | EngineType_.ENGINE_TYPE_NONE class DriverType_: DRIVER_TYPE_EXTERNAL = 3 DRIVER_TYPE_INTEGRATE = 2 DRIVER_TYPE_DISCRETE_FET = 1 entype_settings.DriverType = DriverType_.DRIVER_TYPE_INTEGRATE result = lib.set_entype_settings(id, byref(entype_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result power_settings = power_settings_t() power_settings.HoldCurrent = 50 power_settings.CurrReductDelay = 1000 power_settings.PowerOffDelay = 60 power_settings.CurrentSetTime = 300 class PowerFlags_: POWER_SMOOTH_CURRENT = 4 POWER_OFF_ENABLED = 2 POWER_REDUCT_ENABLED = 1 power_settings.PowerFlags = PowerFlags_.POWER_SMOOTH_CURRENT | PowerFlags_.POWER_REDUCT_ENABLED result = lib.set_power_settings(id, byref(power_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result secure_settings = secure_settings_t() secure_settings.LowUpwrOff = 800 secure_settings.CriticalIpwr = 4000 secure_settings.CriticalUpwr = 5500 secure_settings.CriticalT = 800 secure_settings.CriticalIusb = 450 secure_settings.CriticalUusb = 520 secure_settings.MinimumUusb = 420 class Flags_: ALARM_ENGINE_RESPONSE = 128 ALARM_WINDING_MISMATCH = 64 USB_BREAK_RECONNECT = 32 ALARM_FLAGS_STICKING = 16 ALARM_ON_BORDERS_SWAP_MISSET = 8 H_BRIDGE_ALERT = 4 LOW_UPWR_PROTECTION = 2 ALARM_ON_DRIVER_OVERHEATING = 1 secure_settings.Flags = Flags_.ALARM_ENGINE_RESPONSE | Flags_.ALARM_FLAGS_STICKING | Flags_.ALARM_ON_BORDERS_SWAP_MISSET | Flags_.H_BRIDGE_ALERT | Flags_.ALARM_ON_DRIVER_OVERHEATING result = lib.set_secure_settings(id, byref(secure_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result edges_settings = edges_settings_t() class BorderFlags_: BORDERS_SWAP_MISSET_DETECTION = 8 BORDER_STOP_RIGHT = 4 BORDER_STOP_LEFT = 2 BORDER_IS_ENCODER = 1 edges_settings.BorderFlags = BorderFlags_.BORDER_STOP_RIGHT | BorderFlags_.BORDER_STOP_LEFT class EnderFlags_: ENDER_SW2_ACTIVE_LOW = 4 ENDER_SW1_ACTIVE_LOW = 2 ENDER_SWAP = 1 edges_settings.EnderFlags = EnderFlags_.ENDER_SW2_ACTIVE_LOW | EnderFlags_.ENDER_SW1_ACTIVE_LOW | EnderFlags_.ENDER_SWAP edges_settings.LeftBorder = -971 edges_settings.uLeftBorder = 0 edges_settings.RightBorder = 910 edges_settings.uRightBorder = 0 result = lib.set_edges_settings(id, byref(edges_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result pid_settings = pid_settings_t() pid_settings.KpU = 0 pid_settings.KiU = 0 pid_settings.KdU = 0 pid_settings.Kpf = 0.003599999938160181 pid_settings.Kif = 0.03799999877810478 pid_settings.Kdf = 2.8000000384054147e-05 result = lib.set_pid_settings(id, byref(pid_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_in_settings = sync_in_settings_t() class SyncInFlags_: SYNCIN_GOTOPOSITION = 4 SYNCIN_INVERT = 2 SYNCIN_ENABLED = 1 sync_in_settings.ClutterTime = 4 sync_in_settings.Position = 0 sync_in_settings.uPosition = 0 sync_in_settings.Speed = 0 sync_in_settings.uSpeed = 0 result = lib.set_sync_in_settings(id, byref(sync_in_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_out_settings = sync_out_settings_t() class SyncOutFlags_: SYNCOUT_ONPERIOD = 64 SYNCOUT_ONSTOP = 32 SYNCOUT_ONSTART = 16 SYNCOUT_IN_STEPS = 8 SYNCOUT_INVERT = 4 SYNCOUT_STATE = 2 SYNCOUT_ENABLED = 1 sync_out_settings.SyncOutFlags = SyncOutFlags_.SYNCOUT_ONSTOP | SyncOutFlags_.SYNCOUT_ONSTART sync_out_settings.SyncOutPulseSteps = 100 sync_out_settings.SyncOutPeriod = 2000 sync_out_settings.Accuracy = 0 sync_out_settings.uAccuracy = 0 result = lib.set_sync_out_settings(id, byref(sync_out_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extio_settings = extio_settings_t() class EXTIOSetupFlags_: EXTIO_SETUP_INVERT = 2 EXTIO_SETUP_OUTPUT = 1 extio_settings.EXTIOSetupFlags = EXTIOSetupFlags_.EXTIO_SETUP_OUTPUT class EXTIOModeFlags_: EXTIO_SETUP_MODE_OUT_BITS = 240 EXTIO_SETUP_MODE_OUT_MOTOR_ON = 64 EXTIO_SETUP_MODE_OUT_ALARM = 48 EXTIO_SETUP_MODE_OUT_MOVING = 32 EXTIO_SETUP_MODE_OUT_ON = 16 EXTIO_SETUP_MODE_IN_BITS = 15 EXTIO_SETUP_MODE_IN_ALARM = 5 EXTIO_SETUP_MODE_IN_HOME = 4 EXTIO_SETUP_MODE_IN_MOVR = 3 EXTIO_SETUP_MODE_IN_PWOF = 2 EXTIO_SETUP_MODE_IN_STOP = 1 EXTIO_SETUP_MODE_IN_NOP = 0 EXTIO_SETUP_MODE_OUT_OFF = 0 extio_settings.EXTIOModeFlags = EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_STOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_NOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_OUT_OFF result = lib.set_extio_settings(id, byref(extio_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result brake_settings = brake_settings_t() brake_settings.t1 = 300 brake_settings.t2 = 500 brake_settings.t3 = 300 brake_settings.t4 = 400 class BrakeFlags_: BRAKE_ENG_PWROFF = 2 BRAKE_ENABLED = 1 brake_settings.BrakeFlags = BrakeFlags_.BRAKE_ENG_PWROFF result = lib.set_brake_settings(id, byref(brake_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result control_settings = control_settings_t() control_settings.MaxSpeed[0] = 80 control_settings.MaxSpeed[1] = 800 control_settings.MaxSpeed[2] = 0 control_settings.MaxSpeed[3] = 0 control_settings.MaxSpeed[4] = 0 control_settings.MaxSpeed[5] = 0 control_settings.MaxSpeed[6] = 0 control_settings.MaxSpeed[7] = 0 control_settings.MaxSpeed[8] = 0 control_settings.MaxSpeed[9] = 0 control_settings.uMaxSpeed[0] = 0 control_settings.uMaxSpeed[1] = 0 control_settings.uMaxSpeed[2] = 0 control_settings.uMaxSpeed[3] = 0 control_settings.uMaxSpeed[4] = 0 control_settings.uMaxSpeed[5] = 0 control_settings.uMaxSpeed[6] = 0 control_settings.uMaxSpeed[7] = 0 control_settings.uMaxSpeed[8] = 0 control_settings.uMaxSpeed[9] = 0 control_settings.Timeout[0] = 1000 control_settings.Timeout[1] = 1000 control_settings.Timeout[2] = 1000 control_settings.Timeout[3] = 1000 control_settings.Timeout[4] = 1000 control_settings.Timeout[5] = 1000 control_settings.Timeout[6] = 1000 control_settings.Timeout[7] = 1000 control_settings.Timeout[8] = 1000 control_settings.MaxClickTime = 300 class Flags_: CONTROL_BTN_RIGHT_PUSHED_OPEN = 8 CONTROL_BTN_LEFT_PUSHED_OPEN = 4 CONTROL_MODE_BITS = 3 CONTROL_MODE_LR = 2 CONTROL_MODE_JOY = 1 CONTROL_MODE_OFF = 0 control_settings.Flags = Flags_.CONTROL_MODE_LR | Flags_.CONTROL_MODE_OFF control_settings.DeltaPosition = 1 control_settings.uDeltaPosition = 0 result = lib.set_control_settings(id, byref(control_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result joystick_settings = joystick_settings_t() joystick_settings.JoyLowEnd = 0 joystick_settings.JoyCenter = 5000 joystick_settings.JoyHighEnd = 10000 joystick_settings.ExpFactor = 100 joystick_settings.DeadZone = 50 class JoyFlags_: JOY_REVERSE = 1 result = lib.set_joystick_settings(id, byref(joystick_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result ctp_settings = ctp_settings_t() ctp_settings.CTPMinError = 3 class CTPFlags_: CTP_ERROR_CORRECTION = 16 REV_SENS_INV = 8 CTP_ALARM_ON_ERROR = 4 CTP_BASE = 2 CTP_ENABLED = 1 ctp_settings.CTPFlags = CTPFlags_.CTP_ERROR_CORRECTION result = lib.set_ctp_settings(id, byref(ctp_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result uart_settings = uart_settings_t() uart_settings.Speed = 115200 class UARTSetupFlags_: UART_STOP_BIT = 8 UART_PARITY_BIT_USE = 4 UART_PARITY_BITS = 3 UART_PARITY_BIT_MARK = 3 UART_PARITY_BIT_SPACE = 2 UART_PARITY_BIT_ODD = 1 UART_PARITY_BIT_EVEN = 0 uart_settings.UARTSetupFlags = UARTSetupFlags_.UART_PARITY_BIT_EVEN result = lib.set_uart_settings(id, byref(uart_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result controller_name = controller_name_t() controller_name.ControllerName = bytes([0, 113, 252, 118, 36, 0, 72, 0, 3, 0, 0, 0, 104, 101, 103, 0]) class CtrlFlags_: EEPROM_PRECEDENCE = 1 result = lib.set_controller_name(id, byref(controller_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result emf_settings = emf_settings_t() emf_settings.L = 0 emf_settings.R = 0 emf_settings.Km = 0 class BackEMFFlags_: BACK_EMF_KM_AUTO = 4 BACK_EMF_RESISTANCE_AUTO = 2 BACK_EMF_INDUCTANCE_AUTO = 1 result = lib.set_emf_settings(id, byref(emf_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_advansed_setup = engine_advansed_setup_t() engine_advansed_setup.stepcloseloop_Kw = 50 engine_advansed_setup.stepcloseloop_Kp_low = 1000 engine_advansed_setup.stepcloseloop_Kp_high = 33 result = lib.set_engine_advansed_setup(id, byref(engine_advansed_setup)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extended_settings = extended_settings_t() extended_settings.Param1 = 0 result = lib.set_extended_settings(id, byref(extended_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_name = stage_name_t() stage_name.PositionerName = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_name(id, byref(stage_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_information = stage_information_t() stage_information.Manufacturer = bytes([83, 116, 97, 110, 100, 97, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) stage_information.PartNumber = bytes([56, 77, 73, 68, 49, 50, 45, 49, 45, 65, 82, 0, 95, 49, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_information(id, byref(stage_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_settings = stage_settings_t() stage_settings.LeadScrewPitch = 0.25 stage_settings.Units = bytes([109, 109, 0, 114, 101, 101, 0, 0]) stage_settings.MaxSpeed = 0 stage_settings.TravelRange = 11 stage_settings.SupplyVoltageMin = 5 stage_settings.SupplyVoltageMax = 12 stage_settings.MaxCurrentConsumption = 0 stage_settings.HorizontalLoadCapacity = 0 stage_settings.VerticalLoadCapacity = 0 result = lib.set_stage_settings(id, byref(stage_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_information = motor_information_t() motor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) motor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_motor_information(id, byref(motor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_settings = motor_settings_t() class MotorType_: MOTOR_TYPE_BLDC = 3 MOTOR_TYPE_DC = 2 MOTOR_TYPE_STEP = 1 MOTOR_TYPE_UNKNOWN = 0 motor_settings.MotorType = MotorType_.MOTOR_TYPE_STEP | MotorType_.MOTOR_TYPE_UNKNOWN motor_settings.ReservedField = 0 motor_settings.Poles = 0 motor_settings.Phases = 0 motor_settings.NominalVoltage = 0 motor_settings.NominalCurrent = 0 motor_settings.NominalSpeed = 0 motor_settings.NominalTorque = 0 motor_settings.NominalPower = 0 motor_settings.WindingResistance = 0 motor_settings.WindingInductance = 0 motor_settings.RotorInertia = 0 motor_settings.StallTorque = 0 motor_settings.DetentTorque = 0 motor_settings.TorqueConstant = 0 motor_settings.SpeedConstant = 0 motor_settings.SpeedTorqueGradient = 0 motor_settings.MechanicalTimeConstant = 0 motor_settings.MaxSpeed = 1600 motor_settings.MaxCurrent = 0 motor_settings.MaxCurrentTime = 0 motor_settings.NoLoadCurrent = 0 motor_settings.NoLoadSpeed = 0 result = lib.set_motor_settings(id, byref(motor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_information = encoder_information_t() encoder_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) encoder_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_encoder_information(id, byref(encoder_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_settings = encoder_settings_t() encoder_settings.MaxOperatingFrequency = 0 encoder_settings.SupplyVoltageMin = 0 encoder_settings.SupplyVoltageMax = 0 encoder_settings.MaxCurrentConsumption = 0 encoder_settings.PPR = 1000 class EncoderSettings_: ENCSET_REVOLUTIONSENSOR_ACTIVE_HIGH = 256 ENCSET_REVOLUTIONSENSOR_PRESENT = 64 ENCSET_INDEXCHANNEL_PRESENT = 16 ENCSET_PUSHPULL_OUTPUT = 4 ENCSET_DIFFERENTIAL_OUTPUT = 1 result = lib.set_encoder_settings(id, byref(encoder_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_information = hallsensor_information_t() hallsensor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) hallsensor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_hallsensor_information(id, byref(hallsensor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_settings = hallsensor_settings_t() hallsensor_settings.MaxOperatingFrequency = 0 hallsensor_settings.SupplyVoltageMin = 0 hallsensor_settings.SupplyVoltageMax = 0 hallsensor_settings.MaxCurrentConsumption = 0 hallsensor_settings.PPR = 0 result = lib.set_hallsensor_settings(id, byref(hallsensor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_information = gear_information_t() gear_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) gear_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_gear_information(id, byref(gear_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_settings = gear_settings_t() gear_settings.ReductionIn = 0 gear_settings.ReductionOut = 0 gear_settings.RatedInputTorque = 0 gear_settings.RatedInputSpeed = 0 gear_settings.MaxOutputBacklash = 0 gear_settings.InputInertia = 0 gear_settings.Efficiency = 0 result = lib.set_gear_settings(id, byref(gear_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result accessories_settings = accessories_settings_t() accessories_settings.MagneticBrakeInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.MBRatedVoltage = 0 accessories_settings.MBRatedCurrent = 0 accessories_settings.MBTorque = 0 class MBSettings_: MB_POWERED_HOLD = 2 MB_AVAILABLE = 1 accessories_settings.TemperatureSensorInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.TSMin = 0 accessories_settings.TSMax = 0 accessories_settings.TSGrad = 0 class TSSettings_: TS_AVAILABLE = 8 TS_TYPE_BITS = 7 TS_TYPE_SEMICONDUCTOR = 2 TS_TYPE_THERMOCOUPLE = 1 TS_TYPE_UNKNOWN = 0 accessories_settings.TSSettings = TSSettings_.TS_TYPE_UNKNOWN class LimitSwitchesSettings_: LS_SHORTED = 16 LS_SW2_ACTIVE_LOW = 8 LS_SW1_ACTIVE_LOW = 4 LS_ON_SW2_AVAILABLE = 2 LS_ON_SW1_AVAILABLE = 1 result = lib.set_accessories_settings(id, byref(accessories_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result return worst_result

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def set_profile_8MID12_1_AR(lib, id): worst_result = Result.Ok result = Result.Ok feedback_settings = feedback_settings_t() feedback_settings.IPS = 4000 class FeedbackType_: FEEDBACK_ENCODER_MEDIATED = 6 FEEDBACK_NONE = 5 FEEDBACK_EMF = 4 FEEDBACK_ENCODER = 1 feedback_settings.FeedbackType = FeedbackType_.FEEDBACK_NONE class FeedbackFlags_: FEEDBACK_ENC_TYPE_BITS = 192 FEEDBACK_ENC_TYPE_DIFFERENTIAL = 128 FEEDBACK_ENC_TYPE_SINGLE_ENDED = 64 FEEDBACK_ENC_REVERSE = 1 FEEDBACK_ENC_TYPE_AUTO = 0 feedback_settings.FeedbackFlags = FeedbackFlags_.FEEDBACK_ENC_TYPE_SINGLE_ENDED | FeedbackFlags_.FEEDBACK_ENC_TYPE_AUTO feedback_settings.CountsPerTurn = 4000 result = lib.set_feedback_settings(id, byref(feedback_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result home_settings = home_settings_t() home_settings.FastHome = 50 home_settings.uFastHome = 0 home_settings.SlowHome = 500 home_settings.uSlowHome = 0 home_settings.HomeDelta = 1020 home_settings.uHomeDelta = 0 class HomeFlags_: HOME_USE_FAST = 256 HOME_STOP_SECOND_BITS = 192 HOME_STOP_SECOND_LIM = 192 HOME_STOP_SECOND_SYN = 128 HOME_STOP_SECOND_REV = 64 HOME_STOP_FIRST_BITS = 48 HOME_STOP_FIRST_LIM = 48 HOME_STOP_FIRST_SYN = 32 HOME_STOP_FIRST_REV = 16 HOME_HALF_MV = 8 HOME_MV_SEC_EN = 4 HOME_DIR_SECOND = 2 HOME_DIR_FIRST = 1 home_settings.HomeFlags = HomeFlags_.HOME_USE_FAST | HomeFlags_.HOME_STOP_SECOND_REV | HomeFlags_.HOME_STOP_FIRST_BITS | HomeFlags_.HOME_DIR_SECOND result = lib.set_home_settings(id, byref(home_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result move_settings = move_settings_t() move_settings.Speed = 800 move_settings.uSpeed = 0 move_settings.Accel = 3200 move_settings.Decel = 4800 move_settings.AntiplaySpeed = 800 move_settings.uAntiplaySpeed = 0 class MoveFlags_: RPM_DIV_1000 = 1 result = lib.set_move_settings(id, byref(move_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_settings = engine_settings_t() engine_settings.NomVoltage = 50 engine_settings.NomCurrent = 200 engine_settings.NomSpeed = 1600 engine_settings.uNomSpeed = 0 class EngineFlags_: ENGINE_LIMIT_RPM = 128 ENGINE_LIMIT_CURR = 64 ENGINE_LIMIT_VOLT = 32 ENGINE_ACCEL_ON = 16 ENGINE_ANTIPLAY = 8 ENGINE_MAX_SPEED = 4 ENGINE_CURRENT_AS_RMS = 2 ENGINE_REVERSE = 1 engine_settings.EngineFlags = EngineFlags_.ENGINE_LIMIT_RPM | EngineFlags_.ENGINE_ACCEL_ON | EngineFlags_.ENGINE_REVERSE engine_settings.Antiplay = 326 class MicrostepMode_: MICROSTEP_MODE_FRAC_256 = 9 MICROSTEP_MODE_FRAC_128 = 8 MICROSTEP_MODE_FRAC_64 = 7 MICROSTEP_MODE_FRAC_32 = 6 MICROSTEP_MODE_FRAC_16 = 5 MICROSTEP_MODE_FRAC_8 = 4 MICROSTEP_MODE_FRAC_4 = 3 MICROSTEP_MODE_FRAC_2 = 2 MICROSTEP_MODE_FULL = 1 engine_settings.MicrostepMode = MicrostepMode_.MICROSTEP_MODE_FRAC_256 engine_settings.StepsPerRev = 20 result = lib.set_engine_settings(id, byref(engine_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result entype_settings = entype_settings_t() class EngineType_: ENGINE_TYPE_BRUSHLESS = 5 ENGINE_TYPE_TEST = 4 ENGINE_TYPE_STEP = 3 ENGINE_TYPE_2DC = 2 ENGINE_TYPE_DC = 1 ENGINE_TYPE_NONE = 0 entype_settings.EngineType = EngineType_.ENGINE_TYPE_STEP | EngineType_.ENGINE_TYPE_NONE class DriverType_: DRIVER_TYPE_EXTERNAL = 3 DRIVER_TYPE_INTEGRATE = 2 DRIVER_TYPE_DISCRETE_FET = 1 entype_settings.DriverType = DriverType_.DRIVER_TYPE_INTEGRATE result = lib.set_entype_settings(id, byref(entype_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result power_settings = power_settings_t() power_settings.HoldCurrent = 50 power_settings.CurrReductDelay = 1000 power_settings.PowerOffDelay = 60 power_settings.CurrentSetTime = 300 class PowerFlags_: POWER_SMOOTH_CURRENT = 4 POWER_OFF_ENABLED = 2 POWER_REDUCT_ENABLED = 1 power_settings.PowerFlags = PowerFlags_.POWER_SMOOTH_CURRENT | PowerFlags_.POWER_REDUCT_ENABLED result = lib.set_power_settings(id, byref(power_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result secure_settings = secure_settings_t() secure_settings.LowUpwrOff = 800 secure_settings.CriticalIpwr = 4000 secure_settings.CriticalUpwr = 5500 secure_settings.CriticalT = 800 secure_settings.CriticalIusb = 450 secure_settings.CriticalUusb = 520 secure_settings.MinimumUusb = 420 class Flags_: ALARM_ENGINE_RESPONSE = 128 ALARM_WINDING_MISMATCH = 64 USB_BREAK_RECONNECT = 32 ALARM_FLAGS_STICKING = 16 ALARM_ON_BORDERS_SWAP_MISSET = 8 H_BRIDGE_ALERT = 4 LOW_UPWR_PROTECTION = 2 ALARM_ON_DRIVER_OVERHEATING = 1 secure_settings.Flags = Flags_.ALARM_ENGINE_RESPONSE | Flags_.ALARM_FLAGS_STICKING | Flags_.ALARM_ON_BORDERS_SWAP_MISSET | Flags_.H_BRIDGE_ALERT | Flags_.ALARM_ON_DRIVER_OVERHEATING result = lib.set_secure_settings(id, byref(secure_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result edges_settings = edges_settings_t() class BorderFlags_: BORDERS_SWAP_MISSET_DETECTION = 8 BORDER_STOP_RIGHT = 4 BORDER_STOP_LEFT = 2 BORDER_IS_ENCODER = 1 edges_settings.BorderFlags = BorderFlags_.BORDER_STOP_RIGHT | BorderFlags_.BORDER_STOP_LEFT class EnderFlags_: ENDER_SW2_ACTIVE_LOW = 4 ENDER_SW1_ACTIVE_LOW = 2 ENDER_SWAP = 1 edges_settings.EnderFlags = EnderFlags_.ENDER_SW2_ACTIVE_LOW | EnderFlags_.ENDER_SW1_ACTIVE_LOW | EnderFlags_.ENDER_SWAP edges_settings.LeftBorder = -971 edges_settings.uLeftBorder = 0 edges_settings.RightBorder = 910 edges_settings.uRightBorder = 0 result = lib.set_edges_settings(id, byref(edges_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result pid_settings = pid_settings_t() pid_settings.KpU = 0 pid_settings.KiU = 0 pid_settings.KdU = 0 pid_settings.Kpf = 0.003599999938160181 pid_settings.Kif = 0.03799999877810478 pid_settings.Kdf = 2.8000000384054147e-05 result = lib.set_pid_settings(id, byref(pid_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_in_settings = sync_in_settings_t() class SyncInFlags_: SYNCIN_GOTOPOSITION = 4 SYNCIN_INVERT = 2 SYNCIN_ENABLED = 1 sync_in_settings.ClutterTime = 4 sync_in_settings.Position = 0 sync_in_settings.uPosition = 0 sync_in_settings.Speed = 0 sync_in_settings.uSpeed = 0 result = lib.set_sync_in_settings(id, byref(sync_in_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_out_settings = sync_out_settings_t() class SyncOutFlags_: SYNCOUT_ONPERIOD = 64 SYNCOUT_ONSTOP = 32 SYNCOUT_ONSTART = 16 SYNCOUT_IN_STEPS = 8 SYNCOUT_INVERT = 4 SYNCOUT_STATE = 2 SYNCOUT_ENABLED = 1 sync_out_settings.SyncOutFlags = SyncOutFlags_.SYNCOUT_ONSTOP | SyncOutFlags_.SYNCOUT_ONSTART sync_out_settings.SyncOutPulseSteps = 100 sync_out_settings.SyncOutPeriod = 2000 sync_out_settings.Accuracy = 0 sync_out_settings.uAccuracy = 0 result = lib.set_sync_out_settings(id, byref(sync_out_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extio_settings = extio_settings_t() class EXTIOSetupFlags_: EXTIO_SETUP_INVERT = 2 EXTIO_SETUP_OUTPUT = 1 extio_settings.EXTIOSetupFlags = EXTIOSetupFlags_.EXTIO_SETUP_OUTPUT class EXTIOModeFlags_: EXTIO_SETUP_MODE_OUT_BITS = 240 EXTIO_SETUP_MODE_OUT_MOTOR_ON = 64 EXTIO_SETUP_MODE_OUT_ALARM = 48 EXTIO_SETUP_MODE_OUT_MOVING = 32 EXTIO_SETUP_MODE_OUT_ON = 16 EXTIO_SETUP_MODE_IN_BITS = 15 EXTIO_SETUP_MODE_IN_ALARM = 5 EXTIO_SETUP_MODE_IN_HOME = 4 EXTIO_SETUP_MODE_IN_MOVR = 3 EXTIO_SETUP_MODE_IN_PWOF = 2 EXTIO_SETUP_MODE_IN_STOP = 1 EXTIO_SETUP_MODE_IN_NOP = 0 EXTIO_SETUP_MODE_OUT_OFF = 0 extio_settings.EXTIOModeFlags = EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_STOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_NOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_OUT_OFF result = lib.set_extio_settings(id, byref(extio_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result brake_settings = brake_settings_t() brake_settings.t1 = 300 brake_settings.t2 = 500 brake_settings.t3 = 300 brake_settings.t4 = 400 class BrakeFlags_: BRAKE_ENG_PWROFF = 2 BRAKE_ENABLED = 1 brake_settings.BrakeFlags = BrakeFlags_.BRAKE_ENG_PWROFF result = lib.set_brake_settings(id, byref(brake_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result control_settings = control_settings_t() control_settings.MaxSpeed[0] = 80 control_settings.MaxSpeed[1] = 800 control_settings.MaxSpeed[2] = 0 control_settings.MaxSpeed[3] = 0 control_settings.MaxSpeed[4] = 0 control_settings.MaxSpeed[5] = 0 control_settings.MaxSpeed[6] = 0 control_settings.MaxSpeed[7] = 0 control_settings.MaxSpeed[8] = 0 control_settings.MaxSpeed[9] = 0 control_settings.uMaxSpeed[0] = 0 control_settings.uMaxSpeed[1] = 0 control_settings.uMaxSpeed[2] = 0 control_settings.uMaxSpeed[3] = 0 control_settings.uMaxSpeed[4] = 0 control_settings.uMaxSpeed[5] = 0 control_settings.uMaxSpeed[6] = 0 control_settings.uMaxSpeed[7] = 0 control_settings.uMaxSpeed[8] = 0 control_settings.uMaxSpeed[9] = 0 control_settings.Timeout[0] = 1000 control_settings.Timeout[1] = 1000 control_settings.Timeout[2] = 1000 control_settings.Timeout[3] = 1000 control_settings.Timeout[4] = 1000 control_settings.Timeout[5] = 1000 control_settings.Timeout[6] = 1000 control_settings.Timeout[7] = 1000 control_settings.Timeout[8] = 1000 control_settings.MaxClickTime = 300 class Flags_: CONTROL_BTN_RIGHT_PUSHED_OPEN = 8 CONTROL_BTN_LEFT_PUSHED_OPEN = 4 CONTROL_MODE_BITS = 3 CONTROL_MODE_LR = 2 CONTROL_MODE_JOY = 1 CONTROL_MODE_OFF = 0 control_settings.Flags = Flags_.CONTROL_MODE_LR | Flags_.CONTROL_MODE_OFF control_settings.DeltaPosition = 1 control_settings.uDeltaPosition = 0 result = lib.set_control_settings(id, byref(control_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result joystick_settings = joystick_settings_t() joystick_settings.JoyLowEnd = 0 joystick_settings.JoyCenter = 5000 joystick_settings.JoyHighEnd = 10000 joystick_settings.ExpFactor = 100 joystick_settings.DeadZone = 50 class JoyFlags_: JOY_REVERSE = 1 result = lib.set_joystick_settings(id, byref(joystick_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result ctp_settings = ctp_settings_t() ctp_settings.CTPMinError = 3 class CTPFlags_: CTP_ERROR_CORRECTION = 16 REV_SENS_INV = 8 CTP_ALARM_ON_ERROR = 4 CTP_BASE = 2 CTP_ENABLED = 1 ctp_settings.CTPFlags = CTPFlags_.CTP_ERROR_CORRECTION result = lib.set_ctp_settings(id, byref(ctp_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result uart_settings = uart_settings_t() uart_settings.Speed = 115200 class UARTSetupFlags_: UART_STOP_BIT = 8 UART_PARITY_BIT_USE = 4 UART_PARITY_BITS = 3 UART_PARITY_BIT_MARK = 3 UART_PARITY_BIT_SPACE = 2 UART_PARITY_BIT_ODD = 1 UART_PARITY_BIT_EVEN = 0 uart_settings.UARTSetupFlags = UARTSetupFlags_.UART_PARITY_BIT_EVEN result = lib.set_uart_settings(id, byref(uart_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result controller_name = controller_name_t() controller_name.ControllerName = bytes([0, 113, 252, 118, 36, 0, 72, 0, 3, 0, 0, 0, 104, 101, 103, 0]) class CtrlFlags_: EEPROM_PRECEDENCE = 1 result = lib.set_controller_name(id, byref(controller_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result emf_settings = emf_settings_t() emf_settings.L = 0 emf_settings.R = 0 emf_settings.Km = 0 class BackEMFFlags_: BACK_EMF_KM_AUTO = 4 BACK_EMF_RESISTANCE_AUTO = 2 BACK_EMF_INDUCTANCE_AUTO = 1 result = lib.set_emf_settings(id, byref(emf_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_advansed_setup = engine_advansed_setup_t() engine_advansed_setup.stepcloseloop_Kw = 50 engine_advansed_setup.stepcloseloop_Kp_low = 1000 engine_advansed_setup.stepcloseloop_Kp_high = 33 result = lib.set_engine_advansed_setup(id, byref(engine_advansed_setup)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extended_settings = extended_settings_t() extended_settings.Param1 = 0 result = lib.set_extended_settings(id, byref(extended_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_name = stage_name_t() stage_name.PositionerName = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_name(id, byref(stage_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_information = stage_information_t() stage_information.Manufacturer = bytes([83, 116, 97, 110, 100, 97, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) stage_information.PartNumber = bytes([56, 77, 73, 68, 49, 50, 45, 49, 45, 65, 82, 0, 95, 49, 53, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_information(id, byref(stage_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_settings = stage_settings_t() stage_settings.LeadScrewPitch = 0.25 stage_settings.Units = bytes([109, 109, 0, 114, 101, 101, 0, 0]) stage_settings.MaxSpeed = 0 stage_settings.TravelRange = 11 stage_settings.SupplyVoltageMin = 5 stage_settings.SupplyVoltageMax = 12 stage_settings.MaxCurrentConsumption = 0 stage_settings.HorizontalLoadCapacity = 0 stage_settings.VerticalLoadCapacity = 0 result = lib.set_stage_settings(id, byref(stage_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_information = motor_information_t() motor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) motor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_motor_information(id, byref(motor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_settings = motor_settings_t() class MotorType_: MOTOR_TYPE_BLDC = 3 MOTOR_TYPE_DC = 2 MOTOR_TYPE_STEP = 1 MOTOR_TYPE_UNKNOWN = 0 motor_settings.MotorType = MotorType_.MOTOR_TYPE_STEP | MotorType_.MOTOR_TYPE_UNKNOWN motor_settings.ReservedField = 0 motor_settings.Poles = 0 motor_settings.Phases = 0 motor_settings.NominalVoltage = 0 motor_settings.NominalCurrent = 0 motor_settings.NominalSpeed = 0 motor_settings.NominalTorque = 0 motor_settings.NominalPower = 0 motor_settings.WindingResistance = 0 motor_settings.WindingInductance = 0 motor_settings.RotorInertia = 0 motor_settings.StallTorque = 0 motor_settings.DetentTorque = 0 motor_settings.TorqueConstant = 0 motor_settings.SpeedConstant = 0 motor_settings.SpeedTorqueGradient = 0 motor_settings.MechanicalTimeConstant = 0 motor_settings.MaxSpeed = 1600 motor_settings.MaxCurrent = 0 motor_settings.MaxCurrentTime = 0 motor_settings.NoLoadCurrent = 0 motor_settings.NoLoadSpeed = 0 result = lib.set_motor_settings(id, byref(motor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_information = encoder_information_t() encoder_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) encoder_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_encoder_information(id, byref(encoder_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_settings = encoder_settings_t() encoder_settings.MaxOperatingFrequency = 0 encoder_settings.SupplyVoltageMin = 0 encoder_settings.SupplyVoltageMax = 0 encoder_settings.MaxCurrentConsumption = 0 encoder_settings.PPR = 1000 class EncoderSettings_: ENCSET_REVOLUTIONSENSOR_ACTIVE_HIGH = 256 ENCSET_REVOLUTIONSENSOR_PRESENT = 64 ENCSET_INDEXCHANNEL_PRESENT = 16 ENCSET_PUSHPULL_OUTPUT = 4 ENCSET_DIFFERENTIAL_OUTPUT = 1 result = lib.set_encoder_settings(id, byref(encoder_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_information = hallsensor_information_t() hallsensor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) hallsensor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_hallsensor_information(id, byref(hallsensor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_settings = hallsensor_settings_t() hallsensor_settings.MaxOperatingFrequency = 0 hallsensor_settings.SupplyVoltageMin = 0 hallsensor_settings.SupplyVoltageMax = 0 hallsensor_settings.MaxCurrentConsumption = 0 hallsensor_settings.PPR = 0 result = lib.set_hallsensor_settings(id, byref(hallsensor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_information = gear_information_t() gear_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) gear_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_gear_information(id, byref(gear_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_settings = gear_settings_t() gear_settings.ReductionIn = 0 gear_settings.ReductionOut = 0 gear_settings.RatedInputTorque = 0 gear_settings.RatedInputSpeed = 0 gear_settings.MaxOutputBacklash = 0 gear_settings.InputInertia = 0 gear_settings.Efficiency = 0 result = lib.set_gear_settings(id, byref(gear_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result accessories_settings = accessories_settings_t() accessories_settings.MagneticBrakeInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.MBRatedVoltage = 0 accessories_settings.MBRatedCurrent = 0 accessories_settings.MBTorque = 0 class MBSettings_: MB_POWERED_HOLD = 2 MB_AVAILABLE = 1 accessories_settings.TemperatureSensorInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.TSMin = 0 accessories_settings.TSMax = 0 accessories_settings.TSGrad = 0 class TSSettings_: TS_AVAILABLE = 8 TS_TYPE_BITS = 7 TS_TYPE_SEMICONDUCTOR = 2 TS_TYPE_THERMOCOUPLE = 1 TS_TYPE_UNKNOWN = 0 accessories_settings.TSSettings = TSSettings_.TS_TYPE_UNKNOWN class LimitSwitchesSettings_: LS_SHORTED = 16 LS_SW2_ACTIVE_LOW = 8 LS_SW1_ACTIVE_LOW = 4 LS_ON_SW2_AVAILABLE = 2 LS_ON_SW1_AVAILABLE = 1 result = lib.set_accessories_settings(id, byref(accessories_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result return worst_result

true

1c31164741e00323e319a697268a69b6f3d9f9bb

2,029

py

Python

tests/test_all_notebooks.py

scottprahl/iadpython

df04f6446c73b5c5c1aabed072e986877f81104b

[ "MIT" ]

4

2017-09-13T14:01:32.000Z

2021-11-09T04:48:17.000Z

tests/test_all_notebooks.py

scottprahl/iadpython

df04f6446c73b5c5c1aabed072e986877f81104b

[ "MIT" ]

null

tests/test_all_notebooks.py

scottprahl/iadpython

df04f6446c73b5c5c1aabed072e986877f81104b

[ "MIT" ]

1

2020-06-16T21:09:44.000Z

""" This file is intended to be the target of a pytest run. It will recursively find all .ipynb files in the current directory, ignoring directories that start with . and any files matching patterins found in the file .testignore List patterns to skip in .testignore file: under_construction/* Sample invocations of pytest which make the output nicely readable: pytest --verbose --durations=5 test_all_notebooks.py If you install pytest-xdist you can run tests in parallel with pytest --verbose --durations=5 -n 4 test_all_notebooks.py Original version is licensed under GPL 3.0 so this one is too. The original can be located at https://github.com/alchemyst/Dynamics-and-Control/test_all_notebooks.py """ import os.path import pathlib import pytest import nbformat from nbconvert.preprocessors import ExecutePreprocessor # Default search path is the current directory searchpath = pathlib.Path('./docs/') # all notebooks are in here # Read patterns from .testignore file ignores = '' if os.path.exists('.testignore'): ignores = [line.strip() for line in open('.testignore') if line.strip()] # Ignore hidden folders (startswith('.')) and files matching ignore patterns notebooks = [notebook for notebook in searchpath.glob('**/*.ipynb') if not (any(parent.startswith('.') for parent in notebook.parent.parts) or any(notebook.match(pattern) for pattern in ignores))] notebooks.sort() ids = [str(n) for n in notebooks] @pytest.mark.notebooks @pytest.mark.parametrize("notebook", notebooks, ids=ids) def test_run_notebook(notebook): """Read and execute notebook. The method here is directly from the nbconvert docs Note that there is no error handling in this file as any errors will be caught by pytest """ with open(notebook) as f: nb = nbformat.read(f, as_version=4) ep = ExecutePreprocessor(timeout=600) ep.preprocess(nb, {'metadata': {'path': notebook.parent}})

31.703125

80

0.712666

import os.path import pathlib import pytest import nbformat from nbconvert.preprocessors import ExecutePreprocessor searchpath = pathlib.Path('./docs/') ignores = '' if os.path.exists('.testignore'): ignores = [line.strip() for line in open('.testignore') if line.strip()] notebooks = [notebook for notebook in searchpath.glob('**/*.ipynb') if not (any(parent.startswith('.') for parent in notebook.parent.parts) or any(notebook.match(pattern) for pattern in ignores))] notebooks.sort() ids = [str(n) for n in notebooks] @pytest.mark.notebooks @pytest.mark.parametrize("notebook", notebooks, ids=ids) def test_run_notebook(notebook): with open(notebook) as f: nb = nbformat.read(f, as_version=4) ep = ExecutePreprocessor(timeout=600) ep.preprocess(nb, {'metadata': {'path': notebook.parent}})

true

1c3116918f0e8ccfa2d25dce8b10ccbb99e8d1a0

8,361

py

Python

src/AE/ae.py

goeckslab/MarkerIntensityPredictor

704e4ea782c6653cabb4b37a7b34fea4cd9fe595

[ "MIT" ]

3

2021-02-22T19:26:04.000Z

2022-03-02T22:08:25.000Z

src/AE/ae.py

goeckslab/MarkerIntensityPredictor

704e4ea782c6653cabb4b37a7b34fea4cd9fe595

[ "MIT" ]

1

2021-03-12T22:22:25.000Z

src/AE/ae.py

goeckslab/MarkerIntensityPredictor

704e4ea782c6653cabb4b37a7b34fea4cd9fe595

[ "MIT" ]

1

2021-03-12T20:28:50.000Z

import pickle import sys from pathlib import Path from Shared.data import Data from Shared.data_loader import DataLoader import numpy as np import keras from keras import layers, regularizers from sklearn.preprocessing import StandardScaler, MinMaxScaler import anndata as ad import pandas as pd import umap import tensorflow as tf from sklearn.metrics import r2_score import keract as kt class AutoEncoder: data: Data # The defined encoder encoder: any # The defined decoder decoder: any # The ae ae: any # the training history of the AE history: any input_dim: int encoding_dim: int input_umap: any latent_umap: any r2_scores = pd.DataFrame(columns=["Marker", "Score"]) encoded_data = pd.DataFrame() reconstructed_data = pd.DataFrame() args = None results_folder = Path("results", "ae") def __init__(self, args): self.encoding_dim = 5 self.args = args def normalize(self, data): # Input data contains some zeros which results in NaN (or Inf) # values when their log10 is computed. NaN (or Inf) are problematic # values for downstream analysis. Therefore, zeros are replaced by # a small value; see the following thread for related discussion. # https://www.researchgate.net/post/Log_transformation_of_values_that_include_0_zero_for_statistical_analyses2 data[data == 0] = 1e-32 data = np.log10(data) standard_scaler = StandardScaler() data = standard_scaler.fit_transform(data) data = data.clip(min=-5, max=5) min_max_scaler = MinMaxScaler(feature_range=(0, 1)) data = min_max_scaler.fit_transform(data) return data def load_data(self): print("Loading data...") if self.args.file: inputs, markers = DataLoader.get_data( self.args.file) elif self.args.dir: inputs, markers = DataLoader.load_folder_data( self.args.dir) else: print("Please specify a directory or a file") sys.exit() self.data = Data(np.array(inputs), markers, self.normalize) def build_auto_encoder(self): activation = tf.keras.layers.LeakyReLU() activity_regularizer = regularizers.l1_l2(10e-5) input_layer = keras.Input(shape=(self.data.inputs_dim,)) # Encoder encoded = layers.Dense(self.data.inputs_dim / 2, activation=activation, activity_regularizer=activity_regularizer)(input_layer) encoded = layers.Dense(self.data.inputs_dim / 3, activation=activation, activity_regularizer=activity_regularizer)(encoded) # encoded = layers.Dropout(0.2)(encoded) encoded = layers.Dense(self.encoding_dim, activation=activation, activity_regularizer=activity_regularizer)( encoded) # encoded = layers.Dropout(0.3)(encoded) # Decoder decoded = layers.Dense(self.data.inputs_dim / 3, activation=activation)(encoded) decoded = layers.Dense(self.data.inputs_dim / 2, activation=activation)(decoded) decoded = layers.Dense(self.data.inputs_dim, activation=activation)(decoded) # Auto encoder self.ae = keras.Model(input_layer, decoded, name="AE") self.ae.summary() # Separate encoder model self.encoder = keras.Model(input_layer, encoded, name="encoder") self.encoder.summary() # Separate decoder model encoded_input = keras.Input(shape=(self.encoding_dim,)) deco = self.ae.layers[-3](encoded_input) deco = self.ae.layers[-2](deco) deco = self.ae.layers[-1](deco) # create the decoder model self.decoder = keras.Model(encoded_input, deco, name="decoder") self.decoder.summary() # Compile ae self.ae.compile(optimizer="adam", loss=keras.losses.MeanSquaredError(), metrics=['acc', 'mean_squared_error']) callback = tf.keras.callbacks.EarlyStopping(monitor="val_loss", mode="min", patience=5, restore_best_weights=True) self.history = self.ae.fit(self.data.X_train, self.data.X_train, epochs=500, batch_size=32, shuffle=True, callbacks=[callback], validation_data=(self.data.X_val, self.data.X_val)) def predict(self): # Make some predictions cell = self.data.X_test[0] cell = cell.reshape(1, cell.shape[0]) encoded_cell = self.encoder.predict(cell) decoded_cell = self.decoder.predict(encoded_cell) # var_cell = self.ae.predict(cell) print(f"Epochs: {len(self.history.history['loss'])}") print(f"Input shape:\t{cell.shape}") print(f"Encoded shape:\t{encoded_cell.shape}") print(f"Decoded shape:\t{decoded_cell.shape}") print(f"\nInput:\n{cell[0]}") print(f"\nEncoded:\n{encoded_cell[0]}") print(f"\nDecoded:\n{decoded_cell[0]}") def calculate_r2_score(self): recon_test = self.ae.predict(self.data.X_test) recon_test = pd.DataFrame(data=recon_test, columns=self.data.markers) input_data = pd.DataFrame(data=self.data.X_test, columns=self.data.markers) for marker in self.data.markers: input_marker = input_data[f"{marker}"] var_marker = recon_test[f"{marker}"] score = r2_score(input_marker, var_marker) self.r2_scores = self.r2_scores.append( { "Marker": marker, "Score": score }, ignore_index=True ) def create_h5ad_object(self): # Input fit = umap.UMAP() self.input_umap = input_umap = fit.fit_transform(self.data.X_test) # latent space fit = umap.UMAP() encoded = self.encoder.predict(self.data.X_test) self.latent_umap = fit.fit_transform(encoded) self.__create_h5ad("latent_markers", self.latent_umap, self.data.markers, pd.DataFrame(columns=self.data.markers, data=self.data.X_test)) self.__create_h5ad("input", input_umap, self.data.markers, pd.DataFrame(columns=self.data.markers, data=self.data.X_test)) return def __create_h5ad(self, file_name: str, umap, markers, df): obs = pd.DataFrame(data=df, index=df.index) var = pd.DataFrame(index=markers) obsm = {"X_umap": umap} uns = dict() adata = ad.AnnData(df.to_numpy(), var=var, obs=obs, uns=uns, obsm=obsm) adata.write(Path(f'{self.results_folder}/{file_name}.h5ad')) def create_test_predictions(self): self.encoded_data = pd.DataFrame(self.encoder.predict(self.data.X_test)) self.reconstructed_data = pd.DataFrame(columns=self.data.markers, data=self.decoder.predict(self.encoded_data)) def create_correlation_data(self): inputs = pd.DataFrame(columns=self.data.markers, data=self.data.inputs) corr = inputs.corr() corr.to_csv(Path(f'{self.results_folder}/correlation.csv'), index=False) def write_created_data_to_disk(self): with open(f'{self.results_folder}/ae_history', 'wb') as file_pi: pickle.dump(self.history.history, file_pi) X_test = pd.DataFrame(columns=self.data.markers, data=self.data.X_test) X_test.to_csv(Path(f'{self.results_folder}/test_data.csv'), index=False) self.encoded_data.to_csv(Path(f'{self.results_folder}/encoded_data.csv'), index=False) self.reconstructed_data.to_csv(Path(f'{self.results_folder}/reconstructed_data.csv'), index=False) self.r2_scores.to_csv(Path(f'{self.results_folder}/r2_scores.csv'), index=False) def get_activations(self): cell = self.data.X_test[0] cell = cell.reshape(1, cell.shape[0]) # activations = kt.get_activations(self.encoder, self.data.X_val) activations = kt.get_activations(self.ae, cell) fig = kt.display_activations(activations, cmap="summer", directory=f'{self.results_folder}', save=True)

38.888372

119

0.631384

import pickle import sys from pathlib import Path from Shared.data import Data from Shared.data_loader import DataLoader import numpy as np import keras from keras import layers, regularizers from sklearn.preprocessing import StandardScaler, MinMaxScaler import anndata as ad import pandas as pd import umap import tensorflow as tf from sklearn.metrics import r2_score import keract as kt class AutoEncoder: data: Data encoder: any decoder: any ae: any history: any input_dim: int encoding_dim: int input_umap: any latent_umap: any r2_scores = pd.DataFrame(columns=["Marker", "Score"]) encoded_data = pd.DataFrame() reconstructed_data = pd.DataFrame() args = None results_folder = Path("results", "ae") def __init__(self, args): self.encoding_dim = 5 self.args = args def normalize(self, data): data[data == 0] = 1e-32 data = np.log10(data) standard_scaler = StandardScaler() data = standard_scaler.fit_transform(data) data = data.clip(min=-5, max=5) min_max_scaler = MinMaxScaler(feature_range=(0, 1)) data = min_max_scaler.fit_transform(data) return data def load_data(self): print("Loading data...") if self.args.file: inputs, markers = DataLoader.get_data( self.args.file) elif self.args.dir: inputs, markers = DataLoader.load_folder_data( self.args.dir) else: print("Please specify a directory or a file") sys.exit() self.data = Data(np.array(inputs), markers, self.normalize) def build_auto_encoder(self): activation = tf.keras.layers.LeakyReLU() activity_regularizer = regularizers.l1_l2(10e-5) input_layer = keras.Input(shape=(self.data.inputs_dim,)) encoded = layers.Dense(self.data.inputs_dim / 2, activation=activation, activity_regularizer=activity_regularizer)(input_layer) encoded = layers.Dense(self.data.inputs_dim / 3, activation=activation, activity_regularizer=activity_regularizer)(encoded) encoded = layers.Dense(self.encoding_dim, activation=activation, activity_regularizer=activity_regularizer)( encoded) decoded = layers.Dense(self.data.inputs_dim / 3, activation=activation)(encoded) decoded = layers.Dense(self.data.inputs_dim / 2, activation=activation)(decoded) decoded = layers.Dense(self.data.inputs_dim, activation=activation)(decoded) self.ae = keras.Model(input_layer, decoded, name="AE") self.ae.summary() self.encoder = keras.Model(input_layer, encoded, name="encoder") self.encoder.summary() encoded_input = keras.Input(shape=(self.encoding_dim,)) deco = self.ae.layers[-3](encoded_input) deco = self.ae.layers[-2](deco) deco = self.ae.layers[-1](deco) self.decoder = keras.Model(encoded_input, deco, name="decoder") self.decoder.summary() self.ae.compile(optimizer="adam", loss=keras.losses.MeanSquaredError(), metrics=['acc', 'mean_squared_error']) callback = tf.keras.callbacks.EarlyStopping(monitor="val_loss", mode="min", patience=5, restore_best_weights=True) self.history = self.ae.fit(self.data.X_train, self.data.X_train, epochs=500, batch_size=32, shuffle=True, callbacks=[callback], validation_data=(self.data.X_val, self.data.X_val)) def predict(self): cell = self.data.X_test[0] cell = cell.reshape(1, cell.shape[0]) encoded_cell = self.encoder.predict(cell) decoded_cell = self.decoder.predict(encoded_cell) print(f"Epochs: {len(self.history.history['loss'])}") print(f"Input shape:\t{cell.shape}") print(f"Encoded shape:\t{encoded_cell.shape}") print(f"Decoded shape:\t{decoded_cell.shape}") print(f"\nInput:\n{cell[0]}") print(f"\nEncoded:\n{encoded_cell[0]}") print(f"\nDecoded:\n{decoded_cell[0]}") def calculate_r2_score(self): recon_test = self.ae.predict(self.data.X_test) recon_test = pd.DataFrame(data=recon_test, columns=self.data.markers) input_data = pd.DataFrame(data=self.data.X_test, columns=self.data.markers) for marker in self.data.markers: input_marker = input_data[f"{marker}"] var_marker = recon_test[f"{marker}"] score = r2_score(input_marker, var_marker) self.r2_scores = self.r2_scores.append( { "Marker": marker, "Score": score }, ignore_index=True ) def create_h5ad_object(self): fit = umap.UMAP() self.input_umap = input_umap = fit.fit_transform(self.data.X_test) fit = umap.UMAP() encoded = self.encoder.predict(self.data.X_test) self.latent_umap = fit.fit_transform(encoded) self.__create_h5ad("latent_markers", self.latent_umap, self.data.markers, pd.DataFrame(columns=self.data.markers, data=self.data.X_test)) self.__create_h5ad("input", input_umap, self.data.markers, pd.DataFrame(columns=self.data.markers, data=self.data.X_test)) return def __create_h5ad(self, file_name: str, umap, markers, df): obs = pd.DataFrame(data=df, index=df.index) var = pd.DataFrame(index=markers) obsm = {"X_umap": umap} uns = dict() adata = ad.AnnData(df.to_numpy(), var=var, obs=obs, uns=uns, obsm=obsm) adata.write(Path(f'{self.results_folder}/{file_name}.h5ad')) def create_test_predictions(self): self.encoded_data = pd.DataFrame(self.encoder.predict(self.data.X_test)) self.reconstructed_data = pd.DataFrame(columns=self.data.markers, data=self.decoder.predict(self.encoded_data)) def create_correlation_data(self): inputs = pd.DataFrame(columns=self.data.markers, data=self.data.inputs) corr = inputs.corr() corr.to_csv(Path(f'{self.results_folder}/correlation.csv'), index=False) def write_created_data_to_disk(self): with open(f'{self.results_folder}/ae_history', 'wb') as file_pi: pickle.dump(self.history.history, file_pi) X_test = pd.DataFrame(columns=self.data.markers, data=self.data.X_test) X_test.to_csv(Path(f'{self.results_folder}/test_data.csv'), index=False) self.encoded_data.to_csv(Path(f'{self.results_folder}/encoded_data.csv'), index=False) self.reconstructed_data.to_csv(Path(f'{self.results_folder}/reconstructed_data.csv'), index=False) self.r2_scores.to_csv(Path(f'{self.results_folder}/r2_scores.csv'), index=False) def get_activations(self): cell = self.data.X_test[0] cell = cell.reshape(1, cell.shape[0]) activations = kt.get_activations(self.ae, cell) fig = kt.display_activations(activations, cmap="summer", directory=f'{self.results_folder}', save=True)

true

1c311714daa3855f26e41a441c7cba090913511a

2,127

py

Python

scons-local/SCons/Tool/sgilink.py

bibleuspro/scons

625d446ae8996ff1b3d660c44e2827fc832cf12b

[ "MIT" ]

1

2017-02-10T00:26:44.000Z

scons-local/SCons/Tool/sgilink.py

bibleuspro/scons

625d446ae8996ff1b3d660c44e2827fc832cf12b

[ "MIT" ]

null

scons-local/SCons/Tool/sgilink.py

bibleuspro/scons

625d446ae8996ff1b3d660c44e2827fc832cf12b

[ "MIT" ]

null

"""SCons.Tool.sgilink Tool-specific initialization for the SGI MIPSPro linker on SGI. There normally shouldn't be any need to import this module directly. It will usually be imported through the generic SCons.Tool.Tool() selection method. """ # # Copyright (c) 2001 - 2014 The SCons Foundation # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # __revision__ = "src/engine/SCons/Tool/sgilink.py 2014/07/05 09:42:21 garyo" import SCons.Util import link linkers = ['CC', 'cc'] def generate(env): """Add Builders and construction variables for MIPSPro to an Environment.""" link.generate(env) env['LINK'] = env.Detect(linkers) or 'cc' env['SHLINKFLAGS'] = SCons.Util.CLVar('$LINKFLAGS -shared') # __RPATH is set to $_RPATH in the platform specification if that # platform supports it. env['RPATHPREFIX'] = '-rpath ' env['RPATHSUFFIX'] = '' env['_RPATH'] = '${_concat(RPATHPREFIX, RPATH, RPATHSUFFIX, __env__)}' def exists(env): return env.Detect(linkers) # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

33.761905

80

0.739069

__revision__ = "src/engine/SCons/Tool/sgilink.py 2014/07/05 09:42:21 garyo" import SCons.Util import link linkers = ['CC', 'cc'] def generate(env): link.generate(env) env['LINK'] = env.Detect(linkers) or 'cc' env['SHLINKFLAGS'] = SCons.Util.CLVar('$LINKFLAGS -shared') env['RPATHPREFIX'] = '-rpath ' env['RPATHSUFFIX'] = '' env['_RPATH'] = '${_concat(RPATHPREFIX, RPATH, RPATHSUFFIX, __env__)}' def exists(env): return env.Detect(linkers)

true

1c31177090badb6048b814d1896ef8b6e2323ee1

264

py

Python

tests/artificial/transf_BoxCox/trend_MovingMedian/cycle_12/ar_/test_artificial_128_BoxCox_MovingMedian_12__0.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/artificial/transf_BoxCox/trend_MovingMedian/cycle_12/ar_/test_artificial_128_BoxCox_MovingMedian_12__0.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/artificial/transf_BoxCox/trend_MovingMedian/cycle_12/ar_/test_artificial_128_BoxCox_MovingMedian_12__0.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "MovingMedian", cycle_length = 12, transform = "BoxCox", sigma = 0.0, exog_count = 0, ar_order = 0);

37.714286

164

0.731061

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "MovingMedian", cycle_length = 12, transform = "BoxCox", sigma = 0.0, exog_count = 0, ar_order = 0);

true

1c31198db568de2df98c2405e815b17b0c759b7f

1,784

py

Python

gargantua/apis/query_makers.py

Laisky/laisky-blog

ebe7dadf8fce283ebab0539926ad1be1246e5156

[ "Apache-2.0" ]

18

2015-05-08T02:06:39.000Z

2022-03-05T21:36:48.000Z

gargantua/apis/query_makers.py

Laisky/laisky-blog

ebe7dadf8fce283ebab0539926ad1be1246e5156

[ "Apache-2.0" ]

131

2015-01-22T14:54:59.000Z

2022-02-16T15:14:10.000Z

gargantua/apis/query_makers.py

Laisky/laisky-blog

ebe7dadf8fce283ebab0539926ad1be1246e5156

[ "Apache-2.0" ]

3

2016-01-11T13:52:41.000Z

2019-06-12T08:54:15.000Z

"""Constructors to make query to db""" from abc import ABC, abstractclassmethod import pymongo from bson import ObjectId import tornado from gargantua.utils import logger, is_objectid, debug_wrapper class QueryMakerError(Exception): pass class BaseMaker(ABC): @abstractclassmethod async def update_query(cls, app, query, projection): return query, projection class PostCategoiesFilterMaker(BaseMaker): """Posts' Categories filter""" @staticmethod def get_default_posts_projection(): return { 'post_author': 1, 'post_content': 1, 'link': 1, 'post_id': 1, 'post_name': 1, 'post_status': 1, 'post_title': 1, 'post_type': 1, 'post_menu': 1, 'post_modified_gmt': 1, 'post_created_at': 1, 'post_tags': 1, } @classmethod async def update_query(cls, app, query, projection): try: category = app.get_argument('category', default=None, strip=True) if category and category != 'null': if is_objectid(category): category = ObjectId(category) else: docu = await app.db.categories.find_one({'name': category}) if docu: category = docu['_id'] except Exception as err: raise QueryMakerError(err) if 'category' not in query and category: logger.debug('CategoryFilterMaker.update_query for category %s', category) if category == 'null': query['category'] = {'$exists': False} else: query['category'] = category return query, projection

27.446154

86

0.56222

from abc import ABC, abstractclassmethod import pymongo from bson import ObjectId import tornado from gargantua.utils import logger, is_objectid, debug_wrapper class QueryMakerError(Exception): pass class BaseMaker(ABC): @abstractclassmethod async def update_query(cls, app, query, projection): return query, projection class PostCategoiesFilterMaker(BaseMaker): @staticmethod def get_default_posts_projection(): return { 'post_author': 1, 'post_content': 1, 'link': 1, 'post_id': 1, 'post_name': 1, 'post_status': 1, 'post_title': 1, 'post_type': 1, 'post_menu': 1, 'post_modified_gmt': 1, 'post_created_at': 1, 'post_tags': 1, } @classmethod async def update_query(cls, app, query, projection): try: category = app.get_argument('category', default=None, strip=True) if category and category != 'null': if is_objectid(category): category = ObjectId(category) else: docu = await app.db.categories.find_one({'name': category}) if docu: category = docu['_id'] except Exception as err: raise QueryMakerError(err) if 'category' not in query and category: logger.debug('CategoryFilterMaker.update_query for category %s', category) if category == 'null': query['category'] = {'$exists': False} else: query['category'] = category return query, projection

true

1c311a9d72424ccf04f8719f6f13a013322ccf33

438

py

Python

products/migrations/0006_album_special_offer_price.py

JuanBrachoDev/Vinyl

b988c93f9919371aa151869fef2eed2f7c705b44

[ "Net-SNMP", "Xnet" ]

null

products/migrations/0006_album_special_offer_price.py

JuanBrachoDev/Vinyl

b988c93f9919371aa151869fef2eed2f7c705b44

[ "Net-SNMP", "Xnet" ]

null

products/migrations/0006_album_special_offer_price.py

JuanBrachoDev/Vinyl

b988c93f9919371aa151869fef2eed2f7c705b44

[ "Net-SNMP", "Xnet" ]

1

2021-10-20T21:13:26.000Z

# Generated by Django 3.2 on 2021-10-05 18:02 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0005_auto_20211005_1735'), ] operations = [ migrations.AddField( model_name='album', name='special_offer_price', field=models.DecimalField(blank=True, decimal_places=2, max_digits=6, null=True), ), ]

23.052632

93

0.627854

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0005_auto_20211005_1735'), ] operations = [ migrations.AddField( model_name='album', name='special_offer_price', field=models.DecimalField(blank=True, decimal_places=2, max_digits=6, null=True), ), ]

true

1c311a9ef7a57e39e9cdd6e1cdbd444534088472

2,216

py

Python

hata/discord/guild/flags.py

monoidic/hata

869fdd116360745cd554799d5df3b3a810f156b2

[ "0BSD" ]

1

2022-03-02T03:59:57.000Z

hata/discord/guild/flags.py

m0nk3ybraindead/hata

f87ed3d7009eeae31d6ea158772efd33775c7b1c

[ "0BSD" ]

1

2022-02-08T16:54:39.000Z

hata/discord/guild/flags.py

WizzyBots/hata

f6991afc0bebf7dad932888a536f4d010f8663c7

[ "0BSD" ]

1

2020-09-17T20:10:15.000Z

__all__ = ('SystemChannelFlag',) from ..bases import ReverseFlagBase class SystemChannelFlag(ReverseFlagBase): """ The flags of a ``Guild``'s system channel. For Discord these flags tell, what ``MessageType`-s are not sent to the guild's system channel, but the wrapper reverses this behaviour. The implemented system channel flags are the following: +---------------------------+-------------------+ | Respective name | Bitwise position | +===========================+===================+ | welcome | 0 | +---------------------------+-------------------+ | boost | 1 | +---------------------------+-------------------+ | setup_tips | 2 | +---------------------------+-------------------+ | join_sticker_replies | 3 | +---------------------------+-------------------+ There are also predefined ``SystemChannelFlag``-s: +-----------------------+-----------------------+ | Class attribute name | value | +=======================+=======================+ | NONE | ActivityFlag(0b1111) | +-----------------------+-----------------------+ | ALL | ActivityFlag(0b0000) | +-----------------------+-----------------------+ """ __keys__ = { 'welcome': 0, 'boost': 1, 'setup_tips': 2, 'join_sticker_replies': 3, } @property def none(self): """ Whether the flag not allows any system messages at the respective system channel. Returns ------- none : `bool` """ return (self == self.NONE) @property def all(self): """ Whether the flag allows all the system messages at the respective system channel. Returns ------- none : `bool` """ return (self == self.ALL) NONE = NotImplemented ALL = NotImplemented SystemChannelFlag.NONE = SystemChannelFlag(0b1111) SystemChannelFlag.ALL = SystemChannelFlag(0b0000)

31.657143

115

0.393051

__all__ = ('SystemChannelFlag',) from ..bases import ReverseFlagBase class SystemChannelFlag(ReverseFlagBase): __keys__ = { 'welcome': 0, 'boost': 1, 'setup_tips': 2, 'join_sticker_replies': 3, } @property def none(self): return (self == self.NONE) @property def all(self): return (self == self.ALL) NONE = NotImplemented ALL = NotImplemented SystemChannelFlag.NONE = SystemChannelFlag(0b1111) SystemChannelFlag.ALL = SystemChannelFlag(0b0000)

true

1c311b580482ffa462e622f3c9d59ce48f1417ba

21

py

Python

song/__init__.py

louisgv/song-cli

10186b26f66c2f07e3cf1a3cd7b5212610c33afb

[ "MIT" ]

70

2017-05-17T15:11:27.000Z

2021-01-10T01:09:06.000Z

song/__init__.py

louisgv/song-cli

10186b26f66c2f07e3cf1a3cd7b5212610c33afb

[ "MIT" ]

9

2017-05-12T17:29:46.000Z

2018-03-16T19:21:50.000Z

song/__init__.py

louisgv/song-cli

10186b26f66c2f07e3cf1a3cd7b5212610c33afb

[ "MIT" ]

17

2017-05-28T20:27:35.000Z

2021-07-12T03:41:25.000Z

__version__ = '2.9.1'

21

0.666667

__version__ = '2.9.1'

true

1c311bee07b9b229e65fea6e6239bd3237c48315

341

py

Python

app/api/api_v1/api.py

wlsouza/fastapi-todolist

c7c75bd73754dde8687e1486a80c77d903e33b31

[ "MIT" ]

4

2021-09-09T00:20:21.000Z

2022-01-12T09:08:07.000Z

app/api/api_v1/api.py

wlsouza/fastapi-todolist

c7c75bd73754dde8687e1486a80c77d903e33b31

[ "MIT" ]

null

app/api/api_v1/api.py

wlsouza/fastapi-todolist

c7c75bd73754dde8687e1486a80c77d903e33b31

[ "MIT" ]

null

from fastapi import APIRouter from app.api.api_v1.endpoints import login, users api_v1_router = APIRouter() api_v1_router.include_router(login.router, prefix="/login", tags=["login"]) api_v1_router.include_router(users.router, prefix="/users", tags=["users"]) # api_v1_router.include_router(tasks.router, prefix="/tasks", tags=["tasks"])

34.1

77

0.771261

from fastapi import APIRouter from app.api.api_v1.endpoints import login, users api_v1_router = APIRouter() api_v1_router.include_router(login.router, prefix="/login", tags=["login"]) api_v1_router.include_router(users.router, prefix="/users", tags=["users"])

true

1c311c26c934dbfc350461f0fa8b24cde322d362

9,058

py

Python

src/spaczz/search/tokensearcher.py

JonasHablitzel/spaczz

9f79fe9d35a25787f926b4c4955c2650f4600073

[ "MIT" ]

153

2020-07-07T01:26:25.000Z

2022-03-31T23:47:00.000Z

src/spaczz/search/tokensearcher.py

JonasHablitzel/spaczz

9f79fe9d35a25787f926b4c4955c2650f4600073

[ "MIT" ]

38

2020-07-15T02:29:34.000Z

2021-08-15T21:32:54.000Z

src/spaczz/search/tokensearcher.py

JonasHablitzel/spaczz

9f79fe9d35a25787f926b4c4955c2650f4600073

[ "MIT" ]

20

2020-07-07T15:41:05.000Z

2022-02-21T19:28:22.000Z

"""Module for TokenSearcher: flexible token searching in spaCy `Doc` objects.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Tuple, Union import regex from spacy.tokens import Doc, Token from spacy.vocab import Vocab from ..process import FuzzyFuncs from ..util import n_wise class TokenSearcher: """Class for flexbile token searching in spaCy `Doc` objects. Uses individual (and extended) spaCy token matching patterns to find match candidates. Candidates are used to generate new patterns to add to a spaCy `Matcher`. "FUZZY" and "FREGEX" are the two additional spaCy token pattern options. For example: {"TEXT": {"FREGEX": "(database){e<=1}"}}, {"LOWER": {"FUZZY": "access", "MIN_R": 85, "FUZZY_FUNC": "quick_lev"}} Make sure to use uppercase dictionary keys in patterns. Attributes: vocab (Vocab): The shared vocabulary. Included for consistency and potential future-state. _fuzzy_funcs (FuzzyFuncs): Container class housing fuzzy matching functions. Functions are accessible via the classes `get()` method by their given key name. The following rapidfuzz matching functions with default settings are available: "simple" = `ratio` "quick" = `QRatio` "quick_lev" = `quick_lev_ratio` """ def __init__(self: TokenSearcher, vocab: Vocab) -> None: """Initializes a token searcher. Args: vocab: A spaCy `Vocab` object. Purely for consistency between spaCy and spaczz matcher APIs for now. spaczz matchers are mostly pure-Python currently and do not share vocabulary with spaCy pipelines. """ self.vocab = vocab self._fuzzy_funcs: FuzzyFuncs = FuzzyFuncs(match_type="token") def fuzzy_compare( self: TokenSearcher, a: str, b: str, ignore_case: bool = True, fuzzy_func: str = "simple", ) -> int: """Peforms fuzzy matching between two strings. Applies the given fuzzy matching algorithm (fuzzy_func) to two strings and returns the resulting fuzzy ratio. Args: a: First string for comparison. b: Second string for comparison. ignore_case: Whether to lower-case a and b before comparison or not. Default is `True`. fuzzy_func: Key name of fuzzy matching function to use. The following rapidfuzz matching functions with default settings are available: "simple" = `ratio` "quick" = `QRatio` Default is `"simple"`. Returns: The fuzzy ratio between a and b. Example: >>> import spacy >>> from spaczz.search import TokenSearcher >>> nlp = spacy.blank("en") >>> searcher = TokenSearcher(nlp.vocab) >>> searcher.fuzzy_compare("spaczz", "spacy") 73 """ if ignore_case: a = a.lower() b = b.lower() return round(self._fuzzy_funcs.get(fuzzy_func)(a, b)) def match( self: TokenSearcher, doc: Doc, pattern: List[Dict[str, Any]], min_r: int = 75, fuzzy_func: str = "simple", ) -> List[List[Optional[Tuple[str, str]]]]: """Finds potential token pattern matches in a `Doc` object. Make sure to use uppercase dictionary keys in patterns. Args: doc: `Doc` object to search over. pattern: Individual spaCy token pattern. min_r: Minimum match ratio required for fuzzy matching. Can be overwritten with token pattern options. Default is `75`. fuzzy_func: Fuzzy matching function to use. Can be overwritten with token pattern options. Default is `simple`. Returns: A list of lists with each inner list representing a potential match. The inner lists will be populated with key, value tuples of token matches and `None` for placeholder tokens representing non-fuzzy tokens. Raises: TypeError: doc must be a `Doc` object. TypeError: pattern must be a `Sequence`. ValueError: pattern cannot have zero tokens. Example: >>> import spacy >>> from spaczz.search import TokenSearcher >>> nlp = spacy.blank("en") >>> searcher = TokenSearcher(nlp) >>> doc = nlp("I was prescribed zithramax and advar") >>> pattern = [ {"TEXT": {"FUZZY": "zithromax"}}, {"POS": "CCONJ"}, {"TEXT": {"FREGEX": "(advair){e<=1}"}} ] >>> searcher.match(doc, pattern) [[('TEXT', 'zithramax'), None, ('TEXT', 'advar')]] """ if not isinstance(doc, Doc): raise TypeError("doc must be a Doc object.") if not isinstance(pattern, list): raise TypeError( "pattern must be a list", "Make sure pattern is wrapped in a list.", ) if len(pattern) == 0: raise ValueError("pattern cannot have zero tokens.") matches = [] for seq in n_wise(doc, len(pattern)): seq_matches = self._iter_pattern(seq, pattern, min_r, fuzzy_func) if seq_matches: matches.append(seq_matches) if matches: filtered_matches = [ i for n, i in enumerate(matches) if i not in matches[:n] ] return filtered_matches else: return matches @staticmethod def regex_compare(text: str, pattern: str, ignore_case: bool = False) -> bool: """Performs fuzzy-regex supporting regex matching between two strings. Args: text: The string to match against. pattern: The regex pattern string. ignore_case: Whether to lower-case text before comparison or not. Default is `False`. Returns: `True` if match, `False` if not. Example: >>> import spacy >>> from spaczz.search import TokenSearcher >>> nlp = spacy.blank("en") >>> searcher = TokenSearcher(nlp) >>> searcher.regex_compare("sequel", "(sql){i<=3}") True """ if ignore_case: text = text.lower() if regex.match(pattern, text): return True else: return False def _iter_pattern( self: TokenSearcher, seq: Tuple[Token, ...], pattern: List[Dict[str, Any]], min_r: int, fuzzy_func: str, ) -> List[Optional[Tuple[str, str]]]: """Evaluates each token in a pattern against a doc token sequence.""" seq_matches: List[Optional[Tuple[str, str]]] = [] for i, token in enumerate(pattern): pattern_dict, case, case_bool = self._parse_case(token) if isinstance(pattern_dict, dict): pattern_text, pattern_type = self._parse_type(pattern_dict) if pattern_text and pattern_type == "FUZZY": if ( self.fuzzy_compare( seq[i].text, pattern_text, case_bool, pattern_dict.get("FUZZY_FUNC", fuzzy_func), ) >= pattern_dict.get("MIN_R", min_r) ): seq_matches.append((case, seq[i].text)) else: return [] elif pattern_text and pattern_type == "FREGEX": if self.regex_compare(seq[i].text, pattern_text, case_bool): seq_matches.append((case, seq[i].text)) else: return [] else: seq_matches.append(None) else: seq_matches.append(None) return seq_matches @staticmethod def _parse_case(token: Dict[str, Any]) -> Tuple[Union[str, Dict, None], str, bool]: """Parses the case of a token pattern.""" if token.get("TEXT"): return token.get("TEXT"), "TEXT", False else: return token.get("LOWER"), "LOWER", True @staticmethod def _parse_type(pattern_dict: Dict[str, Any]) -> Tuple[str, str]: """Parses the type of a token pattern.""" fuzzy_text = pattern_dict.get("FUZZY") regex_text = pattern_dict.get("FREGEX") if isinstance(fuzzy_text, str): return fuzzy_text, "FUZZY" elif isinstance(regex_text, str): return regex_text, "FREGEX" else: return "", ""

36.672065

87

0.548465

from __future__ import annotations from typing import Any, Dict, List, Optional, Tuple, Union import regex from spacy.tokens import Doc, Token from spacy.vocab import Vocab from ..process import FuzzyFuncs from ..util import n_wise class TokenSearcher: def __init__(self: TokenSearcher, vocab: Vocab) -> None: self.vocab = vocab self._fuzzy_funcs: FuzzyFuncs = FuzzyFuncs(match_type="token") def fuzzy_compare( self: TokenSearcher, a: str, b: str, ignore_case: bool = True, fuzzy_func: str = "simple", ) -> int: if ignore_case: a = a.lower() b = b.lower() return round(self._fuzzy_funcs.get(fuzzy_func)(a, b)) def match( self: TokenSearcher, doc: Doc, pattern: List[Dict[str, Any]], min_r: int = 75, fuzzy_func: str = "simple", ) -> List[List[Optional[Tuple[str, str]]]]: if not isinstance(doc, Doc): raise TypeError("doc must be a Doc object.") if not isinstance(pattern, list): raise TypeError( "pattern must be a list", "Make sure pattern is wrapped in a list.", ) if len(pattern) == 0: raise ValueError("pattern cannot have zero tokens.") matches = [] for seq in n_wise(doc, len(pattern)): seq_matches = self._iter_pattern(seq, pattern, min_r, fuzzy_func) if seq_matches: matches.append(seq_matches) if matches: filtered_matches = [ i for n, i in enumerate(matches) if i not in matches[:n] ] return filtered_matches else: return matches @staticmethod def regex_compare(text: str, pattern: str, ignore_case: bool = False) -> bool: if ignore_case: text = text.lower() if regex.match(pattern, text): return True else: return False def _iter_pattern( self: TokenSearcher, seq: Tuple[Token, ...], pattern: List[Dict[str, Any]], min_r: int, fuzzy_func: str, ) -> List[Optional[Tuple[str, str]]]: seq_matches: List[Optional[Tuple[str, str]]] = [] for i, token in enumerate(pattern): pattern_dict, case, case_bool = self._parse_case(token) if isinstance(pattern_dict, dict): pattern_text, pattern_type = self._parse_type(pattern_dict) if pattern_text and pattern_type == "FUZZY": if ( self.fuzzy_compare( seq[i].text, pattern_text, case_bool, pattern_dict.get("FUZZY_FUNC", fuzzy_func), ) >= pattern_dict.get("MIN_R", min_r) ): seq_matches.append((case, seq[i].text)) else: return [] elif pattern_text and pattern_type == "FREGEX": if self.regex_compare(seq[i].text, pattern_text, case_bool): seq_matches.append((case, seq[i].text)) else: return [] else: seq_matches.append(None) else: seq_matches.append(None) return seq_matches @staticmethod def _parse_case(token: Dict[str, Any]) -> Tuple[Union[str, Dict, None], str, bool]: if token.get("TEXT"): return token.get("TEXT"), "TEXT", False else: return token.get("LOWER"), "LOWER", True @staticmethod def _parse_type(pattern_dict: Dict[str, Any]) -> Tuple[str, str]: fuzzy_text = pattern_dict.get("FUZZY") regex_text = pattern_dict.get("FREGEX") if isinstance(fuzzy_text, str): return fuzzy_text, "FUZZY" elif isinstance(regex_text, str): return regex_text, "FREGEX" else: return "", ""

true

1c311c684a007f61d10d19b067c8f928b015dca9

1,480

py

Python

doctr/models/detection/predictor/pytorch.py

elejke/doctr

1d62d3f2b1e9a60560af0685fe882a69826503a7

[ "Apache-2.0" ]

null

doctr/models/detection/predictor/pytorch.py

elejke/doctr

1d62d3f2b1e9a60560af0685fe882a69826503a7

[ "Apache-2.0" ]

null

doctr/models/detection/predictor/pytorch.py

elejke/doctr

1d62d3f2b1e9a60560af0685fe882a69826503a7

[ "Apache-2.0" ]

null

# Copyright (C) 2021, Mindee. # This program is licensed under the Apache License version 2. # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details. from typing import Any, List, Union import numpy as np import torch from torch import nn from doctr.models.preprocessor import PreProcessor __all__ = ['DetectionPredictor'] class DetectionPredictor(nn.Module): """Implements an object able to localize text elements in a document Args: pre_processor: transform inputs for easier batched model inference model: core detection architecture """ def __init__( self, pre_processor: PreProcessor, model: nn.Module, ) -> None: super().__init__() self.pre_processor = pre_processor self.model = model.eval() @torch.no_grad() def forward( self, pages: List[Union[np.ndarray, torch.Tensor]], **kwargs: Any, ) -> List[np.ndarray]: # Dimension check if any(page.ndim != 3 for page in pages): raise ValueError("incorrect input shape: all pages are expected to be multi-channel 2D images.") processed_batches = self.pre_processor(pages) predicted_batches = [ self.model(batch, return_boxes=True, **kwargs)['preds'] # type:ignore[operator] for batch in processed_batches ] return [pred for batch in predicted_batches for pred in batch]

28.461538

108

0.658784

from typing import Any, List, Union import numpy as np import torch from torch import nn from doctr.models.preprocessor import PreProcessor __all__ = ['DetectionPredictor'] class DetectionPredictor(nn.Module): def __init__( self, pre_processor: PreProcessor, model: nn.Module, ) -> None: super().__init__() self.pre_processor = pre_processor self.model = model.eval() @torch.no_grad() def forward( self, pages: List[Union[np.ndarray, torch.Tensor]], **kwargs: Any, ) -> List[np.ndarray]: if any(page.ndim != 3 for page in pages): raise ValueError("incorrect input shape: all pages are expected to be multi-channel 2D images.") processed_batches = self.pre_processor(pages) predicted_batches = [ self.model(batch, return_boxes=True, **kwargs)['preds'] for batch in processed_batches ] return [pred for batch in predicted_batches for pred in batch]

true

1c311c9c35666325e1ee647c83484b12a76c0664

37,270

py

Python

sympy/simplify/tests/test_hyperexpand.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

1

2016-02-22T22:46:50.000Z

sympy/simplify/tests/test_hyperexpand.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

7

2017-05-01T14:15:32.000Z

2017-09-06T20:44:24.000Z

sympy/simplify/tests/test_hyperexpand.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

1

2020-09-09T15:20:27.000Z

from random import randrange from sympy.simplify.hyperexpand import (ShiftA, ShiftB, UnShiftA, UnShiftB, MeijerShiftA, MeijerShiftB, MeijerShiftC, MeijerShiftD, MeijerUnShiftA, MeijerUnShiftB, MeijerUnShiftC, MeijerUnShiftD, ReduceOrder, reduce_order, apply_operators, devise_plan, make_derivative_operator, Formula, hyperexpand, Hyper_Function, G_Function, reduce_order_meijer, build_hypergeometric_formula) from sympy import hyper, I, S, meijerg, Piecewise from sympy.abc import z, a, b, c from sympy.utilities.pytest import XFAIL, raises, slow from sympy.utilities.randtest import verify_numerically as tn from sympy.core.compatibility import range from sympy import (cos, sin, log, exp, asin, lowergamma, atanh, besseli, gamma, sqrt, pi, erf, exp_polar) def test_branch_bug(): assert hyperexpand(hyper((-S(1)/3, S(1)/2), (S(2)/3, S(3)/2), -z)) == \ -z**S('1/3')*lowergamma(exp_polar(I*pi)/3, z)/5 \ + sqrt(pi)*erf(sqrt(z))/(5*sqrt(z)) assert hyperexpand(meijerg([S(7)/6, 1], [], [S(2)/3], [S(1)/6, 0], z)) == \ 2*z**S('2/3')*(2*sqrt(pi)*erf(sqrt(z))/sqrt(z) - 2*lowergamma( S(2)/3, z)/z**S('2/3'))*gamma(S(2)/3)/gamma(S(5)/3) def test_hyperexpand(): # Luke, Y. L. (1969), The Special Functions and Their Approximations, # Volume 1, section 6.2 assert hyperexpand(hyper([], [], z)) == exp(z) assert hyperexpand(hyper([1, 1], [2], -z)*z) == log(1 + z) assert hyperexpand(hyper([], [S.Half], -z**2/4)) == cos(z) assert hyperexpand(z*hyper([], [S('3/2')], -z**2/4)) == sin(z) assert hyperexpand(hyper([S('1/2'), S('1/2')], [S('3/2')], z**2)*z) \ == asin(z) def can_do(ap, bq, numerical=True, div=1, lowerplane=False): from sympy import exp_polar, exp r = hyperexpand(hyper(ap, bq, z)) if r.has(hyper): return False if not numerical: return True repl = {} for n, a in enumerate(r.free_symbols - set([z])): repl[a] = randcplx(n)/div [a, b, c, d] = [2, -1, 3, 1] if lowerplane: [a, b, c, d] = [2, -2, 3, -1] return tn( hyper(ap, bq, z).subs(repl), r.replace(exp_polar, exp).subs(repl), z, a=a, b=b, c=c, d=d) def test_roach(): # Kelly B. Roach. Meijer G Function Representations. # Section "Gallery" assert can_do([S(1)/2], [S(9)/2]) assert can_do([], [1, S(5)/2, 4]) assert can_do([-S.Half, 1, 2], [3, 4]) assert can_do([S(1)/3], [-S(2)/3, -S(1)/2, S(1)/2, 1]) assert can_do([-S(3)/2, -S(1)/2], [-S(5)/2, 1]) assert can_do([-S(3)/2, ], [-S(1)/2, S(1)/2]) # shine-integral assert can_do([-S(3)/2, -S(1)/2], [2]) # elliptic integrals @XFAIL def test_roach_fail(): assert can_do([-S(1)/2, 1], [S(1)/4, S(1)/2, S(3)/4]) # PFDD assert can_do([S(3)/2], [S(5)/2, 5]) # struve function assert can_do([-S(1)/2, S(1)/2, 1], [S(3)/2, S(5)/2]) # polylog, pfdd assert can_do([1, 2, 3], [S(1)/2, 4]) # XXX ? assert can_do([S(1)/2], [-S(1)/3, -S(1)/2, -S(2)/3]) # PFDD ? # For the long table tests, see end of file def test_polynomial(): from sympy import oo assert hyperexpand(hyper([], [-1], z)) == oo assert hyperexpand(hyper([-2], [-1], z)) == oo assert hyperexpand(hyper([0, 0], [-1], z)) == 1 assert can_do([-5, -2, randcplx(), randcplx()], [-10, randcplx()]) def test_hyperexpand_bases(): assert hyperexpand(hyper([2], [a], z)) == \ a + z**(-a + 1)*(-a**2 + 3*a + z*(a - 1) - 2)*exp(z)* \ lowergamma(a - 1, z) - 1 # TODO [a+1, a-S.Half], [2*a] assert hyperexpand(hyper([1, 2], [3], z)) == -2/z - 2*log(-z + 1)/z**2 assert hyperexpand(hyper([S.Half, 2], [S(3)/2], z)) == \ -1/(2*z - 2) + atanh(sqrt(z))/sqrt(z)/2 assert hyperexpand(hyper([S(1)/2, S(1)/2], [S(5)/2], z)) == \ (-3*z + 3)/4/(z*sqrt(-z + 1)) \ + (6*z - 3)*asin(sqrt(z))/(4*z**(S(3)/2)) assert hyperexpand(hyper([1, 2], [S(3)/2], z)) == -1/(2*z - 2) \ - asin(sqrt(z))/(sqrt(z)*(2*z - 2)*sqrt(-z + 1)) assert hyperexpand(hyper([-S.Half - 1, 1, 2], [S.Half, 3], z)) == \ sqrt(z)*(6*z/7 - S(6)/5)*atanh(sqrt(z)) \ + (-30*z**2 + 32*z - 6)/35/z - 6*log(-z + 1)/(35*z**2) assert hyperexpand(hyper([1 + S.Half, 1, 1], [2, 2], z)) == \ -4*log(sqrt(-z + 1)/2 + S(1)/2)/z # TODO hyperexpand(hyper([a], [2*a + 1], z)) # TODO [S.Half, a], [S(3)/2, a+1] assert hyperexpand(hyper([2], [b, 1], z)) == \ z**(-b/2 + S(1)/2)*besseli(b - 1, 2*sqrt(z))*gamma(b) \ + z**(-b/2 + 1)*besseli(b, 2*sqrt(z))*gamma(b) # TODO [a], [a - S.Half, 2*a] def test_hyperexpand_parametric(): assert hyperexpand(hyper([a, S(1)/2 + a], [S(1)/2], z)) \ == (1 + sqrt(z))**(-2*a)/2 + (1 - sqrt(z))**(-2*a)/2 assert hyperexpand(hyper([a, -S(1)/2 + a], [2*a], z)) \ == 2**(2*a - 1)*((-z + 1)**(S(1)/2) + 1)**(-2*a + 1) def test_shifted_sum(): from sympy import simplify assert simplify(hyperexpand(z**4*hyper([2], [3, S('3/2')], -z**2))) \ == z*sin(2*z) + (-z**2 + S.Half)*cos(2*z) - S.Half def _randrat(): """ Steer clear of integers. """ return S(randrange(25) + 10)/50 def randcplx(offset=-1): """ Polys is not good with real coefficients. """ return _randrat() + I*_randrat() + I*(1 + offset) @slow def test_formulae(): from sympy.simplify.hyperexpand import FormulaCollection formulae = FormulaCollection().formulae for formula in formulae: h = formula.func(formula.z) rep = {} for n, sym in enumerate(formula.symbols): rep[sym] = randcplx(n) # NOTE hyperexpand returns truly branched functions. We know we are # on the main sheet, but numerical evaluation can still go wrong # (e.g. if exp_polar cannot be evalf'd). # Just replace all exp_polar by exp, this usually works. # first test if the closed-form is actually correct h = h.subs(rep) closed_form = formula.closed_form.subs(rep).rewrite('nonrepsmall') z = formula.z assert tn(h, closed_form.replace(exp_polar, exp), z) # now test the computed matrix cl = (formula.C * formula.B)[0].subs(rep).rewrite('nonrepsmall') assert tn(closed_form.replace( exp_polar, exp), cl.replace(exp_polar, exp), z) deriv1 = z*formula.B.applyfunc(lambda t: t.rewrite( 'nonrepsmall')).diff(z) deriv2 = formula.M * formula.B for d1, d2 in zip(deriv1, deriv2): assert tn(d1.subs(rep).replace(exp_polar, exp), d2.subs(rep).rewrite('nonrepsmall').replace(exp_polar, exp), z) def test_meijerg_formulae(): from sympy.simplify.hyperexpand import MeijerFormulaCollection formulae = MeijerFormulaCollection().formulae for sig in formulae: for formula in formulae[sig]: g = meijerg(formula.func.an, formula.func.ap, formula.func.bm, formula.func.bq, formula.z) rep = {} for sym in formula.symbols: rep[sym] = randcplx() # first test if the closed-form is actually correct g = g.subs(rep) closed_form = formula.closed_form.subs(rep) z = formula.z assert tn(g, closed_form, z) # now test the computed matrix cl = (formula.C * formula.B)[0].subs(rep) assert tn(closed_form, cl, z) deriv1 = z*formula.B.diff(z) deriv2 = formula.M * formula.B for d1, d2 in zip(deriv1, deriv2): assert tn(d1.subs(rep), d2.subs(rep), z) def op(f): return z*f.diff(z) def test_plan(): assert devise_plan(Hyper_Function([0], ()), Hyper_Function([0], ()), z) == [] with raises(ValueError): devise_plan(Hyper_Function([1], ()), Hyper_Function((), ()), z) with raises(ValueError): devise_plan(Hyper_Function([2], [1]), Hyper_Function([2], [2]), z) with raises(ValueError): devise_plan(Hyper_Function([2], []), Hyper_Function([S("1/2")], []), z) # We cannot use pi/(10000 + n) because polys is insanely slow. a1, a2, b1 = (randcplx(n) for n in range(3)) b1 += 2*I h = hyper([a1, a2], [b1], z) h2 = hyper((a1 + 1, a2), [b1], z) assert tn(apply_operators(h, devise_plan(Hyper_Function((a1 + 1, a2), [b1]), Hyper_Function((a1, a2), [b1]), z), op), h2, z) h2 = hyper((a1 + 1, a2 - 1), [b1], z) assert tn(apply_operators(h, devise_plan(Hyper_Function((a1 + 1, a2 - 1), [b1]), Hyper_Function((a1, a2), [b1]), z), op), h2, z) def test_plan_derivatives(): a1, a2, a3 = 1, 2, S('1/2') b1, b2 = 3, S('5/2') h = Hyper_Function((a1, a2, a3), (b1, b2)) h2 = Hyper_Function((a1 + 1, a2 + 1, a3 + 2), (b1 + 1, b2 + 1)) ops = devise_plan(h2, h, z) f = Formula(h, z, h(z), []) deriv = make_derivative_operator(f.M, z) assert tn((apply_operators(f.C, ops, deriv)*f.B)[0], h2(z), z) h2 = Hyper_Function((a1, a2 - 1, a3 - 2), (b1 - 1, b2 - 1)) ops = devise_plan(h2, h, z) assert tn((apply_operators(f.C, ops, deriv)*f.B)[0], h2(z), z) def test_reduction_operators(): a1, a2, b1 = (randcplx(n) for n in range(3)) h = hyper([a1], [b1], z) assert ReduceOrder(2, 0) is None assert ReduceOrder(2, -1) is None assert ReduceOrder(1, S('1/2')) is None h2 = hyper((a1, a2), (b1, a2), z) assert tn(ReduceOrder(a2, a2).apply(h, op), h2, z) h2 = hyper((a1, a2 + 1), (b1, a2), z) assert tn(ReduceOrder(a2 + 1, a2).apply(h, op), h2, z) h2 = hyper((a2 + 4, a1), (b1, a2), z) assert tn(ReduceOrder(a2 + 4, a2).apply(h, op), h2, z) # test several step order reduction ap = (a2 + 4, a1, b1 + 1) bq = (a2, b1, b1) func, ops = reduce_order(Hyper_Function(ap, bq)) assert func.ap == (a1,) assert func.bq == (b1,) assert tn(apply_operators(h, ops, op), hyper(ap, bq, z), z) def test_shift_operators(): a1, a2, b1, b2, b3 = (randcplx(n) for n in range(5)) h = hyper((a1, a2), (b1, b2, b3), z) raises(ValueError, lambda: ShiftA(0)) raises(ValueError, lambda: ShiftB(1)) assert tn(ShiftA(a1).apply(h, op), hyper((a1 + 1, a2), (b1, b2, b3), z), z) assert tn(ShiftA(a2).apply(h, op), hyper((a1, a2 + 1), (b1, b2, b3), z), z) assert tn(ShiftB(b1).apply(h, op), hyper((a1, a2), (b1 - 1, b2, b3), z), z) assert tn(ShiftB(b2).apply(h, op), hyper((a1, a2), (b1, b2 - 1, b3), z), z) assert tn(ShiftB(b3).apply(h, op), hyper((a1, a2), (b1, b2, b3 - 1), z), z) def test_ushift_operators(): a1, a2, b1, b2, b3 = (randcplx(n) for n in range(5)) h = hyper((a1, a2), (b1, b2, b3), z) raises(ValueError, lambda: UnShiftA((1,), (), 0, z)) raises(ValueError, lambda: UnShiftB((), (-1,), 0, z)) raises(ValueError, lambda: UnShiftA((1,), (0, -1, 1), 0, z)) raises(ValueError, lambda: UnShiftB((0, 1), (1,), 0, z)) s = UnShiftA((a1, a2), (b1, b2, b3), 0, z) assert tn(s.apply(h, op), hyper((a1 - 1, a2), (b1, b2, b3), z), z) s = UnShiftA((a1, a2), (b1, b2, b3), 1, z) assert tn(s.apply(h, op), hyper((a1, a2 - 1), (b1, b2, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 0, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1 + 1, b2, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 1, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1, b2 + 1, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 2, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1, b2, b3 + 1), z), z) def can_do_meijer(a1, a2, b1, b2, numeric=True): """ This helper function tries to hyperexpand() the meijer g-function corresponding to the parameters a1, a2, b1, b2. It returns False if this expansion still contains g-functions. If numeric is True, it also tests the so-obtained formula numerically (at random values) and returns False if the test fails. Else it returns True. """ from sympy import unpolarify, expand r = hyperexpand(meijerg(a1, a2, b1, b2, z)) if r.has(meijerg): return False # NOTE hyperexpand() returns a truly branched function, whereas numerical # evaluation only works on the main branch. Since we are evaluating on # the main branch, this should not be a problem, but expressions like # exp_polar(I*pi/2*x)**a are evaluated incorrectly. We thus have to get # rid of them. The expand heuristically does this... r = unpolarify(expand(r, force=True, power_base=True, power_exp=False, mul=False, log=False, multinomial=False, basic=False)) if not numeric: return True repl = {} for n, a in enumerate(meijerg(a1, a2, b1, b2, z).free_symbols - set([z])): repl[a] = randcplx(n) return tn(meijerg(a1, a2, b1, b2, z).subs(repl), r.subs(repl), z) @slow def test_meijerg_expand(): from sympy import combsimp, simplify # from mpmath docs assert hyperexpand(meijerg([[], []], [[0], []], -z)) == exp(z) assert hyperexpand(meijerg([[1, 1], []], [[1], [0]], z)) == \ log(z + 1) assert hyperexpand(meijerg([[1, 1], []], [[1], [1]], z)) == \ z/(z + 1) assert hyperexpand(meijerg([[], []], [[S(1)/2], [0]], (z/2)**2)) \ == sin(z)/sqrt(pi) assert hyperexpand(meijerg([[], []], [[0], [S(1)/2]], (z/2)**2)) \ == cos(z)/sqrt(pi) assert can_do_meijer([], [a], [a - 1, a - S.Half], []) assert can_do_meijer([], [], [a/2], [-a/2], False) # branches... assert can_do_meijer([a], [b], [a], [b, a - 1]) # wikipedia assert hyperexpand(meijerg([1], [], [], [0], z)) == \ Piecewise((0, abs(z) < 1), (1, abs(1/z) < 1), (meijerg([1], [], [], [0], z), True)) assert hyperexpand(meijerg([], [1], [0], [], z)) == \ Piecewise((1, abs(z) < 1), (0, abs(1/z) < 1), (meijerg([], [1], [0], [], z), True)) # The Special Functions and their Approximations assert can_do_meijer([], [], [a + b/2], [a, a - b/2, a + S.Half]) assert can_do_meijer( [], [], [a], [b], False) # branches only agree for small z assert can_do_meijer([], [S.Half], [a], [-a]) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, a + S.Half], [b, b + S.Half]) assert can_do_meijer([], [], [a, -a], [0, S.Half], False) # dito assert can_do_meijer([], [], [a, a + S.Half, b, b + S.Half], []) assert can_do_meijer([S.Half], [], [0], [a, -a]) assert can_do_meijer([S.Half], [], [a], [0, -a], False) # dito assert can_do_meijer([], [a - S.Half], [a, b], [a - S.Half], False) assert can_do_meijer([], [a + S.Half], [a + b, a - b, a], [], False) assert can_do_meijer([a + S.Half], [], [b, 2*a - b, a], [], False) # This for example is actually zero. assert can_do_meijer([], [], [], [a, b]) # Testing a bug: assert hyperexpand(meijerg([0, 2], [], [], [-1, 1], z)) == \ Piecewise((0, abs(z) < 1), (z/2 - 1/(2*z), abs(1/z) < 1), (meijerg([0, 2], [], [], [-1, 1], z), True)) # Test that the simplest possible answer is returned: assert combsimp(simplify(hyperexpand( meijerg([1], [1 - a], [-a/2, -a/2 + S(1)/2], [], 1/z)))) == \ -2*sqrt(pi)*(sqrt(z + 1) + 1)**a/a # Test that hyper is returned assert hyperexpand(meijerg([1], [], [a], [0, 0], z)) == hyper( (a,), (a + 1, a + 1), z*exp_polar(I*pi))*z**a*gamma(a)/gamma(a + 1)**2 def test_meijerg_lookup(): from sympy import uppergamma, Si, Ci assert hyperexpand(meijerg([a], [], [b, a], [], z)) == \ z**b*exp(z)*gamma(-a + b + 1)*uppergamma(a - b, z) assert hyperexpand(meijerg([0], [], [0, 0], [], z)) == \ exp(z)*uppergamma(0, z) assert can_do_meijer([a], [], [b, a + 1], []) assert can_do_meijer([a], [], [b + 2, a], []) assert can_do_meijer([a], [], [b - 2, a], []) assert hyperexpand(meijerg([a], [], [a, a, a - S(1)/2], [], z)) == \ -sqrt(pi)*z**(a - S(1)/2)*(2*cos(2*sqrt(z))*(Si(2*sqrt(z)) - pi/2) - 2*sin(2*sqrt(z))*Ci(2*sqrt(z))) == \ hyperexpand(meijerg([a], [], [a, a - S(1)/2, a], [], z)) == \ hyperexpand(meijerg([a], [], [a - S(1)/2, a, a], [], z)) assert can_do_meijer([a - 1], [], [a + 2, a - S(3)/2, a + 1], []) @XFAIL def test_meijerg_expand_fail(): # These basically test hyper([], [1/2 - a, 1/2 + 1, 1/2], z), # which is *very* messy. But since the meijer g actually yields a # sum of bessel functions, things can sometimes be simplified a lot and # are then put into tables... assert can_do_meijer([], [], [a + S.Half], [a, a - b/2, a + b/2]) assert can_do_meijer([], [], [0, S.Half], [a, -a]) assert can_do_meijer([], [], [3*a - S.Half, a, -a - S.Half], [a - S.Half]) assert can_do_meijer([], [], [0, a - S.Half, -a - S.Half], [S.Half]) assert can_do_meijer([], [], [a, b + S(1)/2, b], [2*b - a]) assert can_do_meijer([], [], [a, b + S(1)/2, b, 2*b - a]) assert can_do_meijer([S.Half], [], [-a, a], [0]) @slow def test_meijerg(): # carefully set up the parameters. # NOTE: this used to fail sometimes. I believe it is fixed, but if you # hit an inexplicable test failure here, please let me know the seed. a1, a2 = (randcplx(n) - 5*I - n*I for n in range(2)) b1, b2 = (randcplx(n) + 5*I + n*I for n in range(2)) b3, b4, b5, a3, a4, a5 = (randcplx() for n in range(6)) g = meijerg([a1], [a3, a4], [b1], [b3, b4], z) assert ReduceOrder.meijer_minus(3, 4) is None assert ReduceOrder.meijer_plus(4, 3) is None g2 = meijerg([a1, a2], [a3, a4], [b1], [b3, b4, a2], z) assert tn(ReduceOrder.meijer_plus(a2, a2).apply(g, op), g2, z) g2 = meijerg([a1, a2], [a3, a4], [b1], [b3, b4, a2 + 1], z) assert tn(ReduceOrder.meijer_plus(a2, a2 + 1).apply(g, op), g2, z) g2 = meijerg([a1, a2 - 1], [a3, a4], [b1], [b3, b4, a2 + 2], z) assert tn(ReduceOrder.meijer_plus(a2 - 1, a2 + 2).apply(g, op), g2, z) g2 = meijerg([a1], [a3, a4, b2 - 1], [b1, b2 + 2], [b3, b4], z) assert tn(ReduceOrder.meijer_minus( b2 + 2, b2 - 1).apply(g, op), g2, z, tol=1e-6) # test several-step reduction an = [a1, a2] bq = [b3, b4, a2 + 1] ap = [a3, a4, b2 - 1] bm = [b1, b2 + 1] niq, ops = reduce_order_meijer(G_Function(an, ap, bm, bq)) assert niq.an == (a1,) assert set(niq.ap) == set([a3, a4]) assert niq.bm == (b1,) assert set(niq.bq) == set([b3, b4]) assert tn(apply_operators(g, ops, op), meijerg(an, ap, bm, bq, z), z) def test_meijerg_shift_operators(): # carefully set up the parameters. XXX this still fails sometimes a1, a2, a3, a4, a5, b1, b2, b3, b4, b5 = (randcplx(n) for n in range(10)) g = meijerg([a1], [a3, a4], [b1], [b3, b4], z) assert tn(MeijerShiftA(b1).apply(g, op), meijerg([a1], [a3, a4], [b1 + 1], [b3, b4], z), z) assert tn(MeijerShiftB(a1).apply(g, op), meijerg([a1 - 1], [a3, a4], [b1], [b3, b4], z), z) assert tn(MeijerShiftC(b3).apply(g, op), meijerg([a1], [a3, a4], [b1], [b3 + 1, b4], z), z) assert tn(MeijerShiftD(a3).apply(g, op), meijerg([a1], [a3 - 1, a4], [b1], [b3, b4], z), z) s = MeijerUnShiftA([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3, a4], [b1 - 1], [b3, b4], z), z) s = MeijerUnShiftC([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3, a4], [b1], [b3 - 1, b4], z), z) s = MeijerUnShiftB([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1 + 1], [a3, a4], [b1], [b3, b4], z), z) s = MeijerUnShiftD([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3 + 1, a4], [b1], [b3, b4], z), z) @slow def test_meijerg_confluence(): def t(m, a, b): from sympy import sympify, Piecewise a, b = sympify([a, b]) m_ = m m = hyperexpand(m) if not m == Piecewise((a, abs(z) < 1), (b, abs(1/z) < 1), (m_, True)): return False if not (m.args[0].args[0] == a and m.args[1].args[0] == b): return False z0 = randcplx()/10 if abs(m.subs(z, z0).n() - a.subs(z, z0).n()).n() > 1e-10: return False if abs(m.subs(z, 1/z0).n() - b.subs(z, 1/z0).n()).n() > 1e-10: return False return True assert t(meijerg([], [1, 1], [0, 0], [], z), -log(z), 0) assert t(meijerg( [], [3, 1], [0, 0], [], z), -z**2/4 + z - log(z)/2 - S(3)/4, 0) assert t(meijerg([], [3, 1], [-1, 0], [], z), z**2/12 - z/2 + log(z)/2 + S(1)/4 + 1/(6*z), 0) assert t(meijerg([], [1, 1, 1, 1], [0, 0, 0, 0], [], z), -log(z)**3/6, 0) assert t(meijerg([1, 1], [], [], [0, 0], z), 0, -log(1/z)) assert t(meijerg([1, 1], [2, 2], [1, 1], [0, 0], z), -z*log(z) + 2*z, -log(1/z) + 2) assert t(meijerg([S(1)/2], [1, 1], [0, 0], [S(3)/2], z), log(z)/2 - 1, 0) def u(an, ap, bm, bq): m = meijerg(an, ap, bm, bq, z) m2 = hyperexpand(m, allow_hyper=True) if m2.has(meijerg) and not (m2.is_Piecewise and len(m2.args) == 3): return False return tn(m, m2, z) assert u([], [1], [0, 0], []) assert u([1, 1], [], [], [0]) assert u([1, 1], [2, 2, 5], [1, 1, 6], [0, 0]) assert u([1, 1], [2, 2, 5], [1, 1, 6], [0]) def test_lerchphi(): from sympy import combsimp, exp_polar, polylog, log, lerchphi assert hyperexpand(hyper([1, a], [a + 1], z)/a) == lerchphi(z, 1, a) assert hyperexpand( hyper([1, a, a], [a + 1, a + 1], z)/a**2) == lerchphi(z, 2, a) assert hyperexpand(hyper([1, a, a, a], [a + 1, a + 1, a + 1], z)/a**3) == \ lerchphi(z, 3, a) assert hyperexpand(hyper([1] + [a]*10, [a + 1]*10, z)/a**10) == \ lerchphi(z, 10, a) assert combsimp(hyperexpand(meijerg([0, 1 - a], [], [0], [-a], exp_polar(-I*pi)*z))) == lerchphi(z, 1, a) assert combsimp(hyperexpand(meijerg([0, 1 - a, 1 - a], [], [0], [-a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 2, a) assert combsimp(hyperexpand(meijerg([0, 1 - a, 1 - a, 1 - a], [], [0], [-a, -a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 3, a) assert hyperexpand(z*hyper([1, 1], [2], z)) == -log(1 + -z) assert hyperexpand(z*hyper([1, 1, 1], [2, 2], z)) == polylog(2, z) assert hyperexpand(z*hyper([1, 1, 1, 1], [2, 2, 2], z)) == polylog(3, z) assert hyperexpand(hyper([1, a, 1 + S(1)/2], [a + 1, S(1)/2], z)) == \ -2*a/(z - 1) + (-2*a**2 + a)*lerchphi(z, 1, a) # Now numerical tests. These make sure reductions etc are carried out # correctly # a rational function (polylog at negative integer order) assert can_do([2, 2, 2], [1, 1]) # NOTE these contain log(1-x) etc ... better make sure we have |z| < 1 # reduction of order for polylog assert can_do([1, 1, 1, b + 5], [2, 2, b], div=10) # reduction of order for lerchphi # XXX lerchphi in mpmath is flaky assert can_do( [1, a, a, a, b + 5], [a + 1, a + 1, a + 1, b], numerical=False) # test a bug from sympy import Abs assert hyperexpand(hyper([S(1)/2, S(1)/2, S(1)/2, 1], [S(3)/2, S(3)/2, S(3)/2], S(1)/4)) == \ Abs(-polylog(3, exp_polar(I*pi)/2) + polylog(3, S(1)/2)) def test_partial_simp(): # First test that hypergeometric function formulae work. a, b, c, d, e = (randcplx() for _ in range(5)) for func in [Hyper_Function([a, b, c], [d, e]), Hyper_Function([], [a, b, c, d, e])]: f = build_hypergeometric_formula(func) z = f.z assert f.closed_form == func(z) deriv1 = f.B.diff(z)*z deriv2 = f.M*f.B for func1, func2 in zip(deriv1, deriv2): assert tn(func1, func2, z) # Now test that formulae are partially simplified. from sympy.abc import a, b, z assert hyperexpand(hyper([3, a], [1, b], z)) == \ (-a*b/2 + a*z/2 + 2*a)*hyper([a + 1], [b], z) \ + (a*b/2 - 2*a + 1)*hyper([a], [b], z) assert tn( hyperexpand(hyper([3, d], [1, e], z)), hyper([3, d], [1, e], z), z) assert hyperexpand(hyper([3], [1, a, b], z)) == \ hyper((), (a, b), z) \ + z*hyper((), (a + 1, b), z)/(2*a) \ - z*(b - 4)*hyper((), (a + 1, b + 1), z)/(2*a*b) assert tn( hyperexpand(hyper([3], [1, d, e], z)), hyper([3], [1, d, e], z), z) def test_hyperexpand_special(): assert hyperexpand(hyper([a, b], [c], 1)) == \ gamma(c)*gamma(c - a - b)/gamma(c - a)/gamma(c - b) assert hyperexpand(hyper([a, b], [1 + a - b], -1)) == \ gamma(1 + a/2)*gamma(1 + a - b)/gamma(1 + a)/gamma(1 + a/2 - b) assert hyperexpand(hyper([a, b], [1 + b - a], -1)) == \ gamma(1 + b/2)*gamma(1 + b - a)/gamma(1 + b)/gamma(1 + b/2 - a) assert hyperexpand(meijerg([1 - z - a/2], [1 - z + a/2], [b/2], [-b/2], 1)) == \ gamma(1 - 2*z)*gamma(z + a/2 + b/2)/gamma(1 - z + a/2 - b/2) \ /gamma(1 - z - a/2 + b/2)/gamma(1 - z + a/2 + b/2) assert hyperexpand(hyper([a], [b], 0)) == 1 assert hyper([a], [b], 0) != 0 def test_Mod1_behavior(): from sympy import Symbol, simplify, lowergamma n = Symbol('n', integer=True) # Note: this should not hang. assert simplify(hyperexpand(meijerg([1], [], [n + 1], [0], z))) == \ lowergamma(n + 1, z) @slow def test_prudnikov_misc(): assert can_do([1, (3 + I)/2, (3 - I)/2], [S(3)/2, 2]) assert can_do([S.Half, a - 1], [S(3)/2, a + 1], lowerplane=True) assert can_do([], [b + 1]) assert can_do([a], [a - 1, b + 1]) assert can_do([a], [a - S.Half, 2*a]) assert can_do([a], [a - S.Half, 2*a + 1]) assert can_do([a], [a - S.Half, 2*a - 1]) assert can_do([a], [a + S.Half, 2*a]) assert can_do([a], [a + S.Half, 2*a + 1]) assert can_do([a], [a + S.Half, 2*a - 1]) assert can_do([S.Half], [b, 2 - b]) assert can_do([S.Half], [b, 3 - b]) assert can_do([1], [2, b]) assert can_do([a, a + S.Half], [2*a, b, 2*a - b + 1]) assert can_do([a, a + S.Half], [S.Half, 2*a, 2*a + S.Half]) assert can_do([a], [a + 1], lowerplane=True) # lowergamma @slow def test_prudnikov_1(): # A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990). # Integrals and Series: More Special Functions, Vol. 3,. # Gordon and Breach Science Publisher # 7.3.1 assert can_do([a, -a], [S.Half]) assert can_do([a, 1 - a], [S.Half]) assert can_do([a, 1 - a], [S(3)/2]) assert can_do([a, 2 - a], [S.Half]) assert can_do([a, 2 - a], [S(3)/2]) assert can_do([a, 2 - a], [S(3)/2]) assert can_do([a, a + S(1)/2], [2*a - 1]) assert can_do([a, a + S(1)/2], [2*a]) assert can_do([a, a + S(1)/2], [2*a + 1]) assert can_do([a, a + S(1)/2], [S(1)/2]) assert can_do([a, a + S(1)/2], [S(3)/2]) assert can_do([a, a/2 + 1], [a/2]) assert can_do([1, b], [2]) assert can_do([1, b], [b + 1], numerical=False) # Lerch Phi # NOTE: branches are complicated for |z| > 1 assert can_do([a], [2*a]) assert can_do([a], [2*a + 1]) assert can_do([a], [2*a - 1]) @slow def test_prudnikov_2(): h = S.Half assert can_do([-h, -h], [h]) assert can_do([-h, h], [3*h]) assert can_do([-h, h], [5*h]) assert can_do([-h, h], [7*h]) assert can_do([-h, 1], [h]) for p in [-h, h]: for n in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: for m in [-h, h, 3*h, 5*h, 7*h]: assert can_do([p, n], [m]) for n in [1, 2, 3, 4]: for m in [1, 2, 3, 4]: assert can_do([p, n], [m]) @slow def test_prudnikov_3(): h = S.Half assert can_do([S(1)/4, S(3)/4], [h]) assert can_do([S(1)/4, S(3)/4], [3*h]) assert can_do([S(1)/3, S(2)/3], [3*h]) assert can_do([S(3)/4, S(5)/4], [h]) assert can_do([S(3)/4, S(5)/4], [3*h]) for p in [1, 2, 3, 4]: for n in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4, 9*h]: for m in [1, 3*h, 2, 5*h, 3, 7*h, 4]: assert can_do([p, m], [n]) @slow def test_prudnikov_4(): h = S.Half for p in [3*h, 5*h, 7*h]: for n in [-h, h, 3*h, 5*h, 7*h]: for m in [3*h, 2, 5*h, 3, 7*h, 4]: assert can_do([p, m], [n]) for n in [1, 2, 3, 4]: for m in [2, 3, 4]: assert can_do([p, m], [n]) @slow def test_prudnikov_5(): h = S.Half for p in [1, 2, 3]: for q in range(p, 4): for r in [1, 2, 3]: for s in range(r, 4): assert can_do([-h, p, q], [r, s]) for p in [h, 1, 3*h, 2, 5*h, 3]: for q in [h, 3*h, 5*h]: for r in [h, 3*h, 5*h]: for s in [h, 3*h, 5*h]: if s <= q and s <= r: assert can_do([-h, p, q], [r, s]) for p in [h, 1, 3*h, 2, 5*h, 3]: for q in [1, 2, 3]: for r in [h, 3*h, 5*h]: for s in [1, 2, 3]: assert can_do([-h, p, q], [r, s]) @slow def test_prudnikov_6(): h = S.Half for m in [3*h, 5*h]: for n in [1, 2, 3]: for q in [h, 1, 2]: for p in [1, 2, 3]: assert can_do([h, q, p], [m, n]) for q in [1, 2, 3]: for p in [3*h, 5*h]: assert can_do([h, q, p], [m, n]) for q in [1, 2]: for p in [1, 2, 3]: for m in [1, 2, 3]: for n in [1, 2, 3]: assert can_do([h, q, p], [m, n]) assert can_do([h, h, 5*h], [3*h, 3*h]) assert can_do([h, 1, 5*h], [3*h, 3*h]) assert can_do([h, 2, 2], [1, 3]) # pages 435 to 457 contain more PFDD and stuff like this @slow def test_prudnikov_7(): assert can_do([3], [6]) h = S.Half for n in [h, 3*h, 5*h, 7*h]: assert can_do([-h], [n]) for m in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: # HERE for n in [-h, h, 3*h, 5*h, 7*h, 1, 2, 3, 4]: assert can_do([m], [n]) @slow def test_prudnikov_8(): h = S.Half # 7.12.2 for a in [1, 2, 3]: for b in [1, 2, 3]: for c in range(1, a + 1): for d in [h, 1, 3*h, 2, 5*h, 3]: assert can_do([a, b], [c, d]) for b in [3*h, 5*h]: for c in [h, 1, 3*h, 2, 5*h, 3]: for d in [1, 2, 3]: assert can_do([a, b], [c, d]) for a in [-h, h, 3*h, 5*h]: for b in [1, 2, 3]: for c in [h, 1, 3*h, 2, 5*h, 3]: for d in [1, 2, 3]: assert can_do([a, b], [c, d]) for b in [h, 3*h, 5*h]: for c in [h, 3*h, 5*h, 3]: for d in [h, 1, 3*h, 2, 5*h, 3]: if c <= b: assert can_do([a, b], [c, d]) def test_prudnikov_9(): # 7.13.1 [we have a general formula ... so this is a bit pointless] for i in range(9): assert can_do([], [(S(i) + 1)/2]) for i in range(5): assert can_do([], [-(2*S(i) + 1)/2]) @slow def test_prudnikov_10(): # 7.14.2 h = S.Half for p in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: for m in [1, 2, 3, 4]: for n in range(m, 5): assert can_do([p], [m, n]) for p in [1, 2, 3, 4]: for n in [h, 3*h, 5*h, 7*h]: for m in [1, 2, 3, 4]: assert can_do([p], [n, m]) for p in [3*h, 5*h, 7*h]: for m in [h, 1, 2, 5*h, 3, 7*h, 4]: assert can_do([p], [h, m]) assert can_do([p], [3*h, m]) for m in [h, 1, 2, 5*h, 3, 7*h, 4]: assert can_do([7*h], [5*h, m]) assert can_do([-S(1)/2], [S(1)/2, S(1)/2]) # shine-integral shi def test_prudnikov_11(): # 7.15 assert can_do([a, a + S.Half], [2*a, b, 2*a - b]) assert can_do([a, a + S.Half], [S(3)/2, 2*a, 2*a - S(1)/2]) assert can_do([S(1)/4, S(3)/4], [S(1)/2, S(1)/2, 1]) assert can_do([S(5)/4, S(3)/4], [S(3)/2, S(1)/2, 2]) assert can_do([S(5)/4, S(3)/4], [S(3)/2, S(3)/2, 1]) assert can_do([S(5)/4, S(7)/4], [S(3)/2, S(5)/2, 2]) assert can_do([1, 1], [S(3)/2, 2, 2]) # cosh-integral chi @slow def test_prudnikov_12(): # 7.16 assert can_do( [], [a, a + S.Half, 2*a], False) # branches only agree for some z! assert can_do([], [a, a + S.Half, 2*a + 1], False) # dito assert can_do([], [S.Half, a, a + S.Half]) assert can_do([], [S(3)/2, a, a + S.Half]) assert can_do([], [S(1)/4, S(1)/2, S(3)/4]) assert can_do([], [S(1)/2, S(1)/2, 1]) assert can_do([], [S(1)/2, S(3)/2, 1]) assert can_do([], [S(3)/4, S(3)/2, S(5)/4]) assert can_do([], [1, 1, S(3)/2]) assert can_do([], [1, 2, S(3)/2]) assert can_do([], [1, S(3)/2, S(3)/2]) assert can_do([], [S(5)/4, S(3)/2, S(7)/4]) assert can_do([], [2, S(3)/2, S(3)/2]) @slow def test_prudnikov_2F1(): h = S.Half # Elliptic integrals for p in [-h, h]: for m in [h, 3*h, 5*h, 7*h]: for n in [1, 2, 3, 4]: assert can_do([p, m], [n]) @XFAIL def test_prudnikov_fail_2F1(): assert can_do([a, b], [b + 1]) # incomplete beta function assert can_do([-1, b], [c]) # Poly. also -2, -3 etc # TODO polys # Legendre functions: assert can_do([a, b], [a + b + S.Half]) assert can_do([a, b], [a + b - S.Half]) assert can_do([a, b], [a + b + S(3)/2]) assert can_do([a, b], [(a + b + 1)/2]) assert can_do([a, b], [(a + b)/2 + 1]) assert can_do([a, b], [a - b + 1]) assert can_do([a, b], [a - b + 2]) assert can_do([a, b], [2*b]) assert can_do([a, b], [S.Half]) assert can_do([a, b], [S(3)/2]) assert can_do([a, 1 - a], [c]) assert can_do([a, 2 - a], [c]) assert can_do([a, 3 - a], [c]) assert can_do([a, a + S(1)/2], [c]) assert can_do([1, b], [c]) assert can_do([1, b], [S(3)/2]) assert can_do([S(1)/4, S(3)/4], [1]) # PFDD o = S(1) assert can_do([o/8, 1], [o/8*9]) assert can_do([o/6, 1], [o/6*7]) assert can_do([o/6, 1], [o/6*13]) assert can_do([o/5, 1], [o/5*6]) assert can_do([o/5, 1], [o/5*11]) assert can_do([o/4, 1], [o/4*5]) assert can_do([o/4, 1], [o/4*9]) assert can_do([o/3, 1], [o/3*4]) assert can_do([o/3, 1], [o/3*7]) assert can_do([o/8*3, 1], [o/8*11]) assert can_do([o/5*2, 1], [o/5*7]) assert can_do([o/5*2, 1], [o/5*12]) assert can_do([o/5*3, 1], [o/5*8]) assert can_do([o/5*3, 1], [o/5*13]) assert can_do([o/8*5, 1], [o/8*13]) assert can_do([o/4*3, 1], [o/4*7]) assert can_do([o/4*3, 1], [o/4*11]) assert can_do([o/3*2, 1], [o/3*5]) assert can_do([o/3*2, 1], [o/3*8]) assert can_do([o/5*4, 1], [o/5*9]) assert can_do([o/5*4, 1], [o/5*14]) assert can_do([o/6*5, 1], [o/6*11]) assert can_do([o/6*5, 1], [o/6*17]) assert can_do([o/8*7, 1], [o/8*15]) @XFAIL def test_prudnikov_fail_3F2(): assert can_do([a, a + S(1)/3, a + S(2)/3], [S(1)/3, S(2)/3]) assert can_do([a, a + S(1)/3, a + S(2)/3], [S(2)/3, S(4)/3]) assert can_do([a, a + S(1)/3, a + S(2)/3], [S(4)/3, S(5)/3]) # page 421 assert can_do([a, a + S(1)/3, a + S(2)/3], [3*a/2, (3*a + 1)/2]) # pages 422 ... assert can_do([-S.Half, S.Half, S.Half], [1, 1]) # elliptic integrals assert can_do([-S.Half, S.Half, 1], [S(3)/2, S(3)/2]) # TODO LOTS more # PFDD assert can_do([S(1)/8, S(3)/8, 1], [S(9)/8, S(11)/8]) assert can_do([S(1)/8, S(5)/8, 1], [S(9)/8, S(13)/8]) assert can_do([S(1)/8, S(7)/8, 1], [S(9)/8, S(15)/8]) assert can_do([S(1)/6, S(1)/3, 1], [S(7)/6, S(4)/3]) assert can_do([S(1)/6, S(2)/3, 1], [S(7)/6, S(5)/3]) assert can_do([S(1)/6, S(2)/3, 1], [S(5)/3, S(13)/6]) assert can_do([S.Half, 1, 1], [S(1)/4, S(3)/4]) # LOTS more @XFAIL def test_prudnikov_fail_other(): # 7.11.2 # 7.12.1 assert can_do([1, a], [b, 1 - 2*a + b]) # ??? # 7.14.2 assert can_do([-S(1)/2], [S(1)/2, 1]) # struve assert can_do([1], [S(1)/2, S(1)/2]) # struve assert can_do([S(1)/4], [S(1)/2, S(5)/4]) # PFDD assert can_do([S(3)/4], [S(3)/2, S(7)/4]) # PFDD assert can_do([1], [S(1)/4, S(3)/4]) # PFDD assert can_do([1], [S(3)/4, S(5)/4]) # PFDD assert can_do([1], [S(5)/4, S(7)/4]) # PFDD # TODO LOTS more # 7.15.2 assert can_do([S(1)/2, 1], [S(3)/4, S(5)/4, S(3)/2]) # PFDD assert can_do([S(1)/2, 1], [S(7)/4, S(5)/4, S(3)/2]) # PFDD # 7.16.1 assert can_do([], [S(1)/3, S(2/3)]) # PFDD assert can_do([], [S(2)/3, S(4/3)]) # PFDD assert can_do([], [S(5)/3, S(4/3)]) # PFDD # XXX this does not *evaluate* right?? assert can_do([], [a, a + S.Half, 2*a - 1]) def test_bug(): h = hyper([-1, 1], [z], -1) assert hyperexpand(h) == (z + 1)/z

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from random import randrange from sympy.simplify.hyperexpand import (ShiftA, ShiftB, UnShiftA, UnShiftB, MeijerShiftA, MeijerShiftB, MeijerShiftC, MeijerShiftD, MeijerUnShiftA, MeijerUnShiftB, MeijerUnShiftC, MeijerUnShiftD, ReduceOrder, reduce_order, apply_operators, devise_plan, make_derivative_operator, Formula, hyperexpand, Hyper_Function, G_Function, reduce_order_meijer, build_hypergeometric_formula) from sympy import hyper, I, S, meijerg, Piecewise from sympy.abc import z, a, b, c from sympy.utilities.pytest import XFAIL, raises, slow from sympy.utilities.randtest import verify_numerically as tn from sympy.core.compatibility import range from sympy import (cos, sin, log, exp, asin, lowergamma, atanh, besseli, gamma, sqrt, pi, erf, exp_polar) def test_branch_bug(): assert hyperexpand(hyper((-S(1)/3, S(1)/2), (S(2)/3, S(3)/2), -z)) == \ -z**S('1/3')*lowergamma(exp_polar(I*pi)/3, z)/5 \ + sqrt(pi)*erf(sqrt(z))/(5*sqrt(z)) assert hyperexpand(meijerg([S(7)/6, 1], [], [S(2)/3], [S(1)/6, 0], z)) == \ 2*z**S('2/3')*(2*sqrt(pi)*erf(sqrt(z))/sqrt(z) - 2*lowergamma( S(2)/3, z)/z**S('2/3'))*gamma(S(2)/3)/gamma(S(5)/3) def test_hyperexpand(): assert hyperexpand(hyper([], [], z)) == exp(z) assert hyperexpand(hyper([1, 1], [2], -z)*z) == log(1 + z) assert hyperexpand(hyper([], [S.Half], -z**2/4)) == cos(z) assert hyperexpand(z*hyper([], [S('3/2')], -z**2/4)) == sin(z) assert hyperexpand(hyper([S('1/2'), S('1/2')], [S('3/2')], z**2)*z) \ == asin(z) def can_do(ap, bq, numerical=True, div=1, lowerplane=False): from sympy import exp_polar, exp r = hyperexpand(hyper(ap, bq, z)) if r.has(hyper): return False if not numerical: return True repl = {} for n, a in enumerate(r.free_symbols - set([z])): repl[a] = randcplx(n)/div [a, b, c, d] = [2, -1, 3, 1] if lowerplane: [a, b, c, d] = [2, -2, 3, -1] return tn( hyper(ap, bq, z).subs(repl), r.replace(exp_polar, exp).subs(repl), z, a=a, b=b, c=c, d=d) def test_roach(): assert can_do([S(1)/2], [S(9)/2]) assert can_do([], [1, S(5)/2, 4]) assert can_do([-S.Half, 1, 2], [3, 4]) assert can_do([S(1)/3], [-S(2)/3, -S(1)/2, S(1)/2, 1]) assert can_do([-S(3)/2, -S(1)/2], [-S(5)/2, 1]) assert can_do([-S(3)/2, ], [-S(1)/2, S(1)/2]) assert can_do([-S(3)/2, -S(1)/2], [2]) @XFAIL def test_roach_fail(): assert can_do([-S(1)/2, 1], [S(1)/4, S(1)/2, S(3)/4]) assert can_do([S(3)/2], [S(5)/2, 5]) assert can_do([-S(1)/2, S(1)/2, 1], [S(3)/2, S(5)/2]) assert can_do([1, 2, 3], [S(1)/2, 4]) assert can_do([S(1)/2], [-S(1)/3, -S(1)/2, -S(2)/3]) def test_polynomial(): from sympy import oo assert hyperexpand(hyper([], [-1], z)) == oo assert hyperexpand(hyper([-2], [-1], z)) == oo assert hyperexpand(hyper([0, 0], [-1], z)) == 1 assert can_do([-5, -2, randcplx(), randcplx()], [-10, randcplx()]) def test_hyperexpand_bases(): assert hyperexpand(hyper([2], [a], z)) == \ a + z**(-a + 1)*(-a**2 + 3*a + z*(a - 1) - 2)*exp(z)* \ lowergamma(a - 1, z) - 1 assert hyperexpand(hyper([1, 2], [3], z)) == -2/z - 2*log(-z + 1)/z**2 assert hyperexpand(hyper([S.Half, 2], [S(3)/2], z)) == \ -1/(2*z - 2) + atanh(sqrt(z))/sqrt(z)/2 assert hyperexpand(hyper([S(1)/2, S(1)/2], [S(5)/2], z)) == \ (-3*z + 3)/4/(z*sqrt(-z + 1)) \ + (6*z - 3)*asin(sqrt(z))/(4*z**(S(3)/2)) assert hyperexpand(hyper([1, 2], [S(3)/2], z)) == -1/(2*z - 2) \ - asin(sqrt(z))/(sqrt(z)*(2*z - 2)*sqrt(-z + 1)) assert hyperexpand(hyper([-S.Half - 1, 1, 2], [S.Half, 3], z)) == \ sqrt(z)*(6*z/7 - S(6)/5)*atanh(sqrt(z)) \ + (-30*z**2 + 32*z - 6)/35/z - 6*log(-z + 1)/(35*z**2) assert hyperexpand(hyper([1 + S.Half, 1, 1], [2, 2], z)) == \ -4*log(sqrt(-z + 1)/2 + S(1)/2)/z assert hyperexpand(hyper([2], [b, 1], z)) == \ z**(-b/2 + S(1)/2)*besseli(b - 1, 2*sqrt(z))*gamma(b) \ + z**(-b/2 + 1)*besseli(b, 2*sqrt(z))*gamma(b) def test_hyperexpand_parametric(): assert hyperexpand(hyper([a, S(1)/2 + a], [S(1)/2], z)) \ == (1 + sqrt(z))**(-2*a)/2 + (1 - sqrt(z))**(-2*a)/2 assert hyperexpand(hyper([a, -S(1)/2 + a], [2*a], z)) \ == 2**(2*a - 1)*((-z + 1)**(S(1)/2) + 1)**(-2*a + 1) def test_shifted_sum(): from sympy import simplify assert simplify(hyperexpand(z**4*hyper([2], [3, S('3/2')], -z**2))) \ == z*sin(2*z) + (-z**2 + S.Half)*cos(2*z) - S.Half def _randrat(): return S(randrange(25) + 10)/50 def randcplx(offset=-1): return _randrat() + I*_randrat() + I*(1 + offset) @slow def test_formulae(): from sympy.simplify.hyperexpand import FormulaCollection formulae = FormulaCollection().formulae for formula in formulae: h = formula.func(formula.z) rep = {} for n, sym in enumerate(formula.symbols): rep[sym] = randcplx(n) # Just replace all exp_polar by exp, this usually works. # first test if the closed-form is actually correct h = h.subs(rep) closed_form = formula.closed_form.subs(rep).rewrite('nonrepsmall') z = formula.z assert tn(h, closed_form.replace(exp_polar, exp), z) # now test the computed matrix cl = (formula.C * formula.B)[0].subs(rep).rewrite('nonrepsmall') assert tn(closed_form.replace( exp_polar, exp), cl.replace(exp_polar, exp), z) deriv1 = z*formula.B.applyfunc(lambda t: t.rewrite( 'nonrepsmall')).diff(z) deriv2 = formula.M * formula.B for d1, d2 in zip(deriv1, deriv2): assert tn(d1.subs(rep).replace(exp_polar, exp), d2.subs(rep).rewrite('nonrepsmall').replace(exp_polar, exp), z) def test_meijerg_formulae(): from sympy.simplify.hyperexpand import MeijerFormulaCollection formulae = MeijerFormulaCollection().formulae for sig in formulae: for formula in formulae[sig]: g = meijerg(formula.func.an, formula.func.ap, formula.func.bm, formula.func.bq, formula.z) rep = {} for sym in formula.symbols: rep[sym] = randcplx() # first test if the closed-form is actually correct g = g.subs(rep) closed_form = formula.closed_form.subs(rep) z = formula.z assert tn(g, closed_form, z) # now test the computed matrix cl = (formula.C * formula.B)[0].subs(rep) assert tn(closed_form, cl, z) deriv1 = z*formula.B.diff(z) deriv2 = formula.M * formula.B for d1, d2 in zip(deriv1, deriv2): assert tn(d1.subs(rep), d2.subs(rep), z) def op(f): return z*f.diff(z) def test_plan(): assert devise_plan(Hyper_Function([0], ()), Hyper_Function([0], ()), z) == [] with raises(ValueError): devise_plan(Hyper_Function([1], ()), Hyper_Function((), ()), z) with raises(ValueError): devise_plan(Hyper_Function([2], [1]), Hyper_Function([2], [2]), z) with raises(ValueError): devise_plan(Hyper_Function([2], []), Hyper_Function([S("1/2")], []), z) # We cannot use pi/(10000 + n) because polys is insanely slow. a1, a2, b1 = (randcplx(n) for n in range(3)) b1 += 2*I h = hyper([a1, a2], [b1], z) h2 = hyper((a1 + 1, a2), [b1], z) assert tn(apply_operators(h, devise_plan(Hyper_Function((a1 + 1, a2), [b1]), Hyper_Function((a1, a2), [b1]), z), op), h2, z) h2 = hyper((a1 + 1, a2 - 1), [b1], z) assert tn(apply_operators(h, devise_plan(Hyper_Function((a1 + 1, a2 - 1), [b1]), Hyper_Function((a1, a2), [b1]), z), op), h2, z) def test_plan_derivatives(): a1, a2, a3 = 1, 2, S('1/2') b1, b2 = 3, S('5/2') h = Hyper_Function((a1, a2, a3), (b1, b2)) h2 = Hyper_Function((a1 + 1, a2 + 1, a3 + 2), (b1 + 1, b2 + 1)) ops = devise_plan(h2, h, z) f = Formula(h, z, h(z), []) deriv = make_derivative_operator(f.M, z) assert tn((apply_operators(f.C, ops, deriv)*f.B)[0], h2(z), z) h2 = Hyper_Function((a1, a2 - 1, a3 - 2), (b1 - 1, b2 - 1)) ops = devise_plan(h2, h, z) assert tn((apply_operators(f.C, ops, deriv)*f.B)[0], h2(z), z) def test_reduction_operators(): a1, a2, b1 = (randcplx(n) for n in range(3)) h = hyper([a1], [b1], z) assert ReduceOrder(2, 0) is None assert ReduceOrder(2, -1) is None assert ReduceOrder(1, S('1/2')) is None h2 = hyper((a1, a2), (b1, a2), z) assert tn(ReduceOrder(a2, a2).apply(h, op), h2, z) h2 = hyper((a1, a2 + 1), (b1, a2), z) assert tn(ReduceOrder(a2 + 1, a2).apply(h, op), h2, z) h2 = hyper((a2 + 4, a1), (b1, a2), z) assert tn(ReduceOrder(a2 + 4, a2).apply(h, op), h2, z) # test several step order reduction ap = (a2 + 4, a1, b1 + 1) bq = (a2, b1, b1) func, ops = reduce_order(Hyper_Function(ap, bq)) assert func.ap == (a1,) assert func.bq == (b1,) assert tn(apply_operators(h, ops, op), hyper(ap, bq, z), z) def test_shift_operators(): a1, a2, b1, b2, b3 = (randcplx(n) for n in range(5)) h = hyper((a1, a2), (b1, b2, b3), z) raises(ValueError, lambda: ShiftA(0)) raises(ValueError, lambda: ShiftB(1)) assert tn(ShiftA(a1).apply(h, op), hyper((a1 + 1, a2), (b1, b2, b3), z), z) assert tn(ShiftA(a2).apply(h, op), hyper((a1, a2 + 1), (b1, b2, b3), z), z) assert tn(ShiftB(b1).apply(h, op), hyper((a1, a2), (b1 - 1, b2, b3), z), z) assert tn(ShiftB(b2).apply(h, op), hyper((a1, a2), (b1, b2 - 1, b3), z), z) assert tn(ShiftB(b3).apply(h, op), hyper((a1, a2), (b1, b2, b3 - 1), z), z) def test_ushift_operators(): a1, a2, b1, b2, b3 = (randcplx(n) for n in range(5)) h = hyper((a1, a2), (b1, b2, b3), z) raises(ValueError, lambda: UnShiftA((1,), (), 0, z)) raises(ValueError, lambda: UnShiftB((), (-1,), 0, z)) raises(ValueError, lambda: UnShiftA((1,), (0, -1, 1), 0, z)) raises(ValueError, lambda: UnShiftB((0, 1), (1,), 0, z)) s = UnShiftA((a1, a2), (b1, b2, b3), 0, z) assert tn(s.apply(h, op), hyper((a1 - 1, a2), (b1, b2, b3), z), z) s = UnShiftA((a1, a2), (b1, b2, b3), 1, z) assert tn(s.apply(h, op), hyper((a1, a2 - 1), (b1, b2, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 0, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1 + 1, b2, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 1, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1, b2 + 1, b3), z), z) s = UnShiftB((a1, a2), (b1, b2, b3), 2, z) assert tn(s.apply(h, op), hyper((a1, a2), (b1, b2, b3 + 1), z), z) def can_do_meijer(a1, a2, b1, b2, numeric=True): from sympy import unpolarify, expand r = hyperexpand(meijerg(a1, a2, b1, b2, z)) if r.has(meijerg): return False # NOTE hyperexpand() returns a truly branched function, whereas numerical # evaluation only works on the main branch. Since we are evaluating on # the main branch, this should not be a problem, but expressions like # exp_polar(I*pi/2*x)**a are evaluated incorrectly. We thus have to get # rid of them. The expand heuristically does this... r = unpolarify(expand(r, force=True, power_base=True, power_exp=False, mul=False, log=False, multinomial=False, basic=False)) if not numeric: return True repl = {} for n, a in enumerate(meijerg(a1, a2, b1, b2, z).free_symbols - set([z])): repl[a] = randcplx(n) return tn(meijerg(a1, a2, b1, b2, z).subs(repl), r.subs(repl), z) @slow def test_meijerg_expand(): from sympy import combsimp, simplify # from mpmath docs assert hyperexpand(meijerg([[], []], [[0], []], -z)) == exp(z) assert hyperexpand(meijerg([[1, 1], []], [[1], [0]], z)) == \ log(z + 1) assert hyperexpand(meijerg([[1, 1], []], [[1], [1]], z)) == \ z/(z + 1) assert hyperexpand(meijerg([[], []], [[S(1)/2], [0]], (z/2)**2)) \ == sin(z)/sqrt(pi) assert hyperexpand(meijerg([[], []], [[0], [S(1)/2]], (z/2)**2)) \ == cos(z)/sqrt(pi) assert can_do_meijer([], [a], [a - 1, a - S.Half], []) assert can_do_meijer([], [], [a/2], [-a/2], False) # branches... assert can_do_meijer([a], [b], [a], [b, a - 1]) # wikipedia assert hyperexpand(meijerg([1], [], [], [0], z)) == \ Piecewise((0, abs(z) < 1), (1, abs(1/z) < 1), (meijerg([1], [], [], [0], z), True)) assert hyperexpand(meijerg([], [1], [0], [], z)) == \ Piecewise((1, abs(z) < 1), (0, abs(1/z) < 1), (meijerg([], [1], [0], [], z), True)) # The Special Functions and their Approximations assert can_do_meijer([], [], [a + b/2], [a, a - b/2, a + S.Half]) assert can_do_meijer( [], [], [a], [b], False) # branches only agree for small z assert can_do_meijer([], [S.Half], [a], [-a]) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, a + S.Half], [b, b + S.Half]) assert can_do_meijer([], [], [a, -a], [0, S.Half], False) # dito assert can_do_meijer([], [], [a, a + S.Half, b, b + S.Half], []) assert can_do_meijer([S.Half], [], [0], [a, -a]) assert can_do_meijer([S.Half], [], [a], [0, -a], False) # dito assert can_do_meijer([], [a - S.Half], [a, b], [a - S.Half], False) assert can_do_meijer([], [a + S.Half], [a + b, a - b, a], [], False) assert can_do_meijer([a + S.Half], [], [b, 2*a - b, a], [], False) # This for example is actually zero. assert can_do_meijer([], [], [], [a, b]) # Testing a bug: assert hyperexpand(meijerg([0, 2], [], [], [-1, 1], z)) == \ Piecewise((0, abs(z) < 1), (z/2 - 1/(2*z), abs(1/z) < 1), (meijerg([0, 2], [], [], [-1, 1], z), True)) # Test that the simplest possible answer is returned: assert combsimp(simplify(hyperexpand( meijerg([1], [1 - a], [-a/2, -a/2 + S(1)/2], [], 1/z)))) == \ -2*sqrt(pi)*(sqrt(z + 1) + 1)**a/a # Test that hyper is returned assert hyperexpand(meijerg([1], [], [a], [0, 0], z)) == hyper( (a,), (a + 1, a + 1), z*exp_polar(I*pi))*z**a*gamma(a)/gamma(a + 1)**2 def test_meijerg_lookup(): from sympy import uppergamma, Si, Ci assert hyperexpand(meijerg([a], [], [b, a], [], z)) == \ z**b*exp(z)*gamma(-a + b + 1)*uppergamma(a - b, z) assert hyperexpand(meijerg([0], [], [0, 0], [], z)) == \ exp(z)*uppergamma(0, z) assert can_do_meijer([a], [], [b, a + 1], []) assert can_do_meijer([a], [], [b + 2, a], []) assert can_do_meijer([a], [], [b - 2, a], []) assert hyperexpand(meijerg([a], [], [a, a, a - S(1)/2], [], z)) == \ -sqrt(pi)*z**(a - S(1)/2)*(2*cos(2*sqrt(z))*(Si(2*sqrt(z)) - pi/2) - 2*sin(2*sqrt(z))*Ci(2*sqrt(z))) == \ hyperexpand(meijerg([a], [], [a, a - S(1)/2, a], [], z)) == \ hyperexpand(meijerg([a], [], [a - S(1)/2, a, a], [], z)) assert can_do_meijer([a - 1], [], [a + 2, a - S(3)/2, a + 1], []) @XFAIL def test_meijerg_expand_fail(): # These basically test hyper([], [1/2 - a, 1/2 + 1, 1/2], z), # which is *very* messy. But since the meijer g actually yields a # sum of bessel functions, things can sometimes be simplified a lot and # are then put into tables... assert can_do_meijer([], [], [a + S.Half], [a, a - b/2, a + b/2]) assert can_do_meijer([], [], [0, S.Half], [a, -a]) assert can_do_meijer([], [], [3*a - S.Half, a, -a - S.Half], [a - S.Half]) assert can_do_meijer([], [], [0, a - S.Half, -a - S.Half], [S.Half]) assert can_do_meijer([], [], [a, b + S(1)/2, b], [2*b - a]) assert can_do_meijer([], [], [a, b + S(1)/2, b, 2*b - a]) assert can_do_meijer([S.Half], [], [-a, a], [0]) @slow def test_meijerg(): # carefully set up the parameters. # NOTE: this used to fail sometimes. I believe it is fixed, but if you # hit an inexplicable test failure here, please let me know the seed. a1, a2 = (randcplx(n) - 5*I - n*I for n in range(2)) b1, b2 = (randcplx(n) + 5*I + n*I for n in range(2)) b3, b4, b5, a3, a4, a5 = (randcplx() for n in range(6)) g = meijerg([a1], [a3, a4], [b1], [b3, b4], z) assert ReduceOrder.meijer_minus(3, 4) is None assert ReduceOrder.meijer_plus(4, 3) is None g2 = meijerg([a1, a2], [a3, a4], [b1], [b3, b4, a2], z) assert tn(ReduceOrder.meijer_plus(a2, a2).apply(g, op), g2, z) g2 = meijerg([a1, a2], [a3, a4], [b1], [b3, b4, a2 + 1], z) assert tn(ReduceOrder.meijer_plus(a2, a2 + 1).apply(g, op), g2, z) g2 = meijerg([a1, a2 - 1], [a3, a4], [b1], [b3, b4, a2 + 2], z) assert tn(ReduceOrder.meijer_plus(a2 - 1, a2 + 2).apply(g, op), g2, z) g2 = meijerg([a1], [a3, a4, b2 - 1], [b1, b2 + 2], [b3, b4], z) assert tn(ReduceOrder.meijer_minus( b2 + 2, b2 - 1).apply(g, op), g2, z, tol=1e-6) # test several-step reduction an = [a1, a2] bq = [b3, b4, a2 + 1] ap = [a3, a4, b2 - 1] bm = [b1, b2 + 1] niq, ops = reduce_order_meijer(G_Function(an, ap, bm, bq)) assert niq.an == (a1,) assert set(niq.ap) == set([a3, a4]) assert niq.bm == (b1,) assert set(niq.bq) == set([b3, b4]) assert tn(apply_operators(g, ops, op), meijerg(an, ap, bm, bq, z), z) def test_meijerg_shift_operators(): # carefully set up the parameters. XXX this still fails sometimes a1, a2, a3, a4, a5, b1, b2, b3, b4, b5 = (randcplx(n) for n in range(10)) g = meijerg([a1], [a3, a4], [b1], [b3, b4], z) assert tn(MeijerShiftA(b1).apply(g, op), meijerg([a1], [a3, a4], [b1 + 1], [b3, b4], z), z) assert tn(MeijerShiftB(a1).apply(g, op), meijerg([a1 - 1], [a3, a4], [b1], [b3, b4], z), z) assert tn(MeijerShiftC(b3).apply(g, op), meijerg([a1], [a3, a4], [b1], [b3 + 1, b4], z), z) assert tn(MeijerShiftD(a3).apply(g, op), meijerg([a1], [a3 - 1, a4], [b1], [b3, b4], z), z) s = MeijerUnShiftA([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3, a4], [b1 - 1], [b3, b4], z), z) s = MeijerUnShiftC([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3, a4], [b1], [b3 - 1, b4], z), z) s = MeijerUnShiftB([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1 + 1], [a3, a4], [b1], [b3, b4], z), z) s = MeijerUnShiftD([a1], [a3, a4], [b1], [b3, b4], 0, z) assert tn( s.apply(g, op), meijerg([a1], [a3 + 1, a4], [b1], [b3, b4], z), z) @slow def test_meijerg_confluence(): def t(m, a, b): from sympy import sympify, Piecewise a, b = sympify([a, b]) m_ = m m = hyperexpand(m) if not m == Piecewise((a, abs(z) < 1), (b, abs(1/z) < 1), (m_, True)): return False if not (m.args[0].args[0] == a and m.args[1].args[0] == b): return False z0 = randcplx()/10 if abs(m.subs(z, z0).n() - a.subs(z, z0).n()).n() > 1e-10: return False if abs(m.subs(z, 1/z0).n() - b.subs(z, 1/z0).n()).n() > 1e-10: return False return True assert t(meijerg([], [1, 1], [0, 0], [], z), -log(z), 0) assert t(meijerg( [], [3, 1], [0, 0], [], z), -z**2/4 + z - log(z)/2 - S(3)/4, 0) assert t(meijerg([], [3, 1], [-1, 0], [], z), z**2/12 - z/2 + log(z)/2 + S(1)/4 + 1/(6*z), 0) assert t(meijerg([], [1, 1, 1, 1], [0, 0, 0, 0], [], z), -log(z)**3/6, 0) assert t(meijerg([1, 1], [], [], [0, 0], z), 0, -log(1/z)) assert t(meijerg([1, 1], [2, 2], [1, 1], [0, 0], z), -z*log(z) + 2*z, -log(1/z) + 2) assert t(meijerg([S(1)/2], [1, 1], [0, 0], [S(3)/2], z), log(z)/2 - 1, 0) def u(an, ap, bm, bq): m = meijerg(an, ap, bm, bq, z) m2 = hyperexpand(m, allow_hyper=True) if m2.has(meijerg) and not (m2.is_Piecewise and len(m2.args) == 3): return False return tn(m, m2, z) assert u([], [1], [0, 0], []) assert u([1, 1], [], [], [0]) assert u([1, 1], [2, 2, 5], [1, 1, 6], [0, 0]) assert u([1, 1], [2, 2, 5], [1, 1, 6], [0]) def test_lerchphi(): from sympy import combsimp, exp_polar, polylog, log, lerchphi assert hyperexpand(hyper([1, a], [a + 1], z)/a) == lerchphi(z, 1, a) assert hyperexpand( hyper([1, a, a], [a + 1, a + 1], z)/a**2) == lerchphi(z, 2, a) assert hyperexpand(hyper([1, a, a, a], [a + 1, a + 1, a + 1], z)/a**3) == \ lerchphi(z, 3, a) assert hyperexpand(hyper([1] + [a]*10, [a + 1]*10, z)/a**10) == \ lerchphi(z, 10, a) assert combsimp(hyperexpand(meijerg([0, 1 - a], [], [0], [-a], exp_polar(-I*pi)*z))) == lerchphi(z, 1, a) assert combsimp(hyperexpand(meijerg([0, 1 - a, 1 - a], [], [0], [-a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 2, a) assert combsimp(hyperexpand(meijerg([0, 1 - a, 1 - a, 1 - a], [], [0], [-a, -a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 3, a) assert hyperexpand(z*hyper([1, 1], [2], z)) == -log(1 + -z) assert hyperexpand(z*hyper([1, 1, 1], [2, 2], z)) == polylog(2, z) assert hyperexpand(z*hyper([1, 1, 1, 1], [2, 2, 2], z)) == polylog(3, z) assert hyperexpand(hyper([1, a, 1 + S(1)/2], [a + 1, S(1)/2], z)) == \ -2*a/(z - 1) + (-2*a**2 + a)*lerchphi(z, 1, a) # Now numerical tests. These make sure reductions etc are carried out # correctly # a rational function (polylog at negative integer order) assert can_do([2, 2, 2], [1, 1]) # NOTE these contain log(1-x) etc ... better make sure we have |z| < 1 # reduction of order for polylog assert can_do([1, 1, 1, b + 5], [2, 2, b], div=10) # reduction of order for lerchphi # XXX lerchphi in mpmath is flaky assert can_do( [1, a, a, a, b + 5], [a + 1, a + 1, a + 1, b], numerical=False) # test a bug from sympy import Abs assert hyperexpand(hyper([S(1)/2, S(1)/2, S(1)/2, 1], [S(3)/2, S(3)/2, S(3)/2], S(1)/4)) == \ Abs(-polylog(3, exp_polar(I*pi)/2) + polylog(3, S(1)/2)) def test_partial_simp(): # First test that hypergeometric function formulae work. a, b, c, d, e = (randcplx() for _ in range(5)) for func in [Hyper_Function([a, b, c], [d, e]), Hyper_Function([], [a, b, c, d, e])]: f = build_hypergeometric_formula(func) z = f.z assert f.closed_form == func(z) deriv1 = f.B.diff(z)*z deriv2 = f.M*f.B for func1, func2 in zip(deriv1, deriv2): assert tn(func1, func2, z) # Now test that formulae are partially simplified. from sympy.abc import a, b, z assert hyperexpand(hyper([3, a], [1, b], z)) == \ (-a*b/2 + a*z/2 + 2*a)*hyper([a + 1], [b], z) \ + (a*b/2 - 2*a + 1)*hyper([a], [b], z) assert tn( hyperexpand(hyper([3, d], [1, e], z)), hyper([3, d], [1, e], z), z) assert hyperexpand(hyper([3], [1, a, b], z)) == \ hyper((), (a, b), z) \ + z*hyper((), (a + 1, b), z)/(2*a) \ - z*(b - 4)*hyper((), (a + 1, b + 1), z)/(2*a*b) assert tn( hyperexpand(hyper([3], [1, d, e], z)), hyper([3], [1, d, e], z), z) def test_hyperexpand_special(): assert hyperexpand(hyper([a, b], [c], 1)) == \ gamma(c)*gamma(c - a - b)/gamma(c - a)/gamma(c - b) assert hyperexpand(hyper([a, b], [1 + a - b], -1)) == \ gamma(1 + a/2)*gamma(1 + a - b)/gamma(1 + a)/gamma(1 + a/2 - b) assert hyperexpand(hyper([a, b], [1 + b - a], -1)) == \ gamma(1 + b/2)*gamma(1 + b - a)/gamma(1 + b)/gamma(1 + b/2 - a) assert hyperexpand(meijerg([1 - z - a/2], [1 - z + a/2], [b/2], [-b/2], 1)) == \ gamma(1 - 2*z)*gamma(z + a/2 + b/2)/gamma(1 - z + a/2 - b/2) \ /gamma(1 - z - a/2 + b/2)/gamma(1 - z + a/2 + b/2) assert hyperexpand(hyper([a], [b], 0)) == 1 assert hyper([a], [b], 0) != 0 def test_Mod1_behavior(): from sympy import Symbol, simplify, lowergamma n = Symbol('n', integer=True) # Note: this should not hang. assert simplify(hyperexpand(meijerg([1], [], [n + 1], [0], z))) == \ lowergamma(n + 1, z) @slow def test_prudnikov_misc(): assert can_do([1, (3 + I)/2, (3 - I)/2], [S(3)/2, 2]) assert can_do([S.Half, a - 1], [S(3)/2, a + 1], lowerplane=True) assert can_do([], [b + 1]) assert can_do([a], [a - 1, b + 1]) assert can_do([a], [a - S.Half, 2*a]) assert can_do([a], [a - S.Half, 2*a + 1]) assert can_do([a], [a - S.Half, 2*a - 1]) assert can_do([a], [a + S.Half, 2*a]) assert can_do([a], [a + S.Half, 2*a + 1]) assert can_do([a], [a + S.Half, 2*a - 1]) assert can_do([S.Half], [b, 2 - b]) assert can_do([S.Half], [b, 3 - b]) assert can_do([1], [2, b]) assert can_do([a, a + S.Half], [2*a, b, 2*a - b + 1]) assert can_do([a, a + S.Half], [S.Half, 2*a, 2*a + S.Half]) assert can_do([a], [a + 1], lowerplane=True) # lowergamma @slow def test_prudnikov_1(): # A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990). # Integrals and Series: More Special Functions, Vol. 3,. # Gordon and Breach Science Publisher # 7.3.1 assert can_do([a, -a], [S.Half]) assert can_do([a, 1 - a], [S.Half]) assert can_do([a, 1 - a], [S(3)/2]) assert can_do([a, 2 - a], [S.Half]) assert can_do([a, 2 - a], [S(3)/2]) assert can_do([a, 2 - a], [S(3)/2]) assert can_do([a, a + S(1)/2], [2*a - 1]) assert can_do([a, a + S(1)/2], [2*a]) assert can_do([a, a + S(1)/2], [2*a + 1]) assert can_do([a, a + S(1)/2], [S(1)/2]) assert can_do([a, a + S(1)/2], [S(3)/2]) assert can_do([a, a/2 + 1], [a/2]) assert can_do([1, b], [2]) assert can_do([1, b], [b + 1], numerical=False) # Lerch Phi # NOTE: branches are complicated for |z| > 1 assert can_do([a], [2*a]) assert can_do([a], [2*a + 1]) assert can_do([a], [2*a - 1]) @slow def test_prudnikov_2(): h = S.Half assert can_do([-h, -h], [h]) assert can_do([-h, h], [3*h]) assert can_do([-h, h], [5*h]) assert can_do([-h, h], [7*h]) assert can_do([-h, 1], [h]) for p in [-h, h]: for n in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: for m in [-h, h, 3*h, 5*h, 7*h]: assert can_do([p, n], [m]) for n in [1, 2, 3, 4]: for m in [1, 2, 3, 4]: assert can_do([p, n], [m]) @slow def test_prudnikov_3(): h = S.Half assert can_do([S(1)/4, S(3)/4], [h]) assert can_do([S(1)/4, S(3)/4], [3*h]) assert can_do([S(1)/3, S(2)/3], [3*h]) assert can_do([S(3)/4, S(5)/4], [h]) assert can_do([S(3)/4, S(5)/4], [3*h]) for p in [1, 2, 3, 4]: for n in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4, 9*h]: for m in [1, 3*h, 2, 5*h, 3, 7*h, 4]: assert can_do([p, m], [n]) @slow def test_prudnikov_4(): h = S.Half for p in [3*h, 5*h, 7*h]: for n in [-h, h, 3*h, 5*h, 7*h]: for m in [3*h, 2, 5*h, 3, 7*h, 4]: assert can_do([p, m], [n]) for n in [1, 2, 3, 4]: for m in [2, 3, 4]: assert can_do([p, m], [n]) @slow def test_prudnikov_5(): h = S.Half for p in [1, 2, 3]: for q in range(p, 4): for r in [1, 2, 3]: for s in range(r, 4): assert can_do([-h, p, q], [r, s]) for p in [h, 1, 3*h, 2, 5*h, 3]: for q in [h, 3*h, 5*h]: for r in [h, 3*h, 5*h]: for s in [h, 3*h, 5*h]: if s <= q and s <= r: assert can_do([-h, p, q], [r, s]) for p in [h, 1, 3*h, 2, 5*h, 3]: for q in [1, 2, 3]: for r in [h, 3*h, 5*h]: for s in [1, 2, 3]: assert can_do([-h, p, q], [r, s]) @slow def test_prudnikov_6(): h = S.Half for m in [3*h, 5*h]: for n in [1, 2, 3]: for q in [h, 1, 2]: for p in [1, 2, 3]: assert can_do([h, q, p], [m, n]) for q in [1, 2, 3]: for p in [3*h, 5*h]: assert can_do([h, q, p], [m, n]) for q in [1, 2]: for p in [1, 2, 3]: for m in [1, 2, 3]: for n in [1, 2, 3]: assert can_do([h, q, p], [m, n]) assert can_do([h, h, 5*h], [3*h, 3*h]) assert can_do([h, 1, 5*h], [3*h, 3*h]) assert can_do([h, 2, 2], [1, 3]) # pages 435 to 457 contain more PFDD and stuff like this @slow def test_prudnikov_7(): assert can_do([3], [6]) h = S.Half for n in [h, 3*h, 5*h, 7*h]: assert can_do([-h], [n]) for m in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: # HERE for n in [-h, h, 3*h, 5*h, 7*h, 1, 2, 3, 4]: assert can_do([m], [n]) @slow def test_prudnikov_8(): h = S.Half # 7.12.2 for a in [1, 2, 3]: for b in [1, 2, 3]: for c in range(1, a + 1): for d in [h, 1, 3*h, 2, 5*h, 3]: assert can_do([a, b], [c, d]) for b in [3*h, 5*h]: for c in [h, 1, 3*h, 2, 5*h, 3]: for d in [1, 2, 3]: assert can_do([a, b], [c, d]) for a in [-h, h, 3*h, 5*h]: for b in [1, 2, 3]: for c in [h, 1, 3*h, 2, 5*h, 3]: for d in [1, 2, 3]: assert can_do([a, b], [c, d]) for b in [h, 3*h, 5*h]: for c in [h, 3*h, 5*h, 3]: for d in [h, 1, 3*h, 2, 5*h, 3]: if c <= b: assert can_do([a, b], [c, d]) def test_prudnikov_9(): # 7.13.1 [we have a general formula ... so this is a bit pointless] for i in range(9): assert can_do([], [(S(i) + 1)/2]) for i in range(5): assert can_do([], [-(2*S(i) + 1)/2]) @slow def test_prudnikov_10(): # 7.14.2 h = S.Half for p in [-h, h, 1, 3*h, 2, 5*h, 3, 7*h, 4]: for m in [1, 2, 3, 4]: for n in range(m, 5): assert can_do([p], [m, n]) for p in [1, 2, 3, 4]: for n in [h, 3*h, 5*h, 7*h]: for m in [1, 2, 3, 4]: assert can_do([p], [n, m]) for p in [3*h, 5*h, 7*h]: for m in [h, 1, 2, 5*h, 3, 7*h, 4]: assert can_do([p], [h, m]) assert can_do([p], [3*h, m]) for m in [h, 1, 2, 5*h, 3, 7*h, 4]: assert can_do([7*h], [5*h, m]) assert can_do([-S(1)/2], [S(1)/2, S(1)/2]) # shine-integral shi def test_prudnikov_11(): # 7.15 assert can_do([a, a + S.Half], [2*a, b, 2*a - b]) assert can_do([a, a + S.Half], [S(3)/2, 2*a, 2*a - S(1)/2]) assert can_do([S(1)/4, S(3)/4], [S(1)/2, S(1)/2, 1]) assert can_do([S(5)/4, S(3)/4], [S(3)/2, S(1)/2, 2]) assert can_do([S(5)/4, S(3)/4], [S(3)/2, S(3)/2, 1]) assert can_do([S(5)/4, S(7)/4], [S(3)/2, S(5)/2, 2]) assert can_do([1, 1], [S(3)/2, 2, 2]) # cosh-integral chi @slow def test_prudnikov_12(): # 7.16 assert can_do( [], [a, a + S.Half, 2*a], False) # branches only agree for some z! assert can_do([], [a, a + S.Half, 2*a + 1], False) # dito assert can_do([], [S.Half, a, a + S.Half]) assert can_do([], [S(3)/2, a, a + S.Half]) assert can_do([], [S(1)/4, S(1)/2, S(3)/4]) assert can_do([], [S(1)/2, S(1)/2, 1]) assert can_do([], [S(1)/2, S(3)/2, 1]) assert can_do([], [S(3)/4, S(3)/2, S(5)/4]) assert can_do([], [1, 1, S(3)/2]) assert can_do([], [1, 2, S(3)/2]) assert can_do([], [1, S(3)/2, S(3)/2]) assert can_do([], [S(5)/4, S(3)/2, S(7)/4]) assert can_do([], [2, S(3)/2, S(3)/2]) @slow def test_prudnikov_2F1(): h = S.Half # Elliptic integrals for p in [-h, h]: for m in [h, 3*h, 5*h, 7*h]: for n in [1, 2, 3, 4]: assert can_do([p, m], [n]) @XFAIL def test_prudnikov_fail_2F1(): assert can_do([a, b], [b + 1]) # incomplete beta function assert can_do([-1, b], [c]) # Poly. also -2, -3 etc # TODO polys # Legendre functions: assert can_do([a, b], [a + b + S.Half]) assert can_do([a, b], [a + b - S.Half]) assert can_do([a, b], [a + b + S(3)/2]) assert can_do([a, b], [(a + b + 1)/2]) assert can_do([a, b], [(a + b)/2 + 1]) assert can_do([a, b], [a - b + 1]) assert can_do([a, b], [a - b + 2]) assert can_do([a, b], [2*b]) assert can_do([a, b], [S.Half]) assert can_do([a, b], [S(3)/2]) assert can_do([a, 1 - a], [c]) assert can_do([a, 2 - a], [c]) assert can_do([a, 3 - a], [c]) assert can_do([a, a + S(1)/2], [c]) assert can_do([1, b], [c]) assert can_do([1, b], [S(3)/2]) assert can_do([S(1)/4, S(3)/4], [1]) # PFDD o = S(1) assert can_do([o/8, 1], [o/8*9]) assert can_do([o/6, 1], [o/6*7]) assert can_do([o/6, 1], [o/6*13]) assert can_do([o/5, 1], [o/5*6]) assert can_do([o/5, 1], [o/5*11]) assert can_do([o/4, 1], [o/4*5]) assert can_do([o/4, 1], [o/4*9]) assert can_do([o/3, 1], [o/3*4]) assert can_do([o/3, 1], [o/3*7]) assert can_do([o/8*3, 1], [o/8*11]) assert can_do([o/5*2, 1], [o/5*7]) assert can_do([o/5*2, 1], [o/5*12]) assert can_do([o/5*3, 1], [o/5*8]) assert can_do([o/5*3, 1], [o/5*13]) assert can_do([o/8*5, 1], [o/8*13]) assert can_do([o/4*3, 1], [o/4*7]) assert can_do([o/4*3, 1], [o/4*11]) assert can_do([o/3*2, 1], [o/3*5]) assert can_do([o/3*2, 1], [o/3*8]) assert can_do([o/5*4, 1], [o/5*9]) assert can_do([o/5*4, 1], [o/5*14]) assert can_do([o/6*5, 1], [o/6*11]) assert can_do([o/6*5, 1], [o/6*17]) assert can_do([o/8*7, 1], [o/8*15]) @XFAIL def test_prudnikov_fail_3F2(): assert can_do([a, a + S(1)/3, a + S(2)/3], [S(1)/3, S(2)/3]) assert can_do([a, a + S(1)/3, a + S(2)/3], [S(2)/3, S(4)/3]) assert can_do([a, a + S(1)/3, a + S(2)/3], [S(4)/3, S(5)/3]) # page 421 assert can_do([a, a + S(1)/3, a + S(2)/3], [3*a/2, (3*a + 1)/2]) # pages 422 ... assert can_do([-S.Half, S.Half, S.Half], [1, 1]) # elliptic integrals assert can_do([-S.Half, S.Half, 1], [S(3)/2, S(3)/2]) # TODO LOTS more # PFDD assert can_do([S(1)/8, S(3)/8, 1], [S(9)/8, S(11)/8]) assert can_do([S(1)/8, S(5)/8, 1], [S(9)/8, S(13)/8]) assert can_do([S(1)/8, S(7)/8, 1], [S(9)/8, S(15)/8]) assert can_do([S(1)/6, S(1)/3, 1], [S(7)/6, S(4)/3]) assert can_do([S(1)/6, S(2)/3, 1], [S(7)/6, S(5)/3]) assert can_do([S(1)/6, S(2)/3, 1], [S(5)/3, S(13)/6]) assert can_do([S.Half, 1, 1], [S(1)/4, S(3)/4]) # LOTS more @XFAIL def test_prudnikov_fail_other(): # 7.11.2 # 7.12.1 assert can_do([1, a], [b, 1 - 2*a + b]) # ??? # 7.14.2 assert can_do([-S(1)/2], [S(1)/2, 1]) # struve assert can_do([1], [S(1)/2, S(1)/2]) # struve assert can_do([S(1)/4], [S(1)/2, S(5)/4]) # PFDD assert can_do([S(3)/4], [S(3)/2, S(7)/4]) # PFDD assert can_do([1], [S(1)/4, S(3)/4]) # PFDD assert can_do([1], [S(3)/4, S(5)/4]) # PFDD assert can_do([1], [S(5)/4, S(7)/4]) # PFDD # TODO LOTS more # 7.15.2 assert can_do([S(1)/2, 1], [S(3)/4, S(5)/4, S(3)/2]) # PFDD assert can_do([S(1)/2, 1], [S(7)/4, S(5)/4, S(3)/2]) # PFDD # 7.16.1 assert can_do([], [S(1)/3, S(2/3)]) # PFDD assert can_do([], [S(2)/3, S(4/3)]) # PFDD assert can_do([], [S(5)/3, S(4/3)]) # PFDD # XXX this does not *evaluate* right?? assert can_do([], [a, a + S.Half, 2*a - 1]) def test_bug(): h = hyper([-1, 1], [z], -1) assert hyperexpand(h) == (z + 1)/z

true

1c311cc4a86d1fcfc51f40fb745398e3291d2ee1

2,144

py

Python

joladnijo/serializers.py

joladnijo/joladnijo-backend

89240e3990ce9cdad86a1d212d28062c07a58edb

[ "MIT" ]

null

joladnijo/serializers.py

joladnijo/joladnijo-backend

89240e3990ce9cdad86a1d212d28062c07a58edb

[ "MIT" ]

40

2022-03-06T19:46:07.000Z

2022-03-27T11:50:02.000Z

joladnijo/serializers.py

joladnijo/joladnijo-backend

89240e3990ce9cdad86a1d212d28062c07a58edb

[ "MIT" ]

1

2022-03-29T08:53:21.000Z

from rest_framework import serializers from . import models class ContactSerializer(serializers.ModelSerializer): class Meta: model = models.Contact exclude = ['organization', 'aid_center'] class OrganizationSerializer(serializers.ModelSerializer): contact = ContactSerializer() class Meta: model = models.Organization fields = '__all__' class AssetCategorySerializer(serializers.ModelSerializer): class Meta: model = models.AssetCategory fields = ['name', 'icon'] read_only_fields = ['name', 'icon'] class AssetTypeSerializer(serializers.ModelSerializer): category = AssetCategorySerializer() icon = serializers.CharField() class Meta: model = models.AssetType fields = '__all__' read_only_fields = ['name', 'icon', 'category'] class AssetRequestSerializer(serializers.ModelSerializer): type = AssetTypeSerializer() class Meta: model = models.AssetRequest exclude = ['aid_center'] def build_standard_field(self, field_name, model_field): field_class, field_kwargs = super(AssetRequestSerializer, self).build_standard_field(field_name, model_field) if field_name == 'status': field_kwargs['required'] = True return field_class, field_kwargs class FeedItemSerializer(serializers.ModelSerializer): aid_center_name = serializers.CharField(source='aid_center.name') aid_center_slug = serializers.CharField(source='aid_center.slug') class Meta: model = models.FeedItem exclude = ['user', 'aid_center'] class AidCenterSerializer(serializers.ModelSerializer): organization = OrganizationSerializer() contact = ContactSerializer() geo_location = serializers.JSONField() assets_requested = AssetRequestSerializer(many=True, read_only=True) assets_urgent = AssetRequestSerializer(many=True, read_only=True) assets_fulfilled = AssetRequestSerializer(many=True, read_only=True) feed = FeedItemSerializer(many=True, read_only=True) class Meta: model = models.AidCenter fields = '__all__'

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117

0.71222

from rest_framework import serializers from . import models class ContactSerializer(serializers.ModelSerializer): class Meta: model = models.Contact exclude = ['organization', 'aid_center'] class OrganizationSerializer(serializers.ModelSerializer): contact = ContactSerializer() class Meta: model = models.Organization fields = '__all__' class AssetCategorySerializer(serializers.ModelSerializer): class Meta: model = models.AssetCategory fields = ['name', 'icon'] read_only_fields = ['name', 'icon'] class AssetTypeSerializer(serializers.ModelSerializer): category = AssetCategorySerializer() icon = serializers.CharField() class Meta: model = models.AssetType fields = '__all__' read_only_fields = ['name', 'icon', 'category'] class AssetRequestSerializer(serializers.ModelSerializer): type = AssetTypeSerializer() class Meta: model = models.AssetRequest exclude = ['aid_center'] def build_standard_field(self, field_name, model_field): field_class, field_kwargs = super(AssetRequestSerializer, self).build_standard_field(field_name, model_field) if field_name == 'status': field_kwargs['required'] = True return field_class, field_kwargs class FeedItemSerializer(serializers.ModelSerializer): aid_center_name = serializers.CharField(source='aid_center.name') aid_center_slug = serializers.CharField(source='aid_center.slug') class Meta: model = models.FeedItem exclude = ['user', 'aid_center'] class AidCenterSerializer(serializers.ModelSerializer): organization = OrganizationSerializer() contact = ContactSerializer() geo_location = serializers.JSONField() assets_requested = AssetRequestSerializer(many=True, read_only=True) assets_urgent = AssetRequestSerializer(many=True, read_only=True) assets_fulfilled = AssetRequestSerializer(many=True, read_only=True) feed = FeedItemSerializer(many=True, read_only=True) class Meta: model = models.AidCenter fields = '__all__'

true

1c311d91ed8d7ddec962d080c66a6e62be771ba9

7,380

py

Python

trainerpi.py

derillina/trainerpi

15268c5765ee5e12f217e9585af7b29e57ba59d8

[ "MIT" ]

33

2019-07-04T19:05:33.000Z

2022-01-12T19:36:27.000Z

trainerpi.py

derillina/trainerpi

15268c5765ee5e12f217e9585af7b29e57ba59d8

[ "MIT" ]

5

2018-07-24T18:36:12.000Z

2021-01-31T05:17:39.000Z

trainerpi.py

derillina/trainerpi

15268c5765ee5e12f217e9585af7b29e57ba59d8

[ "MIT" ]

11

2019-07-21T15:48:38.000Z

2022-03-29T20:08:42.000Z

import asyncio import bleCSC import collections import numpy import os import pygame import time # --------------------------------------------------------------------------- # # SETTINGS # # --------------------------------------------------------------------------- # ROLLING_LENGTH = 2096. # mm POWER_CURVE = numpy.loadtxt("power-4.csv", delimiter=",") SCREEN_SIZE = WIDTH, HEIGHT = 320, 240 BORDER = 10 FONT_NAME = "DejaVuSans" FONT_SIZE = 28 SCREEN_UPDATE_DELAY = 0.05 # Display update should be fast for the timer to "look" right CSC_SENSOR_ADDRESSES = ( "D0:AC:A5:BF:B7:52", "C6:F9:84:6A:C0:8E" ) display_column = collections.namedtuple("display_column", ("title", "data")) display_data = {} SIGNAL_EXIT = False class TrainerThread: def __init__(self): self.display_row = None class CSCTrainer(TrainerThread): def __init__(self, address: str, display_row: int): super().__init__() self.address = address self.display_row = display_row self._location = "" self.should_activity_timer_run = False # Should the activity timer be running? def handle_notification(self, wheel_speed: float, crank_speed: float, cumulative_rotations: int) -> None: global display_data self.should_activity_timer_run = (wheel_speed is not None and wheel_speed > 0) or\ (crank_speed is not None and crank_speed > 0) if "Wheel" in self._location and wheel_speed is not None: speed = wheel_speed * 3600. * ROLLING_LENGTH / 1e+6 power = numpy.interp(speed, POWER_CURVE[:, 0], POWER_CURVE[:, 1]) display_data[(self.display_row, 0)] = display_column( self._location, "{:2.0f} km/h".format( wheel_speed * 3600. * ROLLING_LENGTH / 1e+6 ) ) display_data[(self.display_row, 1)] = display_column( "{:6.2f} km".format(cumulative_rotations * ROLLING_LENGTH / 1e+6), "{:3.0f} W".format(power) ) if "Crank" in self._location and crank_speed is not None: display_data[(self.display_row, 0)] = display_column( self._location, "{:3.0f} RPM".format( crank_speed * 60. ) ) async def worker(self): global SIGNAL_EXIT, display_data display_data[(self.display_row, 0)] = display_column("Connecting for Sensor:", self.address) sensor = bleCSC.CSCSensor() sensor.connect(self.address, self.handle_notification) display_data[(self.display_row, 0)] = display_column("Waiting for Loc'n:", self.address) await asyncio.sleep(0.0) self._location = sensor.get_location() display_data[(self.display_row, 0)] = display_column("Waiting for Data:", self.address) await asyncio.sleep(0.0) sensor.notifications(True) while not SIGNAL_EXIT: await asyncio.sleep(0.0) notify_ret = await sensor.wait_for_notifications(1.0) if notify_ret: continue display_data[(self.display_row, 0)] = display_column("Waiting for Sensor:", self.address) self.should_activity_timer_run = False class ActivityTimer(TrainerThread): def __init__(self, monitor_threads: list, display_row: int): super().__init__() self.monitor_threads = monitor_threads self.prev_accumulated_time = 0 self.running = False self.start_time = 0 self.display_row = display_row async def worker(self): global SIGNAL_EXIT, display_data while not SIGNAL_EXIT: if any([t.should_activity_timer_run for t in self.monitor_threads]): # Timer should be running if not self.running: self.start_time = time.time() self.running = True time_to_display = self.prev_accumulated_time else: time_to_display = self.prev_accumulated_time + time.time() - self.start_time else: # Timer should not be running if self.running: # Timer needs to stop self.prev_accumulated_time += time.time() - self.start_time self.running = False time_to_display = self.prev_accumulated_time display_data[(self.display_row, 0)] = display_column( "Activity Time", time.strftime("%H:%M:%S", time.gmtime(time_to_display)) ) await asyncio.sleep(SCREEN_UPDATE_DELAY) class ScreenUpdateTrainer(TrainerThread): def __init__(self, thread_list): super().__init__() self.thread_list = thread_list self.use_pygame = True try: os.putenv("SDL_FBDEV", "/dev/fb1") pygame.init() pygame.mouse.set_visible(False) self.screen = pygame.display.set_mode(SCREEN_SIZE) self.clock = pygame.time.Clock() self.font = pygame.font.SysFont(FONT_NAME, FONT_SIZE) except pygame.error: self.use_pygame = False async def worker(self): global SIGNAL_EXIT, display_data while not SIGNAL_EXIT: if self.use_pygame: for event in pygame.event.get(): if event.type == pygame.QUIT: SIGNAL_EXIT = True if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: SIGNAL_EXIT = True self.screen.fill((0, 0, 0)) for seg, seg_data in display_data.items(): if seg_data is not None: self.draw_segment(seg, seg_data.title, seg_data.data, (255, 255, 255)) pygame.display.flip() else: for seg, seg_data in display_data.items(): if seg_data is not None: print("{}\t{}\t{}".format(seg, seg_data.title, seg_data.data)) await asyncio.sleep(SCREEN_UPDATE_DELAY) def draw_segment(self, seg: tuple, title: str, data: str, color: tuple): seg_width = WIDTH // 2 seg_height = HEIGHT // 3 x0 = seg_width * seg[1] + BORDER y0 = seg_height * seg[0] + BORDER x1 = seg_width * (seg[1] + 1) - BORDER y1 = seg_height * (seg[0] + 1) - BORDER title_text = self.font.render(title, True, color) self.screen.blit(title_text, (x0, y0)) data_text = self.font.render(data, True, color) self.screen.blit(data_text, (x1 - data_text.get_width(), y1 - data_text.get_height())) def run_trainer(): csc_threads = list( [CSCTrainer(address, i + 1) for (i, address) in enumerate(CSC_SENSOR_ADDRESSES)] ) all_threads = csc_threads.copy() all_threads.append(ActivityTimer(csc_threads, 0)) all_threads.append(ScreenUpdateTrainer(all_threads)) io_loop = asyncio.get_event_loop() tasks = list( [io_loop.create_task(thread.worker()) for thread in all_threads] ) wait_tasks = asyncio.wait(tasks) io_loop.run_until_complete(wait_tasks) io_loop.close() if __name__ == "__main__": run_trainer()

36.9

109

0.581165

import asyncio import bleCSC import collections import numpy import os import pygame import time ROLLING_LENGTH = 2096. POWER_CURVE = numpy.loadtxt("power-4.csv", delimiter=",") SCREEN_SIZE = WIDTH, HEIGHT = 320, 240 BORDER = 10 FONT_NAME = "DejaVuSans" FONT_SIZE = 28 SCREEN_UPDATE_DELAY = 0.05 CSC_SENSOR_ADDRESSES = ( "D0:AC:A5:BF:B7:52", "C6:F9:84:6A:C0:8E" ) display_column = collections.namedtuple("display_column", ("title", "data")) display_data = {} SIGNAL_EXIT = False class TrainerThread: def __init__(self): self.display_row = None class CSCTrainer(TrainerThread): def __init__(self, address: str, display_row: int): super().__init__() self.address = address self.display_row = display_row self._location = "" self.should_activity_timer_run = False def handle_notification(self, wheel_speed: float, crank_speed: float, cumulative_rotations: int) -> None: global display_data self.should_activity_timer_run = (wheel_speed is not None and wheel_speed > 0) or\ (crank_speed is not None and crank_speed > 0) if "Wheel" in self._location and wheel_speed is not None: speed = wheel_speed * 3600. * ROLLING_LENGTH / 1e+6 power = numpy.interp(speed, POWER_CURVE[:, 0], POWER_CURVE[:, 1]) display_data[(self.display_row, 0)] = display_column( self._location, "{:2.0f} km/h".format( wheel_speed * 3600. * ROLLING_LENGTH / 1e+6 ) ) display_data[(self.display_row, 1)] = display_column( "{:6.2f} km".format(cumulative_rotations * ROLLING_LENGTH / 1e+6), "{:3.0f} W".format(power) ) if "Crank" in self._location and crank_speed is not None: display_data[(self.display_row, 0)] = display_column( self._location, "{:3.0f} RPM".format( crank_speed * 60. ) ) async def worker(self): global SIGNAL_EXIT, display_data display_data[(self.display_row, 0)] = display_column("Connecting for Sensor:", self.address) sensor = bleCSC.CSCSensor() sensor.connect(self.address, self.handle_notification) display_data[(self.display_row, 0)] = display_column("Waiting for Loc'n:", self.address) await asyncio.sleep(0.0) self._location = sensor.get_location() display_data[(self.display_row, 0)] = display_column("Waiting for Data:", self.address) await asyncio.sleep(0.0) sensor.notifications(True) while not SIGNAL_EXIT: await asyncio.sleep(0.0) notify_ret = await sensor.wait_for_notifications(1.0) if notify_ret: continue display_data[(self.display_row, 0)] = display_column("Waiting for Sensor:", self.address) self.should_activity_timer_run = False class ActivityTimer(TrainerThread): def __init__(self, monitor_threads: list, display_row: int): super().__init__() self.monitor_threads = monitor_threads self.prev_accumulated_time = 0 self.running = False self.start_time = 0 self.display_row = display_row async def worker(self): global SIGNAL_EXIT, display_data while not SIGNAL_EXIT: if any([t.should_activity_timer_run for t in self.monitor_threads]): # Timer should be running if not self.running: self.start_time = time.time() self.running = True time_to_display = self.prev_accumulated_time else: time_to_display = self.prev_accumulated_time + time.time() - self.start_time else: # Timer should not be running if self.running: # Timer needs to stop self.prev_accumulated_time += time.time() - self.start_time self.running = False time_to_display = self.prev_accumulated_time display_data[(self.display_row, 0)] = display_column( "Activity Time", time.strftime("%H:%M:%S", time.gmtime(time_to_display)) ) await asyncio.sleep(SCREEN_UPDATE_DELAY) class ScreenUpdateTrainer(TrainerThread): def __init__(self, thread_list): super().__init__() self.thread_list = thread_list self.use_pygame = True try: os.putenv("SDL_FBDEV", "/dev/fb1") pygame.init() pygame.mouse.set_visible(False) self.screen = pygame.display.set_mode(SCREEN_SIZE) self.clock = pygame.time.Clock() self.font = pygame.font.SysFont(FONT_NAME, FONT_SIZE) except pygame.error: self.use_pygame = False async def worker(self): global SIGNAL_EXIT, display_data while not SIGNAL_EXIT: if self.use_pygame: for event in pygame.event.get(): if event.type == pygame.QUIT: SIGNAL_EXIT = True if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: SIGNAL_EXIT = True self.screen.fill((0, 0, 0)) for seg, seg_data in display_data.items(): if seg_data is not None: self.draw_segment(seg, seg_data.title, seg_data.data, (255, 255, 255)) pygame.display.flip() else: for seg, seg_data in display_data.items(): if seg_data is not None: print("{}\t{}\t{}".format(seg, seg_data.title, seg_data.data)) await asyncio.sleep(SCREEN_UPDATE_DELAY) def draw_segment(self, seg: tuple, title: str, data: str, color: tuple): seg_width = WIDTH // 2 seg_height = HEIGHT // 3 x0 = seg_width * seg[1] + BORDER y0 = seg_height * seg[0] + BORDER x1 = seg_width * (seg[1] + 1) - BORDER y1 = seg_height * (seg[0] + 1) - BORDER title_text = self.font.render(title, True, color) self.screen.blit(title_text, (x0, y0)) data_text = self.font.render(data, True, color) self.screen.blit(data_text, (x1 - data_text.get_width(), y1 - data_text.get_height())) def run_trainer(): csc_threads = list( [CSCTrainer(address, i + 1) for (i, address) in enumerate(CSC_SENSOR_ADDRESSES)] ) all_threads = csc_threads.copy() all_threads.append(ActivityTimer(csc_threads, 0)) all_threads.append(ScreenUpdateTrainer(all_threads)) io_loop = asyncio.get_event_loop() tasks = list( [io_loop.create_task(thread.worker()) for thread in all_threads] ) wait_tasks = asyncio.wait(tasks) io_loop.run_until_complete(wait_tasks) io_loop.close() if __name__ == "__main__": run_trainer()

true

1c311e21d5aa989de05b6b21c1dec8a37917990b

2,965

py

Python

test/test_melgan_layers.py

Reaiot/kiswahili_tts

1bbbff49f7c6cf899e5e3fd4c8cb7d6a7d1b6e79

[ "Apache-2.0" ]

1,961

2020-07-31T07:31:27.000Z

2022-03-31T20:39:29.000Z

test/test_melgan_layers.py

Reaiot/kiswahili_tts

1bbbff49f7c6cf899e5e3fd4c8cb7d6a7d1b6e79

[ "Apache-2.0" ]

587

2020-07-31T03:24:54.000Z

2022-03-29T02:31:50.000Z

test/test_melgan_layers.py

Reaiot/kiswahili_tts

1bbbff49f7c6cf899e5e3fd4c8cb7d6a7d1b6e79

[ "Apache-2.0" ]

483

2020-07-31T17:48:32.000Z

2022-03-31T13:55:49.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Minh Nguyen (@dathudeptrai) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import numpy as np import pytest import tensorflow as tf from tensorflow_tts.models.melgan import ( TFConvTranspose1d, TFReflectionPad1d, TFResidualStack, ) os.environ["CUDA_VISIBLE_DEVICES"] = "" logging.basicConfig( level=logging.DEBUG, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) @pytest.mark.parametrize("padding_size", [(3), (5)]) def test_padding(padding_size): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) out = TFReflectionPad1d(padding_size=padding_size)(fake_input_1d) assert np.array_equal( tf.keras.backend.int_shape(out), [4, 8000 + 2 * padding_size, 256] ) @pytest.mark.parametrize( "filters,kernel_size,strides,padding,is_weight_norm", [(512, 40, 8, "same", False), (768, 15, 8, "same", True)], ) def test_convtranpose1d(filters, kernel_size, strides, padding, is_weight_norm): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) conv1d_transpose = TFConvTranspose1d( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, is_weight_norm=is_weight_norm, initializer_seed=42, ) out = conv1d_transpose(fake_input_1d) assert np.array_equal(tf.keras.backend.int_shape(out), [4, 8000 * strides, filters]) @pytest.mark.parametrize( "kernel_size,filters,dilation_rate,use_bias,nonlinear_activation,nonlinear_activation_params,is_weight_norm", [ (3, 256, 1, True, "LeakyReLU", {"alpha": 0.3}, True), (3, 256, 3, True, "ReLU", {}, False), ], ) def test_residualblock( kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, ): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) residual_block = TFResidualStack( kernel_size=kernel_size, filters=filters, dilation_rate=dilation_rate, use_bias=use_bias, nonlinear_activation=nonlinear_activation, nonlinear_activation_params=nonlinear_activation_params, is_weight_norm=is_weight_norm, initializer_seed=42, ) out = residual_block(fake_input_1d) assert np.array_equal(tf.keras.backend.int_shape(out), [4, 8000, filters])

31.88172

113

0.707251

import logging import os import numpy as np import pytest import tensorflow as tf from tensorflow_tts.models.melgan import ( TFConvTranspose1d, TFReflectionPad1d, TFResidualStack, ) os.environ["CUDA_VISIBLE_DEVICES"] = "" logging.basicConfig( level=logging.DEBUG, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) @pytest.mark.parametrize("padding_size", [(3), (5)]) def test_padding(padding_size): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) out = TFReflectionPad1d(padding_size=padding_size)(fake_input_1d) assert np.array_equal( tf.keras.backend.int_shape(out), [4, 8000 + 2 * padding_size, 256] ) @pytest.mark.parametrize( "filters,kernel_size,strides,padding,is_weight_norm", [(512, 40, 8, "same", False), (768, 15, 8, "same", True)], ) def test_convtranpose1d(filters, kernel_size, strides, padding, is_weight_norm): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) conv1d_transpose = TFConvTranspose1d( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, is_weight_norm=is_weight_norm, initializer_seed=42, ) out = conv1d_transpose(fake_input_1d) assert np.array_equal(tf.keras.backend.int_shape(out), [4, 8000 * strides, filters]) @pytest.mark.parametrize( "kernel_size,filters,dilation_rate,use_bias,nonlinear_activation,nonlinear_activation_params,is_weight_norm", [ (3, 256, 1, True, "LeakyReLU", {"alpha": 0.3}, True), (3, 256, 3, True, "ReLU", {}, False), ], ) def test_residualblock( kernel_size, filters, dilation_rate, use_bias, nonlinear_activation, nonlinear_activation_params, is_weight_norm, ): fake_input_1d = tf.random.normal(shape=[4, 8000, 256], dtype=tf.float32) residual_block = TFResidualStack( kernel_size=kernel_size, filters=filters, dilation_rate=dilation_rate, use_bias=use_bias, nonlinear_activation=nonlinear_activation, nonlinear_activation_params=nonlinear_activation_params, is_weight_norm=is_weight_norm, initializer_seed=42, ) out = residual_block(fake_input_1d) assert np.array_equal(tf.keras.backend.int_shape(out), [4, 8000, filters])

true

1c311e4869fadf9f2ff3dee2ed081123eb53101a

5,290

py

Python

train.py

endaaman/prostate

e08beb862fc61ab0bcef672ab77d2ff528259094

[ "BSD-2-Clause" ]

null

train.py

endaaman/prostate

e08beb862fc61ab0bcef672ab77d2ff528259094

[ "BSD-2-Clause" ]

1

2020-06-12T07:59:58.000Z

2020-06-12T07:59:59.000Z

train.py

endaaman/prostate

e08beb862fc61ab0bcef672ab77d2ff528259094

[ "BSD-2-Clause" ]

null

import os import math import re import gc import argparse from enum import Enum, auto import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import LambdaLR import torch.nn.functional as F from torch.utils.data import DataLoader from torchvision import datasets, models from torchvision.transforms import ToTensor, Normalize, Compose from models import get_model from datasets import TrainingDataset from store import Store from metrics import Metrics, Coef from formula import * from utils import now_str, pp, CrossEntropyLoss2d parser = argparse.ArgumentParser() parser.add_argument('-w', '--weight') parser.add_argument('-b', '--batch-size', type=int, default=32) parser.add_argument('-e', '--epoch', type=int, default=100) parser.add_argument('-t', '--tile', type=int, default=224) parser.add_argument('-m', '--model', default='unet11') parser.add_argument('-d', '--dest', default='weights') parser.add_argument('--num-workers', type=int, default=4) parser.add_argument('--cpu', action="store_true") parser.add_argument('--fake', action="store_true") parser.add_argument('--target', default='train') args = parser.parse_args() STARTING_WEIGHT = args.weight BATCH_SIZE = args.batch_size NUM_WORKERS = args.num_workers EPOCH_COUNT = args.epoch TILE_SIZE = args.tile MODEL_NAME = args.model DEST_BASE_DIR = args.dest TARGET = args.target FAKE = args.fake USE_GPU = not args.cpu and torch.cuda.is_available() USE_MULTI_GPU = USE_GPU and torch.cuda.device_count() > 1 DEST_DIR = os.path.join(DEST_BASE_DIR, MODEL_NAME) os.makedirs(DEST_DIR, exist_ok=True) if not os.path.isdir(DEST_DIR): print(f'Invalid dest dir: `{DEST_DIR}`') exit(1) store = Store() mode = ('multi' if USE_MULTI_GPU else 'single') if USE_GPU else 'cpu' device = 'cuda' if USE_GPU else 'cpu' # EPOCH first_epoch = 1 if STARTING_WEIGHT: basename = os.path.splitext(os.path.basename(STARTING_WEIGHT))[0] nums = re.findall(r'\d+', basename) if len(nums) > 0 and not nums[-1].isdigit(): print(f'Invalid pt file') exit(1) first_epoch = int(nums[-1]) + 1 store.load(STARTING_WEIGHT) epoch = first_epoch print(f'Preparing MODEL:{MODEL_NAME} BATCH:{BATCH_SIZE} EPOCH:{EPOCH_COUNT} MODE:{mode} ({now_str()})') # MDOEL Model = get_model(MODEL_NAME) model = Model(num_classes=NUM_CLASSES).to(device) if store.weights: model.load_state_dict(store.weights) if USE_MULTI_GPU: model = torch.nn.DataParallel(model) # DATA I = np.identity(NUM_CLASSES, dtype=np.float32) def transform_y(arr): arr[arr > 0] = 1 # to 1bit each color arr = np.sum(np.multiply(arr, (1, 2, 4, 8)), axis=2) # to 4bit each pixel arr = arr - 7 # to 3bit + 1 arr[arr < 0] = 0 # fill overrun return ToTensor()(I[INDEX_MAP[arr]]) data_set = TrainingDataset( transform_x=Compose([ ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]), transform_y=transform_y, tile_size=TILE_SIZE, target=TARGET) data_loader = DataLoader(data_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS) # TRAIN def lr_func_exp(step): return 0.95 ** step optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) if store.optims: optimizer.load_state_dict(store.optims) scheduler = LambdaLR(optimizer, lr_lambda=lr_func_exp, last_epoch=epoch if store.optims else -1) # criterion = nn.BCELoss() # criterion = nn.BCEWithLogitsLoss() criterion = CrossEntropyLoss2d() metrics = Metrics() if store.metrics: metrics.load_state_dict(store.metrics) if FAKE: print('STOP TRAINING') exit(0) # LOOP print(f'Starting ({now_str()})') iter_count = len(data_set) // BATCH_SIZE while epoch < first_epoch + EPOCH_COUNT: iter_metrics = Metrics() lr = scheduler.get_lr()[0] for i, (inputs, labels) in enumerate(data_loader): inputs = inputs.to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(inputs).to(device) loss = criterion(outputs, labels) coef = Coef.calc(outputs, labels) iter_metrics.append_loss(loss.item()) iter_metrics.append_coef(coef) pp('epoch[{ep}]:{i}/{I} iou:{c.pjac:.4f} acc:{c.pdice:.4f} loss:{loss:.4f} lr:{lr:.4f} ({t})'.format( ep=epoch, i=i+1, I=iter_count, lr=lr, t=now_str(), loss=loss.item(), c=coef)) loss.backward() optimizer.step() pp('epoch[{ep}]:Done. iou:{c.pjac:.4f} acc:{c.pdice:.4f} gsi:{c.gsensi:.4f} gsp:{c.gspec:.4f} tsi:{c.tsensi:.4f} tsp:{c.tspec:.4f} loss:{loss:.4f} lr:{lr:.4f} ({t})'.format( ep=epoch, t=now_str(), lr=lr, loss=iter_metrics.avg('losses'), c=iter_metrics.avg_coef() )) gc.collect() print() weight_path = os.path.join(DEST_DIR, f'{Model.__name__.lower()}_{epoch}.pt') weights = model.module.cpu().state_dict() if USE_MULTI_GPU else model.cpu().state_dict() metrics.append_coef(iter_metrics.avg_coef()) metrics.append_loss(iter_metrics.avg_loss()) store.set_states(weights, optimizer.state_dict(), metrics.state_dict()) store.save(weight_path) print(f'save weights to {weight_path}') model = model.to(device) scheduler.step() epoch += 1 print(f'Finished training\n')

33.0625

177

0.693762

import os import math import re import gc import argparse from enum import Enum, auto import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import LambdaLR import torch.nn.functional as F from torch.utils.data import DataLoader from torchvision import datasets, models from torchvision.transforms import ToTensor, Normalize, Compose from models import get_model from datasets import TrainingDataset from store import Store from metrics import Metrics, Coef from formula import * from utils import now_str, pp, CrossEntropyLoss2d parser = argparse.ArgumentParser() parser.add_argument('-w', '--weight') parser.add_argument('-b', '--batch-size', type=int, default=32) parser.add_argument('-e', '--epoch', type=int, default=100) parser.add_argument('-t', '--tile', type=int, default=224) parser.add_argument('-m', '--model', default='unet11') parser.add_argument('-d', '--dest', default='weights') parser.add_argument('--num-workers', type=int, default=4) parser.add_argument('--cpu', action="store_true") parser.add_argument('--fake', action="store_true") parser.add_argument('--target', default='train') args = parser.parse_args() STARTING_WEIGHT = args.weight BATCH_SIZE = args.batch_size NUM_WORKERS = args.num_workers EPOCH_COUNT = args.epoch TILE_SIZE = args.tile MODEL_NAME = args.model DEST_BASE_DIR = args.dest TARGET = args.target FAKE = args.fake USE_GPU = not args.cpu and torch.cuda.is_available() USE_MULTI_GPU = USE_GPU and torch.cuda.device_count() > 1 DEST_DIR = os.path.join(DEST_BASE_DIR, MODEL_NAME) os.makedirs(DEST_DIR, exist_ok=True) if not os.path.isdir(DEST_DIR): print(f'Invalid dest dir: `{DEST_DIR}`') exit(1) store = Store() mode = ('multi' if USE_MULTI_GPU else 'single') if USE_GPU else 'cpu' device = 'cuda' if USE_GPU else 'cpu' first_epoch = 1 if STARTING_WEIGHT: basename = os.path.splitext(os.path.basename(STARTING_WEIGHT))[0] nums = re.findall(r'\d+', basename) if len(nums) > 0 and not nums[-1].isdigit(): print(f'Invalid pt file') exit(1) first_epoch = int(nums[-1]) + 1 store.load(STARTING_WEIGHT) epoch = first_epoch print(f'Preparing MODEL:{MODEL_NAME} BATCH:{BATCH_SIZE} EPOCH:{EPOCH_COUNT} MODE:{mode} ({now_str()})') Model = get_model(MODEL_NAME) model = Model(num_classes=NUM_CLASSES).to(device) if store.weights: model.load_state_dict(store.weights) if USE_MULTI_GPU: model = torch.nn.DataParallel(model) I = np.identity(NUM_CLASSES, dtype=np.float32) def transform_y(arr): arr[arr > 0] = 1 arr = np.sum(np.multiply(arr, (1, 2, 4, 8)), axis=2) arr = arr - 7 arr[arr < 0] = 0 return ToTensor()(I[INDEX_MAP[arr]]) data_set = TrainingDataset( transform_x=Compose([ ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]), transform_y=transform_y, tile_size=TILE_SIZE, target=TARGET) data_loader = DataLoader(data_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS) def lr_func_exp(step): return 0.95 ** step optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) if store.optims: optimizer.load_state_dict(store.optims) scheduler = LambdaLR(optimizer, lr_lambda=lr_func_exp, last_epoch=epoch if store.optims else -1) criterion = CrossEntropyLoss2d() metrics = Metrics() if store.metrics: metrics.load_state_dict(store.metrics) if FAKE: print('STOP TRAINING') exit(0) print(f'Starting ({now_str()})') iter_count = len(data_set) // BATCH_SIZE while epoch < first_epoch + EPOCH_COUNT: iter_metrics = Metrics() lr = scheduler.get_lr()[0] for i, (inputs, labels) in enumerate(data_loader): inputs = inputs.to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(inputs).to(device) loss = criterion(outputs, labels) coef = Coef.calc(outputs, labels) iter_metrics.append_loss(loss.item()) iter_metrics.append_coef(coef) pp('epoch[{ep}]:{i}/{I} iou:{c.pjac:.4f} acc:{c.pdice:.4f} loss:{loss:.4f} lr:{lr:.4f} ({t})'.format( ep=epoch, i=i+1, I=iter_count, lr=lr, t=now_str(), loss=loss.item(), c=coef)) loss.backward() optimizer.step() pp('epoch[{ep}]:Done. iou:{c.pjac:.4f} acc:{c.pdice:.4f} gsi:{c.gsensi:.4f} gsp:{c.gspec:.4f} tsi:{c.tsensi:.4f} tsp:{c.tspec:.4f} loss:{loss:.4f} lr:{lr:.4f} ({t})'.format( ep=epoch, t=now_str(), lr=lr, loss=iter_metrics.avg('losses'), c=iter_metrics.avg_coef() )) gc.collect() print() weight_path = os.path.join(DEST_DIR, f'{Model.__name__.lower()}_{epoch}.pt') weights = model.module.cpu().state_dict() if USE_MULTI_GPU else model.cpu().state_dict() metrics.append_coef(iter_metrics.avg_coef()) metrics.append_loss(iter_metrics.avg_loss()) store.set_states(weights, optimizer.state_dict(), metrics.state_dict()) store.save(weight_path) print(f'save weights to {weight_path}') model = model.to(device) scheduler.step() epoch += 1 print(f'Finished training\n')

true

1c311e9e8f6b393ac57896e04339007088012c4a

4,835

py

Python

cli/sawtooth_cli/transaction.py

mealchain/beta

7dc1a1aea175bfb3f1008939f098a1d58bb455a6

[ "Apache-2.0" ]

1

2017-08-04T10:31:00.000Z

cli/sawtooth_cli/transaction.py

mealchain/beta

7dc1a1aea175bfb3f1008939f098a1d58bb455a6

[ "Apache-2.0" ]

null

cli/sawtooth_cli/transaction.py

mealchain/beta

7dc1a1aea175bfb3f1008939f098a1d58bb455a6

[ "Apache-2.0" ]

null

# Copyright 2017 Intel Corporation # # 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 argparse from base64 import b64decode from sawtooth_cli import format_utils as fmt from sawtooth_cli.rest_client import RestClient from sawtooth_cli.exceptions import CliException from sawtooth_cli.parent_parsers import base_http_parser from sawtooth_cli.parent_parsers import base_list_parser from sawtooth_cli.parent_parsers import base_show_parser def add_transaction_parser(subparsers, parent_parser): """Adds argument parsers for the transaction list and show commands Args: subparsers: Add parsers to this subparser object parent_parser: The parent argparse.ArgumentParser object """ parser = subparsers.add_parser('transaction') grand_parsers = parser.add_subparsers(title='grandchildcommands', dest='subcommand') grand_parsers.required = True epilog = '''details: Lists committed transactions from newest to oldest, including their id (i.e. header_signature), transaction family and version, and their payload. ''' grand_parsers.add_parser( 'list', epilog=epilog, parents=[base_http_parser(), base_list_parser()], formatter_class=argparse.RawDescriptionHelpFormatter) epilog = '''details: Shows the data for a single transaction, or for a particular property within that transaction or its header. Displays data in YAML (default), or JSON formats. ''' show_parser = grand_parsers.add_parser( 'show', epilog=epilog, parents=[base_http_parser(), base_show_parser()], formatter_class=argparse.RawDescriptionHelpFormatter) show_parser.add_argument( 'transaction_id', type=str, help='the id (i.e. header_signature) of the transaction') def do_transaction(args): """Runs the transaction list or show command, printing to the console Args: args: The parsed arguments sent to the command at runtime """ rest_client = RestClient(args.url, args.user) if args.subcommand == 'list': transactions = rest_client.list_transactions() keys = ('transaction_id', 'family', 'version', 'size', 'payload') headers = tuple(k.upper() if k != 'version' else 'VERS' for k in keys) def parse_txn_row(transaction, decode=True): decoded = b64decode(transaction['payload']) return ( transaction['header_signature'], transaction['header']['family_name'], transaction['header']['family_version'], len(decoded), str(decoded) if decode else transaction['payload']) if args.format == 'default': fmt.print_terminal_table(headers, transactions, parse_txn_row) elif args.format == 'csv': fmt.print_csv(headers, transactions, parse_txn_row) elif args.format == 'json' or args.format == 'yaml': data = [{k: d for k, d in zip(keys, parse_txn_row(b, False))} for b in transactions] if args.format == 'yaml': fmt.print_yaml(data) elif args.format == 'json': fmt.print_json(data) else: raise AssertionError('Missing handler: {}'.format(args.format)) else: raise AssertionError('Missing handler: {}'.format(args.format)) if args.subcommand == 'show': output = rest_client.get_transaction(args.transaction_id) if args.key: if args.key == 'payload': output = b64decode(output['payload']) elif args.key in output: output = output[args.key] elif args.key in output['header']: output = output['header'][args.key] else: raise CliException( 'Key "{}" not found in transaction or header'.format( args.key)) if args.format == 'yaml': fmt.print_yaml(output) elif args.format == 'json': fmt.print_json(output) else: raise AssertionError('Missing handler: {}'.format(args.format))

38.373016

80

0.63061

import argparse from base64 import b64decode from sawtooth_cli import format_utils as fmt from sawtooth_cli.rest_client import RestClient from sawtooth_cli.exceptions import CliException from sawtooth_cli.parent_parsers import base_http_parser from sawtooth_cli.parent_parsers import base_list_parser from sawtooth_cli.parent_parsers import base_show_parser def add_transaction_parser(subparsers, parent_parser): parser = subparsers.add_parser('transaction') grand_parsers = parser.add_subparsers(title='grandchildcommands', dest='subcommand') grand_parsers.required = True epilog = '''details: Lists committed transactions from newest to oldest, including their id (i.e. header_signature), transaction family and version, and their payload. ''' grand_parsers.add_parser( 'list', epilog=epilog, parents=[base_http_parser(), base_list_parser()], formatter_class=argparse.RawDescriptionHelpFormatter) epilog = '''details: Shows the data for a single transaction, or for a particular property within that transaction or its header. Displays data in YAML (default), or JSON formats. ''' show_parser = grand_parsers.add_parser( 'show', epilog=epilog, parents=[base_http_parser(), base_show_parser()], formatter_class=argparse.RawDescriptionHelpFormatter) show_parser.add_argument( 'transaction_id', type=str, help='the id (i.e. header_signature) of the transaction') def do_transaction(args): rest_client = RestClient(args.url, args.user) if args.subcommand == 'list': transactions = rest_client.list_transactions() keys = ('transaction_id', 'family', 'version', 'size', 'payload') headers = tuple(k.upper() if k != 'version' else 'VERS' for k in keys) def parse_txn_row(transaction, decode=True): decoded = b64decode(transaction['payload']) return ( transaction['header_signature'], transaction['header']['family_name'], transaction['header']['family_version'], len(decoded), str(decoded) if decode else transaction['payload']) if args.format == 'default': fmt.print_terminal_table(headers, transactions, parse_txn_row) elif args.format == 'csv': fmt.print_csv(headers, transactions, parse_txn_row) elif args.format == 'json' or args.format == 'yaml': data = [{k: d for k, d in zip(keys, parse_txn_row(b, False))} for b in transactions] if args.format == 'yaml': fmt.print_yaml(data) elif args.format == 'json': fmt.print_json(data) else: raise AssertionError('Missing handler: {}'.format(args.format)) else: raise AssertionError('Missing handler: {}'.format(args.format)) if args.subcommand == 'show': output = rest_client.get_transaction(args.transaction_id) if args.key: if args.key == 'payload': output = b64decode(output['payload']) elif args.key in output: output = output[args.key] elif args.key in output['header']: output = output['header'][args.key] else: raise CliException( 'Key "{}" not found in transaction or header'.format( args.key)) if args.format == 'yaml': fmt.print_yaml(output) elif args.format == 'json': fmt.print_json(output) else: raise AssertionError('Missing handler: {}'.format(args.format))

true

1c311eaff781636af920b6c76513759dd4b2e600

3,235

py

Python

setup.py

Caleydo/taco_server

be2d4786fbc8ad62ecb5b599572fe09f8c2ea05e

[ "BSD-3-Clause" ]

2

2017-03-30T05:12:54.000Z

2019-07-11T09:42:06.000Z

setup.py

Caleydo/taco_server

be2d4786fbc8ad62ecb5b599572fe09f8c2ea05e

[ "BSD-3-Clause" ]

11

2016-11-18T17:13:37.000Z

2021-03-26T11:35:43.000Z

setup.py

Caleydo/taco_server

be2d4786fbc8ad62ecb5b599572fe09f8c2ea05e

[ "BSD-3-Clause" ]

2

2018-01-26T09:56:41.000Z

2019-10-26T04:41:31.000Z

############################################################################### # Caleydo - Visualization for Molecular Biology - http://caleydo.org # Copyright (c) The Caleydo Team. All rights reserved. # Licensed under the new BSD license, available at http://caleydo.org/license ############################################################################### from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) def read_it(name): with open(path.join(here, name), encoding='utf-8') as f: return f.read() # read package.json information with open(path.join(here, 'package.json'), encoding='utf-8') as json_data: import json pkg = json.load(json_data) def packaged(*files): r = {} global pkg r[pkg['name']] = list(files) return r def requirements(file): return [r.strip() for r in read_it(file).strip().split('\n') if not r.startswith('-e git+https://')] def to_version(v): import datetime now = datetime.datetime.utcnow() return v.replace('SNAPSHOT', now.strftime('%Y%m%d-%H%M%S')) setup( name=pkg['name'].lower(), version=to_version(pkg['version']), url=pkg['homepage'], description=pkg['description'], long_description=read_it('README.md'), long_description_content_type='text/markdown', keywords=pkg.get('keywords', ''), author=pkg['author']['name'], author_email=pkg['author']['email'], license=pkg['license'], zip_safe=False, entry_points={ 'phovea.registry': ['{0} = {0}:phovea'.format(pkg['name'])], 'phovea.config': ['{0} = {0}:phovea_config'.format(pkg['name'])] }, # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Intended Audience :: Developers', 'Operating System :: OS Independent', # Pick your license as you wish (should match "license" above) 'License :: OSI Approved :: ' + ('BSD License' if pkg['license'] == 'BSD-3-Clause' else pkg['license']), 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4' ], # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). packages=find_packages(exclude=['docs', 'tests*']), # List run-time dependencies here. These will be installed by pip when # your project is installed. For an analysis of "install_requires" vs pip's # requirements files see: # https://packaging.python.org/en/latest/requirements.html install_requires=requirements('requirements.txt'), tests_require=requirements('requirements_dev.txt'), # If there are data files included in your packages that need to be # installed, specify them here. If using Python 2.6 or less, then these # have to be included in MANIFEST.in as well. package_data=packaged('config.json', 'buildInfo.json'), # Although 'package_data' is the preferred approach, in some case you may # need to place data files outside of your packages. See: # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa # In this case, 'data_file' will be installed into '<sys.prefix>/my_data' data_files=[] # [('my_data', ['data/data_file'])], )

34.414894

108

0.662442

true

1c311ef6c77d2d2c696899f5c5518bbfbe901764

1,778

py

Python

tests/integration/preview/acc_security/service/test_verification_check.py

fefi95/twilio-python

b9bfea293b6133fe84d4d8d3ac4e2a75381c3881

[ "MIT" ]

1

2019-12-30T21:46:55.000Z

tests/integration/preview/acc_security/service/test_verification_check.py

fefi95/twilio-python

b9bfea293b6133fe84d4d8d3ac4e2a75381c3881

[ "MIT" ]

null

tests/integration/preview/acc_security/service/test_verification_check.py

fefi95/twilio-python

b9bfea293b6133fe84d4d8d3ac4e2a75381c3881

[ "MIT" ]

null

# coding=utf-8 r""" This code was generated by \ / _ _ _| _ _ | (_)\/(_)(_|\/| |(/_ v1.0.0 / / """ from tests import IntegrationTestCase from tests.holodeck import Request from twilio.base.exceptions import TwilioException from twilio.http.response import Response class VerificationCheckTestCase(IntegrationTestCase): def test_create_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.preview.acc_security.services(sid="VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .verification_checks.create(code="code") values = {'Code': "code", } self.holodeck.assert_has_request(Request( 'post', 'https://preview.twilio.com/Verification/Services/VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/VerificationCheck', data=values, )) def test_verification_checks_response(self): self.holodeck.mock(Response( 201, ''' { "sid": "VEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "service_sid": "VAaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "to": "+15017122661", "channel": "sms", "status": "approved", "valid": false, "date_created": "2015-07-30T20:00:00Z", "date_updated": "2015-07-30T20:00:00Z" } ''' )) actual = self.client.preview.acc_security.services(sid="VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .verification_checks.create(code="code") self.assertIsNotNone(actual)

32.925926

116

0.577615

from tests import IntegrationTestCase from tests.holodeck import Request from twilio.base.exceptions import TwilioException from twilio.http.response import Response class VerificationCheckTestCase(IntegrationTestCase): def test_create_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.preview.acc_security.services(sid="VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .verification_checks.create(code="code") values = {'Code': "code", } self.holodeck.assert_has_request(Request( 'post', 'https://preview.twilio.com/Verification/Services/VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/VerificationCheck', data=values, )) def test_verification_checks_response(self): self.holodeck.mock(Response( 201, ''' { "sid": "VEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "service_sid": "VAaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "to": "+15017122661", "channel": "sms", "status": "approved", "valid": false, "date_created": "2015-07-30T20:00:00Z", "date_updated": "2015-07-30T20:00:00Z" } ''' )) actual = self.client.preview.acc_security.services(sid="VAXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .verification_checks.create(code="code") self.assertIsNotNone(actual)

true

1c311f75778dffa8637a08e97bcd150cd5fab9d0

3,184

py

Python

gpt-example/deps/gpt/src/sample.py

Antaego/gpt-companion

071d1218661cb8dddfd31d50da91c1af7a9be21b

[ "Unlicense" ]

null

gpt-example/deps/gpt/src/sample.py

Antaego/gpt-companion

071d1218661cb8dddfd31d50da91c1af7a9be21b

[ "Unlicense" ]

null

gpt-example/deps/gpt/src/sample.py

Antaego/gpt-companion

071d1218661cb8dddfd31d50da91c1af7a9be21b

[ "Unlicense" ]

null

import tensorflow as tf from deps.gpt.src import model def top_k_logits(logits, k): if k == 0: # no truncation return logits def _top_k(): values, _ = tf.nn.top_k(logits, k=k) min_values = values[:, -1, tf.newaxis] return tf.where( logits < min_values, tf.ones_like(logits, dtype=logits.dtype) * -1e10, logits, ) return tf.cond( tf.equal(k, 0), lambda: logits, lambda: _top_k(), ) def top_p_logits(logits, p): """Nucleus sampling""" batch, _ = logits.shape.as_list() sorted_logits = tf.sort(logits, direction='DESCENDING', axis=-1) cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1) indices = tf.stack([ tf.range(0, batch), # number of indices to include tf.maximum(tf.reduce_sum(tf.cast(cumulative_probs <= p, tf.int32), axis=-1) - 1, 0), ], axis=-1) min_values = tf.gather_nd(sorted_logits, indices) return tf.where( logits < min_values, tf.ones_like(logits) * -1e10, logits, ) def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=1): if start_token is None: assert context is not None, 'Specify exactly one of start_token and context!' else: assert context is None, 'Specify exactly one of start_token and context!' context = tf.fill([batch_size, 1], start_token) def step(hparams, tokens, past=None): lm_output = model.model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE) logits = lm_output['logits'][:, :, :hparams.n_vocab] presents = lm_output['present'] presents.set_shape(model.past_shape(hparams=hparams, batch_size=batch_size)) return { 'logits': logits, 'presents': presents, } with tf.name_scope('sample_sequence'): def body(past, prev, output): next_outputs = step(hparams, prev, past=past) logits = next_outputs['logits'][:, -1, :] / tf.to_float(temperature) logits = top_k_logits(logits, k=top_k) logits = top_p_logits(logits, p=top_p) samples = tf.multinomial(logits, num_samples=1, output_dtype=tf.int32) return [ next_outputs['presents'] if past is None else tf.concat([past, next_outputs['presents']], axis=-2), samples, tf.concat([output, samples], axis=1) ] past, prev, output = body(None, context, context) def cond(*args): return True _, _, tokens = tf.while_loop( cond=cond, body=body, maximum_iterations=length - 1, loop_vars=[ past, prev, output ], shape_invariants=[ tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)), tf.TensorShape([batch_size, None]), tf.TensorShape([batch_size, None]), ], back_prop=False, ) return tokens

33.166667

122

0.576947

import tensorflow as tf from deps.gpt.src import model def top_k_logits(logits, k): if k == 0: return logits def _top_k(): values, _ = tf.nn.top_k(logits, k=k) min_values = values[:, -1, tf.newaxis] return tf.where( logits < min_values, tf.ones_like(logits, dtype=logits.dtype) * -1e10, logits, ) return tf.cond( tf.equal(k, 0), lambda: logits, lambda: _top_k(), ) def top_p_logits(logits, p): batch, _ = logits.shape.as_list() sorted_logits = tf.sort(logits, direction='DESCENDING', axis=-1) cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1) indices = tf.stack([ tf.range(0, batch), tf.maximum(tf.reduce_sum(tf.cast(cumulative_probs <= p, tf.int32), axis=-1) - 1, 0), ], axis=-1) min_values = tf.gather_nd(sorted_logits, indices) return tf.where( logits < min_values, tf.ones_like(logits) * -1e10, logits, ) def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=1): if start_token is None: assert context is not None, 'Specify exactly one of start_token and context!' else: assert context is None, 'Specify exactly one of start_token and context!' context = tf.fill([batch_size, 1], start_token) def step(hparams, tokens, past=None): lm_output = model.model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE) logits = lm_output['logits'][:, :, :hparams.n_vocab] presents = lm_output['present'] presents.set_shape(model.past_shape(hparams=hparams, batch_size=batch_size)) return { 'logits': logits, 'presents': presents, } with tf.name_scope('sample_sequence'): def body(past, prev, output): next_outputs = step(hparams, prev, past=past) logits = next_outputs['logits'][:, -1, :] / tf.to_float(temperature) logits = top_k_logits(logits, k=top_k) logits = top_p_logits(logits, p=top_p) samples = tf.multinomial(logits, num_samples=1, output_dtype=tf.int32) return [ next_outputs['presents'] if past is None else tf.concat([past, next_outputs['presents']], axis=-2), samples, tf.concat([output, samples], axis=1) ] past, prev, output = body(None, context, context) def cond(*args): return True _, _, tokens = tf.while_loop( cond=cond, body=body, maximum_iterations=length - 1, loop_vars=[ past, prev, output ], shape_invariants=[ tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)), tf.TensorShape([batch_size, None]), tf.TensorShape([batch_size, None]), ], back_prop=False, ) return tokens

true

1c311fbb1c23401c4ba13b9d5683ed866307cd23

78,928

py

Python

openmc/lattice.py

openmsr/openmc

831c8d1c50cb4441faf8a0268ec59f6f803bb258

[ "MIT" ]

null

openmc/lattice.py

openmsr/openmc

831c8d1c50cb4441faf8a0268ec59f6f803bb258

[ "MIT" ]

null

openmc/lattice.py

openmsr/openmc

831c8d1c50cb4441faf8a0268ec59f6f803bb258

[ "MIT" ]

null

from abc import ABC from collections import OrderedDict from collections.abc import Iterable from copy import deepcopy from math import sqrt, floor from numbers import Real import types from xml.etree import ElementTree as ET import numpy as np import openmc import openmc.checkvalue as cv from ._xml import get_text from .mixin import IDManagerMixin class Lattice(IDManagerMixin, ABC): """A repeating structure wherein each element is a universe. Parameters ---------- lattice_id : int, optional Unique identifier for the lattice. If not specified, an identifier will automatically be assigned. name : str, optional Name of the lattice. If not specified, the name is the empty string. Attributes ---------- id : int Unique identifier for the lattice name : str Name of the lattice pitch : Iterable of float Pitch of the lattice in each direction in cm outer : openmc.Universe A universe to fill all space outside the lattice universes : Iterable of Iterable of openmc.Universe A two-or three-dimensional list/array of universes filling each element of the lattice """ next_id = 1 used_ids = openmc.Universe.used_ids def __init__(self, lattice_id=None, name=''): # Initialize Lattice class attributes self.id = lattice_id self.name = name self._pitch = None self._outer = None self._universes = None @property def name(self): return self._name @property def pitch(self): return self._pitch @property def outer(self): return self._outer @property def universes(self): return self._universes @name.setter def name(self, name): if name is not None: cv.check_type('lattice name', name, str) self._name = name else: self._name = '' @outer.setter def outer(self, outer): cv.check_type('outer universe', outer, openmc.Universe) self._outer = outer @staticmethod def from_hdf5(group, universes): """Create lattice from HDF5 group Parameters ---------- group : h5py.Group Group in HDF5 file universes : dict Dictionary mapping universe IDs to instances of :class:`openmc.Universe`. Returns ------- openmc.Lattice Instance of lattice subclass """ lattice_type = group['type'][()].decode() if lattice_type == 'rectangular': return openmc.RectLattice.from_hdf5(group, universes) elif lattice_type == 'hexagonal': return openmc.HexLattice.from_hdf5(group, universes) else: raise ValueError(f'Unknown lattice type: {lattice_type}') def get_unique_universes(self): """Determine all unique universes in the lattice Returns ------- universes : collections.OrderedDict Dictionary whose keys are universe IDs and values are :class:`openmc.Universe` instances """ univs = OrderedDict() for k in range(len(self._universes)): for j in range(len(self._universes[k])): if isinstance(self._universes[k][j], openmc.Universe): u = self._universes[k][j] univs[u._id] = u else: for i in range(len(self._universes[k][j])): u = self._universes[k][j][i] assert isinstance(u, openmc.Universe) univs[u._id] = u if self.outer is not None: univs[self.outer._id] = self.outer return univs def get_nuclides(self): """Returns all nuclides in the lattice Returns ------- nuclides : list of str List of nuclide names """ nuclides = [] # Get all unique Universes contained in each of the lattice cells unique_universes = self.get_unique_universes() # Append all Universes containing each cell to the dictionary for universe in unique_universes.values(): for nuclide in universe.get_nuclides(): if nuclide not in nuclides: nuclides.append(nuclide) return nuclides def get_all_cells(self, memo=None): """Return all cells that are contained within the lattice Returns ------- cells : collections.OrderedDict Dictionary whose keys are cell IDs and values are :class:`Cell` instances """ cells = OrderedDict() if memo and self in memo: return cells if memo is not None: memo.add(self) unique_universes = self.get_unique_universes() for universe in unique_universes.values(): cells.update(universe.get_all_cells(memo)) return cells def get_all_materials(self, memo=None): """Return all materials that are contained within the lattice Returns ------- materials : collections.OrderedDict Dictionary whose keys are material IDs and values are :class:`Material` instances """ materials = OrderedDict() # Append all Cells in each Cell in the Universe to the dictionary cells = self.get_all_cells(memo) for cell in cells.values(): materials.update(cell.get_all_materials(memo)) return materials def get_all_universes(self): """Return all universes that are contained within the lattice Returns ------- universes : collections.OrderedDict Dictionary whose keys are universe IDs and values are :class:`Universe` instances """ # Initialize a dictionary of all Universes contained by the Lattice # in each nested Universe level all_universes = OrderedDict() # Get all unique Universes contained in each of the lattice cells unique_universes = self.get_unique_universes() # Add the unique Universes filling each Lattice cell all_universes.update(unique_universes) # Append all Universes containing each cell to the dictionary for universe in unique_universes.values(): all_universes.update(universe.get_all_universes()) return all_universes def get_universe(self, idx): r"""Return universe corresponding to a lattice element index Parameters ---------- idx : Iterable of int Lattice element indices. For a rectangular lattice, the indices are given in the :math:`(x,y)` or :math:`(x,y,z)` coordinate system. For hexagonal lattices, they are given in the :math:`x,\alpha` or :math:`x,\alpha,z` coordinate systems for "y" orientations and :math:`\alpha,y` or :math:`\alpha,y,z` coordinate systems for "x" orientations. Returns ------- openmc.Universe Universe with given indices """ idx_u = self.get_universe_index(idx) if self.ndim == 2: return self.universes[idx_u[0]][idx_u[1]] else: return self.universes[idx_u[0]][idx_u[1]][idx_u[2]] def find(self, point): """Find cells/universes/lattices which contain a given point Parameters ---------- point : 3-tuple of float Cartesian coordinates of the point Returns ------- list Sequence of universes, cells, and lattices which are traversed to find the given point """ idx, p = self.find_element(point) if self.is_valid_index(idx): u = self.get_universe(idx) else: if self.outer is not None: u = self.outer else: return [] return [(self, idx)] + u.find(p) def clone(self, clone_materials=True, clone_regions=True, memo=None): """Create a copy of this lattice with a new unique ID, and clones all universes within this lattice. Parameters ---------- clone_materials : bool Whether to create separate copies of the materials filling cells contained in this lattice and its outer universe. clone_regions : bool Whether to create separate copies of the regions bounding cells contained in this lattice and its outer universe. memo : dict or None A nested dictionary of previously cloned objects. This parameter is used internally and should not be specified by the user. Returns ------- clone : openmc.Lattice The clone of this lattice """ if memo is None: memo = {} # If no memoize'd clone exists, instantiate one if self not in memo: clone = deepcopy(self) clone.id = None if self.outer is not None: clone.outer = self.outer.clone(clone_materials, clone_regions, memo) # Assign universe clones to the lattice clone for i in self.indices: if isinstance(self, RectLattice): clone.universes[i] = self.universes[i].clone( clone_materials, clone_regions, memo) else: if self.ndim == 2: clone.universes[i[0]][i[1]] = \ self.universes[i[0]][i[1]].clone(clone_materials, clone_regions, memo) else: clone.universes[i[0]][i[1]][i[2]] = \ self.universes[i[0]][i[1]][i[2]].clone( clone_materials, clone_regions, memo) # Memoize the clone memo[self] = clone return memo[self] class RectLattice(Lattice): """A lattice consisting of rectangular prisms. To completely define a rectangular lattice, the :attr:`RectLattice.lower_left` :attr:`RectLattice.pitch`, :attr:`RectLattice.outer`, and :attr:`RectLattice.universes` properties need to be set. Most methods for this class use a natural indexing scheme wherein elements are assigned an index corresponding to their position relative to the (x,y,z) axes in a Cartesian coordinate system, i.e., an index of (0,0,0) in the lattice gives the element whose x, y, and z coordinates are the smallest. However, note that when universes are assigned to lattice elements using the :attr:`RectLattice.universes` property, the array indices do not correspond to natural indices. Parameters ---------- lattice_id : int, optional Unique identifier for the lattice. If not specified, an identifier will automatically be assigned. name : str, optional Name of the lattice. If not specified, the name is the empty string. Attributes ---------- id : int Unique identifier for the lattice name : str Name of the lattice pitch : Iterable of float Pitch of the lattice in the x, y, and (if applicable) z directions in cm. outer : openmc.Universe A universe to fill all space outside the lattice universes : Iterable of Iterable of openmc.Universe A two- or three-dimensional list/array of universes filling each element of the lattice. The first dimension corresponds to the z-direction (if applicable), the second dimension corresponds to the y-direction, and the third dimension corresponds to the x-direction. Note that for the y-direction, a higher index corresponds to a lower physical y-value. Each z-slice in the array can be thought of as a top-down view of the lattice. lower_left : Iterable of float The Cartesian coordinates of the lower-left corner of the lattice. If the lattice is two-dimensional, only the x- and y-coordinates are specified. indices : list of tuple A list of all possible (z,y,x) or (y,x) lattice element indices. These indices correspond to indices in the :attr:`RectLattice.universes` property. ndim : int The number of dimensions of the lattice shape : Iterable of int An array of two or three integers representing the number of lattice cells in the x- and y- (and z-) directions, respectively. """ def __init__(self, lattice_id=None, name=''): super().__init__(lattice_id, name) # Initialize Lattice class attributes self._lower_left = None def __repr__(self): string = 'RectLattice\n' string += '{: <16}=\t{}\n'.format('\tID', self._id) string += '{: <16}=\t{}\n'.format('\tName', self._name) string += '{: <16}=\t{}\n'.format('\tShape', self.shape) string += '{: <16}=\t{}\n'.format('\tLower Left', self._lower_left) string += '{: <16}=\t{}\n'.format('\tPitch', self._pitch) string += '{: <16}=\t{}\n'.format( '\tOuter', self._outer._id if self._outer is not None else None) string += '{: <16}\n'.format('\tUniverses') # Lattice nested Universe IDs for i, universe in enumerate(np.ravel(self._universes)): string += f'{universe._id} ' # Add a newline character every time we reach end of row of cells if (i + 1) % self.shape[0] == 0: string += '\n' string = string.rstrip('\n') return string @property def indices(self): if self.ndim == 2: return list(np.broadcast(*np.ogrid[ :self.shape[1], :self.shape[0]])) else: return list(np.broadcast(*np.ogrid[ :self.shape[2], :self.shape[1], :self.shape[0]])) @property def _natural_indices(self): """Iterate over all possible (x,y) or (x,y,z) lattice element indices. This property is used when constructing distributed cell and material paths. Most importantly, the iteration order matches that used on the Fortran side. """ if self.ndim == 2: nx, ny = self.shape for iy in range(ny): for ix in range(nx): yield (ix, iy) else: nx, ny, nz = self.shape for iz in range(nz): for iy in range(ny): for ix in range(nx): yield (ix, iy, iz) @property def lower_left(self): return self._lower_left @property def ndim(self): if self.pitch is not None: return len(self.pitch) else: raise ValueError('Number of dimensions cannot be determined until ' 'the lattice pitch has been set.') @property def shape(self): return self._universes.shape[::-1] @lower_left.setter def lower_left(self, lower_left): cv.check_type('lattice lower left corner', lower_left, Iterable, Real) cv.check_length('lattice lower left corner', lower_left, 2, 3) self._lower_left = lower_left @Lattice.pitch.setter def pitch(self, pitch): cv.check_type('lattice pitch', pitch, Iterable, Real) cv.check_length('lattice pitch', pitch, 2, 3) for dim in pitch: cv.check_greater_than('lattice pitch', dim, 0.0) self._pitch = pitch @Lattice.universes.setter def universes(self, universes): cv.check_iterable_type('lattice universes', universes, openmc.UniverseBase, min_depth=2, max_depth=3) self._universes = np.asarray(universes) def find_element(self, point): """Determine index of lattice element and local coordinates for a point Parameters ---------- point : Iterable of float Cartesian coordinates of point Returns ------- 2- or 3-tuple of int A tuple of the corresponding (x,y,z) lattice element indices 3-tuple of float Carestian coordinates of the point in the corresponding lattice element coordinate system """ ix = floor((point[0] - self.lower_left[0])/self.pitch[0]) iy = floor((point[1] - self.lower_left[1])/self.pitch[1]) if self.ndim == 2: idx = (ix, iy) else: iz = floor((point[2] - self.lower_left[2])/self.pitch[2]) idx = (ix, iy, iz) return idx, self.get_local_coordinates(point, idx) def get_local_coordinates(self, point, idx): """Determine local coordinates of a point within a lattice element Parameters ---------- point : Iterable of float Cartesian coordinates of point idx : Iterable of int (x,y,z) indices of lattice element. If the lattice is 2D, the z index can be omitted. Returns ------- 3-tuple of float Cartesian coordinates of point in the lattice element coordinate system """ x = point[0] - (self.lower_left[0] + (idx[0] + 0.5)*self.pitch[0]) y = point[1] - (self.lower_left[1] + (idx[1] + 0.5)*self.pitch[1]) if self.ndim == 2: z = point[2] else: z = point[2] - (self.lower_left[2] + (idx[2] + 0.5)*self.pitch[2]) return (x, y, z) def get_universe_index(self, idx): """Return index in the universes array corresponding to a lattice element index Parameters ---------- idx : Iterable of int Lattice element indices in the :math:`(x,y,z)` coordinate system Returns ------- 2- or 3-tuple of int Indices used when setting the :attr:`RectLattice.universes` property """ max_y = self.shape[1] - 1 if self.ndim == 2: x, y = idx return (max_y - y, x) else: x, y, z = idx return (z, max_y - y, x) def is_valid_index(self, idx): """Determine whether lattice element index is within defined range Parameters ---------- idx : Iterable of int Lattice element indices in the :math:`(x,y,z)` coordinate system Returns ------- bool Whether index is valid """ if self.ndim == 2: return (0 <= idx[0] < self.shape[0] and 0 <= idx[1] < self.shape[1]) else: return (0 <= idx[0] < self.shape[0] and 0 <= idx[1] < self.shape[1] and 0 <= idx[2] < self.shape[2]) def discretize(self, strategy="degenerate", universes_to_ignore=[], materials_to_clone=[], lattice_neighbors=[], key=lambda univ: univ.id): """Discretize the lattice with either a degenerate or a local neighbor symmetry strategy 'Degenerate' clones every universe in the lattice, thus making them all uniquely defined. This is typically required if depletion or thermal hydraulics will make every universe's environment unique. 'Local neighbor symmetry' groups universes with similar neighborhoods. These clusters of cells and materials provide increased convergence speed to multi-group cross sections tallies. The user can specify the lattice's neighbors to discriminate between two sides of a lattice for example. Parameters ---------- strategy : {'degenerate', 'lns'} Which strategy to adopt when discretizing the lattice universes_to_ignore : Iterable of Universe Lattice universes that need not be discretized materials_to_clone : Iterable of Material List of materials that should be cloned when discretizing lattice_neighbors : Iterable of Universe List of the lattice's neighbors. By default, if present, the lattice outer universe will be used. The neighbors should be ordered as follows [top left, top, top right, left, right, bottom left, bottom, bottom right] key : function Function of argument a universe that is used to extract a comparison key. This function will be called on each universe's neighbors in the lattice to form a neighbor pattern. This pattern is then used to identify unique neighbor symmetries. """ # Check routine inputs if self.ndim != 2: raise NotImplementedError("LNS discretization is not implemented " "for 1D and 3D lattices") cv.check_value('strategy', strategy, ('degenerate', 'lns')) cv.check_type('universes_to_ignore', universes_to_ignore, Iterable, openmc.Universe) cv.check_type('materials_to_clone', materials_to_clone, Iterable, openmc.Material) cv.check_type('lattice_neighbors', lattice_neighbors, Iterable, openmc.Universe) cv.check_value('number of lattice_neighbors', len(lattice_neighbors), (0, 8)) cv.check_type('key', key, types.FunctionType) # Use outer universe if neighbors are missing and outer is defined if self.outer is not None and len(lattice_neighbors) == 0: lattice_neighbors = [key(self.outer) for i in range(8)] elif len(lattice_neighbors) == 8: lattice_neighbors = [key(universe) for universe in lattice_neighbors] # Dictionary that will keep track of where each pattern appears, how # it was rotated and/or symmetrized patterns = {} # Initialize pattern array pattern = np.empty(shape=(3, 3), dtype=type(key(self.universes[0][0]))) # Define an auxiliary function that returns a universe's neighbors # that are outside the lattice def find_edge_neighbors(pattern, i, j): # If no neighbors have been specified, start with an empty array if len(lattice_neighbors) == 0: return # Left edge if i == 0: pattern[:, 0] = lattice_neighbors[3] if j == 0: pattern[0, 0] = lattice_neighbors[0] elif j == self.shape[1] - 1: pattern[2, 0] = lattice_neighbors[5] # Bottom edge if j == 0: pattern[0, 1] = lattice_neighbors[1] if i != 0: pattern[0, 0] = lattice_neighbors[1] if i != self.shape[0] - 1: pattern[0, 2] = lattice_neighbors[1] # Right edge if i == self.shape[0] - 1: pattern[:, 2] = lattice_neighbors[4] if j == 0: pattern[0, 2] = lattice_neighbors[2] elif j == self.shape[1] - 1: pattern[2, 2] = lattice_neighbors[7] # Top edge if j == self.shape[1] - 1: pattern[2, 1] = lattice_neighbors[6] if i != 0: pattern[2, 0] = lattice_neighbors[6] if i != self.shape[0] - 1: pattern[2, 2] = lattice_neighbors[6] # Define an auxiliary function that returns a universe's neighbors # among the universes inside the lattice def find_lattice_neighbors(pattern, i, j): # Away from left edge if i != 0: if j > 0: pattern[0, 0] = key(self.universes[j-1][i-1]) pattern[1, 0] = key(self.universes[j][i-1]) if j < self.shape[1] - 1: pattern[2, 0] = key(self.universes[j+1][i-1]) # Away from bottom edge if j != 0: if i > 0: pattern[0, 0] = key(self.universes[j-1][i-1]) pattern[0, 1] = key(self.universes[j-1][i]) if i < self.shape[0] - 1: pattern[0, 2] = key(self.universes[j-1][i+1]) # Away from right edge if i != self.shape[0] - 1: if j > 0: pattern[0, 2] = key(self.universes[j-1][i+1]) pattern[1, 2] = key(self.universes[j][i+1]) if j < self.shape[1] - 1: pattern[2, 2] = key(self.universes[j+1][i+1]) # Away from top edge if j != self.shape[1] - 1: if i > 0: pattern[2, 0] = key(self.universes[j+1][i-1]) pattern[2, 1] = key(self.universes[j+1][i]) if i < self.shape[0] - 1: pattern[2, 2] = key(self.universes[j+1][i+1]) # Analyze lattice, find unique patterns in groups of universes for j in range(self.shape[1]): for i in range(self.shape[0]): # Skip universes to ignore if self.universes[j][i] in universes_to_ignore: continue # Create a neighborhood pattern based on the universe's # neighbors in the grid, and lattice's neighbors at the edges # Degenerate discretization has all universes be different if strategy == "degenerate": patterns[(i, j)] = {'locations': [(i, j)]} continue # Find neighbors among lattice's neighbors at the edges find_edge_neighbors(pattern, i, j) # Find neighbors among the lattice's universes find_lattice_neighbors(pattern, i, j) pattern[1, 1] = key(self.universes[j][i]) # Look for pattern in dictionary of patterns found found = False for known_pattern, pattern_data in patterns.items(): # Look at all rotations of pattern for rot in range(4): if not found and tuple(map(tuple, pattern)) ==\ known_pattern: found = True # Save location of the pattern in the lattice pattern_data['locations'].append((i, j)) # Rotate pattern pattern = np.rot90(pattern) # Look at transpose of pattern and its rotations pattern = np.transpose(pattern) for rot in range(4): if not found and tuple(map(tuple, pattern)) ==\ known_pattern: found = True # Save location of the pattern in the lattice pattern_data['locations'].append((i, j)) # Rotate pattern pattern = np.rot90(pattern) # Transpose pattern back for the next search pattern = np.transpose(pattern) # Create new pattern and add to the patterns dictionary if not found: patterns[tuple(map(tuple, pattern))] =\ {'locations': [(i, j)]} # Discretize lattice for pattern, pattern_data in patterns.items(): first_pos = pattern_data['locations'][0] # Create a clone of the universe, without cloning materials new_universe = self.universes[first_pos[1]][first_pos[0]].clone( clone_materials=False, clone_regions=False) # Replace only the materials in materials_to_clone for material in materials_to_clone: material_cloned = False for cell in new_universe.get_all_cells().values(): if cell.fill_type == 'material': if cell.fill.id == material.id: # Only a single clone of each material is necessary if not material_cloned: material_clone = material.clone() material_cloned = True cell.fill = material_clone elif cell.fill_type == 'distribmat': raise(ValueError, "Lattice discretization should not " "be used with distributed materials") elif len(cell.temperature) > 1 or len(cell.fill) > 1: raise(ValueError, "Lattice discretization should not " "be used with distributed cells") # Rebuild lattice from list of locations with this pattern for index, location in enumerate(pattern_data['locations']): self.universes[location[1]][location[0]] = new_universe def create_xml_subelement(self, xml_element, memo=None): """Add the lattice xml representation to an incoming xml element Parameters ---------- xml_element : xml.etree.ElementTree.Element XML element to be added to memo : set or None A set of object id's representing geometry entities already written to the xml_element. This parameter is used internally and should not be specified by users. Returns ------- None """ # If the element already contains the Lattice subelement, then return if memo and self in memo: return if memo is not None: memo.add(self) lattice_subelement = ET.Element("lattice") lattice_subelement.set("id", str(self._id)) if len(self._name) > 0: lattice_subelement.set("name", str(self._name)) # Export the Lattice cell pitch pitch = ET.SubElement(lattice_subelement, "pitch") pitch.text = ' '.join(map(str, self._pitch)) # Export the Lattice outer Universe (if specified) if self._outer is not None: outer = ET.SubElement(lattice_subelement, "outer") outer.text = str(self._outer._id) self._outer.create_xml_subelement(xml_element, memo) # Export Lattice cell dimensions dimension = ET.SubElement(lattice_subelement, "dimension") dimension.text = ' '.join(map(str, self.shape)) # Export Lattice lower left lower_left = ET.SubElement(lattice_subelement, "lower_left") lower_left.text = ' '.join(map(str, self._lower_left)) # Export the Lattice nested Universe IDs - column major for Fortran universe_ids = '\n' # 3D Lattices if self.ndim == 3: for z in range(self.shape[2]): for y in range(self.shape[1]): for x in range(self.shape[0]): universe = self._universes[z][y][x] # Append Universe ID to the Lattice XML subelement universe_ids += f'{universe._id} ' # Create XML subelement for this Universe universe.create_xml_subelement(xml_element, memo) # Add newline character when we reach end of row of cells universe_ids += '\n' # Add newline character when we reach end of row of cells universe_ids += '\n' # 2D Lattices else: for y in range(self.shape[1]): for x in range(self.shape[0]): universe = self._universes[y][x] # Append Universe ID to Lattice XML subelement universe_ids += f'{universe._id} ' # Create XML subelement for this Universe universe.create_xml_subelement(xml_element, memo) # Add newline character when we reach end of row of cells universe_ids += '\n' # Remove trailing newline character from Universe IDs string universe_ids = universe_ids.rstrip('\n') universes = ET.SubElement(lattice_subelement, "universes") universes.text = universe_ids # Append the XML subelement for this Lattice to the XML element xml_element.append(lattice_subelement) @classmethod def from_xml_element(cls, elem, get_universe): """Generate rectangular lattice from XML element Parameters ---------- elem : xml.etree.ElementTree.Element `<lattice>` element get_universe : function Function returning universe (defined in :meth:`openmc.Geometry.from_xml`) Returns ------- RectLattice Rectangular lattice """ lat_id = int(get_text(elem, 'id')) name = get_text(elem, 'name') lat = cls(lat_id, name) lat.lower_left = [float(i) for i in get_text(elem, 'lower_left').split()] lat.pitch = [float(i) for i in get_text(elem, 'pitch').split()] outer = get_text(elem, 'outer') if outer is not None: lat.outer = get_universe(int(outer)) # Get array of universes dimension = get_text(elem, 'dimension').split() shape = np.array(dimension, dtype=int)[::-1] uarray = np.array([get_universe(int(i)) for i in get_text(elem, 'universes').split()]) uarray.shape = shape lat.universes = uarray return lat @classmethod def from_hdf5(cls, group, universes): """Create rectangular lattice from HDF5 group Parameters ---------- group : h5py.Group Group in HDF5 file universes : dict Dictionary mapping universe IDs to instances of :class:`openmc.Universe`. Returns ------- openmc.RectLattice Rectangular lattice """ dimension = group['dimension'][...] lower_left = group['lower_left'][...] pitch = group['pitch'][...] outer = group['outer'][()] universe_ids = group['universes'][...] # Create the Lattice lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) name = group['name'][()].decode() if 'name' in group else '' lattice = cls(lattice_id, name) lattice.lower_left = lower_left lattice.pitch = pitch # If the Universe specified outer the Lattice is not void if outer >= 0: lattice.outer = universes[outer] # Build array of Universe pointers for the Lattice uarray = np.empty(universe_ids.shape, dtype=openmc.Universe) for z in range(universe_ids.shape[0]): for y in range(universe_ids.shape[1]): for x in range(universe_ids.shape[2]): uarray[z, y, x] = universes[universe_ids[z, y, x]] # Use 2D NumPy array to store lattice universes for 2D lattices if len(dimension) == 2: uarray = np.squeeze(uarray) uarray = np.atleast_2d(uarray) # Set the universes for the lattice lattice.universes = uarray return lattice class HexLattice(Lattice): r"""A lattice consisting of hexagonal prisms. To completely define a hexagonal lattice, the :attr:`HexLattice.center`, :attr:`HexLattice.pitch`, :attr:`HexLattice.universes`, and :attr:`HexLattice.outer` properties need to be set. Most methods for this class use a natural indexing scheme wherein elements are assigned an index corresponding to their position relative to skewed :math:`(x,\alpha,z)` or :math:`(\alpha,y,z)` bases, depending on the lattice orientation, as described fully in :ref:`hexagonal_indexing`. However, note that when universes are assigned to lattice elements using the :attr:`HexLattice.universes` property, the array indices do not correspond to natural indices. .. versionchanged:: 0.11 The orientation of the lattice can now be changed with the :attr:`orientation` attribute. Parameters ---------- lattice_id : int, optional Unique identifier for the lattice. If not specified, an identifier will automatically be assigned. name : str, optional Name of the lattice. If not specified, the name is the empty string. Attributes ---------- id : int Unique identifier for the lattice name : str Name of the lattice pitch : Iterable of float Pitch of the lattice in cm. The first item in the iterable specifies the pitch in the radial direction and, if the lattice is 3D, the second item in the iterable specifies the pitch in the axial direction. outer : openmc.Universe A universe to fill all space outside the lattice universes : Nested Iterable of openmc.Universe A two- or three-dimensional list/array of universes filling each element of the lattice. Each sub-list corresponds to one ring of universes and should be ordered from outermost ring to innermost ring. The universes within each sub-list are ordered from the "top" and proceed in a clockwise fashion. The :meth:`HexLattice.show_indices` method can be used to help figure out indices for this property. center : Iterable of float Coordinates of the center of the lattice. If the lattice does not have axial sections then only the x- and y-coordinates are specified indices : list of tuple A list of all possible (z,r,i) or (r,i) lattice element indices that are possible, where z is the axial index, r is in the ring index (starting from the outermost ring), and i is the index with a ring starting from the top and proceeding clockwise. orientation : {'x', 'y'} str by default 'y' orientation of main lattice diagonal another option - 'x' num_rings : int Number of radial ring positions in the xy-plane num_axial : int Number of positions along the z-axis. """ def __init__(self, lattice_id=None, name=''): super().__init__(lattice_id, name) # Initialize Lattice class attributes self._num_rings = None self._num_axial = None self._center = None self._orientation = 'y' def __repr__(self): string = 'HexLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) string += '{0: <16}{1}{2}\n'.format('\tOrientation', '=\t', self._orientation) string += '{0: <16}{1}{2}\n'.format('\t# Rings', '=\t', self._num_rings) string += '{0: <16}{1}{2}\n'.format('\t# Axial', '=\t', self._num_axial) string += '{0: <16}{1}{2}\n'.format('\tCenter', '=\t', self._center) string += '{0: <16}{1}{2}\n'.format('\tPitch', '=\t', self._pitch) if self._outer is not None: string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', self._outer._id) else: string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', self._outer) string += '{0: <16}\n'.format('\tUniverses') if self._num_axial is not None: slices = [self._repr_axial_slice(x) for x in self._universes] string += '\n'.join(slices) else: string += self._repr_axial_slice(self._universes) return string @property def num_rings(self): return self._num_rings @property def orientation(self): return self._orientation @property def num_axial(self): return self._num_axial @property def center(self): return self._center @property def indices(self): if self.num_axial is None: return [(r, i) for r in range(self.num_rings) for i in range(max(6*(self.num_rings - 1 - r), 1))] else: return [(z, r, i) for z in range(self.num_axial) for r in range(self.num_rings) for i in range(max(6*(self.num_rings - 1 - r), 1))] @property def _natural_indices(self): """Iterate over all possible (x,alpha) or (x,alpha,z) lattice element indices. This property is used when constructing distributed cell and material paths. Most importantly, the iteration order matches that used on the Fortran side. """ r = self.num_rings if self.num_axial is None: for a in range(-r + 1, r): for x in range(-r + 1, r): idx = (x, a) if self.is_valid_index(idx): yield idx else: for z in range(self.num_axial): for a in range(-r + 1, r): for x in range(-r + 1, r): idx = (x, a, z) if self.is_valid_index(idx): yield idx @property def ndim(self): return 2 if isinstance(self.universes[0][0], openmc.Universe) else 3 @center.setter def center(self, center): cv.check_type('lattice center', center, Iterable, Real) cv.check_length('lattice center', center, 2, 3) self._center = center @orientation.setter def orientation(self, orientation): cv.check_value('orientation', orientation.lower(), ('x', 'y')) self._orientation = orientation.lower() @Lattice.pitch.setter def pitch(self, pitch): cv.check_type('lattice pitch', pitch, Iterable, Real) cv.check_length('lattice pitch', pitch, 1, 2) for dim in pitch: cv.check_greater_than('lattice pitch', dim, 0) self._pitch = pitch @Lattice.universes.setter def universes(self, universes): cv.check_iterable_type('lattice universes', universes, openmc.Universe, min_depth=2, max_depth=3) self._universes = universes # NOTE: This routine assumes that the user creates a "ragged" list of # lists, where each sub-list corresponds to one ring of Universes. # The sub-lists are ordered from outermost ring to innermost ring. # The Universes within each sub-list are ordered from the "top" in a # clockwise fashion. # Set the number of axial positions. if self.ndim == 3: self._num_axial = len(self._universes) else: self._num_axial = None # Set the number of rings and make sure this number is consistent for # all axial positions. if self.ndim == 3: self._num_rings = len(self._universes[0]) for rings in self._universes: if len(rings) != self._num_rings: msg = 'HexLattice ID={0:d} has an inconsistent number of ' \ 'rings per axial position'.format(self._id) raise ValueError(msg) else: self._num_rings = len(self._universes) # Make sure there are the correct number of elements in each ring. if self.ndim == 3: for axial_slice in self._universes: # Check the center ring. if len(axial_slice[-1]) != 1: msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in the innermost ring. Only 1 element is ' \ 'allowed in the innermost ring.'.format(self._id) raise ValueError(msg) # Check the outer rings. for r in range(self._num_rings-1): if len(axial_slice[r]) != 6*(self._num_rings - 1 - r): msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in ring number {1:d} (counting from the '\ 'outermost ring). This ring should have {2:d} ' \ 'elements.'.format(self._id, r, 6*(self._num_rings - 1 - r)) raise ValueError(msg) else: axial_slice = self._universes # Check the center ring. if len(axial_slice[-1]) != 1: msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in the innermost ring. Only 1 element is ' \ 'allowed in the innermost ring.'.format(self._id) raise ValueError(msg) # Check the outer rings. for r in range(self._num_rings-1): if len(axial_slice[r]) != 6*(self._num_rings - 1 - r): msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in ring number {1:d} (counting from the '\ 'outermost ring). This ring should have {2:d} ' \ 'elements.'.format(self._id, r, 6*(self._num_rings - 1 - r)) raise ValueError(msg) def find_element(self, point): r"""Determine index of lattice element and local coordinates for a point Parameters ---------- point : Iterable of float Cartesian coordinates of point Returns ------- 3-tuple of int Indices of corresponding lattice element in :math:`(x,\alpha,z)` or :math:`(\alpha,y,z)` bases numpy.ndarray Carestian coordinates of the point in the corresponding lattice element coordinate system """ # Convert coordinates to skewed bases x = point[0] - self.center[0] y = point[1] - self.center[1] if self._num_axial is None: iz = 1 else: z = point[2] - self.center[2] iz = floor(z/self.pitch[1] + 0.5*self.num_axial) if self._orientation == 'x': alpha = y - x*sqrt(3.) i1 = floor(-alpha/(sqrt(3.0) * self.pitch[0])) i2 = floor(y/(sqrt(0.75) * self.pitch[0])) else: alpha = y - x/sqrt(3.) i1 = floor(x/(sqrt(0.75) * self.pitch[0])) i2 = floor(alpha/self.pitch[0]) # Check four lattice elements to see which one is closest based on local # coordinates indices = [(i1, i2, iz), (i1 + 1, i2, iz), (i1, i2 + 1, iz), (i1 + 1, i2 + 1, iz)] d_min = np.inf for idx in indices: p = self.get_local_coordinates(point, idx) d = p[0]**2 + p[1]**2 if d < d_min: d_min = d idx_min = idx p_min = p return idx_min, p_min def get_local_coordinates(self, point, idx): r"""Determine local coordinates of a point within a lattice element Parameters ---------- point : Iterable of float Cartesian coordinates of point idx : Iterable of int Indices of lattice element in :math:`(x,\alpha,z)` or :math:`(\alpha,y,z)` bases Returns ------- 3-tuple of float Cartesian coordinates of point in the lattice element coordinate system """ if self._orientation == 'x': x = point[0] - (self.center[0] + (idx[0] + 0.5*idx[1])*self.pitch[0]) y = point[1] - (self.center[1] + sqrt(0.75)*self.pitch[0]*idx[1]) else: x = point[0] - (self.center[0] + sqrt(0.75)*self.pitch[0]*idx[0]) y = point[1] - (self.center[1] + (0.5*idx[0] + idx[1])*self.pitch[0]) if self._num_axial is None: z = point[2] else: z = point[2] - (self.center[2] + (idx[2] + 0.5 - 0.5*self.num_axial) * self.pitch[1]) return (x, y, z) def get_universe_index(self, idx): r"""Return index in the universes array corresponding to a lattice element index Parameters ---------- idx : Iterable of int Lattice element indices in the :math:`(x,\alpha,z)` coordinate system in 'y' orientation case, or indices in the :math:`(\alpha,y,z)` coordinate system in 'x' one Returns ------- 2- or 3-tuple of int Indices used when setting the :attr:`HexLattice.universes` property """ # First we determine which ring the index corresponds to. x = idx[0] a = idx[1] z = -a - x g = max(abs(x), abs(a), abs(z)) # Next we use a clever method to figure out where along the ring we are. i_ring = self._num_rings - 1 - g if x >= 0: if a >= 0: i_within = x else: i_within = 2*g + z else: if a <= 0: i_within = 3*g - x else: i_within = 5*g - z if self._orientation == 'x' and g > 0: i_within = (i_within + 5*g) % (6*g) if self.num_axial is None: return (i_ring, i_within) else: return (idx[2], i_ring, i_within) def is_valid_index(self, idx): r"""Determine whether lattice element index is within defined range Parameters ---------- idx : Iterable of int Lattice element indices in the both :math:`(x,\alpha,z)` and :math:`(\alpha,y,z)` coordinate system Returns ------- bool Whether index is valid """ x = idx[0] y = idx[1] z = 0 - y - x g = max(abs(x), abs(y), abs(z)) if self.num_axial is None: return g < self.num_rings else: return g < self.num_rings and 0 <= idx[2] < self.num_axial def create_xml_subelement(self, xml_element, memo=None): # If this subelement has already been written, return if memo and self in memo: return if memo is not None: memo.add(self) lattice_subelement = ET.Element("hex_lattice") lattice_subelement.set("id", str(self._id)) if len(self._name) > 0: lattice_subelement.set("name", str(self._name)) # Export the Lattice cell pitch pitch = ET.SubElement(lattice_subelement, "pitch") pitch.text = ' '.join(map(str, self._pitch)) # Export the Lattice outer Universe (if specified) if self._outer is not None: outer = ET.SubElement(lattice_subelement, "outer") outer.text = str(self._outer._id) self._outer.create_xml_subelement(xml_element, memo) lattice_subelement.set("n_rings", str(self._num_rings)) # If orientation is "x" export it to XML if self._orientation == 'x': lattice_subelement.set("orientation", "x") if self._num_axial is not None: lattice_subelement.set("n_axial", str(self._num_axial)) # Export Lattice cell center center = ET.SubElement(lattice_subelement, "center") center.text = ' '.join(map(str, self._center)) # Export the Lattice nested Universe IDs. # 3D Lattices if self._num_axial is not None: slices = [] for z in range(self._num_axial): # Initialize the center universe. universe = self._universes[z][-1][0] universe.create_xml_subelement(xml_element, memo) # Initialize the remaining universes. for r in range(self._num_rings-1): for theta in range(6*(self._num_rings - 1 - r)): universe = self._universes[z][r][theta] universe.create_xml_subelement(xml_element, memo) # Get a string representation of the universe IDs. slices.append(self._repr_axial_slice(self._universes[z])) # Collapse the list of axial slices into a single string. universe_ids = '\n'.join(slices) # 2D Lattices else: # Initialize the center universe. universe = self._universes[-1][0] universe.create_xml_subelement(xml_element, memo) # Initialize the remaining universes. for r in range(self._num_rings - 1): for theta in range(6*(self._num_rings - 1 - r)): universe = self._universes[r][theta] universe.create_xml_subelement(xml_element, memo) # Get a string representation of the universe IDs. universe_ids = self._repr_axial_slice(self._universes) universes = ET.SubElement(lattice_subelement, "universes") universes.text = '\n' + universe_ids # Append the XML subelement for this Lattice to the XML element xml_element.append(lattice_subelement) @classmethod def from_xml_element(cls, elem, get_universe): """Generate hexagonal lattice from XML element Parameters ---------- elem : xml.etree.ElementTree.Element `<hex_lattice>` element get_universe : function Function returning universe (defined in :meth:`openmc.Geometry.from_xml`) Returns ------- HexLattice Hexagonal lattice """ lat_id = int(get_text(elem, 'id')) name = get_text(elem, 'name') lat = cls(lat_id, name) lat.center = [float(i) for i in get_text(elem, 'center').split()] lat.pitch = [float(i) for i in get_text(elem, 'pitch').split()] lat.orientation = get_text(elem, 'orientation', 'y') outer = get_text(elem, 'outer') if outer is not None: lat.outer = get_universe(int(outer)) # Get nested lists of universes lat._num_rings = n_rings = int(get_text(elem, 'n_rings')) lat._num_axial = n_axial = int(get_text(elem, 'n_axial', 1)) # Create empty nested lists for one axial level univs = [[None for _ in range(max(6*(n_rings - 1 - r), 1))] for r in range(n_rings)] if n_axial > 1: univs = [deepcopy(univs) for i in range(n_axial)] # Get flat array of universes uarray = np.array([get_universe(int(i)) for i in get_text(elem, 'universes').split()]) # Fill nested lists j = 0 for z in range(n_axial): # Get list for a single axial level axial_level = univs[z] if n_axial > 1 else univs if lat.orientation == 'y': # Start iterating from top x, alpha = 0, n_rings - 1 while True: # Set entry in list based on (x,alpha,z) coordinates _, i_ring, i_within = lat.get_universe_index((x, alpha, z)) axial_level[i_ring][i_within] = uarray[j] # Move to the right x += 2 alpha -= 1 if not lat.is_valid_index((x, alpha, z)): # Move down in y direction alpha += x - 1 x = 1 - x if not lat.is_valid_index((x, alpha, z)): # Move to the right x += 2 alpha -= 1 if not lat.is_valid_index((x, alpha, z)): # Reached the bottom break j += 1 else: # Start iterating from top alpha, y = 1 - n_rings, n_rings - 1 while True: # Set entry in list based on (alpha,y,z) coordinates _, i_ring, i_within = lat.get_universe_index((alpha, y, z)) axial_level[i_ring][i_within] = uarray[j] # Move to the right alpha += 1 if not lat.is_valid_index((alpha, y, z)): # Move down to next row alpha = 1 - n_rings y -= 1 # Check if we've reached the bottom if y == -n_rings: break while not lat.is_valid_index((alpha, y, z)): # Move to the right alpha += 1 j += 1 lat.universes = univs return lat def _repr_axial_slice(self, universes): """Return string representation for the given 2D group of universes. The 'universes' argument should be a list of lists of universes where each sub-list represents a single ring. The first list should be the outer ring. """ if self._orientation == 'x': return self._repr_axial_slice_x(universes) else: return self._repr_axial_slice_y(universes) def _repr_axial_slice_x(self, universes): """Return string representation for the given 2D group of universes in 'x' orientation case. The 'universes' argument should be a list of lists of universes where each sub-list represents a single ring. The first list should be the outer ring. """ # Find the largest universe ID and count the number of digits so we can # properly pad the output string later. largest_id = max([max([univ._id for univ in ring]) for ring in universes]) n_digits = len(str(largest_id)) pad = ' '*n_digits id_form = '{: ^' + str(n_digits) + 'd}' # Initialize the list for each row. rows = [[] for i in range(2*self._num_rings - 1)] middle = self._num_rings - 1 # Start with the degenerate first ring. universe = universes[-1][0] rows[middle] = [id_form.format(universe._id)] # Add universes one ring at a time. for r in range(1, self._num_rings): # r_prime increments down while r increments up. r_prime = self._num_rings - 1 - r theta = 0 y = middle # Climb down the bottom-right for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. y += 1 theta += 1 # Climb left across the bottom for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. theta += 1 # Climb up the bottom-left for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. y -= 1 theta += 1 # Climb up the top-left for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. y -= 1 theta += 1 # Climb right across the top for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. theta += 1 # Climb down the top-right for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. y += 1 theta += 1 # Flip the rows and join each row into a single string. rows = [pad.join(x) for x in rows] # Pad the beginning of the rows so they line up properly. for y in range(self._num_rings - 1): rows[y] = (self._num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (self._num_rings - 1 - y)*pad + rows[-1 - y] # Join the rows together and return the string. universe_ids = '\n'.join(rows) return universe_ids def _repr_axial_slice_y(self, universes): """Return string representation for the given 2D group of universes in 'y' orientation case.. The 'universes' argument should be a list of lists of universes where each sub-list represents a single ring. The first list should be the outer ring. """ # Find the largest universe ID and count the number of digits so we can # properly pad the output string later. largest_id = max([max([univ._id for univ in ring]) for ring in universes]) n_digits = len(str(largest_id)) pad = ' '*n_digits id_form = '{: ^' + str(n_digits) + 'd}' # Initialize the list for each row. rows = [[] for i in range(1 + 4 * (self._num_rings-1))] middle = 2 * (self._num_rings - 1) # Start with the degenerate first ring. universe = universes[-1][0] rows[middle] = [id_form.format(universe._id)] # Add universes one ring at a time. for r in range(1, self._num_rings): # r_prime increments down while r increments up. r_prime = self._num_rings - 1 - r theta = 0 y = middle + 2*r # Climb down the top-right. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. y -= 1 theta += 1 # Climb down the right. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. y -= 2 theta += 1 # Climb down the bottom-right. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) # Translate the indices. y -= 1 theta += 1 # Climb up the bottom-left. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. y += 1 theta += 1 # Climb up the left. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. y += 2 theta += 1 # Climb up the top-left. for i in range(r): # Add the universe. universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) # Translate the indices. y += 1 theta += 1 # Flip the rows and join each row into a single string. rows = [pad.join(x) for x in rows[::-1]] # Pad the beginning of the rows so they line up properly. for y in range(self._num_rings - 1): rows[y] = (self._num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (self._num_rings - 1 - y)*pad + rows[-1 - y] for y in range(self._num_rings % 2, self._num_rings, 2): rows[middle + y] = pad + rows[middle + y] if y != 0: rows[middle - y] = pad + rows[middle - y] # Join the rows together and return the string. universe_ids = '\n'.join(rows) return universe_ids @staticmethod def _show_indices_y(num_rings): """Return a diagram of the hexagonal lattice layout with indices. This method can be used to show the proper indices to be used when setting the :attr:`HexLattice.universes` property. For example, running this method with num_rings=3 will return the following diagram:: (0, 0) (0,11) (0, 1) (0,10) (1, 0) (0, 2) (1, 5) (1, 1) (0, 9) (2, 0) (0, 3) (1, 4) (1, 2) (0, 8) (1, 3) (0, 4) (0, 7) (0, 5) (0, 6) Parameters ---------- num_rings : int Number of rings in the hexagonal lattice Returns ------- str Diagram of the hexagonal lattice showing indices """ # Find the largest string and count the number of digits so we can # properly pad the output string later largest_index = 6*(num_rings - 1) n_digits_index = len(str(largest_index)) n_digits_ring = len(str(num_rings - 1)) str_form = '({{:{}}},{{:{}}})'.format(n_digits_ring, n_digits_index) pad = ' '*(n_digits_index + n_digits_ring + 3) # Initialize the list for each row. rows = [[] for i in range(1 + 4 * (num_rings-1))] middle = 2 * (num_rings - 1) # Start with the degenerate first ring. rows[middle] = [str_form.format(num_rings - 1, 0)] # Add universes one ring at a time. for r in range(1, num_rings): # r_prime increments down while r increments up. r_prime = num_rings - 1 - r theta = 0 y = middle + 2*r for i in range(r): # Climb down the top-right. rows[y].append(str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): # Climb down the right. rows[y].append(str_form.format(r_prime, theta)) y -= 2 theta += 1 for i in range(r): # Climb down the bottom-right. rows[y].append(str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): # Climb up the bottom-left. rows[y].insert(0, str_form.format(r_prime, theta)) y += 1 theta += 1 for i in range(r): # Climb up the left. rows[y].insert(0, str_form.format(r_prime, theta)) y += 2 theta += 1 for i in range(r): # Climb up the top-left. rows[y].insert(0, str_form.format(r_prime, theta)) y += 1 theta += 1 # Flip the rows and join each row into a single string. rows = [pad.join(x) for x in rows[::-1]] # Pad the beginning of the rows so they line up properly. for y in range(num_rings - 1): rows[y] = (num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (num_rings - 1 - y)*pad + rows[-1 - y] for y in range(num_rings % 2, num_rings, 2): rows[middle + y] = pad + rows[middle + y] if y != 0: rows[middle - y] = pad + rows[middle - y] # Join the rows together and return the string. return '\n'.join(rows) @staticmethod def _show_indices_x(num_rings): """Return a diagram of the hexagonal lattice with x orientation layout with indices. This method can be used to show the proper indices to be used when setting the :attr:`HexLattice.universes` property. For example,running this method with num_rings=3 will return the similar diagram:: (0, 8) (0, 9) (0,10) (0, 7) (1, 4) (1, 5) (0,11) (0, 6) (1, 3) (2, 0) (1, 0) (0, 0) (0, 5) (1, 2) (1, 1) (0, 1) (0, 4) (0, 3) (0, 2) Parameters ---------- num_rings : int Number of rings in the hexagonal lattice Returns ------- str Diagram of the hexagonal lattice showing indices in OX orientation """ # Find the largest string and count the number of digits so we can # properly pad the output string later largest_index = 6*(num_rings - 1) n_digits_index = len(str(largest_index)) n_digits_ring = len(str(num_rings - 1)) str_form = '({{:{}}},{{:{}}})'.format(n_digits_ring, n_digits_index) pad = ' '*(n_digits_index + n_digits_ring + 3) # Initialize the list for each row. rows = [[] for i in range(2*num_rings - 1)] middle = num_rings - 1 # Start with the degenerate first ring. rows[middle] = [str_form.format(num_rings - 1, 0)] # Add universes one ring at a time. for r in range(1, num_rings): # r_prime increments down while r increments up. r_prime = num_rings - 1 - r theta = 0 y = middle for i in range(r): # Climb down the bottom-right rows[y].append(str_form.format(r_prime, theta)) y += 1 theta += 1 for i in range(r): # Climb left across the bottom rows[y].insert(0, str_form.format(r_prime, theta)) theta += 1 for i in range(r): # Climb up the bottom-left rows[y].insert(0, str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): # Climb up the top-left rows[y].insert(0, str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): # Climb right across the top rows[y].append(str_form.format(r_prime, theta)) theta += 1 for i in range(r): # Climb down the top-right rows[y].append(str_form.format(r_prime, theta)) y += 1 theta += 1 # Flip the rows and join each row into a single string. rows = [pad.join(x) for x in rows] # Pad the beginning of the rows so they line up properly. for y in range(num_rings - 1): rows[y] = (num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (num_rings - 1 - y)*pad + rows[-1 - y] # Join the rows together and return the string. return '\n\n'.join(rows) @staticmethod def show_indices(num_rings, orientation="y"): """Return a diagram of the hexagonal lattice layout with indices. Parameters ---------- num_rings : int Number of rings in the hexagonal lattice orientation : {"x", "y"} Orientation of the hexagonal lattice Returns ------- str Diagram of the hexagonal lattice showing indices """ if orientation == 'x': return HexLattice._show_indices_x(num_rings) else: return HexLattice._show_indices_y(num_rings) @classmethod def from_hdf5(cls, group, universes): """Create rectangular lattice from HDF5 group Parameters ---------- group : h5py.Group Group in HDF5 file universes : dict Dictionary mapping universe IDs to instances of :class:`openmc.Universe`. Returns ------- openmc.RectLattice Rectangular lattice """ n_rings = group['n_rings'][()] n_axial = group['n_axial'][()] center = group['center'][()] pitch = group['pitch'][()] outer = group['outer'][()] if 'orientation' in group: orientation = group['orientation'][()].decode() else: orientation = "y" universe_ids = group['universes'][()] # Create the Lattice lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) name = group['name'][()].decode() if 'name' in group else '' lattice = openmc.HexLattice(lattice_id, name) lattice.center = center lattice.pitch = pitch lattice.orientation = orientation # If the Universe specified outer the Lattice is not void if outer >= 0: lattice.outer = universes[outer] if orientation == "y": # Build array of Universe pointers for the Lattice. Note that # we need to convert between the HDF5's square array of # (x, alpha, z) to the Python API's format of a ragged nested # list of (z, ring, theta). uarray = [] for z in range(n_axial): # Add a list for this axial level. uarray.append([]) x = n_rings - 1 a = 2*n_rings - 2 for r in range(n_rings - 1, 0, -1): # Add a list for this ring. uarray[-1].append([]) # Climb down the top-right. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x += 1 a -= 1 # Climb down the right. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) a -= 1 # Climb down the bottom-right. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x -= 1 # Climb up the bottom-left. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x -= 1 a += 1 # Climb up the left. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) a += 1 # Climb up the top-left. for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x += 1 # Move down to the next ring. a -= 1 # Convert the ids into Universe objects. uarray[-1][-1] = [universes[u_id] for u_id in uarray[-1][-1]] # Handle the degenerate center ring separately. u_id = universe_ids[z, a, x] uarray[-1].append([universes[u_id]]) else: # Build array of Universe pointers for the Lattice. Note that # we need to convert between the HDF5's square array of # (alpha, y, z) to the Python API's format of a ragged nested # list of (z, ring, theta). uarray = [] for z in range(n_axial): # Add a list for this axial level. uarray.append([]) a = 2*n_rings - 2 y = n_rings - 1 for r in range(n_rings - 1, 0, -1): # Add a list for this ring. uarray[-1].append([]) # Climb down the bottom-right. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) y -= 1 # Climb across the bottom. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a -= 1 # Climb up the bottom-left. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a -= 1 y += 1 # Climb up the top-left. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) y += 1 # Climb across the top. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a += 1 # Climb down the top-right. for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a += 1 y -= 1 # Move down to the next ring. a -= 1 # Convert the ids into Universe objects. uarray[-1][-1] = [universes[u_id] for u_id in uarray[-1][-1]] # Handle the degenerate center ring separately. u_id = universe_ids[z, y, a] uarray[-1].append([universes[u_id]]) # Add the universes to the lattice. if len(pitch) == 2: # Lattice is 3D lattice.universes = uarray else: # Lattice is 2D; extract the only axial level lattice.universes = uarray[0] return lattice

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from abc import ABC from collections import OrderedDict from collections.abc import Iterable from copy import deepcopy from math import sqrt, floor from numbers import Real import types from xml.etree import ElementTree as ET import numpy as np import openmc import openmc.checkvalue as cv from ._xml import get_text from .mixin import IDManagerMixin class Lattice(IDManagerMixin, ABC): next_id = 1 used_ids = openmc.Universe.used_ids def __init__(self, lattice_id=None, name=''): self.id = lattice_id self.name = name self._pitch = None self._outer = None self._universes = None @property def name(self): return self._name @property def pitch(self): return self._pitch @property def outer(self): return self._outer @property def universes(self): return self._universes @name.setter def name(self, name): if name is not None: cv.check_type('lattice name', name, str) self._name = name else: self._name = '' @outer.setter def outer(self, outer): cv.check_type('outer universe', outer, openmc.Universe) self._outer = outer @staticmethod def from_hdf5(group, universes): lattice_type = group['type'][()].decode() if lattice_type == 'rectangular': return openmc.RectLattice.from_hdf5(group, universes) elif lattice_type == 'hexagonal': return openmc.HexLattice.from_hdf5(group, universes) else: raise ValueError(f'Unknown lattice type: {lattice_type}') def get_unique_universes(self): univs = OrderedDict() for k in range(len(self._universes)): for j in range(len(self._universes[k])): if isinstance(self._universes[k][j], openmc.Universe): u = self._universes[k][j] univs[u._id] = u else: for i in range(len(self._universes[k][j])): u = self._universes[k][j][i] assert isinstance(u, openmc.Universe) univs[u._id] = u if self.outer is not None: univs[self.outer._id] = self.outer return univs def get_nuclides(self): nuclides = [] unique_universes = self.get_unique_universes() for universe in unique_universes.values(): for nuclide in universe.get_nuclides(): if nuclide not in nuclides: nuclides.append(nuclide) return nuclides def get_all_cells(self, memo=None): cells = OrderedDict() if memo and self in memo: return cells if memo is not None: memo.add(self) unique_universes = self.get_unique_universes() for universe in unique_universes.values(): cells.update(universe.get_all_cells(memo)) return cells def get_all_materials(self, memo=None): materials = OrderedDict() cells = self.get_all_cells(memo) for cell in cells.values(): materials.update(cell.get_all_materials(memo)) return materials def get_all_universes(self): all_universes = OrderedDict() unique_universes = self.get_unique_universes() all_universes.update(unique_universes) for universe in unique_universes.values(): all_universes.update(universe.get_all_universes()) return all_universes def get_universe(self, idx): idx_u = self.get_universe_index(idx) if self.ndim == 2: return self.universes[idx_u[0]][idx_u[1]] else: return self.universes[idx_u[0]][idx_u[1]][idx_u[2]] def find(self, point): idx, p = self.find_element(point) if self.is_valid_index(idx): u = self.get_universe(idx) else: if self.outer is not None: u = self.outer else: return [] return [(self, idx)] + u.find(p) def clone(self, clone_materials=True, clone_regions=True, memo=None): if memo is None: memo = {} if self not in memo: clone = deepcopy(self) clone.id = None if self.outer is not None: clone.outer = self.outer.clone(clone_materials, clone_regions, memo) # Assign universe clones to the lattice clone for i in self.indices: if isinstance(self, RectLattice): clone.universes[i] = self.universes[i].clone( clone_materials, clone_regions, memo) else: if self.ndim == 2: clone.universes[i[0]][i[1]] = \ self.universes[i[0]][i[1]].clone(clone_materials, clone_regions, memo) else: clone.universes[i[0]][i[1]][i[2]] = \ self.universes[i[0]][i[1]][i[2]].clone( clone_materials, clone_regions, memo) # Memoize the clone memo[self] = clone return memo[self] class RectLattice(Lattice): def __init__(self, lattice_id=None, name=''): super().__init__(lattice_id, name) # Initialize Lattice class attributes self._lower_left = None def __repr__(self): string = 'RectLattice\n' string += '{: <16}=\t{}\n'.format('\tID', self._id) string += '{: <16}=\t{}\n'.format('\tName', self._name) string += '{: <16}=\t{}\n'.format('\tShape', self.shape) string += '{: <16}=\t{}\n'.format('\tLower Left', self._lower_left) string += '{: <16}=\t{}\n'.format('\tPitch', self._pitch) string += '{: <16}=\t{}\n'.format( '\tOuter', self._outer._id if self._outer is not None else None) string += '{: <16}\n'.format('\tUniverses') # Lattice nested Universe IDs for i, universe in enumerate(np.ravel(self._universes)): string += f'{universe._id} ' # Add a newline character every time we reach end of row of cells if (i + 1) % self.shape[0] == 0: string += '\n' string = string.rstrip('\n') return string @property def indices(self): if self.ndim == 2: return list(np.broadcast(*np.ogrid[ :self.shape[1], :self.shape[0]])) else: return list(np.broadcast(*np.ogrid[ :self.shape[2], :self.shape[1], :self.shape[0]])) @property def _natural_indices(self): if self.ndim == 2: nx, ny = self.shape for iy in range(ny): for ix in range(nx): yield (ix, iy) else: nx, ny, nz = self.shape for iz in range(nz): for iy in range(ny): for ix in range(nx): yield (ix, iy, iz) @property def lower_left(self): return self._lower_left @property def ndim(self): if self.pitch is not None: return len(self.pitch) else: raise ValueError('Number of dimensions cannot be determined until ' 'the lattice pitch has been set.') @property def shape(self): return self._universes.shape[::-1] @lower_left.setter def lower_left(self, lower_left): cv.check_type('lattice lower left corner', lower_left, Iterable, Real) cv.check_length('lattice lower left corner', lower_left, 2, 3) self._lower_left = lower_left @Lattice.pitch.setter def pitch(self, pitch): cv.check_type('lattice pitch', pitch, Iterable, Real) cv.check_length('lattice pitch', pitch, 2, 3) for dim in pitch: cv.check_greater_than('lattice pitch', dim, 0.0) self._pitch = pitch @Lattice.universes.setter def universes(self, universes): cv.check_iterable_type('lattice universes', universes, openmc.UniverseBase, min_depth=2, max_depth=3) self._universes = np.asarray(universes) def find_element(self, point): ix = floor((point[0] - self.lower_left[0])/self.pitch[0]) iy = floor((point[1] - self.lower_left[1])/self.pitch[1]) if self.ndim == 2: idx = (ix, iy) else: iz = floor((point[2] - self.lower_left[2])/self.pitch[2]) idx = (ix, iy, iz) return idx, self.get_local_coordinates(point, idx) def get_local_coordinates(self, point, idx): x = point[0] - (self.lower_left[0] + (idx[0] + 0.5)*self.pitch[0]) y = point[1] - (self.lower_left[1] + (idx[1] + 0.5)*self.pitch[1]) if self.ndim == 2: z = point[2] else: z = point[2] - (self.lower_left[2] + (idx[2] + 0.5)*self.pitch[2]) return (x, y, z) def get_universe_index(self, idx): max_y = self.shape[1] - 1 if self.ndim == 2: x, y = idx return (max_y - y, x) else: x, y, z = idx return (z, max_y - y, x) def is_valid_index(self, idx): if self.ndim == 2: return (0 <= idx[0] < self.shape[0] and 0 <= idx[1] < self.shape[1]) else: return (0 <= idx[0] < self.shape[0] and 0 <= idx[1] < self.shape[1] and 0 <= idx[2] < self.shape[2]) def discretize(self, strategy="degenerate", universes_to_ignore=[], materials_to_clone=[], lattice_neighbors=[], key=lambda univ: univ.id): # Check routine inputs if self.ndim != 2: raise NotImplementedError("LNS discretization is not implemented " "for 1D and 3D lattices") cv.check_value('strategy', strategy, ('degenerate', 'lns')) cv.check_type('universes_to_ignore', universes_to_ignore, Iterable, openmc.Universe) cv.check_type('materials_to_clone', materials_to_clone, Iterable, openmc.Material) cv.check_type('lattice_neighbors', lattice_neighbors, Iterable, openmc.Universe) cv.check_value('number of lattice_neighbors', len(lattice_neighbors), (0, 8)) cv.check_type('key', key, types.FunctionType) # Use outer universe if neighbors are missing and outer is defined if self.outer is not None and len(lattice_neighbors) == 0: lattice_neighbors = [key(self.outer) for i in range(8)] elif len(lattice_neighbors) == 8: lattice_neighbors = [key(universe) for universe in lattice_neighbors] # Dictionary that will keep track of where each pattern appears, how # it was rotated and/or symmetrized patterns = {} # Initialize pattern array pattern = np.empty(shape=(3, 3), dtype=type(key(self.universes[0][0]))) # Define an auxiliary function that returns a universe's neighbors def find_edge_neighbors(pattern, i, j): if len(lattice_neighbors) == 0: return if i == 0: pattern[:, 0] = lattice_neighbors[3] if j == 0: pattern[0, 0] = lattice_neighbors[0] elif j == self.shape[1] - 1: pattern[2, 0] = lattice_neighbors[5] if j == 0: pattern[0, 1] = lattice_neighbors[1] if i != 0: pattern[0, 0] = lattice_neighbors[1] if i != self.shape[0] - 1: pattern[0, 2] = lattice_neighbors[1] if i == self.shape[0] - 1: pattern[:, 2] = lattice_neighbors[4] if j == 0: pattern[0, 2] = lattice_neighbors[2] elif j == self.shape[1] - 1: pattern[2, 2] = lattice_neighbors[7] if j == self.shape[1] - 1: pattern[2, 1] = lattice_neighbors[6] if i != 0: pattern[2, 0] = lattice_neighbors[6] if i != self.shape[0] - 1: pattern[2, 2] = lattice_neighbors[6] # among the universes inside the lattice def find_lattice_neighbors(pattern, i, j): # Away from left edge if i != 0: if j > 0: pattern[0, 0] = key(self.universes[j-1][i-1]) pattern[1, 0] = key(self.universes[j][i-1]) if j < self.shape[1] - 1: pattern[2, 0] = key(self.universes[j+1][i-1]) # Away from bottom edge if j != 0: if i > 0: pattern[0, 0] = key(self.universes[j-1][i-1]) pattern[0, 1] = key(self.universes[j-1][i]) if i < self.shape[0] - 1: pattern[0, 2] = key(self.universes[j-1][i+1]) # Away from right edge if i != self.shape[0] - 1: if j > 0: pattern[0, 2] = key(self.universes[j-1][i+1]) pattern[1, 2] = key(self.universes[j][i+1]) if j < self.shape[1] - 1: pattern[2, 2] = key(self.universes[j+1][i+1]) # Away from top edge if j != self.shape[1] - 1: if i > 0: pattern[2, 0] = key(self.universes[j+1][i-1]) pattern[2, 1] = key(self.universes[j+1][i]) if i < self.shape[0] - 1: pattern[2, 2] = key(self.universes[j+1][i+1]) # Analyze lattice, find unique patterns in groups of universes for j in range(self.shape[1]): for i in range(self.shape[0]): # Skip universes to ignore if self.universes[j][i] in universes_to_ignore: continue # Create a neighborhood pattern based on the universe's # Degenerate discretization has all universes be different if strategy == "degenerate": patterns[(i, j)] = {'locations': [(i, j)]} continue # Find neighbors among lattice's neighbors at the edges find_edge_neighbors(pattern, i, j) find_lattice_neighbors(pattern, i, j) pattern[1, 1] = key(self.universes[j][i]) # Look for pattern in dictionary of patterns found found = False for known_pattern, pattern_data in patterns.items(): # Look at all rotations of pattern for rot in range(4): if not found and tuple(map(tuple, pattern)) ==\ known_pattern: found = True # Save location of the pattern in the lattice pattern_data['locations'].append((i, j)) # Rotate pattern pattern = np.rot90(pattern) # Look at transpose of pattern and its rotations pattern = np.transpose(pattern) for rot in range(4): if not found and tuple(map(tuple, pattern)) ==\ known_pattern: found = True # Save location of the pattern in the lattice pattern_data['locations'].append((i, j)) # Rotate pattern pattern = np.rot90(pattern) # Transpose pattern back for the next search pattern = np.transpose(pattern) # Create new pattern and add to the patterns dictionary if not found: patterns[tuple(map(tuple, pattern))] =\ {'locations': [(i, j)]} # Discretize lattice for pattern, pattern_data in patterns.items(): first_pos = pattern_data['locations'][0] # Create a clone of the universe, without cloning materials new_universe = self.universes[first_pos[1]][first_pos[0]].clone( clone_materials=False, clone_regions=False) # Replace only the materials in materials_to_clone for material in materials_to_clone: material_cloned = False for cell in new_universe.get_all_cells().values(): if cell.fill_type == 'material': if cell.fill.id == material.id: # Only a single clone of each material is necessary if not material_cloned: material_clone = material.clone() material_cloned = True cell.fill = material_clone elif cell.fill_type == 'distribmat': raise(ValueError, "Lattice discretization should not " "be used with distributed materials") elif len(cell.temperature) > 1 or len(cell.fill) > 1: raise(ValueError, "Lattice discretization should not " "be used with distributed cells") # Rebuild lattice from list of locations with this pattern for index, location in enumerate(pattern_data['locations']): self.universes[location[1]][location[0]] = new_universe def create_xml_subelement(self, xml_element, memo=None): # If the element already contains the Lattice subelement, then return if memo and self in memo: return if memo is not None: memo.add(self) lattice_subelement = ET.Element("lattice") lattice_subelement.set("id", str(self._id)) if len(self._name) > 0: lattice_subelement.set("name", str(self._name)) # Export the Lattice cell pitch pitch = ET.SubElement(lattice_subelement, "pitch") pitch.text = ' '.join(map(str, self._pitch)) # Export the Lattice outer Universe (if specified) if self._outer is not None: outer = ET.SubElement(lattice_subelement, "outer") outer.text = str(self._outer._id) self._outer.create_xml_subelement(xml_element, memo) # Export Lattice cell dimensions dimension = ET.SubElement(lattice_subelement, "dimension") dimension.text = ' '.join(map(str, self.shape)) # Export Lattice lower left lower_left = ET.SubElement(lattice_subelement, "lower_left") lower_left.text = ' '.join(map(str, self._lower_left)) # Export the Lattice nested Universe IDs - column major for Fortran universe_ids = '\n' # 3D Lattices if self.ndim == 3: for z in range(self.shape[2]): for y in range(self.shape[1]): for x in range(self.shape[0]): universe = self._universes[z][y][x] # Append Universe ID to the Lattice XML subelement universe_ids += f'{universe._id} ' # Create XML subelement for this Universe universe.create_xml_subelement(xml_element, memo) # Add newline character when we reach end of row of cells universe_ids += '\n' # Add newline character when we reach end of row of cells universe_ids += '\n' # 2D Lattices else: for y in range(self.shape[1]): for x in range(self.shape[0]): universe = self._universes[y][x] # Append Universe ID to Lattice XML subelement universe_ids += f'{universe._id} ' # Create XML subelement for this Universe universe.create_xml_subelement(xml_element, memo) # Add newline character when we reach end of row of cells universe_ids += '\n' # Remove trailing newline character from Universe IDs string universe_ids = universe_ids.rstrip('\n') universes = ET.SubElement(lattice_subelement, "universes") universes.text = universe_ids # Append the XML subelement for this Lattice to the XML element xml_element.append(lattice_subelement) @classmethod def from_xml_element(cls, elem, get_universe): lat_id = int(get_text(elem, 'id')) name = get_text(elem, 'name') lat = cls(lat_id, name) lat.lower_left = [float(i) for i in get_text(elem, 'lower_left').split()] lat.pitch = [float(i) for i in get_text(elem, 'pitch').split()] outer = get_text(elem, 'outer') if outer is not None: lat.outer = get_universe(int(outer)) # Get array of universes dimension = get_text(elem, 'dimension').split() shape = np.array(dimension, dtype=int)[::-1] uarray = np.array([get_universe(int(i)) for i in get_text(elem, 'universes').split()]) uarray.shape = shape lat.universes = uarray return lat @classmethod def from_hdf5(cls, group, universes): dimension = group['dimension'][...] lower_left = group['lower_left'][...] pitch = group['pitch'][...] outer = group['outer'][()] universe_ids = group['universes'][...] # Create the Lattice lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) name = group['name'][()].decode() if 'name' in group else '' lattice = cls(lattice_id, name) lattice.lower_left = lower_left lattice.pitch = pitch # If the Universe specified outer the Lattice is not void if outer >= 0: lattice.outer = universes[outer] # Build array of Universe pointers for the Lattice uarray = np.empty(universe_ids.shape, dtype=openmc.Universe) for z in range(universe_ids.shape[0]): for y in range(universe_ids.shape[1]): for x in range(universe_ids.shape[2]): uarray[z, y, x] = universes[universe_ids[z, y, x]] # Use 2D NumPy array to store lattice universes for 2D lattices if len(dimension) == 2: uarray = np.squeeze(uarray) uarray = np.atleast_2d(uarray) # Set the universes for the lattice lattice.universes = uarray return lattice class HexLattice(Lattice): def __init__(self, lattice_id=None, name=''): super().__init__(lattice_id, name) # Initialize Lattice class attributes self._num_rings = None self._num_axial = None self._center = None self._orientation = 'y' def __repr__(self): string = 'HexLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) string += '{0: <16}{1}{2}\n'.format('\tOrientation', '=\t', self._orientation) string += '{0: <16}{1}{2}\n'.format('\t string += '{0: <16}{1}{2}\n'.format('\t string += '{0: <16}{1}{2}\n'.format('\tCenter', '=\t', self._center) string += '{0: <16}{1}{2}\n'.format('\tPitch', '=\t', self._pitch) if self._outer is not None: string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', self._outer._id) else: string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', self._outer) string += '{0: <16}\n'.format('\tUniverses') if self._num_axial is not None: slices = [self._repr_axial_slice(x) for x in self._universes] string += '\n'.join(slices) else: string += self._repr_axial_slice(self._universes) return string @property def num_rings(self): return self._num_rings @property def orientation(self): return self._orientation @property def num_axial(self): return self._num_axial @property def center(self): return self._center @property def indices(self): if self.num_axial is None: return [(r, i) for r in range(self.num_rings) for i in range(max(6*(self.num_rings - 1 - r), 1))] else: return [(z, r, i) for z in range(self.num_axial) for r in range(self.num_rings) for i in range(max(6*(self.num_rings - 1 - r), 1))] @property def _natural_indices(self): r = self.num_rings if self.num_axial is None: for a in range(-r + 1, r): for x in range(-r + 1, r): idx = (x, a) if self.is_valid_index(idx): yield idx else: for z in range(self.num_axial): for a in range(-r + 1, r): for x in range(-r + 1, r): idx = (x, a, z) if self.is_valid_index(idx): yield idx @property def ndim(self): return 2 if isinstance(self.universes[0][0], openmc.Universe) else 3 @center.setter def center(self, center): cv.check_type('lattice center', center, Iterable, Real) cv.check_length('lattice center', center, 2, 3) self._center = center @orientation.setter def orientation(self, orientation): cv.check_value('orientation', orientation.lower(), ('x', 'y')) self._orientation = orientation.lower() @Lattice.pitch.setter def pitch(self, pitch): cv.check_type('lattice pitch', pitch, Iterable, Real) cv.check_length('lattice pitch', pitch, 1, 2) for dim in pitch: cv.check_greater_than('lattice pitch', dim, 0) self._pitch = pitch @Lattice.universes.setter def universes(self, universes): cv.check_iterable_type('lattice universes', universes, openmc.Universe, min_depth=2, max_depth=3) self._universes = universes # NOTE: This routine assumes that the user creates a "ragged" list of # lists, where each sub-list corresponds to one ring of Universes. # The sub-lists are ordered from outermost ring to innermost ring. # The Universes within each sub-list are ordered from the "top" in a # clockwise fashion. # Set the number of axial positions. if self.ndim == 3: self._num_axial = len(self._universes) else: self._num_axial = None # Set the number of rings and make sure this number is consistent for # all axial positions. if self.ndim == 3: self._num_rings = len(self._universes[0]) for rings in self._universes: if len(rings) != self._num_rings: msg = 'HexLattice ID={0:d} has an inconsistent number of ' \ 'rings per axial position'.format(self._id) raise ValueError(msg) else: self._num_rings = len(self._universes) # Make sure there are the correct number of elements in each ring. if self.ndim == 3: for axial_slice in self._universes: # Check the center ring. if len(axial_slice[-1]) != 1: msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in the innermost ring. Only 1 element is ' \ 'allowed in the innermost ring.'.format(self._id) raise ValueError(msg) # Check the outer rings. for r in range(self._num_rings-1): if len(axial_slice[r]) != 6*(self._num_rings - 1 - r): msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in ring number {1:d} (counting from the '\ 'outermost ring). This ring should have {2:d} ' \ 'elements.'.format(self._id, r, 6*(self._num_rings - 1 - r)) raise ValueError(msg) else: axial_slice = self._universes # Check the center ring. if len(axial_slice[-1]) != 1: msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in the innermost ring. Only 1 element is ' \ 'allowed in the innermost ring.'.format(self._id) raise ValueError(msg) # Check the outer rings. for r in range(self._num_rings-1): if len(axial_slice[r]) != 6*(self._num_rings - 1 - r): msg = 'HexLattice ID={0:d} has the wrong number of ' \ 'elements in ring number {1:d} (counting from the '\ 'outermost ring). This ring should have {2:d} ' \ 'elements.'.format(self._id, r, 6*(self._num_rings - 1 - r)) raise ValueError(msg) def find_element(self, point): # Convert coordinates to skewed bases x = point[0] - self.center[0] y = point[1] - self.center[1] if self._num_axial is None: iz = 1 else: z = point[2] - self.center[2] iz = floor(z/self.pitch[1] + 0.5*self.num_axial) if self._orientation == 'x': alpha = y - x*sqrt(3.) i1 = floor(-alpha/(sqrt(3.0) * self.pitch[0])) i2 = floor(y/(sqrt(0.75) * self.pitch[0])) else: alpha = y - x/sqrt(3.) i1 = floor(x/(sqrt(0.75) * self.pitch[0])) i2 = floor(alpha/self.pitch[0]) # Check four lattice elements to see which one is closest based on local # coordinates indices = [(i1, i2, iz), (i1 + 1, i2, iz), (i1, i2 + 1, iz), (i1 + 1, i2 + 1, iz)] d_min = np.inf for idx in indices: p = self.get_local_coordinates(point, idx) d = p[0]**2 + p[1]**2 if d < d_min: d_min = d idx_min = idx p_min = p return idx_min, p_min def get_local_coordinates(self, point, idx): if self._orientation == 'x': x = point[0] - (self.center[0] + (idx[0] + 0.5*idx[1])*self.pitch[0]) y = point[1] - (self.center[1] + sqrt(0.75)*self.pitch[0]*idx[1]) else: x = point[0] - (self.center[0] + sqrt(0.75)*self.pitch[0]*idx[0]) y = point[1] - (self.center[1] + (0.5*idx[0] + idx[1])*self.pitch[0]) if self._num_axial is None: z = point[2] else: z = point[2] - (self.center[2] + (idx[2] + 0.5 - 0.5*self.num_axial) * self.pitch[1]) return (x, y, z) def get_universe_index(self, idx): # First we determine which ring the index corresponds to. x = idx[0] a = idx[1] z = -a - x g = max(abs(x), abs(a), abs(z)) # Next we use a clever method to figure out where along the ring we are. i_ring = self._num_rings - 1 - g if x >= 0: if a >= 0: i_within = x else: i_within = 2*g + z else: if a <= 0: i_within = 3*g - x else: i_within = 5*g - z if self._orientation == 'x' and g > 0: i_within = (i_within + 5*g) % (6*g) if self.num_axial is None: return (i_ring, i_within) else: return (idx[2], i_ring, i_within) def is_valid_index(self, idx): x = idx[0] y = idx[1] z = 0 - y - x g = max(abs(x), abs(y), abs(z)) if self.num_axial is None: return g < self.num_rings else: return g < self.num_rings and 0 <= idx[2] < self.num_axial def create_xml_subelement(self, xml_element, memo=None): # If this subelement has already been written, return if memo and self in memo: return if memo is not None: memo.add(self) lattice_subelement = ET.Element("hex_lattice") lattice_subelement.set("id", str(self._id)) if len(self._name) > 0: lattice_subelement.set("name", str(self._name)) # Export the Lattice cell pitch pitch = ET.SubElement(lattice_subelement, "pitch") pitch.text = ' '.join(map(str, self._pitch)) # Export the Lattice outer Universe (if specified) if self._outer is not None: outer = ET.SubElement(lattice_subelement, "outer") outer.text = str(self._outer._id) self._outer.create_xml_subelement(xml_element, memo) lattice_subelement.set("n_rings", str(self._num_rings)) # If orientation is "x" export it to XML if self._orientation == 'x': lattice_subelement.set("orientation", "x") if self._num_axial is not None: lattice_subelement.set("n_axial", str(self._num_axial)) # Export Lattice cell center center = ET.SubElement(lattice_subelement, "center") center.text = ' '.join(map(str, self._center)) # Export the Lattice nested Universe IDs. # 3D Lattices if self._num_axial is not None: slices = [] for z in range(self._num_axial): # Initialize the center universe. universe = self._universes[z][-1][0] universe.create_xml_subelement(xml_element, memo) # Initialize the remaining universes. for r in range(self._num_rings-1): for theta in range(6*(self._num_rings - 1 - r)): universe = self._universes[z][r][theta] universe.create_xml_subelement(xml_element, memo) # Get a string representation of the universe IDs. slices.append(self._repr_axial_slice(self._universes[z])) # Collapse the list of axial slices into a single string. universe_ids = '\n'.join(slices) # 2D Lattices else: # Initialize the center universe. universe = self._universes[-1][0] universe.create_xml_subelement(xml_element, memo) # Initialize the remaining universes. for r in range(self._num_rings - 1): for theta in range(6*(self._num_rings - 1 - r)): universe = self._universes[r][theta] universe.create_xml_subelement(xml_element, memo) # Get a string representation of the universe IDs. universe_ids = self._repr_axial_slice(self._universes) universes = ET.SubElement(lattice_subelement, "universes") universes.text = '\n' + universe_ids # Append the XML subelement for this Lattice to the XML element xml_element.append(lattice_subelement) @classmethod def from_xml_element(cls, elem, get_universe): lat_id = int(get_text(elem, 'id')) name = get_text(elem, 'name') lat = cls(lat_id, name) lat.center = [float(i) for i in get_text(elem, 'center').split()] lat.pitch = [float(i) for i in get_text(elem, 'pitch').split()] lat.orientation = get_text(elem, 'orientation', 'y') outer = get_text(elem, 'outer') if outer is not None: lat.outer = get_universe(int(outer)) # Get nested lists of universes lat._num_rings = n_rings = int(get_text(elem, 'n_rings')) lat._num_axial = n_axial = int(get_text(elem, 'n_axial', 1)) # Create empty nested lists for one axial level univs = [[None for _ in range(max(6*(n_rings - 1 - r), 1))] for r in range(n_rings)] if n_axial > 1: univs = [deepcopy(univs) for i in range(n_axial)] # Get flat array of universes uarray = np.array([get_universe(int(i)) for i in get_text(elem, 'universes').split()]) # Fill nested lists j = 0 for z in range(n_axial): # Get list for a single axial level axial_level = univs[z] if n_axial > 1 else univs if lat.orientation == 'y': # Start iterating from top x, alpha = 0, n_rings - 1 while True: # Set entry in list based on (x,alpha,z) coordinates _, i_ring, i_within = lat.get_universe_index((x, alpha, z)) axial_level[i_ring][i_within] = uarray[j] # Move to the right x += 2 alpha -= 1 if not lat.is_valid_index((x, alpha, z)): # Move down in y direction alpha += x - 1 x = 1 - x if not lat.is_valid_index((x, alpha, z)): # Move to the right x += 2 alpha -= 1 if not lat.is_valid_index((x, alpha, z)): # Reached the bottom break j += 1 else: # Start iterating from top alpha, y = 1 - n_rings, n_rings - 1 while True: # Set entry in list based on (alpha,y,z) coordinates _, i_ring, i_within = lat.get_universe_index((alpha, y, z)) axial_level[i_ring][i_within] = uarray[j] # Move to the right alpha += 1 if not lat.is_valid_index((alpha, y, z)): # Move down to next row alpha = 1 - n_rings y -= 1 # Check if we've reached the bottom if y == -n_rings: break while not lat.is_valid_index((alpha, y, z)): alpha += 1 j += 1 lat.universes = univs return lat def _repr_axial_slice(self, universes): if self._orientation == 'x': return self._repr_axial_slice_x(universes) else: return self._repr_axial_slice_y(universes) def _repr_axial_slice_x(self, universes): largest_id = max([max([univ._id for univ in ring]) for ring in universes]) n_digits = len(str(largest_id)) pad = ' '*n_digits id_form = '{: ^' + str(n_digits) + 'd}' rows = [[] for i in range(2*self._num_rings - 1)] middle = self._num_rings - 1 universe = universes[-1][0] rows[middle] = [id_form.format(universe._id)] for r in range(1, self._num_rings): r_prime = self._num_rings - 1 - r theta = 0 y = middle for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) y += 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) y -= 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) y -= 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) y += 1 theta += 1 rows = [pad.join(x) for x in rows] for y in range(self._num_rings - 1): rows[y] = (self._num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (self._num_rings - 1 - y)*pad + rows[-1 - y] universe_ids = '\n'.join(rows) return universe_ids def _repr_axial_slice_y(self, universes): largest_id = max([max([univ._id for univ in ring]) for ring in universes]) n_digits = len(str(largest_id)) pad = ' '*n_digits id_form = '{: ^' + str(n_digits) + 'd}' rows = [[] for i in range(1 + 4 * (self._num_rings-1))] middle = 2 * (self._num_rings - 1) universe = universes[-1][0] rows[middle] = [id_form.format(universe._id)] for r in range(1, self._num_rings): r_prime = self._num_rings - 1 - r theta = 0 y = middle + 2*r for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) y -= 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) y -= 2 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].append(id_form.format(universe._id)) y -= 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) y += 1 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) y += 2 theta += 1 for i in range(r): universe = universes[r_prime][theta] rows[y].insert(0, id_form.format(universe._id)) y += 1 theta += 1 rows = [pad.join(x) for x in rows[::-1]] for y in range(self._num_rings - 1): rows[y] = (self._num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (self._num_rings - 1 - y)*pad + rows[-1 - y] for y in range(self._num_rings % 2, self._num_rings, 2): rows[middle + y] = pad + rows[middle + y] if y != 0: rows[middle - y] = pad + rows[middle - y] universe_ids = '\n'.join(rows) return universe_ids @staticmethod def _show_indices_y(num_rings): largest_index = 6*(num_rings - 1) n_digits_index = len(str(largest_index)) n_digits_ring = len(str(num_rings - 1)) str_form = '({{:{}}},{{:{}}})'.format(n_digits_ring, n_digits_index) pad = ' '*(n_digits_index + n_digits_ring + 3) rows = [[] for i in range(1 + 4 * (num_rings-1))] middle = 2 * (num_rings - 1) rows[middle] = [str_form.format(num_rings - 1, 0)] for r in range(1, num_rings): r_prime = num_rings - 1 - r theta = 0 y = middle + 2*r for i in range(r): rows[y].append(str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): rows[y].append(str_form.format(r_prime, theta)) y -= 2 theta += 1 for i in range(r): rows[y].append(str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) y += 1 theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) y += 2 theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) y += 1 theta += 1 rows = [pad.join(x) for x in rows[::-1]] for y in range(num_rings - 1): rows[y] = (num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (num_rings - 1 - y)*pad + rows[-1 - y] for y in range(num_rings % 2, num_rings, 2): rows[middle + y] = pad + rows[middle + y] if y != 0: rows[middle - y] = pad + rows[middle - y] return '\n'.join(rows) @staticmethod def _show_indices_x(num_rings): largest_index = 6*(num_rings - 1) n_digits_index = len(str(largest_index)) n_digits_ring = len(str(num_rings - 1)) str_form = '({{:{}}},{{:{}}})'.format(n_digits_ring, n_digits_index) pad = ' '*(n_digits_index + n_digits_ring + 3) rows = [[] for i in range(2*num_rings - 1)] middle = num_rings - 1 rows[middle] = [str_form.format(num_rings - 1, 0)] for r in range(1, num_rings): r_prime = num_rings - 1 - r theta = 0 y = middle for i in range(r): rows[y].append(str_form.format(r_prime, theta)) y += 1 theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): rows[y].insert(0, str_form.format(r_prime, theta)) y -= 1 theta += 1 for i in range(r): rows[y].append(str_form.format(r_prime, theta)) theta += 1 for i in range(r): rows[y].append(str_form.format(r_prime, theta)) y += 1 theta += 1 rows = [pad.join(x) for x in rows] for y in range(num_rings - 1): rows[y] = (num_rings - 1 - y)*pad + rows[y] rows[-1 - y] = (num_rings - 1 - y)*pad + rows[-1 - y] return '\n\n'.join(rows) @staticmethod def show_indices(num_rings, orientation="y"): if orientation == 'x': return HexLattice._show_indices_x(num_rings) else: return HexLattice._show_indices_y(num_rings) @classmethod def from_hdf5(cls, group, universes): n_rings = group['n_rings'][()] n_axial = group['n_axial'][()] center = group['center'][()] pitch = group['pitch'][()] outer = group['outer'][()] if 'orientation' in group: orientation = group['orientation'][()].decode() else: orientation = "y" universe_ids = group['universes'][()] lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) name = group['name'][()].decode() if 'name' in group else '' lattice = openmc.HexLattice(lattice_id, name) lattice.center = center lattice.pitch = pitch lattice.orientation = orientation if outer >= 0: lattice.outer = universes[outer] if orientation == "y": # (x, alpha, z) to the Python API's format of a ragged nested uarray = [] for z in range(n_axial): uarray.append([]) x = n_rings - 1 a = 2*n_rings - 2 for r in range(n_rings - 1, 0, -1): uarray[-1].append([]) for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x += 1 a -= 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) a -= 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x -= 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x -= 1 a += 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) a += 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, a, x]) x += 1 a -= 1 uarray[-1][-1] = [universes[u_id] for u_id in uarray[-1][-1]] u_id = universe_ids[z, a, x] uarray[-1].append([universes[u_id]]) else: # (alpha, y, z) to the Python API's format of a ragged nested uarray = [] for z in range(n_axial): uarray.append([]) a = 2*n_rings - 2 y = n_rings - 1 for r in range(n_rings - 1, 0, -1): uarray[-1].append([]) for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) y -= 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a -= 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a -= 1 y += 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) y += 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a += 1 for i in range(r): uarray[-1][-1].append(universe_ids[z, y, a]) a += 1 y -= 1 a -= 1 uarray[-1][-1] = [universes[u_id] for u_id in uarray[-1][-1]] u_id = universe_ids[z, y, a] uarray[-1].append([universes[u_id]]) if len(pitch) == 2: lattice.universes = uarray else: lattice.universes = uarray[0] return lattice

true

1c31205fde9e085f681e8304eadebb64056c8636

268

py

Python

setup.py

onshoremanover/dist

96a52b23e6e651d6d6b73614c73a5aa0d0c4bd14

[ "MIT" ]

1

2021-11-04T14:02:57.000Z

setup.py

onshoremanover/dcfe

65256aac5a3212a98896cbf0d04533af83bb4ce8

[ "MIT" ]

null

setup.py

onshoremanover/dcfe

65256aac5a3212a98896cbf0d04533af83bb4ce8

[ "MIT" ]

null

from setuptools import setup setup( name = 'dcfe', version = '0.1.4', packages = ['dcfe'], entry_points = { 'console_scripts': [ 'dcfe = dcfe.__main__:main' ] })

20.615385

43

0.399254

from setuptools import setup setup( name = 'dcfe', version = '0.1.4', packages = ['dcfe'], entry_points = { 'console_scripts': [ 'dcfe = dcfe.__main__:main' ] })

true

1c31217294f3c2bc4855de6abfe75bfa6885b338

1,356

py

Python

app/recipe/serializers.py

garden117/recipe-app-api

ce58a993cac38660ddd25b99ae1e6cffeff537eb

[ "MIT" ]

null

app/recipe/serializers.py

garden117/recipe-app-api

ce58a993cac38660ddd25b99ae1e6cffeff537eb

[ "MIT" ]

null

app/recipe/serializers.py

garden117/recipe-app-api

ce58a993cac38660ddd25b99ae1e6cffeff537eb

[ "MIT" ]

null

from rest_framework import serializers from core.models import Tag, Ingredient, Recipe class TagSerializer(serializers.ModelSerializer): """serializer for tag objects""" class Meta: model = Tag fields = ('id', 'name') read_only_fields = ('id',) class IngredientSerializer(serializers.ModelSerializer): """serializer for ingredient objects""" class Meta: model = Ingredient fields = ('id', 'name') read_only_fields = ('id',) class RecipeSerializer(serializers.ModelSerializer): """serializer for recipe objects""" ingredients = serializers.PrimaryKeyRelatedField(many=True, queryset=Ingredient.objects.all()) tags = serializers.PrimaryKeyRelatedField(many=True, queryset=Tag.objects.all()) class Meta: model = Recipe fields = ('id', 'title', 'time_minutes', 'price', 'link', 'ingredients', 'tags') read_only_fields = ('id',) class RecipeDetailSerializer(RecipeSerializer): """serialize a recipe detail""" ingredients = IngredientSerializer(many=True, read_only=True) tags = TagSerializer(many=True, read_only=True) class RecipeImageSerializer(serializers.ModelSerializer): """Serializer for uploading the images""" class Meta: model = Recipe fields = ('id', 'image') read_only_fields = ('id',)

28.25

98

0.676991

from rest_framework import serializers from core.models import Tag, Ingredient, Recipe class TagSerializer(serializers.ModelSerializer): class Meta: model = Tag fields = ('id', 'name') read_only_fields = ('id',) class IngredientSerializer(serializers.ModelSerializer): class Meta: model = Ingredient fields = ('id', 'name') read_only_fields = ('id',) class RecipeSerializer(serializers.ModelSerializer): ingredients = serializers.PrimaryKeyRelatedField(many=True, queryset=Ingredient.objects.all()) tags = serializers.PrimaryKeyRelatedField(many=True, queryset=Tag.objects.all()) class Meta: model = Recipe fields = ('id', 'title', 'time_minutes', 'price', 'link', 'ingredients', 'tags') read_only_fields = ('id',) class RecipeDetailSerializer(RecipeSerializer): ingredients = IngredientSerializer(many=True, read_only=True) tags = TagSerializer(many=True, read_only=True) class RecipeImageSerializer(serializers.ModelSerializer): class Meta: model = Recipe fields = ('id', 'image') read_only_fields = ('id',)

true

1c31223d26a10e794076573f547fb1b5b9a01c27

13,816

py

Python

mypy/stubdoc.py

Phlogistique/mypy

eea4c76de4a67a36e3a2293eae9a2e775c636e1d

[ "PSF-2.0" ]

12,496

2016-02-19T13:38:26.000Z

2022-03-31T23:56:19.000Z

mypy/stubdoc.py

Phlogistique/mypy

eea4c76de4a67a36e3a2293eae9a2e775c636e1d

[ "PSF-2.0" ]

9,429

2016-02-19T13:41:32.000Z

2022-03-31T23:29:38.000Z

mypy/stubdoc.py

Zeckie/baselinedmypy

142c896a7ec0a10697375833fd897b293a748699

[ "PSF-2.0" ]

2,770

2016-02-19T16:18:19.000Z

2022-03-31T08:12:49.000Z

"""Parsing/inferring signatures from documentation. This module provides several functions to generate better stubs using docstrings and Sphinx docs (.rst files). """ import re import io import contextlib import tokenize from typing import ( Optional, MutableMapping, MutableSequence, List, Sequence, Tuple, NamedTuple, Any ) from typing_extensions import Final # Type alias for signatures strings in format ('func_name', '(arg, opt_arg=False)'). Sig = Tuple[str, str] _TYPE_RE: Final = re.compile(r"^[a-zA-Z_][\w\[\], ]*(\.[a-zA-Z_][\w\[\], ]*)*$") _ARG_NAME_RE: Final = re.compile(r"\**[A-Za-z_][A-Za-z0-9_]*$") def is_valid_type(s: str) -> bool: """Try to determine whether a string might be a valid type annotation.""" if s in ('True', 'False', 'retval'): return False if ',' in s and '[' not in s: return False return _TYPE_RE.match(s) is not None class ArgSig: """Signature info for a single argument.""" def __init__(self, name: str, type: Optional[str] = None, default: bool = False): self.name = name if type and not is_valid_type(type): raise ValueError("Invalid type: " + type) self.type = type # Does this argument have a default value? self.default = default def __repr__(self) -> str: return "ArgSig(name={}, type={}, default={})".format(repr(self.name), repr(self.type), repr(self.default)) def __eq__(self, other: Any) -> bool: if isinstance(other, ArgSig): return (self.name == other.name and self.type == other.type and self.default == other.default) return False FunctionSig = NamedTuple('FunctionSig', [ ('name', str), ('args', List[ArgSig]), ('ret_type', str) ]) # States of the docstring parser. STATE_INIT: Final = 1 STATE_FUNCTION_NAME: Final = 2 STATE_ARGUMENT_LIST: Final = 3 STATE_ARGUMENT_TYPE: Final = 4 STATE_ARGUMENT_DEFAULT: Final = 5 STATE_RETURN_VALUE: Final = 6 STATE_OPEN_BRACKET: Final = 7 # For generic types. class DocStringParser: """Parse function signatures in documentation.""" def __init__(self, function_name: str) -> None: # Only search for signatures of function with this name. self.function_name = function_name self.state = [STATE_INIT] self.accumulator = "" self.arg_type: Optional[str] = None self.arg_name = "" self.arg_default: Optional[str] = None self.ret_type = "Any" self.found = False self.args: List[ArgSig] = [] # Valid signatures found so far. self.signatures: List[FunctionSig] = [] def add_token(self, token: tokenize.TokenInfo) -> None: """Process next token from the token stream.""" if (token.type == tokenize.NAME and token.string == self.function_name and self.state[-1] == STATE_INIT): self.state.append(STATE_FUNCTION_NAME) elif (token.type == tokenize.OP and token.string == '(' and self.state[-1] == STATE_FUNCTION_NAME): self.state.pop() self.accumulator = "" self.found = True self.state.append(STATE_ARGUMENT_LIST) elif self.state[-1] == STATE_FUNCTION_NAME: # Reset state, function name not followed by '('. self.state.pop() elif (token.type == tokenize.OP and token.string in ('[', '(', '{') and self.state[-1] != STATE_INIT): self.accumulator += token.string self.state.append(STATE_OPEN_BRACKET) elif (token.type == tokenize.OP and token.string in (']', ')', '}') and self.state[-1] == STATE_OPEN_BRACKET): self.accumulator += token.string self.state.pop() elif (token.type == tokenize.OP and token.string == ':' and self.state[-1] == STATE_ARGUMENT_LIST): self.arg_name = self.accumulator self.accumulator = "" self.state.append(STATE_ARGUMENT_TYPE) elif (token.type == tokenize.OP and token.string == '=' and self.state[-1] in (STATE_ARGUMENT_LIST, STATE_ARGUMENT_TYPE)): if self.state[-1] == STATE_ARGUMENT_TYPE: self.arg_type = self.accumulator self.state.pop() else: self.arg_name = self.accumulator self.accumulator = "" self.state.append(STATE_ARGUMENT_DEFAULT) elif (token.type == tokenize.OP and token.string in (',', ')') and self.state[-1] in (STATE_ARGUMENT_LIST, STATE_ARGUMENT_DEFAULT, STATE_ARGUMENT_TYPE)): if self.state[-1] == STATE_ARGUMENT_DEFAULT: self.arg_default = self.accumulator self.state.pop() elif self.state[-1] == STATE_ARGUMENT_TYPE: self.arg_type = self.accumulator self.state.pop() elif self.state[-1] == STATE_ARGUMENT_LIST: self.arg_name = self.accumulator if not (token.string == ')' and self.accumulator.strip() == '') \ and not _ARG_NAME_RE.match(self.arg_name): # Invalid argument name. self.reset() return if token.string == ')': self.state.pop() # arg_name is empty when there are no args. e.g. func() if self.arg_name: try: self.args.append(ArgSig(name=self.arg_name, type=self.arg_type, default=bool(self.arg_default))) except ValueError: # wrong type, use Any self.args.append(ArgSig(name=self.arg_name, type=None, default=bool(self.arg_default))) self.arg_name = "" self.arg_type = None self.arg_default = None self.accumulator = "" elif token.type == tokenize.OP and token.string == '->' and self.state[-1] == STATE_INIT: self.accumulator = "" self.state.append(STATE_RETURN_VALUE) # ENDMAKER is necessary for python 3.4 and 3.5. elif (token.type in (tokenize.NEWLINE, tokenize.ENDMARKER) and self.state[-1] in (STATE_INIT, STATE_RETURN_VALUE)): if self.state[-1] == STATE_RETURN_VALUE: if not is_valid_type(self.accumulator): self.reset() return self.ret_type = self.accumulator self.accumulator = "" self.state.pop() if self.found: self.signatures.append(FunctionSig(name=self.function_name, args=self.args, ret_type=self.ret_type)) self.found = False self.args = [] self.ret_type = 'Any' # Leave state as INIT. else: self.accumulator += token.string def reset(self) -> None: self.state = [STATE_INIT] self.args = [] self.found = False self.accumulator = "" def get_signatures(self) -> List[FunctionSig]: """Return sorted copy of the list of signatures found so far.""" def has_arg(name: str, signature: FunctionSig) -> bool: return any(x.name == name for x in signature.args) def args_kwargs(signature: FunctionSig) -> bool: return has_arg('*args', signature) and has_arg('**kwargs', signature) # Move functions with (*args, **kwargs) in their signature to last place. return list(sorted(self.signatures, key=lambda x: 1 if args_kwargs(x) else 0)) def infer_sig_from_docstring(docstr: Optional[str], name: str) -> Optional[List[FunctionSig]]: """Convert function signature to list of TypedFunctionSig Look for function signatures of function in docstring. Signature is a string of the format <function_name>(<signature>) -> <return type> or perhaps without the return type. Returns empty list, when no signature is found, one signature in typical case, multiple signatures, if docstring specifies multiple signatures for overload functions. Return None if the docstring is empty. Arguments: * docstr: docstring * name: name of function for which signatures are to be found """ if not docstr: return None state = DocStringParser(name) # Return all found signatures, even if there is a parse error after some are found. with contextlib.suppress(tokenize.TokenError): try: tokens = tokenize.tokenize(io.BytesIO(docstr.encode('utf-8')).readline) for token in tokens: state.add_token(token) except IndentationError: return None sigs = state.get_signatures() def is_unique_args(sig: FunctionSig) -> bool: """return true if function argument names are unique""" return len(sig.args) == len(set((arg.name for arg in sig.args))) # Return only signatures that have unique argument names. Mypy fails on non-unique arg names. return [sig for sig in sigs if is_unique_args(sig)] def infer_arg_sig_from_anon_docstring(docstr: str) -> List[ArgSig]: """Convert signature in form of "(self: TestClass, arg0: str='ada')" to List[TypedArgList].""" ret = infer_sig_from_docstring("stub" + docstr, "stub") if ret: return ret[0].args return [] def infer_ret_type_sig_from_docstring(docstr: str, name: str) -> Optional[str]: """Convert signature in form of "func(self: TestClass, arg0) -> int" to their return type.""" ret = infer_sig_from_docstring(docstr, name) if ret: return ret[0].ret_type return None def infer_ret_type_sig_from_anon_docstring(docstr: str) -> Optional[str]: """Convert signature in form of "(self: TestClass, arg0) -> int" to their return type.""" return infer_ret_type_sig_from_docstring("stub" + docstr.strip(), "stub") def parse_signature(sig: str) -> Optional[Tuple[str, List[str], List[str]]]: """Split function signature into its name, positional an optional arguments. The expected format is "func_name(arg, opt_arg=False)". Return the name of function and lists of positional and optional argument names. """ m = re.match(r'([.a-zA-Z0-9_]+)$([^)]*)$', sig) if not m: return None name = m.group(1) name = name.split('.')[-1] arg_string = m.group(2) if not arg_string.strip(): # Simple case -- no arguments. return name, [], [] args = [arg.strip() for arg in arg_string.split(',')] positional = [] optional = [] i = 0 while i < len(args): # Accept optional arguments as in both formats: x=None and [x]. if args[i].startswith('[') or '=' in args[i]: break positional.append(args[i].rstrip('[')) i += 1 if args[i - 1].endswith('['): break while i < len(args): arg = args[i] arg = arg.strip('[]') arg = arg.split('=')[0] optional.append(arg) i += 1 return name, positional, optional def build_signature(positional: Sequence[str], optional: Sequence[str]) -> str: """Build function signature from lists of positional and optional argument names.""" args: MutableSequence[str] = [] args.extend(positional) for arg in optional: if arg.startswith('*'): args.append(arg) else: args.append('%s=...' % arg) sig = '(%s)' % ', '.join(args) # Ad-hoc fixes. sig = sig.replace('(self)', '') return sig def parse_all_signatures(lines: Sequence[str]) -> Tuple[List[Sig], List[Sig]]: """Parse all signatures in a given reST document. Return lists of found signatures for functions and classes. """ sigs = [] class_sigs = [] for line in lines: line = line.strip() m = re.match(r'\.\. *(function|method|class) *:: *[a-zA-Z_]', line) if m: sig = line.split('::')[1].strip() parsed = parse_signature(sig) if parsed: name, fixed, optional = parsed if m.group(1) != 'class': sigs.append((name, build_signature(fixed, optional))) else: class_sigs.append((name, build_signature(fixed, optional))) return sorted(sigs), sorted(class_sigs) def find_unique_signatures(sigs: Sequence[Sig]) -> List[Sig]: """Remove names with duplicate found signatures.""" sig_map: MutableMapping[str, List[str]] = {} for name, sig in sigs: sig_map.setdefault(name, []).append(sig) result = [] for name, name_sigs in sig_map.items(): if len(set(name_sigs)) == 1: result.append((name, name_sigs[0])) return sorted(result) def infer_prop_type_from_docstring(docstr: Optional[str]) -> Optional[str]: """Check for Google/Numpy style docstring type annotation for a property. The docstring has the format "<type>: <descriptions>". In the type string, we allow the following characters: * dot: because sometimes classes are annotated using full path * brackets: to allow type hints like List[int] * comma/space: things like Tuple[int, int] """ if not docstr: return None test_str = r'^([a-zA-Z0-9_, \.\[\]]*): ' m = re.match(test_str, docstr) return m.group(1) if m else None

37.040214

98

0.585191

import re import io import contextlib import tokenize from typing import ( Optional, MutableMapping, MutableSequence, List, Sequence, Tuple, NamedTuple, Any ) from typing_extensions import Final Sig = Tuple[str, str] _TYPE_RE: Final = re.compile(r"^[a-zA-Z_][\w\[\], ]*(\.[a-zA-Z_][\w\[\], ]*)*$") _ARG_NAME_RE: Final = re.compile(r"\**[A-Za-z_][A-Za-z0-9_]*$") def is_valid_type(s: str) -> bool: if s in ('True', 'False', 'retval'): return False if ',' in s and '[' not in s: return False return _TYPE_RE.match(s) is not None class ArgSig: def __init__(self, name: str, type: Optional[str] = None, default: bool = False): self.name = name if type and not is_valid_type(type): raise ValueError("Invalid type: " + type) self.type = type self.default = default def __repr__(self) -> str: return "ArgSig(name={}, type={}, default={})".format(repr(self.name), repr(self.type), repr(self.default)) def __eq__(self, other: Any) -> bool: if isinstance(other, ArgSig): return (self.name == other.name and self.type == other.type and self.default == other.default) return False FunctionSig = NamedTuple('FunctionSig', [ ('name', str), ('args', List[ArgSig]), ('ret_type', str) ]) STATE_INIT: Final = 1 STATE_FUNCTION_NAME: Final = 2 STATE_ARGUMENT_LIST: Final = 3 STATE_ARGUMENT_TYPE: Final = 4 STATE_ARGUMENT_DEFAULT: Final = 5 STATE_RETURN_VALUE: Final = 6 STATE_OPEN_BRACKET: Final = 7 class DocStringParser: def __init__(self, function_name: str) -> None: self.function_name = function_name self.state = [STATE_INIT] self.accumulator = "" self.arg_type: Optional[str] = None self.arg_name = "" self.arg_default: Optional[str] = None self.ret_type = "Any" self.found = False self.args: List[ArgSig] = [] self.signatures: List[FunctionSig] = [] def add_token(self, token: tokenize.TokenInfo) -> None: if (token.type == tokenize.NAME and token.string == self.function_name and self.state[-1] == STATE_INIT): self.state.append(STATE_FUNCTION_NAME) elif (token.type == tokenize.OP and token.string == '(' and self.state[-1] == STATE_FUNCTION_NAME): self.state.pop() self.accumulator = "" self.found = True self.state.append(STATE_ARGUMENT_LIST) elif self.state[-1] == STATE_FUNCTION_NAME: self.state.pop() elif (token.type == tokenize.OP and token.string in ('[', '(', '{') and self.state[-1] != STATE_INIT): self.accumulator += token.string self.state.append(STATE_OPEN_BRACKET) elif (token.type == tokenize.OP and token.string in (']', ')', '}') and self.state[-1] == STATE_OPEN_BRACKET): self.accumulator += token.string self.state.pop() elif (token.type == tokenize.OP and token.string == ':' and self.state[-1] == STATE_ARGUMENT_LIST): self.arg_name = self.accumulator self.accumulator = "" self.state.append(STATE_ARGUMENT_TYPE) elif (token.type == tokenize.OP and token.string == '=' and self.state[-1] in (STATE_ARGUMENT_LIST, STATE_ARGUMENT_TYPE)): if self.state[-1] == STATE_ARGUMENT_TYPE: self.arg_type = self.accumulator self.state.pop() else: self.arg_name = self.accumulator self.accumulator = "" self.state.append(STATE_ARGUMENT_DEFAULT) elif (token.type == tokenize.OP and token.string in (',', ')') and self.state[-1] in (STATE_ARGUMENT_LIST, STATE_ARGUMENT_DEFAULT, STATE_ARGUMENT_TYPE)): if self.state[-1] == STATE_ARGUMENT_DEFAULT: self.arg_default = self.accumulator self.state.pop() elif self.state[-1] == STATE_ARGUMENT_TYPE: self.arg_type = self.accumulator self.state.pop() elif self.state[-1] == STATE_ARGUMENT_LIST: self.arg_name = self.accumulator if not (token.string == ')' and self.accumulator.strip() == '') \ and not _ARG_NAME_RE.match(self.arg_name): self.reset() return if token.string == ')': self.state.pop() if self.arg_name: try: self.args.append(ArgSig(name=self.arg_name, type=self.arg_type, default=bool(self.arg_default))) except ValueError: self.args.append(ArgSig(name=self.arg_name, type=None, default=bool(self.arg_default))) self.arg_name = "" self.arg_type = None self.arg_default = None self.accumulator = "" elif token.type == tokenize.OP and token.string == '->' and self.state[-1] == STATE_INIT: self.accumulator = "" self.state.append(STATE_RETURN_VALUE) elif (token.type in (tokenize.NEWLINE, tokenize.ENDMARKER) and self.state[-1] in (STATE_INIT, STATE_RETURN_VALUE)): if self.state[-1] == STATE_RETURN_VALUE: if not is_valid_type(self.accumulator): self.reset() return self.ret_type = self.accumulator self.accumulator = "" self.state.pop() if self.found: self.signatures.append(FunctionSig(name=self.function_name, args=self.args, ret_type=self.ret_type)) self.found = False self.args = [] self.ret_type = 'Any' else: self.accumulator += token.string def reset(self) -> None: self.state = [STATE_INIT] self.args = [] self.found = False self.accumulator = "" def get_signatures(self) -> List[FunctionSig]: def has_arg(name: str, signature: FunctionSig) -> bool: return any(x.name == name for x in signature.args) def args_kwargs(signature: FunctionSig) -> bool: return has_arg('*args', signature) and has_arg('**kwargs', signature) return list(sorted(self.signatures, key=lambda x: 1 if args_kwargs(x) else 0)) def infer_sig_from_docstring(docstr: Optional[str], name: str) -> Optional[List[FunctionSig]]: if not docstr: return None state = DocStringParser(name) with contextlib.suppress(tokenize.TokenError): try: tokens = tokenize.tokenize(io.BytesIO(docstr.encode('utf-8')).readline) for token in tokens: state.add_token(token) except IndentationError: return None sigs = state.get_signatures() def is_unique_args(sig: FunctionSig) -> bool: return len(sig.args) == len(set((arg.name for arg in sig.args))) return [sig for sig in sigs if is_unique_args(sig)] def infer_arg_sig_from_anon_docstring(docstr: str) -> List[ArgSig]: ret = infer_sig_from_docstring("stub" + docstr, "stub") if ret: return ret[0].args return [] def infer_ret_type_sig_from_docstring(docstr: str, name: str) -> Optional[str]: ret = infer_sig_from_docstring(docstr, name) if ret: return ret[0].ret_type return None def infer_ret_type_sig_from_anon_docstring(docstr: str) -> Optional[str]: return infer_ret_type_sig_from_docstring("stub" + docstr.strip(), "stub") def parse_signature(sig: str) -> Optional[Tuple[str, List[str], List[str]]]: m = re.match(r'([.a-zA-Z0-9_]+)$([^)]*)$', sig) if not m: return None name = m.group(1) name = name.split('.')[-1] arg_string = m.group(2) if not arg_string.strip(): return name, [], [] args = [arg.strip() for arg in arg_string.split(',')] positional = [] optional = [] i = 0 while i < len(args): if args[i].startswith('[') or '=' in args[i]: break positional.append(args[i].rstrip('[')) i += 1 if args[i - 1].endswith('['): break while i < len(args): arg = args[i] arg = arg.strip('[]') arg = arg.split('=')[0] optional.append(arg) i += 1 return name, positional, optional def build_signature(positional: Sequence[str], optional: Sequence[str]) -> str: args: MutableSequence[str] = [] args.extend(positional) for arg in optional: if arg.startswith('*'): args.append(arg) else: args.append('%s=...' % arg) sig = '(%s)' % ', '.join(args) sig = sig.replace('(self)', '') return sig def parse_all_signatures(lines: Sequence[str]) -> Tuple[List[Sig], List[Sig]]: sigs = [] class_sigs = [] for line in lines: line = line.strip() m = re.match(r'\.\. *(function|method|class) *:: *[a-zA-Z_]', line) if m: sig = line.split('::')[1].strip() parsed = parse_signature(sig) if parsed: name, fixed, optional = parsed if m.group(1) != 'class': sigs.append((name, build_signature(fixed, optional))) else: class_sigs.append((name, build_signature(fixed, optional))) return sorted(sigs), sorted(class_sigs) def find_unique_signatures(sigs: Sequence[Sig]) -> List[Sig]: sig_map: MutableMapping[str, List[str]] = {} for name, sig in sigs: sig_map.setdefault(name, []).append(sig) result = [] for name, name_sigs in sig_map.items(): if len(set(name_sigs)) == 1: result.append((name, name_sigs[0])) return sorted(result) def infer_prop_type_from_docstring(docstr: Optional[str]) -> Optional[str]: if not docstr: return None test_str = r'^([a-zA-Z0-9_, \.\[\]]*): ' m = re.match(test_str, docstr) return m.group(1) if m else None

true

1c31227aa951cf3036977b7f40365ac03c47458f

621

py

Python

src/pythonModules/CsvReader.py

apurva1795/calculator

a72ec7cd961d65da0ebcf3d2c5cea974011ea977

[ "MIT" ]

null

src/pythonModules/CsvReader.py

apurva1795/calculator

a72ec7cd961d65da0ebcf3d2c5cea974011ea977

[ "MIT" ]

null

src/pythonModules/CsvReader.py

apurva1795/calculator

a72ec7cd961d65da0ebcf3d2c5cea974011ea977

[ "MIT" ]

null

import csv from pprint import pprint def ClassFactory(class_name, dictionary): return type(class_name, (object,), dictionary) class CsvReader: data = [] def __init__(self, filepath): self.data = [] with open(filepath) as text_data: csv_data = csv.DictReader(text_data, delimiter=',') for row in csv_data: self.data.append(row) pprint(row) pass def return_data_as_objects(self, class_name): objects = [] for row in self.data: objects.append(ClassFactory(class_name, row)) return objects

27

63

0.605475

import csv from pprint import pprint def ClassFactory(class_name, dictionary): return type(class_name, (object,), dictionary) class CsvReader: data = [] def __init__(self, filepath): self.data = [] with open(filepath) as text_data: csv_data = csv.DictReader(text_data, delimiter=',') for row in csv_data: self.data.append(row) pprint(row) pass def return_data_as_objects(self, class_name): objects = [] for row in self.data: objects.append(ClassFactory(class_name, row)) return objects

true

1c31229798c28406b90c4cd36c7258c85aafd348

3,463

py

Python

ros2/src/roboy_vision/convertions/convertions.py

HackRoboy/dialogic

0c0b766409aeb2b717e4c396000d79909658cbb2

[ "MIT" ]

null

ros2/src/roboy_vision/convertions/convertions.py

HackRoboy/dialogic

0c0b766409aeb2b717e4c396000d79909658cbb2

[ "MIT" ]

1

2018-12-07T09:56:14.000Z

ros2/src/roboy_vision/convertions/convertions.py

ro-boy/ravestate

f67bbb378d327d9e29de21795770fd5e51141608

[ "MIT" ]

1

2018-11-09T19:05:14.000Z

import sys from sensor_msgs.msg import Image import numpy as np from convertions.registry import converts_to_numpy, converts_from_numpy name_to_dtypes = { "rgb8": (np.uint8, 3), "rgba8": (np.uint8, 4), "rgb16": (np.uint16, 3), "rgba16": (np.uint16, 4), "bgr8": (np.uint8, 3), "bgra8": (np.uint8, 4), "bgr16": (np.uint16, 3), "bgra16": (np.uint16, 4), "mono8": (np.uint8, 1), "mono16": (np.uint16, 1), # for bayer image (based on cv_bridge.cpp) "bayer_rggb8": (np.uint8, 1), "bayer_bggr8": (np.uint8, 1), "bayer_gbrg8": (np.uint8, 1), "bayer_grbg8": (np.uint8, 1), "bayer_rggb16": (np.uint16, 1), "bayer_bggr16": (np.uint16, 1), "bayer_gbrg16": (np.uint16, 1), "bayer_grbg16": (np.uint16, 1), # OpenCV CvMat types "8UC1": (np.uint8, 1), "8UC2": (np.uint8, 2), "8UC3": (np.uint8, 3), "8UC4": (np.uint8, 4), "8SC1": (np.int8, 1), "8SC2": (np.int8, 2), "8SC3": (np.int8, 3), "8SC4": (np.int8, 4), "16UC1": (np.int16, 1), "16UC2": (np.int16, 2), "16UC3": (np.int16, 3), "16UC4": (np.int16, 4), "16SC1": (np.uint16, 1), "16SC2": (np.uint16, 2), "16SC3": (np.uint16, 3), "16SC4": (np.uint16, 4), "32SC1": (np.int32, 1), "32SC2": (np.int32, 2), "32SC3": (np.int32, 3), "32SC4": (np.int32, 4), "32FC1": (np.float32, 1), "32FC2": (np.float32, 2), "32FC3": (np.float32, 3), "32FC4": (np.float32, 4), "64FC1": (np.float64, 1), "64FC2": (np.float64, 2), "64FC3": (np.float64, 3), "64FC4": (np.float64, 4) } @converts_to_numpy(Image) def image_to_numpy(msg): if not msg.encoding in name_to_dtypes: raise TypeError('Unrecognized encoding {}'.format(msg.encoding)) dtype_class, channels = name_to_dtypes[msg.encoding] dtype = np.dtype(dtype_class) dtype = dtype.newbyteorder('>' if msg.is_bigendian else '<') shape = (msg.height, msg.width, channels) data = np.array(msg.data, dtype=dtype).reshape(shape) data.strides = ( msg.step, dtype.itemsize * channels, dtype.itemsize ) if channels == 1: data = data[..., 0] return data @converts_from_numpy(Image) def numpy_to_image(arr, encoding): if not encoding in name_to_dtypes: raise TypeError('Unrecognized encoding {}'.format(encoding)) im = Image(encoding=encoding) # extract width, height, and channels dtype_class, exp_channels = name_to_dtypes[encoding] dtype = np.dtype(dtype_class) if len(arr.shape) == 2: im.height, im.width, channels = arr.shape + (1,) elif len(arr.shape) == 3: im.height, im.width, channels = arr.shape else: raise TypeError("Array must be two or three dimensional") # check type and channels if exp_channels != channels: raise TypeError("Array has {} channels, {} requires {}".format( channels, encoding, exp_channels )) if dtype_class != arr.dtype.type: raise TypeError("Array is {}, {} requires {}".format( arr.dtype.type, encoding, dtype_class )) # make the array contiguous in memory, as mostly required by the format contig = np.ascontiguousarray(arr) im.data = contig.tostring() im.step = contig.strides[0] im.is_bigendian = ( arr.dtype.byteorder == '>' or arr.dtype.byteorder == '=' and sys.byteorder == 'big' ) return im

28.154472

75

0.587063

import sys from sensor_msgs.msg import Image import numpy as np from convertions.registry import converts_to_numpy, converts_from_numpy name_to_dtypes = { "rgb8": (np.uint8, 3), "rgba8": (np.uint8, 4), "rgb16": (np.uint16, 3), "rgba16": (np.uint16, 4), "bgr8": (np.uint8, 3), "bgra8": (np.uint8, 4), "bgr16": (np.uint16, 3), "bgra16": (np.uint16, 4), "mono8": (np.uint8, 1), "mono16": (np.uint16, 1), "bayer_rggb8": (np.uint8, 1), "bayer_bggr8": (np.uint8, 1), "bayer_gbrg8": (np.uint8, 1), "bayer_grbg8": (np.uint8, 1), "bayer_rggb16": (np.uint16, 1), "bayer_bggr16": (np.uint16, 1), "bayer_gbrg16": (np.uint16, 1), "bayer_grbg16": (np.uint16, 1), "8UC1": (np.uint8, 1), "8UC2": (np.uint8, 2), "8UC3": (np.uint8, 3), "8UC4": (np.uint8, 4), "8SC1": (np.int8, 1), "8SC2": (np.int8, 2), "8SC3": (np.int8, 3), "8SC4": (np.int8, 4), "16UC1": (np.int16, 1), "16UC2": (np.int16, 2), "16UC3": (np.int16, 3), "16UC4": (np.int16, 4), "16SC1": (np.uint16, 1), "16SC2": (np.uint16, 2), "16SC3": (np.uint16, 3), "16SC4": (np.uint16, 4), "32SC1": (np.int32, 1), "32SC2": (np.int32, 2), "32SC3": (np.int32, 3), "32SC4": (np.int32, 4), "32FC1": (np.float32, 1), "32FC2": (np.float32, 2), "32FC3": (np.float32, 3), "32FC4": (np.float32, 4), "64FC1": (np.float64, 1), "64FC2": (np.float64, 2), "64FC3": (np.float64, 3), "64FC4": (np.float64, 4) } @converts_to_numpy(Image) def image_to_numpy(msg): if not msg.encoding in name_to_dtypes: raise TypeError('Unrecognized encoding {}'.format(msg.encoding)) dtype_class, channels = name_to_dtypes[msg.encoding] dtype = np.dtype(dtype_class) dtype = dtype.newbyteorder('>' if msg.is_bigendian else '<') shape = (msg.height, msg.width, channels) data = np.array(msg.data, dtype=dtype).reshape(shape) data.strides = ( msg.step, dtype.itemsize * channels, dtype.itemsize ) if channels == 1: data = data[..., 0] return data @converts_from_numpy(Image) def numpy_to_image(arr, encoding): if not encoding in name_to_dtypes: raise TypeError('Unrecognized encoding {}'.format(encoding)) im = Image(encoding=encoding) dtype_class, exp_channels = name_to_dtypes[encoding] dtype = np.dtype(dtype_class) if len(arr.shape) == 2: im.height, im.width, channels = arr.shape + (1,) elif len(arr.shape) == 3: im.height, im.width, channels = arr.shape else: raise TypeError("Array must be two or three dimensional") if exp_channels != channels: raise TypeError("Array has {} channels, {} requires {}".format( channels, encoding, exp_channels )) if dtype_class != arr.dtype.type: raise TypeError("Array is {}, {} requires {}".format( arr.dtype.type, encoding, dtype_class )) contig = np.ascontiguousarray(arr) im.data = contig.tostring() im.step = contig.strides[0] im.is_bigendian = ( arr.dtype.byteorder == '>' or arr.dtype.byteorder == '=' and sys.byteorder == 'big' ) return im

true

1c31252b57e09ecac5110c11b73a11a5e8449f6c

3,012

py

Python

server/Pybuilder/env/Lib/site-packages/pybuilder/plugins/python/pylint_plugin.py

abhnvx/DataMetric

adde84ea9b0b7792349ce24eac00b0eee7bbed51

[ "RSA-MD" ]

1,419

2015-01-02T20:51:04.000Z

2022-03-23T21:26:00.000Z

server/Pybuilder/env/Lib/site-packages/pybuilder/plugins/python/pylint_plugin.py

abhnvx/DataMetric

adde84ea9b0b7792349ce24eac00b0eee7bbed51

[ "RSA-MD" ]

670

2015-01-01T10:26:03.000Z

2022-02-23T16:33:13.000Z

src/main/python/pybuilder/plugins/python/pylint_plugin.py

paolodedios/pybuilder

12ea2f54e04f97daada375dc3309a3f525f1b5e1

[ "Apache-2.0" ]

270

2015-01-02T05:01:53.000Z

2022-01-20T10:22:59.000Z

# -*- coding: utf-8 -*- # # This file is part of PyBuilder # # Copyright 2011-2020 PyBuilder Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pybuilder.core import use_plugin, after, init, task from pybuilder.errors import BuildFailedException from pybuilder.pluginhelper.external_command import ExternalCommandBuilder use_plugin("python.core") use_plugin("analysis") DEFAULT_PYLINT_OPTIONS = ["--max-line-length=100", "--no-docstring-rgx=.*"] @init def init_pylint(project): project.plugin_depends_on("pylint") project.set_property_if_unset("pylint_options", DEFAULT_PYLINT_OPTIONS) project.set_property_if_unset("pylint_break_build", False) project.set_property_if_unset("pylint_include_test_sources", False) project.set_property_if_unset("pylint_include_scripts", False) @after("prepare") def check_pylint_availability(project, logger, reactor): logger.debug("Checking availability of PyLint") reactor.pybuilder_venv.verify_can_execute(["pylint"], "pylint", "plugin python.pylint") @task("analyze") def execute_pylint(project, logger, reactor): logger.info("Executing pylint on project sources") verbose = project.get_property("verbose") project.set_property_if_unset("pylint_verbose_output", verbose) command = ExternalCommandBuilder("pylint", project, reactor) for opt in project.get_property("pylint_options"): command.use_argument(opt) include_test_sources = project.get_property("pylint_include_test_sources") include_scripts = project.get_property("pylint_include_scripts") result = command.run_on_production_source_files(logger, include_test_sources=include_test_sources, include_scripts=include_scripts) break_build = project.get_property("pylint_break_build") if result.exit_code == 32 and break_build: raise BuildFailedException("PyLint failed with exit code %s", result.exit_code) warnings = [line.rstrip() for line in result.report_lines if line.find(".py:") >= 0] warning_count = len(warnings) if warning_count: for warning in warnings: logger.warn("pylint: %s", warning) message = "PyLint found {} warning(s).".format(warning_count) if break_build: logger.error(message) raise BuildFailedException(message) else: logger.warn(message)

36.731707

94

0.707503

from pybuilder.core import use_plugin, after, init, task from pybuilder.errors import BuildFailedException from pybuilder.pluginhelper.external_command import ExternalCommandBuilder use_plugin("python.core") use_plugin("analysis") DEFAULT_PYLINT_OPTIONS = ["--max-line-length=100", "--no-docstring-rgx=.*"] @init def init_pylint(project): project.plugin_depends_on("pylint") project.set_property_if_unset("pylint_options", DEFAULT_PYLINT_OPTIONS) project.set_property_if_unset("pylint_break_build", False) project.set_property_if_unset("pylint_include_test_sources", False) project.set_property_if_unset("pylint_include_scripts", False) @after("prepare") def check_pylint_availability(project, logger, reactor): logger.debug("Checking availability of PyLint") reactor.pybuilder_venv.verify_can_execute(["pylint"], "pylint", "plugin python.pylint") @task("analyze") def execute_pylint(project, logger, reactor): logger.info("Executing pylint on project sources") verbose = project.get_property("verbose") project.set_property_if_unset("pylint_verbose_output", verbose) command = ExternalCommandBuilder("pylint", project, reactor) for opt in project.get_property("pylint_options"): command.use_argument(opt) include_test_sources = project.get_property("pylint_include_test_sources") include_scripts = project.get_property("pylint_include_scripts") result = command.run_on_production_source_files(logger, include_test_sources=include_test_sources, include_scripts=include_scripts) break_build = project.get_property("pylint_break_build") if result.exit_code == 32 and break_build: raise BuildFailedException("PyLint failed with exit code %s", result.exit_code) warnings = [line.rstrip() for line in result.report_lines if line.find(".py:") >= 0] warning_count = len(warnings) if warning_count: for warning in warnings: logger.warn("pylint: %s", warning) message = "PyLint found {} warning(s).".format(warning_count) if break_build: logger.error(message) raise BuildFailedException(message) else: logger.warn(message)

true

1c3125571a27dde6334f07b0b8b3ced20a5bab94

21,608

py

Python

pandas/tests/indexes/period/test_construction.py

developing-coder/pandas

9feb3ad92cc0397a04b665803a49299ee7aa1037

[ "BSD-3-Clause" ]

1

2019-05-04T03:42:25.000Z

pandas/tests/indexes/period/test_construction.py

developing-coder/pandas

9feb3ad92cc0397a04b665803a49299ee7aa1037

[ "BSD-3-Clause" ]

null

pandas/tests/indexes/period/test_construction.py

developing-coder/pandas

9feb3ad92cc0397a04b665803a49299ee7aa1037

[ "BSD-3-Clause" ]

null

import numpy as np import pytest from pandas._libs.tslibs.period import IncompatibleFrequency from pandas.compat import lmap, lrange from pandas.core.dtypes.dtypes import PeriodDtype import pandas as pd from pandas import ( Index, Period, PeriodIndex, Series, date_range, offsets, period_range) import pandas.core.indexes.period as period import pandas.util.testing as tm class TestPeriodIndex: def setup_method(self, method): pass def test_construction_base_constructor(self): # GH 13664 arr = [pd.Period('2011-01', freq='M'), pd.NaT, pd.Period('2011-03', freq='M')] tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.PeriodIndex(np.array(arr))) arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.PeriodIndex(np.array(arr))) arr = [pd.Period('2011-01', freq='M'), pd.NaT, pd.Period('2011-03', freq='D')] tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.Index(np.array(arr), dtype=object)) def test_constructor_use_start_freq(self): # GH #1118 p = Period('4/2/2012', freq='B') with tm.assert_produces_warning(FutureWarning): index = PeriodIndex(start=p, periods=10) expected = period_range(start='4/2/2012', periods=10, freq='B') tm.assert_index_equal(index, expected) index = period_range(start=p, periods=10) tm.assert_index_equal(index, expected) def test_constructor_field_arrays(self): # GH #1264 years = np.arange(1990, 2010).repeat(4)[2:-2] quarters = np.tile(np.arange(1, 5), 20)[2:-2] index = PeriodIndex(year=years, quarter=quarters, freq='Q-DEC') expected = period_range('1990Q3', '2009Q2', freq='Q-DEC') tm.assert_index_equal(index, expected) index2 = PeriodIndex(year=years, quarter=quarters, freq='2Q-DEC') tm.assert_numpy_array_equal(index.asi8, index2.asi8) index = PeriodIndex(year=years, quarter=quarters) tm.assert_index_equal(index, expected) years = [2007, 2007, 2007] months = [1, 2] msg = "Mismatched Period array lengths" with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='M') with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='2M') msg = "Can either instantiate from fields or endpoints, but not both" with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='M', start=Period('2007-01', freq='M')) years = [2007, 2007, 2007] months = [1, 2, 3] idx = PeriodIndex(year=years, month=months, freq='M') exp = period_range('2007-01', periods=3, freq='M') tm.assert_index_equal(idx, exp) def test_constructor_U(self): # U was used as undefined period with pytest.raises(ValueError, match="Invalid frequency: X"): period_range('2007-1-1', periods=500, freq='X') def test_constructor_nano(self): idx = period_range(start=Period(ordinal=1, freq='N'), end=Period(ordinal=4, freq='N'), freq='N') exp = PeriodIndex([Period(ordinal=1, freq='N'), Period(ordinal=2, freq='N'), Period(ordinal=3, freq='N'), Period(ordinal=4, freq='N')], freq='N') tm.assert_index_equal(idx, exp) def test_constructor_arrays_negative_year(self): years = np.arange(1960, 2000, dtype=np.int64).repeat(4) quarters = np.tile(np.array([1, 2, 3, 4], dtype=np.int64), 40) pindex = PeriodIndex(year=years, quarter=quarters) tm.assert_index_equal(pindex.year, pd.Index(years)) tm.assert_index_equal(pindex.quarter, pd.Index(quarters)) def test_constructor_invalid_quarters(self): msg = "Quarter must be 1 <= q <= 4" with pytest.raises(ValueError, match=msg): PeriodIndex(year=lrange(2000, 2004), quarter=lrange(4), freq='Q-DEC') def test_constructor_corner(self): msg = "Not enough parameters to construct Period range" with pytest.raises(ValueError, match=msg): PeriodIndex(periods=10, freq='A') start = Period('2007', freq='A-JUN') end = Period('2010', freq='A-DEC') msg = "start and end must have same freq" with pytest.raises(ValueError, match=msg): PeriodIndex(start=start, end=end) msg = ("Of the three parameters: start, end, and periods, exactly two" " must be specified") with pytest.raises(ValueError, match=msg): PeriodIndex(start=start) with pytest.raises(ValueError, match=msg): PeriodIndex(end=end) result = period_range('2007-01', periods=10.5, freq='M') exp = period_range('2007-01', periods=10, freq='M') tm.assert_index_equal(result, exp) def test_constructor_fromarraylike(self): idx = period_range('2007-01', periods=20, freq='M') # values is an array of Period, thus can retrieve freq tm.assert_index_equal(PeriodIndex(idx.values), idx) tm.assert_index_equal(PeriodIndex(list(idx.values)), idx) msg = "freq not specified and cannot be inferred" with pytest.raises(ValueError, match=msg): PeriodIndex(idx._ndarray_values) with pytest.raises(ValueError, match=msg): PeriodIndex(list(idx._ndarray_values)) msg = "'Period' object is not iterable" with pytest.raises(TypeError, match=msg): PeriodIndex(data=Period('2007', freq='A')) result = PeriodIndex(iter(idx)) tm.assert_index_equal(result, idx) result = PeriodIndex(idx) tm.assert_index_equal(result, idx) result = PeriodIndex(idx, freq='M') tm.assert_index_equal(result, idx) result = PeriodIndex(idx, freq=offsets.MonthEnd()) tm.assert_index_equal(result, idx) assert result.freq == 'M' result = PeriodIndex(idx, freq='2M') tm.assert_index_equal(result, idx.asfreq('2M')) assert result.freq == '2M' result = PeriodIndex(idx, freq=offsets.MonthEnd(2)) tm.assert_index_equal(result, idx.asfreq('2M')) assert result.freq == '2M' result = PeriodIndex(idx, freq='D') exp = idx.asfreq('D', 'e') tm.assert_index_equal(result, exp) def test_constructor_datetime64arr(self): vals = np.arange(100000, 100000 + 10000, 100, dtype=np.int64) vals = vals.view(np.dtype('M8[us]')) msg = r"Wrong dtype: datetime64\[us\]" with pytest.raises(ValueError, match=msg): PeriodIndex(vals, freq='D') @pytest.mark.parametrize('box', [None, 'series', 'index']) def test_constructor_datetime64arr_ok(self, box): # https://github.com/pandas-dev/pandas/issues/23438 data = pd.date_range('2017', periods=4, freq="M") if box is None: data = data._values elif box == 'series': data = pd.Series(data) result = PeriodIndex(data, freq='D') expected = PeriodIndex([ '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30' ], freq="D") tm.assert_index_equal(result, expected) def test_constructor_dtype(self): # passing a dtype with a tz should localize idx = PeriodIndex(['2013-01', '2013-03'], dtype='period[M]') exp = PeriodIndex(['2013-01', '2013-03'], freq='M') tm.assert_index_equal(idx, exp) assert idx.dtype == 'period[M]' idx = PeriodIndex(['2013-01-05', '2013-03-05'], dtype='period[3D]') exp = PeriodIndex(['2013-01-05', '2013-03-05'], freq='3D') tm.assert_index_equal(idx, exp) assert idx.dtype == 'period[3D]' # if we already have a freq and its not the same, then asfreq # (not changed) idx = PeriodIndex(['2013-01-01', '2013-01-02'], freq='D') res = PeriodIndex(idx, dtype='period[M]') exp = PeriodIndex(['2013-01', '2013-01'], freq='M') tm.assert_index_equal(res, exp) assert res.dtype == 'period[M]' res = PeriodIndex(idx, freq='M') tm.assert_index_equal(res, exp) assert res.dtype == 'period[M]' msg = 'specified freq and dtype are different' with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(['2011-01'], freq='M', dtype='period[D]') def test_constructor_empty(self): idx = pd.PeriodIndex([], freq='M') assert isinstance(idx, PeriodIndex) assert len(idx) == 0 assert idx.freq == 'M' with pytest.raises(ValueError, match='freq not specified'): pd.PeriodIndex([]) def test_constructor_pi_nat(self): idx = PeriodIndex([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='M')])) tm.assert_index_equal(idx, exp) idx = PeriodIndex([pd.NaT, pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='M')]) exp = PeriodIndex(['NaT', 'NaT', '2011-01', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(np.array([pd.NaT, pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='M')])) tm.assert_index_equal(idx, exp) idx = PeriodIndex([pd.NaT, pd.NaT, '2011-01', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex([pd.NaT, pd.NaT]) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(np.array([pd.NaT, pd.NaT])) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(['NaT', 'NaT']) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(np.array(['NaT', 'NaT'])) def test_constructor_incompat_freq(self): msg = "Input has different freq=D from PeriodIndex\$freq=M\$" with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='D')]) with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='D')])) # first element is pd.NaT with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex([pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='D')]) with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(np.array([pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='D')])) def test_constructor_mixed(self): idx = PeriodIndex(['2011-01', pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(['NaT', pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['NaT', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex([Period('2011-01-01', freq='D'), pd.NaT, '2012-01-01']) exp = PeriodIndex(['2011-01-01', 'NaT', '2012-01-01'], freq='D') tm.assert_index_equal(idx, exp) def test_constructor_simple_new(self): idx = period_range('2007-01', name='p', periods=2, freq='M') result = idx._simple_new(idx, name='p', freq=idx.freq) tm.assert_index_equal(result, idx) result = idx._simple_new(idx.astype('i8'), name='p', freq=idx.freq) tm.assert_index_equal(result, idx) def test_constructor_simple_new_empty(self): # GH13079 idx = PeriodIndex([], freq='M', name='p') result = idx._simple_new(idx, name='p', freq='M') tm.assert_index_equal(result, idx) @pytest.mark.parametrize('floats', [[1.1, 2.1], np.array([1.1, 2.1])]) def test_constructor_floats(self, floats): msg = r"PeriodIndex\._simple_new does not accept floats" with pytest.raises(TypeError, match=msg): pd.PeriodIndex._simple_new(floats, freq='M') msg = "PeriodIndex does not allow floating point in construction" with pytest.raises(TypeError, match=msg): pd.PeriodIndex(floats, freq='M') def test_constructor_nat(self): msg = "start and end must not be NaT" with pytest.raises(ValueError, match=msg): period_range(start='NaT', end='2011-01-01', freq='M') with pytest.raises(ValueError, match=msg): period_range(start='2011-01-01', end='NaT', freq='M') def test_constructor_year_and_quarter(self): year = pd.Series([2001, 2002, 2003]) quarter = year - 2000 idx = PeriodIndex(year=year, quarter=quarter) strs = ['%dQ%d' % t for t in zip(quarter, year)] lops = list(map(Period, strs)) p = PeriodIndex(lops) tm.assert_index_equal(p, idx) @pytest.mark.parametrize('func, warning', [ (PeriodIndex, FutureWarning), (period_range, None) ]) def test_constructor_freq_mult(self, func, warning): # GH #7811 with tm.assert_produces_warning(warning): # must be the same, but for sure... pidx = func(start='2014-01', freq='2M', periods=4) expected = PeriodIndex(['2014-01', '2014-03', '2014-05', '2014-07'], freq='2M') tm.assert_index_equal(pidx, expected) with tm.assert_produces_warning(warning): pidx = func(start='2014-01-02', end='2014-01-15', freq='3D') expected = PeriodIndex(['2014-01-02', '2014-01-05', '2014-01-08', '2014-01-11', '2014-01-14'], freq='3D') tm.assert_index_equal(pidx, expected) with tm.assert_produces_warning(warning): pidx = func(end='2014-01-01 17:00', freq='4H', periods=3) expected = PeriodIndex(['2014-01-01 09:00', '2014-01-01 13:00', '2014-01-01 17:00'], freq='4H') tm.assert_index_equal(pidx, expected) msg = ('Frequency must be positive, because it' ' represents span: -1M') with pytest.raises(ValueError, match=msg): PeriodIndex(['2011-01'], freq='-1M') msg = ('Frequency must be positive, because it' ' represents span: 0M') with pytest.raises(ValueError, match=msg): PeriodIndex(['2011-01'], freq='0M') msg = ('Frequency must be positive, because it' ' represents span: 0M') with pytest.raises(ValueError, match=msg): period_range('2011-01', periods=3, freq='0M') @pytest.mark.parametrize('freq', ['A', 'M', 'D', 'T', 'S']) @pytest.mark.parametrize('mult', [1, 2, 3, 4, 5]) def test_constructor_freq_mult_dti_compat(self, mult, freq): freqstr = str(mult) + freq pidx = period_range(start='2014-04-01', freq=freqstr, periods=10) expected = date_range(start='2014-04-01', freq=freqstr, periods=10).to_period(freqstr) tm.assert_index_equal(pidx, expected) def test_constructor_freq_combined(self): for freq in ['1D1H', '1H1D']: pidx = PeriodIndex(['2016-01-01', '2016-01-02'], freq=freq) expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 00:00'], freq='25H') for freq in ['1D1H', '1H1D']: pidx = period_range(start='2016-01-01', periods=2, freq=freq) expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 01:00'], freq='25H') tm.assert_index_equal(pidx, expected) def test_constructor_range_based_deprecated(self): with tm.assert_produces_warning(FutureWarning): pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') assert len(pi) == 9 def test_constructor_range_based_deprecated_different_freq(self): with tm.assert_produces_warning(FutureWarning) as m: PeriodIndex(start='2000', periods=2) warning, = m assert 'freq="A-DEC"' in str(warning.message) def test_constructor(self): pi = period_range(freq='A', start='1/1/2001', end='12/1/2009') assert len(pi) == 9 pi = period_range(freq='Q', start='1/1/2001', end='12/1/2009') assert len(pi) == 4 * 9 pi = period_range(freq='M', start='1/1/2001', end='12/1/2009') assert len(pi) == 12 * 9 pi = period_range(freq='D', start='1/1/2001', end='12/31/2009') assert len(pi) == 365 * 9 + 2 pi = period_range(freq='B', start='1/1/2001', end='12/31/2009') assert len(pi) == 261 * 9 pi = period_range(freq='H', start='1/1/2001', end='12/31/2001 23:00') assert len(pi) == 365 * 24 pi = period_range(freq='Min', start='1/1/2001', end='1/1/2001 23:59') assert len(pi) == 24 * 60 pi = period_range(freq='S', start='1/1/2001', end='1/1/2001 23:59:59') assert len(pi) == 24 * 60 * 60 start = Period('02-Apr-2005', 'B') i1 = period_range(start=start, periods=20) assert len(i1) == 20 assert i1.freq == start.freq assert i1[0] == start end_intv = Period('2006-12-31', 'W') i1 = period_range(end=end_intv, periods=10) assert len(i1) == 10 assert i1.freq == end_intv.freq assert i1[-1] == end_intv end_intv = Period('2006-12-31', '1w') i2 = period_range(end=end_intv, periods=10) assert len(i1) == len(i2) assert (i1 == i2).all() assert i1.freq == i2.freq end_intv = Period('2006-12-31', ('w', 1)) i2 = period_range(end=end_intv, periods=10) assert len(i1) == len(i2) assert (i1 == i2).all() assert i1.freq == i2.freq end_intv = Period('2005-05-01', 'B') i1 = period_range(start=start, end=end_intv) # infer freq from first element i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) assert len(i2) == 2 assert i2[0] == end_intv i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) assert len(i2) == 2 assert i2[0] == end_intv # Mixed freq should fail vals = [end_intv, Period('2006-12-31', 'w')] msg = r"Input has different freq=W-SUN from PeriodIndex$freq=B$" with pytest.raises(IncompatibleFrequency, match=msg): PeriodIndex(vals) vals = np.array(vals) with pytest.raises(IncompatibleFrequency, match=msg): PeriodIndex(vals) def test_constructor_error(self): start = Period('02-Apr-2005', 'B') end_intv = Period('2006-12-31', ('w', 1)) msg = 'start and end must have same freq' with pytest.raises(ValueError, match=msg): PeriodIndex(start=start, end=end_intv) msg = ('Of the three parameters: start, end, and periods, ' 'exactly two must be specified') with pytest.raises(ValueError, match=msg): PeriodIndex(start=start) @pytest.mark.parametrize('freq', ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'N', 'H']) def test_recreate_from_data(self, freq): org = period_range(start='2001/04/01', freq=freq, periods=1) idx = PeriodIndex(org.values, freq=freq) tm.assert_index_equal(idx, org) def test_map_with_string_constructor(self): raw = [2005, 2007, 2009] index = PeriodIndex(raw, freq='A') expected = Index(lmap(str, raw)) res = index.map(str) # should return an Index assert isinstance(res, Index) # preserve element types assert all(isinstance(resi, str) for resi in res) # lastly, values should compare equal tm.assert_index_equal(res, expected) class TestSeriesPeriod: def setup_method(self, method): self.series = Series(period_range('2000-01-01', periods=10, freq='D')) def test_constructor_cant_cast_period(self): msg = "Cannot cast PeriodArray to dtype float64" with pytest.raises(TypeError, match=msg): Series(period_range('2000-01-01', periods=10, freq='D'), dtype=float) def test_constructor_cast_object(self): s = Series(period_range('1/1/2000', periods=10), dtype=PeriodDtype("D")) exp = Series(period_range('1/1/2000', periods=10)) tm.assert_series_equal(s, exp)

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import numpy as np import pytest from pandas._libs.tslibs.period import IncompatibleFrequency from pandas.compat import lmap, lrange from pandas.core.dtypes.dtypes import PeriodDtype import pandas as pd from pandas import ( Index, Period, PeriodIndex, Series, date_range, offsets, period_range) import pandas.core.indexes.period as period import pandas.util.testing as tm class TestPeriodIndex: def setup_method(self, method): pass def test_construction_base_constructor(self): arr = [pd.Period('2011-01', freq='M'), pd.NaT, pd.Period('2011-03', freq='M')] tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.PeriodIndex(np.array(arr))) arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.PeriodIndex(np.array(arr))) arr = [pd.Period('2011-01', freq='M'), pd.NaT, pd.Period('2011-03', freq='D')] tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) tm.assert_index_equal(pd.Index(np.array(arr)), pd.Index(np.array(arr), dtype=object)) def test_constructor_use_start_freq(self): p = Period('4/2/2012', freq='B') with tm.assert_produces_warning(FutureWarning): index = PeriodIndex(start=p, periods=10) expected = period_range(start='4/2/2012', periods=10, freq='B') tm.assert_index_equal(index, expected) index = period_range(start=p, periods=10) tm.assert_index_equal(index, expected) def test_constructor_field_arrays(self): years = np.arange(1990, 2010).repeat(4)[2:-2] quarters = np.tile(np.arange(1, 5), 20)[2:-2] index = PeriodIndex(year=years, quarter=quarters, freq='Q-DEC') expected = period_range('1990Q3', '2009Q2', freq='Q-DEC') tm.assert_index_equal(index, expected) index2 = PeriodIndex(year=years, quarter=quarters, freq='2Q-DEC') tm.assert_numpy_array_equal(index.asi8, index2.asi8) index = PeriodIndex(year=years, quarter=quarters) tm.assert_index_equal(index, expected) years = [2007, 2007, 2007] months = [1, 2] msg = "Mismatched Period array lengths" with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='M') with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='2M') msg = "Can either instantiate from fields or endpoints, but not both" with pytest.raises(ValueError, match=msg): PeriodIndex(year=years, month=months, freq='M', start=Period('2007-01', freq='M')) years = [2007, 2007, 2007] months = [1, 2, 3] idx = PeriodIndex(year=years, month=months, freq='M') exp = period_range('2007-01', periods=3, freq='M') tm.assert_index_equal(idx, exp) def test_constructor_U(self): with pytest.raises(ValueError, match="Invalid frequency: X"): period_range('2007-1-1', periods=500, freq='X') def test_constructor_nano(self): idx = period_range(start=Period(ordinal=1, freq='N'), end=Period(ordinal=4, freq='N'), freq='N') exp = PeriodIndex([Period(ordinal=1, freq='N'), Period(ordinal=2, freq='N'), Period(ordinal=3, freq='N'), Period(ordinal=4, freq='N')], freq='N') tm.assert_index_equal(idx, exp) def test_constructor_arrays_negative_year(self): years = np.arange(1960, 2000, dtype=np.int64).repeat(4) quarters = np.tile(np.array([1, 2, 3, 4], dtype=np.int64), 40) pindex = PeriodIndex(year=years, quarter=quarters) tm.assert_index_equal(pindex.year, pd.Index(years)) tm.assert_index_equal(pindex.quarter, pd.Index(quarters)) def test_constructor_invalid_quarters(self): msg = "Quarter must be 1 <= q <= 4" with pytest.raises(ValueError, match=msg): PeriodIndex(year=lrange(2000, 2004), quarter=lrange(4), freq='Q-DEC') def test_constructor_corner(self): msg = "Not enough parameters to construct Period range" with pytest.raises(ValueError, match=msg): PeriodIndex(periods=10, freq='A') start = Period('2007', freq='A-JUN') end = Period('2010', freq='A-DEC') msg = "start and end must have same freq" with pytest.raises(ValueError, match=msg): PeriodIndex(start=start, end=end) msg = ("Of the three parameters: start, end, and periods, exactly two" " must be specified") with pytest.raises(ValueError, match=msg): PeriodIndex(start=start) with pytest.raises(ValueError, match=msg): PeriodIndex(end=end) result = period_range('2007-01', periods=10.5, freq='M') exp = period_range('2007-01', periods=10, freq='M') tm.assert_index_equal(result, exp) def test_constructor_fromarraylike(self): idx = period_range('2007-01', periods=20, freq='M') tm.assert_index_equal(PeriodIndex(idx.values), idx) tm.assert_index_equal(PeriodIndex(list(idx.values)), idx) msg = "freq not specified and cannot be inferred" with pytest.raises(ValueError, match=msg): PeriodIndex(idx._ndarray_values) with pytest.raises(ValueError, match=msg): PeriodIndex(list(idx._ndarray_values)) msg = "'Period' object is not iterable" with pytest.raises(TypeError, match=msg): PeriodIndex(data=Period('2007', freq='A')) result = PeriodIndex(iter(idx)) tm.assert_index_equal(result, idx) result = PeriodIndex(idx) tm.assert_index_equal(result, idx) result = PeriodIndex(idx, freq='M') tm.assert_index_equal(result, idx) result = PeriodIndex(idx, freq=offsets.MonthEnd()) tm.assert_index_equal(result, idx) assert result.freq == 'M' result = PeriodIndex(idx, freq='2M') tm.assert_index_equal(result, idx.asfreq('2M')) assert result.freq == '2M' result = PeriodIndex(idx, freq=offsets.MonthEnd(2)) tm.assert_index_equal(result, idx.asfreq('2M')) assert result.freq == '2M' result = PeriodIndex(idx, freq='D') exp = idx.asfreq('D', 'e') tm.assert_index_equal(result, exp) def test_constructor_datetime64arr(self): vals = np.arange(100000, 100000 + 10000, 100, dtype=np.int64) vals = vals.view(np.dtype('M8[us]')) msg = r"Wrong dtype: datetime64\[us\]" with pytest.raises(ValueError, match=msg): PeriodIndex(vals, freq='D') @pytest.mark.parametrize('box', [None, 'series', 'index']) def test_constructor_datetime64arr_ok(self, box): data = pd.date_range('2017', periods=4, freq="M") if box is None: data = data._values elif box == 'series': data = pd.Series(data) result = PeriodIndex(data, freq='D') expected = PeriodIndex([ '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30' ], freq="D") tm.assert_index_equal(result, expected) def test_constructor_dtype(self): idx = PeriodIndex(['2013-01', '2013-03'], dtype='period[M]') exp = PeriodIndex(['2013-01', '2013-03'], freq='M') tm.assert_index_equal(idx, exp) assert idx.dtype == 'period[M]' idx = PeriodIndex(['2013-01-05', '2013-03-05'], dtype='period[3D]') exp = PeriodIndex(['2013-01-05', '2013-03-05'], freq='3D') tm.assert_index_equal(idx, exp) assert idx.dtype == 'period[3D]' idx = PeriodIndex(['2013-01-01', '2013-01-02'], freq='D') res = PeriodIndex(idx, dtype='period[M]') exp = PeriodIndex(['2013-01', '2013-01'], freq='M') tm.assert_index_equal(res, exp) assert res.dtype == 'period[M]' res = PeriodIndex(idx, freq='M') tm.assert_index_equal(res, exp) assert res.dtype == 'period[M]' msg = 'specified freq and dtype are different' with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(['2011-01'], freq='M', dtype='period[D]') def test_constructor_empty(self): idx = pd.PeriodIndex([], freq='M') assert isinstance(idx, PeriodIndex) assert len(idx) == 0 assert idx.freq == 'M' with pytest.raises(ValueError, match='freq not specified'): pd.PeriodIndex([]) def test_constructor_pi_nat(self): idx = PeriodIndex([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='M')])) tm.assert_index_equal(idx, exp) idx = PeriodIndex([pd.NaT, pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='M')]) exp = PeriodIndex(['NaT', 'NaT', '2011-01', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(np.array([pd.NaT, pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='M')])) tm.assert_index_equal(idx, exp) idx = PeriodIndex([pd.NaT, pd.NaT, '2011-01', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex([pd.NaT, pd.NaT]) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(np.array([pd.NaT, pd.NaT])) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(['NaT', 'NaT']) with pytest.raises(ValueError, match='freq not specified'): PeriodIndex(np.array(['NaT', 'NaT'])) def test_constructor_incompat_freq(self): msg = "Input has different freq=D from PeriodIndex\$freq=M\$" with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='D')]) with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(np.array([Period('2011-01', freq='M'), pd.NaT, Period('2011-01', freq='D')])) with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex([pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='D')]) with pytest.raises(period.IncompatibleFrequency, match=msg): PeriodIndex(np.array([pd.NaT, Period('2011-01', freq='M'), Period('2011-01', freq='D')])) def test_constructor_mixed(self): idx = PeriodIndex(['2011-01', pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['2011-01', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex(['NaT', pd.NaT, Period('2011-01', freq='M')]) exp = PeriodIndex(['NaT', 'NaT', '2011-01'], freq='M') tm.assert_index_equal(idx, exp) idx = PeriodIndex([Period('2011-01-01', freq='D'), pd.NaT, '2012-01-01']) exp = PeriodIndex(['2011-01-01', 'NaT', '2012-01-01'], freq='D') tm.assert_index_equal(idx, exp) def test_constructor_simple_new(self): idx = period_range('2007-01', name='p', periods=2, freq='M') result = idx._simple_new(idx, name='p', freq=idx.freq) tm.assert_index_equal(result, idx) result = idx._simple_new(idx.astype('i8'), name='p', freq=idx.freq) tm.assert_index_equal(result, idx) def test_constructor_simple_new_empty(self): idx = PeriodIndex([], freq='M', name='p') result = idx._simple_new(idx, name='p', freq='M') tm.assert_index_equal(result, idx) @pytest.mark.parametrize('floats', [[1.1, 2.1], np.array([1.1, 2.1])]) def test_constructor_floats(self, floats): msg = r"PeriodIndex\._simple_new does not accept floats" with pytest.raises(TypeError, match=msg): pd.PeriodIndex._simple_new(floats, freq='M') msg = "PeriodIndex does not allow floating point in construction" with pytest.raises(TypeError, match=msg): pd.PeriodIndex(floats, freq='M') def test_constructor_nat(self): msg = "start and end must not be NaT" with pytest.raises(ValueError, match=msg): period_range(start='NaT', end='2011-01-01', freq='M') with pytest.raises(ValueError, match=msg): period_range(start='2011-01-01', end='NaT', freq='M') def test_constructor_year_and_quarter(self): year = pd.Series([2001, 2002, 2003]) quarter = year - 2000 idx = PeriodIndex(year=year, quarter=quarter) strs = ['%dQ%d' % t for t in zip(quarter, year)] lops = list(map(Period, strs)) p = PeriodIndex(lops) tm.assert_index_equal(p, idx) @pytest.mark.parametrize('func, warning', [ (PeriodIndex, FutureWarning), (period_range, None) ]) def test_constructor_freq_mult(self, func, warning): with tm.assert_produces_warning(warning): pidx = func(start='2014-01', freq='2M', periods=4) expected = PeriodIndex(['2014-01', '2014-03', '2014-05', '2014-07'], freq='2M') tm.assert_index_equal(pidx, expected) with tm.assert_produces_warning(warning): pidx = func(start='2014-01-02', end='2014-01-15', freq='3D') expected = PeriodIndex(['2014-01-02', '2014-01-05', '2014-01-08', '2014-01-11', '2014-01-14'], freq='3D') tm.assert_index_equal(pidx, expected) with tm.assert_produces_warning(warning): pidx = func(end='2014-01-01 17:00', freq='4H', periods=3) expected = PeriodIndex(['2014-01-01 09:00', '2014-01-01 13:00', '2014-01-01 17:00'], freq='4H') tm.assert_index_equal(pidx, expected) msg = ('Frequency must be positive, because it' ' represents span: -1M') with pytest.raises(ValueError, match=msg): PeriodIndex(['2011-01'], freq='-1M') msg = ('Frequency must be positive, because it' ' represents span: 0M') with pytest.raises(ValueError, match=msg): PeriodIndex(['2011-01'], freq='0M') msg = ('Frequency must be positive, because it' ' represents span: 0M') with pytest.raises(ValueError, match=msg): period_range('2011-01', periods=3, freq='0M') @pytest.mark.parametrize('freq', ['A', 'M', 'D', 'T', 'S']) @pytest.mark.parametrize('mult', [1, 2, 3, 4, 5]) def test_constructor_freq_mult_dti_compat(self, mult, freq): freqstr = str(mult) + freq pidx = period_range(start='2014-04-01', freq=freqstr, periods=10) expected = date_range(start='2014-04-01', freq=freqstr, periods=10).to_period(freqstr) tm.assert_index_equal(pidx, expected) def test_constructor_freq_combined(self): for freq in ['1D1H', '1H1D']: pidx = PeriodIndex(['2016-01-01', '2016-01-02'], freq=freq) expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 00:00'], freq='25H') for freq in ['1D1H', '1H1D']: pidx = period_range(start='2016-01-01', periods=2, freq=freq) expected = PeriodIndex(['2016-01-01 00:00', '2016-01-02 01:00'], freq='25H') tm.assert_index_equal(pidx, expected) def test_constructor_range_based_deprecated(self): with tm.assert_produces_warning(FutureWarning): pi = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') assert len(pi) == 9 def test_constructor_range_based_deprecated_different_freq(self): with tm.assert_produces_warning(FutureWarning) as m: PeriodIndex(start='2000', periods=2) warning, = m assert 'freq="A-DEC"' in str(warning.message) def test_constructor(self): pi = period_range(freq='A', start='1/1/2001', end='12/1/2009') assert len(pi) == 9 pi = period_range(freq='Q', start='1/1/2001', end='12/1/2009') assert len(pi) == 4 * 9 pi = period_range(freq='M', start='1/1/2001', end='12/1/2009') assert len(pi) == 12 * 9 pi = period_range(freq='D', start='1/1/2001', end='12/31/2009') assert len(pi) == 365 * 9 + 2 pi = period_range(freq='B', start='1/1/2001', end='12/31/2009') assert len(pi) == 261 * 9 pi = period_range(freq='H', start='1/1/2001', end='12/31/2001 23:00') assert len(pi) == 365 * 24 pi = period_range(freq='Min', start='1/1/2001', end='1/1/2001 23:59') assert len(pi) == 24 * 60 pi = period_range(freq='S', start='1/1/2001', end='1/1/2001 23:59:59') assert len(pi) == 24 * 60 * 60 start = Period('02-Apr-2005', 'B') i1 = period_range(start=start, periods=20) assert len(i1) == 20 assert i1.freq == start.freq assert i1[0] == start end_intv = Period('2006-12-31', 'W') i1 = period_range(end=end_intv, periods=10) assert len(i1) == 10 assert i1.freq == end_intv.freq assert i1[-1] == end_intv end_intv = Period('2006-12-31', '1w') i2 = period_range(end=end_intv, periods=10) assert len(i1) == len(i2) assert (i1 == i2).all() assert i1.freq == i2.freq end_intv = Period('2006-12-31', ('w', 1)) i2 = period_range(end=end_intv, periods=10) assert len(i1) == len(i2) assert (i1 == i2).all() assert i1.freq == i2.freq end_intv = Period('2005-05-01', 'B') i1 = period_range(start=start, end=end_intv) i2 = PeriodIndex([end_intv, Period('2005-05-05', 'B')]) assert len(i2) == 2 assert i2[0] == end_intv i2 = PeriodIndex(np.array([end_intv, Period('2005-05-05', 'B')])) assert len(i2) == 2 assert i2[0] == end_intv vals = [end_intv, Period('2006-12-31', 'w')] msg = r"Input has different freq=W-SUN from PeriodIndex$freq=B$" with pytest.raises(IncompatibleFrequency, match=msg): PeriodIndex(vals) vals = np.array(vals) with pytest.raises(IncompatibleFrequency, match=msg): PeriodIndex(vals) def test_constructor_error(self): start = Period('02-Apr-2005', 'B') end_intv = Period('2006-12-31', ('w', 1)) msg = 'start and end must have same freq' with pytest.raises(ValueError, match=msg): PeriodIndex(start=start, end=end_intv) msg = ('Of the three parameters: start, end, and periods, ' 'exactly two must be specified') with pytest.raises(ValueError, match=msg): PeriodIndex(start=start) @pytest.mark.parametrize('freq', ['M', 'Q', 'A', 'D', 'B', 'T', 'S', 'L', 'U', 'N', 'H']) def test_recreate_from_data(self, freq): org = period_range(start='2001/04/01', freq=freq, periods=1) idx = PeriodIndex(org.values, freq=freq) tm.assert_index_equal(idx, org) def test_map_with_string_constructor(self): raw = [2005, 2007, 2009] index = PeriodIndex(raw, freq='A') expected = Index(lmap(str, raw)) res = index.map(str) assert isinstance(res, Index) assert all(isinstance(resi, str) for resi in res) tm.assert_index_equal(res, expected) class TestSeriesPeriod: def setup_method(self, method): self.series = Series(period_range('2000-01-01', periods=10, freq='D')) def test_constructor_cant_cast_period(self): msg = "Cannot cast PeriodArray to dtype float64" with pytest.raises(TypeError, match=msg): Series(period_range('2000-01-01', periods=10, freq='D'), dtype=float) def test_constructor_cast_object(self): s = Series(period_range('1/1/2000', periods=10), dtype=PeriodDtype("D")) exp = Series(period_range('1/1/2000', periods=10)) tm.assert_series_equal(s, exp)

true

1c312568dd030c2e506792ee49485e62f3f8f42a

3,182

py

Python

Rendering/Volume/Testing/Python/TestBunykRayCastFunction.py

jasper-yeh/VtkDotNet

84b56f781cb511694e4380cebfb245bbefe2560b

[ "BSD-3-Clause" ]

3

2020-06-20T23:31:06.000Z

2021-01-11T02:17:16.000Z

Rendering/Volume/Testing/Python/TestBunykRayCastFunction.py

jasper-yeh/VtkDotNet

84b56f781cb511694e4380cebfb245bbefe2560b

[ "BSD-3-Clause" ]

1

2020-12-01T23:21:02.000Z

2020-12-02T23:44:43.000Z

Rendering/Volume/Testing/Python/TestBunykRayCastFunction.py

jasper-yeh/VtkDotNet

84b56f781cb511694e4380cebfb245bbefe2560b

[ "BSD-3-Clause" ]

5

2015-10-09T04:12:29.000Z

2021-12-15T16:57:11.000Z

#!/usr/bin/env python import vtk from vtk.test import Testing from vtk.util.misc import vtkGetDataRoot VTK_DATA_ROOT = vtkGetDataRoot() # Create the standard renderer, render window # and interactor ren1 = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren1) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) iren.SetDesiredUpdateRate(3) # Create the reader for the data # This is the data the will be volume rendered reader = vtk.vtkStructuredPointsReader() reader.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/ironProt.vtk") # create a reader for the other data that will # be contoured and displayed as a polygonal mesh reader2 = vtk.vtkSLCReader() reader2.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/neghip.slc") # convert from vtkImageData to vtkUnstructuredGrid, remove # any cells where all values are below 80 thresh = vtk.vtkThreshold() thresh.ThresholdByUpper(80) thresh.AllScalarsOff() thresh.SetInputConnection(reader.GetOutputPort()) # make sure we have only tetrahedra trifilter = vtk.vtkDataSetTriangleFilter() trifilter.SetInputConnection(thresh.GetOutputPort()) # Create transfer mapping scalar value to opacity opacityTransferFunction = vtk.vtkPiecewiseFunction() opacityTransferFunction.AddPoint(80,0.0) opacityTransferFunction.AddPoint(120,0.2) opacityTransferFunction.AddPoint(255,0.2) # Create transfer mapping scalar value to color colorTransferFunction = vtk.vtkColorTransferFunction() colorTransferFunction.AddRGBPoint(80.0,0.0,0.0,0.0) colorTransferFunction.AddRGBPoint(120.0,0.0,0.0,1.0) colorTransferFunction.AddRGBPoint(160.0,1.0,0.0,0.0) colorTransferFunction.AddRGBPoint(200.0,0.0,1.0,0.0) colorTransferFunction.AddRGBPoint(255.0,0.0,1.0,1.0) # The property describes how the data will look volumeProperty = vtk.vtkVolumeProperty() volumeProperty.SetColor(colorTransferFunction) volumeProperty.SetScalarOpacity(opacityTransferFunction) volumeProperty.ShadeOff() volumeProperty.SetInterpolationTypeToLinear() # The mapper / ray cast function know how to render the data volumeMapper = vtk.vtkUnstructuredGridVolumeRayCastMapper() volumeMapper.SetInputConnection(trifilter.GetOutputPort()) # The volume holds the mapper and the property and # can be used to position/orient the volume volume = vtk.vtkVolume() volume.SetMapper(volumeMapper) volume.SetProperty(volumeProperty) # contour the second dataset contour = vtk.vtkContourFilter() contour.SetValue(0,80) contour.SetInputConnection(reader2.GetOutputPort()) # create a mapper for the polygonal data mapper = vtk.vtkPolyDataMapper() mapper.SetInputConnection(contour.GetOutputPort()) mapper.ScalarVisibilityOff() # create an actor for the polygonal data actor = vtk.vtkActor() actor.SetMapper(mapper) ren1.AddViewProp(actor) ren1.AddVolume(volume) renWin.SetSize(300,300) ren1.ResetCamera() ren1.GetActiveCamera().Azimuth(20.0) ren1.GetActiveCamera().Elevation(10.0) ren1.GetActiveCamera().Zoom(1.5) renWin.Render() def TkCheckAbort (__vtk__temp0=0,__vtk__temp1=0): foo = renWin.GetEventPending() if (foo != 0): renWin.SetAbortRender(1) pass renWin.AddObserver("AbortCheckEvent",TkCheckAbort) iren.Initialize() # --- end of script --

37

66

0.803268

import vtk from vtk.test import Testing from vtk.util.misc import vtkGetDataRoot VTK_DATA_ROOT = vtkGetDataRoot() ren1 = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren1) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) iren.SetDesiredUpdateRate(3) reader = vtk.vtkStructuredPointsReader() reader.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/ironProt.vtk") reader2 = vtk.vtkSLCReader() reader2.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/neghip.slc") thresh = vtk.vtkThreshold() thresh.ThresholdByUpper(80) thresh.AllScalarsOff() thresh.SetInputConnection(reader.GetOutputPort()) trifilter = vtk.vtkDataSetTriangleFilter() trifilter.SetInputConnection(thresh.GetOutputPort()) opacityTransferFunction = vtk.vtkPiecewiseFunction() opacityTransferFunction.AddPoint(80,0.0) opacityTransferFunction.AddPoint(120,0.2) opacityTransferFunction.AddPoint(255,0.2) colorTransferFunction = vtk.vtkColorTransferFunction() colorTransferFunction.AddRGBPoint(80.0,0.0,0.0,0.0) colorTransferFunction.AddRGBPoint(120.0,0.0,0.0,1.0) colorTransferFunction.AddRGBPoint(160.0,1.0,0.0,0.0) colorTransferFunction.AddRGBPoint(200.0,0.0,1.0,0.0) colorTransferFunction.AddRGBPoint(255.0,0.0,1.0,1.0) volumeProperty = vtk.vtkVolumeProperty() volumeProperty.SetColor(colorTransferFunction) volumeProperty.SetScalarOpacity(opacityTransferFunction) volumeProperty.ShadeOff() volumeProperty.SetInterpolationTypeToLinear() volumeMapper = vtk.vtkUnstructuredGridVolumeRayCastMapper() volumeMapper.SetInputConnection(trifilter.GetOutputPort()) volume = vtk.vtkVolume() volume.SetMapper(volumeMapper) volume.SetProperty(volumeProperty) contour = vtk.vtkContourFilter() contour.SetValue(0,80) contour.SetInputConnection(reader2.GetOutputPort()) mapper = vtk.vtkPolyDataMapper() mapper.SetInputConnection(contour.GetOutputPort()) mapper.ScalarVisibilityOff() actor = vtk.vtkActor() actor.SetMapper(mapper) ren1.AddViewProp(actor) ren1.AddVolume(volume) renWin.SetSize(300,300) ren1.ResetCamera() ren1.GetActiveCamera().Azimuth(20.0) ren1.GetActiveCamera().Elevation(10.0) ren1.GetActiveCamera().Zoom(1.5) renWin.Render() def TkCheckAbort (__vtk__temp0=0,__vtk__temp1=0): foo = renWin.GetEventPending() if (foo != 0): renWin.SetAbortRender(1) pass renWin.AddObserver("AbortCheckEvent",TkCheckAbort) iren.Initialize()

true

1c3126bad5ba59783881d63b1e1d9ee4ad1c4671

13,205

py

Python

lenstronomy_extensions/Itterative/iterative_source.py

Thomas-01/lenstronomy_extensions

fbbfe24dcfd71eae9e7c2dd60865a9b94db67fe8

[ "MIT" ]

27

2018-02-28T08:54:44.000Z

2022-03-25T00:13:43.000Z

lenstronomy_extensions/Itterative/iterative_source.py

Thomas-01/lenstronomy_extensions

fbbfe24dcfd71eae9e7c2dd60865a9b94db67fe8

[ "MIT" ]

3

2019-03-12T13:37:51.000Z

2020-10-30T03:03:59.000Z

lenstronomy_extensions/Itterative/iterative_source.py

Thomas-01/lenstronomy_extensions

fbbfe24dcfd71eae9e7c2dd60865a9b94db67fe8

[ "MIT" ]

33

2018-03-19T18:47:38.000Z

2022-03-27T02:55:04.000Z

__author__ = 'sibirrer' import numpy as np import lenstronomy.Util.util as util from lenstronomy.LightModel.Profiles.shapelets import Shapelets from lenstronomy.ImSim.image_model import ImageModel class MakeImageIter(ImageModel): """ class to perform an iterative source reconstruction goal: find the floor in the source information (minimal image residuals for a given lens model) Steps: 1: reconstruct source with shapelets 2: find N local maximas in positive residuals (image-model), indicating not enough peaky positive surface brightness 3: compute magnification at this position -> minimum scale to be resolved 4: Add N gaussians with minimal scale at that position 5: Perform reconstruction of source with shapelets and Gaussians 6: iterate over """ def find_max_residuals(self, residuals, ra_coords, dec_coords, N): """ :param residuals: reduced residual map :return: pixel coords of maximas """ ra_mins, dec_mins, values = util.neighborSelect(residuals, ra_coords, dec_coords) ra_pos = util.selectBest(np.array(ra_mins), -np.array(values), N, highest=True) dec_pos = util.selectBest(np.array(dec_mins), -np.array(values), N, highest=True) return ra_pos, dec_pos def check_overlap_in_source(self, x, y, ra_pos, dec_pos, r_min, N): """ check whether different residuals correspond to the same position in the source plane (modulo magnification) :param ra_pos: :param dec_pos: :param kwargs_lens: :param kwargs_else: :return: """ n = len(x) count = 0 i = 0 x_pos_select = [] y_pos_select = [] ra_pos_select = [] dec_pos_select = [] r_min_select = [] while count < N and i < n: if i == 0: x_pos_select.append(x[i]) y_pos_select.append(y[i]) ra_pos_select.append(ra_pos[i]) dec_pos_select.append(dec_pos[i]) r_min_select.append(r_min[i]) count += 1 else: r_delta = np.sqrt((x - x[i])**2 + (y - y[i])**2) if np.min(r_delta[0:i]) > r_min[i]: x_pos_select.append(x[i]) y_pos_select.append(y[i]) ra_pos_select.append(ra_pos[i]) dec_pos_select.append(dec_pos[i]) r_min_select.append(r_min[i]) count += 1 i += 1 return x_pos_select, y_pos_select, r_min_select, ra_pos_select, dec_pos_select def find_clump_param(self, residuals, ra_coords, dec_coords, N, kwargs_lens, kwargs_else, deltaPix, clump_scale): ra_pos, dec_pos = self.find_max_residuals(residuals, ra_coords, dec_coords, 5*N) n = len(ra_pos) x = np.zeros(n) y = np.zeros(n) r_min = np.zeros(n) for i in range(n): x[i], y[i], r_min[i] = self.position_size_estimate(ra_pos[i], dec_pos[i], kwargs_lens, kwargs_else, deltaPix, scale=clump_scale) x_pos, y_pos, sigma, ra_pos_select, dec_pos_select = self.check_overlap_in_source(x, y, ra_pos, dec_pos, r_min, N) return np.array(x_pos), np.array(y_pos), np.array(sigma), np.array(ra_pos_select), np.array(dec_pos_select) def clump_response(self, x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=1): """ response matrix of gaussian clumps :param x_source: :param y_source: :param x_pos: :param y_pos: :param sigma: :return: """ num_param = len(sigma) A = np.zeros((num_param, numPix**2)) for i in range(num_param): image = self.gaussian.function(x_source, y_source, amp=1, sigma_x=sigma[i], sigma_y=sigma[i], center_x=x_pos[i], center_y=y_pos[i]) image = util.array2image(image) image = self.re_size_convolve(image, subgrid_res, kwargs_psf) response = util.image2array(image*mask) A[i, :] = response return A def shapelet_response(self, x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, num_order=1, mask=1): """ returns response matrix for general inputs :param x_grid: :param y_grid: :param kwargs_lens: :param kwargs_source: :param kwargs_psf: :param kwargs_lens_light: :param kwargs_else: :param numPix: :param deltaPix: :param subgrid_res: :return: """ num_clump = len(x_pos) numShapelets = (num_order+2)*(num_order+1)/2 num_param = numShapelets*num_clump A = np.zeros((num_param, numPix**2)) k = 0 for j in range(0, num_clump): H_x, H_y = self.shapelets.pre_calc(x_source, y_source, sigma[j], num_order, x_pos[j], y_pos[j]) n1 = 0 n2 = 0 for i in range(0, numShapelets): kwargs_source_shapelet = {'center_x': x_pos[j], 'center_y': y_pos[j], 'n1': n1, 'n2': n2, 'beta': sigma[j], 'amp': 1} image = self.shapelets.function(H_x, H_y, **kwargs_source_shapelet) image = util.array2image(image) image = self.re_size_convolve(image, numPix, deltaPix, subgrid_res, kwargs_psf) response = util.image2array(image*mask) A[k, :] = response if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 k += 1 return A def make_image_iteration(self, x_grid, y_grid, kwargs_lens, kwargs_source, kwargs_psf, kwargs_lens_light, kwargs_else, numPix, deltaPix, subgrid_res, inv_bool=False, no_lens=False): map_error = self.kwargs_options.get('error_map', False) num_order = self.kwargs_options.get('shapelet_order', 0) data = self.kwargs_data['image_data'] mask = self.kwargs_options['mask'] num_clumps = self.kwargs_options.get('num_clumps', 0) clump_scale = self.kwargs_options.get('clump_scale', 1) if no_lens is True: x_source, y_source = x_grid, y_grid else: x_source, y_source = self.mapping_IS(x_grid, y_grid, kwargs_else, **kwargs_lens) A, error_map, _ = self.get_response_matrix(x_grid, y_grid, x_source, y_source, kwargs_lens, kwargs_source, kwargs_psf, kwargs_lens_light, kwargs_else, numPix, deltaPix, subgrid_res, num_order, mask, map_error=map_error, shapelets_off=self.kwargs_options.get('shapelets_off', False)) d = util.image2array(data*mask) param, cov_param, wls_model = self.DeLens.get_param_WLS(A.T, 1/(self.C_D+error_map), d, inv_bool=inv_bool) if num_clumps > 0: residuals = (wls_model-d)/np.sqrt(self.C_D+error_map) #ra_pos, dec_pos = self.find_max_residuals(residuals, self.ra_coords, self.dec_coords, num_clumps) #x_pos, y_pos, sigma = self.position_size_estimate(ra_pos, dec_pos, kwargs_lens, kwargs_else, deltaPix, clump_scale) x_pos, y_pos, sigma, ra_pos, dec_pos = self.find_clump_param(residuals, self.ra_coords, self.dec_coords, num_clumps, kwargs_lens, kwargs_else, deltaPix, clump_scale) if self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Gaussian': A_clump = self.clump_response(x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=mask) elif self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': A_clump = self.shapelet_response(x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=mask, num_order=self.kwargs_options.get('num_order_clump', 1)) else: raise ValueError("clump_type %s not valid." %(self.kwargs_options['source_clump_type'])) A = np.append(A, A_clump, axis=0) param, cov_param, wls_model = self.DeLens.get_param_WLS(A.T, 1/(self.C_D+error_map), d, inv_bool=inv_bool) else: x_pos, y_pos, sigma, ra_pos, dec_pos = None, None, None, None, None grid_final = util.array2image(wls_model) if not self.kwargs_options['source_type'] == 'NONE': kwargs_source['I0_sersic'] = param[0] i = 1 else: i = 0 kwargs_lens_light['I0_sersic'] = param[i] if self.kwargs_options['lens_light_type'] == 'TRIPLE_SERSIC': kwargs_lens_light['I0_3'] = param[i+1] kwargs_lens_light['I0_2'] = param[i+2] if map_error is True: error_map = util.array2image(error_map) else: error_map = np.zeros_like(grid_final) return grid_final, error_map, cov_param, param, x_pos, y_pos, sigma, ra_pos, dec_pos def get_source_iter(self, param, num_order, beta, x_grid, y_grid, kwargs_source, x_pos, y_pos, sigma, cov_param=None): """ :param param: :param num_order: :param beta: :return: """ if not self.kwargs_options['source_type'] == 'NONE': new = {'I0_sersic': param[0], 'center_x': 0, 'center_y': 0} kwargs_source_new = kwargs_source.copy() kwargs_source_new.update(new) source = self.get_surface_brightness(x_grid, y_grid, **kwargs_source_new) else: source = np.zeros_like(x_grid) x_center = kwargs_source['center_x'] y_center = kwargs_source['center_y'] num_clumps = self.kwargs_options.get('num_clumps', 0) num_param_shapelets = (num_order+2)*(num_order+1)/2 if not self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': numShapelets_clump = 1 else: num_order_clump = self.kwargs_options.get('num_order_clump', 1) numShapelets_clump = (num_order_clump+2)*(num_order_clump+1)/2 shapelets = Shapelets(interpolation=False, precalc=False) error_map_source = np.zeros_like(x_grid) n1 = 0 n2 = 0 basis_functions = np.zeros((len(param), len(x_grid))) for i in range(len(param)-num_param_shapelets-num_clumps*numShapelets_clump, len(param)-num_clumps*numShapelets_clump): source += shapelets.function(x_grid, y_grid, param[i], beta, n1, n2, center_x=0, center_y=0) basis_functions[i, :] = shapelets.function(x_grid, y_grid, 1, beta, n1, n2, center_x=0, center_y=0) if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 if self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Gaussian': for i in range(num_clumps): j = i + len(param) - num_clumps*numShapelets_clump source += self.gaussian.function(x_grid, y_grid, amp=param[j], sigma_x=sigma[i], sigma_y=sigma[i], center_x=x_pos[i]-x_center, center_y=y_pos[i]-y_center) elif self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': i = len(param)-num_clumps*numShapelets_clump for j in range(0, num_clumps): H_x, H_y = self.shapelets.pre_calc(x_grid, y_grid, sigma[j], num_order, x_pos[j]-x_center, y_pos[j]-y_center) n1 = 0 n2 = 0 for k in range(0, numShapelets_clump): kwargs_source_shapelet = {'center_x': x_pos[j], 'center_y': y_pos[j], 'n1': n1, 'n2': n2, 'beta': sigma[j], 'amp': param[i]} source += self.shapelets.function(H_x, H_y, **kwargs_source_shapelet) if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 i += 1 else: raise ValueError("clump_type %s not valid." %(self.kwargs_options['source_clump_type'])) if cov_param is not None: error_map_source = np.zeros_like(x_grid) for i in range(len(error_map_source)): error_map_source[i] = basis_functions[:, i].T.dot(cov_param).dot(basis_functions[:,i]) return util.array2image(source), util.array2image(error_map_source) def position_size_estimate(self, ra_pos, dec_pos, kwargs_lens, kwargs_else, delta, scale=1): """ estimate the magnification at the positions and define resolution limit :param ra_pos: :param dec_pos: :param kwargs_lens: :param kwargs_else: :return: """ x, y = self.LensModel.ray_shooting(ra_pos, dec_pos, kwargs_else, **kwargs_lens) d_x, d_y = util.points_on_circle(delta*2, 10) x_s, y_s = self.LensModel.ray_shooting(ra_pos + d_x, dec_pos + d_y, kwargs_else, **kwargs_lens) x_m = np.mean(x_s) y_m = np.mean(y_s) r_m = np.sqrt((x_s - x_m) ** 2 + (y_s - y_m) ** 2) r_min = np.sqrt(r_m.min(axis=0)*r_m.max(axis=0))/2 * scale return x, y, r_min

48.193431

290

0.605225

__author__ = 'sibirrer' import numpy as np import lenstronomy.Util.util as util from lenstronomy.LightModel.Profiles.shapelets import Shapelets from lenstronomy.ImSim.image_model import ImageModel class MakeImageIter(ImageModel): def find_max_residuals(self, residuals, ra_coords, dec_coords, N): ra_mins, dec_mins, values = util.neighborSelect(residuals, ra_coords, dec_coords) ra_pos = util.selectBest(np.array(ra_mins), -np.array(values), N, highest=True) dec_pos = util.selectBest(np.array(dec_mins), -np.array(values), N, highest=True) return ra_pos, dec_pos def check_overlap_in_source(self, x, y, ra_pos, dec_pos, r_min, N): n = len(x) count = 0 i = 0 x_pos_select = [] y_pos_select = [] ra_pos_select = [] dec_pos_select = [] r_min_select = [] while count < N and i < n: if i == 0: x_pos_select.append(x[i]) y_pos_select.append(y[i]) ra_pos_select.append(ra_pos[i]) dec_pos_select.append(dec_pos[i]) r_min_select.append(r_min[i]) count += 1 else: r_delta = np.sqrt((x - x[i])**2 + (y - y[i])**2) if np.min(r_delta[0:i]) > r_min[i]: x_pos_select.append(x[i]) y_pos_select.append(y[i]) ra_pos_select.append(ra_pos[i]) dec_pos_select.append(dec_pos[i]) r_min_select.append(r_min[i]) count += 1 i += 1 return x_pos_select, y_pos_select, r_min_select, ra_pos_select, dec_pos_select def find_clump_param(self, residuals, ra_coords, dec_coords, N, kwargs_lens, kwargs_else, deltaPix, clump_scale): ra_pos, dec_pos = self.find_max_residuals(residuals, ra_coords, dec_coords, 5*N) n = len(ra_pos) x = np.zeros(n) y = np.zeros(n) r_min = np.zeros(n) for i in range(n): x[i], y[i], r_min[i] = self.position_size_estimate(ra_pos[i], dec_pos[i], kwargs_lens, kwargs_else, deltaPix, scale=clump_scale) x_pos, y_pos, sigma, ra_pos_select, dec_pos_select = self.check_overlap_in_source(x, y, ra_pos, dec_pos, r_min, N) return np.array(x_pos), np.array(y_pos), np.array(sigma), np.array(ra_pos_select), np.array(dec_pos_select) def clump_response(self, x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=1): num_param = len(sigma) A = np.zeros((num_param, numPix**2)) for i in range(num_param): image = self.gaussian.function(x_source, y_source, amp=1, sigma_x=sigma[i], sigma_y=sigma[i], center_x=x_pos[i], center_y=y_pos[i]) image = util.array2image(image) image = self.re_size_convolve(image, subgrid_res, kwargs_psf) response = util.image2array(image*mask) A[i, :] = response return A def shapelet_response(self, x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, num_order=1, mask=1): num_clump = len(x_pos) numShapelets = (num_order+2)*(num_order+1)/2 num_param = numShapelets*num_clump A = np.zeros((num_param, numPix**2)) k = 0 for j in range(0, num_clump): H_x, H_y = self.shapelets.pre_calc(x_source, y_source, sigma[j], num_order, x_pos[j], y_pos[j]) n1 = 0 n2 = 0 for i in range(0, numShapelets): kwargs_source_shapelet = {'center_x': x_pos[j], 'center_y': y_pos[j], 'n1': n1, 'n2': n2, 'beta': sigma[j], 'amp': 1} image = self.shapelets.function(H_x, H_y, **kwargs_source_shapelet) image = util.array2image(image) image = self.re_size_convolve(image, numPix, deltaPix, subgrid_res, kwargs_psf) response = util.image2array(image*mask) A[k, :] = response if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 k += 1 return A def make_image_iteration(self, x_grid, y_grid, kwargs_lens, kwargs_source, kwargs_psf, kwargs_lens_light, kwargs_else, numPix, deltaPix, subgrid_res, inv_bool=False, no_lens=False): map_error = self.kwargs_options.get('error_map', False) num_order = self.kwargs_options.get('shapelet_order', 0) data = self.kwargs_data['image_data'] mask = self.kwargs_options['mask'] num_clumps = self.kwargs_options.get('num_clumps', 0) clump_scale = self.kwargs_options.get('clump_scale', 1) if no_lens is True: x_source, y_source = x_grid, y_grid else: x_source, y_source = self.mapping_IS(x_grid, y_grid, kwargs_else, **kwargs_lens) A, error_map, _ = self.get_response_matrix(x_grid, y_grid, x_source, y_source, kwargs_lens, kwargs_source, kwargs_psf, kwargs_lens_light, kwargs_else, numPix, deltaPix, subgrid_res, num_order, mask, map_error=map_error, shapelets_off=self.kwargs_options.get('shapelets_off', False)) d = util.image2array(data*mask) param, cov_param, wls_model = self.DeLens.get_param_WLS(A.T, 1/(self.C_D+error_map), d, inv_bool=inv_bool) if num_clumps > 0: residuals = (wls_model-d)/np.sqrt(self.C_D+error_map) x_pos, y_pos, sigma, ra_pos, dec_pos = self.find_clump_param(residuals, self.ra_coords, self.dec_coords, num_clumps, kwargs_lens, kwargs_else, deltaPix, clump_scale) if self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Gaussian': A_clump = self.clump_response(x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=mask) elif self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': A_clump = self.shapelet_response(x_source, y_source, x_pos, y_pos, sigma, deltaPix, numPix, subgrid_res, kwargs_psf, mask=mask, num_order=self.kwargs_options.get('num_order_clump', 1)) else: raise ValueError("clump_type %s not valid." %(self.kwargs_options['source_clump_type'])) A = np.append(A, A_clump, axis=0) param, cov_param, wls_model = self.DeLens.get_param_WLS(A.T, 1/(self.C_D+error_map), d, inv_bool=inv_bool) else: x_pos, y_pos, sigma, ra_pos, dec_pos = None, None, None, None, None grid_final = util.array2image(wls_model) if not self.kwargs_options['source_type'] == 'NONE': kwargs_source['I0_sersic'] = param[0] i = 1 else: i = 0 kwargs_lens_light['I0_sersic'] = param[i] if self.kwargs_options['lens_light_type'] == 'TRIPLE_SERSIC': kwargs_lens_light['I0_3'] = param[i+1] kwargs_lens_light['I0_2'] = param[i+2] if map_error is True: error_map = util.array2image(error_map) else: error_map = np.zeros_like(grid_final) return grid_final, error_map, cov_param, param, x_pos, y_pos, sigma, ra_pos, dec_pos def get_source_iter(self, param, num_order, beta, x_grid, y_grid, kwargs_source, x_pos, y_pos, sigma, cov_param=None): if not self.kwargs_options['source_type'] == 'NONE': new = {'I0_sersic': param[0], 'center_x': 0, 'center_y': 0} kwargs_source_new = kwargs_source.copy() kwargs_source_new.update(new) source = self.get_surface_brightness(x_grid, y_grid, **kwargs_source_new) else: source = np.zeros_like(x_grid) x_center = kwargs_source['center_x'] y_center = kwargs_source['center_y'] num_clumps = self.kwargs_options.get('num_clumps', 0) num_param_shapelets = (num_order+2)*(num_order+1)/2 if not self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': numShapelets_clump = 1 else: num_order_clump = self.kwargs_options.get('num_order_clump', 1) numShapelets_clump = (num_order_clump+2)*(num_order_clump+1)/2 shapelets = Shapelets(interpolation=False, precalc=False) error_map_source = np.zeros_like(x_grid) n1 = 0 n2 = 0 basis_functions = np.zeros((len(param), len(x_grid))) for i in range(len(param)-num_param_shapelets-num_clumps*numShapelets_clump, len(param)-num_clumps*numShapelets_clump): source += shapelets.function(x_grid, y_grid, param[i], beta, n1, n2, center_x=0, center_y=0) basis_functions[i, :] = shapelets.function(x_grid, y_grid, 1, beta, n1, n2, center_x=0, center_y=0) if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 if self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Gaussian': for i in range(num_clumps): j = i + len(param) - num_clumps*numShapelets_clump source += self.gaussian.function(x_grid, y_grid, amp=param[j], sigma_x=sigma[i], sigma_y=sigma[i], center_x=x_pos[i]-x_center, center_y=y_pos[i]-y_center) elif self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets': i = len(param)-num_clumps*numShapelets_clump for j in range(0, num_clumps): H_x, H_y = self.shapelets.pre_calc(x_grid, y_grid, sigma[j], num_order, x_pos[j]-x_center, y_pos[j]-y_center) n1 = 0 n2 = 0 for k in range(0, numShapelets_clump): kwargs_source_shapelet = {'center_x': x_pos[j], 'center_y': y_pos[j], 'n1': n1, 'n2': n2, 'beta': sigma[j], 'amp': param[i]} source += self.shapelets.function(H_x, H_y, **kwargs_source_shapelet) if n1 == 0: n1 = n2 + 1 n2 = 0 else: n1 -= 1 n2 += 1 i += 1 else: raise ValueError("clump_type %s not valid." %(self.kwargs_options['source_clump_type'])) if cov_param is not None: error_map_source = np.zeros_like(x_grid) for i in range(len(error_map_source)): error_map_source[i] = basis_functions[:, i].T.dot(cov_param).dot(basis_functions[:,i]) return util.array2image(source), util.array2image(error_map_source) def position_size_estimate(self, ra_pos, dec_pos, kwargs_lens, kwargs_else, delta, scale=1): x, y = self.LensModel.ray_shooting(ra_pos, dec_pos, kwargs_else, **kwargs_lens) d_x, d_y = util.points_on_circle(delta*2, 10) x_s, y_s = self.LensModel.ray_shooting(ra_pos + d_x, dec_pos + d_y, kwargs_else, **kwargs_lens) x_m = np.mean(x_s) y_m = np.mean(y_s) r_m = np.sqrt((x_s - x_m) ** 2 + (y_s - y_m) ** 2) r_min = np.sqrt(r_m.min(axis=0)*r_m.max(axis=0))/2 * scale return x, y, r_min

true

1c3127322990bde39d34eb74ce48ed98c6892598

12,778

py

Python

src/sagemaker/predictor.py

yifeim/sagemaker-python-sdk

d60f8d3889b4bbada745ff67ce4d0aae2013285a

[ "Apache-2.0" ]

null

src/sagemaker/predictor.py

yifeim/sagemaker-python-sdk

d60f8d3889b4bbada745ff67ce4d0aae2013285a

[ "Apache-2.0" ]

null

src/sagemaker/predictor.py

yifeim/sagemaker-python-sdk

d60f8d3889b4bbada745ff67ce4d0aae2013285a

[ "Apache-2.0" ]

1

2020-07-30T13:26:45.000Z

# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. 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. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import print_function, absolute_import import codecs import csv import json import numpy as np import six from six import StringIO, BytesIO from sagemaker.content_types import CONTENT_TYPE_JSON, CONTENT_TYPE_CSV, CONTENT_TYPE_NPY from sagemaker.session import Session class RealTimePredictor(object): """Make prediction requests to an Amazon SageMaker endpoint. """ def __init__(self, endpoint, sagemaker_session=None, serializer=None, deserializer=None, content_type=None, accept=None): """Initialize a ``RealTimePredictor``. Behavior for serialization of input data and deserialization of result data can be configured through initializer arguments. If not specified, a sequence of bytes is expected and the API sends it in the request body without modifications. In response, the API returns the sequence of bytes from the prediction result without any modifications. Args: endpoint (str): Name of the Amazon SageMaker endpoint to which requests are sent. sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain. serializer (callable): Accepts a single argument, the input data, and returns a sequence of bytes. It may provide a ``content_type`` attribute that defines the endpoint request content type. If not specified, a sequence of bytes is expected for the data. deserializer (callable): Accepts two arguments, the result data and the response content type, and returns a sequence of bytes. It may provide a ``content_type`` attribute that defines the endpoint response's "Accept" content type. If not specified, a sequence of bytes is expected for the data. content_type (str): The invocation's "ContentType", overriding any ``content_type`` from the serializer (default: None). accept (str): The invocation's "Accept", overriding any accept from the deserializer (default: None). """ self.endpoint = endpoint self.sagemaker_session = sagemaker_session or Session() self.serializer = serializer self.deserializer = deserializer self.content_type = content_type or getattr(serializer, 'content_type', None) self.accept = accept or getattr(deserializer, 'accept', None) def predict(self, data, initial_args=None): """Return the inference from the specified endpoint. Args: data (object): Input data for which you want the model to provide inference. If a serializer was specified when creating the RealTimePredictor, the result of the serializer is sent as input data. Otherwise the data must be sequence of bytes, and the predict method then sends the bytes in the request body as is. initial_args (dict[str,str]): Optional. Default arguments for boto3 ``invoke_endpoint`` call. Default is None (no default arguments). Returns: object: Inference for the given input. If a deserializer was specified when creating the RealTimePredictor, the result of the deserializer is returned. Otherwise the response returns the sequence of bytes as is. """ request_args = self._create_request_args(data, initial_args) response = self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint(**request_args) return self._handle_response(response) def _handle_response(self, response): response_body = response['Body'] if self.deserializer is not None: # It's the deserializer's responsibility to close the stream return self.deserializer(response_body, response['ContentType']) data = response_body.read() response_body.close() return data def _create_request_args(self, data, initial_args=None): args = dict(initial_args) if initial_args else {} if 'EndpointName' not in args: args['EndpointName'] = self.endpoint if self.content_type and 'ContentType' not in args: args['ContentType'] = self.content_type if self.accept and 'Accept' not in args: args['Accept'] = self.accept if self.serializer is not None: data = self.serializer(data) args['Body'] = data return args def delete_endpoint(self): """Delete the Amazon SageMaker endpoint backing this predictor. """ self.sagemaker_session.delete_endpoint(self.endpoint) class _CsvSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_CSV def __call__(self, data): """Take data of various data formats and serialize them into CSV. Args: data (object): Data to be serialized. Returns: object: Sequence of bytes to be used for the request body. """ # For inputs which represent multiple "rows", the result should be newline-separated CSV rows if _is_mutable_sequence_like(data) and len(data) > 0 and _is_sequence_like(data[0]): return '\n'.join([_CsvSerializer._serialize_row(row) for row in data]) return _CsvSerializer._serialize_row(data) @staticmethod def _serialize_row(data): # Don't attempt to re-serialize a string if isinstance(data, str): return data if isinstance(data, np.ndarray): data = np.ndarray.flatten(data) if hasattr(data, '__len__'): if len(data): return _csv_serialize_python_array(data) else: raise ValueError("Cannot serialize empty array") # files and buffers if hasattr(data, 'read'): return _csv_serialize_from_buffer(data) raise ValueError("Unable to handle input format: ", type(data)) def _csv_serialize_python_array(data): return _csv_serialize_object(data) def _csv_serialize_from_buffer(buff): return buff.read() def _csv_serialize_object(data): csv_buffer = StringIO() csv_writer = csv.writer(csv_buffer, delimiter=',') csv_writer.writerow(data) return csv_buffer.getvalue().rstrip('\r\n') csv_serializer = _CsvSerializer() def _is_mutable_sequence_like(obj): return _is_sequence_like(obj) and hasattr(obj, '__setitem__') def _is_sequence_like(obj): # Need to explicitly check on str since str lacks the iterable magic methods in Python 2 return (hasattr(obj, '__iter__') and hasattr(obj, '__getitem__')) or isinstance(obj, str) def _row_to_csv(obj): if isinstance(obj, str): return obj return ','.join(obj) class BytesDeserializer(object): """Return the response as an undecoded array of bytes. Args: accept (str): The Accept header to send to the server (optional). """ def __init__(self, accept=None): self.accept = accept def __call__(self, stream, content_type): try: return stream.read() finally: stream.close() class StringDeserializer(object): """Return the response as a decoded string. Args: encoding (str): The string encoding to use (default=utf-8). accept (str): The Accept header to send to the server (optional). """ def __init__(self, encoding='utf-8', accept=None): self.encoding = encoding self.accept = accept def __call__(self, stream, content_type): try: return stream.read().decode(self.encoding) finally: stream.close() class StreamDeserializer(object): """Returns the tuple of the response stream and the content-type of the response. It is the receivers responsibility to close the stream when they're done reading the stream. Args: accept (str): The Accept header to send to the server (optional). """ def __init__(self, accept=None): self.accept = accept def __call__(self, stream, content_type): return (stream, content_type) class _JsonSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_JSON def __call__(self, data): """Take data of various formats and serialize them into the expected request body. This uses information about supported input formats for the deployed model. Args: data (object): Data to be serialized. Returns: object: Serialized data used for the request. """ if isinstance(data, dict): # convert each value in dict from a numpy array to a list if necessary, so they can be json serialized return json.dumps({k: _ndarray_to_list(v) for k, v in six.iteritems(data)}) # files and buffers if hasattr(data, 'read'): return _json_serialize_from_buffer(data) return json.dumps(_ndarray_to_list(data)) json_serializer = _JsonSerializer() def _ndarray_to_list(data): return data.tolist() if isinstance(data, np.ndarray) else data def _json_serialize_from_buffer(buff): return buff.read() class _JsonDeserializer(object): def __init__(self): self.accept = CONTENT_TYPE_JSON def __call__(self, stream, content_type): """Decode a JSON object into the corresponding Python object. Args: stream (stream): The response stream to be deserialized. content_type (str): The content type of the response. Returns: object: Body of the response deserialized into a JSON object. """ try: return json.load(codecs.getreader('utf-8')(stream)) finally: stream.close() json_deserializer = _JsonDeserializer() class _NumpyDeserializer(object): def __init__(self, accept=CONTENT_TYPE_NPY, dtype=None): self.accept = accept self.dtype = dtype def __call__(self, stream, content_type=CONTENT_TYPE_NPY): """Decode from serialized data into a Numpy array. Args: stream (stream): The response stream to be deserialized. content_type (str): The content type of the response. Can accept CSV, JSON, or NPY data. Returns: object: Body of the response deserialized into a Numpy array. """ try: if content_type == CONTENT_TYPE_CSV: return np.genfromtxt(codecs.getreader('utf-8')(stream), delimiter=',', dtype=self.dtype) elif content_type == CONTENT_TYPE_JSON: return np.array(json.load(codecs.getreader('utf-8')(stream)), dtype=self.dtype) elif content_type == CONTENT_TYPE_NPY: return np.load(BytesIO(stream.read())) finally: stream.close() numpy_deserializer = _NumpyDeserializer() class _NPYSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_NPY def __call__(self, data, dtype=None): """Serialize data into the request body in NPY format. Args: data (object): Data to be serialized. Can be a numpy array, list, file, or buffer. Returns: object: NPY serialized data used for the request. """ if isinstance(data, np.ndarray): if not data.size > 0: raise ValueError("empty array can't be serialized") return _npy_serialize(data) if isinstance(data, list): if not len(data) > 0: raise ValueError("empty array can't be serialized") return _npy_serialize(np.array(data, dtype)) # files and buffers. Assumed to hold npy-formatted data. if hasattr(data, 'read'): return data.read() return _npy_serialize(np.array(data)) def _npy_serialize(data): buffer = BytesIO() np.save(buffer, data) return buffer.getvalue() npy_serializer = _NPYSerializer()

35.201102

120

0.659649

from __future__ import print_function, absolute_import import codecs import csv import json import numpy as np import six from six import StringIO, BytesIO from sagemaker.content_types import CONTENT_TYPE_JSON, CONTENT_TYPE_CSV, CONTENT_TYPE_NPY from sagemaker.session import Session class RealTimePredictor(object): def __init__(self, endpoint, sagemaker_session=None, serializer=None, deserializer=None, content_type=None, accept=None): self.endpoint = endpoint self.sagemaker_session = sagemaker_session or Session() self.serializer = serializer self.deserializer = deserializer self.content_type = content_type or getattr(serializer, 'content_type', None) self.accept = accept or getattr(deserializer, 'accept', None) def predict(self, data, initial_args=None): request_args = self._create_request_args(data, initial_args) response = self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint(**request_args) return self._handle_response(response) def _handle_response(self, response): response_body = response['Body'] if self.deserializer is not None: return self.deserializer(response_body, response['ContentType']) data = response_body.read() response_body.close() return data def _create_request_args(self, data, initial_args=None): args = dict(initial_args) if initial_args else {} if 'EndpointName' not in args: args['EndpointName'] = self.endpoint if self.content_type and 'ContentType' not in args: args['ContentType'] = self.content_type if self.accept and 'Accept' not in args: args['Accept'] = self.accept if self.serializer is not None: data = self.serializer(data) args['Body'] = data return args def delete_endpoint(self): self.sagemaker_session.delete_endpoint(self.endpoint) class _CsvSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_CSV def __call__(self, data): if _is_mutable_sequence_like(data) and len(data) > 0 and _is_sequence_like(data[0]): return '\n'.join([_CsvSerializer._serialize_row(row) for row in data]) return _CsvSerializer._serialize_row(data) @staticmethod def _serialize_row(data): if isinstance(data, str): return data if isinstance(data, np.ndarray): data = np.ndarray.flatten(data) if hasattr(data, '__len__'): if len(data): return _csv_serialize_python_array(data) else: raise ValueError("Cannot serialize empty array") # files and buffers if hasattr(data, 'read'): return _csv_serialize_from_buffer(data) raise ValueError("Unable to handle input format: ", type(data)) def _csv_serialize_python_array(data): return _csv_serialize_object(data) def _csv_serialize_from_buffer(buff): return buff.read() def _csv_serialize_object(data): csv_buffer = StringIO() csv_writer = csv.writer(csv_buffer, delimiter=',') csv_writer.writerow(data) return csv_buffer.getvalue().rstrip('\r\n') csv_serializer = _CsvSerializer() def _is_mutable_sequence_like(obj): return _is_sequence_like(obj) and hasattr(obj, '__setitem__') def _is_sequence_like(obj): # Need to explicitly check on str since str lacks the iterable magic methods in Python 2 return (hasattr(obj, '__iter__') and hasattr(obj, '__getitem__')) or isinstance(obj, str) def _row_to_csv(obj): if isinstance(obj, str): return obj return ','.join(obj) class BytesDeserializer(object): def __init__(self, accept=None): self.accept = accept def __call__(self, stream, content_type): try: return stream.read() finally: stream.close() class StringDeserializer(object): def __init__(self, encoding='utf-8', accept=None): self.encoding = encoding self.accept = accept def __call__(self, stream, content_type): try: return stream.read().decode(self.encoding) finally: stream.close() class StreamDeserializer(object): def __init__(self, accept=None): self.accept = accept def __call__(self, stream, content_type): return (stream, content_type) class _JsonSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_JSON def __call__(self, data): if isinstance(data, dict): # convert each value in dict from a numpy array to a list if necessary, so they can be json serialized return json.dumps({k: _ndarray_to_list(v) for k, v in six.iteritems(data)}) # files and buffers if hasattr(data, 'read'): return _json_serialize_from_buffer(data) return json.dumps(_ndarray_to_list(data)) json_serializer = _JsonSerializer() def _ndarray_to_list(data): return data.tolist() if isinstance(data, np.ndarray) else data def _json_serialize_from_buffer(buff): return buff.read() class _JsonDeserializer(object): def __init__(self): self.accept = CONTENT_TYPE_JSON def __call__(self, stream, content_type): try: return json.load(codecs.getreader('utf-8')(stream)) finally: stream.close() json_deserializer = _JsonDeserializer() class _NumpyDeserializer(object): def __init__(self, accept=CONTENT_TYPE_NPY, dtype=None): self.accept = accept self.dtype = dtype def __call__(self, stream, content_type=CONTENT_TYPE_NPY): try: if content_type == CONTENT_TYPE_CSV: return np.genfromtxt(codecs.getreader('utf-8')(stream), delimiter=',', dtype=self.dtype) elif content_type == CONTENT_TYPE_JSON: return np.array(json.load(codecs.getreader('utf-8')(stream)), dtype=self.dtype) elif content_type == CONTENT_TYPE_NPY: return np.load(BytesIO(stream.read())) finally: stream.close() numpy_deserializer = _NumpyDeserializer() class _NPYSerializer(object): def __init__(self): self.content_type = CONTENT_TYPE_NPY def __call__(self, data, dtype=None): if isinstance(data, np.ndarray): if not data.size > 0: raise ValueError("empty array can't be serialized") return _npy_serialize(data) if isinstance(data, list): if not len(data) > 0: raise ValueError("empty array can't be serialized") return _npy_serialize(np.array(data, dtype)) # files and buffers. Assumed to hold npy-formatted data. if hasattr(data, 'read'): return data.read() return _npy_serialize(np.array(data)) def _npy_serialize(data): buffer = BytesIO() np.save(buffer, data) return buffer.getvalue() npy_serializer = _NPYSerializer()

true

1c3127e03f7f9fb78734fc9dd2b6659ba51bc514

20,553

py

Python

tests/test_indefinite_freeze_attack.py

KainaatSingh/tuf

08f48d52df95aaaa44ab3f3143c3f148cd65f3aa

[ "Apache-2.0", "MIT" ]

null

tests/test_indefinite_freeze_attack.py

KainaatSingh/tuf

08f48d52df95aaaa44ab3f3143c3f148cd65f3aa

[ "Apache-2.0", "MIT" ]

null

tests/test_indefinite_freeze_attack.py

KainaatSingh/tuf

08f48d52df95aaaa44ab3f3143c3f148cd65f3aa

[ "Apache-2.0", "MIT" ]

null

#!/usr/bin/env python # Copyright 2012 - 2017, New York University and the TUF contributors # SPDX-License-Identifier: MIT OR Apache-2.0 """ <Program Name> test_indefinite_freeze_attack.py <Author> Konstantin Andrianov. <Started> March 10, 2012. April 1, 2014. Refactored to use the 'unittest' module (test conditions in code, rather than verifying text output), use pre-generated repository files, and discontinue use of the old repository tools. -vladimir.v.diaz March 9, 2016. Additional test added relating to issue: https://github.com/theupdateframework/tuf/issues/322 If a metadata file is not updated (no indication of a new version available), the expiration of the pre-existing, locally trusted metadata must still be detected. This additional test complains if such does not occur, and accompanies code in tuf.client.updater:refresh() to detect it. -sebastien.awwad <Copyright> See LICENSE-MIT OR LICENSE for licensing information. <Purpose> Simulate an indefinite freeze attack. In an indefinite freeze attack, attacker is able to respond to client's requests with the same, outdated metadata without the client being aware. """ # Help with Python 3 compatibility, where the print statement is a function, an # implicit relative import is invalid, and the '/' operator performs true # division. Example: print 'hello world' raises a 'SyntaxError' exception. from __future__ import print_function from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import datetime import os import time import tempfile import shutil import json import logging import unittest import sys from urllib import request if sys.version_info >= (3, 3): import unittest.mock as mock else: import mock import tuf.formats import tuf.log import tuf.client.updater as updater import tuf.repository_tool as repo_tool import tuf.unittest_toolbox as unittest_toolbox import tuf.roledb import tuf.keydb import tuf.exceptions from tests import utils import securesystemslib # The repository tool is imported and logs console messages by default. Disable # console log messages generated by this unit test. repo_tool.disable_console_log_messages() logger = logging.getLogger(__name__) class TestIndefiniteFreezeAttack(unittest_toolbox.Modified_TestCase): @classmethod def setUpClass(cls): # Create a temporary directory to store the repository, metadata, and target # files. 'temporary_directory' must be deleted in TearDownModule() so that # temporary files are always removed, even when exceptions occur. cls.temporary_directory = tempfile.mkdtemp(dir=os.getcwd()) # Launch a SimpleHTTPServer (serves files in the current directory). # Test cases will request metadata and target files that have been # pre-generated in 'tuf/tests/repository_data', which will be served by the # SimpleHTTPServer launched here. The test cases of this unit test assume # the pre-generated metadata files have a specific structure, such # as a delegated role 'targets/role1', three target files, five key files, # etc. cls.server_process_handler = utils.TestServerProcess(log=logger) @classmethod def tearDownClass(cls): # Cleans the resources and flush the logged lines (if any). cls.server_process_handler.clean() # Remove the temporary repository directory, which should contain all the # metadata, targets, and key files generated of all the test cases. shutil.rmtree(cls.temporary_directory) def setUp(self): # We are inheriting from custom class. unittest_toolbox.Modified_TestCase.setUp(self) self.repository_name = 'test_repository1' # Copy the original repository files provided in the test folder so that # any modifications made to repository files are restricted to the copies. # The 'repository_data' directory is expected to exist in 'tuf/tests/'. original_repository_files = os.path.join(os.getcwd(), 'repository_data') temporary_repository_root = \ self.make_temp_directory(directory=self.temporary_directory) # The original repository, keystore, and client directories will be copied # for each test case. original_repository = os.path.join(original_repository_files, 'repository') original_client = os.path.join(original_repository_files, 'client') original_keystore = os.path.join(original_repository_files, 'keystore') # Save references to the often-needed client repository directories. # Test cases need these references to access metadata and target files. self.repository_directory = \ os.path.join(temporary_repository_root, 'repository') self.client_directory = os.path.join(temporary_repository_root, 'client') self.keystore_directory = os.path.join(temporary_repository_root, 'keystore') # Copy the original 'repository', 'client', and 'keystore' directories # to the temporary repository the test cases can use. shutil.copytree(original_repository, self.repository_directory) shutil.copytree(original_client, self.client_directory) shutil.copytree(original_keystore, self.keystore_directory) # Set the url prefix required by the 'tuf/client/updater.py' updater. # 'path/to/tmp/repository' -> 'localhost:8001/tmp/repository'. repository_basepath = self.repository_directory[len(os.getcwd()):] url_prefix = 'http://' + utils.TEST_HOST_ADDRESS + ':' \ + str(self.server_process_handler.port) + repository_basepath # Setting 'tuf.settings.repository_directory' with the temporary client # directory copied from the original repository files. tuf.settings.repositories_directory = self.client_directory self.repository_mirrors = {'mirror1': {'url_prefix': url_prefix, 'metadata_path': 'metadata', 'targets_path': 'targets'}} # Create the repository instance. The test cases will use this client # updater to refresh metadata, fetch target files, etc. self.repository_updater = updater.Updater(self.repository_name, self.repository_mirrors) def tearDown(self): # Modified_TestCase.tearDown() automatically deletes temporary files and # directories that may have been created during each test case. unittest_toolbox.Modified_TestCase.tearDown(self) tuf.roledb.clear_roledb(clear_all=True) tuf.keydb.clear_keydb(clear_all=True) # Logs stdout and stderr from the sever subprocess. self.server_process_handler.flush_log() def test_without_tuf(self): # Without TUF, Test 1 and Test 2 are functionally equivalent, so we skip # Test 1 and only perform Test 2. # # Test 1: If we find that the timestamp acquired from a mirror indicates # that there is no new snapshot file, and our current snapshot # file is expired, is it recognized as such? # Test 2: If an expired timestamp is downloaded, is it recognized as such? # Test 2 Begin: # # 'timestamp.json' specifies the latest version of the repository files. A # client should only accept the same version of this file up to a certain # point, or else it cannot detect that new files are available for # download. Modify the repository's timestamp.json' so that it expires # soon, copy it over to the client, and attempt to re-fetch the same # expired version. # # A non-TUF client (without a way to detect when metadata has expired) is # expected to download the same version, and thus the same outdated files. # Verify that the downloaded 'timestamp.json' contains the same file size # and hash as the one available locally. timestamp_path = os.path.join(self.repository_directory, 'metadata', 'timestamp.json') timestamp_metadata = securesystemslib.util.load_json_file(timestamp_path) expiry_time = time.time() - 10 expires = tuf.formats.unix_timestamp_to_datetime(int(expiry_time)) expires = expires.isoformat() + 'Z' timestamp_metadata['signed']['expires'] = expires tuf.formats.check_signable_object_format(timestamp_metadata) with open(timestamp_path, 'wb') as file_object: # Explicitly specify the JSON separators for Python 2 + 3 consistency. timestamp_content = \ json.dumps(timestamp_metadata, indent=1, separators=(',', ': '), sort_keys=True).encode('utf-8') file_object.write(timestamp_content) client_timestamp_path = os.path.join(self.client_directory, 'timestamp.json') shutil.copy(timestamp_path, client_timestamp_path) length, hashes = securesystemslib.util.get_file_details(timestamp_path) fileinfo = tuf.formats.make_targets_fileinfo(length, hashes) url_prefix = self.repository_mirrors['mirror1']['url_prefix'] url_file = os.path.join(url_prefix, 'metadata', 'timestamp.json') request.urlretrieve(url_file.replace('\\', '/'), client_timestamp_path) length, hashes = securesystemslib.util.get_file_details(client_timestamp_path) download_fileinfo = tuf.formats.make_targets_fileinfo(length, hashes) # Verify 'download_fileinfo' is equal to the current local file. self.assertEqual(download_fileinfo, fileinfo) def test_with_tuf(self): # Three tests are conducted here. # # Test 1: If we find that the timestamp acquired from a mirror indicates # that there is no new snapshot file, and our current snapshot # file is expired, is it recognized as such? # Test 2: If an expired timestamp is downloaded, is it recognized as such? # Test 3: If an expired Snapshot is downloaded, is it (1) rejected? (2) the # local Snapshot file deleted? (3) and is the client able to recover when # given a new, valid Snapshot? # Test 1 Begin: # # Addresses this issue: https://github.com/theupdateframework/tuf/issues/322 # # If time has passed and our snapshot or targets role is expired, and # the mirror whose timestamp we fetched doesn't indicate the existence of a # new snapshot version, we still need to check that it's expired and notify # the software update system / application / user. This test creates that # scenario. The correct behavior is to raise an exception. # # Background: Expiration checks (updater._ensure_not_expired) were # previously conducted when the metadata file was downloaded. If no new # metadata file was downloaded, no expiry check would occur. In particular, # while root was checked for expiration at the beginning of each # updater.refresh() cycle, and timestamp was always checked because it was # always fetched, snapshot and targets were never checked if the user did # not receive evidence that they had changed. This bug allowed a class of # freeze attacks. # That bug was fixed and this test tests that fix going forward. # Modify the timestamp file on the remote repository. 'timestamp.json' # must be properly updated and signed with 'repository_tool.py', otherwise # the client will reject it as invalid metadata. # Load the repository repository = repo_tool.load_repository(self.repository_directory) # Load the snapshot and timestamp keys key_file = os.path.join(self.keystore_directory, 'timestamp_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) key_file = os.path.join(self.keystore_directory, 'snapshot_key') snapshot_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.snapshot.load_signing_key(snapshot_private) # sign snapshot with expiry in near future (earlier than e.g. timestamp) expiry = int(time.time() + 60*60) repository.snapshot.expiration = tuf.formats.unix_timestamp_to_datetime( expiry) repository.mark_dirty(['snapshot', 'timestamp']) repository.writeall() # And move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # Refresh metadata on the client. For this refresh, all data is not expired. logger.info('Test: Refreshing #1 - Initial metadata refresh occurring.') self.repository_updater.refresh() logger.info('Test: Refreshing #2 - refresh after local snapshot expiry.') # mock current time to one second after snapshot expiry mock_time = mock.Mock() mock_time.return_value = expiry + 1 with mock.patch('time.time', mock_time): try: self.repository_updater.refresh() # We expect this to fail! except tuf.exceptions.ExpiredMetadataError: logger.info('Test: Refresh #2 - failed as expected. Expired local' ' snapshot case generated a tuf.exceptions.ExpiredMetadataError' ' exception as expected. Test pass.') else: self.fail('TUF failed to detect expired stale snapshot metadata. Freeze' ' attack successful.') # Test 2 Begin: # # 'timestamp.json' specifies the latest version of the repository files. # A client should only accept the same version of this file up to a certain # point, or else it cannot detect that new files are available for download. # Modify the repository's 'timestamp.json' so that it is about to expire, # copy it over the to client, wait a moment until it expires, and attempt to # re-fetch the same expired version. # The same scenario as in test_without_tuf() is followed here, except with # a TUF client. The TUF client performs a refresh of top-level metadata, # which includes 'timestamp.json', and should detect a freeze attack if # the repository serves an outdated 'timestamp.json'. # Modify the timestamp file on the remote repository. 'timestamp.json' # must be properly updated and signed with 'repository_tool.py', otherwise # the client will reject it as invalid metadata. The resulting # 'timestamp.json' should be valid metadata, but expired (as intended). repository = repo_tool.load_repository(self.repository_directory) key_file = os.path.join(self.keystore_directory, 'timestamp_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) # Set timestamp metadata to expire soon. # We cannot set the timestamp expiration with # 'repository.timestamp.expiration = ...' with already-expired timestamp # metadata because of consistency checks that occur during that assignment. expiry_time = time.time() + 60*60 datetime_object = tuf.formats.unix_timestamp_to_datetime(int(expiry_time)) repository.timestamp.expiration = datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # mock current time to one second after timestamp expiry mock_time = mock.Mock() mock_time.return_value = expiry_time + 1 with mock.patch('time.time', mock_time): try: self.repository_updater.refresh() # We expect NoWorkingMirrorError. except tuf.exceptions.NoWorkingMirrorError as e: # Make sure the contained error is ExpiredMetadataError for mirror_url, mirror_error in e.mirror_errors.items(): self.assertTrue(isinstance(mirror_error, tuf.exceptions.ExpiredMetadataError)) else: self.fail('TUF failed to detect expired, stale timestamp metadata.' ' Freeze attack successful.') # Test 3 Begin: # # Serve the client expired Snapshot. The client should reject the given, # expired Snapshot and the locally trusted one, which should now be out of # date. # After the attack, attempt to re-issue a valid Snapshot to verify that # the client is still able to update. A bug previously caused snapshot # expiration or replay to result in an indefinite freeze; see # github.com/theupdateframework/tuf/issues/736 repository = repo_tool.load_repository(self.repository_directory) ts_key_file = os.path.join(self.keystore_directory, 'timestamp_key') snapshot_key_file = os.path.join(self.keystore_directory, 'snapshot_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file( ts_key_file, 'password') snapshot_private = repo_tool.import_ed25519_privatekey_from_file( snapshot_key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) repository.snapshot.load_signing_key(snapshot_private) # Set ts to expire in 1 month. ts_expiry_time = time.time() + 2630000 # Set snapshot to expire in 1 hour. snapshot_expiry_time = time.time() + 60*60 ts_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(ts_expiry_time)) snapshot_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(snapshot_expiry_time)) repository.timestamp.expiration = ts_datetime_object repository.snapshot.expiration = snapshot_datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # mock current time to one second after snapshot expiry mock_time = mock.Mock() mock_time.return_value = snapshot_expiry_time + 1 with mock.patch('time.time', mock_time): try: # We expect the following refresh() to raise a NoWorkingMirrorError. self.repository_updater.refresh() except tuf.exceptions.NoWorkingMirrorError as e: # Make sure the contained error is ExpiredMetadataError for mirror_url, mirror_error in e.mirror_errors.items(): self.assertTrue(isinstance(mirror_error, tuf.exceptions.ExpiredMetadataError)) self.assertTrue(mirror_url.endswith('snapshot.json')) else: self.fail('TUF failed to detect expired, stale Snapshot metadata.' ' Freeze attack successful.') # The client should have rejected the malicious Snapshot metadata, and # distrusted the local snapshot file that is no longer valid. self.assertTrue('snapshot' not in self.repository_updater.metadata['current']) self.assertEqual(sorted(['root', 'targets', 'timestamp']), sorted(self.repository_updater.metadata['current'])) # Verify that the client is able to recover from the malicious Snapshot. # Re-sign a valid Snapshot file that the client should accept. repository = repo_tool.load_repository(self.repository_directory) repository.timestamp.load_signing_key(timestamp_private) repository.snapshot.load_signing_key(snapshot_private) # Set snapshot to expire in 1 month. snapshot_expiry_time = time.time() + 2630000 snapshot_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(snapshot_expiry_time)) repository.snapshot.expiration = snapshot_datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # Verify that the client accepts the valid metadata file. self.repository_updater.refresh() self.assertTrue('snapshot' in self.repository_updater.metadata['current']) self.assertEqual(sorted(['root', 'targets', 'timestamp', 'snapshot']), sorted(self.repository_updater.metadata['current'])) if __name__ == '__main__': utils.configure_test_logging(sys.argv) unittest.main()

43.269474

88

0.724225

from __future__ import print_function from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import datetime import os import time import tempfile import shutil import json import logging import unittest import sys from urllib import request if sys.version_info >= (3, 3): import unittest.mock as mock else: import mock import tuf.formats import tuf.log import tuf.client.updater as updater import tuf.repository_tool as repo_tool import tuf.unittest_toolbox as unittest_toolbox import tuf.roledb import tuf.keydb import tuf.exceptions from tests import utils import securesystemslib repo_tool.disable_console_log_messages() logger = logging.getLogger(__name__) class TestIndefiniteFreezeAttack(unittest_toolbox.Modified_TestCase): @classmethod def setUpClass(cls): cls.temporary_directory = tempfile.mkdtemp(dir=os.getcwd()) cls.server_process_handler = utils.TestServerProcess(log=logger) @classmethod def tearDownClass(cls): cls.server_process_handler.clean() shutil.rmtree(cls.temporary_directory) def setUp(self): unittest_toolbox.Modified_TestCase.setUp(self) self.repository_name = 'test_repository1' original_repository_files = os.path.join(os.getcwd(), 'repository_data') temporary_repository_root = \ self.make_temp_directory(directory=self.temporary_directory) original_repository = os.path.join(original_repository_files, 'repository') original_client = os.path.join(original_repository_files, 'client') original_keystore = os.path.join(original_repository_files, 'keystore') self.repository_directory = \ os.path.join(temporary_repository_root, 'repository') self.client_directory = os.path.join(temporary_repository_root, 'client') self.keystore_directory = os.path.join(temporary_repository_root, 'keystore') shutil.copytree(original_repository, self.repository_directory) shutil.copytree(original_client, self.client_directory) shutil.copytree(original_keystore, self.keystore_directory) repository_basepath = self.repository_directory[len(os.getcwd()):] url_prefix = 'http://' + utils.TEST_HOST_ADDRESS + ':' \ + str(self.server_process_handler.port) + repository_basepath tuf.settings.repositories_directory = self.client_directory self.repository_mirrors = {'mirror1': {'url_prefix': url_prefix, 'metadata_path': 'metadata', 'targets_path': 'targets'}} self.repository_updater = updater.Updater(self.repository_name, self.repository_mirrors) def tearDown(self): unittest_toolbox.Modified_TestCase.tearDown(self) tuf.roledb.clear_roledb(clear_all=True) tuf.keydb.clear_keydb(clear_all=True) self.server_process_handler.flush_log() def test_without_tuf(self): timestamp_path = os.path.join(self.repository_directory, 'metadata', 'timestamp.json') timestamp_metadata = securesystemslib.util.load_json_file(timestamp_path) expiry_time = time.time() - 10 expires = tuf.formats.unix_timestamp_to_datetime(int(expiry_time)) expires = expires.isoformat() + 'Z' timestamp_metadata['signed']['expires'] = expires tuf.formats.check_signable_object_format(timestamp_metadata) with open(timestamp_path, 'wb') as file_object: timestamp_content = \ json.dumps(timestamp_metadata, indent=1, separators=(',', ': '), sort_keys=True).encode('utf-8') file_object.write(timestamp_content) client_timestamp_path = os.path.join(self.client_directory, 'timestamp.json') shutil.copy(timestamp_path, client_timestamp_path) length, hashes = securesystemslib.util.get_file_details(timestamp_path) fileinfo = tuf.formats.make_targets_fileinfo(length, hashes) url_prefix = self.repository_mirrors['mirror1']['url_prefix'] url_file = os.path.join(url_prefix, 'metadata', 'timestamp.json') request.urlretrieve(url_file.replace('\\', '/'), client_timestamp_path) length, hashes = securesystemslib.util.get_file_details(client_timestamp_path) download_fileinfo = tuf.formats.make_targets_fileinfo(length, hashes) self.assertEqual(download_fileinfo, fileinfo) def test_with_tuf(self): # new snapshot version, we still need to check that it's expired and notify repository = repo_tool.load_repository(self.repository_directory) key_file = os.path.join(self.keystore_directory, 'timestamp_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) key_file = os.path.join(self.keystore_directory, 'snapshot_key') snapshot_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.snapshot.load_signing_key(snapshot_private) expiry = int(time.time() + 60*60) repository.snapshot.expiration = tuf.formats.unix_timestamp_to_datetime( expiry) repository.mark_dirty(['snapshot', 'timestamp']) repository.writeall() shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) logger.info('Test: Refreshing #1 - Initial metadata refresh occurring.') self.repository_updater.refresh() logger.info('Test: Refreshing #2 - refresh after local snapshot expiry.') mock_time = mock.Mock() mock_time.return_value = expiry + 1 with mock.patch('time.time', mock_time): try: self.repository_updater.refresh() except tuf.exceptions.ExpiredMetadataError: logger.info('Test: Refresh #2 - failed as expected. Expired local' ' snapshot case generated a tuf.exceptions.ExpiredMetadataError' ' exception as expected. Test pass.') else: self.fail('TUF failed to detect expired stale snapshot metadata. Freeze' ' attack successful.') # copy it over the to client, wait a moment until it expires, and attempt to # re-fetch the same expired version. # The same scenario as in test_without_tuf() is followed here, except with # a TUF client. The TUF client performs a refresh of top-level metadata, # which includes 'timestamp.json', and should detect a freeze attack if # the repository serves an outdated 'timestamp.json'. # Modify the timestamp file on the remote repository. 'timestamp.json' # must be properly updated and signed with 'repository_tool.py', otherwise # the client will reject it as invalid metadata. The resulting # 'timestamp.json' should be valid metadata, but expired (as intended). repository = repo_tool.load_repository(self.repository_directory) key_file = os.path.join(self.keystore_directory, 'timestamp_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file(key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) # Set timestamp metadata to expire soon. # We cannot set the timestamp expiration with # 'repository.timestamp.expiration = ...' with already-expired timestamp # metadata because of consistency checks that occur during that assignment. expiry_time = time.time() + 60*60 datetime_object = tuf.formats.unix_timestamp_to_datetime(int(expiry_time)) repository.timestamp.expiration = datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # mock current time to one second after timestamp expiry mock_time = mock.Mock() mock_time.return_value = expiry_time + 1 with mock.patch('time.time', mock_time): try: self.repository_updater.refresh() # We expect NoWorkingMirrorError. except tuf.exceptions.NoWorkingMirrorError as e: # Make sure the contained error is ExpiredMetadataError for mirror_url, mirror_error in e.mirror_errors.items(): self.assertTrue(isinstance(mirror_error, tuf.exceptions.ExpiredMetadataError)) else: self.fail('TUF failed to detect expired, stale timestamp metadata.' ' Freeze attack successful.') # Test 3 Begin: # # Serve the client expired Snapshot. The client should reject the given, # expired Snapshot and the locally trusted one, which should now be out of # date. # After the attack, attempt to re-issue a valid Snapshot to verify that # the client is still able to update. A bug previously caused snapshot # expiration or replay to result in an indefinite freeze; see # github.com/theupdateframework/tuf/issues/736 repository = repo_tool.load_repository(self.repository_directory) ts_key_file = os.path.join(self.keystore_directory, 'timestamp_key') snapshot_key_file = os.path.join(self.keystore_directory, 'snapshot_key') timestamp_private = repo_tool.import_ed25519_privatekey_from_file( ts_key_file, 'password') snapshot_private = repo_tool.import_ed25519_privatekey_from_file( snapshot_key_file, 'password') repository.timestamp.load_signing_key(timestamp_private) repository.snapshot.load_signing_key(snapshot_private) # Set ts to expire in 1 month. ts_expiry_time = time.time() + 2630000 # Set snapshot to expire in 1 hour. snapshot_expiry_time = time.time() + 60*60 ts_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(ts_expiry_time)) snapshot_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(snapshot_expiry_time)) repository.timestamp.expiration = ts_datetime_object repository.snapshot.expiration = snapshot_datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # mock current time to one second after snapshot expiry mock_time = mock.Mock() mock_time.return_value = snapshot_expiry_time + 1 with mock.patch('time.time', mock_time): try: # We expect the following refresh() to raise a NoWorkingMirrorError. self.repository_updater.refresh() except tuf.exceptions.NoWorkingMirrorError as e: # Make sure the contained error is ExpiredMetadataError for mirror_url, mirror_error in e.mirror_errors.items(): self.assertTrue(isinstance(mirror_error, tuf.exceptions.ExpiredMetadataError)) self.assertTrue(mirror_url.endswith('snapshot.json')) else: self.fail('TUF failed to detect expired, stale Snapshot metadata.' ' Freeze attack successful.') # The client should have rejected the malicious Snapshot metadata, and # distrusted the local snapshot file that is no longer valid. self.assertTrue('snapshot' not in self.repository_updater.metadata['current']) self.assertEqual(sorted(['root', 'targets', 'timestamp']), sorted(self.repository_updater.metadata['current'])) # Verify that the client is able to recover from the malicious Snapshot. # Re-sign a valid Snapshot file that the client should accept. repository = repo_tool.load_repository(self.repository_directory) repository.timestamp.load_signing_key(timestamp_private) repository.snapshot.load_signing_key(snapshot_private) # Set snapshot to expire in 1 month. snapshot_expiry_time = time.time() + 2630000 snapshot_datetime_object = tuf.formats.unix_timestamp_to_datetime( int(snapshot_expiry_time)) repository.snapshot.expiration = snapshot_datetime_object repository.writeall() # Move the staged metadata to the "live" metadata. shutil.rmtree(os.path.join(self.repository_directory, 'metadata')) shutil.copytree(os.path.join(self.repository_directory, 'metadata.staged'), os.path.join(self.repository_directory, 'metadata')) # Verify that the client accepts the valid metadata file. self.repository_updater.refresh() self.assertTrue('snapshot' in self.repository_updater.metadata['current']) self.assertEqual(sorted(['root', 'targets', 'timestamp', 'snapshot']), sorted(self.repository_updater.metadata['current'])) if __name__ == '__main__': utils.configure_test_logging(sys.argv) unittest.main()

true

1c3128678f21598c7caa8347da40ac2a26954faf

32,068

py

Python

test/functional/fundrawtransaction.py

taowen1990/merit

d5cd9ff6c2c77caccf6a6b936884e58f2b88faed

[ "MIT" ]

229

2018-01-01T09:43:38.000Z

2022-03-21T23:11:20.000Z

test/functional/fundrawtransaction.py

taowen1990/merit

d5cd9ff6c2c77caccf6a6b936884e58f2b88faed

[ "MIT" ]

109

2018-01-01T17:23:02.000Z

2020-10-31T00:06:19.000Z

test/functional/fundrawtransaction.py

taowen1990/merit

d5cd9ff6c2c77caccf6a6b936884e58f2b88faed

[ "MIT" ]

26

2018-01-02T22:05:19.000Z

2020-10-30T21:10:55.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the fundrawtransaction RPC.""" from test_framework.test_framework import MeritTestFramework from test_framework.util import * def get_unspent(listunspent, amount): for utx in listunspent: if utx['amount'] == amount: return utx raise AssertionError('Could not find unspent with amount={}'.format(amount)) class RawTransactionsTest(MeritTestFramework): def set_test_params(self): self.num_nodes = 4 self.setup_clean_chain = True def setup_network(self, split=False): self.setup_nodes() connect_nodes_bi(self.nodes, 0, 1) connect_nodes_bi(self.nodes, 1, 2) connect_nodes_bi(self.nodes, 0, 2) connect_nodes_bi(self.nodes, 0, 3) def run_test(self): min_relay_tx_fee = self.nodes[0].getnetworkinfo()['relayfee'] # This test is not meant to test fee estimation and we'd like # to be sure all txs are sent at a consistent desired feerate for node in self.nodes: node.settxfee(min_relay_tx_fee) # if the fee's positive delta is higher than this value tests will fail, # neg. delta always fail the tests. # The size of the signature of every input may be at most 2 bytes larger # than a minimum sized signature. # = 2 bytes * minRelayTxFeePerByte feeTolerance = 2 * min_relay_tx_fee/1000 self.nodes[2].generate(1) self.sync_all() self.nodes[0].generate(121) self.sync_all() # ensure that setting changePosition in fundraw with an exact match is handled properly rawmatch = self.nodes[2].createrawtransaction([], {self.nodes[2].getnewaddress():50}) rawmatch = self.nodes[2].fundrawtransaction(rawmatch, {"changePosition":1, "subtractFeeFromOutputs":[0]}) assert_equal(rawmatch["changepos"], -1) watchonly_address = self.nodes[0].getnewaddress() watchonly_pubkey = self.nodes[0].validateaddress(watchonly_address)["pubkey"] watchonly_amount = Decimal(200) self.nodes[3].importpubkey(watchonly_pubkey, "", True) watchonly_txid = self.nodes[0].sendtoaddress(watchonly_address, watchonly_amount) self.nodes[0].sendtoaddress(self.nodes[3].getnewaddress(), watchonly_amount / 10) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 1.5) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 1.0) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 5.0) self.nodes[0].generate(1) self.sync_all() ############### # simple test # ############### inputs = [ ] outputs = { self.nodes[0].getnewaddress() : 1.0 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) assert(len(dec_tx['vin']) > 0) #test that we have enough inputs ############################## # simple test with two coins # ############################## inputs = [ ] outputs = { self.nodes[0].getnewaddress() : 2.2 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) assert(len(dec_tx['vin']) > 0) #test if we have enough inputs ############################## # simple test with two coins # ############################## inputs = [ ] outputs = { self.nodes[0].getnewaddress() : 2.6 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) assert(len(dec_tx['vin']) > 0) assert_equal(dec_tx['vin'][0]['scriptSig']['hex'], '') ################################ # simple test with two outputs # ################################ inputs = [ ] outputs = { self.nodes[0].getnewaddress() : 2.6, self.nodes[1].getnewaddress() : 2.5 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 for out in dec_tx['vout']: totalOut += out['value'] assert(len(dec_tx['vin']) > 0) assert_equal(dec_tx['vin'][0]['scriptSig']['hex'], '') ######################################################################### # test a fundrawtransaction with a VIN greater than the required amount # ######################################################################### utx = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']}] outputs = { self.nodes[0].getnewaddress() : 1.0 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 for out in dec_tx['vout']: totalOut += out['value'] assert_equal(fee + totalOut, utx['amount']) #compare vin total and totalout+fee ##################################################################### # test a fundrawtransaction with which will not get a change output # ##################################################################### utx = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']}] outputs = { self.nodes[0].getnewaddress() : Decimal(5.0) - fee - feeTolerance } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 for out in dec_tx['vout']: totalOut += out['value'] assert_equal(rawtxfund['changepos'], -1) assert_equal(fee + totalOut, utx['amount']) #compare vin total and totalout+fee #################################################### # test a fundrawtransaction with an invalid option # #################################################### utx = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']} ] outputs = { self.nodes[0].getnewaddress() : Decimal(4.0) } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) assert_raises_jsonrpc(-3, "Unexpected key foo", self.nodes[2].fundrawtransaction, rawtx, {'foo':'bar'}) ############################################################ # test a fundrawtransaction with an invalid change address # ############################################################ utx = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']} ] outputs = { self.nodes[0].getnewaddress() : Decimal(4.0) } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) assert_raises_jsonrpc(-5, "changeAddress must be a valid merit address", self.nodes[2].fundrawtransaction, rawtx, {'changeAddress':'foobar'}) ############################################################ # test a fundrawtransaction with a provided change address # ############################################################ utx = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']} ] outputs = { self.nodes[0].getnewaddress() : Decimal(4.0) } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) change = self.nodes[2].getnewaddress() assert_raises_jsonrpc(-8, "changePosition out of bounds", self.nodes[2].fundrawtransaction, rawtx, {'changeAddress':change, 'changePosition':2}) rawtxfund = self.nodes[2].fundrawtransaction(rawtx, {'changeAddress': change, 'changePosition': 0}) dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) out = dec_tx['vout'][0] assert_equal(change, out['scriptPubKey']['addresses'][0]) ######################################################################### # test a fundrawtransaction with a VIN smaller than the required amount # ######################################################################### utx = get_unspent(self.nodes[2].listunspent(), 1) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']}] outputs = { self.nodes[0].getnewaddress() : 1.0 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) # 4-byte version + 1-byte vin count + 36-byte prevout then script_len rawtx = rawtx[:82] + "0100" + rawtx[84:] dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) assert_equal("00", dec_tx['vin'][0]['scriptSig']['hex']) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 matchingOuts = 0 for i, out in enumerate(dec_tx['vout']): totalOut += out['value'] if out['scriptPubKey']['addresses'][0] in outputs: matchingOuts+=1 else: assert_equal(i, rawtxfund['changepos']) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) assert_equal("00", dec_tx['vin'][0]['scriptSig']['hex']) assert_equal(matchingOuts, 1) assert_equal(len(dec_tx['vout']), 2) ########################################### # test a fundrawtransaction with two VINs # ########################################### utx = get_unspent(self.nodes[2].listunspent(), 1) utx2 = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']},{'txid' : utx2['txid'], 'vout' : utx2['vout']} ] outputs = { self.nodes[0].getnewaddress() : 6.0 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 matchingOuts = 0 for out in dec_tx['vout']: totalOut += out['value'] if out['scriptPubKey']['addresses'][0] in outputs: matchingOuts+=1 assert_equal(matchingOuts, 1) assert_equal(len(dec_tx['vout']), 2) matchingIns = 0 for vinOut in dec_tx['vin']: for vinIn in inputs: if vinIn['txid'] == vinOut['txid']: matchingIns+=1 assert_equal(matchingIns, 2) #we now must see two vins identical to vins given as params ######################################################### # test a fundrawtransaction with two VINs and two vOUTs # ######################################################### utx = get_unspent(self.nodes[2].listunspent(), 1) utx2 = get_unspent(self.nodes[2].listunspent(), 5) inputs = [ {'txid' : utx['txid'], 'vout' : utx['vout']},{'txid' : utx2['txid'], 'vout' : utx2['vout']} ] outputs = { self.nodes[0].getnewaddress() : 6.0, self.nodes[0].getnewaddress() : 1.0 } rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(utx['txid'], dec_tx['vin'][0]['txid']) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) totalOut = 0 matchingOuts = 0 for out in dec_tx['vout']: totalOut += out['value'] if out['scriptPubKey']['addresses'][0] in outputs: matchingOuts+=1 assert_equal(matchingOuts, 2) assert_equal(len(dec_tx['vout']), 3) ############################################## # test a fundrawtransaction with invalid vin # ############################################## inputs = [ {'txid' : "1c7f966dab21119bac53213a2bc7532bff1fa844c124fd750a7d0b1332440bd1", 'vout' : 0} ] #invalid vin! outputs = { self.nodes[0].getnewaddress() : 1.0} rawtx = self.nodes[2].createrawtransaction(inputs, outputs) dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_raises_jsonrpc(-4, "Insufficient funds", self.nodes[2].fundrawtransaction, rawtx) ############################################################ #compare fee of a standard pubkeyhash transaction inputs = [] outputs = {self.nodes[1].getnewaddress():1.1} rawtx = self.nodes[0].createrawtransaction(inputs, outputs) fundedTx = self.nodes[0].fundrawtransaction(rawtx) #create same transaction over sendtoaddress txId = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 1.1) signedFee = self.nodes[0].getrawmempool(True)[txId]['fee'] #compare fee feeDelta = Decimal(fundedTx['fee']) - Decimal(signedFee) assert(feeDelta >= 0 and feeDelta <= feeTolerance) ############################################################ ############################################################ #compare fee of a standard pubkeyhash transaction with multiple outputs inputs = [] outputs = {self.nodes[1].getnewaddress():1.1,self.nodes[1].getnewaddress():1.2,self.nodes[1].getnewaddress():0.1,self.nodes[1].getnewaddress():1.3,self.nodes[1].getnewaddress():0.2,self.nodes[1].getnewaddress():0.3} rawtx = self.nodes[0].createrawtransaction(inputs, outputs) fundedTx = self.nodes[0].fundrawtransaction(rawtx) #create same transaction over sendtoaddress txId = self.nodes[0].sendmany("", outputs) signedFee = self.nodes[0].getrawmempool(True)[txId]['fee'] #compare fee feeDelta = Decimal(fundedTx['fee']) - Decimal(signedFee) assert(feeDelta >= 0 and feeDelta <= feeTolerance) ############################################################ ############################################################ #compare fee of a 2of2 multisig p2sh transaction # create 2of2 addr addr1 = self.nodes[1].getnewaddress() addr2 = self.nodes[1].getnewaddress() addr1Obj = self.nodes[1].validateaddress(addr1) addr2Obj = self.nodes[1].validateaddress(addr2) mSigObj = self.nodes[1].addmultisigaddress(2, [addr1Obj['pubkey'], addr2Obj['pubkey']]) inputs = [] outputs = {mSigObj:1.1} rawtx = self.nodes[0].createrawtransaction(inputs, outputs) fundedTx = self.nodes[0].fundrawtransaction(rawtx) #create same transaction over sendtoaddress txId = self.nodes[0].sendtoaddress(mSigObj, 1.1) signedFee = self.nodes[0].getrawmempool(True)[txId]['fee'] #compare fee feeDelta = Decimal(fundedTx['fee']) - Decimal(signedFee) assert(feeDelta >= 0 and feeDelta <= feeTolerance) ############################################################ ############################################################ #compare fee of a standard pubkeyhash transaction # create 4of5 addr addr1 = self.nodes[1].getnewaddress() addr2 = self.nodes[1].getnewaddress() addr3 = self.nodes[1].getnewaddress() addr4 = self.nodes[1].getnewaddress() addr5 = self.nodes[1].getnewaddress() addr1Obj = self.nodes[1].validateaddress(addr1) addr2Obj = self.nodes[1].validateaddress(addr2) addr3Obj = self.nodes[1].validateaddress(addr3) addr4Obj = self.nodes[1].validateaddress(addr4) addr5Obj = self.nodes[1].validateaddress(addr5) mSigObj = self.nodes[1].addmultisigaddress(4, [addr1Obj['pubkey'], addr2Obj['pubkey'], addr3Obj['pubkey'], addr4Obj['pubkey'], addr5Obj['pubkey']]) inputs = [] outputs = {mSigObj:1.1} rawtx = self.nodes[0].createrawtransaction(inputs, outputs) fundedTx = self.nodes[0].fundrawtransaction(rawtx) #create same transaction over sendtoaddress txId = self.nodes[0].sendtoaddress(mSigObj, 1.1) signedFee = self.nodes[0].getrawmempool(True)[txId]['fee'] #compare fee feeDelta = Decimal(fundedTx['fee']) - Decimal(signedFee) assert(feeDelta >= 0 and feeDelta <= feeTolerance) ############################################################ ############################################################ # spend a 2of2 multisig transaction over fundraw # create 2of2 addr addr1 = self.nodes[2].getnewaddress() addr2 = self.nodes[2].getnewaddress() addr1Obj = self.nodes[2].validateaddress(addr1) addr2Obj = self.nodes[2].validateaddress(addr2) mSigObj = self.nodes[2].addmultisigaddress(2, [addr1Obj['pubkey'], addr2Obj['pubkey']]) # send 1.2 MRT to msig addr txId = self.nodes[0].sendtoaddress(mSigObj, 1.2) self.sync_all() self.nodes[1].generate(1) self.sync_all() oldBalance = self.nodes[1].getbalance() inputs = [] outputs = {self.nodes[1].getnewaddress():1.1} rawtx = self.nodes[2].createrawtransaction(inputs, outputs) fundedTx = self.nodes[2].fundrawtransaction(rawtx) signedTx = self.nodes[2].signrawtransaction(fundedTx['hex']) txId = self.nodes[2].sendrawtransaction(signedTx['hex']) self.sync_all() self.nodes[1].generate(1) self.sync_all() # make sure funds are received at node1 assert_equal(oldBalance+Decimal('1.10000000'), self.nodes[1].getbalance()) ############################################################ # locked wallet test self.stop_node(0) self.nodes[1].node_encrypt_wallet("test") self.stop_node(2) self.stop_node(3) self.start_nodes() # This test is not meant to test fee estimation and we'd like # to be sure all txs are sent at a consistent desired feerate for node in self.nodes: node.settxfee(min_relay_tx_fee) connect_nodes_bi(self.nodes,0,1) connect_nodes_bi(self.nodes,1,2) connect_nodes_bi(self.nodes,0,2) connect_nodes_bi(self.nodes,0,3) self.sync_all() # drain the keypool self.nodes[1].getnewaddress() self.nodes[1].getrawchangeaddress() inputs = [] outputs = {self.nodes[0].getnewaddress():1.1} rawtx = self.nodes[1].createrawtransaction(inputs, outputs) # fund a transaction that requires a new key for the change output # creating the key must be impossible because the wallet is locked assert_raises_jsonrpc(-4, "Keypool ran out, please call keypoolrefill first", self.nodes[1].fundrawtransaction, rawtx) #refill the keypool self.nodes[1].walletpassphrase("test", 100) self.nodes[1].keypoolrefill(8) #need to refill the keypool to get an internal change address self.nodes[1].walletlock() assert_raises_jsonrpc(-13, "walletpassphrase", self.nodes[1].sendtoaddress, self.nodes[0].getnewaddress(), 1.2) oldBalance = self.nodes[0].getbalance() inputs = [] outputs = {self.nodes[0].getnewaddress():1.1} rawtx = self.nodes[1].createrawtransaction(inputs, outputs) fundedTx = self.nodes[1].fundrawtransaction(rawtx) #now we need to unlock self.nodes[1].walletpassphrase("test", 600) signedTx = self.nodes[1].signrawtransaction(fundedTx['hex']) txId = self.nodes[1].sendrawtransaction(signedTx['hex']) self.nodes[1].generate(1) self.sync_all() # make sure funds are received at node1 assert_equal(oldBalance+Decimal('51.10000000'), self.nodes[0].getbalance()) ############################################### # multiple (~19) inputs tx test | Compare fee # ############################################### #empty node1, send some small coins from node0 to node1 self.nodes[1].sendtoaddress(self.nodes[0].getnewaddress(), self.nodes[1].getbalance(), "", "", True) self.sync_all() self.nodes[0].generate(1) self.sync_all() for i in range(0,20): self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.01) self.nodes[0].generate(1) self.sync_all() #fund a tx with ~20 small inputs inputs = [] outputs = {self.nodes[0].getnewaddress():0.15,self.nodes[0].getnewaddress():0.04} rawtx = self.nodes[1].createrawtransaction(inputs, outputs) fundedTx = self.nodes[1].fundrawtransaction(rawtx) #create same transaction over sendtoaddress txId = self.nodes[1].sendmany("", outputs) signedFee = self.nodes[1].getrawmempool(True)[txId]['fee'] #compare fee feeDelta = Decimal(fundedTx['fee']) - Decimal(signedFee) assert(feeDelta >= 0 and feeDelta <= feeTolerance*19) #~19 inputs ############################################# # multiple (~19) inputs tx test | sign/send # ############################################# #again, empty node1, send some small coins from node0 to node1 self.nodes[1].sendtoaddress(self.nodes[0].getnewaddress(), self.nodes[1].getbalance(), "", "", True) self.sync_all() self.nodes[0].generate(1) self.sync_all() for i in range(0,20): self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.01) self.nodes[0].generate(1) self.sync_all() #fund a tx with ~20 small inputs oldBalance = self.nodes[0].getbalance() inputs = [] outputs = {self.nodes[0].getnewaddress():0.15,self.nodes[0].getnewaddress():0.04} rawtx = self.nodes[1].createrawtransaction(inputs, outputs) fundedTx = self.nodes[1].fundrawtransaction(rawtx) fundedAndSignedTx = self.nodes[1].signrawtransaction(fundedTx['hex']) txId = self.nodes[1].sendrawtransaction(fundedAndSignedTx['hex']) self.sync_all() self.nodes[0].generate(1) self.sync_all() assert_equal(oldBalance+Decimal('50.19000000'), self.nodes[0].getbalance()) #0.19+block reward ##################################################### # test fundrawtransaction with OP_RETURN and no vin # ##################################################### rawtx = "0100000000010000000000000000066a047465737400000000" dec_tx = self.nodes[2].decoderawtransaction(rawtx) assert_equal(len(dec_tx['vin']), 0) assert_equal(len(dec_tx['vout']), 1) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) assert_greater_than(len(dec_tx['vin']), 0) # at least one vin assert_equal(len(dec_tx['vout']), 2) # one change output added ################################################## # test a fundrawtransaction using only watchonly # ################################################## inputs = [] outputs = {self.nodes[2].getnewaddress() : watchonly_amount / 2} rawtx = self.nodes[3].createrawtransaction(inputs, outputs) result = self.nodes[3].fundrawtransaction(rawtx, {'includeWatching': True }) res_dec = self.nodes[0].decoderawtransaction(result["hex"]) assert_equal(len(res_dec["vin"]), 1) assert_equal(res_dec["vin"][0]["txid"], watchonly_txid) assert("fee" in result.keys()) assert_greater_than(result["changepos"], -1) ############################################################### # test fundrawtransaction using the entirety of watched funds # ############################################################### inputs = [] outputs = {self.nodes[2].getnewaddress() : watchonly_amount} rawtx = self.nodes[3].createrawtransaction(inputs, outputs) # Backward compatibility test (2nd param is includeWatching) result = self.nodes[3].fundrawtransaction(rawtx, True) res_dec = self.nodes[0].decoderawtransaction(result["hex"]) assert_equal(len(res_dec["vin"]), 2) assert(res_dec["vin"][0]["txid"] == watchonly_txid or res_dec["vin"][1]["txid"] == watchonly_txid) assert_greater_than(result["fee"], 0) assert_greater_than(result["changepos"], -1) assert_equal(result["fee"] + res_dec["vout"][result["changepos"]]["value"], watchonly_amount / 10) signedtx = self.nodes[3].signrawtransaction(result["hex"]) assert(not signedtx["complete"]) signedtx = self.nodes[0].signrawtransaction(signedtx["hex"]) assert(signedtx["complete"]) self.nodes[0].sendrawtransaction(signedtx["hex"]) self.nodes[0].generate(1) self.sync_all() ####################### # Test feeRate option # ####################### # Make sure there is exactly one input so coin selection can't skew the result assert_equal(len(self.nodes[3].listunspent(1)), 1) inputs = [] outputs = {self.nodes[3].getnewaddress() : 1} rawtx = self.nodes[3].createrawtransaction(inputs, outputs) result = self.nodes[3].fundrawtransaction(rawtx) # uses min_relay_tx_fee (set by settxfee) result2 = self.nodes[3].fundrawtransaction(rawtx, {"feeRate": 2*min_relay_tx_fee}) result3 = self.nodes[3].fundrawtransaction(rawtx, {"feeRate": 10*min_relay_tx_fee}) result_fee_rate = result['fee'] * 1000 / count_bytes(result['hex']) assert_fee_amount(result2['fee'], count_bytes(result2['hex']), 2 * result_fee_rate) assert_fee_amount(result3['fee'], count_bytes(result3['hex']), 10 * result_fee_rate) ################################ # Test no address reuse occurs # ################################ result3 = self.nodes[3].fundrawtransaction(rawtx) res_dec = self.nodes[0].decoderawtransaction(result3["hex"]) changeaddress = "" for out in res_dec['vout']: if out['value'] > 1.0: changeaddress += out['scriptPubKey']['addresses'][0] assert(changeaddress != "") nextaddr = self.nodes[3].getnewaddress() # Now the change address key should be removed from the keypool assert(changeaddress != nextaddr) ###################################### # Test subtractFeeFromOutputs option # ###################################### # Make sure there is exactly one input so coin selection can't skew the result assert_equal(len(self.nodes[3].listunspent(1)), 1) inputs = [] outputs = {self.nodes[2].getnewaddress(): 1} rawtx = self.nodes[3].createrawtransaction(inputs, outputs) result = [self.nodes[3].fundrawtransaction(rawtx), # uses min_relay_tx_fee (set by settxfee) self.nodes[3].fundrawtransaction(rawtx, {"subtractFeeFromOutputs": []}), # empty subtraction list self.nodes[3].fundrawtransaction(rawtx, {"subtractFeeFromOutputs": [0]}), # uses min_relay_tx_fee (set by settxfee) self.nodes[3].fundrawtransaction(rawtx, {"feeRate": 2*min_relay_tx_fee}), self.nodes[3].fundrawtransaction(rawtx, {"feeRate": 2*min_relay_tx_fee, "subtractFeeFromOutputs": [0]})] dec_tx = [self.nodes[3].decoderawtransaction(tx['hex']) for tx in result] output = [d['vout'][1 - r['changepos']]['value'] for d, r in zip(dec_tx, result)] change = [d['vout'][r['changepos']]['value'] for d, r in zip(dec_tx, result)] assert_equal(result[0]['fee'], result[1]['fee'], result[2]['fee']) assert_equal(result[3]['fee'], result[4]['fee']) assert_equal(change[0], change[1]) assert_equal(output[0], output[1]) assert_equal(output[0], output[2] + result[2]['fee']) assert_equal(change[0] + result[0]['fee'], change[2]) assert_equal(output[3], output[4] + result[4]['fee']) assert_equal(change[3] + result[3]['fee'], change[4]) inputs = [] outputs = {self.nodes[2].getnewaddress(): value for value in (1.0, 1.1, 1.2, 1.3)} rawtx = self.nodes[3].createrawtransaction(inputs, outputs) result = [self.nodes[3].fundrawtransaction(rawtx), # split the fee between outputs 0, 2, and 3, but not output 1 self.nodes[3].fundrawtransaction(rawtx, {"subtractFeeFromOutputs": [0, 2, 3]})] dec_tx = [self.nodes[3].decoderawtransaction(result[0]['hex']), self.nodes[3].decoderawtransaction(result[1]['hex'])] # Nested list of non-change output amounts for each transaction output = [[out['value'] for i, out in enumerate(d['vout']) if i != r['changepos']] for d, r in zip(dec_tx, result)] # List of differences in output amounts between normal and subtractFee transactions share = [o0 - o1 for o0, o1 in zip(output[0], output[1])] # output 1 is the same in both transactions assert_equal(share[1], 0) # the other 3 outputs are smaller as a result of subtractFeeFromOutputs assert_greater_than(share[0], 0) assert_greater_than(share[2], 0) assert_greater_than(share[3], 0) # outputs 2 and 3 take the same share of the fee assert_equal(share[2], share[3]) # output 0 takes at least as much share of the fee, and no more than 2 satoshis more, than outputs 2 and 3 assert_greater_than_or_equal(share[0], share[2]) assert_greater_than_or_equal(share[2] + Decimal(2e-8), share[0]) # the fee is the same in both transactions assert_equal(result[0]['fee'], result[1]['fee']) # the total subtracted from the outputs is equal to the fee assert_equal(share[0] + share[2] + share[3], result[0]['fee']) if __name__ == '__main__': RawTransactionsTest().main()

43.868673

223

0.569103

from test_framework.test_framework import MeritTestFramework from test_framework.util import * def get_unspent(listunspent, amount): for utx in listunspent: if utx['amount'] == amount: return utx raise AssertionError('Could not find unspent with amount={}'.format(amount)) class RawTransactionsTest(MeritTestFramework): def set_test_params(self): self.num_nodes = 4 self.setup_clean_chain = True def setup_network(self, split=False): self.setup_nodes() connect_nodes_bi(self.nodes, 0, 1) connect_nodes_bi(self.nodes, 1, 2) connect_nodes_bi(self.nodes, 0, 2) connect_nodes_bi(self.nodes, 0, 3) def run_test(self): min_relay_tx_fee = self.nodes[0].getnetworkinfo()['relayfee'] # to be sure all txs are sent at a consistent desired feerate for node in self.nodes: node.settxfee(min_relay_tx_fee) # if the fee's positive delta is higher than this value tests will fail, feeTolerance = 2 * min_relay_tx_fee/1000 self.nodes[2].generate(1) self.sync_all() self.nodes[0].generate(121) self.sync_all() rawmatch = self.nodes[2].createrawtransaction([], {self.nodes[2].getnewaddress():50}) rawmatch = self.nodes[2].fundrawtransaction(rawmatch, {"changePosition":1, "subtractFeeFromOutputs":[0]}) assert_equal(rawmatch["changepos"], -1) watchonly_address = self.nodes[0].getnewaddress() watchonly_pubkey = self.nodes[0].validateaddress(watchonly_address)["pubkey"] watchonly_amount = Decimal(200) self.nodes[3].importpubkey(watchonly_pubkey, "", True) watchonly_txid = self.nodes[0].sendtoaddress(watchonly_address, watchonly_amount) self.nodes[0].sendtoaddress(self.nodes[3].getnewaddress(), watchonly_amount / 10) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 1.5) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 1.0) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 5.0) self.nodes[0].generate(1) self.sync_all() ransaction(rawtx) rawtxfund = self.nodes[2].fundrawtransaction(rawtx) fee = rawtxfund['fee'] dec_tx = self.nodes[2].decoderawtransaction(rawtxfund['hex']) assert(len(dec_tx['vin']) > 0)

true

1c312882f690d45be0acf85dd13c25cd0fdf9bbe

1,154

py

Python

tests/data/source_code/python/default/asciidoxy/default_values.py

RerrerBuub/asciidoxy

3402f37d59e30975e9919653465839e396f05513

[ "Apache-2.0" ]

14

2020-04-28T08:51:43.000Z

2022-02-12T13:40:34.000Z

tests/data/source_code/python/default/asciidoxy/default_values.py

RerrerBuub/asciidoxy

3402f37d59e30975e9919653465839e396f05513

[ "Apache-2.0" ]

47

2020-05-18T14:19:31.000Z

2022-03-04T13:46:46.000Z

tests/data/source_code/python/default/asciidoxy/default_values.py

RerrerBuub/asciidoxy

3402f37d59e30975e9919653465839e396f05513

[ "Apache-2.0" ]

8

2020-05-17T20:52:42.000Z

2022-02-25T16:16:01.000Z

# Copyright (C) 2019-2021, TomTom (http://tomtom.com). # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. class Point: """Class representing a simple point.""" def __init__(self, x: int = 0, y: int = 1): """Construct a point. Args: x: The X coordinate. y: The Y coordinate. """ ... def increment(self, x: int = 2, y: int = 3) -> "Point": """Create a new incremented point. Args: x: Value to increment the X coordinate with. y: Value to increment the Y coordinate with. Returns: A new incremented Point. """ ...

30.368421

74

0.627383

class Point: def __init__(self, x: int = 0, y: int = 1): ... def increment(self, x: int = 2, y: int = 3) -> "Point": ...

true

1c3128bf0ed7b0c9cf485afafd436c3b34c03a80

973

py

Python

taskify/todo/models.py

tricelex/taskify

cf967bbccc39aef65efd13c429d48455f38e1fb6

[ "MIT" ]

null

taskify/todo/models.py

tricelex/taskify

cf967bbccc39aef65efd13c429d48455f38e1fb6

[ "MIT" ]

1

2022-03-01T11:20:20.000Z

taskify/todo/models.py

tricelex/taskify

cf967bbccc39aef65efd13c429d48455f38e1fb6

[ "MIT" ]

null

import uuid from django.conf import settings from django.db import models from django.urls import reverse from taskify.utils.models import BaseAbstractModel class TaskList(BaseAbstractModel): id = models.UUIDField( primary_key=True, default=uuid.uuid4, editable=False, unique=True ) title = models.CharField(max_length=250) description = models.CharField(max_length=125, blank=True) user = models.ForeignKey( settings.AUTH_USER_MODEL, null=True, on_delete=models.CASCADE, related_name="user", ) def __str__(self): return f"{self.title} - {self.user}" def get_absolute_url(self): return reverse("todos:task_list_detail", kwargs={"uuid": self.id}) class Task(BaseAbstractModel): id = models.UUIDField( primary_key=True, default=uuid.uuid4, editable=False, unique=True ) title = models.CharField(max_length=250) status = models.BooleanField(default=False)

27.027778

74

0.700925

import uuid from django.conf import settings from django.db import models from django.urls import reverse from taskify.utils.models import BaseAbstractModel class TaskList(BaseAbstractModel): id = models.UUIDField( primary_key=True, default=uuid.uuid4, editable=False, unique=True ) title = models.CharField(max_length=250) description = models.CharField(max_length=125, blank=True) user = models.ForeignKey( settings.AUTH_USER_MODEL, null=True, on_delete=models.CASCADE, related_name="user", ) def __str__(self): return f"{self.title} - {self.user}" def get_absolute_url(self): return reverse("todos:task_list_detail", kwargs={"uuid": self.id}) class Task(BaseAbstractModel): id = models.UUIDField( primary_key=True, default=uuid.uuid4, editable=False, unique=True ) title = models.CharField(max_length=250) status = models.BooleanField(default=False)

true

1c31290a48f8cfbfa4d86d7e15fa5b566ee63b58

8,954

py

Python

src/pyOpenMS/pyTOPP/MapAlignerPoseClustering.py

liangoaix/OpenMS

cccbc5d872320f197091596db275f35b4d0458cd

[ "Zlib", "Apache-2.0" ]

null

src/pyOpenMS/pyTOPP/MapAlignerPoseClustering.py

liangoaix/OpenMS

cccbc5d872320f197091596db275f35b4d0458cd

[ "Zlib", "Apache-2.0" ]

null

src/pyOpenMS/pyTOPP/MapAlignerPoseClustering.py

liangoaix/OpenMS

cccbc5d872320f197091596db275f35b4d0458cd

[ "Zlib", "Apache-2.0" ]

null

import argparse import pyopenms as pms from common import addDataProcessing, writeParamsIfRequested, updateDefaults def align(in_files, out_files, out_trafos, reference_index, reference_file, params): in_types = set(pms.FileHandler.getType(in_) for in_ in in_files) if in_types <= set((pms.Type.MZML, pms.Type.MZXML, pms.Type.MZDATA)): align_features = False elif in_types == set((pms.Type.FEATUREXML,)): align_features = True else: raise Exception("different kinds of input files") algorithm = pms.MapAlignmentAlgorithmPoseClustering() alignment_params = params.copy("algorithm:", True) algorithm.setParameters(alignment_params) algorithm.setLogType(pms.LogType.CMD) plog = pms.ProgressLogger() plog.setLogType(pms.LogType.CMD) if reference_file: file_ = reference_file elif reference_index > 0: file_ = in_files[reference_index-1] else: sizes = [] if align_features: fh = pms.FeatureXMLFile() plog.startProgress(0, len(in_files), "Determine Reference map") for i, in_f in enumerate(in_files): sizes.append((fh.loadSize(in_f), in_f)) plog.setProgress(i) else: fh = pms.MzMLFile() mse = pms.MSExperiment() plog.startProgress(0, len(in_files), "Determine Reference map") for i, in_f in enumerate(in_files): fh.load(in_f, mse) mse.updateRanges() sizes.append((mse.getSize(), in_f)) plog.setProgress(i) plog.endProgress() __, file_ = max(sizes) f_fmxl = pms.FeatureXMLFile() if not out_files: options = f_fmxl.getOptions() options.setLoadConvexHull(False) options.setLoadSubordinates(False) f_fmxl.setOptions(options) if align_features: map_ref = pms.FeatureMap() f_fxml_tmp = pms.FeatureXMLFile() options = f_fmxl.getOptions() options.setLoadConvexHull(False) options.setLoadSubordinates(False) f_fxml_tmp.setOptions(options) f_fxml_tmp.load(file_, map_ref) algorithm.setReference(map_ref) else: map_ref = pms.MSExperiment() pms.MzMLFile().load(file_, map_ref) algorithm.setReference(map_ref) plog.startProgress(0, len(in_files), "Align input maps") for i, in_file in enumerate(in_files): trafo = pms.TransformationDescription() if align_features: map_ = pms.FeatureMap() f_fxml_tmp = pms.FeatureXMLFile() f_fxml_tmp.setOptions(f_fmxl.getOptions()) f_fxml_tmp.load(in_file, map_) if in_file == file_: trafo.fitModel("identity") else: algorithm.align(map_, trafo) if out_files: pms.MapAlignmentTransformer.transformSingleFeatureMap(map_, trafo) addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT) f_fxml_tmp.store(out_files[i], map_) else: map_ = pms.MSExperiment() pms.MzMLFile().load(in_file, map_) if in_file == file_: trafo.fitModel("identity") else: algorithm.align(map_, trafo) if out_files: pms.MapAlignmentTransformer.transformSinglePeakMap(map_, trafo) addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT) pms.MzMLFile().store(out_files[i], map_) if out_trafos: pms.TransformationXMLFile().store(out_trafos[i], trafo) plog.setProgress(i+1) plog.endProgress() def getModelDefaults(default_model): params = pms.Param() params.setValue("type", default_model, "Type of model") model_types = [ "linear", "interpolated"] if default_model not in model_types: model_types.insert(0, default_model) params.setValidStrings("type", model_types) model_params = pms.Param() pms.TransformationModelLinear.getDefaultParameters(model_params) params.insert("linear:", model_params) params.setSectionDescription("linear", "Parameters for 'linear' model") pms.TransformationModelInterpolated.getDefaultParameters(model_params) entry = model_params.getEntry("interpolation_type") interpolation_types = entry.valid_strings model_params.setValidStrings("interpolation_type", interpolation_types) params.insert("interpolated:", model_params) params.setSectionDescription("interpolated", "Parameters for 'interpolated' model") return params def getDefaultParameters(): model_param = getModelDefaults("linear") algo_param = pms.MapAlignmentAlgorithmPoseClustering().getParameters() default = pms.Param() default.insert("model:", model_param) default.insert("algorithm:", algo_param) return default def main(): parser = argparse.ArgumentParser(description="PeakPickerHiRes") parser.add_argument("-in", action="append", type=str, dest="in_", metavar="input_file", ) parser.add_argument("-seeds", action="store", type=str, metavar="seeds_file", ) parser.add_argument("-out", action="append", type=str, metavar="output_file", ) parser.add_argument("-trafo_out", action="append", type=str, metavar="output_file", ) parser.add_argument("-ini", action="store", type=str, metavar="ini_file", ) parser.add_argument("-dict_ini", action="store", type=str, metavar="python_dict_ini_file", ) parser.add_argument("-write_ini", action="store", type=str, metavar="ini_file", ) parser.add_argument("-write_dict_ini", action="store", type=str, metavar="python_dict_ini_file", ) parser.add_argument("-reference:file", action="store", type=str, metavar="reference_file", dest="reference_file", ) parser.add_argument("-reference:index", action="store", type=int, metavar="reference_index", dest="reference_index", ) args = parser.parse_args() def collect(args): return [f.strip() for arg in args or [] for f in arg.split(",")] in_files = collect(args.in_) out_files = collect(args.out) trafo_out_files = collect(args.trafo_out) run_mode = (in_files and (out_files or trafo_out_files))\ and (args.ini is not None or args.dict_ini is not None) write_mode = args.write_ini is not None or args.write_dict_ini is not None ok = run_mode or write_mode if not ok: parser.error("either specify -in, -(trafo_)out and -(dict)ini for running " "the map aligner\nor -write(dict)ini for creating std " "ini file") defaults = getDefaultParameters() write_requested = writeParamsIfRequested(args, defaults) if not write_requested: updateDefaults(args, defaults) if not out_files and not trafo_out_files: parser.error("need -out or -trafo_out files") if out_files and len(out_files) != len(in_files): parser.error("need as many -out files as -in files") if trafo_out_files and len(trafo_out_files) != len(in_files): parser.error("need as many -trafo_out files as -in files") if args.reference_index is not None and args.reference_file is not None: parser.error("can only handle either reference:index or reference:file") if args.reference_index is not None: if args.reference_index <0 or args.reference_index >= len(in_files): parser.error("reference:index invalid") if args.reference_file is not None: if args.reference_file not in in_files: parser.error("reference_file not in input files") align(in_files, out_files, trafo_out_files, args.reference_index or 0, args.reference_file or "", defaults) if __name__ == "__main__": main()

34.976563

87

0.580634

import argparse import pyopenms as pms from common import addDataProcessing, writeParamsIfRequested, updateDefaults def align(in_files, out_files, out_trafos, reference_index, reference_file, params): in_types = set(pms.FileHandler.getType(in_) for in_ in in_files) if in_types <= set((pms.Type.MZML, pms.Type.MZXML, pms.Type.MZDATA)): align_features = False elif in_types == set((pms.Type.FEATUREXML,)): align_features = True else: raise Exception("different kinds of input files") algorithm = pms.MapAlignmentAlgorithmPoseClustering() alignment_params = params.copy("algorithm:", True) algorithm.setParameters(alignment_params) algorithm.setLogType(pms.LogType.CMD) plog = pms.ProgressLogger() plog.setLogType(pms.LogType.CMD) if reference_file: file_ = reference_file elif reference_index > 0: file_ = in_files[reference_index-1] else: sizes = [] if align_features: fh = pms.FeatureXMLFile() plog.startProgress(0, len(in_files), "Determine Reference map") for i, in_f in enumerate(in_files): sizes.append((fh.loadSize(in_f), in_f)) plog.setProgress(i) else: fh = pms.MzMLFile() mse = pms.MSExperiment() plog.startProgress(0, len(in_files), "Determine Reference map") for i, in_f in enumerate(in_files): fh.load(in_f, mse) mse.updateRanges() sizes.append((mse.getSize(), in_f)) plog.setProgress(i) plog.endProgress() __, file_ = max(sizes) f_fmxl = pms.FeatureXMLFile() if not out_files: options = f_fmxl.getOptions() options.setLoadConvexHull(False) options.setLoadSubordinates(False) f_fmxl.setOptions(options) if align_features: map_ref = pms.FeatureMap() f_fxml_tmp = pms.FeatureXMLFile() options = f_fmxl.getOptions() options.setLoadConvexHull(False) options.setLoadSubordinates(False) f_fxml_tmp.setOptions(options) f_fxml_tmp.load(file_, map_ref) algorithm.setReference(map_ref) else: map_ref = pms.MSExperiment() pms.MzMLFile().load(file_, map_ref) algorithm.setReference(map_ref) plog.startProgress(0, len(in_files), "Align input maps") for i, in_file in enumerate(in_files): trafo = pms.TransformationDescription() if align_features: map_ = pms.FeatureMap() f_fxml_tmp = pms.FeatureXMLFile() f_fxml_tmp.setOptions(f_fmxl.getOptions()) f_fxml_tmp.load(in_file, map_) if in_file == file_: trafo.fitModel("identity") else: algorithm.align(map_, trafo) if out_files: pms.MapAlignmentTransformer.transformSingleFeatureMap(map_, trafo) addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT) f_fxml_tmp.store(out_files[i], map_) else: map_ = pms.MSExperiment() pms.MzMLFile().load(in_file, map_) if in_file == file_: trafo.fitModel("identity") else: algorithm.align(map_, trafo) if out_files: pms.MapAlignmentTransformer.transformSinglePeakMap(map_, trafo) addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT) pms.MzMLFile().store(out_files[i], map_) if out_trafos: pms.TransformationXMLFile().store(out_trafos[i], trafo) plog.setProgress(i+1) plog.endProgress() def getModelDefaults(default_model): params = pms.Param() params.setValue("type", default_model, "Type of model") model_types = [ "linear", "interpolated"] if default_model not in model_types: model_types.insert(0, default_model) params.setValidStrings("type", model_types) model_params = pms.Param() pms.TransformationModelLinear.getDefaultParameters(model_params) params.insert("linear:", model_params) params.setSectionDescription("linear", "Parameters for 'linear' model") pms.TransformationModelInterpolated.getDefaultParameters(model_params) entry = model_params.getEntry("interpolation_type") interpolation_types = entry.valid_strings model_params.setValidStrings("interpolation_type", interpolation_types) params.insert("interpolated:", model_params) params.setSectionDescription("interpolated", "Parameters for 'interpolated' model") return params def getDefaultParameters(): model_param = getModelDefaults("linear") algo_param = pms.MapAlignmentAlgorithmPoseClustering().getParameters() default = pms.Param() default.insert("model:", model_param) default.insert("algorithm:", algo_param) return default def main(): parser = argparse.ArgumentParser(description="PeakPickerHiRes") parser.add_argument("-in", action="append", type=str, dest="in_", metavar="input_file", ) parser.add_argument("-seeds", action="store", type=str, metavar="seeds_file", ) parser.add_argument("-out", action="append", type=str, metavar="output_file", ) parser.add_argument("-trafo_out", action="append", type=str, metavar="output_file", ) parser.add_argument("-ini", action="store", type=str, metavar="ini_file", ) parser.add_argument("-dict_ini", action="store", type=str, metavar="python_dict_ini_file", ) parser.add_argument("-write_ini", action="store", type=str, metavar="ini_file", ) parser.add_argument("-write_dict_ini", action="store", type=str, metavar="python_dict_ini_file", ) parser.add_argument("-reference:file", action="store", type=str, metavar="reference_file", dest="reference_file", ) parser.add_argument("-reference:index", action="store", type=int, metavar="reference_index", dest="reference_index", ) args = parser.parse_args() def collect(args): return [f.strip() for arg in args or [] for f in arg.split(",")] in_files = collect(args.in_) out_files = collect(args.out) trafo_out_files = collect(args.trafo_out) run_mode = (in_files and (out_files or trafo_out_files))\ and (args.ini is not None or args.dict_ini is not None) write_mode = args.write_ini is not None or args.write_dict_ini is not None ok = run_mode or write_mode if not ok: parser.error("either specify -in, -(trafo_)out and -(dict)ini for running " "the map aligner\nor -write(dict)ini for creating std " "ini file") defaults = getDefaultParameters() write_requested = writeParamsIfRequested(args, defaults) if not write_requested: updateDefaults(args, defaults) if not out_files and not trafo_out_files: parser.error("need -out or -trafo_out files") if out_files and len(out_files) != len(in_files): parser.error("need as many -out files as -in files") if trafo_out_files and len(trafo_out_files) != len(in_files): parser.error("need as many -trafo_out files as -in files") if args.reference_index is not None and args.reference_file is not None: parser.error("can only handle either reference:index or reference:file") if args.reference_index is not None: if args.reference_index <0 or args.reference_index >= len(in_files): parser.error("reference:index invalid") if args.reference_file is not None: if args.reference_file not in in_files: parser.error("reference_file not in input files") align(in_files, out_files, trafo_out_files, args.reference_index or 0, args.reference_file or "", defaults) if __name__ == "__main__": main()

true

1c312aba90f987553c7c73457f580416290b3f39

2,727

py

Python

collections/ansible_collections/redhat/satellite/plugins/modules/hardware_model.py

hindman-redhat/automated-smart-management-2

5450ccd71f2a4ba568a7f11b03466e1554ae0087

[ "MIT" ]

null

collections/ansible_collections/redhat/satellite/plugins/modules/hardware_model.py

hindman-redhat/automated-smart-management-2

5450ccd71f2a4ba568a7f11b03466e1554ae0087

[ "MIT" ]

null

collections/ansible_collections/redhat/satellite/plugins/modules/hardware_model.py

hindman-redhat/automated-smart-management-2

5450ccd71f2a4ba568a7f11b03466e1554ae0087

[ "MIT" ]

2

2021-03-30T14:26:02.000Z

2021-04-01T18:17:29.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # (c) 2020, Evgeni Golov <evgeni@golov.de> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' --- module: hardware_model version_added: 1.0.0 short_description: Manage Hardware Models description: - Manage hardware models author: - "Evgeni Golov (@evgeni)" options: name: description: - Name of the hardware model required: true type: str info: description: - General description of the hardware model type: str vendor_class: description: - The class of the machine as reported by the OpenBoot PROM. - This is primarily used by Solaris SPARC builds and can be left blank for other architectures. type: str hardware_model: description: - The class of CPU supplied in this machine. - This is primarily used by Sparc Solaris builds and can be left blank for other architectures. type: str extends_documentation_fragment: - redhat.satellite.foreman - redhat.satellite.foreman.entity_state ''' EXAMPLES = ''' - name: "Create ACME Laptop model" redhat.satellite.hardware_model: username: "admin" password: "changeme" server_url: "https://satellite.example.com" name: "acme laptop" info: "this is the acme laptop" state: present ''' RETURN = ''' entity: description: Final state of the affected entities grouped by their type. returned: success type: dict contains: hardware_models: description: List of hardware models. type: list elements: dict ''' from ansible_collections.redhat.satellite.plugins.module_utils.foreman_helper import ForemanEntityAnsibleModule class ForemanModelModule(ForemanEntityAnsibleModule): pass def main(): module = ForemanModelModule( foreman_spec=dict( name=dict(required=True), info=dict(), vendor_class=dict(), hardware_model=dict(), ), ) with module.api_connection(): module.run() if __name__ == '__main__': main()

26.475728

111

0.703337

from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' --- module: hardware_model version_added: 1.0.0 short_description: Manage Hardware Models description: - Manage hardware models author: - "Evgeni Golov (@evgeni)" options: name: description: - Name of the hardware model required: true type: str info: description: - General description of the hardware model type: str vendor_class: description: - The class of the machine as reported by the OpenBoot PROM. - This is primarily used by Solaris SPARC builds and can be left blank for other architectures. type: str hardware_model: description: - The class of CPU supplied in this machine. - This is primarily used by Sparc Solaris builds and can be left blank for other architectures. type: str extends_documentation_fragment: - redhat.satellite.foreman - redhat.satellite.foreman.entity_state ''' EXAMPLES = ''' - name: "Create ACME Laptop model" redhat.satellite.hardware_model: username: "admin" password: "changeme" server_url: "https://satellite.example.com" name: "acme laptop" info: "this is the acme laptop" state: present ''' RETURN = ''' entity: description: Final state of the affected entities grouped by their type. returned: success type: dict contains: hardware_models: description: List of hardware models. type: list elements: dict ''' from ansible_collections.redhat.satellite.plugins.module_utils.foreman_helper import ForemanEntityAnsibleModule class ForemanModelModule(ForemanEntityAnsibleModule): pass def main(): module = ForemanModelModule( foreman_spec=dict( name=dict(required=True), info=dict(), vendor_class=dict(), hardware_model=dict(), ), ) with module.api_connection(): module.run() if __name__ == '__main__': main()

true

1c312b2883b1becc78033db9aad4d87b74a36a8d

498

py

Python

lib/salus/scanners/report_python_modules.py

greaterninja/salus

2f5f56aa8abe252dea9bfbe8a17e086ae3eae6fa

[ "Apache-2.0" ]

null

lib/salus/scanners/report_python_modules.py

greaterninja/salus

2f5f56aa8abe252dea9bfbe8a17e086ae3eae6fa

[ "Apache-2.0" ]

null

lib/salus/scanners/report_python_modules.py

greaterninja/salus

2f5f56aa8abe252dea9bfbe8a17e086ae3eae6fa

[ "Apache-2.0" ]

null

import json try: # for pip >= 10 from pip._internal.req import parse_requirements except ImportError: # for pip <= 9.0.3 from pip.req import parse_requirements deps = parse_requirements('requirements.txt', session="_") dependencies = {} for dependency in deps: if hasattr(dependency.req, 'key'): dependencies[dependency.req.key] = str(dependency.req.specifier) else: dependencies[dependency.req.name] = str(dependency.req.specifier) print(json.dumps(dependencies))

26.210526

71

0.726908

import json try: from pip._internal.req import parse_requirements except ImportError: from pip.req import parse_requirements deps = parse_requirements('requirements.txt', session="_") dependencies = {} for dependency in deps: if hasattr(dependency.req, 'key'): dependencies[dependency.req.key] = str(dependency.req.specifier) else: dependencies[dependency.req.name] = str(dependency.req.specifier) print(json.dumps(dependencies))

true

1c312b83d918655938a1ccbc2c6b326c2c5b74d4

1,637

py

Python

Vue_api_test/Myapp/views.py

archerckk/Vue_api_test

94d37f2430ff6ead0aa64459079b96429298f8cc

[ "MIT" ]

null

Vue_api_test/Myapp/views.py

archerckk/Vue_api_test

94d37f2430ff6ead0aa64459079b96429298f8cc

[ "MIT" ]

null

Vue_api_test/Myapp/views.py

archerckk/Vue_api_test

94d37f2430ff6ead0aa64459079b96429298f8cc

[ "MIT" ]

null

import json from django.shortcuts import render from django.http import JsonResponse, HttpResponse # Create your views here. def index(request): return render(request, 'index.html') value_list = ['apple', 'pear', 'banana'] def load_login(request): return render(request, 'login.html') def search_key(request): method = request.method if method == 'POST': body = json.loads(request.body) if "key" not in body: return JsonResponse([], safe=False) key = body['key'] ret = [] for i in value_list: if key in i: ret.append(i) return JsonResponse(ret, safe=False) else: return HttpResponse(status=404) fruites = ['apple', 'pear', 'banana', 'orange'] def get_fruits(request): return JsonResponse(fruites, safe=False) def login(request): users = [ {'user': 'user1', 'psw': 'user1'}, {'user': 'user2', 'psw': 'user2'} ] method = request.method if method == 'POST': body = json.loads(request.body) if "name" not in body or "psw" not in body: return JsonResponse({'success': False}, safe=False) for user in users: if user['user'] == body['name'] and user['psw'] == body['psw']: return JsonResponse({'success': True}, safe=False) else: return JsonResponse({'success': False}, safe=False) else: return HttpResponse(status=404) def style_demo(request): return render(request,'style_demo.html') def component_info(request): return render(request,'component_info.html')

21.539474

75

0.597434

import json from django.shortcuts import render from django.http import JsonResponse, HttpResponse def index(request): return render(request, 'index.html') value_list = ['apple', 'pear', 'banana'] def load_login(request): return render(request, 'login.html') def search_key(request): method = request.method if method == 'POST': body = json.loads(request.body) if "key" not in body: return JsonResponse([], safe=False) key = body['key'] ret = [] for i in value_list: if key in i: ret.append(i) return JsonResponse(ret, safe=False) else: return HttpResponse(status=404) fruites = ['apple', 'pear', 'banana', 'orange'] def get_fruits(request): return JsonResponse(fruites, safe=False) def login(request): users = [ {'user': 'user1', 'psw': 'user1'}, {'user': 'user2', 'psw': 'user2'} ] method = request.method if method == 'POST': body = json.loads(request.body) if "name" not in body or "psw" not in body: return JsonResponse({'success': False}, safe=False) for user in users: if user['user'] == body['name'] and user['psw'] == body['psw']: return JsonResponse({'success': True}, safe=False) else: return JsonResponse({'success': False}, safe=False) else: return HttpResponse(status=404) def style_demo(request): return render(request,'style_demo.html') def component_info(request): return render(request,'component_info.html')

true

1c312b94a31e9d253cc05a00ec83f6f345642192

3,506

py

Python

flight/state_settings.py

pieperm/IARC-2020

a90bfe830ea2ceced59e8f2e7b54862dda42f5a3

[ "MIT" ]

12

2019-10-10T22:17:45.000Z

2021-09-14T23:54:02.000Z

flight/state_settings.py

pieperm/IARC-2020

a90bfe830ea2ceced59e8f2e7b54862dda42f5a3

[ "MIT" ]

178

2019-10-29T16:28:02.000Z

2021-07-26T17:15:31.000Z

flight/state_settings.py

pieperm/IARC-2020

a90bfe830ea2ceced59e8f2e7b54862dda42f5a3

[ "MIT" ]

6

2019-10-09T00:20:27.000Z

2021-09-28T00:24:00.000Z

"""Class to contain setters and getters for settings in various flight states""" DEFAULT_EARLY_LAPS: int = 2 DEFAULT_RETURN_LAPS: int = 2 DEFAULT_VISION_TEST: str = "module" DEFAULT_RUN_TITLE: str = "N/A" DEFAULT_RUN_DESCRIPTION: str = "N/A" class StateSettings: def __init__(self): """Default constructor results in default settings""" # Takeoff settings self.simple_takeoff: bool = False # EarlyLaps settings self.do_early_laps: bool = True self.num_early_laps: int = DEFAULT_EARLY_LAPS # ToMast settings self.go_to_mast: bool = False # DetectModule settings self.detect_module: bool = False self.detect_mast_text: bool = False self.vision_test_type: str = DEFAULT_VISION_TEST # ReturnLaps settings self.do_return_laps: bool = False self.num_return_laps: int = DEFAULT_RETURN_LAPS # Other settings self.run_title: str = DEFAULT_RUN_TITLE self.run_description: str = DEFAULT_RUN_DESCRIPTION # ---- Takeoff settings ---- # def enable_simple_takeoff(self, simple_takeoff: bool) -> None: """ Setter for whether to perform simple takeoff instead of regular takeoff simple_takeoff(bool): True for drone to go straight up, False to behave normally """ self.simple_takeoff = simple_takeoff # ---- EarlyLaps settings ---- # def enable_early_laps(self, do_early_laps: bool) -> None: """Setter for whether to do early laps""" self.do_early_laps = do_early_laps def set_number_of_early_laps(self, num_laps: int) -> None: """Setter for how many early laps to do""" self.num_early_laps = num_laps # ---- ToMast settings ---- # def enable_to_mast(self, go_to_mast: bool) -> None: """Setter for whether to go to the mast""" self.go_to_mast = go_to_mast # ---- DetectModule settings ---- # def enable_module_detection(self, detect_module: bool) -> None: """Setter for whether to detect the module""" self.detect_module = detect_module def enable_text_detection(self, detect_text: bool) -> None: """Setter for whether to detect the mast text""" self.detect_mast_text = detect_text def set_vision_test(self, test_type: str) -> None: """ Setter for the type of vision test to run This should only generally only be used with simple takeoff test_type(str) 'module' for module detection or 'text' for mast text detection """ if test_type == "module" or test_type == "text": self.vision_test_type = test_type else: raise ValueError(f"test_type must be 'module' or 'text', got {test_type}") # ---- ReturnLaps settings ---- # def enable_return_laps(self, do_return_laps: bool) -> None: """Setter for whether to do return laps""" self.do_return_laps = do_return_laps def set_number_of_return_laps(self, num_laps: int) -> None: """Setter for how many return laps to do""" self.num_return_laps = num_laps # ---- Other settings ---- # def set_run_title(self, title: str) -> None: """Set a title for the run/test to be output in logging""" self.run_title = title def set_run_description(self, description: str) -> None: """Set a description for the run/test to be output in logging""" self.run_description = description

34.372549

92

0.648032

DEFAULT_EARLY_LAPS: int = 2 DEFAULT_RETURN_LAPS: int = 2 DEFAULT_VISION_TEST: str = "module" DEFAULT_RUN_TITLE: str = "N/A" DEFAULT_RUN_DESCRIPTION: str = "N/A" class StateSettings: def __init__(self): self.simple_takeoff: bool = False self.do_early_laps: bool = True self.num_early_laps: int = DEFAULT_EARLY_LAPS self.go_to_mast: bool = False self.detect_module: bool = False self.detect_mast_text: bool = False self.vision_test_type: str = DEFAULT_VISION_TEST self.do_return_laps: bool = False self.num_return_laps: int = DEFAULT_RETURN_LAPS self.run_title: str = DEFAULT_RUN_TITLE self.run_description: str = DEFAULT_RUN_DESCRIPTION def enable_simple_takeoff(self, simple_takeoff: bool) -> None: self.simple_takeoff = simple_takeoff def enable_early_laps(self, do_early_laps: bool) -> None: self.do_early_laps = do_early_laps def set_number_of_early_laps(self, num_laps: int) -> None: self.num_early_laps = num_laps def enable_to_mast(self, go_to_mast: bool) -> None: self.go_to_mast = go_to_mast def enable_module_detection(self, detect_module: bool) -> None: self.detect_module = detect_module def enable_text_detection(self, detect_text: bool) -> None: self.detect_mast_text = detect_text def set_vision_test(self, test_type: str) -> None: if test_type == "module" or test_type == "text": self.vision_test_type = test_type else: raise ValueError(f"test_type must be 'module' or 'text', got {test_type}") def enable_return_laps(self, do_return_laps: bool) -> None: self.do_return_laps = do_return_laps def set_number_of_return_laps(self, num_laps: int) -> None: self.num_return_laps = num_laps def set_run_title(self, title: str) -> None: self.run_title = title def set_run_description(self, description: str) -> None: self.run_description = description

true

1c312c16399deb036897c7350ef9d6f9245e655e

41,208

py

Python

tests/data_context/test_data_context.py

cicdw/great_expectations

0aecddf7da591df19389c8abadbb1700a51b8739

[ "Apache-2.0" ]

null

tests/data_context/test_data_context.py

cicdw/great_expectations

0aecddf7da591df19389c8abadbb1700a51b8739

[ "Apache-2.0" ]

null

tests/data_context/test_data_context.py

cicdw/great_expectations

0aecddf7da591df19389c8abadbb1700a51b8739

[ "Apache-2.0" ]

null

import json import os import shutil from collections import OrderedDict import pytest from ruamel.yaml import YAML from great_expectations.core import ( ExpectationConfiguration, ExpectationSuite, expectationSuiteSchema, ) from great_expectations.data_context import ( BaseDataContext, DataContext, ExplorerDataContext, ) from great_expectations.data_context.store import ExpectationsStore from great_expectations.data_context.types.base import DataContextConfig from great_expectations.data_context.types.resource_identifiers import ( ExpectationSuiteIdentifier, ) from great_expectations.data_context.util import ( file_relative_path, safe_mmkdir, ) from great_expectations.dataset import Dataset from great_expectations.datasource import Datasource from great_expectations.datasource.types.batch_kwargs import PathBatchKwargs from great_expectations.exceptions import ( BatchKwargsError, ConfigNotFoundError, DataContextError, ) from great_expectations.util import gen_directory_tree_str from tests.test_utils import safe_remove try: from unittest import mock except ImportError: import mock try: from unittest import mock except ImportError: import mock yaml = YAML() @pytest.fixture() def parameterized_expectation_suite(): fixture_path = file_relative_path( __file__, "../test_fixtures/expectation_suites/parameterized_expectation_suite_fixture.json", ) with open(fixture_path, "r",) as suite: return json.load(suite) def test_create_duplicate_expectation_suite(titanic_data_context): # create new expectation suite assert titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite") # attempt to create expectation suite with name that already exists on data asset with pytest.raises(DataContextError): titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite") # create expectation suite with name that already exists on data asset, but pass overwrite_existing=True assert titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite", overwrite_existing=True) def test_get_available_data_asset_names_with_one_datasource_including_a_single_generator(empty_data_context, filesystem_csv): empty_data_context.add_datasource("my_datasource", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": str(filesystem_csv) } } ) available_asset_names = empty_data_context.get_available_data_asset_names() assert set(available_asset_names["my_datasource"]["subdir_reader"]["names"]) == {('f3', 'directory'), ('f2', 'file'), ('f1', 'file')} def test_get_available_data_asset_names_with_one_datasource_without_a_generator_returns_empty_dict( empty_data_context, ): empty_data_context.add_datasource( "my_datasource", module_name="great_expectations.datasource", class_name="PandasDatasource", ) obs = empty_data_context.get_available_data_asset_names() assert obs == {"my_datasource": {}} def test_get_available_data_asset_names_with_multiple_datasources_with_and_without_generators( empty_data_context ): """Test datasources with and without generators.""" context = empty_data_context connection_kwargs = {"drivername": "sqlite"} context.add_datasource( "first", class_name="SqlAlchemyDatasource", generators={"foo": {"class_name": "TableBatchKwargsGenerator", }}, **connection_kwargs ) context.add_datasource( "second", class_name="SqlAlchemyDatasource", **connection_kwargs ) context.add_datasource( "third", class_name="SqlAlchemyDatasource", generators={"bar": {"class_name": "TableBatchKwargsGenerator", }}, **connection_kwargs ) obs = context.get_available_data_asset_names() assert isinstance(obs, dict) assert set(obs.keys()) == {"first", "second", "third"} assert obs == { "first": {"foo": {"is_complete_list": True, "names": []}}, "second": {}, "third": {"bar": {"is_complete_list": True, "names": []}}, } def test_list_expectation_suite_keys(data_context): assert data_context.list_expectation_suites() == [ ExpectationSuiteIdentifier( expectation_suite_name="my_dag_node.default" ) ] def test_get_existing_expectation_suite(data_context): expectation_suite = data_context.get_expectation_suite('my_dag_node.default') assert expectation_suite.expectation_suite_name == 'my_dag_node.default' assert len(expectation_suite.expectations) == 2 def test_get_new_expectation_suite(data_context): expectation_suite = data_context.create_expectation_suite('this_data_asset_does_not_exist.default') assert expectation_suite.expectation_suite_name == 'this_data_asset_does_not_exist.default' assert len(expectation_suite.expectations) == 0 def test_save_expectation_suite(data_context): expectation_suite = data_context.create_expectation_suite('this_data_asset_config_does_not_exist.default') expectation_suite.expectations.append(ExpectationConfiguration( expectation_type="expect_table_row_count_to_equal", kwargs={ "value": 10 })) data_context.save_expectation_suite(expectation_suite) expectation_suite_saved = data_context.get_expectation_suite('this_data_asset_config_does_not_exist.default') assert expectation_suite.expectations == expectation_suite_saved.expectations def test_compile_evaluation_parameter_dependencies(data_context): assert data_context._evaluation_parameter_dependencies == {} data_context._compile_evaluation_parameter_dependencies() assert data_context._evaluation_parameter_dependencies == { 'source_diabetes_data.default': [{ "metric_kwargs_id": { "column=patient_nbr": ["expect_column_unique_value_count_to_be_between.result.observed_value"] } }], 'source_patient_data.default': ["expect_table_row_count_to_equal.result.observed_value"] } def test_list_datasources(data_context): datasources = data_context.list_datasources() assert OrderedDict(datasources) == OrderedDict([ { 'name': 'mydatasource', 'class_name': 'PandasDatasource' } ]) data_context.add_datasource("second_pandas_source", module_name="great_expectations.datasource", class_name="PandasDatasource", ) datasources = data_context.list_datasources() assert OrderedDict(datasources) == OrderedDict([ { 'name': 'mydatasource', 'class_name': 'PandasDatasource' }, { 'name': 'second_pandas_source', 'class_name': 'PandasDatasource' } ]) def test_data_context_get_validation_result(titanic_data_context): """ Test that validation results can be correctly fetched from the configured results store """ profiling_results = titanic_data_context.profile_datasource("mydatasource") all_validation_result = titanic_data_context.get_validation_result( "mydatasource.mygenerator.Titanic.BasicDatasetProfiler", run_id="profiling" ) assert len(all_validation_result.results) == 51 failed_validation_result = titanic_data_context.get_validation_result( "mydatasource.mygenerator.Titanic.BasicDatasetProfiler", run_id="profiling", failed_only=True, ) assert len(failed_validation_result.results) == 8 def test_data_context_get_datasource(titanic_data_context): isinstance(titanic_data_context.get_datasource("mydatasource"), Datasource) def test_data_context_get_datasource_on_non_existent_one_raises_helpful_error(titanic_data_context): with pytest.raises(ValueError): _ = titanic_data_context.get_datasource("fakey_mc_fake") def test_data_context_profile_datasource_on_non_existent_one_raises_helpful_error(titanic_data_context): with pytest.raises(ValueError): _ = titanic_data_context.profile_datasource("fakey_mc_fake") @pytest.mark.rendered_output def test_render_full_static_site_from_empty_project(tmp_path_factory, filesystem_csv_3): # TODO : Use a standard test fixture # TODO : Have that test fixture copy a directory, rather than building a new one from scratch base_dir = str(tmp_path_factory.mktemp("project_dir")) project_dir = os.path.join(base_dir, "project_path") os.mkdir(project_dir) os.makedirs(os.path.join(project_dir, "data")) os.makedirs(os.path.join(project_dir, "data/titanic")) shutil.copy( file_relative_path(__file__, "../test_sets/Titanic.csv"), str(os.path.join(project_dir, "data/titanic/Titanic.csv")) ) os.makedirs(os.path.join(project_dir, "data/random")) shutil.copy( os.path.join(filesystem_csv_3, "f1.csv"), str(os.path.join(project_dir, "data/random/f1.csv")) ) shutil.copy( os.path.join(filesystem_csv_3, "f2.csv"), str(os.path.join(project_dir, "data/random/f2.csv")) ) assert gen_directory_tree_str(project_dir) == """\ project_path/ data/ random/ f1.csv f2.csv titanic/ Titanic.csv """ context = DataContext.create(project_dir) ge_directory = os.path.join(project_dir, "great_expectations") context.add_datasource("titanic", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": os.path.join(project_dir, "data/titanic/") } } ) context.add_datasource("random", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": os.path.join(project_dir, "data/random/") } } ) context.profile_datasource("titanic") # Replicate the batch id of the batch that will be profiled in order to generate the file path of the # validation result titanic_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/titanic/Titanic.csv'), 'datasource': 'titanic'} ).to_id() tree_str = gen_directory_tree_str(project_dir) assert tree_str == """project_path/ data/ random/ f1.csv f2.csv titanic/ Titanic.csv great_expectations/ .gitignore great_expectations.yml expectations/ titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler.json notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler/ profiling/ {}.json """.format(titanic_profiled_batch_id) context.profile_datasource("random") context.build_data_docs() f1_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/random/f1.csv'), 'datasource': 'random'} ).to_id() f2_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/random/f2.csv'), 'datasource': 'random'} ).to_id() data_docs_dir = os.path.join(project_dir, "great_expectations/uncommitted/data_docs") observed = gen_directory_tree_str(data_docs_dir) assert observed == """\ data_docs/ local_site/ index.html expectations/ random/ subdir_reader/ f1/ BasicDatasetProfiler.html f2/ BasicDatasetProfiler.html titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler.html static/ fonts/ HKGrotesk/ HKGrotesk-Bold.otf HKGrotesk-BoldItalic.otf HKGrotesk-Italic.otf HKGrotesk-Light.otf HKGrotesk-LightItalic.otf HKGrotesk-Medium.otf HKGrotesk-MediumItalic.otf HKGrotesk-Regular.otf HKGrotesk-SemiBold.otf HKGrotesk-SemiBoldItalic.otf images/ favicon.ico glossary_scroller.gif iterative-dev-loop.png logo-long-vector.svg logo-long.png short-logo-vector.svg short-logo.png validation_failed_unexpected_values.gif styles/ data_docs_custom_styles_template.css data_docs_default_styles.css validations/ random/ subdir_reader/ f1/ BasicDatasetProfiler/ profiling/ {0:s}.html f2/ BasicDatasetProfiler/ profiling/ {1:s}.html titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler/ profiling/ {2:s}.html """.format(f1_profiled_batch_id, f2_profiled_batch_id, titanic_profiled_batch_id) # save data_docs locally safe_mmkdir("./tests/data_context/output") safe_mmkdir("./tests/data_context/output/data_docs") if os.path.isdir("./tests/data_context/output/data_docs"): shutil.rmtree("./tests/data_context/output/data_docs") shutil.copytree( os.path.join( ge_directory, "uncommitted/data_docs/" ), "./tests/data_context/output/data_docs" ) def test_add_store(empty_data_context): assert "my_new_store" not in empty_data_context.stores.keys() assert "my_new_store" not in empty_data_context.get_config()["stores"] new_store = empty_data_context.add_store( "my_new_store", { "module_name": "great_expectations.data_context.store", "class_name": "ExpectationsStore", } ) assert "my_new_store" in empty_data_context.stores.keys() assert "my_new_store" in empty_data_context.get_config()["stores"] assert isinstance(new_store, ExpectationsStore) @pytest.fixture def basic_data_context_config(): return DataContextConfig(**{ "commented_map": {}, "config_version": 1, "plugins_directory": "plugins/", "evaluation_parameter_store_name": "evaluation_parameter_store", "validations_store_name": "does_not_have_to_be_real", "expectations_store_name": "expectations_store", "config_variables_file_path": "uncommitted/config_variables.yml", "datasources": {}, "stores": { "expectations_store": { "class_name": "ExpectationsStore", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "expectations/", }, }, "evaluation_parameter_store" : { "module_name": "great_expectations.data_context.store", "class_name": "EvaluationParameterStore", } }, "data_docs_sites": {}, "validation_operators": { "default": { "class_name": "ActionListValidationOperator", "action_list": [] } } }) def test_ExplorerDataContext(titanic_data_context): context_root_directory = titanic_data_context.root_directory explorer_data_context = ExplorerDataContext(context_root_directory) assert explorer_data_context._expectation_explorer_manager def test_ConfigOnlyDataContext__initialization(tmp_path_factory, basic_data_context_config): config_path = str(tmp_path_factory.mktemp('test_ConfigOnlyDataContext__initialization__dir')) context = BaseDataContext( basic_data_context_config, config_path, ) assert context.root_directory.split("/")[-1] == "test_ConfigOnlyDataContext__initialization__dir0" assert context.plugins_directory.split("/")[-3:] == ["test_ConfigOnlyDataContext__initialization__dir0", "plugins",""] def test__normalize_absolute_or_relative_path(tmp_path_factory, basic_data_context_config): config_path = str(tmp_path_factory.mktemp('test__normalize_absolute_or_relative_path__dir')) context = BaseDataContext( basic_data_context_config, config_path, ) assert str(os.path.join("test__normalize_absolute_or_relative_path__dir0", "yikes")) in context._normalize_absolute_or_relative_path("yikes") assert "test__normalize_absolute_or_relative_path__dir" not in context._normalize_absolute_or_relative_path("/yikes") assert "/yikes" == context._normalize_absolute_or_relative_path("/yikes") def test_load_data_context_from_environment_variables(tmp_path_factory): curdir = os.path.abspath(os.getcwd()) try: project_path = str(tmp_path_factory.mktemp('data_context')) context_path = os.path.join(project_path, "great_expectations") safe_mmkdir(context_path) os.chdir(context_path) with pytest.raises(DataContextError) as err: DataContext.find_context_root_dir() assert isinstance(err.value, ConfigNotFoundError) shutil.copy(file_relative_path(__file__, "../test_fixtures/great_expectations_basic.yml"), str(os.path.join(context_path, "great_expectations.yml"))) os.environ["GE_HOME"] = context_path assert DataContext.find_context_root_dir() == context_path except Exception: raise finally: # Make sure we unset the environment variable we're using if "GE_HOME" in os.environ: del os.environ["GE_HOME"] os.chdir(curdir) def test_data_context_updates_expectation_suite_names(data_context): # A data context should update the data_asset_name and expectation_suite_name of expectation suites # that it creates when it saves them. expectation_suites = data_context.list_expectation_suites() # We should have a single expectation suite defined assert len(expectation_suites) == 1 expectation_suite_name = expectation_suites[0].expectation_suite_name # We'll get that expectation suite and then update its name and re-save, then verify that everything # has been properly updated expectation_suite = data_context.get_expectation_suite(expectation_suite_name) # Note we codify here the current behavior of having a string data_asset_name though typed ExpectationSuite objects # will enable changing that assert expectation_suite.expectation_suite_name == expectation_suite_name # We will now change the data_asset_name and then save the suite in three ways: # 1. Directly using the new name, # 2. Using a different name that should be overwritten # 3. Using the new name but having the context draw that from the suite # Finally, we will try to save without a name (deleting it first) to demonstrate that saving will fail. expectation_suite.expectation_suite_name = 'a_new_suite_name' data_context.save_expectation_suite( expectation_suite=expectation_suite, expectation_suite_name='a_new_suite_name' ) fetched_expectation_suite = data_context.get_expectation_suite('a_new_suite_name') assert fetched_expectation_suite.expectation_suite_name == 'a_new_suite_name' # 2. Using a different name that should be overwritten data_context.save_expectation_suite( expectation_suite=expectation_suite, expectation_suite_name='a_new_new_suite_name' ) fetched_expectation_suite = data_context.get_expectation_suite('a_new_new_suite_name') assert fetched_expectation_suite.expectation_suite_name == 'a_new_new_suite_name' # Check that the saved name difference is actually persisted on disk with open(os.path.join( data_context.root_directory, "expectations", "a_new_new_suite_name.json" ), 'r') as suite_file: loaded_suite = expectationSuiteSchema.load(json.load(suite_file)).data assert loaded_suite.expectation_suite_name == 'a_new_new_suite_name' # 3. Using the new name but having the context draw that from the suite expectation_suite.expectation_suite_name = "a_third_suite_name" data_context.save_expectation_suite( expectation_suite=expectation_suite ) fetched_expectation_suite = data_context.get_expectation_suite("a_third_suite_name") assert fetched_expectation_suite.expectation_suite_name == "a_third_suite_name" def test_data_context_create_does_not_raise_error_or_warning_if_ge_dir_exists(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) @pytest.fixture() def empty_context(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_dir = os.path.join(project_path, "great_expectations") assert os.path.isdir(ge_dir) assert os.path.isfile(os.path.join(ge_dir, DataContext.GE_YML)) context = DataContext(ge_dir) assert isinstance(context, DataContext) return context def test_data_context_does_ge_yml_exist_returns_true_when_it_does_exist(empty_context): ge_dir = empty_context.root_directory assert DataContext.does_config_exist_on_disk(ge_dir) == True def test_data_context_does_ge_yml_exist_returns_false_when_it_does_not_exist( empty_context, ): ge_dir = empty_context.root_directory # mangle project safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.does_config_exist_on_disk(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_true_when_it_has_a_datasource_configured_in_yml_file_on_disk( empty_context, ): ge_dir = empty_context.root_directory empty_context.add_datasource("arthur", **{"class_name": "PandasDatasource"}) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == True def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_datasource_configured_in_yml_file_on_disk( empty_context, ): ge_dir = empty_context.root_directory assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_ge_yml_file( empty_context, ): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_ge_dir( empty_context, ): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir)) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_the_project_has_an_invalid_config_file( empty_context, ): ge_dir = empty_context.root_directory with open(os.path.join(ge_dir, DataContext.GE_YML), "w") as yml: yml.write("this file: is not a valid ge config") assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_is_project_initialized_returns_true_when_its_valid_context_has_one_datasource_and_one_suite( empty_context, ): context = empty_context ge_dir = context.root_directory context.add_datasource("arthur", class_name="PandasDatasource") context.create_expectation_suite("dent") assert len(context.list_expectation_suites()) == 1 assert DataContext.is_project_initialized(ge_dir) == True def test_data_context_is_project_initialized_returns_true_when_its_valid_context_has_one_datasource_and_no_suites( empty_context, ): context = empty_context ge_dir = context.root_directory context.add_datasource("arthur", class_name="PandasDatasource") assert len(context.list_expectation_suites()) == 0 assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_its_valid_context_has_no_datasource( empty_context, ): ge_dir = empty_context.root_directory assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_config_yml_is_missing(empty_context): ge_dir = empty_context.root_directory # mangle project safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_dir_is_missing(empty_context): ge_dir = empty_context.root_directory # mangle project shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR)) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_data_docs_dir_is_missing(empty_context): ge_dir = empty_context.root_directory # mangle project shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "data_docs")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_validations_dir_is_missing(empty_context): ge_dir = empty_context.root_directory # mangle project shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "validations")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_config_variable_yml_is_missing(empty_context): ge_dir = empty_context.root_directory # mangle project safe_remove(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "config_variables.yml")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_create_raises_warning_and_leaves_existing_yml_untouched(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_yml = os.path.join( project_path, "great_expectations/great_expectations.yml" ) with open(ge_yml, "a") as ff: ff.write("# LOOK I WAS MODIFIED") with pytest.warns(UserWarning): DataContext.create(project_path) with open(ge_yml, "r") as ff: obs = ff.read() assert "# LOOK I WAS MODIFIED" in obs def test_data_context_create_makes_uncommitted_dirs_when_all_are_missing(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) # mangle the existing setup ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") shutil.rmtree(uncommitted_dir) # re-run create to simulate onboarding DataContext.create(project_path) obs = gen_directory_tree_str(ge_dir) print(obs) assert os.path.isdir(uncommitted_dir), "No uncommitted directory created" assert obs == """\ great_expectations/ .gitignore great_expectations.yml expectations/ notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ """ def test_data_context_create_does_nothing_if_all_uncommitted_dirs_exist(tmp_path_factory): expected = """\ great_expectations/ .gitignore great_expectations.yml expectations/ notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ """ project_path = str(tmp_path_factory.mktemp('stuff')) ge_dir = os.path.join(project_path, "great_expectations") DataContext.create(project_path) fixture = gen_directory_tree_str(ge_dir) print(fixture) assert fixture == expected # re-run create to simulate onboarding DataContext.create(project_path) obs = gen_directory_tree_str(ge_dir) assert obs == expected def test_data_context_do_all_uncommitted_dirs_exist(tmp_path_factory): expected = """\ uncommitted/ config_variables.yml data_docs/ validations/ """ project_path = str(tmp_path_factory.mktemp('stuff')) ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") DataContext.create(project_path) fixture = gen_directory_tree_str(uncommitted_dir) print(fixture) assert fixture == expected # Test that all exist assert DataContext.all_uncommitted_directories_exist(ge_dir) # remove a few shutil.rmtree(os.path.join(uncommitted_dir, "data_docs")) shutil.rmtree(os.path.join(uncommitted_dir, "validations")) # Test that not all exist assert not DataContext.all_uncommitted_directories_exist(project_path) def test_data_context_create_does_not_overwrite_existing_config_variables_yml(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") config_vars_yml = os.path.join(uncommitted_dir, "config_variables.yml") # modify config variables with open(config_vars_yml, "a") as ff: ff.write("# LOOK I WAS MODIFIED") # re-run create to simulate onboarding with pytest.warns(UserWarning): DataContext.create(project_path) with open(config_vars_yml, "r") as ff: obs = ff.read() print(obs) assert "# LOOK I WAS MODIFIED" in obs def test_scaffold_directories_and_notebooks(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("test_scaffold_directories_and_notebooks")) DataContext.scaffold_directories(empty_directory) DataContext.scaffold_notebooks(empty_directory) assert set(os.listdir(empty_directory)) == { 'plugins', 'expectations', '.gitignore', 'uncommitted', 'notebooks' } assert set(os.listdir(os.path.join(empty_directory, "uncommitted"))) == { 'data_docs', 'validations' } for subdir in DataContext.NOTEBOOK_SUBDIRECTORIES: subdir_path = os.path.join(empty_directory, "notebooks", subdir) assert set(os.listdir(subdir_path)) == { "validation_playground.ipynb" } def test_build_batch_kwargs(titanic_multibatch_data_context): batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic", partition_id="Titanic_1912") assert os.path.relpath("./data/titanic/Titanic_1912.csv") in batch_kwargs["path"] batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic", partition_id="Titanic_1911") assert os.path.relpath("./data/titanic/Titanic_1911.csv") in batch_kwargs["path"] paths = [] batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic") paths.append(os.path.basename(batch_kwargs["path"])) batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic") paths.append(os.path.basename(batch_kwargs["path"])) assert set(["Titanic_1912.csv", "Titanic_1911.csv"]) == set(paths) def test_existing_local_data_docs_urls_returns_url_on_project_with_no_datasources_and_a_site_configured(tmp_path_factory): """ This test ensures that a url will be returned for a default site even if a datasource is not configured, and docs are not built. """ empty_directory = str(tmp_path_factory.mktemp("another_empty_project")) DataContext.create(empty_directory) context = DataContext(os.path.join(empty_directory, DataContext.GE_DIR)) obs = context.get_docs_sites_urls() assert len(obs) == 1 assert obs[0].endswith("great_expectations/uncommitted/data_docs/local_site/index.html") def test_existing_local_data_docs_urls_returns_single_url_from_customized_local_site(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("yo_yo")) DataContext.create(empty_directory) ge_dir = os.path.join(empty_directory, DataContext.GE_DIR) context = DataContext(ge_dir) context._project_config["data_docs_sites"] = { "my_rad_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/some/local/path/" } } } # TODO Workaround project config programmatic config manipulation # statefulness issues by writing to disk and re-upping a new context context._save_project_config() context = DataContext(ge_dir) context.build_data_docs() expected_path = os.path.join(ge_dir, "uncommitted/data_docs/some/local/path/index.html") assert os.path.isfile(expected_path) obs = context.get_docs_sites_urls() assert obs == ["file://{}".format(expected_path)] def test_existing_local_data_docs_urls_returns_multiple_urls_from_customized_local_site(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("yo_yo_ma")) DataContext.create(empty_directory) ge_dir = os.path.join(empty_directory, DataContext.GE_DIR) context = DataContext(ge_dir) context._project_config["data_docs_sites"] = { "my_rad_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/some/path/" } }, "another_just_amazing_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/another/path/" } } } # TODO Workaround project config programmatic config manipulation # statefulness issues by writing to disk and re-upping a new context context._save_project_config() context = DataContext(ge_dir) context.build_data_docs() data_docs_dir = os.path.join(ge_dir, "uncommitted/data_docs/") path_1 = os.path.join(data_docs_dir, "some/path/index.html") path_2 = os.path.join(data_docs_dir, "another/path/index.html") for expected_path in [path_1, path_2]: assert os.path.isfile(expected_path) obs = context.get_docs_sites_urls() assert set(obs) == set([ "file://{}".format(path_1), "file://{}".format(path_2), ]) def test_load_config_variables_file(basic_data_context_config, tmp_path_factory): # Setup: base_path = str(tmp_path_factory.mktemp('test_load_config_variables_file')) safe_mmkdir(os.path.join(base_path, "uncommitted")) with open(os.path.join(base_path, "uncommitted", "dev_variables.yml"), "w") as outfile: yaml.dump({'env': 'dev'}, outfile) with open(os.path.join(base_path, "uncommitted", "prod_variables.yml"), "w") as outfile: yaml.dump({'env': 'prod'}, outfile) basic_data_context_config["config_variables_file_path"] = "uncommitted/${TEST_CONFIG_FILE_ENV}_variables.yml" try: # We should be able to load different files based on an environment variable os.environ["TEST_CONFIG_FILE_ENV"] = "dev" context = BaseDataContext(basic_data_context_config, context_root_dir=base_path) config_vars = context._load_config_variables_file() assert config_vars['env'] == 'dev' os.environ["TEST_CONFIG_FILE_ENV"] = "prod" context = BaseDataContext(basic_data_context_config, context_root_dir=base_path) config_vars = context._load_config_variables_file() assert config_vars['env'] == 'prod' except Exception: raise finally: # Make sure we unset the environment variable we're using del os.environ["TEST_CONFIG_FILE_ENV"] def test_list_expectation_suite_with_no_suites(titanic_data_context): observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == [] def test_list_expectation_suite_with_one_suite(titanic_data_context): titanic_data_context.create_expectation_suite('warning') observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == ['warning'] def test_list_expectation_suite_with_multiple_suites(titanic_data_context): titanic_data_context.create_expectation_suite('a.warning') titanic_data_context.create_expectation_suite('b.warning') titanic_data_context.create_expectation_suite('c.warning') observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == ['a.warning', 'b.warning', 'c.warning'] assert len(observed) == 3 def test_get_batch_raises_error_when_passed_a_non_string_type_for_suite_parameter( titanic_data_context, ): with pytest.raises(DataContextError): titanic_data_context.get_batch({}, 99) def test_get_batch_raises_error_when_passed_a_non_dict_or_batch_kwarg_type_for_batch_kwarg_parameter( titanic_data_context, ): with pytest.raises(BatchKwargsError): titanic_data_context.get_batch(99, "foo") def test_get_batch_when_passed_a_suite_name(titanic_data_context): context = titanic_data_context root_dir = context.root_directory batch_kwargs = { "datasource": "mydatasource", "path": os.path.join(root_dir, "..", "data", "Titanic.csv"), } context.create_expectation_suite("foo") assert context.list_expectation_suite_names() == ["foo"] batch = context.get_batch(batch_kwargs, "foo") assert isinstance(batch, Dataset) assert isinstance(batch.get_expectation_suite(), ExpectationSuite) def test_get_batch_when_passed_a_suite(titanic_data_context): context = titanic_data_context root_dir = context.root_directory batch_kwargs = { "datasource": "mydatasource", "path": os.path.join(root_dir, "..", "data", "Titanic.csv"), } context.create_expectation_suite("foo") assert context.list_expectation_suite_names() == ["foo"] suite = context.get_expectation_suite("foo") batch = context.get_batch(batch_kwargs, suite) assert isinstance(batch, Dataset) assert isinstance(batch.get_expectation_suite(), ExpectationSuite)

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import json import os import shutil from collections import OrderedDict import pytest from ruamel.yaml import YAML from great_expectations.core import ( ExpectationConfiguration, ExpectationSuite, expectationSuiteSchema, ) from great_expectations.data_context import ( BaseDataContext, DataContext, ExplorerDataContext, ) from great_expectations.data_context.store import ExpectationsStore from great_expectations.data_context.types.base import DataContextConfig from great_expectations.data_context.types.resource_identifiers import ( ExpectationSuiteIdentifier, ) from great_expectations.data_context.util import ( file_relative_path, safe_mmkdir, ) from great_expectations.dataset import Dataset from great_expectations.datasource import Datasource from great_expectations.datasource.types.batch_kwargs import PathBatchKwargs from great_expectations.exceptions import ( BatchKwargsError, ConfigNotFoundError, DataContextError, ) from great_expectations.util import gen_directory_tree_str from tests.test_utils import safe_remove try: from unittest import mock except ImportError: import mock try: from unittest import mock except ImportError: import mock yaml = YAML() @pytest.fixture() def parameterized_expectation_suite(): fixture_path = file_relative_path( __file__, "../test_fixtures/expectation_suites/parameterized_expectation_suite_fixture.json", ) with open(fixture_path, "r",) as suite: return json.load(suite) def test_create_duplicate_expectation_suite(titanic_data_context): assert titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite") with pytest.raises(DataContextError): titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite") assert titanic_data_context.create_expectation_suite(expectation_suite_name="titanic.test_create_expectation_suite", overwrite_existing=True) def test_get_available_data_asset_names_with_one_datasource_including_a_single_generator(empty_data_context, filesystem_csv): empty_data_context.add_datasource("my_datasource", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": str(filesystem_csv) } } ) available_asset_names = empty_data_context.get_available_data_asset_names() assert set(available_asset_names["my_datasource"]["subdir_reader"]["names"]) == {('f3', 'directory'), ('f2', 'file'), ('f1', 'file')} def test_get_available_data_asset_names_with_one_datasource_without_a_generator_returns_empty_dict( empty_data_context, ): empty_data_context.add_datasource( "my_datasource", module_name="great_expectations.datasource", class_name="PandasDatasource", ) obs = empty_data_context.get_available_data_asset_names() assert obs == {"my_datasource": {}} def test_get_available_data_asset_names_with_multiple_datasources_with_and_without_generators( empty_data_context ): context = empty_data_context connection_kwargs = {"drivername": "sqlite"} context.add_datasource( "first", class_name="SqlAlchemyDatasource", generators={"foo": {"class_name": "TableBatchKwargsGenerator", }}, **connection_kwargs ) context.add_datasource( "second", class_name="SqlAlchemyDatasource", **connection_kwargs ) context.add_datasource( "third", class_name="SqlAlchemyDatasource", generators={"bar": {"class_name": "TableBatchKwargsGenerator", }}, **connection_kwargs ) obs = context.get_available_data_asset_names() assert isinstance(obs, dict) assert set(obs.keys()) == {"first", "second", "third"} assert obs == { "first": {"foo": {"is_complete_list": True, "names": []}}, "second": {}, "third": {"bar": {"is_complete_list": True, "names": []}}, } def test_list_expectation_suite_keys(data_context): assert data_context.list_expectation_suites() == [ ExpectationSuiteIdentifier( expectation_suite_name="my_dag_node.default" ) ] def test_get_existing_expectation_suite(data_context): expectation_suite = data_context.get_expectation_suite('my_dag_node.default') assert expectation_suite.expectation_suite_name == 'my_dag_node.default' assert len(expectation_suite.expectations) == 2 def test_get_new_expectation_suite(data_context): expectation_suite = data_context.create_expectation_suite('this_data_asset_does_not_exist.default') assert expectation_suite.expectation_suite_name == 'this_data_asset_does_not_exist.default' assert len(expectation_suite.expectations) == 0 def test_save_expectation_suite(data_context): expectation_suite = data_context.create_expectation_suite('this_data_asset_config_does_not_exist.default') expectation_suite.expectations.append(ExpectationConfiguration( expectation_type="expect_table_row_count_to_equal", kwargs={ "value": 10 })) data_context.save_expectation_suite(expectation_suite) expectation_suite_saved = data_context.get_expectation_suite('this_data_asset_config_does_not_exist.default') assert expectation_suite.expectations == expectation_suite_saved.expectations def test_compile_evaluation_parameter_dependencies(data_context): assert data_context._evaluation_parameter_dependencies == {} data_context._compile_evaluation_parameter_dependencies() assert data_context._evaluation_parameter_dependencies == { 'source_diabetes_data.default': [{ "metric_kwargs_id": { "column=patient_nbr": ["expect_column_unique_value_count_to_be_between.result.observed_value"] } }], 'source_patient_data.default': ["expect_table_row_count_to_equal.result.observed_value"] } def test_list_datasources(data_context): datasources = data_context.list_datasources() assert OrderedDict(datasources) == OrderedDict([ { 'name': 'mydatasource', 'class_name': 'PandasDatasource' } ]) data_context.add_datasource("second_pandas_source", module_name="great_expectations.datasource", class_name="PandasDatasource", ) datasources = data_context.list_datasources() assert OrderedDict(datasources) == OrderedDict([ { 'name': 'mydatasource', 'class_name': 'PandasDatasource' }, { 'name': 'second_pandas_source', 'class_name': 'PandasDatasource' } ]) def test_data_context_get_validation_result(titanic_data_context): profiling_results = titanic_data_context.profile_datasource("mydatasource") all_validation_result = titanic_data_context.get_validation_result( "mydatasource.mygenerator.Titanic.BasicDatasetProfiler", run_id="profiling" ) assert len(all_validation_result.results) == 51 failed_validation_result = titanic_data_context.get_validation_result( "mydatasource.mygenerator.Titanic.BasicDatasetProfiler", run_id="profiling", failed_only=True, ) assert len(failed_validation_result.results) == 8 def test_data_context_get_datasource(titanic_data_context): isinstance(titanic_data_context.get_datasource("mydatasource"), Datasource) def test_data_context_get_datasource_on_non_existent_one_raises_helpful_error(titanic_data_context): with pytest.raises(ValueError): _ = titanic_data_context.get_datasource("fakey_mc_fake") def test_data_context_profile_datasource_on_non_existent_one_raises_helpful_error(titanic_data_context): with pytest.raises(ValueError): _ = titanic_data_context.profile_datasource("fakey_mc_fake") @pytest.mark.rendered_output def test_render_full_static_site_from_empty_project(tmp_path_factory, filesystem_csv_3): base_dir = str(tmp_path_factory.mktemp("project_dir")) project_dir = os.path.join(base_dir, "project_path") os.mkdir(project_dir) os.makedirs(os.path.join(project_dir, "data")) os.makedirs(os.path.join(project_dir, "data/titanic")) shutil.copy( file_relative_path(__file__, "../test_sets/Titanic.csv"), str(os.path.join(project_dir, "data/titanic/Titanic.csv")) ) os.makedirs(os.path.join(project_dir, "data/random")) shutil.copy( os.path.join(filesystem_csv_3, "f1.csv"), str(os.path.join(project_dir, "data/random/f1.csv")) ) shutil.copy( os.path.join(filesystem_csv_3, "f2.csv"), str(os.path.join(project_dir, "data/random/f2.csv")) ) assert gen_directory_tree_str(project_dir) == """\ project_path/ data/ random/ f1.csv f2.csv titanic/ Titanic.csv """ context = DataContext.create(project_dir) ge_directory = os.path.join(project_dir, "great_expectations") context.add_datasource("titanic", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": os.path.join(project_dir, "data/titanic/") } } ) context.add_datasource("random", module_name="great_expectations.datasource", class_name="PandasDatasource", generators={ "subdir_reader": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": os.path.join(project_dir, "data/random/") } } ) context.profile_datasource("titanic") titanic_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/titanic/Titanic.csv'), 'datasource': 'titanic'} ).to_id() tree_str = gen_directory_tree_str(project_dir) assert tree_str == """project_path/ data/ random/ f1.csv f2.csv titanic/ Titanic.csv great_expectations/ .gitignore great_expectations.yml expectations/ titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler.json notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler/ profiling/ {}.json """.format(titanic_profiled_batch_id) context.profile_datasource("random") context.build_data_docs() f1_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/random/f1.csv'), 'datasource': 'random'} ).to_id() f2_profiled_batch_id = PathBatchKwargs({ 'path': os.path.join(project_dir, 'data/random/f2.csv'), 'datasource': 'random'} ).to_id() data_docs_dir = os.path.join(project_dir, "great_expectations/uncommitted/data_docs") observed = gen_directory_tree_str(data_docs_dir) assert observed == """\ data_docs/ local_site/ index.html expectations/ random/ subdir_reader/ f1/ BasicDatasetProfiler.html f2/ BasicDatasetProfiler.html titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler.html static/ fonts/ HKGrotesk/ HKGrotesk-Bold.otf HKGrotesk-BoldItalic.otf HKGrotesk-Italic.otf HKGrotesk-Light.otf HKGrotesk-LightItalic.otf HKGrotesk-Medium.otf HKGrotesk-MediumItalic.otf HKGrotesk-Regular.otf HKGrotesk-SemiBold.otf HKGrotesk-SemiBoldItalic.otf images/ favicon.ico glossary_scroller.gif iterative-dev-loop.png logo-long-vector.svg logo-long.png short-logo-vector.svg short-logo.png validation_failed_unexpected_values.gif styles/ data_docs_custom_styles_template.css data_docs_default_styles.css validations/ random/ subdir_reader/ f1/ BasicDatasetProfiler/ profiling/ {0:s}.html f2/ BasicDatasetProfiler/ profiling/ {1:s}.html titanic/ subdir_reader/ Titanic/ BasicDatasetProfiler/ profiling/ {2:s}.html """.format(f1_profiled_batch_id, f2_profiled_batch_id, titanic_profiled_batch_id) safe_mmkdir("./tests/data_context/output") safe_mmkdir("./tests/data_context/output/data_docs") if os.path.isdir("./tests/data_context/output/data_docs"): shutil.rmtree("./tests/data_context/output/data_docs") shutil.copytree( os.path.join( ge_directory, "uncommitted/data_docs/" ), "./tests/data_context/output/data_docs" ) def test_add_store(empty_data_context): assert "my_new_store" not in empty_data_context.stores.keys() assert "my_new_store" not in empty_data_context.get_config()["stores"] new_store = empty_data_context.add_store( "my_new_store", { "module_name": "great_expectations.data_context.store", "class_name": "ExpectationsStore", } ) assert "my_new_store" in empty_data_context.stores.keys() assert "my_new_store" in empty_data_context.get_config()["stores"] assert isinstance(new_store, ExpectationsStore) @pytest.fixture def basic_data_context_config(): return DataContextConfig(**{ "commented_map": {}, "config_version": 1, "plugins_directory": "plugins/", "evaluation_parameter_store_name": "evaluation_parameter_store", "validations_store_name": "does_not_have_to_be_real", "expectations_store_name": "expectations_store", "config_variables_file_path": "uncommitted/config_variables.yml", "datasources": {}, "stores": { "expectations_store": { "class_name": "ExpectationsStore", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "expectations/", }, }, "evaluation_parameter_store" : { "module_name": "great_expectations.data_context.store", "class_name": "EvaluationParameterStore", } }, "data_docs_sites": {}, "validation_operators": { "default": { "class_name": "ActionListValidationOperator", "action_list": [] } } }) def test_ExplorerDataContext(titanic_data_context): context_root_directory = titanic_data_context.root_directory explorer_data_context = ExplorerDataContext(context_root_directory) assert explorer_data_context._expectation_explorer_manager def test_ConfigOnlyDataContext__initialization(tmp_path_factory, basic_data_context_config): config_path = str(tmp_path_factory.mktemp('test_ConfigOnlyDataContext__initialization__dir')) context = BaseDataContext( basic_data_context_config, config_path, ) assert context.root_directory.split("/")[-1] == "test_ConfigOnlyDataContext__initialization__dir0" assert context.plugins_directory.split("/")[-3:] == ["test_ConfigOnlyDataContext__initialization__dir0", "plugins",""] def test__normalize_absolute_or_relative_path(tmp_path_factory, basic_data_context_config): config_path = str(tmp_path_factory.mktemp('test__normalize_absolute_or_relative_path__dir')) context = BaseDataContext( basic_data_context_config, config_path, ) assert str(os.path.join("test__normalize_absolute_or_relative_path__dir0", "yikes")) in context._normalize_absolute_or_relative_path("yikes") assert "test__normalize_absolute_or_relative_path__dir" not in context._normalize_absolute_or_relative_path("/yikes") assert "/yikes" == context._normalize_absolute_or_relative_path("/yikes") def test_load_data_context_from_environment_variables(tmp_path_factory): curdir = os.path.abspath(os.getcwd()) try: project_path = str(tmp_path_factory.mktemp('data_context')) context_path = os.path.join(project_path, "great_expectations") safe_mmkdir(context_path) os.chdir(context_path) with pytest.raises(DataContextError) as err: DataContext.find_context_root_dir() assert isinstance(err.value, ConfigNotFoundError) shutil.copy(file_relative_path(__file__, "../test_fixtures/great_expectations_basic.yml"), str(os.path.join(context_path, "great_expectations.yml"))) os.environ["GE_HOME"] = context_path assert DataContext.find_context_root_dir() == context_path except Exception: raise finally: if "GE_HOME" in os.environ: del os.environ["GE_HOME"] os.chdir(curdir) def test_data_context_updates_expectation_suite_names(data_context): # A data context should update the data_asset_name and expectation_suite_name of expectation suites # that it creates when it saves them. expectation_suites = data_context.list_expectation_suites() # We should have a single expectation suite defined assert len(expectation_suites) == 1 expectation_suite_name = expectation_suites[0].expectation_suite_name # We'll get that expectation suite and then update its name and re-save, then verify that everything expectation_suite = data_context.get_expectation_suite(expectation_suite_name) assert expectation_suite.expectation_suite_name == expectation_suite_name expectation_suite.expectation_suite_name = 'a_new_suite_name' data_context.save_expectation_suite( expectation_suite=expectation_suite, expectation_suite_name='a_new_suite_name' ) fetched_expectation_suite = data_context.get_expectation_suite('a_new_suite_name') assert fetched_expectation_suite.expectation_suite_name == 'a_new_suite_name' data_context.save_expectation_suite( expectation_suite=expectation_suite, expectation_suite_name='a_new_new_suite_name' ) fetched_expectation_suite = data_context.get_expectation_suite('a_new_new_suite_name') assert fetched_expectation_suite.expectation_suite_name == 'a_new_new_suite_name' with open(os.path.join( data_context.root_directory, "expectations", "a_new_new_suite_name.json" ), 'r') as suite_file: loaded_suite = expectationSuiteSchema.load(json.load(suite_file)).data assert loaded_suite.expectation_suite_name == 'a_new_new_suite_name' expectation_suite.expectation_suite_name = "a_third_suite_name" data_context.save_expectation_suite( expectation_suite=expectation_suite ) fetched_expectation_suite = data_context.get_expectation_suite("a_third_suite_name") assert fetched_expectation_suite.expectation_suite_name == "a_third_suite_name" def test_data_context_create_does_not_raise_error_or_warning_if_ge_dir_exists(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) @pytest.fixture() def empty_context(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_dir = os.path.join(project_path, "great_expectations") assert os.path.isdir(ge_dir) assert os.path.isfile(os.path.join(ge_dir, DataContext.GE_YML)) context = DataContext(ge_dir) assert isinstance(context, DataContext) return context def test_data_context_does_ge_yml_exist_returns_true_when_it_does_exist(empty_context): ge_dir = empty_context.root_directory assert DataContext.does_config_exist_on_disk(ge_dir) == True def test_data_context_does_ge_yml_exist_returns_false_when_it_does_not_exist( empty_context, ): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.does_config_exist_on_disk(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_true_when_it_has_a_datasource_configured_in_yml_file_on_disk( empty_context, ): ge_dir = empty_context.root_directory empty_context.add_datasource("arthur", **{"class_name": "PandasDatasource"}) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == True def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_datasource_configured_in_yml_file_on_disk( empty_context, ): ge_dir = empty_context.root_directory assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_ge_yml_file( empty_context, ): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_it_does_not_have_a_ge_dir( empty_context, ): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir)) assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_does_project_have_a_datasource_in_config_file_returns_false_when_the_project_has_an_invalid_config_file( empty_context, ): ge_dir = empty_context.root_directory with open(os.path.join(ge_dir, DataContext.GE_YML), "w") as yml: yml.write("this file: is not a valid ge config") assert DataContext.does_project_have_a_datasource_in_config_file(ge_dir) == False def test_data_context_is_project_initialized_returns_true_when_its_valid_context_has_one_datasource_and_one_suite( empty_context, ): context = empty_context ge_dir = context.root_directory context.add_datasource("arthur", class_name="PandasDatasource") context.create_expectation_suite("dent") assert len(context.list_expectation_suites()) == 1 assert DataContext.is_project_initialized(ge_dir) == True def test_data_context_is_project_initialized_returns_true_when_its_valid_context_has_one_datasource_and_no_suites( empty_context, ): context = empty_context ge_dir = context.root_directory context.add_datasource("arthur", class_name="PandasDatasource") assert len(context.list_expectation_suites()) == 0 assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_its_valid_context_has_no_datasource( empty_context, ): ge_dir = empty_context.root_directory assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_config_yml_is_missing(empty_context): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir, empty_context.GE_YML)) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_dir_is_missing(empty_context): ge_dir = empty_context.root_directory shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR)) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_data_docs_dir_is_missing(empty_context): ge_dir = empty_context.root_directory shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "data_docs")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_uncommitted_validations_dir_is_missing(empty_context): ge_dir = empty_context.root_directory shutil.rmtree(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "validations")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_is_project_initialized_returns_false_when_config_variable_yml_is_missing(empty_context): ge_dir = empty_context.root_directory safe_remove(os.path.join(ge_dir, empty_context.GE_UNCOMMITTED_DIR, "config_variables.yml")) assert DataContext.is_project_initialized(ge_dir) == False def test_data_context_create_raises_warning_and_leaves_existing_yml_untouched(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_yml = os.path.join( project_path, "great_expectations/great_expectations.yml" ) with open(ge_yml, "a") as ff: ff.write("# LOOK I WAS MODIFIED") with pytest.warns(UserWarning): DataContext.create(project_path) with open(ge_yml, "r") as ff: obs = ff.read() assert "# LOOK I WAS MODIFIED" in obs def test_data_context_create_makes_uncommitted_dirs_when_all_are_missing(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") shutil.rmtree(uncommitted_dir) DataContext.create(project_path) obs = gen_directory_tree_str(ge_dir) print(obs) assert os.path.isdir(uncommitted_dir), "No uncommitted directory created" assert obs == """\ great_expectations/ .gitignore great_expectations.yml expectations/ notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ """ def test_data_context_create_does_nothing_if_all_uncommitted_dirs_exist(tmp_path_factory): expected = """\ great_expectations/ .gitignore great_expectations.yml expectations/ notebooks/ pandas/ validation_playground.ipynb spark/ validation_playground.ipynb sql/ validation_playground.ipynb plugins/ custom_data_docs/ renderers/ styles/ data_docs_custom_styles.css views/ uncommitted/ config_variables.yml data_docs/ validations/ """ project_path = str(tmp_path_factory.mktemp('stuff')) ge_dir = os.path.join(project_path, "great_expectations") DataContext.create(project_path) fixture = gen_directory_tree_str(ge_dir) print(fixture) assert fixture == expected DataContext.create(project_path) obs = gen_directory_tree_str(ge_dir) assert obs == expected def test_data_context_do_all_uncommitted_dirs_exist(tmp_path_factory): expected = """\ uncommitted/ config_variables.yml data_docs/ validations/ """ project_path = str(tmp_path_factory.mktemp('stuff')) ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") DataContext.create(project_path) fixture = gen_directory_tree_str(uncommitted_dir) print(fixture) assert fixture == expected assert DataContext.all_uncommitted_directories_exist(ge_dir) shutil.rmtree(os.path.join(uncommitted_dir, "data_docs")) shutil.rmtree(os.path.join(uncommitted_dir, "validations")) assert not DataContext.all_uncommitted_directories_exist(project_path) def test_data_context_create_does_not_overwrite_existing_config_variables_yml(tmp_path_factory): project_path = str(tmp_path_factory.mktemp('data_context')) DataContext.create(project_path) ge_dir = os.path.join(project_path, "great_expectations") uncommitted_dir = os.path.join(ge_dir, "uncommitted") config_vars_yml = os.path.join(uncommitted_dir, "config_variables.yml") with open(config_vars_yml, "a") as ff: ff.write("# LOOK I WAS MODIFIED") with pytest.warns(UserWarning): DataContext.create(project_path) with open(config_vars_yml, "r") as ff: obs = ff.read() print(obs) assert "# LOOK I WAS MODIFIED" in obs def test_scaffold_directories_and_notebooks(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("test_scaffold_directories_and_notebooks")) DataContext.scaffold_directories(empty_directory) DataContext.scaffold_notebooks(empty_directory) assert set(os.listdir(empty_directory)) == { 'plugins', 'expectations', '.gitignore', 'uncommitted', 'notebooks' } assert set(os.listdir(os.path.join(empty_directory, "uncommitted"))) == { 'data_docs', 'validations' } for subdir in DataContext.NOTEBOOK_SUBDIRECTORIES: subdir_path = os.path.join(empty_directory, "notebooks", subdir) assert set(os.listdir(subdir_path)) == { "validation_playground.ipynb" } def test_build_batch_kwargs(titanic_multibatch_data_context): batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic", partition_id="Titanic_1912") assert os.path.relpath("./data/titanic/Titanic_1912.csv") in batch_kwargs["path"] batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic", partition_id="Titanic_1911") assert os.path.relpath("./data/titanic/Titanic_1911.csv") in batch_kwargs["path"] paths = [] batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic") paths.append(os.path.basename(batch_kwargs["path"])) batch_kwargs = titanic_multibatch_data_context.build_batch_kwargs("mydatasource", "mygenerator", name="titanic") paths.append(os.path.basename(batch_kwargs["path"])) assert set(["Titanic_1912.csv", "Titanic_1911.csv"]) == set(paths) def test_existing_local_data_docs_urls_returns_url_on_project_with_no_datasources_and_a_site_configured(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("another_empty_project")) DataContext.create(empty_directory) context = DataContext(os.path.join(empty_directory, DataContext.GE_DIR)) obs = context.get_docs_sites_urls() assert len(obs) == 1 assert obs[0].endswith("great_expectations/uncommitted/data_docs/local_site/index.html") def test_existing_local_data_docs_urls_returns_single_url_from_customized_local_site(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("yo_yo")) DataContext.create(empty_directory) ge_dir = os.path.join(empty_directory, DataContext.GE_DIR) context = DataContext(ge_dir) context._project_config["data_docs_sites"] = { "my_rad_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/some/local/path/" } } } context._save_project_config() context = DataContext(ge_dir) context.build_data_docs() expected_path = os.path.join(ge_dir, "uncommitted/data_docs/some/local/path/index.html") assert os.path.isfile(expected_path) obs = context.get_docs_sites_urls() assert obs == ["file://{}".format(expected_path)] def test_existing_local_data_docs_urls_returns_multiple_urls_from_customized_local_site(tmp_path_factory): empty_directory = str(tmp_path_factory.mktemp("yo_yo_ma")) DataContext.create(empty_directory) ge_dir = os.path.join(empty_directory, DataContext.GE_DIR) context = DataContext(ge_dir) context._project_config["data_docs_sites"] = { "my_rad_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/some/path/" } }, "another_just_amazing_site": { "class_name": "SiteBuilder", "store_backend": { "class_name": "TupleFilesystemStoreBackend", "base_directory": "uncommitted/data_docs/another/path/" } } } context._save_project_config() context = DataContext(ge_dir) context.build_data_docs() data_docs_dir = os.path.join(ge_dir, "uncommitted/data_docs/") path_1 = os.path.join(data_docs_dir, "some/path/index.html") path_2 = os.path.join(data_docs_dir, "another/path/index.html") for expected_path in [path_1, path_2]: assert os.path.isfile(expected_path) obs = context.get_docs_sites_urls() assert set(obs) == set([ "file://{}".format(path_1), "file://{}".format(path_2), ]) def test_load_config_variables_file(basic_data_context_config, tmp_path_factory): base_path = str(tmp_path_factory.mktemp('test_load_config_variables_file')) safe_mmkdir(os.path.join(base_path, "uncommitted")) with open(os.path.join(base_path, "uncommitted", "dev_variables.yml"), "w") as outfile: yaml.dump({'env': 'dev'}, outfile) with open(os.path.join(base_path, "uncommitted", "prod_variables.yml"), "w") as outfile: yaml.dump({'env': 'prod'}, outfile) basic_data_context_config["config_variables_file_path"] = "uncommitted/${TEST_CONFIG_FILE_ENV}_variables.yml" try: os.environ["TEST_CONFIG_FILE_ENV"] = "dev" context = BaseDataContext(basic_data_context_config, context_root_dir=base_path) config_vars = context._load_config_variables_file() assert config_vars['env'] == 'dev' os.environ["TEST_CONFIG_FILE_ENV"] = "prod" context = BaseDataContext(basic_data_context_config, context_root_dir=base_path) config_vars = context._load_config_variables_file() assert config_vars['env'] == 'prod' except Exception: raise finally: del os.environ["TEST_CONFIG_FILE_ENV"] def test_list_expectation_suite_with_no_suites(titanic_data_context): observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == [] def test_list_expectation_suite_with_one_suite(titanic_data_context): titanic_data_context.create_expectation_suite('warning') observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == ['warning'] def test_list_expectation_suite_with_multiple_suites(titanic_data_context): titanic_data_context.create_expectation_suite('a.warning') titanic_data_context.create_expectation_suite('b.warning') titanic_data_context.create_expectation_suite('c.warning') observed = titanic_data_context.list_expectation_suite_names() assert isinstance(observed, list) assert observed == ['a.warning', 'b.warning', 'c.warning'] assert len(observed) == 3 def test_get_batch_raises_error_when_passed_a_non_string_type_for_suite_parameter( titanic_data_context, ): with pytest.raises(DataContextError): titanic_data_context.get_batch({}, 99) def test_get_batch_raises_error_when_passed_a_non_dict_or_batch_kwarg_type_for_batch_kwarg_parameter( titanic_data_context, ): with pytest.raises(BatchKwargsError): titanic_data_context.get_batch(99, "foo") def test_get_batch_when_passed_a_suite_name(titanic_data_context): context = titanic_data_context root_dir = context.root_directory batch_kwargs = { "datasource": "mydatasource", "path": os.path.join(root_dir, "..", "data", "Titanic.csv"), } context.create_expectation_suite("foo") assert context.list_expectation_suite_names() == ["foo"] batch = context.get_batch(batch_kwargs, "foo") assert isinstance(batch, Dataset) assert isinstance(batch.get_expectation_suite(), ExpectationSuite) def test_get_batch_when_passed_a_suite(titanic_data_context): context = titanic_data_context root_dir = context.root_directory batch_kwargs = { "datasource": "mydatasource", "path": os.path.join(root_dir, "..", "data", "Titanic.csv"), } context.create_expectation_suite("foo") assert context.list_expectation_suite_names() == ["foo"] suite = context.get_expectation_suite("foo") batch = context.get_batch(batch_kwargs, suite) assert isinstance(batch, Dataset) assert isinstance(batch.get_expectation_suite(), ExpectationSuite)

true

1c312caf3764cd4dad921eaa796c13378b4645a6

3,030

py

Python

logging_server/logger.py

Geson-anko/logging_server

4617e6a971c81fc4df1cad1c35cdae5f09e20382

[ "MIT" ]

1

2022-03-29T23:00:56.000Z

logging_server/logger.py

Geson-anko/logging_server

4617e6a971c81fc4df1cad1c35cdae5f09e20382

[ "MIT" ]

1

2022-03-23T11:54:02.000Z

logging_server/logger.py

Geson-anko/logging_server

4617e6a971c81fc4df1cad1c35cdae5f09e20382

[ "MIT" ]

null

""" Logger class for mutiprocessing logging. Usage: from logging_server import SocketLogger logger = SocketLogger(__name__) logger.setLevel(0) logger.debug("debug") logger.info("info") logger.warning("warning") logger.error("error") logger.exception("exception") このロガークラスはlogging.Loggerクラスを継承せずにラップしているため、純粋なロガークラスの様に振る舞わないことに注意してください。ロギングに必要なメソッドのみを提供します。また任意のハンドラーを追加しても正常に機能しないことがあります。 """ import os import logging import logging.handlers from typing import * def _check_pid(func): def check(self,*args, **kwds): if self.logger is None: self.set_logger() pid = os.getpid() if self._pid != pid: self._pid = pid self.reset_logger() func(self,*args, **kwds) return check class SocketLogger: _pid:int = None __logger:logging.Logger = None def __init__( self, name:str, level:int=logging.NOTSET, host="localhost", port:int=logging.handlers.DEFAULT_TCP_LOGGING_PORT, ) -> None: self._pid = os.getpid() self.name = name self.level = level self.host = host self.port = port self.set_logger() @property def logger(self): return self.__logger def setLevel(self, level:int) -> None: self.logger.setLevel(level) def set_logger(self): """set logger class, name, level and socket handler.""" self.__logger = logging.Logger(self.name) self.__logger.setLevel(self.level) socket_handler = logging.handlers.SocketHandler(self.host, self.port) socket_handler.setLevel(logging.NOTSET) self.__logger.addHandler(socket_handler) self.__logger.propagate=False # Because another logger is propagating in server process. def remove_handlers(self): """remove handlers of logger.""" for hdlr in self.__logger.handlers: self.__logger.removeHandler(hdlr) def reset_logger(self): """reset logger class""" self.remove_handlers() self.set_logger() def __reduce__(self): """ Picking helper method. Removes internal Logger class because it is not picklable. """ self.__logger = None return super().__reduce__() @_check_pid def debug(self,*args, **kwds) -> None: self.logger.debug(*args, **kwds) @_check_pid def info(self,*args, **kwds) -> None: self.logger.info(*args, **kwds) @_check_pid def warn(self,*args, **kwds) -> None: self.logger.warn(*args, **kwds) @_check_pid def warning(self,*args, **kwds) -> None: self.logger.warning(*args, **kwds) @_check_pid def error(self,*args, **kwds) -> None: self.logger.error(*args, **kwds) @_check_pid def critical(self,*args, **kwds) -> None: self.logger.critical(*args, **kwds) @_check_pid def exception(self,*args, **kwds) -> None: self.logger.exception(*args, **kwds) @_check_pid def log(self, *args,**kwds) -> None: self.logger.log(*args,**kwds)

29.417476

96

0.639934

import os import logging import logging.handlers from typing import * def _check_pid(func): def check(self,*args, **kwds): if self.logger is None: self.set_logger() pid = os.getpid() if self._pid != pid: self._pid = pid self.reset_logger() func(self,*args, **kwds) return check class SocketLogger: _pid:int = None __logger:logging.Logger = None def __init__( self, name:str, level:int=logging.NOTSET, host="localhost", port:int=logging.handlers.DEFAULT_TCP_LOGGING_PORT, ) -> None: self._pid = os.getpid() self.name = name self.level = level self.host = host self.port = port self.set_logger() @property def logger(self): return self.__logger def setLevel(self, level:int) -> None: self.logger.setLevel(level) def set_logger(self): self.__logger = logging.Logger(self.name) self.__logger.setLevel(self.level) socket_handler = logging.handlers.SocketHandler(self.host, self.port) socket_handler.setLevel(logging.NOTSET) self.__logger.addHandler(socket_handler) self.__logger.propagate=False def remove_handlers(self): for hdlr in self.__logger.handlers: self.__logger.removeHandler(hdlr) def reset_logger(self): self.remove_handlers() self.set_logger() def __reduce__(self): self.__logger = None return super().__reduce__() @_check_pid def debug(self,*args, **kwds) -> None: self.logger.debug(*args, **kwds) @_check_pid def info(self,*args, **kwds) -> None: self.logger.info(*args, **kwds) @_check_pid def warn(self,*args, **kwds) -> None: self.logger.warn(*args, **kwds) @_check_pid def warning(self,*args, **kwds) -> None: self.logger.warning(*args, **kwds) @_check_pid def error(self,*args, **kwds) -> None: self.logger.error(*args, **kwds) @_check_pid def critical(self,*args, **kwds) -> None: self.logger.critical(*args, **kwds) @_check_pid def exception(self,*args, **kwds) -> None: self.logger.exception(*args, **kwds) @_check_pid def log(self, *args,**kwds) -> None: self.logger.log(*args,**kwds)

true

1c312d1dd12914eccb845d112a48c2d3462790c7

135

py

Python

frontend/admin.py

ebmdatalab/openpathology-web

e0620a39b174f2789df2cbea4e12bc413c1723ac

[ "MIT" ]

2

2019-10-08T10:13:25.000Z

2019-10-08T21:55:38.000Z

frontend/admin.py

HDRUK/openpathology-web

e0620a39b174f2789df2cbea4e12bc413c1723ac

[ "MIT" ]

44

2019-09-25T06:36:28.000Z

2021-08-18T11:59:24.000Z

frontend/admin.py

HDRUK/openpathology-web

e0620a39b174f2789df2cbea4e12bc413c1723ac

[ "MIT" ]

4

2019-08-12T14:02:54.000Z

2020-06-16T20:33:11.000Z

from django.contrib import admin from .models import Measure @admin.register(Measure) class MeasureAdmin(admin.ModelAdmin): pass

16.875

37

0.792593

from django.contrib import admin from .models import Measure @admin.register(Measure) class MeasureAdmin(admin.ModelAdmin): pass

true

1c312e102b9cebf8bdc37dfacda2a7151ffc0173

47,881

py

Python

testing/python/collect.py

cristianMeli/pytest

1824349f74298112722396be6f84a121bc9d6d63

[ "MIT" ]

1

2021-11-09T10:45:59.000Z

testing/python/collect.py

cristianMeli/pytest

1824349f74298112722396be6f84a121bc9d6d63

[ "MIT" ]

59

2020-10-27T20:30:33.000Z

2022-03-28T03:02:29.000Z

testing/python/collect.py

symonk/pytest

a53abe93d87083bbd5c183bd654f5787c0376934

[ "MIT" ]

null

import os import sys import textwrap from typing import Any from typing import Dict import _pytest._code import pytest from _pytest.config import ExitCode from _pytest.main import Session from _pytest.monkeypatch import MonkeyPatch from _pytest.nodes import Collector from _pytest.pytester import Pytester from _pytest.python import Class from _pytest.python import Instance class TestModule: def test_failing_import(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("import alksdjalskdjalkjals") pytest.raises(Collector.CollectError, modcol.collect) def test_import_duplicate(self, pytester: Pytester) -> None: a = pytester.mkdir("a") b = pytester.mkdir("b") p1 = a.joinpath("test_whatever.py") p1.touch() p2 = b.joinpath("test_whatever.py") p2.touch() # ensure we don't have it imported already sys.modules.pop(p1.stem, None) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*import*mismatch*", "*imported*test_whatever*", "*%s*" % p1, "*not the same*", "*%s*" % p2, "*HINT*", ] ) def test_import_prepend_append( self, pytester: Pytester, monkeypatch: MonkeyPatch ) -> None: root1 = pytester.mkdir("root1") root2 = pytester.mkdir("root2") root1.joinpath("x456.py").touch() root2.joinpath("x456.py").touch() p = root2.joinpath("test_x456.py") monkeypatch.syspath_prepend(str(root1)) p.write_text( textwrap.dedent( """\ import x456 def test(): assert x456.__file__.startswith({!r}) """.format( str(root2) ) ) ) with monkeypatch.context() as mp: mp.chdir(root2) reprec = pytester.inline_run("--import-mode=append") reprec.assertoutcome(passed=0, failed=1) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_syntax_error_in_module(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("this is a syntax error") pytest.raises(modcol.CollectError, modcol.collect) pytest.raises(modcol.CollectError, modcol.collect) def test_module_considers_pluginmanager_at_import(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("pytest_plugins='xasdlkj',") pytest.raises(ImportError, lambda: modcol.obj) def test_invalid_test_module_name(self, pytester: Pytester) -> None: a = pytester.mkdir("a") a.joinpath("test_one.part1.py").touch() result = pytester.runpytest() result.stdout.fnmatch_lines( [ "ImportError while importing test module*test_one.part1*", "Hint: make sure your test modules/packages have valid Python names.", ] ) @pytest.mark.parametrize("verbose", [0, 1, 2]) def test_show_traceback_import_error( self, pytester: Pytester, verbose: int ) -> None: """Import errors when collecting modules should display the traceback (#1976). With low verbosity we omit pytest and internal modules, otherwise show all traceback entries. """ pytester.makepyfile( foo_traceback_import_error=""" from bar_traceback_import_error import NOT_AVAILABLE """, bar_traceback_import_error="", ) pytester.makepyfile( """ import foo_traceback_import_error """ ) args = ("-v",) * verbose result = pytester.runpytest(*args) result.stdout.fnmatch_lines( [ "ImportError while importing test module*", "Traceback:", "*from bar_traceback_import_error import NOT_AVAILABLE", "*cannot import name *NOT_AVAILABLE*", ] ) assert result.ret == 2 stdout = result.stdout.str() if verbose == 2: assert "_pytest" in stdout else: assert "_pytest" not in stdout def test_show_traceback_import_error_unicode(self, pytester: Pytester) -> None: """Check test modules collected which raise ImportError with unicode messages are handled properly (#2336). """ pytester.makepyfile("raise ImportError('Something bad happened ☺')") result = pytester.runpytest() result.stdout.fnmatch_lines( [ "ImportError while importing test module*", "Traceback:", "*raise ImportError*Something bad happened*", ] ) assert result.ret == 2 class TestClass: def test_class_with_init_warning(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestClass1(object): def __init__(self): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*cannot collect test class 'TestClass1' because it has " "a __init__ constructor (from: test_class_with_init_warning.py)" ] ) def test_class_with_new_warning(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestClass1(object): def __new__(self): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*cannot collect test class 'TestClass1' because it has " "a __new__ constructor (from: test_class_with_new_warning.py)" ] ) def test_class_subclassobject(self, pytester: Pytester) -> None: pytester.getmodulecol( """ class test(object): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*collected 0*"]) def test_static_method(self, pytester: Pytester) -> None: """Support for collecting staticmethod tests (#2528, #2699)""" pytester.getmodulecol( """ import pytest class Test(object): @staticmethod def test_something(): pass @pytest.fixture def fix(self): return 1 @staticmethod def test_fix(fix): assert fix == 1 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*collected 2 items*", "*2 passed in*"]) def test_setup_teardown_class_as_classmethod(self, pytester: Pytester) -> None: pytester.makepyfile( test_mod1=""" class TestClassMethod(object): @classmethod def setup_class(cls): pass def test_1(self): pass @classmethod def teardown_class(cls): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*1 passed*"]) def test_issue1035_obj_has_getattr(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ class Chameleon(object): def __getattr__(self, name): return True chameleon = Chameleon() """ ) colitems = modcol.collect() assert len(colitems) == 0 def test_issue1579_namedtuple(self, pytester: Pytester) -> None: pytester.makepyfile( """ import collections TestCase = collections.namedtuple('TestCase', ['a']) """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( "*cannot collect test class 'TestCase' " "because it has a __new__ constructor*" ) def test_issue2234_property(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestCase(object): @property def prop(self): raise NotImplementedError() """ ) result = pytester.runpytest() assert result.ret == ExitCode.NO_TESTS_COLLECTED class TestFunction: def test_getmodulecollector(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): pass") modcol = item.getparent(pytest.Module) assert isinstance(modcol, pytest.Module) assert hasattr(modcol.obj, "test_func") @pytest.mark.filterwarnings("default") def test_function_as_object_instance_ignored(self, pytester: Pytester) -> None: pytester.makepyfile( """ class A(object): def __call__(self, tmp_path): 0/0 test_a = A() """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "collected 0 items", "*test_function_as_object_instance_ignored.py:2: " "*cannot collect 'test_a' because it is not a function.", ] ) @staticmethod def make_function(pytester: Pytester, **kwargs: Any) -> Any: from _pytest.fixtures import FixtureManager config = pytester.parseconfigure() session = Session.from_config(config) session._fixturemanager = FixtureManager(session) return pytest.Function.from_parent(parent=session, **kwargs) def test_function_equality(self, pytester: Pytester) -> None: def func1(): pass def func2(): pass f1 = self.make_function(pytester, name="name", callobj=func1) assert f1 == f1 f2 = self.make_function( pytester, name="name", callobj=func2, originalname="foobar" ) assert f1 != f2 def test_repr_produces_actual_test_id(self, pytester: Pytester) -> None: f = self.make_function( pytester, name=r"test[\xe5]", callobj=self.test_repr_produces_actual_test_id ) assert repr(f) == r"<Function test[\xe5]>" def test_issue197_parametrize_emptyset(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest @pytest.mark.parametrize('arg', []) def test_function(arg): pass """ ) reprec = pytester.inline_run() reprec.assertoutcome(skipped=1) def test_single_tuple_unwraps_values(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest @pytest.mark.parametrize(('arg',), [(1,)]) def test_function(arg): assert arg == 1 """ ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_issue213_parametrize_value_no_equal(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest class A(object): def __eq__(self, other): raise ValueError("not possible") @pytest.mark.parametrize('arg', [A()]) def test_function(arg): assert arg.__class__.__name__ == "A" """ ) reprec = pytester.inline_run("--fulltrace") reprec.assertoutcome(passed=1) def test_parametrize_with_non_hashable_values(self, pytester: Pytester) -> None: """Test parametrization with non-hashable values.""" pytester.makepyfile( """ archival_mapping = { '1.0': {'tag': '1.0'}, '1.2.2a1': {'tag': 'release-1.2.2a1'}, } import pytest @pytest.mark.parametrize('key value'.split(), archival_mapping.items()) def test_archival_to_version(key, value): assert key in archival_mapping assert value == archival_mapping[key] """ ) rec = pytester.inline_run() rec.assertoutcome(passed=2) def test_parametrize_with_non_hashable_values_indirect( self, pytester: Pytester ) -> None: """Test parametrization with non-hashable values with indirect parametrization.""" pytester.makepyfile( """ archival_mapping = { '1.0': {'tag': '1.0'}, '1.2.2a1': {'tag': 'release-1.2.2a1'}, } import pytest @pytest.fixture def key(request): return request.param @pytest.fixture def value(request): return request.param @pytest.mark.parametrize('key value'.split(), archival_mapping.items(), indirect=True) def test_archival_to_version(key, value): assert key in archival_mapping assert value == archival_mapping[key] """ ) rec = pytester.inline_run() rec.assertoutcome(passed=2) def test_parametrize_overrides_fixture(self, pytester: Pytester) -> None: """Test parametrization when parameter overrides existing fixture with same name.""" pytester.makepyfile( """ import pytest @pytest.fixture def value(): return 'value' @pytest.mark.parametrize('value', ['overridden']) def test_overridden_via_param(value): assert value == 'overridden' @pytest.mark.parametrize('somevalue', ['overridden']) def test_not_overridden(value, somevalue): assert value == 'value' assert somevalue == 'overridden' @pytest.mark.parametrize('other,value', [('foo', 'overridden')]) def test_overridden_via_multiparam(other, value): assert other == 'foo' assert value == 'overridden' """ ) rec = pytester.inline_run() rec.assertoutcome(passed=3) def test_parametrize_overrides_parametrized_fixture( self, pytester: Pytester ) -> None: """Test parametrization when parameter overrides existing parametrized fixture with same name.""" pytester.makepyfile( """ import pytest @pytest.fixture(params=[1, 2]) def value(request): return request.param @pytest.mark.parametrize('value', ['overridden']) def test_overridden_via_param(value): assert value == 'overridden' """ ) rec = pytester.inline_run() rec.assertoutcome(passed=1) def test_parametrize_overrides_indirect_dependency_fixture( self, pytester: Pytester ) -> None: """Test parametrization when parameter overrides a fixture that a test indirectly depends on""" pytester.makepyfile( """ import pytest fix3_instantiated = False @pytest.fixture def fix1(fix2): return fix2 + '1' @pytest.fixture def fix2(fix3): return fix3 + '2' @pytest.fixture def fix3(): global fix3_instantiated fix3_instantiated = True return '3' @pytest.mark.parametrize('fix2', ['2']) def test_it(fix1): assert fix1 == '21' assert not fix3_instantiated """ ) rec = pytester.inline_run() rec.assertoutcome(passed=1) def test_parametrize_with_mark(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.foo @pytest.mark.parametrize('arg', [ 1, pytest.param(2, marks=[pytest.mark.baz, pytest.mark.bar]) ]) def test_function(arg): pass """ ) keywords = [item.keywords for item in items] assert ( "foo" in keywords[0] and "bar" not in keywords[0] and "baz" not in keywords[0] ) assert "foo" in keywords[1] and "bar" in keywords[1] and "baz" in keywords[1] def test_parametrize_with_empty_string_arguments(self, pytester: Pytester) -> None: items = pytester.getitems( """\ import pytest @pytest.mark.parametrize('v', ('', ' ')) @pytest.mark.parametrize('w', ('', ' ')) def test(v, w): ... """ ) names = {item.name for item in items} assert names == {"test[-]", "test[ -]", "test[- ]", "test[ - ]"} def test_function_equality_with_callspec(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.parametrize('arg', [1,2]) def test_function(arg): pass """ ) assert items[0] != items[1] assert not (items[0] == items[1]) def test_pyfunc_call(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): raise ValueError") config = item.config class MyPlugin1: def pytest_pyfunc_call(self): raise ValueError class MyPlugin2: def pytest_pyfunc_call(self): return True config.pluginmanager.register(MyPlugin1()) config.pluginmanager.register(MyPlugin2()) config.hook.pytest_runtest_setup(item=item) config.hook.pytest_pyfunc_call(pyfuncitem=item) def test_multiple_parametrize(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ import pytest @pytest.mark.parametrize('x', [0, 1]) @pytest.mark.parametrize('y', [2, 3]) def test1(x, y): pass """ ) colitems = modcol.collect() assert colitems[0].name == "test1[2-0]" assert colitems[1].name == "test1[2-1]" assert colitems[2].name == "test1[3-0]" assert colitems[3].name == "test1[3-1]" def test_issue751_multiple_parametrize_with_ids(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ import pytest @pytest.mark.parametrize('x', [0], ids=['c']) @pytest.mark.parametrize('y', [0, 1], ids=['a', 'b']) class Test(object): def test1(self, x, y): pass def test2(self, x, y): pass """ ) colitems = modcol.collect()[0].collect()[0].collect() assert colitems[0].name == "test1[a-c]" assert colitems[1].name == "test1[b-c]" assert colitems[2].name == "test2[a-c]" assert colitems[3].name == "test2[b-c]" def test_parametrize_skipif(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skipif('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_skip_if(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 skipped in *"]) def test_parametrize_skip(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skip('') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_skip(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 skipped in *"]) def test_parametrize_skipif_no_skip(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skipif('False') @pytest.mark.parametrize('x', [0, 1, m(2)]) def test_skipif_no_skip(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 1 failed, 2 passed in *"]) def test_parametrize_xfail(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_xfail(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 xfailed in *"]) def test_parametrize_passed(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_xfail(x): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 xpassed in *"]) def test_parametrize_xfail_passed(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('False') @pytest.mark.parametrize('x', [0, 1, m(2)]) def test_passed(x): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 3 passed in *"]) def test_function_originalname(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.parametrize('arg', [1,2]) def test_func(arg): pass def test_no_param(): pass """ ) originalnames = [] for x in items: assert isinstance(x, pytest.Function) originalnames.append(x.originalname) assert originalnames == [ "test_func", "test_func", "test_no_param", ] def test_function_with_square_brackets(self, pytester: Pytester) -> None: """Check that functions with square brackets don't cause trouble.""" p1 = pytester.makepyfile( """ locals()["test_foo[name]"] = lambda: None """ ) result = pytester.runpytest("-v", str(p1)) result.stdout.fnmatch_lines( [ "test_function_with_square_brackets.py::test_foo[[]name[]] PASSED *", "*= 1 passed in *", ] ) class TestSorting: def test_check_equality(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ def test_pass(): pass def test_fail(): assert 0 """ ) fn1 = pytester.collect_by_name(modcol, "test_pass") assert isinstance(fn1, pytest.Function) fn2 = pytester.collect_by_name(modcol, "test_pass") assert isinstance(fn2, pytest.Function) assert fn1 == fn2 assert fn1 != modcol assert hash(fn1) == hash(fn2) fn3 = pytester.collect_by_name(modcol, "test_fail") assert isinstance(fn3, pytest.Function) assert not (fn1 == fn3) assert fn1 != fn3 for fn in fn1, fn2, fn3: assert fn != 3 # type: ignore[comparison-overlap] assert fn != modcol assert fn != [1, 2, 3] # type: ignore[comparison-overlap] assert [1, 2, 3] != fn # type: ignore[comparison-overlap] assert modcol != fn def test_allow_sane_sorting_for_decorators(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ def dec(f): g = lambda: f(2) g.place_as = f return g def test_b(y): pass test_b = dec(test_b) def test_a(y): pass test_a = dec(test_a) """ ) colitems = modcol.collect() assert len(colitems) == 2 assert [item.name for item in colitems] == ["test_b", "test_a"] def test_ordered_by_definition_order(self, pytester: Pytester) -> None: pytester.makepyfile( """\ class Test1: def test_foo(): pass def test_bar(): pass class Test2: def test_foo(): pass test_bar = Test1.test_bar class Test3(Test2): def test_baz(): pass """ ) result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines( [ "*Class Test1*", "*Function test_foo*", "*Function test_bar*", "*Class Test2*", # previously the order was flipped due to Test1.test_bar reference "*Function test_foo*", "*Function test_bar*", "*Class Test3*", "*Function test_foo*", "*Function test_bar*", "*Function test_baz*", ] ) class TestConftestCustomization: def test_pytest_pycollect_module(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyModule(pytest.Module): pass def pytest_pycollect_makemodule(fspath, parent): if fspath.name == "test_xyz.py": return MyModule.from_parent(path=fspath, parent=parent) """ ) pytester.makepyfile("def test_some(): pass") pytester.makepyfile(test_xyz="def test_func(): pass") result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines(["*<Module*test_pytest*", "*<MyModule*xyz*"]) def test_customized_pymakemodule_issue205_subdir(self, pytester: Pytester) -> None: b = pytester.path.joinpath("a", "b") b.mkdir(parents=True) b.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest @pytest.hookimpl(hookwrapper=True) def pytest_pycollect_makemodule(): outcome = yield mod = outcome.get_result() mod.obj.hello = "world" """ ) ) b.joinpath("test_module.py").write_text( textwrap.dedent( """\ def test_hello(): assert hello == "world" """ ) ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_customized_pymakeitem(self, pytester: Pytester) -> None: b = pytester.path.joinpath("a", "b") b.mkdir(parents=True) b.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest @pytest.hookimpl(hookwrapper=True) def pytest_pycollect_makeitem(): outcome = yield if outcome.excinfo is None: result = outcome.get_result() if result: for func in result: func._some123 = "world" """ ) ) b.joinpath("test_module.py").write_text( textwrap.dedent( """\ import pytest @pytest.fixture() def obj(request): return request.node._some123 def test_hello(obj): assert obj == "world" """ ) ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_pytest_pycollect_makeitem(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyFunction(pytest.Function): pass def pytest_pycollect_makeitem(collector, name, obj): if name == "some": return MyFunction.from_parent(name=name, parent=collector) """ ) pytester.makepyfile("def some(): pass") result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines(["*MyFunction*some*"]) def test_issue2369_collect_module_fileext(self, pytester: Pytester) -> None: """Ensure we can collect files with weird file extensions as Python modules (#2369)""" # We'll implement a little finder and loader to import files containing # Python source code whose file extension is ".narf". pytester.makeconftest( """ import sys, os, imp from _pytest.python import Module class Loader(object): def load_module(self, name): return imp.load_source(name, name + ".narf") class Finder(object): def find_module(self, name, path=None): if os.path.exists(name + ".narf"): return Loader() sys.meta_path.append(Finder()) def pytest_collect_file(fspath, parent): if fspath.suffix == ".narf": return Module.from_parent(path=fspath, parent=parent)""" ) pytester.makefile( ".narf", """\ def test_something(): assert 1 + 1 == 2""", ) # Use runpytest_subprocess, since we're futzing with sys.meta_path. result = pytester.runpytest_subprocess() result.stdout.fnmatch_lines(["*1 passed*"]) def test_early_ignored_attributes(self, pytester: Pytester) -> None: """Builtin attributes should be ignored early on, even if configuration would otherwise allow them. This tests a performance optimization, not correctness, really, although it tests PytestCollectionWarning is not raised, while it would have been raised otherwise. """ pytester.makeini( """ [pytest] python_classes=* python_functions=* """ ) pytester.makepyfile( """ class TestEmpty: pass test_empty = TestEmpty() def test_real(): pass """ ) items, rec = pytester.inline_genitems() assert rec.ret == 0 assert len(items) == 1 def test_setup_only_available_in_subdir(pytester: Pytester) -> None: sub1 = pytester.mkpydir("sub1") sub2 = pytester.mkpydir("sub2") sub1.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest def pytest_runtest_setup(item): assert item.path.stem == "test_in_sub1" def pytest_runtest_call(item): assert item.path.stem == "test_in_sub1" def pytest_runtest_teardown(item): assert item.path.stem == "test_in_sub1" """ ) ) sub2.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest def pytest_runtest_setup(item): assert item.path.stem == "test_in_sub2" def pytest_runtest_call(item): assert item.path.stem == "test_in_sub2" def pytest_runtest_teardown(item): assert item.path.stem == "test_in_sub2" """ ) ) sub1.joinpath("test_in_sub1.py").write_text("def test_1(): pass") sub2.joinpath("test_in_sub2.py").write_text("def test_2(): pass") result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) def test_modulecol_roundtrip(pytester: Pytester) -> None: modcol = pytester.getmodulecol("pass", withinit=False) trail = modcol.nodeid newcol = modcol.session.perform_collect([trail], genitems=0)[0] assert modcol.name == newcol.name class TestTracebackCutting: def test_skip_simple(self): with pytest.raises(pytest.skip.Exception) as excinfo: pytest.skip("xxx") assert excinfo.traceback[-1].frame.code.name == "skip" assert excinfo.traceback[-1].ishidden() assert excinfo.traceback[-2].frame.code.name == "test_skip_simple" assert not excinfo.traceback[-2].ishidden() def test_traceback_argsetup(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest @pytest.fixture def hello(request): raise ValueError("xyz") """ ) p = pytester.makepyfile("def test(hello): pass") result = pytester.runpytest(p) assert result.ret != 0 out = result.stdout.str() assert "xyz" in out assert "conftest.py:5: ValueError" in out numentries = out.count("_ _ _") # separator for traceback entries assert numentries == 0 result = pytester.runpytest("--fulltrace", p) out = result.stdout.str() assert "conftest.py:5: ValueError" in out numentries = out.count("_ _ _ _") # separator for traceback entries assert numentries > 3 def test_traceback_error_during_import(self, pytester: Pytester) -> None: pytester.makepyfile( """ x = 1 x = 2 x = 17 asd """ ) result = pytester.runpytest() assert result.ret != 0 out = result.stdout.str() assert "x = 1" not in out assert "x = 2" not in out result.stdout.fnmatch_lines([" *asd*", "E*NameError*"]) result = pytester.runpytest("--fulltrace") out = result.stdout.str() assert "x = 1" in out assert "x = 2" in out result.stdout.fnmatch_lines([">*asd*", "E*NameError*"]) def test_traceback_filter_error_during_fixture_collection( self, pytester: Pytester ) -> None: """Integration test for issue #995.""" pytester.makepyfile( """ import pytest def fail_me(func): ns = {} exec('def w(): raise ValueError("fail me")', ns) return ns['w'] @pytest.fixture(scope='class') @fail_me def fail_fixture(): pass def test_failing_fixture(fail_fixture): pass """ ) result = pytester.runpytest() assert result.ret != 0 out = result.stdout.str() assert "INTERNALERROR>" not in out result.stdout.fnmatch_lines(["*ValueError: fail me*", "* 1 error in *"]) def test_filter_traceback_generated_code(self) -> None: """Test that filter_traceback() works with the fact that _pytest._code.code.Code.path attribute might return an str object. In this case, one of the entries on the traceback was produced by dynamically generated code. See: https://bitbucket.org/pytest-dev/py/issues/71 This fixes #995. """ from _pytest._code import filter_traceback tb = None try: ns: Dict[str, Any] = {} exec("def foo(): raise ValueError", ns) ns["foo"]() except ValueError: _, _, tb = sys.exc_info() assert tb is not None traceback = _pytest._code.Traceback(tb) assert isinstance(traceback[-1].path, str) assert not filter_traceback(traceback[-1]) def test_filter_traceback_path_no_longer_valid(self, pytester: Pytester) -> None: """Test that filter_traceback() works with the fact that _pytest._code.code.Code.path attribute might return an str object. In this case, one of the files in the traceback no longer exists. This fixes #1133. """ from _pytest._code import filter_traceback pytester.syspathinsert() pytester.makepyfile( filter_traceback_entry_as_str=""" def foo(): raise ValueError """ ) tb = None try: import filter_traceback_entry_as_str filter_traceback_entry_as_str.foo() except ValueError: _, _, tb = sys.exc_info() assert tb is not None pytester.path.joinpath("filter_traceback_entry_as_str.py").unlink() traceback = _pytest._code.Traceback(tb) assert isinstance(traceback[-1].path, str) assert filter_traceback(traceback[-1]) class TestReportInfo: def test_itemreport_reportinfo(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyFunction(pytest.Function): def reportinfo(self): return "ABCDE", 42, "custom" def pytest_pycollect_makeitem(collector, name, obj): if name == "test_func": return MyFunction.from_parent(name=name, parent=collector) """ ) item = pytester.getitem("def test_func(): pass") item.config.pluginmanager.getplugin("runner") assert item.location == ("ABCDE", 42, "custom") def test_func_reportinfo(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): pass") path, lineno, modpath = item.reportinfo() assert os.fspath(path) == str(item.path) assert lineno == 0 assert modpath == "test_func" def test_class_reportinfo(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ # lineno 0 class TestClass(object): def test_hello(self): pass """ ) classcol = pytester.collect_by_name(modcol, "TestClass") assert isinstance(classcol, Class) path, lineno, msg = classcol.reportinfo() assert os.fspath(path) == str(modcol.path) assert lineno == 1 assert msg == "TestClass" @pytest.mark.filterwarnings( "ignore:usage of Generator.Function is deprecated, please use pytest.Function instead" ) def test_reportinfo_with_nasty_getattr(self, pytester: Pytester) -> None: # https://github.com/pytest-dev/pytest/issues/1204 modcol = pytester.getmodulecol( """ # lineno 0 class TestClass(object): def __getattr__(self, name): return "this is not an int" def intest_foo(self): pass """ ) classcol = pytester.collect_by_name(modcol, "TestClass") assert isinstance(classcol, Class) instance = list(classcol.collect())[0] assert isinstance(instance, Instance) path, lineno, msg = instance.reportinfo() def test_customized_python_discovery(pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_files=check_*.py python_classes=Check python_functions=check """ ) p = pytester.makepyfile( """ def check_simple(): pass class CheckMyApp(object): def check_meth(self): pass """ ) p2 = p.with_name(p.name.replace("test", "check")) p.rename(p2) result = pytester.runpytest("--collect-only", "-s") result.stdout.fnmatch_lines( ["*check_customized*", "*check_simple*", "*CheckMyApp*", "*check_meth*"] ) result = pytester.runpytest() assert result.ret == 0 result.stdout.fnmatch_lines(["*2 passed*"]) def test_customized_python_discovery_functions(pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_functions=_test """ ) pytester.makepyfile( """ def _test_underscore(): pass """ ) result = pytester.runpytest("--collect-only", "-s") result.stdout.fnmatch_lines(["*_test_underscore*"]) result = pytester.runpytest() assert result.ret == 0 result.stdout.fnmatch_lines(["*1 passed*"]) def test_unorderable_types(pytester: Pytester) -> None: pytester.makepyfile( """ class TestJoinEmpty(object): pass def make_test(): class Test(object): pass Test.__name__ = "TestFoo" return Test TestFoo = make_test() """ ) result = pytester.runpytest() result.stdout.no_fnmatch_line("*TypeError*") assert result.ret == ExitCode.NO_TESTS_COLLECTED @pytest.mark.filterwarnings("default::pytest.PytestCollectionWarning") def test_dont_collect_non_function_callable(pytester: Pytester) -> None: """Test for issue https://github.com/pytest-dev/pytest/issues/331 In this case an INTERNALERROR occurred trying to report the failure of a test like this one because pytest failed to get the source lines. """ pytester.makepyfile( """ class Oh(object): def __call__(self): pass test_a = Oh() def test_real(): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*collected 1 item*", "*test_dont_collect_non_function_callable.py:2: *cannot collect 'test_a' because it is not a function*", "*1 passed, 1 warning in *", ] ) def test_class_injection_does_not_break_collection(pytester: Pytester) -> None: """Tests whether injection during collection time will terminate testing. In this case the error should not occur if the TestClass itself is modified during collection time, and the original method list is still used for collection. """ pytester.makeconftest( """ from test_inject import TestClass def pytest_generate_tests(metafunc): TestClass.changed_var = {} """ ) pytester.makepyfile( test_inject=''' class TestClass(object): def test_injection(self): """Test being parametrized.""" pass ''' ) result = pytester.runpytest() assert ( "RuntimeError: dictionary changed size during iteration" not in result.stdout.str() ) result.stdout.fnmatch_lines(["*1 passed*"]) def test_syntax_error_with_non_ascii_chars(pytester: Pytester) -> None: """Fix decoding issue while formatting SyntaxErrors during collection (#578).""" pytester.makepyfile("☃") result = pytester.runpytest() result.stdout.fnmatch_lines(["*ERROR collecting*", "*SyntaxError*", "*1 error in*"]) def test_collect_error_with_fulltrace(pytester: Pytester) -> None: pytester.makepyfile("assert 0") result = pytester.runpytest("--fulltrace") result.stdout.fnmatch_lines( [ "collected 0 items / 1 error", "", "*= ERRORS =*", "*_ ERROR collecting test_collect_error_with_fulltrace.py _*", "", "> assert 0", "E assert 0", "", "test_collect_error_with_fulltrace.py:1: AssertionError", "*! Interrupted: 1 error during collection !*", ] ) def test_skip_duplicates_by_default(pytester: Pytester) -> None: """Test for issue https://github.com/pytest-dev/pytest/issues/1609 (#1609) Ignore duplicate directories. """ a = pytester.mkdir("a") fh = a.joinpath("test_a.py") fh.write_text( textwrap.dedent( """\ import pytest def test_real(): pass """ ) ) result = pytester.runpytest(str(a), str(a)) result.stdout.fnmatch_lines(["*collected 1 item*"]) def test_keep_duplicates(pytester: Pytester) -> None: """Test for issue https://github.com/pytest-dev/pytest/issues/1609 (#1609) Use --keep-duplicates to collect tests from duplicate directories. """ a = pytester.mkdir("a") fh = a.joinpath("test_a.py") fh.write_text( textwrap.dedent( """\ import pytest def test_real(): pass """ ) ) result = pytester.runpytest("--keep-duplicates", str(a), str(a)) result.stdout.fnmatch_lines(["*collected 2 item*"]) def test_package_collection_infinite_recursion(pytester: Pytester) -> None: pytester.copy_example("collect/package_infinite_recursion") result = pytester.runpytest() result.stdout.fnmatch_lines(["*1 passed*"]) def test_package_collection_init_given_as_argument(pytester: Pytester) -> None: """Regression test for #3749""" p = pytester.copy_example("collect/package_init_given_as_arg") result = pytester.runpytest(p / "pkg" / "__init__.py") result.stdout.fnmatch_lines(["*1 passed*"]) def test_package_with_modules(pytester: Pytester) -> None: """ . └── root ├── __init__.py ├── sub1 │ ├── __init__.py │ └── sub1_1 │ ├── __init__.py │ └── test_in_sub1.py └── sub2 └── test └── test_in_sub2.py """ root = pytester.mkpydir("root") sub1 = root.joinpath("sub1") sub1_test = sub1.joinpath("sub1_1") sub1_test.mkdir(parents=True) for d in (sub1, sub1_test): d.joinpath("__init__.py").touch() sub2 = root.joinpath("sub2") sub2_test = sub2.joinpath("test") sub2_test.mkdir(parents=True) sub1_test.joinpath("test_in_sub1.py").write_text("def test_1(): pass") sub2_test.joinpath("test_in_sub2.py").write_text("def test_2(): pass") # Execute from . result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) # Execute from . with one argument "root" result = pytester.runpytest("-v", "-s", "root") result.assert_outcomes(passed=2) # Chdir into package's root and execute with no args os.chdir(root) result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) def test_package_ordering(pytester: Pytester) -> None: """ . └── root ├── Test_root.py ├── __init__.py ├── sub1 │ ├── Test_sub1.py │ └── __init__.py └── sub2 └── test └── test_sub2.py """ pytester.makeini( """ [pytest] python_files=*.py """ ) root = pytester.mkpydir("root") sub1 = root.joinpath("sub1") sub1.mkdir() sub1.joinpath("__init__.py").touch() sub2 = root.joinpath("sub2") sub2_test = sub2.joinpath("test") sub2_test.mkdir(parents=True) root.joinpath("Test_root.py").write_text("def test_1(): pass") sub1.joinpath("Test_sub1.py").write_text("def test_2(): pass") sub2_test.joinpath("test_sub2.py").write_text("def test_3(): pass") # Execute from . result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=3)

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import os import sys import textwrap from typing import Any from typing import Dict import _pytest._code import pytest from _pytest.config import ExitCode from _pytest.main import Session from _pytest.monkeypatch import MonkeyPatch from _pytest.nodes import Collector from _pytest.pytester import Pytester from _pytest.python import Class from _pytest.python import Instance class TestModule: def test_failing_import(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("import alksdjalskdjalkjals") pytest.raises(Collector.CollectError, modcol.collect) def test_import_duplicate(self, pytester: Pytester) -> None: a = pytester.mkdir("a") b = pytester.mkdir("b") p1 = a.joinpath("test_whatever.py") p1.touch() p2 = b.joinpath("test_whatever.py") p2.touch() sys.modules.pop(p1.stem, None) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*import*mismatch*", "*imported*test_whatever*", "*%s*" % p1, "*not the same*", "*%s*" % p2, "*HINT*", ] ) def test_import_prepend_append( self, pytester: Pytester, monkeypatch: MonkeyPatch ) -> None: root1 = pytester.mkdir("root1") root2 = pytester.mkdir("root2") root1.joinpath("x456.py").touch() root2.joinpath("x456.py").touch() p = root2.joinpath("test_x456.py") monkeypatch.syspath_prepend(str(root1)) p.write_text( textwrap.dedent( """\ import x456 def test(): assert x456.__file__.startswith({!r}) """.format( str(root2) ) ) ) with monkeypatch.context() as mp: mp.chdir(root2) reprec = pytester.inline_run("--import-mode=append") reprec.assertoutcome(passed=0, failed=1) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_syntax_error_in_module(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("this is a syntax error") pytest.raises(modcol.CollectError, modcol.collect) pytest.raises(modcol.CollectError, modcol.collect) def test_module_considers_pluginmanager_at_import(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol("pytest_plugins='xasdlkj',") pytest.raises(ImportError, lambda: modcol.obj) def test_invalid_test_module_name(self, pytester: Pytester) -> None: a = pytester.mkdir("a") a.joinpath("test_one.part1.py").touch() result = pytester.runpytest() result.stdout.fnmatch_lines( [ "ImportError while importing test module*test_one.part1*", "Hint: make sure your test modules/packages have valid Python names.", ] ) @pytest.mark.parametrize("verbose", [0, 1, 2]) def test_show_traceback_import_error( self, pytester: Pytester, verbose: int ) -> None: pytester.makepyfile( foo_traceback_import_error=""" from bar_traceback_import_error import NOT_AVAILABLE """, bar_traceback_import_error="", ) pytester.makepyfile( """ import foo_traceback_import_error """ ) args = ("-v",) * verbose result = pytester.runpytest(*args) result.stdout.fnmatch_lines( [ "ImportError while importing test module*", "Traceback:", "*from bar_traceback_import_error import NOT_AVAILABLE", "*cannot import name *NOT_AVAILABLE*", ] ) assert result.ret == 2 stdout = result.stdout.str() if verbose == 2: assert "_pytest" in stdout else: assert "_pytest" not in stdout def test_show_traceback_import_error_unicode(self, pytester: Pytester) -> None: pytester.makepyfile("raise ImportError('Something bad happened ☺')") result = pytester.runpytest() result.stdout.fnmatch_lines( [ "ImportError while importing test module*", "Traceback:", "*raise ImportError*Something bad happened*", ] ) assert result.ret == 2 class TestClass: def test_class_with_init_warning(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestClass1(object): def __init__(self): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*cannot collect test class 'TestClass1' because it has " "a __init__ constructor (from: test_class_with_init_warning.py)" ] ) def test_class_with_new_warning(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestClass1(object): def __new__(self): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*cannot collect test class 'TestClass1' because it has " "a __new__ constructor (from: test_class_with_new_warning.py)" ] ) def test_class_subclassobject(self, pytester: Pytester) -> None: pytester.getmodulecol( """ class test(object): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*collected 0*"]) def test_static_method(self, pytester: Pytester) -> None: pytester.getmodulecol( """ import pytest class Test(object): @staticmethod def test_something(): pass @pytest.fixture def fix(self): return 1 @staticmethod def test_fix(fix): assert fix == 1 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*collected 2 items*", "*2 passed in*"]) def test_setup_teardown_class_as_classmethod(self, pytester: Pytester) -> None: pytester.makepyfile( test_mod1=""" class TestClassMethod(object): @classmethod def setup_class(cls): pass def test_1(self): pass @classmethod def teardown_class(cls): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["*1 passed*"]) def test_issue1035_obj_has_getattr(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ class Chameleon(object): def __getattr__(self, name): return True chameleon = Chameleon() """ ) colitems = modcol.collect() assert len(colitems) == 0 def test_issue1579_namedtuple(self, pytester: Pytester) -> None: pytester.makepyfile( """ import collections TestCase = collections.namedtuple('TestCase', ['a']) """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( "*cannot collect test class 'TestCase' " "because it has a __new__ constructor*" ) def test_issue2234_property(self, pytester: Pytester) -> None: pytester.makepyfile( """ class TestCase(object): @property def prop(self): raise NotImplementedError() """ ) result = pytester.runpytest() assert result.ret == ExitCode.NO_TESTS_COLLECTED class TestFunction: def test_getmodulecollector(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): pass") modcol = item.getparent(pytest.Module) assert isinstance(modcol, pytest.Module) assert hasattr(modcol.obj, "test_func") @pytest.mark.filterwarnings("default") def test_function_as_object_instance_ignored(self, pytester: Pytester) -> None: pytester.makepyfile( """ class A(object): def __call__(self, tmp_path): 0/0 test_a = A() """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "collected 0 items", "*test_function_as_object_instance_ignored.py:2: " "*cannot collect 'test_a' because it is not a function.", ] ) @staticmethod def make_function(pytester: Pytester, **kwargs: Any) -> Any: from _pytest.fixtures import FixtureManager config = pytester.parseconfigure() session = Session.from_config(config) session._fixturemanager = FixtureManager(session) return pytest.Function.from_parent(parent=session, **kwargs) def test_function_equality(self, pytester: Pytester) -> None: def func1(): pass def func2(): pass f1 = self.make_function(pytester, name="name", callobj=func1) assert f1 == f1 f2 = self.make_function( pytester, name="name", callobj=func2, originalname="foobar" ) assert f1 != f2 def test_repr_produces_actual_test_id(self, pytester: Pytester) -> None: f = self.make_function( pytester, name=r"test[\xe5]", callobj=self.test_repr_produces_actual_test_id ) assert repr(f) == r"<Function test[\xe5]>" def test_issue197_parametrize_emptyset(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest @pytest.mark.parametrize('arg', []) def test_function(arg): pass """ ) reprec = pytester.inline_run() reprec.assertoutcome(skipped=1) def test_single_tuple_unwraps_values(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest @pytest.mark.parametrize(('arg',), [(1,)]) def test_function(arg): assert arg == 1 """ ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_issue213_parametrize_value_no_equal(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest class A(object): def __eq__(self, other): raise ValueError("not possible") @pytest.mark.parametrize('arg', [A()]) def test_function(arg): assert arg.__class__.__name__ == "A" """ ) reprec = pytester.inline_run("--fulltrace") reprec.assertoutcome(passed=1) def test_parametrize_with_non_hashable_values(self, pytester: Pytester) -> None: pytester.makepyfile( """ archival_mapping = { '1.0': {'tag': '1.0'}, '1.2.2a1': {'tag': 'release-1.2.2a1'}, } import pytest @pytest.mark.parametrize('key value'.split(), archival_mapping.items()) def test_archival_to_version(key, value): assert key in archival_mapping assert value == archival_mapping[key] """ ) rec = pytester.inline_run() rec.assertoutcome(passed=2) def test_parametrize_with_non_hashable_values_indirect( self, pytester: Pytester ) -> None: pytester.makepyfile( """ archival_mapping = { '1.0': {'tag': '1.0'}, '1.2.2a1': {'tag': 'release-1.2.2a1'}, } import pytest @pytest.fixture def key(request): return request.param @pytest.fixture def value(request): return request.param @pytest.mark.parametrize('key value'.split(), archival_mapping.items(), indirect=True) def test_archival_to_version(key, value): assert key in archival_mapping assert value == archival_mapping[key] """ ) rec = pytester.inline_run() rec.assertoutcome(passed=2) def test_parametrize_overrides_fixture(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest @pytest.fixture def value(): return 'value' @pytest.mark.parametrize('value', ['overridden']) def test_overridden_via_param(value): assert value == 'overridden' @pytest.mark.parametrize('somevalue', ['overridden']) def test_not_overridden(value, somevalue): assert value == 'value' assert somevalue == 'overridden' @pytest.mark.parametrize('other,value', [('foo', 'overridden')]) def test_overridden_via_multiparam(other, value): assert other == 'foo' assert value == 'overridden' """ ) rec = pytester.inline_run() rec.assertoutcome(passed=3) def test_parametrize_overrides_parametrized_fixture( self, pytester: Pytester ) -> None: pytester.makepyfile( """ import pytest @pytest.fixture(params=[1, 2]) def value(request): return request.param @pytest.mark.parametrize('value', ['overridden']) def test_overridden_via_param(value): assert value == 'overridden' """ ) rec = pytester.inline_run() rec.assertoutcome(passed=1) def test_parametrize_overrides_indirect_dependency_fixture( self, pytester: Pytester ) -> None: pytester.makepyfile( """ import pytest fix3_instantiated = False @pytest.fixture def fix1(fix2): return fix2 + '1' @pytest.fixture def fix2(fix3): return fix3 + '2' @pytest.fixture def fix3(): global fix3_instantiated fix3_instantiated = True return '3' @pytest.mark.parametrize('fix2', ['2']) def test_it(fix1): assert fix1 == '21' assert not fix3_instantiated """ ) rec = pytester.inline_run() rec.assertoutcome(passed=1) def test_parametrize_with_mark(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.foo @pytest.mark.parametrize('arg', [ 1, pytest.param(2, marks=[pytest.mark.baz, pytest.mark.bar]) ]) def test_function(arg): pass """ ) keywords = [item.keywords for item in items] assert ( "foo" in keywords[0] and "bar" not in keywords[0] and "baz" not in keywords[0] ) assert "foo" in keywords[1] and "bar" in keywords[1] and "baz" in keywords[1] def test_parametrize_with_empty_string_arguments(self, pytester: Pytester) -> None: items = pytester.getitems( """\ import pytest @pytest.mark.parametrize('v', ('', ' ')) @pytest.mark.parametrize('w', ('', ' ')) def test(v, w): ... """ ) names = {item.name for item in items} assert names == {"test[-]", "test[ -]", "test[- ]", "test[ - ]"} def test_function_equality_with_callspec(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.parametrize('arg', [1,2]) def test_function(arg): pass """ ) assert items[0] != items[1] assert not (items[0] == items[1]) def test_pyfunc_call(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): raise ValueError") config = item.config class MyPlugin1: def pytest_pyfunc_call(self): raise ValueError class MyPlugin2: def pytest_pyfunc_call(self): return True config.pluginmanager.register(MyPlugin1()) config.pluginmanager.register(MyPlugin2()) config.hook.pytest_runtest_setup(item=item) config.hook.pytest_pyfunc_call(pyfuncitem=item) def test_multiple_parametrize(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ import pytest @pytest.mark.parametrize('x', [0, 1]) @pytest.mark.parametrize('y', [2, 3]) def test1(x, y): pass """ ) colitems = modcol.collect() assert colitems[0].name == "test1[2-0]" assert colitems[1].name == "test1[2-1]" assert colitems[2].name == "test1[3-0]" assert colitems[3].name == "test1[3-1]" def test_issue751_multiple_parametrize_with_ids(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ import pytest @pytest.mark.parametrize('x', [0], ids=['c']) @pytest.mark.parametrize('y', [0, 1], ids=['a', 'b']) class Test(object): def test1(self, x, y): pass def test2(self, x, y): pass """ ) colitems = modcol.collect()[0].collect()[0].collect() assert colitems[0].name == "test1[a-c]" assert colitems[1].name == "test1[b-c]" assert colitems[2].name == "test2[a-c]" assert colitems[3].name == "test2[b-c]" def test_parametrize_skipif(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skipif('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_skip_if(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 skipped in *"]) def test_parametrize_skip(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skip('') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_skip(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 skipped in *"]) def test_parametrize_skipif_no_skip(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.skipif('False') @pytest.mark.parametrize('x', [0, 1, m(2)]) def test_skipif_no_skip(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 1 failed, 2 passed in *"]) def test_parametrize_xfail(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_xfail(x): assert x < 2 """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 xfailed in *"]) def test_parametrize_passed(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('True') @pytest.mark.parametrize('x', [0, 1, pytest.param(2, marks=m)]) def test_xfail(x): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 2 passed, 1 xpassed in *"]) def test_parametrize_xfail_passed(self, pytester: Pytester) -> None: pytester.makepyfile( """ import pytest m = pytest.mark.xfail('False') @pytest.mark.parametrize('x', [0, 1, m(2)]) def test_passed(x): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines(["* 3 passed in *"]) def test_function_originalname(self, pytester: Pytester) -> None: items = pytester.getitems( """ import pytest @pytest.mark.parametrize('arg', [1,2]) def test_func(arg): pass def test_no_param(): pass """ ) originalnames = [] for x in items: assert isinstance(x, pytest.Function) originalnames.append(x.originalname) assert originalnames == [ "test_func", "test_func", "test_no_param", ] def test_function_with_square_brackets(self, pytester: Pytester) -> None: p1 = pytester.makepyfile( """ locals()["test_foo[name]"] = lambda: None """ ) result = pytester.runpytest("-v", str(p1)) result.stdout.fnmatch_lines( [ "test_function_with_square_brackets.py::test_foo[[]name[]] PASSED *", "*= 1 passed in *", ] ) class TestSorting: def test_check_equality(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ def test_pass(): pass def test_fail(): assert 0 """ ) fn1 = pytester.collect_by_name(modcol, "test_pass") assert isinstance(fn1, pytest.Function) fn2 = pytester.collect_by_name(modcol, "test_pass") assert isinstance(fn2, pytest.Function) assert fn1 == fn2 assert fn1 != modcol assert hash(fn1) == hash(fn2) fn3 = pytester.collect_by_name(modcol, "test_fail") assert isinstance(fn3, pytest.Function) assert not (fn1 == fn3) assert fn1 != fn3 for fn in fn1, fn2, fn3: assert fn != 3 # type: ignore[comparison-overlap] assert fn != modcol assert fn != [1, 2, 3] # type: ignore[comparison-overlap] assert [1, 2, 3] != fn # type: ignore[comparison-overlap] assert modcol != fn def test_allow_sane_sorting_for_decorators(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ def dec(f): g = lambda: f(2) g.place_as = f return g def test_b(y): pass test_b = dec(test_b) def test_a(y): pass test_a = dec(test_a) """ ) colitems = modcol.collect() assert len(colitems) == 2 assert [item.name for item in colitems] == ["test_b", "test_a"] def test_ordered_by_definition_order(self, pytester: Pytester) -> None: pytester.makepyfile( """\ class Test1: def test_foo(): pass def test_bar(): pass class Test2: def test_foo(): pass test_bar = Test1.test_bar class Test3(Test2): def test_baz(): pass """ ) result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines( [ "*Class Test1*", "*Function test_foo*", "*Function test_bar*", "*Class Test2*", # previously the order was flipped due to Test1.test_bar reference "*Function test_foo*", "*Function test_bar*", "*Class Test3*", "*Function test_foo*", "*Function test_bar*", "*Function test_baz*", ] ) class TestConftestCustomization: def test_pytest_pycollect_module(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyModule(pytest.Module): pass def pytest_pycollect_makemodule(fspath, parent): if fspath.name == "test_xyz.py": return MyModule.from_parent(path=fspath, parent=parent) """ ) pytester.makepyfile("def test_some(): pass") pytester.makepyfile(test_xyz="def test_func(): pass") result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines(["*<Module*test_pytest*", "*<MyModule*xyz*"]) def test_customized_pymakemodule_issue205_subdir(self, pytester: Pytester) -> None: b = pytester.path.joinpath("a", "b") b.mkdir(parents=True) b.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest @pytest.hookimpl(hookwrapper=True) def pytest_pycollect_makemodule(): outcome = yield mod = outcome.get_result() mod.obj.hello = "world" """ ) ) b.joinpath("test_module.py").write_text( textwrap.dedent( """\ def test_hello(): assert hello == "world" """ ) ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_customized_pymakeitem(self, pytester: Pytester) -> None: b = pytester.path.joinpath("a", "b") b.mkdir(parents=True) b.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest @pytest.hookimpl(hookwrapper=True) def pytest_pycollect_makeitem(): outcome = yield if outcome.excinfo is None: result = outcome.get_result() if result: for func in result: func._some123 = "world" """ ) ) b.joinpath("test_module.py").write_text( textwrap.dedent( """\ import pytest @pytest.fixture() def obj(request): return request.node._some123 def test_hello(obj): assert obj == "world" """ ) ) reprec = pytester.inline_run() reprec.assertoutcome(passed=1) def test_pytest_pycollect_makeitem(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyFunction(pytest.Function): pass def pytest_pycollect_makeitem(collector, name, obj): if name == "some": return MyFunction.from_parent(name=name, parent=collector) """ ) pytester.makepyfile("def some(): pass") result = pytester.runpytest("--collect-only") result.stdout.fnmatch_lines(["*MyFunction*some*"]) def test_issue2369_collect_module_fileext(self, pytester: Pytester) -> None: # We'll implement a little finder and loader to import files containing pytester.makeconftest( """ import sys, os, imp from _pytest.python import Module class Loader(object): def load_module(self, name): return imp.load_source(name, name + ".narf") class Finder(object): def find_module(self, name, path=None): if os.path.exists(name + ".narf"): return Loader() sys.meta_path.append(Finder()) def pytest_collect_file(fspath, parent): if fspath.suffix == ".narf": return Module.from_parent(path=fspath, parent=parent)""" ) pytester.makefile( ".narf", """\ def test_something(): assert 1 + 1 == 2""", ) result = pytester.runpytest_subprocess() result.stdout.fnmatch_lines(["*1 passed*"]) def test_early_ignored_attributes(self, pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_classes=* python_functions=* """ ) pytester.makepyfile( """ class TestEmpty: pass test_empty = TestEmpty() def test_real(): pass """ ) items, rec = pytester.inline_genitems() assert rec.ret == 0 assert len(items) == 1 def test_setup_only_available_in_subdir(pytester: Pytester) -> None: sub1 = pytester.mkpydir("sub1") sub2 = pytester.mkpydir("sub2") sub1.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest def pytest_runtest_setup(item): assert item.path.stem == "test_in_sub1" def pytest_runtest_call(item): assert item.path.stem == "test_in_sub1" def pytest_runtest_teardown(item): assert item.path.stem == "test_in_sub1" """ ) ) sub2.joinpath("conftest.py").write_text( textwrap.dedent( """\ import pytest def pytest_runtest_setup(item): assert item.path.stem == "test_in_sub2" def pytest_runtest_call(item): assert item.path.stem == "test_in_sub2" def pytest_runtest_teardown(item): assert item.path.stem == "test_in_sub2" """ ) ) sub1.joinpath("test_in_sub1.py").write_text("def test_1(): pass") sub2.joinpath("test_in_sub2.py").write_text("def test_2(): pass") result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) def test_modulecol_roundtrip(pytester: Pytester) -> None: modcol = pytester.getmodulecol("pass", withinit=False) trail = modcol.nodeid newcol = modcol.session.perform_collect([trail], genitems=0)[0] assert modcol.name == newcol.name class TestTracebackCutting: def test_skip_simple(self): with pytest.raises(pytest.skip.Exception) as excinfo: pytest.skip("xxx") assert excinfo.traceback[-1].frame.code.name == "skip" assert excinfo.traceback[-1].ishidden() assert excinfo.traceback[-2].frame.code.name == "test_skip_simple" assert not excinfo.traceback[-2].ishidden() def test_traceback_argsetup(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest @pytest.fixture def hello(request): raise ValueError("xyz") """ ) p = pytester.makepyfile("def test(hello): pass") result = pytester.runpytest(p) assert result.ret != 0 out = result.stdout.str() assert "xyz" in out assert "conftest.py:5: ValueError" in out numentries = out.count("_ _ _") # separator for traceback entries assert numentries == 0 result = pytester.runpytest("--fulltrace", p) out = result.stdout.str() assert "conftest.py:5: ValueError" in out numentries = out.count("_ _ _ _") # separator for traceback entries assert numentries > 3 def test_traceback_error_during_import(self, pytester: Pytester) -> None: pytester.makepyfile( """ x = 1 x = 2 x = 17 asd """ ) result = pytester.runpytest() assert result.ret != 0 out = result.stdout.str() assert "x = 1" not in out assert "x = 2" not in out result.stdout.fnmatch_lines([" *asd*", "E*NameError*"]) result = pytester.runpytest("--fulltrace") out = result.stdout.str() assert "x = 1" in out assert "x = 2" in out result.stdout.fnmatch_lines([">*asd*", "E*NameError*"]) def test_traceback_filter_error_during_fixture_collection( self, pytester: Pytester ) -> None: pytester.makepyfile( """ import pytest def fail_me(func): ns = {} exec('def w(): raise ValueError("fail me")', ns) return ns['w'] @pytest.fixture(scope='class') @fail_me def fail_fixture(): pass def test_failing_fixture(fail_fixture): pass """ ) result = pytester.runpytest() assert result.ret != 0 out = result.stdout.str() assert "INTERNALERROR>" not in out result.stdout.fnmatch_lines(["*ValueError: fail me*", "* 1 error in *"]) def test_filter_traceback_generated_code(self) -> None: from _pytest._code import filter_traceback tb = None try: ns: Dict[str, Any] = {} exec("def foo(): raise ValueError", ns) ns["foo"]() except ValueError: _, _, tb = sys.exc_info() assert tb is not None traceback = _pytest._code.Traceback(tb) assert isinstance(traceback[-1].path, str) assert not filter_traceback(traceback[-1]) def test_filter_traceback_path_no_longer_valid(self, pytester: Pytester) -> None: from _pytest._code import filter_traceback pytester.syspathinsert() pytester.makepyfile( filter_traceback_entry_as_str=""" def foo(): raise ValueError """ ) tb = None try: import filter_traceback_entry_as_str filter_traceback_entry_as_str.foo() except ValueError: _, _, tb = sys.exc_info() assert tb is not None pytester.path.joinpath("filter_traceback_entry_as_str.py").unlink() traceback = _pytest._code.Traceback(tb) assert isinstance(traceback[-1].path, str) assert filter_traceback(traceback[-1]) class TestReportInfo: def test_itemreport_reportinfo(self, pytester: Pytester) -> None: pytester.makeconftest( """ import pytest class MyFunction(pytest.Function): def reportinfo(self): return "ABCDE", 42, "custom" def pytest_pycollect_makeitem(collector, name, obj): if name == "test_func": return MyFunction.from_parent(name=name, parent=collector) """ ) item = pytester.getitem("def test_func(): pass") item.config.pluginmanager.getplugin("runner") assert item.location == ("ABCDE", 42, "custom") def test_func_reportinfo(self, pytester: Pytester) -> None: item = pytester.getitem("def test_func(): pass") path, lineno, modpath = item.reportinfo() assert os.fspath(path) == str(item.path) assert lineno == 0 assert modpath == "test_func" def test_class_reportinfo(self, pytester: Pytester) -> None: modcol = pytester.getmodulecol( """ # lineno 0 class TestClass(object): def test_hello(self): pass """ ) classcol = pytester.collect_by_name(modcol, "TestClass") assert isinstance(classcol, Class) path, lineno, msg = classcol.reportinfo() assert os.fspath(path) == str(modcol.path) assert lineno == 1 assert msg == "TestClass" @pytest.mark.filterwarnings( "ignore:usage of Generator.Function is deprecated, please use pytest.Function instead" ) def test_reportinfo_with_nasty_getattr(self, pytester: Pytester) -> None: # https://github.com/pytest-dev/pytest/issues/1204 modcol = pytester.getmodulecol( """ # lineno 0 class TestClass(object): def __getattr__(self, name): return "this is not an int" def intest_foo(self): pass """ ) classcol = pytester.collect_by_name(modcol, "TestClass") assert isinstance(classcol, Class) instance = list(classcol.collect())[0] assert isinstance(instance, Instance) path, lineno, msg = instance.reportinfo() def test_customized_python_discovery(pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_files=check_*.py python_classes=Check python_functions=check """ ) p = pytester.makepyfile( """ def check_simple(): pass class CheckMyApp(object): def check_meth(self): pass """ ) p2 = p.with_name(p.name.replace("test", "check")) p.rename(p2) result = pytester.runpytest("--collect-only", "-s") result.stdout.fnmatch_lines( ["*check_customized*", "*check_simple*", "*CheckMyApp*", "*check_meth*"] ) result = pytester.runpytest() assert result.ret == 0 result.stdout.fnmatch_lines(["*2 passed*"]) def test_customized_python_discovery_functions(pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_functions=_test """ ) pytester.makepyfile( """ def _test_underscore(): pass """ ) result = pytester.runpytest("--collect-only", "-s") result.stdout.fnmatch_lines(["*_test_underscore*"]) result = pytester.runpytest() assert result.ret == 0 result.stdout.fnmatch_lines(["*1 passed*"]) def test_unorderable_types(pytester: Pytester) -> None: pytester.makepyfile( """ class TestJoinEmpty(object): pass def make_test(): class Test(object): pass Test.__name__ = "TestFoo" return Test TestFoo = make_test() """ ) result = pytester.runpytest() result.stdout.no_fnmatch_line("*TypeError*") assert result.ret == ExitCode.NO_TESTS_COLLECTED @pytest.mark.filterwarnings("default::pytest.PytestCollectionWarning") def test_dont_collect_non_function_callable(pytester: Pytester) -> None: pytester.makepyfile( """ class Oh(object): def __call__(self): pass test_a = Oh() def test_real(): pass """ ) result = pytester.runpytest() result.stdout.fnmatch_lines( [ "*collected 1 item*", "*test_dont_collect_non_function_callable.py:2: *cannot collect 'test_a' because it is not a function*", "*1 passed, 1 warning in *", ] ) def test_class_injection_does_not_break_collection(pytester: Pytester) -> None: pytester.makeconftest( """ from test_inject import TestClass def pytest_generate_tests(metafunc): TestClass.changed_var = {} """ ) pytester.makepyfile( test_inject=''' class TestClass(object): def test_injection(self): """Test being parametrized.""" pass ''' ) result = pytester.runpytest() assert ( "RuntimeError: dictionary changed size during iteration" not in result.stdout.str() ) result.stdout.fnmatch_lines(["*1 passed*"]) def test_syntax_error_with_non_ascii_chars(pytester: Pytester) -> None: pytester.makepyfile("☃") result = pytester.runpytest() result.stdout.fnmatch_lines(["*ERROR collecting*", "*SyntaxError*", "*1 error in*"]) def test_collect_error_with_fulltrace(pytester: Pytester) -> None: pytester.makepyfile("assert 0") result = pytester.runpytest("--fulltrace") result.stdout.fnmatch_lines( [ "collected 0 items / 1 error", "", "*= ERRORS =*", "*_ ERROR collecting test_collect_error_with_fulltrace.py _*", "", "> assert 0", "E assert 0", "", "test_collect_error_with_fulltrace.py:1: AssertionError", "*! Interrupted: 1 error during collection !*", ] ) def test_skip_duplicates_by_default(pytester: Pytester) -> None: a = pytester.mkdir("a") fh = a.joinpath("test_a.py") fh.write_text( textwrap.dedent( """\ import pytest def test_real(): pass """ ) ) result = pytester.runpytest(str(a), str(a)) result.stdout.fnmatch_lines(["*collected 1 item*"]) def test_keep_duplicates(pytester: Pytester) -> None: a = pytester.mkdir("a") fh = a.joinpath("test_a.py") fh.write_text( textwrap.dedent( """\ import pytest def test_real(): pass """ ) ) result = pytester.runpytest("--keep-duplicates", str(a), str(a)) result.stdout.fnmatch_lines(["*collected 2 item*"]) def test_package_collection_infinite_recursion(pytester: Pytester) -> None: pytester.copy_example("collect/package_infinite_recursion") result = pytester.runpytest() result.stdout.fnmatch_lines(["*1 passed*"]) def test_package_collection_init_given_as_argument(pytester: Pytester) -> None: p = pytester.copy_example("collect/package_init_given_as_arg") result = pytester.runpytest(p / "pkg" / "__init__.py") result.stdout.fnmatch_lines(["*1 passed*"]) def test_package_with_modules(pytester: Pytester) -> None: root = pytester.mkpydir("root") sub1 = root.joinpath("sub1") sub1_test = sub1.joinpath("sub1_1") sub1_test.mkdir(parents=True) for d in (sub1, sub1_test): d.joinpath("__init__.py").touch() sub2 = root.joinpath("sub2") sub2_test = sub2.joinpath("test") sub2_test.mkdir(parents=True) sub1_test.joinpath("test_in_sub1.py").write_text("def test_1(): pass") sub2_test.joinpath("test_in_sub2.py").write_text("def test_2(): pass") # Execute from . result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) # Execute from . with one argument "root" result = pytester.runpytest("-v", "-s", "root") result.assert_outcomes(passed=2) # Chdir into package's root and execute with no args os.chdir(root) result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=2) def test_package_ordering(pytester: Pytester) -> None: pytester.makeini( """ [pytest] python_files=*.py """ ) root = pytester.mkpydir("root") sub1 = root.joinpath("sub1") sub1.mkdir() sub1.joinpath("__init__.py").touch() sub2 = root.joinpath("sub2") sub2_test = sub2.joinpath("test") sub2_test.mkdir(parents=True) root.joinpath("Test_root.py").write_text("def test_1(): pass") sub1.joinpath("Test_sub1.py").write_text("def test_2(): pass") sub2_test.joinpath("test_sub2.py").write_text("def test_3(): pass") result = pytester.runpytest("-v", "-s") result.assert_outcomes(passed=3)

true

1c312e8c942f41e88f77ac62566074eccc27a9e4

565

py

Python

src/basics/factorial_dffierent_operator.py

sungheeyun/PythonLectures

3a748672bf5b39568b2f42e813a0b9402711ad8e

[ "Unlicense" ]

null

src/basics/factorial_dffierent_operator.py

sungheeyun/PythonLectures

3a748672bf5b39568b2f42e813a0b9402711ad8e

[ "Unlicense" ]

null

src/basics/factorial_dffierent_operator.py

sungheeyun/PythonLectures

3a748672bf5b39568b2f42e813a0b9402711ad8e

[ "Unlicense" ]

null

""" Below we show that the statement result = result * x can be rewritten to result *= x Like this, Python provides operators such as += -= *= /= """ def factorial(n): """ Return the factorial of n. Parameters ---------- n : an integer of which the factorial is evaluated. Returns ------- result : The factorial of n. """ result = 1 for x in range(2, n + 1): result *= x return result if __name__ == "__main__": m = 10 print(m, "! =", factorial(m))

13.139535

55

0.511504

def factorial(n): result = 1 for x in range(2, n + 1): result *= x return result if __name__ == "__main__": m = 10 print(m, "! =", factorial(m))

true

1c3131316358511257cd0a22ed38d1797a92d67b

4,000

py

Python

mycnn/alexnet.py

jacky10001/tf2-mycnn

6a631ee71b2a91fc4e6e7a43f8f9179260a1d7fa

[ "MIT" ]

null

mycnn/alexnet.py

jacky10001/tf2-mycnn

6a631ee71b2a91fc4e6e7a43f8f9179260a1d7fa

[ "MIT" ]

20

2022-01-24T15:28:48.000Z

2022-02-13T14:56:25.000Z

mycnn/alexnet.py

jacky10001/tf2-mycnn

6a631ee71b2a91fc4e6e7a43f8f9179260a1d7fa

[ "MIT" ]

null

# -*- coding: utf-8 -*- import tensorflow as tf from tensorflow.keras import layers from .core.base_model import KerasModel class LRN(layers.Layer): """ Implement Local Response Normalization """ def __init__(self, alpha=0.0001, k=2, beta=0.75, n=5, **kwargs): super(LRN, self).__init__(**kwargs) self.alpha = alpha self.k = k self.beta = beta self.n = n def call(self, x): return tf.nn.lrn(x, depth_radius=self.n, bias=self.k, alpha=self.alpha, beta=self.beta) def get_config(self): config = {"alpha": self.alpha, "k": self.k, "beta": self.beta, "n": self.n} base_config = super(LRN, self).get_config() return dict(list(base_config.items()) + list(config.items())) class AlexNet(KerasModel): """ AlexNet+BN (超參數依照論文設置) """ def __init__(self, input_shape=(227, 227, 3), classes_num=10, **kwargs): self.input_shape = input_shape self.classes_num = classes_num super().__init__(**kwargs) def build(self, **kwargs): x_in = layers.Input(shape=self.input_shape, name="image") x = layers.Conv2D( filters=96, kernel_size=(11, 11), strides=(4, 4), # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), padding='valid' )(x_in) x = layers.BatchNormalization()(x) # x = LRN()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) x = layers.Conv2D( filters=256, kernel_size=(5, 5), # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), # bias_initializer='ones', padding='same' )(x) x = layers.BatchNormalization()(x) # x = LRN()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) x = layers.Conv2D( filters=384, kernel_size=(3, 3), # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.Conv2D( filters=384, kernel_size=(3, 3), # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), # bias_initializer='ones', padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.Conv2D( filters=256, kernel_size=(3, 3), # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), # bias_initializer='ones', padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D( pool_size=(3, 3), strides=(2, 2) )(x) x = layers.Flatten()(x) x = layers.Dense( 4096, # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), # bias_initializer='ones' )(x) x = layers.ReLU()(x) x = layers.Dropout(0.5)(x) x = layers.Dense( 4096, # kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01), # bias_initializer='ones' )(x) x = layers.ReLU()(x) x = layers.Dropout(0.5)(x) x_out = layers.Dense(self.classes_num, activation='softmax')(x) self.setup_model(x_in, x_out, name="AlexNet", **kwargs)

30.534351

86

0.497

import tensorflow as tf from tensorflow.keras import layers from .core.base_model import KerasModel class LRN(layers.Layer): def __init__(self, alpha=0.0001, k=2, beta=0.75, n=5, **kwargs): super(LRN, self).__init__(**kwargs) self.alpha = alpha self.k = k self.beta = beta self.n = n def call(self, x): return tf.nn.lrn(x, depth_radius=self.n, bias=self.k, alpha=self.alpha, beta=self.beta) def get_config(self): config = {"alpha": self.alpha, "k": self.k, "beta": self.beta, "n": self.n} base_config = super(LRN, self).get_config() return dict(list(base_config.items()) + list(config.items())) class AlexNet(KerasModel): def __init__(self, input_shape=(227, 227, 3), classes_num=10, **kwargs): self.input_shape = input_shape self.classes_num = classes_num super().__init__(**kwargs) def build(self, **kwargs): x_in = layers.Input(shape=self.input_shape, name="image") x = layers.Conv2D( filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid' )(x_in) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) x = layers.Conv2D( filters=256, kernel_size=(5, 5), padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x) x = layers.Conv2D( filters=384, kernel_size=(3, 3), padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.Conv2D( filters=384, kernel_size=(3, 3), padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.Conv2D( filters=256, kernel_size=(3, 3), padding='same' )(x) x = layers.BatchNormalization()(x) x = layers.ReLU()(x) x = layers.MaxPooling2D( pool_size=(3, 3), strides=(2, 2) )(x) x = layers.Flatten()(x) x = layers.Dense( 4096, )(x) x = layers.ReLU()(x) x = layers.Dropout(0.5)(x) x = layers.Dense( 4096, )(x) x = layers.ReLU()(x) x = layers.Dropout(0.5)(x) x_out = layers.Dense(self.classes_num, activation='softmax')(x) self.setup_model(x_in, x_out, name="AlexNet", **kwargs)

true

1c3131353ea8a23e84ed78c4de8ea42304b785ec

639

py

Python

venv/lib/python3.5/site-packages/coalib/results/HiddenResult.py

prashant0598/CoffeeApp

4fa006aebf06e12ed34766450ddcfa548ee63307

[ "MIT" ]

null

venv/lib/python3.5/site-packages/coalib/results/HiddenResult.py

prashant0598/CoffeeApp

4fa006aebf06e12ed34766450ddcfa548ee63307

[ "MIT" ]

null

venv/lib/python3.5/site-packages/coalib/results/HiddenResult.py

prashant0598/CoffeeApp

4fa006aebf06e12ed34766450ddcfa548ee63307

[ "MIT" ]

null

from coalib.results.Result import Result class HiddenResult(Result): """ This is a result that is not meant to be shown to the user. It can be used to transfer any data from a dependent bear to others. """ def __init__(self, origin, contents): """ Creates a new HiddenResult. The contents can be accessed with obj.contents later. :param origin: The originating bear. :param contents: Any object that is picklable since it will be transferred across processes. """ Result.__init__(self, origin, '') self.contents = contents

29.045455

78

0.629108

from coalib.results.Result import Result class HiddenResult(Result): def __init__(self, origin, contents): Result.__init__(self, origin, '') self.contents = contents

true

1c31318d50082c5f4a09647c23e41e0b1e41fae2

425

py

Python

numpoly/__init__.py

FredrikMeyer/numpoly

8584d96370dd817df713034cc89a140708dd00a9

[ "BSD-2-Clause" ]

null

numpoly/__init__.py

FredrikMeyer/numpoly

8584d96370dd817df713034cc89a140708dd00a9

[ "BSD-2-Clause" ]

null

numpoly/__init__.py

FredrikMeyer/numpoly

8584d96370dd817df713034cc89a140708dd00a9

[ "BSD-2-Clause" ]

null

# pylint: disable=wildcard-import """Numpoly -- Multivariate polynomials as numpy elements.""" from .baseclass import ndpoly from .align import ( align_polynomials, align_exponents, align_indeterminants, align_shape, align_dtype, ) from .construct import ( polynomial, aspolynomial, clean_attributes, ) from .sympy_ import to_sympy from .array_function import * from .poly_function import *

20.238095

60

0.738824

from .baseclass import ndpoly from .align import ( align_polynomials, align_exponents, align_indeterminants, align_shape, align_dtype, ) from .construct import ( polynomial, aspolynomial, clean_attributes, ) from .sympy_ import to_sympy from .array_function import * from .poly_function import *

true

1c31325282d4dfe6f0247bcb440be73457176259

4,317

py

Python

plugins/pelican-plugins/liquid_tags/pygalcharts.py

dbgriffith01/blog_source

bc5cd3e1ac1ff068de0cbb78b1470a7db743cd53

[ "MIT" ]

4

2018-09-18T19:16:44.000Z

2020-04-30T13:13:29.000Z

plugins/pelican-plugins/liquid_tags/pygalcharts.py

dbgriffith01/blog_source

bc5cd3e1ac1ff068de0cbb78b1470a7db743cd53

[ "MIT" ]

120

2018-09-01T20:27:51.000Z

2021-06-30T16:43:12.000Z

pelican-plugins/liquid_tags/pygalcharts.py

JN-Blog/jn-blog.com

669bf9a9c6813f2b7980792fb137f6718077aea1

[ "MIT" ]

3

2021-03-24T11:58:31.000Z

2022-01-12T16:03:06.000Z

""" pygal Tag --------- This implements a Liquid-style pygal tag for Pelican. JSON is used for the data, and you can pass a bunch of pygal's 'config' items through as-is [1] http://www.pygal.org/ Syntax ------ {% pygal { <graph data> } %} Examples -------- {% pygal { "type": "bar", "title": "Test Chart", "x-labels" : {"from": 2002, "to": 2013}, "data" : [ {"title": "Firefox", "values": [null, null, 0, 16.6, 25, 31, 36.4, 45.5, 46.3, 42.8, 37.1]}, {"title": "Chrome", "values": [null, null, null, null, null, null, 0, 3.9, 10.8, 23.8, 35.3]}, {"title": "IE", "values": [85.8, 84.6, 84.7, 74.5, 66, 58.6, 54.7, 44.8, 36.2, 26.6, 20.1]}, {"title": "Others", "values": [14.2, 15.4, 15.3, 8.9, 9, 10.4, 8.9, 5.8, 6.7, 6.8, 7.5]} ] } %} {% pygal { "type": "pie", "half_pie": true, "title": "Browser usage in February 2012 (in %)", "data" : [ {"title": "IE", "values": 19.5}, {"title": "Firefox", "values": 36.6}, {"title": "Chrome", "values": 36.3}, {"title": "Safari", "values": 4.5}, {"title": "Opera", "values": 2.3} ] } %} {% pygal { "type": "pie", "config": { "show_legend": false, "print_values": true, "show_y_labels": true }, "title": "Browser usage in February 2012 (in %)", "data" : [ {"title": "IE", "values": 19.5}, {"title": "Firefox", "values": 36.6}, {"title": "Chrome", "values": 36.3}, {"title": "Safari", "values": 4.5}, {"title": "Opera", "values": 2.3} ] } %} ... Output ------ <<div class="pygal" style="text-align: center;"><embed type="image/svg+xml" src=SVG_MARKUP_EMBEDDED style="max-width:1000px"/></div> """ import base64 import re from json import loads from .mdx_liquid_tags import LiquidTags SYNTAX = '{% pygal (data) %}' DOT_BLOCK_RE = re.compile(r'^\s*\{\s*(?P<code>.*\})\s*\}$', re.MULTILINE | re.DOTALL) def run_pygal(data, options=[], format='svg'): """ Runs pygal programs and returns image data """ import pygal chart_title = data.get('title', None) chart_type = data.get('type', '').lower() # Config options are pretty much proxied straight through from the JSON dict into the object config = pygal.Config() config_dict = data.get('config', {}) for key in config_dict.keys(): setattr(config, key, config_dict[key]) if chart_type == 'bar': chart = pygal.HorizontalBar(config) if data.get('horizontal', False) else pygal.Bar(config) elif chart_type == 'line': chart = pygal.Line(config) elif chart_type == 'pie': ir=data.get('inner_radius', 0.0) hp=data.get('half_pie', False) chart = pygal.Pie(config, inner_radius=ir, half_pie=hp) else: print('undefined or unknown chart type') if chart is not None: chart.title = data.get('title', None) # Do labels (if present) label_data = data.get('x-labels', None) if isinstance(label_data, list): # use list chart.x_labels = label_data elif isinstance(label_data, dict): # use a range range_from = label_data.get('from', 0) range_to = label_data.get('to', 0) chart.x_labels = map(str, range(range_from, range_to)) # insert data for data_set in data.get('data', []): title = data_set.get('title', None) values = data_set.get('values', None) chart.add(title, values) # now render result = chart.render_data_uri() else: result = None return result @LiquidTags.register('pygal') def pygal_parser(preprocessor, tag, markup): """ Simple pygal parser """ # Find JSON payload data = loads(markup) if tag == 'pygal' and data is not None: # Run generation of chart output = run_pygal(data) # Return embedded SVG image return '<div class="pygal" style="text-align: center;"><embed type="image/svg+xml" src=%s style="max-width:1000px"/></div>' % output else: raise ValueError('Error processing input. \nExpected syntax: {0}'.format(SYNTAX)) #---------------------------------------------------------------------- # This import allows image tag to be a Pelican plugin from .liquid_tags import register

26.163636

140

0.562428

import base64 import re from json import loads from .mdx_liquid_tags import LiquidTags SYNTAX = '{% pygal (data) %}' DOT_BLOCK_RE = re.compile(r'^\s*\{\s*(?P<code>.*\})\s*\}$', re.MULTILINE | re.DOTALL) def run_pygal(data, options=[], format='svg'): import pygal chart_title = data.get('title', None) chart_type = data.get('type', '').lower() config = pygal.Config() config_dict = data.get('config', {}) for key in config_dict.keys(): setattr(config, key, config_dict[key]) if chart_type == 'bar': chart = pygal.HorizontalBar(config) if data.get('horizontal', False) else pygal.Bar(config) elif chart_type == 'line': chart = pygal.Line(config) elif chart_type == 'pie': ir=data.get('inner_radius', 0.0) hp=data.get('half_pie', False) chart = pygal.Pie(config, inner_radius=ir, half_pie=hp) else: print('undefined or unknown chart type') if chart is not None: chart.title = data.get('title', None) label_data = data.get('x-labels', None) if isinstance(label_data, list): chart.x_labels = label_data elif isinstance(label_data, dict): range_from = label_data.get('from', 0) range_to = label_data.get('to', 0) chart.x_labels = map(str, range(range_from, range_to)) for data_set in data.get('data', []): title = data_set.get('title', None) values = data_set.get('values', None) chart.add(title, values) result = chart.render_data_uri() else: result = None return result @LiquidTags.register('pygal') def pygal_parser(preprocessor, tag, markup): data = loads(markup) if tag == 'pygal' and data is not None: output = run_pygal(data) return '<div class="pygal" style="text-align: center;"><embed type="image/svg+xml" src=%s style="max-width:1000px"/></div>' % output else: raise ValueError('Error processing input. \nExpected syntax: {0}'.format(SYNTAX)) from .liquid_tags import register

true

1c313315f6d7710923735974c2cc8f9b448ebeca

7,248

py

Python

networking_bagpipe/agent/bagpipe_ml2/agent_extension.py

daespinel/networking-bagpipe-1

7e96cc651394813c1dc80747186b6cfcaa173f14

[ "Apache-2.0" ]

29

2015-11-09T21:47:52.000Z

2022-01-25T16:03:17.000Z

networking_bagpipe/agent/bagpipe_ml2/agent_extension.py

openstack/networking-bagpipe-l2

d472fb7b5d05b70f9f4e12288eee1a9a01fdc9fd

[ "Apache-2.0" ]

null

networking_bagpipe/agent/bagpipe_ml2/agent_extension.py

openstack/networking-bagpipe-l2

d472fb7b5d05b70f9f4e12288eee1a9a01fdc9fd

[ "Apache-2.0" ]

9

2015-11-17T08:24:32.000Z

2020-10-25T18:59:48.000Z

# Copyright (c) 2015 Orange. # 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. import sys import eventlet eventlet.monkey_patch() # Monkey patch the original current_thread to use the up-to-date _active # global variable. See https://bugs.launchpad.net/bugs/1863021 and # https://github.com/eventlet/eventlet/issues/592 import __original_module_threading as orig_threading # noqa pylint: disable=import-error import threading # noqa orig_threading.current_thread.__globals__['_active'] = threading._active from oslo_concurrency import lockutils # noqa: E402 from oslo_config import cfg # noqa: E402 from oslo_config import types # noqa: E402 from oslo_log import helpers as log_helpers # noqa: E402 from oslo_log import log as logging # noqa: E402 from networking_bagpipe.agent import agent_base_info # noqa: E402 from networking_bagpipe.agent import bagpipe_bgp_agent # noqa: E402 from networking_bagpipe.bagpipe_bgp import \ constants as bbgp_const # noqa: E402 from neutron.agent.linux import ip_lib # noqa: E402 from neutron.common import config as common_config # noqa: E402 from neutron.plugins.ml2.drivers.linuxbridge.agent import \ linuxbridge_neutron_agent as lnx_agt # noqa: E402 from neutron_lib.agent import l2_extension # noqa: E402 from neutron_lib import constants as n_const # noqa: E402 LOG = logging.getLogger(__name__) BAGPIPE_L2_SERVICE = 'bagpipe_l2' opts = [ cfg.ListOpt('as_number', default=[64512], item_type=types.Integer(min=1, max=2**32), help=("Autonomous System number used to generate BGP RTs for" "E-VPNs used by bagpipe ML2 (more than one is possible," "to allow a deployment to do a 2-step transition " "to change the AS number used)") ) ] cfg.CONF.register_opts(opts, "ml2_bagpipe_extension") class BagpipeML2AgentExtension(l2_extension.L2AgentExtension, agent_base_info.BaseInfoManager): def initialize(self, connection, driver_type): self.bagpipe_bgp_agent = ( bagpipe_bgp_agent.BaGPipeBGPAgent.get_instance( n_const.AGENT_TYPE_LINUXBRIDGE) ) self.bagpipe_bgp_agent.register_build_callback( BAGPIPE_L2_SERVICE, self.build_bagpipe_l2_attach_info) self.ports = set() self.bagpipe_bgp_agent.register_port_list(BAGPIPE_L2_SERVICE, self.ports) @log_helpers.log_method_call def build_bagpipe_l2_attach_info(self, port_id): port_info = self.ports_info.get(port_id) if not port_info: LOG.debug("no info for port %s", port_id) return {} LOG.debug("segmentation id: %s", port_info.network.segmentation_id) as_numbers = cfg.CONF.ml2_bagpipe_extension.as_number bagpipe_rts = [ "%s:%s" % (as_number, port_info.network.segmentation_id) for as_number in as_numbers ] attach_info = self._base_attach_info(port_info) attach_info.update({ 'linuxbr': lnx_agt.LinuxBridgeManager.get_bridge_name( port_info.network.id ), 'vni': port_info.network.segmentation_id, bbgp_const.RT_IMPORT: bagpipe_rts, bbgp_const.RT_EXPORT: bagpipe_rts }) return { 'network_id': port_info.network.id, bbgp_const.EVPN: [ attach_info ] } def _base_attach_info(self, port_info): info = { 'mac_address': port_info.mac_address, 'local_port': { 'linuxif': lnx_agt.LinuxBridgeManager.get_tap_device_name( port_info.id) } } if port_info.ip_address: info.update({'ip_address': port_info.ip_address}) return info @lockutils.synchronized('bagpipe-ml2') @log_helpers.log_method_call def handle_port(self, context, data): if data.get('network_type') != n_const.TYPE_VXLAN: LOG.debug("network is not of type vxlan, not handled by this " "extension") return port_id = data['port_id'] tap_device_name = lnx_agt.LinuxBridgeManager.get_tap_device_name( port_id) if not ip_lib.device_exists(tap_device_name): LOG.debug('skip non-existing port %s', port_id) return net_id = data['network_id'] net_info, port_info = ( self._get_network_port_infos(net_id, port_id) ) def delete_hook(): self._delete_port(context, {'port_id': port_info.id}) port_info.update_admin_state(data, delete_hook) if not port_info.admin_state_up: return port_info.mac_address = data['mac_address'] # take the first IPv4 (error if none, warning if many) ip_address = None for alloc in data.get('fixed_ips'): if '.' in alloc['ip_address']: if not ip_address: ip_address = alloc['ip_address'] else: LOG.warning("multiple IPv4 addresses for %s, ignoring %s", port_id, alloc['ip_address']) if ip_address is None: LOG.debug("no IP address for port %s", port_id) port_info.ip_address = ip_address net_info.segmentation_id = data['segmentation_id'] self.bagpipe_bgp_agent.do_port_plug(port_id) self.ports.add(port_id) @lockutils.synchronized('bagpipe-ml2') def delete_port(self, context, data): self._delete_port(context, data) # un-synchronized version, to be called indirectly from handle_port @log_helpers.log_method_call def _delete_port(self, context, data): port_id = data['port_id'] port_info = self.ports_info.get(port_id) if port_info: detach_info = { 'network_id': port_info.network.id, bbgp_const.EVPN: self._base_attach_info(port_info) } self._remove_network_port_infos(port_info.network.id, port_id) self.ports.remove(port_id) self.bagpipe_bgp_agent.do_port_plug_refresh(port_id, detach_info) def main(): common_config.init(sys.argv[1:]) common_config.setup_logging() LOG.warning('This modified agent is not needed anymore. The normal ' 'neutron linuxbridge agent should be used instead, along with' 'networks of type VXLAN, rather than RT.')

35.356098

89

0.639901

import sys import eventlet eventlet.monkey_patch() import __original_module_threading as orig_threading import threading orig_threading.current_thread.__globals__['_active'] = threading._active from oslo_concurrency import lockutils from oslo_config import cfg from oslo_config import types from oslo_log import helpers as log_helpers from oslo_log import log as logging from networking_bagpipe.agent import agent_base_info from networking_bagpipe.agent import bagpipe_bgp_agent from networking_bagpipe.bagpipe_bgp import \ constants as bbgp_const from neutron.agent.linux import ip_lib from neutron.common import config as common_config from neutron.plugins.ml2.drivers.linuxbridge.agent import \ linuxbridge_neutron_agent as lnx_agt from neutron_lib.agent import l2_extension from neutron_lib import constants as n_const LOG = logging.getLogger(__name__) BAGPIPE_L2_SERVICE = 'bagpipe_l2' opts = [ cfg.ListOpt('as_number', default=[64512], item_type=types.Integer(min=1, max=2**32), help=("Autonomous System number used to generate BGP RTs for" "E-VPNs used by bagpipe ML2 (more than one is possible," "to allow a deployment to do a 2-step transition " "to change the AS number used)") ) ] cfg.CONF.register_opts(opts, "ml2_bagpipe_extension") class BagpipeML2AgentExtension(l2_extension.L2AgentExtension, agent_base_info.BaseInfoManager): def initialize(self, connection, driver_type): self.bagpipe_bgp_agent = ( bagpipe_bgp_agent.BaGPipeBGPAgent.get_instance( n_const.AGENT_TYPE_LINUXBRIDGE) ) self.bagpipe_bgp_agent.register_build_callback( BAGPIPE_L2_SERVICE, self.build_bagpipe_l2_attach_info) self.ports = set() self.bagpipe_bgp_agent.register_port_list(BAGPIPE_L2_SERVICE, self.ports) @log_helpers.log_method_call def build_bagpipe_l2_attach_info(self, port_id): port_info = self.ports_info.get(port_id) if not port_info: LOG.debug("no info for port %s", port_id) return {} LOG.debug("segmentation id: %s", port_info.network.segmentation_id) as_numbers = cfg.CONF.ml2_bagpipe_extension.as_number bagpipe_rts = [ "%s:%s" % (as_number, port_info.network.segmentation_id) for as_number in as_numbers ] attach_info = self._base_attach_info(port_info) attach_info.update({ 'linuxbr': lnx_agt.LinuxBridgeManager.get_bridge_name( port_info.network.id ), 'vni': port_info.network.segmentation_id, bbgp_const.RT_IMPORT: bagpipe_rts, bbgp_const.RT_EXPORT: bagpipe_rts }) return { 'network_id': port_info.network.id, bbgp_const.EVPN: [ attach_info ] } def _base_attach_info(self, port_info): info = { 'mac_address': port_info.mac_address, 'local_port': { 'linuxif': lnx_agt.LinuxBridgeManager.get_tap_device_name( port_info.id) } } if port_info.ip_address: info.update({'ip_address': port_info.ip_address}) return info @lockutils.synchronized('bagpipe-ml2') @log_helpers.log_method_call def handle_port(self, context, data): if data.get('network_type') != n_const.TYPE_VXLAN: LOG.debug("network is not of type vxlan, not handled by this " "extension") return port_id = data['port_id'] tap_device_name = lnx_agt.LinuxBridgeManager.get_tap_device_name( port_id) if not ip_lib.device_exists(tap_device_name): LOG.debug('skip non-existing port %s', port_id) return net_id = data['network_id'] net_info, port_info = ( self._get_network_port_infos(net_id, port_id) ) def delete_hook(): self._delete_port(context, {'port_id': port_info.id}) port_info.update_admin_state(data, delete_hook) if not port_info.admin_state_up: return port_info.mac_address = data['mac_address'] ip_address = None for alloc in data.get('fixed_ips'): if '.' in alloc['ip_address']: if not ip_address: ip_address = alloc['ip_address'] else: LOG.warning("multiple IPv4 addresses for %s, ignoring %s", port_id, alloc['ip_address']) if ip_address is None: LOG.debug("no IP address for port %s", port_id) port_info.ip_address = ip_address net_info.segmentation_id = data['segmentation_id'] self.bagpipe_bgp_agent.do_port_plug(port_id) self.ports.add(port_id) @lockutils.synchronized('bagpipe-ml2') def delete_port(self, context, data): self._delete_port(context, data) @log_helpers.log_method_call def _delete_port(self, context, data): port_id = data['port_id'] port_info = self.ports_info.get(port_id) if port_info: detach_info = { 'network_id': port_info.network.id, bbgp_const.EVPN: self._base_attach_info(port_info) } self._remove_network_port_infos(port_info.network.id, port_id) self.ports.remove(port_id) self.bagpipe_bgp_agent.do_port_plug_refresh(port_id, detach_info) def main(): common_config.init(sys.argv[1:]) common_config.setup_logging() LOG.warning('This modified agent is not needed anymore. The normal ' 'neutron linuxbridge agent should be used instead, along with' 'networks of type VXLAN, rather than RT.')

true

1c31346b9eb7cd50c1cd878990e61732e87c10f5

343

py

Python

wandbox/commands/__init__.py

v1nam/wandbox-cli

8ff88944ad3358dc99dd9bf4ac5c0cac2b98179b

[ "MIT" ]

7

2021-01-21T18:45:29.000Z

2021-01-27T06:54:17.000Z

wandbox/commands/__init__.py

v1nam/wandbox-cli

8ff88944ad3358dc99dd9bf4ac5c0cac2b98179b

[ "MIT" ]

null

wandbox/commands/__init__.py

v1nam/wandbox-cli

8ff88944ad3358dc99dd9bf4ac5c0cac2b98179b

[ "MIT" ]

null

from wandbox.commands.frominput import FromInput from wandbox.commands.fromfile import FromFile from wandbox.commands.frombuffer import FromBuffer from wandbox.commands.base import Base commands_dict = { "fromfile": FromFile.runfile, "frominput": FromInput.askinp, "frombuffer": FromBuffer.create_buffer, "base": Base.run, }

26.384615

50

0.77551

from wandbox.commands.frominput import FromInput from wandbox.commands.fromfile import FromFile from wandbox.commands.frombuffer import FromBuffer from wandbox.commands.base import Base commands_dict = { "fromfile": FromFile.runfile, "frominput": FromInput.askinp, "frombuffer": FromBuffer.create_buffer, "base": Base.run, }

true

1c3134fd41be915b03b8899512c41b8f42be8099

11,064

py

Python

deepchem/feat/smiles_tokenizer.py

StashOfCode/deepchem

6c5a5405acea333ee7a65a798ddb5c9df702a0b8

[ "MIT" ]

3

2019-05-29T19:18:25.000Z

2021-01-25T05:44:05.000Z

deepchem/feat/smiles_tokenizer.py

StashOfCode/deepchem

6c5a5405acea333ee7a65a798ddb5c9df702a0b8

[ "MIT" ]

10

2017-02-23T19:39:22.000Z

2017-08-31T22:21:18.000Z

deepchem/feat/smiles_tokenizer.py

StashOfCode/deepchem

6c5a5405acea333ee7a65a798ddb5c9df702a0b8

[ "MIT" ]

1

2018-09-22T00:53:53.000Z

# Requriments - transformers, tokenizers # Right now, the Smiles Tokenizer uses an exiesting vocab file from rxnfp that is fairly comprehensive and from the USPTO dataset. # The vocab may be expanded in the near future import collections import os import re import pkg_resources from typing import List from transformers import BertTokenizer from logging import getLogger logger = getLogger(__name__) """ SMI_REGEX_PATTERN: str SMILES regex pattern for tokenization. Designed by Schwaller et. al. References .. [1] Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction 1572-1583 DOI: 10.1021/acscentsci.9b00576 """ SMI_REGEX_PATTERN = r"""(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|$|$|\.|=| #|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])""" # add vocab_file dict VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} def get_default_tokenizer(): default_vocab_path = (pkg_resources.resource_filename("deepchem", "feat/tests/vocab.txt")) return SmilesTokenizer(default_vocab_path) class SmilesTokenizer(BertTokenizer): """ Creates the SmilesTokenizer class. The tokenizer heavily inherits from the BertTokenizer implementation found in Huggingface's transformers library. It runs a WordPiece tokenization algorithm over SMILES strings using the tokenisation SMILES regex developed by Schwaller et. al. Please see https://github.com/huggingface/transformers and https://github.com/rxn4chemistry/rxnfp for more details. Examples -------- >>> from deepchem.feat.smiles_tokenizer import SmilesTokenizer >>> current_dir = os.path.dirname(os.path.realpath(__file__)) >>> vocab_path = os.path.join(current_dir, 'tests/data', 'vocab.txt') >>> tokenizer = SmilesTokenizer(vocab_path) >>> print(tokenizer.encode("CC(=O)OC1=CC=CC=C1C(=O)O")) [12, 16, 16, 17, 22, 19, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 16, 17, 22, 19, 18, 19, 13] References ---------- .. [1] Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Kreutter, David; Laino, Teodoro; et al. (2019): Mapping the Space of Chemical Reactions using Attention-Based Neural Networks. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.9897365.v3 Notes ---- This class requires huggingface's transformers and tokenizers libraries to be installed. """ vocab_files_names = VOCAB_FILES_NAMES def __init__( self, vocab_file: str = '', # unk_token="[UNK]", # sep_token="[SEP]", # pad_token="[PAD]", # cls_token="[CLS]", # mask_token="[MASK]", **kwargs): """Constructs a SmilesTokenizer. Parameters ---------- vocab_file: str Path to a SMILES character per line vocabulary file. Default vocab file is found in deepchem/feat/tests/data/vocab.txt """ super().__init__(vocab_file, **kwargs) # take into account special tokens in max length self.max_len_single_sentence = self.max_len - 2 self.max_len_sentences_pair = self.max_len - 3 if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocab file at path '{}'.".format(vocab_file)) self.vocab = load_vocab(vocab_file) self.highest_unused_index = max( [i for i, v in enumerate(self.vocab.keys()) if v.startswith("[unused")]) self.ids_to_tokens = collections.OrderedDict( [(ids, tok) for tok, ids in self.vocab.items()]) self.basic_tokenizer = BasicSmilesTokenizer() self.init_kwargs["max_len"] = self.max_len @property def vocab_size(self): return len(self.vocab) @property def vocab_list(self): return list(self.vocab.keys()) def _tokenize(self, text: str): """ Tokenize a string into a list of tokens. Parameters ---------- text: str Input string sequence to be tokenized. """ split_tokens = [token for token in self.basic_tokenizer.tokenize(text)] return split_tokens def _convert_token_to_id(self, token): """ Converts a token (str/unicode) in an id using the vocab. Parameters ---------- token: str String token from a larger sequence to be converted to a numerical id. """ return self.vocab.get(token, self.vocab.get(self.unk_token)) def _convert_id_to_token(self, index): """ Converts an index (integer) in a token (string/unicode) using the vocab. Parameters ---------- index: int Integer index to be converted back to a string-based token as part of a larger sequence. """ return self.ids_to_tokens.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]): """ Converts a sequence of tokens (string) in a single string. Parameters ---------- tokens: List[str] List of tokens for a given string sequence. Returns ------- out_string: str Single string from combined tokens. """ out_string: str = " ".join(tokens).replace(" ##", "").strip() return out_string def add_special_tokens_ids_single_sequence(self, token_ids: List[int]): """ Adds special tokens to the a sequence for sequence classification tasks. A BERT sequence has the following format: [CLS] X [SEP] Parameters ---------- token_ids: list[int] list of tokenized input ids. Can be obtained using the encode or encode_plus methods. """ return [self.cls_token_id] + token_ids + [self.sep_token_id] def add_special_tokens_single_sequence(self, tokens: List[str]): """ Adds special tokens to the a sequence for sequence classification tasks. A BERT sequence has the following format: [CLS] X [SEP] Parameters ---------- tokens: List[str] List of tokens for a given string sequence. """ return [self.cls_token] + tokens + [self.sep_token] def add_special_tokens_ids_sequence_pair(self, token_ids_0: List[int], token_ids_1: List[int]) -> List[int]: """ Adds special tokens to a sequence pair for sequence classification tasks. A BERT sequence pair has the following format: [CLS] A [SEP] B [SEP] Parameters ---------- token_ids_0: List[int] List of ids for the first string sequence in the sequence pair (A). token_ids_1: List[int] List of tokens for the second string sequence in the sequence pair (B). """ sep = [self.sep_token_id] cls = [self.cls_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def add_padding_tokens(self, token_ids: List[int], length: int, right: bool = True) -> List[int]: """ Adds padding tokens to return a sequence of length max_length. By default padding tokens are added to the right of the sequence. Parameters ---------- token_ids: list[int] list of tokenized input ids. Can be obtained using the encode or encode_plus methods. length: int right: bool (True by default) Returns ---------- token_ids : list of tokenized input ids. Can be obtained using the encode or encode_plus methods. padding: int Integer to be added as padding token """ padding = [self.pad_token_id] * (length - len(token_ids)) if right: return token_ids + padding else: return padding + token_ids def save_vocabulary( self, vocab_path: str ): # -> tuple[str]: doctest issue raised with this return type annotation """ Save the tokenizer vocabulary to a file. Parameters ---------- vocab_path: obj: str The directory in which to save the SMILES character per line vocabulary file. Default vocab file is found in deepchem/feat/tests/data/vocab.txt Returns ---------- vocab_file: :obj:`Tuple(str)`: Paths to the files saved. typle with string to a SMILES character per line vocabulary file. Default vocab file is found in deepchem/feat/tests/data/vocab.txt """ index = 0 if os.path.isdir(vocab_path): vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"]) else: vocab_file = vocab_path with open(vocab_file, "w", encoding="utf-8") as writer: for token, token_index in sorted( self.vocab.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( "Saving vocabulary to {}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!".format( vocab_file)) index = token_index writer.write(token + "\n") index += 1 return (vocab_file,) class BasicSmilesTokenizer(object): """ Run basic SMILES tokenization using a regex pattern developed by Schwaller et. al. This tokenizer is to be used when a tokenizer that does not require the transformers library by HuggingFace is required. Examples -------- >>> from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer >>> tokenizer = BasicSmilesTokenizer() >>> print(tokenizer.tokenize("CC(=O)OC1=CC=CC=C1C(=O)O")) ['C', 'C', '(', '=', 'O', ')', 'O', 'C', '1', '=', 'C', 'C', '=', 'C', 'C', '=', 'C', '1', 'C', '(', '=', 'O', ')', 'O'] References ---------- .. [1] Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction 1572-1583 DOI: 10.1021/acscentsci.9b00576 """ def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN): """ Constructs a BasicSMILESTokenizer. Parameters ---------- regex: string SMILES token regex """ self.regex_pattern = regex_pattern self.regex = re.compile(self.regex_pattern) def tokenize(self, text): """ Basic Tokenization of a SMILES. """ tokens = [token for token in self.regex.findall(text)] return tokens def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() with open(vocab_file, "r", encoding="utf-8") as reader: tokens = reader.readlines() for index, token in enumerate(tokens): token = token.rstrip("\n") vocab[token] = index return vocab

33.026866

132

0.62491

import collections import os import re import pkg_resources from typing import List from transformers import BertTokenizer from logging import getLogger logger = getLogger(__name__) SMI_REGEX_PATTERN = r"""(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|$|$|\.|=| #|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])""" VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} def get_default_tokenizer(): default_vocab_path = (pkg_resources.resource_filename("deepchem", "feat/tests/vocab.txt")) return SmilesTokenizer(default_vocab_path) class SmilesTokenizer(BertTokenizer): vocab_files_names = VOCAB_FILES_NAMES def __init__( self, vocab_file: str = '', **kwargs): super().__init__(vocab_file, **kwargs) self.max_len_single_sentence = self.max_len - 2 self.max_len_sentences_pair = self.max_len - 3 if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocab file at path '{}'.".format(vocab_file)) self.vocab = load_vocab(vocab_file) self.highest_unused_index = max( [i for i, v in enumerate(self.vocab.keys()) if v.startswith("[unused")]) self.ids_to_tokens = collections.OrderedDict( [(ids, tok) for tok, ids in self.vocab.items()]) self.basic_tokenizer = BasicSmilesTokenizer() self.init_kwargs["max_len"] = self.max_len @property def vocab_size(self): return len(self.vocab) @property def vocab_list(self): return list(self.vocab.keys()) def _tokenize(self, text: str): split_tokens = [token for token in self.basic_tokenizer.tokenize(text)] return split_tokens def _convert_token_to_id(self, token): return self.vocab.get(token, self.vocab.get(self.unk_token)) def _convert_id_to_token(self, index): return self.ids_to_tokens.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]): out_string: str = " ".join(tokens).replace(" ##", "").strip() return out_string def add_special_tokens_ids_single_sequence(self, token_ids: List[int]): return [self.cls_token_id] + token_ids + [self.sep_token_id] def add_special_tokens_single_sequence(self, tokens: List[str]): return [self.cls_token] + tokens + [self.sep_token] def add_special_tokens_ids_sequence_pair(self, token_ids_0: List[int], token_ids_1: List[int]) -> List[int]: sep = [self.sep_token_id] cls = [self.cls_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def add_padding_tokens(self, token_ids: List[int], length: int, right: bool = True) -> List[int]: padding = [self.pad_token_id] * (length - len(token_ids)) if right: return token_ids + padding else: return padding + token_ids def save_vocabulary( self, vocab_path: str ): # -> tuple[str]: doctest issue raised with this return type annotation index = 0 if os.path.isdir(vocab_path): vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"]) else: vocab_file = vocab_path with open(vocab_file, "w", encoding="utf-8") as writer: for token, token_index in sorted( self.vocab.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( "Saving vocabulary to {}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!".format( vocab_file)) index = token_index writer.write(token + "\n") index += 1 return (vocab_file,) class BasicSmilesTokenizer(object): def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN): self.regex_pattern = regex_pattern self.regex = re.compile(self.regex_pattern) def tokenize(self, text): tokens = [token for token in self.regex.findall(text)] return tokens def load_vocab(vocab_file): vocab = collections.OrderedDict() with open(vocab_file, "r", encoding="utf-8") as reader: tokens = reader.readlines() for index, token in enumerate(tokens): token = token.rstrip("\n") vocab[token] = index return vocab

true

1c31354c7f061a127eb92c549b1b49593a89649b

3,235

py

Python

profiles_project/settings.py

kenbusse1/profiles-rest-api

5344db9a91667f55bbfec87497eb11617afee314

[ "MIT" ]

null

profiles_project/settings.py

kenbusse1/profiles-rest-api

5344db9a91667f55bbfec87497eb11617afee314

[ "MIT" ]

6

2020-02-12T03:12:05.000Z

2021-06-09T18:48:58.000Z

profiles_project/settings.py

kenbusse1/profiles-rest-api

5344db9a91667f55bbfec87497eb11617afee314

[ "MIT" ]

null

""" Django settings for profiles_project project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '-6but+soqw9&)!j1e(cbvmbr+8yfp!+l@9rm$d(fzc^#d0uk#8' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'profiles_api', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'profiles_project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'profiles_project.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'profiles_api.UserProfile'

25.88

91

0.699227

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = '-6but+soqw9&)!j1e(cbvmbr+8yfp!+l@9rm$d(fzc^#d0uk#8' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'profiles_api', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'profiles_project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'profiles_project.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'profiles_api.UserProfile'

true

1c3136359d7c666764adf29db4f5c6f2be46a2e4

3,159

py

Python

Algorithms_Course_1/Assignments/Algorithms_PA_1.py

vivekgoe/stanford_algorithms_courses

79afa0348dc4ecd8f631537b27e34c330abe773a

[ "MIT" ]

null

Algorithms_Course_1/Assignments/Algorithms_PA_1.py

vivekgoe/stanford_algorithms_courses

79afa0348dc4ecd8f631537b27e34c330abe773a

[ "MIT" ]

null

Algorithms_Course_1/Assignments/Algorithms_PA_1.py

vivekgoe/stanford_algorithms_courses

79afa0348dc4ecd8f631537b27e34c330abe773a

[ "MIT" ]

null

import time import math count_naive = 0 count = 0 def mul_large_naive(N1, N2): global count_naive assert(N1.__len__() == N2.__len__()) len = N1.__len__() if len > 1: a = N1[0:int(len/2)] b = N1[int(len/2):int(len)] c = N2[0:int(len/2)] d = N2[int(len/2):int(len)] #recursive calls p1 = mul_large_naive(a,c) p2 = mul_large_naive(a,d) p3 = mul_large_naive(b,c) p4 = mul_large_naive(b,d) #merge step;no real multiplication here; only shifts with base 10 return 10**int(len)*int(p1) + 10**int(len/2)*(int(p2)+int(p3)) + int(p4) #base case; this is the only real multiplication count_naive = count_naive + 1 assert(N1.__len__() == 1) assert(N2.__len__() == 1) return int(N1)*int(N2) def mul_large(N1, N2): global count assert(N1.__len__() == N2.__len__()) len = N1.__len__() if len > 1: p1 = p2 = p3 = 0 a = N1[0:int(len/2)] b = N1[int(len/2):int(len)] c = N2[0:int(len/2)] d = N2[int(len/2):int(len)] #recursive calls if int(a) != 0 and int(c) != 0: p1 = mul_large(a,c) temp1 = str(int(a)+int(b)) temp2 = str(int(c)+int(d)) if int(temp1) != 0 and int(temp2) != 0: if temp1.__len__() > temp2.__len__(): tlen = int(math.ceil(math.log2(int(temp1.__len__())))) tlen = 2**tlen temp1 = '0'*(tlen-temp1.__len__()) + temp1 temp2 = '0'*(tlen-temp2.__len__()) + temp2 else: tlen = int(math.ceil(math.log2(int(temp2.__len__())))) tlen = 2**tlen temp1 = '0'*(tlen-temp1.__len__()) + temp1 temp2 = '0'*(tlen-temp2.__len__()) + temp2 p2 = mul_large(temp1,temp2) if int(b) != 0 and int(d) != 0: p3 = mul_large(b,d) #merge step;no real multiplication here; only shifts with base 10 return 10**int(len)*int(p1) + 10**int(len/2)*(int(p2)-int(p1)-int(p3)) + int(p3) #base case; this is the only real multiplication count = count + 1 assert(N1.__len__() == 1) assert(N2.__len__() == 1) return int(N1)*int(N2) if __name__ == '__main__': Input1 = "3141592653589793238462643383279502884197169399375105820974944592" Input2 = "2718281828459045235360287471352662497757247093699959574966967627" start = time.time() output = mul_large(Input1,Input2) print("% s seconds" % (time.time() - start)) start = time.time() output_naive = mul_large_naive(Input1,Input2) print("% s seconds" % (time.time() - start)) print(output, count) print(output_naive,count_naive) print(output - output_naive) print(output - int(Input1)*int(Input2)) print(output_naive - int(Input1)*int(Input2)) #Solution to Problem Set#1 #1 - nlog(n) #2 - True #3 - Sometimes yes, sometimes no (depending on f & g); yes if f(n) <= g(n) for all sufficiently large n #4 - Omega(nk^2) #5 - 2^(sqrt(log(n)) < sqrt(n) < n^1.5 < n^(5/3) < 10^n

34.714286

104

0.552073

import time import math count_naive = 0 count = 0 def mul_large_naive(N1, N2): global count_naive assert(N1.__len__() == N2.__len__()) len = N1.__len__() if len > 1: a = N1[0:int(len/2)] b = N1[int(len/2):int(len)] c = N2[0:int(len/2)] d = N2[int(len/2):int(len)] p1 = mul_large_naive(a,c) p2 = mul_large_naive(a,d) p3 = mul_large_naive(b,c) p4 = mul_large_naive(b,d) return 10**int(len)*int(p1) + 10**int(len/2)*(int(p2)+int(p3)) + int(p4) count_naive = count_naive + 1 assert(N1.__len__() == 1) assert(N2.__len__() == 1) return int(N1)*int(N2) def mul_large(N1, N2): global count assert(N1.__len__() == N2.__len__()) len = N1.__len__() if len > 1: p1 = p2 = p3 = 0 a = N1[0:int(len/2)] b = N1[int(len/2):int(len)] c = N2[0:int(len/2)] d = N2[int(len/2):int(len)] if int(a) != 0 and int(c) != 0: p1 = mul_large(a,c) temp1 = str(int(a)+int(b)) temp2 = str(int(c)+int(d)) if int(temp1) != 0 and int(temp2) != 0: if temp1.__len__() > temp2.__len__(): tlen = int(math.ceil(math.log2(int(temp1.__len__())))) tlen = 2**tlen temp1 = '0'*(tlen-temp1.__len__()) + temp1 temp2 = '0'*(tlen-temp2.__len__()) + temp2 else: tlen = int(math.ceil(math.log2(int(temp2.__len__())))) tlen = 2**tlen temp1 = '0'*(tlen-temp1.__len__()) + temp1 temp2 = '0'*(tlen-temp2.__len__()) + temp2 p2 = mul_large(temp1,temp2) if int(b) != 0 and int(d) != 0: p3 = mul_large(b,d) return 10**int(len)*int(p1) + 10**int(len/2)*(int(p2)-int(p1)-int(p3)) + int(p3) count = count + 1 assert(N1.__len__() == 1) assert(N2.__len__() == 1) return int(N1)*int(N2) if __name__ == '__main__': Input1 = "3141592653589793238462643383279502884197169399375105820974944592" Input2 = "2718281828459045235360287471352662497757247093699959574966967627" start = time.time() output = mul_large(Input1,Input2) print("% s seconds" % (time.time() - start)) start = time.time() output_naive = mul_large_naive(Input1,Input2) print("% s seconds" % (time.time() - start)) print(output, count) print(output_naive,count_naive) print(output - output_naive) print(output - int(Input1)*int(Input2)) print(output_naive - int(Input1)*int(Input2))

true

1c31365bc2dbe1275ef4e8e056e716303fbecc05

13,411

py

Python

electrum/gui/qt/transaction_dialog.py

traysi/electrum-raven

b2a64a459da32afd2987149460253cfadec03384

[ "MIT" ]

5

2018-10-31T18:47:54.000Z

2021-09-20T02:04:42.000Z

electrum/gui/qt/transaction_dialog.py

project-mynt/electrum-mynt

ca1548e008854f2a3eff900a69365307cc20bd57

[ "MIT" ]

null

electrum/gui/qt/transaction_dialog.py

project-mynt/electrum-mynt

ca1548e008854f2a3eff900a69365307cc20bd57

[ "MIT" ]

11

2018-10-31T19:46:05.000Z

2019-09-25T20:18:37.000Z

#!/usr/bin/env python # # Electrum - lightweight Bitcoin client # Copyright (C) 2012 thomasv@gitorious # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import copy import datetime import json import traceback from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5.QtWidgets import * import qrcode from qrcode import exceptions from electrum.bitcoin import base_encode from electrum.i18n import _ from electrum.plugin import run_hook from electrum import simple_config from electrum.util import bfh from electrum.transaction import SerializationError, Transaction from .util import * SAVE_BUTTON_ENABLED_TOOLTIP = _("Save transaction offline") SAVE_BUTTON_DISABLED_TOOLTIP = _("Please sign this transaction in order to save it") dialogs = [] # Otherwise python randomly garbage collects the dialogs... def show_transaction(tx, parent, desc=None, prompt_if_unsaved=False): try: d = TxDialog(tx, parent, desc, prompt_if_unsaved) except SerializationError as e: traceback.print_exc(file=sys.stderr) parent.show_critical(_("Electrum was unable to deserialize the transaction:") + "\n" + str(e)) else: dialogs.append(d) d.show() class TxDialog(QDialog, MessageBoxMixin): def __init__(self, tx, parent, desc, prompt_if_unsaved): '''Transactions in the wallet will show their description. Pass desc to give a description for txs not yet in the wallet. ''' # We want to be a top-level window QDialog.__init__(self, parent=None) # Take a copy; it might get updated in the main window by # e.g. the FX plugin. If this happens during or after a long # sign operation the signatures are lost. self.tx = tx = copy.deepcopy(tx) # type: Transaction try: self.tx.deserialize() except BaseException as e: raise SerializationError(e) self.main_window = parent self.wallet = parent.wallet self.prompt_if_unsaved = prompt_if_unsaved self.saved = False self.desc = desc # if the wallet can populate the inputs with more info, do it now. # as a result, e.g. we might learn an imported address tx is segwit, # in which case it's ok to display txid self.wallet.add_input_info_to_all_inputs(tx) self.setMinimumWidth(950) self.setWindowTitle(_("Transaction")) vbox = QVBoxLayout() self.setLayout(vbox) vbox.addWidget(QLabel(_("Transaction ID:"))) self.tx_hash_e = ButtonsLineEdit() qr_show = lambda: parent.show_qrcode(str(self.tx_hash_e.text()), 'Transaction ID', parent=self) self.tx_hash_e.addButton(":icons/qrcode.png", qr_show, _("Show as QR code")) self.tx_hash_e.setReadOnly(True) vbox.addWidget(self.tx_hash_e) self.tx_desc = QLabel() vbox.addWidget(self.tx_desc) self.status_label = QLabel() vbox.addWidget(self.status_label) self.date_label = QLabel() vbox.addWidget(self.date_label) self.amount_label = QLabel() vbox.addWidget(self.amount_label) self.size_label = QLabel() vbox.addWidget(self.size_label) self.fee_label = QLabel() vbox.addWidget(self.fee_label) self.add_io(vbox) self.sign_button = b = QPushButton(_("Sign")) b.clicked.connect(self.sign) self.broadcast_button = b = QPushButton(_("Broadcast")) b.clicked.connect(self.do_broadcast) self.save_button = b = QPushButton(_("Save")) save_button_disabled = not tx.is_complete() b.setDisabled(save_button_disabled) if save_button_disabled: b.setToolTip(SAVE_BUTTON_DISABLED_TOOLTIP) else: b.setToolTip(SAVE_BUTTON_ENABLED_TOOLTIP) b.clicked.connect(self.save) self.export_button = b = QPushButton(_("Export")) b.clicked.connect(self.export) self.cancel_button = b = QPushButton(_("Close")) b.clicked.connect(self.close) b.setDefault(True) self.qr_button = b = QPushButton() b.setIcon(QIcon(":icons/qrcode.png")) b.clicked.connect(self.show_qr) self.copy_button = CopyButton(lambda: str(self.tx), parent.app) # Action buttons self.buttons = [self.sign_button, self.broadcast_button, self.cancel_button] # Transaction sharing buttons self.sharing_buttons = [self.copy_button, self.qr_button, self.export_button, self.save_button] run_hook('transaction_dialog', self) hbox = QHBoxLayout() hbox.addLayout(Buttons(*self.sharing_buttons)) hbox.addStretch(1) hbox.addLayout(Buttons(*self.buttons)) vbox.addLayout(hbox) self.update() def do_broadcast(self): self.main_window.push_top_level_window(self) try: self.main_window.broadcast_transaction(self.tx, self.desc) finally: self.main_window.pop_top_level_window(self) self.saved = True self.update() def closeEvent(self, event): if (self.prompt_if_unsaved and not self.saved and not self.question(_('This transaction is not saved. Close anyway?'), title=_("Warning"))): event.ignore() else: event.accept() try: dialogs.remove(self) except ValueError: pass # was not in list already def reject(self): # Override escape-key to close normally (and invoke closeEvent) self.close() def show_qr(self): text = bfh(str(self.tx)) text = base_encode(text, base=43) try: self.main_window.show_qrcode(text, 'Transaction', parent=self) except qrcode.exceptions.DataOverflowError: self.show_error(_('Failed to display QR code.') + '\n' + _('Transaction is too large in size.')) except Exception as e: self.show_error(_('Failed to display QR code.') + '\n' + str(e)) def sign(self): def sign_done(success): # note: with segwit we could save partially signed tx, because they have a txid if self.tx.is_complete(): self.prompt_if_unsaved = True self.saved = False self.save_button.setDisabled(False) self.save_button.setToolTip(SAVE_BUTTON_ENABLED_TOOLTIP) self.update() self.main_window.pop_top_level_window(self) self.sign_button.setDisabled(True) self.main_window.push_top_level_window(self) self.main_window.sign_tx(self.tx, sign_done) def save(self): self.main_window.push_top_level_window(self) if self.main_window.save_transaction_into_wallet(self.tx): self.save_button.setDisabled(True) self.saved = True self.main_window.pop_top_level_window(self) def export(self): name = 'signed_%s.txn' % (self.tx.txid()[0:8]) if self.tx.is_complete() else 'unsigned.txn' fileName = self.main_window.getSaveFileName(_("Select where to save your signed transaction"), name, "*.txn") if fileName: with open(fileName, "w+") as f: f.write(json.dumps(self.tx.as_dict(), indent=4) + '\n') self.show_message(_("Transaction exported successfully")) self.saved = True def update(self): desc = self.desc base_unit = self.main_window.base_unit() format_amount = self.main_window.format_amount tx_hash, status, label, can_broadcast, can_rbf, amount, fee, height, conf, timestamp, exp_n = self.wallet.get_tx_info(self.tx) size = self.tx.estimated_size() self.broadcast_button.setEnabled(can_broadcast) can_sign = not self.tx.is_complete() and \ (self.wallet.can_sign(self.tx) or bool(self.main_window.tx_external_keypairs)) self.sign_button.setEnabled(can_sign) self.tx_hash_e.setText(tx_hash or _('Unknown')) if desc is None: self.tx_desc.hide() else: self.tx_desc.setText(_("Description") + ': ' + desc) self.tx_desc.show() self.status_label.setText(_('Status:') + ' ' + status) if timestamp: time_str = datetime.datetime.fromtimestamp(timestamp).isoformat(' ')[:-3] self.date_label.setText(_("Date: {}").format(time_str)) self.date_label.show() # elif exp_n: # text = '%.2f MB'%(exp_n/1000000) # self.date_label.setText(_('Position in mempool: {} from tip').format(text)) # self.date_label.show() else: self.date_label.hide() if amount is None: amount_str = _("Transaction unrelated to your wallet") elif amount > 0: amount_str = _("Amount received:") + ' %s'% format_amount(amount) + ' ' + base_unit else: amount_str = _("Amount sent:") + ' %s'% format_amount(-amount) + ' ' + base_unit size_str = _("Size:") + ' %d bytes'% size fee_str = _("Fee") + ': %s' % (format_amount(fee) + ' ' + base_unit if fee is not None else _('unknown')) if fee is not None: fee_rate = fee/size*1000 fee_str += ' ( %s ) ' % self.main_window.format_fee_rate(fee_rate) confirm_rate = simple_config.FEERATE_WARNING_HIGH_FEE if fee_rate > confirm_rate: fee_str += ' - ' + _('Warning') + ': ' + _("high fee") + '!' self.amount_label.setText(amount_str) self.fee_label.setText(fee_str) self.size_label.setText(size_str) run_hook('transaction_dialog_update', self) def add_io(self, vbox): if self.tx.locktime > 0: vbox.addWidget(QLabel("LockTime: %d\n" % self.tx.locktime)) vbox.addWidget(QLabel(_("Inputs") + ' (%d)'%len(self.tx.inputs()))) ext = QTextCharFormat() rec = QTextCharFormat() rec.setBackground(QBrush(ColorScheme.GREEN.as_color(background=True))) rec.setToolTip(_("Wallet receive address")) chg = QTextCharFormat() chg.setBackground(QBrush(ColorScheme.YELLOW.as_color(background=True))) chg.setToolTip(_("Wallet change address")) twofactor = QTextCharFormat() twofactor.setBackground(QBrush(ColorScheme.BLUE.as_color(background=True))) twofactor.setToolTip(_("TrustedCoin (2FA) fee for the next batch of transactions")) def text_format(addr): if self.wallet.is_mine(addr): return chg if self.wallet.is_change(addr) else rec elif self.wallet.is_billing_address(addr): return twofactor return ext def format_amount(amt): return self.main_window.format_amount(amt, whitespaces=True) i_text = QTextEditWithDefaultSize() i_text.setFont(QFont(MONOSPACE_FONT)) i_text.setReadOnly(True) cursor = i_text.textCursor() for x in self.tx.inputs(): if x['type'] == 'coinbase': cursor.insertText('coinbase') else: prevout_hash = x.get('prevout_hash') prevout_n = x.get('prevout_n') cursor.insertText(prevout_hash + ":%-4d " % prevout_n, ext) addr = self.wallet.get_txin_address(x) if addr is None: addr = '' cursor.insertText(addr, text_format(addr)) if x.get('value'): cursor.insertText(format_amount(x['value']), ext) cursor.insertBlock() vbox.addWidget(i_text) vbox.addWidget(QLabel(_("Outputs") + ' (%d)'%len(self.tx.outputs()))) o_text = QTextEditWithDefaultSize() o_text.setFont(QFont(MONOSPACE_FONT)) o_text.setReadOnly(True) cursor = o_text.textCursor() for o in self.tx.get_outputs_for_UI(): addr, v = o.address, o.value cursor.insertText(addr, text_format(addr)) if v is not None: cursor.insertText('\t', ext) cursor.insertText(format_amount(v), ext) cursor.insertBlock() vbox.addWidget(o_text) class QTextEditWithDefaultSize(QTextEdit): def sizeHint(self): return QSize(0, 100)

39.560472

134

0.634628

import copy import datetime import json import traceback from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5.QtWidgets import * import qrcode from qrcode import exceptions from electrum.bitcoin import base_encode from electrum.i18n import _ from electrum.plugin import run_hook from electrum import simple_config from electrum.util import bfh from electrum.transaction import SerializationError, Transaction from .util import * SAVE_BUTTON_ENABLED_TOOLTIP = _("Save transaction offline") SAVE_BUTTON_DISABLED_TOOLTIP = _("Please sign this transaction in order to save it") dialogs = [] def show_transaction(tx, parent, desc=None, prompt_if_unsaved=False): try: d = TxDialog(tx, parent, desc, prompt_if_unsaved) except SerializationError as e: traceback.print_exc(file=sys.stderr) parent.show_critical(_("Electrum was unable to deserialize the transaction:") + "\n" + str(e)) else: dialogs.append(d) d.show() class TxDialog(QDialog, MessageBoxMixin): def __init__(self, tx, parent, desc, prompt_if_unsaved): QDialog.__init__(self, parent=None) self.tx = tx = copy.deepcopy(tx) try: self.tx.deserialize() except BaseException as e: raise SerializationError(e) self.main_window = parent self.wallet = parent.wallet self.prompt_if_unsaved = prompt_if_unsaved self.saved = False self.desc = desc self.wallet.add_input_info_to_all_inputs(tx) self.setMinimumWidth(950) self.setWindowTitle(_("Transaction")) vbox = QVBoxLayout() self.setLayout(vbox) vbox.addWidget(QLabel(_("Transaction ID:"))) self.tx_hash_e = ButtonsLineEdit() qr_show = lambda: parent.show_qrcode(str(self.tx_hash_e.text()), 'Transaction ID', parent=self) self.tx_hash_e.addButton(":icons/qrcode.png", qr_show, _("Show as QR code")) self.tx_hash_e.setReadOnly(True) vbox.addWidget(self.tx_hash_e) self.tx_desc = QLabel() vbox.addWidget(self.tx_desc) self.status_label = QLabel() vbox.addWidget(self.status_label) self.date_label = QLabel() vbox.addWidget(self.date_label) self.amount_label = QLabel() vbox.addWidget(self.amount_label) self.size_label = QLabel() vbox.addWidget(self.size_label) self.fee_label = QLabel() vbox.addWidget(self.fee_label) self.add_io(vbox) self.sign_button = b = QPushButton(_("Sign")) b.clicked.connect(self.sign) self.broadcast_button = b = QPushButton(_("Broadcast")) b.clicked.connect(self.do_broadcast) self.save_button = b = QPushButton(_("Save")) save_button_disabled = not tx.is_complete() b.setDisabled(save_button_disabled) if save_button_disabled: b.setToolTip(SAVE_BUTTON_DISABLED_TOOLTIP) else: b.setToolTip(SAVE_BUTTON_ENABLED_TOOLTIP) b.clicked.connect(self.save) self.export_button = b = QPushButton(_("Export")) b.clicked.connect(self.export) self.cancel_button = b = QPushButton(_("Close")) b.clicked.connect(self.close) b.setDefault(True) self.qr_button = b = QPushButton() b.setIcon(QIcon(":icons/qrcode.png")) b.clicked.connect(self.show_qr) self.copy_button = CopyButton(lambda: str(self.tx), parent.app) # Action buttons self.buttons = [self.sign_button, self.broadcast_button, self.cancel_button] # Transaction sharing buttons self.sharing_buttons = [self.copy_button, self.qr_button, self.export_button, self.save_button] run_hook('transaction_dialog', self) hbox = QHBoxLayout() hbox.addLayout(Buttons(*self.sharing_buttons)) hbox.addStretch(1) hbox.addLayout(Buttons(*self.buttons)) vbox.addLayout(hbox) self.update() def do_broadcast(self): self.main_window.push_top_level_window(self) try: self.main_window.broadcast_transaction(self.tx, self.desc) finally: self.main_window.pop_top_level_window(self) self.saved = True self.update() def closeEvent(self, event): if (self.prompt_if_unsaved and not self.saved and not self.question(_('This transaction is not saved. Close anyway?'), title=_("Warning"))): event.ignore() else: event.accept() try: dialogs.remove(self) except ValueError: pass # was not in list already def reject(self): # Override escape-key to close normally (and invoke closeEvent) self.close() def show_qr(self): text = bfh(str(self.tx)) text = base_encode(text, base=43) try: self.main_window.show_qrcode(text, 'Transaction', parent=self) except qrcode.exceptions.DataOverflowError: self.show_error(_('Failed to display QR code.') + '\n' + _('Transaction is too large in size.')) except Exception as e: self.show_error(_('Failed to display QR code.') + '\n' + str(e)) def sign(self): def sign_done(success): # note: with segwit we could save partially signed tx, because they have a txid if self.tx.is_complete(): self.prompt_if_unsaved = True self.saved = False self.save_button.setDisabled(False) self.save_button.setToolTip(SAVE_BUTTON_ENABLED_TOOLTIP) self.update() self.main_window.pop_top_level_window(self) self.sign_button.setDisabled(True) self.main_window.push_top_level_window(self) self.main_window.sign_tx(self.tx, sign_done) def save(self): self.main_window.push_top_level_window(self) if self.main_window.save_transaction_into_wallet(self.tx): self.save_button.setDisabled(True) self.saved = True self.main_window.pop_top_level_window(self) def export(self): name = 'signed_%s.txn' % (self.tx.txid()[0:8]) if self.tx.is_complete() else 'unsigned.txn' fileName = self.main_window.getSaveFileName(_("Select where to save your signed transaction"), name, "*.txn") if fileName: with open(fileName, "w+") as f: f.write(json.dumps(self.tx.as_dict(), indent=4) + '\n') self.show_message(_("Transaction exported successfully")) self.saved = True def update(self): desc = self.desc base_unit = self.main_window.base_unit() format_amount = self.main_window.format_amount tx_hash, status, label, can_broadcast, can_rbf, amount, fee, height, conf, timestamp, exp_n = self.wallet.get_tx_info(self.tx) size = self.tx.estimated_size() self.broadcast_button.setEnabled(can_broadcast) can_sign = not self.tx.is_complete() and \ (self.wallet.can_sign(self.tx) or bool(self.main_window.tx_external_keypairs)) self.sign_button.setEnabled(can_sign) self.tx_hash_e.setText(tx_hash or _('Unknown')) if desc is None: self.tx_desc.hide() else: self.tx_desc.setText(_("Description") + ': ' + desc) self.tx_desc.show() self.status_label.setText(_('Status:') + ' ' + status) if timestamp: time_str = datetime.datetime.fromtimestamp(timestamp).isoformat(' ')[:-3] self.date_label.setText(_("Date: {}").format(time_str)) self.date_label.show() # elif exp_n: # text = '%.2f MB'%(exp_n/1000000) # self.date_label.setText(_('Position in mempool: {} from tip').format(text)) # self.date_label.show() else: self.date_label.hide() if amount is None: amount_str = _("Transaction unrelated to your wallet") elif amount > 0: amount_str = _("Amount received:") + ' %s'% format_amount(amount) + ' ' + base_unit else: amount_str = _("Amount sent:") + ' %s'% format_amount(-amount) + ' ' + base_unit size_str = _("Size:") + ' %d bytes'% size fee_str = _("Fee") + ': %s' % (format_amount(fee) + ' ' + base_unit if fee is not None else _('unknown')) if fee is not None: fee_rate = fee/size*1000 fee_str += ' ( %s ) ' % self.main_window.format_fee_rate(fee_rate) confirm_rate = simple_config.FEERATE_WARNING_HIGH_FEE if fee_rate > confirm_rate: fee_str += ' - ' + _('Warning') + ': ' + _("high fee") + '!' self.amount_label.setText(amount_str) self.fee_label.setText(fee_str) self.size_label.setText(size_str) run_hook('transaction_dialog_update', self) def add_io(self, vbox): if self.tx.locktime > 0: vbox.addWidget(QLabel("LockTime: %d\n" % self.tx.locktime)) vbox.addWidget(QLabel(_("Inputs") + ' (%d)'%len(self.tx.inputs()))) ext = QTextCharFormat() rec = QTextCharFormat() rec.setBackground(QBrush(ColorScheme.GREEN.as_color(background=True))) rec.setToolTip(_("Wallet receive address")) chg = QTextCharFormat() chg.setBackground(QBrush(ColorScheme.YELLOW.as_color(background=True))) chg.setToolTip(_("Wallet change address")) twofactor = QTextCharFormat() twofactor.setBackground(QBrush(ColorScheme.BLUE.as_color(background=True))) twofactor.setToolTip(_("TrustedCoin (2FA) fee for the next batch of transactions")) def text_format(addr): if self.wallet.is_mine(addr): return chg if self.wallet.is_change(addr) else rec elif self.wallet.is_billing_address(addr): return twofactor return ext def format_amount(amt): return self.main_window.format_amount(amt, whitespaces=True) i_text = QTextEditWithDefaultSize() i_text.setFont(QFont(MONOSPACE_FONT)) i_text.setReadOnly(True) cursor = i_text.textCursor() for x in self.tx.inputs(): if x['type'] == 'coinbase': cursor.insertText('coinbase') else: prevout_hash = x.get('prevout_hash') prevout_n = x.get('prevout_n') cursor.insertText(prevout_hash + ":%-4d " % prevout_n, ext) addr = self.wallet.get_txin_address(x) if addr is None: addr = '' cursor.insertText(addr, text_format(addr)) if x.get('value'): cursor.insertText(format_amount(x['value']), ext) cursor.insertBlock() vbox.addWidget(i_text) vbox.addWidget(QLabel(_("Outputs") + ' (%d)'%len(self.tx.outputs()))) o_text = QTextEditWithDefaultSize() o_text.setFont(QFont(MONOSPACE_FONT)) o_text.setReadOnly(True) cursor = o_text.textCursor() for o in self.tx.get_outputs_for_UI(): addr, v = o.address, o.value cursor.insertText(addr, text_format(addr)) if v is not None: cursor.insertText('\t', ext) cursor.insertText(format_amount(v), ext) cursor.insertBlock() vbox.addWidget(o_text) class QTextEditWithDefaultSize(QTextEdit): def sizeHint(self): return QSize(0, 100)

true

1c3136a13a110e5c45f53c6aeb6f0ed4d8822808

358

py

Python

leetcode/0062_unique-paths.py

heyf/cloaked-octo-adventure

8180684a8a1859efb836edd48556b5f3088be398

[ "MIT" ]

null

leetcode/0062_unique-paths.py

heyf/cloaked-octo-adventure

8180684a8a1859efb836edd48556b5f3088be398

[ "MIT" ]

null

leetcode/0062_unique-paths.py

heyf/cloaked-octo-adventure

8180684a8a1859efb836edd48556b5f3088be398

[ "MIT" ]

null

# # @lc app=leetcode id=62 lang=python3 # # [62] Unique Paths # # @lc code=start class Solution: def uniquePaths(self, m: int, n: int) -> int: dp = [ 1 for i in range(n)] for _ in range(1,m): for j in range(1,n): dp[j] += dp[j-1] return dp[-1] # @lc code=end s = Solution() print(s.uniquePaths(7,3))

18.842105

49

0.52514

class Solution: def uniquePaths(self, m: int, n: int) -> int: dp = [ 1 for i in range(n)] for _ in range(1,m): for j in range(1,n): dp[j] += dp[j-1] return dp[-1] s = Solution() print(s.uniquePaths(7,3))

true

1c313710cd0098036ec3618c2755120adfb5e3a8

455

py

Python

util/tools.py

QuietWoods/patent-rewrite

3bf85a3a5a0dfc7caaf602fc32fe3b727da01944

[ "Apache-2.0" ]

null

util/tools.py

QuietWoods/patent-rewrite

3bf85a3a5a0dfc7caaf602fc32fe3b727da01944

[ "Apache-2.0" ]

null

util/tools.py

QuietWoods/patent-rewrite

3bf85a3a5a0dfc7caaf602fc32fe3b727da01944

[ "Apache-2.0" ]

1

2020-09-09T14:49:25.000Z

# -*- coding: utf-8 -*- # @Time : 2018/4/24 16:52 # @Author : Wang Lei # @FileName: tools.py # @Software: PyCharm # @Email ：1258481281@qq.com import os from PatentRewrite.util.gensim_word2vec import Word2vec, Sentences from PatentRewrite.util.settings import WORD2VEC, PATENTS, TEMP_PATENTS def train_word2vec(): model_dir = WORD2VEC word2vec = Word2vec(model_dir) word2vec.train() if __name__ == "__main__": train_word2vec()

20.681818

71

0.705495

import os from PatentRewrite.util.gensim_word2vec import Word2vec, Sentences from PatentRewrite.util.settings import WORD2VEC, PATENTS, TEMP_PATENTS def train_word2vec(): model_dir = WORD2VEC word2vec = Word2vec(model_dir) word2vec.train() if __name__ == "__main__": train_word2vec()

true

1c31377812b00907ea0d4ff34371845055ca7a6c

1,563

py

Python

tests/conftest.py

johnnoone/aiodisque

afb6851ac907783a69b4b2e5c09456ae48a1faba

[ "MIT" ]

null

tests/conftest.py

johnnoone/aiodisque

afb6851ac907783a69b4b2e5c09456ae48a1faba

[ "MIT" ]

null

tests/conftest.py

johnnoone/aiodisque

afb6851ac907783a69b4b2e5c09456ae48a1faba

[ "MIT" ]

null

import os.path from pytest import fixture from tempfile import TemporaryDirectory from subprocess import Popen, PIPE, run from time import sleep class Configuration: def __init__(self, **opts): for k, v in opts.items(): setattr(self, k, v) class DisqueNode: def __init__(self, port, dir): self.port = port self.dir = dir self.proc = None self.socket = os.path.join(dir, 'disque.sock') def start(self): if not self.proc: cmd = ["disque-server", "--port", str(self.port), "--dir", self.dir, "--unixsocket", self.socket, "--unixsocketperm", "755"] self.proc = Popen(cmd, stdout=PIPE, stderr=PIPE) cmd = ['disque', '-p', str(self.port), 'info'] while True: sleep(.01) if self.proc.poll(): raise Exception('already stopped!', self.proc.stderr) resp = run(cmd, stdout=PIPE, stderr=PIPE) if not resp.returncode: break def stop(self): self.proc.kill() self.proc = None @property def configuration(self): return Configuration(port=self.port, dir=self.dir, socket=self.socket) @fixture(scope='function') def node(request): tmp_dir = TemporaryDirectory() node = DisqueNode(port=7711, dir=tmp_dir.name) node.start() def teardown(): node.stop() tmp_dir.cleanup() request.addfinalizer(teardown) return node.configuration

26.05

78

0.568138

import os.path from pytest import fixture from tempfile import TemporaryDirectory from subprocess import Popen, PIPE, run from time import sleep class Configuration: def __init__(self, **opts): for k, v in opts.items(): setattr(self, k, v) class DisqueNode: def __init__(self, port, dir): self.port = port self.dir = dir self.proc = None self.socket = os.path.join(dir, 'disque.sock') def start(self): if not self.proc: cmd = ["disque-server", "--port", str(self.port), "--dir", self.dir, "--unixsocket", self.socket, "--unixsocketperm", "755"] self.proc = Popen(cmd, stdout=PIPE, stderr=PIPE) cmd = ['disque', '-p', str(self.port), 'info'] while True: sleep(.01) if self.proc.poll(): raise Exception('already stopped!', self.proc.stderr) resp = run(cmd, stdout=PIPE, stderr=PIPE) if not resp.returncode: break def stop(self): self.proc.kill() self.proc = None @property def configuration(self): return Configuration(port=self.port, dir=self.dir, socket=self.socket) @fixture(scope='function') def node(request): tmp_dir = TemporaryDirectory() node = DisqueNode(port=7711, dir=tmp_dir.name) node.start() def teardown(): node.stop() tmp_dir.cleanup() request.addfinalizer(teardown) return node.configuration

true

1c3138931e6dc6d58b703a644a6df7d2ebb08023

485

py

Python

TOR/ConnectionsHandler/Models/DNSExitNode.py

AmerJod/Tor_DNS-Servers

bd95ff28bb697a4e0ba7d0276b366e83a6718a13

[ "MIT" ]

2

2019-05-22T09:42:51.000Z

2021-06-15T19:05:22.000Z

TOR/ConnectionsHandler/Models/DNSExitNode.py

txrproject/Tor_DNS-Servers

bd95ff28bb697a4e0ba7d0276b366e83a6718a13

[ "MIT" ]

null

TOR/ConnectionsHandler/Models/DNSExitNode.py

txrproject/Tor_DNS-Servers

bd95ff28bb697a4e0ba7d0276b366e83a6718a13

[ "MIT" ]

1

2020-12-23T05:26:32.000Z

""" This class is for each exitNode which delong to DNS resolver """ class DNSExitNode(): def __init__(self,nodeIP,nodeDomain,nodeModifiedDomainfull): self.exitNodeIP = nodeIP self.nodeDomian = nodeDomain self.nodeModifiedDomian = nodeModifiedDomainfull self.JSON = self.reprExitNodelistJSON() def reprExitNodelistJSON(self): return dict(nodeIP=self.exitNodeIP, nodeDomian=self.nodeDomian, nodeModifiedDomian= self.nodeModifiedDomian)

34.642857

116

0.742268

class DNSExitNode(): def __init__(self,nodeIP,nodeDomain,nodeModifiedDomainfull): self.exitNodeIP = nodeIP self.nodeDomian = nodeDomain self.nodeModifiedDomian = nodeModifiedDomainfull self.JSON = self.reprExitNodelistJSON() def reprExitNodelistJSON(self): return dict(nodeIP=self.exitNodeIP, nodeDomian=self.nodeDomian, nodeModifiedDomian= self.nodeModifiedDomian)

true

1c313bd1421fd2c0959b8883eeab4a41d2c364c8

2,545

py

Python

code/remove_invariant_sites_from_phylip.py

nealplatt/sch_man_nwinvasion

73f7ce5fa4843cc2352fdb709b134f22af28ad19

[ "MIT" ]

null

code/remove_invariant_sites_from_phylip.py

nealplatt/sch_man_nwinvasion

73f7ce5fa4843cc2352fdb709b134f22af28ad19

[ "MIT" ]

null

code/remove_invariant_sites_from_phylip.py

nealplatt/sch_man_nwinvasion

73f7ce5fa4843cc2352fdb709b134f22af28ad19

[ "MIT" ]

null

### RNPlatt ### 05 Sept 2019 ### USAGE: remove_invariant_sites_from_phylip.py <list of invariants> <phylip> <outfile> ### ### This will take a list of invariant sites predetermined by raxml in a single ### site per line file and the original phylip file used in raxml and return ### a phylip file with all the invariant sites removed. ### ### REQUIRES: numpy ### import sys import numpy as np #-------------------- GET CMD LINE OPTIONS-------------------------------------- invariant_file=sys.argv[1] phylip_file=sys.argv[2] out_file=sys.argv[3] #-------------------- GET INVARIANT SITES -------------------------------------- #read in the file of invariant sites generated by raxml inv_sites_infile=open(invariant_file, "r") inv_sites=inv_sites_infile.readlines() i=0 #update each site to 0-based (python) index from 1-based (raxml) for entry in inv_sites: inv_sites[i]=int(entry.rstrip())-1 i=i+1 #-------------------- GET SEQUENCE DATA AND TRIM ------------------------------- #read in the dat from the untrimmed phylip file phylip_infile=open(phylip_file, "r") phylip_data=phylip_infile.readlines() #get the num samples and sites from the header line num_samples, num_sites=phylip_data[0].rstrip('\n').split(" ") #cycle through the seqeunce data into two seperate lists # sample ids are in a list # sequences is a 2d list with each base a seperate position sample_ids=[] sequences=[] for entry in phylip_data[1:]: sample_id, sequence = entry.rstrip('\n').split() sample_ids.append(sample_id) sequences.append(list(sequence)) #convert to 2d array sequences=np.array(sequences) #trim invariant sites trimmed_seqs=np.delete(sequences, inv_sites, 1) #now turn into strings seq_strings=[] for trimmed_seq in trimmed_seqs: #convert trimmed array/list to a string as_string=''.join(list(trimmed_seq)) #add to new list of trimmed sequences seq_strings.append(as_string) #-------------------- CREATING THE OUTPUT FILE --------------------------------- #create an output file trimmed_phylip_outfile=open(out_file, "w") num_sites_after_trimming=len(seq_strings[0]) #print header line trimmed_phylip_outfile.write(str(num_samples) + " " + str(num_sites_after_trimming) + "\n") #print trimmed info to outfile i=0 for sample_id in sample_ids: trimmed_phylip_outfile.write(sample_id + " " + seq_strings[i] + "\n") i=i+1 #-------------------- CLOSING FILES ------------------------------------------- inv_sites_infile.close() phylip_infile.close() trimmed_phylip_outfile.close()

29.252874

91

0.662083

trings=[] for trimmed_seq in trimmed_seqs: as_string=''.join(list(trimmed_seq)) seq_strings.append(as_string) trimmed_phylip_outfile=open(out_file, "w") num_sites_after_trimming=len(seq_strings[0]) trimmed_phylip_outfile.write(str(num_samples) + " " + str(num_sites_after_trimming) + "\n") i=0 for sample_id in sample_ids: trimmed_phylip_outfile.write(sample_id + " " + seq_strings[i] + "\n") i=i+1 inv_sites_infile.close() phylip_infile.close() trimmed_phylip_outfile.close()

true

1c313c2826e1f112676e1337859553b9cf492376

7,335

py

Python

src/utils/chatArchiver.py

ayman2598/GabbyGums

b68ab01610ac399aa2b7daa97d5d71dd0d1b19d6

[ "Apache-2.0" ]

2

2019-12-13T20:06:14.000Z

2022-01-23T00:34:29.000Z

src/utils/chatArchiver.py

ayman2598/GabbyGums

b68ab01610ac399aa2b7daa97d5d71dd0d1b19d6

[ "Apache-2.0" ]

23

2019-10-19T16:55:45.000Z

2020-03-14T16:18:05.000Z

src/utils/chatArchiver.py

amadea-system/GabbyGums

b68ab01610ac399aa2b7daa97d5d71dd0d1b19d6

[ "Apache-2.0" ]

6

2019-12-13T20:06:17.000Z

2021-02-12T16:21:04.000Z

""" Methods for generating HTML and TXT archives of a discord chat from a list of discord messages. Part of the Gabby Gums Discord Logger. """ import hmac import logging import hashlib from functools import partial from datetime import datetime from io import StringIO, SEEK_END, SEEK_SET from typing import TYPE_CHECKING, Optional, Dict, List, Union, Tuple, NamedTuple, Match import regex as re from jinja2 import Template, Environment, FileSystemLoader from utils.discordMarkdownParser import markdown if TYPE_CHECKING: from events.bulkMessageDelete import CompositeMessage, MessageGroups import discord from discord.ext import commands log = logging.getLogger(__name__) auth_key_pattern = re.compile(r"^$") def md(_input): out = markdown.markdown(_input) return out file_loader = FileSystemLoader(searchpath="./htmlTemplates/") env = Environment(loader=file_loader) env.globals['markdown'] = md env.trim_blocks = True env.lstrip_blocks = True template = env.get_template('mainChat.html') class CouldNotFindAuthenticationCode(Exception): pass def generate_txt_archive(messages: List['CompositeMessage'], channel_name) -> StringIO: archive = StringIO() lines = [] for message in messages: if message.content: content = message.content else: content = "----Message contained no text----" if message.is_pk: author_info = f"System ID: {message.system_id}, Member ID: {message.member_id}" else: author: Union['discord.Member', 'discord.User'] = message.author author_info = author.id if author else "None" msg = f"[{message.created_at.strftime('%Y-%m-%d %H:%M:%S-UTC')}] {message.user_name_and_discrim} ({author_info}):" \ f"\n {content}\n\n" lines.append(msg) archive.write(f"{len(lines)} messages archived from #{channel_name} @ {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S-UTC')}\n\n") for line in lines: archive.write(line) archive.seek(0) return archive async def generate_html_archive(bot: 'commands.bot', channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int) -> StringIO: fn = partial(blocking_generate_html_archive, channel, messages, msg_count) archive = await bot.loop.run_in_executor(None, fn) return archive def blocking_generate_html_archive(channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int) -> StringIO: archive = StringIO() ctx = {'guild': channel.guild, 'channel': channel} output = template.render(ctx=ctx, msg_groups=messages, msg_count=msg_count) archive.writelines(output) archive.seek(0) return archive def generate_SHA256_hash(_input: StringIO) -> str: """Generates a SHA256 hash for a StringIO Object and seeks the Object back to 0 at the end.""" _input.seek(0) hasher = hashlib.sha256() hasher.update(str(_input.read()).encode('utf-8')) # 16 _input.seek(0) return hasher.hexdigest() def get_hmac(_input: StringIO, security_key: bytes) -> str: _input.seek(0) msg = str(_input.read()).encode('utf-8') hasher = hmac.new(security_key, msg, hashlib.sha3_256) # Create the HMAC Hasher hash = hasher.hexdigest() # Get the hmac hash _input.seek(0) return hash def write_hmac(_input: StringIO, security_key: bytes): """Generates a Hash-base Message Authentication Code for a given StringIO Object and writes it to the end of the file.""" _input.seek(0) # Seek the StringIO back to the beginning so it can be read. hash = get_hmac(_input, security_key) # log.info(f"Got HMAC: {hash}") _input.seek(0, SEEK_END) # Make sure we are at the end of the file so we can write the hmac _input.write(f"\n") # Write the hash to the StringIO _input.seek(0) # Finally Seek the StringIO back to the beginning so it's ready the next time it needs to be read. def verify_file(file: StringIO, security_key: bytes) -> bool: file.seek(0, SEEK_END) # Seek to the end of the file. so we can iterate backward. pos = file.tell() # store the position that is the end of the file. # log.info(f"Pos: {pos}") file.seek(0, SEEK_END) # Seek back to the end of the file. while pos > 0 and file.read(1) != '\n': # Go backwards through the file until we hit a new line or the start of the file. pos -= 1 file.seek(pos, SEEK_SET) # log.info(f"Pos after seeking: {pos}") auth_code = None if pos > 0: file.seek(pos+1, SEEK_SET) # Go forward one char to get tot he start of the last line. This is where the auth code lies. auth_code = file.readline() # Grab the auth code file.seek(pos, SEEK_SET) # Go back to the new line file.truncate() # And delete everything after it so we can get back to a HMACable file. if auth_code is not None: auth_code_match: Match = auth_key_pattern.match(auth_code) if auth_code_match is not None: auth_code = auth_code_match.group(1) log.info(f"Got auth code: {auth_code}") hash = get_hmac(file, security_key) log.info(f"files hmac: {hash}") # if hash == auth_code: if hmac.compare_digest(hash, auth_code): log.info("File is unmodified.") return True else: log.info("Authentication Code mismatch. File has been modified.") return False log.info("Could not find authentication code in the archive file!") # raise CouldNotFindAuthenticationCode("Could not find authentication code in the archive file!") return False # Unused, for debugging purposes. def save_html_archive(channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int): """This method does the same as generate_html_archive() except instead of returning a StringIO object suitable for passing to Discord, it saves the html for debugging. """ ctx = {'guild': channel.guild, 'channel': channel} output = template.render(ctx=ctx, msg_groups=messages, msg_count=msg_count) with open('archive.html', 'w', encoding="utf-8") as archive: # 16 archive.writelines(output) # Unused, for debugging purposes. def save_htmlDebug_txt_archive(messages: List['CompositeMessage'], channel_name): messages.reverse() lines = [] for message in messages: if message.content: content = message.content else: content = "----Message contained no text----" if message.is_pk: author_info = f"System ID: {message.system_id}, Member ID: {message.member_id}" else: author: Union['discord.Member', 'discord.User'] = message.author author_info = author.id if author else "None" msg = f"[{message.created_at.strftime('%Y-%m-%d %H:%M:%S-UTC')}] {message.user_name_and_discrim} ({author_info}):" \ f"\n\n {content}\n\n" lines.append(msg) with open('debug_archive.html.txt', 'w', encoding="utf-8") as archive: # 16 archive.write(f"{len(lines)} messages archived from #{channel_name} @ {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S-UTC')}\n\n") for line in lines: archive.write(line)

36.675

175

0.667894

import hmac import logging import hashlib from functools import partial from datetime import datetime from io import StringIO, SEEK_END, SEEK_SET from typing import TYPE_CHECKING, Optional, Dict, List, Union, Tuple, NamedTuple, Match import regex as re from jinja2 import Template, Environment, FileSystemLoader from utils.discordMarkdownParser import markdown if TYPE_CHECKING: from events.bulkMessageDelete import CompositeMessage, MessageGroups import discord from discord.ext import commands log = logging.getLogger(__name__) auth_key_pattern = re.compile(r"^$") def md(_input): out = markdown.markdown(_input) return out file_loader = FileSystemLoader(searchpath="./htmlTemplates/") env = Environment(loader=file_loader) env.globals['markdown'] = md env.trim_blocks = True env.lstrip_blocks = True template = env.get_template('mainChat.html') class CouldNotFindAuthenticationCode(Exception): pass def generate_txt_archive(messages: List['CompositeMessage'], channel_name) -> StringIO: archive = StringIO() lines = [] for message in messages: if message.content: content = message.content else: content = "----Message contained no text----" if message.is_pk: author_info = f"System ID: {message.system_id}, Member ID: {message.member_id}" else: author: Union['discord.Member', 'discord.User'] = message.author author_info = author.id if author else "None" msg = f"[{message.created_at.strftime('%Y-%m-%d %H:%M:%S-UTC')}] {message.user_name_and_discrim} ({author_info}):" \ f"\n {content}\n\n" lines.append(msg) archive.write(f"{len(lines)} messages archived from #{channel_name} @ {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S-UTC')}\n\n") for line in lines: archive.write(line) archive.seek(0) return archive async def generate_html_archive(bot: 'commands.bot', channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int) -> StringIO: fn = partial(blocking_generate_html_archive, channel, messages, msg_count) archive = await bot.loop.run_in_executor(None, fn) return archive def blocking_generate_html_archive(channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int) -> StringIO: archive = StringIO() ctx = {'guild': channel.guild, 'channel': channel} output = template.render(ctx=ctx, msg_groups=messages, msg_count=msg_count) archive.writelines(output) archive.seek(0) return archive def generate_SHA256_hash(_input: StringIO) -> str: _input.seek(0) hasher = hashlib.sha256() hasher.update(str(_input.read()).encode('utf-8')) _input.seek(0) return hasher.hexdigest() def get_hmac(_input: StringIO, security_key: bytes) -> str: _input.seek(0) msg = str(_input.read()).encode('utf-8') hasher = hmac.new(security_key, msg, hashlib.sha3_256) hash = hasher.hexdigest() _input.seek(0) return hash def write_hmac(_input: StringIO, security_key: bytes): _input.seek(0) hash = get_hmac(_input, security_key) _input.seek(0, SEEK_END) _input.write(f"\n") _input.seek(0) def verify_file(file: StringIO, security_key: bytes) -> bool: file.seek(0, SEEK_END) # Seek to the end of the file. so we can iterate backward. pos = file.tell() # store the position that is the end of the file. # log.info(f"Pos: {pos}") file.seek(0, SEEK_END) # Seek back to the end of the file. while pos > 0 and file.read(1) != '\n': # Go backwards through the file until we hit a new line or the start of the file. pos -= 1 file.seek(pos, SEEK_SET) # log.info(f"Pos after seeking: {pos}") auth_code = None if pos > 0: file.seek(pos+1, SEEK_SET) # Go forward one char to get tot he start of the last line. This is where the auth code lies. auth_code = file.readline() # Grab the auth code file.seek(pos, SEEK_SET) # Go back to the new line file.truncate() # And delete everything after it so we can get back to a HMACable file. if auth_code is not None: auth_code_match: Match = auth_key_pattern.match(auth_code) if auth_code_match is not None: auth_code = auth_code_match.group(1) log.info(f"Got auth code: {auth_code}") hash = get_hmac(file, security_key) log.info(f"files hmac: {hash}") # if hash == auth_code: if hmac.compare_digest(hash, auth_code): log.info("File is unmodified.") return True else: log.info("Authentication Code mismatch. File has been modified.") return False log.info("Could not find authentication code in the archive file!") # raise CouldNotFindAuthenticationCode("Could not find authentication code in the archive file!") return False # Unused, for debugging purposes. def save_html_archive(channel: 'discord.TextChannel', messages: 'MessageGroups', msg_count: int): ctx = {'guild': channel.guild, 'channel': channel} output = template.render(ctx=ctx, msg_groups=messages, msg_count=msg_count) with open('archive.html', 'w', encoding="utf-8") as archive: # 16 archive.writelines(output) # Unused, for debugging purposes. def save_htmlDebug_txt_archive(messages: List['CompositeMessage'], channel_name): messages.reverse() lines = [] for message in messages: if message.content: content = message.content else: content = "----Message contained no text----" if message.is_pk: author_info = f"System ID: {message.system_id}, Member ID: {message.member_id}" else: author: Union['discord.Member', 'discord.User'] = message.author author_info = author.id if author else "None" msg = f"[{message.created_at.strftime('%Y-%m-%d %H:%M:%S-UTC')}] {message.user_name_and_discrim} ({author_info}):" \ f"\n\n {content}\n\n" lines.append(msg) with open('debug_archive.html.txt', 'w', encoding="utf-8") as archive: # 16 archive.write(f"{len(lines)} messages archived from #{channel_name} @ {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S-UTC')}\n\n") for line in lines: archive.write(line)

true

1c313d9466ba24619116f017bf281b737b3abf9e

1,704

py

Python

vigobusbot/telegram_bot/services/message_generators/stop_message_text.py

David-Lor/VigoBus-TelegramBot

2cbba9a6565e7f8c92953e79b9ca4247d53f4b33

[ "Apache-2.0" ]

8

2019-07-18T21:33:04.000Z

2022-03-26T15:07:14.000Z

vigobusbot/telegram_bot/services/message_generators/stop_message_text.py

EnforcerZhukov/VigoBus-TelegramBot

9a0258edf5ff34ecedab6bcf4a8238f07bc318fa

[ "Apache-2.0" ]

3

2021-09-10T19:53:36.000Z

2021-09-10T19:53:37.000Z

vigobusbot/telegram_bot/services/message_generators/stop_message_text.py

EnforcerZhukov/VigoBus-TelegramBot

9a0258edf5ff34ecedab6bcf4a8238f07bc318fa

[ "Apache-2.0" ]

3

2019-09-24T15:43:23.000Z

2020-04-18T17:48:29.000Z

"""STOP MESSAGE TEXT Helper to generate the Stop Message text body """ # # Native # # import datetime from typing import Optional # # Project # # from vigobusbot.persistence_api import saved_stops from vigobusbot.static_handler import get_messages from vigobusbot.entities import Stop, BusesResponse __all__ = ("generate_stop_message_text",) def generate_stop_message_text( stop: Stop, buses_response: BusesResponse, user_saved_stop: Optional[saved_stops.SavedStop] ) -> str: messages = get_messages() buses = buses_response.buses # Generate Stop Name text if user_saved_stop and user_saved_stop.stop_name: stop_name_text = messages.stop.stop_custom_name.format( stop_custom_name=user_saved_stop.stop_name, stop_original_name=stop.name ) else: stop_name_text = stop.name # Generate Buses text if buses: buses_text_lines = list() for bus in buses: if bus.time == 0: time_text = messages.stop.bus_time_now else: time_text = messages.stop.bus_time_remaining.format(minutes=bus.time) buses_text_lines.append(messages.stop.bus_line.format( line=bus.line, route=bus.route, time=time_text )) buses_text = "\n".join(buses_text_lines) else: buses_text = messages.stop.no_buses_found last_update_text = datetime.datetime.now().strftime(messages.stop.time_format) return messages.stop.message.format( stop_id=stop.stop_id, stop_name=stop_name_text, buses=buses_text, last_update=last_update_text )

28.881356

85

0.663732

from typing import Optional persistence_api import saved_stops from vigobusbot.static_handler import get_messages from vigobusbot.entities import Stop, BusesResponse __all__ = ("generate_stop_message_text",) def generate_stop_message_text( stop: Stop, buses_response: BusesResponse, user_saved_stop: Optional[saved_stops.SavedStop] ) -> str: messages = get_messages() buses = buses_response.buses if user_saved_stop and user_saved_stop.stop_name: stop_name_text = messages.stop.stop_custom_name.format( stop_custom_name=user_saved_stop.stop_name, stop_original_name=stop.name ) else: stop_name_text = stop.name if buses: buses_text_lines = list() for bus in buses: if bus.time == 0: time_text = messages.stop.bus_time_now else: time_text = messages.stop.bus_time_remaining.format(minutes=bus.time) buses_text_lines.append(messages.stop.bus_line.format( line=bus.line, route=bus.route, time=time_text )) buses_text = "\n".join(buses_text_lines) else: buses_text = messages.stop.no_buses_found last_update_text = datetime.datetime.now().strftime(messages.stop.time_format) return messages.stop.message.format( stop_id=stop.stop_id, stop_name=stop_name_text, buses=buses_text, last_update=last_update_text )

true

1c313dedbfd5a753fe40dabaad5a4b121ecda8d2

11,682

py

Python

test/IECore/CompoundData.py

gcodebackups/cortex-vfx

72fa6c6eb3327fce4faf01361c8fcc2e1e892672

[ "BSD-3-Clause" ]

5

2016-07-26T06:09:28.000Z

2022-03-07T03:58:51.000Z

test/IECore/CompoundData.py

turbosun/cortex

4bdc01a692652cd562f3bfa85f3dae99d07c0b15

[ "BSD-3-Clause" ]

null

test/IECore/CompoundData.py

turbosun/cortex

4bdc01a692652cd562f3bfa85f3dae99d07c0b15

[ "BSD-3-Clause" ]

3

2015-03-25T18:45:24.000Z

2020-02-15T15:37:18.000Z

########################################################################## # # Copyright (c) 2007-2013, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * 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. # # * Neither the name of Image Engine Design nor the names of any # other contributors to this software 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 OWNER 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. # ########################################################################## """Unit test for CompoundData binding""" import os import math import unittest import sys import subprocess import IECore class CompoundDataTest(unittest.TestCase): def testConstructors(self): """Test constructors""" v1 = IECore.CompoundData() a = dict() a["1"] = IECore.IntData(1) v3 = IECore.CompoundData(a) self.assertEqual(v3.size(), 1) def testResize(self): """Test resizing""" v = IECore.CompoundData() v["0"] = IECore.FloatData(2) self.assertEqual(v["0"], IECore.FloatData(2)) v["1"] = IECore.FloatData(0) v["2"] = IECore.FloatData(3) v["3"] = IECore.FloatData(2) v["4"] = IECore.FloatData(5) self.assertEqual(v["4"], IECore.FloatData(5)) self.assertEqual(len(v), 5) del(v["0"]) self.assertEqual(len(v), 4) self.assert_(v.has_key("0") == False) v.clear() self.assertEqual(len(v), 0) def testAssignment(self): """Test assignment""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v2 = v1.copy() v3 = v1 v4 = v1.copy() self.assertEqual(len(v1), 2) self.assertEqual(len(v1), len(v2)) self.assertEqual(v1["0"], v2["0"]) self.assertEqual(v1["1"], v2["1"]) self.assertEqual(v1["0"], v4["0"]) self.assertEqual(v1["1"], v4["1"]) self.assertRaises( TypeError, v1.__setitem__, "2", None ) # should prevent setting None as value. def testCopyOnWrite(self): """Test copy-on-write behavior""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v2 = v1.copy() v3 = v1.copy() v3["0"] = IECore.UIntData(5) self.assert_(v3["0"] == IECore.UIntData(5)) self.assert_(v2["0"] == IECore.FloatData(1.2)) v1["2"] = IECore.FloatData(5); self.assertEqual(len(v1), 3) self.assertEqual(len(v2), 2) def testSearch(self): """Test search functions""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) self.assert_("0" in v1) self.assert_("3" not in v1) self.assert_(v1.has_key("1")) self.assert_(not v1.has_key("3")) self.assert_(v1.get("0") == IECore.FloatData(1.2)) self.assert_(v1.get("0", IECore.IntData(10)) == IECore.FloatData(1.2)) self.assert_(v1.get("xx", IECore.IntData(10)) == IECore.IntData(10)) self.assert_(v1.get("xx") == None) self.assert_(v1.get("xx", None ) == None) self.assertEqual(len(v1), 3) def testUpdate(self): """Test update function""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) v2 = IECore.CompoundData() v2["0"] = IECore.UIntData(5) v2["3"] = IECore.UIntData(6) v2.update(v1) self.assertEqual(len(v2), 4) self.assert_(v2["0"] == IECore.FloatData(1.2)) self.assert_(v2["3"] == IECore.UIntData(6)) v3 = dict() v3["1"] = IECore.CharData("a") v3["4"] = IECore.UCharData(9) v2.update(v3) self.assertEqual(len(v2), 5) self.assert_(v2["1"] == IECore.CharData("a")) self.assert_(v2["4"] == IECore.UCharData(9)) def testSetDefault(self): """Test setdefault function""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) v2 = v1.copy() self.assertEqual(len(v1), 3) self.assert_(v1.setdefault("2", IECore.UIntData(10)) == IECore.FloatData(3)) self.assertEqual(len(v1), 3) self.assert_(v1.setdefault("x", IECore.UIntData(10)) == IECore.UIntData(10)) self.assertEqual(len(v1), 4) def testPop(self): """Test pop functions""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) v1["3"] = IECore.FloatData(4) self.assertEqual(len(v1), 4) prev = v1.popitem() self.assertEqual(len(v1), 3) self.assertEqual(v1.pop("x", IECore.UIntData(10)), IECore.UIntData(10)) self.assertEqual(len(v1), 3) def testKeyValues(self): """Test keys/values listing""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1) v1["1"] = IECore.FloatData(2) v1["2"] = IECore.FloatData(3) self.assertEqual( set( v1.keys() ), set( ['0', '1', '2'] ) ) vals = v1.values() self.assertEqual( set( [ x.value for x in vals ] ), set( [ 1, 2, 3 ] ) ) items = v1.items() self.assertEqual( set( [ ( x[0], x[1].value ) for x in items ] ), set( [ ( "0", 1 ), ( "1", 2 ), ( "2", 3 ) ] ) ) def testEquality(self): """Test equality function""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) v2 = IECore.CompoundData() v2["0"] = IECore.FloatData(1.2) v2["1"] = IECore.FloatData(2.3) v2["2"] = IECore.FloatData(3) v3 = v2.copy() del v3["2"] self.assert_(v1 == v2) self.assert_(not v1 != v2) self.assert_(not v1 == v3) self.assert_(not v2 == v3) v2["-1"] = IECore.FloatData(6) self.assert_(v1 != v2) self.assert_(not v1 == v2) del(v1["2"]) self.assert_(v1 == v3) def testByValueItem(self): """Test by value return type""" v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) self.assert_(v1["0"] == IECore.FloatData(1.2)) a = v1["0"] a = IECore.UIntData(255) self.assert_(v1["0"] == IECore.FloatData(1.2)) self.assert_(a == IECore.UIntData(255)) def testLoadSave(self): """Test load/save""" iface = IECore.IndexedIO.create( "test/CompoundData.fio", IECore.IndexedIO.OpenMode.Write ) v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) v1["some:data"] = IECore.FloatData(3) self.assert_(v1["0"] == IECore.FloatData(1.2)) v1.save( iface, "test" ) v2 = IECore.Object.load( iface, "test" ) self.assertEqual( v1, v2 ) def testRepr(self): """Test repr""" v1 = IECore.CompoundData() r1 = repr(v1) self.assertEqual( eval(repr(v1)), v1 ) v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) self.assertEqual( eval(repr(v1)), v1 ) v1 = IECore.CompoundData() v1["0"] = IECore.StringData( "test" ) v1["1"] = IECore.CompoundData( { "0" : IECore.StringData( "test" ), "1" : IECore.M33fData() } ) v1["someMoreData"] = IECore.V3fVectorData() v1["A"] = IECore.Color4fVectorData() self.assertEqual( eval(repr(v1)), v1 ) def testConstructionFromNestedDict( self ) : c = IECore.CompoundData( { "a" : 10, "b" : IECore.BoolData( True ), "c" : { "cc" : IECore.IntData( 20 ), }, "d" : IECore.CompoundData( { "dd" : IECore.IntData( 5 ), } ) } ) self.assertEqual( len( c ), 4 ) self.assertEqual( c["a"], IECore.IntData( 10 ) ) self.assertEqual( c["b"], IECore.BoolData( True ) ) self.assertEqual( len( c["c"] ), 1 ) self.assertEqual( c["c"]["cc"], IECore.IntData( 20 ) ) self.assertEqual( len( c["d"] ), 1 ) self.assertEqual( c["d"]["dd"], IECore.IntData( 5 ) ) def testUpdateFromNestedDict( self ) : c = IECore.CompoundData( { "a" : IECore.IntData( 30 ) } ) d = { "a" : 10, "b" : IECore.BoolData( True ), "c" : { "cc" : IECore.IntData( 20 ), }, "d" : IECore.CompoundData( { "dd" : IECore.IntData( 5 ), } ) } c.update( d ) self.assertEqual( len( c ), 4 ) self.assertEqual( c["a"], IECore.IntData( 10 ) ) self.assertEqual( c["b"], IECore.BoolData( True ) ) self.assertEqual( len( c["c"] ), 1 ) self.assertEqual( c["c"]["cc"], IECore.IntData( 20 ) ) self.assertEqual( len( c["d"] ), 1 ) self.assertEqual( c["d"]["dd"], IECore.IntData( 5 ) ) def testHash( self ) : o1 = IECore.CompoundData() o2 = IECore.CompoundData() o1["a"] = IECore.StringData( "a" ) o1["b"] = IECore.StringData( "b" ) o2["b"] = IECore.StringData( "b" ) o2["a"] = IECore.StringData( "a" ) self.assertEqual( o1.hash(), o2.hash() ) o2["c"] = IECore.StringData( "c" ) self.assertNotEqual( o1.hash(), o2.hash() ) def testHashIndependentFromOrderOfConstruction( self ) : # CompoundData internally uses a map from InternedString to Data. # a naive iteration over this might yield a different order in each # process as it's dependent on the addresses of the InternedStrings. # we need to keep hashes consistent between processes. commands = [ "import IECore; IECore.InternedString( 'a' ); print IECore.CompoundData( { 'a' : IECore.IntData( 10 ), 'b' : IECore.IntData( 20 ) } ).hash()", "import IECore; IECore.InternedString( 'b' ); print IECore.CompoundData( { 'a' : IECore.IntData( 10 ), 'b' : IECore.IntData( 20 ) } ).hash()", ] hashes = set() for command in commands : p = subprocess.Popen( [ sys.executable, "-c", command ], stdout=subprocess.PIPE ) hash, nothing = p.communicate() hashes.add( hash ) self.assertEqual( len( hashes ), 1 ) def testHash( self ) : thingsToAdd = [ ( "a", IECore.IntData( 1 ), True ), ( "a", IECore.UIntData( 1 ), True ), ( "a", IECore.IntData( 1 ), True ), ( "a", IECore.IntData( 1 ), False ), ( "b", IECore.StringVectorData( [ "a", "b", "c" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "b" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "c" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "c" ] ), False ), ( "d", IECore.StringVectorData( [ "a", "c" ] ), True ), ( "d", None, True ), ] o = IECore.CompoundData() for t in thingsToAdd : h = o.hash() for i in range( 0, 10 ) : self.assertEqual( h, o.hash() ) if t[1] is not None : o[t[0]] = t[1] else : del o[t[0]] if t[2] : self.assertNotEqual( h, o.hash() ) else : self.assertEqual( h, o.hash() ) h = o.hash() def tearDown(self): if os.path.isfile("./test/CompoundData.fio") : os.remove("./test/CompoundData.fio") if __name__ == "__main__": unittest.main()

30.421875

145

0.621041

re.Object.load( iface, "test" ) self.assertEqual( v1, v2 ) def testRepr(self): v1 = IECore.CompoundData() r1 = repr(v1) self.assertEqual( eval(repr(v1)), v1 ) v1 = IECore.CompoundData() v1["0"] = IECore.FloatData(1.2) v1["1"] = IECore.FloatData(2.3) v1["2"] = IECore.FloatData(3) self.assertEqual( eval(repr(v1)), v1 ) v1 = IECore.CompoundData() v1["0"] = IECore.StringData( "test" ) v1["1"] = IECore.CompoundData( { "0" : IECore.StringData( "test" ), "1" : IECore.M33fData() } ) v1["someMoreData"] = IECore.V3fVectorData() v1["A"] = IECore.Color4fVectorData() self.assertEqual( eval(repr(v1)), v1 ) def testConstructionFromNestedDict( self ) : c = IECore.CompoundData( { "a" : 10, "b" : IECore.BoolData( True ), "c" : { "cc" : IECore.IntData( 20 ), }, "d" : IECore.CompoundData( { "dd" : IECore.IntData( 5 ), } ) } ) self.assertEqual( len( c ), 4 ) self.assertEqual( c["a"], IECore.IntData( 10 ) ) self.assertEqual( c["b"], IECore.BoolData( True ) ) self.assertEqual( len( c["c"] ), 1 ) self.assertEqual( c["c"]["cc"], IECore.IntData( 20 ) ) self.assertEqual( len( c["d"] ), 1 ) self.assertEqual( c["d"]["dd"], IECore.IntData( 5 ) ) def testUpdateFromNestedDict( self ) : c = IECore.CompoundData( { "a" : IECore.IntData( 30 ) } ) d = { "a" : 10, "b" : IECore.BoolData( True ), "c" : { "cc" : IECore.IntData( 20 ), }, "d" : IECore.CompoundData( { "dd" : IECore.IntData( 5 ), } ) } c.update( d ) self.assertEqual( len( c ), 4 ) self.assertEqual( c["a"], IECore.IntData( 10 ) ) self.assertEqual( c["b"], IECore.BoolData( True ) ) self.assertEqual( len( c["c"] ), 1 ) self.assertEqual( c["c"]["cc"], IECore.IntData( 20 ) ) self.assertEqual( len( c["d"] ), 1 ) self.assertEqual( c["d"]["dd"], IECore.IntData( 5 ) ) def testHash( self ) : o1 = IECore.CompoundData() o2 = IECore.CompoundData() o1["a"] = IECore.StringData( "a" ) o1["b"] = IECore.StringData( "b" ) o2["b"] = IECore.StringData( "b" ) o2["a"] = IECore.StringData( "a" ) self.assertEqual( o1.hash(), o2.hash() ) o2["c"] = IECore.StringData( "c" ) self.assertNotEqual( o1.hash(), o2.hash() ) def testHashIndependentFromOrderOfConstruction( self ) : # we need to keep hashes consistent between processes. commands = [ "import IECore; IECore.InternedString( 'a' ); print IECore.CompoundData( { 'a' : IECore.IntData( 10 ), 'b' : IECore.IntData( 20 ) } ).hash()", "import IECore; IECore.InternedString( 'b' ); print IECore.CompoundData( { 'a' : IECore.IntData( 10 ), 'b' : IECore.IntData( 20 ) } ).hash()", ] hashes = set() for command in commands : p = subprocess.Popen( [ sys.executable, "-c", command ], stdout=subprocess.PIPE ) hash, nothing = p.communicate() hashes.add( hash ) self.assertEqual( len( hashes ), 1 ) def testHash( self ) : thingsToAdd = [ ( "a", IECore.IntData( 1 ), True ), ( "a", IECore.UIntData( 1 ), True ), ( "a", IECore.IntData( 1 ), True ), ( "a", IECore.IntData( 1 ), False ), ( "b", IECore.StringVectorData( [ "a", "b", "c" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "b" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "c" ] ), True ), ( "b", IECore.StringVectorData( [ "a", "c" ] ), False ), ( "d", IECore.StringVectorData( [ "a", "c" ] ), True ), ( "d", None, True ), ] o = IECore.CompoundData() for t in thingsToAdd : h = o.hash() for i in range( 0, 10 ) : self.assertEqual( h, o.hash() ) if t[1] is not None : o[t[0]] = t[1] else : del o[t[0]] if t[2] : self.assertNotEqual( h, o.hash() ) else : self.assertEqual( h, o.hash() ) h = o.hash() def tearDown(self): if os.path.isfile("./test/CompoundData.fio") : os.remove("./test/CompoundData.fio") if __name__ == "__main__": unittest.main()

true

1c313ecf741a8525f0d69119189313d14407c4bb

72

py

Python

oauth_login/login/__init__.py

vinoth3v/In_addon_oauth_login

b7ebfaa8d3a3c455d58300ac7c23da761273aadf

[ "Apache-2.0" ]

1

2015-12-16T03:25:31.000Z

oauth_login/login/__init__.py

vinoth3v/In_addon_oauth_login

b7ebfaa8d3a3c455d58300ac7c23da761273aadf

[ "Apache-2.0" ]

null

oauth_login/login/__init__.py

vinoth3v/In_addon_oauth_login

b7ebfaa8d3a3c455d58300ac7c23da761273aadf

[ "Apache-2.0" ]

1

2019-09-13T10:12:23.000Z

from .oauth_login import * from .google import * from .facebook import *

24

26

0.763889

from .oauth_login import * from .google import * from .facebook import *

true

1c313f615a5d3d57e72c3049381ae4c7671ebbf2

812

py

Python

srv/item/getBudget.py

jphacks/KB_1814

7ae538272f960a5f21460961ebf1112a6e819e3e

[ "MIT" ]

5

2018-10-19T11:09:35.000Z

2020-02-14T07:31:52.000Z

srv/item/getBudget.py

jphacks/KB_1814

7ae538272f960a5f21460961ebf1112a6e819e3e

[ "MIT" ]

null

srv/item/getBudget.py

jphacks/KB_1814

7ae538272f960a5f21460961ebf1112a6e819e3e

[ "MIT" ]

null

import requests import json def getBudget(a, b, c): latitude = a longitude = b name = c url = "https://api.gnavi.co.jp/RestSearchAPI/20150630/?keyid=264a257d88e6a732c7195178e8f86f90&format=json&latitude=" + latitude + "&longitude="+ longitude +"&name=" + name headers = {"content-type": "application/json"} r = requests.get(url, headers=headers) data = r.json() print("aaaa") # print (json.dumps(data, indent=4)) budget = data['rest']['budget'] lunch = data['rest']['lunch'] if budget != {}: print ("budget : " + budget + "円") if lunch != {}: print ("lunch : " + lunch + "円") if __name__ == "__name__": latitude = "34.702492" longitude = "135.4959658" name = "UMEDAI Garden Restaurant" getBudget(latitude, longitude, name)

28

175

0.608374

import requests import json def getBudget(a, b, c): latitude = a longitude = b name = c url = "https://api.gnavi.co.jp/RestSearchAPI/20150630/?keyid=264a257d88e6a732c7195178e8f86f90&format=json&latitude=" + latitude + "&longitude="+ longitude +"&name=" + name headers = {"content-type": "application/json"} r = requests.get(url, headers=headers) data = r.json() print("aaaa") budget = data['rest']['budget'] lunch = data['rest']['lunch'] if budget != {}: print ("budget : " + budget + "円") if lunch != {}: print ("lunch : " + lunch + "円") if __name__ == "__name__": latitude = "34.702492" longitude = "135.4959658" name = "UMEDAI Garden Restaurant" getBudget(latitude, longitude, name)

true

1c31409a6f754c3d642a2e3a3784a98d7c74ce04

3,200

py

Python

OneImage2Video/school_badge.py

HypoX64/bilibili

992029667ad37d7d03131aa2c4c9923da6cca6f2

[ "MIT" ]

24

2020-05-24T10:39:24.000Z

2022-03-09T02:38:09.000Z

OneImage2Video/school_badge.py

HypoX64/bilibili

992029667ad37d7d03131aa2c4c9923da6cca6f2

[ "MIT" ]

null

OneImage2Video/school_badge.py

HypoX64/bilibili

992029667ad37d7d03131aa2c4c9923da6cca6f2

[ "MIT" ]

2

2021-03-24T13:54:17.000Z

2021-08-07T12:23:51.000Z

import os import cv2 import numpy as np import sys sys.path.append("..") from Util import util,ffmpeg # 用校徽看badapple imgs_dir = './pixel_imgs/university/base' highlight_dir = './pixel_imgs/university/highlight' background_dir = './pixel_imgs/university/background' cut_size = 79 pixel_resize = 0 # resize pixel_imgs, if 0, do not resize output_pixel_num = 18 # how many pixels in the output video'width video_path = '../Video/素材/bad_apple_bbkkbk/BadApple.flv' change_frame = 2 # ------------------------- Load Blocks ------------------------- pixels = [] img_names = os.listdir(imgs_dir) img_names.sort() for name in img_names: img = cv2.imread(os.path.join(imgs_dir,name)) for h in range(img.shape[0]//cut_size): for w in range(img.shape[1]//cut_size): pixel = img[h*cut_size:(h+1)*cut_size,w*cut_size:(w+1)*cut_size] if pixel_resize != 0: pixel = cv2.resize(pixel,(pixel_resize,pixel_resize),interpolation=cv2.INTER_AREA) pixels.append(pixel) pixel_size = pixels[0].shape[0] # highlight img_names = os.listdir(highlight_dir) img_names.sort() for name in img_names: pixel = cv2.imread(os.path.join(highlight_dir,name)) pixel = cv2.resize(pixel,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) for i in range(10): pixels.append(pixel) pixels = np.array(pixels) # background background_name = os.listdir(background_dir)[0] background = cv2.imread(os.path.join(background_dir,background_name)) background = cv2.resize(background,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) # ------------------------- Prcessing Video ------------------------- fps,endtime,height,width = ffmpeg.get_video_infos(video_path) scale = height/width util.clean_tempfiles(False) util.makedirs('./tmp/vid2img') util.makedirs('./tmp/output_img') ffmpeg.video2image(video_path, './tmp/vid2img/%05d.png') ffmpeg.video2voice(video_path, './tmp/tmp.mp3') # ------------------------- Video2Block ------------------------- print('Video2Block...') img_names = os.listdir('./tmp/vid2img') img_names.sort() frame = 0 for img_name in img_names: img = cv2.imread(os.path.join('./tmp/vid2img',img_name)) img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img = cv2.resize(img, (output_pixel_num,int(output_pixel_num*scale)),interpolation=cv2.INTER_AREA) h,w = img.shape if frame %change_frame == 0: indexs = np.random.randint(0, pixels.shape[0]-1, (h,w)) out_img = np.zeros((h*pixel_size,w*pixel_size,3), dtype = np.uint8) for i in range(h): for j in range(w): #index = np.clip(img[i,j]//level,0,len(pixels)-1) if img[i,j] < 64: out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = pixels[indexs[i,j]] else: out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = background out_img = out_img[:(h*pixel_size//2)*2,:(w*pixel_size//2)*2] cv2.imwrite(os.path.join('./tmp/output_img',img_name), out_img) frame += 1 # ------------------------- Block2Video ------------------------- ffmpeg.image2video(fps, './tmp/output_img/%05d.png', './tmp/tmp.mp3', './result.mp4')

36.363636

106

0.647188

import os import cv2 import numpy as np import sys sys.path.append("..") from Util import util,ffmpeg imgs_dir = './pixel_imgs/university/base' highlight_dir = './pixel_imgs/university/highlight' background_dir = './pixel_imgs/university/background' cut_size = 79 pixel_resize = 0 output_pixel_num = 18 video_path = '../Video/素材/bad_apple_bbkkbk/BadApple.flv' change_frame = 2 # ------------------------- Load Blocks ------------------------- pixels = [] img_names = os.listdir(imgs_dir) img_names.sort() for name in img_names: img = cv2.imread(os.path.join(imgs_dir,name)) for h in range(img.shape[0]//cut_size): for w in range(img.shape[1]//cut_size): pixel = img[h*cut_size:(h+1)*cut_size,w*cut_size:(w+1)*cut_size] if pixel_resize != 0: pixel = cv2.resize(pixel,(pixel_resize,pixel_resize),interpolation=cv2.INTER_AREA) pixels.append(pixel) pixel_size = pixels[0].shape[0] # highlight img_names = os.listdir(highlight_dir) img_names.sort() for name in img_names: pixel = cv2.imread(os.path.join(highlight_dir,name)) pixel = cv2.resize(pixel,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) for i in range(10): pixels.append(pixel) pixels = np.array(pixels) # background background_name = os.listdir(background_dir)[0] background = cv2.imread(os.path.join(background_dir,background_name)) background = cv2.resize(background,(pixel_size,pixel_size),interpolation=cv2.INTER_AREA) # ------------------------- Prcessing Video ------------------------- fps,endtime,height,width = ffmpeg.get_video_infos(video_path) scale = height/width util.clean_tempfiles(False) util.makedirs('./tmp/vid2img') util.makedirs('./tmp/output_img') ffmpeg.video2image(video_path, './tmp/vid2img/%05d.png') ffmpeg.video2voice(video_path, './tmp/tmp.mp3') # ------------------------- Video2Block ------------------------- print('Video2Block...') img_names = os.listdir('./tmp/vid2img') img_names.sort() frame = 0 for img_name in img_names: img = cv2.imread(os.path.join('./tmp/vid2img',img_name)) img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img = cv2.resize(img, (output_pixel_num,int(output_pixel_num*scale)),interpolation=cv2.INTER_AREA) h,w = img.shape if frame %change_frame == 0: indexs = np.random.randint(0, pixels.shape[0]-1, (h,w)) out_img = np.zeros((h*pixel_size,w*pixel_size,3), dtype = np.uint8) for i in range(h): for j in range(w): #index = np.clip(img[i,j]//level,0,len(pixels)-1) if img[i,j] < 64: out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = pixels[indexs[i,j]] else: out_img[i*pixel_size:(i+1)*pixel_size,j*pixel_size:(j+1)*pixel_size] = background out_img = out_img[:(h*pixel_size//2)*2,:(w*pixel_size//2)*2] cv2.imwrite(os.path.join('./tmp/output_img',img_name), out_img) frame += 1 # ------------------------- Block2Video ------------------------- ffmpeg.image2video(fps, './tmp/output_img/%05d.png', './tmp/tmp.mp3', './result.mp4')

true

1c31419028d0e3ca587e782497408e42320f2b43

18,493

py

Python

tests/unittests/storage/test_storage.py

aimar1986bupt/orion

6d217af1f9002aa671f8a3260a687c540ca5336d

[ "BSD-3-Clause" ]

4

2020-03-25T17:44:40.000Z

2020-04-10T13:53:13.000Z

tests/unittests/storage/test_storage.py

aimar1986bupt/orion

6d217af1f9002aa671f8a3260a687c540ca5336d

[ "BSD-3-Clause" ]

2

2018-06-26T19:17:09.000Z

2022-02-23T13:40:04.000Z

tests/unittests/storage/test_storage.py

aimar1986bupt/orion

6d217af1f9002aa671f8a3260a687c540ca5336d

[ "BSD-3-Clause" ]

2

2019-08-26T11:36:47.000Z

2020-04-07T13:05:48.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """Collection of tests for :mod:`orion.storage`.""" import copy import datetime import json import tempfile import pytest from orion.core.io.database import DuplicateKeyError from orion.core.utils.tests import OrionState from orion.core.worker.trial import Trial from orion.storage.base import FailedUpdate, get_storage, MissingArguments storage_backends = [ None, # defaults to legacy with PickleDB ] base_experiment = { 'name': 'default_name', 'version': 0, 'metadata': { 'user': 'default_user', 'user_script': 'abc', 'datetime': '2017-11-23T02:00:00' } } base_trial = { 'experiment': 'default_name', 'status': 'new', # new, reserved, suspended, completed, broken 'worker': None, 'submit_time': '2017-11-23T02:00:00', 'start_time': None, 'end_time': None, 'heartbeat': None, 'results': [ {'name': 'loss', 'type': 'objective', # objective, constraint 'value': 2} ], 'params': [ {'name': '/encoding_layer', 'type': 'categorical', 'value': 'rnn'}, {'name': '/decoding_layer', 'type': 'categorical', 'value': 'lstm_with_attention'} ] } def _generate(obj, *args, value): if obj is None: return None obj = copy.deepcopy(obj) data = obj for arg in args[:-1]: data = data[arg] data[args[-1]] = value return obj def make_lost_trial(): """Make a lost trial""" obj = copy.deepcopy(base_trial) obj['status'] = 'reserved' obj['heartbeat'] = datetime.datetime.utcnow() - datetime.timedelta(seconds=61 * 2) obj['params'].append({ 'name': '/index', 'type': 'categorical', 'value': 'lost_trial' }) return obj all_status = ['completed', 'broken', 'reserved', 'interrupted', 'suspended', 'new'] def generate_trials(status=None): """Generate Trials with different configurations""" if status is None: status = all_status new_trials = [_generate(base_trial, 'status', value=s) for s in status] # make each trial unique for i, trial in enumerate(new_trials): trial['params'].append({ 'name': '/index', 'type': 'categorical', 'value': i }) return new_trials def generate_experiments(): """Generate a set of experiments""" users = ['a', 'b', 'c'] exps = [_generate(base_experiment, 'metadata', 'user', value=u) for u in users] return [_generate(exp, 'name', value=str(i)) for i, exp in enumerate(exps)] @pytest.mark.parametrize('storage', storage_backends) class TestStorage: """Test all storage backend""" def test_create_experiment(self, storage): """Test create experiment""" with OrionState(experiments=[], database=storage) as cfg: storage = cfg.storage() storage.create_experiment(base_experiment) experiments = storage.fetch_experiments({}) assert len(experiments) == 1, 'Only one experiment in the database' experiment = experiments[0] assert base_experiment == experiment, 'Local experiment and DB should match' def test_create_experiment_fail(self, storage): """Test create experiment""" with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.create_experiment(base_experiment) def test_fetch_experiments(self, storage, name='0', user='a'): """Test fetch experiments""" with OrionState(experiments=generate_experiments(), database=storage) as cfg: storage = cfg.storage() experiments = storage.fetch_experiments({}) assert len(experiments) == len(cfg.experiments) experiments = storage.fetch_experiments({'name': name, 'metadata.user': user}) assert len(experiments) == 1 experiment = experiments[0] assert experiment['name'] == name, 'name should match query' assert experiment['metadata']['user'] == user, 'user name should match query' experiments = storage.fetch_experiments({'name': '-1', 'metadata.user': user}) assert len(experiments) == 0 def test_update_experiment(self, monkeypatch, storage, name='0', user='a'): """Test fetch experiments""" with OrionState(experiments=generate_experiments(), database=storage) as cfg: storage = cfg.storage() class _Dummy(): pass experiment = cfg.experiments[0] mocked_experiment = _Dummy() mocked_experiment._id = experiment['_id'] storage.update_experiment(mocked_experiment, test=True) assert storage.fetch_experiments({'_id': experiment['_id']})[0]['test'] assert 'test' not in storage.fetch_experiments({'_id': cfg.experiments[1]['_id']})[0] storage.update_experiment(uid=experiment['_id'], test2=True) assert storage.fetch_experiments({'_id': experiment['_id']})[0]['test2'] assert 'test2' not in storage.fetch_experiments({'_id': cfg.experiments[1]['_id']})[0] with pytest.raises(MissingArguments): storage.update_experiment() with pytest.raises(AssertionError): storage.update_experiment(experiment=mocked_experiment, uid='123') def test_register_trial(self, storage): """Test register trial""" with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() trial1 = storage.register_trial(Trial(**base_trial)) trial2 = storage.get_trial(trial1) assert trial1.to_dict() == trial2.to_dict(), 'Trials should match after insert' def test_register_duplicate_trial(self, storage): """Test register trial""" with OrionState( experiments=[base_experiment], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.register_trial(Trial(**base_trial)) def test_register_lie(self, storage): """Test register lie""" with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() storage.register_lie(Trial(**base_trial)) def test_register_lie_fail(self, storage): """Test register lie""" with OrionState(experiments=[base_experiment], lies=[base_trial], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.register_lie(Trial(**cfg.lies[0])) def test_reserve_trial_success(self, storage): """Test reserve trial""" with OrionState( experiments=[base_experiment], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trial = storage.reserve_trial(experiment) assert trial is not None assert trial.status == 'reserved' def test_reserve_trial_fail(self, storage): """Test reserve trial""" with OrionState( experiments=[base_experiment], trials=generate_trials(status=['completed', 'reserved']), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trial = storage.reserve_trial(experiment) assert trial is None def test_fetch_trials(self, storage): """Test fetch experiment trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials1 = storage.fetch_trials(experiment=experiment) trials2 = storage.fetch_trials(uid=experiment._id) with pytest.raises(MissingArguments): storage.fetch_trials() with pytest.raises(AssertionError): storage.fetch_trials(experiment=experiment, uid='123') assert len(trials1) == len(cfg.trials), 'trial count should match' assert len(trials2) == len(cfg.trials), 'trial count should match' def test_get_trial(self, storage): """Test get trial""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() trial_dict = cfg.trials[0] trial1 = storage.get_trial(trial=Trial(**trial_dict)) trial2 = storage.get_trial(uid=trial1.id) with pytest.raises(MissingArguments): storage.get_trial() with pytest.raises(AssertionError): storage.get_trial(trial=trial1, uid='123') assert trial1.to_dict() == trial_dict assert trial2.to_dict() == trial_dict def test_fetch_lost_trials(self, storage): """Test update heartbeat""" with OrionState(experiments=[base_experiment], trials=generate_trials() + [make_lost_trial()], database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_lost_trials(experiment) assert len(trials) == 1 def retrieve_result(self, storage, generated_result): """Test retrieve result""" results_file = tempfile.NamedTemporaryFile( mode='w', prefix='results_', suffix='.log', dir='.', delete=True ) # Generate fake result with open(results_file.name, 'w') as file: json.dump([generated_result], file) # -- with OrionState(experiments=[], trials=[], database=storage) as cfg: storage = cfg.storage() trial = Trial(**base_trial) trial = storage.retrieve_result(trial, results_file) results = trial.results assert len(results) == 1 assert results[0].to_dict() == generated_result def test_retrieve_result(self, storage): """Test retrieve result""" self.retrieve_result(storage, generated_result={ 'name': 'loss', 'type': 'objective', 'value': 2}) def test_retrieve_result_incorrect_value(self, storage): """Test retrieve result""" with pytest.raises(ValueError) as exec: self.retrieve_result(storage, generated_result={ 'name': 'loss', 'type': 'objective_unsupported_type', 'value': 2}) assert exec.match(r'Given type, objective_unsupported_type') def test_retrieve_result_nofile(self, storage): """Test retrieve result""" results_file = tempfile.NamedTemporaryFile( mode='w', prefix='results_', suffix='.log', dir='.', delete=True ) with OrionState(experiments=[], trials=[], database=storage) as cfg: storage = cfg.storage() trial = Trial(**base_trial) with pytest.raises(json.decoder.JSONDecodeError) as exec: storage.retrieve_result(trial, results_file) assert exec.match(r'Expecting value: line 1 column 1 $char 0$') def test_push_trial_results(self, storage): """Successfully push a completed trial into database.""" with OrionState(experiments=[], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() trial = storage.get_trial(Trial(**base_trial)) results = [ Trial.Result(name='loss', type='objective', value=2) ] trial.results = results assert storage.push_trial_results(trial), 'should update successfully' trial2 = storage.get_trial(trial) assert trial2.results == results def test_change_status_success(self, storage, exp_config_file): """Change the status of a Trial""" def check_status_change(new_status): with OrionState(from_yaml=exp_config_file, database=storage) as cfg: trial = cfg.get_trial(0) assert trial is not None, 'was not able to retrieve trial for test' get_storage().set_trial_status(trial, status=new_status) assert trial.status == new_status, \ 'Trial status should have been updated locally' trial = get_storage().get_trial(trial) assert trial.status == new_status, \ 'Trial status should have been updated in the storage' check_status_change('completed') check_status_change('broken') check_status_change('reserved') check_status_change('interrupted') check_status_change('suspended') check_status_change('new') def test_change_status_failed_update(self, storage, exp_config_file): """Successfully find new trials in db and reserve one at 'random'.""" def check_status_change(new_status): with OrionState(from_yaml=exp_config_file, database=storage) as cfg: trial = cfg.get_trial(1) assert trial is not None, 'Was not able to retrieve trial for test' assert trial.status != new_status with pytest.raises(FailedUpdate): trial.status = new_status get_storage().set_trial_status(trial, status=new_status) check_status_change('completed') check_status_change('broken') check_status_change('reserved') check_status_change('interrupted') check_status_change('suspended') def test_fetch_pending_trials(self, storage): """Test fetch pending trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_pending_trials(experiment) count = 0 for trial in cfg.trials: if trial['status'] in {'new', 'suspended', 'interrupted'}: count += 1 assert len(trials) == count for trial in trials: assert trial.status in {'new', 'suspended', 'interrupted'} def test_fetch_noncompleted_trials(self, storage): """Test fetch non completed trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_noncompleted_trials(experiment) count = 0 for trial in cfg.trials: if trial['status'] != 'completed': count += 1 assert len(trials) == count for trial in trials: assert trial.status != 'completed' def test_fetch_trial_by_status(self, storage): """Test fetch completed trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'completed': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_trial_by_status(experiment, 'completed') assert len(trials) == count for trial in trials: assert trial.status == 'completed', trial def test_count_completed_trials(self, storage): """Test count completed trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'completed': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.count_completed_trials(experiment) assert trials == count def test_count_broken_trials(self, storage): """Test count broken trials""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'broken': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.count_broken_trials(experiment) assert trials == count def test_update_heartbeat(self, storage): """Test update heartbeat""" with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage_name = storage storage = cfg.storage() exp = cfg.get_experiment(name='default_name') trial1 = storage.fetch_trial_by_status(exp, status='reserved')[0] storage.update_heartbeat(trial1) trial2 = storage.get_trial(trial1) assert trial1.heartbeat is None assert trial2.heartbeat is not None # this checks that heartbeat is the correct type and that it was updated prior to now assert trial2.heartbeat < datetime.datetime.utcnow() if storage_name is None: trial3 = storage.fetch_trial_by_status(exp, status='completed')[0] storage.update_heartbeat(trial3) assert trial3.heartbeat is None, \ 'Legacy does not update trials with a status different from reserved'

36.912176

99

0.607689

import copy import datetime import json import tempfile import pytest from orion.core.io.database import DuplicateKeyError from orion.core.utils.tests import OrionState from orion.core.worker.trial import Trial from orion.storage.base import FailedUpdate, get_storage, MissingArguments storage_backends = [ None, ] base_experiment = { 'name': 'default_name', 'version': 0, 'metadata': { 'user': 'default_user', 'user_script': 'abc', 'datetime': '2017-11-23T02:00:00' } } base_trial = { 'experiment': 'default_name', 'status': 'new', 'worker': None, 'submit_time': '2017-11-23T02:00:00', 'start_time': None, 'end_time': None, 'heartbeat': None, 'results': [ {'name': 'loss', 'type': 'objective', 'value': 2} ], 'params': [ {'name': '/encoding_layer', 'type': 'categorical', 'value': 'rnn'}, {'name': '/decoding_layer', 'type': 'categorical', 'value': 'lstm_with_attention'} ] } def _generate(obj, *args, value): if obj is None: return None obj = copy.deepcopy(obj) data = obj for arg in args[:-1]: data = data[arg] data[args[-1]] = value return obj def make_lost_trial(): obj = copy.deepcopy(base_trial) obj['status'] = 'reserved' obj['heartbeat'] = datetime.datetime.utcnow() - datetime.timedelta(seconds=61 * 2) obj['params'].append({ 'name': '/index', 'type': 'categorical', 'value': 'lost_trial' }) return obj all_status = ['completed', 'broken', 'reserved', 'interrupted', 'suspended', 'new'] def generate_trials(status=None): if status is None: status = all_status new_trials = [_generate(base_trial, 'status', value=s) for s in status] for i, trial in enumerate(new_trials): trial['params'].append({ 'name': '/index', 'type': 'categorical', 'value': i }) return new_trials def generate_experiments(): users = ['a', 'b', 'c'] exps = [_generate(base_experiment, 'metadata', 'user', value=u) for u in users] return [_generate(exp, 'name', value=str(i)) for i, exp in enumerate(exps)] @pytest.mark.parametrize('storage', storage_backends) class TestStorage: def test_create_experiment(self, storage): with OrionState(experiments=[], database=storage) as cfg: storage = cfg.storage() storage.create_experiment(base_experiment) experiments = storage.fetch_experiments({}) assert len(experiments) == 1, 'Only one experiment in the database' experiment = experiments[0] assert base_experiment == experiment, 'Local experiment and DB should match' def test_create_experiment_fail(self, storage): with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.create_experiment(base_experiment) def test_fetch_experiments(self, storage, name='0', user='a'): with OrionState(experiments=generate_experiments(), database=storage) as cfg: storage = cfg.storage() experiments = storage.fetch_experiments({}) assert len(experiments) == len(cfg.experiments) experiments = storage.fetch_experiments({'name': name, 'metadata.user': user}) assert len(experiments) == 1 experiment = experiments[0] assert experiment['name'] == name, 'name should match query' assert experiment['metadata']['user'] == user, 'user name should match query' experiments = storage.fetch_experiments({'name': '-1', 'metadata.user': user}) assert len(experiments) == 0 def test_update_experiment(self, monkeypatch, storage, name='0', user='a'): with OrionState(experiments=generate_experiments(), database=storage) as cfg: storage = cfg.storage() class _Dummy(): pass experiment = cfg.experiments[0] mocked_experiment = _Dummy() mocked_experiment._id = experiment['_id'] storage.update_experiment(mocked_experiment, test=True) assert storage.fetch_experiments({'_id': experiment['_id']})[0]['test'] assert 'test' not in storage.fetch_experiments({'_id': cfg.experiments[1]['_id']})[0] storage.update_experiment(uid=experiment['_id'], test2=True) assert storage.fetch_experiments({'_id': experiment['_id']})[0]['test2'] assert 'test2' not in storage.fetch_experiments({'_id': cfg.experiments[1]['_id']})[0] with pytest.raises(MissingArguments): storage.update_experiment() with pytest.raises(AssertionError): storage.update_experiment(experiment=mocked_experiment, uid='123') def test_register_trial(self, storage): with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() trial1 = storage.register_trial(Trial(**base_trial)) trial2 = storage.get_trial(trial1) assert trial1.to_dict() == trial2.to_dict(), 'Trials should match after insert' def test_register_duplicate_trial(self, storage): with OrionState( experiments=[base_experiment], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.register_trial(Trial(**base_trial)) def test_register_lie(self, storage): with OrionState(experiments=[base_experiment], database=storage) as cfg: storage = cfg.storage() storage.register_lie(Trial(**base_trial)) def test_register_lie_fail(self, storage): with OrionState(experiments=[base_experiment], lies=[base_trial], database=storage) as cfg: storage = cfg.storage() with pytest.raises(DuplicateKeyError): storage.register_lie(Trial(**cfg.lies[0])) def test_reserve_trial_success(self, storage): with OrionState( experiments=[base_experiment], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trial = storage.reserve_trial(experiment) assert trial is not None assert trial.status == 'reserved' def test_reserve_trial_fail(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(status=['completed', 'reserved']), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trial = storage.reserve_trial(experiment) assert trial is None def test_fetch_trials(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials1 = storage.fetch_trials(experiment=experiment) trials2 = storage.fetch_trials(uid=experiment._id) with pytest.raises(MissingArguments): storage.fetch_trials() with pytest.raises(AssertionError): storage.fetch_trials(experiment=experiment, uid='123') assert len(trials1) == len(cfg.trials), 'trial count should match' assert len(trials2) == len(cfg.trials), 'trial count should match' def test_get_trial(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() trial_dict = cfg.trials[0] trial1 = storage.get_trial(trial=Trial(**trial_dict)) trial2 = storage.get_trial(uid=trial1.id) with pytest.raises(MissingArguments): storage.get_trial() with pytest.raises(AssertionError): storage.get_trial(trial=trial1, uid='123') assert trial1.to_dict() == trial_dict assert trial2.to_dict() == trial_dict def test_fetch_lost_trials(self, storage): with OrionState(experiments=[base_experiment], trials=generate_trials() + [make_lost_trial()], database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_lost_trials(experiment) assert len(trials) == 1 def retrieve_result(self, storage, generated_result): results_file = tempfile.NamedTemporaryFile( mode='w', prefix='results_', suffix='.log', dir='.', delete=True ) with open(results_file.name, 'w') as file: json.dump([generated_result], file) with OrionState(experiments=[], trials=[], database=storage) as cfg: storage = cfg.storage() trial = Trial(**base_trial) trial = storage.retrieve_result(trial, results_file) results = trial.results assert len(results) == 1 assert results[0].to_dict() == generated_result def test_retrieve_result(self, storage): self.retrieve_result(storage, generated_result={ 'name': 'loss', 'type': 'objective', 'value': 2}) def test_retrieve_result_incorrect_value(self, storage): with pytest.raises(ValueError) as exec: self.retrieve_result(storage, generated_result={ 'name': 'loss', 'type': 'objective_unsupported_type', 'value': 2}) assert exec.match(r'Given type, objective_unsupported_type') def test_retrieve_result_nofile(self, storage): results_file = tempfile.NamedTemporaryFile( mode='w', prefix='results_', suffix='.log', dir='.', delete=True ) with OrionState(experiments=[], trials=[], database=storage) as cfg: storage = cfg.storage() trial = Trial(**base_trial) with pytest.raises(json.decoder.JSONDecodeError) as exec: storage.retrieve_result(trial, results_file) assert exec.match(r'Expecting value: line 1 column 1 $char 0$') def test_push_trial_results(self, storage): with OrionState(experiments=[], trials=[base_trial], database=storage) as cfg: storage = cfg.storage() trial = storage.get_trial(Trial(**base_trial)) results = [ Trial.Result(name='loss', type='objective', value=2) ] trial.results = results assert storage.push_trial_results(trial), 'should update successfully' trial2 = storage.get_trial(trial) assert trial2.results == results def test_change_status_success(self, storage, exp_config_file): def check_status_change(new_status): with OrionState(from_yaml=exp_config_file, database=storage) as cfg: trial = cfg.get_trial(0) assert trial is not None, 'was not able to retrieve trial for test' get_storage().set_trial_status(trial, status=new_status) assert trial.status == new_status, \ 'Trial status should have been updated locally' trial = get_storage().get_trial(trial) assert trial.status == new_status, \ 'Trial status should have been updated in the storage' check_status_change('completed') check_status_change('broken') check_status_change('reserved') check_status_change('interrupted') check_status_change('suspended') check_status_change('new') def test_change_status_failed_update(self, storage, exp_config_file): def check_status_change(new_status): with OrionState(from_yaml=exp_config_file, database=storage) as cfg: trial = cfg.get_trial(1) assert trial is not None, 'Was not able to retrieve trial for test' assert trial.status != new_status with pytest.raises(FailedUpdate): trial.status = new_status get_storage().set_trial_status(trial, status=new_status) check_status_change('completed') check_status_change('broken') check_status_change('reserved') check_status_change('interrupted') check_status_change('suspended') def test_fetch_pending_trials(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_pending_trials(experiment) count = 0 for trial in cfg.trials: if trial['status'] in {'new', 'suspended', 'interrupted'}: count += 1 assert len(trials) == count for trial in trials: assert trial.status in {'new', 'suspended', 'interrupted'} def test_fetch_noncompleted_trials(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_noncompleted_trials(experiment) count = 0 for trial in cfg.trials: if trial['status'] != 'completed': count += 1 assert len(trials) == count for trial in trials: assert trial.status != 'completed' def test_fetch_trial_by_status(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'completed': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.fetch_trial_by_status(experiment, 'completed') assert len(trials) == count for trial in trials: assert trial.status == 'completed', trial def test_count_completed_trials(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'completed': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.count_completed_trials(experiment) assert trials == count def test_count_broken_trials(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: count = 0 for trial in cfg.trials: if trial['status'] == 'broken': count += 1 storage = cfg.storage() experiment = cfg.get_experiment('default_name', 'default_user', version=None) trials = storage.count_broken_trials(experiment) assert trials == count def test_update_heartbeat(self, storage): with OrionState( experiments=[base_experiment], trials=generate_trials(), database=storage) as cfg: storage_name = storage storage = cfg.storage() exp = cfg.get_experiment(name='default_name') trial1 = storage.fetch_trial_by_status(exp, status='reserved')[0] storage.update_heartbeat(trial1) trial2 = storage.get_trial(trial1) assert trial1.heartbeat is None assert trial2.heartbeat is not None assert trial2.heartbeat < datetime.datetime.utcnow() if storage_name is None: trial3 = storage.fetch_trial_by_status(exp, status='completed')[0] storage.update_heartbeat(trial3) assert trial3.heartbeat is None, \ 'Legacy does not update trials with a status different from reserved'

true

1c31447e628621eb59455f56b1ff028fed8377fa

7,091

py

Python

adanet/core/report_accessor_test.py

intruder1912/adanet

dfa2f0acc253d1de193aaa795b5559bc471f9ed8

[ "Apache-2.0" ]

1

2018-11-02T04:57:02.000Z

adanet/core/report_accessor_test.py

oz99999/adanet

69354c4e961defca790a1ce0e042251dfbe4f410

[ "Apache-2.0" ]

null

adanet/core/report_accessor_test.py

oz99999/adanet

69354c4e961defca790a1ce0e042251dfbe4f410

[ "Apache-2.0" ]

1

2021-12-14T08:18:17.000Z

"""Tests for run_report_accessor.py. Copyright 2018 The AdaNet 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 https://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from adanet.core import subnetwork from adanet.core.report_accessor import _ReportAccessor import tensorflow as tf class ReportAccessorTest(tf.test.TestCase): def test_read_from_empty_file(self): report_accessor = _ReportAccessor(self.get_temp_dir()) self.assertEqual([], list(report_accessor.read_iteration_reports())) def test_add_to_empty_file(self): report_accessor = _ReportAccessor(self.get_temp_dir()) materialized_reports = [ subnetwork.MaterializedReport( iteration_number=0, name="foo", hparams={ "p1": 1, "p2": "hoo", "p3": True, }, attributes={ "a1": 1, "a2": "aoo", "a3": True, }, metrics={ "m1": 1, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ] report_accessor.write_iteration_report( iteration_number=0, materialized_reports=materialized_reports, ) actual_iteration_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(1, len(actual_iteration_reports)) self.assertEqual(materialized_reports, actual_iteration_reports[0]) def test_add_to_existing_file(self): materialized_reports = [ [ subnetwork.MaterializedReport( iteration_number=0, name="foo1", hparams={ "p1": 11, "p2": "hoo", "p3": True, }, attributes={ "a1": 11, "a2": "aoo", "a3": True, }, metrics={ "m1": 11, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), subnetwork.MaterializedReport( iteration_number=0, name="foo2", hparams={ "p1": 12, "p2": "hoo", "p3": True, }, attributes={ "a1": 12, "a2": "aoo", "a3": True, }, metrics={ "m1": 12, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ], [ subnetwork.MaterializedReport( iteration_number=1, name="foo1", hparams={ "p1": 21, "p2": "hoo", "p3": True, }, attributes={ "a1": 21, "a2": "aoo", "a3": True, }, metrics={ "m1": 21, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), subnetwork.MaterializedReport( iteration_number=1, name="foo2", hparams={ "p1": 22, "p2": "hoo", "p3": True, }, attributes={ "a1": 22, "a2": "aoo", "a3": True, }, metrics={ "m1": 22, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), ], [ subnetwork.MaterializedReport( iteration_number=2, name="foo1", hparams={ "p1": 31, "p2": "hoo", "p3": True, }, attributes={ "a1": 31, "a2": "aoo", "a3": True, }, metrics={ "m1": 31, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), subnetwork.MaterializedReport( iteration_number=2, name="foo2", hparams={ "p1": 32, "p2": "hoo", "p3": True, }, attributes={ "a1": 32, "a2": "aoo", "a3": True, }, metrics={ "m1": 32, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ], ] report_accessor = _ReportAccessor(self.get_temp_dir()) report_accessor.write_iteration_report(0, materialized_reports[0]) report_accessor.write_iteration_report(1, materialized_reports[1]) report_accessor.write_iteration_report(2, materialized_reports[2]) actual_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(materialized_reports, actual_reports) def test_write_iteration_report_encoding(self): """Tests GitHub issue #4.""" report_accessor = _ReportAccessor(self.get_temp_dir()) bytes_value = b"\n\x83\x01\n;adanet/iteration_2/ensemble_2_layer_dnn/" materialized_reports = [ subnetwork.MaterializedReport( iteration_number=0, name="foo", hparams={ "p2": bytes_value, }, attributes={ "a2": bytes_value, }, metrics={ "m2": bytes_value, }, included_in_final_ensemble=True, ), ] report_accessor.write_iteration_report( iteration_number=0, materialized_reports=materialized_reports, ) actual_iteration_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(1, len(actual_iteration_reports)) if __name__ == "__main__": tf.test.main()

29.919831

77

0.453674

from __future__ import absolute_import from __future__ import division from __future__ import print_function from adanet.core import subnetwork from adanet.core.report_accessor import _ReportAccessor import tensorflow as tf class ReportAccessorTest(tf.test.TestCase): def test_read_from_empty_file(self): report_accessor = _ReportAccessor(self.get_temp_dir()) self.assertEqual([], list(report_accessor.read_iteration_reports())) def test_add_to_empty_file(self): report_accessor = _ReportAccessor(self.get_temp_dir()) materialized_reports = [ subnetwork.MaterializedReport( iteration_number=0, name="foo", hparams={ "p1": 1, "p2": "hoo", "p3": True, }, attributes={ "a1": 1, "a2": "aoo", "a3": True, }, metrics={ "m1": 1, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ] report_accessor.write_iteration_report( iteration_number=0, materialized_reports=materialized_reports, ) actual_iteration_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(1, len(actual_iteration_reports)) self.assertEqual(materialized_reports, actual_iteration_reports[0]) def test_add_to_existing_file(self): materialized_reports = [ [ subnetwork.MaterializedReport( iteration_number=0, name="foo1", hparams={ "p1": 11, "p2": "hoo", "p3": True, }, attributes={ "a1": 11, "a2": "aoo", "a3": True, }, metrics={ "m1": 11, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), subnetwork.MaterializedReport( iteration_number=0, name="foo2", hparams={ "p1": 12, "p2": "hoo", "p3": True, }, attributes={ "a1": 12, "a2": "aoo", "a3": True, }, metrics={ "m1": 12, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ], [ subnetwork.MaterializedReport( iteration_number=1, name="foo1", hparams={ "p1": 21, "p2": "hoo", "p3": True, }, attributes={ "a1": 21, "a2": "aoo", "a3": True, }, metrics={ "m1": 21, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), subnetwork.MaterializedReport( iteration_number=1, name="foo2", hparams={ "p1": 22, "p2": "hoo", "p3": True, }, attributes={ "a1": 22, "a2": "aoo", "a3": True, }, metrics={ "m1": 22, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), ], [ subnetwork.MaterializedReport( iteration_number=2, name="foo1", hparams={ "p1": 31, "p2": "hoo", "p3": True, }, attributes={ "a1": 31, "a2": "aoo", "a3": True, }, metrics={ "m1": 31, "m2": "moo", "m3": True, }, included_in_final_ensemble=False, ), subnetwork.MaterializedReport( iteration_number=2, name="foo2", hparams={ "p1": 32, "p2": "hoo", "p3": True, }, attributes={ "a1": 32, "a2": "aoo", "a3": True, }, metrics={ "m1": 32, "m2": "moo", "m3": True, }, included_in_final_ensemble=True, ), ], ] report_accessor = _ReportAccessor(self.get_temp_dir()) report_accessor.write_iteration_report(0, materialized_reports[0]) report_accessor.write_iteration_report(1, materialized_reports[1]) report_accessor.write_iteration_report(2, materialized_reports[2]) actual_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(materialized_reports, actual_reports) def test_write_iteration_report_encoding(self): report_accessor = _ReportAccessor(self.get_temp_dir()) bytes_value = b"\n\x83\x01\n;adanet/iteration_2/ensemble_2_layer_dnn/" materialized_reports = [ subnetwork.MaterializedReport( iteration_number=0, name="foo", hparams={ "p2": bytes_value, }, attributes={ "a2": bytes_value, }, metrics={ "m2": bytes_value, }, included_in_final_ensemble=True, ), ] report_accessor.write_iteration_report( iteration_number=0, materialized_reports=materialized_reports, ) actual_iteration_reports = list(report_accessor.read_iteration_reports()) self.assertEqual(1, len(actual_iteration_reports)) if __name__ == "__main__": tf.test.main()

true

1c3145f61b2b8516ef38e743c7ccb56f655d667a

2,816

py

Python

video_statistics_data.py

MrSipahi/Youtube_Statistics_Data

33a08fd334a9c00139727fe6cfa6b0bc95604eba

[ "MIT" ]

null

video_statistics_data.py

MrSipahi/Youtube_Statistics_Data

33a08fd334a9c00139727fe6cfa6b0bc95604eba

[ "MIT" ]

null

video_statistics_data.py

MrSipahi/Youtube_Statistics_Data

33a08fd334a9c00139727fe6cfa6b0bc95604eba

[ "MIT" ]

null

import urllib import urllib3 import requests import json from datetime import datetime import locale import pymysql as MySQLdb db = MySQLdb.connect("ip","user","password","db_names" ) cursor = db.cursor() # keys = ["API_KEYS"] key_numara = 0 API_KEY = keys[key_numara] query="SELECT * FROM kanal" cursor.execute(query) kanallar = cursor.fetchall() kanal_list=[] for i in kanallar: kanal_list.append(i[0]) locale.setlocale(locale.LC_ALL, "") moment = datetime.now() toplam=1 def veri_cek(metadata,toplam,API_KEY): # Here the videoID is printed try: SpecificVideoID = metadata SpecificVideoUrl = 'https://www.googleapis.com/youtube/v3/videos?part=snippet%2CcontentDetails%2Cstatistics&id='+SpecificVideoID+'&key='+API_KEY response = urllib.request.urlopen(SpecificVideoUrl) #makes the call to a specific YouTube except Exception as e: print(e) return 1 videos = json.load(response) for video in videos['items']: if video['kind'] == 'youtube#video': try: ad = video["snippet"]["title"] ad = ad.replace("'","-") goruntulenme= video['statistics']['viewCount'] begenme = video["statistics"]["likeCount"] begenmeme=video["statistics"]["dislikeCount"] yorum = video['statistics']['commentCount'] a = video['snippet']['publishedAt'] b = a.split("T") c = b[1].split(".") d = c[0].split("Z") yuklenme_tarihi = b[0] yuklenme_saati = d[0] tarih = moment.strftime("%Y-%m-%d") query = f"insert into data(videoID,kanal_ID,ad,goruntulenme,begenme,begenmeme,yorum,yuklenme_tarihi,yuklenme_saati,tarih) values ('{metadata}','{i}','{ad}',{goruntulenme},{begenme},{begenmeme},{yorum},'{yuklenme_tarihi}','{yuklenme_saati}','{tarih}')" cursor.execute(query) db.commit() except Exception as a: print(a) continue print(f"Toplam= {toplam}") toplam = toplam + 1 for i in kanal_list: videoMetadata=[] query=f"SELECT DISTINCT videoID FROM videoliste where kanal_ID= '{i}' " cursor.execute(query) for j in cursor.fetchall(): videoid = videoMetadata.append(j[0]) a = veri_cek(j[0],toplam,keys[key_numara]) if a==1: key_numara += 1 if key_numara == 11: key_numara = 0 API_KEY = keys[key_numara] veri_cek(j[0],toplam,keys[key_numara]) cursor.close() db.commit() db.close()

30.945055

268

0.559304

import urllib import urllib3 import requests import json from datetime import datetime import locale import pymysql as MySQLdb db = MySQLdb.connect("ip","user","password","db_names" ) cursor = db.cursor() keys = ["API_KEYS"] key_numara = 0 API_KEY = keys[key_numara] query="SELECT * FROM kanal" cursor.execute(query) kanallar = cursor.fetchall() kanal_list=[] for i in kanallar: kanal_list.append(i[0]) locale.setlocale(locale.LC_ALL, "") moment = datetime.now() toplam=1 def veri_cek(metadata,toplam,API_KEY): try: SpecificVideoID = metadata SpecificVideoUrl = 'https://www.googleapis.com/youtube/v3/videos?part=snippet%2CcontentDetails%2Cstatistics&id='+SpecificVideoID+'&key='+API_KEY response = urllib.request.urlopen(SpecificVideoUrl) except Exception as e: print(e) return 1 videos = json.load(response) for video in videos['items']: if video['kind'] == 'youtube#video': try: ad = video["snippet"]["title"] ad = ad.replace("'","-") goruntulenme= video['statistics']['viewCount'] begenme = video["statistics"]["likeCount"] begenmeme=video["statistics"]["dislikeCount"] yorum = video['statistics']['commentCount'] a = video['snippet']['publishedAt'] b = a.split("T") c = b[1].split(".") d = c[0].split("Z") yuklenme_tarihi = b[0] yuklenme_saati = d[0] tarih = moment.strftime("%Y-%m-%d") query = f"insert into data(videoID,kanal_ID,ad,goruntulenme,begenme,begenmeme,yorum,yuklenme_tarihi,yuklenme_saati,tarih) values ('{metadata}','{i}','{ad}',{goruntulenme},{begenme},{begenmeme},{yorum},'{yuklenme_tarihi}','{yuklenme_saati}','{tarih}')" cursor.execute(query) db.commit() except Exception as a: print(a) continue print(f"Toplam= {toplam}") toplam = toplam + 1 for i in kanal_list: videoMetadata=[] query=f"SELECT DISTINCT videoID FROM videoliste where kanal_ID= '{i}' " cursor.execute(query) for j in cursor.fetchall(): videoid = videoMetadata.append(j[0]) a = veri_cek(j[0],toplam,keys[key_numara]) if a==1: key_numara += 1 if key_numara == 11: key_numara = 0 API_KEY = keys[key_numara] veri_cek(j[0],toplam,keys[key_numara]) cursor.close() db.commit() db.close()

true

1c3146ee467192cfaa82f4f675c16f5fe535c5b7

9,364

py

Python

awx/main/utils/formatters.py

dvaerum/awx

eeab4b90a55864c9c80882e25780a914398b9e51

[ "Apache-2.0" ]

1

2020-04-10T21:29:52.000Z

awx/main/utils/formatters.py

dvaerum/awx

eeab4b90a55864c9c80882e25780a914398b9e51

[ "Apache-2.0" ]

null

awx/main/utils/formatters.py

dvaerum/awx

eeab4b90a55864c9c80882e25780a914398b9e51

[ "Apache-2.0" ]

null

# Copyright (c) 2017 Ansible Tower by Red Hat # All Rights Reserved. from copy import copy import json import logging import traceback import socket from datetime import datetime from dateutil.tz import tzutc from django.core.serializers.json import DjangoJSONEncoder from django.conf import settings class TimeFormatter(logging.Formatter): ''' Custom log formatter used for inventory imports ''' def format(self, record): record.relativeSeconds = record.relativeCreated / 1000.0 return logging.Formatter.format(self, record) class LogstashFormatterBase(logging.Formatter): """Base class taken from python-logstash=0.4.6 modified here since that version For compliance purposes, this was the license at the point of divergence: The MIT License (MIT) Copyright (c) 2013, Volodymyr Klochan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ def __init__(self, message_type='Logstash', fqdn=False): self.message_type = message_type if fqdn: self.host = socket.getfqdn() else: self.host = socket.gethostname() def get_extra_fields(self, record): # The list contains all the attributes listed in # http://docs.python.org/library/logging.html#logrecord-attributes skip_list = ( 'args', 'asctime', 'created', 'exc_info', 'exc_text', 'filename', 'funcName', 'id', 'levelname', 'levelno', 'lineno', 'module', 'msecs', 'msecs', 'message', 'msg', 'name', 'pathname', 'process', 'processName', 'relativeCreated', 'thread', 'threadName', 'extra') easy_types = (str, bool, dict, float, int, list, type(None)) fields = {} for key, value in record.__dict__.items(): if key not in skip_list: if isinstance(value, easy_types): fields[key] = value else: fields[key] = repr(value) return fields def get_debug_fields(self, record): return { 'stack_trace': self.format_exception(record.exc_info), 'lineno': record.lineno, 'process': record.process, 'thread_name': record.threadName, 'funcName': record.funcName, 'processName': record.processName, } @classmethod def format_exception(cls, exc_info): return ''.join(traceback.format_exception(*exc_info)) if exc_info else '' @classmethod def serialize(cls, message): return bytes(json.dumps(message, cls=DjangoJSONEncoder), 'utf-8') class LogstashFormatter(LogstashFormatterBase): def __init__(self, *args, **kwargs): self.cluster_host_id = settings.CLUSTER_HOST_ID self.tower_uuid = None uuid = ( getattr(settings, 'LOG_AGGREGATOR_TOWER_UUID', None) or getattr(settings, 'INSTALL_UUID', None) ) if uuid: self.tower_uuid = uuid super(LogstashFormatter, self).__init__(*args, **kwargs) def reformat_data_for_log(self, raw_data, kind=None): ''' Process dictionaries from various contexts (job events, activity stream changes, etc.) to give meaningful information Output a dictionary which will be passed in logstash or syslog format to the logging receiver ''' if kind == 'activity_stream': try: raw_data['changes'] = json.loads(raw_data.get('changes', '{}')) except Exception: pass # best effort here, if it's not valid JSON, then meh return raw_data elif kind == 'system_tracking': data = copy(raw_data['ansible_facts']) else: data = copy(raw_data) if isinstance(data, str): data = json.loads(data) data_for_log = {} if kind == 'job_events': job_event = raw_data['python_objects']['job_event'] for field_object in job_event._meta.fields: if not field_object.__class__ or not field_object.__class__.__name__: field_class_name = '' else: field_class_name = field_object.__class__.__name__ if field_class_name in ['ManyToOneRel', 'ManyToManyField']: continue fd = field_object.name key = fd if field_class_name == 'ForeignKey': fd = '{}_id'.format(field_object.name) try: data_for_log[key] = getattr(job_event, fd) except Exception as e: data_for_log[key] = 'Exception `{}` producing field'.format(e) data_for_log['event_display'] = job_event.get_event_display2() if hasattr(job_event, 'workflow_job_id'): data_for_log['workflow_job_id'] = job_event.workflow_job_id elif kind == 'system_tracking': data.pop('ansible_python_version', None) if 'ansible_python' in data: data['ansible_python'].pop('version_info', None) data_for_log['ansible_facts'] = data data_for_log['ansible_facts_modified'] = raw_data['ansible_facts_modified'] data_for_log['inventory_id'] = raw_data['inventory_id'] data_for_log['host_name'] = raw_data['host_name'] data_for_log['job_id'] = raw_data['job_id'] elif kind == 'performance': def convert_to_type(t, val): if t is float: val = val[:-1] if val.endswith('s') else val try: return float(val) except ValueError: return val elif t is int: try: return int(val) except ValueError: return val elif t is str: return val request = raw_data['python_objects']['request'] response = raw_data['python_objects']['response'] # Note: All of the below keys may not be in the response "dict" # For example, X-API-Query-Time and X-API-Query-Count will only # exist if SQL_DEBUG is turned on in settings. headers = [ (float, 'X-API-Time'), # may end with an 's' "0.33s" (float, 'X-API-Total-Time'), (int, 'X-API-Query-Count'), (float, 'X-API-Query-Time'), # may also end with an 's' (str, 'X-API-Node'), ] data_for_log['x_api'] = {k: convert_to_type(t, response[k]) for (t, k) in headers if k in response} data_for_log['request'] = { 'method': request.method, 'path': request.path, 'path_info': request.path_info, 'query_string': request.META['QUERY_STRING'], } if hasattr(request, 'data'): data_for_log['request']['data'] = request.data return data_for_log def get_extra_fields(self, record): fields = super(LogstashFormatter, self).get_extra_fields(record) if record.name.startswith('awx.analytics'): log_kind = record.name[len('awx.analytics.'):] fields = self.reformat_data_for_log(fields, kind=log_kind) # General AWX metadata fields['cluster_host_id'] = self.cluster_host_id fields['tower_uuid'] = self.tower_uuid return fields def format(self, record): stamp = datetime.utcfromtimestamp(record.created) stamp = stamp.replace(tzinfo=tzutc()) message = { # Field not included, but exist in related logs # 'path': record.pathname '@timestamp': stamp, 'message': record.getMessage(), 'host': self.host, # Extra Fields 'level': record.levelname, 'logger_name': record.name, } # Add extra fields message.update(self.get_extra_fields(record)) # If exception, add debug info if record.exc_info: message.update(self.get_debug_fields(record)) return self.serialize(message)

37.758065

111

0.595686

from copy import copy import json import logging import traceback import socket from datetime import datetime from dateutil.tz import tzutc from django.core.serializers.json import DjangoJSONEncoder from django.conf import settings class TimeFormatter(logging.Formatter): def format(self, record): record.relativeSeconds = record.relativeCreated / 1000.0 return logging.Formatter.format(self, record) class LogstashFormatterBase(logging.Formatter): def __init__(self, message_type='Logstash', fqdn=False): self.message_type = message_type if fqdn: self.host = socket.getfqdn() else: self.host = socket.gethostname() def get_extra_fields(self, record): ( 'args', 'asctime', 'created', 'exc_info', 'exc_text', 'filename', 'funcName', 'id', 'levelname', 'levelno', 'lineno', 'module', 'msecs', 'msecs', 'message', 'msg', 'name', 'pathname', 'process', 'processName', 'relativeCreated', 'thread', 'threadName', 'extra') easy_types = (str, bool, dict, float, int, list, type(None)) fields = {} for key, value in record.__dict__.items(): if key not in skip_list: if isinstance(value, easy_types): fields[key] = value else: fields[key] = repr(value) return fields def get_debug_fields(self, record): return { 'stack_trace': self.format_exception(record.exc_info), 'lineno': record.lineno, 'process': record.process, 'thread_name': record.threadName, 'funcName': record.funcName, 'processName': record.processName, } @classmethod def format_exception(cls, exc_info): return ''.join(traceback.format_exception(*exc_info)) if exc_info else '' @classmethod def serialize(cls, message): return bytes(json.dumps(message, cls=DjangoJSONEncoder), 'utf-8') class LogstashFormatter(LogstashFormatterBase): def __init__(self, *args, **kwargs): self.cluster_host_id = settings.CLUSTER_HOST_ID self.tower_uuid = None uuid = ( getattr(settings, 'LOG_AGGREGATOR_TOWER_UUID', None) or getattr(settings, 'INSTALL_UUID', None) ) if uuid: self.tower_uuid = uuid super(LogstashFormatter, self).__init__(*args, **kwargs) def reformat_data_for_log(self, raw_data, kind=None): if kind == 'activity_stream': try: raw_data['changes'] = json.loads(raw_data.get('changes', '{}')) except Exception: pass return raw_data elif kind == 'system_tracking': data = copy(raw_data['ansible_facts']) else: data = copy(raw_data) if isinstance(data, str): data = json.loads(data) data_for_log = {} if kind == 'job_events': job_event = raw_data['python_objects']['job_event'] for field_object in job_event._meta.fields: if not field_object.__class__ or not field_object.__class__.__name__: field_class_name = '' else: field_class_name = field_object.__class__.__name__ if field_class_name in ['ManyToOneRel', 'ManyToManyField']: continue fd = field_object.name key = fd if field_class_name == 'ForeignKey': fd = '{}_id'.format(field_object.name) try: data_for_log[key] = getattr(job_event, fd) except Exception as e: data_for_log[key] = 'Exception `{}` producing field'.format(e) data_for_log['event_display'] = job_event.get_event_display2() if hasattr(job_event, 'workflow_job_id'): data_for_log['workflow_job_id'] = job_event.workflow_job_id elif kind == 'system_tracking': data.pop('ansible_python_version', None) if 'ansible_python' in data: data['ansible_python'].pop('version_info', None) data_for_log['ansible_facts'] = data data_for_log['ansible_facts_modified'] = raw_data['ansible_facts_modified'] data_for_log['inventory_id'] = raw_data['inventory_id'] data_for_log['host_name'] = raw_data['host_name'] data_for_log['job_id'] = raw_data['job_id'] elif kind == 'performance': def convert_to_type(t, val): if t is float: val = val[:-1] if val.endswith('s') else val try: return float(val) except ValueError: return val elif t is int: try: return int(val) except ValueError: return val elif t is str: return val request = raw_data['python_objects']['request'] response = raw_data['python_objects']['response'] # Note: All of the below keys may not be in the response "dict" # For example, X-API-Query-Time and X-API-Query-Count will only # exist if SQL_DEBUG is turned on in settings. headers = [ (float, 'X-API-Time'), # may end with an 's' "0.33s" (float, 'X-API-Total-Time'), (int, 'X-API-Query-Count'), (float, 'X-API-Query-Time'), # may also end with an 's' (str, 'X-API-Node'), ] data_for_log['x_api'] = {k: convert_to_type(t, response[k]) for (t, k) in headers if k in response} data_for_log['request'] = { 'method': request.method, 'path': request.path, 'path_info': request.path_info, 'query_string': request.META['QUERY_STRING'], } if hasattr(request, 'data'): data_for_log['request']['data'] = request.data return data_for_log def get_extra_fields(self, record): fields = super(LogstashFormatter, self).get_extra_fields(record) if record.name.startswith('awx.analytics'): log_kind = record.name[len('awx.analytics.'):] fields = self.reformat_data_for_log(fields, kind=log_kind) # General AWX metadata fields['cluster_host_id'] = self.cluster_host_id fields['tower_uuid'] = self.tower_uuid return fields def format(self, record): stamp = datetime.utcfromtimestamp(record.created) stamp = stamp.replace(tzinfo=tzutc()) message = { # Field not included, but exist in related logs # 'path': record.pathname '@timestamp': stamp, 'message': record.getMessage(), 'host': self.host, # Extra Fields 'level': record.levelname, 'logger_name': record.name, } # Add extra fields message.update(self.get_extra_fields(record)) # If exception, add debug info if record.exc_info: message.update(self.get_debug_fields(record)) return self.serialize(message)

true

1c3147546d1847c7fd6da0c2a520a28952b357fb

401

py

Python

open-codegen/opengen/functions/norm2.py

jgillis/optimization-engine

2952af47891204d3cd080a8e7f71e616ac022e52

[ "Apache-2.0", "MIT" ]

null

open-codegen/opengen/functions/norm2.py

jgillis/optimization-engine

2952af47891204d3cd080a8e7f71e616ac022e52

[ "Apache-2.0", "MIT" ]

null

open-codegen/opengen/functions/norm2.py

jgillis/optimization-engine

2952af47891204d3cd080a8e7f71e616ac022e52

[ "Apache-2.0", "MIT" ]

null

import casadi.casadi as cs import numpy as np from .is_numeric import * from .is_symbolic import * def norm2(u): if (isinstance(u, list) and all(is_numeric(x) for x in u))\ or isinstance(u, np.ndarray): # if `u` is a numeric vector return np.linalg.norm(u) elif is_symbolic(u): return cs.norm_2(u) else: raise Exception("Illegal argument")

26.733333

63

0.628429

import casadi.casadi as cs import numpy as np from .is_numeric import * from .is_symbolic import * def norm2(u): if (isinstance(u, list) and all(is_numeric(x) for x in u))\ or isinstance(u, np.ndarray): return np.linalg.norm(u) elif is_symbolic(u): return cs.norm_2(u) else: raise Exception("Illegal argument")

true

1c31489dab3dea9499e3bba760b2a7bdeb0f6ada

1,389

py

Python

ssseg/cfgs/dmnet/cfgs_ade20k_resnet50os16.py

zhizhangxian/sssegmentation

90613f6e0abf4cdd729cf382ab2a915e106d8649

[ "MIT" ]

2

2021-10-31T21:52:30.000Z

2021-12-21T12:35:37.000Z

ssseg/cfgs/dmnet/cfgs_ade20k_resnet50os16.py

zhizhangxian/sssegmentation

90613f6e0abf4cdd729cf382ab2a915e106d8649

[ "MIT" ]

null

ssseg/cfgs/dmnet/cfgs_ade20k_resnet50os16.py

zhizhangxian/sssegmentation

90613f6e0abf4cdd729cf382ab2a915e106d8649

[ "MIT" ]

null

'''define the config file for ade20k and resnet50os16''' import os from .base_cfg import * # modify dataset config DATASET_CFG = DATASET_CFG.copy() DATASET_CFG.update({ 'type': 'ade20k', 'rootdir': os.path.join(os.getcwd(), 'ADE20k'), }) # modify dataloader config DATALOADER_CFG = DATALOADER_CFG.copy() # modify optimizer config OPTIMIZER_CFG = OPTIMIZER_CFG.copy() OPTIMIZER_CFG.update( { 'max_epochs': 130 } ) # modify losses config LOSSES_CFG = LOSSES_CFG.copy() # modify segmentor config SEGMENTOR_CFG = SEGMENTOR_CFG.copy() SEGMENTOR_CFG.update( { 'num_classes': 150, 'backbone': { 'type': 'resnet50', 'series': 'resnet', 'pretrained': True, 'outstride': 16, 'use_stem': True, 'selected_indices': (2, 3), }, } ) # modify inference config INFERENCE_CFG = INFERENCE_CFG.copy() # modify common config COMMON_CFG = COMMON_CFG.copy() COMMON_CFG['train'].update( { 'backupdir': 'dmnet_resnet50os16_ade20k_train', 'logfilepath': 'dmnet_resnet50os16_ade20k_train/train.log', } ) COMMON_CFG['test'].update( { 'backupdir': 'dmnet_resnet50os16_ade20k_test', 'logfilepath': 'dmnet_resnet50os16_ade20k_test/test.log', 'resultsavepath': 'dmnet_resnet50os16_ade20k_test/dmnet_resnet50os16_ade20k_results.pkl' } )

25.722222

96

0.657307

import os from .base_cfg import * DATASET_CFG = DATASET_CFG.copy() DATASET_CFG.update({ 'type': 'ade20k', 'rootdir': os.path.join(os.getcwd(), 'ADE20k'), }) DATALOADER_CFG = DATALOADER_CFG.copy() OPTIMIZER_CFG = OPTIMIZER_CFG.copy() OPTIMIZER_CFG.update( { 'max_epochs': 130 } ) LOSSES_CFG = LOSSES_CFG.copy() SEGMENTOR_CFG = SEGMENTOR_CFG.copy() SEGMENTOR_CFG.update( { 'num_classes': 150, 'backbone': { 'type': 'resnet50', 'series': 'resnet', 'pretrained': True, 'outstride': 16, 'use_stem': True, 'selected_indices': (2, 3), }, } ) INFERENCE_CFG = INFERENCE_CFG.copy() COMMON_CFG = COMMON_CFG.copy() COMMON_CFG['train'].update( { 'backupdir': 'dmnet_resnet50os16_ade20k_train', 'logfilepath': 'dmnet_resnet50os16_ade20k_train/train.log', } ) COMMON_CFG['test'].update( { 'backupdir': 'dmnet_resnet50os16_ade20k_test', 'logfilepath': 'dmnet_resnet50os16_ade20k_test/test.log', 'resultsavepath': 'dmnet_resnet50os16_ade20k_test/dmnet_resnet50os16_ade20k_results.pkl' } )

true

1c3149e38f7b6cc0039c07d18c3679a41298157e

34,017

py

Python

python/interpret_community/mimic/mimic_explainer.py

bethz/interpret-community

3932bfe93aedbc2a6409de1e169e0576cedc8b0d

[ "MIT" ]

2

2020-10-14T01:02:37.000Z

2022-02-17T01:47:49.000Z

python/interpret_community/mimic/mimic_explainer.py

bethz/interpret-community

3932bfe93aedbc2a6409de1e169e0576cedc8b0d

[ "MIT" ]

12

2021-03-10T01:29:02.000Z

2022-02-26T21:11:42.000Z

python/interpret_community/mimic/mimic_explainer.py

bethz/interpret-community

3932bfe93aedbc2a6409de1e169e0576cedc8b0d

[ "MIT" ]

null

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- """Defines the Mimic Explainer for computing explanations on black box models or functions. The mimic explainer trains an explainable model to reproduce the output of the given black box model. The explainable model is called a surrogate model and the black box model is called a teacher model. Once trained to reproduce the output of the teacher model, the surrogate model's explanation can be used to explain the teacher model. """ import numpy as np from ..common.explanation_utils import _order_imp from ..common.model_wrapper import _wrap_model from .._internal.raw_explain.raw_explain_utils import get_datamapper_and_transformed_data, \ transform_with_datamapper from ..common.blackbox_explainer import BlackBoxExplainer from .model_distill import _model_distill from .models import LGBMExplainableModel from ..explanation.explanation import _create_local_explanation, _create_global_explanation, \ _aggregate_global_from_local_explanation, _aggregate_streamed_local_explanations, \ _create_raw_feats_global_explanation, _create_raw_feats_local_explanation, \ _get_raw_explainer_create_explanation_kwargs from ..dataset.decorator import tabular_decorator, init_tabular_decorator from ..dataset.dataset_wrapper import DatasetWrapper from ..common.constants import ExplainParams, ExplainType, ModelTask, \ ShapValuesOutput, MimicSerializationConstants, ExplainableModelType, \ LightGBMParams, Defaults, Extension import logging import json import warnings with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Starting from version 2.2.1', UserWarning) from shap.common import DenseData class MimicExplainer(BlackBoxExplainer): available_explanations = [Extension.GLOBAL, Extension.LOCAL] explainer_type = Extension.BLACKBOX """The Mimic Explainer for explaining black box models or functions. :param model: The black box model or function (if is_function is True) to be explained. Also known as the teacher model. :type model: model that implements sklearn.predict or sklearn.predict_proba or function that accepts a 2d ndarray :param initialization_examples: A matrix of feature vector examples (# examples x # features) for initializing the explainer. :type initialization_examples: numpy.array or pandas.DataFrame or iml.datatypes.DenseData or scipy.sparse.csr_matrix :param explainable_model: The uninitialized surrogate model used to explain the black box model. Also known as the student model. :type explainable_model: interpret_community.mimic.models.BaseExplainableModel :param explainable_model_args: An optional map of arguments to pass to the explainable model for initialization. :type explainable_model_args: dict :param is_function: Default set to false, set to True if passing function instead of model. :type is_function: bool :param augment_data: If true, oversamples the initialization examples to improve surrogate model accuracy to fit teacher model. Useful for high-dimensional data where the number of rows is less than the number of columns. :type augment_data: bool :param max_num_of_augmentations: max number of times we can increase the input data size. :type max_num_of_augmentations: int :param explain_subset: List of feature indices. If specified, only selects a subset of the features in the evaluation dataset for explanation. Note for mimic explainer this will not affect the execution time of getting the global explanation. This argument is not supported when transformations are set. :type explain_subset: list[int] :param features: A list of feature names. :type features: list[str] :param classes: Class names as a list of strings. The order of the class names should match that of the model output. Only required if explaining classifier. :type classes: list[str] :param transformations: sklearn.compose.ColumnTransformer or a list of tuples describing the column name and transformer. When transformations are provided, explanations are of the features before the transformation. The format for list of transformations is same as the one here: https://github.com/scikit-learn-contrib/sklearn-pandas. If the user is using a transformation that is not in the list of sklearn.preprocessing transformations that we support then we cannot take a list of more than one column as input for the transformation. A user can use the following sklearn.preprocessing transformations with a list of columns since these are already one to many or one to one: Binarizer, KBinsDiscretizer, KernelCenterer, LabelEncoder, MaxAbsScaler, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler. Examples for transformations that work:: [ (["col1", "col2"], sklearn_one_hot_encoder), (["col3"], None) #col3 passes as is ] [ (["col1"], my_own_transformer), (["col2"], my_own_transformer), ] Example of transformations that would raise an error since it cannot be interpreted as one to many:: [ (["col1", "col2"], my_own_transformer) ] This would not work since it is hard to make out whether my_own_transformer gives a many to many or one to many mapping when taking a sequence of columns. :type transformations: sklearn.compose.ColumnTransformer or list[tuple] :param shap_values_output: The shap values output from the explainer. Only applies to tree-based models that are in terms of raw feature values instead of probabilities. Can be default, probability or teacher_probability. If probability or teacher_probability are specified, we approximate the feature importance values as probabilities instead of using the default values. If teacher probability is specified, we use the probabilities from the teacher model as opposed to the surrogate model. :type shap_values_output: interpret_community.common.constants.ShapValuesOutput :param categorical_features: Categorical feature names or indexes. If names are passed, they will be converted into indexes first. Note if pandas indexes are categorical, you can either pass the name of the index or the index as if the pandas index was inserted at the end of the input dataframe. :type categorical_features: Union[list[str], list[int]] :param allow_all_transformations: Allow many to many and many to one transformations :type allow_all_transformations: bool :param model_task: Optional parameter to specify whether the model is a classification or regression model. In most cases, the type of the model can be inferred based on the shape of the output, where a classifier has a predict_proba method and outputs a 2 dimensional array, while a regressor has a predict method and outputs a 1 dimensional array. :type model_task: str :param reset_index: Uses the pandas DataFrame index column as part of the features when training the surrogate model. :type reset_index: bool """ @init_tabular_decorator def __init__(self, model, initialization_examples, explainable_model, explainable_model_args=None, is_function=False, augment_data=True, max_num_of_augmentations=10, explain_subset=None, features=None, classes=None, transformations=None, allow_all_transformations=False, shap_values_output=ShapValuesOutput.DEFAULT, categorical_features=None, model_task=ModelTask.Unknown, reset_index=False, **kwargs): """Initialize the MimicExplainer. :param model: The black box model or function (if is_function is True) to be explained. Also known as the teacher model. :type model: model that implements sklearn.predict or sklearn.predict_proba or function that accepts a 2d ndarray :param initialization_examples: A matrix of feature vector examples (# examples x # features) for initializing the explainer. :type initialization_examples: numpy.array or pandas.DataFrame or iml.datatypes.DenseData or scipy.sparse.csr_matrix :param explainable_model: The uninitialized surrogate model used to explain the black box model. Also known as the student model. :type explainable_model: BaseExplainableModel :param explainable_model_args: An optional map of arguments to pass to the explainable model for initialization. :type explainable_model_args: dict :param is_function: Default set to false, set to True if passing function instead of model. :type is_function: bool :param augment_data: If true, oversamples the initialization examples to improve surrogate model accuracy to fit teacher model. Useful for high-dimensional data where the number of rows is less than the number of columns. :type augment_data: bool :param max_num_of_augmentations: max number of times we can increase the input data size. :type max_num_of_augmentations: int :param explain_subset: List of feature indices. If specified, only selects a subset of the features in the evaluation dataset for explanation. Note for mimic explainer this will not affect the execution time of getting the global explanation. This argument is not supported when transformations are set. :type explain_subset: list[int] :param features: A list of feature names. :type features: list[str] :param classes: Class names as a list of strings. The order of the class names should match that of the model output. Only required if explaining classifier. :type classes: list[str] :param transformations: sklearn.compose.ColumnTransformer object or a list of tuples describing the column name and transformer. When transformations are provided, explanations are of the features before the transformation. The format for the list of transformations is same as the one here: https://github.com/scikit-learn-contrib/sklearn-pandas. If the user is using a transformation that is not in the list of sklearn.preprocessing transformations that we support then we cannot take a list of more than one column as input for the transformation. A user can use the following sklearn.preprocessing transformations with a list of columns since these are already one to many or one to one: Binarizer, KBinsDiscretizer, KernelCenterer, LabelEncoder, MaxAbsScaler, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler. Examples for transformations that work: [ (["col1", "col2"], sklearn_one_hot_encoder), (["col3"], None) #col3 passes as is ] [ (["col1"], my_own_transformer), (["col2"], my_own_transformer), ] Example of transformations that would raise an error since it cannot be interpreted as one to many: [ (["col1", "col2"], my_own_transformer) ] This would not work since it is hard to make out whether my_own_transformer gives a many to many or one to many mapping when taking a sequence of columns. :type transformations: sklearn.compose.ColumnTransformer or list[tuple] :param shap_values_output: The shap values output from the explainer. Only applies to tree-based models that are in terms of raw feature values instead of probabilities. Can be default, probability or teacher_probability. If probability or teacher_probability are specified, we approximate the feature importance values as probabilities instead of using the default values. If teacher probability is specified, we use the probabilities from the teacher model as opposed to the surrogate model. :type shap_values_output: interpret_community.common.constants.ShapValuesOutput :param categorical_features: Categorical feature names or indexes. If names are passed, they will be converted into indexes first. Note if pandas indexes are categorical, you can either pass the name of the index or the index as if the pandas index was inserted at the end of the input dataframe. :type categorical_features: Union[list[str], list[int]] :param allow_all_transformations: Allow many to many and many to one transformations :type allow_all_transformations: bool :param model_task: Optional parameter to specify whether the model is a classification or regression model. In most cases, the type of the model can be inferred based on the shape of the output, where a classifier has a predict_proba method and outputs a 2 dimensional array, while a regressor has a predict method and outputs a 1 dimensional array. :type model_task: str :param reset_index: Uses the pandas DataFrame index column as part of the features when training the surrogate model. :type reset_index: bool """ if transformations is not None and explain_subset is not None: raise ValueError("explain_subset not supported with transformations") self.reset_index = reset_index if reset_index: initialization_examples.reset_index() self._datamapper = None if transformations is not None: self._datamapper, initialization_examples = get_datamapper_and_transformed_data( examples=initialization_examples, transformations=transformations, allow_all_transformations=allow_all_transformations) wrapped_model, eval_ml_domain = _wrap_model(model, initialization_examples, model_task, is_function) super(MimicExplainer, self).__init__(wrapped_model, is_function=is_function, model_task=eval_ml_domain, **kwargs) if explainable_model_args is None: explainable_model_args = {} if categorical_features is None: categorical_features = [] self._logger.debug('Initializing MimicExplainer') # Get the feature names from the initialization examples self._init_features = initialization_examples.get_features(features=features) self.features = features # augment the data if necessary if augment_data: initialization_examples.augment_data(max_num_of_augmentations=max_num_of_augmentations) original_training_data = initialization_examples.typed_dataset # If categorical_features is a list of string column names instead of indexes, make sure to convert to indexes if not all(isinstance(categorical_feature, int) for categorical_feature in categorical_features): categorical_features = initialization_examples.get_column_indexes(self._init_features, categorical_features) # Featurize any timestamp columns # TODO: more sophisticated featurization self._timestamp_featurizer = initialization_examples.timestamp_featurizer() # If model is a linear model or isn't able to handle categoricals, one-hot-encode categoricals is_tree_model = explainable_model.explainable_model_type == ExplainableModelType.TREE_EXPLAINABLE_MODEL_TYPE if is_tree_model and self._supports_categoricals(explainable_model): # Index the categorical string columns for training data self._column_indexer = initialization_examples.string_index(columns=categorical_features) self._one_hot_encoder = None explainable_model_args[LightGBMParams.CATEGORICAL_FEATURE] = categorical_features else: # One-hot-encode categoricals for models that don't support categoricals natively self._column_indexer = initialization_examples.string_index(columns=categorical_features) self._one_hot_encoder = initialization_examples.one_hot_encode(columns=categorical_features) self.classes = classes self.explain_subset = explain_subset self.transformations = transformations self._shap_values_output = shap_values_output # Train the mimic model on the given model training_data = initialization_examples.dataset self.initialization_examples = initialization_examples if isinstance(training_data, DenseData): training_data = training_data.data explainable_model_args[ExplainParams.CLASSIFICATION] = self.predict_proba_flag if self._supports_shap_values_output(explainable_model): explainable_model_args[ExplainParams.SHAP_VALUES_OUTPUT] = shap_values_output self.surrogate_model = _model_distill(self.function, explainable_model, training_data, original_training_data, explainable_model_args) self._method = self.surrogate_model._method self._original_eval_examples = None self._allow_all_transformations = allow_all_transformations def _supports_categoricals(self, explainable_model): return issubclass(explainable_model, LGBMExplainableModel) def _supports_shap_values_output(self, explainable_model): return issubclass(explainable_model, LGBMExplainableModel) def _get_explain_global_kwargs(self, evaluation_examples=None, include_local=True, batch_size=Defaults.DEFAULT_BATCH_SIZE): """Get the kwargs for explain_global to create a global explanation. :param evaluation_examples: A matrix of feature vector examples (# examples x # features) on which to explain the model's output. If specified, computes feature importances through aggregation. :type evaluation_examples: numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix :param include_local: Include the local explanations in the returned global explanation. If evaluation examples are specified and include_local is False, will stream the local explanations to aggregate to global. :type include_local: bool :param batch_size: If include_local is False, specifies the batch size for aggregating local explanations to global. :type batch_size: int :return: Args for explain_global. :rtype: dict """ classification = self.predict_proba_flag kwargs = {ExplainParams.METHOD: ExplainType.MIMIC} if classification: kwargs[ExplainParams.CLASSES] = self.classes if evaluation_examples is not None: # Aggregate local explanation to global, either through computing the local # explanation and then aggregating or streaming the local explanation to global if include_local: # Get local explanation local_explanation = self.explain_local(evaluation_examples) kwargs[ExplainParams.LOCAL_EXPLANATION] = local_explanation else: if classification: model_task = ModelTask.Classification else: model_task = ModelTask.Regression if not isinstance(evaluation_examples, DatasetWrapper): self._logger.debug('Eval examples not wrapped, wrapping') evaluation_examples = DatasetWrapper(evaluation_examples) kwargs = _aggregate_streamed_local_explanations(self, evaluation_examples, model_task, self.features, batch_size, **kwargs) return kwargs global_importance_values = self.surrogate_model.explain_global() order = _order_imp(global_importance_values) if classification: kwargs[ExplainParams.MODEL_TASK] = ExplainType.CLASSIFICATION else: kwargs[ExplainParams.MODEL_TASK] = ExplainType.REGRESSION if self.model is not None: kwargs[ExplainParams.MODEL_TYPE] = str(type(self.model)) else: kwargs[ExplainParams.MODEL_TYPE] = ExplainType.FUNCTION kwargs[ExplainParams.EXPECTED_VALUES] = None kwargs[ExplainParams.CLASSIFICATION] = classification kwargs[ExplainParams.GLOBAL_IMPORTANCE_VALUES] = global_importance_values kwargs[ExplainParams.GLOBAL_IMPORTANCE_RANK] = order kwargs[ExplainParams.FEATURES] = self.features return kwargs def explain_global(self, evaluation_examples=None, include_local=True, batch_size=Defaults.DEFAULT_BATCH_SIZE): """Globally explains the blackbox model using the surrogate model. If evaluation_examples are unspecified, retrieves global feature importances from explainable surrogate model. Note this will not include per class feature importances. If evaluation_examples are specified, aggregates local explanations to global from the given evaluation_examples - which computes both global and per class feature importances. :param evaluation_examples: A matrix of feature vector examples (# examples x # features) on which to explain the model's output. If specified, computes feature importances through aggregation. :type evaluation_examples: numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix :param include_local: Include the local explanations in the returned global explanation. If evaluation examples are specified and include_local is False, will stream the local explanations to aggregate to global. :type include_local: bool :param batch_size: If include_local is False, specifies the batch size for aggregating local explanations to global. :type batch_size: int :return: A model explanation object. It is guaranteed to be a GlobalExplanation. If evaluation_examples are passed in, it will also have the properties of a LocalExplanation. If the model is a classifier (has predict_proba), it will have the properties of ClassesMixin, and if evaluation_examples were passed in it will also have the properties of PerClassMixin. :rtype: DynamicGlobalExplanation """ if self._original_eval_examples is None: if isinstance(evaluation_examples, DatasetWrapper): self._original_eval_examples = evaluation_examples.original_dataset_with_type else: self._original_eval_examples = evaluation_examples kwargs = self._get_explain_global_kwargs(evaluation_examples=evaluation_examples, include_local=include_local, batch_size=batch_size) kwargs[ExplainParams.INIT_DATA] = self.initialization_examples if evaluation_examples is not None: kwargs[ExplainParams.EVAL_DATA] = evaluation_examples ys_dict = self._get_ys_dict(self._original_eval_examples, transformations=self.transformations, allow_all_transformations=self._allow_all_transformations) kwargs.update(ys_dict) if include_local: return _aggregate_global_from_local_explanation(**kwargs) explanation = _create_global_explanation(**kwargs) # if transformations have been passed, then return raw features explanation raw_kwargs = _get_raw_explainer_create_explanation_kwargs(kwargs=kwargs) return explanation if self._datamapper is None else _create_raw_feats_global_explanation( explanation, feature_maps=[self._datamapper.feature_map], features=self.features, **raw_kwargs) def _get_explain_local_kwargs(self, evaluation_examples): """Get the kwargs for explain_local to create a local explanation. :param evaluation_examples: A matrix of feature vector examples (# examples x # features) on which to explain the model's output. :type evaluation_examples: numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix :return: Args for explain_local. :rtype: dict """ if self.reset_index: evaluation_examples.reset_index() kwargs = {} original_evaluation_examples = evaluation_examples.typed_dataset probabilities = None if self._shap_values_output == ShapValuesOutput.TEACHER_PROBABILITY: # Outputting shap values in terms of the probabilities of the teacher model probabilities = self.function(original_evaluation_examples) if self._timestamp_featurizer: evaluation_examples.apply_timestamp_featurizer(self._timestamp_featurizer) if self._column_indexer: evaluation_examples.apply_indexer(self._column_indexer, bucket_unknown=True) if self._one_hot_encoder: evaluation_examples.apply_one_hot_encoder(self._one_hot_encoder) dataset = evaluation_examples.dataset kwargs[ExplainParams.NUM_FEATURES] = evaluation_examples.num_features local_importance_values = self.surrogate_model.explain_local(dataset, probabilities=probabilities) classification = isinstance(local_importance_values, list) or self.predict_proba_flag expected_values = self.surrogate_model.expected_values kwargs[ExplainParams.METHOD] = ExplainType.MIMIC self.features = evaluation_examples.get_features(features=self.features) kwargs[ExplainParams.FEATURES] = self.features if self.predict_proba_flag: if self.surrogate_model.multiclass: # For multiclass case, convert to array local_importance_values = np.array(local_importance_values) else: # TODO: Eventually move this back inside the surrogate model # If binary case, we need to reformat the data to have importances per class # and convert the expected values back to the original domain local_importance_values = np.stack((-local_importance_values, local_importance_values)) if classification: kwargs[ExplainParams.CLASSES] = self.classes # Reformat local_importance_values result if explain_subset specified if self.explain_subset: self._logger.debug('Getting subset of local_importance_values') if classification: local_importance_values = local_importance_values[:, :, self.explain_subset] else: local_importance_values = local_importance_values[:, self.explain_subset] if classification: kwargs[ExplainParams.MODEL_TASK] = ExplainType.CLASSIFICATION else: kwargs[ExplainParams.MODEL_TASK] = ExplainType.REGRESSION if self.model is not None: kwargs[ExplainParams.MODEL_TYPE] = str(type(self.model)) else: kwargs[ExplainParams.MODEL_TYPE] = ExplainType.FUNCTION kwargs[ExplainParams.LOCAL_IMPORTANCE_VALUES] = local_importance_values kwargs[ExplainParams.EXPECTED_VALUES] = np.array(expected_values) kwargs[ExplainParams.CLASSIFICATION] = classification kwargs[ExplainParams.INIT_DATA] = self.initialization_examples kwargs[ExplainParams.EVAL_DATA] = original_evaluation_examples ys_dict = self._get_ys_dict(self._original_eval_examples, transformations=self.transformations, allow_all_transformations=self._allow_all_transformations) kwargs.update(ys_dict) return kwargs @tabular_decorator def explain_local(self, evaluation_examples): """Locally explains the blackbox model using the surrogate model. :param evaluation_examples: A matrix of feature vector examples (# examples x # features) on which to explain the model's output. :type evaluation_examples: numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix :return: A model explanation object. It is guaranteed to be a LocalExplanation. If the model is a classifier, it will have the properties of the ClassesMixin. :rtype: DynamicLocalExplanation """ if self._original_eval_examples is None: if isinstance(evaluation_examples, DatasetWrapper): self._original_eval_examples = evaluation_examples.original_dataset_with_type else: self._original_eval_examples = evaluation_examples if self._datamapper is not None: evaluation_examples = transform_with_datamapper(evaluation_examples, self._datamapper) kwargs = self._get_explain_local_kwargs(evaluation_examples) kwargs[ExplainParams.INIT_DATA] = self.initialization_examples kwargs[ExplainParams.EVAL_DATA] = evaluation_examples explanation = _create_local_explanation(**kwargs) # if transformations have been passed, then return raw features explanation raw_kwargs = _get_raw_explainer_create_explanation_kwargs(kwargs=kwargs) return explanation if self._datamapper is None else _create_raw_feats_local_explanation( explanation, feature_maps=[self._datamapper.feature_map], features=self.features, **raw_kwargs) def _save(self): """Return a string dictionary representation of the mimic explainer. Currently only supported scenario is Mimic Explainer with LightGBM surrogate model. :return: A serialized dictionary representation of the mimic explainer. :rtype: dict """ properties = {} # save all of the properties for key, value in self.__dict__.items(): if key in MimicSerializationConstants.nonify_properties: properties[key] = None elif key in MimicSerializationConstants.save_properties: properties[key] = value._save() else: properties[key] = json.dumps(value) # return a dictionary of strings return properties @staticmethod def _load(model, properties): """Load a MimicExplainer from the given properties. Currently only supported scenario is Mimic Explainer with LightGBM surrogate model. :param model: The serialized ONNX model with a scikit-learn like API. :type model: ONNX model. :param properties: A serialized dictionary representation of the mimic explainer. :type properties: dict :return: The deserialized MimicExplainer. :rtype: interpret_community.mimic.MimicExplainer """ # create the MimicExplainer without any properties using the __new__ function, similar to pickle mimic = MimicExplainer.__new__(MimicExplainer) # load all of the properties for key, value in properties.items(): # Regenerate the properties on the fly if key in MimicSerializationConstants.nonify_properties: if key == MimicSerializationConstants.MODEL: mimic.__dict__[key] = model elif key == MimicSerializationConstants.LOGGER: parent = logging.getLogger(__name__) mimic_identity = json.loads(properties[MimicSerializationConstants.IDENTITY]) mimic.__dict__[key] = parent.getChild(mimic_identity) elif key == MimicSerializationConstants.INITIALIZATION_EXAMPLES: mimic.__dict__[key] = None elif key == MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES: mimic.__dict__[key] = None elif key == MimicSerializationConstants.TIMESTAMP_FEATURIZER: mimic.__dict__[key] = None elif key == MimicSerializationConstants.FUNCTION: # TODO add third case if is_function was passed to mimic explainer if json.loads(properties[MimicSerializationConstants.PREDICT_PROBA_FLAG]): mimic.__dict__[key] = model.predict_proba else: mimic.__dict__[key] = model.predict else: raise Exception("Unknown nonify key on deserialize in MimicExplainer: {}".format(key)) elif key in MimicSerializationConstants.save_properties: mimic.__dict__[key] = LGBMExplainableModel._load(value) elif key in MimicSerializationConstants.enum_properties: # NOTE: If more enums added in future, will need to handle this differently mimic.__dict__[key] = ShapValuesOutput(json.loads(value)) else: mimic.__dict__[key] = json.loads(value) if MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES not in mimic.__dict__: mimic.__dict__[MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES] = None if MimicSerializationConstants.TIMESTAMP_FEATURIZER not in mimic.__dict__: mimic.__dict__[MimicSerializationConstants.TIMESTAMP_FEATURIZER] = None return mimic

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import numpy as np from ..common.explanation_utils import _order_imp from ..common.model_wrapper import _wrap_model from .._internal.raw_explain.raw_explain_utils import get_datamapper_and_transformed_data, \ transform_with_datamapper from ..common.blackbox_explainer import BlackBoxExplainer from .model_distill import _model_distill from .models import LGBMExplainableModel from ..explanation.explanation import _create_local_explanation, _create_global_explanation, \ _aggregate_global_from_local_explanation, _aggregate_streamed_local_explanations, \ _create_raw_feats_global_explanation, _create_raw_feats_local_explanation, \ _get_raw_explainer_create_explanation_kwargs from ..dataset.decorator import tabular_decorator, init_tabular_decorator from ..dataset.dataset_wrapper import DatasetWrapper from ..common.constants import ExplainParams, ExplainType, ModelTask, \ ShapValuesOutput, MimicSerializationConstants, ExplainableModelType, \ LightGBMParams, Defaults, Extension import logging import json import warnings with warnings.catch_warnings(): warnings.filterwarnings('ignore', 'Starting from version 2.2.1', UserWarning) from shap.common import DenseData class MimicExplainer(BlackBoxExplainer): available_explanations = [Extension.GLOBAL, Extension.LOCAL] explainer_type = Extension.BLACKBOX @init_tabular_decorator def __init__(self, model, initialization_examples, explainable_model, explainable_model_args=None, is_function=False, augment_data=True, max_num_of_augmentations=10, explain_subset=None, features=None, classes=None, transformations=None, allow_all_transformations=False, shap_values_output=ShapValuesOutput.DEFAULT, categorical_features=None, model_task=ModelTask.Unknown, reset_index=False, **kwargs): if transformations is not None and explain_subset is not None: raise ValueError("explain_subset not supported with transformations") self.reset_index = reset_index if reset_index: initialization_examples.reset_index() self._datamapper = None if transformations is not None: self._datamapper, initialization_examples = get_datamapper_and_transformed_data( examples=initialization_examples, transformations=transformations, allow_all_transformations=allow_all_transformations) wrapped_model, eval_ml_domain = _wrap_model(model, initialization_examples, model_task, is_function) super(MimicExplainer, self).__init__(wrapped_model, is_function=is_function, model_task=eval_ml_domain, **kwargs) if explainable_model_args is None: explainable_model_args = {} if categorical_features is None: categorical_features = [] self._logger.debug('Initializing MimicExplainer') self._init_features = initialization_examples.get_features(features=features) self.features = features if augment_data: initialization_examples.augment_data(max_num_of_augmentations=max_num_of_augmentations) original_training_data = initialization_examples.typed_dataset if not all(isinstance(categorical_feature, int) for categorical_feature in categorical_features): categorical_features = initialization_examples.get_column_indexes(self._init_features, categorical_features) self._timestamp_featurizer = initialization_examples.timestamp_featurizer() is_tree_model = explainable_model.explainable_model_type == ExplainableModelType.TREE_EXPLAINABLE_MODEL_TYPE if is_tree_model and self._supports_categoricals(explainable_model): # Index the categorical string columns for training data self._column_indexer = initialization_examples.string_index(columns=categorical_features) self._one_hot_encoder = None explainable_model_args[LightGBMParams.CATEGORICAL_FEATURE] = categorical_features else: # One-hot-encode categoricals for models that don't support categoricals natively self._column_indexer = initialization_examples.string_index(columns=categorical_features) self._one_hot_encoder = initialization_examples.one_hot_encode(columns=categorical_features) self.classes = classes self.explain_subset = explain_subset self.transformations = transformations self._shap_values_output = shap_values_output training_data = initialization_examples.dataset self.initialization_examples = initialization_examples if isinstance(training_data, DenseData): training_data = training_data.data explainable_model_args[ExplainParams.CLASSIFICATION] = self.predict_proba_flag if self._supports_shap_values_output(explainable_model): explainable_model_args[ExplainParams.SHAP_VALUES_OUTPUT] = shap_values_output self.surrogate_model = _model_distill(self.function, explainable_model, training_data, original_training_data, explainable_model_args) self._method = self.surrogate_model._method self._original_eval_examples = None self._allow_all_transformations = allow_all_transformations def _supports_categoricals(self, explainable_model): return issubclass(explainable_model, LGBMExplainableModel) def _supports_shap_values_output(self, explainable_model): return issubclass(explainable_model, LGBMExplainableModel) def _get_explain_global_kwargs(self, evaluation_examples=None, include_local=True, batch_size=Defaults.DEFAULT_BATCH_SIZE): classification = self.predict_proba_flag kwargs = {ExplainParams.METHOD: ExplainType.MIMIC} if classification: kwargs[ExplainParams.CLASSES] = self.classes if evaluation_examples is not None: if include_local: local_explanation = self.explain_local(evaluation_examples) kwargs[ExplainParams.LOCAL_EXPLANATION] = local_explanation else: if classification: model_task = ModelTask.Classification else: model_task = ModelTask.Regression if not isinstance(evaluation_examples, DatasetWrapper): self._logger.debug('Eval examples not wrapped, wrapping') evaluation_examples = DatasetWrapper(evaluation_examples) kwargs = _aggregate_streamed_local_explanations(self, evaluation_examples, model_task, self.features, batch_size, **kwargs) return kwargs global_importance_values = self.surrogate_model.explain_global() order = _order_imp(global_importance_values) if classification: kwargs[ExplainParams.MODEL_TASK] = ExplainType.CLASSIFICATION else: kwargs[ExplainParams.MODEL_TASK] = ExplainType.REGRESSION if self.model is not None: kwargs[ExplainParams.MODEL_TYPE] = str(type(self.model)) else: kwargs[ExplainParams.MODEL_TYPE] = ExplainType.FUNCTION kwargs[ExplainParams.EXPECTED_VALUES] = None kwargs[ExplainParams.CLASSIFICATION] = classification kwargs[ExplainParams.GLOBAL_IMPORTANCE_VALUES] = global_importance_values kwargs[ExplainParams.GLOBAL_IMPORTANCE_RANK] = order kwargs[ExplainParams.FEATURES] = self.features return kwargs def explain_global(self, evaluation_examples=None, include_local=True, batch_size=Defaults.DEFAULT_BATCH_SIZE): if self._original_eval_examples is None: if isinstance(evaluation_examples, DatasetWrapper): self._original_eval_examples = evaluation_examples.original_dataset_with_type else: self._original_eval_examples = evaluation_examples kwargs = self._get_explain_global_kwargs(evaluation_examples=evaluation_examples, include_local=include_local, batch_size=batch_size) kwargs[ExplainParams.INIT_DATA] = self.initialization_examples if evaluation_examples is not None: kwargs[ExplainParams.EVAL_DATA] = evaluation_examples ys_dict = self._get_ys_dict(self._original_eval_examples, transformations=self.transformations, allow_all_transformations=self._allow_all_transformations) kwargs.update(ys_dict) if include_local: return _aggregate_global_from_local_explanation(**kwargs) explanation = _create_global_explanation(**kwargs) raw_kwargs = _get_raw_explainer_create_explanation_kwargs(kwargs=kwargs) return explanation if self._datamapper is None else _create_raw_feats_global_explanation( explanation, feature_maps=[self._datamapper.feature_map], features=self.features, **raw_kwargs) def _get_explain_local_kwargs(self, evaluation_examples): if self.reset_index: evaluation_examples.reset_index() kwargs = {} original_evaluation_examples = evaluation_examples.typed_dataset probabilities = None if self._shap_values_output == ShapValuesOutput.TEACHER_PROBABILITY: probabilities = self.function(original_evaluation_examples) if self._timestamp_featurizer: evaluation_examples.apply_timestamp_featurizer(self._timestamp_featurizer) if self._column_indexer: evaluation_examples.apply_indexer(self._column_indexer, bucket_unknown=True) if self._one_hot_encoder: evaluation_examples.apply_one_hot_encoder(self._one_hot_encoder) dataset = evaluation_examples.dataset kwargs[ExplainParams.NUM_FEATURES] = evaluation_examples.num_features local_importance_values = self.surrogate_model.explain_local(dataset, probabilities=probabilities) classification = isinstance(local_importance_values, list) or self.predict_proba_flag expected_values = self.surrogate_model.expected_values kwargs[ExplainParams.METHOD] = ExplainType.MIMIC self.features = evaluation_examples.get_features(features=self.features) kwargs[ExplainParams.FEATURES] = self.features if self.predict_proba_flag: if self.surrogate_model.multiclass: local_importance_values = np.array(local_importance_values) else: local_importance_values = np.stack((-local_importance_values, local_importance_values)) if classification: kwargs[ExplainParams.CLASSES] = self.classes if self.explain_subset: self._logger.debug('Getting subset of local_importance_values') if classification: local_importance_values = local_importance_values[:, :, self.explain_subset] else: local_importance_values = local_importance_values[:, self.explain_subset] if classification: kwargs[ExplainParams.MODEL_TASK] = ExplainType.CLASSIFICATION else: kwargs[ExplainParams.MODEL_TASK] = ExplainType.REGRESSION if self.model is not None: kwargs[ExplainParams.MODEL_TYPE] = str(type(self.model)) else: kwargs[ExplainParams.MODEL_TYPE] = ExplainType.FUNCTION kwargs[ExplainParams.LOCAL_IMPORTANCE_VALUES] = local_importance_values kwargs[ExplainParams.EXPECTED_VALUES] = np.array(expected_values) kwargs[ExplainParams.CLASSIFICATION] = classification kwargs[ExplainParams.INIT_DATA] = self.initialization_examples kwargs[ExplainParams.EVAL_DATA] = original_evaluation_examples ys_dict = self._get_ys_dict(self._original_eval_examples, transformations=self.transformations, allow_all_transformations=self._allow_all_transformations) kwargs.update(ys_dict) return kwargs @tabular_decorator def explain_local(self, evaluation_examples): if self._original_eval_examples is None: if isinstance(evaluation_examples, DatasetWrapper): self._original_eval_examples = evaluation_examples.original_dataset_with_type else: self._original_eval_examples = evaluation_examples if self._datamapper is not None: evaluation_examples = transform_with_datamapper(evaluation_examples, self._datamapper) kwargs = self._get_explain_local_kwargs(evaluation_examples) kwargs[ExplainParams.INIT_DATA] = self.initialization_examples kwargs[ExplainParams.EVAL_DATA] = evaluation_examples explanation = _create_local_explanation(**kwargs) raw_kwargs = _get_raw_explainer_create_explanation_kwargs(kwargs=kwargs) return explanation if self._datamapper is None else _create_raw_feats_local_explanation( explanation, feature_maps=[self._datamapper.feature_map], features=self.features, **raw_kwargs) def _save(self): properties = {} for key, value in self.__dict__.items(): if key in MimicSerializationConstants.nonify_properties: properties[key] = None elif key in MimicSerializationConstants.save_properties: properties[key] = value._save() else: properties[key] = json.dumps(value) return properties @staticmethod def _load(model, properties): mimic = MimicExplainer.__new__(MimicExplainer) for key, value in properties.items(): if key in MimicSerializationConstants.nonify_properties: if key == MimicSerializationConstants.MODEL: mimic.__dict__[key] = model elif key == MimicSerializationConstants.LOGGER: parent = logging.getLogger(__name__) mimic_identity = json.loads(properties[MimicSerializationConstants.IDENTITY]) mimic.__dict__[key] = parent.getChild(mimic_identity) elif key == MimicSerializationConstants.INITIALIZATION_EXAMPLES: mimic.__dict__[key] = None elif key == MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES: mimic.__dict__[key] = None elif key == MimicSerializationConstants.TIMESTAMP_FEATURIZER: mimic.__dict__[key] = None elif key == MimicSerializationConstants.FUNCTION: if json.loads(properties[MimicSerializationConstants.PREDICT_PROBA_FLAG]): mimic.__dict__[key] = model.predict_proba else: mimic.__dict__[key] = model.predict else: raise Exception("Unknown nonify key on deserialize in MimicExplainer: {}".format(key)) elif key in MimicSerializationConstants.save_properties: mimic.__dict__[key] = LGBMExplainableModel._load(value) elif key in MimicSerializationConstants.enum_properties: mimic.__dict__[key] = ShapValuesOutput(json.loads(value)) else: mimic.__dict__[key] = json.loads(value) if MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES not in mimic.__dict__: mimic.__dict__[MimicSerializationConstants.ORIGINAL_EVAL_EXAMPLES] = None if MimicSerializationConstants.TIMESTAMP_FEATURIZER not in mimic.__dict__: mimic.__dict__[MimicSerializationConstants.TIMESTAMP_FEATURIZER] = None return mimic

true

1c314a83c757289e9cad510ead448cfc9ded4f58

4,987

py

Python

meidoo/meidoo/apps/orders/serializers.py

amourbrus/meiduo_mall

965b3d4685d1a8fe18a3177cc864f27eeb516081

[ "MIT" ]

null

meidoo/meidoo/apps/orders/serializers.py

amourbrus/meiduo_mall

965b3d4685d1a8fe18a3177cc864f27eeb516081

[ "MIT" ]

null

meidoo/meidoo/apps/orders/serializers.py

amourbrus/meiduo_mall

965b3d4685d1a8fe18a3177cc864f27eeb516081

[ "MIT" ]

null

from decimal import Decimal from django.db import transaction from django.utils import timezone from django_redis import get_redis_connection from rest_framework import serializers from rest_framework.exceptions import ValidationError from goods.models import SKU from orders.models import OrderInfo, OrderGoods from meidoo.utils.exceptions import logger class CartSKUSerializer(serializers.ModelSerializer): """购物车商品数据序列化器""" count = serializers.IntegerField(label='数量') class Meta: model = SKU fields = ('id', 'name', 'default_image_url', 'price', 'count') class OrderSettlementSerializer(serializers.Serializer): """订单结算数据序列化器""" freight = serializers.DecimalField(label='运费', max_digits=10, decimal_places=2) skus = CartSKUSerializer(many=True) class SaveOrderSerializer(serializers.ModelSerializer): """下单数据序列化器""" class Meta: model = OrderInfo fields = ('order_id', 'address', 'pay_method') read_only_fields = ('order_id',) extra_kwargs = { 'address': { 'write_only': True, 'required': True }, 'pay_method':{ 'write_only': True, 'required': True, } } def create(self, validated_data): """保存订单""" # 获取当前下单用户 user = self.context['request'].user # 组装订单编号　　当前时间　+ user.id order_id = timezone.now().strftime('%Y%m%d%H%M%S') + ('%09d' % user.id) address = validated_data['address'] pay_method = validated_data['pay_method'] # 生成订单 with transaction.atomic(): # 创建一个保存点 save_point = transaction.savepoint() try: # 创建订单信息 order = OrderInfo.objects.create( order_id = order_id, user = user, address = address, total_count = 0, total_amount = Decimal(0), freight = Decimal(10), pay_method = pay_method, status=OrderInfo.ORDER_STATUS_ENUM['UNSEND'] if pay_method == OrderInfo.PAY_METHODS_ENUM[ 'CASH'] else OrderInfo.ORDER_STATUS_ENUM['UNPAID'] ) # 获取购物车信息 redis_conn = get_redis_connection('cart') redis_cart = redis_conn.hgetall('cart_%s' % user.id) cart_selected = redis_conn.smembers('cart_selected_%s' % user.id) # 将bytes类型转换为int cart = {} for sku_id in cart_selected: cart[int(sku_id)] = int(redis_cart[sku_id]) # # 一次查询出所有商品数据 # skus = SKU.objects.filter(id__in = cart.keys()) sku_id_list = cart.keys() # 处理订单商品 # for sku in skus: for sku_id in sku_id_list: while True: sku = SKU.objects.get(id=sku_id) sku_count = cart[sku.id] # 判断库存 origin_stock = sku.stock # 原始库存 origin_sales = sku.sales # 原始销量 if sku_count > origin_stock: transaction.savepoint_rollback(save_point) raise serializers.ValidationError('商品库存不足') # 满足条件，则减少库存 new_stock = origin_stock - sku_count new_sales = origin_sales + sku_count sku.stock = new_stock sku.sales = new_sales sku.save() # 累计商品的spu　销量信息 sku.goods.sales += sku_count sku.goods.save() # 累计订单基本信息的数据 order.total_count += sku_count # 累计总金额 order.total_amount += (sku.price * sku_count) # 总金额 # 保存订单商品 OrderGoods.objects.create( order = order, sku = sku, count = sku_count, price = sku.price, ) break # 更新订单的金额数量信息 order.total_amount += order.freight order.save() except ValidationError: raise except Exception as e: logger.error(e) transaction.savepoint_rollback(save_point) raise # 提交事务 transaction.savepoint_commit(save_point) # 更新redis　保存的购物车数据 pl = redis_conn.pipeline() pl.hdel('cart_%s' % user.id, *cart_selected) pl.srem('cart_selected_%s' % user.id, *cart_selected) pl.execute() return order

33.02649

109

0.49609

from decimal import Decimal from django.db import transaction from django.utils import timezone from django_redis import get_redis_connection from rest_framework import serializers from rest_framework.exceptions import ValidationError from goods.models import SKU from orders.models import OrderInfo, OrderGoods from meidoo.utils.exceptions import logger class CartSKUSerializer(serializers.ModelSerializer): count = serializers.IntegerField(label='数量') class Meta: model = SKU fields = ('id', 'name', 'default_image_url', 'price', 'count') class OrderSettlementSerializer(serializers.Serializer): freight = serializers.DecimalField(label='运费', max_digits=10, decimal_places=2) skus = CartSKUSerializer(many=True) class SaveOrderSerializer(serializers.ModelSerializer): class Meta: model = OrderInfo fields = ('order_id', 'address', 'pay_method') read_only_fields = ('order_id',) extra_kwargs = { 'address': { 'write_only': True, 'required': True }, 'pay_method':{ 'write_only': True, 'required': True, } } def create(self, validated_data): user = self.context['request'].user order_id = timezone.now().strftime('%Y%m%d%H%M%S') + ('%09d' % user.id) address = validated_data['address'] pay_method = validated_data['pay_method'] with transaction.atomic(): save_point = transaction.savepoint() try: order = OrderInfo.objects.create( order_id = order_id, user = user, address = address, total_count = 0, total_amount = Decimal(0), freight = Decimal(10), pay_method = pay_method, status=OrderInfo.ORDER_STATUS_ENUM['UNSEND'] if pay_method == OrderInfo.PAY_METHODS_ENUM[ 'CASH'] else OrderInfo.ORDER_STATUS_ENUM['UNPAID'] ) redis_conn = get_redis_connection('cart') redis_cart = redis_conn.hgetall('cart_%s' % user.id) cart_selected = redis_conn.smembers('cart_selected_%s' % user.id) cart = {} for sku_id in cart_selected: cart[int(sku_id)] = int(redis_cart[sku_id]) sku_id_list = cart.keys() for sku_id in sku_id_list: while True: sku = SKU.objects.get(id=sku_id) sku_count = cart[sku.id] origin_stock = sku.stock origin_sales = sku.sales if sku_count > origin_stock: transaction.savepoint_rollback(save_point) raise serializers.ValidationError('商品库存不足') new_stock = origin_stock - sku_count new_sales = origin_sales + sku_count sku.stock = new_stock sku.sales = new_sales sku.save() sku.goods.sales += sku_count sku.goods.save() order.total_count += sku_count order.total_amount += (sku.price * sku_count) OrderGoods.objects.create( order = order, sku = sku, count = sku_count, price = sku.price, ) break order.total_amount += order.freight order.save() except ValidationError: raise except Exception as e: logger.error(e) transaction.savepoint_rollback(save_point) raise transaction.savepoint_commit(save_point) pl = redis_conn.pipeline() pl.hdel('cart_%s' % user.id, *cart_selected) pl.srem('cart_selected_%s' % user.id, *cart_selected) pl.execute() return order

true

1c314b335cc15f8a988e00f70740c268b55cf132

391

py

Python

profile_api/migrations/0002_auto_20210516_0944.py

manishmittal050/profile-rest-api

458806f901e42bfd98fbd14e3da37da7240a01d4

[ "MIT" ]

null

profile_api/migrations/0002_auto_20210516_0944.py

manishmittal050/profile-rest-api

458806f901e42bfd98fbd14e3da37da7240a01d4

[ "MIT" ]

null

profile_api/migrations/0002_auto_20210516_0944.py

manishmittal050/profile-rest-api

458806f901e42bfd98fbd14e3da37da7240a01d4

[ "MIT" ]

null

# Generated by Django 2.2 on 2021-05-16 09:44 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('profile_api', '0001_initial'), ] operations = [ migrations.AlterField( model_name='userprofile', name='is_superuser', field=models.BooleanField(default=False), ), ]

20.578947

53

0.606138

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('profile_api', '0001_initial'), ] operations = [ migrations.AlterField( model_name='userprofile', name='is_superuser', field=models.BooleanField(default=False), ), ]

true

1c314be71dc8f37a5d141751c30c55aed4361499

6,174

py

Python

tests/testshop/settings.py

2000-ion/TIDPP-Lab3

3fc97e6214b6e51f40df39f1692d4deec4bb0cc2

[ "BSD-3-Clause" ]

2,160

2016-01-24T05:08:59.000Z

2022-03-31T12:15:30.000Z

tests/testshop/settings.py

2000-ion/TIDPP-Lab3

3fc97e6214b6e51f40df39f1692d4deec4bb0cc2

[ "BSD-3-Clause" ]

455

2016-01-29T22:41:33.000Z

2022-03-23T08:28:01.000Z

tests/testshop/settings.py

2000-ion/TIDPP-Lab3

3fc97e6214b6e51f40df39f1692d4deec4bb0cc2

[ "BSD-3-Clause" ]

818

2016-02-01T15:09:07.000Z

2022-03-28T19:52:26.000Z

from django.urls import reverse_lazy from django.utils.text import format_lazy DEBUG = True ROOT_URLCONF = 'testshop.urls' SECRET_KEY = 'test' SITE_ID = 1 DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:', } } STATIC_URL = '/static/' MEDIA_URL = '/media/' TEMPLATES = [{ 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'APP_DIRS': True, 'DIRS': [], 'OPTIONS': { 'context_processors': [ 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.template.context_processors.csrf', 'django.template.context_processors.request', 'django.contrib.messages.context_processors.messages', 'sekizai.context_processors.sekizai', 'cms.context_processors.cms_settings', ] } }, { 'BACKEND': 'post_office.template.backends.post_office.PostOfficeTemplates', 'APP_DIRS': True, 'DIRS': [], 'OPTIONS': { 'context_processors': [ 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.template.context_processors.request', ] } }] MIDDLEWARE = [ 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'shop.middleware.CustomerMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.locale.LocaleMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.gzip.GZipMiddleware', 'cms.middleware.language.LanguageCookieMiddleware', 'cms.middleware.user.CurrentUserMiddleware', 'cms.middleware.page.CurrentPageMiddleware', 'cms.middleware.utils.ApphookReloadMiddleware', 'cms.middleware.toolbar.ToolbarMiddleware', ] INSTALLED_APPS = [ 'django.contrib.auth', 'email_auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.admin', 'django.contrib.staticfiles', 'jsonfield', 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'django_fsm', 'fsm_admin', 'filer', 'easy_thumbnails', 'treebeard', 'menus', 'sekizai', 'cms', 'adminsortable2', 'djangocms_text_ckeditor', 'django_select2', 'cmsplugin_cascade', 'cmsplugin_cascade.clipboard', 'cmsplugin_cascade.extra_fields', 'cmsplugin_cascade.icon', 'cmsplugin_cascade.sharable', 'cmsplugin_cascade.segmentation', 'post_office', 'shop', 'testshop', ] USE_I18N = False USE_L10N = True USE_TZ = True TIME_ZONE = 'UTC' X_FRAME_OPTIONS = 'SAMEORIGIN' SILENCED_SYSTEM_CHECKS = ['auth.W004'] LANGUAGES = [ ('en', 'English'), ] LANGUAGE_CODE = 'en' SESSION_ENGINE = 'django.contrib.sessions.backends.cache' CMS_TEMPLATES = [ ('page.html', "Default Page"), ] CMS_PLACEHOLDER_CONF = { 'Main Content': { 'plugins': ['BootstrapContainerPlugin'], }, } CMSPLUGIN_CASCADE_PLUGINS = [ 'cmsplugin_cascade.bootstrap4', 'cmsplugin_cascade.segmentation', 'cmsplugin_cascade.generic', 'cmsplugin_cascade.icon', 'cmsplugin_cascade.leaflet', 'cmsplugin_cascade.link', 'shop.cascade', ] CMSPLUGIN_CASCADE = { 'link_plugin_classes': [ 'shop.cascade.plugin_base.CatalogLinkPluginBase', 'shop.cascade.plugin_base.CatalogLinkForm', ], 'alien_plugins': ['TextPlugin', 'TextLinkPlugin', 'AcceptConditionPlugin'], 'bootstrap4': { 'template_basedir': 'angular-ui', }, 'segmentation_mixins': [ ('shop.cascade.segmentation.EmulateCustomerModelMixin', 'shop.cascade.segmentation.EmulateCustomerAdminMixin'), ], } THUMBNAIL_PROCESSORS = ( 'easy_thumbnails.processors.colorspace', 'easy_thumbnails.processors.autocrop', 'filer.thumbnail_processors.scale_and_crop_with_subject_location', 'easy_thumbnails.processors.filters', ) THUMBNAIL_PRESERVE_EXTENSIONS = True, CKEDITOR_SETTINGS = { 'language': '{{ language }}', 'skin': 'moono', 'toolbar': 'CMS', 'toolbar_HTMLField': [ ['Undo', 'Redo'], ['cmsplugins', '-', 'ShowBlocks'], ['Format', 'Styles'], ['TextColor', 'BGColor', '-', 'PasteText', 'PasteFromWord'], ['Maximize', ''], '/', ['Bold', 'Italic', 'Underline', '-', 'Subscript', 'Superscript', '-', 'RemoveFormat'], ['JustifyLeft', 'JustifyCenter', 'JustifyRight'], ['HorizontalRule'], ['NumberedList', 'BulletedList', '-', 'Outdent', 'Indent', '-', 'Table'], ['Source'] ], 'stylesSet': format_lazy('default:{}', reverse_lazy('admin:cascade_texteditor_config')), } SHOP_APP_LABEL = 'testshop' SHOP_CART_MODIFIERS = [ 'shop.modifiers.defaults.DefaultCartModifier', 'shop.modifiers.taxes.CartIncludeTaxModifier', 'shop.payment.modifiers.PayInAdvanceModifier', 'testshop.modifiers.ComplexPayInAdvanceModifier', 'shop.shipping.modifiers.SelfCollectionModifier', ] SHOP_ORDER_WORKFLOWS = [ 'shop.payment.workflows.ManualPaymentWorkflowMixin', 'shop.payment.workflows.CancelOrderWorkflowMixin', 'shop.shipping.workflows.PartialDeliveryWorkflowMixin', ] AUTH_USER_MODEL = 'email_auth.User' AUTHENTICATION_BACKENDS = [ 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend', ] REST_AUTH_SERIALIZERS = { 'LOGIN_SERIALIZER': 'shop.serializers.auth.LoginSerializer', } POST_OFFICE = { 'TEMPLATE_ENGINE': 'post_office', }

27.5625

119

0.671526

from django.urls import reverse_lazy from django.utils.text import format_lazy DEBUG = True ROOT_URLCONF = 'testshop.urls' SECRET_KEY = 'test' SITE_ID = 1 DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:', } } STATIC_URL = '/static/' MEDIA_URL = '/media/' TEMPLATES = [{ 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'APP_DIRS': True, 'DIRS': [], 'OPTIONS': { 'context_processors': [ 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.template.context_processors.csrf', 'django.template.context_processors.request', 'django.contrib.messages.context_processors.messages', 'sekizai.context_processors.sekizai', 'cms.context_processors.cms_settings', ] } }, { 'BACKEND': 'post_office.template.backends.post_office.PostOfficeTemplates', 'APP_DIRS': True, 'DIRS': [], 'OPTIONS': { 'context_processors': [ 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.template.context_processors.request', ] } }] MIDDLEWARE = [ 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'shop.middleware.CustomerMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.locale.LocaleMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.gzip.GZipMiddleware', 'cms.middleware.language.LanguageCookieMiddleware', 'cms.middleware.user.CurrentUserMiddleware', 'cms.middleware.page.CurrentPageMiddleware', 'cms.middleware.utils.ApphookReloadMiddleware', 'cms.middleware.toolbar.ToolbarMiddleware', ] INSTALLED_APPS = [ 'django.contrib.auth', 'email_auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.admin', 'django.contrib.staticfiles', 'jsonfield', 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'django_fsm', 'fsm_admin', 'filer', 'easy_thumbnails', 'treebeard', 'menus', 'sekizai', 'cms', 'adminsortable2', 'djangocms_text_ckeditor', 'django_select2', 'cmsplugin_cascade', 'cmsplugin_cascade.clipboard', 'cmsplugin_cascade.extra_fields', 'cmsplugin_cascade.icon', 'cmsplugin_cascade.sharable', 'cmsplugin_cascade.segmentation', 'post_office', 'shop', 'testshop', ] USE_I18N = False USE_L10N = True USE_TZ = True TIME_ZONE = 'UTC' X_FRAME_OPTIONS = 'SAMEORIGIN' SILENCED_SYSTEM_CHECKS = ['auth.W004'] LANGUAGES = [ ('en', 'English'), ] LANGUAGE_CODE = 'en' SESSION_ENGINE = 'django.contrib.sessions.backends.cache' CMS_TEMPLATES = [ ('page.html', "Default Page"), ] CMS_PLACEHOLDER_CONF = { 'Main Content': { 'plugins': ['BootstrapContainerPlugin'], }, } CMSPLUGIN_CASCADE_PLUGINS = [ 'cmsplugin_cascade.bootstrap4', 'cmsplugin_cascade.segmentation', 'cmsplugin_cascade.generic', 'cmsplugin_cascade.icon', 'cmsplugin_cascade.leaflet', 'cmsplugin_cascade.link', 'shop.cascade', ] CMSPLUGIN_CASCADE = { 'link_plugin_classes': [ 'shop.cascade.plugin_base.CatalogLinkPluginBase', 'shop.cascade.plugin_base.CatalogLinkForm', ], 'alien_plugins': ['TextPlugin', 'TextLinkPlugin', 'AcceptConditionPlugin'], 'bootstrap4': { 'template_basedir': 'angular-ui', }, 'segmentation_mixins': [ ('shop.cascade.segmentation.EmulateCustomerModelMixin', 'shop.cascade.segmentation.EmulateCustomerAdminMixin'), ], } THUMBNAIL_PROCESSORS = ( 'easy_thumbnails.processors.colorspace', 'easy_thumbnails.processors.autocrop', 'filer.thumbnail_processors.scale_and_crop_with_subject_location', 'easy_thumbnails.processors.filters', ) THUMBNAIL_PRESERVE_EXTENSIONS = True, CKEDITOR_SETTINGS = { 'language': '{{ language }}', 'skin': 'moono', 'toolbar': 'CMS', 'toolbar_HTMLField': [ ['Undo', 'Redo'], ['cmsplugins', '-', 'ShowBlocks'], ['Format', 'Styles'], ['TextColor', 'BGColor', '-', 'PasteText', 'PasteFromWord'], ['Maximize', ''], '/', ['Bold', 'Italic', 'Underline', '-', 'Subscript', 'Superscript', '-', 'RemoveFormat'], ['JustifyLeft', 'JustifyCenter', 'JustifyRight'], ['HorizontalRule'], ['NumberedList', 'BulletedList', '-', 'Outdent', 'Indent', '-', 'Table'], ['Source'] ], 'stylesSet': format_lazy('default:{}', reverse_lazy('admin:cascade_texteditor_config')), } SHOP_APP_LABEL = 'testshop' SHOP_CART_MODIFIERS = [ 'shop.modifiers.defaults.DefaultCartModifier', 'shop.modifiers.taxes.CartIncludeTaxModifier', 'shop.payment.modifiers.PayInAdvanceModifier', 'testshop.modifiers.ComplexPayInAdvanceModifier', 'shop.shipping.modifiers.SelfCollectionModifier', ] SHOP_ORDER_WORKFLOWS = [ 'shop.payment.workflows.ManualPaymentWorkflowMixin', 'shop.payment.workflows.CancelOrderWorkflowMixin', 'shop.shipping.workflows.PartialDeliveryWorkflowMixin', ] AUTH_USER_MODEL = 'email_auth.User' AUTHENTICATION_BACKENDS = [ 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend', ] REST_AUTH_SERIALIZERS = { 'LOGIN_SERIALIZER': 'shop.serializers.auth.LoginSerializer', } POST_OFFICE = { 'TEMPLATE_ENGINE': 'post_office', }

true

1c314c09b523262c1d016eb7b0d051d3bc9ce51c

735

py

Python

image_downloader.py

art-litv/Space-Instagram

98fd162cc7795bf66ca28fa2b112dc0c837914fa

[ "MIT" ]

null

image_downloader.py

art-litv/Space-Instagram

98fd162cc7795bf66ca28fa2b112dc0c837914fa

[ "MIT" ]

null

image_downloader.py

art-litv/Space-Instagram

98fd162cc7795bf66ca28fa2b112dc0c837914fa

[ "MIT" ]

null

import requests import os import urllib3 from pathlib import PurePath def download_image(url: str, path: str, verify=True): ''' Downloads an image into "images" directory ''' ''' Required for fetch_spacex.py and fetch_hubble.py ''' urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) response = requests.get(url, verify=verify) response.raise_for_status() filename = path.split(os.sep)[-1] directories = path[0:path.find(filename)] try: os.makedirs(f"images{os.sep}{directories}", exist_ok=False) except FileExistsError: pass path = path + '.' + url.split(".")[-1] with open(f"images{os.sep}{path}", 'wb') as file: file.write(response.content)

27.222222

71

0.678912

import requests import os import urllib3 from pathlib import PurePath def download_image(url: str, path: str, verify=True): urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) response = requests.get(url, verify=verify) response.raise_for_status() filename = path.split(os.sep)[-1] directories = path[0:path.find(filename)] try: os.makedirs(f"images{os.sep}{directories}", exist_ok=False) except FileExistsError: pass path = path + '.' + url.split(".")[-1] with open(f"images{os.sep}{path}", 'wb') as file: file.write(response.content)

true

1c314d4280a04da568ad4442058981508effe981

2,057

py

Python

test/test_validators.py

srobo/python-dbus-next

934df62b29651cfbf513d244ad7ed138faab6fe4

[ "MIT" ]

1

2021-02-28T15:51:52.000Z

test/test_validators.py

srobo/python-dbus-next

934df62b29651cfbf513d244ad7ed138faab6fe4

[ "MIT" ]

null

test/test_validators.py

srobo/python-dbus-next

934df62b29651cfbf513d244ad7ed138faab6fe4

[ "MIT" ]

1

2021-03-08T14:22:27.000Z

from dbus_next import (is_bus_name_valid, is_object_path_valid, is_interface_name_valid, is_member_name_valid) def test_object_path_validator(): valid_paths = ['/', '/foo', '/foo/bar', '/foo/bar/bat'] invalid_paths = [ None, {}, '', 'foo', 'foo/bar', '/foo/bar/', '/$/foo/bar', '/foo//bar', '/foo$bar/baz' ] for path in valid_paths: assert is_object_path_valid(path), f'path should be valid: "{path}"' for path in invalid_paths: assert not is_object_path_valid(path), f'path should be invalid: "{path}"' def test_bus_name_validator(): valid_names = [ 'foo.bar', 'foo.bar.bat', '_foo._bar', 'foo.bar69', 'foo.bar-69', 'org.mpris.MediaPlayer2.google-play-desktop-player' ] invalid_names = [ None, {}, '', '5foo.bar', 'foo.6bar', '.foo.bar', 'bar..baz', '$foo.bar', 'foo$.ba$r' ] for name in valid_names: assert is_bus_name_valid(name), f'bus name should be valid: "{name}"' for name in invalid_names: assert not is_bus_name_valid(name), f'bus name should be invalid: "{name}"' def test_interface_name_validator(): valid_names = ['foo.bar', 'foo.bar.bat', '_foo._bar', 'foo.bar69'] invalid_names = [ None, {}, '', '5foo.bar', 'foo.6bar', '.foo.bar', 'bar..baz', '$foo.bar', 'foo$.ba$r', 'org.mpris.MediaPlayer2.google-play-desktop-player' ] for name in valid_names: assert is_interface_name_valid(name), f'interface name should be valid: "{name}"' for name in invalid_names: assert not is_interface_name_valid(name), f'interface name should be invalid: "{name}"' def test_member_name_validator(): valid_members = ['foo', 'FooBar', 'Bat_Baz69'] invalid_members = [None, {}, '', 'foo.bar', '5foo', 'foo$bar'] for member in valid_members: assert is_member_name_valid(member), f'member name should be valid: "{member}"' for member in invalid_members: assert not is_member_name_valid(member), f'member name should be invalid: "{member}"'

38.811321

95

0.635391

from dbus_next import (is_bus_name_valid, is_object_path_valid, is_interface_name_valid, is_member_name_valid) def test_object_path_validator(): valid_paths = ['/', '/foo', '/foo/bar', '/foo/bar/bat'] invalid_paths = [ None, {}, '', 'foo', 'foo/bar', '/foo/bar/', '/$/foo/bar', '/foo//bar', '/foo$bar/baz' ] for path in valid_paths: assert is_object_path_valid(path), f'path should be valid: "{path}"' for path in invalid_paths: assert not is_object_path_valid(path), f'path should be invalid: "{path}"' def test_bus_name_validator(): valid_names = [ 'foo.bar', 'foo.bar.bat', '_foo._bar', 'foo.bar69', 'foo.bar-69', 'org.mpris.MediaPlayer2.google-play-desktop-player' ] invalid_names = [ None, {}, '', '5foo.bar', 'foo.6bar', '.foo.bar', 'bar..baz', '$foo.bar', 'foo$.ba$r' ] for name in valid_names: assert is_bus_name_valid(name), f'bus name should be valid: "{name}"' for name in invalid_names: assert not is_bus_name_valid(name), f'bus name should be invalid: "{name}"' def test_interface_name_validator(): valid_names = ['foo.bar', 'foo.bar.bat', '_foo._bar', 'foo.bar69'] invalid_names = [ None, {}, '', '5foo.bar', 'foo.6bar', '.foo.bar', 'bar..baz', '$foo.bar', 'foo$.ba$r', 'org.mpris.MediaPlayer2.google-play-desktop-player' ] for name in valid_names: assert is_interface_name_valid(name), f'interface name should be valid: "{name}"' for name in invalid_names: assert not is_interface_name_valid(name), f'interface name should be invalid: "{name}"' def test_member_name_validator(): valid_members = ['foo', 'FooBar', 'Bat_Baz69'] invalid_members = [None, {}, '', 'foo.bar', '5foo', 'foo$bar'] for member in valid_members: assert is_member_name_valid(member), f'member name should be valid: "{member}"' for member in invalid_members: assert not is_member_name_valid(member), f'member name should be invalid: "{member}"'

true

1c314dfe0048db5f295b09c35b1e27c582e5f4bb

78

py

Python

run.py

TianxiaoHu/GomokuAgent

8cb05025059945692846cbb0541a834e9f985ce2

[ "MIT" ]

15

2017-06-29T07:47:12.000Z

2021-11-09T05:33:59.000Z

run.py

TianxiaoHu/GomokuAgent

8cb05025059945692846cbb0541a834e9f985ce2

[ "MIT" ]

null

run.py

TianxiaoHu/GomokuAgent

8cb05025059945692846cbb0541a834e9f985ce2

[ "MIT" ]

1

2019-12-01T07:53:48.000Z

#!/usr/bin/env python from app import app app.run(debug=True, threaded=True)

15.6

34

0.74359

from app import app app.run(debug=True, threaded=True)

true

1c314fdde8f8337ef25ea5bbed6c290af6543d97

5,429

py

Python

lib/web/ui.py

Juniper/YAPT

b1a54998867c70352001415d5e4b70408480dab9

[ "BSD-3-Clause" ]

33

2018-05-17T04:16:56.000Z

2021-11-25T21:21:02.000Z

lib/web/ui.py

Juniper/YAPT

b1a54998867c70352001415d5e4b70408480dab9

[ "BSD-3-Clause" ]

4

2021-01-10T20:45:31.000Z

2021-09-23T23:21:16.000Z

lib/web/ui.py

Juniper/YAPT

b1a54998867c70352001415d5e4b70408480dab9

[ "BSD-3-Clause" ]

8

2018-09-19T12:18:54.000Z

2021-01-10T03:49:10.000Z

# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER # Copyright (c) 2018 Juniper Networks, Inc. # All rights reserved. # Use is subject to license terms. # # Author: cklewar import socket import jsonpickle import lib.constants as c from lib.amqp.amqpadapter import AMQPBlockingServerAdapter from lib.amqp.amqpmessage import AMQPMessage from lib.web.logviewer import LogViewer from lib.logmsg import LogCommon from lib.logmsg import LogUiProcessor as logmsg from lib.processor import BackendClientProcessor from lib.tools import Tools from lib.web.adapter.amqp2ws import Amqp2ws class UiProcessor(AMQPBlockingServerAdapter): def __init__(self, group=None, target=None, name=None, args=(), kwargs=None): super(UiProcessor, self).__init__(group=group, target=target, name=name, args=args, kwargs=kwargs) self._logger.debug(Tools.create_log_msg(self.__class__.__name__, None, LogCommon.IS_SUBCLASS.format(self.__class__.__name__, issubclass(UiProcessor, AMQPBlockingServerAdapter)))) self.url = 'ws://{0}:{1}/yapt/ws?clientname={2}'.format(c.conf.YAPT.WebUiAddress, str(c.conf.YAPT.WebUiPort), c.conf.YAPT.WebUiPlugin) self.amqp2ws = Amqp2ws(name=c.conf.YAPT.WebUiPlugin, url=self.url) self.backendp = BackendClientProcessor(exchange='', routing_key=c.AMQP_RPC_BACKEND_QUEUE) LogViewer().run_service() def receive_message(self, ch, method, properties, body): if body is not None: ch.basic_ack(delivery_tag=method.delivery_tag) body_decoded = jsonpickle.decode(body) if isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_ADD == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_ADD_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_UPDATE == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_UPDATE_TASK_STATE == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) device_serial = body_decoded.payload[0] task_name = body_decoded.payload[1] task_state = body_decoded.payload[2] message = self.amqp2ws.prepare_device_task_data(device_serial=device_serial, action=c.UI_ACTION_UPDATE_TASK_STATE, task_name=task_name, task_state=task_state) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_AND_RESET == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE_AND_RESET_TASK) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_AND_REBOOT == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_LOG_VIEWER == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) self.conn_hdlr(message=body_decoded.payload) else: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) else: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_AMQP_MSG_NOK) def send_message(self, message, routing_key): pass def conn_hdlr(self, message=None): amqp2ws = Amqp2ws(name=c.conf.YAPT.WebUiPlugin, url=self.url) try: amqp2ws.connect() if message is not None: amqp2ws.send(message) amqp2ws.close() else: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_WS_MSG_NOK) except socket.error as se: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_WS_SOCK_ERR.format(se.message, se.filename, se.strerror, se.args))

47.208696

117

0.640081

import socket import jsonpickle import lib.constants as c from lib.amqp.amqpadapter import AMQPBlockingServerAdapter from lib.amqp.amqpmessage import AMQPMessage from lib.web.logviewer import LogViewer from lib.logmsg import LogCommon from lib.logmsg import LogUiProcessor as logmsg from lib.processor import BackendClientProcessor from lib.tools import Tools from lib.web.adapter.amqp2ws import Amqp2ws class UiProcessor(AMQPBlockingServerAdapter): def __init__(self, group=None, target=None, name=None, args=(), kwargs=None): super(UiProcessor, self).__init__(group=group, target=target, name=name, args=args, kwargs=kwargs) self._logger.debug(Tools.create_log_msg(self.__class__.__name__, None, LogCommon.IS_SUBCLASS.format(self.__class__.__name__, issubclass(UiProcessor, AMQPBlockingServerAdapter)))) self.url = 'ws://{0}:{1}/yapt/ws?clientname={2}'.format(c.conf.YAPT.WebUiAddress, str(c.conf.YAPT.WebUiPort), c.conf.YAPT.WebUiPlugin) self.amqp2ws = Amqp2ws(name=c.conf.YAPT.WebUiPlugin, url=self.url) self.backendp = BackendClientProcessor(exchange='', routing_key=c.AMQP_RPC_BACKEND_QUEUE) LogViewer().run_service() def receive_message(self, ch, method, properties, body): if body is not None: ch.basic_ack(delivery_tag=method.delivery_tag) body_decoded = jsonpickle.decode(body) if isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_ADD == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_ADD_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_UPDATE == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_DEVICE_UPDATE_TASK_STATE == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) device_serial = body_decoded.payload[0] task_name = body_decoded.payload[1] task_state = body_decoded.payload[2] message = self.amqp2ws.prepare_device_task_data(device_serial=device_serial, action=c.UI_ACTION_UPDATE_TASK_STATE, task_name=task_name, task_state=task_state) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_AND_RESET == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE_AND_RESET_TASK) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_AND_REBOOT == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) message = body_decoded.payload.device_to_json(action=c.UI_ACTION_UPDATE_DEVICE) self.conn_hdlr(message=message) elif isinstance(body_decoded, AMQPMessage) and c.AMQP_MSG_TYPE_UI_UPDATE_LOG_VIEWER == body_decoded.message_type: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) self.conn_hdlr(message=body_decoded.payload) else: Tools.amqp_receive_to_logger(routing_key=method.routing_key, body_decoded=body_decoded) else: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_AMQP_MSG_NOK) def send_message(self, message, routing_key): pass def conn_hdlr(self, message=None): amqp2ws = Amqp2ws(name=c.conf.YAPT.WebUiPlugin, url=self.url) try: amqp2ws.connect() if message is not None: amqp2ws.send(message) amqp2ws.close() else: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_WS_MSG_NOK) except socket.error as se: Tools.create_log_msg(self.__class__.__name__, None, logmsg.UIPRO_WS_SOCK_ERR.format(se.message, se.filename, se.strerror, se.args))

true

1c3150901e20732eec2c6bd14f28487bfc0b51c5

1,684

py

Python

freezing/model/migrations/versions/f620a24f5f7e_least_variance.py

freezingsaddles/freezing-model

3bb03739d5bdff418bcf17707a52c9994c45e52f

[ "Apache-2.0" ]

2

2020-01-02T01:23:00.000Z

2022-01-03T20:57:39.000Z

freezing/model/migrations/versions/f620a24f5f7e_least_variance.py

freezingsaddles/freezing-model

3bb03739d5bdff418bcf17707a52c9994c45e52f

[ "Apache-2.0" ]

8

2018-01-19T14:36:05.000Z

2021-11-24T19:22:19.000Z

freezing/model/migrations/versions/f620a24f5f7e_least_variance.py

freezingsaddles/freezing-model

3bb03739d5bdff418bcf17707a52c9994c45e52f

[ "Apache-2.0" ]

1

2018-10-28T16:09:51.000Z

from alembic import op import sqlalchemy as sa """least-variance Revision ID: f620a24f5f7e Revises: b4d003c71167 Create Date: 2020-01-03 23:06:50.491509 """ # revision identifiers, used by Alembic. revision = "f620a24f5f7e" down_revision = "b4d003c71167" def upgrade(): op.execute( """ create or replace view variance_by_day as select ds.athlete_id, sum(case when ds.distance >= 1 then 1 else 0 end) ride_days, sum(distance) total_miles, var_pop(case when dayofweek(ds.ride_date)=1 then ds.distance end) sun_var_pop, var_pop(case when dayofweek(ds.ride_date)=2 then ds.distance end) mon_var_pop, var_pop(case when dayofweek(ds.ride_date)=3 then ds.distance end) tue_var_pop, var_pop(case when dayofweek(ds.ride_date)=4 then ds.distance end) wed_var_pop, var_pop(case when dayofweek(ds.ride_date)=5 then ds.distance end) thu_var_pop, var_pop(case when dayofweek(ds.ride_date)=6 then ds.distance end) fri_var_pop, var_pop(case when dayofweek(ds.ride_date)=7 then ds.distance end) sat_var_pop from daily_scores ds group by ds.athlete_id; """ ) def downgrade(): op.execute( """ drop view variance_by_day ; """ )

31.773585

78

0.515439

from alembic import op import sqlalchemy as sa revision = "f620a24f5f7e" down_revision = "b4d003c71167" def upgrade(): op.execute( """ create or replace view variance_by_day as select ds.athlete_id, sum(case when ds.distance >= 1 then 1 else 0 end) ride_days, sum(distance) total_miles, var_pop(case when dayofweek(ds.ride_date)=1 then ds.distance end) sun_var_pop, var_pop(case when dayofweek(ds.ride_date)=2 then ds.distance end) mon_var_pop, var_pop(case when dayofweek(ds.ride_date)=3 then ds.distance end) tue_var_pop, var_pop(case when dayofweek(ds.ride_date)=4 then ds.distance end) wed_var_pop, var_pop(case when dayofweek(ds.ride_date)=5 then ds.distance end) thu_var_pop, var_pop(case when dayofweek(ds.ride_date)=6 then ds.distance end) fri_var_pop, var_pop(case when dayofweek(ds.ride_date)=7 then ds.distance end) sat_var_pop from daily_scores ds group by ds.athlete_id; """ ) def downgrade(): op.execute( """ drop view variance_by_day ; """ )

true

1c3151b5319a6b48830c7ba8f9a693be51342a4a

98

py

Python

config.py

boada/microblog

84f2d1a71327da3f6283b74a3b3d722e034b2f5f

[ "MIT" ]

1

2020-02-21T16:13:45.000Z

config.py

boada/microblog

84f2d1a71327da3f6283b74a3b3d722e034b2f5f

[ "MIT" ]

null

config.py

boada/microblog

84f2d1a71327da3f6283b74a3b3d722e034b2f5f

[ "MIT" ]

null

import os class Config(object): SECRET_KEY = os.environ.get('SECRET_KEY') or 'my-secret-key'

19.6

64

0.714286

import os class Config(object): SECRET_KEY = os.environ.get('SECRET_KEY') or 'my-secret-key'

true

1c3152ca4581bd3fdb80edd4e4c01537da05cec5

1,256

py

Python

qcloudsdkmonitor/GetMonitorDataRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkmonitor/GetMonitorDataRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkmonitor/GetMonitorDataRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class GetMonitorDataRequest(Request): def __init__(self): super(GetMonitorDataRequest, self).__init__( 'monitor', 'qcloudcliV1', 'GetMonitorData', 'monitor.api.qcloud.com') def get_dimensions(self): return self.get_params().get('dimensions') def set_dimensions(self, dimensions): self.add_param('dimensions', dimensions) def get_endTime(self): return self.get_params().get('endTime') def set_endTime(self, endTime): self.add_param('endTime', endTime) def get_metricName(self): return self.get_params().get('metricName') def set_metricName(self, metricName): self.add_param('metricName', metricName) def get_namespace(self): return self.get_params().get('namespace') def set_namespace(self, namespace): self.add_param('namespace', namespace) def get_period(self): return self.get_params().get('period') def set_period(self, period): self.add_param('period', period) def get_startTime(self): return self.get_params().get('startTime') def set_startTime(self, startTime): self.add_param('startTime', startTime)

27.304348

81

0.667197

from qcloudsdkcore.request import Request class GetMonitorDataRequest(Request): def __init__(self): super(GetMonitorDataRequest, self).__init__( 'monitor', 'qcloudcliV1', 'GetMonitorData', 'monitor.api.qcloud.com') def get_dimensions(self): return self.get_params().get('dimensions') def set_dimensions(self, dimensions): self.add_param('dimensions', dimensions) def get_endTime(self): return self.get_params().get('endTime') def set_endTime(self, endTime): self.add_param('endTime', endTime) def get_metricName(self): return self.get_params().get('metricName') def set_metricName(self, metricName): self.add_param('metricName', metricName) def get_namespace(self): return self.get_params().get('namespace') def set_namespace(self, namespace): self.add_param('namespace', namespace) def get_period(self): return self.get_params().get('period') def set_period(self, period): self.add_param('period', period) def get_startTime(self): return self.get_params().get('startTime') def set_startTime(self, startTime): self.add_param('startTime', startTime)

true

1c3153c25f4173c0d69cf19c9085eee9f382434a

2,768

py

Python

plenum/test/txn_author_agreement/test_get_empty_txn_author_agreement.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

null

plenum/test/txn_author_agreement/test_get_empty_txn_author_agreement.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

1

2019-03-20T14:57:22.000Z

2019-03-20T15:01:55.000Z

plenum/test/txn_author_agreement/test_get_empty_txn_author_agreement.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

null

import pytest from plenum.common.constants import REPLY, CONFIG_LEDGER_ID from plenum.common.exceptions import RequestNackedException from plenum.common.util import get_utc_epoch from plenum.test.delayers import req_delay from plenum.test.stasher import delay_rules from plenum.test.txn_author_agreement.helper import sdk_get_txn_author_agreement, check_state_proof whitelist = ['Unexpected combination of request parameters'] TIMESTAMP_NONE = None @pytest.fixture(scope='module') def nodeSetWithoutTaaAlwaysResponding(txnPoolNodeSet, looper): global TIMESTAMP_NONE # Simulate freshness update txnPoolNodeSet[0].master_replica._do_send_3pc_batch(ledger_id=CONFIG_LEDGER_ID) looper.runFor(1) # Make sure we have long enough gap between updates TIMESTAMP_NONE = get_utc_epoch() return txnPoolNodeSet @pytest.fixture(scope='function', params=['all_responding', 'one_responding']) def nodeSetWithoutTaa(request, nodeSetWithoutTaaAlwaysResponding): if request.param == 'all_responding': yield nodeSetWithoutTaaAlwaysResponding else: stashers = [node.clientIbStasher for node in nodeSetWithoutTaaAlwaysResponding[1:]] with delay_rules(stashers, req_delay()): yield nodeSetWithoutTaaAlwaysResponding @pytest.mark.parametrize(argnames="params, state_key", argvalues=[ ({}, '2:latest'), ({'digest': 'some_digest'}, '2:d:some_digest'), ({'version': 'some_version'}, '2:v:some_version'), ({'timestamp': TIMESTAMP_NONE}, '2:latest') ]) def test_get_txn_author_agreement_works_on_clear_state(params, state_key, looper, nodeSetWithoutTaa, sdk_pool_handle, sdk_wallet_client): reply = sdk_get_txn_author_agreement(looper, sdk_pool_handle, sdk_wallet_client, **params)[1] assert reply['op'] == REPLY result = reply['result'] assert result['data'] is None check_state_proof(result, state_key, None) @pytest.mark.parametrize(argnames="params", argvalues=[ {'digest': 'some_digest', 'version': 'some_version'}, {'digest': 'some_digest', 'timestamp': 374273}, {'version': 'some_version', 'timestamp': 374273}, {'digest': 'some_digest', 'version': 'some_version', 'timestamp': 374273} ]) def test_get_txn_author_agreement_cannot_have_more_than_one_parameter(params, looper, nodeSetWithoutTaa, sdk_pool_handle, sdk_wallet_client): with pytest.raises(RequestNackedException) as e: sdk_get_txn_author_agreement(looper, sdk_pool_handle, sdk_wallet_client, **params) assert e.match("GET_TXN_AUTHOR_AGREEMENT request can have at most one " "of the following parameters: version, digest, timestamp")

41.939394

106

0.725072

import pytest from plenum.common.constants import REPLY, CONFIG_LEDGER_ID from plenum.common.exceptions import RequestNackedException from plenum.common.util import get_utc_epoch from plenum.test.delayers import req_delay from plenum.test.stasher import delay_rules from plenum.test.txn_author_agreement.helper import sdk_get_txn_author_agreement, check_state_proof whitelist = ['Unexpected combination of request parameters'] TIMESTAMP_NONE = None @pytest.fixture(scope='module') def nodeSetWithoutTaaAlwaysResponding(txnPoolNodeSet, looper): global TIMESTAMP_NONE txnPoolNodeSet[0].master_replica._do_send_3pc_batch(ledger_id=CONFIG_LEDGER_ID) looper.runFor(1) TIMESTAMP_NONE = get_utc_epoch() return txnPoolNodeSet @pytest.fixture(scope='function', params=['all_responding', 'one_responding']) def nodeSetWithoutTaa(request, nodeSetWithoutTaaAlwaysResponding): if request.param == 'all_responding': yield nodeSetWithoutTaaAlwaysResponding else: stashers = [node.clientIbStasher for node in nodeSetWithoutTaaAlwaysResponding[1:]] with delay_rules(stashers, req_delay()): yield nodeSetWithoutTaaAlwaysResponding @pytest.mark.parametrize(argnames="params, state_key", argvalues=[ ({}, '2:latest'), ({'digest': 'some_digest'}, '2:d:some_digest'), ({'version': 'some_version'}, '2:v:some_version'), ({'timestamp': TIMESTAMP_NONE}, '2:latest') ]) def test_get_txn_author_agreement_works_on_clear_state(params, state_key, looper, nodeSetWithoutTaa, sdk_pool_handle, sdk_wallet_client): reply = sdk_get_txn_author_agreement(looper, sdk_pool_handle, sdk_wallet_client, **params)[1] assert reply['op'] == REPLY result = reply['result'] assert result['data'] is None check_state_proof(result, state_key, None) @pytest.mark.parametrize(argnames="params", argvalues=[ {'digest': 'some_digest', 'version': 'some_version'}, {'digest': 'some_digest', 'timestamp': 374273}, {'version': 'some_version', 'timestamp': 374273}, {'digest': 'some_digest', 'version': 'some_version', 'timestamp': 374273} ]) def test_get_txn_author_agreement_cannot_have_more_than_one_parameter(params, looper, nodeSetWithoutTaa, sdk_pool_handle, sdk_wallet_client): with pytest.raises(RequestNackedException) as e: sdk_get_txn_author_agreement(looper, sdk_pool_handle, sdk_wallet_client, **params) assert e.match("GET_TXN_AUTHOR_AGREEMENT request can have at most one " "of the following parameters: version, digest, timestamp")

true

1c31541017e2e3db5152ae18abbb5211d1ab50d4

6,481

py

Python

analyze_tls.py

khushhallchandra/CN-project

405ce86e4e65e116531aa19287b8d05c959b1441

[ "MIT" ]

null

analyze_tls.py

khushhallchandra/CN-project

405ce86e4e65e116531aa19287b8d05c959b1441

[ "MIT" ]

null

analyze_tls.py

khushhallchandra/CN-project

405ce86e4e65e116531aa19287b8d05c959b1441

[ "MIT" ]

null

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)

54.923729

257

0.738158

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.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.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)

true