xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

96ecdeb7b91b53fb476cd19d209335bddd30ed72

21,284

py

Python

uatu/watchers/networks.py

mclaughlin6464/uatu

c6f4e76c2477bb0e5221d575a9e0f4eb5a249e7b

[ "MIT" ]

1

2018-05-07T17:16:18.000Z

uatu/watchers/networks.py

mclaughlin6464/uatu

c6f4e76c2477bb0e5221d575a9e0f4eb5a249e7b

[ "MIT" ]

1

2018-05-07T17:16:40.000Z

2018-06-01T02:20:37.000Z

uatu/watchers/networks.py

mclaughlin6464/uatu

c6f4e76c2477bb0e5221d575a9e0f4eb5a249e7b

[ "MIT" ]

null

""" This module holds all the neural network models for uatu. To start, their architecture will be mostly hardcoded, but I may generalize it in the futuere. """ try: import tensorflow as tf except: pass def standard_convnet_init_fn(inputs, training= False): #TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? #prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=62, padding='same') # kernel_initializer=initializer) bn1_out = tf.layers.batch_normalization(conv1_out, axis = axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(31, 31, 31), strides = 2) conv2_out = tf.layers.conv3d(ap1_out, 12, kernel_size=(28, 28, 28), padding='same') # kernel_initializer=initializer) bn2_out = tf.layers.batch_normalization(conv2_out, axis = axis, training=training) lr2_out = tf.nn.leaky_relu(bn2_out, alpha=0.01) ap2_out = tf.layers.average_pooling3d(lr2_out, pool_size=(14, 14, 14), strides = 2) conv3_out = tf.layers.conv3d(ap2_out, 64, kernel_size=(6, 6, 6), padding='same') # kernel_initializer=initializer) bn3_out = tf.layers.batch_normalization(conv3_out, axis = axis, training=training) lr3_out = tf.nn.leaky_relu(bn3_out, alpha=0.01) conv4_out = tf.layers.conv3d(lr3_out, 64, kernel_size=(4, 4, 4), padding='same') # kernel_initializer=initializer) bn4_out = tf.layers.batch_normalization(conv4_out, axis = axis, training=training) lr4_out = tf.nn.leaky_relu(bn4_out, alpha=0.01) conv5_out = tf.layers.conv3d(lr4_out, 128, kernel_size=(3, 3, 3), padding='same') # kernel_initializer=initializer) bn5_out= tf.layers.batch_normalization(conv5_out, axis = axis, training=training) lr5_out = tf.nn.leaky_relu(bn5_out, alpha=0.01) conv6_out = tf.layers.conv3d(lr5_out, 128, kernel_size=(2, 2, 2), padding='same') # kernel_initializer=initializer) bn6_out = tf.layers.batch_normalization(conv6_out, axis = axis, training= training) lr6_out = tf.nn.leaky_relu(bn6_out, alpha=0.01) flat_out = tf.layers.flatten(lr6_out) dense1_out = tf.layers.dense(flat_out, 1024)# kernel_initializer=initializer) #drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(dense1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 256)# kernel_initializer=initializer) #drop2_out = tf.layers.dropout(dense2_out, training=training) lr8_out = tf.nn.leaky_relu(dense2_out, alpha=0.01) dense3_out = tf.layers.dense(lr8_out, 2)# kernel_initializer=initializer) return dense3_out def bayesian_convnet_init_fn(inputs, bayes_prob=0.95, training= False): #TODO add more customization #initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? #prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=62, padding='same') # kernel_initializer=initializer) bd1_out = tf.nn.dropout(conv1_out, keep_prob = bayes_prob)#, training = True) bn1_out = tf.layers.batch_normalization(bd1_out, axis = axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(31, 31, 31), strides = 2) conv2_out = tf.layers.conv3d(ap1_out, 12, kernel_size=(28, 28, 28), padding='same') # kernel_initializer=initializer) bd2_out = tf.layers.dropout(conv2_out, rate = bayes_prob, training = True) bn2_out = tf.layers.batch_normalization(bd2_out, axis = axis, training=training) lr2_out = tf.nn.leaky_relu(bn2_out, alpha=0.01) ap2_out = tf.layers.average_pooling3d(lr2_out, pool_size=(14, 14, 14), strides = 2) conv3_out = tf.layers.conv3d(ap2_out, 64, kernel_size=(6, 6, 6), padding='same') # kernel_initializer=initializer) bd3_out = tf.layers.dropout(conv3_out, rate = bayes_prob, training = True) bn3_out = tf.layers.batch_normalization(bd3_out, axis = axis, training=training) lr3_out = tf.nn.leaky_relu(bn3_out, alpha=0.01) conv4_out = tf.layers.conv3d(lr3_out, 64, kernel_size=(4, 4, 4), padding='same') # kernel_initializer=initializer) bd4_out = tf.layers.dropout(conv4_out, rate = bayes_prob, training = True) bn4_out = tf.layers.batch_normalization(bd4_out, axis = axis, training=training) lr4_out = tf.nn.leaky_relu(bn4_out, alpha=0.01) conv5_out = tf.layers.conv3d(lr4_out, 128, kernel_size=(3, 3, 3), padding='same') # kernel_initializer=initializer) bd5_out = tf.layers.dropout(conv5_out, rate = bayes_prob, training = True) bn5_out= tf.layers.batch_normalization(bd5_out, axis = axis, training=training) lr5_out = tf.nn.leaky_relu(bn5_out, alpha=0.01) conv6_out = tf.layers.conv3d(lr5_out, 128, kernel_size=(2, 2, 2), padding='same') # kernel_initializer=initializer) bd6_out = tf.layers.dropout(conv6_out, rate = bayes_prob, training = True) bn6_out = tf.layers.batch_normalization(bd6_out, axis = axis, training= training) lr6_out = tf.nn.leaky_relu(bn6_out, alpha=0.01) flat_out = tf.layers.flatten(lr6_out) dense1_out = tf.layers.dense(flat_out, 1024)# kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 256)# kernel_initializer=initializer) drop2_out = tf.layers.dropout(dense2_out, training=training) lr8_out = tf.nn.leaky_relu(drop2_out, alpha=0.01) dense3_out = tf.layers.dense(lr8_out, 5)# kernel_initializer=initializer) return dense3_out def shallow_convnet_init_fn(inputs, training=False): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=32, padding='same', kernel_initializer=initializer) bn1_out = tf.layers.batch_normalization(conv1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(24,24,24), strides=2) conv2_out = tf.layers.conv3d(ap1_out, 12, kernel_size=16, padding='same', kernel_initializer=initializer) bn2_out = tf.layers.batch_normalization(conv2_out, axis=axis, training=training) lr2_out = tf.nn.leaky_relu(bn2_out, alpha=0.01) ap2_out = tf.layers.average_pooling3d(lr2_out, pool_size=(8, 8, 8), strides=2) conv3_out = tf.layers.conv3d(ap2_out, 64, kernel_size=4, padding='same', kernel_initializer=initializer) bn3_out = tf.layers.batch_normalization(conv3_out, axis=axis, training=training) lr3_out = tf.nn.leaky_relu(bn3_out, alpha=0.01) # conv4_out = tf.layers.conv3d(lr3_out, 64, kernel_size=(4, 4, 4), padding='same') # kernel_initializer=initializer) # bn4_out = tf.layers.batch_normalization(conv4_out, axis = axis, training=training) # lr4_out = tf.nn.leaky_relu(bn4_out, alpha=0.01) # conv5_out = tf.layers.conv3d(lr4_out, 128, kernel_size=(3, 3, 3), padding='same') # kernel_initializer=initializer) # bn5_out= tf.layers.batch_normalization(conv5_out, axis = axis, training=training) # lr5_out = tf.nn.leaky_relu(bn5_out, alpha=0.01) # conv6_out = tf.layers.conv3d(lr5_out, 128, kernel_size=(2, 2, 2), padding='same') # kernel_initializer=initializer) # bn6_out = tf.layers.batch_normalization(conv6_out, axis = axis, training= training) # lr6_out = tf.nn.leaky_relu(bn6_out, alpha=0.01) flat_out = tf.layers.flatten(lr3_out) dense1_out = tf.layers.dense(flat_out, 1024) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 256) # kernel_initializer=initializer) drop2_out = tf.layers.dropout(dense2_out, training=training) lr8_out = tf.nn.leaky_relu(drop2_out, alpha=0.01) dense3_out = tf.layers.dense(lr8_out, 2) # kernel_initializer=initializer) return dense3_out def shallow_bayesian_convnet_init_fn(inputs, training=False, keep_prob = 0.95): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=32, padding='same', kernel_initializer=initializer) bd1_out = tf.layers.dropout(conv1_out, rate= 1-keep_prob, training = True) bn1_out = tf.layers.batch_normalization(bd1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(24,24,24), strides=2) conv2_out = tf.layers.conv3d(ap1_out, 12, kernel_size=16, padding='same', kernel_initializer=initializer) bd2_out = tf.layers.dropout(conv2_out, rate= 1-keep_prob, training = True) bn2_out = tf.layers.batch_normalization(bd2_out, axis=axis, training=training) lr2_out = tf.nn.leaky_relu(bn2_out, alpha=0.01) ap2_out = tf.layers.average_pooling3d(lr2_out, pool_size=(8, 8, 8), strides=2) conv3_out = tf.layers.conv3d(ap2_out, 64, kernel_size=4, padding='same', kernel_initializer=initializer) bd3_out = tf.layers.dropout(conv3_out, rate= 1-keep_prob, training = True) bn3_out = tf.layers.batch_normalization(bd3_out, axis=axis, training=training) lr3_out = tf.nn.leaky_relu(bn3_out, alpha=0.01) # conv4_out = tf.layers.conv3d(lr3_out, 64, kernel_size=(4, 4, 4), padding='same') # kernel_initializer=initializer) # bn4_out = tf.layers.batch_normalization(conv4_out, axis = axis, training=training) # lr4_out = tf.nn.leaky_relu(bn4_out, alpha=0.01) # conv5_out = tf.layers.conv3d(lr4_out, 128, kernel_size=(3, 3, 3), padding='same') # kernel_initializer=initializer) # bn5_out= tf.layers.batch_normalization(conv5_out, axis = axis, training=training) # lr5_out = tf.nn.leaky_relu(bn5_out, alpha=0.01) # conv6_out = tf.layers.conv3d(lr5_out, 128, kernel_size=(2, 2, 2), padding='same') # kernel_initializer=initializer) # bn6_out = tf.layers.batch_normalization(conv6_out, axis = axis, training= training) # lr6_out = tf.nn.leaky_relu(bn6_out, alpha=0.01) flat_out = tf.layers.flatten(lr3_out) dense1_out = tf.layers.dense(flat_out, 1024) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 256) # kernel_initializer=initializer) drop2_out = tf.layers.dropout(dense2_out, training=training) lr8_out = tf.nn.leaky_relu(drop2_out, alpha=0.01) dense3_out = tf.layers.dense(lr8_out, 4) # kernel_initializer=initializer) return dense3_out def shallow_original_bayesian_convnet_init_fn(inputs, training=False, keep_prob = 0.95): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=32, padding='same', kernel_initializer=initializer) bd1_out = tf.layers.dropout(conv1_out, rate= 1-keep_prob, training = True) bn1_out = tf.layers.batch_normalization(bd1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(24,24,24), strides=2) conv2_out = tf.layers.conv3d(ap1_out, 12, kernel_size=16, padding='same', kernel_initializer=initializer) bd2_out = tf.layers.dropout(conv2_out, rate= 1-keep_prob, training = True) bn2_out = tf.layers.batch_normalization(bd2_out, axis=axis, training=training) lr2_out = tf.nn.leaky_relu(bn2_out, alpha=0.01) ap2_out = tf.layers.average_pooling3d(lr2_out, pool_size=(8, 8, 8), strides=2) conv3_out = tf.layers.conv3d(ap2_out, 64, kernel_size=4, padding='same', kernel_initializer=initializer) bd3_out = tf.layers.dropout(conv3_out, rate= 1-keep_prob, training = True) bn3_out = tf.layers.batch_normalization(bd3_out, axis=axis, training=training) lr3_out = tf.nn.leaky_relu(bn3_out, alpha=0.01) # conv4_out = tf.layers.conv3d(lr3_out, 64, kernel_size=(4, 4, 4), padding='same') # kernel_initializer=initializer) # bn4_out = tf.layers.batch_normalization(conv4_out, axis = axis, training=training) # lr4_out = tf.nn.leaky_relu(bn4_out, alpha=0.01) # conv5_out = tf.layers.conv3d(lr4_out, 128, kernel_size=(3, 3, 3), padding='same') # kernel_initializer=initializer) # bn5_out= tf.layers.batch_normalization(conv5_out, axis = axis, training=training) # lr5_out = tf.nn.leaky_relu(bn5_out, alpha=0.01) # conv6_out = tf.layers.conv3d(lr5_out, 128, kernel_size=(2, 2, 2), padding='same') # kernel_initializer=initializer) # bn6_out = tf.layers.batch_normalization(conv6_out, axis = axis, training= training) # lr6_out = tf.nn.leaky_relu(bn6_out, alpha=0.01) flat_out = tf.layers.flatten(lr3_out) dense1_out = tf.layers.dense(flat_out, 1024) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 256) # kernel_initializer=initializer) drop2_out = tf.layers.dropout(dense2_out, training=training) lr8_out = tf.nn.leaky_relu(drop2_out, alpha=0.01) dense3_out = tf.layers.dense(lr8_out, 5) # kernel_initializer=initializer) return dense3_out def very_shallow_convnet_init_fn(inputs, training=False): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=32, padding='same', kernel_initializer=initializer) bn1_out = tf.layers.batch_normalization(conv1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(24,24,24), strides=2) flat_out = tf.layers.flatten(ap1_out) dense1_out = tf.layers.dense(flat_out, 1024) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 2) # kernel_initializer=initializer) return dense2_out def very_shallow_bayesian_convnet_init_fn(inputs, training=False, keep_prob = 0.95): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=32, padding='same', kernel_initializer=initializer) bd1_out = tf.layers.dropout(conv1_out, rate= 1-keep_prob, training = True) bn1_out = tf.layers.batch_normalization(bd1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(24,24,24), strides=2) flat_out = tf.layers.flatten(ap1_out) dense1_out = tf.layers.dense(flat_out, 1024) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 4) # kernel_initializer=initializer) return dense2_out def most_shallow_bayesian_convnet_init_fn(inputs, training=False, keep_prob = 0.95): # TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? # prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? axis = -1 # NOTE ask waren if i need separate relus conv1_out = tf.layers.conv3d(inputs, 2, kernel_size=16, padding='same', kernel_initializer=initializer) bd1_out = tf.layers.dropout(conv1_out, rate= 1-keep_prob, training = True) bn1_out = tf.layers.batch_normalization(bd1_out, axis=axis, training=training) lr1_out = tf.nn.leaky_relu(bn1_out, alpha=0.01) ap1_out = tf.layers.average_pooling3d(lr1_out, pool_size=(10,10,10), strides=2) flat_out = tf.layers.flatten(ap1_out) dense1_out = tf.layers.dense(flat_out, 512) # kernel_initializer=initializer) drop1_out = tf.layers.dropout(dense1_out, training=training) lr7_out = tf.nn.leaky_relu(drop1_out, alpha=0.01) dense2_out = tf.layers.dense(lr7_out, 4) # kernel_initializer=initializer) return dense2_out def standard_convnet_init_ob(inputs, training= False): #TODO add more customization initializer = tf.variance_scaling_initializer(scale=2.0) # TODO gotta be a better way to do this? prob = tf.cond(training, lambda : 0.5, lambda : 1.0) #should i do some fancier tf stuff? # NOTE ask waren if i need separate relus layers = [ tf.keras.layers.Conv3D(2,input_shape = (64,64,64,1), kernel_size=62, padding='same', kernel_initializer=initializer, name = 'Dickhead'), tf.keras.layers.BatchNormalization(name = 'Butts'), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.AveragePooling3D(pool_size=(31, 31, 31), strides = 2), tf.layers.Conv3D(12, kernel_size=(28, 28, 28), padding='same', kernel_initializer=initializer), #tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.AveragePooling3D(pool_size=(14, 14, 14), strides = 2), tf.layers.Conv3D(64, kernel_size=(6, 6, 6), padding='same', kernel_initializer=initializer), tf.keras.keras.layers.BatchNormalization(axis=1, gamma_initializer=initializer), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.Conv3D(64, kernel_size=(4, 4, 4), padding='same', kernel_initializer=initializer), #tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.Conv3D(128, kernel_size=(3, 3, 3), padding='same', kernel_initializer=initializer), #tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.Conv3D(128, kernel_size=(2, 2, 2), padding='same', kernel_initializer=initializer), #tf.keras.layers.BatchNormalization(), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.Flatten(), tf.keras.layers.Dense(1024, kernel_initializer=initializer), tf.keras.layers.Dropout(rate=prob), tf.keras.layers.LeakyReLU(alpha=0.01), tf.keras.layers.Dense(256, kernel_initializer=initializer), tf.keras.layers.Dropout(rate=prob), tf.keras.layers.LeakyReLU(alpha=0.01), tf.layers.Dense(2, kernel_initializer=initializer),] model = tf.keras.Sequential(layers) return model(inputs) def standard_optimizer_init_fn(lr = 0.0005): return tf.train.AdamOptimizer(learning_rate=lr)

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8c42eab5d6bfdc6f4afcb76c2a7725ae21350a4b

13,217

py

Python

aydin/it/test/test_cnn.py

royerloic/aydin

f9c61a24030891d008c318b250da5faec69fcd7d

[ "BSD-3-Clause" ]

78

2021-11-08T16:11:23.000Z

2022-03-27T17:51:04.000Z

aydin/it/test/test_cnn.py

royerloic/aydin

f9c61a24030891d008c318b250da5faec69fcd7d

[ "BSD-3-Clause" ]

19

2021-11-08T17:15:40.000Z

2022-03-30T17:46:55.000Z

aydin/it/test/test_cnn.py

royerloic/aydin

f9c61a24030891d008c318b250da5faec69fcd7d

[ "BSD-3-Clause" ]

7

2021-11-09T17:42:32.000Z

2022-03-09T00:37:57.000Z

import time import numpy import pytest import tensorflow as tf # noqa: F401 from skimage.data import camera from skimage.exposure import rescale_intensity from skimage.metrics import peak_signal_noise_ratio as psnr from skimage.metrics import structural_similarity as ssim from tensorflow.python.keras.backend import clear_session from aydin.io import io from aydin.io.datasets import normalise, add_noise, examples_single from aydin.it.cnn import ImageTranslatorCNN def test_it_cnn_history(): """ Check if training history is properly recorded. """ start = time.time() max_epochs = 2 data = numpy.zeros((64, 64)) it = ImageTranslatorCNN( model_architecture="unet", training_architecture='checkran', nb_unet_levels=1, patch_size=64, batch_size=1, mask_size=3, total_num_patches=1, patience=1, max_epochs=max_epochs, ) it.train(data, data) history = it.loss_history for key, val in history.history.items(): assert len(val) == max_epochs assert len(history.epoch) == max_epochs stop = time.time() print(f"Total elapsed time: {stop - start} ") clear_session() def test_it_cnn_shiftconv_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 30 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) print("noisy shape: ", noisy.shape) it = ImageTranslatorCNN( model_architecture="unet", training_architecture='shiftconv', nb_unet_levels=2, batch_norm=None, # 'instance', max_epochs=max_epochs, ) it.train(noisy, noisy) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy clear_session() def test_it_cnn_checkerbox_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 5 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture="unet", training_architecture='checkerbox', nb_unet_levels=2, mask_size=3, batch_norm='instance', max_epochs=max_epochs, ) it.train(noisy, noisy) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy clear_session() def test_it_cnn_random_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 5 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture="unet", training_architecture='random', nb_unet_levels=2, batch_norm='instance', max_epochs=max_epochs, ) it.train(noisy, noisy) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy * 0.9 and ssim_denoised > ssim_noisy * 0.9 clear_session() def test_it_cnn_checkran_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 5 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] # Test with arbitrary input shape arbitrary_shape = (1, 1) + image.shape batch_dims = tuple([True if i == 1 else False for i in arbitrary_shape]) image = image.reshape(arbitrary_shape) noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture="unet", training_architecture='checkran', nb_unet_levels=2, mask_size=3, batch_norm='instance', max_epochs=max_epochs, ) it.train(noisy, noisy, batch_axes=batch_dims) denoised = it.translate(noisy, tile_size=image_width, batch_axes=batch_dims) assert denoised.shape == noisy.shape denoised = denoised.squeeze() noisy = noisy.squeeze() image = image.squeeze() image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy clear_session() def test_it_cnn_jinet2D_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 30 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture='jinet', patch_size=image_width, max_epochs=max_epochs ) it.train(noisy, noisy) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy clear_session() def test_it_cnn_jinet2D_supervised_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 30 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture='jinet', patch_size=image_width, max_epochs=max_epochs ) it.train(noisy, image) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy clear_session() def test_it_cnn_jinet3D_light(): """ Demo for self-supervised denoising """ start = time.time() max_epochs = 30 image_width = 64 image_path = examples_single.royerlab_hcr.get_path() image, metadata = io.imread(image_path) image = image[10:20, 1:2, 100 : 100 + image_width, 200 : 200 + image_width] image = rescale_intensity( image.astype(numpy.float32), in_range='image', out_range=(0, 1) ) noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture='jinet', patch_size=image_width, max_epochs=max_epochs ) it.train( noisy, noisy, batch_axes=metadata.batch_axes, channel_axes=metadata.channel_axes ) denoised = it.translate( noisy, tile_size=image_width, batch_axes=metadata.batch_axes, channel_axes=metadata.channel_axes, ) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) noisy = numpy.squeeze(noisy) image = numpy.squeeze(image) denoised = numpy.squeeze(denoised) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > (psnr_noisy * 0.5) and ssim_denoised > (ssim_noisy * 0.5) clear_session() def test_it_cnn_jinet3D_supervised_light(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 30 image_width = 64 image_path = examples_single.royerlab_hcr.get_path() image, metadata = io.imread(image_path) image = image[10:20, 1:2, 100 : 100 + image_width, 200 : 200 + image_width] image = rescale_intensity( image.astype(numpy.float32), in_range='image', out_range=(0, 1) ) noisy = add_noise(image) it = ImageTranslatorCNN( model_architecture='jinet', patch_size=image_width, max_epochs=max_epochs ) it.train( noisy, image, batch_axes=metadata.batch_axes, channel_axes=metadata.channel_axes ) denoised = it.translate( noisy, tile_size=image_width, batch_axes=metadata.batch_axes, channel_axes=metadata.channel_axes, ) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) noisy = numpy.squeeze(noisy) image = numpy.squeeze(image) denoised = numpy.squeeze(denoised) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image, multichannel=True) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image, multichannel=True) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > (psnr_noisy * 0.5) and ssim_denoised > (ssim_noisy * 0.5) clear_session() @pytest.mark.heavy def test_it_cnn_random_patching(): """ Demo for self-supervised denoising using camera image with synthetic noise """ start = time.time() max_epochs = 16 image_width = 100 image = normalise(camera()) H0, W0 = (numpy.array(image.shape) - image_width) // 2 image = image[H0 : H0 + image_width, W0 : W0 + image_width] noisy = add_noise(image) it = ImageTranslatorCNN( training_architecture='random', nb_unet_levels=2, batch_norm='instance', max_epochs=max_epochs, patch_size=64, ) it.train(noisy, noisy) denoised = it.translate(noisy, tile_size=image_width) image = numpy.clip(image, 0, 1) noisy = numpy.clip(noisy.reshape(image.shape), 0, 1) denoised = numpy.clip(denoised, 0, 1) psnr_noisy = psnr(noisy, image) ssim_noisy = ssim(noisy, image) print("noisy", psnr_noisy, ssim_noisy) psnr_denoised = psnr(denoised, image) ssim_denoised = ssim(denoised, image) print("denoised", psnr_denoised, ssim_denoised) stop = time.time() print(f"Total elapsed time: {stop - start} ") assert psnr_denoised > psnr_noisy and ssim_denoised > ssim_noisy

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4fcaecaf2b7662077956627384842b20e16293eb

211

py

Python

microsetta_admin/_model.py

dhakim87/microsetta-admin

306efb273e8fc7efa99f6bfd28372da3f3cf5f2e

[ "BSD-3-Clause" ]

null

microsetta_admin/_model.py

dhakim87/microsetta-admin

306efb273e8fc7efa99f6bfd28372da3f3cf5f2e

[ "BSD-3-Clause" ]

null

microsetta_admin/_model.py

dhakim87/microsetta-admin

306efb273e8fc7efa99f6bfd28372da3f3cf5f2e

[ "BSD-3-Clause" ]

null

from microsetta_private_api.model.sample import Sample from microsetta_private_api.model.source import Source from microsetta_private_api.model.account import Account __all__ = ['Sample', 'Source', 'Account']

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py

Python

mecab/__init__.py

DataLama/custom-python-mecab-ko

5766a0e2369165dd12cc24fadc64a2e93cdebffd

[ "MIT" ]

null

mecab/__init__.py

DataLama/custom-python-mecab-ko

5766a0e2369165dd12cc24fadc64a2e93cdebffd

[ "MIT" ]

null

mecab/__init__.py

DataLama/custom-python-mecab-ko

5766a0e2369165dd12cc24fadc64a2e93cdebffd

[ "MIT" ]

null

from .mecab import MeCabError from .mecab import MeCab from .add_userdict import update_custom_dictionary

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py

Python

entity/cards/BARL_017H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

102

2021-10-20T09:06:39.000Z

2022-03-28T13:35:11.000Z

entity/cards/BARL_017H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

98

2021-10-19T16:13:27.000Z

2022-03-27T13:27:49.000Z

entity/cards/BARL_017H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

55

2021-10-19T03:56:50.000Z

2022-03-25T08:25:26.000Z

# -*- coding: utf-8 -*- import entity.cards.BARL_017H.LETL_470 import entity.cards.BARL_017H.LETL_471 import entity.cards.BARL_017H.LETL_472 import entity.cards.BARL_017H.LETL_706 import entity.cards.BARL_017H.LETL_707 import entity.cards.BARL_017H.LETL_709

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Python

tests/L0/run_amp/test_fused_sgd.py

Mahathi-Vatsal/apex

063d720f1a41f1b5437f0cf7cbbc5e4a81392538

[ "BSD-3-Clause" ]

6

2020-06-01T17:27:13.000Z

2022-01-10T08:59:50.000Z

tests/L0/run_amp/test_fused_sgd.py

Mahathi-Vatsal/apex

063d720f1a41f1b5437f0cf7cbbc5e4a81392538

[ "BSD-3-Clause" ]

43

2020-04-28T17:09:02.000Z

2022-03-31T18:10:01.000Z

tests/L0/run_amp/test_fused_sgd.py

Mahathi-Vatsal/apex

063d720f1a41f1b5437f0cf7cbbc5e4a81392538

[ "BSD-3-Clause" ]

9

2020-05-14T18:41:24.000Z

2022-03-30T00:09:42.000Z

import unittest import functools as ft import itertools as it from apex import amp from apex.amp import _amp_state import torch from torch import nn import torch.nn.functional as F from torch.nn import Parameter from utils import common_init, HALF, FLOAT,\ ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT try: import amp_C disabled = False from apex.optimizers import FusedSGD as FusedSGD except ImportError as err: print("amp_C fused kernels unavailable, disabling TestMultiTensorApply. ImportError was ", err) disabled = True class MyModel(torch.nn.Module): def __init__(self, unique): super(MyModel, self).__init__() self.weight0 = Parameter(unique + torch.arange(2, device='cuda', dtype=torch.float32)) self.weight1 = Parameter(1. + unique + torch.arange(2, device='cuda', dtype=torch.float16)) @staticmethod def ops(input, weight0, weight1): return ((input*(weight0.float()))*(weight1.float())).sum() def forward(self, input): return self.ops(input, self.weight0, self.weight1) # Abandon all hope, ye who enter here. # This is hands down the ugliest code I have ever written, but it succeeds in testing # multiple models/optimizers/losses fairly thoroughly. Many of the different test cases # require slightly divergent code in a way that seems near-impossible to genericize into a simple # cross product or nested loops. class TestMultipleModelsOptimizersLosses(unittest.TestCase): def setUp(self): self.x = torch.ones((2), device='cuda', dtype=torch.float32) common_init(self) def tearDown(self): pass @unittest.skipIf(disabled, "amp_C is unavailable") def test_2models2losses1optimizer(self): model0 = MyModel(1) model1 = MyModel(2) optimizer = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 0.5}], momentum=0.125) reference_grads = [] for i in range(2): optimizer.zero_grad() loss0 = model0(self.x) loss1 = model1(self.x) loss0.backward() loss1.backward() reference_grads.append([param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()]) optimizer.step() final_params = [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] for materialize_master_grads in (False, True): for opt_level in ("O0", "O1", "O2", "O3"): for how_to_zero in ("none", "model", "optimizer"): for use_multiple_loss_scalers in (False, True): if opt_level == "O1" or opt_level == "O2": inject_inf_iters = (-1, 0, 1) else: inject_inf_iters = (-1,) for inject_inf in inject_inf_iters: if inject_inf >= 0: inject_inf_locs = ("fp16", "fp32") which_backwards = (0, 1) else: inject_inf_locs = ("fdsa",) which_backwards = (None,) for inject_inf_loc in inject_inf_locs: for which_backward in which_backwards: if use_multiple_loss_scalers: num_losses = 2 loss_ids = [0, 1] else: num_losses = 1 loss_ids = [0, 0] if inject_inf >= 0: iters = 3 else: iters = 2 model0 = MyModel(1) model1 = MyModel(2) models = [model0, model1] optimizer = FusedSGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 0.5}], momentum=0.125, materialize_master_grads=materialize_master_grads) _amp_state.allow_incoming_model_not_fp32 = True [model0, model1], optimizer = amp.initialize( [model0, model1], optimizer, opt_level=opt_level, verbosity=0, cast_model_type=False, num_losses=num_losses) _amp_state.allow_incoming_model_not_fp32 = False _amp_state.loss_scalers[0]._loss_scale = 4.0 if use_multiple_loss_scalers: _amp_state.loss_scalers[1]._loss_scale = 16.0 unskipped = 0 for i in range(iters): if how_to_zero == "none": for model in models: for param in model.parameters(): param.grad = None elif how_to_zero == "model": for model in models: model.zero_grad() else: optimizer.zero_grad() loss0 = model0(self.x) loss1 = model1(self.x) with amp.scale_loss(loss0, optimizer, loss_id=loss_ids[0]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 0: if inject_inf_loc == "fp32": model0.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": model0.weight1.grad[0] = float('inf') with amp.scale_loss(loss1, optimizer, loss_id=loss_ids[1]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 1: if inject_inf_loc == "fp32": model1.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": model1.weight1.grad[0] = float('inf') if i != inject_inf: master_params = amp.master_params(optimizer) for param, reference_grad in zip(master_params, reference_grads[unskipped]): if opt_level == "O2" and not materialize_master_grads: continue else: self.assertTrue(torch.allclose(param.grad.float(), reference_grad.float()), "opt_level {} i {} inject_inf {} which_backward {} inject_inf_loc {} use_multiple_loss_scalers {}".format(opt_level, i, inject_inf, which_backward, inject_inf_loc, use_multiple_loss_scalers)) unskipped += 1 optimizer.step() model_params = [p for p in model0.parameters()] + [p for p in model1.parameters()] for model, master, reference in zip( model_params, amp.master_params(optimizer), final_params): self.assertTrue(torch.allclose(model, reference)) self.assertTrue(torch.allclose(model, master.to(model.dtype))) if opt_level == "O1": _amp_state.handle._deactivate() @unittest.skipIf(disabled, "amp_C is unavailable") def test_3models2losses1optimizer(self): model0 = MyModel(1) model1 = MyModel(2) model2 = MyModel(3) optimizer = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 0.5}, {'params' : model2.parameters(), 'lr' : 0.125}], momentum=0.125) reference_grads = [] for i in range(2): optimizer.zero_grad() loss0 = model0(self.x) + model2(self.x) loss1 = model1(self.x) + model2(self.x) loss0.backward() loss1.backward() reference_grads.append([param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()] + [param.grad.data.clone() for param in model2.parameters()]) optimizer.step() final_params = [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] + \ [param.data.clone() for param in model2.parameters()] for materialize_master_grads in (False, True): for opt_level in ("O0", "O1", "O2", "O3"): for how_to_zero in ("none", "model", "optimizer"): for use_multiple_loss_scalers in (False, True): if opt_level == "O1" or opt_level == "O2": inject_inf_iters = (-1, 0, 1) else: inject_inf_iters = (-1,) for inject_inf in inject_inf_iters: if inject_inf >= 0: inject_inf_locs = ("fp16", "fp32") which_backwards = (0, 1) else: inject_inf_locs = ("fdsa",) which_backwards = (None,) for inject_inf_loc in inject_inf_locs: for which_backward in which_backwards: if use_multiple_loss_scalers: num_losses = 2 loss_ids = [0, 1] else: num_losses = 1 loss_ids = [0, 0] if inject_inf >= 0: iters = 3 if which_backward == 0: which_models = (0, 2) elif which_backward == 1: which_models = (1, 2) else: iters = 2 which_models = (None,) for which_model in which_models: model0 = MyModel(1) model1 = MyModel(2) model2 = MyModel(3) models = [model0, model1, model2] optimizer = FusedSGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 0.5}, {'params' : model2.parameters(), 'lr' : 0.125}], momentum=0.125, materialize_master_grads=materialize_master_grads) _amp_state.allow_incoming_model_not_fp32 = True [model0, model1, model2], optimizer = amp.initialize( [model0, model1, model2], optimizer, opt_level=opt_level, verbosity=0, cast_model_type=False, num_losses=num_losses) _amp_state.allow_incoming_model_not_fp32 = False _amp_state.loss_scalers[0]._loss_scale = 4.0 if use_multiple_loss_scalers: _amp_state.loss_scalers[1]._loss_scale = 16.0 unskipped = 0 for i in range(iters): if how_to_zero == "none": for model in models: for param in model.parameters(): param.grad = None elif how_to_zero == "model": for model in models: model.zero_grad() else: optimizer.zero_grad() loss0 = model0(self.x) + model2(self.x) loss1 = model1(self.x) + model2(self.x) with amp.scale_loss(loss0, optimizer, loss_id=loss_ids[0]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 0: if which_model == 0: inj_model = model0 elif which_model == 2: inj_model = model2 else: raise RuntimeError(which_model + " invalid for loss 0") if inject_inf_loc == "fp32": inj_model.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": inj_model.weight1.grad[0] = float('inf') with amp.scale_loss(loss1, optimizer, loss_id=loss_ids[1]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 1: if which_model == 1: inj_model = model1 elif which_model == 2: inj_model = model2 else: raise RuntimeError(which_model + " invalid for loss 1 ") if inject_inf_loc == "fp32": inj_model.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": inj_model.weight1.grad[0] = float('inf') if i != inject_inf: master_params = amp.master_params(optimizer) for param, reference_grad in zip(master_params, reference_grads[unskipped]): if opt_level == "O2" and not materialize_master_grads: continue else: self.assertTrue(torch.allclose(param.grad.float(), reference_grad.float()), "opt_level {} i {} inject_inf {} which_backward {} inject_inf_loc {} which_model {} use_multiple_loss_scalers {}".format(opt_level, i, inject_inf, which_backward, inject_inf_loc, which_model, use_multiple_loss_scalers)) unskipped += 1 optimizer.step() model_params = [p for p in model0.parameters()] + \ [p for p in model1.parameters()] + \ [p for p in model2.parameters()] for model, master, reference in zip( model_params, amp.master_params(optimizer), final_params): self.assertTrue(torch.allclose(model, reference)) self.assertTrue(torch.allclose(model, master.to(model.dtype))) if opt_level == "O1": _amp_state.handle._deactivate() @unittest.skipIf(disabled, "amp_C is unavailable") def test_2models2losses2optimizers(self): model0 = MyModel(1) model1 = MyModel(2) optimizer0 = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}], momentum=0.125) optimizer1 = torch.optim.SGD([{'params' : model1.parameters(), 'lr' : 0.5}], momentum=0.25) # Don't do it like this: reference_grads = [[]]*5 # because then it creates a list of 5 references to the same "[]" and appending # to any of them effectively makes you append to all of them, which multiplies # the resulting size of reference_grads by 5x and needless to say makes the test fail. reference_grads = [[], [], [], [], []] final_params = [None, None, None, None, None] for i in range(2): optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) loss1 = model1(self.x) loss0.backward() loss1.backward() reference_grads[0].append([param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()]) optimizer0.step() optimizer1.step() final_params[0] = [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] def what_got_skipped(which_iter, which_backward): if which_iter == 0 and which_backward == 0: return 1 if which_iter == 0 and which_backward == 1: return 2 if which_iter == 1 and which_backward == 0: return 3 if which_iter == 1 and which_backward == 1: return 4 return 0 for which_iter in (0,1): for which_backward in (0,1): model0 = MyModel(1) model1 = MyModel(2) optimizer0 = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}], momentum=0.125) optimizer1 = torch.optim.SGD([{'params' : model1.parameters(), 'lr' : 0.5}], momentum=0.25) for i in range(3): optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) loss1 = model1(self.x) loss0.backward() loss1.backward() if i != which_iter: reference_grads[what_got_skipped(which_iter, which_backward)].append( [param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()]) if i == which_iter: if which_backward == 0: optimizer1.step() else: optimizer0.step() else: optimizer0.step() optimizer1.step() final_params[what_got_skipped(which_iter, which_backward)] = \ [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] for materialize_master_grads in (False, True): for opt_level in ("O0", "O1", "O2", "O3"): for how_to_zero in ("none", "model", "optimizer"): for use_multiple_loss_scalers in (False, True): if opt_level == "O1" or opt_level == "O2": inject_inf_iters = (-1, 0, 1) else: inject_inf_iters = (-1,) for inject_inf in inject_inf_iters: if inject_inf >= 0: inject_inf_locs = ("fp16", "fp32") which_backwards = (0, 1) else: inject_inf_locs = ("fdsa",) which_backwards = (None,) for inject_inf_loc in inject_inf_locs: for which_backward in which_backwards: if use_multiple_loss_scalers: num_losses = 2 loss_ids = [0, 1] else: num_losses = 1 loss_ids = [0, 0] if inject_inf >= 0: iters = 3 else: iters = 2 model0 = MyModel(1) model1 = MyModel(2) models = [model0, model1] optimizer0 = FusedSGD([{'params' : model0.parameters(), 'lr' : 0.25}], momentum=0.125, materialize_master_grads=materialize_master_grads) optimizer1 = FusedSGD([{'params' : model1.parameters(), 'lr' : 0.5}], momentum=0.25, materialize_master_grads=materialize_master_grads) _amp_state.allow_incoming_model_not_fp32 = True [model0, model1], [optimizer0, optimizer1] = amp.initialize( [model0, model1], [optimizer0, optimizer1], opt_level=opt_level, verbosity=0, cast_model_type=False, num_losses=num_losses) _amp_state.allow_incoming_model_not_fp32 = False _amp_state.loss_scalers[0]._loss_scale = 4.0 if use_multiple_loss_scalers: _amp_state.loss_scalers[1]._loss_scale = 16.0 unskipped = 0 for i in range(iters): if how_to_zero == "none": for model in models: for param in model.parameters(): param.grad = None elif how_to_zero == "model": for model in models: model.zero_grad() else: optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) loss1 = model1(self.x) with amp.scale_loss(loss0, optimizer0, loss_id=loss_ids[0]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 0: if inject_inf_loc == "fp32": model0.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": model0.weight1.grad[0] = float('inf') with amp.scale_loss(loss1, optimizer1, loss_id=loss_ids[1]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 1: if inject_inf_loc == "fp32": model1.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": model1.weight1.grad[0] = float('inf') # print("opt_level {} i {} inject_inf {} which_backward {} inject_inf_loc {} use_multiple_loss_scalers {}".format(opt_level, i, inject_inf, which_backward, inject_inf_loc, use_multiple_loss_scalers)) if i != inject_inf: master_params = list(amp.master_params(optimizer0)) + \ list(amp.master_params(optimizer1)) for param, reference_grad in zip(master_params, reference_grads[what_got_skipped(inject_inf, which_backward)][unskipped]): if opt_level == "O2" and not materialize_master_grads: continue else: self.assertTrue(torch.allclose(param.grad.float(), reference_grad.float())) unskipped += 1 optimizer0.step() optimizer1.step() model_params = [p for p in model0.parameters()] + [p for p in model1.parameters()] master_params = [p for p in amp.master_params(optimizer0)] + \ [p for p in amp.master_params(optimizer1)] for model, master, reference in zip( model_params, master_params, final_params[what_got_skipped(inject_inf, which_backward)]): self.assertTrue(torch.allclose(model, reference)) self.assertTrue(torch.allclose(model, master.to(model.dtype))) if opt_level == "O1": _amp_state.handle._deactivate() @unittest.skipIf(disabled, "amp_C is unavailable") def test_3models2losses2optimizers(self): model0 = MyModel(1) model1 = MyModel(2) model2 = MyModel(3) optimizer0 = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 1.0}], momentum=0.5) optimizer1 = torch.optim.SGD([{'params' : model2.parameters(), 'lr' : 0.5}], momentum=0.25) # Again, can't do this: reference_grads = [[]]*9 reference_grads = [[], [], [], [], [], [], [], [], []] final_params = [None, None, None, None, None, None, None, None, None] for i in range(2): optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) + model1(self.x) loss1 = model2(self.x) + model1(self.x) loss0.backward() loss1.backward() reference_grads[0].append([param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()]) optimizer0.step() optimizer1.step() final_params[0] = \ [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] + \ [param.data.clone() for param in model2.parameters()] def what_got_skipped(which_iter, which_backward, which_model): if which_iter == 0: if which_backward == 0: if which_model == 0: return 1 if which_model == 1: return 2 if which_backward == 1: if which_model == 2: return 3 if which_model == 1: return 4 if which_iter == 1: if which_backward == 0: if which_model == 0: return 5 if which_model == 1: return 6 if which_backward == 1: if which_model == 2: return 7 if which_model == 1: return 8 return 0 for which_iter in (0,1): for which_backward in (0,1): if which_backward == 0: which_models = (0,1) if which_backward == 1: which_models = (2,1) for which_model in which_models: model0 = MyModel(1) model1 = MyModel(2) model2 = MyModel(3) optimizer0 = torch.optim.SGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 1.0}], momentum=0.5) optimizer1 = torch.optim.SGD([{'params' : model2.parameters(), 'lr' : 0.5}], momentum=0.25) for i in range(3): optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) + model1(self.x) loss1 = model2(self.x) + model1(self.x) loss0.backward() loss1.backward() if i != which_iter: reference_grads[what_got_skipped(which_iter, which_backward, which_model)].append( [param.grad.data.clone() for param in model0.parameters()] + [param.grad.data.clone() for param in model1.parameters()]) if i == which_iter: if which_backward == 0: # if which_model == 0: optimizer1.step() # if which_model == 1: # optimizer1.step() if which_backward == 1: # if which_model == 2: # optimizer0.step() # if which_model == 1: continue else: optimizer0.step() optimizer1.step() final_params[what_got_skipped(which_iter, which_backward, which_model)] = \ [param.data.clone() for param in model0.parameters()] + \ [param.data.clone() for param in model1.parameters()] + \ [param.data.clone() for param in model2.parameters()] for materialize_master_grads in (False, True): for opt_level in ("O0", "O1", "O2", "O3"): for how_to_zero in ("none", "model", "optimizer"): for use_multiple_loss_scalers in (False, True): if opt_level == "O1" or opt_level == "O2": inject_inf_iters = (-1, 0, 1) else: inject_inf_iters = (-1,) for inject_inf in inject_inf_iters: if inject_inf >= 0: inject_inf_locs = ("fp16", "fp32") which_backwards = (0, 1) else: inject_inf_locs = ("fdsa",) which_backwards = (None,) for inject_inf_loc in inject_inf_locs: for which_backward in which_backwards: if use_multiple_loss_scalers: num_losses = 2 loss_ids = [0, 1] else: num_losses = 1 loss_ids = [0, 0] if inject_inf >= 0: iters = 3 if which_backward == 0: which_models = (0, 1) elif which_backward == 1: which_models = (2, 1) else: iters = 2 which_models = (None,) for which_model in which_models: model0 = MyModel(1) model1 = MyModel(2) model2 = MyModel(3) models = [model0, model1, model2] optimizer0 = FusedSGD([{'params' : model0.parameters(), 'lr' : 0.25}, {'params' : model1.parameters(), 'lr' : 1.0}], momentum=0.5, materialize_master_grads=materialize_master_grads) optimizer1 = FusedSGD([{'params' : model2.parameters(), 'lr' : 0.5}], momentum=0.25, materialize_master_grads=materialize_master_grads) _amp_state.allow_incoming_model_not_fp32 = True [model0, model1, model2], [optimizer0, optimizer1] = amp.initialize( [model0, model1, model2], [optimizer0, optimizer1], opt_level=opt_level, verbosity=0, cast_model_type=False, num_losses=num_losses) _amp_state.allow_incoming_model_not_fp32 = False _amp_state.loss_scalers[0]._loss_scale = 4.0 if use_multiple_loss_scalers: _amp_state.loss_scalers[1]._loss_scale = 16.0 unskipped = 0 for i in range(iters): if how_to_zero == "none": for model in models: for param in model.parameters(): param.grad = None elif how_to_zero == "model": for model in models: model.zero_grad() else: optimizer0.zero_grad() optimizer1.zero_grad() loss0 = model0(self.x) + model1(self.x) loss1 = model2(self.x) + model1(self.x) with amp.scale_loss(loss0, optimizer0, loss_id=loss_ids[0]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 0: if which_model == 0: inj_model = model0 elif which_model == 1: inj_model = model1 else: raise RuntimeError(which_model + " invalid for loss 0") if inject_inf_loc == "fp32": inj_model.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": inj_model.weight1.grad[0] = float('inf') with amp.scale_loss(loss1, [optimizer0, optimizer1], loss_id=loss_ids[1]) as scaled_loss: scaled_loss.backward() if i == inject_inf and which_backward == 1: if which_model == 2: inj_model = model2 elif which_model == 1: inj_model = model1 else: raise RuntimeError(which_model + " invalid for loss 1 ") if inject_inf_loc == "fp32": inj_model.weight0.grad[0] = float('inf') elif inject_inf_loc == "fp16": inj_model.weight1.grad[0] = float('inf') if i != inject_inf: master_params = list(amp.master_params(optimizer0)) + \ list(amp.master_params(optimizer1)) for param, reference_grad in zip(master_params, reference_grads[what_got_skipped(inject_inf, which_backward, which_model)][unskipped]): if opt_level == "O2" and not materialize_master_grads: continue else: self.assertTrue(torch.allclose(param.grad.float(), reference_grad.float())) unskipped += 1 optimizer0.step() optimizer1.step() model_params = [p for p in model0.parameters()] + \ [p for p in model1.parameters()] + \ [p for p in model2.parameters()] master_params = [p for p in amp.master_params(optimizer0)] + \ [p for p in amp.master_params(optimizer1)] # print("opt_level {} i {} inject_inf {} which_backward {} inject_inf_loc {} use_multiple_loss_scalers {} which_model {}".format(opt_level, i, inject_inf, which_backward, inject_inf_loc, use_multiple_loss_scalers, which_model)) for model, master, reference in zip( model_params, master_params, final_params[what_got_skipped(inject_inf, which_backward, which_model)]): self.assertTrue(torch.allclose(model, reference)) self.assertTrue(torch.allclose(model, master.to(model.dtype))) if opt_level == "O1": _amp_state.handle._deactivate() if __name__ == '__main__': unittest.main()

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Python

benchmarks/SimResults/combinations_splash_mylocality/oldstuff/cmp_choleskybarnesfftraytrace/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/combinations_splash_mylocality/oldstuff/cmp_choleskybarnesfftraytrace/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/combinations_splash_mylocality/oldstuff/cmp_choleskybarnesfftraytrace/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.161722, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.329713, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.908078, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.575982, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.997392, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.572033, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 2.14541, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.430112, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 7.34003, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.171555, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0208798, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.210121, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.154419, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.381677, 'Execution Unit/Register Files/Runtime Dynamic': 0.175299, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.552045, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 1.38374, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155, 'Execution Unit/Runtime Dynamic': 4.43953, 'Execution Unit/Subthreshold Leakage': 1.83518, 'Execution Unit/Subthreshold Leakage with power gating': 0.709678, 'Gate Leakage': 0.372997, 'Instruction Fetch Unit/Area': 5.86007, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.0024986, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.0024986, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00217213, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.0008386, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00221824, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00938758, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0241046, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0590479, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.148447, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.43323, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.438245, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.504192, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 8.96874, 'Instruction Fetch Unit/Runtime Dynamic': 1.12438, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932587, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0654484, 'L2/Runtime Dynamic': 0.0146108, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80969, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 6.01103, 'Load Store Unit/Data Cache/Runtime Dynamic': 2.30575, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0351387, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.154448, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.154447, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 6.74334, 'Load Store Unit/Runtime Dynamic': 3.22188, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.380841, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.761682, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591622, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283406, 'Memory Management Unit/Area': 0.434579, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.135162, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.136134, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00813591, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.399995, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0718762, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.792162, 'Memory Management Unit/Runtime Dynamic': 0.20801, 'Memory Management Unit/Subthreshold Leakage': 0.0769113, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462, 'Peak Dynamic': 28.4714, 'Renaming Unit/Area': 0.369768, 'Renaming Unit/FP Front End RAT/Area': 0.168486, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.598518, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925, 'Renaming Unit/Free List/Area': 0.0414755, 'Renaming Unit/Free List/Gate Leakage': 4.15911e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0401324, 'Renaming Unit/Free List/Runtime Dynamic': 0.0366547, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987, 'Renaming Unit/Gate Leakage': 0.00863632, 'Renaming Unit/Int Front End RAT/Area': 0.114751, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.289168, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781, 'Renaming Unit/Peak Dynamic': 4.56169, 'Renaming Unit/Runtime Dynamic': 0.924341, 'Renaming Unit/Subthreshold Leakage': 0.070483, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779, 'Runtime Dynamic': 9.93275, 'Subthreshold Leakage': 6.21877, 'Subthreshold Leakage with power gating': 2.58311}, {'Area': 32.0201, 'Execution Unit/Area': 7.68434, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0230561, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.220798, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.139744, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.191082, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.308208, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.155573, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.654863, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.197117, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 4.4461, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0264006, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00801483, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0659696, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0592746, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.0923703, 'Execution Unit/Register Files/Runtime Dynamic': 0.0672894, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.144745, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.368414, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 1.71674, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00203883, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00203883, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00183308, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000740937, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000851484, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00676222, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0175021, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0569822, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 3.62455, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.189809, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.193537, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 6.01898, 'Instruction Fetch Unit/Runtime Dynamic': 0.464592, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932286, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0183571, 'L2/Runtime Dynamic': 0.00401057, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80901, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 2.80455, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.755572, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.05071, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0507099, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 3.04401, 'Load Store Unit/Runtime Dynamic': 1.05637, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate 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'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.011878, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0693182, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.40923, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.204476, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.235436, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 6.84174, 'Instruction Fetch Unit/Runtime 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'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.195105, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.014485, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.133676, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.343266, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 4.66416, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 1.8508442514106407, 'Runtime Dynamic': 1.8508442514106407, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.122426, 'Runtime Dynamic': 0.0751825, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 86.8973, 'Peak Power': 120.009, 'Runtime Dynamic': 23.1843, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 86.7748, 'Total Cores/Runtime Dynamic': 23.1091, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.122426, 'Total L3s/Runtime Dynamic': 0.0751825, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}

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py

Python

pylibrary/stats/__init__.py

pbmanis/pylibrary

d6cb41386cd39b7a1b6678a71a704f3b9d09faef

[ "MIT" ]

1

2016-06-24T18:32:40.000Z

pylibrary/stats/__init__.py

pbmanis/pylibrary

d6cb41386cd39b7a1b6678a71a704f3b9d09faef

[ "MIT" ]

null

pylibrary/stats/__init__.py

pbmanis/pylibrary

d6cb41386cd39b7a1b6678a71a704f3b9d09faef

[ "MIT" ]

1

2019-03-20T18:03:20.000Z

#!/usr/bin/env python __author__ = "Paul B. Manis" __version__ = "0.4" import pylibrary.stats.bootstrap import pylibrary.stats.permutation import pylibrary.stats.permutation_test

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7

0fef6c8bf3faf9ef2b9da765a82eb4453d920361

29,298

py

Python

tests/api/test_api.py

binh-vu/serene-python-client

efc72dc36ffd9224ec7d6780821e0302cda661af

[ "Apache-2.0" ]

6

2017-10-20T19:55:43.000Z

2021-06-06T14:47:24.000Z

tests/api/test_api.py

binh-vu/serene-python-client

efc72dc36ffd9224ec7d6780821e0302cda661af

[ "Apache-2.0" ]

2

2019-11-17T22:51:23.000Z

2019-11-24T06:05:37.000Z

tests/api/test_api.py

binh-vu/serene-python-client

efc72dc36ffd9224ec7d6780821e0302cda661af

[ "Apache-2.0" ]

5

2017-11-30T01:08:40.000Z

2020-05-22T22:07:16.000Z

from functools import partial from io import TextIOBase from mock import MagicMock, Mock, call, patch from requests import Response, Session from unittest2 import TestCase from serene.api.data_api import DataSetAPI from serene.api.exceptions import InternalError from serene.api.model_api import ModelAPI from serene.api.octopus_api import OctopusAPI from serene.api.ontology_api import OntologyAPI, OwlFormat from serene.api.ssd_api import SsdAPI from serene.elements import DataSet, Ontology, SSD class TestDataSetAPI(TestCase): def setUp(self): self.connection = Mock(Session()) self.root_uri = "http://localhost/" self.dataset_path = "dataset/" self.uri = self.root_uri + self.dataset_path self.api = DataSetAPI(self.root_uri, self.connection) self.response = Mock(Response()) self.description = "this python file" self.file_path = __file__ self.type_map = {"a": "int"} self.update_description = "another python file" self.update_type_map = {"b": "string"} def test_keys(self): keys = [1, 2] self.response.status_code = 200 self.response.json = Mock(return_value=keys) self.connection.get = Mock(return_value=self.response) result = self.api.keys() self.assertEqual(result, keys) self.connection.get.assert_called_with(self.uri) def test_keys_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) self.assertRaises(InternalError, self.api.keys) def test_post(self): message = "Created" self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.post(self.description, self.file_path, self.type_map) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri) self.assertEqual( args[1]["data"], {"description": self.description, "typeMap": self.type_map}) self.assertIsNotNone(args[1]["files"]) self.assertEqual(result, message) def test_post_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_post = partial( self.api.post, self.description, self.file_path, self.type_map) self.assertRaises(InternalError, api_post) def test_update(self): message = "Updated" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.update( key, self.update_description, self.update_type_map) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri + str(key)) self.assertEqual( args[1]["data"], { "description": self.update_description, "typeMap": self.update_type_map }) self.assertEqual(result, message) def test_update_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_update = partial( self.api.update, 1, self.update_description, self.update_type_map) self.assertRaises(InternalError, api_update) def test_item(self): key = 1 item = { "description": self.description, "typeMap": self.type_map } self.response.status_code = 200 self.response.json = Mock(return_value=item) self.connection.get = Mock(return_value=self.response) result = self.api.item(key) self.assertEqual(result, item) self.connection.get.assert_called_with(self.uri + str(key)) def test_item_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) api_item = partial(self.api.item, 1) self.assertRaises(InternalError, api_item) def test_delete(self): message = "Deleted" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.delete = Mock(return_value=self.response) result = self.api.delete(key) self.assertEqual(result, message) self.connection.delete.assert_called_with(self.uri + str(key)) def test_delete_with_connection_exception(self): self.connection.delete = Mock(side_effect=Exception) api_delete = partial(self.api.delete, 1) self.assertRaises(InternalError, api_delete) class TestModelAPI(TestCase): def setUp(self): self.connection = Mock(Session()) self.root_uri = "http://localhost/" self.model_path = "model/" self.uri = self.root_uri + self.model_path self.api = ModelAPI(self.root_uri, self.connection) self.response = Mock(Response()) self.feature_config = { "activeFeatures": [ "num-unique-vals" ] } self.description = "test model" self.classes = ["name", "age"] self.model_type = "randomForest" self.labels = { "123": "name", "666": "age" } self.cost_matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] self.resampling_strategy = "ResampleToMean" self.num_bags = 60 self.bag_size = 90 self.data = { "features": self.feature_config, "description": self.description, "classes": ["unknown"], "modelType": self.model_type, "labelData": self.labels, "costMatrix": self.cost_matrix, "resamplingStrategy": self.resampling_strategy, "numBags": self.num_bags, "bagSize": self.bag_size } def test_post(self): message = "Created" self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.post( feature_config=self.feature_config, description=self.description, classes=None, model_type=self.model_type, labels=self.labels, cost_matrix=self.cost_matrix, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size) self.assertEqual(result, message) self.connection.post.assert_called_with(self.uri, json=self.data) def test_post_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_post = partial( self.api.post, feature_config=self.feature_config, description=self.description, classes=None, model_type=self.model_type, labels=self.labels, cost_matrix=self.cost_matrix, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags) self.assertRaises(InternalError, api_post) def test_update(self): message = "Updated" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.update( key, feature_config=self.feature_config, description=self.description, classes=["unknown"], model_type=self.model_type, labels=self.labels, cost_matrix=self.cost_matrix, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size) self.assertEqual(result, message) self.connection.post.assert_called_with( self.uri + str(key), json=self.data) def test_update_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_update = partial( self.api.update, 1, feature_config=self.feature_config, description=self.description, classes=["unknown"], model_type=self.model_type, labels=self.labels, cost_matrix=self.cost_matrix, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags) self.assertRaises(InternalError, api_update) def test_item(self): key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=self.data) self.connection.get = Mock(return_value=self.response) result = self.api.item(key) self.assertEqual(result, self.data) self.connection.get.assert_called_with(self.uri + str(key)) def test_item_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) api_item = partial(self.api.item, 1) self.assertRaises(InternalError, api_item) def test_delete(self): message = "Deleted" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.delete = Mock(return_value=self.response) result = self.api.delete(key) self.assertEqual(result, message) self.connection.delete.assert_called_with(self.uri + str(key)) def test_delete_with_connection_exception(self): self.connection.delete = Mock(side_effect=Exception) api_delete = partial(self.api.delete, 1) self.assertRaises(InternalError, api_delete) def test_keys(self): keys = [1, 2] self.response.status_code = 200 self.response.json = Mock(return_value=keys) self.connection.get = Mock(return_value=self.response) result = self.api.keys() self.assertEqual(result, keys) self.connection.get.assert_called_with(self.uri) def test_keys_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) self.assertRaises(InternalError, self.api.keys) def test_train(self): key = 1 self.response.status_code = 200 self.connection.post = Mock(return_value=self.response) result = self.api.train(key) self.assertEqual(result, True) self.connection.post.assert_called_with( self.uri + str(key) + "/train") def test_train_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) self.assertRaises(InternalError, partial(self.api.train, 1)) def test_predict(self): modelKey = 1 dataSetKey = 2 predictions = { "modelID": modelKey, "dataSetID": dataSetKey, "predictions": { "label": "name", "confidence": 0.6, "scores": {"name": 0.6}, "features": {} } } self.response.status_code = 200 self.response.json = Mock(return_value=predictions) self.connection.post = Mock(return_value=self.response) result = self.api.predict(modelKey, dataSetKey) self.assertEqual(result, predictions) self.connection.post.assert_called_with( self.uri + str(modelKey) + "/predict/" + str(dataSetKey)) def test_predict_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) self.assertRaises(InternalError, partial(self.api.predict, 1, 2)) class TestOntologyAPI(TestCase): def setUp(self): self.connection = Mock(Session()) self.root_uri = "http://localhost/" self.model_path = "owl/" self.uri = self.root_uri + self.model_path self.api = OntologyAPI(self.root_uri, self.connection) self.response = Mock(Response()) self.description = "test ontology" self.file_path = __file__ self.owl_format = OwlFormat.TURTLE.value def test_post(self): message = "Created" self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.post( self.description, self.file_path, self.owl_format) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri) self.assertEqual( args[1]["data"], {"description": self.description, "format": self.owl_format}) self.assertIsNotNone(args[1]["files"]["file"]) self.assertEqual(result, message) def test_post_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_post = partial( self.api.post, self.description, self.file_path, self.owl_format) self.assertRaises(InternalError, api_post) def test_post_with_unsupported_format(self): api_post = partial( self.api.post, self.description, self.file_path, "unknown") self.assertRaises(ValueError, api_post) def test_update(self): message = "Updated" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.update( key, self.description, self.file_path, self.owl_format) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri + str(key)) self.assertEqual( args[1]["data"], {"description": self.description, "format": self.owl_format}) self.assertIsNotNone(args[1]["files"]["file"]) self.assertEqual(result, message) def test_update_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_update = partial( self.api.update, 1, self.description, self.file_path, self.owl_format) self.assertRaises(InternalError, api_update) def test_update_with_unsupported_format(self): api_update = partial( self.api.update, 1, self.description, self.file_path, "unknown") self.assertRaises(ValueError, api_update) def test_delete(self): message = "Deleted" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.delete = Mock(return_value=self.response) result = self.api.delete(key) self.assertEqual(result, message) self.connection.delete.assert_called_with(self.uri + str(key)) def test_delete_with_connection_exception(self): self.connection.delete = Mock(side_effect=Exception) api_delete = partial(self.api.delete, 1) self.assertRaises(InternalError, api_delete) def test_keys(self): keys = [1, 2] self.response.status_code = 200 self.response.json = Mock(return_value=keys) self.connection.get = Mock(return_value=self.response) result = self.api.keys() self.assertEqual(result, keys) self.connection.get.assert_called_with(self.uri) def test_keys_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) self.assertRaises(InternalError, self.api.keys) def test_item(self): key = 1 item = { "description": self.description, "format": self.owl_format } self.response.status_code = 200 self.response.json = Mock(return_value=item) self.connection.get = Mock(return_value=self.response) result = self.api.item(key) self.assertEqual(result, item) self.connection.get.assert_called_with(self.uri + str(key)) def test_item_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) api_item = partial(self.api.item, 1) self.assertRaises(InternalError, api_item) @patch("requests.get") def test_owl_file(self, get): key = 3 self.api.item = Mock(return_value={"name": "test.ttl"}) self.response.status_code = 200 chunks = ["1", "2"] self.response.iter_content = Mock(return_value=chunks) get.configure_mock(return_value=self.response) f = MagicMock(spec=TextIOBase) f.__enter__ = Mock(return_value=f) self.api._create_local_owl_file = Mock(return_value=f) result = self.api.owl_file(key) get.assert_called_with(self.uri + str(key) + "/file", stream=True) self.api._create_local_owl_file.assert_called_with(result) f.write.assert_has_calls([call(chunk) for chunk in chunks]) @patch("requests.get") def test_owl_file_with_connection_exception(self, get): self.api.item = Mock(return_value={"name": "test.ttl"}) get.configure_mock(side_effect=Exception) api_owl_file = partial(self.api.owl_file, 1) self.assertRaises(InternalError, api_owl_file) def init_ssd(target): target.dataset_json = { "dateCreated": "2017-03-16T15:29:03.388", "dateModified": "2017-03-16T15:29:03.388", "description": "", "filename": "businessInfo.csv", "id": 2035625835, "path": "/Users/li151/Dev/serene/./storage/datasets/2035625835/businessinfo.csv", "typeMap": {}, "columns": [ { "datasetID": 2035625835, "id": 1246005714, "index": 0, "logicalType": "string", "name": "company", "path": "/Users/li151/Dev/serene/./storage/datasets/2035625835/businessinfo.csv", "sample": ["Data61"], "size": 59 }, { "datasetID": 2035625835, "id": 281689915, "index": 1, "logicalType": "string", "name": "ceo", "path": "/Users/li151/Dev/serene/./storage/datasets/2035625835/businessinfo.csv", "sample": ["Garv Mcowen"], "size": 59 } ] } target.dataset = DataSet(target.dataset_json) target.ontology = Ontology().update({ "name": __file__, "id": 123, "description": "test ontology", "dateCreated": "2017-03-16T15:29:03.388", "dateModified": "2017-03-16T15:29:03.388" }) target.ssd = SSD(target.dataset, target.ontology, "test ssd") target.ssd_json = target.ssd.json class TestSsdAPI(TestCase): def setUp(self): self.connection = Mock(Session()) self.root_uri = "http://localhost/" self.ssd_path = "ssd/" self.uri = self.root_uri + self.ssd_path self.api = SsdAPI(self.root_uri, self.connection) self.response = Mock(Response()) init_ssd(self) def test_keys(self): keys = [1, 2] self.response.status_code = 200 self.response.json = Mock(return_value=keys) self.connection.get = Mock(return_value=self.response) result = self.api.keys() self.assertEqual(result, keys) self.connection.get.assert_called_with(self.uri) def test_keys_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) self.assertRaises(InternalError, self.api.keys) def test_post(self): message = "Created" self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.post(self.ssd_json) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri) self.assertEqual( args[1]["data"], self.ssd_json) self.assertEqual(result, message) def test_post_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_post = partial( self.api.post, self.ssd_json) self.assertRaises(InternalError, api_post) def test_update(self): message = "Updated" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.update(key, self.ssd_json) args = self.connection.post.call_args self.assertEqual(args[0][0], self.uri + str(key)) self.assertEqual(args[1]["data"], self.ssd_json) self.assertEqual(result, message) def test_update_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_update = partial(self.api.update, 1, self.ssd_json) self.assertRaises(InternalError, api_update) def test_item(self): key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=self.ssd_json) self.connection.get = Mock(return_value=self.response) result = self.api.item(key) self.assertEqual(result, self.ssd_json) self.connection.get.assert_called_with(self.uri + str(key)) def test_item_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) api_item = partial(self.api.item, 1) self.assertRaises(InternalError, api_item) def test_delete(self): message = "Deleted" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.delete = Mock(return_value=self.response) result = self.api.delete(key) self.assertEqual(result, message) self.connection.delete.assert_called_with(self.uri + str(key)) def test_delete_with_connection_exception(self): self.connection.delete = Mock(side_effect=Exception) api_delete = partial(self.api.delete, 1) self.assertRaises(InternalError, api_delete) class TestOctopusAPI(TestCase): def setUp(self): self.connection = Mock(Session()) self.root_uri = "http://localhost/" self.octopus_path = "octopus/" self.uri = self.root_uri + self.octopus_path self.api = OctopusAPI(self.root_uri, self.connection) self.response = Mock(Response()) init_ssd(self) self.name = "test octopus" self.feature_config = { "activeFeatures": [ "num-unique-vals" ] } self.description = "test model" self.model_type = "randomForest" self.resampling_strategy = "ResampleToMean" self.num_bags = 60 self.bag_size = 90 self.modeling_props = {"topkSteinerTrees": 1} self.data = { "ssds": [self.ssd], "name": self.name, "description": self.description, "modelType": self.model_type, "resamplingStrategy": self.resampling_strategy, "features": self.feature_config, "numBags": self.num_bags, "bagSize": self.bag_size, "ontologies": [self.ontology], "modelingProps": self.modeling_props } def test_post(self): message = "Created" self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.post( ssds=[self.ssd], name=self.name, description=self.description, feature_config=self.feature_config, model_type=self.model_type, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size, ontologies=[self.ontology], modeling_props=self.modeling_props) self.assertEqual(result, message) self.connection.post.assert_called_with(self.uri, json=self.data) def test_post_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_post = partial( self.api.post, ssds=[self.ssd], name=self.name, description=self.description, feature_config=self.feature_config, model_type=self.model_type, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size, ontologies=[self.ontology], modeling_props=self.modeling_props) self.assertRaises(InternalError, api_post) def test_update(self): message = "Updated" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.post = Mock(return_value=self.response) result = self.api.update( key, ssds=[self.ssd], name=self.name, description=self.description, feature_config=self.feature_config, model_type=self.model_type, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size, ontologies=[self.ontology], modeling_props=self.modeling_props) self.assertEqual(result, message) self.connection.post.assert_called_with( self.uri + str(key), json=self.data) def test_update_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) api_update = partial( self.api.update, 1, ssds=[self.ssd], name=self.name, description=self.description, feature_config=self.feature_config, model_type=self.model_type, resampling_strategy=self.resampling_strategy, num_bags=self.num_bags, bag_size=self.bag_size, ontologies=[self.ontology], modeling_props=self.modeling_props) self.assertRaises(InternalError, api_update) def test_item(self): key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=self.data) self.connection.get = Mock(return_value=self.response) result = self.api.item(key) self.assertEqual(result, self.data) self.connection.get.assert_called_with(self.uri + str(key)) def test_item_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) api_item = partial(self.api.item, 1) self.assertRaises(InternalError, api_item) def test_delete(self): message = "Deleted" key = 1 self.response.status_code = 200 self.response.json = Mock(return_value=message) self.connection.delete = Mock(return_value=self.response) result = self.api.delete(key) self.assertEqual(result, message) self.connection.delete.assert_called_with(self.uri + str(key)) def test_delete_with_connection_exception(self): self.connection.delete = Mock(side_effect=Exception) api_delete = partial(self.api.delete, 1) self.assertRaises(InternalError, api_delete) def test_keys(self): keys = [1, 2] self.response.status_code = 200 self.response.json = Mock(return_value=keys) self.connection.get = Mock(return_value=self.response) result = self.api.keys() self.assertEqual(result, keys) self.connection.get.assert_called_with(self.uri) def test_keys_with_connection_exception(self): self.connection.get = Mock(side_effect=Exception) self.assertRaises(InternalError, self.api.keys) def test_train(self): key = 1 self.response.status_code = 200 self.connection.post = Mock(return_value=self.response) result = self.api.train(key) self.assertEqual(result, True) self.connection.post.assert_called_with( self.uri + str(key) + "/train") def test_train_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) self.assertRaises(InternalError, partial(self.api.train, 1)) def test_predict(self): octopusKey = 1 dataSetKey = 2 predictions = { "predictions": [{ "ssd": { "name": "test ssd", }, "score": { "linkCost": 0.5 } }] } self.response.status_code = 200 self.response.json = Mock(return_value=predictions) self.connection.post = Mock(return_value=self.response) result = self.api.predict(octopusKey, dataSetKey) self.assertEqual(result, predictions) self.connection.post.assert_called_with( self.uri + str(octopusKey) + "/predict/" + str(dataSetKey)) def test_predict_with_connection_exception(self): self.connection.post = Mock(side_effect=Exception) self.assertRaises(InternalError, partial(self.api.predict, 1, 2))

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97

0.626015

3,361

29,298

5.274918

0.060994

0.076598

0.052456

0.035366

0.880083

0.863557

0.854983

0.846015

0.833606

0.826668

0

0.017018

0.265923

29,298

845

98

34.672189

0.807319

0

0.765557

0

0.053894

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0.098408

false

0

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0.12301

0

null

0

1

0

1

0

null

0

7

e83a65a438b6a13194bbb441caeb795d640cb798

2,442

py

Python

test/test_collapses.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

21

2019-07-16T15:03:43.000Z

2021-11-16T10:51:58.000Z

test/test_collapses.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

null

test/test_collapses.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

null

from markyp_bootstrap4.collapses import * def test_a_args_for(): assert a_args_for("foo") == { "href": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": False } assert a_args_for("foo", expanded=True) == { "href": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": True } assert a_args_for("foo", foo="foo", bar=42) == { "foo": "foo", "bar": 42, "href": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": False } def test_button_args_for(): assert button_args_for("foo") == { "data-target": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": False } assert button_args_for("foo", expanded=True) == { "data-target": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": True } assert button_args_for("foo", foo="foo", bar=42) == { "foo": "foo", "bar": 42, "data-target": "#foo", "data-toggle": "collapse", "aria-controls": "foo", "aria-expanded": False } def test_collapse(): assert collapse(identifier="collapse-id").markup ==\ '<div id="collapse-id" class="collapse"></div>' assert collapse("First", "Second", identifier="collapse-id").markup ==\ '<div id="collapse-id" class="collapse">\nFirst\nSecond\n</div>' assert collapse("First", "Second", identifier="collapse-id", class_="my-collapse").markup ==\ '<div id="collapse-id" class="collapse my-collapse">\nFirst\nSecond\n</div>' assert collapse("First", "Second", identifier="collapse-id", class_="my-collapse", show=True).markup ==\ '<div id="collapse-id" class="collapse show my-collapse">\nFirst\nSecond\n</div>' assert collapse("First", "Second", identifier="collapse-id", class_="my-collapse", show=True, foo="foo", bar="bar").markup ==\ '<div id="collapse-id" foo="foo" bar="bar" class="collapse show my-collapse">\nFirst\nSecond\n</div>' assert collapse("First", "Second", identifier="collapse-id", accordion_id="acc-1", class_="my-collapse", show=True, foo="foo", bar="bar").markup ==\ '<div id="collapse-id" foo="foo" bar="bar" data-parent="#acc-1" class="collapse show my-collapse">\nFirst\nSecond\n</div>'

40.7

152

0.580262

293

2,442

4.750853

0.139932

0.086207

0.051724

0.090517

0.918103

0.84842

0.808908

0.788793

0.757184

0

0.00572

0.212531

2,442

59

153

41.389831

0.718149

0

0.5

0

0.071429

0.43407

0.084767

0

0.214286

1

0.053571

true

0

0.017857

0

0.071429

0

null

0

1

0

null

0

1

0

7

e899c683c384970166dde42cf599b2b782a4e3e9

4,262

py

Python

tests/test_Counter.py

mouckatron/pyprogress

11b1a2f427bb67b17409acc5ea117a041e9630e4

[ "MIT" ]

null

tests/test_Counter.py

mouckatron/pyprogress

11b1a2f427bb67b17409acc5ea117a041e9630e4

[ "MIT" ]

1

2015-12-09T16:40:43.000Z

2017-02-01T13:00:31.000Z

tests/test_Counter.py

mouckatron/pyprogress

11b1a2f427bb67b17409acc5ea117a041e9630e4

[ "MIT" ]

null

from . import TestStdoutReader import pyprogress class TestCounter(TestStdoutReader): def tearDown(self): self.c.stop() self.c.join() TestStdoutReader.tearDown(self) def test_counter_no_total(self): output = ['0', '\b1', '\b2', '\b3', '\b4', '\b5'] self.c = pyprogress.Counter() self.c.start() assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): self.c.inc() self.c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_with_total(self): output = ['0/5', '\b\b\b1/5', '\b\b\b2/5', '\b\b\b3/5', '\b\b\b4/5', '\b\b\b5/5'] self.c = pyprogress.Counter(total=5) self.c.start() assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): self.c.inc() self.c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_initial(self): output = ['2', '\b3', '\b4', '\b5'] self.c = pyprogress.Counter(initial=2) self.c.start() assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 4): self.c.inc() self.c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_inc_2(self): output = ['0/10', '\b\b\b\b2/10', '\b\b\b\b4/10', '\b\b\b\b6/10', '\b\b\b\b8/10', '\b\b\b\b10/10'] self.c = pyprogress.Counter(total=10) self.c.start() assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): self.c.inc(2) self.c.write() assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) class TestCounter(TestStdoutReader): def test_counter_no_total(self): output = ['0', '\b1', '\b2', '\b3', '\b4', '\b5'] with pyprogress.Counter() as c: assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): c.inc() c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_with_total(self): output = ['0/5', '\b\b\b1/5', '\b\b\b2/5', '\b\b\b3/5', '\b\b\b4/5', '\b\b\b5/5'] with pyprogress.Counter(total=5) as c: assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): c.inc() c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_initial(self): output = ['2', '\b3', '\b4', '\b5'] with pyprogress.Counter(initial=2) as c: assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 4): c.inc() c.write() # force write output assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0) def test_counter_inc_2(self): output = ['0/10', '\b\b\b\b2/10', '\b\b\b\b4/10', '\b\b\b\b6/10', '\b\b\b\b8/10', '\b\b\b\b10/10'] with pyprogress.Counter(total=10) as c: assert self.stdout.getvalue().strip() == output[0] self.stdout.truncate(0) for x in range(1, 6): c.inc(2) c.write() assert self.stdout.getvalue().strip('\x00').strip() == output[x] self.stdout.truncate(0)

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89

0.499061

555

4,262

3.792793

0.09009

0.152019

0.121615

0.182423

0.88266

0.857007

0.837055

0.816152

0

0.052265

0.326607

4,262

117

90

36.42735

0.681185

0.026513

0

0.843137

0

0.074861

0

0.156863

1

0.088235

false

0

0.019608

0

0.127451

0

null

0

1

0

1

0

null

0

8

e8b00ff9035fefe6e4f3e61af93453f299e81a52

17,379

py

Python

samplesheets/tests/test_views_ajax_taskflow.py

bihealth/sodar-server

0c6a03c274ab34cd8987280fe97dc8989551d4bd

[ "MIT" ]

null

samplesheets/tests/test_views_ajax_taskflow.py

bihealth/sodar-server

0c6a03c274ab34cd8987280fe97dc8989551d4bd

[ "MIT" ]

1

2021-05-28T10:59:49.000Z

2021-06-03T12:30:23.000Z

samplesheets/tests/test_views_ajax_taskflow.py

bihealth/sodar-server

0c6a03c274ab34cd8987280fe97dc8989551d4bd

[ "MIT" ]

null

"""Tests for Ajax API views in the samplesheets app with Taskflow enabled""" import os from django.conf import settings from django.urls import reverse from unittest.case import skipIf from samplesheets.models import IrodsDataRequest from samplesheets.tests.test_views import ( IRODS_BACKEND_ENABLED, IRODS_BACKEND_SKIP_MSG, ) from samplesheets.tests.test_views_taskflow import ( TestIrodsRequestViewsBase, TEST_FILE_NAME2, ) # Local constants IRODS_NON_PROJECT_PATH = ( '/' + settings.IRODS_ZONE + '/home/' + settings.IRODS_USER ) IRODS_FAIL_COLL = 'xeiJ1Vie' @skipIf(not IRODS_BACKEND_ENABLED, IRODS_BACKEND_SKIP_MSG) class TestIrodsRequestCreateAjaxView(TestIrodsRequestViewsBase): """Tests for IrodsRequestCreateAjaxView""" def test_create_request(self): """Test creating a delete request on a data object""" self.assertEqual(IrodsDataRequest.objects.count(), 0) self.assertEqual(self._get_create_alert_count(self.user), 0) self.assertEqual(self._get_create_alert_count(self.user_delegate), 0) with self.login(self.user): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) # Assert response self.assertEqual(response.status_code, 200) self.assertEqual(response.data['detail'], 'ok') self.assertEqual(response.data['status'], 'ACTIVE') self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual(self._get_create_alert_count(self.user_delegate), 1) def test_create_exists_same_user(self): """Test creating delete request if request for same user exists""" with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual(self._get_create_alert_count(self.user_delegate), 1) with self.login(self.user_contrib): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) # Assert response self.assertEqual(response.status_code, 400) self.assertEqual( response.data['detail'], 'active request for path already exists' ) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual(self._get_create_alert_count(self.user_delegate), 1) def test_create_exists_as_admin_by_contributor(self): """Test creating request as admin if request from contributor exists""" with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) with self.login(self.user): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) # Assert response self.assertEqual(response.status_code, 400) self.assertEqual( response.data['detail'], 'active request for path already exists' ) def test_create_exists_as_contributor_by_contributor2(self): """Test creating request as contributor if request by contributor2 exists""" with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) with self.login(self.user_contrib2): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) # Assert response self.assertEqual(response.status_code, 400) self.assertEqual( response.data['detail'], 'active request for path already exists' ) def test_create_multiple(self): """Test creating multiple delete requests""" path2 = os.path.join(self.assay_path, TEST_FILE_NAME2) path2_md5 = os.path.join(self.assay_path, TEST_FILE_NAME2 + '.md5') self.irods_session.data_objects.create(path2) self.irods_session.data_objects.create(path2_md5) self.assertEqual(IrodsDataRequest.objects.count(), 0) self.assertEqual(self._get_create_alert_count(self.user), 0) self.assertEqual(self._get_create_alert_count(self.user_delegate), 0) with self.login(self.user): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) with self.login(self.user): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': path2}, ) self.assertEqual(IrodsDataRequest.objects.count(), 2) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual(self._get_create_alert_count(self.user_delegate), 1) @skipIf(not IRODS_BACKEND_ENABLED, IRODS_BACKEND_SKIP_MSG) class TestIrodsRequestDeleteAjaxView(TestIrodsRequestViewsBase): """Tests for IrodsRequestDeleteAjaxView""" def test_delete_request(self): """Test GET request for deleting an existing delete request""" # Create request with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual(self._get_create_alert_count(self.user_delegate), 1) # Delete request with self.login(self.user_contrib): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_delete', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 0) # Assert response self.assertEqual(response.status_code, 200) self.assertEqual(response.data['detail'], 'ok') self.assertEqual(response.data['status'], None) self.assertEqual(self._get_create_alert_count(self.user), 0) self.assertEqual(self._get_create_alert_count(self.user_delegate), 0) def test_delete_request_as_admin_by_contributor(self): """Test deleting an existing delete request""" # Create request with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) # Delete request with self.login(self.user): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_delete', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 0) # Assert response self.assertEqual(response.status_code, 200) self.assertEqual(response.data['detail'], 'ok') self.assertEqual(response.data['status'], None) def test_delete_request_as_contributor_by_contributor2(self): """Test GET request for deleting an existing delete request""" # Create request with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) # Delete request with self.login(self.user_contrib2): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_delete', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) # Assert response self.assertEqual(response.status_code, 403) self.assertEqual( response.data['detail'], 'User not allowed to delete request' ) def test_delete_request_doesnt_exist(self): """Test deleting a delete request that doesn't exist""" self.assertEqual(IrodsDataRequest.objects.count(), 0) # Delete request with self.login(self.user): response = self.client.post( reverse( 'samplesheets:ajax_irods_request_delete', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) # Assert response self.assertEqual(response.status_code, 404) self.assertEqual(response.data['detail'], 'Request not found') def test_delete_one_of_multiple(self): """Test deleting one of multiple requests""" path2 = os.path.join(self.assay_path, TEST_FILE_NAME2) path2_md5 = os.path.join(self.assay_path, TEST_FILE_NAME2 + '.md5') self.irods_session.data_objects.create(path2) self.irods_session.data_objects.create(path2_md5) self.assertEqual(IrodsDataRequest.objects.count(), 0) self.assertEqual(self._get_create_alert_count(self.user), 0) self.assertEqual(self._get_create_alert_count(self.user_delegate), 0) with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': path2}, ) self.assertEqual(IrodsDataRequest.objects.count(), 2) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual( self._get_create_alert_count(self.user_delegate), 1 ) self.client.post( reverse( 'samplesheets:ajax_irods_request_delete', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) self.assertEqual(self._get_create_alert_count(self.user), 1) self.assertEqual( self._get_create_alert_count(self.user_delegate), 1 ) @skipIf(not IRODS_BACKEND_ENABLED, IRODS_BACKEND_SKIP_MSG) class TestIrodsObjectListAjaxView(TestIrodsRequestViewsBase): """Tests for IrodsObjectListAjaxView""" def test_get_coll_obj_with_delete_request(self): """Test listing collection with data object with delete request""" # Create request with self.login(self.user_contrib): self.client.post( reverse( 'samplesheets:ajax_irods_request_create', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.path}, ) self.assertEqual(IrodsDataRequest.objects.count(), 1) with self.login(self.user_contrib): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.assay_path}, ) self.assertEqual(response.status_code, 200) self.assertEqual(response.json()['irods_data'][0]['name'], 'test1') self.assertEqual(response.json()['irods_data'][0]['path'], self.path) self.assertEqual( response.json()['irods_data'][0]['irods_request_status'], 'ACTIVE', ) def test_get_empty_coll(self): """Test GET request for listing an empty collection in iRODS""" self.irods_session.data_objects.get(self.path).unlink(force=True) self.irods_session.data_objects.get(self.path_md5).unlink(force=True) with self.login(self.user): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.assay_path}, ) self.assertEqual(response.status_code, 200) self.assertEqual(len(response.data['irods_data']), 0) def test_get_coll_obj(self): """Test GET request for listing a collection with a data object""" with self.login(self.user): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.assay_path}, ) self.assertEqual(response.status_code, 200) self.assertEqual(len(response.data['irods_data']), 1) list_obj = response.data['irods_data'][0] self.assertNotIn('md5_file', list_obj) self.assertEqual(self.file_obj.name, list_obj['name']) self.assertEqual(self.file_obj.path, list_obj['path']) self.assertEqual(self.file_obj.size, 0) def test_get_coll_not_found(self): """Test GET request for listing a collection which doesn't exist""" fail_path = self.assay_path + '/' + IRODS_FAIL_COLL self.assertEqual( self.irods_session.collections.exists(fail_path), False ) with self.login(self.user): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': fail_path}, ) self.assertEqual(response.status_code, 404) def test_get_coll_not_in_project(self): """Test GET request for listing a collection not belonging to project""" self.assertEqual( self.irods_session.collections.exists(IRODS_NON_PROJECT_PATH), True ) with self.login(self.user): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': IRODS_NON_PROJECT_PATH}, ) self.assertEqual(response.status_code, 400) def test_get_no_access(self): """Test GET request for listing with no acces for the iRODS folder""" new_user = self.make_user('new_user') self._make_assignment( self.project, new_user, self.role_contributor ) # No taskflow with self.login(new_user): response = self.client.get( reverse( 'samplesheets:ajax_irods_objects', kwargs={'project': self.project.sodar_uuid}, ), data={'path': self.assay_path}, ) self.assertEqual(response.status_code, 403)

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e8ce61e2a727c52c683fc9027e14ddf6172505fb

12,921

py

Python

halotools/mock_observables/pair_counters/test_pair_counters/test_positional_marked_npairs_xy_z.py

nehapjoshi/halotools

ad9e183ee7471f7876201ce83fdc36a76e653902

[ "BSD-3-Clause" ]

null

halotools/mock_observables/pair_counters/test_pair_counters/test_positional_marked_npairs_xy_z.py

nehapjoshi/halotools

ad9e183ee7471f7876201ce83fdc36a76e653902

[ "BSD-3-Clause" ]

null

halotools/mock_observables/pair_counters/test_pair_counters/test_positional_marked_npairs_xy_z.py

nehapjoshi/halotools

ad9e183ee7471f7876201ce83fdc36a76e653902

[ "BSD-3-Clause" ]

null

""" """ from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np import pytest from ..positional_marked_npairs_xy_z import positional_marked_npairs_xy_z from ..npairs_xy_z import npairs_xy_z from ...tests.cf_helpers import ( generate_3d_regular_mesh, generate_locus_of_3d_points, ) from ....utils.vector_utilities import ( normalized_vectors, angles_between_list_of_vectors, ) slow = pytest.mark.slow __all__ = ( "test_1", "test_2", "test_3", "test_4", "test_threading", "test_unweighted_counts", ) def generate_interlacing_grids(npts_per_dim, period=1.0): """ return two sets of interlaced points on a grid """ dmin, dmax = 0.0, period dx = (dmax - dmin) / float(npts_per_dim) npts_mesh1 = npts_per_dim ** 3 mesh1_points = generate_3d_regular_mesh(npts_per_dim, dmin=dmin, dmax=dmax) mesh2_points = mesh1_points + dx / 2.0 npts_mesh2 = mesh2_points.shape[0] return mesh1_points, mesh2_points def generate_aligned_vectors(npts, dim=2): """ return a set of aligned vectors, all pointing in a random direction """ vector = normalized_vectors(np.random.random(dim)) vectors = np.tile(vector, npts).reshape((npts, dim)) return vectors def test_1(): """ test weighting function 1 """ # generate two locusts of points npts = 100 epsilon = 0.001 # #cluster 1 coords1 = generate_locus_of_3d_points(npts, 0.1, 0.1, 0.1, epsilon=epsilon) # cluster 2 coords2 = generate_locus_of_3d_points(npts, 0.9, 0.9, 0.9, epsilon=epsilon) # generate orientation vectors for cluster 1 vectors1 = generate_aligned_vectors(len(coords1)) # calculate dot product between vectors1 and cluster 2 rp = np.sqrt((0.9 - 0.1) ** 2 + (0.9 - 0.1) ** 2) pi = 0.9 - 0.1 # s, vector between coords1 and cluster2 sp = np.zeros((npts, 2)) sp[:, 0] = 0.9 - coords1[:, 0] sp[:, 1] = 0.9 - coords1[:, 1] # calculate dot product between orientation and direction between cluster 1 and 2 angles = angles_between_list_of_vectors(vectors1, sp) costheta = np.cos(angles) # dot product between vectors avg_costheta = np.mean(costheta) # define radial bins rp_bins = np.array([0.0, 0.1, rp + 2.0 * epsilon]) pi_bins = np.array([0.0, 0.1, pi + 2.0 * epsilon]) # define weights appropiate for weighting function weights1 = np.ones((npts, 3)) weights1[:, 1] = vectors1[:, 0] weights1[:, 2] = vectors1[:, 1] weights2 = np.ones(npts) # calculate weighted counts weighted_counts, counts = positional_marked_npairs_xy_z( coords1, coords2, rp_bins, pi_bins, period=None, weights1=weights1, weights2=weights2, weight_func_id=1, num_threads=1, ) msg = "weighted counts do not match expected result given the weighting function" print(weighted_counts) assert np.isclose( weighted_counts[-1, -1], avg_costheta * counts[-1, -1], rtol=1.0 / npts ), msg def test_2(): """ test weighting function 2 """ # generate two locusts of points npts = 100 epsilon = 0.001 # #cluster 1 coords1 = generate_locus_of_3d_points(npts, 0.1, 0.1, 0.1, epsilon=epsilon) # cluster 2 coords2 = generate_locus_of_3d_points(npts, 0.9, 0.9, 0.9, epsilon=epsilon) # generate orientation vectors for cluster 1 vectors1 = generate_aligned_vectors(len(coords1)) # calculate dot product between vectors1 and cluster 2 rp = np.sqrt((0.9 - 0.1) ** 2 + (0.9 - 0.1) ** 2) pi = 0.9 - 0.1 # s, vector between coords1 and cluster2 sp = np.zeros((npts, 2)) sp[:, 0] = 0.9 - coords1[:, 0] sp[:, 1] = 0.9 - coords1[:, 1] # calculate dot product between orientation and direction between cluster 1 and 2 angles = angles_between_list_of_vectors(vectors1, sp) avg_two_costheta_1 = np.mean(np.cos(2.0 * angles)) avg_two_costheta_2 = np.mean(2.0 * np.cos(angles) * np.cos(angles) - 1.0) assert np.isclose( avg_two_costheta_1, avg_two_costheta_2 ) # test trig identify used in weighting function avg_two_costheta = avg_two_costheta_2 # define radial bins rp_bins = np.array([0.0, 0.1, rp + 2.0 * epsilon]) pi_bins = np.array([0.0, 0.1, pi + 2.0 * epsilon]) # define weights appropiate for weighting function weights1 = np.ones((npts, 3)) weights1[:, 1] = vectors1[:, 0] weights1[:, 2] = vectors1[:, 1] weights2 = np.ones(npts) # calculate weighted counts weighted_counts, counts = positional_marked_npairs_xy_z( coords1, coords2, rp_bins, pi_bins, period=None, weights1=weights1, weights2=weights2, weight_func_id=2, num_threads=1, ) msg = "weighted counts do not match expected result given the weighting function" assert np.isclose( weighted_counts[-1, -1], avg_two_costheta * counts[-1, -1], rtol=1.0 / npts ), msg def test_3(): """ test weighting function 3 """ # generate two locusts of points npts = 100 epsilon = 0.001 # #cluster 1 coords1 = generate_locus_of_3d_points(npts, 0.1, 0.1, 0.1, epsilon=epsilon) # cluster 2 coords2 = generate_locus_of_3d_points(npts, 0.9, 0.9, 0.9, epsilon=epsilon) # generate orientation vectors for cluster 1 vectors1 = generate_aligned_vectors(len(coords1)) # calculate dot product between vectors1 and cluster 2 rp = np.sqrt((0.9 - 0.1) ** 2 + (0.9 - 0.1) ** 2) pi = 0.9 - 0.1 # s, vector between coords1 and cluster2 sp = np.zeros((npts, 2)) sp[:, 0] = 0.9 - coords1[:, 0] sp[:, 1] = 0.9 - coords1[:, 1] # calculate dot product between orientation and direction between cluster 1 and 2 angles = angles_between_list_of_vectors(vectors1, sp) avg_two_sintheta = np.mean(np.sin(2.0 * angles)) # define radial bins rp_bins = np.array([0.0, 0.1, rp + 2.0 * epsilon]) pi_bins = np.array([0.0, 0.1, pi + 2.0 * epsilon]) # define weights appropiate for weighting function weights1 = np.ones((npts, 3)) weights1[:, 1] = vectors1[:, 0] weights1[:, 2] = vectors1[:, 1] weights2 = np.ones(npts) # calculate weighted counts weighted_counts, counts = positional_marked_npairs_xy_z( coords1, coords2, rp_bins, pi_bins, period=None, weights1=weights1, weights2=weights2, weight_func_id=3, num_threads=1, ) msg = "weighted counts do not match expected result given the weighting function" assert np.isclose( weighted_counts[-1, -1], avg_two_sintheta * counts[-1, -1], rtol=1.0 / npts ), msg def test_4(): """ test weighting function 4 """ # generate two locusts of points npts = 100 epsilon = 0.001 # #cluster 1 coords1 = generate_locus_of_3d_points(npts, 0.1, 0.1, 0.1, epsilon=epsilon) # cluster 2 coords2 = generate_locus_of_3d_points(npts, 0.9, 0.9, 0.9, epsilon=epsilon) # generate orientation vectors for cluster 1 vectors1 = generate_aligned_vectors(len(coords1)) # calculate dot product between vectors1 and cluster 2 rp = np.sqrt((0.9 - 0.1) ** 2 + (0.9 - 0.1) ** 2) pi = 0.9 - 0.1 # s, vector between coords1 and cluster2 sp = np.zeros((npts, 2)) sp[:, 0] = 0.9 - coords1[:, 0] sp[:, 1] = 0.9 - coords1[:, 1] # calculate dot product between orientation and direction between cluster 1 and 2 angles = angles_between_list_of_vectors(vectors1, sp) costheta_squared = np.cos(angles) * np.cos(angles) # dot product between vectors avg_costheta_squared = np.mean(costheta_squared) # define radial bins rp_bins = np.array([0.0, 0.1, rp + 2.0 * epsilon]) pi_bins = np.array([0.0, 0.1, pi + 2.0 * epsilon]) # define weights appropiate for weighting function weights1 = np.ones((npts, 3)) weights1[:, 1] = vectors1[:, 0] weights1[:, 2] = vectors1[:, 1] weights2 = np.ones(npts) # calculate weighted counts weighted_counts, counts = positional_marked_npairs_xy_z( coords1, coords2, rp_bins, pi_bins, period=None, weights1=weights1, weights2=weights2, weight_func_id=4, num_threads=1, ) msg = "weighted counts do not match expected result given the weighting function" assert np.isclose( weighted_counts[-1, -1], avg_costheta_squared * counts[-1, -1], rtol=1.0 / npts ), msg def test_threading(): """ test to make sure the result is the same with and without threading for each weighting function """ npts = 100 random_coords = np.random.random((npts, 3)) random_vectors = np.random.random((npts, 3)) * 2.0 - 1.0 period = np.array([1.0, 1.0, 1.0]) rp_bins = np.linspace(0.0, 0.3, 5) pi_bins = np.linspace(0.0, 0.3, 5) weights1 = np.ones((npts, 3)) weights1[:, 1] = random_vectors[:, 0] weights1[:, 2] = random_vectors[:, 1] weights2 = np.ones(npts) msg = "counts do not match for different ``num_threads``." weighted_counts_1, counts_1 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=1, num_threads=1, ) weighted_counts_2, counts_2 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=1, num_threads=3, ) assert np.allclose(weighted_counts_1, weighted_counts_2), msg assert np.allclose(counts_1, counts_2), msg weighted_counts_1, counts_1 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=2, num_threads=1, ) weighted_counts_2, counts_2 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=2, num_threads=3, ) assert np.allclose(weighted_counts_1, weighted_counts_2), msg assert np.allclose(counts_1, counts_2), msg weighted_counts_1, counts_1 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=3, num_threads=1, ) weighted_counts_2, counts_2 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=3, num_threads=3, ) assert np.allclose(weighted_counts_1, weighted_counts_2), msg assert np.allclose(counts_1, counts_2), msg weighted_counts_1, counts_1 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=4, num_threads=1, ) weighted_counts_2, counts_2 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=4, num_threads=3, ) assert np.allclose(weighted_counts_1, weighted_counts_2), msg assert np.allclose(counts_1, counts_2), msg def test_unweighted_counts(): """ test to make sure the unweighted counts result is the same as npairs_3d """ npts = 100 random_coords = np.random.random((npts, 3)) random_vectors = np.random.random((npts, 3)) * 2.0 - 1.0 period = np.array([1.0, 1.0, 1.0]) rp_bins = np.linspace(0.0, 0.3, 5) pi_bins = np.linspace(0.0, 0.3, 5) weights1 = np.ones((npts, 3)) weights1[:, 1] = random_vectors[:, 0] weights1[:, 2] = random_vectors[:, 1] weights2 = np.ones(npts) weighted_counts_1, counts_1 = positional_marked_npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, weights1=weights1, weights2=weights2, weight_func_id=1, num_threads=1, ) counts_2 = npairs_xy_z( random_coords, random_coords, rp_bins, pi_bins, period=period, num_threads=3 ) msg = "unweighted counts do no match npairs_3d result" assert np.allclose(counts_1, counts_2), msg

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2cd1a23052ac78acdf138f8c8c37f8dbfa1490c8

139

py

Python

cvxpy/cvxcore/python/__init__.py

jasondark/cvxpy

56aaa01b0e9d98ae5a91a923708129a7b37a6f18

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

3,285

2015-01-03T04:02:29.000Z

2021-04-19T14:51:29.000Z

cvxpy/cvxcore/python/__init__.py

h-vetinari/cvxpy

86307f271819bb78fcdf64a9c3a424773e8269fa

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

1,138

2015-01-01T19:40:14.000Z

2021-04-18T23:37:31.000Z

cvxpy/cvxcore/python/__init__.py

h-vetinari/cvxpy

86307f271819bb78fcdf64a9c3a424773e8269fa

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

765

2015-01-02T19:29:39.000Z

2021-04-20T00:50:43.000Z

# TODO(akshayka): This is a hack; the swig-auto-generated cvxcore.py # tries to import cvxcore as `from . import _cvxcore` import _cvxcore

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py

Python

tests/test_parse_stimulus_elements.py

learningsimulator/learningsimulator

79b00bb0155537a4219637e68d5092fd10a1017f

[ "MIT" ]

7

2020-07-14T20:30:23.000Z

2022-02-14T05:58:22.000Z

tests/test_parse_stimulus_elements.py

learningsimulator/learningsimulator

79b00bb0155537a4219637e68d5092fd10a1017f

[ "MIT" ]

89

2020-11-25T18:38:21.000Z

2022-02-25T12:37:45.000Z

tests/test_parse_stimulus_elements.py

learningsimulator/learningsimulator

79b00bb0155537a4219637e68d5092fd10a1017f

[ "MIT" ]

null

from .testutil import LsTestCase from keywords import STIMULUS_ELEMENTS from parsing import Script def parse(text): script = Script(text) script.parse() return script.script_parser.parameters.val[STIMULUS_ELEMENTS] class TestBasic(LsTestCase): def setUp(self): pass def test_simple(self): text = ''' stimulus_elements: B1, b1, B2, b2 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'B1', 'b1', 'B2', 'b2'}) text = ''' stimulus_elements : B1,b1, B2, b2 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'B1', 'b1', 'B2', 'b2'}) def test_multiline(self): text = ''' stimulus_elements: b1, b2, b3, b4 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'b1', 'b2', 'b3', 'b4'}) text = ''' stimulus_elements : b1, b2, b3, b4, b5 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'b1', 'b2', 'b3', 'b4', 'b5'}) def test_redefinition(self): text = ''' stimulus_elements: b1, b2, b3, b4 stimulus_elements: x1, x2 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'x1', 'x2'}) text = ''' stimulus_elements: x1, x2 stimulus_elements: b1, b2, b3, b4 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'b1', 'b2', 'b3', 'b4'}) text = ''' stimulus_elements: b1, b2, b3, b4 stimulus_elements: x1, x2 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'x1', 'x2'}) text = ''' stimulus_elements: x1, x2 stimulus_elements: b1, b2, b3, b4 ''' stimulus_elements = parse(text) self.assertEqual(stimulus_elements, {'b1', 'b2', 'b3', 'b4'}) class TestParsestimulus_elementsErrors(LsTestCase): def setUp(self): pass def test_empty_name(self): text = ''' stimulus_elements: b1, , b2, b3 ''' msg = "Error on line 2: Found empty stimulus element name." with self.assertRaisesMsg(msg): parse(text) def test_duplicate(self): text = ''' stimulus_elements: b1, b2, b3, b4, b2, b1 ''' msg = "Error on line 2: The stimulus element name 'b2' occurs more than once." with self.assertRaisesMsg(msg): parse(text) text = ''' stimulus_elements: b1, b2, b3, b4, b2, b1 ''' msg = "Error on line 3: The stimulus element name 'b2' occurs more than once." with self.assertRaisesMsg(msg): parse(text) def test_stimulus_element_is_behavior(self): text = ''' behaviors: e1, e2, e3 stimulus_elements: b1, b2, b3, b4, e2 ''' msg = "Error on line 3: The stimulus element name 'e2' is invalid, since it is a behavior name." with self.assertRaisesMsg(msg): parse(text) text = ''' behaviors: e1, e2, e3 stimulus_elements: b1, b2, b3, b4, e2, b1 ''' msg = "Error on line 4: The stimulus element name 'e2' is invalid, since it is a behavior name." with self.assertRaisesMsg(msg): parse(text) def test_behavior_is_variable(self): text = ''' @variables v1:1.2, v2:2.3, v3:3.4 stimulus_elements: b1, b2, b3, b4, v2, v3, v1 ''' msg = "Error on line 3: The stimulus element name 'v2' is invalid, since it is a variable name." with self.assertRaisesMsg(msg): parse(text) text = ''' @variables v1:1.2, v2:2.3, v3:3.4 stimulus_elements: b1, b2, b3, b4, v2 ''' msg = "Error on line 4: The stimulus element name 'v2' is invalid, since it is a variable name." with self.assertRaisesMsg(msg): parse(text) def test_invalid_identifier(self): text = ''' @variables v1:1.2, v2:2.3, v3:3.4 stimulus_elements: b1, b2, b3, b4, v2. v3, v1 ''' msg = "Error on line 3: Stimulus element name 'v2. v3' is not a valid identifier." with self.assertRaisesMsg(msg): parse(text)

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fa38962ce8a587a96d8da67e88203fb5ed0597f5

3,203

py

Python

tests/manage/z_cluster/conftest.py

annagitel/ocs-ci

284fe04aeb6e3d6cb70c99e65fec8ff1b1ea1dd5

[ "MIT" ]

130

2019-04-08T06:22:53.000Z

2022-03-23T06:11:19.000Z

tests/manage/z_cluster/conftest.py

annagitel/ocs-ci

284fe04aeb6e3d6cb70c99e65fec8ff1b1ea1dd5

[ "MIT" ]

4,359

2019-04-09T18:48:47.000Z

2022-03-31T20:04:55.000Z

tests/manage/z_cluster/conftest.py

annagitel/ocs-ci

284fe04aeb6e3d6cb70c99e65fec8ff1b1ea1dd5

[ "MIT" ]

183

2019-04-18T15:55:30.000Z

2022-03-11T06:16:50.000Z

# -*- coding: utf8 -*- import logging import pytest from ocs_ci.ocs.fiojob import workload_fio_storageutilization logger = logging.getLogger(__name__) @pytest.fixture(scope="function") def workload_storageutilization_rbd( request, project, fio_pvc_dict, fio_job_dict, fio_configmap_dict, measurement_dir, tmp_path, supported_configuration, ): """ In order to use this fixture you need to pass 3 indirect parameters: target_percentage (float): the percentage storage utilization(from 0.01 to 0.99). keep_fio_data (bool): indicate if you want to keep the fio data after the test is finished. minimal_time (int): Minimal number of seconds to monitor a system (See more details in the function 'measure_operation'). For example: Let's say I want to use workload_storageutilization_rbd fixture with 'target_percentage'=0.25, 'keep_fio_data'=True, 'minimal_time'=120 then In my test I will specify these parameters: @pytest.mark.parametrize("workload_storageutilization_rbd", [(0.25, True, 120)], indirect=["workload_storageutilization_rbd"]) """ target_percentage, keep_fio_data, minimal_time = request.param percent_to_fill = int(target_percentage * 100) fixture_name = f"workload_storageutilization_{percent_to_fill}p_rbd" measured_op = workload_fio_storageutilization( fixture_name, project, fio_pvc_dict, fio_job_dict, fio_configmap_dict, measurement_dir, tmp_path, target_percentage=target_percentage, keep_fio_data=keep_fio_data, minimal_time=minimal_time, ) return measured_op @pytest.fixture(scope="function") def workload_storageutilization_cephfs( request, project, fio_pvc_dict, fio_job_dict, fio_configmap_dict, measurement_dir, tmp_path, supported_configuration, ): """ In order to use this fixture you need to pass 3 indirect parameters: target_percentage (float): the percentage storage utilization(from 0.01 to 0.99). keep_fio_data (bool): indicate if you want to keep the fio data after the test is finished. minimal_time (int): Minimal number of seconds to monitor a system (See more details in the function 'measure_operation'). For example: Let's say I want to use workload_storageutilization_cephfs fixture with 'target_percentage'=0.25, 'keep_fio_data'=True, 'minimal_time'=120 then In my test I will specify these parameters: @pytest.mark.parametrize("workload_storageutilization_cephfs", [(0.25, True, 120)], indirect=["workload_storageutilization_cephfs"]) """ target_percentage, keep_fio_data, minimal_time = request.param percent_to_fill = int(target_percentage * 100) fixture_name = f"workload_storageutilization_{percent_to_fill}p_cephfs" measured_op = workload_fio_storageutilization( fixture_name, project, fio_pvc_dict, fio_job_dict, fio_configmap_dict, measurement_dir, tmp_path, target_percentage=target_percentage, keep_fio_data=keep_fio_data, minimal_time=minimal_time, ) return measured_op

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d72fd93f383719c11d34cdc9edb8f03595e70f1a

164

py

Python

ptrlib/pwn/__init__.py

alissonbezerra/ptrlib

67a557acfa5069a66dd26670f53d94e63b023642

[ "MIT" ]

null

ptrlib/pwn/__init__.py

alissonbezerra/ptrlib

67a557acfa5069a66dd26670f53d94e63b023642

[ "MIT" ]

null

ptrlib/pwn/__init__.py

alissonbezerra/ptrlib

67a557acfa5069a66dd26670f53d94e63b023642

[ "MIT" ]

null

# coding: utf-8 from ptrlib.pwn.fsb import * from ptrlib.pwn.sock import * from ptrlib.pwn.proc import * from ptrlib.pwn.robot import * from ptrlib.pwn.dl import *

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8

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py

Python

lib/dfext.py

VisualComputingInstitute/reid-tracking

13c90ec698c6ce39aff8bc88d1ca9510b94bf931

[ "MIT" ]

81

2017-05-12T14:56:39.000Z

2021-03-23T03:25:27.000Z

lib/dfext.py

AsuradaYuci/towards-reid-tracking

13c90ec698c6ce39aff8bc88d1ca9510b94bf931

[ "MIT" ]

3

2017-08-23T10:19:21.000Z

2018-06-10T14:13:06.000Z

lib/dfext.py

AsuradaYuci/towards-reid-tracking

13c90ec698c6ce39aff8bc88d1ca9510b94bf931

[ "MIT" ]

19

2017-05-22T00:13:22.000Z

2020-06-16T02:58:52.000Z

import DeepFried2 as df def resblock(chan_in, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_in return df.Sequential( df.RepeatInput( df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_mid, (3,3), border='same', stride=stride, init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_out, (3,3), border='same', init=df.init.prelu()), ), df.Identity() if chan_in == chan_out else df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride) ), df.zoo.resnet.Add() ) def resblock2(chan_in, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_in identity_or_projection = df.Identity() if chan_in != chan_out: identity_or_projection = df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride, init=df.init.prelu()), ) return df.Sequential( df.RepeatInput( df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_mid, (3,3), border='same', stride=stride, init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_out, (3,3), border='same', init=df.init.prelu()), ), identity_or_projection, ), df.zoo.resnet.Add() ) def resblock_bottle(chan_in, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_out//4 return df.Sequential( df.RepeatInput( df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_mid, (1,1), stride=stride, init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_mid, (3,3), border='same', init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_out, (1,1), init=df.init.prelu()), ), df.Identity() if chan_in == chan_out else df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride) ), df.zoo.resnet.Add() ) def resblock_bottle2(chan_in, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_out//4 identity_or_projection = df.Identity() if chan_in != chan_out: identity_or_projection = df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride, init=df.init.prelu()), ) return df.Sequential( df.RepeatInput( df.Sequential( mkbn(chan_in), mknl(), df.SpatialConvolutionCUDNN(chan_in, chan_mid, (1,1), init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_mid, (3,3), stride=stride, border='same', init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_out, (1,1), init=df.init.prelu()), ), identity_or_projection, ), df.zoo.resnet.Add() ) def repeat_apply_merge(modules, merger, *tail): return df.Sequential(df.RepeatInput(*modules), merger, *tail) def nextblock_a(chan_in, cardin, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_out//cardin//2 identity_or_projection = df.Identity() if chan_in != chan_out: identity_or_projection = df.Sequential( df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride, init=df.init.prelu()), mkbn(chan_out), ) return repeat_apply_merge([ repeat_apply_merge([ df.Sequential( df.SpatialConvolutionCUDNN(chan_in, chan_mid, (1,1), init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_mid, (3,3), init=df.init.prelu(), bias=False, stride=stride, border='same'), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_out, (1,1), init=df.init.prelu(), bias=False), ) for _ in range(cardin) ], df.zoo.resnet.Add(), mkbn(chan_out)), identity_or_projection ], df.zoo.resnet.Add(), mknl()) def nextblock_b(chan_in, cardin, chan_out=None, chan_mid=None, stride=1, mkbn=lambda chan: df.BatchNormalization(chan, 0.95), mknl=lambda: df.ReLU()): chan_out = chan_out or chan_in chan_mid = chan_mid or chan_out//cardin//2 identity_or_projection = df.Identity() if chan_in != chan_out: identity_or_projection = df.Sequential( df.SpatialConvolutionCUDNN(chan_in, chan_out, (1,1), stride=stride, init=df.init.prelu()), mkbn(chan_out), ) return repeat_apply_merge([ repeat_apply_merge([ df.Sequential( df.SpatialConvolutionCUDNN(chan_in, chan_mid, (1,1), init=df.init.prelu(), bias=False), mkbn(chan_mid), mknl(), df.SpatialConvolutionCUDNN(chan_mid, chan_mid, (3,3), init=df.init.prelu(), bias=False, stride=stride, border='same'), mkbn(chan_mid), mknl(), ) for _ in range(cardin) ], df.Concat(), df.SpatialConvolutionCUDNN(chan_mid*cardin, chan_out, (1,1), init=df.init.prelu(), bias=False), mkbn(chan_out) ), identity_or_projection ], df.zoo.resnet.Add(), mknl())

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ad1cad2844c39eae53cedc4dc7a7400a1e79bcc3

50,912

py

Python

radiomanager_sdk/api/item_api.py

Pluxbox/radiomanager-python-client

a25450c079110fb12d8e5b00f8b96c2619ed6172

[ "MIT" ]

null

radiomanager_sdk/api/item_api.py

Pluxbox/radiomanager-python-client

a25450c079110fb12d8e5b00f8b96c2619ed6172

[ "MIT" ]

1

2018-09-05T08:51:24.000Z

2018-09-06T14:56:30.000Z

radiomanager_sdk/api/item_api.py

Pluxbox/radiomanager-python-client

a25450c079110fb12d8e5b00f8b96c2619ed6172

[ "MIT" ]

null

# coding: utf-8 """ RadioManager RadioManager # noqa: E501 OpenAPI spec version: 2.0 Contact: support@pluxbox.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from radiomanager_sdk.api_client import ApiClient class ItemApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_item(self, **kwargs): # noqa: E501 """Create an new item. # noqa: E501 Create item. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.create_item(async=True) >>> result = thread.get() :param async bool :param ItemDataInput data: Data *(Optional)* :return: PostSuccess If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.create_item_with_http_info(**kwargs) # noqa: E501 else: (data) = self.create_item_with_http_info(**kwargs) # noqa: E501 return data def create_item_with_http_info(self, **kwargs): # noqa: E501 """Create an new item. # noqa: E501 Create item. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.create_item_with_http_info(async=True) >>> result = thread.get() :param async bool :param ItemDataInput data: Data *(Optional)* :return: PostSuccess If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_item" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PostSuccess', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def current_item_post_structure(self, **kwargs): # noqa: E501 """Post a current playing item, keep structure # noqa: E501 Post a current playing item, keep structure # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.current_item_post_structure(async=True) >>> result = thread.get() :param async bool :param ImportItem data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.current_item_post_structure_with_http_info(**kwargs) # noqa: E501 else: (data) = self.current_item_post_structure_with_http_info(**kwargs) # noqa: E501 return data def current_item_post_structure_with_http_info(self, **kwargs): # noqa: E501 """Post a current playing item, keep structure # noqa: E501 Post a current playing item, keep structure # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.current_item_post_structure_with_http_info(async=True) >>> result = thread.get() :param async bool :param ImportItem data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method current_item_post_structure" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/current/structure', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Success', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def current_item_post_timing(self, **kwargs): # noqa: E501 """Post a current playing item # noqa: E501 Post a current playing item # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.current_item_post_timing(async=True) >>> result = thread.get() :param async bool :param ImportItem data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.current_item_post_timing_with_http_info(**kwargs) # noqa: E501 else: (data) = self.current_item_post_timing_with_http_info(**kwargs) # noqa: E501 return data def current_item_post_timing_with_http_info(self, **kwargs): # noqa: E501 """Post a current playing item # noqa: E501 Post a current playing item # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.current_item_post_timing_with_http_info(async=True) >>> result = thread.get() :param async bool :param ImportItem data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method current_item_post_timing" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/current/timing', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Success', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_item_by_id(self, id, **kwargs): # noqa: E501 """Delete item by ID. # noqa: E501 Delete item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.delete_item_by_id(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :return: Success If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.delete_item_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.delete_item_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def delete_item_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Delete item by ID. # noqa: E501 Delete item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.delete_item_by_id_with_http_info(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :return: Success If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_item_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `delete_item_by_id`") # noqa: E501 if 'id' in params and params['id'] < 0: # noqa: E501 raise ValueError("Invalid value for parameter `id` when calling `delete_item_by_id`, must be a value greater than or equal to `0`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Success', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_current_item(self, **kwargs): # noqa: E501 """Get current Item # noqa: E501 Get current Item # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_current_item(async=True) >>> result = thread.get() :param async bool :param bool lastplayed: Show last played item if there is no current item*(Optional)* :return: ItemResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.get_current_item_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_current_item_with_http_info(**kwargs) # noqa: E501 return data def get_current_item_with_http_info(self, **kwargs): # noqa: E501 """Get current Item # noqa: E501 Get current Item # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_current_item_with_http_info(async=True) >>> result = thread.get() :param async bool :param bool lastplayed: Show last played item if there is no current item*(Optional)* :return: ItemResult If the method is called asynchronously, returns the request thread. """ all_params = ['lastplayed'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_current_item" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'lastplayed' in params: query_params.append(('lastplayed', params['lastplayed'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/current', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ItemResult', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_item_by_id(self, id, **kwargs): # noqa: E501 """Get extended item details by ID. # noqa: E501 Read item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_item_by_id(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :param int external_station_id: Query on a different (content providing) station *(Optional)* :return: ItemResult If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.get_item_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_item_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def get_item_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Get extended item details by ID. # noqa: E501 Read item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_item_by_id_with_http_info(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :param int external_station_id: Query on a different (content providing) station *(Optional)* :return: ItemResult If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'external_station_id'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_item_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `get_item_by_id`") # noqa: E501 if 'id' in params and params['id'] < 0: # noqa: E501 raise ValueError("Invalid value for parameter `id` when calling `get_item_by_id`, must be a value greater than or equal to `0`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] if 'external_station_id' in params: query_params.append(('_external_station_id', params['external_station_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ItemResult', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_items(self, **kwargs): # noqa: E501 """Get a list of all the items currently in your station. # noqa: E501 Get a list of all the items currently in your station. This feature supports pagination and will give a maximum results of 50 items back. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.list_items(async=True) >>> result = thread.get() :param async bool :param int page: Current page *(Optional)* :param int block_id: Search on Block ID *(Optional)* `(Relation)` :param int broadcast_id: Search on Broadcast ID *(Optional)* `(Relation)` :param int model_type_id: Search on ModelType ID *(Optional)* `(Relation)` :param int tag_id: Search on Tag ID *(Optional)* `(Relation)` :param int campaign_id: Search on Campaign ID *(Optional)* `(Relation)` :param int contact_id: Search on Contact ID *(Optional)* `(Relation)` :param int program_draft_id: Search on Program Draft ID *(Optional)* :param int user_draft_id: Search on User Draft ID *(Optional)* :param int station_draft_id: Search on Station Draft ID *(Optional)* :param int program_id: Search on Program ID *(Optional)* `(Relation)` :param str external_id: Search on External ID *(Optional)* :param datetime start_min: Minimum start date *(Optional)* :param datetime start_max: Maximum start date *(Optional)* :param int duration_min: Minimum duration (seconds) *(Optional)* :param int duration_max: Maximum duration (seconds) *(Optional)* :param str status: Play Status of item *(Optional)* :param int limit: Results per page *(Optional)* :param str order_by: Field to order the results *(Optional)* :param str order_direction: Direction of ordering *(Optional)* :param int external_station_id: Query on a different (content providing) station *(Optional)* :return: ItemResults If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.list_items_with_http_info(**kwargs) # noqa: E501 else: (data) = self.list_items_with_http_info(**kwargs) # noqa: E501 return data def list_items_with_http_info(self, **kwargs): # noqa: E501 """Get a list of all the items currently in your station. # noqa: E501 Get a list of all the items currently in your station. This feature supports pagination and will give a maximum results of 50 items back. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.list_items_with_http_info(async=True) >>> result = thread.get() :param async bool :param int page: Current page *(Optional)* :param int block_id: Search on Block ID *(Optional)* `(Relation)` :param int broadcast_id: Search on Broadcast ID *(Optional)* `(Relation)` :param int model_type_id: Search on ModelType ID *(Optional)* `(Relation)` :param int tag_id: Search on Tag ID *(Optional)* `(Relation)` :param int campaign_id: Search on Campaign ID *(Optional)* `(Relation)` :param int contact_id: Search on Contact ID *(Optional)* `(Relation)` :param int program_draft_id: Search on Program Draft ID *(Optional)* :param int user_draft_id: Search on User Draft ID *(Optional)* :param int station_draft_id: Search on Station Draft ID *(Optional)* :param int program_id: Search on Program ID *(Optional)* `(Relation)` :param str external_id: Search on External ID *(Optional)* :param datetime start_min: Minimum start date *(Optional)* :param datetime start_max: Maximum start date *(Optional)* :param int duration_min: Minimum duration (seconds) *(Optional)* :param int duration_max: Maximum duration (seconds) *(Optional)* :param str status: Play Status of item *(Optional)* :param int limit: Results per page *(Optional)* :param str order_by: Field to order the results *(Optional)* :param str order_direction: Direction of ordering *(Optional)* :param int external_station_id: Query on a different (content providing) station *(Optional)* :return: ItemResults If the method is called asynchronously, returns the request thread. """ all_params = ['page', 'block_id', 'broadcast_id', 'model_type_id', 'tag_id', 'campaign_id', 'contact_id', 'program_draft_id', 'user_draft_id', 'station_draft_id', 'program_id', 'external_id', 'start_min', 'start_max', 'duration_min', 'duration_max', 'status', 'limit', 'order_by', 'order_direction', 'external_station_id'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_items" % key ) params[key] = val del params['kwargs'] if 'page' in params and params['page'] < 1: # noqa: E501 raise ValueError("Invalid value for parameter `page` when calling `list_items`, must be a value greater than or equal to `1`") # noqa: E501 if 'limit' in params and params['limit'] > 50: # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `list_items`, must be a value less than or equal to `50`") # noqa: E501 if 'limit' in params and params['limit'] < 1: # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `list_items`, must be a value greater than or equal to `1`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 if 'block_id' in params: query_params.append(('block_id', params['block_id'])) # noqa: E501 if 'broadcast_id' in params: query_params.append(('broadcast_id', params['broadcast_id'])) # noqa: E501 if 'model_type_id' in params: query_params.append(('model_type_id', params['model_type_id'])) # noqa: E501 if 'tag_id' in params: query_params.append(('tag_id', params['tag_id'])) # noqa: E501 if 'campaign_id' in params: query_params.append(('campaign_id', params['campaign_id'])) # noqa: E501 if 'contact_id' in params: query_params.append(('contact_id', params['contact_id'])) # noqa: E501 if 'program_draft_id' in params: query_params.append(('program_draft_id', params['program_draft_id'])) # noqa: E501 if 'user_draft_id' in params: query_params.append(('user_draft_id', params['user_draft_id'])) # noqa: E501 if 'station_draft_id' in params: query_params.append(('station_draft_id', params['station_draft_id'])) # noqa: E501 if 'program_id' in params: query_params.append(('program_id', params['program_id'])) # noqa: E501 if 'external_id' in params: query_params.append(('external_id', params['external_id'])) # noqa: E501 if 'start_min' in params: query_params.append(('start-min', params['start_min'])) # noqa: E501 if 'start_max' in params: query_params.append(('start-max', params['start_max'])) # noqa: E501 if 'duration_min' in params: query_params.append(('duration-min', params['duration_min'])) # noqa: E501 if 'duration_max' in params: query_params.append(('duration-max', params['duration_max'])) # noqa: E501 if 'status' in params: query_params.append(('status', params['status'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 if 'order_by' in params: query_params.append(('order-by', params['order_by'])) # noqa: E501 if 'order_direction' in params: query_params.append(('order-direction', params['order_direction'])) # noqa: E501 if 'external_station_id' in params: query_params.append(('_external_station_id', params['external_station_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ItemResults', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def playlist_post_merge(self, **kwargs): # noqa: E501 """Post a playlist, do not remove previously imported items # noqa: E501 Post a playlist, do not remove previously imported items # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_merge(async=True) >>> result = thread.get() :param async bool :param Data2 data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.playlist_post_merge_with_http_info(**kwargs) # noqa: E501 else: (data) = self.playlist_post_merge_with_http_info(**kwargs) # noqa: E501 return data def playlist_post_merge_with_http_info(self, **kwargs): # noqa: E501 """Post a playlist, do not remove previously imported items # noqa: E501 Post a playlist, do not remove previously imported items # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_merge_with_http_info(async=True) >>> result = thread.get() :param async bool :param Data2 data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method playlist_post_merge" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/playlist/merge', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='InlineResponse202', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def playlist_post_structure(self, **kwargs): # noqa: E501 """Post a playlist, keep current structure # noqa: E501 Post a playlist, keep current structure # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_structure(async=True) >>> result = thread.get() :param async bool :param Data1 data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.playlist_post_structure_with_http_info(**kwargs) # noqa: E501 else: (data) = self.playlist_post_structure_with_http_info(**kwargs) # noqa: E501 return data def playlist_post_structure_with_http_info(self, **kwargs): # noqa: E501 """Post a playlist, keep current structure # noqa: E501 Post a playlist, keep current structure # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_structure_with_http_info(async=True) >>> result = thread.get() :param async bool :param Data1 data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method playlist_post_structure" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/playlist/structure', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='InlineResponse202', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def playlist_post_timing(self, **kwargs): # noqa: E501 """Post a playlist # noqa: E501 Post a playlist # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_timing(async=True) >>> result = thread.get() :param async bool :param Data data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.playlist_post_timing_with_http_info(**kwargs) # noqa: E501 else: (data) = self.playlist_post_timing_with_http_info(**kwargs) # noqa: E501 return data def playlist_post_timing_with_http_info(self, **kwargs): # noqa: E501 """Post a playlist # noqa: E501 Post a playlist # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.playlist_post_timing_with_http_info(async=True) >>> result = thread.get() :param async bool :param Data data: Data *(Optional)* :return: InlineResponse202 If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method playlist_post_timing" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/playlist/timing', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='InlineResponse202', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def stop_current_item(self, **kwargs): # noqa: E501 """Stop an Item # noqa: E501 Set a current playing or specific item on played # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.stop_current_item(async=True) >>> result = thread.get() :param async bool :param Data3 data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.stop_current_item_with_http_info(**kwargs) # noqa: E501 else: (data) = self.stop_current_item_with_http_info(**kwargs) # noqa: E501 return data def stop_current_item_with_http_info(self, **kwargs): # noqa: E501 """Stop an Item # noqa: E501 Set a current playing or specific item on played # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.stop_current_item_with_http_info(async=True) >>> result = thread.get() :param async bool :param Data3 data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ all_params = ['data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method stop_current_item" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/stopcurrent', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Success', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_item_by_id(self, id, **kwargs): # noqa: E501 """Update extended item details by ID. # noqa: E501 Update item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.update_item_by_id(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :param ItemDataInput data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.update_item_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.update_item_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def update_item_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Update extended item details by ID. # noqa: E501 Update item by id. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.update_item_by_id_with_http_info(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of Item **(Required)** (required) :param ItemDataInput data: Data *(Optional)* :return: Success If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'data'] # noqa: E501 all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_item_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `update_item_by_id`") # noqa: E501 if 'id' in params and params['id'] < 0: # noqa: E501 raise ValueError("Invalid value for parameter `id` when calling `update_item_by_id`, must be a value greater than or equal to `0`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API Key'] # noqa: E501 return self.api_client.call_api( '/items/{id}', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Success', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

39.558664

344

0.600664

5,912

50,912

4.943674

0.040088

0.057481

0.022992

0.029562

0.948986

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0.916413

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0.903411

0

0.01939

0.30207

50,912

1,286

345

39.589425

0.803141

0.055331

0

0.77551

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0.008746

0.188565

0.031894

0

null

0

0.005831

null

0

null

0

1

0

1

0

null

0

1

0

9

ad24262598cd1db5442ebbdc9cfd32249c1f9c58

454

py

Python

codepack/service/__init__.py

ihnokim/codepack

9d043b2db977de503faf7f5f1370c1424c6cb19f

[ "MIT" ]

2

2021-04-18T17:51:49.000Z

2021-06-22T10:21:30.000Z

codepack/service/__init__.py

ihnokim/codepack

9d043b2db977de503faf7f5f1370c1424c6cb19f

[ "MIT" ]

24

2021-12-23T18:02:01.000Z

2022-03-27T03:03:38.000Z

codepack/service/__init__.py

ihnokim/codepack

9d043b2db977de503faf7f5f1370c1424c6cb19f

[ "MIT" ]

1

2021-09-13T12:56:40.000Z

from codepack.service.delivery_service import MemoryDeliveryService, FileDeliveryService, MongoDeliveryService, DeliveryServiceAlias from codepack.service.storage_service import MemoryStorageService, FileStorageService, MongoStorageService, StorageServiceAlias from codepack.service.snapshot_service import MemorySnapshotService, FileSnapshotService, MongoSnapshotService, SnapshotServiceAlias from codepack.service.default_service import DefaultService

90.8

132

0.907489

37

454

11.027027

0.567568

0.117647

0.186275

0

0.055066

454

4

133

113.5

0.951049

0

1

0

true

0

1

0

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

1

0

1

0

7

ad2a6821c20abdcb5572aca4ced07b93de500795

39,556

py

Python

tests/asg/test_asg_actions.py

mvollman/chaostoolkit-aws

17029b763fc20e36bc4ebee35ef1012b9d28bc14

[ "Apache-2.0" ]

3

2020-08-04T18:45:23.000Z

2021-11-12T16:14:49.000Z

tests/asg/test_asg_actions.py

mvollman/chaostoolkit-aws

17029b763fc20e36bc4ebee35ef1012b9d28bc14

[ "Apache-2.0" ]

1

2021-03-18T18:07:37.000Z

tests/asg/test_asg_actions.py

mvollman/chaostoolkit-aws

17029b763fc20e36bc4ebee35ef1012b9d28bc14

[ "Apache-2.0" ]

1

2020-09-14T10:43:46.000Z

# -*- coding: utf-8 -*- from unittest.mock import MagicMock, patch from chaoslib.exceptions import FailedActivity from chaosaws.asg.actions import ( suspend_processes, resume_processes, terminate_random_instances, detach_random_instances, change_subnets, detach_random_volume, attach_volume, stop_random_instances) import pytest def test_suspend_process_no_name_or_tag(): with pytest.raises(FailedActivity) as x: suspend_processes() assert 'one of the following arguments are required: ' \ 'asg_names or tags' in str(x.value) def test_suspend_process_both_name_and_tag_one(): with pytest.raises(FailedActivity) as x: suspend_processes( asg_names=['AutoScalingGroup-A'], tags=[{"Key": "TagKey", "Values": ["TagValues"]}]) assert 'only one of the following arguments are allowed: ' \ 'asg_names/tags' in str(x.value) def test_suspend_process_invalid_process(): with pytest.raises(FailedActivity) as x: suspend_processes( asg_names=['AutoScalingGroup-A'], process_names=['Lunch']) assert "invalid process(es): ['Lunch'] not in" in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_suspend_process_asg_names(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [] }] } suspend_processes(asg_names=asg_names) client.suspend_processes.assert_called_with( AutoScalingGroupName=asg_names[0]) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_suspend_process_asg_tags(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}] }] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [] }] } suspend_processes(tags=[{'Key': 'TargetKey', 'Value': 'TargetValue'}]) client.suspend_processes.assert_called_with( AutoScalingGroupName=asg_names[0]) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_suspend_process_asg_invalid_names(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": []} with pytest.raises(FailedActivity) as x: suspend_processes(asg_names=asg_names, process_names=["Launch"]) assert 'Unable to locate ASG(s): %s' % asg_names in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_suspend_process_asg_invalid_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A', 'AutoScalingGroup-B'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [] }] } with pytest.raises(FailedActivity) as x: suspend_processes(asg_names=asg_names, process_names=["Launch"]) assert 'No ASG(s) found with name(s): %s' % ([asg_names[1]]) in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_suspend_process_asg_invalid_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{'Tags': []}] with pytest.raises(FailedActivity) as x: suspend_processes(tags=tags) assert 'No ASG(s) found with matching tag(s): %s.' % tags in str(x.value) def test_resume_process_no_name_or_tag(): with pytest.raises(FailedActivity) as x: resume_processes() assert 'one of the following arguments are required: ' \ 'asg_names or tags' in str(x.value) def test_resume_process_both_name_and_tag(): with pytest.raises(FailedActivity) as x: resume_processes( asg_names=['AutoScalingGroup-A'], tags=[{"Key": "TagKey", "Values": ["TagValues"]}]) assert 'only one of the following arguments are allowed: ' \ 'asg_names/tags' in str(x.value) def test_resume_process_invalid_process(): with pytest.raises(FailedActivity) as x: resume_processes( asg_names=['AutoScalingGroup-A'], process_names=['Lunch']) assert "invalid process(es): ['Lunch'] not in" in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_resume_process_asg_names(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [{"ProcessName": "Launch"}] }] } resume_processes(asg_names=asg_names, process_names=["Launch"]) client.resume_processes.assert_called_with( AutoScalingGroupName=asg_names[0], ScalingProcesses=["Launch"]) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_resume_process_asg_tags(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}] }] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [{"ProcessName": "Launch"}] }] } resume_processes(tags=tags, process_names=["Launch"]) client.resume_processes.assert_called_with( AutoScalingGroupName=asg_names[0], ScalingProcesses=["Launch"]) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_resume_process_asg_invalid_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{'Tags': []}] with pytest.raises(FailedActivity) as x: resume_processes(tags=tags) assert 'No ASG(s) found with matching tag(s): %s.' % tags in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_resume_process_asg_invalid_names(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": []} with pytest.raises(FailedActivity) as x: resume_processes(asg_names=asg_names, process_names=["Launch"]) assert 'Unable to locate ASG(s): ' in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_resume_process_asg_invalid_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A', 'AutoScalingGroup-B'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "DesiredCapacity": 1, "Instances": [{ "HealthStatus": "Healthy", "LifecycleState": "InService" }], "SuspendedProcesses": [{"ProcessName": "Launch"}] }] } with pytest.raises(FailedActivity) as x: resume_processes(asg_names=asg_names, process_names=["Launch"]) assert 'No ASG(s) found with name(s): %s' % ([asg_names[1]]) in str(x.value) def test_terminate_instances_no_asgs(): with pytest.raises(FailedActivity) as x: terminate_random_instances(instance_count=10) assert 'one of the following arguments are required: ' \ 'asg_names or tags' in str(x.value) def test_terminate_instances_no_numbers(): asg_names = ['AutoScalingGroup-A', 'AutoScalingGroup-B'] with pytest.raises(FailedActivity) as x: terminate_random_instances(asg_names) assert 'Must specify one of "instance_count", ' \ '"instance_percent", "az"' in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_count_pass(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } terminate_random_instances(asg_names=asg_names, instance_count=2) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003'] ] ex = None for i in instance_calls: try: client.terminate_instances.assert_called_with( InstanceIds=sorted(i)) return True except AssertionError as e: ex = e.args raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_percent_pass(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } terminate_random_instances(asg_names=asg_names, instance_percent=50) instance_calls = [ 'i-00000000000000001', 'i-00000000000000002', 'i-00000000000000003'] ex = None for i in instance_calls: try: client.terminate_instances.assert_called_with( InstanceIds=[i]) return True except AssertionError as e: ex = e.args raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_valid_az(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } terminate_random_instances(asg_names=asg_names, az='us-east-1a') client.terminate_instances.assert_called_with( InstanceIds=['i-00000000000000001']) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_invalid_az(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } with pytest.raises(FailedActivity) as x: terminate_random_instances(asg_names=asg_names, az='us-east-1d') assert 'No instances found in Availability Zone: us-east-1d' in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_invalid_count(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" } ] } ] } with pytest.raises(FailedActivity) as x: terminate_random_instances(asg_names=asg_names, instance_count=2) assert 'Not enough healthy instances in {} to satisfy ' \ 'termination count {} ({})'.format(asg_names[0], 2, 1) in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_terminate_instances_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}] }] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] }] } terminate_random_instances(tags=tags, instance_count=2) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003'] ] ex = None for i in instance_calls: try: client.terminate_instances.assert_called_with( InstanceIds=sorted(i)) return True except AssertionError as e: ex = e.args raise AssertionError(ex) def test_detach_instance_no_name_or_tag(): with pytest.raises(FailedActivity) as x: detach_random_instances() assert 'one of the following arguments are required: ' \ 'asg_names or tags' in str(x.value) def test_detach_instance_both_name_and_tag_one(): with pytest.raises(FailedActivity) as x: detach_random_instances( asg_names=['AutoScalingGroup-A'], tags=[{"Key": "TagKey", "Values": ["TagValues"]}]) assert 'only one of the following arguments are allowed: ' \ 'asg_names/tags' in str(x.value) def test_detach_instance_no_count(): with pytest.raises(FailedActivity) as x: detach_random_instances( asg_names=['AutoScalingGroup-A']) assert 'You must specify either "instance_count" or ' \ '"instance_percent"' in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_instances_invalid_count(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" } ] } ] } with pytest.raises(FailedActivity) as x: detach_random_instances(asg_names, instance_count=3) assert 'You are attempting to detach more instances than exist on ' \ 'asg %s' % asg_names[0] in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_instances_count(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } detach_random_instances(asg_names, instance_count=2) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003']] ex = None for i in instance_calls: try: client.detach_instances.assert_called_with( AutoScalingGroupName=asg_names[0], InstanceIds=sorted(i), ShouldDecrementDesiredCapacity=False) return True except AssertionError as e: ex = str(e.args) raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_instances_percent(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } detach_random_instances(asg_names, instance_percent=67) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003']] ex = None for i in instance_calls: try: client.detach_instances.assert_called_with( AutoScalingGroupName=asg_names[0], InstanceIds=sorted(i), ShouldDecrementDesiredCapacity=False) return True except AssertionError as e: ex = str(e.args) raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_instances_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}] }] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] }] } detach_random_instances(tags=tags, instance_count=2) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003'] ] ex = None for i in instance_calls: try: client.detach_instances.assert_called_with( AutoScalingGroupName='AutoScalingGroup-A', InstanceIds=sorted(i), ShouldDecrementDesiredCapacity=False) return True except AssertionError as e: ex = e.args raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_change_subnets_valid_names(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] params = dict( asg_names=asg_names, subnets=['subnet-123456789', 'subnet-23456789a']) client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "VPCZoneIdentifier": "subnet-012345678,subnet-123456789"}]} change_subnets(**params) client.update_auto_scaling_group.assert_called_with( AutoScalingGroupName=asg_names[0], VPCZoneIdentifier="subnet-123456789,subnet-23456789a") @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_change_subnets_valid_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] params = dict( tags=tags, subnets=['subnet-123456789', 'subnet-23456789a']) client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue'}]}] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "VPCZoneIdentifier": "subnet-012345678,subnet-123456789"}]} change_subnets(**params) client.update_auto_scaling_group.assert_called_with( AutoScalingGroupName="AutoScalingGroup-A", VPCZoneIdentifier="subnet-123456789,subnet-23456789a") def test_change_subnets_no_subnet(): asg_names = ['AutoScalingGroup-A'] with pytest.raises(TypeError) as x: change_subnets(asg_names=asg_names) assert "missing 1 required positional argument: 'subnets'" in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_random_volume_asg_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ {"InstanceId": "i-00000000000000001"}]}]} client.describe_instances.return_value = { 'Reservations': [{ 'Instances': [{ 'InstanceId': 'i-00000000000000001', 'BlockDeviceMappings': [ { 'DeviceName': '/dev/xvda', 'Ebs': {'VolumeId': 'vol-00000001'} }, { 'DeviceName': '/dev/sdc', 'Ebs': {'VolumeId': 'vol-00000002'} }]}]}]} client.detach_volume.return_value = { 'Device': '/dev/sdc', 'InstanceId': 'i-00000000000000001', 'State': 'detaching', 'VolumeId': 'vol-00000002'} results = detach_random_volume(asg_names=asg_names) client.describe_auto_scaling_groups.assert_called_with( AutoScalingGroupNames=asg_names) client.describe_instances.assert_called_with( InstanceIds=['i-00000000000000001']) client.detach_volume.assert_called_with( Device='/dev/sdc', Force=True, InstanceId='i-00000000000000001', VolumeId='vol-00000002') assert results[0]['Device'] == '/dev/sdc' @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_random_volume_asg_tags(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}]}] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ {"InstanceId": "i-00000000000000001"}]}]} client.describe_instances.return_value = { 'Reservations': [{ 'Instances': [{ 'InstanceId': 'i-00000000000000001', 'BlockDeviceMappings': [ { 'DeviceName': '/dev/xvda', 'Ebs': {'VolumeId': 'vol-00000001'} }, { 'DeviceName': '/dev/sdb', 'Ebs': {'VolumeId': 'vol-00000002'} }]}]}]} client.detach_volume.return_value = { 'Device': '/dev/sdb', 'InstanceId': 'i-00000000000000001', 'State': 'detaching', 'VolumeId': 'vol-00000002'} results = detach_random_volume(tags=tags) client.describe_auto_scaling_groups.assert_called_with( AutoScalingGroupNames=asg_names) client.describe_instances.assert_called_with( InstanceIds=['i-00000000000000001']) client.detach_volume.assert_called_with( Device='/dev/sdb', Force=True, InstanceId='i-00000000000000001', VolumeId='vol-00000002') assert results[0]['Device'] == '/dev/sdb' @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_random_volume_asg_invalid_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": []} with pytest.raises(FailedActivity) as x: detach_random_volume(asg_names=asg_names) assert "Unable to locate ASG(s): %s" % asg_names in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_detach_random_volume_asg_invalid_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.describe_instances.return_value = {'Reservations': []} with pytest.raises(FailedActivity) as x: detach_random_volume(tags=tags) assert "No ASG(s) found with matching tag(s): %s" % tags in str(x.value) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_attach_volume_asg_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": asg_names[0], "Instances": [ {"InstanceId": "i-00000000000000001"}]}]} client.describe_volumes.return_value = { 'Volumes': [ { 'VolumeId': 'vol-00000001', 'Tags': [{ 'Key': 'ChaosToolkitDetached', 'Value': 'DeviceName=/dev/sdc;InstanceId=%s;ASG=%s' % ( 'i-987654321fabcde', asg_names[0])}] }, { 'VolumeId': 'vol-00000002', 'Tags': [{ 'Key': 'ChaosToolkitDetached', 'Value': 'DeviceName=/dev/sdb;InstanceId=' 'i-987654321fefghi' }]}]} client.attach_volume.return_value = { 'DeviceName': '/dev/sdc', 'InstanceId': 'i-987654321fabcde', 'State': 'attaching', 'VolumeId': 'vol-00000001'} results = attach_volume(asg_names=asg_names) client.describe_auto_scaling_groups.assert_called_with( AutoScalingGroupNames=asg_names) client.describe_volumes.assert_called_with( Filters=[{'Name': 'tag-key', 'Values': ['ChaosToolkitDetached']}]) client.attach_volume.assert_called_with( Device='/dev/sdc', InstanceId='i-987654321fabcde', VolumeId='vol-00000001') assert results[0]['DeviceName'] == '/dev/sdc' @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_attach_volume_asg_tags(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': asg_names[0], 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}]}] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": asg_names[0], "Instances": [ {"InstanceId": "i-00000000000000001"}]}]} client.describe_volumes.return_value = { 'Volumes': [ { 'VolumeId': 'vol-00000001', 'Tags': [{ 'Key': 'ChaosToolkitDetached', 'Value': 'DeviceName=/dev/sdb;InstanceId=%s;ASG=%s' % ( 'i-00000000000000001', asg_names[0])}] }, { 'VolumeId': 'vol-00000002', 'Tags': [{ 'Key': 'ChaosToolkitDetached', 'Value': 'DeviceName=/dev/sdb;InstanceId=' 'i-987654321fghij' }]}]} client.attach_volume.return_value = { 'DeviceName': '/dev/sdb', 'InstanceId': 'i-00000000000000001', 'State': 'attaching', 'VolumeId': 'vol-00000001'} results = attach_volume(tags=tags) client.describe_auto_scaling_groups.assert_called_with( AutoScalingGroupNames=asg_names) client.get_paginator.return_value.paginate.assert_called_with( Filters=[ {'Name': 'key', 'Values': ['TargetKey']}, {'Name': 'value', 'Values': ['TargetValue']}]) client.describe_volumes.assert_called_with( Filters=[{'Name': 'tag-key', 'Values': ['ChaosToolkitDetached']}]) client.attach_volume.assert_called_with( Device='/dev/sdb', InstanceId='i-00000000000000001', VolumeId='vol-00000001') assert results[0]['DeviceName'] == '/dev/sdb' @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_asg_stop_random_instance_name(aws_client): client = MagicMock() aws_client.return_value = client asg_names = ['AutoScalingGroup-A'] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [ { "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }, ] } ] } stop_random_instances(asg_names=asg_names, instance_percent=50) instance_calls = [ 'i-00000000000000001', 'i-00000000000000002', 'i-00000000000000003'] ex = None for i in instance_calls: try: client.stop_instances.assert_called_with( Force=False, InstanceIds=[i]) return True except AssertionError as e: ex = e.args raise AssertionError(ex) @patch('chaosaws.asg.actions.aws_client', autospec=True) def test_asg_stop_random_instance_tags(aws_client): client = MagicMock() aws_client.return_value = client tags = [{'Key': 'TargetKey', 'Value': 'TargetValue'}] client.get_paginator.return_value.paginate.return_value = [{ 'Tags': [{ 'ResourceId': 'AutoScalingGroup-A', 'ResourceType': 'auto-scaling-group', 'Key': 'TargetKey', 'Value': 'TargetValue', 'PropagateAtLaunch': False}]}] client.describe_auto_scaling_groups.return_value = { "AutoScalingGroups": [{ "AutoScalingGroupName": "AutoScalingGroup-A", "Instances": [ { "InstanceId": "i-00000000000000001", "AvailabilityZone": "us-east-1a", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000002", "AvailabilityZone": "us-east-1b", "LifecycleState": "InService" }, { "InstanceId": "i-00000000000000003", "AvailabilityZone": "us-east-1c", "LifecycleState": "InService" }]}]} stop_random_instances(tags=tags, instance_count=2) instance_calls = [ ['i-00000000000000001', 'i-00000000000000002'], ['i-00000000000000001', 'i-00000000000000003'], ['i-00000000000000002', 'i-00000000000000003']] ex = None for i in instance_calls: try: client.stop_instances.assert_called_with( Force=False, InstanceIds=sorted(i)) return True except AssertionError as e: ex = e.args raise AssertionError(ex)

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null

0

7

ad4e5d33eff0c656a4d5b49343eff6d835b7dd82

5,216

py

Python

tests/test_visitors/test_tokenize/test_comments/test_shebang.py

cdhiraj40/wemake-python-styleguide

7cef9be081d594c30045b7a98cae77a9be46e1aa

[ "MIT" ]

1,931

2018-03-17T13:52:45.000Z

2022-03-27T09:39:17.000Z

tests/test_visitors/test_tokenize/test_comments/test_shebang.py

amansr02/wemake-python-styleguide

681035ed21fbe28ebfb32b8807b98e8de76b64aa

[ "MIT" ]

2,231

2018-03-09T21:19:05.000Z

2022-03-31T08:35:37.000Z

tests/test_visitors/test_tokenize/test_comments/test_shebang.py

amansr02/wemake-python-styleguide

681035ed21fbe28ebfb32b8807b98e8de76b64aa

[ "MIT" ]

492

2018-05-18T21:20:28.000Z

2022-03-20T14:11:50.000Z

import pytest from wemake_python_styleguide.violations.best_practices import ShebangViolation from wemake_python_styleguide.visitors.tokenize import comments template_empty = '' template_newlines = '\n\n' template_regular = '{0}' template_with_leading_comment = """{0} # some other """ template_regular_comment = 'x = 1{0}' @pytest.mark.parametrize('template', [ template_regular, template_with_leading_comment, ]) @pytest.mark.parametrize(('code', 'executable'), [ ('x = 1', False), ('#!/bin/python', True), ]) def test_correct_shebang_executable1( make_file, assert_errors, parse_file_tokens, default_options, template, code, executable, ): """Testing cases when no errors should be reported.""" path_to_file = make_file('test_file.py', template.format(code), executable) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename=path_to_file, file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, []) @pytest.mark.parametrize('template', [ template_regular_comment, template_empty, ]) @pytest.mark.parametrize(('code', 'executable'), [ ('#!/bin/some', False), ('#!/bin/python', False), ('# any text', False), (' # any text with padding', False), ]) def test_correct_shebang_executable2( make_file, assert_errors, parse_file_tokens, default_options, template, code, executable, ): """Testing cases when no errors should be reported.""" path_to_file = make_file('test_file.py', template.format(code), executable) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename=path_to_file, file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, []) @pytest.mark.parametrize('template', [ template_regular, template_with_leading_comment, template_regular_comment, template_empty, ]) @pytest.mark.parametrize(('code', 'executable'), [ ('#!/bin/python', False), ('#!/bin/python', True), ('# any text', False), ('# any text', True), ]) def test_shebang_on_windows( make_file, monkeypatch, assert_errors, parse_file_tokens, default_options, template, code, executable, ): """Testing cases when no errors should be reported.""" monkeypatch.setattr(comments, 'is_windows', lambda: True) path_to_file = make_file('test_file.py', template.format(code), executable) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename=path_to_file, file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, []) @pytest.mark.parametrize('template', [ template_regular, template_with_leading_comment, template_regular_comment, template_empty, ]) @pytest.mark.parametrize(('code', 'executable'), [ ('#!/bin/python', False), ('#!/bin/python', True), ('# any text', False), ('# any text', True), ]) def test_shebang_with_stdin( make_file, monkeypatch, assert_errors, parse_file_tokens, default_options, template, code, executable, ): """Testing cases when no errors should be reported.""" path_to_file = make_file('test_file.py', template.format(code), executable) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename='stdin', file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, []) @pytest.mark.parametrize('template', [ template_regular, template_with_leading_comment, ]) @pytest.mark.parametrize(('code', 'executable'), [ ('#!/bin/python', False), ('# regular comment', True), ]) def test_wrong_shebang_executable( make_file, assert_errors, parse_file_tokens, default_options, template, code, executable, ): """Testing cases when no errors should be reported.""" path_to_file = make_file('test_file.py', template.format(code), executable) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename=path_to_file, file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, [ShebangViolation]) @pytest.mark.parametrize('template', [ template_with_leading_comment, ]) @pytest.mark.parametrize('code', [ '#!/bin/other', # does not include `python` ' #!/bin/python', # has extra whitespace '\n\n#!python', # has extra newlines ]) def test_wrong_shebang_format( make_file, assert_errors, parse_file_tokens, default_options, template, code, ): """Testing cases when no errors should be reported.""" path_to_file = make_file( 'test_file.py', template.format(code), is_executable=True, ) file_tokens = parse_file_tokens(path_to_file) visitor = comments.ShebangVisitor( default_options, filename=path_to_file, file_tokens=file_tokens, ) visitor.run() assert_errors(visitor, [ShebangViolation])

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7

ad8f694ed91f7b40ca4c8720c5f1ecd12978adf2

4,563

py

Python

comapv/tests/tests_viz.py

co-map-v/co-map-v.github.io

8431b20d64605d07b59734da77fb22f7fc9587c0

[ "BSD-2-Clause" ]

null

comapv/tests/tests_viz.py

co-map-v/co-map-v.github.io

8431b20d64605d07b59734da77fb22f7fc9587c0

[ "BSD-2-Clause" ]

6

2020-12-04T03:11:06.000Z

2021-06-02T03:34:51.000Z

comapv/tests/tests_viz.py

co-map-v/co-map-v.github.io

8431b20d64605d07b59734da77fb22f7fc9587c0

[ "BSD-2-Clause" ]

1

2020-12-15T07:02:54.000Z

""" Unit tests to ensure that each function in app.py generates a plotly figure. Pylint = 9.83 """ import unittest import os import json import urllib.request import pathlib import pandas as pd from .. import app class UnitTests(unittest.TestCase): def test_smoke1 (self): """Smoke Test: Death Counts Map Should check to see if the function generates a plotly plot """ #URLs left long, ouside of PEP8 compliance to favour readability! # Load data from Github Repo with urllib.request.urlopen('https://raw.githubusercontent.com/co-map-v/co-map-v.github.io/main/comapv/data/ma_map.geojson') as response: # pylint: disable=line-too-long counties_1 = json.load(response) wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.death_counts_map(df_time_1,counties_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") def test_smoke2 (self): """Smoke Test: Case Counts Map Should check to see if the function generates a plotly plot """ #URLs left long, ouside of PEP8 compliance to favour readability! # Load data from Github Repo with urllib.request.urlopen('https://raw.githubusercontent.com/co-map-v/co-map-v.github.io/main/comapv/data/ma_map.geojson') as response: # pylint: disable=line-too-long counties_1 = json.load(response) wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.case_count_map(df_time_1,counties_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") def test_smoke3 (self): """Smoke Test: Pop Counts Map Should check to see if the function generates a plotly plot """ #URLs left long, ouside of PEP8 compliance to favour readability! # Load data from Github Repo with urllib.request.urlopen('https://raw.githubusercontent.com/co-map-v/co-map-v.github.io/main/comapv/data/ma_map.geojson') as response: # pylint: disable=line-too-long counties_1 = json.load(response) wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.population_map(df_time_1,counties_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") def test_smoke4 (self): """Smoke Test: Pop Counts Chart Should check to see if the function generates a plotly plot """ wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.population_histogram(df_time_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") def test_smoke5 (self): """Smoke Test: Death Counts Chart Should check to see if the function generates a plotly plot """ wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.deaths_histogram(df_time_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") def test_smoke6 (self): """Smoke Test: Case Counts Chart Should check to see if the function generates a plotly plot """ wd_of_script = pathlib.Path(__file__).parent.absolute() filepath_read = os.path.join(wd_of_script, './', 'smoketest_data.csv')# pylint: disable=line-too-long df_time_1 = pd.read_csv(filepath_read) fig = app.case_histogram(df_time_1) string = str(type(fig)) self.assertEqual(string, "<class 'plotly.graph_objs._figure.Figure'>") suite = unittest.TestLoader().loadTestsFromTestCase(UnitTests) _ = unittest.TextTestRunner().run(suite)

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null

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d171cfb9994369c889653b5c2c50677d646c6278

11,968

py

Python

Python Code/Algorithm Range 0 - 20000/bubble.py

Roisin-Fallon/Sorting_Algorithms

5ebb0fb3175982bbaa991556a5b09bb443636422

[ "Apache-2.0" ]

null

Python Code/Algorithm Range 0 - 20000/bubble.py

Roisin-Fallon/Sorting_Algorithms

5ebb0fb3175982bbaa991556a5b09bb443636422

[ "Apache-2.0" ]

null

Python Code/Algorithm Range 0 - 20000/bubble.py

Roisin-Fallon/Sorting_Algorithms

5ebb0fb3175982bbaa991556a5b09bb443636422

[ "Apache-2.0" ]

null

from random import * # Import python random module def random_array(n): # Function takes as input a value n array = [] # create an array variable for i in range(0, n, 1): # i start at 0 stop at n an increment by 1 (e.g. if n=4 0,1,2,3) array.append(randint(0,100)) # Add random generated integers with values between 0 and 99 to the array return array # assign the random array to alist alist1= random_array(100) alist2= random_array(250) alist3= random_array(500) alist4 = random_array(750) alist5 = random_array(1000) alist6 = random_array(1250) alist7 = random_array(2500) alist8 = random_array(3750) alist9 = random_array(5000) alist10 = random_array(6250) alist11 = random_array(7500) alist12 = random_array(8750) alist13 = random_array(10000) alist14 = random_array(15000) alist15 = random_array(20000) # Code adapted from: https://www.geeksforgeeks.org/bubble-sort/ def bubbleSort(alist): n = len(alist) for i in range(n): # Traverse through all elements in the array for j in range(0, n-i-1): # Last i elements are already in place if alist[j] > alist[j+1]: # Swap if the element is greater than the next element alist[j], alist[j+1] = alist[j+1], alist[j] import time # import time module num_runs = 10 # Number of times to test the function i.e. we want 10 runs results = [] # array to store results for each test bubble_avglist = [] def benchmark_bubble(): for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist1) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist2) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist3) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist4) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist5) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist6) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist7) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist8) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist9) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist10) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist11) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist12) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist13) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist14) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) for r in range(num_runs): # Benchmark the function start_time = time.time() # Log the start time in seconds bubbleSort(alist15) # Call the function insertion to benchmark end_time = time.time() # Log the end time in seconds time_elapsed= end_time - start_time # Calculate the elapsed time results.append(time_elapsed) b = sum(results) # Sum the results of the 10 runs average = (b/num_runs) # Calculate the average of a run bubble_avglist.append(average) print(bubble_avglist) benchmark_bubble()

55.665116

128

0.528576

1,368

11,968

4.510234

0.096491

0.077796

0.058347

0.072934

0.823177

0.816856

0

0.022302

0.415525

11,968

214

129

55.925234

0.859757

0.312834

0

0.710059

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false

0

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0

null

0

1

0

1

0

null

0

7

66ffeb367675e14a4d37bf1160fbabcd0cd6e4aa

8,310

py

Python

web-scraping/converter.py

stivenramireza/nutibara-web-scraping

48be40b735f011ac93901a2ca97fcacc6d2d5ca6

[ "MIT" ]

1

2019-10-02T14:29:48.000Z

web-scraping/converter.py

stivenramireza/nutibara-web-scraping

48be40b735f011ac93901a2ca97fcacc6d2d5ca6

[ "MIT" ]

1

2019-10-02T14:42:31.000Z

web-scraping/converter.py

stivenramireza/anutibara-web-scraping

48be40b735f011ac93901a2ca97fcacc6d2d5ca6

[ "MIT" ]

null

import crawl import generator from datetime import datetime import json, re date = datetime.now() scraping_date = str(date.strftime("%d")) + '/' + str(date.strftime("%m")) + '/' + str(date.strftime("%Y")) scraping_hour = str(date.strftime("%X")) def convert_string_to_json(url): soup = crawl.scrape_html(url) pattern = re.compile("var sfAdvert = \{.*\:.*\:.*\};") json_property = '' for script in soup.find_all("script", type="text/javascript"): if(pattern.findall(script.text)): json_property = pattern.findall(script.text)[0].split()[3:] json_to_strip = (json_property[-1])[0:-1] json_property = json_property[0:-1] json_property.append(json_to_strip) json_property = " ".join(json_property) json_property_agency = json.loads(json_property) return json_property_agency def convert_12_to_24(str1): if str1[-2:] == "AM" and str1[:2] == "12": return "00" + str1[2:-2] elif str1[-2:] == "AM": return str1[:-2] elif str1[-2:] == "PM" and str1[:2] == "12": return str1[:-2] else: return str(int(str1[:2]) + 12) + str1[2:8] def convert_new_property_to_json(json_property_agency, property_location, owner_property, property_features, property_hidden_features, array_offers_type, url): modify_date = json_property_agency["ModifyDate"].split()[0] modify_date = modify_date.split('/') modify_date = modify_date[1] + '/' + modify_date[0] + '/' + modify_date[2] modify_date_object = datetime.strptime(modify_date, '%d/%m/%Y') modify_date = datetime.strftime(modify_date_object, '%d/%m/%Y') hour = json_property_agency["ModifyDate"].split()[1] am_pm = json_property_agency["ModifyDate"].split()[2] modify_hour_object = datetime.strptime(hour, '%H:%M:%S') modify_hour_str = datetime.strftime(modify_hour_object, '%H:%M:%S') modify_hour = modify_hour_str + " " + am_pm modify_hour = convert_12_to_24(modify_hour) array_interior_features = '' array_exterior_features = '' array_sector_features = '' for key in property_hidden_features: if(key == 'interiorFeatures'): array_interior_features = property_hidden_features[key] elif(key == 'exteriorFeatures'): array_exterior_features = property_hidden_features[key] else: array_sector_features = property_hidden_features[key] new_property_dict = { 'urlProperty': url, 'scrapingDate': scraping_date, 'scrapingHour': scraping_hour, 'modifyDate': modify_date, 'modifyHour': modify_hour, 'code': int(json_property_agency["AdvertId"]), 'status': json_property_agency["Status"], 'type': json_property_agency["TransactionType"], 'use': 'Nuevo', 'nameProject': json_property_agency["Title"], 'country': property_location['country'], 'department': property_location['department'], 'city': property_location['city'], 'sector': property_location['sector'], 'neighborhood': property_location['neighborhood'], 'address': property_location['address'], 'latitude': property_location['latitude'], 'longitude': property_location['longitude'], 'idOwnerProperty': owner_property['id'], 'nameOwnerProperty': owner_property['name'], 'contractType': owner_property['contractType'], 'financing': owner_property['financing'], 'schedule': owner_property['schedule'], 'description': json_property_agency["Description"], 'price': property_features['price'], 'squareMeters': property_features['squareMeters'], 'rooms': property_features['rooms'], 'bathrooms': property_features['bathrooms'], 'garages': property_features['garages'], 'privateArea': property_features['privateArea'], 'constructionArea': property_features['constructionArea'], 'squareMetersPrice': property_features['squareMetersPrice'], 'stratum': property_features['stratum'], 'condition': property_features['condition'], 'antiquity': property_features['antiquity'], 'floor': property_features['floor'], 'interiorFloors': property_features['interiorFloors'], 'weather': property_features['weather'], 'includesAdministration': property_features['includesAdministration'], 'admonPrice': property_features['admonPrice'], 'interiorFeatures': array_interior_features, 'exteriorFeatures': array_exterior_features, 'sectorFeatures': array_sector_features, 'offersType': array_offers_type[1:] } generator.create_json(new_property_dict) def convert_old_property_to_json(json_property_agency, property_location, owner_property, property_features, property_hidden_features, array_offers_type, url): modify_date = json_property_agency["ModifyDate"].split()[0] modify_date = modify_date.split('/') modify_date = modify_date[1] + '/' + modify_date[0] + '/' + modify_date[2] modify_date_object = datetime.strptime(modify_date, '%d/%m/%Y') modify_date = datetime.strftime(modify_date_object, '%d/%m/%Y') hour = json_property_agency["ModifyDate"].split()[1] am_pm = json_property_agency["ModifyDate"].split()[2] modify_hour_object = datetime.strptime(hour, '%H:%M:%S') modify_hour_str = datetime.strftime(modify_hour_object, '%H:%M:%S') modify_hour = modify_hour_str + " " + am_pm modify_hour = convert_12_to_24(modify_hour) array_interior_features = '' array_exterior_features = '' array_sector_features = '' for key in property_hidden_features: if(key == 'interiorFeatures'): array_interior_features = property_hidden_features[key] elif(key == 'exteriorFeatures'): array_exterior_features = property_hidden_features[key] else: array_sector_features = property_hidden_features[key] old_property_dict = { 'urlProperty': url, 'scrapingDate': scraping_date, 'scrapingHour': scraping_hour, 'modifyDate': modify_date, 'modifyHour': modify_hour, 'code': int(json_property_agency["AdvertId"]), 'status': json_property_agency["Status"], 'type': json_property_agency["TransactionType"], 'use': 'Usado', 'nameProject': json_property_agency["Title"], 'country': property_location['country'], 'department': property_location['department'], 'city': property_location['city'], 'sector': property_location['sector'], 'neighborhood': property_location['neighborhood'], 'address': property_location['address'], 'latitude': property_location['latitude'], 'longitude': property_location['longitude'], 'idOwnerProperty': owner_property['id'], 'nameOwnerProperty': owner_property['name'], 'contractType': owner_property['contractType'], 'financing': owner_property['financing'], 'schedule': owner_property['schedule'], 'description': json_property_agency["Description"], 'price': property_features['price'], 'squareMeters': property_features['squareMeters'], 'rooms': property_features['rooms'], 'bathrooms': property_features['bathrooms'], 'garages': property_features['garages'], 'privateArea': property_features['privateArea'], 'constructionArea': property_features['constructionArea'], 'squareMetersPrice': property_features['squareMetersPrice'], 'stratum': property_features['stratum'], 'condition': property_features['condition'], 'antiquity': property_features['antiquity'], 'floor': property_features['floor'], 'interiorFloors': property_features['interiorFloors'], 'weather': property_features['weather'], 'includesAdministration': property_features['includesAdministration'], 'admonPrice': property_features['admonPrice'], 'interiorFeatures': array_interior_features, 'exteriorFeatures': array_exterior_features, 'sectorFeatures': array_sector_features, 'offersType': array_offers_type[1:] } generator.create_json(old_property_dict)

46.424581

159

0.667268

845

8,310

6.236686

0.153846

0.103226

0.068311

0.045541

0.872865

0.858065

0

0.009537

0.192419

8,310

179

160

46.424581

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0

0.784431

0

0.213452

0.010588

0

1

0.023952

false

0

0.023952

0

0.077844

0

null

0

1

0

1

0

null

0

7

0f58e6280f341628cc9f1c7135ec9b84af946a00

21,451

py

Python

core/views/tasks.py

ruizdiazever/inter-webapp

dc79c9e1d7bfb296d6c5326d2e52d83e14f3a5a7

[ "MIT" ]

null

core/views/tasks.py

ruizdiazever/inter-webapp

dc79c9e1d7bfb296d6c5326d2e52d83e14f3a5a7

[ "MIT" ]

null

core/views/tasks.py

ruizdiazever/inter-webapp

dc79c9e1d7bfb296d6c5326d2e52d83e14f3a5a7

[ "MIT" ]

null

import json from datetime import datetime, timedelta, timezone import pytz from zoneinfo import ZoneInfo from flask import request, jsonify, make_response from core.settings import VERSION_API, TIME_ZONE, ISO_8601, KEY_DELETE_ALL_TASKS from core.models import Unit, User, Task from flask import jsonify from core.instance import app from core.session import * # GET ALL TASK OF CALENDAR @app.route(f'/api/{VERSION_API}/tasks', methods=['GET', 'POST']) @token_required def get_all_tasks(current_unit): tasks = Task.query.all() units = Unit.query.all() tz = ZoneInfo(TIME_ZONE) result = [] for task in tasks: task_data = {} task_data['id'] = task.id task_data['user'] = task.user # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) task_data['startTime'] = start_ba.strftime(ISO_8601) # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) task_data['endTime'] = end_ba.strftime(ISO_8601) for unit in units: if unit.id == task.unit_id: task_data['unit_name'] = unit.name task_data['public_id'] = unit.public_id result.append(task_data) return jsonify({'tasks': result}) # GET ALL TASK OF CALENDAR IN CURRENT UNIT @app.route(f'/api/{VERSION_API}/tasks/current', methods=['GET', 'POST']) @token_required def get_all_current_tasks(current_unit): tz = ZoneInfo(TIME_ZONE) tasks = Task.query.filter_by(unit_id=current_unit.id) users = User.query.all() result = [] for task in tasks: task_data = {} for user in users: if user.id == int(task.user): task_data['phone'] = user.phone task_data['specialty'] = user.specialty task_data['name'] = user.name task_data['lastName'] = user.last_name task_data['position'] = user.position task_data['id'] = task.id task_data['user'] = task.user task_data['unitName'] = task.unit_name # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) task_data['startTime'] = start_ba.strftime("%d-%m-%Y %H:%M") task_data['dateStart'] = start_ba.strftime("%Y-%m-%d") task_data['dateStartArg'] = start_ba.strftime("%d-%m-%Y") task_data['hourStart'] = start_ba.strftime("%H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) task_data['endTime'] = end_ba.strftime("%d-%m-%Y %H:%M") task_data['dateEndArg'] = end_ba.strftime("%d-%m-%Y") task_data['hourEnd'] = end_ba.strftime("%H:%M") # Special task_data['rangeHour'] = f'{start_ba.strftime("%H:%M")} a {end_ba.strftime("%H:%M")}' result.append(task_data) return jsonify({'tasks': result}) # GET TASKS OF TODAY @app.route(f'/api/{VERSION_API}/tasks/current/today/v2', methods=['GET']) @token_required def get_task_today_v2(current_unit): tasks = Task.query.filter_by(unit_id=current_unit.id) users = User.query.filter_by(unit_id=current_unit.id) result = { "before": [], "after" : [], "current" : [], "out" : [] } time = {} time['timeZone'] = TIME_ZONE tz = ZoneInfo(TIME_ZONE) now = datetime.utcnow() limit = datetime.utcnow() + timedelta(hours=24) todayArg = datetime.now(pytz.timezone(TIME_ZONE)) limit = limit.replace(tzinfo=None) time['dateUtc'] = now.strftime("%d-%m-%Y %H:%M") time['dateArg'] = todayArg.strftime("%d-%m-%Y %H:%M") time['limitUtc'] = limit.strftime("%d-%m-%Y %H:%M") time['limitArg'] = (todayArg + timedelta(hours=24)).strftime("%d-%m-%Y a las %H:%M") for task in tasks: if task.start < limit: # BEFORE if (task.end < now): before = {} before['startUtc'] = task.start before['endUtc'] = task.end before['id'] = task.id before['unitName'] = task.unit_name for user in users: if user.id == int(task.user): before['phone'] = user.phone before['specialty'] = user.specialty before['name'] = user.name before['lastName'] = user.last_name before['user'] = task.user before['position'] = user.position start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) before['start'] = start_ba.strftime("%H:%M") before['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) before['end'] = end_ba.strftime("%H:%M") before['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") before['beforeFormat'] = end_ba.strftime("%H:%M del %d/%m") result['before'].append(before) # CURRENT elif (task.start <= now and task.end >= now): current = {} current['startUtc'] = task.start current['endUtc'] = task.end current['id'] = task.id current['unitName'] = task.unit_name for user in users: if user.id == int(task.user): current['phone'] = user.phone current['specialty'] = user.specialty current['name'] = user.name current['lastName'] = user.last_name current['user'] = task.user current['position'] = user.position # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) current['start'] = start_ba.strftime("%H:%M") current['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) current['end'] = end_ba.strftime("%H:%M") current['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result['current'].append(current) # AFTER elif (task.start > now and task.start < limit): after = {} after['startUtc'] = task.start after['endUtc'] = task.end after['id'] = task.id after['unitName'] = task.unit_name for user in users: if user.id == int(task.user): after['phone'] = user.phone after['specialty'] = user.specialty after['name'] = user.name after['lastName'] = user.last_name after['user'] = task.user after['position'] = user.position # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) after['start'] = start_ba.strftime("%H:%M") after['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) after['end'] = end_ba.strftime("%H:%M") after['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") after['afterFormat'] = start_ba.strftime("%H:%M del %d/%m") result['after'].append(after) else: out = {} out['startUtc'] = task.start.strftime("%d-%m-%Y %H:%M") out['endUtc'] = task.end.strftime("%d-%m-%Y %H:%M") out['unitName'] = task.unit_name for user in users: if user.id == int(task.user): out['name'] = user.name # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) out['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) out['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result['out'].append(out) if not tasks: return jsonify({'message': 'Task does not exist.'}) return jsonify({'tasks': result, 'time': time}) # GET TASKS OF TODAY WITH PUBLIC ID @app.route(f'/api/{VERSION_API}/tasks/current/<public_id>', methods=['GET']) @token_required def get_task_today_main(current_unit, public_id): current_unit = None tasks = Task.query.filter_by(public_id=public_id) users = User.query.filter_by(public_id=public_id) result = { "before": [], "after" : [], "current" : [], "out" : [] } time = {} time['timeZone'] = TIME_ZONE tz = ZoneInfo(TIME_ZONE) now = datetime.utcnow() limit = datetime.utcnow() + timedelta(hours=24) todayArg = datetime.now(pytz.timezone(TIME_ZONE)) limit = limit.replace(tzinfo=None) time['dateUtc'] = now.strftime("%d-%m-%Y %H:%M") time['dateArg'] = todayArg.strftime("%d-%m-%Y %H:%M") time['limitUtc'] = limit.strftime("%d-%m-%Y %H:%M") time['limitArg'] = (todayArg + timedelta(hours=24)).strftime("%d-%m-%Y a las %H:%M") for task in tasks: if task.start < limit: # BEFORE if (task.end < now): before = {} before['startUtc'] = task.start before['endUtc'] = task.end before['id'] = task.id before['unitName'] = task.unit_name for user in users: if user.id == int(task.user): before['phone'] = user.phone before['specialty'] = user.specialty before['name'] = user.name before['lastName'] = user.last_name before['user'] = task.user before['position'] = user.position start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) before['start'] = start_ba.strftime("%H:%M") before['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) before['end'] = end_ba.strftime("%H:%M") before['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") before['beforeFormat'] = end_ba.strftime("%H:%M del %d/%m") result['before'].append(before) # CURRENT elif (task.start <= now and task.end >= now): current = {} current['startUtc'] = task.start current['endUtc'] = task.end current['id'] = task.id current['unitName'] = task.unit_name for user in users: if user.id == int(task.user): current['phone'] = user.phone current['specialty'] = user.specialty current['name'] = user.name current['lastName'] = user.last_name current['user'] = task.user current['position'] = user.position # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) current['start'] = start_ba.strftime("%H:%M") current['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) current['end'] = end_ba.strftime("%H:%M") current['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result['current'].append(current) # AFTER elif (task.start > now and task.start < limit): after = {} after['startUtc'] = task.start after['endUtc'] = task.end after['id'] = task.id after['unitName'] = task.unit_name for user in users: if user.id == int(task.user): after['phone'] = user.phone after['specialty'] = user.specialty after['name'] = user.name after['lastName'] = user.last_name after['user'] = task.user after['position'] = user.position # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) after['start'] = start_ba.strftime("%H:%M") after['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") after['afterFormat'] = start_ba.strftime("%H:%M del %d/%m") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) after['end'] = end_ba.strftime("%H:%M") after['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result['after'].append(after) else: out = {} out['startUtc'] = task.start.strftime("%d-%m-%Y %H:%M") out['endUtc'] = task.end.strftime("%d-%m-%Y %H:%M") out['unitName'] = task.unit_name for user in users: if user.id == int(task.user): out['name'] = user.name # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) out['startFull'] = start_ba.strftime("%d-%m-%Y %H:%M") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) out['endFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result['out'].append(out) if not tasks: return jsonify({'message': 'Task does not exist.'}) return jsonify({'tasks': result, 'time': time}) # GET ALL TASK OF CALENDAR IN CURRENT UNIT @app.route(f'/api/{VERSION_API}/tasks/<public_id>', methods=['GET']) @token_required def get_all_tasks_id(current_unit, public_id): current_unit = None tz = ZoneInfo(TIME_ZONE) tasks = Task.query.filter_by(public_id=public_id) users = User.query.all() result = [] for task in tasks: task_data = {} for user in users: if user.id == int(task.user): task_data['phone'] = user.phone task_data['specialty'] = user.specialty task_data['name'] = user.name task_data['lastName'] = user.last_name task_data['position'] = user.position task_data['id'] = task.id task_data['user'] = task.user task_data['unitName'] = task.unit_name # START DATETIME start_utc = datetime(int(task.start.year), int(task.start.month), int(task.start.day), int(task.start.hour), int(task.start.minute), tzinfo=timezone.utc) start_ba = start_utc.astimezone(tz) task_data['startTime'] = start_ba.strftime("%H:%M") task_data['startTimeFull'] = start_ba.strftime("%d-%m-%Y %H:%M") task_data['dateStart'] = start_ba.strftime("%Y-%m-%d") # END DATETIME end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_ba = end_utc.astimezone(tz) task_data['endTime'] = end_ba.strftime("%H:%M") task_data['date'] = start_ba.strftime("%d-%m-%Y") task_data['endTimeFull'] = end_ba.strftime("%d-%m-%Y %H:%M") result.append(task_data) return jsonify({'tasks': result}) # CREATE TASK @app.route(f'/api/{VERSION_API}/create/task', methods=['POST']) @token_required def create_task(current_unit): data = request.get_json() new_task = Task(start=datetime.strptime(data['start'], ISO_8601), end=datetime.strptime(data['end'], ISO_8601), user=data['user'], public_id=current_unit.public_id, unit_id=current_unit.id, unit_name=current_unit.name) db.session.add(new_task) db.session.commit() return make_response(jsonify({'message': 'New task created.'}), 201) # UPDATE TASK @app.route(f'/api/{VERSION_API}/tasks/update/<task_id>', methods=['PUT']) @token_required def update_task(current_unit, task_id): data = request.get_json() task = Task.query.filter_by(id=task_id, unit_id=current_unit.id).first() if not task: return jsonify({'message': 'Task does not exist.'}) task.start = datetime.strptime(data['start'], ISO_8601) task.end = datetime.strptime(data['end'], ISO_8601) task.user = data['user'] db.session.merge(task) db.session.flush() db.session.commit() return jsonify({'message': 'Task updated.'}) # DELETE TASK @app.route(f'/api/{VERSION_API}/tasks/delete/<task_id>', methods=['DELETE']) @token_required def delete_task(current_unit, task_id): task = Task.query.filter_by(id=task_id, unit_id=current_unit.id).first() if not task: return jsonify({'message': 'Task does not exist.'}) db.session.delete(task) db.session.commit() return jsonify({'message': 'Task deleted.'}) # USED IN DELETE EXPIRE TASKS def task_expired(task, hours=24): tz = ZoneInfo(TIME_ZONE) end_utc = datetime(int(task.end.year), int(task.end.month), int(task.end.day), int(task.end.hour), int(task.end.minute), tzinfo=timezone.utc) end_arg = end_utc.astimezone(tz) return end_arg + timedelta(hours=hours) # DELETE EXPIRED TASKS @app.route(f'/api/{VERSION_API}/tasks/delete/expired', methods=['DELETE']) @token_required def delete_task_expired(current_unit): tasks = Task.query.all() nowArg = datetime.now(pytz.timezone(TIME_ZONE)) deleted = 0 for task in tasks: if task_expired(task) <= nowArg: deleted = deleted + 1 db.session.delete(task) db.session.commit() if not tasks: return jsonify({'message': 'Task does not exist.'}) return jsonify({'tasks': f'{deleted} expired tasks deleted.', 'argTimeNow': nowArg}) # DELETE ALL TASKS @app.route(f'/api/{VERSION_API}/tasks/delete/{KEY_DELETE_ALL_TASKS}', methods=['DELETE']) @token_required def delete_all_tasks(current_unit): try: num_rows_deleted = db.session.query(Task).delete() db.session.commit() except: db.session.rollback() return jsonify({'message': 'All tasks of the Unit deleted.'})

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Python

projects/faces/insight/insight/common/__init__.py

Bingwen-Hu/hackaway

69727d76fd652390d9660e9ea4354ba5cc76dd5c

[ "BSD-2-Clause" ]

null

projects/faces/insight/insight/common/__init__.py

Bingwen-Hu/hackaway

69727d76fd652390d9660e9ea4354ba5cc76dd5c

[ "BSD-2-Clause" ]

null

projects/faces/insight/insight/common/__init__.py

Bingwen-Hu/hackaway

69727d76fd652390d9660e9ea4354ba5cc76dd5c

[ "BSD-2-Clause" ]

null

from . import face_image from . import face_preprocess

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py

Python

nnpy/utils/math_utils.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

nnpy/utils/math_utils.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

nnpy/utils/math_utils.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

import numpy as np def get_random_array(*dims, offset=0): return np.random.rand(*dims) + offset

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py

Python

symphony/bdk/gen/pod_api/app_entitlement_api.py

SymphonyOSF/symphony-api-client-python

70137a893f4385381a3158ef80e1be156e0fc4bd

[ "Apache-2.0" ]

null

symphony/bdk/gen/pod_api/app_entitlement_api.py

SymphonyOSF/symphony-api-client-python

70137a893f4385381a3158ef80e1be156e0fc4bd

[ "Apache-2.0" ]

null

symphony/bdk/gen/pod_api/app_entitlement_api.py

SymphonyOSF/symphony-api-client-python

70137a893f4385381a3158ef80e1be156e0fc4bd

[ "Apache-2.0" ]

null

""" Pod API This document refers to Symphony API calls that do not need encryption or decryption of content. - sessionToken can be obtained by calling the authenticationAPI on the symphony back end and the key manager respectively. Refer to the methods described in authenticatorAPI.yaml. - Actions are defined to be atomic, ie will succeed in their entirety or fail and have changed nothing. - If it returns a 40X status then it will have made no change to the system even if ome subset of the request would have succeeded. - If this contract cannot be met for any reason then this is an error and the response code will be 50X. # noqa: E501 The version of the OpenAPI document: 20.14.1 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from symphony.bdk.gen.api_client import ApiClient, Endpoint as _Endpoint from symphony.bdk.gen.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from symphony.bdk.gen.pod_model.error import Error from symphony.bdk.gen.pod_model.pod_app_entitlement_list import PodAppEntitlementList from symphony.bdk.gen.pod_model.user_app_entitlement_list import UserAppEntitlementList from symphony.bdk.gen.pod_model.user_app_entitlements_patch_list import UserAppEntitlementsPatchList class AppEntitlementApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.v1_admin_app_entitlement_list_get_endpoint = _Endpoint( settings={ 'response_type': (PodAppEntitlementList,), 'auth': [], 'endpoint_path': '/v1/admin/app/entitlement/list', 'operation_id': 'v1_admin_app_entitlement_list_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'session_token', ], 'required': [ 'session_token', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'session_token': (str,), }, 'attribute_map': { 'session_token': 'sessionToken', }, 'location_map': { 'session_token': 'header', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.v1_admin_app_entitlement_list_post_endpoint = _Endpoint( settings={ 'response_type': (PodAppEntitlementList,), 'auth': [], 'endpoint_path': '/v1/admin/app/entitlement/list', 'operation_id': 'v1_admin_app_entitlement_list_post', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'session_token', 'payload', ], 'required': [ 'session_token', 'payload', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'session_token': (str,), 'payload': (PodAppEntitlementList,), }, 'attribute_map': { 'session_token': 'sessionToken', }, 'location_map': { 'session_token': 'header', 'payload': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.v1_admin_user_uid_app_entitlement_list_get_endpoint = _Endpoint( settings={ 'response_type': (UserAppEntitlementList,), 'auth': [], 'endpoint_path': '/v1/admin/user/{uid}/app/entitlement/list', 'operation_id': 'v1_admin_user_uid_app_entitlement_list_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'session_token', 'uid', ], 'required': [ 'session_token', 'uid', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'session_token': (str,), 'uid': (int,), }, 'attribute_map': { 'session_token': 'sessionToken', 'uid': 'uid', }, 'location_map': { 'session_token': 'header', 'uid': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.v1_admin_user_uid_app_entitlement_list_patch_endpoint = _Endpoint( settings={ 'response_type': (UserAppEntitlementList,), 'auth': [], 'endpoint_path': '/v1/admin/user/{uid}/app/entitlement/list', 'operation_id': 'v1_admin_user_uid_app_entitlement_list_patch', 'http_method': 'PATCH', 'servers': None, }, params_map={ 'all': [ 'session_token', 'uid', 'payload', ], 'required': [ 'session_token', 'uid', 'payload', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'session_token': (str,), 'uid': (int,), 'payload': (UserAppEntitlementsPatchList,), }, 'attribute_map': { 'session_token': 'sessionToken', 'uid': 'uid', }, 'location_map': { 'session_token': 'header', 'uid': 'path', 'payload': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.v1_admin_user_uid_app_entitlement_list_post_endpoint = _Endpoint( settings={ 'response_type': (UserAppEntitlementList,), 'auth': [], 'endpoint_path': '/v1/admin/user/{uid}/app/entitlement/list', 'operation_id': 'v1_admin_user_uid_app_entitlement_list_post', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'session_token', 'uid', 'payload', ], 'required': [ 'session_token', 'uid', 'payload', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'session_token': (str,), 'uid': (int,), 'payload': (UserAppEntitlementList,), }, 'attribute_map': { 'session_token': 'sessionToken', 'uid': 'uid', }, 'location_map': { 'session_token': 'header', 'uid': 'path', 'payload': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) def v1_admin_app_entitlement_list_get( self, session_token, **kwargs ): """Get the list of application entitlements for the company # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = pod_api.v1_admin_app_entitlement_list_get(session_token, async_req=True) >>> result = thread.get() Args: session_token (str): Session authentication token. Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: PodAppEntitlementList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['session_token'] = \ session_token return self.v1_admin_app_entitlement_list_get_endpoint.call_with_http_info(**kwargs) def v1_admin_app_entitlement_list_post( self, session_token, payload, **kwargs ): """Update the application entitlements for the company # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = pod_api.v1_admin_app_entitlement_list_post(session_token, payload, async_req=True) >>> result = thread.get() Args: session_token (str): Session authentication token. payload (PodAppEntitlementList): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: PodAppEntitlementList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['session_token'] = \ session_token kwargs['payload'] = \ payload return self.v1_admin_app_entitlement_list_post_endpoint.call_with_http_info(**kwargs) def v1_admin_user_uid_app_entitlement_list_get( self, session_token, uid, **kwargs ): """Get the list of application entitlements for this user # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = pod_api.v1_admin_user_uid_app_entitlement_list_get(session_token, uid, async_req=True) >>> result = thread.get() Args: session_token (str): Session authentication token. uid (int): User ID as a decimal integer Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: UserAppEntitlementList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['session_token'] = \ session_token kwargs['uid'] = \ uid return self.v1_admin_user_uid_app_entitlement_list_get_endpoint.call_with_http_info(**kwargs) def v1_admin_user_uid_app_entitlement_list_patch( self, session_token, uid, payload, **kwargs ): """Update unique entitlement of an app for this user. Entitlement can be installation, visibility or product # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = pod_api.v1_admin_user_uid_app_entitlement_list_patch(session_token, uid, payload, async_req=True) >>> result = thread.get() Args: session_token (str): Session authentication token. uid (int): User ID as a decimal integer payload (UserAppEntitlementsPatchList): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: UserAppEntitlementList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['session_token'] = \ session_token kwargs['uid'] = \ uid kwargs['payload'] = \ payload return self.v1_admin_user_uid_app_entitlement_list_patch_endpoint.call_with_http_info(**kwargs) def v1_admin_user_uid_app_entitlement_list_post( self, session_token, uid, payload, **kwargs ): """Update the application entitlements for this user # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = pod_api.v1_admin_user_uid_app_entitlement_list_post(session_token, uid, payload, async_req=True) >>> result = thread.get() Args: session_token (str): Session authentication token. uid (int): User ID as a decimal integer payload (UserAppEntitlementList): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: UserAppEntitlementList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['session_token'] = \ session_token kwargs['uid'] = \ uid kwargs['payload'] = \ payload return self.v1_admin_user_uid_app_entitlement_list_post_endpoint.call_with_http_info(**kwargs)

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null

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7

0e1f5070f5bbd8c5e4bbd6103600e30f75d7b0eb

3,967

py

Python

python3/lib/python3.6/site-packages/tensorflow/_api/v1/saved_model/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

3

2020-10-12T15:47:01.000Z

2022-01-14T19:51:26.000Z

python3/lib/python3.6/site-packages/tensorflow/_api/v1/saved_model/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

null

python3/lib/python3.6/site-packages/tensorflow/_api/v1/saved_model/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

2

2020-08-03T13:02:06.000Z

2020-11-04T03:15:44.000Z

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.saved_model namespace. """ from __future__ import print_function as _print_function from tensorflow._api.v1.saved_model import builder from tensorflow._api.v1.saved_model import constants from tensorflow._api.v1.saved_model import experimental from tensorflow._api.v1.saved_model import loader from tensorflow._api.v1.saved_model import main_op from tensorflow._api.v1.saved_model import signature_constants from tensorflow._api.v1.saved_model import signature_def_utils from tensorflow._api.v1.saved_model import tag_constants from tensorflow._api.v1.saved_model import utils from tensorflow.lite.python.lite import _load as load_v2 from tensorflow.python.saved_model.builder import SavedModelBuilder as Builder from tensorflow.python.saved_model.constants import ASSETS_DIRECTORY from tensorflow.python.saved_model.constants import ASSETS_KEY from tensorflow.python.saved_model.constants import LEGACY_INIT_OP_KEY from tensorflow.python.saved_model.constants import MAIN_OP_KEY from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PB from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PBTXT from tensorflow.python.saved_model.constants import SAVED_MODEL_SCHEMA_VERSION from tensorflow.python.saved_model.constants import VARIABLES_DIRECTORY from tensorflow.python.saved_model.constants import VARIABLES_FILENAME from tensorflow.python.saved_model.loader import load from tensorflow.python.saved_model.loader import maybe_saved_model_directory from tensorflow.python.saved_model.loader import maybe_saved_model_directory as contains_saved_model from tensorflow.python.saved_model.main_op import main_op_with_restore from tensorflow.python.saved_model.save import save from tensorflow.python.saved_model.saved_model import simple_save from tensorflow.python.saved_model.signature_constants import CLASSIFY_INPUTS from tensorflow.python.saved_model.signature_constants import CLASSIFY_METHOD_NAME from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_CLASSES from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_SCORES from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS from tensorflow.python.saved_model.signature_constants import PREDICT_METHOD_NAME from tensorflow.python.saved_model.signature_constants import PREDICT_OUTPUTS from tensorflow.python.saved_model.signature_constants import REGRESS_INPUTS from tensorflow.python.saved_model.signature_constants import REGRESS_METHOD_NAME from tensorflow.python.saved_model.signature_constants import REGRESS_OUTPUTS from tensorflow.python.saved_model.signature_def_utils import build_signature_def from tensorflow.python.saved_model.signature_def_utils import classification_signature_def from tensorflow.python.saved_model.signature_def_utils import is_valid_signature from tensorflow.python.saved_model.signature_def_utils import predict_signature_def from tensorflow.python.saved_model.signature_def_utils import regression_signature_def from tensorflow.python.saved_model.tag_constants import GPU from tensorflow.python.saved_model.tag_constants import SERVING from tensorflow.python.saved_model.tag_constants import TPU from tensorflow.python.saved_model.tag_constants import TRAINING from tensorflow.python.saved_model.utils import build_tensor_info from tensorflow.python.saved_model.utils import get_tensor_from_tensor_info del _print_function import sys as _sys from tensorflow.python.util import deprecation_wrapper as _deprecation_wrapper if not isinstance(_sys.modules[__name__], _deprecation_wrapper.DeprecationWrapper): _sys.modules[__name__] = _deprecation_wrapper.DeprecationWrapper( _sys.modules[__name__], "saved_model")

61.030769

100

0.887825

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3,967

5.958855

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0.2852

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0.002696

0.065037

3,967

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null

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1

0

1

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8

0e63e60147b4222e24b5c8b326fa3f541c362d63

203

py

Python

backend_rest/tracking/admin.py

ezrankayamba/twiga_distribution

ac4fd3d4f6b111e734a932398be564c863582be2

[ "MIT" ]

null

backend_rest/tracking/admin.py

ezrankayamba/twiga_distribution

ac4fd3d4f6b111e734a932398be564c863582be2

[ "MIT" ]

16

2020-03-23T13:24:11.000Z

2022-03-12T00:17:58.000Z

backend_rest/tracking/admin.py

ezrankayamba/twiga_distribution

ac4fd3d4f6b111e734a932398be564c863582be2

[ "MIT" ]

null

from django.contrib import admin from . import models admin.site.register(models.Record) admin.site.register(models.Customer) admin.site.register(models.Contact) admin.site.register(models.Description)

25.375

39

0.827586

28

203

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0.428571

0.214286

0.404762

0.547619

0

0.064039

203

7

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0.884211

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true

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1

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null

0

1

0

1

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7

0e864d836d6730653f652cf674dea361c6aa70e1

33

py

Python

function_20373822.py

Dludora/Study-19

35a35dcf8e73828bce7153be3dc5e51c0296456e

[ "MIT" ]

1

2022-03-19T08:03:06.000Z

function_20373822.py

Dludora/Study-19

35a35dcf8e73828bce7153be3dc5e51c0296456e

[ "MIT" ]

null

function_20373822.py

Dludora/Study-19

35a35dcf8e73828bce7153be3dc5e51c0296456e

[ "MIT" ]

1

2022-03-19T07:25:31.000Z

print('My student_id: 20373822')

16.5

32

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33

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null

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1

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1

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7

7ebf46afd1ee3c3ed793024cf961c90428229d55

3,744

py

Python

Doc/includes/test.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

2,293

2015-01-02T12:46:10.000Z

2022-03-29T09:45:43.000Z

Doc/includes/test.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

315

2015-05-31T11:55:46.000Z

2022-01-12T08:36:37.000Z

Doc/includes/test.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

1,033

2015-01-04T07:48:40.000Z

2022-03-24T09:34:37.000Z

"""Test module for the noddy examples Noddy 1: >>> import noddy >>> n1 = noddy.Noddy() >>> n2 = noddy.Noddy() >>> del n1 >>> del n2 Noddy 2 >>> import noddy2 >>> n1 = noddy2.Noddy('jim', 'fulton', 42) >>> n1.first 'jim' >>> n1.last 'fulton' >>> n1.number 42 >>> n1.name() 'jim fulton' >>> n1.first = 'will' >>> n1.name() 'will fulton' >>> n1.last = 'tell' >>> n1.name() 'will tell' >>> del n1.first >>> n1.name() Traceback (most recent call last): ... AttributeError: first >>> n1.first Traceback (most recent call last): ... AttributeError: first >>> n1.first = 'drew' >>> n1.first 'drew' >>> del n1.number Traceback (most recent call last): ... TypeError: can't delete numeric/char attribute >>> n1.number=2 >>> n1.number 2 >>> n1.first = 42 >>> n1.name() '42 tell' >>> n2 = noddy2.Noddy() >>> n2.name() ' ' >>> n2.first '' >>> n2.last '' >>> del n2.first >>> n2.first Traceback (most recent call last): ... AttributeError: first >>> n2.first Traceback (most recent call last): ... AttributeError: first >>> n2.name() Traceback (most recent call last): File "<stdin>", line 1, in ? AttributeError: first >>> n2.number 0 >>> n3 = noddy2.Noddy('jim', 'fulton', 'waaa') Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: an integer is required >>> del n1 >>> del n2 Noddy 3 >>> import noddy3 >>> n1 = noddy3.Noddy('jim', 'fulton', 42) >>> n1 = noddy3.Noddy('jim', 'fulton', 42) >>> n1.name() 'jim fulton' >>> del n1.first Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: Cannot delete the first attribute >>> n1.first = 42 Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: The first attribute value must be a string >>> n1.first = 'will' >>> n1.name() 'will fulton' >>> n2 = noddy3.Noddy() >>> n2 = noddy3.Noddy() >>> n2 = noddy3.Noddy() >>> n3 = noddy3.Noddy('jim', 'fulton', 'waaa') Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: an integer is required >>> del n1 >>> del n2 Noddy 4 >>> import noddy4 >>> n1 = noddy4.Noddy('jim', 'fulton', 42) >>> n1.first 'jim' >>> n1.last 'fulton' >>> n1.number 42 >>> n1.name() 'jim fulton' >>> n1.first = 'will' >>> n1.name() 'will fulton' >>> n1.last = 'tell' >>> n1.name() 'will tell' >>> del n1.first >>> n1.name() Traceback (most recent call last): ... AttributeError: first >>> n1.first Traceback (most recent call last): ... AttributeError: first >>> n1.first = 'drew' >>> n1.first 'drew' >>> del n1.number Traceback (most recent call last): ... TypeError: can't delete numeric/char attribute >>> n1.number=2 >>> n1.number 2 >>> n1.first = 42 >>> n1.name() '42 tell' >>> n2 = noddy4.Noddy() >>> n2 = noddy4.Noddy() >>> n2 = noddy4.Noddy() >>> n2 = noddy4.Noddy() >>> n2.name() ' ' >>> n2.first '' >>> n2.last '' >>> del n2.first >>> n2.first Traceback (most recent call last): ... AttributeError: first >>> n2.first Traceback (most recent call last): ... AttributeError: first >>> n2.name() Traceback (most recent call last): File "<stdin>", line 1, in ? AttributeError: first >>> n2.number 0 >>> n3 = noddy4.Noddy('jim', 'fulton', 'waaa') Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: an integer is required Test cyclic gc(?) >>> import gc >>> gc.disable() >>> x = [] >>> l = [x] >>> n2.first = l >>> n2.first [[]] >>> l.append(n2) >>> del l >>> del n1 >>> del n2 >>> sys.getrefcount(x) 3 >>> ignore = gc.collect() >>> sys.getrefcount(x) 2 >>> gc.enable() """ import os import sys from distutils.util import get_platform PLAT_SPEC = "%s-%s" % (get_platform(), sys.version[0:3]) src = os.path.join("build", "lib.%s" % PLAT_SPEC) sys.path.append(src) if __name__ == "__main__": import doctest, __main__ doctest.testmod(__main__)

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0.051739

0.140435

0.17

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0

0.04672

0.165331

3,744

213

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0.68928

0.923878

0

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0

1

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false

0

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0.444444

0

null

0

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1

0

1

0

null

0

1

0

8

7ecc84fd811f0c4bbbca745557b86487db4fba56

7,898

py

Python

tests/odeint_tests.py

morgatron/tfdiffeq

ef646f85cbd0821749a03e7ab51e03e16798fab1

[ "MIT" ]

214

2019-02-10T08:24:12.000Z

2022-03-31T06:15:05.000Z

tests/odeint_tests.py

morgatron/tfdiffeq

ef646f85cbd0821749a03e7ab51e03e16798fab1

[ "MIT" ]

14

2019-03-02T14:56:29.000Z

2021-12-28T13:06:45.000Z

tests/odeint_tests.py

morgatron/tfdiffeq

ef646f85cbd0821749a03e7ab51e03e16798fab1

[ "MIT" ]

40

2019-03-03T12:55:09.000Z

2022-02-11T02:14:47.000Z

import unittest import tensorflow as tf import tfdiffeq from tests import problems if not tf.executing_eagerly(): tf.enable_v2_behavior() error_tol = 1e-4 # torch.set_default_dtype(torch.float64) TEST_DEVICE = "gpu:0" if tf.test.is_gpu_available() else "cpu" def max_abs(tensor): return tf.reduce_max(tf.abs(tensor)) def rel_error(true, estimate): return max_abs((true - estimate) / true) class TestSolverError(unittest.TestCase): def test_euler(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE) y = tfdiffeq.odeint(f, y0, t_points, method='euler') self.assertLess(rel_error(sol, y), error_tol) def test_midpoint(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE) y = tfdiffeq.odeint(f, y0, t_points, method='midpoint') self.assertLess(rel_error(sol, y), error_tol) def test_huen(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE) y = tfdiffeq.odeint(f, y0, t_points, method='huen') self.assertLess(rel_error(sol, y), error_tol) def test_bosh3(self): for ode in problems.PROBLEMS.keys(): if ode == 'sine': # Sine test never finishes. continue f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, ode=ode) y = tfdiffeq.odeint(f, y0, t_points, method='bosh3') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_adaptive_heun(self): for ode in problems.PROBLEMS.keys(): if ode == 'sine': # Sine test never finishes. continue f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, ode=ode) y = tfdiffeq.odeint(f, y0, t_points, method='adaptive_heun') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_dopri8(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, ode=ode) y = tfdiffeq.odeint(f, y0, t_points, method='dopri8', rtol=1e-12, atol=1e-14) with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_rk4(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE) y = tfdiffeq.odeint(f, y0, t_points, method='rk4') self.assertLess(rel_error(sol, y), error_tol) def test_explicit_adams(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE) y = tfdiffeq.odeint(f, y0, t_points, method='explicit_adams') self.assertLess(rel_error(sol, y), error_tol) def test_adams(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, ode=ode) y = tfdiffeq.odeint(f, y0, t_points, method='adams') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_dopri5(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, ode=ode) y = tfdiffeq.odeint(f, y0, t_points, method='dopri5') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_adjoint(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y0 = tf.cast(y0, tf.float64) t_points = tf.cast(t_points, tf.float64) sol = tf.cast(sol, tf.float64) y = tfdiffeq.odeint_adjoint(f, y0, t_points, method='dopri5') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) class TestSolverBackwardsInTimeError(unittest.TestCase): def test_euler(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='euler') self.assertLess(rel_error(sol, y), error_tol) def test_midpoint(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='midpoint') self.assertLess(rel_error(sol, y), error_tol) def test_rk4(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='rk4') self.assertLess(rel_error(sol, y), error_tol) def test_explicit_adams(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='explicit_adams') self.assertLess(rel_error(sol, y), error_tol) def test_adams(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='adams') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_dopri5(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='dopri5') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_dopri8(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points, method='dopri8') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) def test_adjoint(self): for ode in problems.PROBLEMS.keys(): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y0 = tf.cast(y0, tf.float64) t_points = tf.cast(t_points, tf.float64) sol = tf.cast(sol, tf.float64) y = tfdiffeq.odeint_adjoint(f, y0, t_points, method='dopri5') with self.subTest(ode=ode): self.assertLess(rel_error(sol, y), error_tol) class TestNoIntegration(unittest.TestCase): def test_midpoint(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='midpoint') self.assertLess(max_abs(sol[0] - y), error_tol) def test_rk4(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='rk4') self.assertLess(max_abs(sol[0] - y), error_tol) def test_explicit_adams(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='explicit_adams') self.assertLess(max_abs(sol[0] - y), error_tol) def test_adams(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='adams') self.assertLess(max_abs(sol[0] - y), error_tol) def test_dopri5(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='dopri5') self.assertLess(max_abs(sol[0] - y), error_tol) def test_dopri8(self): f, y0, t_points, sol = problems.construct_problem(TEST_DEVICE, reverse=True) y = tfdiffeq.odeint(f, y0, t_points[0:1], method='dopri8') self.assertLess(max_abs(sol[0] - y), error_tol) if __name__ == '__main__': tf.enable_eager_execution() unittest.main()

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Python

backend/tests/baserow/contrib/database/api/views/test_view_views.py

ericderace/baserow

7b35e81f75166d914d07ef4ad0c30c625b6bb396

[ "MIT" ]

null

backend/tests/baserow/contrib/database/api/views/test_view_views.py

ericderace/baserow

7b35e81f75166d914d07ef4ad0c30c625b6bb396

[ "MIT" ]

6

2021-04-08T22:03:06.000Z

2022-01-13T03:38:17.000Z

backend/tests/baserow/contrib/database/api/views/test_view_views.py

ericderace/baserow

7b35e81f75166d914d07ef4ad0c30c625b6bb396

[ "MIT" ]

null

import pytest from rest_framework.status import HTTP_200_OK, HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND from django.shortcuts import reverse from baserow.contrib.database.views.models import ViewFilter, ViewSort, GridView from baserow.contrib.database.views.registries import ( view_type_registry, view_filter_type_registry ) @pytest.mark.django_db def test_list_views(api_client, data_fixture): user, token = data_fixture.create_user_and_token( email='test@test.nl', password='password', first_name='Test1') table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() view_1 = data_fixture.create_grid_view(table=table_1, order=1) view_2 = data_fixture.create_grid_view(table=table_1, order=3) view_3 = data_fixture.create_grid_view( table=table_1, order=2, filter_type='OR', filters_disabled=True ) data_fixture.create_grid_view(table=table_2, order=1) response = api_client.get( reverse('api:database:views:list', kwargs={'table_id': table_1.id}), **{ 'HTTP_AUTHORIZATION': f'JWT {token}' } ) assert response.status_code == HTTP_200_OK response_json = response.json() assert len(response_json) == 3 assert response_json[0]['id'] == view_1.id assert response_json[0]['type'] == 'grid' assert response_json[0]['filter_type'] == 'AND' assert response_json[0]['filters_disabled'] is False assert response_json[1]['id'] == view_3.id assert response_json[1]['type'] == 'grid' assert response_json[1]['filter_type'] == 'OR' assert response_json[1]['filters_disabled'] is True assert response_json[2]['id'] == view_2.id assert response_json[2]['type'] == 'grid' assert response_json[2]['filter_type'] == 'AND' assert response_json[2]['filters_disabled'] is False response = api_client.get( reverse('api:database:views:list', kwargs={'table_id': table_2.id}), **{ 'HTTP_AUTHORIZATION': f'JWT {token}' } ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.get( reverse('api:database:views:list', kwargs={'table_id': 999999}), **{ 'HTTP_AUTHORIZATION': f'JWT {token}' } ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_TABLE_DOES_NOT_EXIST' @pytest.mark.django_db def test_list_views_including_filters(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_1) field_3 = data_fixture.create_text_field(table=table_2) view_1 = data_fixture.create_grid_view(table=table_1, order=1) view_2 = data_fixture.create_grid_view(table=table_1, order=2) view_3 = data_fixture.create_grid_view(table=table_2, order=1) filter_1 = data_fixture.create_view_filter(view=view_1, field=field_1) filter_2 = data_fixture.create_view_filter(view=view_1, field=field_2) filter_3 = data_fixture.create_view_filter(view=view_2, field=field_1) data_fixture.create_view_filter(view=view_3, field=field_3) response = api_client.get( '{}'.format(reverse( 'api:database:views:list', kwargs={'table_id': table_1.id} )), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_200_OK response_json = response.json() assert len(response_json) == 2 assert 'filters' not in response_json[0] assert 'filters' not in response_json[1] response = api_client.get( '{}?includes=filters'.format(reverse( 'api:database:views:list', kwargs={'table_id': table_1.id} )), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_200_OK response_json = response.json() assert len(response_json[0]['filters']) == 2 assert response_json[0]['filters'][0]['id'] == filter_1.id assert response_json[0]['filters'][0]['view'] == view_1.id assert response_json[0]['filters'][0]['field'] == field_1.id assert response_json[0]['filters'][0]['type'] == filter_1.type assert response_json[0]['filters'][0]['value'] == filter_1.value assert response_json[0]['filters'][1]['id'] == filter_2.id assert len(response_json[1]['filters']) == 1 assert response_json[1]['filters'][0]['id'] == filter_3.id @pytest.mark.django_db def test_list_views_including_sortings(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_1) field_3 = data_fixture.create_text_field(table=table_2) view_1 = data_fixture.create_grid_view(table=table_1, order=1) view_2 = data_fixture.create_grid_view(table=table_1, order=2) view_3 = data_fixture.create_grid_view(table=table_2, order=1) sort_1 = data_fixture.create_view_sort(view=view_1, field=field_1) sort_2 = data_fixture.create_view_sort(view=view_1, field=field_2) sort_3 = data_fixture.create_view_sort(view=view_2, field=field_1) data_fixture.create_view_sort(view=view_3, field=field_3) response = api_client.get( '{}'.format(reverse( 'api:database:views:list', kwargs={'table_id': table_1.id} )), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_200_OK response_json = response.json() assert len(response_json) == 2 assert 'sortings' not in response_json[0] assert 'sortings' not in response_json[1] response = api_client.get( '{}?includes=sortings'.format(reverse( 'api:database:views:list', kwargs={'table_id': table_1.id} )), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_200_OK response_json = response.json() assert len(response_json[0]['sortings']) == 2 assert response_json[0]['sortings'][0]['id'] == sort_1.id assert response_json[0]['sortings'][0]['view'] == view_1.id assert response_json[0]['sortings'][0]['field'] == field_1.id assert response_json[0]['sortings'][0]['order'] == sort_1.order assert response_json[0]['sortings'][1]['id'] == sort_2.id assert len(response_json[1]['sortings']) == 1 assert response_json[1]['sortings'][0]['id'] == sort_3.id @pytest.mark.django_db def test_create_view(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() response = api_client.post( reverse('api:database:views:list', kwargs={'table_id': table.id}), { 'name': 'Test 1', 'type': 'NOT_EXISTING' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_REQUEST_BODY_VALIDATION' assert response_json['detail']['type'][0]['code'] == 'invalid_choice' response = api_client.post( reverse('api:database:views:list', kwargs={'table_id': 99999}), {'name': 'Test 1', 'type': 'grid'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_TABLE_DOES_NOT_EXIST' response = api_client.post( reverse('api:database:views:list', kwargs={'table_id': table_2.id}), {'name': 'Test 1', 'type': 'grid'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.post( reverse('api:database:views:list', kwargs={'table_id': table.id}), { 'name': 'Test 1', 'type': 'grid', 'filter_type': 'OR', 'filters_disabled': True }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['type'] == 'grid' assert response_json['filter_type'] == 'OR' assert response_json['filters_disabled'] is True grid = GridView.objects.filter()[0] assert response_json['id'] == grid.id assert response_json['name'] == grid.name assert response_json['order'] == grid.order assert response_json['filter_type'] == grid.filter_type assert response_json['filters_disabled'] == grid.filters_disabled assert 'filters' not in response_json assert 'sortings' not in response_json response = api_client.post( '{}?includes=filters,sortings'.format( reverse('api:database:views:list', kwargs={'table_id': table.id}) ), { 'name': 'Test 2', 'type': 'grid', 'filter_type': 'AND', 'filters_disabled': False }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['name'] == 'Test 2' assert response_json['type'] == 'grid' assert response_json['filter_type'] == 'AND' assert response_json['filters_disabled'] is False assert response_json['filters'] == [] assert response_json['sortings'] == [] response = api_client.post( '{}'.format(reverse('api:database:views:list', kwargs={'table_id': table.id})), { 'name': 'Test 3', 'type': 'grid' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['name'] == 'Test 3' assert response_json['type'] == 'grid' assert response_json['filter_type'] == 'AND' assert response_json['filters_disabled'] is False assert 'filters' not in response_json assert 'sortings' not in response_json @pytest.mark.django_db def test_get_view(api_client, data_fixture): user, token = data_fixture.create_user_and_token() user_2, token_2 = data_fixture.create_user_and_token() table = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table(user=user_2) view = data_fixture.create_grid_view(table=table) view_2 = data_fixture.create_grid_view(table=table_2) filter = data_fixture.create_view_filter(view=view) url = reverse('api:database:views:item', kwargs={'view_id': view_2.id}) response = api_client.get( url, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' url = reverse('api:database:views:item', kwargs={'view_id': 99999}) response = api_client.get( url, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.get( url, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['id'] == view.id assert response_json['table_id'] == view.table_id assert response_json['type'] == 'grid' assert response_json['table']['id'] == table.id assert response_json['filter_type'] == 'AND' assert not response_json['filters_disabled'] assert 'filters' not in response_json assert 'sortings' not in response_json url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.get( '{}?includes=filters,sortings'.format(url), format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['id'] == view.id assert len(response_json['filters']) == 1 assert response_json['filters'][0]['id'] == filter.id assert response_json['filters'][0]['view'] == filter.view_id assert response_json['filters'][0]['field'] == filter.field_id assert response_json['filters'][0]['type'] == filter.type assert response_json['filters'][0]['value'] == filter.value assert response_json['sortings'] == [] @pytest.mark.django_db def test_update_view(api_client, data_fixture): user, token = data_fixture.create_user_and_token() user_2, token_2 = data_fixture.create_user_and_token() table = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table(user=user_2) view = data_fixture.create_grid_view(table=table) view_2 = data_fixture.create_grid_view(table=table_2) url = reverse('api:database:views:item', kwargs={'view_id': view_2.id}) response = api_client.patch( url, {'name': 'Test 1'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_USER_NOT_IN_GROUP' url = reverse('api:database:views:item', kwargs={'view_id': 999999}) response = api_client.patch( url, {'name': 'Test 1'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.patch( url, {'UNKNOWN_FIELD': 'Test 1'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_200_OK url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.patch( url, {'name': 'Test 1'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['id'] == view.id assert response_json['name'] == 'Test 1' assert response_json['filter_type'] == 'AND' assert not response_json['filters_disabled'] view.refresh_from_db() assert view.name == 'Test 1' assert view.filter_type == 'AND' assert not view.filters_disabled url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.patch( url, { 'filter_type': 'OR', 'filters_disabled': True, }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['id'] == view.id assert response_json['filter_type'] == 'OR' assert response_json['filters_disabled'] assert 'filters' not in response_json assert 'sortings' not in response_json view.refresh_from_db() assert view.filter_type == 'OR' assert view.filters_disabled filter_1 = data_fixture.create_view_filter(view=view) url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.patch( '{}?includes=filters,sortings'.format(url), {'filter_type': 'AND'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['id'] == view.id assert response_json['filter_type'] == 'AND' assert response_json['filters_disabled'] is True assert response_json['filters'][0]['id'] == filter_1.id assert response_json['sortings'] == [] @pytest.mark.django_db def test_delete_view(api_client, data_fixture): user, token = data_fixture.create_user_and_token() user_2, token_2 = data_fixture.create_user_and_token() table = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table(user=user_2) view = data_fixture.create_grid_view(table=table) view_2 = data_fixture.create_grid_view(table=table_2) url = reverse('api:database:views:item', kwargs={'view_id': view_2.id}) response = api_client.delete(url, HTTP_AUTHORIZATION=f'JWT {token}') response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_USER_NOT_IN_GROUP' url = reverse('api:database:views:item', kwargs={'view_id': 99999}) response = api_client.delete(url, HTTP_AUTHORIZATION=f'JWT {token}') assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' url = reverse('api:database:views:item', kwargs={'view_id': view.id}) response = api_client.delete(url, HTTP_AUTHORIZATION=f'JWT {token}') assert response.status_code == 204 assert GridView.objects.all().count() == 1 @pytest.mark.django_db def test_list_view_filters(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_1) field_3 = data_fixture.create_text_field(table=table_2) view_1 = data_fixture.create_grid_view(table=table_1, order=1) view_2 = data_fixture.create_grid_view(table=table_1, order=2) view_3 = data_fixture.create_grid_view(table=table_2, order=1) filter_1 = data_fixture.create_view_filter(view=view_1, field=field_1) filter_2 = data_fixture.create_view_filter(view=view_1, field=field_2) data_fixture.create_view_filter(view=view_2, field=field_1) data_fixture.create_view_filter(view=view_3, field=field_3) response = api_client.get( reverse( 'api:database:views:list_filters', kwargs={'view_id': view_3.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.get( reverse( 'api:database:views:list_filters', kwargs={'view_id': 999999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' response = api_client.get( reverse( 'api:database:views:list_filters', kwargs={'view_id': view_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert len(response_json) == 2 assert response_json[0]['id'] == filter_1.id assert response_json[0]['view'] == view_1.id assert response_json[0]['field'] == field_1.id assert response_json[0]['type'] == filter_1.type assert response_json[0]['value'] == filter_1.value assert response_json[1]['id'] == filter_2.id @pytest.mark.django_db def test_create_view_filter(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_2) view_1 = data_fixture.create_grid_view(table=table_1) view_2 = data_fixture.create_grid_view(table=table_2) response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_2.id}), { 'field': field_2.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': 99999}), { 'field': field_1.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': 9999999, 'type': 'NOT_EXISTING', 'not_value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_REQUEST_BODY_VALIDATION' assert response_json['detail']['field'][0]['code'] == 'does_not_exist' assert response_json['detail']['type'][0]['code'] == 'invalid_choice' response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_2.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_FIELD_NOT_IN_TABLE' grid_view_type = view_type_registry.get('grid') grid_view_type.can_filter = False response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_FILTER_NOT_SUPPORTED' grid_view_type.can_filter = True equal_filter_type = view_filter_type_registry.get('equal') allowed = equal_filter_type.compatible_field_types equal_filter_type.compatible_field_types = [] response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_FILTER_TYPE_NOT_ALLOWED_FOR_FIELD' equal_filter_type.compatible_field_types = allowed response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'type': 'equal', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewFilter.objects.all().count() == 1 first = ViewFilter.objects.all().first() assert response_json['id'] == first.id assert response_json['view'] == view_1.id assert response_json['field'] == field_1.id assert response_json['type'] == 'equal' assert response_json['value'] == 'test' response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'type': 'equal', 'value': '' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['value'] == '' response = api_client.post( reverse('api:database:views:list_filters', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'type': 'equal' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['value'] == '' @pytest.mark.django_db def test_get_view_filter(api_client, data_fixture): user, token = data_fixture.create_user_and_token() filter_1 = data_fixture.create_view_filter(user=user, value='test') filter_2 = data_fixture.create_view_filter() response = api_client.get( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_2.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.get( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': 99999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_FILTER_DOES_NOT_EXIST' response = api_client.get( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewFilter.objects.all().count() == 2 first = ViewFilter.objects.get(pk=filter_1.id) assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == first.field_id assert response_json['type'] == 'equal' assert response_json['value'] == 'test' @pytest.mark.django_db def test_update_view_filter(api_client, data_fixture): user, token = data_fixture.create_user_and_token() filter_1 = data_fixture.create_view_filter(user=user, value='test') filter_2 = data_fixture.create_view_filter() field_1 = data_fixture.create_text_field(table=filter_1.view.table) field_2 = data_fixture.create_text_field() response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_2.id} ), {'value': 'test'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': 9999} ), {'value': 'test'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_FILTER_DOES_NOT_EXIST' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), { 'field': 9999999, 'type': 'NOT_EXISTING', }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_REQUEST_BODY_VALIDATION' assert response_json['detail']['field'][0]['code'] == 'does_not_exist' assert response_json['detail']['type'][0]['code'] == 'invalid_choice' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), {'field': field_2.id}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_FIELD_NOT_IN_TABLE' equal_filter_type = view_filter_type_registry.get('not_equal') allowed = equal_filter_type.compatible_field_types equal_filter_type.compatible_field_types = [] grid_view_type = view_type_registry.get('grid') grid_view_type.can_filter = False response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), {'type': 'not_equal'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_FILTER_TYPE_NOT_ALLOWED_FOR_FIELD' equal_filter_type.compatible_field_types = allowed response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), { 'field': field_1.id, 'type': 'not_equal', 'value': 'test 2' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewFilter.objects.all().count() == 2 first = ViewFilter.objects.get(pk=filter_1.id) assert first.field_id == field_1.id assert first.type == 'not_equal' assert first.value == 'test 2' assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == field_1.id assert response_json['type'] == 'not_equal' assert response_json['value'] == 'test 2' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), {'type': 'equal'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK first = ViewFilter.objects.get(pk=filter_1.id) assert first.field_id == field_1.id assert first.type == 'equal' assert first.value == 'test 2' assert response_json['id'] == first.id assert response_json['field'] == field_1.id assert response_json['type'] == 'equal' assert response_json['value'] == 'test 2' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), {'value': 'test 3'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK first = ViewFilter.objects.get(pk=filter_1.id) assert first.field_id == field_1.id assert first.type == 'equal' assert first.value == 'test 3' assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == field_1.id assert response_json['type'] == 'equal' assert response_json['value'] == 'test 3' response = api_client.patch( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), {'value': ''}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK first = ViewFilter.objects.get(pk=filter_1.id) assert first.value == '' assert response_json['value'] == '' @pytest.mark.django_db def test_delete_view_filter(api_client, data_fixture): user, token = data_fixture.create_user_and_token() filter_1 = data_fixture.create_view_filter(user=user, value='test') filter_2 = data_fixture.create_view_filter() response = api_client.delete( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_2.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.delete( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': 9999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_FILTER_DOES_NOT_EXIST' response = api_client.delete( reverse( 'api:database:views:filter_item', kwargs={'view_filter_id': filter_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == 204 assert ViewFilter.objects.all().count() == 1 @pytest.mark.django_db def test_list_view_sortings(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_1) field_3 = data_fixture.create_text_field(table=table_2) view_1 = data_fixture.create_grid_view(table=table_1, order=1) data_fixture.create_grid_view(table=table_1, order=2) view_3 = data_fixture.create_grid_view(table=table_2, order=1) sort_1 = data_fixture.create_view_sort(view=view_1, field=field_1) sort_2 = data_fixture.create_view_sort(view=view_1, field=field_2) data_fixture.create_view_sort(view=view_3, field=field_3) response = api_client.get( reverse( 'api:database:views:list_sortings', kwargs={'view_id': view_3.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.get( reverse( 'api:database:views:list_sortings', kwargs={'view_id': 999999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' response = api_client.get( reverse( 'api:database:views:list_sortings', kwargs={'view_id': view_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert len(response_json) == 2 assert response_json[0]['id'] == sort_1.id assert response_json[0]['view'] == view_1.id assert response_json[0]['field'] == field_1.id assert response_json[0]['order'] == sort_1.order assert response_json[1]['id'] == sort_2.id @pytest.mark.django_db def test_create_view_sort(api_client, data_fixture): user, token = data_fixture.create_user_and_token() table_1 = data_fixture.create_database_table(user=user) table_2 = data_fixture.create_database_table() field_1 = data_fixture.create_text_field(table=table_1) field_2 = data_fixture.create_text_field(table=table_2) field_3 = data_fixture.create_text_field(table=table_1) field_4 = data_fixture.create_text_field(table=table_1) link_row_field = data_fixture.create_link_row_field(table=table_1) view_1 = data_fixture.create_grid_view(table=table_1) view_2 = data_fixture.create_grid_view(table=table_2) response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_2.id}), { 'field': field_2.id, 'order': 'ASC', }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': 99999}), { 'field': field_1.id, 'order': 'ASC', 'value': 'test' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_DOES_NOT_EXIST' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': 9999999, 'order': 'NOT_EXISTING' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_REQUEST_BODY_VALIDATION' assert response_json['detail']['field'][0]['code'] == 'does_not_exist' assert response_json['detail']['order'][0]['code'] == 'invalid_choice' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_2.id, 'order': 'ASC', }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_FIELD_NOT_IN_TABLE' grid_view_type = view_type_registry.get('grid') grid_view_type.can_sort = False response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'order': 'ASC' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_SORT_NOT_SUPPORTED' grid_view_type.can_sort = True response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': link_row_field.id, 'order': 'ASC' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_SORT_FIELD_NOT_SUPPORTED' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'order': 'ASC' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewSort.objects.all().count() == 1 first = ViewSort.objects.all().first() assert response_json['id'] == first.id assert response_json['view'] == view_1.id assert response_json['field'] == field_1.id assert response_json['order'] == 'ASC' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_1.id, 'order': 'ASC' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_SORT_FIELD_ALREADY_EXISTS' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_3.id, 'order': 'DESC' }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['order'] == 'DESC' response = api_client.post( reverse('api:database:views:list_sortings', kwargs={'view_id': view_1.id}), { 'field': field_4.id, }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert response_json['order'] == 'ASC' assert ViewSort.objects.all().count() == 3 @pytest.mark.django_db def test_get_view_sort(api_client, data_fixture): user, token = data_fixture.create_user_and_token() sort_1 = data_fixture.create_view_sort(user=user, order='DESC') sort_2 = data_fixture.create_view_sort() response = api_client.get( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_2.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.get( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': 99999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_SORT_DOES_NOT_EXIST' response = api_client.get( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewSort.objects.all().count() == 2 first = ViewSort.objects.get(pk=sort_1.id) assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == first.field_id assert response_json['order'] == 'DESC' @pytest.mark.django_db def test_update_view_sort(api_client, data_fixture): user, token = data_fixture.create_user_and_token() sort_1 = data_fixture.create_view_sort(user=user, order='DESC') sort_2 = data_fixture.create_view_sort() sort_3 = data_fixture.create_view_sort(view=sort_1.view, order='ASC') field_1 = data_fixture.create_text_field(table=sort_1.view.table) link_row_field = data_fixture.create_link_row_field(table=sort_1.view.table) field_2 = data_fixture.create_text_field() response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_2.id} ), {'order': 'ASC'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': 9999} ), {'order': 'ASC'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_SORT_DOES_NOT_EXIST' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), { 'field': 9999999, 'order': 'EXISTING', }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_REQUEST_BODY_VALIDATION' assert response_json['detail']['field'][0]['code'] == 'does_not_exist' assert response_json['detail']['order'][0]['code'] == 'invalid_choice' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), {'field': field_2.id}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_FIELD_NOT_IN_TABLE' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), {'field': link_row_field.id}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_SORT_FIELD_NOT_SUPPORTED' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_3.id} ), {'field': sort_1.field_id}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_400_BAD_REQUEST assert response_json['error'] == 'ERROR_VIEW_SORT_FIELD_ALREADY_EXISTS' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), { 'field': field_1.id, 'order': 'ASC', }, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK assert ViewSort.objects.all().count() == 3 first = ViewSort.objects.get(pk=sort_1.id) assert first.field_id == field_1.id assert first.order == 'ASC' assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == field_1.id assert response_json['order'] == 'ASC' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), {'order': 'DESC'}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK first = ViewSort.objects.get(pk=sort_1.id) assert first.field_id == field_1.id assert first.order == 'DESC' assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == field_1.id assert response_json['order'] == 'DESC' response = api_client.patch( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), {}, format='json', HTTP_AUTHORIZATION=f'JWT {token}' ) response_json = response.json() assert response.status_code == HTTP_200_OK first = ViewSort.objects.get(pk=sort_1.id) assert first.field_id == field_1.id assert first.order == 'DESC' assert response_json['id'] == first.id assert response_json['view'] == first.view_id assert response_json['field'] == field_1.id assert response_json['order'] == 'DESC' @pytest.mark.django_db def test_delete_view_sort(api_client, data_fixture): user, token = data_fixture.create_user_and_token() sort_1 = data_fixture.create_view_sort(user=user, order='DESC') sort_2 = data_fixture.create_view_sort() response = api_client.delete( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_2.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_400_BAD_REQUEST assert response.json()['error'] == 'ERROR_USER_NOT_IN_GROUP' response = api_client.delete( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': 9999} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == HTTP_404_NOT_FOUND assert response.json()['error'] == 'ERROR_VIEW_SORT_DOES_NOT_EXIST' response = api_client.delete( reverse( 'api:database:views:sort_item', kwargs={'view_sort_id': sort_1.id} ), HTTP_AUTHORIZATION=f'JWT {token}' ) assert response.status_code == 204 assert ViewSort.objects.all().count() == 1

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1

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null

0

7

7d2166249a94b72f089dc3f17b99cd5b7b467886

13,315

py

Python

great_expectations/expectations/util.py

Lee-W/great_expectations

bd9cb27d1caa752364d298f5057e85b6b604b622

[ "Apache-2.0" ]

null

great_expectations/expectations/util.py

Lee-W/great_expectations

bd9cb27d1caa752364d298f5057e85b6b604b622

[ "Apache-2.0" ]

null

great_expectations/expectations/util.py

Lee-W/great_expectations

bd9cb27d1caa752364d298f5057e85b6b604b622

[ "Apache-2.0" ]

null

import numpy as np from great_expectations.validator.validation_graph import MetricConfiguration legacy_method_parameters = { "expect_column_bootstrapped_ks_test_p_value_to_be_greater_than": ( "column", "partition_object", "p", "bootstrap_samples", "bootstrap_sample_size", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_chisquare_test_p_value_to_be_greater_than": ( "column", "partition_object", "p", "tail_weight_holdout", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_distinct_values_to_be_in_set": ( "column", "value_set", "parse_strings_as_datetimes", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_distinct_values_to_contain_set": ( "column", "value_set", "parse_strings_as_datetimes", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_distinct_values_to_equal_set": ( "column", "value_set", "parse_strings_as_datetimes", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_kl_divergence_to_be_less_than": ( "column", "partition_object", "threshold", "tail_weight_holdout", "internal_weight_holdout", "bucketize_data", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_max_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "parse_strings_as_datetimes", "output_strftime_format", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_mean_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_median_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_min_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "parse_strings_as_datetimes", "output_strftime_format", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_most_common_value_to_be_in_set": ( "column", "value_set", "ties_okay", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_pair_cramers_phi_value_to_be_less_than": ( "column_A", "column_B", "bins_A", "bins_B", "n_bins_A", "n_bins_B", "threshold", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_pair_values_A_to_be_greater_than_B": ( "column_A", "column_B", "or_equal", "parse_strings_as_datetimes", "allow_cross_type_comparisons", "ignore_row_if", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_pair_values_to_be_equal": ( "column_A", "column_B", "ignore_row_if", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_pair_values_to_be_in_set": ( "column_A", "column_B", "value_pairs_set", "ignore_row_if", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_parameterized_distribution_ks_test_p_value_to_be_greater_than": ( "column", "distribution", "p_value", "params", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_proportion_of_unique_values_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_quantile_values_to_be_between": ( "column", "quantile_ranges", "allow_relative_error", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_stdev_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_sum_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_to_exist": ( "column", "column_index", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_unique_value_count_to_be_between": ( "column", "min_value", "max_value", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_value_lengths_to_be_between": ( "column", "min_value", "max_value", "mostly", "row_condition", "condition_parser", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_value_lengths_to_equal": ( "column", "value", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_between": ( "column", "min_value", "max_value", "strict_min", "strict_max", "allow_cross_type_comparisons", "parse_strings_as_datetimes", "output_strftime_format", "mostly", "row_condition", "condition_parser", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_dateutil_parseable": ( "column", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_decreasing": ( "column", "strictly", "parse_strings_as_datetimes", "mostly", "row_condition", "condition_parser", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_in_set": ( "column", "value_set", "mostly", "parse_strings_as_datetimes", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_in_type_list": ( "column", "type_list", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_increasing": ( "column", "strictly", "parse_strings_as_datetimes", "mostly", "row_condition", "condition_parser", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_json_parseable": ( "column", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_null": ( "column", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_of_type": ( "column", "type_", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_be_unique": ( "column", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_match_json_schema": ( "column", "json_schema", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_match_regex": ( "column", "regex", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_match_regex_list": ( "column", "regex_list", "match_on", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_match_strftime_format": ( "column", "strftime_format", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_not_be_in_set": ( "column", "value_set", "mostly", "parse_strings_as_datetimes", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_not_be_null": ( "column", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_not_match_regex": ( "column", "regex", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_column_values_to_not_match_regex_list": ( "column", "regex_list", "mostly", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_compound_columns_to_be_unique": ( "column_list", "ignore_row_if", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_multicolumn_sum_to_equal": ( "column_list", "sum_total", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_multicolumn_values_to_be_unique": ( "column_list", "ignore_row_if", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_select_column_values_to_be_unique_within_record": ( "column_list", "ignore_row_if", "result_format", "row_condition", "condition_parser", "include_config", "catch_exceptions", "meta", ), "expect_table_column_count_to_be_between": ( "min_value", "max_value", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_table_column_count_to_equal": ( "value", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_table_columns_to_match_ordered_list": ( "column_list", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_table_columns_to_match_set": ( "column_set", "exact_match", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_table_row_count_to_be_between": ( "min_value", "max_value", "result_format", "include_config", "catch_exceptions", "meta", ), "expect_table_row_count_to_equal": ( "value", "result_format", "include_config", "catch_exceptions", "meta", ), }

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addc5055476cd3df1b30b6a33696f129bf6f9243

119

py

Python

utils/QtCore.py

JaviCDiaz/Crypto-Info

72b9343946efe6df135abf48e5a43b2a440b8d59

[ "MIT" ]

null

utils/QtCore.py

JaviCDiaz/Crypto-Info

72b9343946efe6df135abf48e5a43b2a440b8d59

[ "MIT" ]

null

utils/QtCore.py

JaviCDiaz/Crypto-Info

72b9343946efe6df135abf48e5a43b2a440b8d59

[ "MIT" ]

null

from PySide6.QtCore import * from PySide6.QtGui import * from PySide6.QtWidgets import * from PySide6.QtCharts import *

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7

bc30d6e498352c14509d7223b59dfb8c1c5b9cc2

4,574

py

Python

tests/test_options.py

tcmetzger/sphinx-favicon

e8613e04bbe3b6f6816da4efd297302d20aa8cae

[ "MIT" ]

9

2021-09-30T14:06:08.000Z

2022-02-19T05:23:16.000Z

tests/test_options.py

tcmetzger/sphinx-favicon

e8613e04bbe3b6f6816da4efd297302d20aa8cae

[ "MIT" ]

6

2021-11-10T14:17:09.000Z

2021-11-18T22:01:51.000Z

tests/test_options.py

tcmetzger/sphinx-favicon

e8613e04bbe3b6f6816da4efd297302d20aa8cae

[ "MIT" ]

1

2021-11-10T17:05:48.000Z

from itertools import chain from pathlib import Path import pytest import conftest @pytest.mark.sphinx("html", testroot="list_of_three_dicts") def test_list_of_three_dicts(favicon_tags): # this test should have 3 favicons assert len(favicon_tags) == 3 # all favicons should have rel, href, type, and sizes attributes for favicon_tag in favicon_tags: assert favicon_tag["rel"] assert favicon_tag["href"] assert favicon_tag["type"] assert favicon_tag["sizes"] # check first favicon in more detail assert favicon_tags[0]["rel"] == ["icon"] assert ( favicon_tags[0]["href"] == "https://secure.example.com/favicon/favicon-16x16.png" ) assert favicon_tags[0]["type"] == "image/png" assert favicon_tags[0]["sizes"] == "16x16" @pytest.mark.sphinx("html", testroot="list_of_three_dicts_automated_values") def test_list_of_three_dicts_automated_values(favicon_tags): # this test should have 3 favicons assert len(favicon_tags) == 3 # all favicons should have rel, href, type, and sizes attributes for favicon_tag in favicon_tags: assert favicon_tag["rel"] assert favicon_tag["href"] assert favicon_tag["type"] assert favicon_tag["sizes"] # check first favicon in more detail assert favicon_tags[0]["rel"] == ["icon"] assert ( favicon_tags[0]["href"] == "https://secure.example.com/favicon/favicon-16x16.png" ) assert favicon_tags[0]["type"] == "image/png" assert favicon_tags[0]["sizes"] == "16x16" @pytest.mark.sphinx("html", testroot="single_dict") def test_single_dict(favicon_tags): # this test should have 1 favicon assert len(favicon_tags) == 1 # check favicon assert favicon_tags[0]["rel"] == ["apple-touch-icon"] assert ( favicon_tags[0]["href"] == "https://secure.example.com/favicon/apple-touch-icon-180x180.png" ) assert favicon_tags[0]["type"] == "image/png" assert favicon_tags[0]["sizes"] == "180x180" @pytest.mark.sphinx("html", testroot="list_of_urls") def test_list_of_urls(favicon_tags): # this test should have 3 favicons assert len(favicon_tags) == 3 # all favicons should have rel, href, and type attributes for favicon_tag in favicon_tags: assert favicon_tag["rel"] assert favicon_tag["href"] assert favicon_tag["type"] # check first favicon in more detail assert favicon_tags[0]["rel"] == ["icon"] assert ( favicon_tags[0]["href"] == "https://secure.example.com/favicon/favicon-16x16.gif" ) assert favicon_tags[0]["type"] == "image/gif" @pytest.mark.sphinx("html", testroot="static_files") def test_static_files(app, favicon_tags, favicon_tags_for_nested): # this test should have 2 favicons assert len(favicon_tags) == 2 # all favicons should have rel, href, type, and sizes attributes for favicon_tag in chain(favicon_tags, favicon_tags_for_nested): assert favicon_tag["rel"] == ["icon"] assert "_static" in favicon_tag["href"] assert favicon_tag["type"] == "image/svg+xml" assert favicon_tag["sizes"] assert "static-file" not in favicon_tag for favicon_tag in favicon_tags: assert favicon_tag["href"].startswith("_static") for favicon_tag in favicon_tags_for_nested: assert favicon_tag["href"].startswith("../_static") static = Path(app.outdir, "_static") assert (static / "square.svg").exists() assert (static / "nested/triangle.svg").exists() @pytest.mark.sphinx("html", testroot="href_and_static") def test_href_and_static(app, favicon_tags, favicon_tags_for_nested): # this test should have 3 favicons assert len(favicon_tags) == 2 # all favicons should have rel, href, type, and sizes attributes for favicon_tag in chain(favicon_tags, favicon_tags_for_nested): assert favicon_tag["rel"] == ["icon"] assert "_static" in favicon_tag["href"] assert favicon_tag["type"] == "image/svg+xml" assert favicon_tag["sizes"] assert "static-file" not in favicon_tag for favicon_tag in favicon_tags: assert favicon_tag["href"].startswith("_static") for favicon_tag in favicon_tags_for_nested: assert favicon_tag["href"].startswith("../_static") # favicons should use relative paths, ignoring paths provided with `href` static = Path(app.outdir, "_static") assert (static / "square.svg").exists() assert (static / "nested/triangle.svg").exists()

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null

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70d810762bddeff22a1b0a3448efa398a2582901

2,188

py

Python

tests/test_importer.py

vecmezoni/import_monster

8ada4394d44e0d7413e5776506b483da567eb410

[ "MIT" ]

null

tests/test_importer.py

vecmezoni/import_monster

8ada4394d44e0d7413e5776506b483da567eb410

[ "MIT" ]

null

tests/test_importer.py

vecmezoni/import_monster

8ada4394d44e0d7413e5776506b483da567eb410

[ "MIT" ]

null

# -*- coding: utf-8 -*- import cmath import itertools import math import pytest from import_monster import methods_importer class TestMethodsImporter: def test_raises_error_on_incorrect_module_input(self): with pytest.raises(TypeError): methods_importer('cool_method', [123]) def test_raises_error_on_non_existing_module_name(self): with pytest.raises(ModuleNotFoundError): methods_importer('cool_method', ['non_existing_module']) def test_returns_nothing_for_non_existing_method_for_module_name(self): assert methods_importer('non_existing_method', ['math']) == [] def test_returns_nothing_for_non_existing_method_for_module(self): assert methods_importer('non_existing_method', [math]) == [] def test_returns_nothing_for_non_callable_property_for_module_name(self): assert methods_importer('pi', ['math']) == [] def test_returns_nothing_for_non_callable_property_for_module(self): assert methods_importer('pi', [math]) == [] def test_returns_method_for_single_module_name(self): assert methods_importer('exp', ['math']) == [math.exp] def test_returns_only_existing_method_for_several_module_names(self): assert methods_importer('exp', ['math', 'itertools']) == [math.exp] def test_returns_several_existing_method_for_several_module_names(self): assert methods_importer('exp', ['math', 'cmath']) == [ math.exp, cmath.exp] def test_keeps_the_order_of_modules_for_module_names(self): assert methods_importer('exp', ['cmath', 'math']) == [ cmath.exp, math.exp] def test_returns_method_for_single_module(self): assert methods_importer('exp', [math]) == [math.exp] def test_returns_only_existing_method_for_several_modules(self): assert methods_importer('exp', [math, itertools]) == [math.exp] def test_returns_several_existing_method_for_several_modules(self): assert methods_importer('exp', [math, cmath]) == [math.exp, cmath.exp] def test_keeps_the_order_of_modules_for_modules(self): assert methods_importer('exp', [cmath, math]) == [cmath.exp, math.exp]

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9

cb916feb84a117f7d641121b0ba4427c4ecdba77

21

py

Python

sphinx-docs/__init__.py

czbiohub/reconstruct-order

e729ae3871aea0a5ec2d42744a9448c7f0a93037

[ "Unlicense" ]

6

2019-10-30T23:00:01.000Z

2021-03-02T19:09:07.000Z

sphinx-docs/__init__.py

czbiohub/ReconstructOrder

e729ae3871aea0a5ec2d42744a9448c7f0a93037

[ "Unlicense" ]

14

2019-07-08T22:51:29.000Z

2019-07-13T15:44:01.000Z

sphinx-docs/__init__.py

mehta-lab/reconstruct-order

e729ae3871aea0a5ec2d42744a9448c7f0a93037

[ "Unlicense" ]

2

2020-05-02T23:28:36.000Z

2020-07-16T23:46:46.000Z

# bchhun, {4/17/19}

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cbb54b0d0840b7d04738992656c79b3ddd60c44f

14,891

py

Python

examples/mixer-calibration/auto_mixer_tools_visa.py

qua-platform/qua-libs

805a3b1a69980b939b370b3ba09434bc26dc45ec

[ "BSD-3-Clause" ]

21

2021-05-21T08:23:34.000Z

2022-03-25T11:30:55.000Z

examples/mixer-calibration/auto_mixer_tools_visa.py

qua-platform/qua-libs

805a3b1a69980b939b370b3ba09434bc26dc45ec

[ "BSD-3-Clause" ]

9

2021-05-13T19:56:00.000Z

2021-12-21T05:11:04.000Z

examples/mixer-calibration/auto_mixer_tools_visa.py

qua-platform/qua-libs

805a3b1a69980b939b370b3ba09434bc26dc45ec

[ "BSD-3-Clause" ]

2

2021-06-21T10:56:40.000Z

2021-12-19T14:21:33.000Z

# This file contains classes of spectrum analyzers using the VISA interface to communicate with the computers. # They should have almost uniform commands, making adaptions to new models/brands quite easy from qm.qua import * from abc import ABC, abstractmethod import numpy as np import pyvisa as visa class VisaSA(ABC): def __init__(self, address, qm): # Gets an existing qm, assumes there is an element called "qubit" with an operation named "test_pulse" which # plays a constant pulse super().__init__() rm = visa.ResourceManager() self.sa = rm.open_resource(address) self.sa.timeout = 100000 with program() as mixer_cal: with infinite_loop_(): play("test_pulse", "qubit") self.qm = qm self.job = qm.execute(mixer_cal) self.method = None def IQ_imbalance_correction(self, g, phi): c = np.cos(phi) s = np.sin(phi) N = 1 / ((1 - g ** 2) * (2 * c ** 2 - 1)) return [ float(N * x) for x in [(1 - g) * c, (1 + g) * s, (1 - g) * s, (1 + g) * c] ] def get_leakage(self, i0, q0): self.qm.set_dc_offset_by_qe("qubit", "I", i0) self.qm.set_dc_offset_by_qe("qubit", "Q", q0) amp_ = self.get_amp() return amp_ def get_image(self, g, p): self.job.set_element_correction("qubit", self.IQ_imbalance_correction(g, p)) amp_ = self.get_amp() return amp_ def __del__(self): self.sa.clear() self.sa.close() @abstractmethod def get_amp(self): pass @abstractmethod def set_automatic_video_bandwidth(self, state: int): # State should be 1 or 0 pass @abstractmethod def set_automatic_bandwidth(self, state: int): # State should be 1 or 0 pass @abstractmethod def set_bandwidth(self, bw: int): # Sets the bandwidth pass @abstractmethod def set_sweep_points(self, n_points: int): # Sets the number of points for a sweep pass @abstractmethod def set_center_freq(self, freq: int): # Sets the central frequency pass @abstractmethod def set_span(self, span: int): # Sets the span pass @abstractmethod def set_cont_off(self): # Sets continuous mode off pass @abstractmethod def set_cont_on(self): # Sets continuous mode on pass @abstractmethod def get_single_trigger(self): # Performs a single sweep pass @abstractmethod def active_marker(self, marker: int): # Active the given marker pass @abstractmethod def set_marker_freq(self, marker: int, freq: int): # Sets the marker's frequency pass @abstractmethod def query_marker(self, marker: int): # Query the marker pass @abstractmethod def get_full_trace(self): # Returns the full trace pass @abstractmethod def enable_measurement(self): # Sets the measurement to channel power pass @abstractmethod def disables_measurement(self): # Sets the measurement to none pass @abstractmethod def sets_measurement_integration_bw(self, ibw: int): # Sets the measurement integration bandwidth pass @abstractmethod def disables_measurement_averaging(self): # Disables averaging in the measurement pass @abstractmethod def get_measurement_data(self): # Returns the result of the measurement pass class RohdeSchwarzFPC1000(VisaSA): def get_amp(self): self.get_single_trigger() if self.method == 1: # Channel power sig = self.get_measurement_data() elif self.method == 2: # Marker sig = self.query_marker(1) else: sig = float("NaN") return sig def set_automatic_video_bandwidth(self, state: int): # State should be 1 or 0 self.sa.write(f"SENS:BAND:VID:AUTO {int(state)}") def set_automatic_bandwidth(self, state: int): # State should be 1 or 0. Resolution (or measurement) bandwidth self.sa.write(f"SENS:BAND:AUTO {int(state)}") def set_bandwidth(self, bw: int): # Sets the resolution (or measurement) bandwidth, 1 Hz to 3 MHz, default unit is Hz # Example SENS:BAND 100000 self.sa.write(f"SENS:BAND {int(bw)}") def set_sweep_points(self, n_points: int): # Sets the number of points for a sweep, allowed range 101 to 2501, default is 201 self.sa.write(f"SENS:SWE:POIN {int(n_points)}") def set_center_freq(self, freq: int): # Sets the central frequency, default unit is Hz self.sa.write(f"SENS:FREQ:CENT {int(freq)}") def set_span(self, span: int): # Sets the span, default unit is Hz self.sa.write(f"SENS:FREQ:SPAN {int(span)}") def set_cont_off(self): # This command selects the sweep mode (but does not start the measurement!) # OFF or 0 is a single sweep mode # *OPC? is to make sure there is no overlapping execution return self.sa.query("INIT:CONT OFF;*OPC?") def set_cont_on(self): # This command selects the sweep mode (but does not start the measurement!) # ON or 1 is a continuous sweep mode # *OPC? is to make sure there is no overlapping execution return self.sa.query("INIT:CONT ON;*OPC?") def get_single_trigger(self): # Initiates a new measurement sequence (starts the sweep) return self.sa.query("INIT:IMM;*OPC?") def active_marker(self, marker: int): # Activate the given marker self.sa.write(f"CALC:MARK{int(marker)} ON") def set_marker_freq(self, marker: int, freq: int): # Sets the marker's frequency. Default unit is Hz self.get_single_trigger() self.sa.write(f"CALC:MARK{int(marker)}:X {int(freq)}") def query_marker(self, marker: int): # Query the amplitude (default unit is dBm) of the marker return float(self.sa.query(f"CALC:MARK{int(marker)}:Y?")) def get_full_trace(self): # Returns the full trace. Implicit assumption that this is trace1 (there could be 1-4) self.sa.write("FORM ASC") # data format needs to be in ASCII ff_SA_Trace_Data = self.sa.query("TRAC:DATA? TRACE1") # Data from the FPC comes out as a string of 1183 values separated by ',': # '-1.97854112E+01,-3.97854112E+01,-2.97454112E+01,-4.92543112E+01,-5.17254112E+01,-1.91254112E+01...\n' # The code below turns it into an a python list of floats # Use split to turn long string to an array of values ff_SA_Trace_Data_Array = ff_SA_Trace_Data.split(",") amp = [float(i) for i in ff_SA_Trace_Data_Array] return amp def enable_measurement(self): # Sets the measurement to channel power self.sa.write( "CALC:MARK:FUNC:POW:SEL CPOW; CALC:MARK:FUNC:LEV:ONCE; CALC:MARK:FUNC:CPOW:UNIT DBM; CALC:MARK:FUNC:POW:RES:PHZ ON" ) def disables_measurement(self): # Sets the channel power measurement to none self.sa.write("CALC:MARK:FUNC:POW OFF") def sets_measurement_integration_bw(self, ibw: int): # Sets the measurement integration bandwidth for channel power measurements self.sa.write(f"CALC:MARK:FUNC:CPOW:BAND {int(ibw)}") def disables_measurement_averaging(self): # disables averaging in the measurement pass def get_measurement_data(self): # Returns the result of the measurement return self.sa.query(f"CALC:MARK:FUNC:POW:RES? CPOW") class KeysightFieldFox(VisaSA): def get_amp(self): self.get_single_trigger() if self.method == 1: # Channel power sig = self.get_measurement_data() elif self.method == 2: # Marker sig = self.query_marker(1) else: sig = float("NaN") return sig def set_automatic_video_bandwidth(self, state: int): # State should be 1 or 0 self.sa.write(f"SENS:BAND:VID:AUTO {int(state)}") def set_automatic_bandwidth(self, state: int): # State should be 1 or 0 self.sa.write(f"SENS:BAND:AUTO {int(state)}") def set_bandwidth(self, bw: int): # Sets the bandwidth self.sa.write(f"SENS:BAND {int(bw)}") def set_sweep_points(self, n_points: int): # Sets the number of points for a sweep self.sa.write(f"SENS:SWE:POIN {int(n_points)}") def set_center_freq(self, freq: int): # Sets the central frequency self.sa.write(f"SENS:FREQ:CENT {int(freq)}") def set_span(self, span: int): # Sets the span self.sa.write(f"SENS:FREQ:SPAN {int(span)}") def set_cont_off(self): return self.sa.query("INIT:CONT OFF;*OPC?") def set_cont_on(self): # Sets continuous mode on return self.sa.query("INIT:CONT ON;*OPC?") def get_single_trigger(self): # Performs a single sweep return self.sa.query("INIT:IMM;*OPC?") def active_marker(self, marker: int): # Active the given marker self.sa.write(f"CALC:MARK{int(marker)}:ACT") def set_marker_freq(self, marker: int, freq: int): # Sets the marker's frequency self.get_single_trigger() self.sa.write(f"CALC:MARK{int(marker)}:X {int(freq)}") def query_marker(self, marker: int): # Query the marker return float(self.sa.query(f"CALC:MARK{int(marker)}:Y?")) def get_full_trace(self): # Returns the full trace ff_SA_Trace_Data = self.sa.query("TRACE:DATA?") # Data from the Fieldfox comes out as a string separated by ',': # '-1.97854112E+01,-3.97854112E+01,-2.97454112E+01,-4.92543112E+01,-5.17254112E+01,-1.91254112E+01...\n' # The code below turns it into an a python list of floats # Use split to turn long string to an array of values ff_SA_Trace_Data_Array = ff_SA_Trace_Data.split(",") amp = [float(i) for i in ff_SA_Trace_Data_Array] return amp def enable_measurement(self): # Sets the measurement to channel power self.sa.write("SENS:MEAS:CHAN CHP") def disables_measurement(self): # Sets the measurement to none self.sa.write("SENS:MEAS:CHAN NONE") def sets_measurement_integration_bw(self, ibw: int): # Sets the measurement integration bandwidth self.sa.write(f"SENS:CME:IBW {int(ibw)}") def disables_measurement_averaging(self): # disables averaging in the measurement self.sa.write("SENS:CME:AVER:ENAB 0") def get_measurement_data(self): # Returns the result of the measurement return float(self.sa.query("CALC:MEAS:DATA?").split(",")[0]) # Data from the Fieldfox comes out as a string separated by ',': # '-1.97854112E+01,-3.97854112E+01\n' # The code above takes the first value and converts to float. class KeysightXSeries(VisaSA): def get_amp(self): self.get_single_trigger() if self.method == 1: # Channel power sig = self.get_measurement_data() elif self.method == 2: # Marker sig = self.query_marker(1) else: sig = float("NaN") return sig def set_automatic_video_bandwidth(self, state: int): # State should be 1 or 0 self.sa.write(f"SENS:BAND:VID:AUTO {int(state)}") def set_automatic_bandwidth(self, state: int): # State should be 1 or 0 self.sa.write(f"SENS:BAND:AUTO {int(state)}") def set_bandwidth(self, bw: int): # Sets the bandwidth self.sa.write(f"SENS:BAND {int(bw)}") def set_sweep_points(self, n_points: int): # Sets the number of points for a sweep self.sa.write(f"SENS:SWE:POIN {int(n_points)}") def set_center_freq(self, freq: int): # Sets the central frequency self.sa.write(f"SENS:FREQ:CENT {int(freq)}") def set_span(self, span: int): # Sets the span self.sa.write(f"SENS:FREQ:SPAN {int(span)}") def set_cont_off(self): return self.sa.query("INIT:CONT OFF;*OPC?") def set_cont_on(self): # Sets continuous mode on return self.sa.query("INIT:CONT ON;*OPC?") def get_single_trigger(self): # Performs a single sweep return self.sa.query("INIT:IMM;*OPC?") def active_marker(self, marker: int): # Active the given marker self.sa.write(f"CALC:MARK{int(marker)}:MODE POS") def set_marker_freq(self, marker: int, freq: int): # Sets the marker's frequency self.get_single_trigger() self.sa.write(f"CALC:MARK{int(marker)}:X {int(freq)}") def query_marker(self, marker: int): # Query the marker return float(self.sa.query(f"CALC:MARK{int(marker)}:Y?")) def get_full_trace(self): # Returns the full trace ff_SA_Trace_Data = self.sa.query("TRACE:DATA? TRACE1") # Data from the Keysight comes out as a string separated by ',': # '-1.97854112E+01,-3.97854112E+01,-2.97454112E+01,-4.92543112E+01,-5.17254112E+01,-1.91254112E+01...\n' # The code below turns it into an a python list of floats # Use split to turn long string to an array of values ff_SA_Trace_Data_Array = ff_SA_Trace_Data.split(",") amp = [float(i) for i in ff_SA_Trace_Data_Array] return amp def enable_measurement(self): # Sets the measurement to channel power self.sa.write(":CONF:CHP") def disables_measurement(self): # Sets the measurement to none self.sa.write(":CONF:CHP NONE") def sets_measurement_integration_bw(self, ibw: int): # Sets the measurement integration bandwidth self.sa.write(f"SENS:CHP:BAND:INT {int(ibw)}") def disables_measurement_averaging(self): # disables averaging in the measurement self.sa.write("SENS:CHP:AVER 0") def get_measurement_data(self): # Returns the result of the measurement return float(self.sa.query("READ:CHP?").split(",")[0]) # Data from the Keysight comes out as a string separated by ',': # '-1.97854112E+01,-3.97854112E+01\n' # The code above takes the first value and converts to float.

34.3903

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null

0

1

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1

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9

cbbd10b4d257bc705753d53e71d9522e4d20bea3

4,774

py

Python

tests/dictlrn/test_cbpdndlmd.py

manvhah/sporco

9237d7fc37e75089a2a65ebfe02b7491410da7d4

[ "BSD-3-Clause" ]

1

2019-07-23T11:27:41.000Z

tests/dictlrn/test_cbpdndlmd.py

wxwoods/sporco

7b0eefea8b6c720ab9a4998a7c55237445765738

[ "BSD-3-Clause" ]

null

tests/dictlrn/test_cbpdndlmd.py

wxwoods/sporco

7b0eefea8b6c720ab9a4998a7c55237445765738

[ "BSD-3-Clause" ]

null

from __future__ import division from builtins import object import numpy as np from sporco.dictlrn import cbpdndlmd class TestSet01(object): def setup_method(self, method): N = 16 Nd = 5 M = 4 K = 3 np.random.seed(12345) self.D0 = np.random.randn(Nd, Nd, M) self.S = np.random.randn(N, N, K) def test_01(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({'MaxMainIter': 10}) try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt) b.solve() except Exception as e: print(e) assert 0 def test_02(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options( {'MaxMainIter': 5, 'CCMOD': {'CG': {'MaxIter': 1}}}, dmethod='cg') try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt, dmethod='cg') b.solve() except Exception as e: print(e) assert 0 def test_03(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({'MaxMainIter': 10}, dmethod='cns') try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt, dmethod='cns') b.solve() except Exception as e: print(e) assert 0 def test_04(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options( {'AccurateDFid': True, 'MaxMainIter': 10}) try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt) b.solve() except Exception as e: print(e) assert 0 def test_05(self): N = 16 Nc = 3 Nd = 5 M = 4 K = 3 D0 = np.random.randn(Nd, Nd, Nc, M) S = np.random.randn(N, N, Nc, K) lmbda = 1e-1 W = np.ones((N, N, 1, K, 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({'MaxMainIter': 10}) try: b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, S, lmbda, W, opt=opt) b.solve() except Exception as e: print(e) assert 0 def test_06(self): N = 16 Nc = 3 Nd = 5 M = 4 K = 3 D0 = np.random.randn(Nd, Nd, 1, M) S = np.random.randn(N, N, Nc, K) lmbda = 1e-1 W = np.ones((N, N, Nc, K, 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({'MaxMainIter': 10}) try: b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, S, lmbda, W, opt=opt) b.solve() except Exception as e: print(e) assert 0 def test_07(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options( {'AccurateDFid': True, 'MaxMainIter': 10}, dmethod='fista') try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt, dmethod='fista') b.solve() except Exception as e: print(e) assert 0 def test_08(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options( {'AccurateDFid': True, 'MaxMainIter': 10}, xmethod='fista') try: b = cbpdndlmd.ConvBPDNMaskDictLearn(self.D0, self.S, lmbda, W, opt=opt, xmethod='fista') b.solve() except Exception as e: print(e) assert 0 def test_09(self): lmbda = 1e-1 W = np.ones(self.S.shape[0:2] + (1, self.S.shape[2], 1)) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options( {'AccurateDFid': True, 'MaxMainIter': 10}, xmethod='fista', dmethod='cns') try: b = cbpdndlmd.ConvBPDNMaskDictLearn( self.D0, self.S, lmbda, W, opt=opt, xmethod='fista', dmethod='cns') b.solve() except Exception as e: print(e) assert 0

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null

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1

0

1

0

null

0

7

381021e874fe9cb88508ca834c01ad594be4bd74

117

py

Python

ramda/min_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

56

2018-08-06T08:44:58.000Z

2022-03-17T09:49:03.000Z

ramda/min_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

28

2019-06-17T11:09:52.000Z

2022-02-18T16:59:21.000Z

ramda/min_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

5

2019-09-18T09:24:38.000Z

2021-07-21T08:40:23.000Z

from .min import min from ramda.private.asserts import assert_equal def min_test(): assert_equal(min(3, 1), 1)

16.714286

46

0.735043

20

117

4.15

0.6

0.26506

0

0.030612

0.162393

117

6

47

19.5

0.816327

0

0.5

1

0.25

true

0

0.5

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

1

0

8

381a9ca944d257ff19d0f2450a4db9fd8a13e9e5

34,713

py

Python

src/data_cleaners/cleaning_Encuesta_C2_Inicial.py

Grupo-Informatica-Educativa/CFK

8fa09fa4c5259b358326ab364bd79d3564123ca7

[ "MIT" ]

null

src/data_cleaners/cleaning_Encuesta_C2_Inicial.py

Grupo-Informatica-Educativa/CFK

8fa09fa4c5259b358326ab364bd79d3564123ca7

[ "MIT" ]

null

src/data_cleaners/cleaning_Encuesta_C2_Inicial.py

Grupo-Informatica-Educativa/CFK

8fa09fa4c5259b358326ab364bd79d3564123ca7

[ "MIT" ]

null

import pandas as pd save_new = True pd.set_option('display.max_rows', 50) pd.set_option('display.max_columns', 50) pd.set_option('display.width', 1000) def add_columns(df1, df2): for col in df2.columns: df1[col] = df2[col] def add_equal_columns(pivot_inicial): # Pregunta 9 col = "9. ¿Cuáles de las siguientes áreas enseña y en qué grado?" pivot_inicial[col] = pivot_inicial[col].str.replace('-1', '0') df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.rename({ 1: '9.1 Ciencias naturales y educación ambiental', 2: '9.2 Ciencias sociales, historia, geografía, constitución política y democracia', 3: '9.3 Educación artística', 4: '9.4 Educación ética y en valores humanos', 5: '9.5 Educación física, recreación y deportes', 6: '9.6 Educación religiosa', 7: '9.7 Humanidades, lengua castellana e idiomas extranjeros', 8: '9.8 Matemáticas', 9: '9.9 Tecnología e informática', 10: '9.10 Otro', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 12 col = "12. ¿Cuáles de las siguientes estrategias usted ha usado en sus clases?" opciones_preg10 = [ "Realizar clubes y actividades extracurriculares para niñas y jóvenes como refuerzo de lo visto en las clases de áreas STEM.", "Destacar y reconocer los logros de las niñas y jóvenes, por ejemplo, promover concursos diferenciados por género, como, premio a la niña científica y el niño científico.", "Dar referencias o modelos de mujeres destacadas en las áreas STEM, por ejemplo, mostrar la película de Marie Curie.", "Motivar que las niñas participen y sean escuchadas, por ejemplo, alternándolas con los niños.", "Estimular el liderazgo femenino, por ejemplo, que las niñas y adolescentes sean representantes de grupo.", "Generar espacios de confianza para las niñas, por ejemplo, realizando reflexiones sobre el género al comienza de la clase", "Prohibir y corregir los comentarios, actitudes y acciones sexistas.", 'Utilizar lenguaje inclusivo y no realizar estereotipos de género, por ejemplo, decir "Todas las personas" en vez de "todos los niños" o evitar decir que las niñas son delicadas.', "Tratos y estímulos igualitarios a toda y todo estudiante independientemente de su género.", "Observar el comportamiento de los niños hacia las niñas porque a ellas no se les puede tocar ni con pétalo de ua rosa." ] for count, subpregunta in enumerate(opciones_preg10): pivot_inicial[f'12.{count+1} {subpregunta}'] = pivot_inicial[col].str.contains(subpregunta).replace({ True: "Si", False: "No" }) # Pregunta 13 col = "13. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia" df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.replace({ "1": "Totalmente en desacuerdo", "2": "En desacuerdo", "3": "Neutro", "4": "De acuerdo", "5": "Totalmente de acuerdo", },inplace=True) df2.rename({ 1: '13.1 Es preferible que las mujeres enseñen ciencias sociales y los hombres ciencias exactas', 2: '13.2 Es normal que la mayoría de los ingenieros mecánicos sean varones porque los hombres son mejores para los números', 3: '13.3 Por su esencia una mujer tiene mejor desempeño en un proyecto de alto impacto social que en un proyecto de robótica industrial.', 4: '13.4 Los hombres son mejores para la tecnología que las mujeres.', 5: '13.5 Las mujeres tienen mayores habilidades para proyectos sociales que tecnológicos.', 6: '13.6 Los grandes aportes en la computación han sido hechos por hombres.', 7: '13.7 Que la mayoría de mujeres no opte por áreas exactas es simplemente cuestión de preferencias.', 8: '13.8 Que la mayoría de personas en artes y humanidades sean mujeres es muestra de su sensibilidad.', 9: '13.9 Es natural que los hombres sea buenos para los números y las mujeres para las letras', 10: '13.10 Los hombres son muy ágiles tomando decisiones importantes.', 11: '13.11 Las niñas son más ordenadas que los niños.', 12: '13.12 Muchas mujeres se caracterizan por una pureza que pocos hombres poseen', 13: '13.13 Las mujeres deben ser queridas y protegidas por los hombres', 14: '13.14 Todo hombre debe tener una mujer a quien amar', 15: '13.15 El hombre está incompleto sin la mujer', 16: '13.16 Las mujeres en comparación con los hombres tienden a tener un sentido más refinado de la cultura y el buen gusto', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 15 col = "15. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia:" df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.replace({ "1": "Totalmente en desacuerdo", "2": "En desacuerdo", "3": "Neutro", "4": "De acuerdo", "5": "Totalmente de acuerdo", },inplace=True) df2.rename({ 1: '15.1 Sé cómo resolver los problemas técnicos cuando fallan las TIC', 2: '15.2 Puedo aprender sobre nuevas tecnologías fácilmente', 3: '15.3 Sé cómo usar las TIC con los estudiantes en clase', 4: '15.4 Me apoyo en mis colegas para resolver problemas sobre cómo trabajar algún tema', 5: '15.5 Puedo hablar con otros docentes sobre el diseño de cursos', 6: '15.6 Siento que tengo apoyo de otros docentes para el diseño de mis cursos', 7: '15.7 No tengo con quién conversar sobre el diseño de mis cursos', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 17 col = "17. Por favor evalúe las siguientes afirmaciones según qué tan de acuerdo está usted con enseñar las siguientes prácticas como objetivos de aprendizaje relacionados con el pensamiento computacional" df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.replace({ "1": "Totalmente en desacuerdo", "2": "En desacuerdo", "3": "Neutro", "4": "De acuerdo", "5": "Totalmente de acuerdo", },inplace=True) df2.rename({ 1: '17.1 Usar el correo electrónico', 2: '17.2 Crear y usar de modelos y simulaciones', 3: '17.3 Automatizar tareas', 4: '17.4 Usar Word', 5: '17.5 Procesar Datos', 6: '17.6 Resolver problemas a través de herramientas computacionales (como simulaciones)', 7: '17.7 Resolver problemas a través de herramientas computacionales (como lenguajes de programación)', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 18 col = "18. Por favor evalúe los siguientes enunciados de acuerdo con qué tan preparado(a) se siente para integrar el pensamiento computacional en sus cursos" df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.replace({ "1": "Totalmente en desacuerdo", "2": "En desacuerdo", "3": "Neutro", "4": "De acuerdo", "5": "Totalmente de acuerdo", },inplace=True) df2.rename({ 1: '18.1 Puedo aplicar las prácticas y habilidades del pensamiento computacional a mi trabajo', 2: '18.2 Puedo definir el pensamiento computacional', 3: '18.3 Puedo describir las prácticas y habilidades que componen el pensamiento computacional a mis estudiantes', 4: '18.4 Puedo aplicar las prácticas y habilidades del pensamiento computacional a mi vida diaria', 5: '18.5 Creo que tengo las habilidades para desarrollar el pensamiento computacional en mis estudiantes', 6: '18.6 Puedo enseñar fácilmente sobre nuevas prácticas computacionales', 7: '18.7 Puedo diseñar una clase que desarrolle el pensamiento computacional en los estudiantes', 8: '18.8 Puedo seleccionar tecnologías para usar en mi salón de clases, que me permitan mejorar qué enseño y cómo enseño pensamiento computacional', 9: '18.9 Puedo aplicar mis habilidades en pensamiento computacional para ayudar a los estudiantes a perseguir sus intereses individuales', 10: '18.10 Puedo implementar y evaluar la idoneidad de una estrategia pedagógica que le permita a los estudiantes desarrollar pensamiento computacional' }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 20 col = "20. En una escala de 1 a 10 (donde 10 es muy a menudo), con qué frecuencia utilizarías las siguientes prácticas pedagógicas para enseñar pensamiento computacional" pivot_inicial[col] = pivot_inicial[col].str.replace('-1', '0') df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.rename({ 1: '20.1 Actividades desconectadas', 2: '20.2 Usa-Modifica-Crea', 3: '20.3 Clase magistral', 4: '20.4 Enseñanza explícita y sin ambigüedades', 5: '20.5 Marcha Silenciosa', 6: '20.6 Aprendizaje basado en proyectos', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) # Pregunta 22 col = "22. Cuando un estudiante se enfrenta a una dificultad creando un programa y no sabe si está correcto, qué tan a menudo, en una escala de 1-10 (donde 10 es siempre), usted:" pivot_inicial[col] = pivot_inicial[col].str.replace('-1', '0') df2 = pivot_inicial[col].str.split(r'\b\D+\b', expand=True) df2.rename({ 1: '22.1 Le explicaría la respuesta correcta', 2: '22.2 Le sugeriría ir paso a paso por el programa simulando su ejecución', 3: '22.3 Le diría que revise sus notas', 4: '22.4 Le sugeriría que revise las memorias colectivas', 5: '22.5 Le sugeriría volver a leer el problema', 6: '22.6 Le sugeriría intentar con varios valores para evaluar el programa', 7: '22.7 Le explicaría el problema nuevamente', }, axis=1, inplace=True) df2 = df2.drop([0], axis=1) add_columns(pivot_inicial, df2) otras = [ "9. ¿Cuáles de las siguientes áreas enseña y en qué grado?", '9.1 Ciencias naturales y educación ambiental', '9.2 Ciencias sociales, historia, geografía, constitución política y democracia', '9.3 Educación artística', '9.4 Educación ética y en valores humanos', '9.5 Educación física, recreación y deportes', '9.6 Educación religiosa', '9.7 Humanidades, lengua castellana e idiomas extranjeros', '9.8 Matemáticas', '9.9 Tecnología e informática', '9.10 Otro', "12. ¿Cuáles de las siguientes estrategias usted ha usado en sus clases?", "12.1 Realizar clubes y actividades extracurriculares para niñas y jóvenes como refuerzo de lo visto en las clases de áreas STEM.", "12.2 Destacar y reconocer los logros de las niñas y jóvenes, por ejemplo, promover concursos diferenciados por género, como, premio a la niña científica y el niño científico.", "12.3 Dar referencias o modelos de mujeres destacadas en las áreas STEM, por ejemplo, mostrar la película de Marie Curie.", "12.4 Motivar que las niñas participen y sean escuchadas, por ejemplo, alternándolas con los niños.", "12.5 Estimular el liderazgo femenino, por ejemplo, que las niñas y adolescentes sean representantes de grupo.", "12.6 Generar espacios de confianza para las niñas, por ejemplo, realizando reflexiones sobre el género al comienza de la clase", "12.7 Prohibir y corregir los comentarios, actitudes y acciones sexistas.", '12.8 Utilizar lenguaje inclusivo y no realizar estereotipos de género, por ejemplo, decir "Todas las personas" en vez de "todos los niños" o evitar decir que las niñas son delicadas.', "12.9 Tratos y estímulos igualitarios a toda y todo estudiante independientemente de su género.", "12.10 Observar el comportamiento de los niños hacia las niñas porque a ellas no se les puede tocar ni con pétalo de ua rosa.", "13. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia", '13.1 Es preferible que las mujeres enseñen ciencias sociales y los hombres ciencias exactas', '13.2 Es normal que la mayoría de los ingenieros mecánicos sean varones porque los hombres son mejores para los números', '13.3 Por su esencia una mujer tiene mejor desempeño en un proyecto de alto impacto social que en un proyecto de robótica industrial.', '13.4 Los hombres son mejores para la tecnología que las mujeres.', '13.5 Las mujeres tienen mayores habilidades para proyectos sociales que tecnológicos.', '13.6 Los grandes aportes en la computación han sido hechos por hombres.', '13.7 Que la mayoría de mujeres no opte por áreas exactas es simplemente cuestión de preferencias.', '13.8 Que la mayoría de personas en artes y humanidades sean mujeres es muestra de su sensibilidad.', '13.9 Es natural que los hombres sea buenos para los números y las mujeres para las letras', '13.10 Los hombres son muy ágiles tomando decisiones importantes.', '13.11 Las niñas son más ordenadas que los niños.', '13.12 Muchas mujeres se caracterizan por una pureza que pocos hombres poseen', '13.13 Las mujeres deben ser queridas y protegidas por los hombres', '13.14 Todo hombre debe tener una mujer a quien amar', '13.15 El hombre está incompleto sin la mujer', '13.16 Las mujeres en comparación con los hombres tienden a tener un sentido más refinado de la cultura y el buen gusto', '15.1 Sé cómo resolver los problemas técnicos cuando fallan las TIC', '15.2 Puedo aprender sobre nuevas tecnologías fácilmente', '15.3 Sé cómo usar las TIC con los estudiantes en clase', '15.4 Me apoyo en mis colegas para resolver problemas sobre cómo trabajar algún tema', '15.5 Puedo hablar con otros docentes sobre el diseño de cursos', '15.6 Siento que tengo apoyo de otros docentes para el diseño de mis cursos', '15.7 No tengo con quién conversar sobre el diseño de mis cursos', "17. Por favor evalúe las siguientes afirmaciones según qué tan de acuerdo está usted con enseñar las siguientes prácticas como objetivos de aprendizaje relacionados con el pensamiento computacional", '17.1 Usar el correo electrónico', '17.2 Crear y usar de modelos y simulaciones', '17.3 Automatizar tareas', '17.4 Usar Word', '17.5 Procesar Datos', '17.6 Resolver problemas a través de herramientas computacionales (como simulaciones)', '17.7 Resolver problemas a través de herramientas computacionales (como lenguajes de programación)', "18. Por favor evalúe los siguientes enunciados de acuerdo con qué tan preparado(a) se siente para integrar el pensamiento computacional en sus cursos", '18.1 Puedo aplicar las prácticas y habilidades del pensamiento computacional a mi trabajo', '18.2 Puedo definir el pensamiento computacional', '18.3 Puedo describir las prácticas y habilidades que componen el pensamiento computacional a mis estudiantes', '18.4 Puedo aplicar las prácticas y habilidades del pensamiento computacional a mi vida diaria', '18.5 Creo que tengo las habilidades para desarrollar el pensamiento computacional en mis estudiantes', '18.6 Puedo enseñar fácilmente sobre nuevas prácticas computacionales', '18.7 Puedo diseñar una clase que desarrolle el pensamiento computacional en los estudiantes', '18.8 Puedo seleccionar tecnologías para usar en mi salón de clases, que me permitan mejorar qué enseño y cómo enseño pensamiento computacional', '18.9 Puedo aplicar mis habilidades en pensamiento computacional para ayudar a los estudiantes a perseguir sus intereses individuales', '18.10 Puedo implementar y evaluar la idoneidad de una estrategia pedagógica que le permita a los estudiantes desarrollar pensamiento computacional', "20. En una escala de 1 a 10 (donde 10 es muy a menudo), con qué frecuencia utilizarías las siguientes prácticas pedagógicas para enseñar pensamiento computacional", '20.1 Actividades desconectadas', '20.2 Usa-Modifica-Crea', '20.3 Clase magistral', '20.4 Enseñanza explícita y sin ambigüedades', '20.5 Marcha Silenciosa', '20.6 Aprendizaje basado en proyectos', "22. Cuando un estudiante se enfrenta a una dificultad creando un programa y no sabe si está correcto, qué tan a menudo, en una escala de 1-10 (donde 10 es siempre), usted:", '22.1 Le explicaría la respuesta correcta', '22.2 Le sugeriría ir paso a paso por el programa simulando su ejecución', '22.3 Le diría que revise sus notas', '22.4 Le sugeriría que revise las memorias colectivas', '22.5 Le sugeriría volver a leer el problema', '22.6 Le sugeriría intentar con varios valores para evaluar el programa', '22.7 Le explicaría el problema nuevamente' ] ######################### df_inicial = pd.read_csv('data/crudos/Inicial.csv', error_bad_lines=False, warn_bad_lines=False, low_memory=False) df_inicial["Pregunta"] = df_inicial["Pregunta"].str.replace("\n", " ").replace("\b", " ") _items = df_inicial[df_inicial["Pregunta"] == "Por favor evalúe los siguientes enunciados de acuerdo con su experiencia: "]["Respuesta"] _items = _items.str.contains("TIC") df_inicial["temp"] = _items.copy() df_inicial["temp"] = df_inicial["temp"].fillna(False) df_inicial.loc[df_inicial["temp"],"Pregunta"] = "15. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia:" df_inicial = df_inicial.drop(["temp"],axis=1) pivot_inicial = df_inicial.pivot_table( index=['Nombre', 'Apellido', 'Correo Electrónico', 'Curso', 'ID Asignado Por Moodle', 'Nombre De Usuario'], columns='Pregunta', values='Respuesta', aggfunc='first' ).reset_index() pivot_inicial.columns = [col.replace("\n", " ").strip() for col in pivot_inicial.columns] pivot_inicial.columns = [col.replace("\r", " ").strip() for col in pivot_inicial.columns] pivot_inicial.columns = [col.replace("\b", " ").strip() for col in pivot_inicial.columns] df_inicial.columns = [col.replace("\n", " ").strip() for col in df_inicial.columns] ######################### encuesta_caraterizacion = { '¿Cómo prefieres que te llamen?': '2. ¿Cómo prefieres que te llamen?', 'Número de Cédula': '3. Número de Cédula', 'Rango de edad': '4. Rango de edad', 'Mi primera lengua es español:': '5. Mi primera lengua es español:', 'Departamento de residencia': '6. Departamento de residencia', 'Municipio de residencia:': '7. Municipio de residencia:', 'Institución Educativa en la que laboro': '8. Institución Educativa en la que laboro', '¿A qué estatuto docente pertenece?': '9. ¿A qué estatuto docente pertenece?', 'Por favor evalúa tus conocimientos de herramienta digitales del 1 al 10, según tu grado de familiarización en el manejo de los mismos (10 es muy hábil)': '10. Por favor evalúa tus conocimientos de herramienta digitales del 1 al 10, según tu grado de familiarización en el manejo de los mismos (10 es muy hábil)', 'Por favor evalúa, en la escala del 1 al 10, tus conocimientos previos sobre los contenidos pedagógicos que se estudiarán en el curso, según tu nivel de experiencia (10 es experto)': '11. Por favor evalúa, en la escala del 1 al 10, tus conocimientos previos sobre los contenidos pedagógicos que se estudiarán en el curso, según tu nivel de experiencia (10 es experto)', 'Por favor evalúa tus habilidades previas en programación, según la siguiente escala': '12. Por favor evalúa tus habilidades previas en programación, según la siguiente escala', 'Agrega cualquier comentario adicional que quieras hacer, con relación a tus conocimientos previos y/o cómo esperas beneficiarte de los contenidos que estudiarás.': "13. Agrega cualquier comentario adicional que quieras hacer, con relación a tus conocimientos previos y/o cómo espera beneficiarse de los contenidos que estudiarás.", 'Considero que tengo la autorregulación, disciplina y responsabilidad que se requieren para ser exitoso(a) en este programa de formación virtual': '14. Considero que tengo la autorregulación, disciplina y responsabilidad que se requieren para ser exitoso(a) en este programa de formación virtual', 'Considero que los conocimientos y materiales que adquiriré durante el programa serán relevantes para mi trabajo como docente.': "15. Considero que los conocimientos y materiales que adquiriré durante el programa serán relevantes para mi trabajo como docente.", 'Considero que lo que aprenderé en el curso lo podre aplicar fácilmente en mi contexto de enseñanza/aprendizaje.': "16. Considero que lo que aprenderé en el curso lo podre aplicar fácilmente en mi contexto de enseñanza/aprendizaje.", 'Considero que los recursos de internet y equipos con los que cuento serán suficientes para participar en las actividades del curso.': "17. Considero que los recursos de internet y equipos con los que cuento serán suficientes para participar en las actividades del curso.", 'He hecho arreglos para disponer, cabalmente, del tiempo semanal requerido para desarrollar las actividades propuestas de forma adecuada.': "18. He hecho arreglos para disponer, cabalmente, del tiempo semanal requerido para desarrollar las actividades propuestas de forma adecuada.", 'El o los horarios que me resultan más adecuados para asistir a los encuentros sincrónicos es/son: (Marca todas las opciones que te resulten adecuadas)': "19. El o los horarios que me resultan más adecuados para asistir a los encuentros sincrónicos es/son: (Marca todas las opciones que te resulten adecuadas)" } for a in pivot_inicial.columns: if "Por favor evalúa tus habilidades previas en programación" in a: aux = a pivot_inicial2 = pivot_inicial.rename(columns={aux:'Por favor evalúa tus habilidades previas en programación, según la siguiente escala'}) to_drop = list(encuesta_caraterizacion.keys()) ######################### preguntas_info_inicial = { "ID Asignado Por Moodle": "ID Moodle", "Nombre": "Nombre", "Apellido": "Apellido", "Correo Electrónico": "Correo Electrónico", "Curso": "Curso", "Nombre De Usuario": "1. Cédula", "Edad (Años)": "2. Edad", "Su institución está en un contexto:": "3. Contexto IE", "Género:": "4. Género", '¿Es usted cabeza de hogar?': '5. ¿Es usted cabeza de hogar? ', '¿Cuál es su estado civil?': '6. ¿Cuál es su estado civil?', 'Número de horas de clases semanales que orienta (Solo números)': '7. Número de horas de clases semanales que orienta', '¿Es usted líder comunitario?': '8. ¿Es usted líder comunitario?', "¿Cuáles de las siguientes áreas enseña y en qué grado? (Marque 'NS/NC' si no enseña el área)": "9. ¿Cuáles de las siguientes áreas enseña y en qué grado?", '¿De acuerdo con lo anterior, usted es docente de áreas STEM (ciencias naturales, matemática, tecnología e informática) o No STEM (ciencias sociales, educación artística, educación física, educación religiosa, humanidades e idiomas extranjeros)?': '10. ¿De acuerdo con lo anterior, usted es docente de áreas STEM o No STEM?', 'Su formación es en áreas' : '11. Su formación es en áreas', "¿Cuáles de las siguientes estrategias usted ha usado en sus clases? (Opción múltiple)": "12. ¿Cuáles de las siguientes estrategias usted ha usado en sus clases?", "Por favor evalúe los siguientes enunciados de acuerdo con su experiencia:": "13. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia", "Agregue cualquier comentario o aclaración sobre las preguntas anteriores.": "14 .Comentario o clarificación sobre las preguntas anteriores", "15. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia:": "15. Por favor evalúe los siguientes enunciados de acuerdo con su experiencia:", #"Agrega cualquier comentario o clarificación sobre las preguntas anteriores.": #"16. Comentario o clarificación sobre las preguntas anteriores", "Por favor evalúe las siguientes afirmaciones según qué tan de acuerdo está usted con enseñar las siguientes prácticas como objetivos de aprendizaje relacionados con el pensamiento computacional:": "17. Por favor evalúe las siguientes afirmaciones según qué tan de acuerdo está usted con enseñar las siguientes prácticas como objetivos de aprendizaje relacionados con el pensamiento computacional", "Por favor evalúe los siguientes enunciados de acuerdo con qué tan preparado(a) se siente para integrar el pensamiento computacional en sus cursos:": "18. Por favor evalúe los siguientes enunciados de acuerdo con qué tan preparado(a) se siente para integrar el pensamiento computacional en sus cursos", #"Agrega cualquier comentario o clarificación sobre las preguntas anteriores.": #"19 .Comentario o clarificación sobre las preguntas anteriores", "En una escala de 1 a 10 (donde 10 es muy a menudo), con qué frecuencia utilizarías las siguientes prácticas pedagógicas para enseñar pensamiento computacional. Si no conoce alguna práctica pedagógica, por favor elija la opción NS/NC.": "20. En una escala de 1 a 10 (donde 10 es muy a menudo), con qué frecuencia utilizarías las siguientes prácticas pedagógicas para enseñar pensamiento computacional", #"Agrega cualquier comentario o clarificación adicional sobre las estrategias de enseñanza de la pregunta anterior.": #"21. Comentario o clarificación adicional sobre las estrategias de enseñanza de la pregunta anterior", "Cuando un estudiante se enfrenta a una dificultad creando un programa y no sabe si está correcto, qué tan a menudo, en una escala de 1-10 (donde 10 es siempre), usted:": "22. Cuando un estudiante se enfrenta a una dificultad creando un programa y no sabe si está correcto, qué tan a menudo, en una escala de 1-10 (donde 10 es siempre), usted:", } preguntas_propias_rename = { "La docente Margarita decidió hacer que sus estudiantes de segundo de primaria utilicen los computadores del colegio para predecir el clima de una semana (temperatura, precipitaciones, y viento). Cada estudiante debe dibujar cómo se verá el clima en la ciudad en dicha semana. Margarita, creó un archivo compartido donde los estudiantes ingresarán la información. Luego tomaron las predicciones de modelos de Internet y los ingresaron en el mismo documento compartido. Durante una semana tomaron los datos reales, y luego, proyectaron en el tablero los datos predichos por los estudiantes, los del modelo de Internet, y los datos reales. Al finalizar, Margarita les mostró a los estudiantes cómo hacer un gráfico para comparar los diferentes datos. ¿Está Margarita desarrollando el pensamiento computacional de sus estudiantes? Seleccione todas las respuestas que considere correctas.": "24. La docente Margarita decidió hacer que sus estudiantes de segundo de primaria utilicen los computadores del colegio para predecir el clima de una semana (temperatura, precipitaciones, y viento). Cada estudiante debe dibujar cómo se verá el clima en la ciudad en dicha semana. Margarita, creó un archivo compartido donde los estudiantes ingresarán la información. Luego tomaron las predicciones de modelos de Internet y los ingresaron en el mismo documento compartido. Durante una semana tomaron los datos reales, y luego, proyectaron en el tablero los datos predichos por los estudiantes, los del modelo de Internet, y los datos reales. Al finalizar, Margarita les mostró a los estudiantes cómo hacer un gráfico para comparar los diferentes datos. ¿Está Margarita desarrollando el pensamiento computacional de sus estudiantes? Seleccione todas las respuestas que considere correctas.", "La cafetería del colegio empacó almuerzos iguales para todos los estudiantes, menos los de Juan Arias y María Vásquez que no pueden comer huevo. Los almuerzos están marcados con el apellido de los estudiantes y organizados alfabéticamente. Para verificar que su almuerzo cumple con la restricción alimenticia María con ayuda de su profesor buscan en las cajas. María sabe que su almuerzo debe estar al final, así que busca hasta que encuentre una caja que comience por una letra cerca de la V. Cuando encuentra una que comienza con Trujillo, mira el último almuerzo de esa caja y se da cuenta que termina en Zapata. Así, María se da cuenta que su almuerzo debe estar allí. ¿Está María usando el pensamiento computacional para encontrar su almuerzo? Seleccione todas las respuestas que considere correctas.": "25. La cafetería del colegio empacó almuerzos iguales para todos los estudiantes, menos los de Juan Arias y María Vásquez que no pueden comer huevo. Los almuerzos están marcados con el apellido de los estudiantes y organizados alfabéticamente. Para verificar que su almuerzo cumple con la restricción alimenticia María con ayuda de su profesor buscan en las cajas. María sabe que su almuerzo debe estar al final, así que busca hasta que encuentre una caja que comience por una letra cerca de la V. Cuando encuentra una que comienza con Trujillo, mira el último almuerzo de esa caja y se da cuenta que termina en Zapata. Así, María se da cuenta que su almuerzo debe estar allí. ¿Está María usando el pensamiento computacional para encontrar su almuerzo? Seleccione todas las respuestas que considere correctas.", "Un ratón robot ha sido programado para seguir las siguientes instrucciones: (1) Sigue hacia abajo hasta que haya un cruce a uno de los lados (2) Cuando encuentres un cruce, atraviésalo (3) Vuelve al paso (1). Considera el siguiente laberinto para nuestro ratón robot. ¿En cuál de los tubos debería comenzar el robot para llegar al queso?": "26. Un ratón robot ha sido programado para seguir instrucciones. ¿En cuál de los tubos debería comenzar el robot para llegar al queso?", "Andrea hizo un diagrama de flujo para diseñar el algoritmo que le permitirá encender automáticamente el ventilador cuando esté muy caliente su habitación. Sin embargo, no está segura de que funcione. ¿Qué le podrías recomendar?": "27. Andrea hizo un diagrama de flujo para diseñar el algoritmo que le permitirá encender automáticamente el ventilador cuando esté muy caliente su habitación. Sin embargo, no está segura de que funcione. ¿Qué le podrías recomendar?", "Considera el siguiente segmento de código¿Después de que el anterior código se ejecuta, cual es el valor de la variable secuela?": "28. Considera el siguiente segmento de código ¿Después de que el anterior código se ejecuta, cual es el valor de la variable secuela?", "Considera el siguiente código: Si a=3, b=8 y c=10, ¿Qué imprimirá el programa?": "29. Considera el siguiente código: Si a=3, b=8 y c=10, ¿Qué imprimirá el programa?", "Considera el siguiente código: Después de que se ejecute el código anterior, ¿Cuáles de los siguientes enunciados sonverdaderos?": "30. Considera el siguiente código: Después de que se ejecute el código anterior, ¿Cuáles de los siguientes enunciados son verdaderos?", "Suponiendo que “a” y “b” son variables booleanas. Considera la siguiente expresión lógica:¿Cuál de las siguientes afirmaciones describe de manera más precisa la evaluación de las expresiones?": "31. Suponiendo que “a” y “b” son variables booleanas. Considera la siguiente expresión lógica:¿Cuál de las siguientes afirmaciones describe de manera más precisa la evaluación de las expresiones?", "La alcaldía acaba de contratar a Valeria para hacer un programa en la Micro:bit que controle el alumbrado público de su ciudad. Utilizando el sensor de luz de la tarjeta Micro:bit, ella se dio cuenta que cuando mide niveles de luz con un valor por debajo de 100, ya está suficientemente oscuro como para prender el alumbrado público. El programa que hizo funciona bien para prender el alumbrado de la ciudad, pero luego cuando amanece, las luces siguen encendidas durante todo el día. Valeria no está segura cómo solucionarlo, pero tiene algunas ideas que cree que podrían funcionar. ¿Cuál de las siguientes opciones crees que debería usar Valeria? Imagen 1 Imagen 2 Imagen 3 Imagen 4": "32. La alcaldía acaba de contratar a Valeria para hacer un programa en la Micro:bit que controle el alumbrado público de su ciudad. Utilizando el sensor de luz de la tarjeta Micro:bit, ella se dio cuenta que cuando mide niveles de luz con un valor por debajo de 100, ya está suficientemente oscuro como para prender el alumbrado público. El programa que hizo funciona bien para prender el alumbrado de la ciudad, pero luego cuando amanece, las luces siguen encendidas durante todo el día. Valeria no está segura cómo solucionarlo, pero tiene algunas ideas que cree que podrían funcionar. ¿Cuál de las siguientes opciones crees que debería usar Valeria?", "¿Qué botella debe cambiarse de color para que el resultado final sea una botella de color blanco? Tenga en cuenta lo que hace cada máquina recicladora que se usa en este sistema.": "33. ¿Qué botella debe cambiarse de color para que el resultado final sea una botella de color blanco? Tenga en cuenta lo que hace cada máquina recicladora que se usa en este sistema.", "Teniendo en cuenta el siguiente fragmento de código, Alejandra responde a la pregunta ¿Cuál será el valor final de “Y”? afirmando que el valor final será 44. El código retorna 120¿Qué opinas de la respuesta de Alejandra?": "34. Teniendo en cuenta el siguiente fragmento de código, Alejandra responde a la pregunta ¿Cuál será el valor final de “Y”? afirmando que el valor final será 44. El código retorna 120 ¿Qué opinas de la respuesta de Alejandra?", } ######################### col_inicial = [i for i in pivot_inicial2.columns if i.startswith( 'Por favor evalúa tus habilidades previas en programación')][0] pivot_inicial3 = pivot_inicial2.reset_index() pivot_inicial3 = pivot_inicial2.drop(to_drop, axis=1) merged = preguntas_info_inicial | preguntas_propias_rename cols =[] for e in merged: cols.append(merged[e]) pivot_inicial.rename(merged, axis=1, inplace=True) add_equal_columns(pivot_inicial) merge = [] merge.extend(otras) merge.extend(cols) pivot_inicial[merge].to_excel("PretestInicial.xlsx", encoding='utf-8-sig')

69.84507

901

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5,189

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4.864521

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0.008755

0.891451

0.87398

0.859401

0.834641

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0.783337

0

0.02951

0.199522

34,713

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69.985887

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null

0

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0

1

0

null

0

8

3825d4bedf9a74ff5e2aac10fc1d31d79b6ad756

649

py

Python

chesschallenge/chess/tests/test_coordinate_validation.py

Mika-IO/-backend-technical-assessment

1503883e3a925d63113fad73599972078fd62932

[ "MIT" ]

null

chesschallenge/chess/tests/test_coordinate_validation.py

Mika-IO/-backend-technical-assessment

1503883e3a925d63113fad73599972078fd62932

[ "MIT" ]

2

2021-08-31T16:27:53.000Z

2021-08-31T17:21:17.000Z

chesschallenge/chess/tests/test_coordinate_validation.py

Mika-IO/backend-technical-assessment

1503883e3a925d63113fad73599972078fd62932

[ "MIT" ]

null

import pytest from chesschallenge.chess.chess import validate_coordinate def test_a2_is_valid_coordenate(): assert validate_coordinate("a2") == True def test_c5_is_valid_coordenate(): assert validate_coordinate("c5") == True def test_34_is_valid_coordenate(): assert validate_coordinate("34") == False def test_345_is_valid_coordenate(): assert validate_coordinate("345") == False def test_ans_is_valid_coordenate(): assert validate_coordinate("ans") == False def test_bb_is_valid_coordenate(): assert validate_coordinate("bb") == False def test_z9_is_valid_coordenate(): assert validate_coordinate("z9") == False

27.041667

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649

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27.041667

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null

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1

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1

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7

698e6b66490d4797b771389cf6da605c3385c4bd

370

py

Python

Project/new/proj_spec/submission.py

suryaavala/17s1-cs9318

fdfa84a5f3330d189af213d670479c65d6c60a28

[ "MIT" ]

null

Project/new/proj_spec/submission.py

suryaavala/17s1-cs9318

fdfa84a5f3330d189af213d670479c65d6c60a28

[ "MIT" ]

null

Project/new/proj_spec/submission.py

suryaavala/17s1-cs9318

fdfa84a5f3330d189af213d670479c65d6c60a28

[ "MIT" ]

2

2018-04-04T10:36:55.000Z

2019-08-23T05:53:55.000Z

## import modules here ################# training ################# def train(data, classifier_file):# do not change the heading of the function pass # **replace** this line with your code ################# testing ################# def test(data, classifier_file):# do not change the heading of the function pass # **replace** this line with your code

30.833333

76

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45

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370

12

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0.698361

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0

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1

0

null

0

1

0

1

0

8

69d9090869ace69da074296ad7953eddfe80e6ff

13,957

py

Python

tests.py

Atterratio/flake8-prevent-fails

068502a1542d03e60d9d9a9853dfdc1f0883f9cb

[ "MIT" ]

null

tests.py

Atterratio/flake8-prevent-fails

068502a1542d03e60d9d9a9853dfdc1f0883f9cb

[ "MIT" ]

null

tests.py

Atterratio/flake8-prevent-fails

068502a1542d03e60d9d9a9853dfdc1f0883f9cb

[ "MIT" ]

null

import ast import unittest from flake8_prevent_fails import FailsChecker, MESSAGES class TestIndexes(unittest.TestCase): def test_dirty_list(self): data = ast.parse('test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('try:\n' ' test_var = test_list[0]\n' 'except AttributeError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('try:\n' ' test_var = test_list[0]\n' 'except (AttributeError, Error):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except AttributeError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except (AttributeError, Error):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) def test_cleaned_except_list_with_num(self): data = ast.parse('try:\n' ' test_var = test_list[0]\n' 'except:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list[0]\n' 'except IndexError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list[0]\n' 'except (AttributeError, IndexError):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) def test_cleaned_except_list_with_name(self): data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except IndexError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except (AttributeError, IndexError):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) def test_cleaned_if_lt_list_with_num(self): data = ast.parse('if 0 < len(test_list):\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('if 0 > len(over_list):\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('if 0 < len(test_list):\n' ' test_var = test_list[1]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('if 0 < len(over_list):\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) def test_cleaned_if_gt_list_with_num(self): data = ast.parse('if len(test_list) > 0:\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('if len(test_list) < 0:\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('if len(test_list) > 0:\n' ' test_var = test_list[1]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) data = ast.parse('if len(over_list) > 0:\n' ' test_var = test_list[0]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF101'), result) def test_dirty_dict(self): data = ast.parse('test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) data = ast.parse('try:\n' ' test_var = test_list["test"]\n' 'except AttributeError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) data = ast.parse('try:\n' ' test_var = test_list["test"]\n' 'except (AttributeError, Error):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) def test_cleaned_except_dict_with_str(self): data = ast.parse('try:\n' ' test_var = test_list["test"]\n' 'except:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list["test"]\n' 'except KeyError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list["test"]\n' 'except (AttributeError, KeyError):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) def test_cleaned_except_dict_with_name(self): data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except KeyError:\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('try:\n' ' test_var = test_list[var]\n' 'except (AttributeError, KeyError):\n' ' pass') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) def test_cleaned_if_dict_with_str(self): data = ast.parse('if test_list.get("test"):\n' ' test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('if test_list.get("tests"):\n' ' test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) data = ast.parse('if tests_list.get("test"):\n' ' test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) data = ast.parse('if test_list.let("test"):\n' ' test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) def test_cleaned_if_dict_with_name(self): data = ast.parse('if test_list.get(var):\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('if test_list.get(vars):\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('if test_list.get("tests"):\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('if tests_list.get(var):\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('if test_list.let(var):\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) def test_cleaned_for_dict(self): data = ast.parse('for var in test_list:\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 0) data = ast.parse('for vars in test_list:\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('for var in tests_list:\n' ' test_var = test_list[var]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF103'), result) data = ast.parse('for var in tests_list:\n' ' test_var = test_list["test"]') checker = FailsChecker(data, None, None) results = list(e for e in checker.run()) self.assertEqual(len(results), 1) for result in results: self.assertIn(MESSAGES.get('PF102'), result) if __name__ == '__main__': unittest.main()

40.929619

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0.523393

1,631

13,957

4.380748

0.036174

0.060462

0.06718

0.083975

0.976487

0.973548

0.968509

0.961652

0.943457

0

0.01499

0.354732

13,957

340

66

41.05

0.77837

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0.892256

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0.179265

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0.037037

false

0.057239

0.010101

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0

1

0

1

0

null

0

1

0

8

69db47a83f332f756d460a7aca32bb1e0ce6bc4d

2,228

py

Python

tests/test_sim_league_browser.py

League-Advisor/league-advisor

b77895833075ff13b075875eff421ec9fef9770e

[ "MIT" ]

null

tests/test_sim_league_browser.py

League-Advisor/league-advisor

b77895833075ff13b075875eff421ec9fef9770e

[ "MIT" ]

10

2021-11-04T16:45:45.000Z

2021-11-12T11:11:15.000Z

tests/test_sim_league_browser.py

League-Advisor/league-advisor

b77895833075ff13b075875eff421ec9fef9770e

[ "MIT" ]

null

"""This module will tests LeagueBrowser class methodes""" from league_advisor.league_browser import LeagueBrowser from tests.flo import diff def test_import_class(): assert LeagueBrowser() def test_leaguebrowser_receive_user_input_method_quit(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_user_input,path="tests/simulations/leaguebrowser_receive_user_input_method_quit.sim.txt") assert not diffs, diffs def test_leaguebrowser_receive_user_input_method_item_class(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_user_input, path="tests/simulations/leaguebrowser_receive_user_input_method_item.sim.txt") assert not diffs, diffs def test_leaguebrowser_receive_item_method_classes(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_user_input, path="tests/simulations/leaguebrowser_receive_item_method_classes.sim.txt") assert not diffs, diffs # def test_leaguebrowser_receive_item_method_names(): # leaguebrowser = LeagueBrowser() # diffs = diff(leaguebrowser.receive_user_input, path="tests/simulations/leaguebrowser_receive_item_method_names.sim.txt") # assert not diffs, diffs def test_leaguebrowser_receive_item_method_names_backmenu(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_user_input, path="tests/simulations/leaguebrowser_receive_item_method_nanes_backmenu.sim.txt") assert not diffs, diffs def test_leaguebrowser_receive_item_method_classes_backmenu(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_user_input, path="tests/simulations/leaguebrowser_receive_item_method_classes_backmenu.sim.txt") assert not diffs, diffs def test_leaguebrowser_receive_champions_start(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_champions,path="tests/simulations/browser_recieve_champions_start.sim.txt") assert not diffs, diffs def test_leaguebrowser_receive_champions_info(): leaguebrowser = LeagueBrowser() diffs = diff(leaguebrowser.receive_champions,path="tests/simulations/browser_recieve_champions_info.sim.txt") assert not diffs, diffs

40.509091

129

0.798474

260

2,228

6.476923

0.142308

0.261283

0.142518

0.172209

0.892518

0.889549

0.865202

0.831354

0

0.125673

2,228

55

130

40.509091

0.864476

0.131508

0

0.470588

0

0.243902

0

0.235294

1

0.235294

false

0

0.088235

0

0.323529

0

null

1

0

1

0

null

0

1

0

9

384eba14f4998ea63dd57ca896eef627c273e04d

724

py

Python

problems/dfs/Solution695.py

akalu/cs-problems-python

9b1bd8e3932be62135a38a77f955ded9a766b654

[ "MIT" ]

null

problems/dfs/Solution695.py

akalu/cs-problems-python

9b1bd8e3932be62135a38a77f955ded9a766b654

[ "MIT" ]

null

problems/dfs/Solution695.py

akalu/cs-problems-python

9b1bd8e3932be62135a38a77f955ded9a766b654

[ "MIT" ]

null

""" Given a non-empty 2D array grid of 0's and 1's, an island is a group of 1's (representing land) connected 4-directionally (horizontal or vertical.) You may assume all four edges of the grid are surrounded by water. Find the maximum area of an island in the given 2D array. (If there is no island, the maximum area is 0.) Example 1: [ [0,0,1,0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,0,0,1,1,1,0,0,0], [0,1,1,0,1,0,0,0,0,0,0,0,0], [0,1,0,0,1,1,0,0,1,0,1,0,0], [0,1,0,0,1,1,0,0,1,1,1,0,0], [0,0,0,0,0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,1,1,1,0,0,0], [0,0,0,0,0,0,0,1,1,0,0,0,0] ] output: 6 IDEA: use dfs to traverse all cells """ class Solution695: pass

26.814815

81

0.569061

182

724

2.263736

0.296703

0.305825

0.356796

0.38835

0.254854

0.252427

0.240291

0.225728

0

0.207143

0.226519

724

26

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27.846154

0.528571

0.856354

0

1

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true

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1

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1

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null

0

1

0

7

385c3048d9a4773ed0d837c4eebfacdbb3aad296

5,897

py

Python

lib/django/tests/regressiontests/httpwrappers/tests.py

vin/gerbilcount

fdffe648c3e9ad2667a6edfe0e19d4446c522395

[ "Apache-2.0" ]

2

2016-05-08T08:57:01.000Z

2020-02-08T07:39:48.000Z

lib/django/tests/regressiontests/httpwrappers/tests.py

Arachnid/google_appengine

2e950619f5027f414131fafc3cc253af4875a0fe

[ "Apache-2.0" ]

null

lib/django/tests/regressiontests/httpwrappers/tests.py

Arachnid/google_appengine

2e950619f5027f414131fafc3cc253af4875a0fe

[ "Apache-2.0" ]

null

""" ################### # Empty QueryDict # ################### >>> q = QueryDict('') >>> q['foo'] Traceback (most recent call last): ... MultiValueDictKeyError: "Key 'foo' not found in <MultiValueDict: {}>" >>> q['something'] = 'bar' Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.get('foo', 'default') 'default' >>> q.getlist('foo') [] >>> q.setlist('foo', ['bar', 'baz']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.appendlist('foo', ['bar']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.has_key('foo') False >>> q.items() [] >>> q.lists() [] >>> q.keys() [] >>> q.values() [] >>> len(q) 0 >>> q.update({'foo': 'bar'}) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.pop('foo') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.popitem() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.clear() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.setdefault('foo', 'bar') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.urlencode() '' ################################### # Mutable copy of empty QueryDict # ################################### >>> q = q.copy() >>> q['foo'] Traceback (most recent call last): ... MultiValueDictKeyError: "Key 'foo' not found in <MultiValueDict: {}>" >>> q['name'] = 'john' >>> q['name'] 'john' >>> q.get('foo', 'default') 'default' >>> q.get('name', 'default') 'john' >>> q.getlist('name') ['john'] >>> q.getlist('foo') [] >>> q.setlist('foo', ['bar', 'baz']) >>> q.get('foo', 'default') 'baz' >>> q.getlist('foo') ['bar', 'baz'] >>> q.appendlist('foo', 'another') >>> q.getlist('foo') ['bar', 'baz', 'another'] >>> q['foo'] 'another' >>> q.has_key('foo') True >>> q.items() [('foo', 'another'), ('name', 'john')] >>> q.lists() [('foo', ['bar', 'baz', 'another']), ('name', ['john'])] >>> q.keys() ['foo', 'name'] >>> q.values() ['another', 'john'] >>> len(q) 2 >>> q.update({'foo': 'hello'}) # Displays last value >>> q['foo'] 'hello' >>> q.get('foo', 'not available') 'hello' >>> q.getlist('foo') ['bar', 'baz', 'another', 'hello'] >>> q.pop('foo') ['bar', 'baz', 'another', 'hello'] >>> q.get('foo', 'not there') 'not there' >>> q.setdefault('foo', 'bar') 'bar' >>> q['foo'] 'bar' >>> q.getlist('foo') ['bar'] >>> q.urlencode() 'foo=bar&name=john' >>> q.clear() >>> len(q) 0 ##################################### # QueryDict with one key/value pair # ##################################### >>> q = QueryDict('foo=bar') >>> q['foo'] 'bar' >>> q['bar'] Traceback (most recent call last): ... MultiValueDictKeyError: "Key 'bar' not found in <MultiValueDict: {'foo': ['bar']}>" >>> q['something'] = 'bar' Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.get('foo', 'default') 'bar' >>> q.get('bar', 'default') 'default' >>> q.getlist('foo') ['bar'] >>> q.getlist('bar') [] >>> q.setlist('foo', ['bar', 'baz']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.appendlist('foo', ['bar']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.has_key('foo') True >>> q.has_key('bar') False >>> q.items() [('foo', 'bar')] >>> q.lists() [('foo', ['bar'])] >>> q.keys() ['foo'] >>> q.values() ['bar'] >>> len(q) 1 >>> q.update({'foo': 'bar'}) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.pop('foo') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.popitem() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.clear() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.setdefault('foo', 'bar') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.urlencode() 'foo=bar' ##################################################### # QueryDict with two key/value pairs with same keys # ##################################################### >>> q = QueryDict('vote=yes&vote=no') >>> q['vote'] 'no' >>> q['something'] = 'bar' Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.get('vote', 'default') 'no' >>> q.get('foo', 'default') 'default' >>> q.getlist('vote') ['yes', 'no'] >>> q.getlist('foo') [] >>> q.setlist('foo', ['bar', 'baz']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.appendlist('foo', ['bar']) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.has_key('vote') True >>> q.has_key('foo') False >>> q.items() [('vote', 'no')] >>> q.lists() [('vote', ['yes', 'no'])] >>> q.keys() ['vote'] >>> q.values() ['no'] >>> len(q) 1 >>> q.update({'foo': 'bar'}) Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.pop('foo') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.popitem() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.clear() Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.setdefault('foo', 'bar') Traceback (most recent call last): ... AttributeError: This QueryDict instance is immutable >>> q.urlencode() 'vote=yes&vote=no' """ from django.http import QueryDict if __name__ == "__main__": import doctest doctest.testmod()

16.426184

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0.102672

0.050159

0.147881

0.179014

0.780917

0.75036

0.71548

0.680888

0.672816

0

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5,897

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0.682293

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0

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true

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0

null

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0

1

0

null

0

1

0

1

0

7

386e50c4d69a8d0a05ce983dd399235c595fc130

67,060

py

Python

BenchmarkEvalPaper/RunTests.py

bmd2007/benchmark_eval

aa42bb3369e79db4cb63e1963afcc8af6d8f5696

[ "MIT" ]

1

2022-01-11T08:03:32.000Z

BenchmarkEvalPaper/RunTests.py

bmd2007/benchmark_eval

aa42bb3369e79db4cb63e1963afcc8af6d8f5696

[ "MIT" ]

null

BenchmarkEvalPaper/RunTests.py

bmd2007/benchmark_eval

aa42bb3369e79db4cb63e1963afcc8af6d8f5696

[ "MIT" ]

null

import os import sys currentdir = os.path.dirname(os.path.realpath(__file__)) parentdir = os.path.dirname(currentdir) sys.path.append(parentdir) currentDir = currentdir + '/' import PPIPUtils from Methods.Tian2019SVM.Tian2019SVM import Tian2019SVM from Methods.Guo2008.GuoSVM import GuoSVM from Methods.Li2020DeepEnsemble.LiDeepNetwork import LiDeepNetworkModule from Methods.Sun2017AutoEncoderNetwork.SunStackAutoEncoder import SunStackAutoEncoderAC from Methods.Sun2017AutoEncoderNetwork.SunStackAutoEncoder import SunStackAutoEncoderCT from Methods.Chen2019RNNNetwork.ChenNetwork import ChenNetworkModule from Methods.RichouxDeepNetwork.RichouxDeepNetwork import RichouxNetworkModuleLSTM from Methods.RichouxDeepNetwork.RichouxDeepNetwork import RichouxNetworkModuleFULL from Methods.Li2018DeepNetwork.Li2018DeepNetwork import Li2018DeepNetworkModule from Methods.Czibula2021AutoPPI.Czibula2021AutoPPI import Czibula2021AutoPPIModule from Methods.Czibula2021AutoPPI.Czibula2021AutoPPI import Czibula2021AutoPPIModuleSS from Methods.Czibula2021AutoPPI.Czibula2021AutoPPI import Czibula2021AutoPPIModuleJJ from Methods.Czibula2021AutoPPI.Czibula2021AutoPPI import Czibula2021AutoPPIModuleSJ from Methods.Zhang2019DeepEnsemble.Zhang2019DeepEnsemble import ZhangDeepModule from Methods.Yao2019DeepNetwork.Yao2019DeepNetwork import Yao2019NetworkModule from Methods.Zhou2011SVM.ZhouSVM import ZhouSVM from Methods.GonzalezLopez2019DeepNetwork.GonzalezLopez2019DeepNetwork import GonzalezLopez2019Module from Methods.Zhao2012SVM.Zhao2012SVM import Zhao2012SVM from Methods.Hashemifar2018DeepNetwork.Hashemifar2018DeepNetwork import Hashemifar2018DeepNetworkModule from Methods.Goktepe2018SVM.Goktepe2018SVM import Goktepe2018SVM from Methods.Pan2010.Pan2010 import Pan2010ModuleLDACTRANDFOREST, Pan2010ModuleLDACTROTFOREST, Pan2010ModuleLDACTSVM, Pan2010ModuleACRANDFOREST, Pan2010ModuleACROTFOREST, Pan2010ModuleACSVM,Pan2010ModulePSAACRANDFOREST,Pan2010ModulePSAACROTFOREST,Pan2010ModulePSAACSVM from Methods.Du2017DeepNetwork.Du2017DeepNetwork import Du2017DeepNetworkModuleComb, Du2017DeepNetworkModuleSep from Methods.Jia2015.Jia2015RF import Jia2015RFModule from Methods.You2015RF.You2015RF import You2015RFModule from Methods.Ding2016RF.Ding2016RF import Ding2016RFModule from Methods.Wang2017RotF.Wang2017RotF import Wang2017RotFModule from Methods.Chen2019LGBM.Chen2019LGBM import Chen2019LGBMModule from Methods.Jia2019RF.Jia2019RF import Jia2019RFModule from Methods.RandomNetwork.RandomNetwork import RandomNetworkModule from Methods.RandomRF.RandomRF import RandomRFModule from Methods.BiasModules.BasicBiasModuleGOSimSeqSim import BasicBiasModuleGOSimSeqSim from Methods.BiasModules.BasicBiasModuleSeqSim import BasicBiasModuleSeqSim from Methods.BiasModules.BasicBiasModule import BasicBiasModule from Methods.MaetschkeVar2011.MaetschkeVar2011 import MaetschkeVar2011Module from Methods.Chen2005RF.Chen2005RF import Chen2005RFModule from Methods.GouVar2006GOLR.GouVar2006GOLR import GouVar2006GOLRModule from Methods.ZhangDomainVar2016.ZhangDomainVar2016 import ZhangDomainVar2016AllModule, ZhangDomainVar2016NonTestModule, ZhangDomainVar2016HeldOutModule from Methods.Zhang2016GO.Zhang2016GO import Zhang2016GOModule from Methods.SimpleEnsemble.SimpleEnsemble import SimpleEnsembleAllModule, SimpleEnsembleNonTestModule, SimpleEnsembleHeldOutModule import time import numpy as np from ProjectDataLoader import * from PreProcessDatasets import createFeatures from RunTrainTest import * #algorithms guo2008Test = True li2020Test = True sun2017Test = True tian2019Test = True Chen2019RNN = True richouxANN = True li2018Deep = True Czibula2021AutoPPI = True ZhangDeep2019 = True YaoDeep2019 = True zhou2011SVM = True GonzalezLopez2019 = True Zhao2012SVMTest = True Hashemifar2018Test = True Goktepe2018SVMTest = True pan2010TestForests = True pan2010TestSVMs = True du2017DeepNetworkTest = True jia2015RandomForestTest = True you2015RandomForestTest = True ding2016RandomForestTest = True wang2017RotFTest = True chen2019LGBMTest = True jia2019RandomForestTest = True randomNetworkTest = True randomRFTest = True biasTests = True MaetschkeVarTest = True Chen2005RF = True GouVar2006GOLRTest = True ZhangDomainVar2016Test = True Zhang2016GOTest = True SimpleEnsembleTest = True #data Types orgData = True HumanRandom50 = True HumanRandom20 = True HumanHeldOut50 = True HumanHeldOut20 = True baseResultsFolderName = 'results/' #runs based on global variables #can be toggled before calling function def RunAll(): if guo2008Test: #create results folders if they do not exist PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Guo2008Results/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(GuoSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(GuoSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTest(GuoSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(GuoSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTest(GuoSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if li2020Test: #create results folders if they do not exist PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName= baseResultsFolderName+'Li2020Results/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True,'deviceType':'cuda'} if orgData: trainSets, testSets, saves, pfs, folderName = loadLiADData(resultsFolderName) runTest(LiDeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(LiDeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTest(LiDeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(LiDeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTest(LiDeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if tian2019Test: #create results folders if they do not exist PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'tian2019Results/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTest(Tian2019SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if sun2017Test: PPIPUtils.makeDir(baseResultsFolderName) PPIPUtils.makeDir(baseResultsFolderName+'Sun2017Results/') for pair in [(SunStackAutoEncoderAC,'SunResults2017AC'),(SunStackAutoEncoderCT,'SunResults2017CT')]: resultsFolderName = baseResultsFolderName+'Sun2017Results/'+pair[1]+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True,'deviceType':'cuda'} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(pair[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(pair[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(pair[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(pair[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(pair[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs,startIdx=13) if Chen2019RNN: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Chen2019Results/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(ChenNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) hyp = {'fullGPU':True,'schedPatience':1} if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(ChenNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTest(ChenNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(ChenNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTest(ChenNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if richouxANN: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Richoux2019Results/' PPIPUtils.makeDir(resultsFolderName) resultsFolderName1= resultsFolderName+'LSTM/' resultsFolderName2= resultsFolderName+'FULL/' PPIPUtils.makeDir(resultsFolderName1) PPIPUtils.makeDir(resultsFolderName2) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadRichouxHumanDataStrict(resultsFolderName1) runTest(RichouxNetworkModuleLSTM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadRichouxHumanDataStrict(resultsFolderName2) runTest(RichouxNetworkModuleFULL, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName1,augment=True) runTest(RichouxNetworkModuleLSTM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName2,augment=True) runTest(RichouxNetworkModuleFULL, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName1,augment=True) runTestLst(RichouxNetworkModuleLSTM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName2,augment=True) runTestLst(RichouxNetworkModuleFULL, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName1,augment=True) runTest(RichouxNetworkModuleLSTM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName2,augment=True) runTest(RichouxNetworkModuleFULL, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName1,augment=True) runTestLst(RichouxNetworkModuleLSTM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName2,augment=True) runTestLst(RichouxNetworkModuleFULL, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if li2018Deep: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Li2018DeepResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(Li2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Li2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Li2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Li2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Li2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if Czibula2021AutoPPI: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Czibula2021AutoPPI/' PPIPUtils.makeDir(resultsFolderName) resultsFolderNames = [resultsFolderName+'Czibula2021AutoPPISS/',resultsFolderName+'Czibula2021AutoPPISJ/',resultsFolderName+'Czibula2021AutoPPIJJ/'] modelTypes = [Czibula2021AutoPPIModuleSS,Czibula2021AutoPPIModuleSJ,Czibula2021AutoPPIModuleJJ] for i in range(0,3): PPIPUtils.makeDir(resultsFolderNames[i]) hyp = {'fullGPU':True} if orgData: for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderNames[i]) runTest(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadGuoMultiSpeciesChen(resultsFolderNames[i]) runTest(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderNames[i]) runTest(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderNames[i]) runTestLst(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderNames[i]) runTest(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: for i in range(0,3): trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderNames[i]) runTestLst(modelTypes[i], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if ZhangDeep2019: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'ZhangDeep2019/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(ZhangDeepModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(ZhangDeepModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(ZhangDeepModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(ZhangDeepModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(ZhangDeepModule, None,trainSets,testSets,folderName,hyp,saveModels=convertToFolder(saves),predictionsFLst = pfs) if YaoDeep2019: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'YaoDeep2019/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Yao2019NetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if zhou2011SVM: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'zhou2011SVMResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(ZhouSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(ZhouSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(ZhouSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(ZhouSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(ZhouSVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if GonzalezLopez2019: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'GonzalezLopez2019/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(GonzalezLopez2019Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if Zhao2012SVMTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'zhao2012SVMResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiuFruitFly(resultsFolderName) runTest(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Zhao2012SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if Hashemifar2018Test: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Hashemifar2018DeepResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Hashemifar2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Hashemifar2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Hashemifar2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Hashemifar2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Hashemifar2018DeepNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if Goktepe2018SVMTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Goktepe2018SVMResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'ThunderSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=5) runTest(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName,kfolds=5) runTest(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Goktepe2018SVM, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if pan2010TestForests: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Pan2010/' PPIPUtils.makeDir(resultsFolderName) pan2010TestForestModules = [Pan2010ModuleLDACTRANDFOREST, Pan2010ModuleLDACTROTFOREST, Pan2010ModuleACRANDFOREST, Pan2010ModuleACROTFOREST, Pan2010ModulePSAACRANDFOREST,Pan2010ModulePSAACROTFOREST] pan2010ResultsFolderNames = [resultsFolderName+'LDARand/',resultsFolderName+'LDARot/',resultsFolderName+'ACRand/',resultsFolderName+'ACRot/',resultsFolderName+'PSAACRand/',resultsFolderName+'PSAACRot'] for i in range(0,len(pan2010TestForestModules)): modName = pan2010TestForestModules[i] resultsFolderName = pan2010ResultsFolderNames[i] PPIPUtils.makeDir(resultsFolderName) hyp={} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) saves=None runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName,kfolds=5) saves=None runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName,kfolds=5) saves=None runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) hyp={} if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) saves=None runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) saves=None runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) saves=None runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) saves=None runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if pan2010TestSVMs: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Pan2010/' PPIPUtils.makeDir(resultsFolderName) PPIPUtils.makeDir('Results/') PPIPUtils.makeDir('Results/Pan2010/') pan2010TestSVMModules = [Pan2010ModuleLDACTSVM, Pan2010ModuleACSVM, Pan2010ModulePSAACSVM] pan2010ResultsFolderNames = [baseResultsFolderName+'LDASVM/',baseResultsFolderName+'ACSVM/',baseResultsFolderName+'PSAACSVM/'] for i in range(0,len(pan2010TestSVMModules)): modName = pan2010TestSVMModules[i] resultsFolderName = pan2010ResultsFolderNames[i] PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName,kfolds=5) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName,kfolds=5) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if du2017DeepNetworkTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Du2017/' PPIPUtils.makeDir(resultsFolderName) modLst = [Du2017DeepNetworkModuleSep, Du2017DeepNetworkModuleComb] resultFolders = [resultsFolderName+'Sep/',resultsFolderName+'Comb/'] for i in range(0,len(modLst)): modName = modLst[i] resultsFolderName = resultFolders[i] PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(modName, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs,startIdx=2) if jia2015RandomForestTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Jia2015RFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadJiaYeast(resultsFolderName) runTest(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=10) runTest(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Jia2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if you2015RandomForestTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'You2015RFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=10) runTest(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(You2015RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if ding2016RandomForestTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Ding2016RFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=5) runTest(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Ding2016RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if wang2017RotFTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Wang2017RotFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=5) runTest(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Wang2017RotFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if chen2019LGBMTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Chen2019LGBMTest/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=5) runTest(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Chen2019LGBMModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if jia2019RandomForestTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Jia2019RFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadJiaYeast(resultsFolderName,trainDataPerClass='Max',full=False) runTest(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName,kfolds=10) runTest(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Jia2019RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if randomNetworkTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'RandomNetworkResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiADData(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadRichouxHumanDataStrict(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoMultiSpeciesChen(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiuFruitFly(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadJiaYeast(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(RandomNetworkModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if randomRFTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'RandomRFResults/' PPIPUtils.makeDir(resultsFolderName) hyp = {'fullGPU':True} if orgData: trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiADData(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadRichouxHumanDataStrict(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoMultiSpeciesChen(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiuFruitFly(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadJiaYeast(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(RandomRFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if biasTests: for mod in [(BasicBiasModule,'BasicBiasModule'),(BasicBiasModuleSeqSim,'BasicBiasModuleSeqSim'),(BasicBiasModuleGOSimSeqSim,'BasicBiasModuleGOSimSeqSim')]: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+mod[1]+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if orgData: trainSets, testSets, saves, pfs, folderName = loadMartinHPylori(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataTian(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanLarge(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanHumanSmall(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadPanMartinHuman(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoYeastDataChen(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiADData(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadRichouxHumanDataStrict(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadGuoMultiSpeciesChen(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadLiuFruitFly(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadDuYeast(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) trainSets, testSets, saves, pfs, folderName = loadJiaYeast(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) testSets = [testSets[0]] pfs = [pfs[0]] runTestLst(mod[0], None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if MaetschkeVarTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'MaetschkeVarResults'+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(MaetschkeVar2011Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(MaetschkeVar2011Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(MaetschkeVar2011Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(MaetschkeVar2011Module, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if Chen2005RF: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Chen2005RFResults'+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if HumanRandom50: trainSets, testSets, saves, pfs, folderName = loadHumanRandom50(resultsFolderName) runTest(Chen2005RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderName = loadHumanRandom20(resultsFolderName) runTestLst(Chen2005RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut50(resultsFolderName) runTest(Chen2005RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderName = loadHumanHeldOut20(resultsFolderName) runTestLst(Chen2005RFModule, None,trainSets,testSets,folderName,hyp,saveModels=saves,predictionsFLst = pfs) if GouVar2006GOLRTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Guo2007SimResults'+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {} if HumanRandom50: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(GouVar2006GOLRModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom20(resultsFolderName,dirLst=True) loads = [None]*len(saves) #since Semantic Similarities do not change based on test set, can skip doing training 2nd time loads[len(saves)//2:] = saves[:len(saves)//2] runTestPairwiseFoldersLst(GouVar2006GOLRModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs,loads=loads) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(GouVar2006GOLRModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut20(resultsFolderName,dirLst=True) loads = [None]*len(saves) #since Semantic Similarities do not change based on test set, can skip doing training 2nd time loads[len(saves)//2:] = saves[:len(saves)//2] runTestPairwiseFoldersLst(GouVar2006GOLRModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs,loads=loads) if Zhang2016GOTest: PPIPUtils.makeDir(baseResultsFolderName) resultsFolderName = baseResultsFolderName+'Zhang2016GO'+'/' PPIPUtils.makeDir(resultsFolderName) hyp = {'Model':'THUNDERSVM'} if HumanRandom50: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(Zhang2016GOModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom20(resultsFolderName,dirLst=True) loads = [None]*len(saves) #since Semantic Similarities do not change based on test set, can skip doing training 2nd time loads[len(saves)//2:] = saves[:len(saves)//2] runTestPairwiseFoldersLst(Zhang2016GOModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs,loads=loads,startIdx=8) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(Zhang2016GOModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut20(resultsFolderName,dirLst=True) loads = [None]*len(saves) #since Semantic Similarities do not change based on test set, can skip doing training 2nd time loads[len(saves)//2:] = saves[:len(saves)//2] runTestPairwiseFoldersLst(Zhang2016GOModule, None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs,loads=loads) if ZhangDomainVar2016Test: PPIPUtils.makeDir(baseResultsFolderName) midFolder = baseResultsFolderName + 'ZhangDomainVar2016Results/' PPIPUtils.makeDir(midFolder) idx = 0 for pair in [(ZhangDomainVar2016AllModule,midFolder+'All/'), (ZhangDomainVar2016NonTestModule,midFolder+'NonTest/'), (ZhangDomainVar2016HeldOutModule,midFolder+'HeldOut/')]: resultsFolderName = pair[1] PPIPUtils.makeDir(resultsFolderName) hyp = {} if HumanRandom50 and idx !=2: #idx=2 is held out data, which only works on the held out protein datasets trainSets, testSets, saves, pfs, folderNames = loadHumanRandom50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20 and idx !=2: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom20(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut20(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) idx += 1 if SimpleEnsembleTest: PPIPUtils.makeDir(baseResultsFolderName) midFolder = baseResultsFolderName + 'SimpleEnsembleResults/' PPIPUtils.makeDir(midFolder) idx = 0 for pair in [(SimpleEnsembleAllModule,midFolder+'All/'), (SimpleEnsembleNonTestModule,midFolder+'NonTest/'), (SimpleEnsembleHeldOutModule,midFolder+'HeldOut/')]: resultsFolderName = pair[1] PPIPUtils.makeDir(resultsFolderName) hyp = {} if HumanRandom50 and idx !=2: #idx=2 is held out data, which only works on the held out protein datasets trainSets, testSets, saves, pfs, folderNames = loadHumanRandom50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanRandom20 and idx !=2: trainSets, testSets, saves, pfs, folderNames = loadHumanRandom20(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut50: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut50(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) if HumanHeldOut20: trainSets, testSets, saves, pfs, folderNames = loadHumanHeldOut20(resultsFolderName,dirLst=True) runTestPairwiseFoldersLst(pair[0], None,None,None,folderNames,hyp,saveModels=saves,predictionsFLst = pfs) idx += 1 def genSequenceFeatures(): createFeatures(currentDir+'PPI_Datasets/Guo_Data_Yeast_Tian/',set(['EGBW11','AC30','MMI','LD10_CTD','PSAAC15','Moran','Geary','AC11','PSAAC9','PSSMAAC','PSSMDPC','SkipGramAA25H20','LD10_CTD','NumericEncoding20Skip3','MCD4CTD','PSSMLST','PSSMAAC','PSSMDPC','JIA_DWT','MLD4CTD','NMBROTO_6_30','AAC20','PSSMDCT','NMBROTO_9','MORAN_9','GEARY_9','PSEAAC_3','conjointTriad','CHAOS','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Guo_Data_Yeast_Chen/',set(['SkipGramAA7','OneHotEncoding7','SkipGramAA25H20','NumericEncoding20Skip3','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Guo_MultiSpecies_Chen/',set(['AC14_30','conjointTriad','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Du_Yeast/',set(['MCD5CTD','LD10_CTD','AC30','AAC20','AAC400','DUMULTIGROUPCTD','Grantham_Sequence_Order_30','Schneider_Sequence_Order_30','Grantham_Quasi_30','Schneider_Quasi_30','APSAAC30_2','PSEAAC_3','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Jia_Data_Yeast/',set(['JIA_DWT','CHAOS','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Martin_H_pylori/',set(['EGBW11','AC11','PSAAC9','NumericEncoding20Skip3','MCD4CTD','Grantham_Sequence_Order_30','Schneider_Sequence_Order_30','Grantham_Quasi_30','Schneider_Quasi_30','Geary_Zhao_30','NMBroto_Zhao_30','Moran_Zhao_30','PSEAAC_Zhao_30','PSSMDPC','SkipWeightedConjointTriad','PSAAC20','AAC20','AAC400','DUMULTIGROUPCTD','APSAAC30_2','JIA_DWT','MLD4CTD','NMBROTO_6_30','MMI','PSSMDCT','NMBROTO_9','MORAN_9','GEARY_9','PSEAAC_3','LD10_CTD','conjointTriad','CHAOS','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Liu_Fruit_Fly/',set(['Grantham_Sequence_Order_30','Schneider_Sequence_Order_30','Grantham_Quasi_30','Schneider_Quasi_30','Geary_Zhao_30','NMBroto_Zhao_30','Moran_Zhao_30','PSEAAC_Zhao_30','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Li_AD/',set(['AC30','LD10_CTD','PSAAC15','conjointTriad','PSEAAC_3','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Pan_Human_Data/Pan_Large/',set(['AC30','NumericEncoding22','AC14_30','conjointTriad','PSAAC20','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Pan_Human_Data/Pan_Small/',set(['SkipGramAA25H20','NumericEncoding20Skip3','PSSMLST','PSSMDPC','SkipWeightedConjointTriad','PSAAC20','conjointTriad','AC30','AAC20','AAC400','DUMULTIGROUPCTD','Grantham_Sequence_Order_30','Schneider_Sequence_Order_30','Grantham_Quasi_30','Schneider_Quasi_30','APSAAC30_2','NMBROTO_6_30','MMI','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Pan_Human_Data/Martin_Human/',set(['PSSMDPC','SkipWeightedConjointTriad','PSAAC20','conjointTriad','AC30','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Richoux_Human_Data/',set(['OneHotEncoding24','Random500','AllvsAllSim'])) createFeatures(currentDir+'PPI_Datasets/Human2021/',set(['EGBW11','AC30','LD10_CTD','PSAAC15','conjointTriad','MMI','Moran','Geary','PSSMAAC','PSSMDPC','AC11','PSAAC9','SkipGramAA7','OneHotEncoding7','OneHotEncoding24','NumericEncoding22','AC14_30','MCD5CTD','SkipGramAA25H20','NumericEncoding20Skip3','Geary_Zhao_30','NMBroto_Zhao_30','Moran_Zhao_30','PSEAAC_Zhao_30','Grantham_Quasi_30','Schneider_Quasi_30','MCD4CTD','Grantham_Sequence_Order_30','Schneider_Sequence_Order_30','PSSMLST','SkipWeightedConjointTriad','PSAAC20','AAC20','AAC400','DUMULTIGROUPCTD','APSAAC30_2','JIA_DWT','MLD4CTD','NMBROTO_6_30','PSSMDCT','NMBROTO_9','MORAN_9','GEARY_9','PSEAAC_3','CHAOS','Random500','AllvsAllSim'])) if __name__ == '__main__': genSequenceFeatures() RunAll()

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38af88e07eac26ceb429d8a929803fa99d93e6ad

14,710

py

Python

models.py

KNU-BrainAI/AD

ce2c778039d47a01baa1adf3bc00d9d448e0b3bd

[ "MIT" ]

null

models.py

KNU-BrainAI/AD

ce2c778039d47a01baa1adf3bc00d9d448e0b3bd

[ "MIT" ]

null

models.py

KNU-BrainAI/AD

ce2c778039d47a01baa1adf3bc00d9d448e0b3bd

[ "MIT" ]

null

#!/usr/bin/env python3 # author: jinhee import torch import torch.nn as nn import torch.nn.functional as F import numpy as np torch.set_default_tensor_type(torch.DoubleTensor) """ for within-subject : Deep_ConvNEt, EEGNet, EEG-TCNet, CCRNN for cross-subject : with 'sub_*' """ class ConstrainedConv2d(nn.Conv2d): def forward(self, input): return F.conv2d(input, self.weight.clamp(min=-1.0, max=1.0), self.bias, self.stride, self.padding, self.dilation, self.groups) class ConstrainedLinear(nn.Linear): def forward(self, input): return F.linear(input, self.weight.clamp(min=-0.25, max=0.25), self.bias) class Deep_ConvNet(nn.Module): def __init__(self, bias=False, num_class=2): super(Deep_ConvNet, self).__init__() self.conv_split = nn.Sequential( nn.Conv2d(1, 25, (1,10), 1), nn.Conv2d(25, 25, (32,1), 1, bias=False), ) self.post_conv = nn.Sequential( nn.BatchNorm2d(25), nn.ELU(), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool1 = nn.Sequential( nn.Conv2d(25, 50, (1,10), 1, bias=False), nn.BatchNorm2d(50), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool2 = nn.Sequential( nn.Conv2d(50, 100, (1,10), 1, bias=False), nn.BatchNorm2d(100), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool3 = nn.Sequential( nn.Conv2d(100, 200, (1,10), 1, bias=False), nn.BatchNorm2d(200), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_fc = nn.Sequential( ConstrainedLinear(200*1*1, num_class) ) def forward(self, x): out = self.conv_split(x) out = self.post_conv(out) out = self.conv_pool1(out) out = self.conv_pool2(out) out = self.conv_pool3(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class EEGNet(nn.Module): def __init__(self, num_class=2, bias=False, drop_ratio=.5, F1=8, D=2): super(EEGNet, self).__init__() F2 = F1*D self.conv_temporal = nn.Sequential( nn.ZeroPad2d((((250-1)//2)+1, ((250-1)//2), 0, 0)), nn.Conv2d(1, F1, (1,250), 1, bias=bias), nn.BatchNorm2d(F1), ) self.conv_spatial = nn.Sequential( ConstrainedConv2d(F1, F1*D, (32,1), 1, bias=bias, groups=F1), nn.BatchNorm2d(F1*D), nn.ELU(), nn.AvgPool2d((1,4)), nn.Dropout(drop_ratio) ) self.conv_separable = nn.Sequential( nn.ZeroPad2d((((125-1)//2)+1, ((125-1)//2), 0, 0)), nn.Conv2d(F1*D, F2, (1,125), 1, bias=bias, groups=F1*D), #depthwise nn.Conv2d(F2, F2, 1, 1), #pointwise = 1dconv nn.BatchNorm2d(F2), nn.ELU(), nn.AvgPool2d((1,8)), #(12) nn.Dropout(drop_ratio) ) self.conv_fc = nn.Sequential( ConstrainedLinear(F2*1*15, num_class) #nn.Linear(F2*1*15, num_class) #(16*1*10) ) def forward(self, x): out = self.conv_temporal(x) out = self.conv_spatial(out) out = self.conv_separable(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class EEG_TCNet(nn.Module): def __init__(self, bias=False, num_class=2, drop_ratio=.5, F1=8, D=2): super(EEG_TCNet, self).__init__() F2 = F1*D self.conv_temporal = nn.Sequential( nn.ZeroPad2d((((250-1)//2)+1, ((250-1)//2), 0, 0)), nn.Conv2d(1, F1, (1,250), 1, bias=bias), nn.BatchNorm2d(F1), ) self.conv_spatial = nn.Sequential( ConstrainedConv2d(F1, F1*D, (32,1), 1, bias=bias, groups=F1), nn.BatchNorm2d(F1*D), nn.ELU(), nn.AvgPool2d((1,4)), nn.Dropout(drop_ratio) ) self.conv_separable = nn.Sequential( nn.ZeroPad2d((((125-1)//2)+1, ((125-1)//2), 0, 0)), nn.Conv2d(F1*D, F2, (1,125), 1, bias=bias, groups=F1*D), #depthwise nn.Conv2d(F2, F2, 1, 1), #pointwise = 1dconv nn.BatchNorm2d(F2), nn.ELU(), nn.AvgPool2d((1,8)), #(12) nn.Dropout(drop_ratio) ) self.conv_fc = nn.Sequential( ConstrainedLinear(F2*1*15, num_class) #nn.Linear(F2*1*15, num_class) #(16*1*10) ) # TCN-block self.tcn_block1 = nn.Sequential( nn.ZeroPad2d((2,1,0,0)), nn.Conv1d(F2, F2, 4, 1), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), nn.ZeroPad2d((2,1,0,0)), nn.Conv1d(F2, F2, 4, 1), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), ) self.tcn_block2 = nn.Sequential( nn.ZeroPad2d((3,3,0,0)), nn.Conv1d(F2, F2, 4, 1, dilation=2), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), nn.ZeroPad2d((3,3,0,0)), nn.Conv1d(F2, F2, 4, 1, dilation=2), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), ) def forward(self, x): out = self.conv_temporal(x) out = self.conv_spatial(out) out = self.conv_separable(out) out = torch.squeeze(out, axis=2) tcn = self.tcn_block1(out) out = out + tcn out = nn.ELU()(out) tcn = self.tcn_block2(out) out = out + tcn out = nn.ELU()(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class CCRNN(nn.Module): def __init__(self, num_classes=2, drop_ratio=0.5, nSeg=30): super(CCRNN, self).__init__() self.nSeg = nSeg self.conv_module = nn.Sequential( nn.Conv2d(1, 32, 3, 1, padding=(3-1)//2), nn.ELU(), nn.Conv2d(32, 64, 3, 1, padding=(3-1)//2), nn.ELU(), nn.Conv2d(64, 128, 3, 1, padding=(3-1)//2), nn.ELU() ) self.conv_fc = nn.Sequential( nn.Linear(128*7*5, 1024), nn.ELU(), nn.Dropout(drop_ratio) ) self.rnn_module = nn.Sequential( nn.LSTM(1024, 64, 2, batch_first=True, dropout=drop_ratio) ) self.rnn_fc = nn.Sequential( nn.Linear(64, 1024), nn.ELU(), nn.Dropout(drop_ratio) ) self.readout = nn.Sequential( nn.Linear(1024, num_classes) ) def forward(self, x): out = self.conv_module(x) out = out.reshape(out.shape[0], np.prod(out.shape[1:])) out = self.conv_fc(out) out = out.reshape(-1, self.nSeg, out.shape[-1]) out, (hn, cn) = self.rnn_module(out) out = out[:, -1] out = self.rnn_fc(out) out = self.readout(out) return out class sub_Deep_ConvNet(nn.Module): def __init__(self, bias=False, drop_ratio=0.5, num_class=2): super(sub_Deep_ConvNet, self).__init__() self.conv_split = nn.Sequential( nn.Conv2d(1, 25, (1,10), 1), nn.Conv2d(25, 25, (32,1), 1, bias=False), ) self.post_conv = nn.Sequential( nn.BatchNorm2d(25), nn.ELU(), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool1 = nn.Sequential( nn.Conv2d(25, 50, (1,10), 1, bias=False), nn.BatchNorm2d(50), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool2 = nn.Sequential( nn.Conv2d(50, 100, (1,10), 1, bias=False), nn.BatchNorm2d(100), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool3 = nn.Sequential( nn.Conv2d(100, 200, (1,10), 1, bias=False), nn.BatchNorm2d(200), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_pool3 = nn.Sequential( nn.Conv2d(100, 200, (1,10), 1, bias=False), nn.BatchNorm2d(200), nn.MaxPool2d((1,3), 3), nn.Dropout(0.3) ) self.conv_fc = nn.Sequential( ConstrainedLinear(200*1*1, 1024), nn.Dropout(drop_ratio), ConstrainedLinear(1024, 512), nn.Dropout(drop_ratio), ConstrainedLinear(512, num_class) ) def forward(self, x): out = self.conv_split(x) out = self.post_conv(out) out = self.conv_pool1(out) out = self.conv_pool2(out) out = self.conv_pool3(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class sub_EEGNet(nn.Module): def __init__(self, drop_ratio=.5, bias=False, num_class=2, F1=8, D=2): super(sub_EEGNet, self).__init__() F2 = F1*D self.conv_temporal = nn.Sequential( nn.ZeroPad2d((((250-1)//2)+1, ((250-1)//2), 0, 0)), nn.Conv2d(1, F1, (1,250), 1, bias=bias), nn.BatchNorm2d(F1), ) self.conv_spatial = nn.Sequential( ConstrainedConv2d(F1, F1*D, (32,1), 1, bias=bias, groups=F1), nn.BatchNorm2d(F1*D), nn.ELU(), nn.AvgPool2d((1,4)), nn.Dropout(drop_ratio) ) self.conv_separable = nn.Sequential( nn.ZeroPad2d((((125-1)//2)+1, ((125-1)//2), 0, 0)), nn.Conv2d(F1*D, F2, (1,125), 1, bias=bias, groups=F1*D), #depthwise nn.Conv2d(F2, F2, 1, 1), #pointwise = 1dconv nn.BatchNorm2d(F2), nn.ELU(), nn.AvgPool2d((1,8)), #(12) nn.Dropout(drop_ratio) ) self.conv_fc = nn.Sequential( ConstrainedLinear(F2*1*15, 1024), #nn.Linear(F2*1*15, 1024), nn.Dropout(drop_ratio), ConstrainedLinear(1024, 512), #nn.Linear(1024, 512), nn.Dropout(drop_ratio), ConstrainedLinear(512, num_class) #nn.Linear(512, num_class) ) def forward(self, x): out = self.conv_temporal(x) out = self.conv_spatial(out) out = self.conv_separable(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class sub_EEG_TCNet(nn.Module): def __init__(self, bias=False, drop_ratio=.5, num_class=2, F1=8, D=2): super(sub_EEG_TCNet, self).__init__() F2 = F1*D self.conv_temporal = nn.Sequential( nn.ZeroPad2d((((250-1)//2)+1, ((250-1)//2), 0, 0)), nn.Conv2d(1, F1, (1,250), 1, bias=bias), nn.BatchNorm2d(F1), ) self.conv_spatial = nn.Sequential( ConstrainedConv2d(F1, F1*D, (32,1), 1, bias=bias, groups=F1), nn.BatchNorm2d(F1*D), nn.ELU(), nn.AvgPool2d((1,4)), nn.Dropout(drop_ratio) ) self.conv_separable = nn.Sequential( nn.ZeroPad2d((((125-1)//2)+1, ((125-1)//2), 0, 0)), nn.Conv2d(F1*D, F2, (1,125), 1, bias=bias, groups=F1*D), #depthwise nn.Conv2d(F2, F2, 1, 1), #pointwise = 1dconv nn.BatchNorm2d(F2), nn.ELU(), nn.AvgPool2d((1,8)), #(12) nn.Dropout(drop_ratio) ) self.conv_fc = nn.Sequential( ConstrainedLinear(F2*1*15, 1024), nn.Dropout(drop_ratio), ConstrainedLinear(1024, 512), nn.Dropout(drop_ratio), ConstrainedLinear(512, num_class) ) # TCN-block self.tcn_block1 = nn.Sequential( nn.ZeroPad2d((2,1,0,0)), nn.Conv1d(F2, F2, 4, 1), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), nn.ZeroPad2d((2,1,0,0)), nn.Conv1d(F2, F2, 4, 1), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), ) self.tcn_block2 = nn.Sequential( nn.ZeroPad2d((3,3,0,0)), nn.Conv1d(F2, F2, 4, 1, dilation=2), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), nn.ZeroPad2d((3,3,0,0)), nn.Conv1d(F2, F2, 4, 1, dilation=2), nn.BatchNorm1d(F2), nn.ELU(), nn.Dropout(0.3), ) def forward(self, x): out = self.conv_temporal(x) out = self.conv_spatial(out) out = self.conv_separable(out) out = torch.squeeze(out, axis=2) tcn = self.tcn_block1(out) out = out + tcn out = nn.ELU()(out) tcn = self.tcn_block2(out) out = out + tcn out = nn.ELU()(out) out = out.view(-1, np.prod(out.shape[1:])) out = self.conv_fc(out) return out class sub_CCRNN(nn.Module): def __init__(self, drop_ratio=0.5, nSeg=30, num_classes=2): super(sub_CCRNN, self).__init__() self.nSeg = nSeg self.conv_module = nn.Sequential( nn.Conv2d(1, 32, 3, 1, padding=(3-1)//2), nn.ELU(), nn.Conv2d(32, 64, 3, 1, padding=(3-1)//2), nn.ELU(), nn.Conv2d(64, 128, 3, 1, padding=(3-1)//2), nn.ELU() ) self.conv_fc = nn.Sequential( nn.Linear(128*7*5, 1024), nn.ELU(), nn.Dropout(drop_ratio) ) self.rnn_module = nn.Sequential( nn.LSTM(1024, 64, 2, batch_first=True, dropout=drop_ratio) ) self.rnn_fc = nn.Sequential( nn.Linear(64, 1024), nn.ELU(), nn.Dropout(drop_ratio) ) self.readout = nn.Sequential( nn.Linear(1024, 128), nn.Dropout(drop_ratio), nn.Linear(128, 128), nn.Dropout(drop_ratio), nn.Linear(128, num_classes) ) def forward(self, x): out = self.conv_module(x) out = out.reshape(out.shape[0], np.prod(out.shape[1:])) out = self.conv_fc(out) out = out.reshape(-1, self.nSeg, out.shape[-1]) out, (hn, cn) = self.rnn_module(out) out = out[:, -1] out = self.rnn_fc(out) out = self.readout(out) return out

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null

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7

38e722462ed84e6769a401e82b6799009c7fd399

205

py

Python

Codewars/6kyu/micro-world/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/6kyu/micro-world/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/6kyu/micro-world/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 Test.assert_equals(micro_world([101, 53, 42, 102, 101, 55, 54], 1), 3) Test.assert_equals(micro_world([20, 15, 10, 15, 20, 25], 5), 1) Test.assert_equals(micro_world([5, 3, 1, 5], 1), 4)

34.166667

70

0.643902

42

205

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null

0

1

0

1

0

8

2a32aa2ae6dd29c7fb71209109c9605f27fd33b2

70

py

Python

celery_config.py

hoslo/ocr

4f78ae7013beb2cab8fb9391ba25ba5e6e78967c

[ "Apache-2.0" ]

4

2019-05-27T10:23:55.000Z

2020-01-19T10:03:14.000Z

celery_config.py

dun933/ocr

4f78ae7013beb2cab8fb9391ba25ba5e6e78967c

[ "Apache-2.0" ]

null

celery_config.py

dun933/ocr

4f78ae7013beb2cab8fb9391ba25ba5e6e78967c

[ "Apache-2.0" ]

3

2019-08-16T18:24:02.000Z

2020-05-15T06:35:45.000Z

broker = 'redis://127.0.0.1:6379/0' backend='redis://127.0.0.1:6379/1'

35

0.642857

16

70

2.8125

0.4375

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0.444444

0.666667

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0.328358

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35

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1

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1

0

1

0

1

null

0

9

2a4dddfd454aed1911e285f795bfb926288de8d2

34

py

Python

passlocker/__init__.py

chrislee35/passlocker

337b225db3b9281ea58c54c9334658b8c7b27f72

[ "MIT" ]

2

2020-11-23T17:49:38.000Z

2020-12-27T12:47:08.000Z

passlocker/__init__.py

chrislee35/passlocker

337b225db3b9281ea58c54c9334658b8c7b27f72

[ "MIT" ]

null

passlocker/__init__.py

chrislee35/passlocker

337b225db3b9281ea58c54c9334658b8c7b27f72

[ "MIT" ]

null

from .passlocker import PassLocker

34

0.882353

4

34

7.5

0.75

0

0.088235

34

1

34

0.967742

0

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true

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null

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1

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null

0

1

0

1

0

7

2a6495d0d4b0a64a39ffbda616ca7132327d7f5b

6,362

py

Python

caffe/mnist-gpu/model/test_predict.py

melatonin355/models

fc2500aab024f71fa8cf33e13d748703338612a8

[ "Apache-2.0" ]

null

caffe/mnist-gpu/model/test_predict.py

melatonin355/models

fc2500aab024f71fa8cf33e13d748703338612a8

[ "Apache-2.0" ]

null

caffe/mnist-gpu/model/test_predict.py

melatonin355/models

fc2500aab024f71fa8cf33e13d748703338612a8

[ "Apache-2.0" ]

1

2019-06-10T22:57:15.000Z

import pipeline_predict json_bytes = b'{"image": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05098039656877518, 0.529411792755127, 0.3960784673690796, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}' print(pipeline_predict.predict(json_bytes))

1,060.333333

6,291

0.626847

1,581

6,362

2.519924

0.054396

0.630522

0.926205

1.209839

0.620231

0.571787

0.536145

0.518825

0

0.702334

0.124489

6,362

5

6,292

1,272.4

0.012926

0

0.333333

0.986325

0.019805

0

1

0

false

0

0.333333

0

0.333333

0

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1

0

1

0

1

0

1

0

null

0

1

0

11

aa7842242752b6f74dd5a35b2eb0c5fb4bedcbb7

58

py

Python

core-python/Core_Python/extraknowledge/TypeTest.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-23T05:24:19.000Z

2022-02-17T16:37:51.000Z

core-python/Core_Python/extraknowledge/TypeTest.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

5

2020-10-01T05:08:37.000Z

2020-10-12T03:18:10.000Z

core-python/Core_Python/extraknowledge/TypeTest.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-28T14:06:41.000Z

2021-10-19T18:32:28.000Z

print(type(type(int))) print(type(int)) print(type(float))

19.333333

22

0.724138

10

58

4.2

0.4

0.642857

0.571429

0.761905

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7

2aec9a17238d8649b07f43484a27516e162a0699

9,199

py

Python

advanced/part12-15_hockey_statistics/test/test_hockey_statistics2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

advanced/part12-15_hockey_statistics/test/test_hockey_statistics2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

advanced/part12-15_hockey_statistics/test/test_hockey_statistics2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

import unittest from unittest.mock import patch from tmc import points, reflect from tmc.utils import load, load_module, reload_module, get_stdout, check_source from functools import reduce import os import os.path import textwrap from random import choice, randint from datetime import date, datetime, timedelta exercise = 'src.hockey_statistics' def s(l: list): return "\n".join(l) @points('12.hockey_statistics2') class HockeyStatistics2Test(unittest.TestCase): @classmethod def setUpClass(cls): with patch('builtins.input', side_effect=["partial.json", "0"]): cls.module = load_module(exercise, 'fi') def test_01_team_players_1(self): input_values = ["partial.json", "4" , "WSH", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() self.assertFalse(len(output)==0,'Your code does not output anything. Check that it is not inside if __name__ == "__main__" block.') exp = """Jakub Vrana WSH 25 + 27 = 52 Jonas Siegenthaler WSH 2 + 7 = 9""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") def test_02_team_players_2(self): input_values = ["partial.json", "4" , "DAL", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() exp = """John Klingberg DAL 6 + 26 = 32 Taylor Fedun DAL 2 + 7 = 9""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") def test_03_country_players_1(self): input_values = ["partial.json", "5" , "CAN", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() exp = """Jared McCann PIT 14 + 21 = 35 Travis Zajac NJD 9 + 16 = 25 Taylor Fedun DAL 2 + 7 = 9 Mark Jankowski CGY 5 + 2 = 7 Logan Shaw WPG 3 + 2 = 5""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") def test_04_country_players_2(self): input_values = ["partial.json", "5" , "SWE", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() exp = """John Klingberg DAL 6 + 26 = 32 Jonathan Davidsson OTT 0 + 1 = 1""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") def test_05_country_players_big_file_1(self): input_values = ["all.json", "5" , "AUS", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() exp = """Nathan Walker STL 1 + 1 = 2""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") def test_06_country_players_big_file_2(self): input_values = ["all.json", "5" , "AUT", "0"] with patch('builtins.input', side_effect=input_values): try: reload_module(self.module) except: self.fail(f"Check that your program works with input\n{s(input_values)}") output = get_stdout() exp = """Michael Raffl PHI 8 + 12 = 20 Michael Grabner ARI 8 + 3 = 11""" for line in exp.split("\n"): if not line in output: self.fail(f"Your program should output line\n{line}\nwhen the program is executed with input\n{s(input_values)}\nNow the output was\n{output}") output_lines = output.split('\n') exp_lines = exp.split("\n") n = output_lines.index(exp_lines[0]) for i in range(len(exp_lines)): try: oo = output_lines[n+i] except: self.fail(f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") ee = exp_lines[i] self.assertEqual(oo, ee, f"when the program is executed with input\n{s(input_values)}\nOutput \n{output}\nis not in correct order, it should be\n{exp}") if __name__ == '__main__': unittest.main()

45.315271

170

0.558865

1,250

9,199

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0.048067

0.052874

0.828961

0.806329

0.779091

0

0.016069

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9,199

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45.315271

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0.385761

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null

0

1

0

1

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null

0

7

2afaa17e5456401e6879f89535d519d6ec5c0972

2,391

py

Python

project/iotd/workish/all_views/apis/tasks.py

balangheorghe/CloudComputing

b4ca2209e1a2292abffcb559dc942430a2862296

[ "Apache-2.0" ]

null

project/iotd/workish/all_views/apis/tasks.py

balangheorghe/CloudComputing

b4ca2209e1a2292abffcb559dc942430a2862296

[ "Apache-2.0" ]

null

project/iotd/workish/all_views/apis/tasks.py

balangheorghe/CloudComputing

b4ca2209e1a2292abffcb559dc942430a2862296

[ "Apache-2.0" ]

null

from django.shortcuts import render from ...utils import get_user_role, check_admin from django.contrib.auth.decorators import login_required @login_required(login_url='login') def task_view(request): isAdmin = check_admin(get_user_role(request)) api_name = "view_not_admin" if isAdmin: api_name = "view" if request.method == 'POST': return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) else: return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) @login_required(login_url='login') def task_create(request): isAdmin = check_admin(get_user_role(request)) working_on_it = "working_on_it" if isAdmin: working_on_it = "working_on_it_admin" if not isAdmin: return render(request, 'workish/views/auth/{}.html'.format(working_on_it), {'error': 'Access Denied!'}) api_name = 'create' if request.method == 'POST': return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) else: return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) @login_required(login_url='login') def task_request(request): isAdmin = check_admin(get_user_role(request)) working_on_it = "working_on_it" if isAdmin: working_on_it = "working_on_it_admin" api_name = 'request' if request.method == 'POST': return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) else: return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) @login_required(login_url='login') def task_to_approve(request): isAdmin = check_admin(get_user_role(request)) working_on_it = "working_on_it" if isAdmin: working_on_it = "working_on_it_admin" if not isAdmin: return render(request, 'workish/views/auth/{}.html'.format(working_on_it), {'error': 'Access Denied!'}) api_name = 'to_approve' if request.method == 'POST': return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'}) else: return render(request, 'workish/views/apis/tasks/{}.html'.format(api_name), {'error': 'Working on it!'})

35.161765

112

0.677959

325

2,391

4.753846

0.138462

0.128155

0.156634

0.168285

0.86343

0.842071

0.814887

0

0.167712

2,391

67

113

35.686567

0.776382

0

0.76

0

0.280636

0.128816

0

1

0.08

false

0

0.06

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0.34

0

null

0

1

0

1

0

null

0

8

2dd06cfd36fd6dff170b0c31dbf01e51606153fc

200

py

Python

reVX/hybrid_stats/__init__.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

7

2020-04-06T00:29:55.000Z

2022-01-23T20:00:14.000Z

reVX/hybrid_stats/__init__.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

67

2020-02-28T20:15:35.000Z

2022-03-31T21:34:52.000Z

reVX/hybrid_stats/__init__.py

NREL/reVX

4d62eb2c003c3b53b959f7a58bdc342d18098884

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Sub-package to compute hybrid solar-wind generation stats """ from reVX.hybrid_stats.hybrid_stats import HybridStats from reVX.hybrid_stats.temporal_agg import TemporalAgg

28.571429

57

0.785

28

200

5.464286

0.678571

0.215686

0.183007

0.248366

0

0.005618

0.11

200

6

58

33.333333

0.853933

0.4

0

1

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true

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1

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1

0

null

0

1

0

1

0

1

0

8

9356181b70c3d16eeed53436f20bd9f3be44436d

24,211

py

Python

django_flex_user/tests/views/test_endpoint_users_user.py

ebenh/django-flex-user

efffb21e4ce33d2ea8665756334e2a391f4b5a72

[ "MIT" ]

1

2021-09-13T20:26:02.000Z

django_flex_user/tests/views/test_endpoint_users_user.py

ebenh/django-flex-user

efffb21e4ce33d2ea8665756334e2a391f4b5a72

[ "MIT" ]

null

django_flex_user/tests/views/test_endpoint_users_user.py

ebenh/django-flex-user

efffb21e4ce33d2ea8665756334e2a391f4b5a72

[ "MIT" ]

null

from rest_framework.test import APITestCase from rest_framework import status class TestFlexUserRetrieveUpdate(APITestCase): """ This class is designed to test django_flex_user.views.FlexUser """ _REST_ENDPOINT_PATH = '/api/accounts/users/user/' def test_method_get(self): response = self.client.get(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_method_post(self): response = self.client.post(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_method_put(self): response = self.client.put(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_method_patch(self): response = self.client.patch(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_method_delete(self): response = self.client.delete(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_method_options(self): response = self.client.options(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) class TestFlexUserRetrieveUpdateAuthenticated(APITestCase): """ This class is designed to test django_flex_user.views.FlexUser """ _REST_ENDPOINT_PATH = '/api/accounts/users/user/' class _ContentType: class ApplicationJSON: username_values = [{}, {'username': None}, {'username': ''}, {'username': 'validUsername'}, {'username': 'invalidUsername+'}] email_values = [{}, {'email': None}, {'email': ''}, {'email': 'validEmail@example.com'}, {'email': 'invalidEmail'}] phone_values = [{}, {'phone': None}, {'phone': ''}, {'phone': '+12025551234'}, {'phone': 'invalidPhoneNumber'}] password_values = [{}, {'password': None}, {'password': ''}, {'password': 'validPassword'}, {'password': 'invalid'}] class MultipartFormData: username_values = [{}, {'username': ''}, {'username': 'validUsername'}, {'username': 'invalidUsername+'}] email_values = [{}, {'email': ''}, {'email': 'validEmail@example.com'}, {'email': 'invalidEmail'}] phone_values = [{}, {'phone': ''}, {'phone': '+12025551234'}, {'phone': 'invalidPhoneNumber'}] password_values = [{}, {'password': ''}, {'password': 'validPassword'}, {'password': 'invalid'}] def setUp(self): from django_flex_user.models.user import FlexUser self.user = FlexUser.objects.create_user(username='validUsername', password='validPassword') def test_method_get(self): is_authenticated = self.client.login(username='validUsername', password='validPassword') self.assertIs(is_authenticated, True) response = self.client.get(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data, { 'username': 'validUsername', 'email': None, 'email_verified': None, 'phone': None, 'phone_verified': None } ) self.client.logout() def test_method_post(self): is_authenticated = self.client.login(username='validUsername', password='validPassword') self.assertIs(is_authenticated, True) response = self.client.post(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) self.client.logout() def test_method_patch_format_application_json(self): from django.db import transaction for i in self._ContentType.ApplicationJSON.username_values: for j in self._ContentType.ApplicationJSON.email_values: for k in self._ContentType.ApplicationJSON.phone_values: for l in self._ContentType.ApplicationJSON.password_values: data = {} data.update(i) data.update(j) data.update(k) data.update(l) with self.subTest(**data), transaction.atomic(): """ Special considerations for password changes: By default, updating a user's password invalidates all sessions for the user. To make it so that the user is *not* signed out by a password change, in django_flex_user.serializers.FlexUserSerializer.update we call django.contrib.auth.update_session_auth_hash which (1) generates a new session key for the user's current session (2) updates the current session's _auth_user_hash with a value based on the user's new password (because the value of _auth_user_hash for all other sessions are not based on the user's latest password, those sessions are implicitly invalidated). The session key for the newly created session is returned to the client in a 'set-cookie' response header. Because this call is wrapped in a transaction that rolls back all database changes at the end of each iteration, the changes to the user's session will not be persisted to the database. This means that on iterations following a password change, the session key that was returned to the client will not match any session in the django_session table. Therefore it is insufficient to log in the test client once before the execution of this loop. Instead we have to call django.test.client.Client.force_login on each iteration to ensure the client always has a valid session. We could instead call django.test.client.Client.login, but it significantly impacts execution time. For good measure/symmetry we also call django.test.client.Client.logout at the end of each iteration. """ self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') response = self.client.patch(self._REST_ENDPOINT_PATH, data=data, format='json') if 'password' in data and not data['password']: """ If the supplied password is defined and either None or the empty string, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() elif ('username' in data and data['username'] is None) and \ ('email' not in data or data['email'] is None) and \ ('phone' not in data or data['phone'] is None): """ If the supplied username is None, and the supplied email and phone are simultaneously undefined or None, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() elif data.get('username') == '' or \ data.get('email') == '' or \ data.get('phone') == '': """ If any of the supplied username, email or phone are the empty string django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() elif (data.get('username') and 'invalid' in data['username']) or \ (data.get('email') and 'invalid' in data['email']) or \ (data.get('phone') and 'invalid' in data['phone']) or \ (data.get('password') and 'invalid' in data['password']): """ If any of the supplied username, email, phone or password are defined and invalid, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() else: """ This case encompasses all possible permutations of supplied username, email, phone and password for which django_flex_user.views.FlexUser.put should return HTTP status code HTTP_200_OK. """ self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data, { 'username': data.get('username', 'validUsername'), 'email': data.get('email'), 'email_verified': False if data.get('email') else None, 'phone': data.get('phone'), 'phone_verified': False if data.get('phone') else None } ) self.client.logout() transaction.set_rollback(True) def test_method_patch_format_multipart_form_data(self): from django.db import transaction for i in self._ContentType.MultipartFormData.username_values: for j in self._ContentType.MultipartFormData.email_values: for k in self._ContentType.MultipartFormData.phone_values: for l in self._ContentType.MultipartFormData.password_values: data = {} data.update(i) data.update(j) data.update(k) data.update(l) with self.subTest(**data), transaction.atomic(): """ Special considerations for password changes: By default, updating a user's password invalidates all sessions for the user. To make it so that the user is *not* signed out by a password change, in django_flex_user.serializers.FlexUserSerializer.update we call django.contrib.auth.update_session_auth_hash which (1) generates a new session key for the user's current session (2) updates the current session's _auth_user_hash with a value based on the user's new password (because the value of _auth_user_hash for all other sessions are not based on the user's latest password, those sessions are implicitly invalidated). The session key for the newly created session is returned to the client in a 'set-cookie' response header. Because this call is wrapped in a transaction that rolls back all database changes at the end of each iteration, the changes to the user's session will not be persisted to the database. This means that on iterations following a password change, the session key that was returned to the client will not match any session in the django_session table. Therefore it is insufficient to log in the test client once before the execution of this loop. Instead we have to call django.test.client.Client.force_login on each iteration to ensure the client always has a valid session. We could instead call django.test.client.Client.login, but it significantly impacts execution time. For good measure/symmetry we also call django.test.client.Client.logout at the end of each iteration. """ self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') response = self.client.patch(self._REST_ENDPOINT_PATH, data=data, format='multipart') if 'password' in data and data['password'] == '': """ If the supplied password is defined and blank, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() elif ('username' in data and data['username'] == '') and \ ('email' not in data or data['email'] == '') and \ ('phone' not in data or data['phone'] == ''): """ If the supplied username is blank, and the supplied email and phone are simultaneously undefined or blank, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() elif (data.get('username') and 'invalid' in data['username']) or \ (data.get('email') and 'invalid' in data['email']) or \ (data.get('phone') and 'invalid' in data['phone']) or \ (data.get('password') and 'invalid' in data['password']): """ If any of the supplied username, email, phone or password are defined and invalid, django_flex_user.views.FlexUser.put should return HTTP status code HTTP_400_BAD_REQUEST. """ self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() else: """ This case encompasses all possible permutations of supplied username, email, phone and password for which django_flex_user.views.FlexUser.put should return HTTP status code HTTP_200_OK. """ self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data, { 'username': data.get('username', 'validUsername') or None, 'email': data.get('email') or None, 'email_verified': False if data.get('email') else None, 'phone': data.get('phone') or None, 'phone_verified': False if data.get('phone') else None } ) self.client.logout() transaction.set_rollback(True) def test_method_patch_username_case_insensitivity(self): from django_flex_user.models.user import FlexUser FlexUser.objects.create_user(username='validUsername2', password='validPassword') self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'username': 'VALIDUSERNAME2'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_duplicate_username(self): from django_flex_user.models.user import FlexUser FlexUser.objects.create_user(username='validUsername2', password='validPassword') self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'username': 'validUsername2'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_duplicate_email(self): from django_flex_user.models.user import FlexUser FlexUser.objects.create_user(email='validEmail@example.com', password='validPassword') self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'email': 'validEmail@example.com'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_duplicate_phone(self): from django_flex_user.models.user import FlexUser FlexUser.objects.create_user(phone='+12025551234', password='validPassword') self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'phone': '+12025551234'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_ambiguous_username(self): """ Verify that an email address or phone number cannot form a valid username. :return: """ self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'username': 'validEmail@example.com'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) data = {'username': '+12025551234'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_ambiguous_email(self): """ Verify that a username or phone number cannot form a valid email. :return: """ self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'email': 'validUsername'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) data = {'email': '+12025551234'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_ambiguous_phone(self): """ Verify that a username or email address cannot form a valid phone. :return: """ self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'phone': 'validUsername'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) data = {'phone': 'validEmail@example.com'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.client.logout() def test_method_patch_normalize_username(self): self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') nfd = 'validUsérname' # é = U+0065 U+0301 nfkc = 'validUsérname' # é = U+00e9 data = {'username': nfd} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data['username'], nfkc) self.client.logout() def test_method_patch_normalize_email(self): self.client.force_login(self.user, 'django_flex_user.backends.FlexUserModelBackend') data = {'email': 'validEmail@bücher.example'} response = self.client.patch(self._REST_ENDPOINT_PATH, data=data) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data['email'], 'validEmail@xn--bcher-kva.example') self.client.logout() def test_method_put(self): is_authenticated = self.client.login(username='validUsername', password='validPassword') self.assertIs(is_authenticated, True) response = self.client.put(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) self.client.logout() def test_method_delete(self): is_authenticated = self.client.login(username='validUsername', password='validPassword') self.assertIs(is_authenticated, True) response = self.client.delete(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) self.client.logout() def test_method_options(self): is_authenticated = self.client.login(username='validUsername', password='validPassword') self.assertIs(is_authenticated, True) response = self.client.options(self._REST_ENDPOINT_PATH) self.assertEqual(response.status_code, status.HTTP_200_OK) self.client.logout()

50.650628

129

0.549585

2,409

24,211

5.342466

0.096721

0.049728

0.066123

0.072106

0.923077

0.897514

0.889277

0.866589

0.832012

0.821445

0

0.01363

0.369708

24,211

477

130

50.756813

0.829751

0.016274

0

0.685185

0

0.124457

0.044364

0

0.155556

1

0.085185

false

0.092593

0.033333

0

0.144444

0

null

0

1

0

null

0

1

0

7

9369c745bf0136b92dba325fdb228c97367af112

84

py

Python

c.py

usha324/python

7aa967b8dac8cd0c466652db448cb7e405821389

[ "bzip2-1.0.6" ]

null

c.py

usha324/python

7aa967b8dac8cd0c466652db448cb7e405821389

[ "bzip2-1.0.6" ]

null

c.py

usha324/python

7aa967b8dac8cd0c466652db448cb7e405821389

[ "bzip2-1.0.6" ]

null

print (7 > 10) print (4 < 16) print (4 == 4) print (4 <= 4) print (4 != 4)

14

17

0.440476

15

84

2.466667

0.333333

0.648649

0.567568

0.648649

0.567568

0

0.218182

0.345238

84

5

18

16.8

0.454545

0

1

0

true

0

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

9

938333b03cc7b78e7463ac545c3eddf8b5815a4b

2,861

py

Python

eugene/tests/test_initial_conditions.py

jantzen/eugene

a5fdc8cfb31e1fa4e48b2f882be84347cc8a7d69

[ "MIT" ]

3

2017-04-11T22:12:41.000Z

2021-06-29T20:08:59.000Z

eugene/tests/test_initial_conditions.py

jantzen/eugene

a5fdc8cfb31e1fa4e48b2f882be84347cc8a7d69

[ "MIT" ]

null

eugene/tests/test_initial_conditions.py

jantzen/eugene

a5fdc8cfb31e1fa4e48b2f882be84347cc8a7d69

[ "MIT" ]

1

2021-04-09T08:51:14.000Z

import eugene as eu import numpy as np import warnings import pdb def test_choose_untrans_trans(): # test 1-D t = np.linspace(0., 10., 100) x = [] y = [] z = [] ics = np.random.normal(size=10000) for ic in ics: x.append(ic + np.exp(0.2 * t) - 1.) ics = np.random.normal(size=10000) for ic in ics: y.append(ic + np.exp(0.3 * t) - 1.) ics = np.random.normal(size=10000) for ic in ics: z.append(ic + np.exp(0.2 * t) + 0.2 * t -1.) data = [x, y, z] untrans, trans = eu.initial_conditions.choose_untrans_trans(data, 100) assert len(untrans[0]) == 100 ## verify that warnings are properly triggered and reported with warnings.catch_warnings(record=True) as w: x = [] y = [] ics = np.random.normal(size=10000) for ic in ics: x.append(ic + np.exp(0.2 * t) - 1.) ics = np.random.normal(size=10000) + 20. for ic in ics: y.append(ic + np.exp(0.3 * t) - 1.) data = [x, y] untrans, transm, error_flag = eu.initial_conditions.choose_untrans_trans(data, 100, report=True) print("Number of warnings captured = " + str(len(w))) for warn in w: print(warn.message) assert len(w) == 3 assert error_flag[0,1] == 3 # test 3-D t = np.concatenate([np.linspace(0., 10., 100).reshape(1,-1), np.linspace(0., 10., 100).reshape(1,-1),np.linspace(0., 10., 100).reshape(1,-1)], axis=0) x = [] y = [] z = [] ics = np.random.normal(size=(3,10000)) for ic in ics.T: ic = ic.reshape(-1,1) x.append(ic + np.exp(0.2 * t) - 1.) ics = np.random.normal(size=(3,10000)) for ic in ics.T: ic = ic.reshape(-1,1) y.append(ic + np.exp(0.3 * t) - 1.) ics = np.random.normal(size=(3,10000)) for ic in ics.T: ic = ic.reshape(-1,1) z.append(ic + np.exp(0.2 * t) + 0.2 * t -1.) data = [x, y, z] untrans, trans = eu.initial_conditions.choose_untrans_trans(data, 100) assert len(untrans[0]) == 100 ## verify that warnings are properly triggered and reported with warnings.catch_warnings(record=True) as w: x = [] y = [] ics = np.random.normal(size=(3,10000)) for ic in ics.T: ic = ic.reshape(-1,1) x.append(ic + np.exp(0.2 * t) - 1.) ics = np.random.normal(size=(3,10000)) + 20. for ic in ics.T: ic = ic.reshape(-1,1) y.append(ic + np.exp(0.3 * t) - 1.) data = [x, y] untrans, trans, error_flag = eu.initial_conditions.choose_untrans_trans(data, 100, report=True) print("Number of warnings captured = " + str(len(w))) for warn in w: print(warn.message) assert len(w) == 3 assert error_flag[0,1] == 3

28.61

91

0.539322

455

2,861

3.345055

0.147253

0.032852

0.072273

0.111695

0.919842

0.90933

0.906702

0.90276

0

0.082082

0.301643

2,861

99

92

28.89899

0.67968

0.045788

0

0.833333

0

0.022043

0

0.076923

1

0.012821

false

0

0.051282

0

0.064103

0.051282

0

null

0

1

0

1

0

null

0

7

878740beb31b3cef055b4c67bdff3247e7e288a9

156

py

Python

test_password_security.py

Schots/password_security

35ecb12a055b3b0169f37a8c9095f49b1f6e82e8

[ "MIT" ]

null

test_password_security.py

Schots/password_security

35ecb12a055b3b0169f37a8c9095f49b1f6e82e8

[ "MIT" ]

1

2021-03-24T22:59:48.000Z

test_password_security.py

Schots/password_security

35ecb12a055b3b0169f37a8c9095f49b1f6e82e8

[ "MIT" ]

null

import hashlib from password_security import password_checker,get_pwnd_count def test_password_checker(): assert password_checker("123") == 1078184

17.333333

61

0.807692

20

156

5.95

0.7

0.378151

0

0.073529

0.128205

156

8

62

19.5

0.801471

0

0.019608

0

0.25

1

0.25

true

0.75

0.5

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

879ed2c06a475e4d5af527ef1424feb10812e062

13,831

py

Python

tf_api/PtGrey.py

abhineet123/animal_detection_

be0dd60d2b56b267f329b7be71d7f037499f98bc

[ "CC-BY-4.0" ]

6

2020-06-18T16:41:40.000Z

2022-03-10T07:15:13.000Z

tf_api/PtGrey.py

abhineet123/animal_detection_

be0dd60d2b56b267f329b7be71d7f037499f98bc

[ "CC-BY-4.0" ]

1

2021-08-11T08:42:28.000Z

tf_api/PtGrey.py

abhineet123/animal_detection_

be0dd60d2b56b267f329b7be71d7f037499f98bc

[ "CC-BY-4.0" ]

1

2022-02-25T11:06:17.000Z

def runPySpinCam(cam_id, _mode=0): global height, width, image_converted, cap_fps system = PtGrey.System.GetInstance() cam_list = system.GetCameras() num_cameras = cam_list.GetSize() print("Number of cameras detected: {:d}".format(num_cameras)) if num_cameras == 0: cam_list.Clear() system.ReleaseInstance() raise IOError("Not enough cameras!") cam = cam_list.GetByIndex(cam_id) try: nodemap_tldevice = cam.GetTLDeviceNodeMap() try: node_device_information = PtGrey.CCategoryPtr(nodemap_tldevice.GetNode("DeviceInformation")) if PtGrey.IsAvailable(node_device_information) and PtGrey.IsReadable(node_device_information): features = node_device_information.GetFeatures() for feature in features: node_feature = PtGrey.CValuePtr(feature) print("%s: %s" % (node_feature.GetName(), node_feature.ToString() if PtGrey.IsReadable(node_feature) else "Node not readable")) else: print("Device control information not available.") except PtGrey.SpinnakerException as ex: raise IOError("Error in getting device info: %s" % ex) cam.Init() nodemap = cam.GetNodeMap() if rgb_mode == 1: pix_format_txt = "RGB8Packed" elif rgb_mode == 2: pix_format_txt = "BayerRG8" else: pix_format_txt = "Mono8" pixel_format_mode = PtGrey.CEnumerationPtr(nodemap.GetNode("PixelFormat")) if not PtGrey.IsAvailable(pixel_format_mode) or not PtGrey.IsWritable(pixel_format_mode): raise IOError("Unable to set pixel format mode to RGB (enum retrieval). Aborting...") node_pixel_format_mode_rgb8 = pixel_format_mode.GetEntryByName(pix_format_txt) if not PtGrey.IsAvailable(node_pixel_format_mode_rgb8) or not PtGrey.IsReadable( node_pixel_format_mode_rgb8): raise IOError("Unable to set pixel format mode to RGB (entry retrieval). Aborting...") pixel_format_mode.SetIntValue(node_pixel_format_mode_rgb8.GetValue()) print("pixel format mode set to {:s}...".format(pix_format_txt)) video_mode_txt = 'Mode{:d}'.format(video_mode) video_mode_node = PtGrey.CEnumerationPtr(nodemap.GetNode("VideoMode")) if not PtGrey.IsAvailable(video_mode_node) or not PtGrey.IsWritable(video_mode_node): raise IOError("Unable to set video mode to {} (enum retrieval). Aborting...".format(video_mode_txt)) node_video_mode_node = video_mode_node.GetEntryByName(video_mode_txt) if not PtGrey.IsAvailable(node_video_mode_node) or not PtGrey.IsReadable(node_video_mode_node): raise IOError("Unable to set video mode to {} (entry retrieval). Aborting...".format(video_mode_txt)) video_mode_node.SetIntValue(node_video_mode_node.GetValue()) print("video mode set to {:s}...".format(video_mode_txt)) node_acquisition_mode = PtGrey.CEnumerationPtr(nodemap.GetNode("AcquisitionMode")) if not PtGrey.IsAvailable(node_acquisition_mode) or not PtGrey.IsWritable(node_acquisition_mode): raise IOError( "Unable to set acquisition mode to continuous (enum retrieval). Aborting...") node_acquisition_mode_continuous = node_acquisition_mode.GetEntryByName("Continuous") if not PtGrey.IsAvailable(node_acquisition_mode_continuous) or not PtGrey.IsReadable( node_acquisition_mode_continuous): raise IOError("Unable to set acquisition mode to continuous (entry retrieval). Aborting...") acquisition_mode_continuous = node_acquisition_mode_continuous.GetValue() node_acquisition_mode.SetIntValue(acquisition_mode_continuous) print("acquisition mode set to continuous...") cam.BeginAcquisition() # get first image while True: try: # print('Getting the first image') image_result = cam.GetNextImage() if image_result.IsIncomplete(): print("Image incomplete with image status %d ..." % image_result.GetImageStatus()) continue width = image_result.GetWidth() height = image_result.GetHeight() image_converted = image_result # if rgb_mode == 2: # image_converted = image_result.Convert(PtGrey.PixelFormat_RGB8Packed, PtGrey.HQ_LINEAR) # else: # image_converted = image_result # image_result.Release() break except PtGrey.SpinnakerException as ex: raise IOError("Error in acquiring image: %s" % ex) while True: if stop_pt_grey_cam: break try: cap_start_t = time.time() image_result = cam.GetNextImage() if image_result.IsIncomplete(): print("Image incomplete with image status %d ..." % image_result.GetImageStatus()) continue width = image_result.GetWidth() height = image_result.GetHeight() cap_end_t = time.time() cap_fps = 1.0 / float(cap_end_t - cap_start_t) with ptgrey_mutex: # if rgb_mode == 2: # image_converted = image_result.Convert(PtGrey.PixelFormat_RGB8Packed, PtGrey.HQ_LINEAR) # else: # image_converted = image_result image_converted = image_result # cap_end_t2 = time.time() # cap_fps2 = 1.0 / float(cap_end_t2 - cap_start_t) if _mode == 1: image_np_gray = np.array(image_converted.GetData(), dtype=np.uint8).reshape( (height, width)).copy() image_np = cv2.cvtColor(image_np_gray, cv2.COLOR_GRAY2RGB) cv2.imshow(win_title, image_np) k = cv2.waitKey(1) if k == ord('q') or k == 27: break # image_result.Release() except PtGrey.SpinnakerException as ex: raise IOError("Error in acquiring image: %s" % ex) except PtGrey.SpinnakerException as ex: raise IOError("Error: %s" % ex) cam.EndAcquisition() cam.DeInit() del cam cam_list.Clear() system.ReleaseInstance() def runPySpinCam(cam_id, _mode=0): global height, width, image_converted, cap_fps system = PtGrey.System.GetInstance() cam_list = system.GetCameras() num_cameras = cam_list.GetSize() print("Number of cameras detected: {:d}".format(num_cameras)) if num_cameras == 0: cam_list.Clear() system.ReleaseInstance() raise IOError("Not enough cameras!") cam = cam_list.GetByIndex(cam_id) try: nodemap_tldevice = cam.GetTLDeviceNodeMap() try: node_device_information = PtGrey.CCategoryPtr(nodemap_tldevice.GetNode("DeviceInformation")) if PtGrey.IsAvailable(node_device_information) and PtGrey.IsReadable(node_device_information): features = node_device_information.GetFeatures() for feature in features: node_feature = PtGrey.CValuePtr(feature) print("%s: %s" % (node_feature.GetName(), node_feature.ToString() if PtGrey.IsReadable(node_feature) else "Node not readable")) else: print("Device control information not available.") except PtGrey.SpinnakerException as ex: raise IOError("Error in getting device info: %s" % ex) cam.Init() nodemap = cam.GetNodeMap() if rgb_mode == 1: pix_format_txt = "RGB8Packed" elif rgb_mode == 2: pix_format_txt = "BayerRG8" else: pix_format_txt = "Mono8" pixel_format_mode = PtGrey.CEnumerationPtr(nodemap.GetNode("PixelFormat")) if not PtGrey.IsAvailable(pixel_format_mode) or not PtGrey.IsWritable(pixel_format_mode): raise IOError("Unable to set pixel format mode to RGB (enum retrieval). Aborting...") node_pixel_format_mode_rgb8 = pixel_format_mode.GetEntryByName(pix_format_txt) if not PtGrey.IsAvailable(node_pixel_format_mode_rgb8) or not PtGrey.IsReadable( node_pixel_format_mode_rgb8): raise IOError("Unable to set pixel format mode to RGB (entry retrieval). Aborting...") pixel_format_mode.SetIntValue(node_pixel_format_mode_rgb8.GetValue()) print("pixel format mode set to {:s}...".format(pix_format_txt)) video_mode_txt = 'Mode{:d}'.format(video_mode) video_mode_node = PtGrey.CEnumerationPtr(nodemap.GetNode("VideoMode")) if not PtGrey.IsAvailable(video_mode_node) or not PtGrey.IsWritable(video_mode_node): raise IOError("Unable to set video mode to {} (enum retrieval). Aborting...".format(video_mode_txt)) node_video_mode_node = video_mode_node.GetEntryByName(video_mode_txt) if not PtGrey.IsAvailable(node_video_mode_node) or not PtGrey.IsReadable(node_video_mode_node): raise IOError( "Unable to set video mode to {} (entry retrieval). Aborting...".format(video_mode_txt)) video_mode_node.SetIntValue(node_video_mode_node.GetValue()) print("video mode set to {:s}...".format(video_mode_txt)) node_acquisition_mode = PtGrey.CEnumerationPtr(nodemap.GetNode("AcquisitionMode")) if not PtGrey.IsAvailable(node_acquisition_mode) or not PtGrey.IsWritable(node_acquisition_mode): raise IOError( "Unable to set acquisition mode to continuous (enum retrieval). Aborting...") node_acquisition_mode_continuous = node_acquisition_mode.GetEntryByName("Continuous") if not PtGrey.IsAvailable(node_acquisition_mode_continuous) or not PtGrey.IsReadable( node_acquisition_mode_continuous): raise IOError("Unable to set acquisition mode to continuous (entry retrieval). Aborting...") acquisition_mode_continuous = node_acquisition_mode_continuous.GetValue() node_acquisition_mode.SetIntValue(acquisition_mode_continuous) print("acquisition mode set to continuous...") cam.BeginAcquisition() # get first image while True: try: # print('Getting the first image') image_result = cam.GetNextImage() if image_result.IsIncomplete(): print("Image incomplete with image status %d ..." % image_result.GetImageStatus()) continue width = image_result.GetWidth() height = image_result.GetHeight() image_converted = image_result # if rgb_mode == 2: # image_converted = image_result.Convert(PtGrey.PixelFormat_RGB8Packed, PtGrey.HQ_LINEAR) # else: # image_converted = image_result # image_result.Release() break except PtGrey.SpinnakerException as ex: raise IOError("Error in acquiring image: %s" % ex) while True: if stop_pt_grey_cam: break try: cap_start_t = time.time() image_result = cam.GetNextImage() if image_result.IsIncomplete(): print("Image incomplete with image status %d ..." % image_result.GetImageStatus()) continue width = image_result.GetWidth() height = image_result.GetHeight() cap_end_t = time.time() cap_fps = 1.0 / float(cap_end_t - cap_start_t) with ptgrey_mutex: # if rgb_mode == 2: # image_converted = image_result.Convert(PtGrey.PixelFormat_RGB8Packed, PtGrey.HQ_LINEAR) # else: # image_converted = image_result image_converted = image_result # cap_end_t2 = time.time() # cap_fps2 = 1.0 / float(cap_end_t2 - cap_start_t) if _mode == 1: image_np_gray = np.array(image_converted.GetData(), dtype=np.uint8).reshape( (height, width)).copy() image_np = cv2.cvtColor(image_np_gray, cv2.COLOR_GRAY2RGB) cv2.imshow(win_title, image_np) k = cv2.waitKey(1) if k == ord('q') or k == 27: break # image_result.Release() except PtGrey.SpinnakerException as ex: raise IOError("Error in acquiring image: %s" % ex) except PtGrey.SpinnakerException as ex: raise IOError("Error: %s" % ex) cam.EndAcquisition() cam.DeInit() del cam cam_list.Clear() system.ReleaseInstance()

48.024306

117

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1,444

13,831

5.364266

0.108033

0.041828

0.046476

0.034082

1

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0.006904

0.329766

13,831

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48.024306

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py

Python

src/radical/ensemblemd/tests/kernels/simple_tests.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

src/radical/ensemblemd/tests/kernels/simple_tests.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

src/radical/ensemblemd/tests/kernels/simple_tests.py

chemlove/radical.ensemblemd

0ec4b127760d2fee88d4eae1768fecec4bdd6b21

[ "MIT" ]

null

""" Tests cases """ import os import sys import glob import unittest import radical.ensemblemd #----------------------------------------------------------------------------- # class SimpleKernelTests(unittest.TestCase): def setUp(self): # clean up fragments from previous tests pass def tearDown(self): # clean up after ourselves pass #------------------------------------------------------------------------- # def test__amber_kernel(self): """Basic test of the AMBER kernel. """ k = radical.ensemblemd.Kernel(name="md.amber") k.arguments = ["--mininfile=abc", "--mdinfile=def", "--topfile=ghi","--cycle=1"] _kernel = k._bind_to_resource("*") assert type(_kernel) == radical.ensemblemd.kernel_plugins.md.amber.Kernel, _kernel # Test kernel specifics here: k = radical.ensemblemd.Kernel(name="md.amber") k.arguments = ["--mininfile=abc", "--mdinfile=def", "--topfile=ghi","--cycle=1"] k._bind_to_resource("*") assert k._cu_def_executable == "/bin/bash", k._cu_def_executable assert k.arguments == ['-l','-c','pmemd -O -i abc -o min1.out -inf min1.inf -r md1.crd -p ghi -c min1.crd -ref min1.crd && pmemd -O -i def -o md1.out -inf md1.inf -x md1.ncdf -r md1.rst -p ghi -c md1.crd'], k.arguments assert k._cu_def_pre_exec == [], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec k._bind_to_resource("stampede.tacc.utexas.edu") assert k._cu_def_executable == "/bin/bash", k._cu_def_executable assert k.arguments == ['-l','-c','pmemd -O -i abc -o min1.out -inf min1.inf -r md1.crd -p ghi -c min1.crd -ref min1.crd && pmemd -O -i def -o md1.out -inf md1.inf -x md1.ncdf -r md1.rst -p ghi -c md1.crd'], k.arguments assert k._cu_def_pre_exec == ["module load TACC", "module load amber"], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec #------------------------------------------------------------------------- # def test__coco_kernel(self): """Basic test of the CoCo kernel. """ k = radical.ensemblemd.Kernel(name="md.coco") k.arguments = ["--grid=3", "--dims=3", "--frontpoints=8","--topfile=abc","--mdfile=def", "--output=xyz","--cycle=1"] _kernel = k._bind_to_resource("*") assert type(_kernel) == radical.ensemblemd.kernel_plugins.md.coco.Kernel, _kernel # Test kernel specifics here: k = radical.ensemblemd.Kernel(name="md.coco") k.arguments = ["--grid=3", "--dims=3", "--frontpoints=8","--topfile=abc","--mdfile=def", "--output=xyz","--cycle=1"] k._bind_to_resource("*") assert k._cu_def_executable == "/bin/bash", k._cu_def_executable assert k.arguments == ['-l','-c','pyCoCo --grid 3 --dims 3 --frontpoints 8 --topfile abc --mdfile def --output xyz && python postexec.py 8 1'], k.arguments assert k._cu_def_pre_exec == [], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec k._bind_to_resource("stampede.tacc.utexas.edu") assert k._cu_def_executable == "/bin/bash", k._cu_def_executable assert k.arguments == ['-l','-c','pyCoCo --grid 3 --dims 3 --frontpoints 8 --topfile abc --mdfile def --output xyz && python postexec.py 8 1'], k.arguments assert k._cu_def_pre_exec == ["module load intel/13.0.2.146","module load python","module load netcdf/4.3.2", "module load hdf5/1.8.13","module load amber", "export PYTHONPATH=/work/02998/ardi/coco_installation/lib/python2.7/site-packages:$PYTHONPATH", "export PATH=/work/02998/ardi/coco_installation/bin:$PATH"], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec #------------------------------------------------------------------------- # def test__gromacs_kernel(self): """Basic test of the GROMACS kernel. """ k = radical.ensemblemd.Kernel(name="md.gromacs") k.arguments = ["--grompp=grompp.mdp","--topol=topol.top"] _kernel = k._bind_to_resource("*") assert type(_kernel) == radical.ensemblemd.kernel_plugins.md.gromacs.Kernel, _kernel # Test kernel specifics here: k = radical.ensemblemd.Kernel(name="md.gromacs") k.arguments = ["--grompp=grompp.mdp","--topol=topol.top"] k._bind_to_resource("*") assert k._cu_def_executable == "python", k._cu_def_executable assert k.arguments == ['run.py','--mdp','grompp.mdp','--gro','start.gro','--top','topol.top','--out','out.gro'], k.arguments assert k._cu_def_pre_exec == [], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec k._bind_to_resource("stampede.tacc.utexas.edu") assert k._cu_def_executable == ["python"], k._cu_def_executable assert k.arguments == ['run.py','--mdp','grompp.mdp','--gro','start.gro','--top','topol.top','--out','out.gro'], k.arguments assert k._cu_def_pre_exec == ["module load gromacs python mpi4py"], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec #------------------------------------------------------------------------- # def test__lsdmap_kernel(self): """Basic test of the LSDMAP kernel. """ k = radical.ensemblemd.Kernel(name="md.lsdmap") k.arguments = ["--config=config.ini"] _kernel = k._bind_to_resource("*") assert type(_kernel) == radical.ensemblemd.kernel_plugins.md.lsdmap.Kernel, _kernel # Test kernel specifics here: k = radical.ensemblemd.Kernel(name="md.lsdmap") k.arguments = ["--config=config.ini"] k._bind_to_resource("*") assert k._cu_def_executable == "lsdmap", k._cu_def_executable assert k.arguments == ['lsdm.py', '-f','config.ini','-c','tmpha.gro','-n','out.nn','-w','weight.w'], k.arguments assert k._cu_def_pre_exec == [], k._cu_def_pre_exec assert k._cu_def_post_exec == None, k._cu_def_post_exec k._bind_to_resource("stampede.tacc.utexas.edu") assert k.arguments == ['lsdm.py', '-f','config.ini','-c','tmpha.gro','-n','out.nn','-w','weight.w'], k.arguments assert k._cu_def_post_exec == None, k._cu_def_post_exec

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e201bac45ab5a564032360c104a84888e9619a26

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py

Python

tests/experiments/datasets.py

laudv/veritas

ba1761cc333b08b4381afa720b24ace065a9f106

[ "Apache-2.0" ]

6

2020-10-29T10:20:48.000Z

2022-03-31T13:39:47.000Z

tests/experiments/datasets.py

laudv/veritas

ba1761cc333b08b4381afa720b24ace065a9f106

[ "Apache-2.0" ]

1

2021-11-25T13:15:11.000Z

2021-12-08T09:23:24.000Z

tests/experiments/datasets.py

laudv/veritas

ba1761cc333b08b4381afa720b24ace065a9f106

[ "Apache-2.0" ]

null

import os, json import util import pickle import numpy as np import pandas as pd import sklearn.metrics as metrics from sklearn import preprocessing from veritas import addtree_from_xgb_model, addtrees_from_multiclass_xgb_model class Dataset: models_dir = "tests/experiments/models" def __init__(self, special_name_tag=""): self.special_tag = special_name_tag # special parameters, name indication self.X = None self.y = None def load_dataset(self): # populate X, y raise RuntimeError("not implemented") def load_model(self, num_trees, tree_depth): # populate self.model, self.at, self.feat2id """ populate self.model, self.at """ raise RuntimeError("not implemented") def get_model_name(self, num_trees, tree_depth): return f"{type(self).__name__}{self.special_tag}-{num_trees}-{tree_depth}" def minmax_normalize(self): X = self.X.values min_max_scaler = preprocessing.MinMaxScaler() X_scaled = min_max_scaler.fit_transform(X) df = pd.DataFrame(X_scaled, columns=self.X.columns) self.X = df class Calhouse(Dataset): def __init__(self): super().__init__() self.params = { "objective": "reg:squarederror", "tree_method": "hist", "seed": 14, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: self.X, self.y = util.load_openml("calhouse", data_id=537) self.y = np.log(self.y) def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): #maximized return -metrics.mean_squared_error(y, raw_yhat) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class Allstate(Dataset): def __init__(self): super().__init__() self.params = { "objective": "reg:squarederror", "tree_method": "hist", "seed": 14, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: allstate_data_path = os.path.join(os.environ["VERITAS_DATA_DIR"], "allstate.h5") data = pd.read_hdf(allstate_data_path) self.X = data.drop(columns=["loss"]) self.y = data.loss def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): #maximized return -metrics.mean_squared_error(y, raw_yhat) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class Covtype(Dataset): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 235, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: self.X, self.y = util.load_openml("covtype", data_id=1596) self.y = (self.y==2) def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class CovtypeNormalized(Covtype): def __init__(self): super().__init__() def load_dataset(self): if self.X is None or self.y is None: super().load_dataset() self.minmax_normalize() class Higgs(Dataset): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 220, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: higgs_data_path = os.path.join(os.environ["VERITAS_DATA_DIR"], "higgs.h5") self.X = pd.read_hdf(higgs_data_path, "X") self.y = pd.read_hdf(higgs_data_path, "y") def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class LargeHiggs(Dataset): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 220, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: higgs_data_path = os.path.join(os.environ["VERITAS_DATA_DIR"], "higgs_large.h5") data = pd.read_hdf(higgs_data_path) self.y = data[0] self.X = data.drop(columns=[0]) columns = [f"a{i}" for i in range(self.X.shape[1])] self.X.columns = columns self.minmax_normalize() def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class Mnist(Dataset): def __init__(self): super().__init__() self.params = { "num_class": 10, "objective": "multi:softmax", "tree_method": "hist", "eval_metric": "merror", "seed": 53589, "nthread": 4, } def load_dataset(self): if self.X is None or self.y is None: self.X, self.y = util.load_openml("mnist", data_id=554) def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, yhat): #maximized return metrics.accuracy_score(y, yhat) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtrees_from_multiclass_xgb_model(self.model, 10, feat2id_map=self.feat2id) for at in self.at: at.base_score = 0 class MnistNormalized(Mnist): def __init__(self): super().__init__() def load_dataset(self): if self.X is None or self.y is None: super().load_dataset() self.minmax_normalize() class Mnist2v6(Mnist): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 235, "nthread": 4, "subsample": 0.5, "colsample_bytree": 0.8, } def load_dataset(self): if self.X is None or self.y is None: super().load_dataset() self.X = self.X.loc[(self.y==2) | (self.y==6), :] self.y = self.y[(self.y==2) | (self.y==6)] self.y = (self.y == 2.0).astype(float) self.X.reset_index(inplace=True, drop=True) self.y.reset_index(inplace=True, drop=True) def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class FashionMnist(Dataset): def __init__(self): super().__init__() self.params = { "num_class": 10, "objective": "multi:softmax", "tree_method": "hist", "eval_metric": "merror", "seed": 132955, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: self.X, self.y = util.load_openml("fashion_mnist", data_id=40996) #self.minmax_normalize() def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, yhat): #maximized return metrics.accuracy_score(y, yhat) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtrees_from_multiclass_xgb_model(self.model, 10, feat2id_map=self.feat2id) for at in self.at: at.base_score = 0 class FashionMnist2v6(FashionMnist): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 235, "nthread": 4, "subsample": 0.5, "colsample_bytree": 0.8, } def load_dataset(self): if self.X is None or self.y is None: super().load_dataset() self.X = self.X.loc[(self.y==2) | (self.y==6), :] self.y = self.y[(self.y==2) | (self.y==6)] self.y = (self.y == 2.0).astype(float) self.X.reset_index(inplace=True, drop=True) self.y.reset_index(inplace=True, drop=True) def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class Ijcnn1(Dataset): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 235, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: ijcnn1_data_path = os.path.join(os.environ["VERITAS_DATA_DIR"], "ijcnn1.h5") self.X = pd.read_hdf(ijcnn1_data_path, "Xtrain") self.Xtest = pd.read_hdf(ijcnn1_data_path, "Xtest") columns = [f"a{i}" for i in range(self.X.shape[1])] self.X.columns = columns self.Xtest.columns = columns self.y = pd.read_hdf(ijcnn1_data_path, "ytrain") self.ytest = pd.read_hdf(ijcnn1_data_path, "ytest") self.minmax_normalize() def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0 class Webspam(Dataset): def __init__(self): super().__init__() self.params = { "objective": "binary:logistic", "eval_metric": "error", "tree_method": "hist", "seed": 732, "nthread": 1, } def load_dataset(self): if self.X is None or self.y is None: data_path = os.path.join(os.environ["VERITAS_DATA_DIR"], "webspam_wc_normalized_unigram.h5") self.X = pd.read_hdf(data_path, "X") self.X.columns = [f"a{i}" for i in range(self.X.shape[1])] self.y = pd.read_hdf(data_path, "y") self.minmax_normalize() def load_model(self, num_trees, tree_depth): model_name = self.get_model_name(num_trees, tree_depth) if not os.path.isfile(os.path.join(self.models_dir, f"{model_name}.xgb")): self.load_dataset() print(f"training model depth={tree_depth}, num_trees={num_trees}") def metric(y, raw_yhat): return metrics.accuracy_score(y, raw_yhat > 0) self.params["max_depth"] = tree_depth self.model, lr, metric_value = util.optimize_learning_rate(self.X, self.y, self.params, num_trees, metric) self.meta = {"lr": lr, "metric": metric_value, "columns": list(self.X.columns)} with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "wb") as f: pickle.dump(self.model, f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "w") as f: json.dump(self.meta, f) else: print(f"loading model from file: {model_name}") with open(os.path.join(self.models_dir, f"{model_name}.xgb"), "rb") as f: self.model = pickle.load(f) with open(os.path.join(self.models_dir, f"{model_name}.meta"), "r") as f: self.meta = json.load(f) feat2id_dict = {v: i for i, v in enumerate(self.meta["columns"])} self.feat2id = lambda x: feat2id_dict[x] self.at = addtree_from_xgb_model(self.model, feat2id_map=self.feat2id) self.at.base_score = 0

41.421227

104

0.577331

3,458

24,977

3.969057

0.054078

0.058361

0.043716

0.056102

0.92867

0.908998

0.894572

0.892168

0

0.010408

0.284502

24,977

602

105

41.490033

0.757596

0.007287

0

0.847695

0

0.138856

0.014164

0

1

0.106212

false

0

0.016032

0.024048

0.176353

0.044088

0

null

0

1

0

1

0

null

0

7

35d4f93c85bbe4dfb2f35081822fb95a5247a189

8,988

py

Python

robust_rmab/baselines/nature_baselines_sis.py

sjohnsonyu/cluster-level-robust-rmab

75bd09332907d5e215ab325e118856d5ada03405

[ "MIT", "BSD-3-Clause" ]

2

2021-07-10T08:31:28.000Z

2021-11-24T16:38:45.000Z

robust_rmab/baselines/nature_baselines_sis.py

sjohnsonyu/cluster-level-robust-rmab

75bd09332907d5e215ab325e118856d5ada03405

[ "MIT", "BSD-3-Clause" ]

null

robust_rmab/baselines/nature_baselines_sis.py

sjohnsonyu/cluster-level-robust-rmab

75bd09332907d5e215ab325e118856d5ada03405

[ "MIT", "BSD-3-Clause" ]

1

2021-11-24T17:21:27.000Z

import numpy as np import itertools # Don't use if you need deterministic (e.g., in main loop of double oracle) class RandomNaturePolicy: def __init__(self, nature_params, ind): self.nature_params=nature_params self.ind = ind self.name="Random_Nature" def __repr__(self): return "%s_%i"%(self.name, self.ind) def get_nature_action(self, o): actions = np.zeros((self.nature_params.shape[0],self.nature_params.shape[1])) for arm_i in range(actions.shape[0]): for param_i in range(actions.shape[1]): param_range = self.nature_params[arm_i, param_i, 1] - self.nature_params[arm_i, param_i, 0] param_lower = self.nature_params[arm_i, param_i, 0] actions[arm_i, param_i] = np.random.rand()*param_range + param_lower return actions def bound_nature_actions(self, actions, state=None, reshape=True): return actions # this is never going to work for SIS, too big # def get_policy_array(self, state_dim=0, N=0): # N = self.nature_params.shape[0] # S = self.nature_params.shape[1] # param_i = self.nature_params.shape[2] # all_states = list(itertools.product(np.arange(S), repeat=N)) # policy_array = np.zeros((len(all_states),N*A),dtype=float) # tup_to_ind = dict(zip(all_states,np.arange(len(all_states)))) # all_states = np.array(all_states) # for i, state in enumerate(all_states): # policy_array[i] = self.get_nature_action(state).reshape(-1) # return policy_array, tup_to_ind class PessimisticNaturePolicy: def __init__(self, nature_params, ind): self.nature_params=nature_params self.ind = ind self.name="Pessimist_Nature" self.param_setting = self.set_params() def __repr__(self): return "%s_%i"%(self.name, self.ind) def get_nature_action(self, o): return self.param_setting def set_params(self): param_setting = np.zeros((self.nature_params.shape[0],self.nature_params.shape[1])) for arm_i in range(param_setting.shape[0]): # infectivity -- pess is higher param_setting[arm_i, 0] = self.nature_params[arm_i, 0, 1] # num_contacts -- pess is higher param_setting[arm_i, 1] = self.nature_params[arm_i, 1, 1] # action effect -- pess is lower param_setting[arm_i, 2] = self.nature_params[arm_i, 2, 0] # action effect -- pess is lower param_setting[arm_i, 3] = self.nature_params[arm_i, 3, 0] return param_setting def bound_nature_actions(self, actions, state=None, reshape=True): return actions # def get_policy_array(self, state_dim=0, N=0): # N = self.nature_params.shape[0] # S = self.nature_params.shape[1] # A = self.nature_params.shape[2] # all_states = list(itertools.product(np.arange(S), repeat=N)) # policy_array = np.zeros((len(all_states),N*A),dtype=float) # tup_to_ind = dict(zip(all_states,np.arange(len(all_states)))) # all_states = np.array(all_states) # for i, state in enumerate(all_states): # policy_array[i] = self.get_nature_action(state).reshape(-1) # return policy_array, tup_to_ind class OptimisticNaturePolicy: def __init__(self, nature_params, ind): self.nature_params=nature_params self.ind = ind self.name="Optimist_Nature" self.param_setting = self.set_params() def __repr__(self): return "%s_%i"%(self.name, self.ind) def get_nature_action(self, o): return self.param_setting def set_params(self): param_setting = np.zeros((self.nature_params.shape[0],self.nature_params.shape[1])) for arm_i in range(param_setting.shape[0]): # infectivity -- optim is lower param_setting[arm_i, 0] = self.nature_params[arm_i, 0, 0] # num_contacts -- optim is lower param_setting[arm_i, 1] = self.nature_params[arm_i, 1, 0] # action effect -- optim is higher param_setting[arm_i, 2] = self.nature_params[arm_i, 2, 1] # action effect -- optim is higher param_setting[arm_i, 3] = self.nature_params[arm_i, 3, 1] return param_setting def bound_nature_actions(self, actions, state=None, reshape=True): return actions # def get_policy_array(self, state_dim=0, N=0): # N = self.nature_params.shape[0] # S = self.nature_params.shape[1] # A = self.nature_params.shape[2] # all_states = list(itertools.product(np.arange(S), repeat=N)) # policy_array = np.zeros((len(all_states),N*A),dtype=float) # tup_to_ind = dict(zip(all_states,np.arange(len(all_states)))) # all_states = np.array(all_states) # for i, state in enumerate(all_states): # policy_array[i] = self.get_nature_action(state).reshape(-1) # return policy_array, tup_to_ind class MiddleNaturePolicy: def __init__(self, nature_params, ind, perturbations=None, perturbation_size=0.1): self.nature_params=nature_params self.ind = ind self.perturbations = perturbations self.perturbation_size = perturbation_size self.name="Middle_Nature" self.param_setting = self.set_params() def __repr__(self): return "%s_%i"%(self.name, self.ind) def get_nature_action(self, o): return self.param_setting def set_params(self): param_setting = np.zeros((self.nature_params.shape[0],self.nature_params.shape[1])) for arm_i in range(param_setting.shape[0]): for param_i in range(param_setting.shape[1]): param_mean = self.nature_params[arm_i, param_i].mean() if self.perturbations is not None: param_range = np.ptp(self.nature_params[arm_i, param_i]) perturb_width = param_range*self.perturbation_size perturbation = self.perturbations[arm_i, param_i] perturbation = perturbation*perturb_width*2 - perturb_width print(self.nature_params[arm_i, param_i]) print(perturbation) print('before',param_mean) param_mean = param_mean + perturbation print('after',param_mean) print() param_setting[arm_i, param_i] = param_mean return param_setting def bound_nature_actions(self, actions, state=None, reshape=True): return actions # def get_policy_array(self, state_dim=0, N=0): # N = self.nature_params.shape[0] # S = self.nature_params.shape[1] # A = self.nature_params.shape[2] # all_states = list(itertools.product(np.arange(S), repeat=N)) # policy_array = np.zeros((len(all_states),N*A),dtype=float) # tup_to_ind = dict(zip(all_states,np.arange(len(all_states)))) # all_states = np.array(all_states) # for i, state in enumerate(all_states): # policy_array[i] = self.get_nature_action(state).reshape(-1) # return policy_array, tup_to_ind class SampledRandomNaturePolicy: def __init__(self, nature_params, ind): self.nature_params=nature_params self.param_setting=None self.ind = ind self.name="Sampled_Random_Nature_sis" # only run this once def sample_param_setting(self, seed): assert self.param_setting is None rand_state = np.random.RandomState() rand_state.seed(seed) shape = self.nature_params.shape[:-1] sample = rand_state.rand(*shape) range_upper = self.nature_params[:, :, 1] range_lower = self.nature_params[:, :, 0] sample = sample*(range_upper - range_lower) + range_lower self.param_setting = sample def __repr__(self): return "%s_%i"%(self.name, self.ind) def get_nature_action(self, o): return self.param_setting def bound_nature_actions(self, actions, state=None, reshape=True): return actions # we'll just settle for getting one sample from each... # def get_policy_array(self, state_dim=0, N=0): # N = self.nature_params.shape[0] # S = self.nature_params.shape[1] # A = self.nature_params.shape[2] # all_states = list(itertools.product(np.arange(S), repeat=N)) # policy_array = np.zeros((len(all_states),N*A),dtype=float) # tup_to_ind = dict(zip(all_states,np.arange(len(all_states)))) # all_states = np.array(all_states) # for i, state in enumerate(all_states): # policy_array[i] = self.get_nature_action(state).reshape(-1) # return policy_array, tup_to_ind

33.537313

107

0.624833

1,230

8,988

4.298374

0.102439

0.124835

0.151315

0.095328

0.781918

0.770758

0.762625

0.726499

0.716285

0.690183

0

0.011631

0.263462

8,988

267

108

33.662921

0.787009

0.346239

0

0.522124

0

0.020317

0.004304

0

0.00885

1

0.212389

false

0

0.017699

0.123894

0.433628

0.044248

0

null

0

1

0

null

0

1

0

1

0

7

ea31e19ebf0cde7622964b661c7077889dae0d9d

83

py

Python

basic/output.py

sdyz5210/python

78f9999f94d92d9ca7fde6f18acec7d3abd422ef

[ "BSD-3-Clause" ]

null

basic/output.py

sdyz5210/python

78f9999f94d92d9ca7fde6f18acec7d3abd422ef

[ "BSD-3-Clause" ]

null

basic/output.py

sdyz5210/python

78f9999f94d92d9ca7fde6f18acec7d3abd422ef

[ "BSD-3-Clause" ]

null

print 300 print 100+200 print '100 + 200 =',100+200 print 'my','name','is','summer'

20.75

31

0.662651

15

83

3.666667

0.533333

0.327273

0.4

0

0.287671

0.120482

83

4

31

20.75

0.465753

0

0.297619

0

null

0

null

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

8

ea37b015cd46684c28d356372205dd39554e8fca

181,901

py

Python

fireworks/fireworks_compare_bounds.py

ermongroup/adaptive_hashing

b6e0cfb9c81881d7935e4ff7444438bddd56e7dc

[ "MIT" ]

null

fireworks/fireworks_compare_bounds.py

ermongroup/adaptive_hashing

b6e0cfb9c81881d7935e4ff7444438bddd56e7dc

[ "MIT" ]

1

2019-07-27T08:47:56.000Z

fireworks/fireworks_compare_bounds.py

ermongroup/adaptive_hashing

b6e0cfb9c81881d7935e4ff7444438bddd56e7dc

[ "MIT" ]

1

2020-08-13T21:12:07.000Z

from __future__ import division # from sat import SAT, get_variable_subset import time import os import math import resource import decimal from fireworks import Firework, Workflow, FWorker, LaunchPad from fireworks.utilities.fw_utilities import explicit_serialize from fireworks.core.firework import FWAction, FireTaskBase import sys sys.path.insert(0, "/atlas/u/jkuck/F2/src/python/") from f2 import sharp_sat_call_from_python, find_lower_bound_call_from_python, execute_cmd #True: run locally #False: run remotely on cluster TEST_LOCAL = False DSHARP_EXECUTABLE = '/atlas/u/jkuck/dsharp/dsharp' if TEST_LOCAL: from fireworks.core.rocket_launcher import rapidfire else: from fireworks.queue.queue_launcher import rapidfire from fireworks.user_objects.queue_adapters.common_adapter import CommonAdapter from fw_tutorials.dynamic_wf.fibadd_task import FibonacciAdderTask from cluster_config import HOME_DIRECTORY, MONGODB_USERNAME, MONGODB_PASSWORD from experiment_config import MONGODB_HOST, MONGODB_PORT, MONGODB_NAME import numpy as np # Add the following lines to the file ~/.bashrc.user on Atlas: # export PYTHONPATH="/atlas/u/jkuck/F2:$PYTHONPATH" # export PYTHONPATH="/atlas/u/jkuck/F2/fireworks:$PYTHONPATH" # $ source ~/.bashrc.user # $ cd /atlas/u/jkuck/F2/fireworks/venv_f2 # $ source bin/activate # $ cd ../ # $ python fireworks_compare_bounds.py NJOBS_QUEUE = 200 m_ranges = {#'c432.isc': range(25, 42), #log_2(Z) = 36.1 'c432.isc': range(25, 46), #log_2(Z) = 36.1 'c499.isc': range(30, 51), #log_2(Z) = 41.0 'c880.isc': range(50, 71), #log_2(Z) = 60.0 'c1355.isc': range(30, 51), #log_2(Z) = 41.0 'c1908.isc': range(20, 44), #log_2(Z) = 33.0 'c2670.isc': range(220, 265), #log_2(Z) = 233 'sat-grid-pbl-0010.cnf': range(65, 95), #log_2(Z) = 78.9 'sat-grid-pbl-0015.cnf': range(170, 210), #log_2(Z) = 180.9 'sat-grid-pbl-0020.cnf': range(310, 350), #log_2(Z) = 318 'ra.cnf': range(920, 1000), #log_2(Z) = 951.0 'tire-1.cnf': range(20, 40), #log_2(Z) = 29.4 #range(27, 32), #range(20, 40), 'tire-2.cnf': range(30, 55), #log_2(Z) = 39.4 #range(27, 32), #range(20, 40), 'tire-3.cnf': range(25, 55), #log_2(Z) = 37.7 #range(27, 32), #range(20, 40), 'tire-4.cnf': range(35, 60), #log_2(Z) = 46.6 #range(27, 32), #range(20, 40), 'log-1.cnf': range(60, 85), #log_2(Z) = 69.0 'log-2.cnf': range(30, 45), #log_2(Z) = 34.9 'lang12.cnf': range(10, 26), #log_2(Z) = 'hypercube.cnf': range(80, 100), #log_2(Z) = 90 'hypercube1.cnf': range(40, 60), #log_2(Z) = 50 'hypercube2.cnf': range(1, 20), #log_2(Z) = 10 'hypercube3.cnf': range(1, 30), #log_2(Z) = 10 'hypercube4.cnf': range(10, 40), #log_2(Z) = 20 'hypercube5.cnf': range(40, 70), #log_2(Z) = 50 'hypercube6.cnf': range(90, 120), #log_2(Z) = 100 'hypercube7.cnf': range(490, 530), #log_2(Z) = 500 } m_ranges = {#'c432.isc': range(25, 42), #log_2(Z) = 36.1 'c432.isc': range(18, 46), #log_2(Z) = 36.1 'c499.isc': range(20, 49), #log_2(Z) = 41.0 'c880.isc': range(40, 62), #log_2(Z) = 60.0 'c1355.isc': range(31, 38), #log_2(Z) = 41.0 'c1908.isc': range(20, 44), #log_2(Z) = 33.0 'c2670.isc': range(180, 240), #log_2(Z) = 233 'sat-grid-pbl-0010.cnf': range(55, 85), #log_2(Z) = 78.9 'sat-grid-pbl-0015.cnf': range(150, 190), #log_2(Z) = 180.9 'sat-grid-pbl-0020.cnf': range(270, 325), #log_2(Z) = 318 'ra.cnf': range(870, 1000), #log_2(Z) = 951.0 'tire-1.cnf': range(15, 38), #log_2(Z) = 29.4 #range(27, 32), #range(20, 40), 'tire-2.cnf': range(23, 47), #log_2(Z) = 39.4 #range(27, 32), #range(20, 40), 'tire-3.cnf': range(24, 46), #log_2(Z) = 37.7 #range(27, 32), #range(20, 40), 'tire-4.cnf': range(28, 55), #log_2(Z) = 46.6 #range(27, 32), #range(20, 40), 'log-1.cnf': range(55, 75), #log_2(Z) = 69.0 'log-2.cnf': range(22, 43), #log_2(Z) = 34.9 'lang12.cnf': range(6, 26), #log_2(Z) = 'hypercube.cnf': range(70, 100), #log_2(Z) = 90 'hypercube1.cnf': range(33, 60), #log_2(Z) = 50 'hypercube2.cnf': range(1, 20), #log_2(Z) = 10 'hypercube3.cnf': range(1, 30), #log_2(Z) = 10 'hypercube4.cnf': range(10, 40), #log_2(Z) = 20 'hypercube5.cnf': range(40, 70), #log_2(Z) = 50 'hypercube6.cnf': range(90, 120), #log_2(Z) = 100 'hypercube7.cnf': range(490, 530), #log_2(Z) = 500 } if TEST_LOCAL: f_ranges = {'c432.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], #'c432.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 7)], #'c432.isc': [.0001, .001], 'c499.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'lang12.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c880.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c1355.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c1908.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c2670.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'sat-grid-pbl-0010.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], # 'sat-grid-pbl-0015.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'sat-grid-pbl-0015.cnf': [i/2000.0 for i in range(20,40)], 'sat-grid-pbl-0020.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'ra.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-3.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-4.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], # 'log-1.cnf': [i/100.0 for i in range(20, 50)], 'log-1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'log-2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube3.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube4.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube5.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube6.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube7.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], } else: f_ranges = {'c432.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], #'c432.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 7)], #'c432.isc': [.0001, .001], 'c499.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'lang12.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c880.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c1355.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c1908.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'c2670.isc': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'sat-grid-pbl-0010.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], # 'sat-grid-pbl-0015.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'sat-grid-pbl-0015.cnf': [i/2000.0 for i in range(20,60)], 'sat-grid-pbl-0020.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'ra.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-3.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'tire-4.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'log-1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'log-2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube1.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube2.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube3.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube4.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube5.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube6.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], 'hypercube7.cnf': [i/1000.0 for i in range(1,10)] + [i/100.0 for i in range(1, 50)], } #logger = open('heatmap_result_moreModels2/speed=%d.txt' % (m), "w") @explicit_serialize class RunSpecificExperimentBatch(FireTaskBase): def run_task(self, fw_spec): # RESULTS_DIRECTORY = '/atlas/u/jkuck/XORModelCount/SATModelCount/fireworks/postNIPS/extended_MF_valsTEST/%s' % fw_spec['problem_name'].split('.')[0] if TEST_LOCAL: RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/local_results' else: # RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_fixSharpSat' # RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_orderVarsByMarginals_chunksRandom' # RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_orderVarsByDOUBLEMarginals_chunksAssignmentProblem' # RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_orderVarsByMarginals_randomInChunks_postUAI1' # RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_UAIcameraReadyDsharp1' RESULTS_DIRECTORY = '/atlas/u/jkuck/F2/fireworks/cluster_results_UAIcameraReadyDsharp_LBconfidenceFixed' if not os.path.exists(RESULTS_DIRECTORY): os.makedirs(RESULTS_DIRECTORY) filename = '%s/%s.txt'%\ (RESULTS_DIRECTORY, fw_spec['problem_name'].split('.cnf')[0]) logger = open(filename, 'w') logger.write('repeats_of_randomized_hashing_methods: %s\n' % (fw_spec['repeats'])) logger.close() ####### CHECK IF DSHARP CAN SOLVE THE PROBLEM QUICKLY, IF SO RETURN EARLY ####### time_out, solution_count, dsharp_time = dsharp_call_from_python(problem_name=fw_spec['problem_name'], time_limit=2) if not time_out: logger = open(filename, 'a') logger.write("dsharp time_out: %s solution_count: %s dsharp_time: %s\n" % (time_out, solution_count, dsharp_time)) logger.close() return 0 # ####### CHECK IF SHARPSAT CAN SOLVE THE PROBLEM QUICKLY, IF SO RETURN EARLY ####### # time_out, solution_count, sharp_sat_time = sharp_sat_call_from_python(problem_name=fw_spec['problem_name'], time_limit=2) # if not time_out: # logger = open(filename, 'a') # logger.write("sharpSAT time_out: %s solution_count: %s sharp_sat_time: %s\n" % (time_out, solution_count, sharp_sat_time)) # logger.close() # return 0 # ####### RUN EXPERIMENT: F2 with 1 ones per column, order variables by marginals, T=1 solutions ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # 'sum_of_T_solutions':1, # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_1_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) # logger.close() # if sat_solver_time > 500: # break ####### RUN EXPERIMENT: F2 with 1 ones per column, T=1 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':1, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=1, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_1_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 5000: break ####### RUN EXPERIMENT: F2 with 1 ones per column, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=1, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_1_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 5000: break ####### RUN EXPERIMENT: F2 with 1 ones per column, order variables by marginals, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=1, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_1_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 5000: break TEST_FEWER_REPEATS = False if TEST_FEWER_REPEATS: ####### RUN EXPERIMENT: F2 with 1 ones per column, order variables by marginals, T=3 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':3, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=1, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_1_Tsol_3 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break ####### RUN EXPERIMENT: F2 with 1 ones per column, order variables by marginals, T=1 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':1, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=1, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_1_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break # ####### RUN EXPERIMENT: F2 with 1 ones per column, order variables by 'double' marginals ####### # for random_seed in range(fw_spec['repeats']): # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1, method='bi_regular_order_vars_by_double_marginals', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("biregular_order_vars_by_doubleMarginals_assignmentProblem_variable_degree_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) # logger.close() # if sat_solver_time > 500: # break TEST_HIGHER_DENSITY = False if TEST_HIGHER_DENSITY: #try longer constraints, 2 ones per column ####### RUN EXPERIMENT: F2 with 2 ones per column, T=1 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':1, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=2, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_2_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break ####### RUN EXPERIMENT: F2 with 2 ones per column, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=2, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_2_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break ####### RUN EXPERIMENT: F2 with 2 ones per column, order variables by marginals, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=2, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_2_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break #try longer constraints, 3 ones per column ####### RUN EXPERIMENT: F2 with 3 ones per column, T=1 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':1, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=3, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_3_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break ####### RUN EXPERIMENT: F2 with 3 ones per column, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=3, method='original', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_variable_degree_3_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break ####### RUN EXPERIMENT: F2 with 3 ones per column, order variables by marginals, T=10 solutions ####### for random_seed in range(fw_spec['repeats']): extra_configs = { 'sum_of_T_solutions':10, } lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ random_seed=random_seed, var_degree=3, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=extra_configs, time_limit=5000) logger = open(filename, 'a') logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_3_Tsol_10 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) logger.close() if sat_solver_time > 500: break # ####### RUN EXPERIMENT: F2 with 1.5 ones per column, T=1 solutions ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # 'sum_of_T_solutions':1, # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1.5, method='original', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("biregular_variable_degree_1.5_Tsol_1 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) # logger.close() # if sat_solver_time > 500: # break # ####### RUN EXPERIMENT: F2 with 1.5 ones per column, order variables by marginals ####### # for random_seed in range(fw_spec['repeats']): # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1.5, method='bi_regular_order_vars_by_marginals_randomChunks', extra_configs=None, time_limit=5000) # logger = open(filename, 'a') # logger.write("biregular_order_vars_by_marginals_assignmentProblem_variable_degree_1.5 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s parallel_runtime: %s\n" % (time_out, lb, sat_solver_time, random_seed, parallel_runtime)) # logger.close() # if sat_solver_time > 500: # break # exit(0) # ####### RUN EXPERIMENT: F2 with 3 ones per column ####### # for random_seed in range(fw_spec['repeats']): # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=3, method='original', extra_configs=None, time_limit=5000) # logger = open(filename, 'a') # logger.write("biregular_variable_degree_3 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s\n" % (time_out, lb, sat_solver_time, random_seed)) # logger.close() # if sat_solver_time > 500: # break # ####### RUN EXPERIMENT: F2 with long (iid .5) constraints ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # #density of ones in constraint matrix for method = 'iid' # 'f': .5, # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=3, method='iid', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("long_iid_.5 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s\n" % (time_out, lb, sat_solver_time, random_seed)) # logger.close() # if sat_solver_time > 500: # break # ####### RUN EXPERIMENT: F2 biregular constraints, sample entire matrix and look at marginals ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # #when sampling biregular matrices such that each constraint has a good marginal, # #how do we deal with the 'problem constraints' at the end? # # - 'iid': sample them iid, give up biregular # # - 'keep_biregular': leave them as biregular, give up good marginals # 'biregular_marginal_problem_constraint': 'iid', # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1.5, method='bi_regular_marginals_joint_constraint', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("bi_regular_marginals_joint_constraint_variable_degree_1.5 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s\n" % (time_out, lb, sat_solver_time, random_seed)) # logger.close() # if sat_solver_time > 500: # break # ####### RUN EXPERIMENT: F2 biregular constraints, sample entire matrix and look at marginals of each constraint ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # #when sampling biregular matrices such that each constraint has a good marginal, # #how do we deal with the 'problem constraints' at the end? # # - 'iid': sample them iid, give up biregular # # - 'keep_biregular': leave them as biregular, give up good marginals # 'biregular_marginal_problem_constraint': 'iid', # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1.5, method='bi_regular_marginals_per_constraint', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("bi_regular_marginals_per_constraint_iid_variable_degree_1.5 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s\n" % (time_out, lb, sat_solver_time, random_seed)) # logger.close() # if sat_solver_time > 500: # break # ####### RUN EXPERIMENT: F2 biregular constraints, sample entire matrix and look at marginals of each constraint ####### # for random_seed in range(fw_spec['repeats']): # extra_configs = { # #when sampling biregular matrices such that each constraint has a good marginal, # #how do we deal with the 'problem constraints' at the end? # # - 'iid': sample them iid, give up biregular # # - 'keep_biregular': leave them as biregular, give up good marginals # 'biregular_marginal_problem_constraint': 'keep_biregular', # } # lb, sat_solver_time, time_out, parallel_runtime = find_lower_bound_call_from_python(problem_name=fw_spec['problem_name'],\ # random_seed=random_seed, var_degree=1.5, method='bi_regular_marginals_per_constraint', extra_configs=extra_configs, time_limit=5000) # logger = open(filename, 'a') # logger.write("bi_regular_marginals_per_constraint_keep_biregular_variable_degree_1.5 time_out: %s lower_bound: %s sat_solver_time: %s random_seed: %s\n" % (time_out, lb, sat_solver_time, random_seed)) # logger.close() # if sat_solver_time > 500: # break ####### RUN EXPERIMENT IN ONE FILE: SHARPSAT ####### time_out, solution_count, sharp_sat_time = sharp_sat_call_from_python(problem_name=fw_spec['problem_name'], time_limit=5000) logger = open(filename, 'a') logger.write("sharpSAT time_out: %s solution_count: %s sharp_sat_time: %s\n" % (time_out, solution_count, sharp_sat_time)) logger.close() #etc.... ####### CHECK IF DSHARP CAN SOLVE THE PROBLEM QUICKLY, IF SO RETURN EARLY ####### time_out, solution_count, dsharp_time = dsharp_call_from_python(problem_name=fw_spec['problem_name'], time_limit=5000) logger = open(filename, 'a') logger.write("dsharp time_out: %s solution_count: %s dsharp_time: %s\n" % (time_out, solution_count, dsharp_time)) logger.close() def create_launchpad(): with open('./my_launchpad.yaml', 'w') as f: f.write('host: %s\n' % MONGODB_HOST) f.write('port: %d\n' % MONGODB_PORT) f.write('name: %s\n' % MONGODB_NAME) f.write('username: %s\n' % MONGODB_USERNAME) f.write('password: %s\n' % MONGODB_PASSWORD) f.write('logdir: null\n') f.write('strm_lvl: INFO\n') def run_experiment(): ''' ''' # write new launchpad file create_launchpad() # set up the LaunchPad and reset it launchpad = LaunchPad(host=MONGODB_HOST, port=MONGODB_PORT, name=MONGODB_NAME, username=MONGODB_USERNAME, password=MONGODB_PASSWORD, logdir=None, strm_lvl='INFO', user_indices=None, wf_user_indices=None) # logdir=None, strm_lvl='INFO', user_indices=None, wf_user_indices=None, ssl_ca_file=None) launchpad.reset('', require_password=False) all_fireworks = [] if TEST_LOCAL: PROBLEM_NAMES = ['01A-1.cnf.gz.no_w.cnf'] # PROBLEM_NAMES = ['54.sk_12_97.cnf.gz.no_w.cnf'] # PROBLEM_NAMES = ['01A-1.cnf.gz.no_w.cnf', '01B-1.cnf.gz.no_w.cnf', '01B-2.cnf.gz.no_w.cnf', '01B-3.cnf.gz.no_w.cnf', '01B-4.cnf.gz.no_w.cnf', '01B-5.cnf.gz.no_w.cnf', '02A-1.cnf.gz.no_w.cnf', '02A-2.cnf.gz.no_w.cnf', '02A-3.cnf.gz.no_w.cnf', '02B-1.cnf.gz.no_w.cnf', '02B-2.cnf.gz.no_w.cnf', '02B-3.cnf.gz.no_w.cnf', '02B-4.cnf.gz.no_w.cnf', '02B-5.cnf.gz.no_w.cnf', '03A-1.cnf.gz.no_w.cnf', '03A-2.cnf.gz.no_w.cnf', '03B-1.cnf.gz.no_w.cnf', '03B-2.cnf.gz.no_w.cnf', '03B-3.cnf.gz.no_w.cnf', '03B-4.cnf.gz.no_w.cnf', '04A-1.cnf.gz.no_w.cnf', '04A-2.cnf.gz.no_w.cnf', '04A-3.cnf.gz.no_w.cnf', '04B-1.cnf.gz.no_w.cnf', '04B-2.cnf.gz.no_w.cnf', '04B-3.cnf.gz.no_w.cnf', '04B-3.cnf.gz.no_w.cnf.gz.log', '04B-4.cnf.gz.no_w.cnf', '05A-1.cnf.gz.no_w.cnf', '05A-2.cnf.gz.no_w.cnf', '05B-1.cnf.gz.no_w.cnf', '05B-2.cnf.gz.no_w.cnf', '05B-3.cnf.gz.no_w.cnf', '06A-1.cnf.gz.no_w.cnf', '06A-2.cnf.gz.no_w.cnf', '06A-3.cnf.gz.no_w.cnf', '06A-4.cnf.gz.no_w.cnf', '06B-1.cnf.gz.no_w.cnf', '06B-2.cnf.gz.no_w.cnf', '06B-3.cnf.gz.no_w.cnf', '06B-4.cnf.gz.no_w.cnf', '07A-1.cnf.gz.no_w.cnf', '07A-2.cnf.gz.no_w.cnf', '07A-3.cnf.gz.no_w.cnf', '07A-4.cnf.gz.no_w.cnf', '07A-5.cnf.gz.no_w.cnf', '07B-1.cnf.gz.no_w.cnf', '07B-2.cnf.gz.no_w.cnf', '07B-3.cnf.gz.no_w.cnf', '07B-4.cnf.gz.no_w.cnf', '07B-5.cnf.gz.no_w.cnf', '07B-6.cnf.gz.no_w.cnf', '08A-1.cnf.gz.no_w.cnf', '08A-2.cnf.gz.no_w.cnf', '08A-3.cnf.gz.no_w.cnf', '08A-4.cnf.gz.no_w.cnf', '08B-1.cnf.gz.no_w.cnf', '08B-2.cnf.gz.no_w.cnf', '08B-3.cnf.gz.no_w.cnf', '08B-4.cnf.gz.no_w.cnf', '09A-1.cnf.gz.no_w.cnf', '09A-2.cnf.gz.no_w.cnf', '09A-3.cnf.gz.no_w.cnf', '09B-1.cnf.gz.no_w.cnf', '09B-2.cnf.gz.no_w.cnf', '09B-3.cnf.gz.no_w.cnf', '09B-4.cnf.gz.no_w.cnf', '09B-5.cnf.gz.no_w.cnf', '09B-6.cnf.gz.no_w.cnf', '107.sk_3_90.cnf.gz.no_w.cnf', '109.sk_4_36.cnf.gz.no_w.cnf', '10A-1.cnf.gz.no_w.cnf', '10A-2.cnf.gz.no_w.cnf', '10A-3.cnf.gz.no_w.cnf', '10A-4.cnf.gz.no_w.cnf', '10B-10.cnf.gz.no_w.cnf', '10B-11.cnf.gz.no_w.cnf', '10B-1.cnf.gz.no_w.cnf', '10B-2.cnf.gz.no_w.cnf', '10B-3.cnf.gz.no_w.cnf', '10B-4.cnf.gz.no_w.cnf', '10B-5.cnf.gz.no_w.cnf', '10B-6.cnf.gz.no_w.cnf', '10B-7.cnf.gz.no_w.cnf', '10B-8.cnf.gz.no_w.cnf', '10B-9.cnf.gz.no_w.cnf', '10.sk_1_46.cnf.gz.no_w.cnf', '110.sk_3_88.cnf.gz.no_w.cnf', '111.sk_2_36.cnf.gz.no_w.cnf', '11A-1.cnf.gz.no_w.cnf', '11A-2.cnf.gz.no_w.cnf', '11A-3.cnf.gz.no_w.cnf', '11A-4.cnf.gz.no_w.cnf', '11B-1.cnf.gz.no_w.cnf', '11B-2.cnf.gz.no_w.cnf', '11B-3.cnf.gz.no_w.cnf', '11B-4.cnf.gz.no_w.cnf', '11B-5.cnf.gz.no_w.cnf', '12A-1.cnf.gz.no_w.cnf', '12A-2.cnf.gz.no_w.cnf', '12A-3.cnf.gz.no_w.cnf', '12A-4.cnf.gz.no_w.cnf', '12B-1.cnf.gz.no_w.cnf', '12B-2.cnf.gz.no_w.cnf', '12B-3.cnf.gz.no_w.cnf', '12B-4.cnf.gz.no_w.cnf', '12B-5.cnf.gz.no_w.cnf', '12B-6.cnf.gz.no_w.cnf', '13A-1.cnf.gz.no_w.cnf', '13A-2.cnf.gz.no_w.cnf', '13A-3.cnf.gz.no_w.cnf', '13A-4.cnf.gz.no_w.cnf', '13B-1.cnf.gz.no_w.cnf', '13B-2.cnf.gz.no_w.cnf', '13B-3.cnf.gz.no_w.cnf', '13B-4.cnf.gz.no_w.cnf', '13B-5.cnf.gz.no_w.cnf', '14A-1.cnf.gz.no_w.cnf', '14A-2.cnf.gz.no_w.cnf', '14A-3.cnf.gz.no_w.cnf', '15A-1.cnf.gz.no_w.cnf', '15A-2.cnf.gz.no_w.cnf', '15A-3.cnf.gz.no_w.cnf', '15A-4.cnf.gz.no_w.cnf', '15B-1.cnf.gz.no_w.cnf', '15B-2.cnf.gz.no_w.cnf', '15B-3.cnf.gz.no_w.cnf', '15B-4.cnf.gz.no_w.cnf', '15B-5.cnf.gz.no_w.cnf', '17A-1.cnf.gz.no_w.cnf', '17A-2.cnf.gz.no_w.cnf', '17A-3.cnf.gz.no_w.cnf', '17A-4.cnf.gz.no_w.cnf', '17A-5.cnf.gz.no_w.cnf', '17A-6.cnf.gz.no_w.cnf', '17B-1.cnf.gz.no_w.cnf', '17B-2.cnf.gz.no_w.cnf', '17B-3.cnf.gz.no_w.cnf', '17B-4.cnf.gz.no_w.cnf', '17B-5.cnf.gz.no_w.cnf', '17.sk_3_45.cnf.gz.no_w.cnf', '18A-1.cnf.gz.no_w.cnf', '18A-2.cnf.gz.no_w.cnf', '18A-3.cnf.gz.no_w.cnf', '18A-4.cnf.gz.no_w.cnf', '19.sk_3_48.cnf.gz.no_w.cnf', '20.sk_1_51.cnf.gz.no_w.cnf', '27.sk_3_32.cnf.gz.no_w.cnf', '29.sk_3_45.cnf.gz.no_w.cnf', '30.sk_5_76.cnf.gz.no_w.cnf', '32.sk_4_38.cnf.gz.no_w.cnf', '35.sk_3_52.cnf.gz.no_w.cnf', '36.sk_3_77.cnf.gz.no_w.cnf', '4step.cnf.gz.no_w.cnf', '50-10-10-q.cnf.gz.no_w.cnf', '50-10-1-q.cnf.gz.no_w.cnf', '50-10-2-q.cnf.gz.no_w.cnf', '50-10-3-q.cnf.gz.no_w.cnf', '50-10-4-q.cnf.gz.no_w.cnf', '50-10-5-q.cnf.gz.no_w.cnf', '50-10-6-q.cnf.gz.no_w.cnf', '50-10-7-q.cnf.gz.no_w.cnf', '50-10-8-q.cnf.gz.no_w.cnf', '50-10-9-q.cnf.gz.no_w.cnf', '50-12-10-q.cnf.gz.no_w.cnf', '50-12-1-q.cnf.gz.no_w.cnf', '50-12-2-q.cnf.gz.no_w.cnf', '50-12-3-q.cnf.gz.no_w.cnf', '50-12-4-q.cnf.gz.no_w.cnf', '50-12-5-q.cnf.gz.no_w.cnf', '50-12-6-q.cnf.gz.no_w.cnf', '50-12-7-q.cnf.gz.no_w.cnf', '50-12-8-q.cnf.gz.no_w.cnf', '50-12-9-q.cnf.gz.no_w.cnf', '50-14-10-q.cnf.gz.no_w.cnf', '50-14-1-q.cnf.gz.no_w.cnf', '50-14-2-q.cnf.gz.no_w.cnf', '50-14-3-q.cnf.gz.no_w.cnf', '50-14-4-q.cnf.gz.no_w.cnf', '50-14-5-q.cnf.gz.no_w.cnf', 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's382_3_2.cnf.gz.no_w.cnf', 's382_7_4.cnf.gz.no_w.cnf', 's38417_15_7.cnf.gz.no_w.cnf', 's38417_3_2.cnf.gz.no_w.cnf', 's38417_7_4.cnf.gz.no_w.cnf', 's38584_15_7.cnf.gz.no_w.cnf', 's38584_3_2.cnf.gz.no_w.cnf', 's38584_7_4.cnf.gz.no_w.cnf', 's420_15_7.cnf.gz.no_w.cnf', 's420_3_2.cnf.gz.no_w.cnf', 's420_7_4.cnf.gz.no_w.cnf', 's420_new1_15_7.cnf.gz.no_w.cnf', 's420_new1_3_2.cnf.gz.no_w.cnf', 's420_new_15_7.cnf.gz.no_w.cnf', 's420_new1_7_4.cnf.gz.no_w.cnf', 's420_new_3_2.cnf.gz.no_w.cnf', 's420_new_7_4.cnf.gz.no_w.cnf', 's444_15_7.cnf.gz.no_w.cnf', 's444_3_2.cnf.gz.no_w.cnf', 's444_7_4.cnf.gz.no_w.cnf', 's510_15_7.cnf.gz.no_w.cnf', 's510_3_2.cnf.gz.no_w.cnf', 's510_7_4.cnf.gz.no_w.cnf', 's526_15_7.cnf.gz.no_w.cnf', 's526_3_2.cnf.gz.no_w.cnf', 's526_7_4.cnf.gz.no_w.cnf', 's526a_15_7.cnf.gz.no_w.cnf', 's526a_3_2.cnf.gz.no_w.cnf', 's526a_7_4.cnf.gz.no_w.cnf', 's5378a_15_7.cnf.gz.no_w.cnf', 's5378a_3_2.cnf.gz.no_w.cnf', 's5378a_7_4.cnf.gz.no_w.cnf', 's641_15_7.cnf.gz.no_w.cnf', 's641_3_2.cnf.gz.no_w.cnf', 's641_7_4.cnf.gz.no_w.cnf', 's713_15_7.cnf.gz.no_w.cnf', 's713_3_2.cnf.gz.no_w.cnf', 's713_7_4.cnf.gz.no_w.cnf', 's820a_15_7.cnf.gz.no_w.cnf', 's820a_3_2.cnf.gz.no_w.cnf', 's820a_7_4.cnf.gz.no_w.cnf', 's832a_15_7.cnf.gz.no_w.cnf', 's832a_3_2.cnf.gz.no_w.cnf', 's832a_7_4.cnf.gz.no_w.cnf', 's838_15_7.cnf.gz.no_w.cnf', 's838_3_2.cnf.gz.no_w.cnf', 's838_7_4.cnf.gz.no_w.cnf', 's9234a_15_7.cnf.gz.no_w.cnf', 's9234a_3_2.cnf.gz.no_w.cnf', 's9234a_7_4.cnf.gz.no_w.cnf', 's953a_15_7.cnf.gz.no_w.cnf', 's953a_3_2.cnf.gz.no_w.cnf', 's953a_7_4.cnf.gz.no_w.cnf', 'SetTest.sk_9_21.cnf.gz.no_w.cnf', 'signedAvg.sk_8_1020.cnf.gz.no_w.cnf', 'sort.sk_8_52.cnf.gz.no_w.cnf', 'tableBasedAddition.sk_240_1024.cnf.gz.no_w.cnf', 'tire-1.cnf.gz.no_w.cnf', 'tire-2.cnf.gz.no_w.cnf', 'tire-3.cnf.gz.no_w.cnf', 'tire-4.cnf.gz.no_w.cnf', 'tutorial1.sk_1_1.cnf.gz.no_w.cnf', 'tutorial2.sk_3_4.cnf.gz.no_w.cnf', 'tutorial3.sk_4_31.cnf.gz.no_w.cnf', 'UserServiceImpl.sk_8_32.cnf.gz.no_w.cnf', 'xpose.sk_6_134.cnf.gz.no_w.cnf'] else: # PROBLEM_NAMES = ['01A-1.cnf.gz.no_w.cnf', '01B-1.cnf.gz.no_w.cnf'] # PROBLEM_NAMES = ['75-18-7-q.cnf.gz.no_w.cnf'] # all problems PROBLEM_NAMES = ['01A-1.cnf.gz.no_w.cnf', '01B-1.cnf.gz.no_w.cnf', '01B-2.cnf.gz.no_w.cnf', '01B-3.cnf.gz.no_w.cnf', '01B-4.cnf.gz.no_w.cnf', '01B-5.cnf.gz.no_w.cnf', '02A-1.cnf.gz.no_w.cnf', '02A-2.cnf.gz.no_w.cnf', '02A-3.cnf.gz.no_w.cnf', '02B-1.cnf.gz.no_w.cnf', '02B-2.cnf.gz.no_w.cnf', '02B-3.cnf.gz.no_w.cnf', '02B-4.cnf.gz.no_w.cnf', '02B-5.cnf.gz.no_w.cnf', '03A-1.cnf.gz.no_w.cnf', '03A-2.cnf.gz.no_w.cnf', '03B-1.cnf.gz.no_w.cnf', '03B-2.cnf.gz.no_w.cnf', '03B-3.cnf.gz.no_w.cnf', '03B-4.cnf.gz.no_w.cnf', '04A-1.cnf.gz.no_w.cnf', '04A-2.cnf.gz.no_w.cnf', '04A-3.cnf.gz.no_w.cnf', '04B-1.cnf.gz.no_w.cnf', '04B-2.cnf.gz.no_w.cnf', '04B-3.cnf.gz.no_w.cnf', '04B-3.cnf.gz.no_w.cnf.gz.log', '04B-4.cnf.gz.no_w.cnf', '05A-1.cnf.gz.no_w.cnf', '05A-2.cnf.gz.no_w.cnf', '05B-1.cnf.gz.no_w.cnf', '05B-2.cnf.gz.no_w.cnf', '05B-3.cnf.gz.no_w.cnf', '06A-1.cnf.gz.no_w.cnf', '06A-2.cnf.gz.no_w.cnf', '06A-3.cnf.gz.no_w.cnf', '06A-4.cnf.gz.no_w.cnf', '06B-1.cnf.gz.no_w.cnf', '06B-2.cnf.gz.no_w.cnf', '06B-3.cnf.gz.no_w.cnf', '06B-4.cnf.gz.no_w.cnf', '07A-1.cnf.gz.no_w.cnf', '07A-2.cnf.gz.no_w.cnf', '07A-3.cnf.gz.no_w.cnf', '07A-4.cnf.gz.no_w.cnf', '07A-5.cnf.gz.no_w.cnf', '07B-1.cnf.gz.no_w.cnf', '07B-2.cnf.gz.no_w.cnf', '07B-3.cnf.gz.no_w.cnf', '07B-4.cnf.gz.no_w.cnf', '07B-5.cnf.gz.no_w.cnf', '07B-6.cnf.gz.no_w.cnf', '08A-1.cnf.gz.no_w.cnf', '08A-2.cnf.gz.no_w.cnf', '08A-3.cnf.gz.no_w.cnf', '08A-4.cnf.gz.no_w.cnf', '08B-1.cnf.gz.no_w.cnf', '08B-2.cnf.gz.no_w.cnf', '08B-3.cnf.gz.no_w.cnf', '08B-4.cnf.gz.no_w.cnf', '09A-1.cnf.gz.no_w.cnf', '09A-2.cnf.gz.no_w.cnf', '09A-3.cnf.gz.no_w.cnf', '09B-1.cnf.gz.no_w.cnf', '09B-2.cnf.gz.no_w.cnf', '09B-3.cnf.gz.no_w.cnf', '09B-4.cnf.gz.no_w.cnf', '09B-5.cnf.gz.no_w.cnf', '09B-6.cnf.gz.no_w.cnf', '107.sk_3_90.cnf.gz.no_w.cnf', '109.sk_4_36.cnf.gz.no_w.cnf', '10A-1.cnf.gz.no_w.cnf', '10A-2.cnf.gz.no_w.cnf', '10A-3.cnf.gz.no_w.cnf', '10A-4.cnf.gz.no_w.cnf', '10B-10.cnf.gz.no_w.cnf', '10B-11.cnf.gz.no_w.cnf', '10B-1.cnf.gz.no_w.cnf', '10B-2.cnf.gz.no_w.cnf', '10B-3.cnf.gz.no_w.cnf', '10B-4.cnf.gz.no_w.cnf', '10B-5.cnf.gz.no_w.cnf', '10B-6.cnf.gz.no_w.cnf', '10B-7.cnf.gz.no_w.cnf', '10B-8.cnf.gz.no_w.cnf', '10B-9.cnf.gz.no_w.cnf', '10.sk_1_46.cnf.gz.no_w.cnf', '110.sk_3_88.cnf.gz.no_w.cnf', '111.sk_2_36.cnf.gz.no_w.cnf', '11A-1.cnf.gz.no_w.cnf', '11A-2.cnf.gz.no_w.cnf', '11A-3.cnf.gz.no_w.cnf', '11A-4.cnf.gz.no_w.cnf', '11B-1.cnf.gz.no_w.cnf', '11B-2.cnf.gz.no_w.cnf', '11B-3.cnf.gz.no_w.cnf', '11B-4.cnf.gz.no_w.cnf', '11B-5.cnf.gz.no_w.cnf', '12A-1.cnf.gz.no_w.cnf', '12A-2.cnf.gz.no_w.cnf', '12A-3.cnf.gz.no_w.cnf', '12A-4.cnf.gz.no_w.cnf', '12B-1.cnf.gz.no_w.cnf', '12B-2.cnf.gz.no_w.cnf', '12B-3.cnf.gz.no_w.cnf', '12B-4.cnf.gz.no_w.cnf', '12B-5.cnf.gz.no_w.cnf', '12B-6.cnf.gz.no_w.cnf', '13A-1.cnf.gz.no_w.cnf', '13A-2.cnf.gz.no_w.cnf', '13A-3.cnf.gz.no_w.cnf', '13A-4.cnf.gz.no_w.cnf', '13B-1.cnf.gz.no_w.cnf', '13B-2.cnf.gz.no_w.cnf', '13B-3.cnf.gz.no_w.cnf', '13B-4.cnf.gz.no_w.cnf', '13B-5.cnf.gz.no_w.cnf', '14A-1.cnf.gz.no_w.cnf', '14A-2.cnf.gz.no_w.cnf', '14A-3.cnf.gz.no_w.cnf', '15A-1.cnf.gz.no_w.cnf', '15A-2.cnf.gz.no_w.cnf', '15A-3.cnf.gz.no_w.cnf', '15A-4.cnf.gz.no_w.cnf', '15B-1.cnf.gz.no_w.cnf', '15B-2.cnf.gz.no_w.cnf', '15B-3.cnf.gz.no_w.cnf', '15B-4.cnf.gz.no_w.cnf', '15B-5.cnf.gz.no_w.cnf', '17A-1.cnf.gz.no_w.cnf', '17A-2.cnf.gz.no_w.cnf', '17A-3.cnf.gz.no_w.cnf', '17A-4.cnf.gz.no_w.cnf', '17A-5.cnf.gz.no_w.cnf', '17A-6.cnf.gz.no_w.cnf', '17B-1.cnf.gz.no_w.cnf', '17B-2.cnf.gz.no_w.cnf', '17B-3.cnf.gz.no_w.cnf', '17B-4.cnf.gz.no_w.cnf', '17B-5.cnf.gz.no_w.cnf', '17.sk_3_45.cnf.gz.no_w.cnf', '18A-1.cnf.gz.no_w.cnf', '18A-2.cnf.gz.no_w.cnf', '18A-3.cnf.gz.no_w.cnf', '18A-4.cnf.gz.no_w.cnf', '19.sk_3_48.cnf.gz.no_w.cnf', '20.sk_1_51.cnf.gz.no_w.cnf', '27.sk_3_32.cnf.gz.no_w.cnf', '29.sk_3_45.cnf.gz.no_w.cnf', '30.sk_5_76.cnf.gz.no_w.cnf', '32.sk_4_38.cnf.gz.no_w.cnf', '35.sk_3_52.cnf.gz.no_w.cnf', '36.sk_3_77.cnf.gz.no_w.cnf', '4step.cnf.gz.no_w.cnf', '50-10-10-q.cnf.gz.no_w.cnf', '50-10-1-q.cnf.gz.no_w.cnf', '50-10-2-q.cnf.gz.no_w.cnf', '50-10-3-q.cnf.gz.no_w.cnf', '50-10-4-q.cnf.gz.no_w.cnf', '50-10-5-q.cnf.gz.no_w.cnf', '50-10-6-q.cnf.gz.no_w.cnf', '50-10-7-q.cnf.gz.no_w.cnf', '50-10-8-q.cnf.gz.no_w.cnf', '50-10-9-q.cnf.gz.no_w.cnf', '50-12-10-q.cnf.gz.no_w.cnf', '50-12-1-q.cnf.gz.no_w.cnf', '50-12-2-q.cnf.gz.no_w.cnf', '50-12-3-q.cnf.gz.no_w.cnf', '50-12-4-q.cnf.gz.no_w.cnf', '50-12-5-q.cnf.gz.no_w.cnf', '50-12-6-q.cnf.gz.no_w.cnf', '50-12-7-q.cnf.gz.no_w.cnf', '50-12-8-q.cnf.gz.no_w.cnf', '50-12-9-q.cnf.gz.no_w.cnf', '50-14-10-q.cnf.gz.no_w.cnf', '50-14-1-q.cnf.gz.no_w.cnf', '50-14-2-q.cnf.gz.no_w.cnf', '50-14-3-q.cnf.gz.no_w.cnf', '50-14-4-q.cnf.gz.no_w.cnf', '50-14-5-q.cnf.gz.no_w.cnf', '50-14-6-q.cnf.gz.no_w.cnf', '50-14-7-q.cnf.gz.no_w.cnf', '50-14-8-q.cnf.gz.no_w.cnf', 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's641_3_2.cnf.gz.no_w.cnf', 's641_7_4.cnf.gz.no_w.cnf', 's713_15_7.cnf.gz.no_w.cnf', 's713_3_2.cnf.gz.no_w.cnf', 's713_7_4.cnf.gz.no_w.cnf', 's820a_15_7.cnf.gz.no_w.cnf', 's820a_3_2.cnf.gz.no_w.cnf', 's820a_7_4.cnf.gz.no_w.cnf', 's832a_15_7.cnf.gz.no_w.cnf', 's832a_3_2.cnf.gz.no_w.cnf', 's832a_7_4.cnf.gz.no_w.cnf', 's838_15_7.cnf.gz.no_w.cnf', 's838_3_2.cnf.gz.no_w.cnf', 's838_7_4.cnf.gz.no_w.cnf', 's9234a_15_7.cnf.gz.no_w.cnf', 's9234a_3_2.cnf.gz.no_w.cnf', 's9234a_7_4.cnf.gz.no_w.cnf', 's953a_15_7.cnf.gz.no_w.cnf', 's953a_3_2.cnf.gz.no_w.cnf', 's953a_7_4.cnf.gz.no_w.cnf', 'SetTest.sk_9_21.cnf.gz.no_w.cnf', 'signedAvg.sk_8_1020.cnf.gz.no_w.cnf', 'sort.sk_8_52.cnf.gz.no_w.cnf', 'tableBasedAddition.sk_240_1024.cnf.gz.no_w.cnf', 'tire-1.cnf.gz.no_w.cnf', 'tire-2.cnf.gz.no_w.cnf', 'tire-3.cnf.gz.no_w.cnf', 'tire-4.cnf.gz.no_w.cnf', 'tutorial1.sk_1_1.cnf.gz.no_w.cnf', 'tutorial2.sk_3_4.cnf.gz.no_w.cnf', 'tutorial3.sk_4_31.cnf.gz.no_w.cnf', 'UserServiceImpl.sk_8_32.cnf.gz.no_w.cnf', 'xpose.sk_6_134.cnf.gz.no_w.cnf'] # PROBLEM_NAMES = ['hypercube.cnf', 'hypercube1.cnf', 'hypercube2.cnf', 'hypercube3.cnf', 'hypercube4.cnf', 'hypercube5.cnf', 'hypercube6.cnf', 'hypercube7.cnf'] #these problems report timeout for using marginals with random chunks, but SAT time is less than 100 and testing 1 or 2 they seem fast # PROBLEM_NAMES = ['90-26-10-q.cnf.gz.no_w.cnf', '90-42-7-q.cnf.gz.no_w.cnf', '50-20-9-q.cnf.gz.no_w.cnf', '75-25-4-q.cnf.gz.no_w.cnf', '75-22-10-q.cnf.gz.no_w.cnf', '75-21-4-q.cnf.gz.no_w.cnf', '75-23-2-q.cnf.gz.no_w.cnf', '75-22-1-q.cnf.gz.no_w.cnf', '90-50-3-q.cnf.gz.no_w.cnf', '90-26-4-q.cnf.gz.no_w.cnf', 's38417_7_4.cnf.gz.no_w.cnf', '90-25-1-q.cnf.gz.no_w.cnf', '75-21-8-q.cnf.gz.no_w.cnf', '90-30-1-q.cnf.gz.no_w.cnf', '90-34-1-q.cnf.gz.no_w.cnf', '90-21-10-q.cnf.gz.no_w.cnf', '75-21-5-q.cnf.gz.no_w.cnf', '75-24-6-q.cnf.gz.no_w.cnf', '75-23-3-q.cnf.gz.no_w.cnf', '90-22-5-q.cnf.gz.no_w.cnf', '75-25-10-q.cnf.gz.no_w.cnf', '90-26-5-q.cnf.gz.no_w.cnf', '75-25-9-q.cnf.gz.no_w.cnf', '75-23-10-q.cnf.gz.no_w.cnf', '90-46-5-q.cnf.gz.no_w.cnf', '90-42-5-q.cnf.gz.no_w.cnf', '90-34-2-q.cnf.gz.no_w.cnf', '90-42-9-q.cnf.gz.no_w.cnf', '90-46-9-q.cnf.gz.no_w.cnf', '90-30-2-q.cnf.gz.no_w.cnf', '75-26-3-q.cnf.gz.no_w.cnf', '90-50-1-q.cnf.gz.no_w.cnf', '75-22-3-q.cnf.gz.no_w.cnf', '75-25-6-q.cnf.gz.no_w.cnf', '75-24-5-q.cnf.gz.no_w.cnf', '75-21-6-q.cnf.gz.no_w.cnf', '75-24-9-q.cnf.gz.no_w.cnf', '90-38-10-q.cnf.gz.no_w.cnf', '90-25-3-q.cnf.gz.no_w.cnf', '75-20-9-q.cnf.gz.no_w.cnf', '90-26-6-q.cnf.gz.no_w.cnf', '90-23-5-q.cnf.gz.no_w.cnf', '90-42-4-q.cnf.gz.no_w.cnf', '50-20-6-q.cnf.gz.no_w.cnf', '75-17-6-q.cnf.gz.no_w.cnf', '75-24-10-q.cnf.gz.no_w.cnf', '90-30-3-q.cnf.gz.no_w.cnf', '90-34-3-q.cnf.gz.no_w.cnf', '90-23-8-q.cnf.gz.no_w.cnf', '75-22-2-q.cnf.gz.no_w.cnf', '90-30-10-q.cnf.gz.no_w.cnf', '75-26-2-q.cnf.gz.no_w.cnf', '75-23-1-q.cnf.gz.no_w.cnf', '75-21-7-q.cnf.gz.no_w.cnf', '75-24-4-q.cnf.gz.no_w.cnf', '75-20-4-q.cnf.gz.no_w.cnf', '75-25-7-q.cnf.gz.no_w.cnf', '75-20-8-q.cnf.gz.no_w.cnf', '75-24-8-q.cnf.gz.no_w.cnf', '90-30-4-q.cnf.gz.no_w.cnf', '90-34-4-q.cnf.gz.no_w.cnf', '90-38-4-q.cnf.gz.no_w.cnf', '90-38-8-q.cnf.gz.no_w.cnf', '90-19-2-q.cnf.gz.no_w.cnf', '90-42-3-q.cnf.gz.no_w.cnf', '90-30-8-q.cnf.gz.no_w.cnf', '75-26-9-q.cnf.gz.no_w.cnf', '75-22-9-q.cnf.gz.no_w.cnf', '90-25-5-q.cnf.gz.no_w.cnf', '75-24-3-q.cnf.gz.no_w.cnf', '90-25-9-q.cnf.gz.no_w.cnf', '75-22-5-q.cnf.gz.no_w.cnf', '75-26-5-q.cnf.gz.no_w.cnf', '75-23-6-q.cnf.gz.no_w.cnf', '90-34-5-q.cnf.gz.no_w.cnf', '90-30-5-q.cnf.gz.no_w.cnf', '75-19-6-q.cnf.gz.no_w.cnf', '90-38-5-q.cnf.gz.no_w.cnf', '75-18-9-q.cnf.gz.no_w.cnf', '90-34-10-q.cnf.gz.no_w.cnf', '90-46-2-q.cnf.gz.no_w.cnf', '90-30-9-q.cnf.gz.no_w.cnf', '90-34-9-q.cnf.gz.no_w.cnf', '75-22-8-q.cnf.gz.no_w.cnf', '75-20-10-q.cnf.gz.no_w.cnf', '90-25-4-q.cnf.gz.no_w.cnf', '90-24-7-q.cnf.gz.no_w.cnf', '75-25-1-q.cnf.gz.no_w.cnf', '75-20-2-q.cnf.gz.no_w.cnf', '75-21-1-q.cnf.gz.no_w.cnf', '75-23-7-q.cnf.gz.no_w.cnf', '75-26-4-q.cnf.gz.no_w.cnf', '90-50-6-q.cnf.gz.no_w.cnf', '75-18-6-q.cnf.gz.no_w.cnf', '90-38-6-q.cnf.gz.no_w.cnf', '90-30-6-q.cnf.gz.no_w.cnf', '90-34-6-q.cnf.gz.no_w.cnf', '90-46-1-q.cnf.gz.no_w.cnf', 's38417_3_2.cnf.gz.no_w.cnf', '90-24-4-q.cnf.gz.no_w.cnf', '90-22-2-q.cnf.gz.no_w.cnf', '75-23-8-q.cnf.gz.no_w.cnf', '90-50-9-q.cnf.gz.no_w.cnf', '75-22-7-q.cnf.gz.no_w.cnf', '90-50-5-q.cnf.gz.no_w.cnf', '75-26-7-q.cnf.gz.no_w.cnf', '75-23-4-q.cnf.gz.no_w.cnf', '50-18-8-q.cnf.gz.no_w.cnf', '75-21-2-q.cnf.gz.no_w.cnf', '75-24-1-q.cnf.gz.no_w.cnf', '75-25-2-q.cnf.gz.no_w.cnf', '90-22-10-q.cnf.gz.no_w.cnf', '90-38-7-q.cnf.gz.no_w.cnf', '90-34-7-q.cnf.gz.no_w.cnf', '90-30-7-q.cnf.gz.no_w.cnf', '75-21-10-q.cnf.gz.no_w.cnf', '90-42-10-q.cnf.gz.no_w.cnf', '90-25-6-q.cnf.gz.no_w.cnf', '90-26-3-q.cnf.gz.no_w.cnf', '90-22-3-q.cnf.gz.no_w.cnf', '90-25-10-q.cnf.gz.no_w.cnf', '75-23-9-q.cnf.gz.no_w.cnf', '50-18-9-q.cnf.gz.no_w.cnf', '75-23-5-q.cnf.gz.no_w.cnf', '75-22-6-q.cnf.gz.no_w.cnf', '90-50-4-q.cnf.gz.no_w.cnf', '90-24-9-q.cnf.gz.no_w.cnf', '75-21-3-q.cnf.gz.no_w.cnf'] for problem_name in PROBLEM_NAMES: for repeats_per_experiment in [2]: cur_spec = { 'problem_name': problem_name, 'repeats': repeats_per_experiment, #the } all_fireworks.append(Firework(RunSpecificExperimentBatch(), spec=cur_spec)) firework_dependencies = {} workflow = Workflow(all_fireworks, firework_dependencies) if TEST_LOCAL: launchpad.add_wf(workflow) rapidfire(launchpad, FWorker()) else: launchpad.add_wf(workflow) qadapter = CommonAdapter.from_file("%s/my_qadapter.yaml" % HOME_DIRECTORY) rapidfire(launchpad, FWorker(), qadapter, launch_dir='.', nlaunches='infinite', njobs_queue=NJOBS_QUEUE, njobs_block=500, sleep_time=None, reserve=False, strm_lvl='INFO', timeout=None, fill_mode=False) def dsharp_call_from_python(problem_name, time_limit, problem_directory='/atlas/u/jkuck/approxmc/counting2/'): global SAT_SOLVER_TIME SAT_SOLVER_TIME = 0 input_filename = '%s/%s' % (problem_directory, problem_name) t0 = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime time_out, solution_count = dsharp_count(formula=input_filename, time_limit=time_limit) t1 = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime dsharp_time = t1-t0 return time_out, solution_count, dsharp_time def dsharp_count(formula, time_limit): """ Count the number of solutions of a cnf formula by invoking sharpsat. :param formula: The full pathname of the formula file :param time_limit: Maximum run time (in sec) allowed :return: a pair (t, n) such that: t: [boolean] has the time_limit been reached without finishing? n: number of solutions (if t is False) """ sh_cmd = '{dsharp_exe} {form}'.format(form=formula, dsharp_exe=DSHARP_EXECUTABLE) n_sol, _ = execute_cmd(sh_cmd, time_limit, parse_dsharp_output, plain_timeout=True, count_time=False) return n_sol is math.nan, n_sol def parse_dsharp_output(output): """ Parse the output of dsharp If it finds the number of solutions it is reported in the first element of the returning pair, otherwise it is math.nan. The second element of the returning pair is 0, to conform with the protocol for the last element of the returning tuple from output parsers :param output: The sharpsat output. If None then a timeout occured :return: A pair of values """ if output is None: return math.nan, 0 nsol = math.nan all_lines = output.split('\n') for i, line in enumerate(all_lines): line = line.strip() if line.startswith('# of solutions:'): solution_count = line.split('\t')[-1] if solution_count in ['-nan', 'inf']: nsol = np.nan else: # nsol = int(solution_count) nsol = int(decimal.Decimal(solution_count)) return nsol, 0 if __name__=="__main__": run_experiment() ######################### Fireworks info copied from anothor project ######################### # If the database thinks a firework is still running, but no jobs are running on the cluster, try: # $ lpad detect_lostruns --time 1 --refresh # # If a firework fizzles and you are trying to find the error/output, note the fireworks fw_id # in the online database, then search for this fw_id in the launcher block, e.g.: # $ cd block_2017-11-01-07-30-53-457640 # $ pt 'fw_id: 34' # or on atlas-ws-6 use silver searcher: # $ ag 'fw_id: 34' # #Note, on Atlas before this script: # start a krbscreen session: # $ krbscreen #reattach using $ screen -rx # $ reauth #important so that jobs can be submitted after logging out, enter password # # $ export PATH=/opt/rh/python27/root/usr/bin:$PATH # $ export LD_LIBRARY_PATH=/opt/rh/python27/root/usr/lib64/:$LD_LIBRARY_PATH # $ PACKAGE_DIR=/atlas/u/jkuck/software # $ export PATH=$PACKAGE_DIR/anaconda2/bin:$PATH # $ export LD_LIBRARY_PATH=$PACKAGE_DIR/anaconda2/local:$LD_LIBRARY_PATH # $ source activate anaconda_venv # $ cd /atlas/u/jkuck/rbpf_fireworks/ # # To install anaconda packages run, e.g.: # $ conda install -c matsci fireworks=1.3.9 # #May need to run $ kinit -r 30d # # Add the following line to the file ~/.bashrc.user on Atlas: # export PYTHONPATH="/atlas/u/jkuck/rbpf_fireworks:$PYTHONPATH" # Weird, but to run commands like "lpad -l my_launchpad.yaml get_fws", # add the following line to the file ~/.bashrc.user on Atlas: # export PYTHONPATH="${PYTHONPATH}:/atlas/u/jkuck/rbpf_fireworks/KITTI_helpers/" # # To install cvxpy on atlas run (hopefully): # #$ export PATH=/opt/rh/python27/root/usr/bin:$PATH #$ export LD_LIBRARY_PATH=/opt/rh/python27/root/usr/lib64/:$LD_LIBRARY_PATH #$ pip install --user numpy #$ pip install --user cvxpy # # Install pymatgen: #$ pip install --user pymatgen ########################################################################################## # #Note, on Sherlock before this script: #$ ml load python/2.7.5 #$ easy_install-2.7 --user pip #$ export PATH=~/.local/bin:$PATH #$ pip2.7 install --user fireworks #and others #$ pip2.7 install --user filterpy #$ pip2.7 install --user scipy --upgrade #$ pip2.7 install --user munkres #$ pip2.7 install --user pymatgen #$ cd /scratch/users/kuck/rbpf_fireworks/ # # Add the following line to the file ~/.bashrc on Sherlock: # export PYTHONPATH="/scratch/users/kuck/rbpf_fireworks:$PYTHONPATH" # Weird, but to run commands like "lpad -l my_launchpad.yaml get_fws", # add the following line to the file ~/.bashrc.user on Atlas: # export PYTHONPATH="${PYTHONPATH}:/scratch/users/kuck/rbpf_fireworks/KITTI_helpers/" # # # When setting up: # - make cluster_config.py file # - make my_qadapter.yaml file (look at fireworks workflow manager website for info) # # To install cvxpy on sherlock run: # $ pip2.7 install --user cvxpy

253.69735

68,602

0.680579

43,523

181,901

2.638995

0.022655

0.136831

0.171039

0.273523

0.951505

0.948736

0.94676

0.944296

0.942058

0.93841

0

0.135163

0.081319

181,901

716

68,603

254.051676

0.552155

0.485654

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0.515581

0

0.031161

0.703196

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0.016997

false

0.011331

0.050992

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0.084986

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null

0

1

0

1

0

1

null

0

11

ea3b43871f9cbbf6c5b0bd64763911bf44739912

133

py

Python

tasks/models/template/__init__.py

heolin123/funcrowd

20167783de208394c09ed0429a5f02ec6dd79c42

[ "MIT" ]

null

tasks/models/template/__init__.py

heolin123/funcrowd

20167783de208394c09ed0429a5f02ec6dd79c42

[ "MIT" ]

11

2019-11-12T23:26:45.000Z

2021-06-10T17:37:23.000Z

tasks/models/template/__init__.py

heolin123/funcrowd

20167783de208394c09ed0429a5f02ec6dd79c42

[ "MIT" ]

null

from tasks.models.template.item_template import ItemTemplate from tasks.models.template.item_template_field import ItemTemplateField

44.333333

71

0.894737

17

133

6.823529

0.529412

0.155172

0.258621

0.396552

0.603448

0

0.06015

133

2

72

66.5

0.928

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true

0

1

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1

0

1

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null

0

1

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1

0

null

0

1

0

1

0

1

0

7

ea42729534366c88e976303730ebfda5ae5f6c80

134

py

Python

src/icemac/ab/calendar/roles.py

icemac/icemac.ab.calendar

c0cdedd3a8fdd39520156c2ea7cf83aca742e3d9

[ "BSD-2-Clause" ]

1

2020-04-21T19:34:04.000Z

src/icemac/ab/calendar/roles.py

icemac/icemac.ab.calendar

c0cdedd3a8fdd39520156c2ea7cf83aca742e3d9

[ "BSD-2-Clause" ]

null

src/icemac/ab/calendar/roles.py

icemac/icemac.ab.calendar

c0cdedd3a8fdd39520156c2ea7cf83aca742e3d9

[ "BSD-2-Clause" ]

null

def editor_role(ignored): return 'icemac.ab.calendar.Editor' def visitor_role(ignored): return 'icemac.ab.calendar.Visitor'

19.142857

39

0.746269

18

134

5.444444

0.5

0.22449

0.346939

0.469388

0.673469

0

0.134328

134

6

40

22.333333

0.844828

0

0.380597

0

1

0.5

false

0

0.5

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

8

ea5aeb9ca3734a446502ce60ba1d7e97f842ab84

40,189

py

Python

retrieval/AIR-retriever/Graph_nodes.py

dair-iitd/ECQA

a74bb658194e647e5d7955a84cc895bf5a5d8b3e

[ "Apache-2.0" ]

3

2021-07-28T01:13:23.000Z

2022-01-27T15:51:49.000Z

retrieval/AIR-retriever/Graph_nodes.py

dair-iitd/ECQA

a74bb658194e647e5d7955a84cc895bf5a5d8b3e

[ "Apache-2.0" ]

null

retrieval/AIR-retriever/Graph_nodes.py

dair-iitd/ECQA

a74bb658194e647e5d7955a84cc895bf5a5d8b3e

[ "Apache-2.0" ]

null

import numpy as np import collections from Overlap_analysis import calculate_overlap, calculate_all_overlap, calculate_overlap_labels, get_union, get_intersection, get_intersection_withIDF, calculate_kappa, calculate_alignment_overlap \ , calculate_alignment_union # from Compute_F1 import mean_confidence_interval, meta_voting_ensemble, meta_voting_ensemble_BECKY from itertools import combinations from Compute_F1 import get_differences_list import math ######################################## These functions are to implement 2^n combinations of subgraphs and select the best one out of it. def get_all_combination_best_graph(pred_labels_over_runs, performance_runs): ## Edge based model runs = list(pred_labels_over_runs.keys()) meta_subgraphs = [] for i in range(len(runs)-1): meta_subgraphs += list(combinations(runs, i+2)) print ("len of meta subgraphs are ", len(meta_subgraphs)) meta_graph_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_score = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_score.append ( performance_runs[rk1]+performance_runs[rk2] / float(calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]) ) ) meta_graph_scores.append(sum(current_subgraph_score)/float(len(current_subgraph_score))) ## taking average of subgraph scores print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] def get_all_combination_Vikas_EdgeAPPROACH_withCoverage_best_graph(pred_labels_over_runs, performance_runs, gold_labels): runs = list(pred_labels_over_runs.keys()) meta_subgraphs = [] for i in range(len(runs)-1): meta_subgraphs += list(combinations(runs, i+2)) print ("len of meta subgraphs are ", len(meta_subgraphs)) meta_graph_scores = [] meta_graph_coverage_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_score = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = pred_labels_over_runs[rk1] for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_score.append(performance_runs[rk1] + performance_runs[rk2] / float(calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]))) prediction_coverage = get_union(prediction_coverage, pred_labels_over_runs[rk2]) final_coverage = sum(get_intersection(prediction_coverage, gold_labels))/float(sum(gold_labels)) meta_graph_coverage_scores.append(final_coverage) meta_graph_scores.append( (sum(current_subgraph_score)/float(len(current_subgraph_score)) ) * final_coverage ) ## taking average of subgraph scores print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores),meta_graph_coverage_scores[best_sub_graph_index]) return meta_subgraphs[best_sub_graph_index] def get_all_combination_STEVE_best_graph(pred_labels_over_runs, performance_runs): ## steve's suggestions runs = list(pred_labels_over_runs.keys()) meta_subgraphs = [] for i in range(len(runs)-1): meta_subgraphs += list(combinations(runs, i+2)) print ("len of meta subgraphs are ", len(meta_subgraphs)) meta_graph_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_overlap.append (float(calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]) ) ) avg_score = sum(current_subgraph_perf)/float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap)/float(len(current_subgraph_overlap)) meta_graph_scores.append(avg_score/float(avg_overlap)) ## taking average of subgraph scores print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] def get_all_combination_withCoverage_best_graph(KB_terms, performance_runs, Ques_terms, Ans_terms): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] # for i in range(len(runs)-1): # meta_subgraphs += list(combinations(runs, i+2)) meta_subgraphs += list(combinations(runs, 4)) meta_graph_scores = [] meta_graph_coverage_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = KB_terms[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1+1:-1]: ##### This is equivalent to M C 2 current_subgraph_overlap.append (float(calculate_overlap(KB_terms[rk1], KB_terms[rk2]) ) ) prediction_coverage = get_union(prediction_coverage, KB_terms[rk2]) avg_score = sum(current_subgraph_perf)/float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap)/float(len(current_subgraph_overlap)) # print ("the ") final_coverage = len(get_intersection(prediction_coverage, gold_labels))/float(len(gold_labels)) meta_graph_coverage_scores.append(final_coverage) # meta_graph_scores.append( (avg_score/float(avg_overlap+1)) * final_coverage ) ## taking average of subgraph scores meta_graph_scores.append( avg_score * final_coverage ) ## taking average of subgraph scores # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] except ValueError: return "Crashed" ####################################### def get_all_combination_withCoverage_best_graph_Cand_boost(KB_terms, performance_runs, Ques_terms, Ans_terms, subgraph_size): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(subgraph_size-2): meta_subgraphs += list(combinations(runs, i+2)) # for i in range(subgraph_size): ## for taking best subgraph amongst subgraphs of size 3,4,5 # meta_subgraphs += list(combinations(runs, i+3)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = KB_terms[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 current_subgraph_overlap.append(float(calculate_overlap(KB_terms[rk1], KB_terms[rk2]))) prediction_coverage = get_union(prediction_coverage, KB_terms[rk2]) avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # print ("the ") final_query_coverage = len(get_intersection(prediction_coverage, Ques_terms)) / max(1,float(len(Ques_terms))) final_ans_coverage = len(get_intersection(prediction_coverage, Ans_terms)) / max(1,float(len(Ans_terms))) meta_graph_coverage_scores.append(final_query_coverage) # meta_graph_scores.append( avg_score * final_ans_coverage * final_query_coverage) ## taking average of subgraph scores # if subgraph_size>2: # print ("the avg score, overlap and coverage looks like: ", avg_score, avg_overlap, final_query_coverage, final_ans_coverage) # meta_graph_scores.append( (avg_score/float(1+avg_overlap)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores meta_graph_scores.append( avg_score * (1+12*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] except ValueError: return "Crashed" ####################################### ####################################### def get_all_combination_withCoverage_best_graph_Cand_boost_withIDF(KB_terms, performance_runs, Ques_terms, Ans_terms, subgraph_size, IDF_vals): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(subgraph_size-1): meta_subgraphs += list(combinations(runs, i+2)) # for i in range(subgraph_size): ## for taking best subgraph amongst subgraphs of size 3,4,5 # meta_subgraphs += list(combinations(runs, i+3)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] meta_graph_ans_coverage_scores = [] meta_graph_overlap_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = KB_terms[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 current_subgraph_overlap.append(float(calculate_overlap(KB_terms[rk1], KB_terms[rk2]))) prediction_coverage = get_union(prediction_coverage, KB_terms[rk2]) avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # print ("the ") final_query_coverage = get_intersection_withIDF(prediction_coverage, Ques_terms, IDF_vals) / max(1,float(len(Ques_terms))) final_ans_coverage = get_intersection_withIDF(prediction_coverage, Ans_terms, IDF_vals) / max(1,float(len(Ans_terms))) meta_graph_coverage_scores.append(final_query_coverage) meta_graph_ans_coverage_scores.append(final_ans_coverage) meta_graph_overlap_scores.append(avg_overlap) # meta_graph_scores.append( avg_score * final_ans_coverage * final_query_coverage) ## taking average of subgraph scores # if subgraph_size>2: # print ("the avg score, overlap and coverage looks like: ", avg_score, avg_overlap, final_query_coverage, final_ans_coverage) # meta_graph_scores.append( (avg_score/float(1+avg_overlap)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores meta_graph_scores.append( (1+avg_score/float(1+avg_overlap)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores ## * * # 1+avg_overlap # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) # print ("checking weather this returns any overlap val or not ", meta_graph_overlap_scores) return meta_subgraphs[best_sub_graph_index], meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] except ValueError: return "Crashed" ####################################### ##################################### def get_all_combination_withCoverage_Alignment_IDF(Justification_ans_scores, ans_IDF_mat, Justification_ques_scores, ques_IDF_mat, Justification_ques_ans_scores_together, performance_runs, Ques_terms, Ans_terms, subgraph_size, IDF_vals): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(subgraph_size): meta_subgraphs += list(combinations(runs, i+2)) # for i in range(subgraph_size): ## for taking best subgraph amongst subgraphs of size 3,4,5 # meta_subgraphs += list(combinations(runs, i+3)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] meta_graph_ans_coverage_scores = [] meta_graph_overlap_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list ques_coverage = Justification_ques_scores[rk1] ans_coverage = Justification_ans_scores[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 if len(Justification_ques_ans_scores_together[rk1]) > 0 and len(Justification_ques_ans_scores_together[rk2]) > 0 : # if len(Justification_ques_ans_scores_together[rk1])>0 and len(Justification_ques_ans_scores_together[rk2]) > 0: if len(Justification_ques_ans_scores_together[rk1]) == len(Justification_ques_ans_scores_together[rk2]): current_subgraph_overlap.append(float(calculate_alignment_overlap(Justification_ques_ans_scores_together[rk1], Justification_ques_ans_scores_together[rk2]))) ques_coverage = calculate_alignment_union(ques_coverage, Justification_ques_scores[rk2]) ans_coverage = calculate_alignment_union(ans_coverage, Justification_ans_scores[rk2]) else: print ("find out why this is happening", len(Justification_ques_ans_scores_together[rk1]), len(Justification_ques_ans_scores_together[rk2])) else: pass avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # final_query_coverage = get_intersection_withIDF(prediction_coverage, Ques_terms, IDF_vals) / max(1,float(len(Ques_terms))) # final_ans_coverage = get_intersection_withIDF(prediction_coverage, Ans_terms, IDF_vals) / max(1,float(len(Ans_terms))) # if len(ques_coverage) > 0 and len(ans_coverage) > 0 : if len(ques_coverage) == len(ques_IDF_mat): ques_coverage = [a*b for a,b in zip(ques_IDF_mat, ques_coverage)] # print ("yes, we do come here ") # else: # print ("The len of cov vector and idf vector are different, checkout why", len(ques_coverage), len(ques_IDF_mat)) if len(ans_coverage) == len(ans_IDF_mat): ans_coverage = [a*b for a,b in zip(ans_IDF_mat, ans_coverage)] # else: # print ("yep, we had these cases: ") final_query_coverage = (sum(ques_coverage)) / float(max(1, len(ques_coverage))) final_ans_coverage = (sum(ans_coverage)) / float(max(1, len(ans_coverage))) # final_query_coverage = math.log(max(1,sum(ques_coverage)))/float(max(1,len(ques_coverage))) # final_ans_coverage = math.log(max(1,sum(ans_coverage)))/float(max(1,len(ans_coverage))) meta_graph_coverage_scores.append(final_query_coverage) meta_graph_ans_coverage_scores.append(final_ans_coverage) meta_graph_overlap_scores.append(avg_overlap) # meta_graph_scores.append( avg_score * final_ans_coverage * final_query_coverage) ## taking average of subgraph scores # if subgraph_size>2: # print ("the avg score, overlap and coverage looks like: ", avg_score, avg_overlap, final_query_coverage, final_ans_coverage) # meta_graph_scores.append( (1+avg_score) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores meta_graph_scores.append( ((1+avg_score)/float(1+0)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores ## * * # 1+avg_overlap # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) list_of_top_subgraphs = list(np.argsort(meta_graph_scores))[::-1] vicinity_scores = [] for top_graph_ind in list_of_top_subgraphs[0:20]: vicinity_scores.append(sum(get_differences_list(meta_subgraphs[top_graph_ind]))) # print ("checking weather this returns any overlap val or not ", meta_graph_overlap_scores) # print("The first calculated index was: ", best_sub_graph_index, "then", list_of_top_subgraphs[vicinity_scores.index(min(vicinity_scores))]) return meta_subgraphs[list_of_top_subgraphs[vicinity_scores.index(min(vicinity_scores))]], meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] # return meta_subgraphs[best_sub_graph_index], meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] # return meta_subgraphs[best_sub_graph_index], meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] except ValueError: return "Crashed" ####################################### ##################################### def get_all_combination_withCoverage_Alignment_Regression(Justification_ans_scores, ans_IDF_mat, Justification_ques_scores, ques_IDF_mat, Justification_ques_ans_scores_together, performance_runs, Ques_terms, Ans_terms, subgraph_size, IDF_vals, n_top_ranked_sets): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(subgraph_size): meta_subgraphs += list(combinations(runs, i+2)) # for i in range(subgraph_size): ## for taking best subgraph amongst subgraphs of size 3,4,5 # meta_subgraphs += list(combinations(runs, i+3)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] meta_graph_ans_coverage_scores = [] meta_graph_overlap_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list ques_coverage = Justification_ques_scores[rk1] ans_coverage = Justification_ans_scores[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 if len(Justification_ques_ans_scores_together[rk1]) > 0 and len(Justification_ques_ans_scores_together[rk2]) > 0 : # if len(Justification_ques_ans_scores_together[rk1])>0 and len(Justification_ques_ans_scores_together[rk2]) > 0: if len(Justification_ques_ans_scores_together[rk1]) == len(Justification_ques_ans_scores_together[rk2]): current_subgraph_overlap.append(float(calculate_alignment_overlap(Justification_ques_ans_scores_together[rk1], Justification_ques_ans_scores_together[rk2]))) ques_coverage = calculate_alignment_union(ques_coverage, Justification_ques_scores[rk2]) ans_coverage = calculate_alignment_union(ans_coverage, Justification_ans_scores[rk2]) else: print ("find out why this is happening", len(Justification_ques_ans_scores_together[rk1]), len(Justification_ques_ans_scores_together[rk2])) else: pass avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # final_query_coverage = get_intersection_withIDF(prediction_coverage, Ques_terms, IDF_vals) / max(1,float(len(Ques_terms))) # final_ans_coverage = get_intersection_withIDF(prediction_coverage, Ans_terms, IDF_vals) / max(1,float(len(Ans_terms))) # if len(ques_coverage) > 0 and len(ans_coverage) > 0 : if len(ques_coverage) == len(ques_IDF_mat): ques_coverage = [a*b for a,b in zip(ques_IDF_mat, ques_coverage)] # print ("yes, we do come here ") # else: # print ("The len of cov vector and idf vector are different, checkout why", len(ques_coverage), len(ques_IDF_mat)) if len(ans_coverage) == len(ans_IDF_mat): ans_coverage = [a*b for a,b in zip(ans_IDF_mat, ans_coverage)] # else: # print ("yep, we had these cases: ") final_query_coverage = (sum(ques_coverage)) / float(max(1, len(ques_coverage))) final_ans_coverage = (sum(ans_coverage)) / float(max(1, len(ans_coverage))) # final_query_coverage = math.log(max(1,sum(ques_coverage)))/float(max(1,len(ques_coverage))) # final_ans_coverage = math.log(max(1,sum(ans_coverage)))/float(max(1,len(ans_coverage))) meta_graph_coverage_scores.append(final_query_coverage) meta_graph_ans_coverage_scores.append(final_ans_coverage) meta_graph_overlap_scores.append(avg_overlap) # vicinity_score = sum(get_differences_list(meta_sub_graph1))/float(len(get_differences_list(meta_sub_graph1))) # vicinity_score = min(get_differences_list(meta_sub_graph1)) meta_graph_scores.append( ((1+avg_score) / float(1+0)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores ## * * # 1+avg_overlap # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) list_of_top_subgraphs = list(np.argsort(meta_graph_scores))[::-1] """ ## adding vicinity_scores_here: top_ranked_by_ROCC_meta_subgraphs = [meta_subgraphs[i1] for i1 in list_of_top_subgraphs[0:100]] vicinity_scores = [] for top_graph_1 in top_ranked_by_ROCC_meta_subgraphs: vicinity_scores.append(sum(get_differences_list(top_graph_1))/float(len(get_differences_list(top_graph_1)))) list_of_top_subgraphs_from_vicinity_scores = list(np.argsort(vicinity_scores))[::-1] top_10percent_subgraphs = [] # for top_subgraph1 in list_of_top_subgraphs[0:math.floor(0.1*len(list_of_top_subgraphs))]: for top_subgraph1 in list_of_top_subgraphs_from_vicinity_scores[0:n_top_ranked_sets]: top_10percent_subgraphs.append(top_ranked_by_ROCC_meta_subgraphs[top_subgraph1]) """ top_10percent_subgraphs = [] # for top_subgraph1 in list_of_top_subgraphs[0:math.floor(0.1*len(list_of_top_subgraphs))]: for top_subgraph1 in list_of_top_subgraphs[0:n_top_ranked_sets]: top_10percent_subgraphs.append(meta_subgraphs[top_subgraph1]) # print ("checking weather this returns any overlap val or not ", meta_graph_overlap_scores) # print("The first calculated index was: ", best_sub_graph_index, "then", list_of_top_subgraphs[vicinity_scores.index(min(vicinity_scores))]) return meta_subgraphs[best_sub_graph_index], top_10percent_subgraphs, meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] except ValueError: return "Crashed" ##################################### def get_all_combination_withCoverage_SOFT_Alignment_IDF(Justification_ans_scores, ans_IDF_mat, Justification_ques_scores, ques_IDF_mat, Justification_ques_ans_scores_together, performance_runs, Ques_terms, Ans_terms, subgraph_size, IDF_vals): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(subgraph_size): meta_subgraphs += list(combinations(runs, i+2)) # for i in range(subgraph_size): ## for taking best subgraph amongst subgraphs of size 3,4,5 # meta_subgraphs += list(combinations(runs, i+3)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] meta_graph_ans_coverage_scores = [] meta_graph_overlap_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list ques_coverage = Justification_ques_scores[rk1] ans_coverage = Justification_ans_scores[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 if len(Justification_ques_ans_scores_together[rk1]) > 0 and len(Justification_ques_ans_scores_together[rk2]) > 0 : if len(Justification_ques_ans_scores_together[rk1]) == len(Justification_ques_ans_scores_together[rk2]): current_subgraph_overlap.append(float(calculate_alignment_overlap(Justification_ques_ans_scores_together[rk1], Justification_ques_ans_scores_together[rk2]))) ques_coverage = calculate_alignment_union(ques_coverage, Justification_ques_scores[rk2]) ans_coverage = calculate_alignment_union(ans_coverage, Justification_ans_scores[rk2]) else: print ("find out why this is happening", len(Justification_ques_ans_scores_together[rk1]), len(Justification_ques_ans_scores_together[rk2])) else: pass avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # final_query_coverage = get_intersection_withIDF(prediction_coverage, Ques_terms, IDF_vals) / max(1,float(len(Ques_terms))) # final_ans_coverage = get_intersection_withIDF(prediction_coverage, Ans_terms, IDF_vals) / max(1,float(len(Ans_terms))) # if len(ques_coverage) > 0 and len(ans_coverage) > 0 : if len(ques_coverage) == len(ques_IDF_mat): ques_coverage = [a*b for a,b in zip(ques_IDF_mat, ques_coverage)] # print ("yes, we do come here ") # else: # print ("The len of cov vector and idf vector are different, checkout why", len(ques_coverage), len(ques_IDF_mat)) if len(ans_coverage) == len(ans_IDF_mat): ans_coverage = [a*b for a,b in zip(ans_IDF_mat, ans_coverage)] # else: # print ("yep, we had these cases: ") final_query_coverage = (sum(ques_coverage)) / float(max(1, len(ques_coverage)*len(current_subgraph_overlap))) final_ans_coverage = (sum(ans_coverage)) / float(max(1, len(ans_coverage)*len(current_subgraph_overlap))) # final_query_coverage = math.log(max(1,sum(ques_coverage)))/float(max(1,len(ques_coverage))) # final_ans_coverage = math.log(max(1,sum(ans_coverage)))/float(max(1,len(ans_coverage))) meta_graph_coverage_scores.append(final_query_coverage) meta_graph_ans_coverage_scores.append(final_ans_coverage) meta_graph_overlap_scores.append(avg_overlap) # meta_graph_scores.append( avg_score * final_ans_coverage * final_query_coverage) ## taking average of subgraph scores # if subgraph_size>2: # print ("the avg score, overlap and coverage looks like: ", avg_score, avg_overlap, final_query_coverage, final_ans_coverage) # meta_graph_scores.append( (1+avg_score) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores meta_graph_scores.append( ((1+avg_score)/float(1)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores ## * * # 1+avg_overlap # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) # print ("checking weather this returns any overlap val or not ", meta_graph_overlap_scores) return meta_subgraphs[best_sub_graph_index], meta_graph_overlap_scores[best_sub_graph_index], meta_graph_coverage_scores[best_sub_graph_index], meta_graph_ans_coverage_scores[best_sub_graph_index] except ValueError: return "Crashed" ####################################### ####################################### def get_all_combination_withCoverage_best_graph_Cand_boost_ALL(KB_terms, performance_runs, Ques_terms, Ans_terms): ## gold_labels_list is QA terms and pred_labels_over_runs is justification terms runs = list(performance_runs.keys()) gold_labels = Ques_terms + Ans_terms # print("the gold_labels list looks like: ", runs) meta_subgraphs = [] for i in range(len(runs)-1): meta_subgraphs += list(combinations(runs, i+2)) # meta_subgraphs += list(combinations(runs, subgraph_size)) meta_graph_scores = [] meta_graph_coverage_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = KB_terms[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1 + 1:-1]: ##### This is equivalent to M C 2 current_subgraph_overlap.append(float(calculate_overlap(KB_terms[rk1], KB_terms[rk2]))) prediction_coverage = get_union(prediction_coverage, KB_terms[rk2]) avg_score = sum(current_subgraph_perf) / float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap) / float(max(1,len(current_subgraph_overlap))) # print ("the ") final_query_coverage = len(get_intersection(prediction_coverage, Ques_terms)) / float(len(Ques_terms)) final_ans_coverage = len(get_intersection(prediction_coverage, Ans_terms)) / float(len(Ans_terms)) meta_graph_coverage_scores.append(final_query_coverage) # meta_graph_scores.append( avg_score * final_ans_coverage * final_query_coverage) ## taking average of subgraph scores # if subgraph_size>2: # print ("the avg score, overlap and coverage looks like: ", avg_score, avg_overlap, final_query_coverage, final_ans_coverage) meta_graph_scores.append( (avg_score/float(1+avg_overlap)) * (1+1*final_ans_coverage) * (1+final_query_coverage) ) ## taking average of subgraph scores # print ("the len of meta graph scores are : ", len(meta_graph_scores)) try: best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] except ValueError: return "Crashed" ####################################### def get_all_combination_forN_sizes_withCoverage_best_graph(pred_labels_over_runs, all_prediction_label_runs, performance_runs, gold_labels_list, BiNODE_overlap, mean_score): runs = list(pred_labels_over_runs.keys()) best_subgraphs_diff_sizes = {} ### (P/O)*C best_subgraphs_overlaps = {} ## just 1/O factor best_subgraphs_Perf_Over = {} ## Just (P/O) factor, no coverage gold_labels = list(range(len(gold_labels_list))) all_subgraphs = [] feature_x = [] label_y = [] POC_score = 0 POC_subgraph = [] for i in range(len(runs)-1): meta_subgraphs = list(combinations(runs, i+2)) meta_graph_scores = [] ### same sequence as above meta_graph_Overlap_scores = [] meta_graph_PERF_Overlap_scores = [] meta_graph_coverage_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_overlap = [] current_subgraph_perf = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): if ik1 == 0: ## initializing the coverage list prediction_coverage = pred_labels_over_runs[rk1] current_subgraph_perf.append(performance_runs[rk1]) for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_overlap.append (BiNODE_overlap[str(rk1)+str(rk2)] ) prediction_coverage = get_union(prediction_coverage, pred_labels_over_runs[rk2]) avg_score = sum(current_subgraph_perf)/float(len(current_subgraph_perf)) avg_overlap = sum(current_subgraph_overlap)/float(len(current_subgraph_overlap)) # print ("the ") final_coverage = len(get_intersection(prediction_coverage, gold_labels))/float(len(gold_labels)) meta_graph_coverage_scores.append(final_coverage) meta_graph_scores.append( (avg_score/float(avg_overlap)) * final_coverage ) ## taking average of subgraph scores ############### for linear regression statistics and feature generation # feature_x.append([avg_score, 1/float(avg_overlap), final_coverage, avg_score/float(avg_overlap),(avg_score/float(avg_overlap))*final_coverage, avg_score*final_coverage]) feature_x.append([avg_score, avg_overlap, final_coverage]) best_subgraph_preds = {mn1: all_prediction_label_runs[mn1] for mn1 in meta_sub_graph1} subgraph_ensemble_performance = meta_voting_ensemble(best_subgraph_preds, gold_labels_list, math.ceil(len(meta_sub_graph1) / 2)) # print("the subgraph ensemble performance looks like: ", subgraph_ensemble_performance) label_y.append(subgraph_ensemble_performance - mean_score) all_subgraphs.append(meta_sub_graph1) ################### meta_graph_Overlap_scores.append(1/float(avg_overlap)) meta_graph_PERF_Overlap_scores.append(avg_score/float(avg_overlap)) # print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) if max(meta_graph_scores)>POC_score: POC_score = max(meta_graph_scores) POC_subgraph = meta_subgraphs[best_sub_graph_index] print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores),meta_graph_coverage_scores[best_sub_graph_index]) best_subgraphs_diff_sizes.update({i+2: meta_subgraphs[best_sub_graph_index]}) best_subgraphs_overlaps.update({i+2: meta_subgraphs[meta_graph_Overlap_scores.index(max(meta_graph_Overlap_scores))]}) best_subgraphs_Perf_Over.update({i+2:meta_subgraphs[meta_graph_PERF_Overlap_scores.index(max(meta_graph_PERF_Overlap_scores))]}) return best_subgraphs_diff_sizes, best_subgraphs_overlaps, best_subgraphs_Perf_Over, feature_x, label_y, all_subgraphs, POC_subgraph ######################################## def get_ensemble_perf_based_best_subgraph(meta_nodes, prediction_label_runs, gold_labels, best_performance, final_best_graph): best_subgraph_preds = {mn1: prediction_label_runs[mn1] for mn1 in meta_nodes} meta_ensemble_performance = meta_voting_ensemble(best_subgraph_preds, gold_labels, math.ceil(len(meta_nodes) / 2)) # print("the final meta ensemble performance is: ", meta_ensemble_performance) if meta_ensemble_performance > best_performance: final_best_graph = meta_nodes best_performance = meta_ensemble_performance return best_performance, final_best_graph ######################################## def get_complete_graph(pred_labels_over_runs, performance_runs, subgraph_size=4): runs = list(pred_labels_over_runs.keys()) meta_subgraphs = list(combinations(runs, subgraph_size)) print ("len of meta subgraphs are ", len(meta_subgraphs)) meta_graph_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_score = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_score.append ( performance_runs[rk1]*performance_runs[rk2] / float(calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]) ) ) meta_graph_scores.append(sum(current_subgraph_score)/float(len(current_subgraph_score))) print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] def get_unsupervised_sub_graph(pred_labels_over_runs, subgraph_size=4): runs = list(pred_labels_over_runs.keys()) meta_subgraphs = list(combinations(runs, subgraph_size)) print ("len of meta subgraphs are ", len(meta_subgraphs)) meta_graph_scores = [] for meta_sub_graph1 in meta_subgraphs: current_subgraph_score = [] for ik1, rk1 in enumerate(meta_sub_graph1[:-1]): for rk2 in meta_sub_graph1[ik1+1:]: current_subgraph_score.append ( 1 / float(calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]) ) ) meta_graph_scores.append(sum(current_subgraph_score)) print ("the len of meta graph scores are : ", len(meta_graph_scores)) best_sub_graph_index = meta_graph_scores.index(max(meta_graph_scores)) print ("best subgraph is: ", meta_subgraphs[best_sub_graph_index], max(meta_graph_scores)) return meta_subgraphs[best_sub_graph_index] ## dummy function - complete this later def get_node_pair_score(runs): for ik1, rk1 in enumerate(runs[:-1]): for rk2 in runs[ik1+1:]: meta_graph.update({rk1+" " + rk2 : calculate_overlap_labels(pred_labels_over_runs[rk1], pred_labels_over_runs[rk2]) })

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ea62811b648ea3b6ef5104ab32f082f06010a937

11,077

py

Python

Python/Classification Evaluation/printPDF.py

DrMoe/Evaluation-of-satellite-imagery-based-crop-classification

ca7324ee6e5c399ea08d2c3ac11497e4ed95f473

[ "MIT" ]

9

2018-01-07T14:51:19.000Z

2021-05-06T18:58:13.000Z

Python/Classification Evaluation/printPDF.py

DrMoe/Evaluation-of-satellite-imagery-based-crop-classification

ca7324ee6e5c399ea08d2c3ac11497e4ed95f473

[ "MIT" ]

null

Python/Classification Evaluation/printPDF.py

DrMoe/Evaluation-of-satellite-imagery-based-crop-classification

ca7324ee6e5c399ea08d2c3ac11497e4ed95f473

[ "MIT" ]

5

2017-05-31T15:01:42.000Z

2019-12-27T07:27:44.000Z

import timeit import os import errno import socket import datetime import time import csv import numpy as np import shutil from pylatex import Document, Section, Subsection, Description, Tabular, MultiColumn,\ MultiRow, Itemize, Enumerate, Command, NoEscape class printPDF: def __init__(self, metrics_dict): self.metrics_dict = metrics_dict def create_pdf(self, test_spec_dict, test_description, path, name): array_dimension = self.metrics_dict['con_matrix'].shape row_dimensions = array_dimension[0] array = self.metrics_dict['TpFpFnTn'][0] code = self.metrics_dict['class_code'][0] doc = Document("metrics") with doc.create(Section('Test description')): doc.append(test_description) with doc.create(Description()) as desc: for key, value in test_spec_dict.iteritems(): desc.add_item(key, value) section = Section('Metrics overview') test1 = Subsection('Rate matrix') # Create TN, TP, FP, FN table table1 = Tabular('cccccc') table1.add_hline() table1.add_row(("Class",'True-Positive','False-Positive','False-Negative','True-Negative', 'Accuracy')) table1.add_hline() for x in range(0, row_dimensions): table1.add_row([self.metrics_dict['class_code'][x], self.metrics_dict['TpFpFnTn'][x][0], self.metrics_dict['TpFpFnTn'][x][1], self.metrics_dict['TpFpFnTn'][x][2], self.metrics_dict['TpFpFnTn'][x][3], self.metrics_dict['Acc_Indi'][x]]) table1.add_hline() test1.append(table1) test3 = Subsection('Class metrics') table3 = Tabular('ccccc') table3.add_hline() table3.add_row(("Class",'True-Positive Rate (TPR)','Precision','True-Negative Rate (TNR)','F1-Score')) table3.add_hline() for x in range(0, row_dimensions): table3.add_row([self.metrics_dict['class_code'][x], self.metrics_dict['recall_all'][x], self.metrics_dict['precision_all'][x], self.metrics_dict['TNR'][x], self.metrics_dict['f1_score_all'][x]]) table3.add_hline() test3.append(table3) test2 = Subsection('Other') table2 = Tabular('cc') table2.add_hline() table2.add_row(("Class", "Value")) table2.add_hline() table2.add_row(["F1 Micro (Globally)", self.metrics_dict['f1_score_micro']]) table2.add_row(["F1 Macro (Each label)", self.metrics_dict['f1_score_macro']]) table2.add_row(["F1 Weighted (Each label)", self.metrics_dict['f1_score_weighted']]) table2.add_row(["F1 Micro (Globally) Std", self.metrics_dict['f1_score_micro_std']]) table2.add_hline() table2.add_row(["Recall Micro (Globally)", self.metrics_dict['recall_micro']]) table2.add_row(["Recall Macro (Each label)", self.metrics_dict['recall_macro']]) table2.add_row(["Recall Weighted (Each label)", self.metrics_dict['recall_weighted']]) table2.add_hline() table2.add_row(["Precision Micro (Globally)", self.metrics_dict['precision_micro']]) table2.add_row(["Precision Macro (Each label)", self.metrics_dict['precision_macro']]) table2.add_row(["Precision Weighted (Each label)", self.metrics_dict['precision_weighted']]) table2.add_hline() table2.add_row(["Kappa", self.metrics_dict['kappa_all']]) table2.add_row(["Kappa (Linear weighted)", self.metrics_dict['kappa_linear']]) table2.add_row(["Kappa (Quadratic weighted)", self.metrics_dict['kappa_quadratic']]) table2.add_row(["Kappa Std", self.metrics_dict['kappa_all_std']]) table2.add_hline() table2.add_row(["Accuracy (Correct classified)", self.metrics_dict['accuracy_all']]) table2.add_row(["Accuracy (Normalized)", self.metrics_dict['accuracy_normalized']]) table2.add_row(["Accuracy (Normalized) Std", self.metrics_dict['accuracy_normalized_std']]) table2.add_row(["Confidence Level(95%)", self.metrics_dict['confidence_level']]) table2.add_hline() table2.add_row(["Jaccard (Sum)", self.metrics_dict['jaccard_all']]) table2.add_row(["Jaccard (Average)", self.metrics_dict['jaccard_normalized']]) table2.add_hline() table2.add_row(["Zero-one classification loss (Misclassifications)", self.metrics_dict['zero_one_all']]) table2.add_row(["Zero-one classification loss (Fraction of misclassifications)", self.metrics_dict['zero_one_normalize']]) table2.add_hline() table2.add_row(["Hamming loss", self.metrics_dict['hamming_loss']]) table2.add_hline() table2.add_row(["Run Time (MSec)", self.metrics_dict['Run Time(MSec)']]) test2.append(table2) section.append(test1) section.append(test3) section.append(test2) doc.append(section) try: doc.generate_pdf(name + '_' + 'Metrics', compiler='pdflatex') except Exception: print "" shutil.move(name + '_' + 'Metrics' + '.pdf', path) try: os.remove(name + '_' + 'Metrics' + '.tex') except OSError: pass try: os.remove(name + '_' + 'Metrics' + '.log') except OSError: pass try: os.remove(name + '_' + 'Metrics' + '.aux') except OSError: pass return def create_pdf_indi(self, test_spec_dict, test_description, path, name): array_dimension = self.metrics_dict['con_matrix'].shape row_dimensions = array_dimension[0] array = self.metrics_dict['TpFpFnTn'][0] code = self.metrics_dict['class_code'][0] doc = Document("metrics") with doc.create(Section('Test description')): doc.append(test_description) with doc.create(Description()) as desc: for key, value in test_spec_dict.iteritems(): desc.add_item(key, value) section = Section('Metrics overview') test4 = Subsection('Confusion Matrix') crop_array = np.array( ['Spring Barly(1)', 'Winter Barley(10)', 'Winter Wheat(11)', 'Winter Rape(22)', 'Maize(216)']) # Create TN, TP, FP, FN table table4 = Tabular('cccccc') table4.add_hline() table4.add_row(('', 'Spring Barly', 'Winter Barley', 'Winter Wheat', 'Winter Rape', 'Maize')) table4.add_hline() for x in range(0, row_dimensions): table4.add_row([crop_array[x], self.metrics_dict['con_matrix'][x][0], self.metrics_dict['con_matrix'][x][1], self.metrics_dict['con_matrix'][x][2], self.metrics_dict['con_matrix'][x][3], self.metrics_dict['con_matrix'][x][4]]) table4.add_hline() test4.append(table4) test1 = Subsection('Rate matrix') # Create TN, TP, FP, FN table table1 = Tabular('cccccc') table1.add_hline() table1.add_row(("Class", 'True-Positive', 'False-Positive', 'False-Negative', 'True-Negative', 'Accuracy')) table1.add_hline() for x in range(0, row_dimensions): table1.add_row([self.metrics_dict['class_code'][x], self.metrics_dict['TpFpFnTn'][x][0], self.metrics_dict['TpFpFnTn'][x][1], self.metrics_dict['TpFpFnTn'][x][2], self.metrics_dict['TpFpFnTn'][x][3], self.metrics_dict['Acc_Indi'][x]]) table1.add_hline() test1.append(table1) test3 = Subsection('Class metrics') table3 = Tabular('ccccc') table3.add_hline() table3.add_row(("Class", 'True-Positive Rate (TPR)', 'Precision', 'True-Negative Rate (TNR)', 'F1-Score')) table3.add_hline() for x in range(0, row_dimensions): table3.add_row([self.metrics_dict['class_code'][x], self.metrics_dict['recall_all'][x], self.metrics_dict['precision_all'][x], self.metrics_dict['TNR'][x], self.metrics_dict['f1_score_all'][x]]) table3.add_hline() test3.append(table3) test2 = Subsection('Other') table2 = Tabular('cc') table2.add_hline() table2.add_row(("Class", "Value")) table2.add_hline() table2.add_row(["F1 Micro (Globally)", self.metrics_dict['f1_score_micro']]) table2.add_row(["F1 Macro (Each label)", self.metrics_dict['f1_score_macro']]) table2.add_row(["F1 Weighted (Each label)", self.metrics_dict['f1_score_weighted']]) table2.add_hline() table2.add_row(["Recall Micro (Globally)", self.metrics_dict['recall_micro']]) table2.add_row(["Recall Macro (Each label)", self.metrics_dict['recall_macro']]) table2.add_row(["Recall Weighted (Each label)", self.metrics_dict['recall_weighted']]) table2.add_hline() table2.add_row(["Precision Micro (Globally)", self.metrics_dict['precision_micro']]) table2.add_row(["Precision Macro (Each label)", self.metrics_dict['precision_macro']]) table2.add_row(["Precision Weighted (Each label)", self.metrics_dict['precision_weighted']]) table2.add_hline() table2.add_row(["Kappa", self.metrics_dict['kappa_all']]) table2.add_row(["Kappa (Linear weighted)", self.metrics_dict['kappa_linear']]) table2.add_row(["Kappa (Quadratic weighted)", self.metrics_dict['kappa_quadratic']]) table2.add_hline() table2.add_row(["Accuracy (Correct classified)", self.metrics_dict['accuracy_all']]) table2.add_row(["Accuracy (Normalized)", self.metrics_dict['accuracy_normalized']]) table2.add_hline() table2.add_row(["Jaccard (Sum)", self.metrics_dict['jaccard_all']]) table2.add_row(["Jaccard (Average)", self.metrics_dict['jaccard_normalized']]) table2.add_hline() table2.add_row(["Zero-one classification loss (Misclassifications)", self.metrics_dict['zero_one_all']]) table2.add_row( ["Zero-one classification loss (Fraction of misclassifications)", self.metrics_dict['zero_one_normalize']]) table2.add_hline() table2.add_row(["Hamming loss", self.metrics_dict['hamming_loss']]) test2.append(table2) section.append(test4) section.append(test1) section.append(test3) section.append(test2) doc.append(section) try: doc.generate_pdf(name + '_' + 'Metrics', compiler='pdflatex') except Exception: print "" shutil.move(name + '_' + 'Metrics' + '.pdf', path) try: os.remove(name + '_' + 'Metrics' + '.tex') except OSError: pass try: os.remove(name + '_' + 'Metrics' + '.log') except OSError: pass try: os.remove(name + '_' + 'Metrics' + '.aux') except OSError: pass return

43.439216

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1,314

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4.959665

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0.058309

0.895351

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0.842719

0.841798

0.836735

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0.021058

0.236887

11,077

255

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1

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null

0

1

0

8

ea7352330f9dc1ff03b3f68222b9167f70a01f45

5,145

py

Python

tests/modules/test_firewall.py

The-Cracker-Technology/CANToolz

1773cf8b7ef906da245461f0768007e43e4bc02d

[ "Apache-2.0" ]

194

2017-08-17T06:51:30.000Z

2022-03-23T09:01:29.000Z

tests/modules/test_firewall.py

The-Cracker-Technology/CANToolz

1773cf8b7ef906da245461f0768007e43e4bc02d

[ "Apache-2.0" ]

32

2017-08-17T06:23:19.000Z

2022-03-03T14:44:39.000Z

tests/modules/test_firewall.py

The-Cracker-Technology/CANToolz

1773cf8b7ef906da245461f0768007e43e4bc02d

[ "Apache-2.0" ]

42

2017-08-19T10:22:41.000Z

2022-02-23T04:34:16.000Z

import time from ..utils import TestCANToolz class TestFirewall(TestCANToolz): def test_blocked_body_hex(self): self.CANEngine.load_config('tests/configurations/conf_analyze.py') self.CANEngine.edit_module(2, {'pipe': 2, 'hex_black_body': ['0102030605']}) self.CANEngine.start_loop() index = 3 self.CANEngine.call_module(0, 't 4:6:010203060505') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) == 0, "We should find message in PIPE") self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 4:5:0102030605') # blocked mod = self.CANEngine.actions[index][1].CANList self.assertTrue(len(mod) == 0, "We should NOT find message in PIPE") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.edit_module(2, {'pipe': 2, 'hex_white_body': ['0102030605']}) self.CANEngine.call_module(0, 't 4:5:0102030605') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) == 0, "We should find message in PIPE") self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 4:6:010203060505') # blocked mod = self.CANEngine.actions[index][1].CANList self.assertTrue(len(mod) == 0, "We should NOT find message in PIPE") self.CANEngine.actions[index][1].CANList = [] def test_blocked_body(self): self.CANEngine.load_config('tests/configurations/conf_analyze.py') self.CANEngine.edit_module(2, {'pipe': 2, 'black_body': [[1, 2, 3, 6, 5]]}) self.CANEngine.start_loop() index = 3 self.CANEngine.call_module(0, 't 4:6:010203060505') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) == 0, "We should find message in PIPE") self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 4:5:0102030605') # blocked mod = self.CANEngine.actions[index][1].CANList self.assertTrue(len(mod) == 0, "We should NOT find message in PIPE") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.edit_module(2, {'pipe': 2, 'white_body': [[1, 2, 3, 6, 5]]}) self.CANEngine.call_module(0, 't 4:5:0102030605') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) == 0, "We should find message in PIPE") self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 4:6:010203060505') # blocked mod = self.CANEngine.actions[index][1].CANList self.assertTrue(len(mod) == 0, "We should NOT find message in PIPE") self.CANEngine.actions[index][1].CANList = [] def test_blocked_id(self): self.CANEngine.load_config('tests/configurations/conf_analyze.py') self.CANEngine.edit_module(2, {'pipe': 2, 'black_list': [1, 2, 3, 6, 5]}) self.CANEngine.start_loop() index = 3 self.CANEngine.call_module(0, 't 4:4:11223344') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) == 0, "We should find message in PIPE") self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 1:4:11223344') time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) > 0, "Message number 1 should not pass") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 7:4:11223344') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertTrue(mod[-1].frame_id == 7, "We should be able to find ID 7") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 1:4:11223344') time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) > 0, "Message number 1 should not pass") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 1:8:1122334411223344') time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertFalse(len(mod) > 0, "Message number 1 should not pass") self.CANEngine.actions[index][1].CANList = [] self.CANEngine.call_module(0, 't 4:4:11223344') # pass time.sleep(1) mod = self.CANEngine.actions[index][1].CANList self.assertTrue(mod[-1].frame_id == 4, "We should be able to find ID 4") self.CANEngine.actions[index][1].CANList = []

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5,145

117

85

43.974359

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null

1

0

1

0

1

0

null

0

9

57664ae71afb3e6edf9d10681a4085ea023a296d

14,091

py

Python

rnnmodels.py

georgeyiasemis/Recurrent-Neural-Networks-from-scratch-in-Pytorch

ea0ae4d8bd876a8d619303f250e0f05061e4eef5

[ "MIT" ]

11

2021-04-12T07:10:24.000Z

2022-03-08T21:44:29.000Z

rnnmodels.py

georgeyiasemis/Recurrent-Neural-Networks-from-scratch-in-Pytorch

ea0ae4d8bd876a8d619303f250e0f05061e4eef5

[ "MIT" ]

null

rnnmodels.py

georgeyiasemis/Recurrent-Neural-Networks-from-scratch-in-Pytorch

ea0ae4d8bd876a8d619303f250e0f05061e4eef5

[ "MIT" ]

1

2022-02-25T21:18:16.000Z

import torch import torch.nn as nn from torch.autograd import Variable import numpy as np from rnncells import LSTMCell, GRUCell, RNNCell class SimpleRNN(nn.Module): def __init__(self, input_size, hidden_size, num_layers, bias, output_size, activation='tanh'): super(SimpleRNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.output_size = output_size self.rnn_cell_list = nn.ModuleList() if activation == 'tanh': self.rnn_cell_list.append(RNNCell(self.input_size, self.hidden_size, self.bias, "tanh")) for l in range(1, self.num_layers): self.rnn_cell_list.append(RNNCell(self.hidden_size, self.hidden_size, self.bias, "tanh")) elif activation == 'relu': self.rnn_cell_list.append(RNNCell(self.input_size, self.hidden_size, self.bias, "relu")) for l in range(1, self.num_layers): self.rnn_cell_list.append(RNNCell(self.hidden_size, self.hidden_size, self.bias, "relu")) else: raise ValueError("Invalid activation.") self.fc = nn.Linear(self.hidden_size, self.output_size) def forward(self, input, hx=None): # Input of shape (batch_size, seqence length, input_size) # # Output of shape (batch_size, output_size) if hx is None: if torch.cuda.is_available(): h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size).cuda()) else: h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size)) else: h0 = hx outs = [] hidden = list() for layer in range(self.num_layers): hidden.append(h0[layer, :, :]) for t in range(input.size(1)): for layer in range(self.num_layers): if layer == 0: hidden_l = self.rnn_cell_list[layer](input[:, t, :], hidden[layer]) else: hidden_l = self.rnn_cell_list[layer](hidden[layer - 1],hidden[layer]) hidden[layer] = hidden_l hidden[layer] = hidden_l outs.append(hidden_l) # Take only last time step. Modify for seq to seq out = outs[-1].squeeze() out = self.fc(out) return out class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, bias, output_size): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.output_size = output_size self.rnn_cell_list = nn.ModuleList() self.rnn_cell_list.append(LSTMCell(self.input_size, self.hidden_size, self.bias)) for l in range(1, self.num_layers): self.rnn_cell_list.append(LSTMCell(self.hidden_size, self.hidden_size, self.bias)) self.fc = nn.Linear(self.hidden_size, self.output_size) def forward(self, input, hx=None): # Input of shape (batch_size, seqence length , input_size) # # Output of shape (batch_size, output_size) if hx is None: if torch.cuda.is_available(): h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size).cuda()) else: h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size)) else: h0 = hx outs = [] hidden = list() for layer in range(self.num_layers): hidden.append((h0[layer, :, :], h0[layer, :, :])) for t in range(input.size(1)): for layer in range(self.num_layers): if layer == 0: hidden_l = self.rnn_cell_list[layer]( input[:, t, :], (hidden[layer][0],hidden[layer][1]) ) else: hidden_l = self.rnn_cell_list[layer]( hidden[layer - 1][0], (hidden[layer][0], hidden[layer][1]) ) hidden[layer] = hidden_l outs.append(hidden_l[0]) out = outs[-1].squeeze() out = self.fc(out) return out class GRU(nn.Module): def __init__(self, input_size, hidden_size, num_layers, bias, output_size): super(GRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.output_size = output_size self.rnn_cell_list = nn.ModuleList() self.rnn_cell_list.append(GRUCell(self.input_size, self.hidden_size, self.bias)) for l in range(1, self.num_layers): self.rnn_cell_list.append(GRUCell(self.hidden_size, self.hidden_size, self.bias)) self.fc = nn.Linear(self.hidden_size, self.output_size) def forward(self, input, hx=None): # Input of shape (batch_size, seqence length, input_size) # # Output of shape (batch_size, output_size) if hx is None: if torch.cuda.is_available(): h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size).cuda()) else: h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size)) else: h0 = hx outs = [] hidden = list() for layer in range(self.num_layers): hidden.append(h0[layer, :, :]) for t in range(input.size(1)): for layer in range(self.num_layers): if layer == 0: hidden_l = self.rnn_cell_list[layer](input[:, t, :], hidden[layer]) else: hidden_l = self.rnn_cell_list[layer](hidden[layer - 1],hidden[layer]) hidden[layer] = hidden_l hidden[layer] = hidden_l outs.append(hidden_l) # Take only last time step. Modify for seq to seq out = outs[-1].squeeze() out = self.fc(out) return out class BidirRecurrentModel(nn.Module): def __init__(self, mode, input_size, hidden_size, num_layers, bias, output_size): super(BidirRecurrentModel, self).__init__() self.mode = mode self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.output_size = output_size self.rnn_cell_list = nn.ModuleList() if mode == 'LSTM': self.rnn_cell_list.append(LSTMCell(self.input_size, self.hidden_size, self.bias)) for l in range(1, self.num_layers): self.rnn_cell_list.append(LSTMCell(self.hidden_size, self.hidden_size, self.bias)) elif mode == 'GRU': self.rnn_cell_list.append(GRUCell(self.input_size, self.hidden_size, self.bias)) for l in range(1, self.num_layers): self.rnn_cell_list.append(GRUCell(self.hidden_size, self.hidden_size, self.bias)) elif mode == 'RNN_TANH': self.rnn_cell_list.append(RNNCell(self.input_size, self.hidden_size, self.bias, "tanh")) for l in range(1, self.num_layers): self.rnn_cell_list.append(RNNCell(self.hidden_size, self.hidden_size, self.bias, "tanh")) elif mode == 'RNN_RELU': self.rnn_cell_list.append(RNNCell(self.input_size, self.hidden_size, self.bias, "relu")) for l in range(1, self.num_layers): self.rnn_cell_list.append(RNNCell(self.hidden_size, self.hidden_size, self.bias, "relu")) else: raise ValueError("Invalid RNN mode selected.") self.fc = nn.Linear(self.hidden_size * 2, self.output_size) def forward(self, input, hx=None): # Input of shape (batch_size, sequence length, input_size) # # Output of shape (batch_size, output_size) if torch.cuda.is_available(): h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size).cuda()) else: h0 = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size)) if torch.cuda.is_available(): hT = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size).cuda()) else: hT = Variable(torch.zeros(self.num_layers, input.size(0), self.hidden_size)) outs = [] outs_rev = [] hidden_forward = list() for layer in range(self.num_layers): if self.mode == 'LSTM': hidden_forward.append((h0[layer, :, :], h0[layer, :, :])) else: hidden_forward.append(h0[layer, :, :]) hidden_backward = list() for layer in range(self.num_layers): if self.mode == 'LSTM': hidden_backward.append((hT[layer, :, :], hT[layer, :, :])) else: hidden_backward.append(hT[layer, :, :]) for t in range(input.shape[1]): for layer in range(self.num_layers): if self.mode == 'LSTM': # If LSTM if layer == 0: # Forward net h_forward_l = self.rnn_cell_list[layer]( input[:, t, :], (hidden_forward[layer][0], hidden_forward[layer][1]) ) # Backward net h_back_l = self.rnn_cell_list[layer]( input[:, -(t + 1), :], (hidden_backward[layer][0], hidden_backward[layer][1]) ) else: # Forward net h_forward_l = self.rnn_cell_list[layer]( hidden_forward[layer - 1][0], (hidden_forward[layer][0], hidden_forward[layer][1]) ) # Backward net h_back_l = self.rnn_cell_list[layer]( hidden_backward[layer - 1][0], (hidden_backward[layer][0], hidden_backward[layer][1]) ) else: # If RNN{_TANH/_RELU} / GRU if layer == 0: # Forward net h_forward_l = self.rnn_cell_list[layer](input[:, t, :], hidden_forward[layer]) # Backward net h_back_l = self.rnn_cell_list[layer](input[:, -(t + 1), :], hidden_backward[layer]) else: # Forward net h_forward_l = self.rnn_cell_list[layer](hidden_forward[layer - 1], hidden_forward[layer]) # Backward net h_back_l = self.rnn_cell_list[layer](hidden_backward[layer - 1], hidden_backward[layer]) hidden_forward[layer] = h_forward_l hidden_backward[layer] = h_back_l if self.mode == 'LSTM': outs.append(h_forward_l[0]) outs_rev.append(h_back_l[0]) else: outs.append(h_forward_l) outs_rev.append(h_back_l) # Take only last time step. Modify for seq to seq out = outs[-1].squeeze() out_rev = outs_rev[0].squeeze() out = torch.cat((out, out_rev), 1) out = self.fc(out) return out

38.083784

114

0.455255

1,443

14,091

4.222453

0.06237

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Python

qipr/registry/migrations/0001_initial.py

ctsit/qipr

3f0ef102d81a859c955f918b74037d199b4d6a00

[ "Apache-2.0" ]

2

2017-02-10T15:07:51.000Z

2017-02-10T15:08:01.000Z

qipr/registry/migrations/0001_initial.py

ctsit/qipr

3f0ef102d81a859c955f918b74037d199b4d6a00

[ "Apache-2.0" ]

11

2016-08-03T13:18:08.000Z

2017-01-24T14:19:59.000Z

qipr/registry/migrations/0001_initial.py

ctsit/qipr

3f0ef102d81a859c955f918b74037d199b4d6a00

[ "Apache-2.0" ]

5

2016-07-29T17:12:43.000Z

2016-12-19T15:56:14.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-01-17 20:10 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion import registry.utils class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='AccessLog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('gatorlink', models.CharField(max_length=50, null=True)), ('http_verb', models.CharField(max_length=10)), ('ip', models.GenericIPAddressField()), ('request_body', models.TextField(null=True)), ('response_code', models.IntegerField(null=True)), ('time_requested', models.DateTimeField(auto_now_add=True)), ('time_responded', models.DateTimeField(auto_now=True)), ('url', models.TextField()), ('previous_log', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='next_log', to='registry.AccessLog')), ], ), migrations.CreateModel( name='Address', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('address1', models.CharField(max_length=50)), ('address2', models.CharField(max_length=50)), ('city', models.CharField(max_length=50)), ('zip_code', models.CharField(blank=True, max_length=10, null=True)), ('state', models.CharField(blank=True, max_length=2, null=True)), ('country', models.CharField(blank=True, max_length=2, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='AuditTrail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('datetime', models.DateTimeField(auto_now=True)), ('json_before', models.TextField(null=True)), ('json_after', models.TextField(null=True)), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='audit', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='BigAim', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=400)), ('description', models.CharField(max_length=400, null=True)), ('sort_order', models.IntegerField(null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='ClinicalArea', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='ClinicalDepartment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('sort_order', models.IntegerField(null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='ClinicalSetting', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Descriptor', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('date_added', models.DateField(null=True)), ('major_revision_date', models.DateField(null=True)), ('ui', models.CharField(max_length=10)), ('cas_registry_number', models.CharField(max_length=40, null=True)), ('descriptor_class', models.CharField(max_length=1, null=True)), ('descriptor_entry_version', models.CharField(max_length=100, null=True)), ('descriptor_sort_version', models.CharField(max_length=300, null=True)), ('major_descriptor_date', models.DateField(null=True)), ('mesh_heading', models.CharField(max_length=150)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Entry', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=50, null=True)), ('pipe_separated', models.CharField(max_length=300, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Expertise', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='FocusArea', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('sort_order', models.IntegerField(null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Keyword', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='MeshTreeNumber', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('value', models.CharField(max_length=100)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Organization', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('org_name', models.CharField(max_length=400)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Person', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('account_expiration_time', models.DateTimeField(null=True)), ('business_phone', models.CharField(max_length=50, null=True)), ('contact_phone', models.CharField(max_length=50, null=True)), ('email_address', models.CharField(max_length=100, null=True)), ('first_name', models.CharField(max_length=30)), ('gatorlink', models.CharField(max_length=50, null=True)), ('last_login_time', models.DateTimeField(null=True)), ('last_name', models.CharField(max_length=30)), ('training', models.CharField(max_length=50, null=True)), ('webpage_url', models.CharField(max_length=50, null=True)), ('title', models.CharField(max_length=50, null=True)), ('department', models.CharField(max_length=50, null=True)), ('qi_required', models.SmallIntegerField(null=True)), ('other_self_classification', models.CharField(max_length=100, null=True)), ('is_admin', models.BooleanField(default=False)), ('clinical_area', models.ManyToManyField(to='registry.ClinicalArea')), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('expertise', models.ManyToManyField(to='registry.Expertise')), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('organization', models.ManyToManyField(to='registry.Organization')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='PharmacologicalAction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=250)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Position', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Project', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('approval_date', models.DateTimeField(null=True)), ('archived', models.BooleanField(default=False)), ('description', models.TextField(null=True)), ('measures', models.TextField(null=True)), ('overall_goal', models.TextField(null=True)), ('proposed_end_date', models.DateTimeField(null=True)), ('proposed_start_date', models.DateTimeField(null=True)), ('title', models.CharField(max_length=300)), ('advisor', models.ManyToManyField(related_name='advised_projects', to='registry.Person')), ('big_aim', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='projects', to='registry.BigAim')), ('category', models.ManyToManyField(related_name='projects', to='registry.Category')), ('clinical_area', models.ManyToManyField(related_name='projects', to='registry.ClinicalArea')), ('clinical_setting', models.ManyToManyField(related_name='projects', to='registry.ClinicalSetting')), ('collaborator', models.ManyToManyField(related_name='collaborations', to='registry.Person')), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('owner', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='projects', to='registry.Person')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='QI_Interest', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Qualifier', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('date_added', models.DateField(null=True)), ('major_revision_date', models.DateField(null=True)), ('ui', models.CharField(max_length=10)), ('qualifier_established', models.CharField(max_length=25, null=True)), ('abbreviation', models.CharField(max_length=2)), ('sub_heading', models.CharField(max_length=50)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='RegistryNumber', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=200)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='SCR', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('date_added', models.DateField(null=True)), ('major_revision_date', models.DateField(null=True)), ('ui', models.CharField(max_length=10)), ('cas_registry_number', models.CharField(max_length=40, null=True)), ('frequency', models.IntegerField(null=True)), ('note', models.TextField()), ('substance_name', models.CharField(max_length=300, null=True)), ('substance_name_term_thesaurus', models.CharField(max_length=40, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('heading_mapped_to', models.ManyToManyField(related_name='scr', to='registry.Descriptor')), ('indexing_information', models.ManyToManyField(related_name='scr_indexing', to='registry.Descriptor')), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('pharmacological_action', models.ManyToManyField(to='registry.PharmacologicalAction')), ('related_registry_number', models.ManyToManyField(to='registry.RegistryNumber')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Self_Classification', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=400)), ('description', models.CharField(max_length=400, null=True)), ('sort_order', models.IntegerField(null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='SemanticType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('value', models.CharField(max_length=10)), ('description', models.CharField(max_length=50, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Source', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=200)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Speciality', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Suffix', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('description', models.CharField(max_length=100, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Synonym', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=50, null=True)), ('pipe_separated', models.CharField(max_length=400, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Training', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('last_modified', models.DateTimeField(auto_now=True)), ('guid', models.CharField(default=registry.utils.get_guid, editable=False, max_length=32)), ('name', models.CharField(max_length=200)), ('description', models.CharField(max_length=200, null=True)), ('created_by', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ('last_modified_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='UserAgent', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('ua_string', models.TextField()), ('ua_hash', models.CharField(editable=False, max_length=32)), ], ), migrations.AlterUniqueTogether( name='useragent', unique_together=set([('id', 'ua_hash')]), ), migrations.AddField( model_name='scr', name='semantic_type', field=models.ManyToManyField(to='registry.SemanticType'), ), migrations.AddField( model_name='scr', name='source', field=models.ManyToManyField(to='registry.Source'), ), migrations.AddField( model_name='scr', name='synonym', field=models.ManyToManyField(to='registry.Synonym'), ), migrations.AddField( model_name='person', name='position', field=models.ManyToManyField(to='registry.Position'), ), migrations.AddField( model_name='person', name='qi_interest', field=models.ManyToManyField(to='registry.QI_Interest'), ), migrations.AddField( model_name='person', name='self_classification', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='person', to='registry.Self_Classification'), ), migrations.AddField( model_name='person', name='speciality', field=models.ManyToManyField(to='registry.Speciality'), ), migrations.AddField( model_name='person', name='suffix', field=models.ManyToManyField(to='registry.Suffix'), ), migrations.AddField( model_name='descriptor', name='allowable_qualifiers', field=models.ManyToManyField(related_name='_descriptor_allowable_qualifiers_+', to='registry.Qualifier'), ), migrations.AddField( model_name='descriptor', name='created_by', field=models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='descriptor', name='entry', field=models.ManyToManyField(related_name='descriptor', to='registry.Entry'), ), migrations.AddField( model_name='descriptor', name='forward_reference', field=models.ManyToManyField(related_name='_descriptor_forward_reference_+', to='registry.Descriptor'), ), migrations.AddField( model_name='descriptor', name='last_modified_by', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='descriptor', name='mesh_tree_number', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='descriptor', to='registry.MeshTreeNumber'), ), migrations.AddField( model_name='descriptor', name='pharmacological_action', field=models.ManyToManyField(to='registry.PharmacologicalAction'), ), migrations.AddField( model_name='descriptor', name='projects', field=models.ManyToManyField(null=True, related_name='mesh_keyword', to='registry.Project'), ), migrations.AddField( model_name='descriptor', name='related_registry_number', field=models.ManyToManyField(to='registry.RegistryNumber'), ), migrations.AddField( model_name='descriptor', name='semantic_type', field=models.ManyToManyField(to='registry.SemanticType'), ), migrations.AddField( model_name='address', name='organization', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='org_address', to='registry.Organization'), ), migrations.AddField( model_name='address', name='person', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='business_address', to='registry.Person'), ), migrations.AddField( model_name='accesslog', name='user_agent', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to='registry.UserAgent'), ), ]

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159

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38,589

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0.075148

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0.748882

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38,589

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160

58.379728

0.772289

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null

0

8

aa0e681d5f93082cf250932f6fe98fd6df1917ea

4,105

py

Python

tests/test_respawnTracker.py

fenugrec/dirt-rally-time-recorder

0f91fc49e9ed9b34afd9a11676ecd51a58a6d596

[ "CC-BY-3.0", "Apache-2.0", "MIT" ]

null

tests/test_respawnTracker.py

fenugrec/dirt-rally-time-recorder

0f91fc49e9ed9b34afd9a11676ecd51a58a6d596

[ "CC-BY-3.0", "Apache-2.0", "MIT" ]

null

tests/test_respawnTracker.py

fenugrec/dirt-rally-time-recorder

0f91fc49e9ed9b34afd9a11676ecd51a58a6d596

[ "CC-BY-3.0", "Apache-2.0", "MIT" ]

null

import unittest from timerecorder.respawnTracker import RespawnTracker fieldCount = 66 class TestRespawnTracker(unittest.TestCase): def __init__(self, methodName): unittest.TestCase.__init__(self, methodName) def setUp(self): self.thing = RespawnTracker() def tearDown(self): pass def testNoRespawnForFirstStats(self): stats = [0] * fieldCount stats[4] = 100.0 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) def testNoRespawnForLowXDeltas(self): stats = [0] * fieldCount stats[4] = 100.0 self.thing.track(stats) stats[4] = 101.1 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) stats[4] = 100.8 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) stats[4] = 99.9 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) def testNoRespawnForLowYDeltas(self): stats = [0] * fieldCount stats[5] = 100.0 self.thing.track(stats) stats[5] = 101.1 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) stats[5] = 100.8 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) stats[5] = 99.9 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) def testNoRespawnForCombinedDeltas(self): stats = [0] * fieldCount stats[4] = 100.0 stats[5] = 100.0 self.thing.track(stats) stats[4] = 101.1 stats[5] = 101.0 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) stats[4] = 100.8 stats[5] = 102.2 self.thing.track(stats) self.assertFalse(self.thing.isRecover() or self.thing.isRestart()) def testSmallDeltaIsRecover(self): stats = [0] * fieldCount stats[4] = 100.0 stats[5] = 100.0 self.thing.track(stats) stats[4] = 95.0 stats[5] = 100.0 self.thing.track(stats) self.assertTrue(self.thing.isRecover()) self.assertFalse(self.thing.isRestart()) stats[4] = 96.8 stats[5] = 99.9 self.thing.track(stats) self.assertFalse(self.thing.isRecover()) self.assertFalse(self.thing.isRestart()) stats[4] = 97.0 stats[5] = 105.0 self.thing.track(stats) self.assertTrue(self.thing.isRecover()) self.assertFalse(self.thing.isRestart()) def testLargeDeltaIsRestartForDistanceValueNearZero(self): stats = [0] * fieldCount stats[2] = 13 stats[4] = 100.0 stats[5] = 100.0 self.thing.track(stats) stats[2] = 5 stats[4] = 20.0 stats[5] = 100.0 self.thing.track(stats) self.assertFalse(self.thing.isRecover()) self.assertTrue(self.thing.isRestart()) stats[4] = 10.0 stats[5] = 15.0 self.thing.track(stats) self.assertFalse(self.thing.isRecover()) self.assertTrue(self.thing.isRestart()) def testLargeDeltaIsRecoverForHigherDistanceValue(self): stats = [0] * fieldCount stats[2] = 25 stats[4] = 100.0 stats[5] = 100.0 self.thing.track(stats) stats[4] = 20.0 stats[5] = 100.0 self.thing.track(stats) self.assertTrue(self.thing.isRecover()) self.assertFalse(self.thing.isRestart()) stats[4] = 10.0 stats[5] = 15.0 self.thing.track(stats) self.assertTrue(self.thing.isRecover()) self.assertFalse(self.thing.isRestart()) if __name__ == '__main__': unittest.main()

29.746377

74

0.574421

467

4,105

5.014989

0.117773

0.211358

0.131512

0.17848

0.777541

0.769001

0.746798

0.73228

0

0.06263

0.299878

4,105

138

75

29.746377

0.752262

0

0.715596

0

0.001948

0

0.211009

1

0.091743

false

0.009174

0.018349

0

0.119266

0

null

1

0

1

0

1

0

1

0

null

0

8

aa1a88ac9dfb6656e1227a9de6e17e6ecf5d91b4

119,046

py

Python

cryptonic.py

Septillioner/cryptonic

b4d69bf9c38d934606b862ab99b44c18642446c3

[ "MIT" ]

5

2017-10-22T14:22:09.000Z

2018-08-27T21:02:40.000Z

cryptonic.py

Septillioner/cryptonic

b4d69bf9c38d934606b862ab99b44c18642446c3

[ "MIT" ]

null

cryptonic.py

Septillioner/cryptonic

b4d69bf9c38d934606b862ab99b44c18642446c3

[ "MIT" ]

null

# -*- coding: utf-8 -*- import random import shlex from base64 import b64encode,b64decode import string import os import md5 import threading import json import zlib # CRYPTONIC WRITTEN BY SEPTILLIONER # THX FOR SUPPORTS HeykLog && Рнаитом # BINARY def charToBin(data): try: return bin(ord(data))[2:] except TypeError: return data def strToBin(data): if(len(data) != 1): str_data = str() for i in data: str_data+= bin(ord(i))[2:]+" " str_data = str_data[:-1] return str_data elif(len(data) > 1): return charToBin(data) def binToChar(data): return chr(int(data,2)) def binToStr(data): if(len(data)!= 1): str_data = str() for i in data.split(" "): str_data+= chr(int(i,2)) return str_data else: return binToChar(data) # # OCTAL # def charToOct(data): return oct(ord(data)) def strToOct(data): if(len(data) != 1): str_data = str() for i in data: str_data+= oct(ord(i))+" " str_data = str_data[:-1] return str_data elif(len(data) > 1): return charToOct(data) def octToChar(data): return chr(int(data,8)) def octToStr(data): if(len(data)!= 1): str_data = str() for i in data.split(" "): str_data+= chr(int(i,8)) return str_data else: return octToChar(data) # # DECIMAL # def charToDec(data): return ord(data) def strToDec(data): if(len(data) != 1): str_data = str() for i in data: str_data+= str(ord(i))+" " str_data = str_data[:-1] return str_data elif(len(data) > 1): return charToDec(data) def decToChar(data): return chr(int(data,10)) def decToStr(data): if(len(data)!= 1): str_data = str() for i in data.split(" "): str_data+= chr(int(i,10)) return str_data else: return decToChar(data) # # HEXDECIMAL # def charToHex(data): return ord(data) def strToHex(data): if(len(data) != 1): str_data = str() for i in data: str_data+= hex(ord(i))[2:]+" " str_data = str_data[:-1] return str_data elif(len(data) > 1): return charToDec(data) def hexToChar(data): return chr(int(data,16)) def hexToStr(data): if(len(data)!= 1): str_data = str() for i in data.split(" "): str_data+= chr(int(i,16)) return str_data else: return decToChar(data) # # TORS # tors = 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def randEncode(data): global tors randBit = list(tors)[random.randint(0,len(tors)-1)] return string.translate(data,tors[randBit]["tencode"]),binToStr(randBit) def randByteChar(): global tors return binToChar(list(tors)[random.randint(0,len(tors)-1)]) def torEncode(text,eChar): global tors return string.translate(text,tors[charToBin(eChar)]["tencode"]) def torDecode(text,eChar): global tors return string.translate(text,tors[charToBin(eChar)]["tdecode"]) def md5_(data): md_ = md5.md5() md_.update(str(data)) return md_.hexdigest() minLen = 16 class cryptonic: def __init__(self,pattern=False,patternFilename=False,cryptonicLen=False): if(pattern != False): self.pattern = pattern elif(patternFilename != False): self.pattern = patternRead(patternFilename) elif(cryptonicLen != False): self.pattern = createCryptonicKey(codecLen = cryptonicLen) def encode(self,data=""): if(len(data) >= 0): if(data == 0): data = "Niye dosyayi bos birakiyosun dosya israfi degilmi utan...Program bozuluyo sonra" pattern = b64decode(self.pattern) encodedData = b64encode(data) for item in pattern: encodedData = torEncode(encodedData,item) return True,b64encode(encodedData) else: return False,False def decode(self,data=""): if(len(data) > 0): pattern = b64decode(self.pattern)[::-1] decodedData = b64decode(data) for eChar in pattern: decodedData = torDecode(decodedData,eChar) try: return True,b64decode(decodedData) except: return False,None else: return False,None def encodeFile(self,fname): with open(fname,"rb") as fileVarR: with open("%s.cryptonicted"%(fname),"wb") as fileVar: fileVar.write("-----BEGIN CRYPTONIC DATA-----\n%s\n-----END CRYPTONIC DATA-----"%(self.encode(data=fileVarR.read())[1])) return "%s.cryptonicted"%(fname) def decodeFile(self,fname,nt=True): try: if(nt): with open("%s.cryptonicted" % fname, "rb") as fileVar: state, decodedFileData = self.decode( data=str(fileVar.read().split("-----")[1:-1][1].split("\n")[1:-1])) if (not state): return False, None with open("(decoded)"+".".join(fname.split(".")),"wb") as fileVar: fileVar.write("%s"%(decodedFileData)) return True,"(decoded)%s.%s"%(fname.split(".")[0],fname.split(".")[1]) else: with open("%s" % fname, "rb") as fileVar: data = str(fileVar.read().split("-----")[1:-1][1].split("\n")[1:-1]) state, decodedFileData = self.decode(data=data) if (not state): return False, None with open(".".join(fname.split(".")[:-1]),"wb") as fileVar: fileVar.write("%s"%(decodedFileData)) return True,"%s.%s"%(fname.split(".")[0],fname.split(".")[1]) except IOError: return False,None except IndexError: return False,None except None: return False,None def patternSave(self,fname): if(len(self.pattern) > 4): if(os.path.isfile("%s.cton"%(fname))): question = raw_input("%s.cton adli dosya uzerine yazilsinmi ?(e/h)"%(fname)) if(question.lower() == "e"): with open("%s.cton"%(fname),"wb") as fileVar: fileVar.write("-----BEGIN CRYPTONIC KEY-----\n%s\n-----END CRYPTONIC KEY-----"%(self.pattern)) elif(question.lower() == "h"): i = 0 while True: if(not os.path.isfile("%s(%s).cton"%(fname,i))): with open("%s(%s).cton"%(fname,i),"wb") as fileVar: fileVar.write("-----BEGIN CRYPTONIC KEY-----\n%s\n-----END CRYPTONIC KEY-----"%(self.pattern)) break i += 1 else: with open("%s.cton"%(fname),"wb") as fileVar: fileVar.write("-----BEGIN CRYPTONIC KEY-----\n%s\n-----END CRYPTONIC KEY-----"%(self.pattern)) class fusion(object): """docstring for fusion""" fsrule = "***" def __init__(self, filename): self.filename = filename def triggerFusion(self,fileTree): with open(self.filename,"wb+") as fp: for path in fileTree: for fpath in fileTree[path]: try: with open("%s\\%s"%(path,fpath),"rb") as fpa: fp.write("%s%s%s"%(self.fsrule,b64encode(json.dumps({"filename":"%s\\%s"%(path,fpath),"data":self.encode(fpa.read())})),self.fsrule)) except None: print "Permission Denied" def encode(self,data): return b64encode(zlib.compress(data)) def decode(self,data): return zlib.decompress(b64decode(data)) def triggerFission(self): with open(self.filename,"rb") as fp: fname = os.path.split(self.filename)[1] for i in fp.read().split(self.fsrule): if(i != ""): djson = json.loads(b64decode(i)) if not os.path.exists(os.path.split(djson["filename"])[0]): os.makedirs(os.path.split(djson["filename"])[0]) with open(djson["filename"],"wb") as fpa: fpa.write(self.decode(djson["data"])) def createCryptonicKey(codecLen = 4): if(codecLen >= 16): lastcodec = "" pattern = "" eChar = "" eChar =randByteChar() lastcodec=eChar for item in xrange(codecLen): while True: eChar =randByteChar() if(lastcodec != eChar): pattern += eChar break else:continue lastcodec = eChar return b64encode(pattern) else: lastcodec = "" pattern = "" eChar = "" eChar =randByteChar() lastcodec=eChar for item in xrange(16): while True: eChar =randByteChar() if(lastcodec != eChar): pattern += eChar break else:continue lastcodec = eChar return b64encode(pattern) def getFileData(fname): with open(fname,"rb") as fileVar: return fileVar.read() def patternRead(fname): try: with open("%s.cton"%(fname),"rb") as fileVar: return str(fileVar.read().split("-----")[1:-1][1].split("\n")[1:-1]) except: print "%s.cton not found so created on %s.cton.Pattern_"%(fname,fname) patternWriteInFile(fname,createPattern(1024)) return patternRead(fname) def consoleApplication(): global tors nowPattern = "" while True: cmd = raw_input("Cryptonic > ") for commander in cmd.split(";"): commands=shlex.split(commander) if(commands[0] == "pattern"): if(commands[1] == "set"): if(commands[2] == "create"): try: nowPattern = createCryptonicKey(int(commands[3])) pattern = cryptonic(pattern=nowPattern) print "Successfully Loaded at cache" except IndexError: print "Please enter integer of create method" elif(commands[2] == "ctonfile"): try: nowPattern = patternRead(commands[3]) pattern = cryptonic(pattern=nowPattern) print "Successfully Loaded at cache" except IndexError: print "Please enter string." elif(commands[2] == "seton"): try: nowPattern = b64encode(commands[3]) pattern = cryptonic(pattern=nowPattern) print "Successfully Loaded at cache" except TypeError: print "Base64 Error !" elif(commands[2] == "getfile"): try: ffname = commands[3] if(os.path.isfile(ffname)): with open(ffname,"rb") as fileVar: nowPattern = b64encode(fileVar.read()) pattern = cryptonic(pattern=nowPattern) print "Successfuly created pattern with %s. Loaded at cache"%(ffname) else: print "File not found." except: print "Please enter filename." else: print "Command wrong using" elif(commands[1] == "get"): if(commands[2] == "key"): if(len(nowPattern) < 1024*5 and len(nowPattern) != 0): print "-----BEGIN CRYPTONIC KEY-----\n%s\n-----END CRYPTONIC KEY-----"%(nowPattern) elif(len(nowPattern) > 1024*5): print "Pattern size bigger than 5KB so didn't print." else: print "Pattern don't loaded." elif(commands[2] == "size"): if(len(nowPattern) != 0): print "Pattern Size : %s Byte"%(len(b64decode(nowPattern))) else: print "Pattern don't loaded." elif(commands[2] == "md5"): if(len(nowPattern) != 0): print "Pattern MD5 Hash : %s"%(md5_(b64decode(nowPattern))) else: print "Pattern don't loaded." elif(commands[2] == "info"): if(len(nowPattern) != 0): print "Pattern Size : %s Byte"%(len(b64decode(nowPattern))) print "Pattern MD5 Hash : %s"%(md5_(b64decode(nowPattern))) else: print "Pattern don't loaded." elif(commands[1] == "save"): if(len(nowPattern) != 0): if(os.path.isfile(commands[2])): print "This file already created." else: try: pattern.patternSave(commands[2]) print "Sucessfully created cryptonic key file > %s.cton"%(commands[2]) except: print "Error on saving." else: print "Please load pattern key." else: print "Please enter valid command." elif(commands[0] == "encode"): if(commands[1] == "file"): if(len(nowPattern) != 0): if(os.path.isfile(commands[2])): fname = pattern.encodeFile(commands[2]) print "Encoded File Name : %s"%(fname) else: print "File not found." else: print "Please load pattern key." elif(commands[1] == "text"): if(len(nowPattern) != 0): try: encodedText = pattern.encode(data=commands[2]) print "\nPattern MD5 : %s\nEncoded Text : %s\nRaw Text : %s\n"%(md5_(nowPattern),encodedText[1],commands[2]) except: print "Error excepted on encode text" else: print "Please load cryptonic key." elif(commands[1] == "allpath"): if(len(nowPattern) != 0): folderName = commands[2] fileTree = getFolderTree(folderName) for i in fileTree: for j in fileTree[i]: filename = "%s\\%s"%(i,j) fname = pattern.encodeFile(filename) os.remove(filename) os.rename(folderName,"%s-cryptonicted"%(folderName)) else: print "Please load cryptonic key." elif(commands[0] == "decode"): if(commands[1] == "file"): if(len(nowPattern) != 0): if(os.path.isfile(commands[2])): try: state,fname = pattern.decodeFile(commands[2]) if(state): print "\nDecoded Filename : %s\nEncoded File Name : %s"%(fname,commands[2]) else: print "Failed." except: print "Cryptonic key not valid." else: print "File not found." else: print "Please load pattern key." elif(commands[1] == "text"): if(len(nowPattern) != 0): try: state,decodedText = pattern.decode(data=commands[2]) if(state): print "\nPattern MD5 : %s\nDecoded Text : %s\nEncoded Text : %s\n"%(md5_(nowPattern),decodedText,commands[2]) else: print "Failed\n" except: print "Error excepted on decode text" else: print "Please load pattern key." elif(commands[1] == "allpath"): if(len(nowPattern) != 0): folderName = commands[2] fileTree = getFolderTree(folderName) for i in fileTree: for j in fileTree[i]: if(j.split(".")[-1] == "cryptonicted"): filename = "%s\\%s"%(i,j) state,fname = pattern.decodeFile(fname=filename,nt=False) if(state == False): print "Failed." break os.remove(filename) else: pass if(folderName.split("-")[-1] == "cryptonicted"): os.rename(folderName,"-".join(folderName.split("-")[:-1])) else: pass else: print "Please load pattern key." elif(commands[0] == "cry"): if(commands[1] == "compress"): try: path = commands[2] except: print "Please enter path." continue fs = fusion(raw_input("Please enter file name > ")) fs.triggerFusion(getFolderTree(path)) elif(commands[1] == "decompress"): try: filename = commands[2] except: print "Please enter filename." continue fs = fusion(filename) fs.triggerFission() elif(commands[0] == "help"): print getHelp() else: print "Please Enter valid command." # # MAIN # def getHelp(): return """ TR--------- pattern BÃ¶lÃ¼mÃ¼-Section set BÃ¶lÃ¼mÃ¼-Section create - Verilen boyuta gÃ¶re Cryptonic key oluÃ¾turur. PatternSize ctonfile - Key dosyasÃ½nÃ½ loadlar. Filename getfile - GirdiÃ°iniz dosyanÃ½n binarysine gÃ¶re pattern oluÃ¾turur. Filename seton - Komut isteminde base64 kodu girerek manuel key loadlayÃ½n. Base64PatternKey get key - Dosya 5 KB den bÃ¼yÃ¼k deÃ°ilse base64 iÃ§eriÃ°ini yazdÃ½rÃ½r. size -Cryptonic key'in Boyutunu YazdÃ½rÃ½r. md5 - Cryptonic key'in MD5 hashini yazdirir. info - Dosyanin ozelliklerini yazdidir. save - VerdiÃ°iniz isme .cton uzantÃ½sÃ½nÃ½ koyarak patterni kaydeder. Filename encode text - Konsolda girdiÃ°iniz veriyi encodeler. string file - GirdiÃ°iniz dosyayÃ½ encodeler. Dizin girebilirsiniz. Filename allpath - GirdiÃ°iniz klasÃ¶rÃ¼ iÃ§indeki Ã¾ifrelennmiÃ¾ verilerin hepsini Ã§Ã¶zer. Folder decode text - Konsolda girdiÃ°iniz veriyi decodeler. string file - GirdiÃ°iniz dosyayÃ½ decodeler. Dizin girebilirsiniz. Filename allpath - GirdiÃ°iniz klasÃ¶rÃ¼ iÃ§indeki Ã¾ifrelennmiÃ¾ verilerin hepsini Ã§Ã¶zer. Folder cry compress path - GirdiÄŸiniz klasÃ¶rÃ¼n iÃ§indeki dosyalar ve klasÃ¶rler doÄŸrudan sizin belirlediÄŸiniz dosyanÄ±n iÃ§ine sÄ±kÄ±ÅŸtÄ±rÄ±lÄ±r. decompress path - GirdiÄŸiniz dosyanÄ±n iÃ§indeki dosyalar ve klasÃ¶rler doÄŸrudan sizin Ã¶nceden belirlediÄŸiniz klasÃ¶r iÃ§ine yerleÅŸtirilir. EN--------- pattern set - set patterns for encoding,decoding.(cache from import) create - create a pattern of the desired length Argument-Input - pattern length(Integer) ctonfile Argument-Input - cton Filename(String) getfile - creating pattern with according any file Argument-Input - Filename(String) seton - setting pattern with entered key Argument-Input - Pattern-base64(String) get - Getting pattern information key - If greater than 5 KB does not print Console-Output - Pattern-base64(String) size - Pattern key size Console-Output - Pattern-size(String) md5 - Returns pattern md5 Console-Output - Pattern-MD5-Hexdigest(String) info - Returns pattern md5 and size Console-Output - Pattern-info(String) save - Saves last loaded pattern as file Argument-Input - Filename(String) encode - Encoding with last loaded pattern text - Encoding simple text Argument-Input - Text(String) file - Encoding file with entered file path Argument-Input - Filename(String) allpath - Encoding all files and folders with entered folder path Argument-Input - Folder Path(String) decode - Decoding with last loaded pattern text - Decoding simple crypted text(needs pattern key on used to encoding for decoding) Argument-Input - Encrypted text(String) file - Decoding file with entered file path(needs pattern key on used to encoding for decoding) Argument-Input - Filename(String) allpath - Decoding crypted all files and folders with entered file path(needs pattern key on used to encoding for decoding) Argument-Input - Folder path(String) cry - Compressing paths compress - Compressing all paths and files with entered file path Argument-Input - Folder path(String) Console-Input - Filename for keeping files and folders(String) decompress - Decompressing all paths and files with entered compress file name Argument-Input - Filename for decompressing files and folders(String) """ def getFolderTree(path): fileTree = dict() for root, dirs, files in os.walk(path): fileTree[root] = files return fileTree def main(): consoleApplication() if(__name__ == "__main__"): main()

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a4aa543b312725f6f816bb0eb7e529e4069903d7

605

py

Python

tests/mock/s3.py

Wambosa/tetrauniversal-functions

4d63ab7b45afbdb67f569e2bc513cb91feaa0e17

[ "MIT" ]

3

2020-01-30T21:25:35.000Z

2020-02-26T21:05:05.000Z

tests/mock/s3.py

Wambosa/tetrauniversal-functions

4d63ab7b45afbdb67f569e2bc513cb91feaa0e17

[ "MIT" ]

null

tests/mock/s3.py

Wambosa/tetrauniversal-functions

4d63ab7b45afbdb67f569e2bc513cb91feaa0e17

[ "MIT" ]

null

from box import Box class VoidS3: def get_object(self, Bucket='', Key=''): def read(): return b'1234567,http://web.uk,left,2019-01-01T00:01:000Z,87646675465\n8901234,https://web.com,right,2020-01-01T00:00:000Z,99999999999' return { 'Body': Box({ 'read': read }) } class DiffDelimiterS3: def get_object(self, Bucket='', Key=''): def read(): return b'1234567!http://web.uk!left!2019-01-01T00:01:000Z!87646675465\n8901234!https://web.com!right!2020-01-01T00:00:000Z!99999999999' return { 'Body': Box({ 'read': read }) }

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0.402439

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0.087193

0.871935

0

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605

28

142

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a4d338d42d94c49c2b517211d1d1347eacded247

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Python

tests/reg_tests/test_DVGeometryESP.py

jrram/pygeo

ed15c848703a90055d38130b6d05cef8080a9d68

[ "Apache-2.0" ]

41

2019-04-18T00:49:42.000Z

2022-03-27T10:06:47.000Z

tests/reg_tests/test_DVGeometryESP.py

jrram/pygeo

ed15c848703a90055d38130b6d05cef8080a9d68

[ "Apache-2.0" ]

90

2019-05-01T19:08:26.000Z

2022-03-28T15:27:12.000Z

tests/reg_tests/test_DVGeometryESP.py

jrram/pygeo

ed15c848703a90055d38130b6d05cef8080a9d68

[ "Apache-2.0" ]

35

2019-04-30T19:06:42.000Z

2022-03-18T14:26:57.000Z

import unittest import os import numpy as np from stl import mesh from baseclasses import BaseRegTest from baseclasses.utils import Error from parameterized import parameterized_class import time try: from mpi4py import MPI except ImportError: MPI = None if MPI: try: import pyOCSM from pygeo import DVGeometryESP except ImportError: pyOCSM = None test_params = [{"N_PROCS": 1, "name": "serial"}, {"N_PROCS": 4, "name": "parallel_4procs"}] @unittest.skipUnless(MPI and pyOCSM, "MPI and pyOCSM are required.") @parameterized_class(test_params) class TestPyGeoESP_BasicCube(unittest.TestCase): # to be tested in serial and parallel automatically N_PROCS = 1 def setUp(self): # Store the path where this current script lives # This all paths in the script are relative to this path # This is needed to support testflo running directories and files as inputs self.input_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) def setup_cubemodel(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) # add a point set on the surface vertex1 = np.array([-2.0, -2.0, -2.0]) vertex2 = np.array([1.5, 1.5, 1.5]) left = np.array([-2.0, -1.1, -1.1]) right = np.array([1.5, -1.2, -0.1]) front = np.array([0.25, 1.5, 0.3]) back = np.array([1.2, -2.0, -0.3]) top = np.array([0.0, 0.1, 1.5]) bottom = np.array([-1.9, -1.1, -2.0]) initpts = np.vstack([vertex1, vertex2, left, right, front, back, top, bottom, left, right]) distglobal = DVGeo.addPointSet(initpts, "mypts", cache_projections=False) self.assertAlmostEqual(distglobal, 0.0, 8) # evaluate the points and check that they match DVGeo._updateESPModel() DVGeo._updateProjectedPts() self.assertTrue(DVGeo.pointSetUpToDate) self.assertAlmostEqual(np.linalg.norm(initpts - DVGeo.pointSets["mypts"].proj_pts), 0.0, 10) return DVGeo, initpts def setup_cubemodel_analytic_jac(self): jacpt0 = np.array( [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]] # x # y ) # z jacpt1 = np.array( [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0]] # x # y ) # z jacpt2 = np.array( [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.9 / 3.5], ] ) # z jacpt3 = np.array( [ [1.0, 0.0, 0.0, 1.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.8 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.9 / 3.5], ] ) # z jacpt4 = np.array( [ [1.0, 0.0, 0.0, 2.25 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 2.30 / 3.50], ] ) # z jacpt5 = np.array( [ [1.0, 0.0, 0.0, 3.20 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.70 / 3.50], ] ) # z jacpt6 = np.array( [ [1.0, 0.0, 0.0, 2.0 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 2.1 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.0], ] ) # z jacpt7 = np.array( [ [1.0, 0.0, 0.0, 0.1 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], ] ) # z ordered_analytic_jac = np.concatenate( [jacpt0, jacpt1, jacpt2, jacpt3, jacpt4, jacpt5, jacpt6, jacpt7, jacpt2, jacpt3], axis=0 ).reshape(10, 3, 6) return ordered_analytic_jac def test_load_a_model(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeometryESP(csmFile) def test_save_cadfile(self): write_fullpath = os.path.join(self.input_path, "reg_tests/fullpath_" + str(self.N_PROCS) + ".step") DVGeo, initpts = self.setup_cubemodel() if DVGeo.comm.rank == 0: try: os.remove(write_fullpath) except OSError: pass DVGeo.writeCADFile(write_fullpath) DVGeo.comm.barrier() time.sleep(0.1) self.assertTrue(os.path.exists(write_fullpath)) # check that bad file extension raises a Python error with self.assertRaises(IOError): DVGeo.writeCADFile("relpath.wrongext") def test_write_csmfile(self): DVGeo, initpts = self.setup_cubemodel() write_fullpath = os.path.join(self.input_path, "reg_tests/fullpath_" + str(self.N_PROCS) + ".csm") if DVGeo.comm.rank == 0: try: os.remove(write_fullpath) except OSError: pass DVGeo.writeCSMFile(write_fullpath) DVGeo.comm.barrier() time.sleep(0.1) self.assertTrue(os.path.exists(write_fullpath)) # check that bad file extension raises a Python error with self.assertRaises(IOError): DVGeo.writeCADFile("relpath.wrongext") def test_add_desvars(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) # add variables with a mix of optional arguments DVGeo.addVariable("cubex0", lower=np.array([-10.0]), upper=np.array([10.0]), scale=0.1, dh=0.0001) self.assertEqual(DVGeo.getNDV(), 1) DVGeo.addVariable("cubey0") self.assertEqual(DVGeo.getNDV(), 2) DVGeo.addVariable("cubez0", lower=np.array([-10.0]), upper=np.array([10.0])) self.assertEqual(DVGeo.getNDV(), 3) # try to add a variable that isn't in the CSM file with self.assertRaises(Error): DVGeo.addVariable("cubew0") def test_add_pointset(self): DVGeo, initpts = self.setup_cubemodel() def test_updated_points(self): DVGeo, initpts = self.setup_cubemodel() DVGeo.addVariable("cubey0") DVGeo.setDesignVars({"cubey0": np.array([4.2000])}, updateJacobian=False) npts = initpts.shape[0] self.assertAlmostEqual(np.sum(DVGeo.pointSets["mypts"].proj_pts[:, 1] - initpts[:, 1]) / npts, 6.2, 10) DVGeo.addVariable("cubedz") DVGeo.setDesignVars({"cubedz": np.array([9.5])}, updateJacobian=False) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2], 7.5) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 2], -2.0) def test_finite_precision(self): DVGeo, initpts = self.setup_cubemodel() DVGeo.addVariable("cubey0") DVGeo.setDesignVars({"cubey0": np.array([4.2 + 1e-12])}, updateJacobian=False) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 1] - 4.2, 1e-12, 15) DVGeo.addVariable("cubedz") DVGeo.setDesignVars({"cubedz": np.array([9.5 - 1e-12])}, updateJacobian=False) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2] - 7.5, -1e-12, 15) def test_serial_finite_difference(self): # this test checks the underlying jacobian itself, not the public API # TODO write tests for the public API DVGeo, initpts = self.setup_cubemodel() for designvarname in ["cubex0", "cubey0", "cubez0", "cubedx", "cubedy", "cubedz"]: DVGeo.addVariable(designvarname) # check the FD derivatives initpts_cache = initpts.copy() dvdict_cache = DVGeo.DVs.copy() self.assertFalse(DVGeo.updatedJac["mypts"]) DVGeo._computeSurfJacobian(fd=True) self.assertTrue(DVGeo.updatedJac["mypts"]) npts = initpts.shape[0] ndvs = DVGeo.getNDV() # check the jacobian results match analytic result testjac = DVGeo.pointSets["mypts"].jac.reshape(npts, 3, ndvs) analyticjac = self.setup_cubemodel_analytic_jac() for ipt in range(npts): self.assertAlmostEqual(np.sum(np.abs(testjac[ipt, :, :] - analyticjac[ipt, :, :])), 0) # check that the point set hasn't changed after running the FDs self.assertAlmostEqual(np.sum(np.abs(initpts_cache - DVGeo.pointSets["mypts"].proj_pts)), 0.0) # check that the DV dict hasn't changed for key in dvdict_cache: self.assertAlmostEqual(np.sum(np.abs(DVGeo.DVs[key].value - dvdict_cache[key].value)), 0.0) def test_jacobian_arbitrary_added_order(self): # this test checks the underlying jacobian itself, not the public API DVGeo, initpts = self.setup_cubemodel() # switch up the order of DVs added for designvarname in ["cubey0", "cubedx", "cubedy", "cubex0", "cubedz", "cubez0"]: DVGeo.addVariable(designvarname) # check the FD derivatives DVGeo._computeSurfJacobian(fd=True) npts = initpts.shape[0] ndvs = DVGeo.getNDV() # check the jacobian results match analytic result testjac = DVGeo.pointSets["mypts"].jac.reshape(npts, 3, ndvs) ordered_analyticjac = self.setup_cubemodel_analytic_jac() analyticjac = np.zeros((npts, 3, ndvs)) # get original variable ordering orig_var_order = ["cubex0", "cubey0", "cubez0", "cubedx", "cubedy", "cubedz"] # reorder the analytic jacobian for idv, designvarname in enumerate(orig_var_order): dv_ind = DVGeo.DVs[designvarname].globalStartInd analyticjac[:, :, dv_ind] = ordered_analyticjac[:, :, idv] self.assertNotEqual(dv_ind, idv) for ipt in range(npts): self.assertAlmostEqual(np.sum(np.abs(testjac[ipt, :, :] - analyticjac[ipt, :, :])), 0) @unittest.skipUnless(MPI and pyOCSM, "MPI and pyOCSM are required.") class TestPyGeoESP_BasicCube_Distributed(unittest.TestCase): N_PROCS = 3 def setUp(self): # Store the path where this current script lives # This all paths in the script are relative to this path # This is needed to support testflo running directories and files as inputs self.input_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) self.comm = MPI.COMM_WORLD def setup_cubemodel(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) # add a point set on the surface # distri vertex1 = np.array([-2.0, -2.0, -2.0]) vertex2 = np.array([1.5, 1.5, 1.5]) left = np.array([-2.0, -1.1, -1.1]) right = np.array([1.5, -1.2, -0.1]) front = np.array([0.25, 1.5, 0.3]) back = np.array([1.2, -2.0, -0.3]) top = np.array([0.0, 0.1, 1.5]) bottom = np.array([-1.9, -1.1, -2.0]) # distribute the pointset if self.comm.rank == 0: initpts = np.vstack([vertex1, vertex2, left, right]) elif self.comm.rank == 1: initpts = np.vstack([front, back, top]) elif self.comm.rank == 2: initpts = np.vstack([bottom, left, right]) else: raise ValueError("Too many procs") distglobal = DVGeo.addPointSet(initpts, "mypts", cache_projections=False) self.assertAlmostEqual(distglobal, 0.0, 8) # evaluate the points and check that they match DVGeo._updateESPModel() DVGeo._updateProjectedPts() self.assertTrue(DVGeo.pointSetUpToDate) self.assertAlmostEqual(np.linalg.norm(initpts - DVGeo.pointSets["mypts"].proj_pts), 0.0, 10) return DVGeo, initpts def setup_cubemodel_analytic_jac(self): jacpt0 = np.array( [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]] # x # y ) # z jacpt1 = np.array( [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0]] # x # y ) # z jacpt2 = np.array( [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.9 / 3.5], ] ) # z jacpt3 = np.array( [ [1.0, 0.0, 0.0, 1.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.8 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.9 / 3.5], ] ) # z jacpt4 = np.array( [ [1.0, 0.0, 0.0, 2.25 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 2.30 / 3.50], ] ) # z jacpt5 = np.array( [ [1.0, 0.0, 0.0, 3.20 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.70 / 3.50], ] ) # z jacpt6 = np.array( [ [1.0, 0.0, 0.0, 2.0 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 2.1 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.0], ] ) # z jacpt7 = np.array( [ [1.0, 0.0, 0.0, 0.1 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], ] ) # z if self.comm.rank == 0: ordered_analytic_jac = np.concatenate([jacpt0, jacpt1, jacpt2, jacpt3], axis=0).reshape(4, 3, 6) elif self.comm.rank == 1: ordered_analytic_jac = np.concatenate([jacpt4, jacpt5, jacpt6], axis=0).reshape(3, 3, 6) elif self.comm.rank == 2: ordered_analytic_jac = np.concatenate([jacpt7, jacpt2, jacpt3], axis=0).reshape(3, 3, 6) return ordered_analytic_jac def test_load_a_model(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeometryESP(csmFile) def test_add_desvars(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) # add variables with a mix of optional arguments DVGeo.addVariable("cubex0", lower=np.array([-10.0]), upper=np.array([10.0]), scale=0.1, dh=0.0001) self.assertEqual(DVGeo.getNDV(), 1) DVGeo.addVariable("cubey0") self.assertEqual(DVGeo.getNDV(), 2) DVGeo.addVariable("cubez0", lower=np.array([-10.0]), upper=np.array([10.0])) self.assertEqual(DVGeo.getNDV(), 3) # try to add a variable that isn't in the CSM file with self.assertRaises(Error): DVGeo.addVariable("cubew0") def test_add_pointset(self): DVGeo, initpts = self.setup_cubemodel() def test_updated_points(self): DVGeo, initpts = self.setup_cubemodel() DVGeo.addVariable("cubey0") DVGeo.setDesignVars({"cubey0": np.array([4.2000])}, updateJacobian=False) npts = initpts.shape[0] self.assertAlmostEqual(np.sum(DVGeo.pointSets["mypts"].proj_pts[:, 1] - initpts[:, 1]) / npts, 6.2, 10) DVGeo.addVariable("cubedz") DVGeo.setDesignVars({"cubedz": np.array([9.5])}, updateJacobian=False) if self.comm.rank == 0: self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2], 7.5) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 2], -2.0) def test_parallel_finite_difference(self): # this test checks the underlying jacobian itself, not the public API # TODO write tests for the public API DVGeo, initpts = self.setup_cubemodel() for designvarname in ["cubex0", "cubey0", "cubez0", "cubedx", "cubedy", "cubedz"]: DVGeo.addVariable(designvarname) # check the FD derivatives initpts_cache = initpts.copy() dvdict_cache = DVGeo.DVs.copy() self.assertFalse(DVGeo.updatedJac["mypts"]) DVGeo._computeSurfJacobian(fd=True) self.assertTrue(DVGeo.updatedJac["mypts"]) npts = initpts.shape[0] ndvs = DVGeo.getNDV() # check the jacobian results match analytic result testjac = DVGeo.pointSets["mypts"].jac.reshape(npts, 3, ndvs) analyticjac = self.setup_cubemodel_analytic_jac() for ipt in range(npts): self.assertAlmostEqual(np.sum(np.abs(testjac[ipt, :, :] - analyticjac[ipt, :, :])), 0) # check that the point set hasn't changed after running the FDs self.assertAlmostEqual(np.sum(np.abs(initpts_cache - DVGeo.pointSets["mypts"].proj_pts)), 0.0) # check that the DV dict hasn't changed for key in dvdict_cache: self.assertAlmostEqual(np.sum(np.abs(DVGeo.DVs[key].value - dvdict_cache[key].value)), 0.0) def test_jacobian_arbitrary_added_order(self): # this test checks the underlying jacobian itself, not the public API DVGeo, initpts = self.setup_cubemodel() # switch up the order of DVs added for designvarname in ["cubey0", "cubedx", "cubedy", "cubex0", "cubedz", "cubez0"]: DVGeo.addVariable(designvarname) # check the FD derivatives DVGeo._computeSurfJacobian(fd=True) npts = initpts.shape[0] ndvs = DVGeo.getNDV() # check the jacobian results match analytic result testjac = DVGeo.pointSets["mypts"].jac.reshape(npts, 3, ndvs) ordered_analyticjac = self.setup_cubemodel_analytic_jac() analyticjac = np.zeros((npts, 3, ndvs)) # get original variable ordering orig_var_order = ["cubex0", "cubey0", "cubez0", "cubedx", "cubedy", "cubedz"] # reorder the analytic jacobian for idv, designvarname in enumerate(orig_var_order): dv_ind = DVGeo.DVs[designvarname].globalStartInd analyticjac[:, :, dv_ind] = ordered_analyticjac[:, :, idv] self.assertNotEqual(dv_ind, idv) for ipt in range(npts): self.assertAlmostEqual(np.sum(np.abs(testjac[ipt, :, :] - analyticjac[ipt, :, :])), 0) @unittest.skipUnless(MPI and pyOCSM, "MPI and pyOCSM are required.") class TestPyGeoESP_BasicCube_Distributed_OneProcBlank(unittest.TestCase): N_PROCS = 4 def setUp(self): # Store the path where this current script lives # This all paths in the script are relative to this path # This is needed to support testflo running directories and files as inputs self.input_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) self.comm = MPI.COMM_WORLD def setup_cubemodel(self): # load the box model and build the box model csmFile = os.path.join(self.input_path, "../input_files/esp/box.csm") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) # add a point set on the surface # distri vertex1 = np.array([-2.0, -2.0, -2.0]) vertex2 = np.array([1.5, 1.5, 1.5]) left = np.array([-2.0, -1.1, -1.1]) right = np.array([1.5, -1.2, -0.1]) front = np.array([0.25, 1.5, 0.3]) back = np.array([1.2, -2.0, -0.3]) top = np.array([0.0, 0.1, 1.5]) bottom = np.array([-1.9, -1.1, -2.0]) # distribute the pointset if self.comm.rank == 0: initpts = np.vstack([vertex1, vertex2, left, right]) elif self.comm.rank == 1: initpts = np.vstack([front, back, top]) elif self.comm.rank == 2: initpts = np.array([]).reshape((0, 3)) elif self.comm.rank == 3: initpts = np.vstack([bottom, left, right]) else: raise ValueError("Too many procs") distglobal = DVGeo.addPointSet(initpts, "mypts", cache_projections=False) self.assertAlmostEqual(distglobal, 0.0, 8) # evaluate the points and check that they match DVGeo._updateESPModel() DVGeo._updateProjectedPts() self.assertTrue(DVGeo.pointSetUpToDate) self.assertAlmostEqual(np.linalg.norm(initpts - DVGeo.pointSets["mypts"].proj_pts), 0.0, 10) return DVGeo, initpts def setup_cubemodel_analytic_jac(self): jacpt0 = np.array( [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]] # x # y ) # z jacpt1 = np.array( [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0]] # x # y ) # z jacpt2 = np.array( [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.9 / 3.5], ] ) # z jacpt3 = np.array( [ [1.0, 0.0, 0.0, 1.0, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.8 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.9 / 3.5], ] ) # z jacpt4 = np.array( [ [1.0, 0.0, 0.0, 2.25 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 2.30 / 3.50], ] ) # z jacpt5 = np.array( [ [1.0, 0.0, 0.0, 3.20 / 3.50, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.70 / 3.50], ] ) # z jacpt6 = np.array( [ [1.0, 0.0, 0.0, 2.0 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 2.1 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 1.0], ] ) # z jacpt7 = np.array( [ [1.0, 0.0, 0.0, 0.1 / 3.5, 0.0, 0.0], # x [0.0, 1.0, 0.0, 0.0, 0.9 / 3.5, 0.0], # y [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], ] ) # z if self.comm.rank == 0: ordered_analytic_jac = np.concatenate([jacpt0, jacpt1, jacpt2, jacpt3], axis=0).reshape(4, 3, 6) elif self.comm.rank == 1: ordered_analytic_jac = np.concatenate([jacpt4, jacpt5, jacpt6], axis=0).reshape(3, 3, 6) elif self.comm.rank == 2: ordered_analytic_jac = np.array([]).reshape(0, 3, 6) elif self.comm.rank == 3: ordered_analytic_jac = np.concatenate([jacpt7, jacpt2, jacpt3], axis=0).reshape(3, 3, 6) return ordered_analytic_jac def test_add_pointset(self): DVGeo, initpts = self.setup_cubemodel() def test_updated_points(self): DVGeo, initpts = self.setup_cubemodel() DVGeo.addVariable("cubey0") DVGeo.setDesignVars({"cubey0": np.array([4.2000])}, updateJacobian=False) npts = initpts.shape[0] if self.comm.rank != 2: self.assertAlmostEqual(np.sum(DVGeo.pointSets["mypts"].proj_pts[:, 1] - initpts[:, 1]) / npts, 6.2, 10) DVGeo.addVariable("cubedz") DVGeo.setDesignVars({"cubedz": np.array([9.5])}, updateJacobian=False) if self.comm.rank == 0: self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2], 7.5) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 2], -2.0) elif self.comm.rank == 1: self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 2], -2.0 + (0.3 + 2.0) * (9.5 / 3.5)) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2], -2.0 + (-0.3 + 2.0) * (9.5 / 3.5)) elif self.comm.rank == 3: self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[0, 2], -2.0) self.assertAlmostEqual(DVGeo.pointSets["mypts"].proj_pts[1, 2], -2.0 + (-1.1 + 2.0) * (9.5 / 3.5)) def test_parallel_finite_difference(self): # this test checks the underlying jacobian itself, not the public API # TODO write tests for the public API DVGeo, initpts = self.setup_cubemodel() for designvarname in ["cubex0", "cubey0", "cubez0", "cubedx", "cubedy", "cubedz"]: DVGeo.addVariable(designvarname) # check the FD derivatives initpts_cache = initpts.copy() dvdict_cache = DVGeo.DVs.copy() self.assertFalse(DVGeo.updatedJac["mypts"]) DVGeo._computeSurfJacobian(fd=True) self.assertTrue(DVGeo.updatedJac["mypts"]) npts = initpts.shape[0] ndvs = DVGeo.getNDV() # check the jacobian results match analytic result testjac = DVGeo.pointSets["mypts"].jac.reshape(npts, 3, ndvs) analyticjac = self.setup_cubemodel_analytic_jac() if self.comm.rank != 2: for ipt in range(npts): self.assertAlmostEqual(np.sum(np.abs(testjac[ipt, :, :] - analyticjac[ipt, :, :])), 0) # check that the point set hasn't changed after running the FDs self.assertAlmostEqual(np.sum(np.abs(initpts_cache - DVGeo.pointSets["mypts"].proj_pts)), 0.0) # check that the DV dict hasn't changed for key in dvdict_cache: self.assertAlmostEqual(np.sum(np.abs(DVGeo.DVs[key].value - dvdict_cache[key].value)), 0.0) @unittest.skipUnless(MPI and pyOCSM, "MPI and pyOCSM are required.") @parameterized_class(test_params) class TestPyGeoESP_NACAFoil(unittest.TestCase): # serial and parallel handled automatically N_PROCS = 1 def setUp(self): # Store the path where this current script lives # This all paths in the script are relative to this path # This is needed to support testflo running directories and files as inputs self.input_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) self.comm = MPI.COMM_WORLD def setup_airfoilmodel(self, kulfan=False, projtol=0.01): # load the csm file and pointset file if kulfan: csmFile = os.path.join(self.input_path, "../input_files/esp/naca0012_kulfan.csm") max_dist_tol = 2 else: csmFile = os.path.join(self.input_path, "../input_files/esp/naca0012.csm") max_dist_tol = 3 stlFile = os.path.join(self.input_path, "../input_files/esp/naca0012_esp.stl") DVGeo = DVGeometryESP(csmFile, projTol=projtol) self.assertIsNotNone(DVGeo) testobj = mesh.Mesh.from_file(stlFile) # test mesh dim 0 is triangle index # dim 1 is each vertex of the triangle # dim 2 is x, y, z dimension p0 = testobj.vectors[:, 0, :] p1 = testobj.vectors[:, 1, :] p2 = testobj.vectors[:, 2, :] distglobal1 = DVGeo.addPointSet(p0, "airfoil_p0") distglobal2 = DVGeo.addPointSet(p1, "airfoil_p1") distglobal3 = DVGeo.addPointSet(p2, "airfoil_p2") distglobal = np.max(np.array([distglobal1, distglobal2, distglobal3])) self.assertAlmostEqual(distglobal, 0.0, max_dist_tol) # evaluate the points and check that they match DVGeo._updateESPModel() DVGeo._updateProjectedPts() self.assertTrue(DVGeo.pointSetUpToDate) updated_dist_max = np.max(np.sqrt(np.sum((p0 - DVGeo.pointSets["airfoil_p0"].proj_pts) ** 2, axis=1))) self.assertAlmostEqual(updated_dist_max, 0.0, max_dist_tol) updated_dist_max = np.max(np.sqrt(np.sum((p1 - DVGeo.pointSets["airfoil_p1"].proj_pts) ** 2, axis=1))) self.assertAlmostEqual(updated_dist_max, 0.0, max_dist_tol) updated_dist_max = np.max(np.sqrt(np.sum((p2 - DVGeo.pointSets["airfoil_p2"].proj_pts) ** 2, axis=1))) self.assertAlmostEqual(updated_dist_max, 0.0, max_dist_tol) return DVGeo, [p0, p1, p2] def test_add_pointset(self): DVGeo, initpts = self.setup_airfoilmodel() def test_add_pointset_tighter_tolerance(self): with self.assertRaises(ValueError): DVGeo, initpts = self.setup_airfoilmodel(projtol=1e-5) def test_add_desvars(self): DVGeo, initpts = self.setup_airfoilmodel() DVGeo.addVariable("nacacode", lower=np.array([8]), upper=np.array([15]), scale=1, dh=0.001) self.assertEqual(DVGeo.getNDV(), 1) def test_point_mismatch(self): # load the wrong pointset on purpose csmFile = os.path.join(self.input_path, "../input_files/esp/naca0010.csm") stlFile = os.path.join(self.input_path, "../input_files/esp/naca0012_esp.stl") DVGeo = DVGeometryESP(csmFile) self.assertIsNotNone(DVGeo) testobj = mesh.Mesh.from_file(stlFile) # test mesh dim 0 is triangle index # dim 1 is each vertex of the triangle # dim 2 is x, y, z dimension p0 = testobj.vectors[:, 0, :] with self.assertRaises(ValueError): distglobal1 = DVGeo.addPointSet(p0, "airfoil_p0") self.assertGreater(distglobal1, 0.01) def test_parallel_finite_difference(self, train=False): np.random.seed(1) DVGeo, initpts = self.setup_airfoilmodel(kulfan=True) DVGeo.addVariable("cst_u", lower=np.zeros((13,)), upper=np.ones((13,)), scale=1, dh=0.0001) DVGeo.addVariable("cst_l", lower=-np.ones((13,)), upper=np.zeros((13,)), scale=1, dh=0.0001) refFile = os.path.join(self.input_path, "reg_tests/ref/test_DVGeometryESP_01.ref") pointset_names = ["airfoil_p0", "airfoil_p1", "airfoil_p2"] for pointset_name in pointset_names: self.assertFalse(DVGeo.updatedJac[pointset_name]) DVGeo._computeSurfJacobian(fd=True) for pointset_name in pointset_names: self.assertTrue(DVGeo.updatedJac[pointset_name]) with BaseRegTest(refFile, train=train) as handler: handler.root_print("ESP NACA 0012 derivative test") npts = initpts[0].shape[0] dIdpt = np.random.rand(1, npts, 3) for pointset_name in pointset_names: dIdx = DVGeo.totalSensitivity(dIdpt, pointset_name) handler.root_add_dict("dIdx_" + pointset_name, dIdx, rtol=1e-7, atol=1e-7) # TODO test pointset caching? # TODO test total derivative API on an actual distributed pointset? if __name__ == "__main__": unittest.main()

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Python

rfvision/core/visualizer3d/__init__.py

mvig-robotflow/rfvision

cc662f213dfe5a3e8864a6b5685a668a4436e397

[ "Apache-2.0" ]

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2021-09-25T03:53:06.000Z

2022-02-19T03:25:11.000Z

rfvision/core/visualizer3d/__init__.py

mvig-robotflow/rfvision

cc662f213dfe5a3e8864a6b5685a668a4436e397

[ "Apache-2.0" ]

1

2021-07-21T13:14:54.000Z

rfvision/core/visualizer3d/__init__.py

mvig-robotflow/rfvision

cc662f213dfe5a3e8864a6b5685a668a4436e397

[ "Apache-2.0" ]

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2021-07-16T03:25:04.000Z

2021-11-22T06:04:01.000Z

from .show_result import show_result, show_multi_modality_result, show_seg_result __all__ = ['show_result', 'show_multi_modality_result', 'show_seg_result']

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pyclesperanto_prototype/_tier0/_set_wait_for_kernel_finish.py

haesleinhuepf/pyclesperanto_prototype

65bc3035d3b2b61a2722c93b95bae310bfbd190e

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pyclesperanto_prototype/_tier0/_set_wait_for_kernel_finish.py

haesleinhuepf/pyclesperanto_prototype

65bc3035d3b2b61a2722c93b95bae310bfbd190e

[ "BSD-3-Clause" ]

null

pyclesperanto_prototype/_tier0/_set_wait_for_kernel_finish.py

haesleinhuepf/pyclesperanto_prototype

65bc3035d3b2b61a2722c93b95bae310bfbd190e

[ "BSD-3-Clause" ]

null

def set_wait_for_kernel_finish(wait_for_kernel_finish : bool = None): from ._pycl import OCLProgram OCLProgram._wait_for_kernel_finish = wait_for_kernel_finish

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tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2016] 1.py

gour/holidata

89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13

[ "MIT" ]

32

2019-04-12T08:01:34.000Z

2022-02-28T04:41:50.000Z

tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2016] 1.py

gour/holidata

89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13

[ "MIT" ]

74

2019-07-09T16:35:20.000Z

2022-03-09T16:41:34.000Z

tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[es_ES-2016] 1.py

gour/holidata

89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13

[ "MIT" ]

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2019-01-28T07:41:02.000Z

2022-02-16T02:38:57.000Z

[ { 'date': '2016-01-01', 'description': 'Año Nuevo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2016-01-06', 'description': 'Epifanía del Señor', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2016-02-29', 'description': 'Día de Andalucía', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'F' }, { 'date': '2016-03-01', 'description': 'Día de las Illes Balears', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'F' }, { 'date': '2016-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'RF' }, { 'date': '2016-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RF' }, { 'date': '2016-03-19', 'description': 'San José', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RF' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'RV' }, { 'date': '2016-03-24', 'description': 'Jueves Santo', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RV' }, { 'date': '2016-03-25', 'description': 'Viernes Santo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRV' }, { 'date': '2016-03-27', 'description': 'Pascua', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'RV' }, { 'date': '2016-03-28', 'description': 'Lunes de Pascua', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RV' }, { 'date': '2016-04-23', 'description': 'Fiesta de Castilla y León', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'F' }, { 'date': '2016-04-23', 'description': 'San Jorge / Día de Aragón', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'RF' }, { 'date': '2016-05-01', 'description': 'Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'F' }, { 'date': '2016-05-02', 'description': 'Lunes siguiente a la Fiesta del Trabajo', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'F' }, { 'date': '2016-05-16', 'description': 'Lunes de Pascua Granada', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'F' }, { 'date': '2016-05-17', 'description': 'Día de las Letras Gallegas', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'F' }, { 'date': '2016-05-26', 'description': 'Corpus Christi', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RV' }, { 'date': '2016-05-30', 'description': 'Día de Canarias', 'locale': 'es-ES', 'notes': '', 'region': 'CN', 'type': 'F' }, { 'date': '2016-05-31', 'description': 'Día de Castilla-La Mancha', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'F' }, { 'date': '2016-06-09', 'description': 'Día de la Región de Murcia', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'F' }, { 'date': '2016-06-09', 'description': 'Día de La Rioja', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'F' }, { 'date': '2016-06-24', 'description': 'San Juan', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RF' }, { 'date': '2016-06-24', 'description': 'San Juan', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RF' }, { 'date': '2016-07-25', 'description': 'Santiago Apóstol', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'RF' }, { 'date': '2016-07-25', 'description': 'Santiago Apóstol', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RF' }, { 'date': '2016-07-25', 'description': 'Santiago Apóstol', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'RF' }, { 'date': '2016-07-25', 'description': 'Santiago Apóstol', 'locale': 'es-ES', 'notes': '', 'region': 'RI', 'type': 'RF' }, { 'date': '2016-07-25', 'description': 'Santiago Apóstol / Día Nacional de Galicia', 'locale': 'es-ES', 'notes': '', 'region': 'GA', 'type': 'RF' }, { 'date': '2016-07-28', 'description': 'Día de las Instituciones de Cantabria', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'F' }, { 'date': '2016-08-15', 'description': 'Asunción de la Virgen', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2016-09-02', 'description': 'Día de Ceuta', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'F' }, { 'date': '2016-09-08', 'description': 'Día de Asturias', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'F' }, { 'date': '2016-09-08', 'description': 'Día de Extremadura', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'F' }, { 'date': '2016-09-12', 'description': 'Fiesta del Sacrificio (Aid El Kebir)', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RV' }, { 'date': '2016-09-12', 'description': 'Fiesta del Sacrificio (Eidul Adha)', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'RV' }, { 'date': '2016-09-15', 'description': 'La Bien Aparecida', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RF' }, { 'date': '2016-10-07', 'description': '80º aniversario del primer Gobierno Vasco', 'locale': 'es-ES', 'notes': '', 'region': 'PV', 'type': 'F' }, { 'date': '2016-10-12', 'description': 'Fiesta Nacional de España', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2016-11-01', 'description': 'Todos los Santos', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2016-12-06', 'description': 'Día de la Constitución Española', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NF' }, { 'date': '2016-12-08', 'description': 'Inmaculada Concepción', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2016-12-25', 'description': 'Natividad del Señor', 'locale': 'es-ES', 'notes': '', 'region': '', 'type': 'NRF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'AN', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'AR', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'AS', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CB', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CE', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CL', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CM', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'CT', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'EX', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'IB', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'MC', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'MD', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'ML', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'NC', 'type': 'RF' }, { 'date': '2016-12-26', 'description': 'San Esteban', 'locale': 'es-ES', 'notes': '', 'region': 'VC', 'type': 'RF' } ]

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Python

yawhois/parser/jobswhois_verisign_grs_com.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

yawhois/parser/jobswhois_verisign_grs_com.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

yawhois/parser/jobswhois_verisign_grs_com.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

from .base_verisign import VerisignParserBase class JobswhoisVerisignGrsComParser(VerisignParserBase): pass

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py

Python

checkio/Codeship/Mono Captcha/test_mono_captcha.py

KenMercusLai/checkio

c7702221e1bc0b0b30425859ffa6c09722949d65

[ "MIT" ]

39

2015-02-09T13:24:12.000Z

2019-05-16T17:51:19.000Z

checkio/Codeship/Mono Captcha/test_mono_captcha.py

KenMercusLai/checkio

c7702221e1bc0b0b30425859ffa6c09722949d65

[ "MIT" ]

1

2019-10-21T16:18:14.000Z

checkio/Codeship/Mono Captcha/test_mono_captcha.py

KenMercusLai/checkio

c7702221e1bc0b0b30425859ffa6c09722949d65

[ "MIT" ]

22

2015-01-30T18:00:05.000Z

2021-05-22T02:57:23.000Z

import unittest from mono_captcha import checkio class Tests(unittest.TestCase): TESTS = { "Basics": [ { "input": [ [0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0], ], "answer": 394, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0], ], "answer": 394, "explanation": " 3,1 3,5 0,10 ", }, ], "Clear": [ { "input": [ [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], ], "answer": 123, "explanation": "", }, { "input": [ [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0], ], "answer": 456, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0], ], "answer": 789, "explanation": "", }, { "input": [ [0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0], ], "answer": 1034, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0], ], "answer": 52678, "explanation": "", }, { "input": [ [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], ], "answer": 911, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], ], "answer": 777, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0], [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0], ], "answer": 21312, "explanation": "", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0], ], "answer": 80808, "explanation": "", }, ], "Noise": [ { "input": [ [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], ], "answer": 123, "explanation": " 1,3 1,5 2,11 ", }, { "input": [ [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0], ], "answer": 456, "explanation": " 4,2 3,5 3,9 ", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0], ], "answer": 789, "explanation": " 2,2 2,6 1,10 ", }, { "input": [ [0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0], ], "answer": 1034, "explanation": " 1,1 4,7 1,10 2,14 ", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0], ], "answer": 52678, "explanation": " 2,1 2,5 1,9 3,15 2,17 ", }, { "input": [ [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], ], "answer": 911, "explanation": " 4,1 4,6 0,10 ", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], ], "answer": 777, "explanation": " 1,2 1,5 2,9 ", }, { "input": [ [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], ], "answer": 21312, "explanation": " 3,3 3,5 0,11 1,14 4,17 ", }, { "input": [ [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0], ], "answer": 80808, "explanation": " 1,2 4,5 3,10 0,15 2,18 ", }, ], } def test_Basics(self): for i in self.TESTS['Basics']: assert checkio(i['input']) == i['answer'] def test_Clear(self): for i in self.TESTS['Clear']: assert checkio(i['input']) == i['answer'] def test_Noise(self): for i in self.TESTS['Noise']: assert checkio(i['input']) == i['answer'] if __name__ == "__main__": # pragma: no cover unittest.main()

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52dbee006f3c8ec7cc2a6ae3cc4ad76306096180

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py

Python

nidaqmx/_task_modules/triggering/start_trigger.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

252

2017-03-22T02:43:16.000Z

2022-03-27T14:44:44.000Z

nidaqmx/_task_modules/triggering/start_trigger.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

133

2017-03-21T20:57:59.000Z

2022-03-31T16:08:12.000Z

nidaqmx/_task_modules/triggering/start_trigger.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

124

2017-04-01T18:35:24.000Z

2022-03-25T06:30:00.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import ctypes import numpy from nidaqmx._lib import ( lib_importer, wrapped_ndpointer, ctypes_byte_str, c_bool32) from nidaqmx.system.physical_channel import PhysicalChannel from nidaqmx.errors import ( check_for_error, is_string_buffer_too_small, is_array_buffer_too_small) from nidaqmx.constants import ( Coupling, DigitalPatternCondition, DigitalWidthUnits, Edge, Slope, TriggerType, WindowTriggerCondition1) class StartTrigger(object): """ Represents the start trigger configurations for a DAQmx task. """ def __init__(self, task_handle): self._handle = task_handle @property def anlg_edge_coupling(self): """ :class:`nidaqmx.constants.Coupling`: Specifies the coupling for the source signal of the trigger if the source is a terminal rather than a virtual channel. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return Coupling(val.value) @anlg_edge_coupling.setter def anlg_edge_coupling(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_coupling.deleter def anlg_edge_coupling(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_dig_fltr_enable(self): """ bool: Specifies whether to apply a digital filter to the digital output of the analog triggering circuitry (the Analog Comparison Event). When enabled, the analog signal must stay above or below the trigger level for the minimum pulse width before being recognized. Use filtering for noisy trigger signals that transition in and out of the hysteresis window rapidly. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_dig_fltr_enable.setter def anlg_edge_dig_fltr_enable(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_dig_fltr_enable.deleter def anlg_edge_dig_fltr_enable(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_dig_fltr_min_pulse_width(self): """ float: Specifies in seconds the minimum pulse width the filter recognizes. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetAnlgEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_dig_fltr_min_pulse_width.setter def anlg_edge_dig_fltr_min_pulse_width(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_dig_fltr_min_pulse_width.deleter def anlg_edge_dig_fltr_min_pulse_width(self): cfunc = (lib_importer.windll. DAQmxResetAnlgEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_dig_fltr_timebase_rate(self): """ float: Specifies in hertz the rate of the digital filter timebase. NI-DAQmx uses this value to compute settings for the filter. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetAnlgEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_dig_fltr_timebase_rate.setter def anlg_edge_dig_fltr_timebase_rate(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_dig_fltr_timebase_rate.deleter def anlg_edge_dig_fltr_timebase_rate(self): cfunc = (lib_importer.windll. DAQmxResetAnlgEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_dig_fltr_timebase_src(self): """ str: Specifies the terminal of the signal to use as the timebase of the digital filter. """ cfunc = (lib_importer.windll. DAQmxGetAnlgEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @anlg_edge_dig_fltr_timebase_src.setter def anlg_edge_dig_fltr_timebase_src(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_dig_fltr_timebase_src.deleter def anlg_edge_dig_fltr_timebase_src(self): cfunc = (lib_importer.windll. DAQmxResetAnlgEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_dig_sync_enable(self): """ bool: Specifies whether to synchronize recognition of transitions in the signal to the internal timebase of the device. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_dig_sync_enable.setter def anlg_edge_dig_sync_enable(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_dig_sync_enable.deleter def anlg_edge_dig_sync_enable(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_hyst(self): """ float: Specifies a hysteresis level in the units of the measurement or generation. If **anlg_edge_slope** is **Slope1.RISING**, the trigger does not deassert until the source signal passes below **anlg_edge_lvl** minus the hysteresis. If **anlg_edge_slope** is **Slope1.FALLING**, the trigger does not deassert until the source signal passes above **anlg_edge_lvl** plus the hysteresis. Hysteresis is always enabled. Set this property to a non-zero value to use hysteresis. """ val = ctypes.c_double() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigHyst if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_hyst.setter def anlg_edge_hyst(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigHyst if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_hyst.deleter def anlg_edge_hyst(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigHyst if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_lvl(self): """ float: Specifies at what threshold in the units of the measurement or generation to start acquiring or generating samples. Use **anlg_edge_slope** to specify on which slope to trigger on this threshold. """ val = ctypes.c_double() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigLvl if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_edge_lvl.setter def anlg_edge_lvl(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigLvl if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_lvl.deleter def anlg_edge_lvl(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigLvl if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_slope(self): """ :class:`nidaqmx.constants.Slope`: Specifies on which slope of the trigger signal to start acquiring or generating samples. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigSlope if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return Slope(val.value) @anlg_edge_slope.setter def anlg_edge_slope(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigSlope if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_slope.deleter def anlg_edge_slope(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigSlope if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_edge_src(self): """ str: Specifies the name of a virtual channel or terminal where there is an analog signal to use as the source of the Start Trigger. """ cfunc = lib_importer.windll.DAQmxGetAnlgEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @anlg_edge_src.setter def anlg_edge_src(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_edge_src.deleter def anlg_edge_src(self): cfunc = lib_importer.windll.DAQmxResetAnlgEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_btm(self): """ float: Specifies the lower limit of the window. Specify this value in the units of the measurement or generation. """ val = ctypes.c_double() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigBtm if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_btm.setter def anlg_win_btm(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigBtm if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_btm.deleter def anlg_win_btm(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigBtm if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_coupling(self): """ :class:`nidaqmx.constants.Coupling`: Specifies the coupling for the source signal of the trigger if the source is a terminal rather than a virtual channel. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return Coupling(val.value) @anlg_win_coupling.setter def anlg_win_coupling(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_coupling.deleter def anlg_win_coupling(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigCoupling if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_dig_fltr_enable(self): """ bool: Specifies whether to apply a digital filter to the digital output of the analog triggering circuitry (the Analog Comparison Event). When enabled, the analog signal must stay within the trigger window for the minimum pulse width before being recognized. Use filtering for noisy trigger signals that transition in and out of the window rapidly. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_dig_fltr_enable.setter def anlg_win_dig_fltr_enable(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_dig_fltr_enable.deleter def anlg_win_dig_fltr_enable(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_dig_fltr_min_pulse_width(self): """ float: Specifies in seconds the minimum pulse width the filter recognizes. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetAnlgWinStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_dig_fltr_min_pulse_width.setter def anlg_win_dig_fltr_min_pulse_width(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgWinStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_dig_fltr_min_pulse_width.deleter def anlg_win_dig_fltr_min_pulse_width(self): cfunc = (lib_importer.windll. DAQmxResetAnlgWinStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_dig_fltr_timebase_rate(self): """ float: Specifies in hertz the rate of the digital filter timebase. NI-DAQmx uses this value to compute settings for the filter. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetAnlgWinStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_dig_fltr_timebase_rate.setter def anlg_win_dig_fltr_timebase_rate(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgWinStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_dig_fltr_timebase_rate.deleter def anlg_win_dig_fltr_timebase_rate(self): cfunc = (lib_importer.windll. DAQmxResetAnlgWinStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_dig_fltr_timebase_src(self): """ str: Specifies the terminal of the signal to use as the timebase of the digital filter. """ cfunc = (lib_importer.windll. DAQmxGetAnlgWinStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @anlg_win_dig_fltr_timebase_src.setter def anlg_win_dig_fltr_timebase_src(self, val): cfunc = (lib_importer.windll. DAQmxSetAnlgWinStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_dig_fltr_timebase_src.deleter def anlg_win_dig_fltr_timebase_src(self): cfunc = (lib_importer.windll. DAQmxResetAnlgWinStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_dig_sync_enable(self): """ bool: Specifies whether to synchronize recognition of transitions in the signal to the internal timebase of the device. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_dig_sync_enable.setter def anlg_win_dig_sync_enable(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_dig_sync_enable.deleter def anlg_win_dig_sync_enable(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_src(self): """ str: Specifies the name of a virtual channel or terminal where there is an analog signal to use as the source of the Start Trigger. """ cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @anlg_win_src.setter def anlg_win_src(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_src.deleter def anlg_win_src(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_top(self): """ float: Specifies the upper limit of the window. Specify this value in the units of the measurement or generation. """ val = ctypes.c_double() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigTop if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @anlg_win_top.setter def anlg_win_top(self, val): cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigTop if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_top.deleter def anlg_win_top(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigTop if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def anlg_win_trig_when(self): """ :class:`nidaqmx.constants.WindowTriggerCondition1`: Specifies whether the task starts acquiring or generating samples when the signal enters or leaves the window you specify with **anlg_win_btm** and **anlg_win_top**. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetAnlgWinStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return WindowTriggerCondition1(val.value) @anlg_win_trig_when.setter def anlg_win_trig_when(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetAnlgWinStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @anlg_win_trig_when.deleter def anlg_win_trig_when(self): cfunc = lib_importer.windll.DAQmxResetAnlgWinStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def delay(self): """ float: Specifies an amount of time to wait after the Start Trigger is received before acquiring or generating the first sample. This value is in the units you specify with **delay_units**. """ val = ctypes.c_double() cfunc = lib_importer.windll.DAQmxGetStartTrigDelay if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @delay.setter def delay(self, val): cfunc = lib_importer.windll.DAQmxSetStartTrigDelay if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @delay.deleter def delay(self): cfunc = lib_importer.windll.DAQmxResetStartTrigDelay if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def delay_units(self): """ :class:`nidaqmx.constants.DigitalWidthUnits`: Specifies the units of **delay**. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetStartTrigDelayUnits if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return DigitalWidthUnits(val.value) @delay_units.setter def delay_units(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetStartTrigDelayUnits if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @delay_units.deleter def delay_units(self): cfunc = lib_importer.windll.DAQmxResetStartTrigDelayUnits if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_dig_fltr_enable(self): """ bool: Specifies whether to apply a digital filter to the trigger signal. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetDigEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @dig_edge_dig_fltr_enable.setter def dig_edge_dig_fltr_enable(self, val): cfunc = lib_importer.windll.DAQmxSetDigEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_dig_fltr_enable.deleter def dig_edge_dig_fltr_enable(self): cfunc = lib_importer.windll.DAQmxResetDigEdgeStartTrigDigFltrEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_dig_fltr_min_pulse_width(self): """ float: Specifies in seconds the minimum pulse width the filter recognizes. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetDigEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @dig_edge_dig_fltr_min_pulse_width.setter def dig_edge_dig_fltr_min_pulse_width(self, val): cfunc = (lib_importer.windll. DAQmxSetDigEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_dig_fltr_min_pulse_width.deleter def dig_edge_dig_fltr_min_pulse_width(self): cfunc = (lib_importer.windll. DAQmxResetDigEdgeStartTrigDigFltrMinPulseWidth) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_dig_fltr_timebase_rate(self): """ float: Specifies in hertz the rate of the pulse width filter timebase. NI-DAQmx uses this value to compute settings for the filter. """ val = ctypes.c_double() cfunc = (lib_importer.windll. DAQmxGetDigEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_double)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @dig_edge_dig_fltr_timebase_rate.setter def dig_edge_dig_fltr_timebase_rate(self, val): cfunc = (lib_importer.windll. DAQmxSetDigEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_double] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_dig_fltr_timebase_rate.deleter def dig_edge_dig_fltr_timebase_rate(self): cfunc = (lib_importer.windll. DAQmxResetDigEdgeStartTrigDigFltrTimebaseRate) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_dig_fltr_timebase_src(self): """ str: Specifies the input terminal of the signal to use as the timebase of the pulse width filter. """ cfunc = (lib_importer.windll. DAQmxGetDigEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @dig_edge_dig_fltr_timebase_src.setter def dig_edge_dig_fltr_timebase_src(self, val): cfunc = (lib_importer.windll. DAQmxSetDigEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_dig_fltr_timebase_src.deleter def dig_edge_dig_fltr_timebase_src(self): cfunc = (lib_importer.windll. DAQmxResetDigEdgeStartTrigDigFltrTimebaseSrc) if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_dig_sync_enable(self): """ bool: Specifies whether to synchronize recognition of transitions in the signal to the internal timebase of the device. If you set this property to True, the device does not recognize and act upon the trigger until the next pulse of the internal timebase. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetDigEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @dig_edge_dig_sync_enable.setter def dig_edge_dig_sync_enable(self, val): cfunc = lib_importer.windll.DAQmxSetDigEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_dig_sync_enable.deleter def dig_edge_dig_sync_enable(self): cfunc = lib_importer.windll.DAQmxResetDigEdgeStartTrigDigSyncEnable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_edge(self): """ :class:`nidaqmx.constants.Edge`: Specifies on which edge of a digital pulse to start acquiring or generating samples. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetDigEdgeStartTrigEdge if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return Edge(val.value) @dig_edge_edge.setter def dig_edge_edge(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetDigEdgeStartTrigEdge if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_edge.deleter def dig_edge_edge(self): cfunc = lib_importer.windll.DAQmxResetDigEdgeStartTrigEdge if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_edge_src(self): """ str: Specifies the name of a terminal where there is a digital signal to use as the source of the Start Trigger. """ cfunc = lib_importer.windll.DAQmxGetDigEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @dig_edge_src.setter def dig_edge_src(self, val): cfunc = lib_importer.windll.DAQmxSetDigEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_edge_src.deleter def dig_edge_src(self): cfunc = lib_importer.windll.DAQmxResetDigEdgeStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_pattern_pattern(self): """ str: Specifies the digital pattern that must be met for the Start Trigger to occur. """ cfunc = lib_importer.windll.DAQmxGetDigPatternStartTrigPattern if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @dig_pattern_pattern.setter def dig_pattern_pattern(self, val): cfunc = lib_importer.windll.DAQmxSetDigPatternStartTrigPattern if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_pattern_pattern.deleter def dig_pattern_pattern(self): cfunc = lib_importer.windll.DAQmxResetDigPatternStartTrigPattern if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_pattern_src(self): """ :class:`nidaqmx.system.physical_channel.PhysicalChannel`: Specifies the physical channels to use for pattern matching. The order of the physical channels determines the order of the pattern. If a port is included, the order of the physical channels within the port is in ascending order. """ cfunc = lib_importer.windll.DAQmxGetDigPatternStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return PhysicalChannel(val.value.decode('ascii')) @dig_pattern_src.setter def dig_pattern_src(self, val): val = val.name cfunc = lib_importer.windll.DAQmxSetDigPatternStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_pattern_src.deleter def dig_pattern_src(self): cfunc = lib_importer.windll.DAQmxResetDigPatternStartTrigSrc if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def dig_pattern_trig_when(self): """ :class:`nidaqmx.constants.DigitalPatternCondition`: Specifies whether the Start Trigger occurs when the physical channels specified with **dig_pattern_src** match or differ from the digital pattern specified with **dig_pattern_pattern**. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetDigPatternStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return DigitalPatternCondition(val.value) @dig_pattern_trig_when.setter def dig_pattern_trig_when(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetDigPatternStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @dig_pattern_trig_when.deleter def dig_pattern_trig_when(self): cfunc = lib_importer.windll.DAQmxResetDigPatternStartTrigWhen if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def retriggerable(self): """ bool: Specifies whether a finite task resets and waits for another Start Trigger after the task completes. When you set this property to True, the device performs a finite acquisition or generation each time the Start Trigger occurs until the task stops. The device ignores a trigger if it is in the process of acquiring or generating signals. """ val = c_bool32() cfunc = lib_importer.windll.DAQmxGetStartTrigRetriggerable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(c_bool32)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return val.value @retriggerable.setter def retriggerable(self, val): cfunc = lib_importer.windll.DAQmxSetStartTrigRetriggerable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, c_bool32] error_code = cfunc( self._handle, val) check_for_error(error_code) @retriggerable.deleter def retriggerable(self): cfunc = lib_importer.windll.DAQmxResetStartTrigRetriggerable if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) @property def term(self): """ str: Indicates the name of the internal Start Trigger terminal for the task. This property does not return the name of the trigger source terminal. """ cfunc = lib_importer.windll.DAQmxGetStartTrigTerm if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_char_p, ctypes.c_uint] temp_size = 0 while True: val = ctypes.create_string_buffer(temp_size) size_or_code = cfunc( self._handle, val, temp_size) if is_string_buffer_too_small(size_or_code): # Buffer size must have changed between calls; check again. temp_size = 0 elif size_or_code > 0 and temp_size == 0: # Buffer size obtained, use to retrieve data. temp_size = size_or_code else: break check_for_error(size_or_code) return val.value.decode('ascii') @property def trig_type(self): """ :class:`nidaqmx.constants.TriggerType`: Specifies the type of trigger to use to start a task. """ val = ctypes.c_int() cfunc = lib_importer.windll.DAQmxGetStartTrigType if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.POINTER(ctypes.c_int)] error_code = cfunc( self._handle, ctypes.byref(val)) check_for_error(error_code) return TriggerType(val.value) @trig_type.setter def trig_type(self, val): val = val.value cfunc = lib_importer.windll.DAQmxSetStartTrigType if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes.c_int] error_code = cfunc( self._handle, val) check_for_error(error_code) @trig_type.deleter def trig_type(self): cfunc = lib_importer.windll.DAQmxResetStartTrigType if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code) def cfg_anlg_edge_start_trig( self, trigger_source="", trigger_slope=Slope.RISING, trigger_level=0.0): """ Configures the task to start acquiring or generating samples when an analog signal crosses the level you specify. Args: trigger_source (Optional[str]): Is the name of a virtual channel or terminal where there is an analog signal to use as the source of the trigger. trigger_slope (Optional[nidaqmx.constants.Slope]): Specifies on which slope of the signal to start acquiring or generating samples when the signal crosses **trigger_level**. trigger_level (Optional[float]): Specifies at what threshold to start acquiring or generating samples. Specify this value in the units of the measurement or generation. Use **trigger_slope** to specify on which slope to trigger at this threshold. """ cfunc = lib_importer.windll.DAQmxCfgAnlgEdgeStartTrig if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str, ctypes.c_int, ctypes.c_double] error_code = cfunc( self._handle, trigger_source, trigger_slope.value, trigger_level) check_for_error(error_code) def cfg_anlg_window_start_trig( self, window_top, window_bottom, trigger_source="", trigger_when=WindowTriggerCondition1.ENTERING_WINDOW): """ Configures the task to start acquiring or generating samples when an analog signal enters or leaves a range you specify. Args: window_top (float): Is the upper limit of the window. Specify this value in the units of the measurement or generation. window_bottom (float): Is the lower limit of the window. Specify this value in the units of the measurement or generation. trigger_source (Optional[str]): Is the name of a virtual channel or terminal where there is an analog signal to use as the source of the trigger. trigger_when (Optional[nidaqmx.constants.WindowTriggerCondition1]): Specifies whether the task starts measuring or generating samples when the signal enters the window or when it leaves the window. Use **window_bottom** and **window_top** to specify the limits of the window. """ cfunc = lib_importer.windll.DAQmxCfgAnlgWindowStartTrig if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str, ctypes.c_int, ctypes.c_double, ctypes.c_double] error_code = cfunc( self._handle, trigger_source, trigger_when.value, window_top, window_bottom) check_for_error(error_code) def cfg_dig_edge_start_trig( self, trigger_source, trigger_edge=Edge.RISING): """ Configures the task to start acquiring or generating samples on a rising or falling edge of a digital signal. Args: trigger_source (str): Specifies the name of a terminal where there is a digital signal to use as the source of the trigger. trigger_edge (Optional[nidaqmx.constants.Edge]): Specifies on which edge of the digital signal to start acquiring or generating samples. """ cfunc = lib_importer.windll.DAQmxCfgDigEdgeStartTrig if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str, ctypes.c_int] error_code = cfunc( self._handle, trigger_source, trigger_edge.value) check_for_error(error_code) def cfg_dig_pattern_start_trig( self, trigger_source, trigger_pattern, trigger_when=DigitalPatternCondition.PATTERN_MATCHES): """ Configures a task to start acquiring or generating samples when a digital pattern is matched. Args: trigger_source (str): Specifies the physical channels to use for pattern matching. The order of the physical channels determines the order of the pattern. If a port is included, the order of the physical channels within the port is in ascending order. trigger_pattern (str): Specifies the digital pattern that must be met for the trigger to occur. trigger_when (Optional[nidaqmx.constants.DigitalPatternCondition]): Specifies the condition under which the trigger occurs. """ cfunc = lib_importer.windll.DAQmxCfgDigPatternStartTrig if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle, ctypes_byte_str, ctypes_byte_str, ctypes.c_int] error_code = cfunc( self._handle, trigger_source, trigger_pattern, trigger_when.value) check_for_error(error_code) def disable_start_trig(self): """ Configures the task to start acquiring or generating samples immediately upon starting the task. """ cfunc = lib_importer.windll.DAQmxDisableStartTrig if cfunc.argtypes is None: with cfunc.arglock: if cfunc.argtypes is None: cfunc.argtypes = [ lib_importer.task_handle] error_code = cfunc( self._handle) check_for_error(error_code)

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null

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1

0

null

0

7

5e182410daf815402a887144abd96e20403a4fa5

7,621

py

Python

microsoft_atp/komand_microsoft_atp/actions/get_file_id_from_alert_id/schema.py

emartin-merrill-r7/insightconnect-plugins

a589745dbcc9f01d3e601431e77ab7221a84c117

[ "MIT" ]

1

2020-03-18T09:14:55.000Z

microsoft_atp/komand_microsoft_atp/actions/get_file_id_from_alert_id/schema.py

OSSSP/insightconnect-plugins

846758dab745170cf1a8c146211a8bea9592e8ff

[ "MIT" ]

null

microsoft_atp/komand_microsoft_atp/actions/get_file_id_from_alert_id/schema.py

OSSSP/insightconnect-plugins

846758dab745170cf1a8c146211a8bea9592e8ff

[ "MIT" ]

null

# GENERATED BY KOMAND SDK - DO NOT EDIT import komand import json class Component: DESCRIPTION = "Retrieve the file ID related to an alert" class Input: ALERT_ID = "alert_id" class Output: FILE_INFORMATION = "file_information" class GetFileIdFromAlertIdInput(komand.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "alert_id": { "type": "string", "title": "Alert ID", "description": "Alert ID to get files from", "order": 1 } }, "required": [ "alert_id" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class GetFileIdFromAlertIdOutput(komand.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "file_information": { "$ref": "#/definitions/file_information", "title": "File Information", "description": "The file ID related to the given alert ID", "order": 1 } }, "required": [ "file_information" ], "definitions": { "file_information": { "type": "object", "title": "file_information", "properties": { "@odata.context": { "type": "string", "title": "OData Context", "description": "OData context", "order": 2 }, "file_list": { "type": "array", "title": "File List", "description": "List of file information entities", "items": { "$ref": "#/definitions/file_list_entry" }, "order": 1 } }, "definitions": { "file_list_entry": { "type": "object", "title": "file_list_entry", "properties": { "fileProductName": { "type": "string", "title": "File Product Name", "description": "File product name", "order": 1 }, "filePublisher": { "type": "string", "title": "File Publisher", "description": "File publisher", "order": 2 }, "fileType": { "type": "string", "title": "File Type", "description": "File type", "order": 3 }, "globalFirstObserved": { "type": "string", "title": "Global First Observed", "description": "Global first observed", "order": 4 }, "globalLastObserved": { "type": "string", "title": "Global Last Observed", "description": "Global last observed", "order": 5 }, "globalPrevalence": { "type": "integer", "title": "Global Prevalence", "description": "Global prevalence", "order": 6 }, "isPeFile": { "type": "boolean", "title": "Is PE File", "description": "Is PE file", "order": 7 }, "isValidCertificate": { "type": "boolean", "title": "Is Valid Certificate", "description": "Is valid certificate", "order": 8 }, "issuer": { "type": "string", "title": "Issuer", "description": "Issuer", "order": 9 }, "md5": { "type": "string", "title": "MD5", "description": "MD5", "order": 10 }, "sha1": { "type": "string", "title": "SHA1", "description": "SHA1", "order": 11 }, "sha256": { "type": "string", "title": "SHA256", "description": "SHA256", "order": 12 }, "signer": { "type": "string", "title": "Signer", "description": "Signer", "order": 13 }, "signerHash": { "type": "string", "title": "Signer Hash", "description": "Signer hash", "order": 14 }, "size": { "type": "integer", "title": "Size", "description": "Size", "order": 15 }, "windowsDefenderAVThreatName": { "type": "string", "title": "Windows Defender AV Threat Name", "description": "Windows Defender AV threat name", "order": 16 } } } } }, "file_list_entry": { "type": "object", "title": "file_list_entry", "properties": { "fileProductName": { "type": "string", "title": "File Product Name", "description": "File product name", "order": 1 }, "filePublisher": { "type": "string", "title": "File Publisher", "description": "File publisher", "order": 2 }, "fileType": { "type": "string", "title": "File Type", "description": "File type", "order": 3 }, "globalFirstObserved": { "type": "string", "title": "Global First Observed", "description": "Global first observed", "order": 4 }, "globalLastObserved": { "type": "string", "title": "Global Last Observed", "description": "Global last observed", "order": 5 }, "globalPrevalence": { "type": "integer", "title": "Global Prevalence", "description": "Global prevalence", "order": 6 }, "isPeFile": { "type": "boolean", "title": "Is PE File", "description": "Is PE file", "order": 7 }, "isValidCertificate": { "type": "boolean", "title": "Is Valid Certificate", "description": "Is valid certificate", "order": 8 }, "issuer": { "type": "string", "title": "Issuer", "description": "Issuer", "order": 9 }, "md5": { "type": "string", "title": "MD5", "description": "MD5", "order": 10 }, "sha1": { "type": "string", "title": "SHA1", "description": "SHA1", "order": 11 }, "sha256": { "type": "string", "title": "SHA256", "description": "SHA256", "order": 12 }, "signer": { "type": "string", "title": "Signer", "description": "Signer", "order": 13 }, "signerHash": { "type": "string", "title": "Signer Hash", "description": "Signer hash", "order": 14 }, "size": { "type": "integer", "title": "Size", "description": "Size", "order": 15 }, "windowsDefenderAVThreatName": { "type": "string", "title": "Windows Defender AV Threat Name", "description": "Windows Defender AV threat name", "order": 16 } } } } } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

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0

1

0

1

0

1

0

null

0

8

5e28b730031236c92538dd6aee0a1934f03e16b6

3,884

py

Python

tests/tools/assigner/actions/fixtures.py

bringhurst/kafka-tools

5472a89d5a6702ae7a692211053a55dfba63072b

[ "Apache-2.0" ]

null

tests/tools/assigner/actions/fixtures.py

bringhurst/kafka-tools

5472a89d5a6702ae7a692211053a55dfba63072b

[ "Apache-2.0" ]

null

tests/tools/assigner/actions/fixtures.py

bringhurst/kafka-tools

5472a89d5a6702ae7a692211053a55dfba63072b

[ "Apache-2.0" ]

5

2019-10-24T06:54:44.000Z

2021-07-25T03:20:49.000Z

import argparse from kafka.tools.assigner.models.broker import Broker from kafka.tools.assigner.models.cluster import Cluster from kafka.tools.assigner.models.topic import Topic def set_up_cluster(): cluster = Cluster() cluster.add_broker(Broker(1, "brokerhost1.example.com")) cluster.add_broker(Broker(2, "brokerhost2.example.com")) cluster.brokers[1].rack = "a" cluster.brokers[2].rack = "b" cluster.add_topic(Topic("testTopic1", 2)) cluster.add_topic(Topic("testTopic2", 2)) partition = cluster.topics['testTopic1'].partitions[0] partition.add_replica(cluster.brokers[1], 0) partition.add_replica(cluster.brokers[2], 1) partition = cluster.topics['testTopic1'].partitions[1] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[1], 1) partition = cluster.topics['testTopic2'].partitions[0] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[1], 1) partition = cluster.topics['testTopic2'].partitions[1] partition.add_replica(cluster.brokers[1], 0) partition.add_replica(cluster.brokers[2], 1) return cluster def set_up_cluster_4broker(): cluster = Cluster() cluster.add_broker(Broker(1, "brokerhost1.example.com")) cluster.add_broker(Broker(2, "brokerhost2.example.com")) cluster.add_broker(Broker(3, "brokerhost3.example.com")) cluster.add_broker(Broker(4, "brokerhost4.example.com")) cluster.brokers[1].rack = "a" cluster.brokers[2].rack = "a" cluster.brokers[3].rack = "b" cluster.brokers[4].rack = "b" cluster.add_topic(Topic("testTopic1", 4)) cluster.add_topic(Topic("testTopic2", 4)) cluster.add_topic(Topic("testTopic3", 4)) partition = cluster.topics['testTopic1'].partitions[0] partition.add_replica(cluster.brokers[1], 0) partition.add_replica(cluster.brokers[2], 1) partition = cluster.topics['testTopic1'].partitions[1] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[3], 1) partition = cluster.topics['testTopic1'].partitions[2] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[3], 1) partition = cluster.topics['testTopic1'].partitions[3] partition.add_replica(cluster.brokers[4], 0) partition.add_replica(cluster.brokers[1], 1) partition = cluster.topics['testTopic2'].partitions[0] partition.add_replica(cluster.brokers[4], 0) partition.add_replica(cluster.brokers[3], 1) partition = cluster.topics['testTopic2'].partitions[1] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[4], 1) partition = cluster.topics['testTopic2'].partitions[2] partition.add_replica(cluster.brokers[2], 0) partition.add_replica(cluster.brokers[1], 1) partition = cluster.topics['testTopic2'].partitions[3] partition.add_replica(cluster.brokers[3], 0) partition.add_replica(cluster.brokers[1], 1) partition = cluster.topics['testTopic3'].partitions[0] partition.add_replica(cluster.brokers[3], 0) partition.add_replica(cluster.brokers[2], 1) partition = cluster.topics['testTopic3'].partitions[1] partition.add_replica(cluster.brokers[4], 0) partition.add_replica(cluster.brokers[2], 1) partition = cluster.topics['testTopic3'].partitions[2] partition.add_replica(cluster.brokers[1], 0) partition.add_replica(cluster.brokers[2], 1) partition = cluster.topics['testTopic3'].partitions[3] partition.add_replica(cluster.brokers[3], 0) partition.add_replica(cluster.brokers[4], 1) return cluster def set_up_subparser(): aparser = argparse.ArgumentParser(prog='kafka-assigner', description='Rejigger Kafka cluster partitions') subparsers = aparser.add_subparsers(help='Select manipulation module to use') return (aparser, subparsers)

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0

null

0

1

0

null

0

8

eab49adb56bb0dfa1ac3ae14fed884e5a4d768a6

99,881

py

Python

selfdrive/car/hyundai/values.py

baldwalker/openpilot-opkr

367c663fbea7d5bdb059e0bfbb467684d501d844

[ "MIT" ]

1

2021-12-01T23:50:06.000Z

selfdrive/car/hyundai/values.py

baldwalker/openpilot-opkr

367c663fbea7d5bdb059e0bfbb467684d501d844

[ "MIT" ]

1

2022-02-13T07:28:46.000Z

2022-02-13T07:40:59.000Z

selfdrive/car/hyundai/values.py

baldwalker/openpilot-opkr

367c663fbea7d5bdb059e0bfbb467684d501d844

[ "MIT" ]

1

2022-02-27T06:04:07.000Z

from dataclasses import dataclass from typing import Dict, List, Union from cereal import car from common.conversions import Conversions as CV from selfdrive.car import dbc_dict from selfdrive.car.docs_definitions import CarInfo, Harness from common.params import Params Ecu = car.CarParams.Ecu # Steer torque limits class CarControllerParams: ACCEL_MIN = -4.0 # m/s ACCEL_MAX = 2.0 # m/s def __init__(self, CP): self.STEER_MAX = int(Params().get("SteerMaxAdj", encoding="utf8")) # default 384 self.STEER_DELTA_UP = int(Params().get("SteerDeltaUpAdj", encoding="utf8")) # default 3 self.STEER_DELTA_DOWN = int(Params().get("SteerDeltaDownAdj", encoding="utf8")) # default 7 self.STEER_DRIVER_ALLOWANCE = 50 self.STEER_DRIVER_MULTIPLIER = 2 self.STEER_DRIVER_FACTOR = 1 class CAR: # HYUNDAI AVANTE_AD = "HYUNDAI AVANTE (AD)" AVANTE_CN7 = "HYUNDAI AVANTE (CN7)" AVANTE_HEV_CN7 = "HYUNDAI AVANTE HYBRID (CN7)" I30_PD = "HYUNDAI I30 (PD)" SONATA_DN8 = "HYUNDAI SONATA (DN8)" SONATA_HEV_DN8 = "HYUNDAI SONATA HYBRID (DN8)" SONATA_LF = "HYUNDAI SONATA (LF)" SONATA_TURBO_LF = "HYUNDAI SONATA TURBO (LF)" SONATA_HEV_LF = "HYUNDAI SONATA HYBRID (LF)" KONA_OS = "HYUNDAI KONA (OS)" KONA_EV_OS = "HYUNDAI KONA EV (OS)" KONA_HEV_OS = "HYUNDAI KONA HYBRID (OS)" IONIQ_EV_AE = "HYUNDAI IONIQ ELECTRIC (AE)" IONIQ_HEV_AE = "HYUNDAI IONIQ HYBRID (AE)" SANTAFE_TM = "HYUNDAI SANTAFE (TM)" SANTAFE_HEV_TM = "HYUNDAI SANTAFE HYBRID (TM)" PALISADE_LX2 = "HYUNDAI PALISADE (LX2)" VELOSTER_JS = "HYUNDAI VELOSTER (JS)" GRANDEUR_IG = "HYUNDAI GRANDEUR (IG)" GRANDEUR_HEV_IG = "HYUNDAI GRANDEUR HYBRID (IG)" GRANDEUR_FL_IG = "HYUNDAI GRANDEUR FL (IG)" GRANDEUR_HEV_FL_IG = "HYUNDAI GRANDEUR HYBRID FL (IG)" TUCSON_TL = "HYUNDAI TUCSON (TL)" NEXO_FE = "HYUNDAI NEXO (FE)" # KIA KIA_FORTE = "KIA FORTE E 2018 & GT 2021" K3_BD = "KIA K3 (BD)" K5_JF = "KIA K5 (JF)" K5_HEV_JF = "KIA K5 HYBRID (JF)" K5_DL3 = "KIA K5 (DL3)" SPORTAGE_QL = "KIA SPORTAGE (QL)" SORENTO_UM = "KIA SORENTO (UM)" STINGER_CK = "KIA STINGER (CK)" NIRO_EV_DE = "KIA NIRO EV (DE)" NIRO_HEV_DE = "KIA NIRO HYBRID (DE)" K7_YG = "KIA K7 (YG)" K7_HEV_YG = "KIA K7 HYBRID (YG)" SELTOS_SP2 = "KIA SELTOS (SP2)" SOUL_EV_SK3 = "KIA SOUL EV (SK3)" MOHAVE_HM = "KIA MOHAVE (HM)" # GENESIS GENESIS_DH = "GENESIS (DH)" GENESIS_G70_IK = "GENESIS G70 (IK)" GENESIS_G70_2020 = "GENESIS G70 2020" GENESIS_G80_DH = "GENESIS G80 (DH)" GENESIS_G90_HI = "GENESIS G90 (HI)" GENESIS_EQ900_HI = "GENESIS EQ900 (HI)" @dataclass class HyundaiCarInfo(CarInfo): package: str="SCC + LKAS" good_torque: bool = True CAR_INFO: Dict[str, Union[HyundaiCarInfo, List[HyundaiCarInfo]]] = { # hyundai CAR.AVANTE_AD: HyundaiCarInfo("Hyundai Avante", video_link="https://youtu.be/_EdYQtV52-c"), CAR.AVANTE_CN7: HyundaiCarInfo("Hyundai Avante 2021", video_link="https://youtu.be/_EdYQtV52-c"), CAR.AVANTE_HEV_CN7: HyundaiCarInfo("Hyundai Avante Hybrid 2021"), CAR.I30_PD: HyundaiCarInfo("Hyundai I30", "All"), CAR.SONATA_DN8: HyundaiCarInfo("Hyundai Sonata 2020-22", "All", video_link="https://www.youtube.com/watch?v=ix63r9kE3Fw", harness=Harness.hyundai_a), CAR.SONATA_HEV_DN8: HyundaiCarInfo("Hyundai Sonata Hybrid 2021-22", "All", harness=Harness.hyundai_a), CAR.SONATA_LF: HyundaiCarInfo("Hyundai LF Sonata"), CAR.SONATA_TURBO_LF: HyundaiCarInfo("Hyundai LF Sonata Turbo"), CAR.SONATA_HEV_LF: HyundaiCarInfo("Hyundai LF Sonata Hybrid"), CAR.KONA_OS: HyundaiCarInfo("Hyundai Kona 2020", harness=Harness.hyundai_b), CAR.KONA_EV_OS: HyundaiCarInfo("Hyundai Kona Electric 2018-19", harness=Harness.hyundai_g), CAR.KONA_HEV_OS: HyundaiCarInfo("Hyundai Kona Hybrid 2020", video_link="https://youtu.be/_EdYQtV52-c", harness=Harness.hyundai_i), CAR.IONIQ_EV_AE: HyundaiCarInfo("Hyundai Ioniq Electric 2019", "All", harness=Harness.hyundai_c), CAR.IONIQ_HEV_AE: HyundaiCarInfo("Hyundai Ioniq Hybrid 2020-22", "SCC + LFA", harness=Harness.hyundai_h), CAR.SANTAFE_TM: HyundaiCarInfo("Hyundai Santa Fe 2019-20", "All", harness=Harness.hyundai_d), CAR.SANTAFE_HEV_TM: HyundaiCarInfo("Hyundai Santa Fe Hybrid 2022", "All", harness=Harness.hyundai_l), CAR.PALISADE_LX2: [ HyundaiCarInfo("Hyundai Palisade 2020-21", "All", video_link="https://youtu.be/TAnDqjF4fDY?t=456", harness=Harness.hyundai_h), HyundaiCarInfo("Kia Telluride 2020", harness=Harness.hyundai_h), ], CAR.VELOSTER_JS: HyundaiCarInfo("Hyundai Veloster 2019-20", "All", min_enable_speed=5. * CV.MPH_TO_MS, harness=Harness.hyundai_e), CAR.GRANDEUR_IG: HyundaiCarInfo("Hyundai Grandeur IG", "All", harness=Harness.hyundai_c), CAR.GRANDEUR_HEV_IG: HyundaiCarInfo("Hyundai Grandeur IG Hybrid", "All", harness=Harness.hyundai_c), CAR.GRANDEUR_FL_IG: HyundaiCarInfo("Hyundai Grandeur IG FL", "All", harness=Harness.hyundai_k), CAR.GRANDEUR_HEV_FL_IG: HyundaiCarInfo("Hyundai Grandeur IG FL Hybrid", "All", harness=Harness.hyundai_k), CAR.TUCSON_TL: HyundaiCarInfo("Hyundai Tucson", "All"), CAR.NEXO_FE: HyundaiCarInfo("Hyundai Nexo", "All"), # Kia CAR.KIA_FORTE: [ HyundaiCarInfo("Kia Forte 2018", harness=Harness.hyundai_b), HyundaiCarInfo("Kia Forte 2019-21", harness=Harness.hyundai_g), ], CAR.K3_BD: HyundaiCarInfo("Kia K3 2018-21"), CAR.K5_JF: HyundaiCarInfo("Kia K5 2021-22", "SCC + LFA", harness=Harness.hyundai_a), CAR.K5_HEV_JF: HyundaiCarInfo("Kia K5 Hybrid 2017"), CAR.K5_DL3: HyundaiCarInfo("Kia K5 2021"), CAR.SPORTAGE_QL: HyundaiCarInfo("Kia Sportage"), CAR.SORENTO_UM: HyundaiCarInfo("Kia Sorento 2018-19", video_link="https://www.youtube.com/watch?v=Fkh3s6WHJz8"), CAR.STINGER_CK: HyundaiCarInfo("Kia Stinger 2018", video_link="https://www.youtube.com/watch?v=MJ94qoofYw0", harness=Harness.hyundai_c), CAR.NIRO_EV_DE: HyundaiCarInfo("Kia Niro Electric 2019-22", "All", video_link="https://www.youtube.com/watch?v=lT7zcG6ZpGo"), CAR.NIRO_HEV_DE: HyundaiCarInfo("Kia Niro Plug-In Hybrid 2019", min_enable_speed=10. * CV.MPH_TO_MS, harness=Harness.hyundai_c), CAR.K7_YG: HyundaiCarInfo("Kia K7 2016-19"), CAR.K7_HEV_YG: HyundaiCarInfo("Kia K7 Hybrid 2016-19"), CAR.SELTOS_SP2: HyundaiCarInfo("Kia Seltos 2021", harness=Harness.hyundai_a), CAR.SOUL_EV_SK3: HyundaiCarInfo("Kia Soul EV 2019"), CAR.MOHAVE_HM: HyundaiCarInfo("Kia Mohave 2019"), # genesis CAR.GENESIS_DH: HyundaiCarInfo("Genesis 2015-2016", min_enable_speed=19 * CV.MPH_TO_MS, harness=Harness.hyundai_j), CAR.GENESIS_G70_IK: HyundaiCarInfo("Genesis G70 2018", "All", harness=Harness.hyundai_f), CAR.GENESIS_G70_2020: HyundaiCarInfo("Genesis G70 2020", "All", harness=Harness.hyundai_f), CAR.GENESIS_G80_DH: HyundaiCarInfo("Genesis G80 2017", "All", harness=Harness.hyundai_h), CAR.GENESIS_G90_HI: HyundaiCarInfo("Genesis G90 2017", "All", harness=Harness.hyundai_c), CAR.GENESIS_EQ900_HI: HyundaiCarInfo("Genesis EQ900", "All"), } class Buttons: NONE = 0 RES_ACCEL = 1 SET_DECEL = 2 GAP_DIST = 3 CANCEL = 4 FINGERPRINTS = { # genesis CAR.GENESIS_DH: [{ 67: 8, 68: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 7, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 5, 897: 8, 902: 8, 903: 6, 916: 8, 1024: 2, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1287: 4, 1292: 8, 1312: 8, 1322: 8, 1331: 8, 1332: 8, 1333: 8, 1334: 8, 1335: 8, 1342: 6, 1345: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 5, 1407: 8, 1419: 8, 1427: 6, 1434: 2, 1456: 4 },{ 67: 8, 68: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 7, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 5, 897: 8, 902: 8, 903: 6, 916: 8, 1024: 2, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 3, 1287: 4, 1292: 8, 1312: 8, 1322: 8, 1331: 8, 1332: 8, 1333: 8, 1334: 8, 1335: 8, 1345: 8, 1363: 8, 1369: 8, 1370: 8, 1378: 4, 1379: 8, 1384: 5, 1407: 8, 1419: 8, 1427: 6, 1434: 2, 1456: 4 },{ 67: 8, 68: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 7, 593: 8, 608: 8, 688: 5, 809: 8, 854: 7, 870: 7, 871: 8, 872: 5, 897: 8, 902: 8, 903: 6, 912: 7, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1268: 8, 1280: 1, 1281: 3, 1287: 4, 1292: 8, 1312: 8, 1322: 8, 1331: 8, 1332: 8, 1333: 8, 1334: 8, 1335: 8, 1345: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 5, 1407: 8, 1419: 8, 1427: 6, 1434: 2, 1437: 8, 1456: 4 },{ 67: 8, 68: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 7, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 5, 897: 8, 902: 8, 903: 6, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1287: 4, 1292: 8, 1312: 8, 1322: 8, 1331: 8, 1332: 8, 1333: 8, 1334: 8, 1335: 8, 1345: 8, 1363: 8, 1369: 8, 1370: 8, 1378: 4, 1379: 8, 1384: 5, 1407: 8, 1425: 2, 1427: 6, 1437: 8, 1456: 4 },{ 67: 8, 68: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 7, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 5, 897: 8, 902: 8, 903: 6, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1287: 4, 1292: 8, 1312: 8, 1322: 8, 1331: 8, 1332: 8, 1333: 8, 1334: 8, 1335: 8, 1345: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 5, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1437: 8, 1456: 4 }], CAR.GENESIS_G70_IK: [{ 67: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 544: 8, 576: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832:8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 909: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1168: 7, 1170: 8, 1173:8, 1184: 8, 1186: 2, 1191: 2, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1379: 8, 1384: 8, 1407: 8, 1419:8, 1427: 6, 1456: 4, 1470: 8, 1988: 8, 1996: 8, 2000: 8, 2004: 8, 2008: 8, 2012: 8, 2015: 8 }], CAR.GENESIS_G80_DH: [{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 544: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1024: 2, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1191: 2, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1434: 2, 1456: 4, 1470: 8 },{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 359: 8, 544: 8, 546: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1157: 4, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 3, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1434: 2, 1437: 8, 1456: 4, 1470: 8 },{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 544: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1157: 4, 1162: 8, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1193: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1437: 8, 1456: 4, 1470: 8 }], CAR.GENESIS_G90_HI: [{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 359: 8, 544: 8, 593: 8, 608: 8, 688: 5, 809: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1162: 4, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 3, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1434: 2, 1456: 4, 1470: 8, 1988: 8, 2000: 8, 2003: 8, 2004: 8, 2005: 8, 2008: 8, 2011: 8, 2012: 8, 2013: 8 },{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 359: 8, 544: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1157: 4, 1162: 4, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 3, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1370: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1434: 2, 1456: 4, 1470: 8 }], # hyundai CAR.AVANTE_CN7: [{ 66: 8, 67: 8, 68: 8, 127: 8, 273: 8, 274: 8, 275: 8, 339: 8, 356: 4, 399: 8, 512: 6, 544: 8, 593: 8, 608: 8, 688: 5, 790: 8, 809: 8, 897: 8, 832: 8, 899: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1170: 8, 1265: 4, 1280: 1, 1282: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1314: 8, 1322: 8, 1345: 8, 1349: 8, 1351: 8, 1353: 8, 1363: 8, 1366: 8, 1367: 8, 1369: 8, 1407: 8, 1415: 8, 1419: 8, 1425: 2, 1427: 6, 1440: 8, 1456: 4, 1472: 8, 1486: 8, 1487: 8, 1491: 8, 1530: 8, 1532: 5, 2001: 8, 2003: 8, 2004: 8, 2009: 8, 2012: 8, 2016: 8, 2017: 8, 2024: 8, 2025: 8 }], CAR.I30_PD: [{ 66: 8, 67: 8, 68: 8, 127: 8, 128: 8, 129: 8, 273: 8, 274: 8, 275: 8, 339: 8, 354: 3, 356: 4, 399: 8, 512: 6, 544: 8, 593: 8, 608: 8, 688: 5, 790: 8, 809: 8, 884: 8, 897: 8, 899: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1151: 6, 1168: 7, 1170: 8, 1193: 8, 1265: 4, 1280: 1, 1282: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1345: 8, 1348: 8, 1349: 8, 1351: 8, 1353: 8, 1356: 8, 1363: 8, 1365: 8, 1366: 8, 1367: 8, 1369: 8, 1407: 8, 1414: 3, 1415: 8, 1427: 6, 1440: 8, 1456: 4, 1470: 8, 1486: 8, 1487: 8, 1491: 8, 1530: 8, 1952: 8, 1960: 8, 1988: 8, 2000: 8, 2001: 8, 2005: 8, 2008: 8, 2009: 8, 2013: 8, 2017: 8, 2025: 8 },{ 66: 8, 67: 8, 68: 8, 127: 8, 128: 8, 129: 8, 273: 8, 274: 8, 275: 8, 339: 8, 354: 3, 356: 4, 399: 8, 512: 6, 544: 8, 593: 8, 608: 8, 688: 5, 790: 8, 809: 8, 832: 8, 897: 8, 899: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1151: 6, 1168: 7, 1170: 8, 1265: 4, 1280: 1, 1282: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1349: 8, 1351: 8, 1353: 8, 1356: 8, 1363: 8, 1366: 8, 1367: 8, 1369: 8, 1407: 8, 1414: 3, 1415: 8, 1419: 8, 1440: 8, 1456: 4, 1470: 8, 1486: 8, 1487: 8, 1491: 8, 1530: 8 },{ 66: 8, 67: 8, 68: 8, 127: 8, 128: 8, 129: 8, 273: 8, 274: 8, 275: 8, 339: 8, 354: 3, 356: 4, 399: 8, 512: 6, 544: 8, 593: 8, 608: 8, 688: 5, 790: 8, 809: 8, 832: 8, 897: 8, 899: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1151: 6, 1168: 7, 1170: 8, 1265: 4, 1280: 1, 1282: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1349: 8, 1351: 8, 1353: 8, 1356: 8, 1363: 8, 1366: 8, 1367: 8, 1369: 8, 1407: 8, 1414: 3, 1419: 8, 1427: 6, 1440: 8, 1456: 4, 1470: 8, 1486: 8, 1487: 8, 1491: 8, 1960: 8, 1990: 8, 1998: 8, 2000: 8, 2001: 8, 2004: 8, 2005: 8, 2008: 8, 2009: 8, 2012: 8, 2013: 8, 2015: 8, 2016: 8, 2017: 8, 2024: 8, 2025: 8 },{ 67: 8, 68: 8, 127: 8, 128: 8, 129: 8, 273: 8, 274: 8, 275: 8, 339: 8, 354: 3, 356: 4, 399: 8, 512: 6, 544: 8, 608: 8, 790: 8, 809: 8, 832: 8, 899: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1151: 6, 1168: 7, 1170: 8, 1265: 4, 1280: 1, 1282: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1349: 8, 1351: 8, 1353: 8, 1356: 8, 1363: 8, 1365: 8, 1366: 8, 1367: 8, 1369: 8, 1407: 8, 1414: 3, 1415: 8, 1419: 8, 1427: 6, 1440: 8, 1456: 4, 1470: 8, 1486: 8, 1487: 8, 1491: 8, 1530: 8 }], CAR.SONATA_DN8: [{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 8, 545: 8, 546: 8, 547: 8, 548: 8, 549: 8, 550: 8, 576: 8, 593: 8, 608: 8, 688: 6, 809: 8, 832: 8, 854: 8, 865: 8, 870: 7, 871: 8, 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4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 4, 1379: 8, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1456: 4, 1470: 8 },{ 67: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 359: 8, 544: 8, 576: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 909: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1157: 4, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1427: 6, 1437: 8, 1456: 4, 1470: 8 },{ 67: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 358: 6, 359: 8, 544: 8, 576: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 909: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1168: 7, 1170: 8, 1173: 8, 1184: 8, 1265: 4, 1280: 1, 1281: 4, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1378: 4, 1379: 8, 1384: 8, 1407: 8, 1419: 8, 1425: 2, 1427: 6, 1456: 4, 1470: 8 }], CAR.NIRO_EV_DE: [{ 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 593: 8, 688: 5, 832: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 8, 1151: 6, 1157: 4, 1168: 7, 1173: 8, 1186: 2, 1191: 2, 1225: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1407: 8, 1419: 8, 1426: 8, 1427: 6, 1429: 8, 1430: 8, 1456: 4, 1470: 8, 1473: 8, 1507: 8, 1535: 8 },{ 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 546: 8, 593: 8, 688: 5, 832: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 905: 8, 909: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 8, 1151: 6, 1157: 4, 1168: 7, 1173: 8, 1186: 2, 1191: 2, 1225: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1407: 8, 1419: 8, 1426: 8, 1427: 6, 1429: 8, 1430: 8, 1456: 4, 1470: 8, 1473: 8, 1507: 8, 1535: 8 }], CAR.NIRO_HEV_DE: [{ 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 576: 8, 593: 8, 688: 5, 832: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 6, 1173: 8, 1225: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1419: 8, 1427: 6, 1429: 8, 1430: 8, 1448: 8, 1456: 4, 1470: 8, 1476: 8, 1535: 8 },{ 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 576: 8, 593: 8, 688: 5, 832: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 6, 1173: 8, 1225: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1407: 8, 1419: 8, 1427: 6, 1429: 8, 1430: 8, 1448: 8, 1456: 4, 1470: 8, 1476: 8, 1535: 8 },{ 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 546: 8, 576: 8, 832: 8, 881: 8, 882: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 6, 1173: 8, 1225: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1407: 8, 1419: 8, 1427: 6, 1429: 8, 1430: 8, 1448: 8, 1456: 4, 1470: 8, 1476: 8, 1535: 8 }], CAR.K7_YG: [{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 8, 546: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1162: 4, 1168: 7, 1170: 8, 1173: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1427: 6, 1444: 8, 1456: 4, 1470: 8 },{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 8, 546: 8, 608: 8, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1162: 4, 1168: 7, 1170: 8, 1173: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1427: 6, 1444: 8, 1456: 4, 1470: 8 },{ 67: 8, 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 8, 546: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 7, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 903: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1078: 4, 1107: 5, 1136: 8, 1151: 6, 1156: 8, 1157: 4, 1162: 4, 1168: 7, 1170: 8, 1173: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 4, 1384: 8, 1407: 8, 1419: 8, 1427: 6, 1444: 8, 1456: 4, 1470: 8 }], CAR.K7_HEV_YG: [{ 68: 8, 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 576: 8, 593: 8, 688: 5, 832: 8, 865: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 905: 8, 909: 8, 913: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1078: 4, 1096: 8, 1102: 8, 1108: 8, 1136: 6, 1138: 5, 1151: 8, 1155: 8, 1156: 8, 1157: 4, 1162: 8, 1164: 8, 1168: 7, 1173: 8, 1180: 8, 1186: 2, 1191: 2, 1210: 8, 1227: 8, 1265: 4, 1268: 8, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1343: 8, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 8, 1379: 8, 1407: 8, 1419: 8, 1427: 6, 1429: 8, 1430: 8, 1448: 8, 1456: 4, 1470: 8, 1476: 8, 1535: 8 }], CAR.SELTOS_SP2: [{ 67: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 524: 8, 544: 8, 593: 8, 608: 8, 688: 6, 809: 8, 832: 8, 854: 8, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 905: 8, 909: 8, 910: 5, 911: 5, 913: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1102: 8, 1107: 5, 1114: 8, 1136: 8, 1145: 8, 1151: 8, 1155: 8, 1156: 8, 1157: 4, 1162: 8, 1164: 8, 1168: 8, 1170: 8, 1173: 8, 1186: 2, 1191: 2, 1225: 8, 1265: 4, 1280: 8, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1379: 8, 1384: 8, 1394: 8, 1407: 8, 1419: 8, 1427: 6, 1446: 8, 1456: 4, 1470: 8, 1485: 8, 1988: 8, 1996: 8, 2000: 8, 2004: 8, 2008: 8, 2012: 8, 2015: 8 }], CAR.SOUL_EV_SK3: [{ 127: 8, 304: 8, 320: 8, 339: 8, 352: 8, 356: 4, 544: 8, 546: 8, 548: 8, 549: 8, 593: 8, 688: 6, 832: 8, 881: 8, 882: 8, 897: 8, 902: 8, 903: 8, 905: 8, 909: 8, 913: 8, 916: 8, 1040: 8, 1042: 8, 1056: 8, 1057: 8, 1078: 4, 1136: 8, 1151: 6, 1155: 8, 1156: 8, 1157: 4, 1162: 8, 1164: 8, 1168: 7, 1173: 8, 1186: 2, 1191: 2, 1193: 8, 1225: 8, 1227: 8, 1265: 4, 1280: 1, 1287: 4, 1290: 8, 1291: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1355: 8, 1363: 8, 1369: 8, 1378: 8, 1379: 8, 1407: 8, 1419: 8, 1426: 8, 1427: 6, 1429: 8, 1430: 8, 1456: 4, 1470: 8, 1473: 8, 1507: 8, 1535: 8 }], CAR.MOHAVE_HM: [{ 67: 8, 127: 8, 304: 8, 320: 8, 339: 8, 356: 4, 544: 8, 593: 8, 608: 8, 688: 5, 809: 8, 832: 8, 854: 8, 870: 7, 871: 8, 872: 8, 897: 8, 902: 8, 905: 8, 909: 8, 913: 8, 916: 8, 1040: 8, 1056: 8, 1057: 8, 1064: 8, 1078: 4, 1107: 5, 1123: 8, 1136: 8, 1145: 8, 1151: 8, 1155: 8, 1156: 8, 1157: 4, 1162: 8, 1164: 8, 1168: 8, 1170: 8, 1173: 8, 1180: 8, 1186: 2, 1191: 2, 1193: 8, 1210: 8, 1225: 8, 1227: 8, 1265: 4, 1280: 8, 1287: 4, 1290: 8, 1292: 8, 1294: 8, 1312: 8, 1322: 8, 1342: 6, 1345: 8, 1348: 8, 1363: 8, 1369: 8, 1371: 8, 1378: 8, 1384: 8, 1407: 8, 1419: 8, 1427: 6, 1456: 4, 1470: 8, 1479: 8 }] } if Params().get_bool("FingerprintTwoSet"): FW_VERSIONS = { # genesis CAR.GENESIS_G70_IK: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00IK__ SCC F-CUP 1.00 1.02 96400-G9100 \xf1\xa01.02',], (Ecu.esp, 0x7d1, None): [b'\xf1\x00\x00\x00\x00\x00\x00\x00',], (Ecu.engine, 0x7e0, None): [b'\xf1\x81640F0051\x00\x00\x00\x00\x00\x00\x00\x00',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00IK MDPS R 1.00 1.06 57700-G9420 4I4VL106',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00IK MFC AT USA LHD 1.00 1.01 95740-G9000 170920',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x87VDJLT17895112DN4\x88fVf\x99\x88\x88\x88\x87fVe\x88vhwwUFU\x97eFex\x99\xff\xb7\x82\xf1\x81E25\x00\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 E25\x00\x00\x00\x00\x00\x00\x00SIK0T33NB2\x11\x1am\xda',], }, CAR.GENESIS_G70_2020: { (Ecu.eps, 0x7d4, None): [ b'\xf1\x00IK MDPS R 1.00 1.07 57700-G9220 4I2VL107', b'\xf1\x00IK MDPS R 1.00 1.07 57700-G9420 4I4VL107', b'\xf1\x00IK MDPS R 1.00 1.08 57700-G9420 4I4VL108', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x87VCJLP18407832DN3\x88vXfvUVT\x97eFU\x87d7v\x88eVeveFU\x89\x98\x7f\xff\xb2\xb0\xf1\x81E25\x00\x00\x00', b'\x00\x00\x00\x00\xf1\x00bcsh8p54 E25\x00\x00\x00\x00\x00\x00\x00SIK0T33NB4\xecE\xefL', b'\xf1\x87VDKLT18912362DN4wfVfwefeveVUwfvw\x88vWfvUFU\x89\xa9\x8f\xff\x87w\xf1\x81E25\x00\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 E25\x00\x00\x00\x00\x00\x00\x00SIK0T33NB4\xecE\xefL', b'\xf1\x87VDJLC18480772DK9\x88eHfwfff\x87eFUeDEU\x98eFe\x86T5DVyo\xff\x87s\xf1\x81E25\x00\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 E25\x00\x00\x00\x00\x00\x00\x00SIK0T33KB5\x9f\xa5&\x81', ], (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00IK__ SCC F-CUP 1.00 1.02 96400-G9100 ', b'\xf1\x00IK__ SCC F-CUP 1.00 1.02 96400-G9100 \xf1\xa01.02', b'\xf1\x00IK__ SCC FHCUP 1.00 1.02 96400-G9000 ', ], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00IK MFC AT USA LHD 1.00 1.01 95740-G9000 170920', b'\xf1\x00IK MFC AT KOR LHD 1.00 1.01 95740-G9000 170920', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x81640J0051\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x81640H0051\x00\x00\x00\x00\x00\x00\x00\x00', ], }, # hyundai CAR.AVANTE_CN7: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00CN7_ SCC F-CUP 1.00 1.01 99110-AA000 ', b'\xf1\x00CN7_ SCC FHCUP 1.00 1.01 99110-AA000 ', b'\xf1\x8799110AA000\xf1\x00CN7_ SCC FHCUP 1.00 1.01 99110-AA000 ', b'\xf1\x8799110AA000\xf1\x00CN7_ SCC F-CUP 1.00 1.01 99110-AA000 ', ], (Ecu.eps, 0x7d4, None): [ b'\xf1\x87\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf1\x00CN7 MDPS C 1.00 1.06 \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00 4CNDC106', b'\xf1\x8756310/AA070\xf1\x00CN7 MDPS C 1.00 1.06 56310/AA070 4CNDC106', b'\xf1\x8756310AA050\x00\xf1\x00CN7 MDPS C 1.00 1.06 56310AA050\x00 4CNDC106', ], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00CN7 MFC AT USA LHD 1.00 1.00 99210-AB000 200819', b'\xf1\x00CN7 MFC AT USA LHD 1.00 1.03 99210-AA000 200819', b'\xf1\x00CN7 MFC AT USA LHD 1.00 1.01 99210-AB000 210205', ], (Ecu.esp, 0x7d1, None): [ b'\xf1\x00CN ESC \t 101 \x10\x03 58910-AB800', b'\xf1\x8758910-AA800\xf1\x00CN ESC \t 104 \x08\x03 58910-AA800', b'\xf1\x8758910-AB800\xf1\x00CN ESC \t 101 \x10\x03 58910-AB800', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x00HT6WA280BLHT6VA640A1CCN0N20NS5\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x00HT6WA280BLHT6VA640A1CCN0N20NS5\x00\x00\x00\x00\x00\x00\xe8\xba\xce\xfa', b'\xf1\x87CXMQFM2135005JB2E\xb9\x89\x98W\xa9y\x97h\xa9\x98\x99wxvwh\x87\177\xffx\xff\xff\xff,,\xf1\x89HT6VA640A1\xf1\x82CCN0N20NS5\x00\x00\x00\x00\x00\x00', b'\xf1\x87CXMQFM1916035JB2\x88vvgg\x87Wuwgev\xa9\x98\x88\x98h\x99\x9f\xffh\xff\xff\xff\xa5\xee\xf1\x89HT6VA640A1\xf1\x82CCN0N20NS5\x00\x00\x00\x00\x00\x00', b'\xf1\x87CXLQF40189012JL2f\x88\x86\x88\x88vUex\xb8\x88\x88\x88\x87\x88\x89fh?\xffz\xff\xff\xff\x08z\xf1\x89HT6VA640A1\xf1\x82CCN0N20NS5\x00\x00\x00\x00\x00\x00', b'\xf1\x87CXMQFM2728305JB2E\x97\x87xw\x87vwgw\x84x\x88\x88w\x89EI\xbf\xff{\xff\xff\xff\xe6\x0e\xf1\x89HT6VA640A1\xf1\x82CCN0N20NS5\x00\x00\x00\x00\x00\x00', b'\xf1\x87CXMQFM3806705JB2\x89\x87wwx\x88g\x86\x99\x87\x86xwwv\x88yv\x7f\xffz\xff\xff\xffV\x15\xf1\x89HT6VA640A1\xf1\x82CCN0N20NS5\x00\x00\x00\x00\x00\x00', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x82CNCWD0AMFCXCSFFA', b'\xf1\x82CNCWD0AMFCXCSFFB', b'\xf1\x82CNCVD0AMFCXCSFFB', b'\xf1\x870\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf1\x82CNDWD0AMFCXCSG8A', ], }, CAR.I30_PD: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00PD__ SCC F-CUP 1.00 1.01 99110-G3100 ',], (Ecu.esp, 0x7d1, None): [b'\xf1\x00PD ESC \x11 100 \a\x03 58910-G3AC0',], (Ecu.engine, 0x7e0, None): [b'\x01TPD-1A506F000H00',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00PDu MDPS C 1.00 1.01 56310/G3690 4PDUC101',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00PDP LKAS AT AUS RHD 1.00 1.01 99211-G4000 v60',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x816U2VA051\x00\x00\xf1\x006U2V0_C2\x00\x006U2VA051\x00\x00DPD0H16US0\x00\x00\x00\x00',], }, CAR.SONATA_DN8: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00DN8_ SCC FHCUP 1.00 1.01 99110-L1000 ', b'\xf1\x00DN8_ SCC FHCUP 1.00 1.00 99110-L0000 ', b'\xf1\x00DN8_ SCC F-CU- 1.00 1.00 99110-L0000 ', ], (Ecu.esp, 0x7d1, None): [ b'\xf1\x00DN ESC \x01 102\x19\x04\x13 58910-L1300\xf1\xa01.02', b'\xf1\x8758910-L0100\xf1\x00DN ESC \x06 104\x19\x08\x01 58910-L0100\xf1\xa01.04', ], (Ecu.engine, 0x7e0, None): [ b'HM6M2_0a0_BD0', b'\xf1\x87391162M003\xf1\xa0000F', b'\xf1\x87391162M003\xf1\xa00240', ], (Ecu.eps, 0x7d4, None): [ b'\xf1\x8756310-L1010\xf1\x00DN8 MDPS C 1.00 1.03 56310-L1010 4DNDC103\xf1\xa01.03', b'\xf1\x8756310L0010\x00\xf1\x00DN8 MDPS C 1.00 1.01 56310L0010\x00 4DNAC101\xf1\xa01.01', b'\xf1\x8756310-L0010\xf1\x00DN8 MDPS C 1.00 1.01 56310-L0010 4DNAC101\xf1\xa01.01', ], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00DN8 MFC AT KOR LHD 1.00 1.02 99211-L1000 190422', b'\xf1\x00DN8 MFC AT USA LHD 1.00 1.00 99211-L0000 190716', b'\xf1\x00DN8 MFC AT USA LHD 1.00 1.01 99211-L0000 191016', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x00HT6TA260BLHT6TA800A1TDN8C20KS4\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x00bcsh8p54 U903\x00\x00\x00\x00\x00\x00SDN8T16NB0z{\xd4v', ], }, CAR.SONATA_HEV_DN8: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00DNhe SCC FHCUP 1.00 1.02 99110-L5000 ',], (Ecu.esp, 0x7d1, None): [b'\xf1\x8758910-L0100\xf1\x00DN ESC \x06 104\x19\x08\x01 58910-L0100\xf1\xa01.04',], (Ecu.engine, 0x7e0, None): [b'\xf1\x87391062J002\xf1\xa0000P',], (Ecu.eps, 0x7d4, None): [b'\xf1\x8756310-L5500\xf1\x00DN8 MDPS C 1.00 1.02 56310-L5500 4DNHC102\xf1\xa01.02',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00DN8HMFC AT USA LHD 1.00 1.04 99211-L1000 191016',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x00PSBG2323 E09\x00\x00\x00\x00\x00\x00\x00TDN2H20SA5\x97R\x88\x9e',], }, CAR.KONA_HEV_OS: { (Ecu.esp, 0x7d1, None): [b'\xf1\x00OS IEB \x01 104 \x11 58520-CM000\xf1\xa01.04',], (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00OShe SCC FNCUP 1.00 1.01 99110-CM000 \xf1\xa01.01',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00OS MDPS C 1.00 1.00 56310CM030\x00 4OHDC100',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00OSH LKAS AT KOR LHD 1.00 1.01 95740-CM000 l31',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x816U3J9051\x00\x00\xf1\x006U3H1_C2\x00\x006U3J9051\x00\x00HOS0G16DS1\x16\xc7\xb0\xd9',], (Ecu.engine, 0x7e0, None): [b'\xf1\x816H6F6051\x00\x00\x00\x00\x00\x00\x00\x00',], }, CAR.KONA_OS: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00OS__ SCC F-CUP 1.00 1.00 95655-J9200 \xf1\xa01.00',], (Ecu.esp, 0x7d1, None): [b'\xf1\x816V5RAK00018.ELF\xf1\x00\x00\x00\x00\x00\x00\x00\xf1\xa01.05',], (Ecu.engine, 0x7e0, None): [b'"\x01TOS-0NU06F301J02',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00OS MDPS C 1.00 1.05 56310J9030\x00 4OSDC105',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00OS9 LKAS AT USA LHD 1.00 1.00 95740-J9300 g21',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x816U2VE051\x00\x00\xf1\x006U2V0_C2\x00\x006U2VE051\x00\x00DOS4T16NS3\x00\x00\x00\x00',], }, CAR.KONA_EV_OS: { (Ecu.fwdRadar, 0x7D0, None): [b'\xf1\x00OSev SCC FNCUP 1.00 1.01 99110-K4000 \xf1\xa01.01',], (Ecu.esp, 0x7D1, None): [b'\xf1\xa02.06',], (Ecu.eps, 0x7D4, None): [ b'\xf1\x00OS MDPS C 1.00 1.04 56310K4000\x00 4OEDC104', b'\xf1\x00OS MDPS C 1.00 1.04 56310K4050\x00 4OEDC104', ], (Ecu.fwdCamera, 0x7C4, None): [b'\xf1\x00OSE LKAS AT KOR LHD 1.00 1.00 95740-K4100 W40',], }, CAR.IONIQ_HEV_AE: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00AEhe SCC F-CUP 1.00 1.00 99110-G2200 ', b'\xf1\x00AEhe SCC H-CUP 1.01 1.01 96400-G2000 ', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x816H6F6051\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x816H6F2051\x00\x00\x00\x00\x00\x00\x00\x00', ], (Ecu.eps, 0x7D4, None): [ b'\xf1\x00AE MDPS C 1.00 1.07 56310/G2301 4AEHC107', b'\xf1\x00AE MDPS C 1.00 1.01 56310/G2310 4APHC101', ], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00AEH MFC AT EUR LHD 1.00 1.01 95740-G2600 190819', b'\xf1\x00AEH MFC AT EUR LHD 1.00 1.00 95740-G2400 180222', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x816U3J8051\x00\x00\xf1\x006U3H1_C2\x00\x006U3J8051\x00\x00HAE0G16UL0Nd\xed:', b'\xf1\x816U3H1051\x00\x00\xf1\x006U3H0_C2\x00\x006U3H1051\x00\x00HAE0G16US2\x95\xa2^$', ], }, CAR.SANTAFE_TM: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00TM__ SCC F-CUP 1.00 1.02 99110-S2000 \xf1\xa01.02',], (Ecu.esp, 0x7d1, None): [b'\xf1\x00TM ESC \x02 100\x18\x030 58910-S2600\xf1\xa01.00',], (Ecu.engine, 0x7e0, None): [b'\xf1\x81606EA051\x00\x00\x00\x00\x00\x00\x00\x00',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00TM MDPS C 1.00 1.00 56340-S2000 8409',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00TM MFC AT USA LHD 1.00 1.00 99211-S2000 180409',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x87SBJWAA6562474GG0ffvgeTeFx\x88\x97\x88ww\x87www\x87w\x84o\xfa\xff\x87fO\xff\xc2 \xf1\x816W3C2051\x00\x00\xf1\x006W351_C2\x00\x006W3C2051\x00\x00TTM2G24NS1\x00\x00\x00\x00',], }, CAR.PALISADE_LX2: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00LX2_ SCC FHCUP 1.00 1.04 99110-S8100 \xf1\xa01.04', b'\xf1\x00LX2 SCC FHCUP 1.00 1.04 99110-S8100 \xf1\xa01.04', b'\xf1\x00LX2_ SCC FHCUP 1.00 1.04 99110-S8100 \xf1\xa01.04', ], (Ecu.esp, 0x7d1, None): [ b'\xf1\x00LX ESC \v 102\x19\x05\a 58910-S8330\xf1\xa01.02', b'\xf1\x00LX ESC \v 103\x19\t\x10 58910-S8360\xf1\xa01.03', b'\xf1\x00LX ESC \x01 103\x19\t\x10 58910-S8360\xf1\xa01.03', b'\xf1\x00LX ESC \x0b 102\x19\x05\x07 58910-S8330', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x81640J0051\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x81640K0051\x00\x00\x00\x00\x00\x00\x00\x00', ], (Ecu.eps, 0x7d4, None): [ b'\xf1\x00LX2 MDPS C 1.00 1.03 56310-S8020 4LXDC103', ], (Ecu.engine, 0x7e0, None): [b'\xf1\x81640J0051\x00\x00\x00\x00\x00\x00\x00\x00',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00LX2 MDPS C 1.00 1.03 56310-S8020 4LXDC103',], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00LX2 MFC AT USA LHD 1.00 1.03 99211-S8100 190125', b'\xf1\x00LX2 MFC AT USA LHD 1.00 1.05 99211-S8100 190909', b'\xf1\x00LX2 MFC AT USA LHD 1.00 1.05 99211-S8100 190909', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x87LBLUFN650868KF36\xa9\x98\x89\x88\xa8\x88\x88\x88h\x99\xa6\x89fw\x86gw\x88\x97x\xaa\x7f\xf6\xff\xbb\xbb\x8f\xff+\x82\xf1\x81U891\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 U891\x00\x00\x00\x00\x00\x00SLX2G38NB3\xd1\xc3\xf8\xa8', b'\xf1\x87LDKVBN424201KF26\xba\xaa\x9a\xa9\x99\x99\x89\x98\x89\x99\xa8\x99\x88\x99\x98\x89\x88\x99\xa8\x89v\x7f\xf7\xffwf_\xffq\xa6\xf1\x81U891\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 U891\x00\x00\x00\x00\x00\x00SLX4G38NB2\xafL]\xe7', b'\xf1\x87LDLVBN560098KF26\x86fff\x87vgfg\x88\x96xfw\x86gfw\x86g\x95\xf6\xffeU_\xff\x92c\xf1\x81U891\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 U891\x00\x00\x00\x00\x00\x00SLX4G38NB2\xafL]\xe7', b'\xf1\x87LDLVBN5600981KF26\x86fff\x87vgfg\x88\x96xfw\x86gfw\x86g\x95\xf6\xffeU_\xff\x92c\xf1\x81U891\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 U891\x00\x00\x00\x00\x00\x00SLX4G38NB2\xafL]\xe7', b'\xf1\x87LBLUFN655162KF36\x98\x88\x88\x88\x98\x88\x88\x88x\x99\xa7\x89x\x99\xa7\x89x\x99\x97\x89g\xf7\xffwU_\xff\xe9!\xf1\x81U891\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 U891\x00\x00\x00\x00\x00\x00SLX2G38NB3\xd1\xc3\xf8\xa8', ], }, CAR.VELOSTER_JS: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00JS__ SCC H-CUP 1.00 1.02 95650-J3200 ',], (Ecu.esp, 0x7d1, None): [b'\xf1\x00\x00\x00\x00\x00\x00\x00',], (Ecu.engine, 0x7e0, None): [b'\x01TJS-JNU06F200H0A',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00JSL MDPS C 1.00 1.03 56340-J3000 8308',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00JS LKAS AT USA LHD 1.00 1.02 95740-J3000 K32',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x816U2V8051\x00\x00\xf1\x006U2V0_C2\x00\x006U2V8051\x00\x00DJS0T16NS1\xba\x02\xb8\x80',], }, # kia CAR.KIA_FORTE: { (Ecu.eps, 0x7D4, None): [ b'\xf1\x00BD MDPS C 1.00 1.02 56310-XX000 4BD2C102', b'\xf1\x00BD MDPS C 1.00 1.08 56310/M6300 4BDDC108', b'\xf1\x00BD MDPS C 1.00 1.08 56310M6300\x00 4BDDC108', ], (Ecu.fwdCamera, 0x7C4, None): [ b'\xf1\x00BD LKAS AT USA LHD 1.00 1.04 95740-M6000 J33', ], (Ecu.fwdRadar, 0x7D0, None): [ b'\xf1\x00BD__ SCC H-CUP 1.00 1.02 99110-M6000 ', ], (Ecu.engine, 0x7e0, None): [ b'\x01TBDM1NU06F200H01', b'391182B945\x00', ], (Ecu.esp, 0x7d1, None): [ b'\xf1\x816VGRAH00018.ELF\xf1\x00\x00\x00\x00\x00\x00\x00', ], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x816U2VC051\x00\x00\xf1\x006U2V0_C2\x00\x006U2VC051\x00\x00DBD0T16SS0\x00\x00\x00\x00', b"\xf1\x816U2VC051\x00\x00\xf1\x006U2V0_C2\x00\x006U2VC051\x00\x00DBD0T16SS0\xcf\x1e'\xc3", ], }, CAR.K5_JF: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x00JF__ SCC F-CUP 1.00 1.00 96400-D4110 ',], (Ecu.esp, 0x7d1, None): [b'\xf1\x00JF ESC \v 11 \x18\x030 58920-D5180',], (Ecu.engine, 0x7e0, None): [b'\x01TJFAJNU06F201H03',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00TM MDPS C 1.00 1.00 56340-S2000 8409',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00JFA LKAS AT USA LHD 1.00 1.02 95895-D5000 h31',], (Ecu.transmission, 0x7e1, None): [b'\xf1\x816U2V8051\x00\x00\xf1\x006U2V0_C2\x00\x006U2V8051\x00\x00DJF0T16NL0\t\xd2GW',], }, CAR.K5_HEV_JF: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00DEhe SCC H-CUP 1.01 1.02 96400-G5100 ', b'\xf1\x00JFhe SCC F-CUP 1.00 1.00 96400-A8000 ', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x816H6F4051\x00\x00\x00\x00\x00\x00\x00\x00', b'\xf1\x816H673051\x00\x00\x00\x00\x00\x00\x00\x00', ], (Ecu.eps, 0x7d4, None): [ b'\xf1\x00DE MDPS C 1.00 1.09 56310G5301\x00 4DEHC109', b'\xf1\x00JF MDPS C 1.00 1.02 56310-XX000\x00 4JFHC102', ], (Ecu.fwdCamera, 0x7c4, None): [ b'\xf1\x00DEP MFC AT USA LHD 1.00 1.01 95740-G5010 170424', b'\xf1\x00JFP MFC AT EUR LHD 1.00 1.03 95895-A8100 180608', ], (Ecu.transmission, 0x7e1, None): [ b"\xf1\x816U3J2051\x00\x00\xf1\x006U3H0_C2\x00\x006U3J2051\x00\x00PDE0G16NS2\xf4'\\\x91", b"\xf1\x816T7B0051\x00\x00\xf1\x006T7B0_C2\x00\x006T7B0051\x00\x00TJF2H20KA0\xf4'\\\x91", ], }, CAR.STINGER_CK: { (Ecu.fwdRadar, 0x7d0, None): [ b'\xf1\x00CK__ SCC F_CUP 1.00 1.01 96400-J5100 \xf1\xa01.01',], (Ecu.engine, 0x7e0, None): [ b'\xf1\x81640E0051\x00\x00\x00\x00\x00\x00\x00\x00',], (Ecu.eps, 0x7d4, None): [b'\xf1\x00CK MDPS R 1.00 1.04 57700-J5420 4C4VL104',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\x00CK MFC AT USA LHD 1.00 1.03 95740-J5000 170822',], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x87VDHLG17118862DK2\x8awWwgu\x96wVfUVwv\x97xWvfvUTGTx\x87o\xff\xc9\xed\xf1\x81E21\x00\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 E21\x00\x00\x00\x00\x00\x00\x00SCK0T33NB0\x88\xa2\xe6\xf0', b'\xf1\x87VDHLG17000192DK2xdFffT\xa5VUD$DwT\x86wveVeeD&T\x99\xba\x8f\xff\xcc\x99\xf1\x81E21\x00\x00\x00\x00\x00\x00\x00\xf1\x00bcsh8p54 E21\x00\x00\x00\x00\x00\x00\x00SCK0T33NB0\x88\xa2\xe6\xf0', ], }, CAR.NIRO_EV_DE: { (Ecu.fwdRadar, 0x7D0, None): [ b'\xf1\x00DEev SCC F-CUP 1.00 1.03 96400-Q4100 \xf1\xa01.03', b'\xf1\x00DEev SCC F-CUP 1.00 1.00 99110-Q4000 \xf1\xa01.00', ], (Ecu.esp, 0x7D1, None): [ b'\xf1\xa01.06', b'\xf1\xa01.07', ], (Ecu.eps, 0x7D4, None): [ b'\xf1\x00DE MDPS C 1.00 1.05 56310Q4000\x00 4DEEC105', b'\xf1\x00DE MDPS C 1.00 1.05 56310Q4100\x00 4DEEC105', ], (Ecu.fwdCamera, 0x7C4, None): [ b'\xf1\x00DEE MFC AT USA LHD 1.00 1.03 95740-Q4000 180821', b'\xf1\x00DEE MFC AT EUR LHD 1.00 1.00 99211-Q4000 191211', ], }, CAR.SELTOS_SP2: { (Ecu.fwdRadar, 0x7d0, None): [b'\xf1\x8799110Q5100\xf1\000SP2_ SCC FHCUP 1.01 1.05 99110-Q5100 \xf1\xa01.05',], (Ecu.esp, 0x7d1, None): [ b'\xf1\x8758910-Q5450\xf1\000SP ESC \a 101\031\t\005 58910-Q5450\xf1\xa01.01', b'\xf1\x8758910-Q5450\xf1\000SP ESC \t 101\031\t\005 58910-Q5450\xf1\xa01.01', ], (Ecu.engine, 0x7e0, None): [ b'\xf1\x81616D2051\000\000\000\000\000\000\000\000', b'\001TSP2KNL06F100J0K', ], (Ecu.eps, 0x7d4, None): [b'\xf1\000SP2 MDPS C 1.00 1.04 56300Q5200 ',], (Ecu.fwdCamera, 0x7c4, None): [b'\xf1\000SP2 MFC AT USA LHD 1.00 1.04 99210-Q5000 191114',], (Ecu.transmission, 0x7e1, None): [ b'\xf1\x87CZLUB49370612JF7h\xa8y\x87\x99\xa7hv\x99\x97fv\x88\x87x\x89x\x96O\xff\x88\xff\xff\xff.@\xf1\x816V2C2051\000\000\xf1\0006V2B0_C2\000\0006V2C2051\000\000CSP4N20NS3\000\000\000\000', b'\xf1\x87954A22D200\xf1\x81T01950A1 \xf1\000T0190XBL T01950A1 DSP2T16X4X950NS6\xd30\xa5\xb9', ], }, } CHECKSUM = { "crc8": [CAR.SANTAFE_TM, CAR.SONATA_DN8, CAR.PALISADE_LX2, CAR.SONATA_HEV_DN8, CAR.SELTOS_SP2, CAR.AVANTE_CN7, CAR.SOUL_EV_SK3, CAR.AVANTE_HEV_CN7, CAR.SANTAFE_HEV_TM, CAR.K5_DL3], "6B": [CAR.SORENTO_UM, CAR.GENESIS_DH], } FEATURES = { # Use Cluster for Gear Selection, rather than Transmission "use_cluster_gears": {CAR.AVANTE_AD, CAR.KONA_OS, CAR.I30_PD, CAR.K7_YG, CAR.GRANDEUR_IG, CAR.GRANDEUR_FL_IG}, # Use TCU Message for Gear Selection "use_tcu_gears": {CAR.K5_JF, CAR.SONATA_LF, CAR.VELOSTER_JS, CAR.SONATA_TURBO_LF, CAR.STINGER_CK}, # Use E_GEAR Message for Gear Selection "use_elect_gears": {CAR.SONATA_HEV_DN8, CAR.SONATA_HEV_LF, CAR.KONA_EV_OS, CAR.KONA_HEV_OS, CAR.IONIQ_EV_AE, CAR.IONIQ_HEV_AE, CAR.GRANDEUR_HEV_IG, CAR.GRANDEUR_HEV_FL_IG, CAR.NEXO_FE, CAR.K5_HEV_JF, CAR.K7_HEV_YG, CAR.NIRO_EV_DE, CAR.NIRO_HEV_DE, CAR.SOUL_EV_SK3, CAR.AVANTE_HEV_CN7, CAR.SANTAFE_HEV_TM}, # send LFA MFA message for new HKG models # Insert your car in this if you want turn LFA icon on. # need to add lfa modded cars which are changed from lkas to lfa cam "send_lfahda_mfa": {CAR.GRANDEUR_HEV_FL_IG, CAR.GRANDEUR_FL_IG, CAR.SONATA_DN8, CAR.PALISADE_LX2, CAR.SONATA_HEV_DN8, CAR.SANTAFE_TM, CAR.KONA_EV_OS, CAR.NIRO_EV_DE, CAR.KONA_HEV_OS, CAR.SELTOS_SP2, CAR.SOUL_EV_SK3, CAR.NEXO_FE, CAR.MOHAVE_HM, CAR.STINGER_CK, CAR.AVANTE_CN7, CAR.AVANTE_HEV_CN7, CAR.K5_DL3, CAR.SANTAFE_HEV_TM, CAR.GENESIS_G70_IK}, "send_hda_mfa": {CAR.GRANDEUR_IG, CAR.GRANDEUR_HEV_IG}, # these cars use the FCA11 message for the AEB and FCW signals, all others use SCC12 # Insert your car in this if you see front collision error on your cluster. "use_fca": {CAR.GRANDEUR_HEV_FL_IG, CAR.GRANDEUR_FL_IG, CAR.SONATA_DN8, CAR.AVANTE_CN7, CAR.I30_PD, CAR.PALISADE_LX2, CAR.GENESIS_G70_IK, CAR.GENESIS_G70_2020, CAR.GENESIS_G90_HI, CAR.KONA_HEV_OS, CAR.KONA_EV_OS, CAR.SELTOS_SP2, CAR.MOHAVE_HM, CAR.KIA_FORTE}, } HYBRID_CAR = {CAR.K5_HEV_JF, CAR.IONIQ_HEV_AE, CAR.SONATA_HEV_DN8, CAR.SONATA_HEV_LF, CAR.K7_HEV_YG, CAR.GRANDEUR_HEV_IG, CAR.GRANDEUR_HEV_FL_IG, CAR.NIRO_HEV_DE, CAR.KONA_HEV_OS, CAR.AVANTE_HEV_CN7} EV_CAR = {CAR.IONIQ_EV_AE, CAR.KONA_EV_OS, CAR.NIRO_EV_DE, CAR.NEXO_FE, CAR.SOUL_EV_SK3} if Params().get_bool("UseRadarTrack"): DBC = { # genesis CAR.GENESIS_DH: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GENESIS_G70_IK: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GENESIS_G70_2020: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GENESIS_G80_DH: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GENESIS_G90_HI: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GENESIS_EQ900_HI: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), # hyundai CAR.AVANTE_AD: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.AVANTE_CN7: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.AVANTE_HEV_CN7: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.I30_PD: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SONATA_DN8: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SONATA_HEV_DN8: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SONATA_LF: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SONATA_TURBO_LF: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SONATA_HEV_LF: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.KONA_OS: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.KONA_EV_OS: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.KONA_HEV_OS: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.IONIQ_EV_AE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.IONIQ_HEV_AE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SANTAFE_TM: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.PALISADE_LX2: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.VELOSTER_JS: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GRANDEUR_IG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GRANDEUR_HEV_IG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GRANDEUR_FL_IG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.GRANDEUR_HEV_FL_IG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.TUCSON_TL: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.NEXO_FE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), # kia CAR.KIA_FORTE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K3_BD: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K5_JF: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K5_HEV_JF: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K5_DL3: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SPORTAGE_QL: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SORENTO_UM: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.STINGER_CK: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.NIRO_EV_DE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.NIRO_HEV_DE: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K7_YG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.K7_HEV_YG: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SELTOS_SP2: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.SOUL_EV_SK3: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), CAR.MOHAVE_HM: dbc_dict('hyundai_kia_generic', 'hyundai_kia_mando_front_radar'), } else: DBC = { # genesis CAR.GENESIS_DH: dbc_dict('hyundai_kia_generic', None), CAR.GENESIS_G70_IK: dbc_dict('hyundai_kia_generic', None), CAR.GENESIS_G70_2020: dbc_dict('hyundai_kia_generic', None), # 'hyundai_kia_mando_front_radar'), CAR.GENESIS_G80_DH: dbc_dict('hyundai_kia_generic', None), CAR.GENESIS_G90_HI: dbc_dict('hyundai_kia_generic', None), CAR.GENESIS_EQ900_HI: dbc_dict('hyundai_kia_generic', None), # hyundai CAR.AVANTE_AD: dbc_dict('hyundai_kia_generic', None), CAR.AVANTE_CN7: dbc_dict('hyundai_kia_generic', None), CAR.AVANTE_HEV_CN7: dbc_dict('hyundai_kia_generic', None), CAR.I30_PD: dbc_dict('hyundai_kia_generic', None), CAR.SONATA_DN8: dbc_dict('hyundai_kia_generic', None), CAR.SONATA_HEV_DN8: dbc_dict('hyundai_kia_generic', None), CAR.SONATA_LF: dbc_dict('hyundai_kia_generic', None), CAR.SONATA_TURBO_LF: dbc_dict('hyundai_kia_generic', None), CAR.SONATA_HEV_LF: dbc_dict('hyundai_kia_generic', None), CAR.KONA_OS: dbc_dict('hyundai_kia_generic', None), CAR.KONA_EV_OS: dbc_dict('hyundai_kia_generic', None), CAR.KONA_HEV_OS: dbc_dict('hyundai_kia_generic', None), CAR.IONIQ_EV_AE: dbc_dict('hyundai_kia_generic', None), CAR.IONIQ_HEV_AE: dbc_dict('hyundai_kia_generic', None), CAR.SANTAFE_TM: dbc_dict('hyundai_kia_generic', None), CAR.PALISADE_LX2: dbc_dict('hyundai_kia_generic', None), CAR.VELOSTER_JS: dbc_dict('hyundai_kia_generic', None), CAR.GRANDEUR_IG: dbc_dict('hyundai_kia_generic', None), CAR.GRANDEUR_HEV_IG: dbc_dict('hyundai_kia_generic', None), CAR.GRANDEUR_FL_IG: dbc_dict('hyundai_kia_generic', None), CAR.GRANDEUR_HEV_FL_IG: dbc_dict('hyundai_kia_generic', None), CAR.TUCSON_TL: dbc_dict('hyundai_kia_generic', None), CAR.NEXO_FE: dbc_dict('hyundai_kia_generic', None), # kia CAR.KIA_FORTE: dbc_dict('hyundai_kia_generic', None), CAR.K3_BD: dbc_dict('hyundai_kia_generic', None), CAR.K5_JF: dbc_dict('hyundai_kia_generic', None), CAR.K5_HEV_JF: dbc_dict('hyundai_kia_generic', None), CAR.K5_DL3: dbc_dict('hyundai_kia_generic', None), CAR.SPORTAGE_QL: dbc_dict('hyundai_kia_generic', None), CAR.SORENTO_UM: dbc_dict('hyundai_kia_generic', None), CAR.STINGER_CK: dbc_dict('hyundai_kia_generic', None), CAR.NIRO_EV_DE: dbc_dict('hyundai_kia_generic', None), CAR.NIRO_HEV_DE: dbc_dict('hyundai_kia_generic', None), CAR.K7_YG: dbc_dict('hyundai_kia_generic', None), CAR.K7_HEV_YG: dbc_dict('hyundai_kia_generic', None), CAR.SELTOS_SP2: dbc_dict('hyundai_kia_generic', None), CAR.SOUL_EV_SK3: dbc_dict('hyundai_kia_generic', None), CAR.MOHAVE_HM: dbc_dict('hyundai_kia_generic', None), } STEER_THRESHOLD = int(Params().get("SteerThreshold", encoding="utf8"))

114.93786

901

0.582553

20,214

99,881

2.825022

0.051351

0.035198

0.041923

0.043919

0.83427

0.812083

0.778653

0.756203

0.733719

0.697645

0

0.505264

0.214485

99,881

868

902

115.070277

0.222552

0.006618

0

0.27907

0

0.084455

0.178553

0.078973

0

0.005444

0

1

0.001224

false

0

0.008568

0

0.080783

0.001224

0

null

0

1

0

1

0

1

0

null

0

9

d838705d92ab1411ce7e82cde060426c9d770747

1,396

py

Python

runs/batchshipyard/snake/3d/scripts/visit_database_views.py

mesnardo/FlyingSnake2Cloud

c76d83226327476a17ba244040cc6338a4dbe022

[ "CC-BY-4.0", "BSD-3-Clause" ]

null

runs/batchshipyard/snake/3d/scripts/visit_database_views.py

mesnardo/FlyingSnake2Cloud

c76d83226327476a17ba244040cc6338a4dbe022

[ "CC-BY-4.0", "BSD-3-Clause" ]

null

runs/batchshipyard/snake/3d/scripts/visit_database_views.py

mesnardo/FlyingSnake2Cloud

c76d83226327476a17ba244040cc6338a4dbe022

[ "CC-BY-4.0", "BSD-3-Clause" ]

null

""" List of predefined view attributes. """ def set_view3d_attributes(View3DAtts, name): if name == 'domain': View3DAtts.viewNormal = (-0.31, 0.41, 0.86) View3DAtts.focus = (0, 0, 1.6) View3DAtts.viewUp = (0.24, 0.91, -0.34) View3DAtts.viewAngle = 30 View3DAtts.parallelScale = 21 View3DAtts.nearPlane = -42.1555 View3DAtts.farPlane = 42.1555 View3DAtts.imagePan = (-0.06, -0.014) View3DAtts.imageZoom = 5.56 View3DAtts.perspective = 1 View3DAtts.eyeAngle = 2 View3DAtts.centerOfRotationSet = 0 View3DAtts.centerOfRotation = (0.0146802, 0, 1.6) View3DAtts.axis3DScaleFlag = 0 View3DAtts.axis3DScales = (1, 1, 1) View3DAtts.shear = (0, 0, 1) View3DAtts.windowValid = 1 elif name == 'crop': View3DAtts.viewNormal = (-0.31, 0.41, 0.86) View3DAtts.focus = (0, 0, 1.6) View3DAtts.viewUp = (0.24, 0.91, -0.34) View3DAtts.viewAngle = 30 View3DAtts.parallelScale = 21 View3DAtts.nearPlane = -42.1555 View3DAtts.farPlane = 42.1555 View3DAtts.imagePan = (0.06, -0.014) View3DAtts.imageZoom = 1.2 View3DAtts.perspective = 1 View3DAtts.eyeAngle = 2 View3DAtts.centerOfRotationSet = 0 View3DAtts.centerOfRotation = (0.0146802, 0, 1.6) View3DAtts.axis3DScaleFlag = 0 View3DAtts.axis3DScales = (1, 1, 1) View3DAtts.shear = (0, 0, 1) View3DAtts.windowValid = 1 return

31.727273

53

0.662607

174

1,396

5.304598

0.293103

0.013001

0.056338

0.890574

0

0.161991

0.208453

1,396

43

54

32.465116

0.673303

0.025072

0

0.789474

0

0.007391

0

1

0.026316

false

0

0.052632

0

null

0

1

0

1

0

null

0

7

dc402bcdf48ceca405cf343cbb3b62ba16303ff7

68

py

Python

python/testData/codeInsight/controlflow/assertfalse.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

null

python/testData/codeInsight/controlflow/assertfalse.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

11

2017-02-27T22:35:32.000Z

2021-12-24T08:07:40.000Z

python/testData/codeInsight/controlflow/assertfalse.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

1

2020-11-27T10:36:50.000Z

assert false print("Unreachable") assert False print("Unreachable2")

17

21

0.808824

8

68

6.875

0.625

0.4

0.581818

0

0.015873

0.073529

68

4

21

17

0.857143

0

0.333333

0

0.5

1

0

true

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

f49cd1defcfd9740e4dd22673cf42c7f4ddb4c0e

18,004

py

Python

sdk/python/pulumi_rancher2/project.py

mitchellmaler/pulumi-rancher2

e6ca44b58b5b10c12a4e628e61aa8d98330f0863

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

null

sdk/python/pulumi_rancher2/project.py

mitchellmaler/pulumi-rancher2

e6ca44b58b5b10c12a4e628e61aa8d98330f0863

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

null

sdk/python/pulumi_rancher2/project.py

mitchellmaler/pulumi-rancher2

e6ca44b58b5b10c12a4e628e61aa8d98330f0863

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from . import utilities, tables class Project(pulumi.CustomResource): annotations: pulumi.Output[dict] """ Annotations for Node Pool object (map) """ cluster_id: pulumi.Output[str] """ The cluster id where create project (string) """ container_resource_limit: pulumi.Output[dict] """ Default containers resource limits on project (List maxitem:1) * `limitsCpu` (`str`) - Limit for limits cpu in project (string) * `limitsMemory` (`str`) - Limit for limits memory in project (string) * `requestsCpu` (`str`) - Limit for requests cpu in project (string) * `requestsMemory` (`str`) - Limit for requests memory in project (string) """ description: pulumi.Output[str] """ A project description (string) """ enable_project_monitoring: pulumi.Output[bool] """ Enable built-in project monitoring. Default `false` (bool) """ labels: pulumi.Output[dict] """ Labels for Node Pool object (map) """ name: pulumi.Output[str] """ The name of the project (string) """ pod_security_policy_template_id: pulumi.Output[str] """ Default Pod Security Policy ID for the project (string) """ project_monitoring_input: pulumi.Output[dict] """ Project monitoring config. Any parameter defined in [rancher-monitoring charts](https://github.com/rancher/system-charts/tree/dev/charts/rancher-monitoring) could be configured (list maxitems:1) * `answers` (`dict`) - Key/value answers for monitor input (map) """ resource_quota: pulumi.Output[dict] """ Resource quota for project. Rancher v2.1.x or higher (list maxitems:1) * `namespaceDefaultLimit` (`dict`) - Default resource quota limit for namespaces in project (list maxitems:1) * `configMaps` (`str`) - Limit for config maps in project (string) * `limitsCpu` (`str`) - Limit for limits cpu in project (string) * `limitsMemory` (`str`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`str`) - Limit for persistent volume claims in project (string) * `pods` (`str`) - Limit for pods in project (string) * `replicationControllers` (`str`) - Limit for replication controllers in project (string) * `requestsCpu` (`str`) - Limit for requests cpu in project (string) * `requestsMemory` (`str`) - Limit for requests memory in project (string) * `requestsStorage` (`str`) - Limit for requests storage in project (string) * `secrets` (`str`) - Limit for secrets in project (string) * `services` (`str`) * `servicesLoadBalancers` (`str`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`str`) - Limit for services node ports in project (string) * `projectLimit` (`dict`) - Resource quota limit for project (list maxitems:1) * `configMaps` (`str`) - Limit for config maps in project (string) * `limitsCpu` (`str`) - Limit for limits cpu in project (string) * `limitsMemory` (`str`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`str`) - Limit for persistent volume claims in project (string) * `pods` (`str`) - Limit for pods in project (string) * `replicationControllers` (`str`) - Limit for replication controllers in project (string) * `requestsCpu` (`str`) - Limit for requests cpu in project (string) * `requestsMemory` (`str`) - Limit for requests memory in project (string) * `requestsStorage` (`str`) - Limit for requests storage in project (string) * `secrets` (`str`) - Limit for secrets in project (string) * `services` (`str`) * `servicesLoadBalancers` (`str`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`str`) - Limit for services node ports in project (string) """ wait_for_cluster: pulumi.Output[bool] """ Wait for cluster becomes active. Default `false` (bool) """ def __init__(__self__, resource_name, opts=None, annotations=None, cluster_id=None, container_resource_limit=None, description=None, enable_project_monitoring=None, labels=None, name=None, pod_security_policy_template_id=None, project_monitoring_input=None, resource_quota=None, wait_for_cluster=None, __props__=None, __name__=None, __opts__=None): """ Provides a Rancher v2 Project resource. This can be used to create projects for Rancher v2 environments and retrieve their information. > This content is derived from https://github.com/terraform-providers/terraform-provider-rancher2/blob/master/website/docs/r/project.html.markdown. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[dict] annotations: Annotations for Node Pool object (map) :param pulumi.Input[str] cluster_id: The cluster id where create project (string) :param pulumi.Input[dict] container_resource_limit: Default containers resource limits on project (List maxitem:1) :param pulumi.Input[str] description: A project description (string) :param pulumi.Input[bool] enable_project_monitoring: Enable built-in project monitoring. Default `false` (bool) :param pulumi.Input[dict] labels: Labels for Node Pool object (map) :param pulumi.Input[str] name: The name of the project (string) :param pulumi.Input[str] pod_security_policy_template_id: Default Pod Security Policy ID for the project (string) :param pulumi.Input[dict] project_monitoring_input: Project monitoring config. Any parameter defined in [rancher-monitoring charts](https://github.com/rancher/system-charts/tree/dev/charts/rancher-monitoring) could be configured (list maxitems:1) :param pulumi.Input[dict] resource_quota: Resource quota for project. Rancher v2.1.x or higher (list maxitems:1) :param pulumi.Input[bool] wait_for_cluster: Wait for cluster becomes active. Default `false` (bool) The **container_resource_limit** object supports the following: * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) The **project_monitoring_input** object supports the following: * `answers` (`pulumi.Input[dict]`) - Key/value answers for monitor input (map) The **resource_quota** object supports the following: * `namespaceDefaultLimit` (`pulumi.Input[dict]`) - Default resource quota limit for namespaces in project (list maxitems:1) * `configMaps` (`pulumi.Input[str]`) - Limit for config maps in project (string) * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`pulumi.Input[str]`) - Limit for persistent volume claims in project (string) * `pods` (`pulumi.Input[str]`) - Limit for pods in project (string) * `replicationControllers` (`pulumi.Input[str]`) - Limit for replication controllers in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) * `requestsStorage` (`pulumi.Input[str]`) - Limit for requests storage in project (string) * `secrets` (`pulumi.Input[str]`) - Limit for secrets in project (string) * `services` (`pulumi.Input[str]`) * `servicesLoadBalancers` (`pulumi.Input[str]`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`pulumi.Input[str]`) - Limit for services node ports in project (string) * `projectLimit` (`pulumi.Input[dict]`) - Resource quota limit for project (list maxitems:1) * `configMaps` (`pulumi.Input[str]`) - Limit for config maps in project (string) * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`pulumi.Input[str]`) - Limit for persistent volume claims in project (string) * `pods` (`pulumi.Input[str]`) - Limit for pods in project (string) * `replicationControllers` (`pulumi.Input[str]`) - Limit for replication controllers in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) * `requestsStorage` (`pulumi.Input[str]`) - Limit for requests storage in project (string) * `secrets` (`pulumi.Input[str]`) - Limit for secrets in project (string) * `services` (`pulumi.Input[str]`) * `servicesLoadBalancers` (`pulumi.Input[str]`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`pulumi.Input[str]`) - Limit for services node ports in project (string) """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['annotations'] = annotations if cluster_id is None: raise TypeError("Missing required property 'cluster_id'") __props__['cluster_id'] = cluster_id __props__['container_resource_limit'] = container_resource_limit __props__['description'] = description __props__['enable_project_monitoring'] = enable_project_monitoring __props__['labels'] = labels __props__['name'] = name __props__['pod_security_policy_template_id'] = pod_security_policy_template_id __props__['project_monitoring_input'] = project_monitoring_input __props__['resource_quota'] = resource_quota __props__['wait_for_cluster'] = wait_for_cluster super(Project, __self__).__init__( 'rancher2:index/project:Project', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, annotations=None, cluster_id=None, container_resource_limit=None, description=None, enable_project_monitoring=None, labels=None, name=None, pod_security_policy_template_id=None, project_monitoring_input=None, resource_quota=None, wait_for_cluster=None): """ Get an existing Project resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[dict] annotations: Annotations for Node Pool object (map) :param pulumi.Input[str] cluster_id: The cluster id where create project (string) :param pulumi.Input[dict] container_resource_limit: Default containers resource limits on project (List maxitem:1) :param pulumi.Input[str] description: A project description (string) :param pulumi.Input[bool] enable_project_monitoring: Enable built-in project monitoring. Default `false` (bool) :param pulumi.Input[dict] labels: Labels for Node Pool object (map) :param pulumi.Input[str] name: The name of the project (string) :param pulumi.Input[str] pod_security_policy_template_id: Default Pod Security Policy ID for the project (string) :param pulumi.Input[dict] project_monitoring_input: Project monitoring config. Any parameter defined in [rancher-monitoring charts](https://github.com/rancher/system-charts/tree/dev/charts/rancher-monitoring) could be configured (list maxitems:1) :param pulumi.Input[dict] resource_quota: Resource quota for project. Rancher v2.1.x or higher (list maxitems:1) :param pulumi.Input[bool] wait_for_cluster: Wait for cluster becomes active. Default `false` (bool) The **container_resource_limit** object supports the following: * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) The **project_monitoring_input** object supports the following: * `answers` (`pulumi.Input[dict]`) - Key/value answers for monitor input (map) The **resource_quota** object supports the following: * `namespaceDefaultLimit` (`pulumi.Input[dict]`) - Default resource quota limit for namespaces in project (list maxitems:1) * `configMaps` (`pulumi.Input[str]`) - Limit for config maps in project (string) * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`pulumi.Input[str]`) - Limit for persistent volume claims in project (string) * `pods` (`pulumi.Input[str]`) - Limit for pods in project (string) * `replicationControllers` (`pulumi.Input[str]`) - Limit for replication controllers in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) * `requestsStorage` (`pulumi.Input[str]`) - Limit for requests storage in project (string) * `secrets` (`pulumi.Input[str]`) - Limit for secrets in project (string) * `services` (`pulumi.Input[str]`) * `servicesLoadBalancers` (`pulumi.Input[str]`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`pulumi.Input[str]`) - Limit for services node ports in project (string) * `projectLimit` (`pulumi.Input[dict]`) - Resource quota limit for project (list maxitems:1) * `configMaps` (`pulumi.Input[str]`) - Limit for config maps in project (string) * `limitsCpu` (`pulumi.Input[str]`) - Limit for limits cpu in project (string) * `limitsMemory` (`pulumi.Input[str]`) - Limit for limits memory in project (string) * `persistentVolumeClaims` (`pulumi.Input[str]`) - Limit for persistent volume claims in project (string) * `pods` (`pulumi.Input[str]`) - Limit for pods in project (string) * `replicationControllers` (`pulumi.Input[str]`) - Limit for replication controllers in project (string) * `requestsCpu` (`pulumi.Input[str]`) - Limit for requests cpu in project (string) * `requestsMemory` (`pulumi.Input[str]`) - Limit for requests memory in project (string) * `requestsStorage` (`pulumi.Input[str]`) - Limit for requests storage in project (string) * `secrets` (`pulumi.Input[str]`) - Limit for secrets in project (string) * `services` (`pulumi.Input[str]`) * `servicesLoadBalancers` (`pulumi.Input[str]`) - Limit for services load balancers in project (string) * `servicesNodePorts` (`pulumi.Input[str]`) - Limit for services node ports in project (string) """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["annotations"] = annotations __props__["cluster_id"] = cluster_id __props__["container_resource_limit"] = container_resource_limit __props__["description"] = description __props__["enable_project_monitoring"] = enable_project_monitoring __props__["labels"] = labels __props__["name"] = name __props__["pod_security_policy_template_id"] = pod_security_policy_template_id __props__["project_monitoring_input"] = project_monitoring_input __props__["resource_quota"] = resource_quota __props__["wait_for_cluster"] = wait_for_cluster return Project(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

64.530466

352

0.673906

2,108

18,004

5.600095

0.102467

0.102414

0.078272

0.090131

0.843456

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null

0

1

0

1

0

null

0

7

f4ba0bfd41f7be174d9c3b88ba26f3cd4e291b58

212

py

Python

dpckan/tests/__init__.py

danielfeloiola/dpckan

9aea7aa1d7137dca5adf7ad95d8a6d148ab337e5

[ "MIT" ]

6

2021-07-04T08:53:12.000Z

2022-01-27T21:53:05.000Z

dpckan/tests/__init__.py

danielfeloiola/dpckan

9aea7aa1d7137dca5adf7ad95d8a6d148ab337e5

[ "MIT" ]

81

2021-06-22T17:01:23.000Z

2022-01-31T20:41:45.000Z

dpckan/tests/__init__.py

danielfeloiola/dpckan

9aea7aa1d7137dca5adf7ad95d8a6d148ab337e5

[ "MIT" ]

2

2021-10-07T14:42:36.000Z

2022-01-27T14:43:48.000Z

from dpckan.tests.dpckan_test import clone_online_repo from dpckan.tests.dpckan_test import get_file_name from dpckan.tests.dpckan_test import get_file_path from dpckan.tests.dpckan_test import get_ckan_instance

42.4

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0.886792

36

212

4.888889

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0.227273

0.340909

0.477273

0.801136

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0.431818

0

0.075472

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1

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true

0

1

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1

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null

1

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1

0

null

0

1

0

1

0

8

761b3c4ef9f0a778f094b8c8be79a8f4f964a233

2,586

py

Python

csc121/lab6/chapter9.py

rbranford/csc121

52aee4940fb01778670c25fb6180a8641e14949e

[ "CC0-1.0" ]

null

csc121/lab6/chapter9.py

rbranford/csc121

52aee4940fb01778670c25fb6180a8641e14949e

[ "CC0-1.0" ]

null

csc121/lab6/chapter9.py

rbranford/csc121

52aee4940fb01778670c25fb6180a8641e14949e

[ "CC0-1.0" ]

null

def print_10_stars(): for _ in range(10): print('*', end=' ') print() def print_5_stars(): for _ in range(5): print('*', end=' ') print() def print_20_stars(): for _ in range(20): print('*', end=' ') print() def problem_2(): print_10_stars() print_5_stars() print_20_stars() def problem_3(): for _ in range(10): for _ in range(10): print('*', end=' ') print() def problem_4(): for _ in range(10): for _ in range(5): print('*', end=' ') print() def problem_5(): for _ in range(5): for _ in range(20): print('*', end=' ') print() def problem_6(): for _ in range(10): for i in range(10): print(i, end=' ') print() def problem_7(): for i in range(10): for _ in range(10): print(i, end=' ') print() def problem_8(): for i in range(10): for j in range(i+1): print(j, end=' ') print() def problem_9(): for i in range(10): for j in range(i): print(' ', end=' ') for j in range(10-i): print(j, end=' ') print() def problem_10(): for i in range(1, 10): for j in range(1, 10): if i*j < 10: print(' ', end=' ') print(i*j, end=' ') print() def problem_11(): for i in range (10): for j in range(10-i): print (' ', end=' ') for j in range(1, i+1): print(j, end=' ') for j in range(i-1, 0, -1): print(j, end=' ') print() def problem_12(): for i in range(10): for j in range(10-i): print (' ', end=' ') for j in range(1,i+1): print (j, end=' ') for j in range(i-1,0,-1): print (j, end=' ') print() for i in range(10): for j in range(i+2): print (' ', end=' ') for j in range(1,9-i): print (j, end=' ') print() def problem_13(): for i in range(10): for j in range(10-i): print (' ', end=' ') for j in range(1, i+1): print (j, end=' ') for j in range(i-1,0,-1): print (j, end=' ') print() for i in range(10): for j in range(i+2): print (' ', end=' ') for j in range(1, 9-i): print (j, end=' ') for j in range(7-i, 0, -1): print (j, end=' ') print()

19.892308

35

0.41686

361

2,586

2.889197

0.072022

0.261745

0.16395

0.189837

0.882071

0.799616

0.780441

0.663471

0.537872

0.407478

0

0.068829

0.4157

2,586

129

36

20.046512

0.621443

0

0.752475

0

0.015486

0

1

0.148515

false

0

0.148515

0.485149

0

null

1

0

1

0

1

0

null

0

1

0

7

76275a557d6b3344e79bbeb0277cef0f9f902b41

249

py

Python

v3/Libraries/builtin/replace/inline replace characters.py

TheShellLand/python

a35e9b32bec3a3ff03d6f0f4c2c2cc891180e516

[ "MIT" ]

null

v3/Libraries/builtin/replace/inline replace characters.py

TheShellLand/python

a35e9b32bec3a3ff03d6f0f4c2c2cc891180e516

[ "MIT" ]

1

2021-06-01T22:50:19.000Z

v3/Libraries/builtin/replace/inline replace characters.py

TheShellLand/python

a35e9b32bec3a3ff03d6f0f4c2c2cc891180e516

[ "MIT" ]

null

#!/usr/bin/env python2.7 # -*- coding: utf8 -*- # '0c a8 f0 d6 02 00 00 00 00 d0 1c d1 10 d2 00 d3 00 d7 01 d4 78 20 ff'.replace(' ', '').decode('hex') print('0c a8 f0 d6 02 00 00 00 00 d0 1c d1 10 d2 00 d3 00 d7 01 d4 78 20 ff'.replace(' ', ''))

35.571429

103

0.590361

58

249

2.534483

0.5

0.163265

0.108844

0.721088

0

0.365079

0.240964

249

6

104

41.5

0.412698

0.586345

0

1

0.69

0

1

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

1

0

1

0

null

1

0

1

0

1

0

13

5202ba9389d59e26e377cfcc4b11bff8cde3a1a2

10,804

py

Python

integration/data/test_ha.py

ywei88/longhorn-engine

552d4b46cb8ae88f202b5697afc2d4590dc9f1cd

[ "Apache-2.0" ]

null

integration/data/test_ha.py

ywei88/longhorn-engine

552d4b46cb8ae88f202b5697afc2d4590dc9f1cd

[ "Apache-2.0" ]

null

integration/data/test_ha.py

ywei88/longhorn-engine

552d4b46cb8ae88f202b5697afc2d4590dc9f1cd

[ "Apache-2.0" ]

null

import cmd import common from common import grpc_controller, grpc_replica1, grpc_replica2 # NOQA from common import grpc_backing_replica1, grpc_backing_replica2 # NOQA from common import prepare_backup_dir, BACKUP_DIR # NOQA from common import open_replica, get_blockdev, cleanup_replica from common import verify_read, verify_data, verify_async, VOLUME_HEAD from snapshot_tree import snapshot_tree_build, snapshot_tree_verify def test_ha_single_replica_failure(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA open_replica(grpc_replica1) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, common.REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, data) cleanup_replica(grpc_replica2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "ERR") verify_read(dev, data_offset, data) def test_ha_single_replica_rebuild(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA open_replica(grpc_replica1) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, common.REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, data) # Cleanup replica2 cleanup_replica(grpc_replica2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "ERR") verify_read(dev, data_offset, data) grpc_controller.replica_delete(replicas[1].address) # Rebuild replica2 open_replica(grpc_replica2) cmd.add_replica(common.REPLICA2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "RW") verify_read(dev, data_offset, data) # WORKAROUND for unable to remove the parent of volume head newsnap = cmd.snapshot_create() info = cmd.snapshot_info() assert len(info) == 3 sysnap = info[newsnap]["parent"] assert info[sysnap]["parent"] == "" assert newsnap in info[sysnap]["children"] assert info[sysnap]["usercreated"] is False assert info[sysnap]["removed"] is False cmd.snapshot_purge() info = cmd.snapshot_info() assert len(info) == 2 assert info[newsnap] is not None assert info[VOLUME_HEAD] is not None def test_ha_double_replica_rebuild(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA open_replica(grpc_replica1) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, common.REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() data1 = common.random_string(128) data1_offset = 1024 verify_data(dev, data1_offset, data1) # Close replica2 r2 = grpc_replica2.replica_get() assert r2.revisionCounter == 1 grpc_replica2.replica_close() verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "ERR") verify_read(dev, data1_offset, data1) data2 = common.random_string(128) data2_offset = 512 verify_data(dev, data2_offset, data2) # Close replica1 r1 = grpc_replica1.replica_get() assert r1.revisionCounter == 12 # 1 + 10 + 1 grpc_replica1.replica_close() # Restart volume common.cleanup_controller(grpc_controller) replicas = grpc_controller.replica_list() assert len(replicas) == 0 # NOTE the order is reversed here v = grpc_controller.volume_start(replicas=[ common.REPLICA2, common.REPLICA1 ]) assert v.replicaCount == 2 # replica2 is out because of lower revision counter replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "ERR" assert replicas[1].mode == "RW" verify_read(dev, data1_offset, data1) verify_read(dev, data2_offset, data2) # Rebuild replica2 r2 = grpc_replica2.replica_get() assert r2.revisionCounter == 1 grpc_replica2.replica_close() grpc_controller.replica_delete(replicas[0].address) cmd.add_replica(common.REPLICA2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "RW") verify_read(dev, data1_offset, data1) verify_read(dev, data2_offset, data2) r1 = grpc_replica1.replica_get() r2 = grpc_replica2.replica_get() assert r1.revisionCounter == 22 # 1 + 10 + 1 + 10 assert r2.revisionCounter == 22 # must be in sync with r1 def test_ha_revision_counter_consistency(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA open_replica(grpc_replica1) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, common.REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() common.verify_async(dev, 10, 128, 100) r1 = grpc_replica1.replica_get() r2 = grpc_replica2.replica_get() # kernel can merge requests so backend may not receive 1000 writes assert r1.revisionCounter > 0 assert r1.revisionCounter == r2.revisionCounter def test_snapshot_tree_rebuild(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA offset = 0 length = 128 open_replica(grpc_replica1) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, common.REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() snap, snap_data = snapshot_tree_build(dev, offset, length) data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, data) # Cleanup replica2 cleanup_replica(grpc_replica2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "ERR") verify_read(dev, data_offset, data) grpc_controller.replica_delete(replicas[1].address) # Rebuild replica2 open_replica(grpc_replica2) cmd.add_replica(common.REPLICA2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "RW") snapshot_tree_verify(dev, offset, length, snap, snap_data) def test_ha_single_backing_replica_rebuild(grpc_controller, # NOQA grpc_backing_replica1, # NOQA grpc_backing_replica2): # NOQA prepare_backup_dir(BACKUP_DIR) open_replica(grpc_backing_replica1) open_replica(grpc_backing_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.BACKED_REPLICA1, common.BACKED_REPLICA2 ]) assert v.replicaCount == 2 replicas = grpc_controller.replica_list() assert len(replicas) == 2 assert replicas[0].mode == "RW" assert replicas[1].mode == "RW" dev = get_blockdev() data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, data) # Cleanup replica2 cleanup_replica(grpc_backing_replica2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "ERR") verify_read(dev, data_offset, data) grpc_controller.replica_delete(replicas[1].address) # Rebuild replica2 open_replica(grpc_backing_replica2) cmd.add_replica(common.BACKED_REPLICA2) verify_async(dev, 10, 128, 1) common.verify_replica_state(grpc_controller, 1, "RW") verify_read(dev, data_offset, data) # WORKAROUND for unable to remove the parent of volume head newsnap = cmd.snapshot_create() info = cmd.snapshot_info() assert len(info) == 3 sysnap = info[newsnap]["parent"] assert info[sysnap]["parent"] == "" assert newsnap in info[sysnap]["children"] assert info[sysnap]["usercreated"] is False assert info[sysnap]["removed"] is False cmd.snapshot_purge() info = cmd.snapshot_info() assert len(info) == 2 assert info[newsnap] is not None assert info[VOLUME_HEAD] is not None def test_ha_remove_extra_disks(grpc_controller, # NOQA grpc_replica1, grpc_replica2): # NOQA prepare_backup_dir(BACKUP_DIR) open_replica(grpc_replica1) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA1, ]) assert v.replicaCount == 1 replicas = grpc_controller.replica_list() assert len(replicas) == 1 assert replicas[0].mode == "RW" dev = get_blockdev() wasted_data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, wasted_data) # now replica1 contains extra data in a snapshot cmd.snapshot_create() common.cleanup_controller(grpc_controller) open_replica(grpc_replica2) replicas = grpc_controller.replica_list() assert len(replicas) == 0 v = grpc_controller.volume_start(replicas=[ common.REPLICA2, ]) assert v.replicaCount == 1 replicas = grpc_controller.replica_list() assert len(replicas) == 1 assert replicas[0].mode == "RW" dev = get_blockdev() data = common.random_string(128) data_offset = 1024 verify_data(dev, data_offset, data) r1 = grpc_replica1.replica_reload() print(r1) cmd.add_replica(common.REPLICA1) verify_data(dev, data_offset, data)

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521945c2757073bdf5c69a830c1b5b28c25afe20

38,049

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Python

tests/test_blobxfer_models_upload.py

temporaer/blobxfer

8602006192c0f8f7bb078e3d6da20396c07f302a

[ "MIT" ]

null

tests/test_blobxfer_models_upload.py

temporaer/blobxfer

8602006192c0f8f7bb078e3d6da20396c07f302a

[ "MIT" ]

null

tests/test_blobxfer_models_upload.py

temporaer/blobxfer

8602006192c0f8f7bb078e3d6da20396c07f302a

[ "MIT" ]

null

# coding=utf-8 """Tests for models upload""" # stdlib imports import hashlib try: import unittest.mock as mock except ImportError: # noqa import mock try: import pathlib2 as pathlib except ImportError: # noqa import pathlib # non-stdlib imports import bitstring import pytest # local imports import blobxfer.models.azure as azmodels import blobxfer.models.metadata as metadata import blobxfer.models.options as options import blobxfer.operations.azure as azops import blobxfer.util as util # module under test import blobxfer.models.upload as upload def test_vectorediodistributionmode(): a = upload.VectoredIoDistributionMode('stripe') assert a == upload.VectoredIoDistributionMode.Stripe assert str(a) == 'stripe' def test_localpath(tmpdir): tmpdir.join('a').write('zz') pp = pathlib.Path(str(tmpdir)) rp = pathlib.Path('a') file = pp / rp stat = file.stat() lp = upload.LocalPath(pp, rp, use_stdin=True, view=None) assert lp.absolute_path == file assert lp.size == 0 assert lp.total_size == 0 assert lp.lmt == 0 assert lp.mode.replace('o', '') == '00' assert lp.uid == 0 assert lp.gid == 0 lp = upload.LocalPath(pp, rp, use_stdin=False, view=None) assert lp.absolute_path == file assert lp.size == stat.st_size assert lp.total_size == stat.st_size assert lp.lmt == stat.st_mtime assert lp.mode.replace('o', '') == str(oct(stat.st_mode)).replace('o', '') assert lp.uid == stat.st_uid assert lp.gid == stat.st_gid lpview = upload.LocalPathView( fd_start=1, fd_end=2, slice_num=1, mode=upload.VectoredIoDistributionMode.Stripe, total_slices=2, next=None, ) lp = upload.LocalPath(pp, rp, use_stdin=False, view=lpview) assert lp.absolute_path == file assert lp.size == 1 assert lp.total_size == stat.st_size assert lp.lmt == stat.st_mtime assert lp.mode.replace('o', '') == str(oct(stat.st_mode)).replace('o', '') assert lp.uid == stat.st_uid assert lp.gid == stat.st_gid def _resolve_pypath(path): return str(pathlib.Path(str(path)).resolve()) def test_localsourcepaths_files(tmpdir): tmpdir.mkdir('abc') tmpdir.join('moo.cow').write('z') abcpath = tmpdir.join('abc') abcpath.join('hello.txt').write('hello') abcpath.join('blah.x').write('x') abcpath.join('blah.y').write('x') abcpath.join('blah.z').write('x') abcpath.mkdir('def') defpath = abcpath.join('def') defpath.join('world.txt').write('world') defpath.join('moo.cow').write('y') a = upload.LocalSourcePath() a.add_includes('**') a.add_includes('*.txt') a.add_includes(('moo.cow', '*blah*')) with pytest.raises(ValueError): a.add_includes('**/**/*') a.add_excludes('**') a.add_excludes('**/blah.x') with pytest.raises(ValueError): a.add_excludes('**/**/blah.x') a.add_excludes(['world.txt']) a.add_path(str(tmpdir)) a_set = set() for file in a.files(True): sfile = str(file.parent_path / file.relative_path) a_set.add(sfile) assert len(a._include) == 3 assert len(a._exclude) == 2 assert not a.can_rename() assert len(a.paths) == 1 assert _resolve_pypath(abcpath.join('blah.x')) in a_set assert _resolve_pypath(defpath.join('world.txt')) in a_set assert _resolve_pypath(defpath.join('moo.cow')) not in a_set b = upload.LocalSourcePath() b.add_includes(['moo.cow', '*blah*']) b.add_includes('*.txt') b.add_excludes(('world.txt',)) b.add_excludes('**/blah.x') b.add_paths([pathlib.Path(str(tmpdir))]) for file in a.files(True): sfile = str(file.parent_path / file.relative_path) assert sfile in a_set assert upload.LocalSourcePath.is_stdin('-') assert upload.LocalSourcePath.is_stdin('/dev/stdin') assert not upload.LocalSourcePath.is_stdin('/') a = upload.LocalSourcePath() a.add_includes('z') a.add_path(str(tmpdir) + '/abc/hello.txt') a_set = set() for file in a.files(True): sfile = str(file.parent_path / file.relative_path) a_set.add(sfile) assert len(a_set) == 0 c = upload.LocalSourcePath() c.add_path('-') for file in c.files(False): assert file.use_stdin d = upload.LocalSourcePath() d.add_path(str(tmpdir.join('moo.cow'))) i = 0 for file in d.files(True): assert str(file.parent_path.absolute()) == str(tmpdir) assert str(file.relative_path) == 'moo.cow' assert not file.use_stdin i += 1 assert i == 1 tmpdir.join('moo.cow2').ensure(file=True) d.add_path(str(tmpdir.join('moo.cow2'))) i = 0 for file in d.files(True): i += 1 assert i == 2 def test_specification(tmpdir): lsp = upload.LocalSourcePath() lsp.add_paths(['-', '/dev/stdin']) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=4194304, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=0, overwrite=True, recursive=True, rename=True, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control='cc', content_type='ct', lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) lsp = upload.LocalSourcePath() lsp.add_path(str(tmpdir)) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=4194304, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=0, overwrite=True, recursive=True, rename=True, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control='cc', content_type='ct', lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) lsp = upload.LocalSourcePath() lsp.add_path(str(tmpdir)) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=-1, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=0, overwrite=True, recursive=True, rename=False, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control='cc', content_type='ct', lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES + 1, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=0, overwrite=True, recursive=True, rename=False, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control='cc', content_type='ct', lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=4194304, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=-1, overwrite=True, recursive=True, rename=False, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control='cc', content_type='ct', lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) with pytest.raises(ValueError): upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=4194304, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=upload._MAX_BLOCK_BLOB_ONESHOT_BYTES + 1, overwrite=True, recursive=True, rename=False, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control=None, content_type=None, lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) spec = upload.Specification( upload_options=options.Upload( access_tier=None, chunk_size_bytes=4194304, delete_extraneous_destination=False, delete_only=False, mode=azmodels.StorageModes.Auto, one_shot_bytes=0, overwrite=True, recursive=True, rename=False, rsa_public_key=None, stdin_as_page_blob_size=0, store_file_properties=options.FileProperties( attributes=True, cache_control=None, content_type=None, lmt=None, md5=True, ), strip_components=0, vectored_io=None, ), skip_on_options=options.SkipOn( filesize_match=True, lmt_ge=False, md5_match=True, ), local_source_path=lsp, ) spec.add_azure_destination_path(azops.DestinationPath()) assert len(spec.destinations) == 1 def test_descriptor(tmpdir): size = 32 tmpdir.join('a').write('z' * size) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 8 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = False opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._size = size ase._encryption = None ase2 = azmodels.StorageEntity('cont') ase2._mode = azmodels.StorageModes.Block ase2._name = 'name2' ase2._size = size ase2._encryption = None ase.replica_targets = [ase2] ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud.hmac is None assert ud.md5 is None assert ud._outstanding_ops == 4 * 2 assert ud._completed_chunks is not None assert ud._md5_cache is not None assert ud._replica_counters is not None assert ud.entity == ase assert not ud.must_compute_md5 assert not ud.all_operations_completed assert ud.last_block_num == -1 assert ud.is_resumable assert not ud.remote_is_file assert not ud.remote_is_page_blob assert not ud.remote_is_append_blob assert not ud.is_one_shot_block_blob assert ud.requires_put_block_list assert not ud.requires_non_encrypted_md5_put assert not ud.requires_set_file_properties_md5 assert not ud.requires_access_tier_set assert ud.requires_resize() == (False, ud._offset) # test sym key ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._size = size ase._encryption = mock.MagicMock() opts.rsa_public_key = None with pytest.raises(RuntimeError): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) def test_descriptor_complete_offset_upload(tmpdir): tmpdir.join('a').write('z' * 32) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 16 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._size = 32 ase._encryption = None ase.replica_targets = [ase] ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._md5_cache[0] = 'md50' ud._md5_cache[1] = 'md51' ud.complete_offset_upload(0) assert ud._outstanding_ops == 3 assert ud._replica_counters[0] == 0 ud.complete_offset_upload(1) assert ud._outstanding_ops == 2 assert ud._replica_counters[1] == 0 # fill md5 cache with junk to trigger gc on next complete for i in range(-30, -1): ud._md5_cache[i] = '' ud.complete_offset_upload(0) assert ud._outstanding_ops == 1 assert 0 not in ud._replica_counters assert len(ud._md5_cache) == 2 ud.complete_offset_upload(1) assert ud._outstanding_ops == 0 assert 1 not in ud._replica_counters assert len(ud._md5_cache) == 0 def test_descriptor_hmac_data(tmpdir): tmpdir.join('a').write('z' * 32) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 16 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._size = 32 ase._encryption = mock.MagicMock() ase._encryption.symmetric_key = 'abc' ase.replica_targets = [ase] ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud.hmac is not None ud.hmac_data(b'\0') def test_descriptor_initialize_encryption(tmpdir): tmpdir.join('a').write('z' * 32) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 16 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = 'abc' ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._size = 32 ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud.hmac is not None assert ud.entity.is_encrypted def test_descriptor_compute_remote_size(tmpdir): tmpdir.join('a').write('z' * 32) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) # encrypted remote size with replica opts = mock.MagicMock() opts.chunk_size_bytes = 16 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = 'abc' ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = mock.MagicMock() ase._encryption.symmetric_key = 'abc' ase2 = azmodels.StorageEntity('cont') ase2._mode = azmodels.StorageModes.Block ase2._name = 'name2' ase.replica_targets = [ase2] ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._compute_remote_size(opts) assert ud.entity.size == 48 for rt in ase.replica_targets: assert rt.size == ud.entity.size # remote size opts = mock.MagicMock() opts.chunk_size_bytes = 16 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._compute_remote_size(opts) assert ud.entity.size == 32 # remote size of zero tmpdir.join('b').ensure(file=True) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('b')) ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._compute_remote_size(opts) assert ud.entity.size == 0 # stdin as page, resize lp = upload.LocalPath(pathlib.Path('-'), pathlib.Path('-'), use_stdin=True) opts.stdin_as_page_blob_size = 0 ase._mode = azmodels.StorageModes.Page ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._compute_remote_size(opts) assert ud.entity.size == upload._MAX_PAGE_BLOB_SIZE assert ud._needs_resize # stdin as page, no resize lp = upload.LocalPath(pathlib.Path('-'), pathlib.Path('-'), use_stdin=True) opts.stdin_as_page_blob_size = 32 ase._mode = azmodels.StorageModes.Page ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._compute_remote_size(opts) assert ud.entity.size == 32 assert not ud._needs_resize def test_descriptor_adjust_chunk_size(tmpdir): tmpdir.join('a').ensure(file=True) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 0 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 0 with mock.patch('blobxfer.models.upload._DEFAULT_AUTO_CHUNKSIZE_BYTES', 1): with mock.patch( 'blobxfer.models.upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES', 3): with mock.patch('blobxfer.models.upload._MAX_NUM_CHUNKS', 2): tmpdir.join('a').write('z' * 4) lp = upload.LocalPath( pathlib.Path(str(tmpdir)), pathlib.Path('a')) ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 2 lp = upload.LocalPath( pathlib.Path(str(tmpdir)), pathlib.Path('-'), use_stdin=True) ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES tmpdir.join('a').write('z' * 32) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Page ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 32 ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Append ase._name = 'name' ase._encryption = None opts.chunk_size_bytes = upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES + 1 with mock.patch( 'blobxfer.models.upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES', 4): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 4 ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None opts.chunk_size_bytes = 32 opts.one_shot_bytes = 32 ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 32 opts.one_shot_bytes = 31 with mock.patch( 'blobxfer.models.upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES', 4): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 4 ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.File ase._name = 'name' ase._encryption = None opts.chunk_size_bytes = upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES + 1 with mock.patch( 'blobxfer.models.upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES', 4): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 4 ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Page ase._name = 'name' ase._encryption = None opts.chunk_size_bytes = upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES + 1 with mock.patch( 'blobxfer.models.upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES', 4): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) assert ud._chunk_size == 4 with mock.patch('blobxfer.models.upload._MAX_PAGE_BLOB_SIZE', 4): with pytest.raises(RuntimeError): upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) def test_compute_total_chunks(tmpdir): tmpdir.join('a').ensure(file=True) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 0 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud.entity.size = upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES with pytest.raises(RuntimeError): ud._compute_total_chunks(1) ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud.entity.size = upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES ud._chunk_size = upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES with pytest.raises(RuntimeError): ud._compute_total_chunks(1) ase._mode = azmodels.StorageModes.Append ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud.entity.size = upload._MAX_BLOCK_BLOB_CHUNKSIZE_BYTES ud._chunk_size = upload._MAX_NONBLOCK_BLOB_CHUNKSIZE_BYTES with pytest.raises(RuntimeError): ud._compute_total_chunks(1) def test_resume(tmpdir): tmpdir.join('a').write('zz') lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 0 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None # test no resume ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), None) assert ud._resume() is None # check if path exists in resume db resume = mock.MagicMock() resume.get_record.return_value = None ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() is None # check same lengths bad = mock.MagicMock() bad.length = 0 resume.get_record.return_value = bad assert ud._resume() is None # check completed resume comp = mock.MagicMock() comp.length = 2 comp.completed = True comp.total_chunks = 1 comp.chunk_size = 2 comp.completed_chunks = 1 resume.get_record.return_value = comp ud._completed_chunks = mock.MagicMock() ud._src_ase = ase assert ud._resume() == 2 ase.replica_targets = [ase] ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) ud._completed_chunks = mock.MagicMock() ud._src_ase = ase assert ud._resume() == 4 # check no encryption ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' opts.rsa_public_key = 'abc' nc = mock.MagicMock() nc.length = 16 nc.completed = False nc.total_chunks = 2 nc.chunk_size = 1 nc.completed_chunks = 1 resume.get_record.return_value = nc ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() is None # check rr path exists ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' nc.length = 2 nc.local_path = pathlib.Path('yyy') opts.rsa_public_key = None resume.get_record.return_value = nc ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() is None # check resume no md5 opts.store_file_properties.md5 = False ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' nc = mock.MagicMock() nc.length = 2 nc.completed = False nc.total_chunks = 2 nc.chunk_size = 1 cc = bitstring.BitArray(length=nc.total_chunks) cc.set(True, 0) nc.completed_chunks = cc.int nc.local_path = lp.absolute_path resume.get_record.return_value = nc ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() == 1 # check resume with md5 mismatch opts.store_file_properties.md5 = True ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' nc = mock.MagicMock() nc.length = 2 nc.completed = False nc.total_chunks = 2 nc.chunk_size = 1 cc = bitstring.BitArray(length=nc.total_chunks) cc.set(True, 0) nc.completed_chunks = cc.int nc.local_path = lp.absolute_path resume.get_record.return_value = nc ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() is None # check resume with md5 match ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' nc = mock.MagicMock() nc.length = 2 nc.completed = False nc.total_chunks = 2 nc.chunk_size = 1 cc = bitstring.BitArray(length=nc.total_chunks) cc.set(True, 0) nc.completed_chunks = cc.int nc.local_path = lp.absolute_path md5 = hashlib.md5() md5.update(b'z') nc.md5hexdigest = md5.hexdigest() resume.get_record.return_value = nc ud = upload.Descriptor(lp, ase, 'uid', opts, mock.MagicMock(), resume) assert ud._resume() == 1 def test_descriptor_next_offsets(tmpdir): tmpdir.join('a').write('ab') lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None # test normal ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._resume = mock.MagicMock() ud._resume.return_value = None offsets, rb = ud.next_offsets() assert rb is None assert offsets.chunk_num == 0 assert offsets.num_bytes == 1 assert offsets.range_start == 0 assert offsets.range_end == 0 assert not offsets.pad assert ud._offset == 1 assert ud._chunk_num == 1 offsets, rb = ud.next_offsets() assert rb is None assert offsets.chunk_num == 1 assert offsets.num_bytes == 1 assert offsets.range_start == 1 assert offsets.range_end == 1 assert not offsets.pad assert ud._offset == 2 assert ud._chunk_num == 2 offsets, rb = ud.next_offsets() assert rb is None assert offsets is None # test chunk size exceeds size lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts.chunk_size_bytes = 3 ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._chunk_size = 3 ud._resume = mock.MagicMock() ud._resume.return_value = None offsets, rb = ud.next_offsets() assert rb is None assert offsets.chunk_num == 0 assert offsets.num_bytes == 2 assert offsets.range_start == 0 assert offsets.range_end == 1 assert not offsets.pad assert ud._offset == 2 assert ud._chunk_num == 1 # test encrypted tmpdir.join('a').write('z' * 16) lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) opts.chunk_size_bytes = 16 opts.rsa_public_key = 'abc' ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._resume = mock.MagicMock() ud._resume.return_value = None offsets, rb = ud.next_offsets() assert rb is None assert offsets.chunk_num == 0 assert offsets.num_bytes == 16 assert offsets.range_start == 0 assert offsets.range_end == 15 assert not offsets.pad assert ud._offset == 16 assert ud._chunk_num == 1 offsets, rb = ud.next_offsets() assert rb is None assert offsets.chunk_num == 1 assert offsets.num_bytes == 16 assert offsets.range_start == 16 assert offsets.range_end == 31 assert offsets.pad assert ud._offset == 32 assert ud._chunk_num == 2 def test_descriptor_read_data(tmpdir): tmpdir.join('a').write('ab') lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) # test normal opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._resume = mock.MagicMock() ud._resume.return_value = None # test no data to read mockoffsets = mock.MagicMock() mockoffsets.num_bytes = 0 data, newoffset = ud.read_data(mockoffsets) assert data is None assert newoffset is None # test normal data to read offsets, rb = ud.next_offsets() assert rb is None data, newoffset = ud.read_data(offsets) assert data == b'a' assert newoffset is None # test stdin with mock.patch( 'blobxfer.STDIN', new_callable=mock.PropertyMock) as patched_stdin: patched_stdin.read = mock.MagicMock() patched_stdin.read.return_value = b'z' ud.local_path.use_stdin = True data, newoffset = ud.read_data(offsets) assert data == b'z' assert newoffset.chunk_num == 0 assert newoffset.num_bytes == 1 assert newoffset.range_start == 0 assert newoffset.range_end == 0 assert not newoffset.pad assert ud._total_chunks == 3 assert ud._outstanding_ops == 3 assert ud._offset == 1 assert ud.entity.size == 2 with mock.patch( 'blobxfer.STDIN', new_callable=mock.PropertyMock) as patched_stdin: patched_stdin.read = mock.MagicMock() patched_stdin.read.return_value = None ud.local_path.use_stdin = True data, newoffset = ud.read_data(offsets) assert data is None assert newoffset is None assert ud._total_chunks == 2 assert ud._outstanding_ops == 2 assert ud._chunk_num == 0 def test_descriptor_generate_metadata(tmpdir): tmpdir.join('a').write('ab') lp = upload.LocalPath(pathlib.Path(str(tmpdir)), pathlib.Path('a')) # test nothing opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.attributes = False opts.store_file_properties.md5 = False opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) meta = ud.generate_metadata() assert meta is None # test page md5 align opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.attributes = False opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Page ase._name = 'name' ase._encryption = None ase._size = 1 ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud._offset = 1 ud.md5 = hashlib.md5() ud.md5.update(b'z') meta = ud.generate_metadata() assert meta is None md5 = hashlib.md5() md5.update(b'z' + b'\0' * 511) assert ud.md5.digest() == md5.digest() # test fileattr meta opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.attributes = True opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None # file attr store is not avail on windows if not util.on_windows(): ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) meta = ud.generate_metadata() assert metadata.JSON_KEY_BLOBXFER_METADATA in meta assert metadata._JSON_KEY_FILE_ATTRIBUTES in meta[ metadata.JSON_KEY_BLOBXFER_METADATA] # test enc meta opts.store_file_properties.attributes = False opts.store_file_properties.md5 = False opts.rsa_public_key = 'abc' ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ase.encryption_metadata = mock.MagicMock() ase.encryption_metadata.convert_to_json_with_mac.return_value = { 'encmeta': 'encmeta' } meta = ud.generate_metadata() assert 'encmeta' in meta ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ud.hmac = None ase.encryption_metadata = mock.MagicMock() ase.encryption_metadata.convert_to_json_with_mac.return_value = { 'encmeta': 'encmeta' } meta = ud.generate_metadata() assert 'encmeta' in meta opts.store_file_properties.md5 = True ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) ase.encryption_metadata = mock.MagicMock() ase.encryption_metadata.convert_to_json_with_mac.return_value = { 'encmeta': 'encmeta' } meta = ud.generate_metadata() assert 'encmeta' in meta # test vio meta opts = mock.MagicMock() opts.chunk_size_bytes = 1 opts.one_shot_bytes = 0 opts.store_file_properties.md5 = True opts.rsa_public_key = None ase = azmodels.StorageEntity('cont') ase._mode = azmodels.StorageModes.Block ase._name = 'name' ase._encryption = None lp.view = mock.MagicMock() lp.view.mode = upload.VectoredIoDistributionMode.Stripe ud = upload.Descriptor( lp, ase, 'uid', opts, mock.MagicMock(), mock.MagicMock()) with mock.patch( 'blobxfer.models.metadata.generate_vectored_io_stripe_metadata', return_value={'viometa': 'viometa'}): meta = ud.generate_metadata() assert metadata.JSON_KEY_BLOBXFER_METADATA in meta assert 'viometa' in meta[metadata.JSON_KEY_BLOBXFER_METADATA]

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524dea420c5168f9fa23870f0c0412f330ef81ca

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py

Python

layint_api/apis/event_api.py

LayeredInsight/layint_api_python

a5c9a5b24098bd823c5102b7ab9e4745432f19b4

[ "Apache-2.0" ]

null

layint_api/apis/event_api.py

LayeredInsight/layint_api_python

a5c9a5b24098bd823c5102b7ab9e4745432f19b4

[ "Apache-2.0" ]

null

layint_api/apis/event_api.py

LayeredInsight/layint_api_python

a5c9a5b24098bd823c5102b7ab9e4745432f19b4

[ "Apache-2.0" ]

null

# coding: utf-8 """ Layered Insight Assessment, Compliance, Witness & Control LI Assessment & Compliance performs static vulnerability analysis, license and package compliance. LI Witness provides deep insight and analytics into containerized applications. Control provides dynamic runtime security and analytics for containerized applications. You can find out more about the Layered Insight Suite at [http://layeredinsight.com](http://layeredinsight.com). OpenAPI spec version: 0.10 Contact: help@layeredinsight.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import sys import os import re # python 2 and python 3 compatibility library from six import iteritems from ..configuration import Configuration from ..api_client import ApiClient class EventApi(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): config = Configuration() if api_client: self.api_client = api_client else: if not config.api_client: config.api_client = ApiClient() self.api_client = config.api_client def describe_event(self, event_id, **kwargs): """ Gets description about specific event Describes an event in a manner that can be understood by humans. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.describe_event(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: AlertEvents If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.describe_event_with_http_info(event_id, **kwargs) else: (data) = self.describe_event_with_http_info(event_id, **kwargs) return data def describe_event_with_http_info(self, event_id, **kwargs): """ Gets description about specific event Describes an event in a manner that can be understood by humans. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.describe_event_with_http_info(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: AlertEvents If the method is called asynchronously, returns the request thread. """ all_params = ['event_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method describe_event" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'event_id' is set if ('event_id' not in params) or (params['event_id'] is None): raise ValueError("Missing the required parameter `event_id` when calling `describe_event`") collection_formats = {} path_params = {} if 'event_id' in params: path_params['eventID'] = params['event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['ApiKey'] return self.api_client.call_api('/Events/{eventID}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AlertEvents', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_file_accessors(self, event_id, **kwargs): """ Get programs accessing a file Get a list of programs attempting to access the file in this event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_file_accessors(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_file_accessors_with_http_info(event_id, **kwargs) else: (data) = self.get_file_accessors_with_http_info(event_id, **kwargs) return data def get_file_accessors_with_http_info(self, event_id, **kwargs): """ Get programs accessing a file Get a list of programs attempting to access the file in this event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_file_accessors_with_http_info(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ all_params = ['event_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_file_accessors" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'event_id' is set if ('event_id' not in params) or (params['event_id'] is None): raise ValueError("Missing the required parameter `event_id` when calling `get_file_accessors`") collection_formats = {} path_params = {} if 'event_id' in params: path_params['eventID'] = params['event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['ApiKey'] return self.api_client.call_api('/Events/{eventID}/FileAccessors', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_file_executors(self, event_id, **kwargs): """ Get programs executing a file Get a list of programs attempting to execute the file in this event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_file_executors(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_file_executors_with_http_info(event_id, **kwargs) else: (data) = self.get_file_executors_with_http_info(event_id, **kwargs) return data def get_file_executors_with_http_info(self, event_id, **kwargs): """ Get programs executing a file Get a list of programs attempting to execute the file in this event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_file_executors_with_http_info(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ all_params = ['event_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_file_executors" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'event_id' is set if ('event_id' not in params) or (params['event_id'] is None): raise ValueError("Missing the required parameter `event_id` when calling `get_file_executors`") collection_formats = {} path_params = {} if 'event_id' in params: path_params['eventID'] = params['event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['ApiKey'] return self.api_client.call_api('/Events/{eventID}/FileExecutors', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_source_ip(self, event_id, **kwargs): """ Get IP address used in event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_source_ip(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_source_ip_with_http_info(event_id, **kwargs) else: (data) = self.get_source_ip_with_http_info(event_id, **kwargs) return data def get_source_ip_with_http_info(self, event_id, **kwargs): """ Get IP address used in event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_source_ip_with_http_info(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['event_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_source_ip" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'event_id' is set if ('event_id' not in params) or (params['event_id'] is None): raise ValueError("Missing the required parameter `event_id` when calling `get_source_ip`") collection_formats = {} path_params = {} if 'event_id' in params: path_params['eventID'] = params['event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['ApiKey'] return self.api_client.call_api('/Events/{eventID}/SourceIP', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_source_log(self, event_id, **kwargs): """ Get log that resulted in an event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_source_log(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: ContainerLog If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_source_log_with_http_info(event_id, **kwargs) else: (data) = self.get_source_log_with_http_info(event_id, **kwargs) return data def get_source_log_with_http_info(self, event_id, **kwargs): """ Get log that resulted in an event This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_source_log_with_http_info(event_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str event_id: hexadecimal ID of event to get description of (required) :return: ContainerLog If the method is called asynchronously, returns the request thread. """ all_params = ['event_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_source_log" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'event_id' is set if ('event_id' not in params) or (params['event_id'] is None): raise ValueError("Missing the required parameter `event_id` when calling `get_source_log`") collection_formats = {} path_params = {} if 'event_id' in params: path_params['eventID'] = params['event_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['ApiKey'] return self.api_client.call_api('/Events/{eventID}/SourceLog', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ContainerLog', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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526c881000c40b4d03fed764d59bc592ecd7697e

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Python

model.py

tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow

d0abf521d8b21bc2b8e30952c19652b63150ddd9

[ "MIT" ]

null

model.py

tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow

d0abf521d8b21bc2b8e30952c19652b63150ddd9

[ "MIT" ]

null

model.py

tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow

d0abf521d8b21bc2b8e30952c19652b63150ddd9

[ "MIT" ]

null

# This U-Net implementation is originally imported from zhixuhao's 'unet' GitHub repository and modified for # 3D convolutions instead of 3D convolutions. # https://github.com/zhixuhao/unet/blob/master/model.py from tensorflow.keras.models import Model from tensorflow.keras.layers import ( Input, Conv3D, MaxPooling3D, UpSampling3D, Dropout, Conv3DTranspose, BatchNormalization, concatenate ) def unet_3d_upsampling_dropout(input_size=(240, 240, 160, 4), unet_resize_factor=2, unet_dropout_rate=0.3, num_classes=4, binary_model=False): """Constructs a U-Net 3D segmentation model with Dropout layers and UpSampling3D -> Conv3D layers. Args: input_size: (tuple) Keras model input shape is (batch_size, height, width, length, channels) with 'channels_last', (default: (240, 240, 160, 4)). Note: depth must be a multiple of 16. Source: 'data_format' parameter documentation: https://keras.io/api/layers/convolution_layers/convolution3d/ unet_resize_factor: (int) Resize factor of the number of filters (channels) per Convolutional layer in the U-Net model (must be >= 1, such that 1 means retaining the original number of filters (channels) per Convolutional layer in the U-Net model) (default: 2 (half-size)) unet_dropout_rate: (float) Dropout rate for the Dropout layers in the U-Net model (default: 0.3). num_classes: (int) Number of classes in the training dataset (default: 4). binary_model: (boolean) If True, make the last layer have one filter with 'sigmoid' activation for a 3D binary segmentation model. """ inputs = Input(shape=input_size) # Contractive path conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3) pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4) drop4 = Dropout(rate=unet_dropout_rate)(conv4) pool4 = MaxPooling3D(pool_size=(2, 2, 2))(drop4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5) drop5 = Dropout(rate=unet_dropout_rate)(conv5) # Expansive path up6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(drop5)) merge6 = concatenate([drop4, up6], axis=4) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6) up7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv6)) merge7 = concatenate([conv3, up7], axis=4) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7) up8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv7)) merge8 = concatenate([conv2, up8], axis=4) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8) up9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(conv8)) merge9 = concatenate([conv1, up9], axis=4) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9) conv9 = Conv3D(filters=2, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9) # Final layer if binary_model: conv10 = Conv3D(filters=1, kernel_size=1, activation="sigmoid")(conv9) else: conv10 = Conv3D(filters=num_classes, kernel_size=1, activation="softmax")(conv9) model = Model(inputs=inputs, outputs=conv10) return model def unet_3d_conv3dtranspose_dropout(input_size=(240, 240, 160, 4), unet_resize_factor=2, unet_dropout_rate=0.3, num_classes=4, binary_model=False): """Constructs a U-Net 3D segmentation model with Dropout layers and Conv3DTranspose layers instead of UpSampling3D -> Conv3D layers. Args: input_size: (tuple) Keras model input shape is (batch_size, height, width, length, channels) with 'channels_last', (default: (240, 240, 160, 4)). Note: depth must be a multiple of 16. Source: 'data_format' parameter documentation: https://keras.io/api/layers/convolution_layers/convolution3d/ unet_resize_factor: (int) Resize factor of the number of filters (channels) per Convolutional layer in the U-Net model (must be >= 1, such that 1 means retaining the original number of filters (channels) per Convolutional layer in the U-Net model) (default: 2 (half-size)) unet_dropout_rate: (float) Dropout rate for the Dropout layers in the U-Net model (default: 0.3). num_classes: (int) Number of classes in the training dataset (default: 4). binary_model: (boolean) If True, make the last layer have one filter with 'sigmoid' activation for a 3D binary segmentation model. """ inputs = Input(shape=input_size) # Contractive path conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3) pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4) drop4 = Dropout(rate=unet_dropout_rate)(conv4) pool4 = MaxPooling3D(pool_size=(2, 2, 2))(drop4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5) drop5 = Dropout(rate=unet_dropout_rate)(conv5) # Expansive path up6 = Conv3DTranspose(filters=512 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(drop5) merge6 = concatenate([drop4, up6], axis=4) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6) up7 = Conv3DTranspose(filters=128 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(conv6) merge7 = concatenate([conv3, up7], axis=4) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7) up8 = Conv3DTranspose(filters=64 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(conv7) merge8 = concatenate([conv2, up8], axis=4) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8) up9 = Conv3DTranspose(filters=32 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(conv8) merge9 = concatenate([conv1, up9], axis=4) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9) conv9 = Conv3D(filters=2, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9) # Final layer if binary_model: conv10 = Conv3D(filters=1, kernel_size=1, activation="sigmoid")(conv9) else: conv10 = Conv3D(filters=num_classes, kernel_size=1, activation="softmax")(conv9) model = Model(inputs=inputs, outputs=conv10) return model def unet_3d_upsampling_batchnormalization(input_size=(240, 240, 160, 4), unet_resize_factor=2, num_classes=4, binary_model=False): """Constructs a U-Net 3D segmentation model with BatchNormalization layers after each Conv3D layer instead of using Dropout layers in the expansive path and with using UpSampling3D -> Conv3D layers. Args: input_size: (tuple) Keras model input shape is (batch_size, height, width, length, channels) with 'channels_last', (default: (240, 240, 160, 4)). Note: depth must be a multiple of 16. Source: 'data_format' parameter documentation: https://keras.io/api/layers/convolution_layers/convolution3d/ unet_resize_factor: (int) Resize factor of the number of filters (channels) per Convolutional layer in the U-Net model (must be >= 1, such that 1 means retaining the original number of filters (channels) per Convolutional layer in the U-Net model) (default: 2 (half-size)). num_classes: (int) Number of classes in the training dataset (default: 4). binary_model: (boolean) If True, make the last layer have one filter with 'sigmoid' activation for a 3D binary segmentation model. """ inputs = Input(shape=input_size) # Contractive path conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) bn1 = BatchNormalization()(conv1) conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn1) bn1 = BatchNormalization()(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(bn1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) bn2 = BatchNormalization()(conv2) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn2) bn2 = BatchNormalization()(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(bn2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) bn3 = BatchNormalization()(conv3) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn3) bn3 = BatchNormalization()(conv3) pool3 = MaxPooling3D(pool_size=(2, 2, 2))(bn3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) bn4 = BatchNormalization()(conv4) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn4) bn4 = BatchNormalization()(conv4) pool4 = MaxPooling3D(pool_size=(2, 2, 2))(bn4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) bn5 = BatchNormalization()(conv5) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn5) bn5 = BatchNormalization()(conv5) # Expansive path up6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(bn5)) bn6 = BatchNormalization()(up6) merge6 = concatenate([bn4, bn6], axis=4) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) bn6 = BatchNormalization()(conv6) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn6) bn6 = BatchNormalization()(conv6) up7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(bn6)) bn7 = BatchNormalization()(up7) merge7 = concatenate([conv3, bn7], axis=4) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) bn7 = BatchNormalization()(conv7) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn7) bn7 = BatchNormalization()(conv7) up8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(bn7)) bn8 = BatchNormalization()(up8) merge8 = concatenate([conv2, bn8], axis=4) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) bn8 = BatchNormalization()(conv8) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn8) bn8 = BatchNormalization()(conv8) up9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling3D(size=(2, 2, 2))(bn8)) bn9 = BatchNormalization()(up9) merge9 = concatenate([conv1, bn9], axis=4) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) bn9 = BatchNormalization()(conv9) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn9) bn9 = BatchNormalization()(conv9) conv9 = Conv3D(filters=2, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn9) bn9 = BatchNormalization()(conv9) # Final layer if binary_model: conv10 = Conv3D(filters=1, kernel_size=1, activation="sigmoid")(bn9) else: conv10 = Conv3D(filters=num_classes, kernel_size=1, activation="softmax")(bn9) model = Model(inputs=inputs, outputs=conv10) return model def unet_3d_conv3dtranspose_batchnormalization(input_size=(240, 240, 160, 4), unet_resize_factor=2, num_classes=4, binary_model=False): """Constructs a U-Net 3D segmentation model with BatchNormalization layers after each Conv3D layer instead of using Dropout layers in the expansive path and with using Conv3DTranspose layers instead of UpSampling3D -> Conv3D layers. Args: input_size: (tuple) Keras model input shape is (batch_size, height, width, length, channels) with 'channels_last', (default: (240, 240, 160, 4)). Note: depth must be a multiple of 16. Source: 'data_format' parameter documentation: https://keras.io/api/layers/convolution_layers/convolution3d/ unet_resize_factor: (int) Resize factor of the number of filters (channels) per Convolutional layer in the U-Net model (must be >= 1, such that 1 means retaining the original number of filters (channels) per Convolutional layer in the U-Net model) (default: 2 (half-size)). num_classes: (int) Number of classes in the training dataset (default: 4). binary_model: (boolean) If True, make the last layer have one filter with 'sigmoid' activation for a 3D binary segmentation model. """ inputs = Input(shape=input_size) # Contractive path conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) bn1 = BatchNormalization()(conv1) conv1 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn1) bn1 = BatchNormalization()(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(bn1) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) bn2 = BatchNormalization()(conv2) conv2 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn2) bn2 = BatchNormalization()(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(bn2) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) bn3 = BatchNormalization()(conv3) conv3 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn3) bn3 = BatchNormalization()(conv3) pool3 = MaxPooling3D(pool_size=(2, 2, 2))(bn3) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) bn4 = BatchNormalization()(conv4) conv4 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn4) bn4 = BatchNormalization()(conv4) pool4 = MaxPooling3D(pool_size=(2, 2, 2))(bn4) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) bn5 = BatchNormalization()(conv5) conv5 = Conv3D(filters=1024 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn5) bn5 = BatchNormalization()(conv5) # Expansive path up6 = Conv3DTranspose(filters=512 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(bn5) bn6 = BatchNormalization()(up6) merge6 = concatenate([bn4, bn6], axis=4) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) bn6 = BatchNormalization()(conv6) conv6 = Conv3D(filters=512 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn6) bn6 = BatchNormalization()(conv6) up7 = Conv3DTranspose(filters=128 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(bn6) bn7 = BatchNormalization()(up7) merge7 = concatenate([conv3, bn7], axis=4) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) bn7 = BatchNormalization()(conv7) conv7 = Conv3D(filters=256 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn7) bn7 = BatchNormalization()(conv7) up8 = Conv3DTranspose(filters=64 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(bn7) bn8 = BatchNormalization()(up8) merge8 = concatenate([conv2, bn8], axis=4) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) bn8 = BatchNormalization()(conv8) conv8 = Conv3D(filters=128 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn8) bn8 = BatchNormalization()(conv8) up9 = Conv3DTranspose(filters=32 // unet_resize_factor, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="same", kernel_initializer='he_normal')(bn8) bn9 = BatchNormalization()(up9) merge9 = concatenate([conv1, bn9], axis=4) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) bn9 = BatchNormalization()(conv9) conv9 = Conv3D(filters=64 // unet_resize_factor, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn9) bn9 = BatchNormalization()(conv9) conv9 = Conv3D(filters=2, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(bn9) bn9 = BatchNormalization()(conv9) # Final layer if binary_model: conv10 = Conv3D(filters=1, kernel_size=1, activation="sigmoid")(bn9) else: conv10 = Conv3D(filters=num_classes, kernel_size=1, activation="softmax")(bn9) model = Model(inputs=inputs, outputs=conv10) return model

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