xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

6d793f7eed23a8e32819461276be9643de946e28

54

py

Python

acmicpc/3053/3053.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

3

2019-03-09T05:19:23.000Z

2019-04-06T09:26:36.000Z

acmicpc/3053/3053.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

1

2020-02-23T10:38:04.000Z

acmicpc/3053/3053.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

1

2019-05-22T13:47:53.000Z

n=int(input())**2 print(n*3.14159265359) print(n*2.0)

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6d8309d0525efb522da2ea48d66f0b90afb3437e

83

py

Python

exe.curso em video/Aula 11.py

Lorenzo-Lopes/Python-Estudo

7ee623ce29b6a0e9fac48189fbd9c641be84d418

[ "MIT" ]

null

exe.curso em video/Aula 11.py

Lorenzo-Lopes/Python-Estudo

7ee623ce29b6a0e9fac48189fbd9c641be84d418

[ "MIT" ]

null

exe.curso em video/Aula 11.py

Lorenzo-Lopes/Python-Estudo

7ee623ce29b6a0e9fac48189fbd9c641be84d418

[ "MIT" ]

null

print('oi') n = int(input('\033[4;34;43mteste\033[1;31;47m /033[0;0;0m')) print(n)

20.75

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0.626506

19

83

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py

Python

src/main.py

StephenGemin/discord-bot

b85254b0f1fdf5e1415d2ee026601e7557c73856

[ "MIT" ]

null

src/main.py

StephenGemin/discord-bot

b85254b0f1fdf5e1415d2ee026601e7557c73856

[ "MIT" ]

null

src/main.py

StephenGemin/discord-bot

b85254b0f1fdf5e1415d2ee026601e7557c73856

[ "MIT" ]

null

from discord_bot.view import start_discord_bot if __name__ == "__main__": start_discord_bot()

16.666667

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099b402200da1c4ee1d94cd464ea8b0137f56001

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py

Python

scrapers/lords/__init__.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

2

2015-04-11T12:22:41.000Z

2016-08-18T11:12:06.000Z

scrapers/lords/__init__.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

84

2015-01-22T14:33:49.000Z

2015-04-01T23:15:29.000Z

scrapers/lords/__init__.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

1

2015-04-16T03:10:39.000Z

from fetch_lords import fetch from scrape_lords import scrape

20.666667

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61eda79cd22fed4633d1aff056fe430f646f69a4

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py

Python

difi/tests/test_metrics.py

moeyensj/difi

4108ee93f35030174eb456b9e5a8a2e9cbbd04a0

[ "BSD-3-Clause" ]

1

2019-02-14T20:10:44.000Z

difi/tests/test_metrics.py

moeyensj/difi

4108ee93f35030174eb456b9e5a8a2e9cbbd04a0

[ "BSD-3-Clause" ]

22

2019-02-06T22:22:20.000Z

2021-05-12T17:13:21.000Z

difi/tests/test_metrics.py

moeyensj/difi

4108ee93f35030174eb456b9e5a8a2e9cbbd04a0

[ "BSD-3-Clause" ]

1

2020-10-05T05:02:26.000Z

import pytest import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from ..metrics import calcFindableMinObs from ..metrics import calcFindableNightlyLinkages from .create_test_data import createTestDataSet MIN_OBS = range(5, 10) def test_calcFindableMinObs(): ### Test calcFindableMinObs against the test data set column_mapping = { "truth" : "truth", "obs_id" : "obs_id", } for min_obs in MIN_OBS: # Generate test data set observations_test, all_truths_test, linkage_members_test, all_linkages_test, summary_test = createTestDataSet( min_obs, 5, 20) findable_observations = calcFindableMinObs(observations_test, min_obs=min_obs, column_mapping=column_mapping) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure all objects with not findable are not included in the findable_observations dataframe not_findable_truths_test = all_truths_test[all_truths_test["findable"] == 0]["truth"].values assert len(findable_observations[findable_observations[column_mapping["truth"]].isin(not_findable_truths_test)]) == 0 return def test_calcFindableNightlyLinkages(): ### Test calcFindableNightlyLinkages against the test data set column_mapping = { "truth" : "truth", "obs_id" : "obs_id", "time" : "time", "night" : "night", } # Generate test data set observations_test, all_truths_test, linkage_members_test, all_linkages_test, summary_test = createTestDataSet( 5, 5, 20) # For every single truth in blue, their observations are seperated by a half day for truth in observations_test[observations_test["class"] == "blue"]["truth"].unique(): mask = (observations_test["truth"] == truth) observations_test.loc[mask, "time"] = np.arange(0, len(observations_test[mask])/2, 0.5) # For every single truth in red, their observations are seperated by a quarter day for truth in observations_test[observations_test["class"] == "red"]["truth"].unique(): mask = (observations_test["truth"] == truth) observations_test.loc[mask, "time"] = np.arange(0, len(observations_test[mask])/4, 0.25) # Observation times for greens are selected at random from the available ones in blues and greens observations_test.loc[observations_test["class"] == "green", "time"] = np.random.choice( observations_test[~observations_test["time"].isna()]["time"].values, len(observations_test[observations_test["class"] == "green"]), replace=True) # Lets add a night column which is simply the floor of the observation time observations_test["night"] = np.floor(observations_test["time"]).astype(int) # With a maximum separation of 0.25 only reds should be findable findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=1, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure that only reds were found classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red"])) # With a maximum separation of 0.5 reds and blues should be findable findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.5, min_linkage_nights=1, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure that only reds and blues were found classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red", "blue"])) # With a minimum linkage length of 1, everything should be findable findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=1, max_obs_separation=0.5, min_linkage_nights=1, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure that all reds, blues, and greens were found classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red", "blue", "green"])) # With a minimum linkage length of 100, nothing should be findable findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=100, max_obs_separation=0.5, min_linkage_nights=1, column_mapping=column_mapping ) assert len(findable_observations) == 0 ### These next few tests focus on red05 which has the following observations: # obs_id truth class time night # obs00000 red05 red 0.00 0 # obs00008 red05 red 0.25 0 # obs00013 red05 red 0.50 0 # obs00024 red05 red 0.75 0 # obs00049 red05 red 1.00 1 # obs00051 red05 red 1.25 1 # obs00057 red05 red 1.50 1 # obs00070 red05 red 1.75 1 # obs00085 red05 red 2.00 2 # obs00096 red05 red 2.25 2 # Lets set min_linkage nights to 3 with a maximum separation of 0.25, only red05 should be findable findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=3, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure that only red05 should be findable classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red"])) np.testing.assert_array_equal(findable_observations["truth"].values, np.array(["red05"])) # Keep min_linkage nights to 3 with a maximum separation of 0.25, set the last of red05's observations to be outside the time separation # resulting in only two viable tracklet nights, it should no longer be findable observations_test.loc[observations_test["obs_id"] == "obs00096", "time"] = 2.26 # obs_id truth class time night findable # obs00000 red05 red 0.00 0 Y # obs00008 red05 red 0.25 0 Y # obs00013 red05 red 0.50 0 Y # obs00024 red05 red 0.75 0 Y # obs00049 red05 red 1.00 1 Y # obs00051 red05 red 1.25 1 Y # obs00057 red05 red 1.50 1 Y # obs00070 red05 red 1.75 1 Y # obs00085 red05 red 2.00 2 N # obs00096 red05 red 2.26 2 N # red05 findable : N findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=3, column_mapping=column_mapping ) # Red05 should no longer be findable classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array([])) # Set the observation back to its original time observations_test.loc[observations_test["obs_id"] == "obs00096", "time"] = 2.25 # Keep min_linkage nights to 3 with a maximum separation of 0.25, set the two of the observations on night 1 to not be # findable, red05 should still be findable with the remaining observations but those unfindable observations should not # be returned as findable observations observations_test.loc[observations_test["obs_id"] == "obs00057", "time"] = 1.51 observations_test.loc[observations_test["obs_id"] == "obs00070", "time"] = 1.77 # This observation needs to be shifted so that it is more than 0.25 from the previous exposure time # so we dont count a linkage across nights observations_test.loc[observations_test["obs_id"] == "obs00085", "time"] = 2.10 # obs_id truth class time night findable # obs00000 red05 red 0.00 0 Y # obs00008 red05 red 0.25 0 Y # obs00013 red05 red 0.50 0 Y # obs00024 red05 red 0.75 0 Y # obs00049 red05 red 1.00 1 Y # obs00051 red05 red 1.25 1 Y # obs00057 red05 red 1.51 1 N # obs00070 red05 red 1.76 1 N # obs00085 red05 red 2.10 2 Y # obs00096 red05 red 2.25 2 Y # red05 findable : Y findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=3, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[(observations_test["truth"] == truth) & (~observations_test["obs_id"].isin(["obs00057", "obs00070"]))]["obs_id"].values) # Make sure that only red05 should be findable classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red"])) np.testing.assert_array_equal(findable_observations["truth"].values, np.array(["red05"])) # Set the observations back to their previous values observations_test.loc[observations_test["obs_id"] == "obs00057", "time"] = 1.50 observations_test.loc[observations_test["obs_id"] == "obs00070", "time"] = 1.75 observations_test.loc[observations_test["obs_id"] == "obs00085", "time"] = 2.00 # Keep min_linkage nights to 3 with a maximum separation of 0.25, remove some of red05's observations # so that there are only two observations on each night -- it should still be the only object findable observations_test = observations_test[~observations_test["obs_id"].isin(["obs00000", "obs00008", "obs00057", "obs00070"])] # obs_id truth class time night findable # obs00013 red05 red 0.50 0 Y # obs00024 red05 red 0.75 0 Y # obs00049 red05 red 1.00 1 Y # obs00070 red05 red 1.75 1 Y # obs00085 red05 red 2.00 2 Y # obs00096 red05 red 2.25 2 Y # red05 findable : Y findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=3, column_mapping=column_mapping ) for truth in findable_observations[column_mapping["truth"]].unique(): # Make sure all observations are correctly identified as findable obs_ids = findable_observations[findable_observations[column_mapping["truth"]].isin([truth])]["obs_ids"].values[0] np.testing.assert_array_equal(obs_ids, observations_test[observations_test["truth"] == truth]["obs_id"].values) # Make sure that only red05 should be findable classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array(["red"])) np.testing.assert_array_equal(findable_observations["truth"].values, np.array(["red05"])) # Keep min_linkage nights to 3 with a maximum separation of 0.25, set one of red05's observations to be outside the time # separation for a linkage -- it now should not be findable observations_test.loc[observations_test["obs_id"] == "obs00096", "time"] = 2.26 # obs_id truth class time night findable # obs00013 red05 red 0.50 0 Y # obs00024 red05 red 0.75 0 Y # obs00049 red05 red 1.00 1 Y # obs00070 red05 red 1.75 1 Y # obs00085 red05 red 2.00 2 N # obs00096 red05 red 2.26 2 N # red05 findable : N findable_observations = calcFindableNightlyLinkages( observations_test, linkage_min_obs=2, max_obs_separation=0.25, min_linkage_nights=3, column_mapping=column_mapping ) # Red05 should no longer be findable classes_found = observations_test[observations_test["truth"].isin(findable_observations[column_mapping["truth"]].values)]["class"].unique() np.testing.assert_array_equal(classes_found, np.array([])) return

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182

py

Python

quran/usecase/image/find_image.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

quran/usecase/image/find_image.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

quran/usecase/image/find_image.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

class FindImage: def __init__(self, image_repo): self.image_repo = image_repo def by_ayah_id(self, ayah_id): return self.image_repo.find_by_ayah_id(ayah_id)

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1141e514f4e00f71b3246547bc4756e21bd24313

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py

Python

_lib/godot/bindings/tools.py

WilliamTambellini/godopy

7b4142ddf7acafa66e1b2b201afa5fa37a4c7f4e

[ "MIT" ]

30

2020-02-09T22:30:06.000Z

2022-01-26T04:23:09.000Z

_lib/godot/bindings/tools.py

WilliamTambellini/godopy

7b4142ddf7acafa66e1b2b201afa5fa37a4c7f4e

[ "MIT" ]

1

2020-10-12T04:12:52.000Z

2020-12-19T07:07:51.000Z

_lib/godot/bindings/tools.py

WilliamTambellini/godopy

7b4142ddf7acafa66e1b2b201afa5fa37a4c7f4e

[ "MIT" ]

5

2020-02-10T02:49:13.000Z

2021-01-25T18:18:16.000Z

from .python.tools import *

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py

Python

Section10_Facade/Practice/Generator.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

1

2020-10-20T07:41:51.000Z

Section10_Facade/Practice/Generator.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

Section10_Facade/Practice/Generator.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

from random import randint class Generator: def generate(self, count): return [randint(1,9) for x in range(count)]

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py

Python

pygem/tests/test_basics.py

lilianschuster/PyGEM

c805d09960f937fe6e35cdd1587f9089d4bec6b8

[ "MIT" ]

25

2019-06-12T21:08:24.000Z

2022-03-01T08:05:14.000Z

pygem/tests/test_basics.py

lilianschuster/PyGEM

c805d09960f937fe6e35cdd1587f9089d4bec6b8

[ "MIT" ]

2

2020-04-23T14:08:00.000Z

2020-06-04T13:52:44.000Z

pygem/tests/test_basics.py

lilianschuster/PyGEM

c805d09960f937fe6e35cdd1587f9089d4bec6b8

[ "MIT" ]

24

2019-06-12T19:48:40.000Z

2022-02-16T03:42:53.000Z

import pygem def test_version_string(): # simple test to check that the verion number is available assert type(pygem.__version__ ) == str

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e90fdc92d0651cb3d0b6aeb5b5752a662b2453ba

13,225

py

Python

model_dropout.py

soufiomario/keras-cnn

6ba72bf89dfc746d8a30634219160843b16c5fb8

[ "CC0-1.0" ]

5

2020-03-18T09:33:17.000Z

2022-03-20T15:19:18.000Z

model_dropout.py

soufiomario/keras-cnn

6ba72bf89dfc746d8a30634219160843b16c5fb8

[ "CC0-1.0" ]

1

2021-09-07T14:47:07.000Z

2021-09-07T14:47:44.000Z

model_dropout.py

soufiomario/keras-cnn

6ba72bf89dfc746d8a30634219160843b16c5fb8

[ "CC0-1.0" ]

11

2020-02-14T04:15:51.000Z

2021-12-11T12:31:38.000Z

import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K from keras.constraints import max_norm # Model configuration img_width, img_height = 32, 32 batch_size = 250 no_epochs = 55 no_classes = 10 validation_split = 0.2 verbosity = 1 max_norm_value = 2.0 # Load CIFAR10 dataset (input_train, target_train), (input_test, target_test) = cifar10.load_data() # Reshape data based on channels first / channels last strategy. # This is dependent on whether you use TF, Theano or CNTK as backend. # Source: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py if K.image_data_format() == 'channels_first': input_train = input_train.reshape(input_train.shape[0],3, img_width, img_height) input_test = input_test.reshape(input_test.shape[0], 3, img_width, img_height) input_shape = (3, img_width, img_height) else: input_train = input_train.reshape(input_train.shape[0], img_width, img_height, 3) input_test = input_test.reshape(input_test.shape[0], img_width, img_height, 3) input_shape = (img_width , img_height, 3) # Parse numbers as floats input_train = input_train.astype('float32') input_test = input_test.astype('float32') # Normalize data input_train = input_train / 255 input_test = input_test / 255 # Convert target vectors to categorical targets target_train = keras.utils.to_categorical(target_train, no_classes) target_test = keras.utils.to_categorical(target_test, no_classes) # Create the model model = Sequential() model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.50)) model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.50)) model.add(Flatten()) model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) model.add(Dense(no_classes, activation='softmax')) # Compile the model model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(), metrics=['accuracy']) # Fit data to model model.fit(input_train, target_train, batch_size=batch_size, epochs=no_epochs, verbose=verbosity, validation_split=validation_split ) # Generate generalization metrics score = model.evaluate(input_test, target_test, verbose=0) print(f'Test loss: {score[0]} / Test accuracy: {score[1]}') # ============ # Test loss: 0.8185625041961669 / Test accuracy: 0.7193999886512756 # ============ # model = Sequential() # model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ============ # Test loss: 1.3634590747833253 / Test accuracy: 0.5906000137329102 # ============ # model = Sequential() # model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(Dropout(0.50)) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_uniform')) # model.add(Dropout(0.50)) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_initializer='he_uniform')) # model.add(Dropout(0.50)) # model.add(Dense(no_classes, activation='softmax')) # ============ # Test loss: 0.8021318348884583 / Test accuracy: 0.7243000268936157 # ============ # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ============ # Test loss: 1.0652880083084106 / Test accuracy: 0.623199999332428 # ============ # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ============ # Test loss: 0.7692169213294983 / Test accuracy: 0.7314000129699707 # ============ # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(2.0), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(2.0), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(2.0), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # Test loss: 0.8035776728630066 / Test accuracy: 0.7233999967575073 # maxnorm=1.0 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # Test loss: 0.8003060577392578 / Test accuracy: 0.7250000238418579 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # Test loss: 0.7669966766357422 / Test accuracy: 0.7365999817848206 # maxnorm = 2.5 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) #Test loss: 2.3026647621154783 / Test accuracy: 0.10000000149011612 # maxnorm = 2.5 # lr 10e-2 && decay linear # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) #Test loss: 2.302865937805176 / Test accuracy: 0.10000000149011612 #maxnorm = 2.5 # lre0 && decay linear # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ================== # Test loss: Test loss: 0.9980722076416015 / Test accuracy: 0.6534000039100647 # SGD, momentum 0.99, Nesterov false, LR 10e-2, LR decay linear # maxnorm=2.5 # ================== # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ================== # Test loss: Test loss: 0.965770835018158 / Test accuracy: 0.6678000092506409 # SGD, momentum 0.99, Nesterov true, LR 10e-2, LR decay linear # maxnorm=2.5 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ================== # Test loss: Test loss: 1.0010871562957764 / Test accuracy: 0.6502000093460083 # SGD, momentum 0.99, Nesterov true, default LR settings # MAXNORM=2.5 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax')) # ================== # Test loss: Test loss: 0.9282757438659668 / Test accuracy: 0.6773999929428101 # SGD, momentum 0.99, Nesterov true, default LR settings # MAXNORM=2.0 # model = Sequential() # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', input_shape=input_shape, kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Conv2D(64, kernel_size=(3, 3), kernel_constraint=max_norm(max_norm_value), activation='relu', kernel_initializer='he_uniform')) # model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.50)) # model.add(Flatten()) # model.add(Dense(256, activation='relu', kernel_constraint=max_norm(max_norm_value), kernel_initializer='he_uniform')) # model.add(Dense(no_classes, activation='softmax'))

49.163569

164

0.733837

1,891

13,225

4.927552

0.088842

0.115905

0.089719

0.122773

0.807684

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49.163569

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null

0

1

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null

0

6

e927b0dfc553dadb3b4e6922e4c7c716d582e644

1,417

py

Python

tests/test_a0095uniquebinarysearchtreesii.py

nirofang/pyleet

600d58ad97028c9a14148af4ef468683a011a515

[ "MIT" ]

3

2019-11-06T13:10:38.000Z

2021-11-17T07:29:54.000Z

tests/test_a0095uniquebinarysearchtreesii.py

nirofang/pyleet

600d58ad97028c9a14148af4ef468683a011a515

[ "MIT" ]

1

2020-12-17T22:18:05.000Z

tests/test_a0095uniquebinarysearchtreesii.py

nirofang/pyleet

600d58ad97028c9a14148af4ef468683a011a515

[ "MIT" ]

1

2019-11-06T13:10:45.000Z

from solutions.a0095uniquebinarysearchtreesii import Solution from utils.tree.TreeNode import TreeNode import json solution = Solution() # def test_generateTrees1(): # n = 3 # expect = sorted([TreeNode.integerListToString(nums) for nums in [ # [1, None, 3, 2], # [3, 2, None, 1], # [3, 1, None, None, 2], # [2, 1, 3], # [1, None, 2, None, 3] # ]]) # actual = solution.generateTrees(n) # assert len(actual) == len(expect) # actual = sorted([TreeNode.integerListToString(nums) for nums in [TreeNode.treeToList(node) for node in actual]]) # assert actual == expect def test_generateTrees2(): n = 0 expect = [] actual = solution.generateTrees(n) assert len(actual) == len(expect) assert actual == expect def test_generateTrees3(): n = 1 expect = sorted([TreeNode.integerListToString(nums) for nums in [[1]]]) actual = solution.generateTrees(n) assert len(actual) == len(expect) actual = sorted([TreeNode.integerListToString(nums) for nums in [TreeNode.treeToList(node) for node in actual]]) assert actual == expect def test_generateTrees4(): n = 2 expect = sorted([TreeNode.integerListToString(nums) for nums in [[2, 1], [1, None, 2]]]) actual = solution.generateTrees(n) assert len(actual) == len(expect) actual = sorted([TreeNode.integerListToString(nums) for nums in [TreeNode.treeToList(node) for node in actual]]) assert actual == expect

29.520833

116

0.685251

180

1,417

5.372222

0.183333

0.086867

0.204757

0.229576

0.750776

0.724922

0.671148

0.507756

0

0.027444

0.177135

1,417

47

117

30.148936

0.801887

0.304869

0

0.458333

1

0

0.25

1

0.125

false

0

0.125

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0

null

0

1

0

1

0

1

0

null

0

6

e94363fca85ee7931d530cd96b67366486d585e0

216

py

Python

src/common/new_user_creation.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

4

2021-11-14T17:16:03.000Z

2022-03-17T21:01:42.000Z

src/common/new_user_creation.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

null

src/common/new_user_creation.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

5

2021-07-30T14:27:37.000Z

2021-12-15T12:08:46.000Z

from common.owner import Owner owner = Owner() print(f"private key: {owner.private_key.export_key(format='DER')}") print(f"public key hash: {owner.public_key_hash}") print(f"public key hex: {owner.public_key_hex}")

30.857143

67

0.75463

36

216

4.361111

0.388889

0.229299

0.152866

0.191083

0

0.087963

216

6

68

36

0.796954

0

0.625

0.412037

0

1

0

false

0

0.2

0

0.2

0.6

0

null

1

0

1

0

1

0

1

null

0

1

0

6

3a6858d2ab63ec58a60118a8aa3ec8637f6fb6b9

102

py

Python

core/crawler/crawl.py

abdallah-elsharif/WRock

7cfd4bf29e932bf0048ee357c16cf6c021e7fb81

[ "MIT" ]

14

2022-03-13T19:51:24.000Z

2022-03-18T07:36:39.000Z

core/crawler/crawl.py

abdallah-elsharif/WRock

7cfd4bf29e932bf0048ee357c16cf6c021e7fb81

[ "MIT" ]

null

core/crawler/crawl.py

abdallah-elsharif/WRock

7cfd4bf29e932bf0048ee357c16cf6c021e7fb81

[ "MIT" ]

3

2022-03-14T05:58:06.000Z

2022-03-14T11:46:47.000Z

from core.crawler.crawler import WebCrawler def crawl(config): return WebCrawler(config).Start()

20.4

43

0.77451

13

102

6.076923

0.769231

0

0.127451

102

5

44

20.4

0.88764

0

1

0.333333

false

0

0.333333

1

0

1

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null

0

1

0

null

0

1

0

1

0

6

3a92f8edda04a6262981d6d6424b66f06b3a8120

157

py

Python

src/frontend/controllers/kitchen.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

1

2018-09-11T19:32:25.000Z

src/frontend/controllers/kitchen.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

null

src/frontend/controllers/kitchen.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

null

from flask import render_template from .. import app @app.route('/kitchen/', methods=['GET']) def render_kitchen_view(): return render_template('')

14.272727

40

0.707006

20

157

5.35

0.65

0.261682

0

0.146497

157

10

41

15.7

0.798507

0

0.077419

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

1

0

null

0

1

0

1

0

6

3ac71ae0feb98dfe47ceb8f67c4588c0520d9ece

230

py

Python

wazimap_ng/points/admin/__init__.py

arghyaiitb/wazimap-ng

2a77860526d865b8fd0c22a2204f121fdb3b28a0

[ "Apache-2.0" ]

11

2019-12-31T20:27:22.000Z

2022-03-10T03:55:38.000Z

wazimap_ng/points/admin/__init__.py

arghyaiitb/wazimap-ng

2a77860526d865b8fd0c22a2204f121fdb3b28a0

[ "Apache-2.0" ]

164

2020-02-06T15:02:22.000Z

2022-03-30T22:42:00.000Z

wazimap_ng/points/admin/__init__.py

arghyaiitb/wazimap-ng

2a77860526d865b8fd0c22a2204f121fdb3b28a0

[ "Apache-2.0" ]

16

2020-01-03T20:30:24.000Z

2022-01-11T11:05:15.000Z

from .theme_admin import ThemeAdmin from .location_admin import LocationAdmin from .profilecategory_admin import ProfileCategoryAdmin from .category_admin import CategoryAdmin from .coordinate_file_admin import CoordinateFileAdmin

46

55

0.895652

26

230

7.692308

0.538462

0.275

0

0.082609

230

5

56

46

0.947867

0

1

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true

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

3af33daab91cbdd24da8da2beb3ea99484b254b8

23

py

Python

PyColored/__init__.py

MrHedryX/PyColours

b1363f7354ad938343cf8953ebffe0479aa7a4f6

[ "MIT" ]

null

PyColored/__init__.py

MrHedryX/PyColours

b1363f7354ad938343cf8953ebffe0479aa7a4f6

[ "MIT" ]

null

PyColored/__init__.py

MrHedryX/PyColours

b1363f7354ad938343cf8953ebffe0479aa7a4f6

[ "MIT" ]

null

from .colours import *

11.5

22

0.73913

3

23

5.666667

1

0

0.173913

23

1

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

aaf1c6fe8814afe42f0c78715a2b16a28f4063c4

169

py

Python

ggpy/cruft/autocode/Event.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

1

2015-01-26T19:07:45.000Z

ggpy/cruft/autocode/Event.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

null

ggpy/cruft/autocode/Event.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

null

#!/usr/bin/env python """ generated source for module Event """ # package: org.ggp.base.util.observer class Event(object): """ generated source for class Event """

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c92db475a620b89f1b7440b904315aef1e24579d

93

py

Python

data/__init__.py

ssydasheng/Neural-Kernel-Network

2b1540f20445e05705769cfd5808d2810eac8a4f

[ "MIT" ]

67

2018-07-03T14:01:08.000Z

2021-11-08T10:40:55.000Z

data/__init__.py

ssydasheng/Neural-Kernel-Network

2b1540f20445e05705769cfd5808d2810eac8a4f

[ "MIT" ]

1

2019-10-13T12:33:42.000Z

2019-10-15T07:14:51.000Z

data/__init__.py

ssydasheng/Neural-Kernel-Network

2b1540f20445e05705769cfd5808d2810eac8a4f

[ "MIT" ]

8

2018-07-04T18:57:40.000Z

2020-07-31T11:14:11.000Z

from .register import * from .hparams import * from .data import * from .timeSeries import *

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6

a3415feadfd677e09cc64c4cb3f96e2fa0a9a1ea

160

py

Python

fuzzyfloat/types.py

keystonetowersystems/fuzzyfloat

551f324180d1b107149e3d3d3b8076f6397cdfb3

[ "MIT" ]

null

fuzzyfloat/types.py

keystonetowersystems/fuzzyfloat

551f324180d1b107149e3d3d3b8076f6397cdfb3

[ "MIT" ]

null

fuzzyfloat/types.py

keystonetowersystems/fuzzyfloat

551f324180d1b107149e3d3d3b8076f6397cdfb3

[ "MIT" ]

null

from .meta import FuzzyFloatMeta class rel_fp(metaclass=FuzzyFloatMeta): pass class abs_fp(metaclass=FuzzyFloatMeta, rel_tol=0.0, atol=1e-07): pass

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160

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1

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6

6e6f48fa31ca0cb89aadc6b7b4522a4dc7f65a98

251

py

Python

sdk/lusid_configuration/api/__init__.py

finbourne/lusid-configuration-sdk-python-preview

f9ed2bc71042ce1b6f85baf62c9f6603150000dc

[ "MIT" ]

null

sdk/lusid_configuration/api/__init__.py

finbourne/lusid-configuration-sdk-python-preview

f9ed2bc71042ce1b6f85baf62c9f6603150000dc

[ "MIT" ]

null

sdk/lusid_configuration/api/__init__.py

finbourne/lusid-configuration-sdk-python-preview

f9ed2bc71042ce1b6f85baf62c9f6603150000dc

[ "MIT" ]

1

2021-12-09T18:53:23.000Z

from __future__ import absolute_import # flake8: noqa # import apis into api package from lusid_configuration.api.application_metadata_api import ApplicationMetadataApi from lusid_configuration.api.configuration_sets_api import ConfigurationSetsApi

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6

6eb6377c66738d2e3e66614c58f3bc58e7ec6530

1,353

py

Python

app/core/forms.py

mihalw28/book_manager

553397a4c25e5f90a02bd794722f77b423c346e5

[ "MIT" ]

1

2020-04-22T18:05:14.000Z

app/core/forms.py

mihalw28/book_manager

553397a4c25e5f90a02bd794722f77b423c346e5

[ "MIT" ]

null

app/core/forms.py

mihalw28/book_manager

553397a4c25e5f90a02bd794722f77b423c346e5

[ "MIT" ]

null

from flask_wtf import FlaskForm from wtforms import IntegerField, StringField, SubmitField, TextAreaField from wtforms.validators import DataRequired, InputRequired, ValidationError class AddBookForm(FlaskForm): title = StringField("Title", validators=[DataRequired()]) author = StringField("Author", validators=[DataRequired()]) description = TextAreaField("Description", validators=[DataRequired()]) pages_no = IntegerField("Number of pages", validators=[InputRequired()]) submit = SubmitField("Add to library") def validate_pages_no(self, pages_no): """Method validates if number of pages is a positive one.""" if pages_no.data < 1: raise ValidationError("A book cannot have less than 1 page.") class UpdateBookForm(FlaskForm): title = StringField("Title", validators=[DataRequired()]) author = StringField("Author", validators=[DataRequired()]) description = TextAreaField("Description", validators=[DataRequired()]) pages_no = IntegerField("Number of pages", validators=[InputRequired()]) submit = SubmitField("Update book") def validate_pages_no(self, pages_no): """Method validates if number of pages is a positive one.""" if pages_no.data < 1: raise ValidationError("A book cannot have less than 1 page.")

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1,353

29

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null

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1

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6

42fe7e20aa1719e1564e1e8a7b24964f711a720d

48,427

py

Python

heat_cfntools/tests/test_cfn_helper.py

citrix-openstack-build/heat-cfntools

8ef88a6e864fa49cdc6dda9b1c701a29e0557253

[ "Apache-2.0" ]

null

heat_cfntools/tests/test_cfn_helper.py

citrix-openstack-build/heat-cfntools

8ef88a6e864fa49cdc6dda9b1c701a29e0557253

[ "Apache-2.0" ]

null

heat_cfntools/tests/test_cfn_helper.py

citrix-openstack-build/heat-cfntools

8ef88a6e864fa49cdc6dda9b1c701a29e0557253

[ "Apache-2.0" ]

null

# # Copyright 2013 Hewlett-Packard Development Company, L.P. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import boto.cloudformation as cfn import fixtures import json from mox3 import mox import os import subprocess import tempfile import testtools import testtools.matchers as ttm from heat_cfntools.cfntools import cfn_helper class FakePOpen(): def __init__(self, stdout='', stderr='', returncode=0): self.returncode = returncode self.stdout = stdout self.stderr = stderr def communicate(self): return (self.stdout, self.stderr) def wait(self): pass class MockPopenTestCase(testtools.TestCase): def mock_cmd_run(self, command, cwd=None, env=None): return subprocess.Popen( command, cwd=cwd, env=env, stderr=-1, stdout=-1) def mock_unorder_cmd_run(self, command, cwd=None, env=None): return subprocess.Popen( command, cwd=cwd, env=env, stderr=-1, stdout=-1).InAnyOrder() def setUp(self): super(MockPopenTestCase, self).setUp() self.m = mox.Mox() self.m.StubOutWithMock(subprocess, 'Popen') self.addCleanup(self.m.UnsetStubs) class TestCommandRunner(MockPopenTestCase): def test_command_runner(self): self.mock_cmd_run(['su', 'root', '-c', '/bin/command1']).AndReturn( FakePOpen('All good')) self.mock_cmd_run(['su', 'root', '-c', '/bin/command2']).AndReturn( FakePOpen('Doing something', 'error', -1)) self.m.ReplayAll() cmd2 = cfn_helper.CommandRunner('/bin/command2') cmd1 = cfn_helper.CommandRunner('/bin/command1', cmd2) cmd1.run('root') self.assertEqual( 'CommandRunner:\n\tcommand: /bin/command1\n\tstdout: All good', str(cmd1)) self.assertEqual( 'CommandRunner:\n\tcommand: /bin/command2\n\tstatus: -1\n' '\tstdout: Doing something\n\tstderr: error', str(cmd2)) self.m.VerifyAll() class TestPackages(MockPopenTestCase): def test_yum_install(self): install_list = [] for pack in ('httpd', 'wordpress', 'mysql-server'): self.mock_unorder_cmd_run( ['su', 'root', '-c', 'rpm -q %s' % pack]) \ .AndReturn(FakePOpen(returncode=1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', 'yum -y --showduplicates list available %s' % pack]) \ .AndReturn(FakePOpen(returncode=0)) install_list.append(pack) # This mock call corresponding to 'su root -c yum -y install .*' # But there is no way to ignore the order of the parameters, so only # check the return value. self.mock_cmd_run(mox.IgnoreArg()).AndReturn(FakePOpen( returncode=0)) self.m.ReplayAll() packages = { "yum": { "mysql-server": [], "httpd": [], "wordpress": [] } } cfn_helper.PackagesHandler(packages).apply_packages() self.m.VerifyAll() def test_zypper_install(self): install_list = [] for pack in ('httpd', 'wordpress', 'mysql-server'): self.mock_unorder_cmd_run( ['su', 'root', '-c', 'rpm -q %s' % pack]) \ .AndReturn(FakePOpen(returncode=1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', 'zypper -n --no-refresh search %s' % pack]) \ .AndReturn(FakePOpen(returncode=0)) install_list.append(pack) # This mock call corresponding to 'su root -c zypper -n install .*' # But there is no way to ignore the order of the parameters, so only # check the return value. self.mock_cmd_run(mox.IgnoreArg()).AndReturn(FakePOpen( returncode=0)) self.m.ReplayAll() packages = { "zypper": { "mysql-server": [], "httpd": [], "wordpress": [] } } cfn_helper.PackagesHandler(packages).apply_packages() self.m.VerifyAll() def test_apt_install(self): # This mock call corresponding to # 'DEBIAN_FRONTEND=noninteractive su root -c apt-get -y install .*' # But there is no way to ignore the order of the parameters, so only # check the return value. self.mock_cmd_run(mox.IgnoreArg()).AndReturn(FakePOpen( returncode=0)) self.m.ReplayAll() packages = { "apt": { "mysql-server": [], "httpd": [], "wordpress": [] } } cfn_helper.PackagesHandler(packages).apply_packages() self.m.VerifyAll() class TestServicesHandler(MockPopenTestCase): def test_services_handler_systemd(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(True) # apply_services self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl enable httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl start httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl enable mysqld.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status mysqld.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl start mysqld.service'] ).AndReturn(FakePOpen()) # monitor_services not running self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl start httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/services_restarted'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status mysqld.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl start mysqld.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/services_restarted'] ).AndReturn(FakePOpen()) # monitor_services running self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status mysqld.service'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "systemd": { "mysqld": {"enabled": "true", "ensureRunning": "true"}, "httpd": {"enabled": "true", "ensureRunning": "true"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() # services not running sh.monitor_services() # services running sh.monitor_services() self.m.VerifyAll() def test_services_handler_systemd_disabled(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(True) # apply_services self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl disable httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl stop httpd.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl disable mysqld.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl status mysqld.service'] ).AndReturn(FakePOpen()) self.mock_unorder_cmd_run( ['su', 'root', '-c', '/bin/systemctl stop mysqld.service'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "systemd": { "mysqld": {"enabled": "false", "ensureRunning": "false"}, "httpd": {"enabled": "false", "ensureRunning": "false"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() self.m.VerifyAll() def test_services_handler_sysv_service_chkconfig(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(False) os.path.exists('/sbin/service').MultipleTimes().AndReturn(True) os.path.exists('/sbin/chkconfig').MultipleTimes().AndReturn(True) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/sbin/chkconfig httpd on'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd status'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd start'] ).AndReturn(FakePOpen()) # monitor_services not running self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd status'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd start'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/services_restarted'] ).AndReturn(FakePOpen()) # monitor_services running self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd status'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "true", "ensureRunning": "true"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() # services not running sh.monitor_services() # services running sh.monitor_services() self.m.VerifyAll() def test_services_handler_sysv_disabled_service_chkconfig(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(False) os.path.exists('/sbin/service').MultipleTimes().AndReturn(True) os.path.exists('/sbin/chkconfig').MultipleTimes().AndReturn(True) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/sbin/chkconfig httpd off'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd status'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/sbin/service httpd stop'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "false", "ensureRunning": "false"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() self.m.VerifyAll() def test_services_handler_sysv_systemctl(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(True) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl enable httpd.service'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl start httpd.service'] ).AndReturn(FakePOpen()) # monitor_services not running self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl start httpd.service'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/services_restarted'] ).AndReturn(FakePOpen()) # monitor_services running self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "true", "ensureRunning": "true"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() # services not running sh.monitor_services() # services running sh.monitor_services() self.m.VerifyAll() def test_services_handler_sysv_disabled_systemctl(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(True) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl disable httpd.service'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl status httpd.service'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/systemctl stop httpd.service'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "false", "ensureRunning": "false"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() self.m.VerifyAll() def test_services_handler_sysv_service_updaterc(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(False) os.path.exists('/sbin/service').MultipleTimes().AndReturn(False) os.path.exists('/sbin/chkconfig').MultipleTimes().AndReturn(False) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/update-rc.d httpd enable'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd status'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd start'] ).AndReturn(FakePOpen()) # monitor_services not running self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd status'] ).AndReturn(FakePOpen(returncode=-1)) self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd start'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/bin/services_restarted'] ).AndReturn(FakePOpen()) # monitor_services running self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd status'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "true", "ensureRunning": "true"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() # services not running sh.monitor_services() # services running sh.monitor_services() self.m.VerifyAll() def test_services_handler_sysv_disabled_service_updaterc(self): self.m.StubOutWithMock(os.path, 'exists') os.path.exists('/bin/systemctl').MultipleTimes().AndReturn(False) os.path.exists('/sbin/service').MultipleTimes().AndReturn(False) os.path.exists('/sbin/chkconfig').MultipleTimes().AndReturn(False) # apply_services self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/update-rc.d httpd disable'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd status'] ).AndReturn(FakePOpen()) self.mock_cmd_run( ['su', 'root', '-c', '/usr/sbin/service httpd stop'] ).AndReturn(FakePOpen()) self.m.ReplayAll() services = { "sysvinit": { "httpd": {"enabled": "false", "ensureRunning": "false"} } } hooks = [ cfn_helper.Hook( 'hook1', 'service.restarted', 'Resources.resource1.Metadata', 'root', '/bin/services_restarted') ] sh = cfn_helper.ServicesHandler(services, 'resource1', hooks) sh.apply_services() self.m.VerifyAll() class TestHupConfig(MockPopenTestCase): def test_load_main_section(self): fcreds = tempfile.NamedTemporaryFile() fcreds.write('AWSAccessKeyId=foo\nAWSSecretKey=bar\n') fcreds.flush() main_conf = tempfile.NamedTemporaryFile() main_conf.write('''[main] stack=teststack credential-file=%s''' % fcreds.name) main_conf.flush() mainconfig = cfn_helper.HupConfig([open(main_conf.name)]) self.assertEqual( '{stack: teststack, credential_file: %s, ' 'region: nova, interval:10}' % fcreds.name, str(mainconfig)) main_conf.close() main_conf = tempfile.NamedTemporaryFile() main_conf.write('''[main] stack=teststack region=region1 credential-file=%s-invalid interval=120''' % fcreds.name) main_conf.flush() e = self.assertRaises(Exception, cfn_helper.HupConfig, [open(main_conf.name)]) self.assertIn('invalid credentials file', str(e)) fcreds.close() def test_hup_config(self): self.mock_cmd_run( ['su', 'root', '-c', '/bin/cfn-http-restarted']).AndReturn( FakePOpen('All good')) self.mock_cmd_run(['su', 'root', '-c', '/bin/hook1']).AndReturn( FakePOpen('All good')) self.mock_cmd_run(['su', 'root', '-c', '/bin/hook2']).AndReturn( FakePOpen('All good')) self.mock_cmd_run(['su', 'root', '-c', '/bin/hook3']).AndReturn( FakePOpen('All good')) self.m.ReplayAll() hooks_conf = tempfile.NamedTemporaryFile() def write_hook_conf(f, name, triggers, path, action): f.write( '[%s]\ntriggers=%s\npath=%s\naction=%s\nrunas=root\n\n' % ( name, triggers, path, action)) write_hook_conf( hooks_conf, 'hook2', 'service2.restarted', 'Resources.resource2.Metadata', '/bin/hook2') write_hook_conf( hooks_conf, 'hook1', 'service1.restarted', 'Resources.resource1.Metadata', '/bin/hook1') write_hook_conf( hooks_conf, 'hook3', 'service3.restarted', 'Resources.resource3.Metadata', '/bin/hook3') write_hook_conf( hooks_conf, 'cfn-http-restarted', 'service.restarted', 'Resources.resource.Metadata', '/bin/cfn-http-restarted') hooks_conf.flush() fcreds = tempfile.NamedTemporaryFile() fcreds.write('AWSAccessKeyId=foo\nAWSSecretKey=bar\n') fcreds.flush() main_conf = tempfile.NamedTemporaryFile() main_conf.write('''[main] stack=teststack credential-file=%s region=region1 interval=120''' % fcreds.name) main_conf.flush() mainconfig = cfn_helper.HupConfig([ open(main_conf.name), open(hooks_conf.name)]) unique_resources = mainconfig.unique_resources_get() self.assertThat([ 'resource', 'resource1', 'resource2', 'resource3', ], ttm.Equals(sorted(unique_resources))) hooks = sorted(mainconfig.hooks, key=lambda hook: hook.resource_name_get()) self.assertEqual(len(hooks), 4) self.assertEqual( '{cfn-http-restarted, service.restarted,' ' Resources.resource.Metadata, root, /bin/cfn-http-restarted}', str(hooks[0])) self.assertEqual( '{hook1, service1.restarted, Resources.resource1.Metadata,' ' root, /bin/hook1}', str(hooks[1])) self.assertEqual( '{hook2, service2.restarted, Resources.resource2.Metadata,' ' root, /bin/hook2}', str(hooks[2])) self.assertEqual( '{hook3, service3.restarted, Resources.resource3.Metadata,' ' root, /bin/hook3}', str(hooks[3])) for hook in hooks: hook.event(hook.triggers, None, hook.resource_name_get()) hooks_conf.close() fcreds.close() main_conf.close() self.m.VerifyAll() class TestCfnHelper(testtools.TestCase): def _check_metadata_content(self, content, value): with tempfile.NamedTemporaryFile() as metadata_info: metadata_info.write(content) metadata_info.flush() port = cfn_helper.metadata_server_port(metadata_info.name) self.assertEqual(value, port) def test_metadata_server_port(self): self._check_metadata_content("http://172.20.42.42:8000\n", 8000) def test_metadata_server_port_https(self): self._check_metadata_content("https://abc.foo.bar:6969\n", 6969) def test_metadata_server_port_noport(self): self._check_metadata_content("http://172.20.42.42\n", None) def test_metadata_server_port_justip(self): self._check_metadata_content("172.20.42.42", None) def test_metadata_server_port_weird(self): self._check_metadata_content("::::", None) self._check_metadata_content("beforecolons:aftercolons", None) def test_metadata_server_port_emptyfile(self): self._check_metadata_content("\n", None) self._check_metadata_content("", None) def test_metadata_server_nofile(self): random_filename = self.getUniqueString() self.assertEqual(None, cfn_helper.metadata_server_port(random_filename)) def test_to_boolean(self): self.assertTrue(cfn_helper.to_boolean(True)) self.assertTrue(cfn_helper.to_boolean('true')) self.assertTrue(cfn_helper.to_boolean('yes')) self.assertTrue(cfn_helper.to_boolean('1')) self.assertTrue(cfn_helper.to_boolean(1)) self.assertFalse(cfn_helper.to_boolean(False)) self.assertFalse(cfn_helper.to_boolean('false')) self.assertFalse(cfn_helper.to_boolean('no')) self.assertFalse(cfn_helper.to_boolean('0')) self.assertFalse(cfn_helper.to_boolean(0)) self.assertFalse(cfn_helper.to_boolean(None)) self.assertFalse(cfn_helper.to_boolean('fingle')) def test_parse_creds_file(self): def parse_creds_test(file_contents, creds_match): with tempfile.NamedTemporaryFile(mode='w') as fcreds: fcreds.write(file_contents) fcreds.flush() creds = cfn_helper.parse_creds_file(fcreds.name) self.assertThat(creds_match, ttm.Equals(creds)) parse_creds_test( 'AWSAccessKeyId=foo\nAWSSecretKey=bar\n', {'AWSAccessKeyId': 'foo', 'AWSSecretKey': 'bar'} ) parse_creds_test( 'AWSAccessKeyId =foo\nAWSSecretKey= bar\n', {'AWSAccessKeyId': 'foo', 'AWSSecretKey': 'bar'} ) parse_creds_test( 'AWSAccessKeyId = foo\nAWSSecretKey = bar\n', {'AWSAccessKeyId': 'foo', 'AWSSecretKey': 'bar'} ) class TestMetadataRetrieve(testtools.TestCase): def setUp(self): super(TestMetadataRetrieve, self).setUp() self.tdir = self.useFixture(fixtures.TempDir()) self.last_file = os.path.join(self.tdir.path, 'last_metadata') def test_metadata_retrieve_files(self): md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) with tempfile.NamedTemporaryFile(mode='w+') as default_file: default_file.write(md_str) default_file.flush() self.assertThat(default_file.name, ttm.FileContains(md_str)) self.assertTrue( md.retrieve(default_path=default_file.name, last_path=self.last_file)) self.assertThat(self.last_file, ttm.FileContains(md_str)) self.assertThat(md_data, ttm.Equals(md._metadata)) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(default_path=default_file.name, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) def test_metadata_retrieve_none(self): md = cfn_helper.Metadata('teststack', None) default_file = os.path.join(self.tdir.path, 'default_file') self.assertFalse(md.retrieve(default_path=default_file, last_path=self.last_file)) self.assertIsNone(md._metadata) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display() fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "") def test_metadata_retrieve_passed(self): md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display() fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "{\"AWS::CloudFormation::Init\": {\"config\": {" "\"files\": {\"/tmp/foo\": {\"content\": \"bar\"}" "}}}}\n") def test_metadata_retrieve_by_key_passed(self): md_data = {"foo": {"bar": {"fred.1": "abcd"}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display("foo") fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "{\"bar\": {\"fred.1\": \"abcd\"}}\n") def test_metadata_retrieve_by_nested_key_passed(self): md_data = {"foo": {"bar": {"fred.1": "abcd"}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display("foo.bar.'fred.1'") fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), '"abcd"\n') def test_metadata_retrieve_key_none(self): md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display("no_key") fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "") def test_metadata_retrieve_by_nested_key_none(self): md_data = {"foo": {"bar": {"fred.1": "abcd"}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display("foo.fred") fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "") def test_metadata_retrieve_by_nested_key_none_with_matching_string(self): md_data = {"foo": "bar"} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertTrue(md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertEqual(md_str, str(md)) displayed = self.useFixture(fixtures.StringStream('stdout')) fake_stdout = displayed.stream self.useFixture(fixtures.MonkeyPatch('sys.stdout', fake_stdout)) md.display("foo.bar") fake_stdout.flush() self.assertEqual(displayed.getDetails()['stdout'].as_text(), "") def test_metadata_creates_cache(self): temp_home = tempfile.mkdtemp() def cleanup_temp_home(thome): os.unlink(os.path.join(thome, 'cache', 'last_metadata')) os.rmdir(os.path.join(thome, 'cache')) os.rmdir(os.path.join(thome)) self.addCleanup(cleanup_temp_home, temp_home) last_path = os.path.join(temp_home, 'cache', 'last_metadata') md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} md_str = json.dumps(md_data) md = cfn_helper.Metadata('teststack', None) self.assertFalse(os.path.exists(last_path), "last_metadata file already exists") self.assertTrue(md.retrieve(meta_str=md_str, last_path=last_path)) self.assertTrue(os.path.exists(last_path), "last_metadata file should exist") # Ensure created dirs and file have right perms self.assertTrue(os.stat(last_path).st_mode & 0o600 == 0o600) self.assertTrue( os.stat(os.path.dirname(last_path)).st_mode & 0o700 == 0o700) def test_is_valid_metadata(self): md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} md = cfn_helper.Metadata('teststack', None) self.assertTrue( md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) self.assertTrue(md._is_valid_metadata()) self.assertThat( md_data['AWS::CloudFormation::Init'], ttm.Equals(md._metadata)) def test_remote_metadata(self): md_data = {"AWS::CloudFormation::Init": {"config": {"files": { "/tmp/foo": {"content": "bar"}}}}} m = mox.Mox() m.StubOutWithMock( cfn.CloudFormationConnection, 'describe_stack_resource') cfn.CloudFormationConnection.describe_stack_resource( 'teststack', None).MultipleTimes().AndReturn({ 'DescribeStackResourceResponse': { 'DescribeStackResourceResult': { 'StackResourceDetail': {'Metadata': md_data}}}}) m.ReplayAll() try: md = cfn_helper.Metadata( 'teststack', None, access_key='foo', secret_key='bar') self.assertTrue(md.retrieve(last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) with tempfile.NamedTemporaryFile(mode='w') as fcreds: fcreds.write('AWSAccessKeyId=foo\nAWSSecretKey=bar\n') fcreds.flush() md = cfn_helper.Metadata( 'teststack', None, credentials_file=fcreds.name) self.assertTrue(md.retrieve(last_path=self.last_file)) self.assertThat(md_data, ttm.Equals(md._metadata)) m.VerifyAll() finally: m.UnsetStubs() def test_nova_meta_with_cache(self): meta_in = {"uuid": "f9431d18-d971-434d-9044-5b38f5b4646f", "availability_zone": "nova", "hostname": "as-wikidatabase-4ykioj3lgi57.novalocal", "launch_index": 0, "meta": {}, "public_keys": {"heat_key": "ssh-rsa etc...\n"}, "name": "as-WikiDatabase-4ykioj3lgi57"} md_str = json.dumps(meta_in) md = cfn_helper.Metadata('teststack', None) with tempfile.NamedTemporaryFile(mode='w+') as default_file: default_file.write(md_str) default_file.flush() self.assertThat(default_file.name, ttm.FileContains(md_str)) meta_out = md.get_nova_meta(cache_path=default_file.name) self.assertEqual(meta_in, meta_out) def test_nova_meta_curl(self): url = 'http://169.254.169.254/openstack/2012-08-10/meta_data.json' temp_home = tempfile.mkdtemp() cache_path = os.path.join(temp_home, 'meta_data.json') def cleanup_temp_home(thome): os.unlink(cache_path) os.rmdir(thome) self.m = mox.Mox() self.addCleanup(self.m.UnsetStubs) self.addCleanup(cleanup_temp_home, temp_home) meta_in = {"uuid": "f9431d18-d971-434d-9044-5b38f5b4646f", "availability_zone": "nova", "hostname": "as-wikidatabase-4ykioj3lgi57.novalocal", "launch_index": 0, "meta": {"freddy": "is hungry"}, "public_keys": {"heat_key": "ssh-rsa etc...\n"}, "name": "as-WikiDatabase-4ykioj3lgi57"} md_str = json.dumps(meta_in) def write_cache_file(*params, **kwargs): with open(cache_path, 'w+') as cache_file: cache_file.write(md_str) cache_file.flush() self.assertThat(cache_file.name, ttm.FileContains(md_str)) self.m.StubOutWithMock(subprocess, 'Popen') subprocess.Popen(['su', 'root', '-c', 'curl -o %s %s' % (cache_path, url)], cwd=None, env=None, stderr=-1, stdout=-1)\ .WithSideEffects(write_cache_file)\ .AndReturn(FakePOpen('Downloaded', '', 0)) self.m.ReplayAll() md = cfn_helper.Metadata('teststack', None) meta_out = md.get_nova_meta(cache_path=cache_path) self.assertEqual(meta_in, meta_out) self.m.VerifyAll() def test_nova_meta_curl_corrupt(self): url = 'http://169.254.169.254/openstack/2012-08-10/meta_data.json' temp_home = tempfile.mkdtemp() cache_path = os.path.join(temp_home, 'meta_data.json') def cleanup_temp_home(thome): os.unlink(cache_path) os.rmdir(thome) self.m = mox.Mox() self.addCleanup(self.m.UnsetStubs) self.addCleanup(cleanup_temp_home, temp_home) md_str = "this { is not really json" def write_cache_file(*params, **kwargs): with open(cache_path, 'w+') as cache_file: cache_file.write(md_str) cache_file.flush() self.assertThat(cache_file.name, ttm.FileContains(md_str)) self.m.StubOutWithMock(subprocess, 'Popen') subprocess.Popen(['su', 'root', '-c', 'curl -o %s %s' % (cache_path, url)], cwd=None, env=None, stderr=-1, stdout=-1)\ .WithSideEffects(write_cache_file)\ .AndReturn(FakePOpen('Downloaded', '', 0)) self.m.ReplayAll() md = cfn_helper.Metadata('teststack', None) meta_out = md.get_nova_meta(cache_path=cache_path) self.assertEqual(None, meta_out) self.m.VerifyAll() def test_nova_meta_curl_failed(self): url = 'http://169.254.169.254/openstack/2012-08-10/meta_data.json' temp_home = tempfile.mkdtemp() cache_path = os.path.join(temp_home, 'meta_data.json') def cleanup_temp_home(thome): os.rmdir(thome) self.m = mox.Mox() self.addCleanup(self.m.UnsetStubs) self.addCleanup(cleanup_temp_home, temp_home) self.m.StubOutWithMock(subprocess, 'Popen') subprocess.Popen(['su', 'root', '-c', 'curl -o %s %s' % (cache_path, url)], cwd=None, env=None, stderr=-1, stdout=-1)\ .AndReturn(FakePOpen('Failed', '', 1)) self.m.ReplayAll() md = cfn_helper.Metadata('teststack', None) meta_out = md.get_nova_meta(cache_path=cache_path) self.assertEqual(None, meta_out) self.m.VerifyAll() def test_get_tags(self): self.m = mox.Mox() self.addCleanup(self.m.UnsetStubs) fake_tags = {'foo': 'fee', 'apple': 'red'} md_data = {"uuid": "f9431d18-d971-434d-9044-5b38f5b4646f", "availability_zone": "nova", "hostname": "as-wikidatabase-4ykioj3lgi57.novalocal", "launch_index": 0, "meta": fake_tags, "public_keys": {"heat_key": "ssh-rsa etc...\n"}, "name": "as-WikiDatabase-4ykioj3lgi57"} tags_expect = fake_tags tags_expect['InstanceId'] = md_data['uuid'] md = cfn_helper.Metadata('teststack', None) self.m.StubOutWithMock(md, 'get_nova_meta') md.get_nova_meta().AndReturn(md_data) self.m.ReplayAll() tags = md.get_tags() self.assertEqual(tags_expect, tags) self.m.VerifyAll() def test_get_instance_id(self): self.m = mox.Mox() self.addCleanup(self.m.UnsetStubs) uuid = "f9431d18-d971-434d-9044-5b38f5b4646f" md_data = {"uuid": uuid, "availability_zone": "nova", "hostname": "as-wikidatabase-4ykioj3lgi57.novalocal", "launch_index": 0, "public_keys": {"heat_key": "ssh-rsa etc...\n"}, "name": "as-WikiDatabase-4ykioj3lgi57"} md = cfn_helper.Metadata('teststack', None) self.m.StubOutWithMock(md, 'get_nova_meta') md.get_nova_meta().AndReturn(md_data) self.m.ReplayAll() self.assertEqual(md.get_instance_id(), uuid) self.m.VerifyAll() class TestCfnInit(MockPopenTestCase): def setUp(self): super(TestCfnInit, self).setUp() self.tdir = self.useFixture(fixtures.TempDir()) self.last_file = os.path.join(self.tdir.path, 'last_metadata') def test_cfn_init(self): with tempfile.NamedTemporaryFile(mode='w+') as foo_file: md_data = {"AWS::CloudFormation::Init": {"config": {"files": { foo_file.name: {"content": "bar"}}}}} md = cfn_helper.Metadata('teststack', None) self.assertTrue( md.retrieve(meta_str=md_data, last_path=self.last_file)) md.cfn_init() self.assertThat(foo_file.name, ttm.FileContains('bar')) def test_cfn_init_with_ignore_errors_false(self): self.mock_cmd_run(['su', 'root', '-c', '/bin/command1']).AndReturn( FakePOpen('Doing something', 'error', -1)) self.m.ReplayAll() md_data = {"AWS::CloudFormation::Init": {"config": {"commands": { "00_foo": {"command": "/bin/command1", "ignoreErrors": "false"}}}}} md = cfn_helper.Metadata('teststack', None) self.assertTrue( md.retrieve(meta_str=md_data, last_path=self.last_file)) self.assertRaises(cfn_helper.CommandsHandlerRunError, md.cfn_init) def test_cfn_init_with_ignore_errors_true(self): self.mock_cmd_run(['su', 'root', '-c', '/bin/command1']).AndReturn( FakePOpen('Doing something', 'error', -1)) self.mock_cmd_run(['su', 'root', '-c', '/bin/command2']).AndReturn( FakePOpen('All good')) self.m.ReplayAll() md_data = {"AWS::CloudFormation::Init": {"config": {"commands": { "00_foo": {"command": "/bin/command1", "ignoreErrors": "true"}, "01_bar": {"command": "/bin/command2", "ignoreErrors": "false"} }}}} md = cfn_helper.Metadata('teststack', None) self.assertTrue( md.retrieve(meta_str=md_data, last_path=self.last_file)) md.cfn_init() class TestSourcesHandler(MockPopenTestCase): def test_apply_sources_empty(self): sh = cfn_helper.SourcesHandler({}) sh.apply_sources() def _test_apply_sources(self, url, end_file): dest = tempfile.mkdtemp() self.addCleanup(os.rmdir, dest) sources = {dest: url} td = os.path.dirname(end_file) self.m.StubOutWithMock(tempfile, 'mkdtemp') tempfile.mkdtemp().AndReturn(td) er = "mkdir -p '%s'; cd '%s'; curl -s '%s' | gunzip | tar -xvf -" cmd = ['su', 'root', '-c', er % (dest, dest, url)] self.mock_cmd_run(cmd).AndReturn(FakePOpen('Curl good')) self.m.ReplayAll() sh = cfn_helper.SourcesHandler(sources) sh.apply_sources() def test_apply_sources_github(self): url = "https://github.com/NoSuchProject/tarball/NoSuchTarball" td = tempfile.mkdtemp() self.addCleanup(os.rmdir, td) end_file = '%s/NoSuchProject-NoSuchTarball.tar.gz' % td self._test_apply_sources(url, end_file) def test_apply_sources_general(self): url = "https://website.no.existe/a/b/c/file.tar.gz" td = tempfile.mkdtemp() self.addCleanup(os.rmdir, td) end_file = '%s/file.tar.gz' % td self._test_apply_sources(url, end_file) def test_apply_source_cmd(self): sh = cfn_helper.SourcesHandler({}) er = "mkdir -p '%s'; cd '%s'; curl -s '%s' | %s | tar -xvf -" dest = '/tmp' # test tgz url = 'http://www.example.com/a.tgz' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "gunzip"), cmd) # test tar.gz url = 'http://www.example.com/a.tar.gz' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "gunzip"), cmd) # test github - tarball 1 url = 'https://github.com/openstack/heat-cfntools/tarball/master' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "gunzip"), cmd) # test github - tarball 2 url = 'https://github.com/openstack/heat-cfntools/tarball/master/' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "gunzip"), cmd) # test tbz2 url = 'http://www.example.com/a.tbz2' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "bunzip2"), cmd) # test tar.bz2 url = 'http://www.example.com/a.tar.bz2' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "bunzip2"), cmd) # test zip er = "mkdir -p '%s'; cd '%s'; curl -s -o '%s' '%s' && unzip -o '%s'" url = 'http://www.example.com/a.zip' d = "/tmp/tmp2I0yNK" tmp = "%s/a.zip" % d self.m.StubOutWithMock(tempfile, 'mkdtemp') tempfile.mkdtemp().AndReturn(d) self.m.ReplayAll() cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, tmp, url, tmp), cmd) # test gz er = "mkdir -p '%s'; cd '%s'; curl -s '%s' | %s > '%s'" url = 'http://www.example.com/a.sh.gz' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "gunzip", "a.sh"), cmd) # test bz2 url = 'http://www.example.com/a.sh.bz2' cmd = sh._apply_source_cmd(dest, url) self.assertEqual(er % (dest, dest, url, "bunzip2", "a.sh"), cmd) # test other url = 'http://www.example.com/a.sh' cmd = sh._apply_source_cmd(dest, url) self.assertEqual("", cmd)

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0.574803

5,261

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5.110815

0.089717

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0.018224

0.028117

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0.72683

0.708346

0.695701

0

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0.282611

48,427

1,305

78

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0.762154

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0.671815

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0.0427

0

0.092664

1

0.062741

false

0.003861

0.009653

0.002896

0.084942

0

null

0

1

0

null

0

6

6e12bf3c6a96788f08fd718e490c142c0c3be633

1,259

py

Python

ffprobe/FFVideoFrame.py

ifij775/ffprobe-python

73868263717825b08345446f2dc90bc8011a7624

[ "MIT" ]

null

ffprobe/FFVideoFrame.py

ifij775/ffprobe-python

73868263717825b08345446f2dc90bc8011a7624

[ "MIT" ]

null

ffprobe/FFVideoFrame.py

ifij775/ffprobe-python

73868263717825b08345446f2dc90bc8011a7624

[ "MIT" ]

null

from ffprobe.FFFrame import FFFrame class FFVideoFrame(FFFrame): def width(self): return int(self._data['width']) def height(self): return int(self._data['height']) def frame_size(self): return (self.width(), self.height()) def pixel_format(self): return self._data['pix_fmt'] def sample_aspect_ratio(self): return self._data['sample_aspect_ratio'] def pict_type(self): return self._data['pict_type'] def coded_picture_number(self): return int(self._data['coded_picture_number']) def display_picture_number(self): return int(self._data['display_picture_number']) def interlaced(self): return (self._data['interlaced_frame']=='1') def top_field_first(self): return (self._data['top_field_first']=='1') def repeat_pict(self): return (self._data['repeat_pict']=='1') def color_range(self): return self._data['color_range'] def color_space(self): return self._data['color_space'] def color_transfer(self): return self._data['color_transfer'] def color_primaries(self): return self._data['color_primaries'] def chroma_location(self): return self._data['chroma_location']

34.972222

56

0.661636

162

1,259

4.839506

0.253086

0.204082

0.214286

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0.257653

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1,259

35

57

35.971429

0.784925

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false

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null

1

0

null

0

1

0

1

0

6

282e93476958c14847d0387442847c1084f198d0

10,691

py

Python

astm/tests/test_client.py

Iskander1b/python-astm

606a77407e59c2f2dd12d65a7b2d2e3c141ad8d9

[ "BSD-3-Clause" ]

38

2015-06-11T06:43:02.000Z

2022-03-01T18:21:07.000Z

astm/tests/test_client.py

Iskander1b/python-astm

606a77407e59c2f2dd12d65a7b2d2e3c141ad8d9

[ "BSD-3-Clause" ]

7

2016-08-12T10:16:34.000Z

2021-02-11T15:43:34.000Z

astm/tests/test_client.py

Iskander1b/python-astm

606a77407e59c2f2dd12d65a7b2d2e3c141ad8d9

[ "BSD-3-Clause" ]

39

2015-08-10T16:49:33.000Z

2021-12-26T10:27:07.000Z

# -*- coding: utf-8 -*- # # Copyright (C) 2012 Alexander Shorin # All rights reserved. # # This software is licensed as described in the file COPYING, which # you should have received as part of this distribution. # import unittest from astm import codec from astm import constants from astm.exceptions import NotAccepted from astm.client import Client from astm.tests.utils import DummyMixIn class DummyClient(DummyMixIn, Client): def __init__(self, *args, **kwargs): super(DummyClient, self).__init__(*args, **kwargs) self.timeout = None def create_socket(self, family, type): pass def connect(self, address): pass class emitter(object): def __init__(self, *args): self.outbox = list(args) self.pos = 0 self.inbox = [] def __iter__(self): return self def next(self): if self.pos >= len(self.outbox): raise StopIteration item = self.outbox[self.pos] self.pos += 1 return item __next__ = next def send(self, value): self.inbox.append(value) return self.next() def put(self, record): self.outbox.append(record) def simple_emitter(): yield ['H'] yield ['L'] class ClientTestCase(unittest.TestCase): def test_open_connection(self): client = DummyClient(simple_emitter) client.handle_connect() self.assertEqual(client.outbox[0], constants.ENQ) def test_fail_on_enq(self): client = DummyClient(emitter) self.assertRaises(NotAccepted, client.on_enq) def test_fail_on_eot(self): client = DummyClient(emitter) self.assertRaises(NotAccepted, client.on_eot) def test_fail_on_message(self): client = DummyClient(emitter) self.assertRaises(NotAccepted, client.on_message) def test_callback_on_sent_failure(self): def emitter(): yield ['H'] assert not (yield ['P']) yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() client.on_nak() def test_emitter_may_send_new_record_after_nak_response(self): def emitter(): yield ['H'] assert (yield ['P']) ok = yield ['O'] if not ok: yield ['R'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() client.on_ack() client.on_nak() self.assertEqual(client.outbox[-1][2:3], b'R') def test_empty_emitter(self): def emitter(): if False: yield client = DummyClient(emitter) client.handle_connect() self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) def test_early_yield(self): def emitter(): yield ['P'] if False: yield ['H'] yield ['L'] client = DummyClient(emitter) client.handle_connect() self.assertRaises(AssertionError, client.on_ack) def test_late_ack(self): def emitter(): if False: yield ['H'] yield ['L'] client = DummyClient(emitter) client.handle_connect() self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) client.on_ack() self.assertEqual(client.outbox[-1], None) def test_dummy_usage(self): def emitter(): yield ['H'] ok = yield ['P'] assert ok ok = yield ['O'] assert ok yield ['L'] client = DummyClient(emitter) client.handle_connect() self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-1][1:3], b'1H') client.on_ack() self.assertEqual(client.outbox[-1][1:3], b'2P') client.on_ack() self.assertEqual(client.outbox[-1][1:3], b'3O') client.on_ack() self.assertEqual(client.outbox[-1][1:3], b'4L') client.on_ack() self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) def test_reject_header(self): def emitter(): assert (yield ['H']) yield ['P'] yield ['O'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() self.assertRaises(AssertionError, client.on_nak) def test_nak_callback(self): def emitter(): yield ['H'] assert not (yield ['P']) yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() client.on_nak() client.on_ack() def test_emit_after_nak(self): def emitter(): yield ['H'] assert not (yield ['P']) yield ['O'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() client.on_nak() client.on_ack() def test_terminate_on_exception_after_nake(self): def emitter(): yield ['H'] assert (yield ['P']) yield ['O'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() self.assertRaises(AssertionError, client.on_nak) self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) def test_messages_workflow(self): def emitter(): yield ['H'] yield ['C'] yield ['P'] yield ['O'] yield ['O'] yield ['P'] yield ['C'] yield ['O'] yield ['O'] yield ['C'] yield ['R'] yield ['C'] yield ['R'] yield ['R'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() while client.outbox[-1] is not None: client.on_ack() def test_session_in_loop(self): def emitter(): for i in range(2): yield ['H'] yield ['P'] yield ['O'] yield ['L'] client = DummyClient(emitter) client.handle_connect() client.on_ack() while client.outbox[-1] is not None: client.on_ack() self.assertEqual(list(client.outbox), [b'\x05', b'\x021H\r\x0389\r\n', b'\x022P\r\x0392\r\n', b'\x023O\r\x0392\r\n', b'\x024L\r\x0390\r\n', b'\x04', b'\x05', b'\x021H\r\x0389\r\n', b'\x022P\r\x0392\r\n', b'\x023O\r\x0392\r\n', b'\x024L\r\x0390\r\n', b'\x04', b'\x05', b'\x04', None]) def test_reject_terminator(self): def emitter(): assert (yield ['H']) assert (yield ['P']) assert (yield ['O']) assert (yield ['L']) client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_ack() client.on_ack() client.on_ack() self.assertEqual(client.outbox[-1][1:3], b'4L') self.assertRaises(AssertionError, client.on_nak) self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) def test_timeout_handler(self): def emitter(): assert (yield ['H']) assert (yield ['P']) assert (yield ['O']) assert (yield ['L']) client = DummyClient(emitter) client.handle_connect() client.on_ack() client.on_timeout() self.assertEqual(client.outbox[-2], constants.EOT) self.assertEqual(client.outbox[-1], None) def test_chunked_response(self): def emitter(): assert (yield ['H', 'foo', 'bar']) assert (yield ['L', 'bar', 'baz']) client = DummyClient(emitter, chunk_size=12) client.handle_connect() client.on_ack() self.assertTrue(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x021H|foo\x1750\r\n') client.on_ack() self.assertFalse(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x022|bar\r\x03F3\r\n') client.on_ack() self.assertTrue(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x023L|bar\x1747\r\n') client.on_ack() self.assertFalse(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x024|baz\r\x03FD\r\n') client.on_ack() self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-1], None) def test_bulk_mode(self): def emitter(): assert (yield ['H', 'foo', 'bar']) assert (yield ['L', 'bar', 'baz']) client = DummyClient(emitter, chunk_size=12, bulk_mode=True) client.handle_connect() client.on_ack() self.assertTrue(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x021H|foo\x1750\r\n') client.on_ack() self.assertTrue(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x022|bar\r\x1707\r\n') client.on_ack() self.assertTrue(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x023L|bar\x1747\r\n') client.on_ack() self.assertFalse(codec.is_chunked_message(client.outbox[-1])) self.assertEqual(client.outbox[-1], b'\x024|baz\r\x03FD\r\n') client.on_ack() self.assertEqual(client.outbox[-1], constants.ENQ) client.on_ack() self.assertEqual(client.outbox[-1], None) if __name__ == '__main__': unittest.main()

30.286119

69

0.55542

1,251

10,691

4.603517

0.138289

0.077791

0.085952

0.173468

0.76489

0.730682

0.717659

0.712971

0.693697

0.653238

0

0.026376

0.308484

10,691

352

70

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0.752604

0.018614

0

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0

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0.008013

0

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1

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false

0.006623

0.019868

0.003311

0.192053

0

null

0

1

0

1

0

1

0

1

0

null

0

6

9544ea04693dd462ba5326ad18ba1eb6b2058f6a

35,662

py

Python

tests/test_query.py

Yiling-J/pharos

a6dd80bd7c0475d78e6490735e3e5fd6eebc28c5

[ "BSD-3-Clause" ]

1

2021-12-03T16:28:41.000Z

tests/test_query.py

Yiling-J/pharos

a6dd80bd7c0475d78e6490735e3e5fd6eebc28c5

[ "BSD-3-Clause" ]

14

2021-04-08T14:16:27.000Z

2021-05-24T15:15:11.000Z

tests/test_query.py

Yiling-J/pharos

a6dd80bd7c0475d78e6490735e3e5fd6eebc28c5

[ "BSD-3-Clause" ]

null

import yaml from unittest import TestCase, mock from jinja2 import PackageLoader, Environment, FileSystemLoader from kubernetes.dynamic import exceptions as api_exceptions from pharos import models, fields, exceptions, lookups, backend, jinja from pharos.jinja import to_yaml from pharos.backend import TemplateBackend class BaseCase(TestCase): def setUp(self): self.dynamic_client = mock.Mock() self.client = mock.Mock() self.client.settings.enable_chunk = True self.client.settings.chunk_size = 100 self.client.settings.jinja_loader = PackageLoader("tests", "./") self.client.settings.template_engine = "pharos.jinja.JinjaEngine" self.client.dynamic_client = self.dynamic_client class DeploymentTestCase(BaseCase): def test_no_client(self): with self.assertRaises(exceptions.ClientNotSet): len(models.Deployment.objects.all()) def test_chunk_iterator(self): mock_response = mock.Mock() response_lambda = lambda token: { "metadata": {"continue": token}, "items": [ { "id": token, "metadata": { "ownerReferences": [{"kind": "Apple", "uid": "123"}], "name": "test", }, } ], } # should call 6 times, and get END signal, so 7 won't be called mock_response.to_dict.side_effect = [ response_lambda(f"{i}") for i in [1, 2, 3, 4, 5, "END", 7] ] self.dynamic_client.resources.get.return_value.get.return_value = mock_response query = models.Deployment.objects.using(self.client).all() self.assertEqual(len(query), 6) expected_call = [ mock.call.get(_continue=None, limit=100), mock.call.get(_continue="1", limit=100), mock.call.get(_continue="2", limit=100), mock.call.get(_continue="3", limit=100), mock.call.get(_continue="4", limit=100), mock.call.get(_continue="5", limit=100), ] self.assertEqual( self.dynamic_client.resources.get.return_value.method_calls, expected_call ) def test_limit_with_iterator(self): mock_response = mock.Mock() response_lambda = lambda token: { "metadata": {"continue": token}, "items": [ { "id": token, "metadata": { "ownerReferences": [{"kind": "Apple", "uid": "123"}], "name": "test", }, } ], } # should call 3 times only mock_response.to_dict.side_effect = [ response_lambda(f"{i}") for i in [1, 2, 3, 4, 5, "END", 7] ] self.dynamic_client.resources.get.return_value.get.return_value = mock_response query = models.Deployment.objects.using(self.client).limit(3) self.assertEqual(len(query), 3) expected_call = [ mock.call.get(_continue=None, limit=100), mock.call.get(_continue="1", limit=100), mock.call.get(_continue="2", limit=100), ] self.assertEqual( self.dynamic_client.resources.get.return_value.method_calls, expected_call ) def test_deployment_query_basic(self): test_cases = [ { "query": models.Deployment.objects.using(self.client).all(), "api_call": {}, }, { "query": models.Deployment.objects.using(self.client).filter( name="apple" ), "api_call": { "name": "apple", }, }, { "query": models.Deployment.objects.using(self.client).filter( name="apple", namespace="orange" ), "api_call": { "name": "apple", "namespace": "orange", }, }, { "query": models.Deployment.objects.using(self.client) .filter(name="apple") .filter(namespace="orange"), "api_call": { "name": "apple", "namespace": "orange", }, }, { "query": models.Deployment.objects.using(self.client).filter( selector="app in (a)" ), "api_call": { "label_selector": "app in (a)", }, }, { "query": models.Deployment.objects.using(self.client) .filter(selector="app in (a)") .filter(selector="app=b"), "api_call": { "label_selector": "app in (a),app=b", }, }, { "query": models.Deployment.objects.using(self.client).filter( field_selector="name=foo" ), "api_call": { "field_selector": "name=foo", }, }, { "query": models.Deployment.objects.using(self.client) .filter(field_selector="name=foo") .filter(field_selector="type=bar"), "api_call": { "field_selector": "name=foo,type=bar", }, }, ] self.dynamic_client.resources.get.return_value.get.return_value.to_dict.side_effect = lambda: { "metadata": {}, "items": ["test"], } for case in test_cases: with self.subTest(case=case): len(case["query"]) self.assertEqual( self.dynamic_client.resources.method_calls, [mock.call.get(api_version="v1", kind="Deployment")], ) self.assertEqual( self.dynamic_client.resources.get.return_value.method_calls, [mock.call.get(**case["api_call"], _continue=None, limit=100)], ) self.dynamic_client.reset_mock() models.Deployment.objects.using(self.client).get( name="apple", namespace="orange" ) self.assertEqual( self.dynamic_client.resources.get.return_value.method_calls, [ mock.call.get( name="apple", namespace="orange", _continue=None, limit=100 ) ], ) def test_owner(self): mock_data = {"kind": "Apple", "metadata": {"uid": "123"}} mock_owner = models.Deployment(client=None, k8s_object=mock_data) mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ { "id": 1, "metadata": { "ownerReferences": [{"kind": "Apple", "uid": "123"}], "name": "test", }, }, { "id": 2, "metadata": {"ownerReferences": [{"kind": "Appl", "uid": "124"}]}, }, { "id": 3, "metadata": {"ownerReferences": [{"kind": "Apple", "uid": "125"}]}, }, {"id": 4, "metadata": {"ownerReferences": [{"kind": "Apple"}]}}, { "id": 6, "metadata": {"ownerReferences": [{"kind": "Apple", "uid": "123"}]}, }, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response query = models.Deployment.objects.using(self.client).filter(owner=mock_owner) self.assertEqual(len(query), 2) mock_owner2 = models.Deployment( client=None, k8s_object={"kind": "Apple", "metadata": {"uid": "124"}} ) query = models.Deployment.objects.using(self.client).filter( owner__in=[mock_owner, mock_owner2] ) self.assertEqual(len(query), 3) deployment = query[0] self.assertEqual(deployment.name, "test") def test_deployment_pods(self): deployment = models.Deployment( client=self.client, k8s_object={ "metadata": {"uid": "123"}, "spec": {"selector": {"matchLabels": {"app": "test"}}}, }, ) mock_rs_response = mock.Mock() mock_rs_response.to_dict.return_value = { "metadata": {}, "items": [ { "id": 1, "metadata": { "ownerReferences": [{"kind": "ReplicaSet", "uid": "123"}], "uid": "234", }, }, { "id": 2, "metadata": { "ownerReferences": [{"kind": "ReplicaSet", "uid": "124"}], "uid": "235", }, }, { "id": 3, "metadata": { "ownerReferences": [{"kind": "ReplicaSet", "uid": "123"}], "uid": "236", }, }, ], } mock_pod_response = mock.Mock() mock_pod_response.to_dict.return_value = { "metadata": {}, "items": [ { "id": 1, "metadata": { "ownerReferences": [{"kind": "ReplicaSet", "uid": "234"}] }, }, { "id": 2, "metadata": { "ownerReferences": [{"kind": "ReplicaSet", "uid": "235"}] }, }, {"id": 4, "metadata": {"ownerReferences": [{"kind": "ReplicaSet"}]}}, ], } # pod come first because owner filter is POST operator self.dynamic_client.resources.get.return_value.get.side_effect = [ mock_pod_response, mock_rs_response, ] self.assertEqual(len(deployment.pods.all()), 1) def test_refresh(self): deployment = models.Deployment( client=self.client, k8s_object={ "metadata": {"uid": "123", "name": "foo"}, "spec": {"selector": {"matchLabels": {"app": "test"}}}, }, ) self.assertEqual(deployment.name, "foo") mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: {"metadata": {"name": "bar"}} self.dynamic_client.resources.get.return_value.get.return_value = mock_response deployment.refresh() self.assertEqual(deployment.name, "bar") def test_delete(self): deployment = models.Deployment( client=self.client, k8s_object={ "metadata": { "name": "nginx-deployment", "annotations": { "deployment.kubernetes.io/revision": "1", "pharos.py/template": "test.yaml", "pharos.py/variable": "deployment-nginx-deployment-default", }, "spec": {"selector": {"matchLabels": {"app": "test"}}}, } }, ) mock_response = { "metadata": { "name": "nginx-deployment", "namespace": "default", "annotations": { "deployment.kubernetes.io/revision": "1", "pharos.py/template": "test.yaml", "pharos.py/variable": "deployment-nginx-deployment-default", }, }, "json": {"label_name": "foo"}, } self.dynamic_client.resources.get.return_value.get.return_value.to_dict.return_value = ( mock_response ) deployment.delete() self.assertSequenceEqual( self.dynamic_client.resources.method_calls, [ mock.call.get(api_version="v1", kind="Deployment"), mock.call.get(api_version="pharos.py/v1", kind="Variable"), mock.call.get(api_version="v1", kind="Deployment"), ], ) self.assertSequenceEqual( self.dynamic_client.resources.get.return_value.method_calls, [ mock.call.get(name="nginx-deployment", namespace="default"), mock.call.delete("deployment-nginx-deployment-default", None), mock.call.delete("nginx-deployment", "default"), ], ) def test_create_deployment_wrong_resource(self): mock_response = { "metadata": { "name": "foobar", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, } } self.dynamic_client.resources.get.return_value.create.return_value.to_dict.return_value = ( mock_response ) with self.assertRaises(exceptions.ResourceNotMatch): models.Service.objects.using(self.client).create( "test.yaml", {"label_name": "foo"} ) class ServicePodsTestCase(BaseCase): def test_service_pods(self): service = models.Service( client=self.client, k8s_object={ "metadata": {"uid": "123"}, "spec": {"selector": {"foo": "bar"}}, }, ) mock_rs_response = mock.Mock() mock_rs_response.to_dict.return_value = {} self.dynamic_client.resources.get.return_value.get.return_value = ( mock_rs_response ) len(service.pods.all()) self.assertEqual( self.dynamic_client.resources.get.return_value.method_calls, [mock.call.get(_continue=None, label_selector="foo=bar", limit=100, namespace=None)], ) class CustomLookup(lookups.Lookup): name = "foo" type = lookups.Lookup.POST def validate(self, obj, data): return True fields.JsonPathField.add_lookup(CustomLookup) class CustomModel(models.Model): id = fields.JsonPathField(path="id") task = fields.JsonPathField(path="job.task") class Meta: api_version = "v1" kind = "CustomModel" class CustomModelTestCase(BaseCase): def test_custom_model(self): mock_data = { "kind": "CustomModel", "job": {"task": "task1"}, "metadata": {"name": "custom", "namespace": "default"}, } mock_obj = CustomModel(client=None, k8s_object=mock_data) self.assertEqual(mock_obj.task, "task1") self.assertEqual(mock_obj.name, "custom") self.assertEqual(mock_obj.namespace, "default") def test_custom_filed_filter(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ {"id": 1, "job": {"task": "task1"}}, {"id": 2, "job": {"task": "task2"}}, {"id": 3, "job": {"task": "task3"}}, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter(task="task3") self.assertEqual(len(queryset), 1) self.assertEqual(queryset[0].task, "task3") queryset = CustomModel.objects.using(self.client).filter( task__in=["task1", "task3"] ) self.assertEqual(len(queryset), 2) self.assertEqual(queryset[0].task, "task1") self.assertEqual(queryset[1].task, "task3") def test_custom_lookup(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [{"id": 1, "job": {"task": "task1"}}], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter(task__foo="task3") self.assertEqual(len(queryset), 1) def test_contains(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ {"id": 1, "job": {"task": "foo"}}, {"id": 2, "job": {"task": "bar"}}, {"id": 3, "job": {"task": "barfoobar"}}, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter(task__contains="foo") self.assertEqual(len(queryset), 2) def test_contains_list(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ {"id": 1, "job": {"task": ["foo"]}}, {"id": 2, "job": {"task": ["foo", "bar"]}}, {"id": 3, "job": {"task": ["foo", "bar", "new"]}}, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter( task__contains=["foo", "new"] ) self.assertEqual(len(queryset), 1) self.assertEqual(queryset[0].task, ["foo", "bar", "new"]) def test_startswith(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ {"id": 1, "job": {"task": "foofoo"}}, {"id": 2, "job": {"task": "fobar"}}, {"id": 3, "job": {"task": "barfoobar"}}, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter(task__startswith="foo") self.assertEqual(len(queryset), 1) def test_compare(self): mock_response = mock.Mock() mock_response.to_dict.side_effect = lambda: { "metadata": {}, "items": [ {"id": 1, "job": {"task": "foofoo"}}, {"id": 2, "job": {"task": "fobar"}}, {"id": 3, "job": {"task": "barfoobar"}}, ], } self.dynamic_client.resources.get.return_value.get.return_value = mock_response queryset = CustomModel.objects.using(self.client).filter(id__gt=1) self.assertEqual(len(queryset), 2) queryset = CustomModel.objects.using(self.client).filter(id__gt=2) self.assertEqual(len(queryset), 1) queryset = CustomModel.objects.using(self.client).filter(id__gte=2) self.assertEqual(len(queryset), 2) queryset = CustomModel.objects.using(self.client).filter(id__lt=4) self.assertEqual(len(queryset), 3) queryset = CustomModel.objects.using(self.client).filter(id__lt=1) self.assertEqual(len(queryset), 0) queryset = CustomModel.objects.using(self.client).filter(id__lte=1) self.assertEqual(len(queryset), 1) class Step: parent = None client = None class GetSpec(Step): parent = mock.call.resources def __init__(self, api_version, kind, inherit=False): self.api_version = api_version self.kind = kind self.inherit = inherit @property def call(self): return self.parent.get(api_version=self.api_version, kind=self.kind) class GetResource(Step): parent = mock.call.resources.get() def __init__(self, name, namespace, inherit=False, limit=False): self.name = name self.namespace = namespace self.inherit = inherit self.limit = limit @property def call(self): params = {'name': self.name, 'namespace': self.namespace} if self.limit: params['_continue'] = None params['limit'] = 100 return self.parent.get(**params) class CreateResource(Step): parent = mock.call.resources.get() def __init__( self, template, variable, namespace="default", inherit=False, internal=False, dry_run=False, ): self.template = template self.variable = variable self.namespace = namespace self.inherit = inherit self.internal = internal self.dry_run = dry_run @property def call(self): loader = FileSystemLoader("./tests") engine = jinja.JinjaEngine(self.client, loader=loader, internal=self.internal) template_backend = backend.TemplateBackend() template_backend.set_engine(engine) body = template_backend.render( self.namespace, self.template, self.variable, self.internal ) params = {"body": body, "namespace": self.namespace} if self.dry_run: params["query_params"] = [("dryRun", "All")] return self.parent.create(**params) class UpdateResource(Step): parent = mock.call.resources.get() def __init__( self, template, variable, namespace="default", inherit=False, internal=False, dry_run=False, resource_version=None ): self.template = template self.variable = variable self.namespace = namespace self.inherit = inherit self.internal = internal self.dry_run = dry_run self.resource_version = resource_version @property def call(self): loader = FileSystemLoader("./tests") engine = jinja.JinjaEngine(self.client, loader=loader, internal=self.internal) template_backend = backend.TemplateBackend() template_backend.set_engine(engine) body = template_backend.render( self.namespace, self.template, self.variable, self.internal ) body["metadata"]["resourceVersion"] = self.resource_version params = {"body": body, "namespace": self.namespace} params["query_params"] = [] if self.dry_run: params["query_params"] = [("dryRun", "All")] return self.parent.replace(**params) class DeleteResource(Step): parent = mock.call.resources.get() def __init__(self, name, namespace, inherit=False): self.name = name self.namespace = namespace self.inherit = inherit @property def call(self): return self.parent.delete(self.name, self.namespace) class ToDict(Step): parent = mock.call.resources.get().create() def __init__(self, inherit=False): self.inherit = inherit @property def call(self): return self.parent.to_dict() class ResourceCreateTestCase(BaseCase): def assertQuery(self, steps, query): expected_calls = [] for step in steps: step.client = self.client if step.inherit: step.parent = expected_calls[-1] expected_calls.append(step.call) query() self.assertSequenceEqual(self.dynamic_client.mock_calls, expected_calls) def test_create_deployment(self): mock_response = { "metadata": { "name": "foobar", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, } } self.dynamic_client.resources.get.return_value.create.return_value.to_dict.return_value = ( mock_response ) expected_steps = [ GetSpec("v1", "Deployment"), CreateResource("test.yaml", {"label_name": "foo"}, inherit=True), ToDict(inherit=True), GetSpec("apiextensions.k8s.io/v1", "CustomResourceDefinition"), CreateResource("variable_crd.yaml", {}, inherit=True, internal=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), CreateResource( "variables.yaml", {"name": "deployment-foobar-default", "value": {"label_name": "foo"}}, inherit=True, internal=True, ), ToDict(inherit=True), ] query = lambda: models.Deployment.objects.using(self.client).create( "test.yaml", {"label_name": "foo"} ) self.assertQuery(expected_steps, query) def test_create_deployment_namespace(self): mock_response = { "metadata": { "name": "foobar", "namespace": "test", "annotations": {"pharos.py/template": "test.yaml"}, } } self.dynamic_client.resources.get.return_value.create.return_value.to_dict.return_value = ( mock_response ) expected_steps = [ GetSpec("v1", "Deployment"), CreateResource( "test.yaml", {"label_name": "foo"}, inherit=True, namespace="test" ), ToDict(inherit=True), GetSpec("apiextensions.k8s.io/v1", "CustomResourceDefinition"), CreateResource("variable_crd.yaml", {}, inherit=True, internal=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), CreateResource( "variables.yaml", {"name": "deployment-foobar-test", "value": {"label_name": "foo"}}, inherit=True, internal=True, namespace="test", ), ToDict(inherit=True), ] query = lambda: models.Deployment.objects.using(self.client).create( "test.yaml", {"label_name": "foo"}, namespace="test" ) self.assertQuery(expected_steps, query) def test_create_deployment_dry(self): mock_response = { "metadata": { "name": "foobar", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, } } self.dynamic_client.resources.get.return_value.create.return_value.to_dict.return_value = ( mock_response ) expected_steps = [ GetSpec("v1", "Deployment"), CreateResource( "test.yaml", {"label_name": "foo"}, inherit=True, dry_run=True ), ToDict(inherit=True), ] query = lambda: models.Deployment.objects.using(self.client).create( "test.yaml", {"label_name": "foo"}, dry_run=True ) self.assertQuery(expected_steps, query) class ResourceUpdateTestCase(BaseCase): def assertQuery(self, steps, query): expected_calls = [] for step in steps: step.client = self.client if step.inherit: step.parent = expected_calls[-1] expected_calls.append(step.call) query() self.assertSequenceEqual(self.dynamic_client.mock_calls, expected_calls) def test_sync_deployment(self): mock_response = { "metadata": { "name": "foobar", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, } } self.dynamic_client.resources.get.return_value.create.return_value.to_dict.return_value = ( mock_response ) self.dynamic_client.resources.get.return_value.replace.return_value.to_dict.return_value = ( mock_response ) deployment = models.Deployment( client=self.client, k8s_object={ "metadata": { "name": "nginx-deployment", "annotations": { "deployment.kubernetes.io/revision": "1", }, "spec": {"selector": {"matchLabels": {"app": "test"}}}, } }, ) query = lambda: deployment.sync("test.yaml", {"label_name": "foo"}) expected_steps = [ GetSpec("v1", "Deployment"), GetResource('nginx-deployment', 'default', inherit=True), ToDict(inherit=True), GetSpec("v1", "Deployment"), UpdateResource( "test.yaml", {"label_name": "foo"}, inherit=True ), ToDict(inherit=True), GetSpec("apiextensions.k8s.io/v1", "CustomResourceDefinition"), CreateResource("variable_crd.yaml", {}, inherit=True, internal=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), DeleteResource('deployment-foobar-default', None), GetSpec("pharos.py/v1", "Variable"), CreateResource( "variables.yaml", {"name": "deployment-foobar-default", "value": {"label_name": "foo"}}, inherit=True, internal=True, ), ToDict(inherit=True), ] self.assertQuery(expected_steps, query) def test_update_deployment(self): mock_response = { "metadata": { "name": "nginx-deployment", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, }, "json": {"label_name": "foo"}, } self.dynamic_client.resources.get.return_value.get.return_value.to_dict.return_value = ( mock_response ) self.dynamic_client.resources.get.return_value.replace.return_value.to_dict.return_value = ( mock_response ) deployment = models.Deployment( client=self.client, k8s_object={ "metadata": { "name": "nginx-deployment", "annotations": { "deployment.kubernetes.io/revision": "1", "pharos.py/template": "test.yaml", "pharos.py/variable": "deployment-nginx-deployment-default", }, "spec": {"selector": {"matchLabels": {"app": "test"}}}, } }, ) query = lambda: deployment.deploy() expected_steps = [ GetSpec("v1", "Deployment"), GetResource('nginx-deployment', 'default', inherit=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), GetResource('deployment-nginx-deployment-default', 'default', inherit=True, limit=True), ToDict(inherit=True), GetSpec("v1", "Deployment"), UpdateResource( "test.yaml", {"label_name": "foo"}, inherit=True ), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), UpdateResource( "variables.yaml", {"name": "deployment-nginx-deployment-default", "value": {"label_name": "foo"}}, namespace='default', inherit=True, internal=True, ), ToDict(inherit=True) ] self.assertQuery(expected_steps, query) def test_update_deployment_dry(self): mock_response = { "metadata": { "name": "nginx-deployment", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, }, "json": {"label_name": "foo"}, } self.dynamic_client.resources.get.return_value.get.return_value.to_dict.return_value = ( mock_response ) self.dynamic_client.resources.get.return_value.replace.return_value.to_dict.return_value = ( mock_response ) deployment = models.Deployment( client=self.client, k8s_object={ "metadata": { "name": "nginx-deployment", "annotations": { "deployment.kubernetes.io/revision": "1", "pharos.py/template": "test.yaml", "pharos.py/variable": "deployment-nginx-deployment-default", }, "spec": {"selector": {"matchLabels": {"app": "test"}}}, } }, ) query = lambda: deployment.deploy(dry_run=True) expected_steps = [ GetSpec("v1", "Deployment"), GetResource('nginx-deployment', 'default', inherit=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), GetResource('deployment-nginx-deployment-default', 'default', inherit=True, limit=True), ToDict(inherit=True), GetSpec("v1", "Deployment"), UpdateResource( "test.yaml", {"label_name": "foo"}, inherit=True, dry_run=True ), ToDict(inherit=True), ] self.assertQuery(expected_steps, query) def test_update_deployment_variable(self): mock_response = { "metadata": { "name": "nginx-deployment", "namespace": "default", "annotations": {"pharos.py/template": "test.yaml"}, }, "json": {"label_name": "foo"}, } self.dynamic_client.resources.get.return_value.get.return_value.to_dict.return_value = ( mock_response ) self.dynamic_client.resources.get.return_value.replace.return_value.to_dict.return_value = ( mock_response ) deployment = models.Deployment( client=self.client, k8s_object={ "metadata": { "name": "nginx-deployment", "annotations": { "deployment.kubernetes.io/revision": "1", "pharos.py/template": "test.yaml", "pharos.py/variable": "deployment-nginx-deployment-default", }, "spec": {"selector": {"matchLabels": {"app": "test"}}}, } }, ) deployment.set_variable({"label_name": "bar"}) query = lambda: deployment.deploy() expected_steps = [ GetSpec("v1", "Deployment"), GetResource('nginx-deployment', 'default', inherit=True), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), GetResource('deployment-nginx-deployment-default', 'default', inherit=True, limit=True), ToDict(inherit=True), GetSpec("v1", "Deployment"), UpdateResource( "test.yaml", {"label_name": "bar"}, inherit=True ), ToDict(inherit=True), GetSpec("pharos.py/v1", "Variable"), UpdateResource( "variables.yaml", {"name": "deployment-nginx-deployment-default", "value": {"label_name": "bar"}}, namespace='default', inherit=True, internal=True, ), ToDict(inherit=True) ] self.assertQuery(expected_steps, query)

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6

959f6e1cc40b940116e8a1e57c955d1368605067

58

py

Python

__init__.py

hoefkensj/reroot

890e6086272577d599ced9fe52ddac1bfce60d85

[ "Unlicense" ]

null

__init__.py

hoefkensj/reroot

890e6086272577d599ced9fe52ddac1bfce60d85

[ "Unlicense" ]

null

__init__.py

hoefkensj/reroot

890e6086272577d599ced9fe52ddac1bfce60d85

[ "Unlicense" ]

null

#!/usr/bin/env python from . import main from . import cfg

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null

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1

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0

1

0

6

252201a349373fe37cf0e804e0412ea1052bd627

236

py

Python

pages/urls.py

Louis86/ecommerce

d47f13bfe4772b33a9cb6b8ec08183525fc4655d

[ "MIT" ]

null

pages/urls.py

Louis86/ecommerce

d47f13bfe4772b33a9cb6b8ec08183525fc4655d

[ "MIT" ]

null

pages/urls.py

Louis86/ecommerce

d47f13bfe4772b33a9cb6b8ec08183525fc4655d

[ "MIT" ]

null

from django.urls import path from . import views #add parmelink urlpatterns = [ path('', views.index, name='index'), #path('', views.index, {'pagename':''}, name='home'), #path('<str:pagename>', views.index, name='index') ]

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16,530

py

Python

AuxillaryFunctions/GWASPlots.py

daverblair/CrypticPhenotypeAnalysisScripts

0d722f079549ac68b7863ac885f7295d236806b0

[ "MIT" ]

null

AuxillaryFunctions/GWASPlots.py

daverblair/CrypticPhenotypeAnalysisScripts

0d722f079549ac68b7863ac885f7295d236806b0

[ "MIT" ]

null

AuxillaryFunctions/GWASPlots.py

daverblair/CrypticPhenotypeAnalysisScripts

0d722f079549ac68b7863ac885f7295d236806b0

[ "MIT" ]

null

import pandas as pd import numpy as np from scipy.stats.mstats import mquantiles from scipy.stats import spearmanr,chi2,beta import matplotlib.pyplot as plt from matplotlib import cm import seaborn as sns sns.set(context='talk',color_codes=True,style='ticks',font='Arial',font_scale=2,rc={'axes.linewidth':5,"font.weight":"bold",'axes.labelweight':"bold",'xtick.major.width':4,'xtick.minor.width': 2}) cmap = cm.get_cmap('viridis', 12) color_list=[cmap(x) for x in [0.0,0.1,0.25,0.5,0.75,0.9,1.0]] grey_color=(0.25, 0.25, 0.25) red_color = '#d10e00' blue_color='#5DA4FF' def LambdaGC(p_val_vec,quantile_list=[0.5],scaling_factor=None): quantile_list=np.array(quantile_list) obs_pval_quantiles=mquantiles(p_val_vec,prob=quantile_list,alphap=1.0,betap=1.0) if scaling_factor is not None: lambda_unscaled = chi2.ppf(1.0-obs_pval_quantiles, 1) / chi2.ppf(1.0-quantile_list,1) return 1+(lambda_unscaled-1.0)*(scaling_factor/p_val_vec.shape[0]) else: return chi2.ppf(1.0-obs_pval_quantiles, 1) / chi2.ppf(1.0-quantile_list,1) def QQPlot(data_table,p_value_column=None,maf_column=None,freq_bins=None,n_quantiles=1000,error_ci=0.95,min_p=1e-30,hide_hla=False,error_type='experimental',lambda_gc_scale=None): f, axis = plt.subplots(1, 1,figsize=(8,8)) axis.spines['right'].set_visible(False) axis.spines['top'].set_visible(False) if p_value_column==None: p_value_column='P' if maf_column==None: if 'MAF' in data_table.columns: maf_column='MAF' else: data_table['MAF']=np.zeros(len(data_table))*np.nan if hide_hla: chr6 = data_table.loc[(data_table.CHROM==6)] excluded=chr6.index[np.logical_and(chr6.POS>=28477797,chr6.POS<=33448354)] p_maf_table=data_table.drop(excluded)[[maf_column,p_value_column]] elif maf_column is not None: p_maf_table=data_table[[maf_column,p_value_column]] else: p_maf_table=data_table[[p_value_column]] assert error_type in ['experimental','theoretical'],"Error type must be in ['experimental','theoretical']" min_vals_obs=[] min_vals_exp=[] if freq_bins is None: p_input= p_maf_table[p_value_column].values p_input[p_input<min_p]=min_p quantile_thresholds = np.concatenate([np.arange(1,np.floor(0.5*n_quantiles))/p_input.shape[0], np.logspace(np.log10(np.floor(0.5*n_quantiles)/p_input.shape[0]), 0, int(np.ceil(0.5*n_quantiles))+1)[:-1]]) obs_quantiles = mquantiles(p_input, prob=quantile_thresholds, alphap=0.0, betap=1.0, limit=(0.0, 1.0)) axis.plot(-np.log10(quantile_thresholds),-np.log10(obs_quantiles),'.',color=color_list[0],ms=15) if lambda_gc_scale is not None: axis.text(1,5,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0])+' ('+r'$\lambda^{'+'{0:d}'.format(lambda_gc_scale)+'}_{IF}$'+'={0:1.3f}'.format(LambdaGC(p_input,scaling_factor=lambda_gc_scale)[0])+')',fontsize=24,fontweight='bold',color=color_list[0]) else: axis.text(1,5,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0]),fontsize=24,fontweight='bold',color=color_list[0]) min_vals_obs+=[obs_quantiles.min()] min_vals_exp+=[quantile_thresholds.min()] if error_type=='experimental': ci_vecs = beta.interval(error_ci, len(p_maf_table)*quantile_thresholds, len(p_maf_table) - len(p_maf_table)*quantile_thresholds) axis.fill_between( -np.log10(quantile_thresholds), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[0]), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[1]), color=color_list[0], alpha=0.25, label='{0:2d}% CI'.format(int(100*error_ci))) else: for i in range(len(freq_bins)-2): p_input= p_maf_table[np.logical_and(p_maf_table[maf_column]>=freq_bins[i],p_maf_table[maf_column]<freq_bins[i+1])][p_value_column].values p_input[p_input<min_p]=min_p quantile_thresholds = np.concatenate([np.arange(1,np.floor(0.5*n_quantiles))/p_input.shape[0], np.logspace(np.log10(np.floor(0.5*n_quantiles)/p_input.shape[0]), 0, int(np.ceil(0.5*n_quantiles))+1)[:-1]]) obs_quantiles = mquantiles(p_input, prob=quantile_thresholds, alphap=0.0, betap=1.0, limit=(0.0, 1.0)) axis.plot(-np.log10(quantile_thresholds),-np.log10(obs_quantiles),'.',ms=15,color=color_list[(i*2)%len(color_list)],label=r'{0:.1e}$\leq$ MAF$<${1:.1e}'.format(freq_bins[i],freq_bins[i+1])) if error_type=='experimental': ci_vecs = beta.interval(error_ci, len(p_maf_table)*quantile_thresholds, len(p_maf_table) - len(p_maf_table)*quantile_thresholds) axis.fill_between( -np.log10(quantile_thresholds), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[0]), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[1]), color=color_list[(i*2)%len(color_list)], alpha=0.25, label='{0:2d}% CI'.format(int(100*error_ci))) if lambda_gc_scale is not None: axis.text(1,5-i,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0])+' ('+r'$\lambda^{'+'{0:d}'.format(lambda_gc_scale)+'}_{IF}$'+'={0:1.3f}'.format(LambdaGC(p_input,scaling_factor=lambda_gc_scale)[0])+')',fontsize=24,fontweight='bold',color=color_list[i*2]) else: axis.text(1,5-i,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0]),fontsize=24,fontweight='bold',color=color_list[i*2]) min_vals_obs+=[obs_quantiles.min()] min_vals_exp+=[quantile_thresholds.min()] i+=1 p_input= p_maf_table[np.logical_and(p_maf_table[maf_column]>=freq_bins[i],p_maf_table[maf_column]<=freq_bins[i+1])][p_value_column].values p_input[p_input<min_p]=min_p quantile_thresholds = np.concatenate([np.arange(1,np.floor(0.5*n_quantiles))/p_input.shape[0], np.logspace(np.log10(np.floor(0.5*n_quantiles)/p_input.shape[0]), 0, int(np.ceil(0.5*n_quantiles))+1)[:-1]]) obs_quantiles = mquantiles(p_input, prob=quantile_thresholds, alphap=0.0, betap=1.0, limit=(0.0, 1.0)) axis.plot(-np.log10(quantile_thresholds),-np.log10(obs_quantiles),'o',color=color_list[(i*2)%len(color_list)],mew=0.0,label=r'{0:.1e}$\leq$ MAF$\leq${1:.1e}'.format(freq_bins[i],0.5)) if error_type=='experimental': ci_vecs = beta.interval(error_ci, len(p_maf_table)*quantile_thresholds, len(p_maf_table) - len(p_maf_table)*quantile_thresholds) axis.fill_between( -np.log10(quantile_thresholds), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[0]), -np.log10(obs_quantiles/quantile_thresholds*ci_vecs[1]), color=color_list[(i*2)%len(color_list)], alpha=0.25, label='{0:2d}% CI'.format(int(100*error_ci))) if lambda_gc_scale is not None: axis.text(1,5-i,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0])+' ('+r'$\lambda^{'+'{0:d}'.format(lambda_gc_scale)+'}_{IF}$'+'={0:1.3f}'.format(LambdaGC(p_input,scaling_factor=lambda_gc_scale)[0])+')',fontsize=24,fontweight='bold',color=color_list[i*2]) else: axis.text(1,5-i,r'$\lambda_{IF}$'+'={0:1.2f}'.format(LambdaGC(p_input)[0]),fontsize=24,fontweight='bold',color=color_list[i*2]) min_vals_obs+=[obs_quantiles.min()] min_vals_exp+=[quantile_thresholds.min()] axis.set_xlim(0.0,np.ceil(-np.log10(min(min_vals_exp)))) exp_p_vals = np.linspace(0,axis.get_xlim()[1],100) if error_type=='theoretical': ci_vecs = beta.interval(error_ci, len(p_maf_table)*(10**(-1.0*exp_p_vals)), len(p_maf_table) - len(p_maf_table)*(10**(-1.0*exp_p_vals))) axis.fill_between(exp_p_vals, -np.log10(ci_vecs[0]), -np.log10(ci_vecs[1]), color=grey_color, alpha=0.25, label='{0:2d}% CI'.format(int(100*error_ci))) axis.plot(exp_p_vals,exp_p_vals,'--',color=red_color,lw=3.0) axis.set_ylim(0.0,np.ceil(-np.log10(min(min(min_vals_obs),ci_vecs[0].min(),min(min_vals_obs))))+1) axis.legend(loc='upper left',frameon=False,fontsize=14) axis.set_xlabel(r'$\log_{10}$(P-Value)'+'\nExpected',fontsize=24) axis.set_ylabel(r'$\log_{10}$(P-Value)'+'\nObserved',fontsize=24) return f,axis def ManhattanPlot(data_table,p_value_column='P',chrom_column='CHROM',pos_column='POS',allele_freq_window=None,maf_column=None,marked_column=None,all_sig_thresh=[5e-8],chrom_colors = [color_list[0],color_list[3]],alpha_min=1.0,min_p=1e-30,hide_hla=False,thin_data=True,thin_data_thresh=1e-4): f, axis = plt.subplots(1, 1,figsize=(24,8)) axis.spines['right'].set_visible(False) axis.spines['top'].set_visible(False) axis.spines['bottom'].set_visible(False) included_columns=[chrom_column,pos_column,p_value_column] if marked_column is not None: included_columns+=[marked_column] if allele_freq_window is not None: if maf_column is None: fig_table=data_table[np.logical_and(data_table.MAF>=allele_freq_window[0],data_table.MAF<allele_freq_window[1])][included_columns] else: fig_table=data_table[np.logical_and(data_table[maf_column]>=allele_freq_window[0],data_table[maf_column]<allele_freq_window[1])][included_columns] else: fig_table=data_table[included_columns] if hide_hla: chr6 = fig_table.loc[(fig_table[chrom_column]==6)] excluded=chr6.index[np.logical_and(chr6[pos_column]>=28477797,chr6[pos_column]<=33448354)] fig_table=fig_table.drop(excluded)[included_columns] axis.set_xlim(0.0,1.05) axis.set_ylim(0.0,np.ceil(min(-np.log10(fig_table[p_value_column].min()),-np.log10(min_p)))) all_chrom = np.unique(fig_table[chrom_column]) all_chrom.sort() offsets = np.zeros(all_chrom.shape[0]) total_bps=0.0 for i,c in enumerate(all_chrom): offsets[i]=total_bps total_bps+=np.max(fig_table.loc[fig_table[chrom_column]==c][pos_column].values) offsets/=total_bps offsets=np.append(offsets,1.0) for i,c in enumerate(all_chrom): current_chrom = fig_table.loc[fig_table[chrom_column]==c] plot_table=pd.DataFrame(index=current_chrom.index) plot_table['logP']=-np.log10(current_chrom[p_value_column]) plot_table.loc[plot_table.logP>(-np.log10(min_p))]=-np.log10(min_p) plot_table['pos']=(current_chrom[pos_column]/total_bps)+offsets[i] if marked_column is not None: plot_table['mark']=current_chrom[marked_column] if thin_data: #thins p-values less than 1e-5 by rounding and taking only unique values plot_table['logP_R']=plot_table['logP'].values plot_table.loc[plot_table.logP<(-np.log10(thin_data_thresh)),'logP_R']=np.round(plot_table.loc[plot_table.logP<(-np.log10(thin_data_thresh))]['logP_R'].values,1) plot_table['pos_R']=np.round(plot_table['pos']*5,2) plot_table=plot_table.drop_duplicates(['logP_R','pos_R']) rgba_colors = np.zeros((plot_table.shape[0],4)) rgba_colors[:,0:4] = np.array(chrom_colors[i%2]) alpha_levels = (1.0-alpha_min)*(plot_table.logP)/(-np.log10(min_p))+alpha_min alpha_levels[alpha_levels>1.0]=1.0 rgba_colors[:,3]=alpha_levels axis.scatter(plot_table.pos,plot_table.logP,s=75.0,marker='o',color=rgba_colors,lw=0.0) if marked_column is not None: axis.scatter(plot_table.loc[plot_table['mark']==True].pos,plot_table.loc[plot_table['mark']==True].logP,s=75.0,marker='*',color=np.array(red_color),lw=0.0) for sig_thresh in all_sig_thresh: axis.hlines(-np.log10(sig_thresh),0.0,1.0,linestyle='--',color=red_color,alpha=0.75,lw=2.0) axis.text(0.1,-np.log10(sig_thresh)+0.05*axis.get_ylim()[1],r'Sig. Level {0:.1e}'.format(sig_thresh),fontsize=12) axis.set_ylabel(r"$P$-Value"+'\n'+r'($-\log_{10}$-Scale)',fontsize=24) axis.set_xlabel('Chromsome',fontsize=24) chrom_locators = offsets[:-1]+(offsets[1:]-offsets[:-1])/2.0 axis.xaxis.set_major_locator(plt.FixedLocator(chrom_locators[0::2])) axis.xaxis.set_major_formatter(plt.FixedFormatter(np.array(all_chrom,dtype=np.str)[0::2])) axis.xaxis.set_minor_locator(plt.FixedLocator(chrom_locators[1::2])) return f,axis def ManhattanPlot(data_table,p_value_column='P',chrom_column='CHROM',pos_column='POS',allele_freq_window=None,maf_column=None,marked_column=None,snp_to_gene=None,all_sig_thresh=[5e-8],chrom_colors = [color_list[0],color_list[3]],alpha_min=1.0,min_p=1e-30,hide_hla=False,thin_data=True,thin_data_thresh=1e-6): f, axis = plt.subplots(1, 1,figsize=(24,8)) axis.spines['right'].set_visible(False) axis.spines['top'].set_visible(False) axis.spines['bottom'].set_visible(False) included_columns=[chrom_column,pos_column,p_value_column] if marked_column is not None: included_columns+=[marked_column] if allele_freq_window is not None: if maf_column is None: fig_table=data_table[np.logical_and(data_table.MAF>=allele_freq_window[0],data_table.MAF<allele_freq_window[1])][included_columns] else: fig_table=data_table[np.logical_and(data_table[maf_column]>=allele_freq_window[0],data_table[maf_column]<allele_freq_window[1])][included_columns] else: fig_table=data_table[included_columns] if hide_hla: chr6 = fig_table.loc[(fig_table[chrom_column]==6)] excluded=chr6.index[np.logical_and(chr6[pos_column]>=28477797,chr6[pos_column]<=33448354)] fig_table=fig_table.drop(excluded)[included_columns] axis.set_xlim(0.0,1.05) axis.set_ylim(0.0,np.ceil(min(-np.log10(fig_table[p_value_column].min()),-np.log10(min_p)))) all_chrom = np.unique(fig_table[chrom_column]) all_chrom.sort() offsets = np.zeros(all_chrom.shape[0]) total_bps=0.0 for i,c in enumerate(all_chrom): offsets[i]=total_bps total_bps+=np.max(fig_table.loc[fig_table[chrom_column]==c][pos_column].values) offsets/=total_bps offsets=np.append(offsets,1.0) for i,c in enumerate(all_chrom): current_chrom = fig_table.loc[fig_table[chrom_column]==c] plot_table=pd.DataFrame(index=current_chrom.index) plot_table['logP']=-np.log10(current_chrom[p_value_column]) plot_table.loc[plot_table.logP>(-np.log10(min_p))]=-np.log10(min_p) plot_table['pos']=(current_chrom[pos_column]/total_bps)+offsets[i] if marked_column is not None: plot_table['mark']=current_chrom[marked_column] if thin_data: #thins p-values less than thin_data_thresh by rounding and taking only unique values plot_table['logP_R']=plot_table['logP'].values plot_table.loc[plot_table.logP<(-np.log10(thin_data_thresh)),'logP_R']=np.round(plot_table.loc[plot_table.logP<(-np.log10(thin_data_thresh))]['logP_R'].values,1) plot_table['pos_R']=np.round(plot_table['pos']*5,2) plot_table=plot_table.drop_duplicates(['logP_R','pos_R']) rgba_colors = np.zeros((plot_table.shape[0],4)) rgba_colors[:,0:4] = np.array(chrom_colors[i%2]) alpha_levels = (1.0-alpha_min)*(plot_table.logP)/(-np.log10(min_p))+alpha_min alpha_levels[alpha_levels>1.0]=1.0 rgba_colors[:,3]=alpha_levels axis.scatter(plot_table.pos,plot_table.logP,s=75.0,marker='o',color=rgba_colors,lw=0.0) if marked_column is not None: axis.scatter(plot_table.loc[plot_table['mark']==True].pos,plot_table.loc[plot_table['mark']==True].logP,s=75.0,marker='*',color=red_color,lw=0.0) for sig_thresh in all_sig_thresh: axis.hlines(-np.log10(sig_thresh),0.0,1.0,linestyle='--',color=red_color,alpha=0.75,lw=2.0) axis.text(0.1,-np.log10(sig_thresh)+0.05*axis.get_ylim()[1],r'Sig. Level {0:.1e}'.format(sig_thresh),fontsize=18) if snp_to_gene is not None: for snp,gene_list in snp_to_gene.items(): x_loc=((fig_table.loc[snp][pos_column]/total_bps)+offsets[int(fig_table.loc[snp][chrom_column])-1])+0.001*axis.get_ylim()[1] y_loc=-np.log10(fig_table.loc[snp][p_value_column]) axis.text(x_loc,y_loc,'\n'.join(gene_list),fontsize=18,fontstyle='italic') axis.set_ylabel(r"$P$-Value"+'\n'+r'($-\log_{10}$-Scale)',fontsize=24) axis.set_xlabel('Chromsome',fontsize=24) chrom_locators = offsets[:-1]+(offsets[1:]-offsets[:-1])/2.0 axis.xaxis.set_major_locator(plt.FixedLocator(chrom_locators[0::2])) axis.xaxis.set_major_formatter(plt.FixedFormatter(np.array(all_chrom,dtype=np.str)[0::2])) axis.xaxis.set_minor_locator(plt.FixedLocator(chrom_locators[1::2])) return f,axis

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null

0

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c2812c4acc0e3d78a5fbc26ae842cc83cc6fc435

40

py

Python

double_debias/__init__.py

joe5saia/double_debias

d01ad21c1c5ca9b3790cfb62f68657a7604768f4

[ "MIT" ]

3

2021-06-08T06:46:58.000Z

2022-02-26T13:34:47.000Z

double_debias/__init__.py

joe5saia/double_debias

d01ad21c1c5ca9b3790cfb62f68657a7604768f4

[ "MIT" ]

1

2020-12-01T13:09:39.000Z

double_debias/__init__.py

joe5saia/double_debias

d01ad21c1c5ca9b3790cfb62f68657a7604768f4

[ "MIT" ]

1

2022-02-26T13:34:48.000Z

from .double_debias import DoubleDebias

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6

c28deef028683bbb5f7d3760f80e67e0907a5c97

23,477

py

Python

ipychart/plots.py

nicohlr/ipychart

fab49fb798363e775c0ad2456f9d22ac0048d6fb

[ "MIT" ]

45

2020-08-05T19:32:13.000Z

2022-03-25T09:17:41.000Z

ipychart/plots.py

nicohlr/ipychart

fab49fb798363e775c0ad2456f9d22ac0048d6fb

[ "MIT" ]

3

2021-05-07T12:45:37.000Z

2022-01-21T20:58:37.000Z

ipychart/plots.py

nicohlr/ipychart

fab49fb798363e775c0ad2456f9d22ac0048d6fb

[ "MIT" ]

7

2020-08-29T02:56:03.000Z

2021-10-04T21:20:21.000Z

import pandas as pd import numpy as np from typing import Union from pandas.api.types import is_numeric_dtype from sklearn.neighbors import KernelDensity from sklearn.model_selection import GridSearchCV from .chart import Chart from .utils.plots_utils import (_create_chart_options, _create_chart_data_agg, _create_chart_data_count) def countplot(data: pd.DataFrame, x: str, hue: str = None, horizontal: bool = False, dataset_options: dict = None, options: dict = None, colorscheme: str = None, zoom: bool = True) -> Chart: """ Show the counts of observations in each categorical bin using bars. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. horizontal (bool, optional): Draw the bar chart horizontally. Defaults to False. dataset_options (dict, optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_count( data=data, x=x, hue=hue, dataset_options=dataset_options ) if horizontal: options = _create_chart_options( kind='count', options=options, x='Count', y=x, hue=hue ) else: options = _create_chart_options( kind='count', options=options, x=x, y='Count', hue=hue ) kind = 'horizontalBar' if horizontal else 'bar' return Chart( data=data, kind=kind, options=options, colorscheme=colorscheme, zoom=zoom ) def distplot(data: pd.DataFrame, x: str, bandwidth: Union[float, str] = 'auto', gridsize: int = 1000, dataset_options: dict = None, options: dict = None, colorscheme: str = None, zoom: bool = True, **kwargs) -> Chart: """ Fit and plot a univariate kernel density estimate on a line chart. This is useful to have a representation of the distribution of the data. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. bandwidth ([float, str], optional): Parameter which affect how “smooth” the resulting curve is. If set to 'auto', the optimal bandwidth is found using gridsearch. Defaults to 'auto'. gridsize (int, optional): Number of discrete points in the evaluation grid. Defaults to 1000. dataset_options (dict, optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. kwargs (optionnal): Other keyword arguments are passed down to scikit-learn's KernelDensity class. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ assert is_numeric_dtype(data[x]), 'x must be a numeric column' if isinstance(bandwidth, str): assert bandwidth == 'auto', "bandwidth must be an int or 'auto'" if dataset_options is None: dataset_options = {} # Remove outliers to find max and min values for the x axis iqr = data[x].quantile(0.95) - data[x].quantile(0.05) data_truncated = data[x][ ~((data[x] < (data[x].quantile(0.05) - 0.5 * iqr)) | (data[x] > (data[x].quantile(0.95) + 0.5 * iqr))) ] max_val, min_val = (int(data_truncated.max()) + 1, int(data_truncated.min())) max_val, min_val = (max_val + 0.05 * (max_val + abs(min_val)), min_val - 0.05 * (max_val + abs(min_val))) # Create grid which will be used to compute kde _, step = np.linspace(min_val, max_val, num=gridsize, retstep=True) x_grid = np.round(np.arange(min_val, max_val, step), 5) # If bandwidth is 'auto', find the best bandwidh using gridsearch if bandwidth == 'auto': grid = GridSearchCV(KernelDensity(), {'bandwidth': np.linspace(0.1, 2, 30)}, cv=5) grid.fit(data[x].dropna().to_numpy()[:, None]) bandwidth = grid.best_params_['bandwidth'] kde_skl = KernelDensity(bandwidth=bandwidth, **kwargs) kde_skl.fit(data[x].dropna().to_numpy()[:, np.newaxis]) pdf = np.exp(kde_skl.score_samples(x_grid[:, np.newaxis])) data = { 'labels': x_grid.tolist(), 'datasets': [{'data': pdf.tolist(), 'pointRadius': 0, **dataset_options}] } options = _create_chart_options( kind='count', options=options, x=x, y=f'Density (bandwidth: {bandwidth.round(4)})', hue=None ) # Add ticks formatting to options if not already set # This will not break because keys are created in the # _create_chart_options method called previouly maxtickslimit = 10 ticks_format_function = ( "function(value, index, values) {if (Math.abs(value) >= 1) {" "return Math.round(value);} else {return value.toFixed(3);}}" ) if 'ticks' not in options['scales']['xAxes'][0]: options['scales']['xAxes'][0].update( {'ticks': {'maxTicksLimit': maxtickslimit, 'callback': ticks_format_function}} ) else: ticks_options = options['scales']['xAxes'][0]['ticks'] if 'maxTicksLimit' not in ticks_options: ticks_options['maxTicksLimit'] = maxtickslimit if 'callback' not in ticks_options: ticks_options['callback'] = ticks_format_function return Chart( data=data, kind='line', options=options, colorscheme=colorscheme, zoom=zoom ) def lineplot(data: pd.DataFrame, x: str, y: str, hue: str = None, agg: str = 'mean', dataset_options: Union[dict, list] = None, options: dict = None, colorscheme: str = None, zoom: bool = True) -> Chart: """ A line chart is a way of plotting data points on a line. Often, it is used to show a trend in the data, or the comparison of two data sets. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options ([dict, list], optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_agg( data=data, kind='line', x=x, y=y, hue=hue, agg=agg, dataset_options=dataset_options ) options = _create_chart_options( kind='line', options=options, x=x, y=y, hue=hue, agg=agg ) return Chart( data=data, kind='line', options=options, colorscheme=colorscheme, zoom=zoom ) def barplot(data: pd.DataFrame, x: str, y: str, hue: str = None, horizontal: bool = False, agg: str = 'mean', dataset_options: Union[dict, list] = None, options: dict = None, colorscheme: str = None, zoom: bool = True) -> Chart: """ A bar chart provides a way of showing data values represented as vertical bars. It is sometimes used to show a trend in the data, and the comparison of multiple data sets side by side. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. horizontal (bool): Draw the bar chart horizontally. Defaults to False. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options ([dict, list], optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_agg( data=data, kind='bar', x=x, y=y, hue=hue, agg=agg, dataset_options=dataset_options ) options = _create_chart_options( kind='bar', options=options, x=x, y=y, hue=hue, agg=agg ) kind = 'horizontalBar' if horizontal else 'bar' return Chart( data=data, kind=kind, options=options, colorscheme=colorscheme, zoom=zoom ) def radarplot(data: pd.DataFrame, x: str, y: str, hue: str = None, agg: str = 'mean', dataset_options: Union[dict, list] = None, options: dict = None, colorscheme: str = None) -> Chart: """ A radar chart is a way of showing multiple data points and the variation between them. They are often useful for comparing the points of two or more different data sets. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options ([dict, list], optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_agg( data=data, kind='radar', x=x, y=y, hue=hue, agg=agg, dataset_options=dataset_options ) options = _create_chart_options( kind='radar', options=options, x=x, y=y, hue=hue, agg=agg ) return Chart( data=data, kind='radar', options=options, colorscheme=colorscheme ) def doughnutplot(data: pd.DataFrame, x: str, y: str, agg: str = 'mean', dataset_options: dict = None, options: dict = None, colorscheme: str = None) -> Chart: """ Pie and doughnut charts are excellent at showing the relational proportions between data. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options (dict, optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} if y: data = _create_chart_data_agg( data=data, kind='doughnut', x=x, y=y, agg=agg, dataset_options=dataset_options ) else: data = _create_chart_data_count( data=data, x=x, dataset_options=dataset_options ) options = _create_chart_options( kind='doughnut', options=options, x=x, y=y, hue=None, agg=agg ) return Chart( data=data, kind='doughnut', options=options, colorscheme=colorscheme ) def pieplot(data: pd.DataFrame, x: str, y: str = None, agg: str = 'mean', dataset_options: dict = None, options: dict = None, colorscheme: str = None) -> Chart: """ Pie and doughnut charts are excellent at showing the relational proportions between data. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options (dict, optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} if y: data = _create_chart_data_agg( data=data, kind='pie', x=x, y=y, agg=agg, dataset_options=dataset_options ) else: data = _create_chart_data_count( data=data, x=x, dataset_options=dataset_options ) options = _create_chart_options( kind='pie', options=options, x=x, y=y, hue=None, agg=agg ) return Chart( data=data, kind='pie', options=options, colorscheme=colorscheme ) def polarplot(data: pd.DataFrame, x: str, y: str = None, agg: str = 'mean', dataset_options: dict = None, options: dict = None, colorscheme: str = None) -> Chart: """ Polar area charts are similar to pie charts, but each segment has the same angle - the radius of the segment differs depending on the value. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. agg (str, optional): The aggregator used to gather data (ex: 'median' or 'mean'). Defaults to None. dataset_options (dict, optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} if y: data = _create_chart_data_agg( data=data, kind='polarArea', x=x, y=y, agg=agg, dataset_options=dataset_options ) else: data = _create_chart_data_count( data=data, x=x, dataset_options=dataset_options ) options = _create_chart_options( kind='polarArea', options=options, x=x, y=y, hue=None, agg=agg ) return Chart( data=data, kind='polarArea', options=options, colorscheme=colorscheme ) def scatterplot(data: pd.DataFrame, x: str, y: str, hue: str = None, dataset_options: Union[dict, list] = None, options: dict = None, colorscheme: str = None, zoom: bool = True) -> Chart: """ Scatter charts are based on basic line charts with the x axis changed to a linear axis. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. dataset_options ([dict, list], optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_agg( data=data, kind='scatter', x=x, y=y, hue=hue, dataset_options=dataset_options ) options = _create_chart_options( kind='scatter', options=options, x=x, y=y, hue=hue ) return Chart( data=data, kind='scatter', options=options, colorscheme=colorscheme, zoom=zoom ) def bubbleplot(data: pd.DataFrame, x: str, y: str, r: str, hue: str = None, dataset_options: Union[dict, list] = None, options: dict = None, colorscheme: str = None, zoom: bool = True) -> Chart: """ A bubble chart is used to display three-dimension data. The location of the bubble is determined by the first two dimensions and the corresponding horizontal and vertical axes. The third dimension is represented by the radius of the individual bubbles. Args: data (pd.DataFrame): The dataframe used to draw the chart. x (str): Column of the dataframe used as datapoints for x Axis. y (str): Column of the dataframe used as datapoints for y Axis. r (str, optional): Column of the dataframe used as radius for bubbles. hue (str, optional): Grouping variable that will produce points with different colors. Defaults to None. dataset_options ([dict, list], optional): Options related to the dataset object (i.e. options concerning your data). Defaults to {}. options (dict, optional): Options to configure the chart. This dictionary corresponds to the "options" argument of Chart.js. Defaults to None. colorscheme (str, optional): Colorscheme to use when drawing the chart. Defaults to None. zoom (bool, optional): Allow the user to zoom on the Chart once it is created. Defaults to True. Returns: [ipychart.Chart]: A chart which display the data using ipychart. """ if dataset_options is None: dataset_options = {} data = _create_chart_data_agg( data=data, kind='bubble', x=x, y=y, r=r, hue=hue, dataset_options=dataset_options ) options = _create_chart_options( kind='bubble', options=options, x=x, y=y, hue=hue ) return Chart( data=data, kind='bubble', options=options, colorscheme=colorscheme, zoom=zoom )

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null

0

6

c2f8a70ab8519221d14ff44835b8084e47d21ee7

283

py

Python

tests/__init__.py

alekLukanen/pyDist

ffb2c3feb20afba078fec7381c8785eb1e2b0543

[ "MIT" ]

5

2017-12-24T08:11:16.000Z

2019-02-07T22:13:26.000Z

tests/__init__.py

alekLukanen/pyDist

ffb2c3feb20afba078fec7381c8785eb1e2b0543

[ "MIT" ]

1

2021-06-01T23:17:31.000Z

tests/__init__.py

alekLukanen/pyDist

ffb2c3feb20afba078fec7381c8785eb1e2b0543

[ "MIT" ]

null

import tests.test_clusterStructures import tests.test_basePackage import tests.test_nodeEndpoints print('(TEST DIR __INIT__.py) loaded the test functions into' 'the current namespace') print('+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+' '+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+')

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null

0

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0

1

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6

6c0fb0f7a3bb2112370a7551deae91b5e52d23b9

8,152

py

Python

test/api/test_resize.py

mrz1988/lilies

9525770fabab7e142ebedc40ab5d0c8027aa90ba

[ "MIT" ]

null

test/api/test_resize.py

mrz1988/lilies

9525770fabab7e142ebedc40ab5d0c8027aa90ba

[ "MIT" ]

51

2019-06-18T16:35:56.000Z

2021-02-23T00:32:23.000Z

test/api/test_resize.py

mrz1988/lilies

9525770fabab7e142ebedc40ab5d0c8027aa90ba

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import unittest from lilies import wilt, resize from lilies.objects.lilyblock import LilyBlock class TestResize(unittest.TestCase): def setUp(self): self.strings = [ "hello", "dfDfEEFdfaC", "Mister John", "mr. jOhn", "iSn't it", "comma,separated,values", "trailing,comma,", ",", ] self.block_str = os.linesep.join(self.strings) self.twenty_chars = "12345678901234567890" def test_empty_align_fails(self): with self.assertRaises(TypeError): resize(self.twenty_chars, 20, align="") def test_garbage_align_fails(self): with self.assertRaises(TypeError): resize(self.twenty_chars, 20, align="garbage") def test_garbage_x_align_fails(self): with self.assertRaises(TypeError): resize(self.twenty_chars, align="top garbage") def test_garbage_y_align_fails(self): with self.assertRaises(TypeError): resize(self.twenty_chars, align="garbage left") def test_resize_vertically_respects_fill_character(self): resized = resize(" ", 5, 3, "center", character="a") expected = "\n".join(["aaaaa", "a a", "aaaaa"]) self.assertEqual(expected, wilt(resized)) def test_resize_to_same_size_leaves_unchanged(self): resized = resize(self.twenty_chars, 20, add_elipsis=False) self.assertEqual(self.twenty_chars, wilt(resized)) def test_resize_same_width_with_elipsis_leaves_unchanged(self): resized = resize(self.twenty_chars, 20, add_elipsis=True) self.assertEqual(self.twenty_chars, wilt(resized)) def test_resize_same_width_centered_leaves_unchanged(self): resized = resize(self.twenty_chars, 20, align="center") self.assertEqual(self.twenty_chars, wilt(resized)) def test_resize_smaller_left_no_elipsis_truncates(self): expected = self.twenty_chars[:10] resized = resize( self.twenty_chars, 10, align="left", add_elipsis=False ) self.assertEqual(expected, wilt(resized)) def test_resize_smaller_left_with_elipsis_truncates(self): expected = self.twenty_chars[:8] + ".." resized = resize(self.twenty_chars, 10, align="left", add_elipsis=True) self.assertEqual(expected, wilt(resized)) def test_resize_smaller_right_with_elipsis_truncates(self): # we basically expect this to be the same. Don't truncate left side. expected = self.twenty_chars[:8] + ".." resized = resize( self.twenty_chars, 10, align="right", add_elipsis=True ) self.assertEqual(expected, wilt(resized)) def test_resize_smaller_center_with_elipsis_truncates(self): # we basically expect this to be the same. Don't truncate left side. expected = self.twenty_chars[:8] + ".." resized = resize( self.twenty_chars, 10, align="center", add_elipsis=True ) self.assertEqual(expected, wilt(resized)) def test_resize_horiz_larger_left(self): expected = self.twenty_chars + " " * 20 resized = resize(self.twenty_chars, 40, align="left") self.assertEqual(expected, wilt(resized)) def test_resize_horiz_larger_right(self): expected = " " * 20 + self.twenty_chars resized = resize(self.twenty_chars, 40, align="right") self.assertEqual(expected, wilt(resized)) def test_resize_horiz_larger_center_even(self): expected = " " * 10 + self.twenty_chars + " " * 10 resized = resize(self.twenty_chars, 40, align="center") self.assertEqual(expected, wilt(resized)) def test_resize_horiz_larger_center_odd(self): expected = " " * 9 + self.twenty_chars + " " * 10 resized = resize(self.twenty_chars, 39, align="center") self.assertEqual(expected, wilt(resized)) def test_resize_block_horiz_larger(self): control_group = [ "hello ", "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values ", "trailing,comma, ", ", ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, width=23) self.assertEqual(control, wilt(result)) def test_resize_block_vert_top_larger(self): control_group = [ "hello ", "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values", "trailing,comma, ", ", ", " ", " ", " ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=11, align="top") self.assertEqual(control, wilt(result)) def test_resize_block_vert_bottom_larger(self): control_group = [ " ", " ", "hello ", "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values", "trailing,comma, ", ", ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=10, align="bottom") self.assertEqual(control, wilt(result)) def test_resize_block_vert_center_larger(self): control_group = [ " ", " ", "hello ", "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values", "trailing,comma, ", ", ", " ", " ", " ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=13, align="center left") self.assertEqual(control, wilt(result)) def test_resize_block_vert_top_smaller(self): control_group = [ "hello ", "dfDfEEFdfaC", "Mister John", "mr. jOhn ", "iSn't it ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=5, align="top") self.assertEqual(control, wilt(result)) def test_resize_block_vert_bottom_smaller(self): control_group = [ "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values", "trailing,comma, ", ", ", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=7, align="bottom") self.assertEqual(control, wilt(result)) def test_resize_block_vert_center_smaller(self): control_group = [ "dfDfEEFdfaC ", "Mister John ", "mr. jOhn ", "iSn't it ", "comma,separated,values", ] control = os.linesep.join(control_group) block = LilyBlock(self.block_str) result = resize(block, height=5, align="center left") self.assertEqual(control, wilt(result))

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79

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0.097685

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0.84274

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0.785094

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0

0.014652

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8,152

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false

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0

null

0

1

0

null

0

6

dd25412f0d4b82222c1d80577095b8b5b8fa3a83

3,659

py

Python

tests/test_bootstrap_field_parameters.py

akx/django-bootstrap5

2fbe688c30061122e08981f65bf87ec35fcf28ad

[ "BSD-3-Clause" ]

118

2021-03-15T14:01:49.000Z

2022-03-29T06:40:46.000Z

tests/test_bootstrap_field_parameters.py

akx/django-bootstrap5

2fbe688c30061122e08981f65bf87ec35fcf28ad

[ "BSD-3-Clause" ]

97

2021-03-19T05:44:28.000Z

2022-03-31T09:05:29.000Z

tests/test_bootstrap_field_parameters.py

akx/django-bootstrap5

2fbe688c30061122e08981f65bf87ec35fcf28ad

[ "BSD-3-Clause" ]

33

2021-03-22T14:45:08.000Z

2022-02-23T18:12:23.000Z

from django import forms from tests.base import BootstrapTestCase class CharFieldTestForm(forms.Form): test = forms.CharField() class BootstrapFieldParameterTestCase(BootstrapTestCase): """Test `bootstrap_field` parameters`.""" def test_wrapper_class(self): """Test field with default CharField widget.""" form = CharFieldTestForm() self.assertHTMLEqual( self.render("{% bootstrap_field form.test %}", context={"form": form}), ( '<div class="django_bootstrap5-req mb-3">' '<label for="id_test" class="form-label">Test</label>' '<input class="form-control" id="id_test" name="test" placeholder="Test" required type="text">' "</div>" ), ) self.assertHTMLEqual( self.render("{% bootstrap_field form.test inline_wrapper_class='foo' %}", context={"form": form}), ( '<div class="django_bootstrap5-req mb-3">' '<label for="id_test" class="form-label">Test</label>' '<input class="form-control" id="id_test" name="test" placeholder="Test" required type="text">' "</div>" ), ) self.assertHTMLEqual( self.render("{% bootstrap_field form.test wrapper_class='foo' %}", context={"form": form}), ( '<div class="django_bootstrap5-req foo">' '<label for="id_test" class="form-label">Test</label>' '<input class="form-control" id="id_test" name="test" placeholder="Test" required type="text">' "</div>" ), ) self.assertHTMLEqual( self.render("{% bootstrap_field form.test wrapper_class=None %}", context={"form": form}), ( '<div class="django_bootstrap5-req">' '<label for="id_test" class="form-label">Test</label>' '<input class="form-control" id="id_test" name="test" placeholder="Test" required type="text">' "</div>" ), ) def test_inline_wrapper_class(self): """Test field with default CharField widget.""" form = CharFieldTestForm() self.assertHTMLEqual( self.render("{% bootstrap_field form.test layout='inline' %}", context={"form": form}), ( '<div class="col-12 django_bootstrap5-req">' '<label class="visually-hidden" for="id_test">Test</label>' '<input type="text" name="test" class="form-control" placeholder="Test" required id="id_test">' "</div>" ), ) self.assertHTMLEqual( self.render("{% bootstrap_field form.test layout='inline' wrapper_class='foo' %}", context={"form": form}), ( '<div class="col-12 django_bootstrap5-req">' '<label class="visually-hidden" for="id_test">Test</label>' '<input type="text" name="test" class="form-control" placeholder="Test" required id="id_test">' "</div>" ), ) self.assertHTMLEqual( self.render( "{% bootstrap_field form.test layout='inline' inline_wrapper_class='foo' %}", context={"form": form} ), ( '<div class="col-12 django_bootstrap5-req foo">' '<label class="visually-hidden" for="id_test">Test</label>' '<input type="text" name="test" class="form-control" placeholder="Test" required id="id_test">' "</div>" ), )

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0

null

0

1

0

null

0

6

dd44ea40086f4538136724946734eeeeabbb5c69

27

py

Python

api/tasks/__init__.py

C-Canchola/texel

9ebb12cf38b78608394f44767f55236845f0f9bc

[ "MIT" ]

null

api/tasks/__init__.py

C-Canchola/texel

9ebb12cf38b78608394f44767f55236845f0f9bc

[ "MIT" ]

null

api/tasks/__init__.py

C-Canchola/texel

9ebb12cf38b78608394f44767f55236845f0f9bc

[ "MIT" ]

null

from . import task_manager

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null

0

1

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1

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1

0

6

06d5871238bbd12943d1289a2c8967575b92cdbb

43

py

Python

run.py

williamjacksn/inventory

1422307b12db671c73b8bf43193151b3c19df9ee

[ "MIT" ]

3

2017-11-26T17:04:46.000Z

2020-08-05T23:57:07.000Z

run.py

williamjacksn/inventory

1422307b12db671c73b8bf43193151b3c19df9ee

[ "MIT" ]

17

2019-12-22T14:38:18.000Z

2022-03-25T23:01:49.000Z

run.py

williamjacksn/inventory

1422307b12db671c73b8bf43193151b3c19df9ee

[ "MIT" ]

1

2019-09-09T09:48:39.000Z

import inventory.app inventory.app.main()

10.75

20

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0.666667

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0.093023

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null

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1

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6

06fa3683acad0246167de299a7577258d9b3f919

119

py

Python

rascil/processing_components/util/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

7

2019-12-14T13:42:33.000Z

2022-01-28T03:31:45.000Z

rascil/processing_components/util/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

6

2020-01-08T09:40:08.000Z

2020-06-11T14:56:13.000Z

rascil/processing_components/util/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

3

2020-01-14T11:14:16.000Z

2020-09-15T05:21:06.000Z

from .array_functions import * from .compass_bearing import * from .coordinate_support import * from .sizeof import *

19.833333

33

0.789916

15

119

6.066667

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119

5

34

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0.25

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null

0

1

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6

660eab63eb3ca2052064ee9c7ef03b71f457fa7e

2,361

py

Python

PyBank/main.py

erictonian/python-challenge

04318a90e619f5944792672d2587d2f211367dda

[ "MIT" ]

null

PyBank/main.py

erictonian/python-challenge

04318a90e619f5944792672d2587d2f211367dda

[ "MIT" ]

null

PyBank/main.py

erictonian/python-challenge

04318a90e619f5944792672d2587d2f211367dda

[ "MIT" ]

null

import csv pybank = "budget_data.csv" with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: count = sum(1 for row in csvreader) with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: total_sum = sum(int(row[1]) for row in csvreader) rows = [] rows_stagger = [] with open(pybank, 'r', newline='') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') next(csvfile) for row in csvreader: rows.append(int(row[1])) with open(pybank, 'r', newline='') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') next(csvfile) next(csvfile) for row in csvreader: rows_stagger.append(int(row[1])) dfrows = [x1-x2 for (x1,x2) in zip(rows_stagger, rows)] dfrows_avg = (sum(dfrows))/(count -1) with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: max_val = max(int(row[1]) for row in csvreader) with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: max_month = [str(row[0]) for row in csvreader if int(row[1]) == max_val] with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: min_val = min(int(row[1]) for row in csvreader) with open(pybank, 'r') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: min_month = [str(row[0]) for row in csvreader if int(row[1]) == min_val] print(f"Total Months: {count}") print(f"Total Sum: ${total_sum}.00") print(f"Average Change: ${round(dfrows_avg, 2)}") print(f"Greatest Increase in Profits: {max_month[0]} (${max_val}.00)") print(f"Greatest Decrease in Profits: {min_month[0]} (${min_val}.00)") f = open("pybank_results.txt", 'w') f.write(f"Total Months: {count}\n") f = open("pybank_results.txt", 'a') f.write(f"Total Sum: ${total_sum}.00\n") f = open("pybank_results.txt", 'a') f.write(f"Average Change: ${round(dfrows_avg, 2)}\n") f = open("pybank_results.txt", 'a') f.write(f"Greatest Increase in Profits: {max_month[0]} (${max_val}.00)\n") f = open("pybank_results.txt", 'a') f.write(f"Greatest Decrease in Profits: {min_month[0]} (${min_val}.00)\n") f.close

34.217391

80

0.643795

362

2,361

4.118785

0.157459

0.056338

0.075117

0.159624

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2,361

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null

0

1

0

null

0

6

661bc7e21529e78baaf002daea130f0c9404d9d0

139

py

Python

data_construction_allInOne/error.py

DiracSea/multi_linear_regression

ab047c2c0769e0389c5e01719f1afbc1db70beb0

[ "MIT" ]

null

data_construction_allInOne/error.py

DiracSea/multi_linear_regression

ab047c2c0769e0389c5e01719f1afbc1db70beb0

[ "MIT" ]

null

data_construction_allInOne/error.py

DiracSea/multi_linear_regression

ab047c2c0769e0389c5e01719f1afbc1db70beb0

[ "MIT" ]

null

class EmptyRainfallError(ValueError): pass class EmptyWaterlevelError(ValueError): pass class NoMethodError(ValueError): pass

17.375

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139

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139

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1

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1

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null

0

1

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6

b0758f8598d3ea2a1e90c9b938da03feb778cbd6

122

py

Python

data/hospital_level/raw/DH_hospital/download.py

666Chao666/covid19-severity-prediction

f7ef4ea5f3109fdb4246d2bf90d07fbf048d3706

[ "MIT" ]

2

2020-05-15T14:42:02.000Z

2020-05-22T08:51:47.000Z

data_new/hospital_level/raw/DH_hospital/download.py

rahul263-stack/covid19-severity-prediction

f581adb2fccb12d5ab3f3c59ee120f484703edf5

[ "MIT" ]

null

data_new/hospital_level/raw/DH_hospital/download.py

rahul263-stack/covid19-severity-prediction

f581adb2fccb12d5ab3f3c59ee120f484703edf5

[ "MIT" ]

null

#!/usr/bin/python3 import os os.system('wget https://data.medicare.gov/api/views/xubh-q36u/rows.csv -O DH_hospital.csv')

24.4

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6

b090c6ef25b52c5b25c8e27f639cfd5284798c8e

37,258

py

Python

keras_contrib/applications/fully_convolutional_networks.py

ahundt/keras-contrib

65ade7af19c86f4b9888acfd2ed31245f4f5c770

[ "MIT" ]

7

2017-07-22T09:05:44.000Z

2019-04-30T02:08:04.000Z

keras_contrib/applications/fully_convolutional_networks.py

ahundt/keras-contrib

65ade7af19c86f4b9888acfd2ed31245f4f5c770

[ "MIT" ]

1

2017-12-26T02:59:59.000Z

keras_contrib/applications/fully_convolutional_networks.py

ahundt/keras-contrib

65ade7af19c86f4b9888acfd2ed31245f4f5c770

[ "MIT" ]

11

2017-07-06T14:11:51.000Z

2021-08-21T23:18:20.000Z

""" Fully Convolutional Networks Based on the paper: - [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1605.06211) Implementation adapted from [Keras-FCN](https://github.com/aurora95/Keras-FCN). """ import numpy as np import matplotlib.pyplot as plt import os import sys from keras import backend as K from keras_contrib.applications import densenet from keras.models import Model from keras.regularizers import l2 from keras.layers import Conv2D from keras.layers import BatchNormalization from keras.layers import Activation from keras.layers import MaxPooling2D from keras.layers import Add from keras.engine import Layer from keras_applications.imagenet_utils import _obtain_input_shape import keras.backend as K import tensorflow as tf def conv_relu(nb_filter, nb_row, nb_col, subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('conv_relu'): x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), stride=subsample, use_bias=bias, kernel_initializer="he_normal", W_regularizer=l2(w_decay), border_mode=border_mode)(x) x = Activation("relu")(x) return x return f def conv_bn(nb_filter, nb_row, nb_col, subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('conv_bn'): x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), stride=subsample, use_bias=bias, kernel_initializer="he_normal", W_regularizer=l2(w_decay), border_mode=border_mode)(x) x = BatchNormalization(mode=0, axis=-1)(x) return x return f def conv_bn_relu(nb_filter, nb_row, nb_col, subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('conv_bn_relu'): x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), stride=subsample, use_bias=bias, kernel_initializer="he_normal", W_regularizer=l2(w_decay), border_mode=border_mode)(x) x = BatchNormalization(mode=0, axis=-1)(x) x = Activation("relu")(x) return x return f def bn_relu_conv(nb_filter, nb_row, nb_col, subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('bn_relu_conv'): x = BatchNormalization(mode=0, axis=-1)(x) x = Activation("relu")(x) x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), stride=subsample, use_bias=bias, kernel_initializer="he_normal", W_regularizer=l2(w_decay), border_mode=border_mode)(x) return x return f def atrous_conv_bn(nb_filter, nb_row, nb_col, atrous_rate=(2, 2), subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('atrous_conv_bn'): x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), dilation_rate=atrous_rate, stride=subsample, use_bias=bias, kernel_initializer="he_normal", kernel_regularizer=l2(w_decay), padding=border_mode)(x) x = BatchNormalization(mode=0, axis=-1)(x) return x return f def atrous_conv_bn_relu(nb_filter, nb_row, nb_col, atrous_rate=(2, 2), subsample=(1, 1), border_mode='same', bias=True, w_decay=0.01): def f(x): with K.name_scope('atrous_conv_bn_relu'): x = Conv2D(filters=nb_filter, kernel_size=(nb_row, nb_col), dilation_rate=atrous_rate, stride=subsample, use_bias=bias, kernel_initializer="he_normal", kernel_regularizer=l2(w_decay), padding=border_mode)(x) x = BatchNormalization(mode=0, axis=-1)(x) x = Activation("relu")(x) return x return f def get_weights_path_vgg16(): TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' weights_path = get_file( 'vgg16_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models') return weights_path def get_weights_path_resnet(): TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5' weights_path = get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models') def resize_images_bilinear(X, height_factor=1, width_factor=1, target_height=None, target_width=None, data_format='default'): '''Resizes the images contained in a 4D tensor of shape - [batch, channels, height, width] (for 'channels_first' data_format) - [batch, height, width, channels] (for 'channels_last' data_format) by a factor of (height_factor, width_factor). Both factors should be positive integers. ''' if data_format == 'default': data_format = K.image_data_format() if data_format == 'channels_first': original_shape = K.int_shape(X) if target_height and target_width: new_shape = tf.constant( np.array((target_height, target_width)).astype('int32')) else: new_shape = tf.shape(X)[2:] new_shape *= tf.constant( np.array([height_factor, width_factor]).astype('int32')) X = K.permute_dimensions(X, [0, 2, 3, 1]) X = tf.image.resize_bilinear(X, new_shape) X = K.permute_dimensions(X, [0, 3, 1, 2]) if target_height and target_width: X.set_shape((None, None, target_height, target_width)) else: X.set_shape( (None, None, original_shape[2] * height_factor, original_shape[3] * width_factor)) return X elif data_format == 'channels_last': original_shape = K.int_shape(X) if target_height and target_width: new_shape = tf.constant( np.array((target_height, target_width)).astype('int32')) else: new_shape = tf.shape(X)[1:3] new_shape *= tf.constant( np.array([height_factor, width_factor]).astype('int32')) X = tf.image.resize_bilinear(X, new_shape) if target_height and target_width: X.set_shape((None, target_height, target_width, None)) else: X.set_shape( (None, original_shape[1] * height_factor, original_shape[2] * width_factor, None)) return X else: raise Exception('Invalid data_format: ' + data_format) class BilinearUpSampling2D(Layer): """ Bilinear Upsampling TODO: remove and replace with UpSampling2D when https://github.com/keras-team/keras/pull/9303 is available """ def __init__(self, size=(1, 1), target_size=None, data_format='default', **kwargs): if data_format == 'default': data_format = K.image_data_format() self.size = tuple(size) if target_size is not None: self.target_size = tuple(target_size) else: self.target_size = None assert data_format in { 'channels_last', 'channels_first'}, 'data_format must be in {tf, th}' self.data_format = data_format self.input_spec = [InputSpec(ndim=4)] super(BilinearUpSampling2D, self).__init__(**kwargs) def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': width = int(self.size[0] * input_shape[2] if input_shape[2] is not None else None) height = int(self.size[1] * input_shape[3] if input_shape[3] is not None else None) if self.target_size is not None: width = self.target_size[0] height = self.target_size[1] return (input_shape[0], input_shape[1], width, height) elif self.data_format == 'channels_last': width = int(self.size[0] * input_shape[1] if input_shape[1] is not None else None) height = int(self.size[1] * input_shape[2] if input_shape[2] is not None else None) if self.target_size is not None: width = self.target_size[0] height = self.target_size[1] return (input_shape[0], width, height, input_shape[3]) else: raise Exception('Invalid data_format: ' + self.data_format) def call(self, x, mask=None): if self.target_size is not None: return resize_images_bilinear(x, target_height=self.target_size[0], target_width=self.target_size[1], data_format=self.data_format) else: return resize_images_bilinear(x, height_factor=self.size[0], width_factor=self.size[1], data_format=self.data_format) def get_config(self): config = {'size': self.size, 'target_size': self.target_size} base_config = super(BilinearUpSampling2D, self).get_config() return dict(list(base_config.items()) + list(config.items())) # The original help functions from keras does not have weight regularizers, so I modified them. # Also, I changed these two functions into functional style def identity_block(kernel_size, filters, stage, block, weight_decay=0., batch_momentum=0.99): '''The identity_block is the block that has no conv layer at shortcut # Arguments kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names ''' def f(input_tensor): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x) x = Add()([x, input_tensor]) x = Activation('relu')(x) return x return f def conv_block(kernel_size, filters, stage, block, weight_decay=0., strides=(2, 2), batch_momentum=0.99): '''conv_block is the block that has a conv layer at shortcut # Arguments kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names Note that from stage 3, the first conv layer at main path is with strides=(2,2) And the shortcut should have strides=(2,2) as well ''' def f(input_tensor): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x) shortcut = Conv2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', kernel_regularizer=l2(weight_decay))(input_tensor) shortcut = BatchNormalization( axis=bn_axis, name=bn_name_base + '1', momentum=batch_momentum)(shortcut) x = Add()([x, shortcut]) x = Activation('relu')(x) return x return f # Atrous-Convolution version of residual blocks def atrous_identity_block(kernel_size, filters, stage, block, weight_decay=0., atrous_rate=(2, 2), batch_momentum=0.99): '''The identity_block is the block that has no conv layer at shortcut # Arguments kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names ''' def f(input_tensor): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), dilation_rate=atrous_rate, padding='same', name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x) x = Add()([x, input_tensor]) x = Activation('relu')(x) return x return f def atrous_conv_block(kernel_size, filters, stage, block, weight_decay=0., strides=(1, 1), atrous_rate=(2, 2), batch_momentum=0.99): '''conv_block is the block that has a conv layer at shortcut # Arguments kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names ''' def f(input_tensor): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', dilation_rate=atrous_rate, name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x) x = Activation('relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization( axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x) shortcut = Conv2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', kernel_regularizer=l2(weight_decay))(input_tensor) shortcut = BatchNormalization( axis=bn_axis, name=bn_name_base + '1', momentum=batch_momentum)(shortcut) x = Add()([x, shortcut]) x = Activation('relu')(x) return x return f def top(x, input_shape, classes, activation, weight_decay): x = Conv2D(classes, (1, 1), activation='linear', padding='same', kernel_regularizer=l2(weight_decay), use_bias=False)(x) if K.image_data_format() == 'channels_first': channel, row, col = input_shape else: row, col, channel = input_shape # TODO(ahundt) this is modified for the sigmoid case! also use loss_shape if activation is 'sigmoid': x = Reshape((row * col * classes,))(x) return x def FCN_Vgg16_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21): if batch_shape: img_input = Input(batch_shape=batch_shape) image_size = batch_shape[1:3] else: img_input = Input(shape=input_shape) image_size = input_shape[0:2] # Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer=l2(weight_decay))(img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) # Convolutional layers transfered from fully-connected layers x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x) x = Dropout(0.5)(x) x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x) x = Dropout(0.5)(x) # classifying layer x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x) x = BilinearUpSampling2D(size=(32, 32))(x) model = Model(img_input, x) weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5')) model.load_weights(weights_path, by_name=True) return model def AtrousFCN_Vgg16_16s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21, weights_path=None, upsample=True, input_tensor=None, include_top=False, dilation_rate=(2, 2), name=''): if batch_shape: img_input = Input(tensor=input_tensor, batch_shape=batch_shape) if upsample: image_size = batch_shape[1:3] else: img_input = Input(tensor=input_tensor, shape=input_shape) if upsample: image_size = input_shape[0:2] # Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name=name + 'block1_conv1', kernel_regularizer=l2(weight_decay))(img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name=name + 'block1_conv2', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name=name + 'block2_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name=name + 'block2_conv2', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name=name + 'block3_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name=name + 'block3_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name=name + 'block3_conv3', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block4_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block4_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block4_conv3', kernel_regularizer=l2(weight_decay))(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block5_conv1', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block5_conv2', kernel_regularizer=l2(weight_decay))(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name=name + 'block5_conv3', kernel_regularizer=l2(weight_decay))(x) if dilation_rate == 1 or dilation_rate == (1, 1): x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) if include_top: # Convolutional layers transfered from fully-connected layers x = Conv2D(4096, (7, 7), activation='relu', padding='same', dilation_rate=dilation_rate, name='fc1', kernel_regularizer=l2(weight_decay))(x) x = Dropout(0.5)(x) x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x) x = Dropout(0.5)(x) # classifying layer x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x) if upsample: x = BilinearUpSampling2D(target_size=tuple(image_size))(x) model = Model(img_input, x) if weights_path is None: weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')) if not os.path.exists(weights_path): temp_weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5')) if not os.path.exists(temp_weights_path): # download the model if we don't have it yet temp_model = keras.applications.vgg16.VGG16(include_top=False) temp_model.save_weights(weights_path) del(temp_model) model.load_weights(weights_path, by_name=True, reshape=True) model.save_weights(weights_path) else: model.load_weights(weights_path) else: model.load_weights(weights_path) return model def FCN_Resnet50_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21): if batch_shape: img_input = Input(batch_shape=batch_shape) image_size = batch_shape[1:3] else: img_input = Input(shape=input_shape) image_size = input_shape[0:2] bn_axis = 3 x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1', kernel_regularizer=l2(weight_decay))(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(3, [64, 64, 256], stage=2, block='a', strides=(1, 1))(x) x = identity_block(3, [64, 64, 256], stage=2, block='b')(x) x = identity_block(3, [64, 64, 256], stage=2, block='c')(x) x = conv_block(3, [128, 128, 512], stage=3, block='a')(x) x = identity_block(3, [128, 128, 512], stage=3, block='b')(x) x = identity_block(3, [128, 128, 512], stage=3, block='c')(x) x = identity_block(3, [128, 128, 512], stage=3, block='d')(x) x = conv_block(3, [256, 256, 1024], stage=4, block='a')(x) x = identity_block(3, [256, 256, 1024], stage=4, block='b')(x) x = identity_block(3, [256, 256, 1024], stage=4, block='c')(x) x = identity_block(3, [256, 256, 1024], stage=4, block='d')(x) x = identity_block(3, [256, 256, 1024], stage=4, block='e')(x) x = identity_block(3, [256, 256, 1024], stage=4, block='f')(x) x = conv_block(3, [512, 512, 2048], stage=5, block='a')(x) x = identity_block(3, [512, 512, 2048], stage=5, block='b')(x) x = identity_block(3, [512, 512, 2048], stage=5, block='c')(x) # classifying layer x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x) x = BilinearUpSampling2D(size=(32, 32))(x) model = Model(img_input, x) weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/fcn_resnet50_weights_tf_dim_ordering_tf_kernels.h5')) model.load_weights(weights_path, by_name=True) return model def AtrousFCN_Resnet50_16s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21, include_top=False, upsample=False): if input_shape is None and input_tensor is not None: batch_shape = keras.backend.int_shape(input_tensor) if batch_shape: img_input = Input(batch_shape=batch_shape) image_size = batch_shape[1:3] elif input_shape is not None: img_input = Input(shape=input_shape) image_size = input_shape[0:2] bn_axis = 3 x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1', kernel_regularizer=l2(weight_decay))(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1', momentum=batch_momentum)(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(3, [64, 64, 256], stage=2, block='a', weight_decay=weight_decay, strides=( 1, 1), batch_momentum=batch_momentum)(x) x = identity_block(3, [64, 64, 256], stage=2, block='b', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [64, 64, 256], stage=2, block='c', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = conv_block(3, [128, 128, 512], stage=3, block='a', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [128, 128, 512], stage=3, block='b', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [128, 128, 512], stage=3, block='c', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [128, 128, 512], stage=3, block='d', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = conv_block(3, [256, 256, 1024], stage=4, block='a', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [256, 256, 1024], stage=4, block='b', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [256, 256, 1024], stage=4, block='c', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [256, 256, 1024], stage=4, block='d', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [256, 256, 1024], stage=4, block='e', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = identity_block(3, [256, 256, 1024], stage=4, block='f', weight_decay=weight_decay, batch_momentum=batch_momentum)(x) x = atrous_conv_block(3, [512, 512, 2048], stage=5, block='a', weight_decay=weight_decay, atrous_rate=( 2, 2), batch_momentum=batch_momentum)(x) x = atrous_identity_block(3, [512, 512, 2048], stage=5, block='b', weight_decay=weight_decay, atrous_rate=( 2, 2), batch_momentum=batch_momentum)(x) x = atrous_identity_block(3, [512, 512, 2048], stage=5, block='c', weight_decay=weight_decay, atrous_rate=( 2, 2), batch_momentum=batch_momentum)(x) # classifying layer # x = Conv2D(classes, (3, 3), dilation_rate=(2, 2), kernel_initializer='normal', activation='linear', padding='same', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x) x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='same', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x) if upsample: x = BilinearUpSampling2D(target_size=tuple(image_size))(x) model = Model(img_input, x) if weights_path is None: weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')) if not os.path.exists(weights_path): temp_weights_path = os.path.expanduser(os.path.join( '~', '.keras/models/fcn_resnet50_weights_tf_dim_ordering_tf_kernels.h5')) if not os.path.exists(temp_weights_path): # download the model if we don't have it yet temp_model = keras.applications.resnet50.ResNet50(include_top=False) temp_model.save_weights(weights_path) del(temp_model) model.load_weights(weights_path, by_name=True, reshape=True) model.save_weights(weights_path) else: model.load_weights(weights_path) else: model.load_weights(weights_path) return model def Atrous_DenseNet(input_shape=None, weight_decay=1E-4, batch_momentum=0.9, batch_shape=None, classes=21, include_top=False, activation='sigmoid'): # TODO(ahundt) pass the parameters but use defaults for now if include_top is True: # TODO(ahundt) Softmax is pre-applied, so need different train, inference, evaluate. # TODO(ahundt) for multi-label try per class sigmoid top as follows: # x = Reshape((row * col * classes))(x) # x = Activation('sigmoid')(x) # x = Reshape((row, col, classes))(x) return densenet.DenseNet(depth=None, nb_dense_block=3, growth_rate=32, nb_filter=-1, nb_layers_per_block=[6, 12, 24, 16], bottleneck=True, reduction=0.5, dropout_rate=0.2, weight_decay=1E-4, include_top=True, top='segmentation', weights=None, input_tensor=None, input_shape=input_shape, classes=classes, transition_dilation_rate=2, transition_kernel_size=(1, 1), transition_pooling=None) # if batch_shape: # img_input = Input(batch_shape=batch_shape) # image_size = batch_shape[1:3] # else: # img_input = Input(shape=input_shape) # image_size = input_shape[0:2] input_shape = _obtain_input_shape(input_shape, default_size=32, min_size=16, data_format=K.image_data_format(), include_top=False) img_input = Input(shape=input_shape) x = densenet.__create_dense_net(classes, img_input, depth=None, nb_dense_block=3, growth_rate=32, nb_filter=-1, nb_layers_per_block=[6, 12, 24, 16], bottleneck=True, reduction=0.5, dropout_rate=0.2, weight_decay=1E-4, top='segmentation', input_shape=input_shape, transition_dilation_rate=2, transition_kernel_size=(1, 1), transition_pooling=None, include_top=include_top) x = top(x, input_shape, classes, activation, weight_decay) model = Model(img_input, x, name='Atrous_DenseNet') # TODO(ahundt) add weight loading return model def DenseNet_FCN(input_shape=None, weight_decay=1E-4, batch_momentum=0.9, batch_shape=None, classes=21, include_top=False, activation='sigmoid'): if include_top is True: # TODO(ahundt) Softmax is pre-applied, so need different train, inference, evaluate. # TODO(ahundt) for multi-label try per class sigmoid top as follows: # x = Reshape((row * col * classes))(x) # x = Activation('sigmoid')(x) # x = Reshape((row, col, classes))(x) return densenet.DenseNetFCN(input_shape=input_shape, weights=None, classes=classes, nb_layers_per_block=[4, 5, 7, 10, 12, 15], growth_rate=16, dropout_rate=0.2) # if batch_shape: # img_input = Input(batch_shape=batch_shape) # image_size = batch_shape[1:3] # else: # img_input = Input(shape=input_shape) # image_size = input_shape[0:2] input_shape = _obtain_input_shape(input_shape, default_size=32, min_size=16, data_format=K.image_data_format(), include_top=False) img_input = Input(shape=input_shape) x = densenet.__create_fcn_dense_net(classes, img_input, input_shape=input_shape, nb_layers_per_block=[ 4, 5, 7, 10, 12, 15], growth_rate=16, dropout_rate=0.2, include_top=include_top) x = top(x, input_shape, classes, activation, weight_decay) # TODO(ahundt) add weight loading model = Model(img_input, x, name='DenseNet_FCN') return model

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py

Python

robit/__init__.py

ToxicFrazzles/robit

5f727b9f191e06fe51e78a8eb75a65b94a571202

[ "MIT" ]

null

robit/__init__.py

ToxicFrazzles/robit

5f727b9f191e06fe51e78a8eb75a65b94a571202

[ "MIT" ]

null

robit/__init__.py

ToxicFrazzles/robit

5f727b9f191e06fe51e78a8eb75a65b94a571202

[ "MIT" ]

null

from .robit import Robit

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py

Python

src/API/__init__.py

Public-Health-Bioinformatics/sequdas-upload

a22f090f9cd3b5ecfe0bae487016622b9b80651d

[ "MIT" ]

9

2015-11-24T21:51:42.000Z

2020-10-21T20:16:24.000Z

src/API/__init__.py

Public-Health-Bioinformatics/sequdas-upload

a22f090f9cd3b5ecfe0bae487016622b9b80651d

[ "MIT" ]

6

2016-09-13T20:38:57.000Z

2019-02-21T18:31:22.000Z

src/API/__init__.py

Public-Health-Bioinformatics/sequdas-upload

a22f090f9cd3b5ecfe0bae487016622b9b80651d

[ "MIT" ]

1

2018-10-07T00:55:43.000Z

from apiCalls import ApiCalls from config import read_config_option, write_config_option

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py

Python

tests/test_buildmaps.py

LSSTDESC/healsparse

f6b15f570ab6335328e34006f69c3919d9fcf1c8

[ "BSD-3-Clause" ]

8

2019-05-06T11:42:41.000Z

2021-10-08T14:57:12.000Z

tests/test_buildmaps.py

LSSTDESC/healsparse

f6b15f570ab6335328e34006f69c3919d9fcf1c8

[ "BSD-3-Clause" ]

75

2019-03-01T23:25:26.000Z

2022-01-29T21:40:27.000Z

tests/test_buildmaps.py

LSSTDESC/healsparse

f6b15f570ab6335328e34006f69c3919d9fcf1c8

[ "BSD-3-Clause" ]

3

2020-01-30T19:10:19.000Z

2022-03-08T14:57:38.000Z

from __future__ import division, absolute_import, print_function import unittest import numpy.testing as testing import numpy as np import healpy as hp from numpy import random import healsparse class BuildMapsTestCase(unittest.TestCase): def test_build_maps_single(self): """ Test building a map for a single-value field """ random.seed(seed=12345) nside_coverage = 32 nside_map = 64 n_rand = 1000 ra = np.random.random(n_rand) * 360.0 dec = np.random.random(n_rand) * 180.0 - 90.0 # Create an empty map sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map, np.float64) # Look up all the values, make sure they're all UNSEEN testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True), hp.UNSEEN) # Fail to append because of wrong dtype pixel = np.arange(4000, 20000) values = np.ones_like(pixel, dtype=np.float32) self.assertRaises(ValueError, sparse_map.update_values_pix, pixel, values) # Append a bunch of pixels values = np.ones_like(pixel, dtype=np.float64) sparse_map.update_values_pix(pixel, values) # Make a healpix map for comparison hpmap = np.zeros(hp.nside2npix(nside_map)) + hp.UNSEEN hpmap[pixel] = values theta = np.radians(90.0 - dec) phi = np.radians(ra) ipnest_test = hp.ang2pix(nside_map, theta, phi, nest=True) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True), hpmap[ipnest_test]) # Replace the pixels values += 1 sparse_map.update_values_pix(pixel, values) hpmap[pixel] = values testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True), hpmap[ipnest_test]) # Replace and append more pixels # Note that these are lower-number pixels, so the map is out of order pixel2 = np.arange(3000) + 2000 values2 = np.ones_like(pixel2, dtype=np.float64) sparse_map.update_values_pix(pixel2, values2) hpmap[pixel2] = values2 testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True), hpmap[ipnest_test]) # Test making empty maps sparse_map2 = healsparse.HealSparseMap.make_empty_like(sparse_map) self.assertEqual(sparse_map2.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2.dtype, sparse_map.dtype) self.assertEqual(sparse_map2._sentinel, sparse_map._sentinel) sparse_map2b = healsparse.HealSparseMap.make_empty_like(sparse_map, cov_pixels=[0, 2]) self.assertEqual(sparse_map2b.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2b.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2b.dtype, sparse_map.dtype) self.assertEqual(sparse_map2b._sentinel, sparse_map._sentinel) self.assertEqual(len(sparse_map2b._sparse_map), sparse_map2._cov_map.nfine_per_cov*3) testing.assert_array_equal(sparse_map2b._sparse_map, sparse_map._sentinel) sparse_map2 = healsparse.HealSparseMap.make_empty_like(sparse_map, nside_coverage=16) self.assertEqual(sparse_map2.nside_coverage, 16) self.assertEqual(sparse_map2.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2.dtype, sparse_map.dtype) self.assertEqual(sparse_map2._sentinel, sparse_map._sentinel) sparse_map2 = healsparse.HealSparseMap.make_empty_like(sparse_map, nside_sparse=128) self.assertEqual(sparse_map2.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2.nside_sparse, 128) self.assertEqual(sparse_map2.dtype, sparse_map.dtype) self.assertEqual(sparse_map2._sentinel, sparse_map._sentinel) sparse_map2 = healsparse.HealSparseMap.make_empty_like(sparse_map, dtype=np.int32, sentinel=0) self.assertEqual(sparse_map2.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2.dtype, np.int32) def test_build_maps_recarray(self): """ Testing building a map for a recarray """ random.seed(seed=12345) nside_coverage = 32 nside_map = 64 n_rand = 1000 ra = np.random.random(n_rand) * 360.0 dec = np.random.random(n_rand) * 180.0 - 90.0 # Create an empty map dtype = [('col1', 'f4'), ('col2', 'f8')] self.assertRaises(RuntimeError, healsparse.HealSparseMap.make_empty, nside_coverage, nside_map, dtype) sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map, dtype, primary='col1') # Look up all the values, make sure they're all UNSEEN testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col1'], hp.UNSEEN) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col2'], hp.UNSEEN) pixel = np.arange(4000, 20000) values = np.zeros_like(pixel, dtype=dtype) values['col1'] = 1.0 values['col2'] = 2.0 sparse_map.update_values_pix(pixel, values) # Make healpix maps for comparison hpmapCol1 = np.zeros(hp.nside2npix(nside_map), dtype=np.float32) + hp.UNSEEN hpmapCol2 = np.zeros(hp.nside2npix(nside_map)) + hp.UNSEEN hpmapCol1[pixel] = values['col1'] hpmapCol2[pixel] = values['col2'] theta = np.radians(90.0 - dec) phi = np.radians(ra) ipnest_test = hp.ang2pix(nside_map, theta, phi, nest=True) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col1'], hpmapCol1[ipnest_test]) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col2'], hpmapCol2[ipnest_test]) # Replace the pixels values['col1'] += 1 values['col2'] += 1 sparse_map.update_values_pix(pixel, values) hpmapCol1[pixel] = values['col1'] hpmapCol2[pixel] = values['col2'] testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col1'], hpmapCol1[ipnest_test]) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col2'], hpmapCol2[ipnest_test]) # Replace and append more pixels # Note that these are lower-number pixels, so the map is out of order pixel2 = np.arange(3000) + 2000 values2 = np.zeros_like(pixel2, dtype=dtype) values2['col1'] = 1.0 values2['col2'] = 2.0 sparse_map.update_values_pix(pixel2, values2) hpmapCol1[pixel2] = values2['col1'] hpmapCol2[pixel2] = values2['col2'] testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col1'], hpmapCol1[ipnest_test]) testing.assert_almost_equal(sparse_map.get_values_pos(ra, dec, lonlat=True)['col2'], hpmapCol2[ipnest_test]) # Test making empty maps sparse_map2 = healsparse.HealSparseMap.make_empty_like(sparse_map) self.assertEqual(sparse_map2.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2.dtype, sparse_map.dtype) self.assertEqual(sparse_map2._sentinel, sparse_map._sentinel) sparse_map2b = healsparse.HealSparseMap.make_empty_like(sparse_map, cov_pixels=[0, 2]) self.assertEqual(sparse_map2b.nside_coverage, sparse_map.nside_coverage) self.assertEqual(sparse_map2b.nside_sparse, sparse_map.nside_sparse) self.assertEqual(sparse_map2b.dtype, sparse_map.dtype) self.assertEqual(sparse_map2b._sentinel, sparse_map._sentinel) self.assertEqual(len(sparse_map2b._sparse_map), sparse_map2._cov_map.nfine_per_cov*3) testing.assert_array_equal(sparse_map2b._sparse_map['col1'], sparse_map._sentinel) testing.assert_array_equal(sparse_map2b._sparse_map['col2'], hp.UNSEEN) if __name__ == '__main__': unittest.main()

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b0233970b3c27cfa9c2c7592b2e023a2a52b57ce

135

py

Python

src/modules/__init__.py

Nobregaigor/Optical-Mark-Registration-PDF-Reader

0c6d8d652fa4e52d04098787c022b7235cd2b8ac

[ "MIT" ]

null

src/modules/__init__.py

Nobregaigor/Optical-Mark-Registration-PDF-Reader

0c6d8d652fa4e52d04098787c022b7235cd2b8ac

[ "MIT" ]

null

src/modules/__init__.py

Nobregaigor/Optical-Mark-Registration-PDF-Reader

0c6d8d652fa4e52d04098787c022b7235cd2b8ac

[ "MIT" ]

null

from .check_unumber import * from .check_email import * from .rename_pdfs import * from .test_email import * from .send_emails import *

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0

null

0

1

0

1

0

6

c65d1896c0352cbebf8019534651e9a56036fa8e

504

py

Python

espnet2/gan_tts/hifigan/__init__.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

5,053

2017-12-13T06:21:41.000Z

2022-03-31T13:38:29.000Z

espnet2/gan_tts/hifigan/__init__.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

3,666

2017-12-14T05:58:50.000Z

2022-03-31T22:11:49.000Z

espnet2/gan_tts/hifigan/__init__.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

1,709

2017-12-13T01:02:42.000Z

2022-03-31T11:57:45.000Z

from espnet2.gan_tts.hifigan.hifigan import HiFiGANGenerator # NOQA from espnet2.gan_tts.hifigan.hifigan import HiFiGANMultiPeriodDiscriminator # NOQA from espnet2.gan_tts.hifigan.hifigan import HiFiGANMultiScaleDiscriminator # NOQA from espnet2.gan_tts.hifigan.hifigan import ( # NOQA HiFiGANMultiScaleMultiPeriodDiscriminator, # NOQA ) from espnet2.gan_tts.hifigan.hifigan import HiFiGANPeriodDiscriminator # NOQA from espnet2.gan_tts.hifigan.hifigan import HiFiGANScaleDiscriminator # NOQA

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504

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null

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null

0

1

0

1

0

1

0

6

c6a23b3dd64ffdfe9143394f3d5d5ef32284969e

1,022

py

Python

tests/test_permission.py

dmelo/Flask-ACL

7339b89f96ad8686d1526e25c138244ad912e12d

[ "BSD-3-Clause" ]

25

2015-02-06T20:00:16.000Z

2022-03-17T15:59:47.000Z

tests/test_permission.py

dmelo/Flask-ACL

7339b89f96ad8686d1526e25c138244ad912e12d

[ "BSD-3-Clause" ]

2

2015-03-30T06:03:27.000Z

2018-01-05T17:03:12.000Z

tests/test_permission.py

dmelo/Flask-ACL

7339b89f96ad8686d1526e25c138244ad912e12d

[ "BSD-3-Clause" ]

17

2015-03-20T19:48:58.000Z

2020-03-29T15:47:42.000Z

from . import * from flask_acl.permission import is_permission_in_set class TestPermissions(TestCase): def test_strings(self): self.assertTrue(is_permission_in_set('xxx', 'xxx')) self.assertFalse(is_permission_in_set('xxx', 'axxx')) self.assertFalse(is_permission_in_set('xxx', 'xxxb')) def test_containers(self): self.assertTrue(is_permission_in_set('xxx', ('a', 'xxx', 'b'))) self.assertTrue(is_permission_in_set('xxx', ['a', 'xxx', 'b'])) self.assertTrue(is_permission_in_set('xxx', set(['a', 'xxx', 'b']))) self.assertFalse(is_permission_in_set('xxx', ('a', 'b'))) self.assertFalse(is_permission_in_set('xxx', ['a', 'b'])) self.assertFalse(is_permission_in_set('xxx', set(['a', 'b']))) def test_callables(self): self.assertTrue(is_permission_in_set('xxx', lambda p: True)) self.assertTrue(is_permission_in_set('xxx', lambda p: 'x' in p)) self.assertFalse(is_permission_in_set('xxx', lambda p: 'X' in p))

40.88

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143

1,022

4.384615

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0.290271

0.352472

0.76236

0.614035

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0.452951

0

0.169276

1,022

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null

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0

1

0

null

0

1

0

6

c6a51c5697d169beff79ebf57f4064354d0ce4ea

28

py

Python

example/boot.py

jdtsmith/autoftp

5d06749ba6132f64cbb2231334553ea17c814f41

[ "MIT" ]

1

2021-03-14T03:12:12.000Z

example/boot.py

jdtsmith/autoftp

5d06749ba6132f64cbb2231334553ea17c814f41

[ "MIT" ]

null

example/boot.py

jdtsmith/autoftp

5d06749ba6132f64cbb2231334553ea17c814f41

[ "MIT" ]

null

print("MyModule Starting!")

14

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true

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null

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1

0

1

0

null

0

1

0

1

0

6

c6b05e8ef9e2ab103120c4a9405847a2dc073555

39

py

Python

tests/environment_tests/dataset_handler/__init__.py

doslindos/ml_crapwrap

f9daa2904234492921c6c344bfcd24992e2ff421

[ "MIT" ]

null

tests/environment_tests/dataset_handler/__init__.py

doslindos/ml_crapwrap

f9daa2904234492921c6c344bfcd24992e2ff421

[ "MIT" ]

null

tests/environment_tests/dataset_handler/__init__.py

doslindos/ml_crapwrap

f9daa2904234492921c6c344bfcd24992e2ff421

[ "MIT" ]

null

from .util import DatasetHandlerTester

19.5

38

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4

39

8.5

1

0

0.102564

39

1

39

0.971429

0

1

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true

0

1

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

05b50cf561283569860addc2e70867e8bb843ad7

122

py

Python

src/dlqmc/tools.py

noegroup/dlqmc-project

ed7561ec0156df6d6309e49c1276646173ec8641

[ "MIT" ]

3

2020-12-22T16:26:36.000Z

2021-08-11T16:54:46.000Z

src/dlqmc/tools.py

noegroup/dlqmc-project

ed7561ec0156df6d6309e49c1276646173ec8641

[ "MIT" ]

5

2020-07-26T23:13:16.000Z

2020-07-26T23:13:45.000Z

src/dlqmc/tools.py

noegroup/dlqmc-project

ed7561ec0156df6d6309e49c1276646173ec8641

[ "MIT" ]

1

2021-06-18T05:00:39.000Z

import uncertainties def short_fmt(x): return f'{x:S}' if isinstance(x, uncertainties.core.AffineScalarFunc) else x

20.333333

80

0.754098

18

122

5.055556

0.777778

0

0.139344

122

5

81

24.4

0.866667

0

0.040984

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

05cae0dbf5c34f27a96d0017a1f5a55c10de89ff

74

py

Python

besspin/cyberPhys/cyberphyslib/cyberphyslib/canlib/__init__.py

mikkowus/BESSPIN-Tool-Suite

e87e9abb1156a8627aacc3272f1925b034129146

[ "Apache-2.0" ]

null

besspin/cyberPhys/cyberphyslib/cyberphyslib/canlib/__init__.py

mikkowus/BESSPIN-Tool-Suite

e87e9abb1156a8627aacc3272f1925b034129146

[ "Apache-2.0" ]

null

besspin/cyberPhys/cyberphyslib/cyberphyslib/canlib/__init__.py

mikkowus/BESSPIN-Tool-Suite

e87e9abb1156a8627aacc3272f1925b034129146

[ "Apache-2.0" ]

null

from .canlib import * from .canspecs import * from .componentids import *

18.5

27

0.756757

9

74

6.222222

0.555556

0.357143

0

0.162162

74

3

28

24.666667

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

05f416b3b4673cf189674c943bdc0c5032d17a65

25,179

py

Python

tests/test_time.py

colinahill/terrapyn

77a2bba2b6365d289d894f0aa722880ae36c52bd

[ "BSD-3-Clause" ]

null

tests/test_time.py

colinahill/terrapyn

77a2bba2b6365d289d894f0aa722880ae36c52bd

[ "BSD-3-Clause" ]

null

tests/test_time.py

colinahill/terrapyn

77a2bba2b6365d289d894f0aa722880ae36c52bd

[ "BSD-3-Clause" ]

null

import datetime as dt import unittest import zoneinfo from pathlib import Path import numpy as np import pandas as pd import pytest import xarray as xr from freezegun import freeze_time import terrapyn as tp PACKAGE_ROOT_DIR = Path(__file__).resolve().parent.parent TEST_DATA_PATH = PACKAGE_ROOT_DIR / "tests" / "data" idx = pd.IndexSlice class TestConvertDatetime64(unittest.TestCase): def test_object_type(self): result = tp.time.datetime64_to_datetime(np.datetime64("2013-04-05 07:12:34.056789")) self.assertEqual(result, dt.datetime(2013, 4, 5, 7, 12, 34, 56789)) class TestConvertDatetime(unittest.TestCase): def test_object_type(self): result = tp.time.datetime_to_datetime64(dt.datetime(2013, 4, 5, 7, 12, 34, 123)) self.assertEqual(result, np.datetime64("2013-04-05 07:12:34.000123")) class TestGetTimeFromData(unittest.TestCase): expected = pd.DatetimeIndex( ["2019-03-15", "2019-03-16", "2019-03-17"], dtype="datetime64[ns]", name="time", freq=None ) df = pd.DataFrame( { "time": expected, "id": [123, 456, 789], "val": [1, 3, 5], } ).set_index(["time", "id"]) def test_dataframe(self): results = tp.time.get_time_from_data(self.df.reset_index(drop=False)) pd.testing.assert_index_equal(results, self.expected) def test_dataframe_time_column(self): results = tp.time.get_time_from_data(self.df) pd.testing.assert_index_equal(results, self.expected) def test_dataset(self): results = tp.time.get_time_from_data(self.df.to_xarray()) pd.testing.assert_index_equal(results, self.expected) def test_list(self): results = tp.time.get_time_from_data(list(self.expected.to_pydatetime())) pd.testing.assert_index_equal(results, self.expected) def test_dataarray(self): results = tp.time.get_time_from_data(self.df.to_xarray()["val"]) pd.testing.assert_index_equal(results, self.expected) def test_series_time_index(self): results = tp.time.get_time_from_data(self.df["val"]) pd.testing.assert_index_equal(results, self.expected) def test_series_time_column(self): results = tp.time.get_time_from_data(pd.Series(self.expected)) pd.testing.assert_index_equal(results, self.expected) def test_datetime(self): results = tp.time.get_time_from_data(dt.datetime(2019, 3, 15)) pd.testing.assert_index_equal(results, pd.DatetimeIndex([dt.datetime(2019, 3, 15)], name="time")) def test_ndarray(self): results = tp.time.get_time_from_data(self.expected.to_numpy()) pd.testing.assert_index_equal(results, self.expected) def test_datetimeindex(self): results = tp.time.get_time_from_data(self.expected) pd.testing.assert_index_equal(results, self.expected) def test_multiindex(self): results = tp.time.get_time_from_data(self.df.index) pd.testing.assert_index_equal(results, self.expected) def test_missing_from_multiindex(self): with self.assertRaises(ValueError): tp.time.get_time_from_data(self.df.index, time_dim="date") def test_invalid_datatype(self): with self.assertRaises(TypeError): tp.time.get_time_from_data(1) class TestGroupbyTime(unittest.TestCase): ds = xr.open_dataset(TEST_DATA_PATH / "lat_2_lon_2_time_15_D_test_data.nc") def test_dataset_groupby_week(self): result = tp.time.groupby_time(self.ds, grouping="week") self.assertEqual(result.groups, {8: [0, 1, 2, 3, 4], 9: [5, 6, 7, 8, 9, 10, 11], 10: [12, 13, 14]}) def test_dataarray_groupby_week(self): result = tp.time.groupby_time(self.ds["var"], grouping="week") self.assertEqual(result.groups, {8: [0, 1, 2, 3, 4], 9: [5, 6, 7, 8, 9, 10, 11], 10: [12, 13, 14]}) def test_dataset_groupby_month(self): result = tp.time.groupby_time(self.ds, grouping="month") self.assertEqual(result.groups, {2: [0, 1, 2, 3, 4, 5, 6, 7, 8], 3: [9, 10, 11, 12, 13, 14]}) def test_dataframe_groupby_pentad(self): result = tp.time.groupby_time(self.ds.to_dataframe(), grouping="pentad") np.testing.assert_almost_equal(result.sum().values, np.array([[272.59223017], [257.44398154], [295.72954042]])) def test_series_groupby_dekad(self): result = tp.time.groupby_time(self.ds.to_dataframe()["var"], grouping="dekad") np.testing.assert_almost_equal(result.sum().values, np.array([80.23334597, 412.85991647, 332.67248968])) def test_groupby_year(self): result = tp.time.groupby_time(self.ds, grouping="year") self.assertEqual(result.groups, {2019: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]}) def test_groupby_dayofyear(self): result = tp.time.groupby_time(self.ds, grouping="dayofyear") self.assertEqual(result.groups[55], [4]) def test_invalid_grouping(self): with self.assertRaises(ValueError): tp.time.groupby_time(self.ds, grouping="10d") def test_groupby_other_keys(self): result = tp.time.groupby_time(self.ds.to_dataframe(), grouping="year", other_grouping_keys="lon").groups[ (2019, 30) ][0] self.assertEqual(result, (pd.Timestamp("2019-02-20 00:00:00"), 2, 30)) def test_groupby_multiple_other_keys(self): result = tp.time.groupby_time( self.ds.to_dataframe(), grouping="year", other_grouping_keys=["lat", "lon"] ).groups[(2019, 2, 30)][0] self.assertEqual(result, (pd.Timestamp("2019-02-20 00:00:00"), 2, 30)) @freeze_time("2012-01-14 11:12:13") class TestDailyDateRange(unittest.TestCase): def test_date_today(self): result = tp.time.daily_date_range() self.assertEqual(result, [dt.datetime(2012, 1, 14, 0, 0)]) def test_delta_days(self): result = tp.time.daily_date_range(delta_days=-1) self.assertEqual(result, [dt.datetime(2012, 1, 13, 0, 0), dt.datetime(2012, 1, 14, 0, 0)]) def test_delta_days_future(self): result = tp.time.daily_date_range(delta_days=1) self.assertEqual(result, [dt.datetime(2012, 1, 14, 0, 0), dt.datetime(2012, 1, 15, 0, 0)]) def test_future_dates(self): result = tp.time.daily_date_range(end_time=dt.datetime(2012, 1, 16), reset_time=True, hours=6) self.assertEqual( result, [dt.datetime(2012, 1, 14, 6, 0), dt.datetime(2012, 1, 15, 6, 0), dt.datetime(2012, 1, 16, 6, 0)] ) @freeze_time("2012-01-14 11:12:13") class TestMonthlyDateRange(unittest.TestCase): def test_date_today_ignore_days(self): result = tp.time.monthly_date_range() self.assertEqual(result, [dt.datetime(2012, 1, 1, 0, 0)]) def test_date_today_include_days(self): result = tp.time.monthly_date_range(reset_time=False) self.assertEqual(result, [dt.datetime(2012, 1, 14, 11, 12, 13)]) def test_zero_delta_months(self): result = tp.time.monthly_date_range(delta_months=0) self.assertEqual(result, [dt.datetime(2012, 1, 1, 0, 0)]) def test_delta_months(self): result = tp.time.monthly_date_range(delta_months=-2) self.assertEqual( result, [ dt.datetime(2011, 11, 1, 0, 0), dt.datetime(2011, 12, 1, 0, 0), dt.datetime(2012, 1, 1, 0, 0), ], ) class TestAddDayOfYearVariable(unittest.TestCase): ds = xr.open_dataset(TEST_DATA_PATH / "lat_2_lon_2_time_366_D_test_data.nc") def test_dataset_modify_ordinal_days(self): result = tp.time.add_day_of_year_variable(self.ds) np.testing.assert_equal(result["dayofyear"].values[58:62], np.array([59, 60, 60, 61])) def test_dataset_no_modify_ordinal_days(self): result = tp.time.add_day_of_year_variable(self.ds, modify_ordinal_days=False) np.testing.assert_equal(result["dayofyear"].values[58:62], np.array([59, 60, 61, 62])) def test_dataarray(self): result = tp.time.add_day_of_year_variable(self.ds["var"], modify_ordinal_days=False) np.testing.assert_equal(result["dayofyear"].values[58:62], np.array([59, 60, 61, 62])) class TestCheckStartEndTimeValidity(unittest.TestCase): def test_datetime64_with_datetime64(self): result = tp.time.check_start_end_time_validity( np.datetime64("2013-04-05 07:12:34.056789"), np.datetime64("2013-04-05 07:12:35") ) self.assertTrue(result) def test_datetime64_with_datetime64_invalid(self): result = tp.time.check_start_end_time_validity( np.datetime64("2013-04-05 07:12:35"), np.datetime64("2013-04-05 07:12:34.056789") ) self.assertFalse(result) def test_datetime_with_datetime64(self): result = tp.time.check_start_end_time_validity( dt.datetime(2013, 4, 5, 7, 12, 34, 56789), np.datetime64("2013-04-05 07:12:35") ) self.assertTrue(result) @pytest.fixture(autouse=True) def capfd(self, capfd): self.capfd = capfd def test_verbose_warning(self): result = tp.time.check_start_end_time_validity(dt.datetime(2014, 1, 2), dt.datetime(2014, 1, 1), verbose=True) out, err = self.capfd.readouterr() self.assertFalse(result) assert out == "Warning: End time 2014-01-01 00:00:00 before start time 2014-01-02 00:00:00\n" def test_missing_time(self): with self.assertRaises(ValueError): tp.time.check_start_end_time_validity(None, dt.datetime(2014, 1, 2)) class TestGetDayOfYear(unittest.TestCase): ds = xr.open_dataset(TEST_DATA_PATH / "lat_2_lon_2_time_366_D_test_data.nc") def test_dataset_ordinal_days(self): result = tp.time.get_day_of_year(self.ds, modify_ordinal_days=False)[58:62] np.testing.assert_equal(result, np.array([59, 60, 61, 62])) def test_dataset_modify_ordinal_days(self): result = tp.time.get_day_of_year(self.ds, modify_ordinal_days=True)[58:62] np.testing.assert_equal(result, np.array([59, 60, 60, 61])) def test_dataarray_ordinal_days(self): result = tp.time.get_day_of_year(self.ds["var"], modify_ordinal_days=False)[58:62] np.testing.assert_equal(result, np.array([59, 60, 61, 62])) def test_dataframe_ordinal_days(self): result = tp.time.get_day_of_year(self.ds.to_dataframe(), time_dim="time", modify_ordinal_days=False)[58:62] np.testing.assert_equal(result, np.array([15, 15, 16, 16])) def test_series_ordinal_days(self): result = tp.time.get_day_of_year(self.ds.to_dataframe()["var"].index, modify_ordinal_days=False)[58:62] np.testing.assert_equal(result, np.array([15, 15, 16, 16])) def test_datetime_ordinal_days(self): result = tp.time.get_day_of_year(dt.datetime(2004, 3, 1), modify_ordinal_days=False) np.testing.assert_equal(result, np.array([61])) class TestTimeToLocalTime(unittest.TestCase): def test_invalid_string(self): with self.assertRaises(TypeError): tp.time.time_to_local_time(dt.datetime(2019, 3, 15, 1, 0), timezone_name=1) class TestDataToLocalTime(unittest.TestCase): expected = pd.DatetimeIndex( ["2019-03-15 01:00:00", "2019-03-15 02:00:00"], dtype="datetime64[ns]", name="time", freq=None ) df = pd.DataFrame( {"time": pd.date_range("2019-03-15", freq="h", periods=2), "id": ["a", "b"], "val": [1, 2]} ).set_index(["time", "id"]) def test_dataframe_multiindex(self): results = tp.time.data_to_local_time(self.df.copy(), "CET").index.get_level_values("time") pd.testing.assert_index_equal(results, self.expected) def test_dataframe(self): test_df = self.df.copy().reset_index(drop=False).set_index("time") results = tp.time.data_to_local_time(test_df, "CET").index.get_level_values("time") pd.testing.assert_index_equal(results, self.expected) def test_dataframe_column(self): results = tp.time.data_to_local_time(self.df.reset_index(drop=False).copy(), "CET", time_dim="time")["time"] np.testing.assert_equal(results.values, self.expected.values) def test_series(self): results = tp.time.data_to_local_time(self.df.copy()["val"], "CET").index.get_level_values("time") pd.testing.assert_index_equal(results, self.expected) def test_series_values(self): results = tp.time.data_to_local_time(pd.Series(self.expected), "CET").values np.testing.assert_equal(results, pd.Series(self.expected) + pd.Timedelta("1h")) def test_dataset(self): results = tp.time.data_to_local_time(self.df.copy().to_xarray(), "CET").indexes["time"] pd.testing.assert_index_equal(results, self.expected) def test_dataarray(self): results = tp.time.data_to_local_time(self.df.copy().to_xarray(), "CET")["val"].indexes["time"] pd.testing.assert_index_equal(results, self.expected) def test_datetime(self): results = tp.time.data_to_local_time(dt.datetime(2019, 3, 15, 1, 0), "CET")[0] self.assertTrue(results, dt.datetime(2019, 3, 15, 2, 0)) def test_missing_timezone(self): with self.assertRaises(ValueError): tp.time.data_to_local_time(dt.datetime(2019, 3, 15, 1, 0), None) def test_datetimeindex(self): results = tp.time.data_to_local_time(self.expected - pd.Timedelta("1h"), "CET") pd.testing.assert_index_equal(results, self.expected) def test_ndarray(self): results = tp.time.data_to_local_time(self.expected.values - np.timedelta64(1, "h"), "CET") pd.testing.assert_index_equal(results, self.expected) def test_list(self): results = tp.time.data_to_local_time([dt.datetime(2019, 3, 15, 1, 0)], "CET") self.assertEqual(results.to_pydatetime(), [dt.datetime(2019, 3, 15, 2, 0)]) @pytest.fixture(autouse=True) def capfd(self, capfd): self.capfd = capfd def test_invalid_data_type(self): with self.assertRaises(TypeError): out, err = self.capfd.readouterr() _ = tp.time.data_to_local_time(1, "CET") assert out == "Data type of int not implemented" class TestListTimezones(unittest.TestCase): def test_dict_type(self): result = tp.time.list_timezones() self.assertTrue(isinstance(result, set)) class TestEnsureDatetimeIndex(unittest.TestCase): def test_datetime(self): result = tp.time._ensure_datetimeindex(dt.datetime(2021, 4, 5)) expected = pd.DatetimeIndex(["2021-04-05 00:00:00"], dtype="datetime64[ns]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) def test_list_of_datetimes(self): result = tp.time._ensure_datetimeindex([dt.datetime(2021, 4, 5), dt.datetime(2021, 4, 6)]) expected = pd.DatetimeIndex( ["2021-04-05 00:00:00", "2021-04-06 00:00:00"], dtype="datetime64[ns]", name="time", freq=None ) pd.testing.assert_index_equal(result, expected) def test_datetimeindex(self): expected = pd.DatetimeIndex(["2021-04-05 00:00:00"], dtype="datetime64[ns]", name="time", freq=None) result = tp.time._ensure_datetimeindex(expected) pd.testing.assert_index_equal(result, expected) class TestDatetimeToUTC(unittest.TestCase): def test_no_timezone(self): result = tp.time._datetime_to_UTC(dt.datetime(2021, 4, 5)) expected = pd.DatetimeIndex(["2021-04-05 00:00:00+00:00"], dtype="datetime64[ns, UTC]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) def test_timezone_set(self): result = tp.time._datetime_to_UTC(dt.datetime(2021, 4, 5, tzinfo=zoneinfo.ZoneInfo("CET"))) expected = pd.DatetimeIndex(["2021-04-04 22:00:00+00:00"], dtype="datetime64[ns, UTC]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) class TestDatetimeindexToLocalTimeTzAware(unittest.TestCase): def test_no_timezone(self): result = tp.time._datetimeindex_to_local_time_tz_aware(dt.datetime(2021, 4, 5)) expected = pd.DatetimeIndex(["2021-04-05 00:00:00+00:00"], dtype="datetime64[ns, UTC]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) def test_timezone_set(self): result = tp.time._datetimeindex_to_local_time_tz_aware( dt.datetime(2021, 4, 5, tzinfo=zoneinfo.ZoneInfo("CET")), "EST" ) expected = pd.DatetimeIndex(["2021-04-04 17:00:00-05:00"], dtype="datetime64[ns, EST]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) class TestDatetimeindexToLocalTimeTzNaive(unittest.TestCase): def test_no_timezone(self): result = tp.time._datetimeindex_to_local_time_tz_naive(dt.datetime(2021, 4, 5)) expected = pd.DatetimeIndex(["2021-04-05"], dtype="datetime64[ns]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) def test_timezone_set(self): result = tp.time._datetimeindex_to_local_time_tz_naive(dt.datetime(2021, 4, 5, tzinfo=zoneinfo.ZoneInfo("CET"))) expected = pd.DatetimeIndex(["2021-04-04 22:00:00"], dtype="datetime64[ns]", name="time", freq=None) pd.testing.assert_index_equal(result, expected) class TestSetTimeInData(unittest.TestCase): df = pd.DataFrame( {"time": pd.date_range("2019-03-15 06:00", freq="D", periods=2), "id": ["a", "b"], "val": [1, 2]} ).set_index(["time", "id"]) def test_replace_times(self): results = tp.time._set_time_in_data( self.df, new_times=pd.date_range("2021-01-1 06:00", freq="D", periods=2) ).index.get_level_values("time") expected = pd.DatetimeIndex( ["2021-01-01 06:00:00", "2021-01-02 06:00:00"], dtype="datetime64[ns]", name="time", freq=None ) pd.testing.assert_index_equal(results, expected) def test_set_times_to_midnight(self): results = tp.time._set_time_in_data( self.df, set_time_to_midnight=True, hours_to_subtract=None ).index.get_level_values("time") expected = pd.DatetimeIndex(["2021-01-01", "2021-01-02"], dtype="datetime64[ns]", name="time", freq=None) pd.testing.assert_index_equal(results, expected) def test_subtract_hours(self): results = tp.time._set_time_in_data( self.df, set_time_to_midnight=True, hours_to_subtract=5 ).index.get_level_values("time") expected = pd.DatetimeIndex( ["2020-12-31 19:00:00", "2021-01-01 19:00:00"], dtype="datetime64[ns]", name="time", freq=None ) pd.testing.assert_index_equal(results, expected) def test_no_modification(self): results = tp.time._set_time_in_data(self.df).index.get_level_values("time") expected = self.df.index.get_level_values("time") pd.testing.assert_index_equal(results, expected) class TestUTCOffsetInHours(unittest.TestCase): def test_datetimeindex_no_timezone(self): result = tp.time.utc_offset_in_hours(pd.date_range("2019-01-02", freq="6H", periods=2), "Asia/Kolkata") self.assertEqual(result, 5.5) def test_datetime_no_timezone(self): result = tp.time.utc_offset_in_hours(dt.datetime(2021, 4, 5), "Asia/Kolkata") self.assertEqual(result, 5.5) def test_datetimeindex_timezone_set(self): result = tp.time.utc_offset_in_hours( pd.date_range("2019-01-02", freq="6H", periods=2, tz="CET"), "Asia/Kolkata" ) self.assertEqual(result, 5.5) def test_datetimeindex_return_multiple_offsets(self): result = tp.time.utc_offset_in_hours( pd.date_range("2019-01-02", freq="6H", periods=2), "Asia/Kolkata", return_single_value=False ) self.assertEqual(result, [5.5, 5.5]) class TestGroupbyFreq(unittest.TestCase): ds = xr.Dataset( data_vars={"var": (("lat", "lon", "time"), np.ones((1, 1, 100)))}, coords={"lat": [1], "lon": [2], "time": pd.date_range("2022-01-01", periods=100)}, ) ds_hourly = xr.Dataset( data_vars={"var": (("lat", "lon", "time"), np.arange(100)[np.newaxis, np.newaxis, :])}, coords={"lat": [1], "lon": [2], "time": pd.date_range("2022-01-01", periods=100, freq="h")}, ) ds_hourly_multicoord = xr.Dataset( data_vars={"var": (("lat", "lon", "time"), np.full((2, 2, 100), np.arange(100)))}, coords={"lat": [1, 2], "lon": [3, 4], "time": pd.date_range("2022-01-01", periods=100, freq="h")}, ) def test_dataset(self): result = tp.time.groupby_freq(self.ds, freq="M").sum()["var"].values.flatten() expected = np.array([30.0, 28.0, 31.0, 11.0]) np.testing.assert_array_equal(result, expected) def test_dataarray(self): result = tp.time.groupby_freq(self.ds["var"], freq="M").sum().values.flatten() expected = np.array([30.0, 28.0, 31.0, 11.0]) np.testing.assert_array_equal(result, expected) def test_dataframe(self): result = tp.time.groupby_freq(self.ds["var"].to_dataframe(), freq="M").sum()["var"].values expected = np.array([30.0, 28.0, 31.0, 11.0]) np.testing.assert_array_equal(result, expected) def test_series(self): result = tp.time.groupby_freq(self.ds["var"].to_series(), freq="M").sum().values expected = np.array([30.0, 28.0, 31.0, 11.0]) np.testing.assert_array_equal(result, expected) def test_dataset_hourly_to_daily(self): result = tp.time.groupby_freq(self.ds_hourly, freq="D", day_start_hour=6).sum()["var"].values.flatten() expected = np.array([15, 420, 996, 1572, 1947]) np.testing.assert_array_equal(result, expected) def test_dataframe_hourly_to_daily(self): result = tp.time.groupby_freq(self.ds_hourly.to_dataframe(), freq="D", day_start_hour=6).sum()["var"] expected = np.array([15, 420, 996, 1572, 1947]) np.testing.assert_array_equal(result.values, expected) self.assertEqual(result.index.names, ["time", "lat", "lon"]) def test_series_hourly_to_daily_single_index(self): result = tp.time.groupby_freq( self.ds_hourly["var"].to_series().reset_index(drop=True, level=["lat", "lon"]), freq="D", day_start_hour=6 ).sum() expected = np.array([15, 420, 996, 1572, 1947]) np.testing.assert_array_equal(result.values, expected) self.assertEqual(result.index.name, "time") def test_dataframe_no_time_dim(self): with self.assertRaises(ValueError): tp.time.groupby_freq(self.ds["var"].to_dataframe(), freq="M", time_dim="x") def test_series_no_time_dim(self): with self.assertRaises(ValueError): tp.time.groupby_freq(self.ds["var"].to_series(), freq="M", time_dim="x") def test_dataframe_time_column(self): result = ( tp.time.groupby_freq(self.ds["var"].to_dataframe().reset_index(drop=False), freq="M").sum()["var"].values ) expected = np.array([30.0, 28.0, 31.0, 11.0]) np.testing.assert_array_equal(result, expected) def test_dataframe_time_column_other_cols(self): result = ( tp.time.groupby_freq( self.ds_hourly_multicoord.to_dataframe().reset_index(drop=False), freq="D", other_grouping_columns="lat" ) .sum()["lon"] .values ) expected = np.array([168, 168, 168, 168, 168, 168, 168, 168, 28, 28]) np.testing.assert_array_equal(result, expected) def test_invalid_datatype(self): with self.assertRaises(TypeError): tp.time.groupby_freq([1]) class TestResampleTime(unittest.TestCase): ds_hourly = xr.Dataset( data_vars={"var": (("lat", "lon", "time"), np.arange(100)[np.newaxis, np.newaxis, :])}, coords={"lat": [1], "lon": [2], "time": pd.date_range("2022-01-01", periods=100, freq="h")}, ) def test_dataset_sum(self): result = tp.time.resample_time(self.ds_hourly, freq="D", day_start_hour=6, resample_method="sum") expected = np.array([15, 420, 996, 1572, 1947]) self.assertEqual(result["time"].values[0], np.datetime64("2021-12-31T06:00:00.00")) np.testing.assert_array_equal(result["var"].values.flatten(), expected) def test_dataset_mean(self): result = tp.time.resample_time(self.ds_hourly, resample_method="mean")["var"].values.flatten() np.testing.assert_array_equal(result, np.array([11.5, 35.5, 59.5, 83.5, 97.5])) def test_dataset_max(self): result = tp.time.resample_time(self.ds_hourly, resample_method="max")["var"].values.flatten() np.testing.assert_array_equal(result, np.array([23, 47, 71, 95, 99])) def test_dataset_min(self): result = tp.time.resample_time(self.ds_hourly, resample_method="min")["var"].values.flatten() np.testing.assert_array_equal(result, np.array([0, 24, 48, 72, 96])) def test_method_not_implemented(self): with self.assertRaises(ValueError): tp.time.resample_time(self.ds_hourly, resample_method="foobar")

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05f9dbcdf439cc82456bb49424264d138e6ec2cf

20,660

py

Python

scoreprobability/crawl.py

htpauleta/ScoreProbability

eae1d76e3607bb4cb19eff6a7ad72d5b2c20f4f7

[ "Apache-2.0" ]

7

2018-12-29T06:39:28.000Z

2021-10-12T01:42:37.000Z

scoreprobability/crawl.py

htpauleta/ScoreProbability

eae1d76e3607bb4cb19eff6a7ad72d5b2c20f4f7

[ "Apache-2.0" ]

2

2021-03-31T18:56:32.000Z

2021-06-01T23:30:22.000Z

scoreprobability/crawl.py

htpauleta/ScoreProbability

eae1d76e3607bb4cb19eff6a7ad72d5b2c20f4f7

[ "Apache-2.0" ]

null

""" @Project : ScoreProbability @Module : crawl.py @Author : HjwGivenLyy [1752929469@qq.com] @Created : 12/17/18 4:54 PM @Desc : crawl data from qtw net """ import datetime import re import loguru import requests import yaml from bs4 import BeautifulSoup from pymongo import MongoClient from base import SUPPORT_LEAGUE_NAME_ID, SERVER_FILE_PATH # qtw company bet QTW_COMPANY_BET = [8, 23, 24, 31] YP_URL = "http://vip.win007.com/changeDetail/handicap.aspx?" \ "id={qtw_match_id}&companyID={company_id}&l=0" DXQ_URL = "http://vip.win007.com/changeDetail/overunder.aspx?" \ "id={qtw_match_id}&companyID={company_id}&l=0" LEAGUE_URL = "http://zq.win007.com/cn/SubLeague/{league_id}.html" SEASON_URL = "http://zq.win007.com/jsData/matchResult/2018-2019/" \ "s{id}.js?version={value}" SPECIAL_SEASON_URL = { 'fy': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s12_1778.js?version={value}", 'dy': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s9_132.js?version={value}", 'yg': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s37_87.js?version={value}", 'bj': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s5_114.js?version={value}", 'pc': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s23_1123.js?version={value}", 'ac': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s273_462.js?version={value}", 'xy': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s33_546.js?version={value}", 'yy': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s40_261.js?version={value}", 'hj': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s16_98.js?version={value}", 'hy': "http://zq.win007.com/jsData/matchResult/2018-2019/" "s17_94.js?version={value}", } logger = loguru.logger def get_latest_odds_by_qtw_match_id(qtw_match_id: int, odd_type: str): """ according to qtw match id get latest odds :param qtw_match_id: :param odd_type: "yp" or "dxq" :return: """ def get_latest_odd_by_company_id(match_id: int, company_id: int, odd: str): """get certain bet company odd""" if odd == "yp": url = YP_URL.format(qtw_match_id=match_id, company_id=company_id) elif odd == "dxq": url = DXQ_URL.format(qtw_match_id=match_id, company_id=company_id) else: return "odds type must be in ['yp', 'dxq']" headers1 = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; ' 'rv:2.0.1) Gecko/20100101 Firefox/4.0.1' } try: content = requests.get(url, headers=headers1) page = BeautifulSoup(content.content, "lxml") odds2_lst = page.find_all(id="odds2") td_lst = odds2_lst[0].find_all("tr")[1].find_all("td") if td_lst[-1].text.encode("utf-8") != "滚": return td_lst[3].text else: return "match have begin!" except Exception as e: logger.exception(e) logger.error( "get qtw_match_id = {0} odds failure".format(qtw_match_id)) return None for company_id_value in QTW_COMPANY_BET: rtn = get_latest_odd_by_company_id( match_id=qtw_match_id, company_id=company_id_value, odd=odd_type) if rtn is not None: return rtn else: return None def save_match_info_to_mongodb(db_client: MongoClient, league_name: str): """ save qtw match information data to mongodb :param db_client: mongodb client :param league_name: league name --> "yc", "dj", "fj", "xj", "yj" :return: run insert into """ tb = db_client["xscore"]["match_info"] page_text = spare_url(league_name) pattern = re.compile('jh.* = .*]') match_lst = re.findall(pattern, page_text) game_week = 0 for match_str in match_lst: game_week += 1 match_info_str = match_str.replace(",,,", ",'','',") match_info_lst = eval(str(match_info_str).split(" = ")[1]) for match_information in match_info_lst: qtw_match_id = int(match_information[0]) result = tb.find_one( filter={"qtw_match_id": qtw_match_id}, projection={'status': 1} ) if result is not None and result["status"] == 2: continue elif result is not None and result["status"] != 2: if match_information[2] == -1: new_status = 2 new_status_text = "Played" score_lst = match_information[6].split("-") tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text, "home_score": int(score_lst[0]), "away_score": int(score_lst[1]) } } ) elif match_information[2] == -14: new_status = 3 new_status_text = "Delay" tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text } } ) else: new_status = 3 new_status_text = "Fixture" tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text } } ) logger.info( "qtw_match_id = {0} have finished update!".format( qtw_match_id)) else: result_dict = dict() result_dict["qtw_match_id"] = qtw_match_id result_dict["qtw_league_id"] = int(match_information[1]) result_dict["match_time"] = match_information[3] result_dict["home_id"] = int(match_information[4]) result_dict["away_id"] = int(match_information[5]) result_dict["game_week"] = int(game_week) if match_information[2] == -1: score_lst = match_information[6].split("-") result_dict["home_score"] = int(score_lst[0]) result_dict["away_score"] = int(score_lst[1]) result_dict["status"] = 2 result_dict["status_text"] = "Played" elif match_information[2] == -14: result_dict["home_score"] = -1 result_dict["away_score"] = -1 result_dict["status"] = 3 result_dict["status_text"] = "Delay" else: result_dict["home_score"] = -1 result_dict["away_score"] = -1 result_dict["status"] = 1 result_dict["status_text"] = "Fixture" logger.info("result_dict = {0}".format(result_dict)) tb.insert_one(result_dict) def save_team_info_to_mongodb(db_client: MongoClient, league_name: str): """ save qtw team information data to mongodb :param db_client: mongodb client :param league_name: league name --> "yc", "dj", "fj", "xj", "yj" :return: run insert into """ tb = db_client["xscore"]["team_info"] league_id = int(SUPPORT_LEAGUE_NAME_ID[league_name]) page_text = spare_url(league_name) pattern = re.compile("arrTeam = .*]") match_str = re.search(pattern, page_text) match_group = match_str.group() team_information_lst = eval(str(match_group).split(" = ")[1]) for team_information in team_information_lst: team_id = int(team_information[0]) result = tb.find_one( filter={ "league_id": league_id, "team_id": team_id }, projection={'league_id': 1} ) if result is not None: logger.info("team_id = {0} exist in mongodb".format( team_information[0])) else: result_dict = dict() result_dict["league_id"] = league_id result_dict["league_name"] = league_name result_dict['team_id'] = team_id result_dict["team_cn_name"] = team_information[1] result_dict["team_en_name"] = team_information[3] logger.info("result_dict = ", result_dict) tb.insert_one(result_dict) def save_league_info_to_mongodb(db_client: MongoClient): """save qtw league information data to mongodb""" result_lst = [] tb = db_client["xscore"]["league_info"] for key, value in SUPPORT_LEAGUE_NAME_ID.items(): result = tb.find_one( filter={"league_id": int(value)}, projection={'league_id': 1} ) if result is not None: logger.info("league_id = {0} exist in mongodb".format(value)) else: result_dct = dict() result_dct["league_name"] = key result_dct["league_id"] = int(value) result_lst.append(result_dct) if result_lst: tb.insert_many(result_lst) else: logger.info("no league info need update !!!") def spare_url(league_name: str): """spare url""" now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") version_value = now.split(":")[0].replace("-", "").replace(" ", "") league_id = SUPPORT_LEAGUE_NAME_ID[league_name] headers1 = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) ' 'Gecko/20100101 Firefox/4.0.1' } headers2 = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) ' 'Gecko/20100101 Firefox/4.0.1', 'Host': 'zq.win007.com', 'Connection': 'keep-alive', 'Accept': '*/*', 'Referer': 'http://zq.win007.com/cn/SubLeague/37.html', 'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.9' } session = requests.Session() league_home_page_url = LEAGUE_URL.format( league_id=SUPPORT_LEAGUE_NAME_ID[league_name] ) session.get(league_home_page_url, headers=headers1) if league_name in ['yc', 'dj', 'fj', 'xj', 'yj', 'sc']: season_home_page_url = SEASON_URL.format( id=league_id, value=version_value) else: season_home_page_url = SPECIAL_SEASON_URL[league_name].format( value=version_value) chi = session.get(season_home_page_url, headers=headers2) page = BeautifulSoup(chi.text, "lxml") page_text = page.p.text return page_text # Another way of code implementation class CrawlDataBase: def __init__(self, league_name: str): """ Initialization parameters :param league_name: league name --> "yc", "dj", "fj", "xj", "yj" """ self._db_client = self._get_db_client() self._league_name = league_name def __del__(self): self._db_client.close() @staticmethod def _get_db_client(): config = yaml.load(open(SERVER_FILE_PATH, encoding="utf-8")) config_dct = config.get("mongodb") client = MongoClient( "mongodb://{user}:{pwd}@{host}:{port}/{db}" "?readPreference=primary".format( user=config_dct.get("user"), pwd=config_dct.get("pwd"), host=config_dct.get("host"), port=config_dct.get("port"), db=config_dct.get("db"))) return client def spare_url(self): """spare url""" now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") version_value = now.split(":")[0].replace("-", "").replace(" ", "") league_id = SUPPORT_LEAGUE_NAME_ID[self._league_name] headers1 = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; ' 'rv:2.0.1) Gecko/20100101 Firefox/4.0.1' } headers2 = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; ' 'rv:2.0.1) Gecko/20100101 Firefox/4.0.1', 'Host': 'zq.win007.com', 'Connection': 'keep-alive', 'Accept': '*/*', 'Referer': 'http://zq.win007.com/cn/SubLeague/37.html', 'Accept-Encoding': 'gzip, deflate', 'Accept-Language': 'zh-CN,zh;q=0.9' } session = requests.Session() league_home_page_url = LEAGUE_URL.format( league_id=SUPPORT_LEAGUE_NAME_ID[self._league_name] ) session.get(league_home_page_url, headers=headers1) if self._league_name in ['yc', 'dj', 'fj', 'xj', 'yj', 'sc']: season_home_page_url = SEASON_URL.format( id=league_id, value=version_value) else: season_home_page_url = SPECIAL_SEASON_URL[ self._league_name].format( value=version_value) chi = session.get(season_home_page_url, headers=headers2) page = BeautifulSoup(chi.text, "lxml") page_text = page.p.text return page_text def save_info_to_mongodb(self): raise NotImplementedError class LeagueInfo(CrawlDataBase): def save_info_to_mongodb(self): """save qtw league information data to mongodb""" result_lst = [] tb = self._db_client["xscore"]["league_info"] for key, value in SUPPORT_LEAGUE_NAME_ID.items(): result_dct = dict() result_dct["league_name"] = key result_dct["league_id"] = int(value) result_lst.append(result_dct) tb.insert_many(result_lst) class MatchInfo(CrawlDataBase): def save_info_to_mongodb(self): """save qtw match information data to mongodb""" tb = self._db_client["xscore"]["match_info"] page_text = self.spare_url() pattern = re.compile('jh.* = .*]') match_lst = re.findall(pattern, page_text) game_week = 0 for match_str in match_lst: game_week += 1 match_info_str = match_str.replace(",,,", ",'','',") match_info_lst = eval(str(match_info_str).split(" = ")[1]) for match_information in match_info_lst: qtw_match_id = int(match_information[0]) result = tb.find_one( filter={"qtw_match_id": qtw_match_id}, projection={'status': 1} ) if result is not None and result["status"] == 2: continue elif result is not None and result["status"] != 2: if match_information[2] == -1: new_status = 2 new_status_text = "Played" score_lst = match_information[6].split("-") tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text, "home_score": int(score_lst[0]), "away_score": int(score_lst[1]) } } ) elif match_information[2] == -14: new_status = 3 new_status_text = "Delay" tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text } } ) else: new_status = 3 new_status_text = "Fixture" tb.update( {"qtw_match_id": qtw_match_id}, { "$set": { "match_time": match_information[3], "status": new_status, "status_text": new_status_text } } ) logger.info( "qtw_match_id = {0} have finished update!".format( qtw_match_id)) else: result_dict = dict() result_dict["qtw_match_id"] = qtw_match_id result_dict["qtw_league_id"] = int(match_information[1]) result_dict["match_time"] = match_information[3] result_dict["home_id"] = int(match_information[4]) result_dict["away_id"] = int(match_information[5]) result_dict["game_week"] = int(game_week) if match_information[2] == -1: score_lst = match_information[6].split("-") result_dict["home_score"] = int(score_lst[0]) result_dict["away_score"] = int(score_lst[1]) result_dict["status"] = 2 result_dict["status_text"] = "Played" elif match_information[2] == -14: result_dict["home_score"] = -1 result_dict["away_score"] = -1 result_dict["status"] = 3 result_dict["status_text"] = "Delay" else: result_dict["home_score"] = -1 result_dict["away_score"] = -1 result_dict["status"] = 1 result_dict["status_text"] = "Fixture" logger.info("result_dict = {0}".format(result_dict)) tb.insert_one(result_dict) class TeamInfo(CrawlDataBase): def save_info_to_mongodb(self): """save qtw team information data to mongodb""" tb = self._db_client["xscore"]["team_info"] league_id = int(SUPPORT_LEAGUE_NAME_ID[self._league_name]) page_text = self.spare_url() pattern = re.compile("arrTeam = .*]") match_str = re.search(pattern, page_text) match_group = match_str.group() team_information_lst = eval(str(match_group).split(" = ")[1]) for team_information in team_information_lst: result = tb.find_one( filter={ "league_id": league_id, "team_id": int(team_information[0]) }, projection={'league_id': 1} ) if result is not None: logger.info("team_id = {0} exist in mongodb".format( team_information[0])) else: result_dict = dict() result_dict["league_id"] = league_id result_dict["league_name"] = self._league_name result_dict['team_id'] = int(team_information[0]) result_dict["team_cn_name"] = team_information[1] result_dict["team_en_name"] = team_information[3] print("result_dict = ", result_dict) tb.insert_one(result_dict) if __name__ == "__main__": yp_odd = get_latest_odds_by_qtw_match_id(1585238, "yp") print(yp_odd)

37.225225

79

0.512391

2,302

20,660

4.31364

0.124674

0.062437

0.038268

0.016918

0.808258

0.802719

0.774824

0.757402

0.703021

0.667271

0

0.034554

0.366844

20,660

554

80

37.292419

0.724562

0.046515

0

0.63109

0

0.00464

0.182214

0.022207

0

1

0.032483

false

0

0.018561

0

0.081207

0.00464

0

null

0

1

0

null

0

6

af2b91feb5701de094d9cc85ec6cd74a6e2eb062

56

py

Python

mcunet/tinynas/elastic_nn/modules/__init__.py

1999michael/tinyml

e8a5c9baef3d8a4890bb7ddbed4f5655cb4fa535

[ "MIT" ]

306

2021-01-15T07:49:40.000Z

2022-03-31T03:13:20.000Z

tinynas/elastic_nn/modules/__init__.py

liuyy3364/mcunet

f53f9e20e8e912bdb111b4c32da75e71e9a59597

[ "Apache-2.0" ]

9

2021-02-04T00:58:33.000Z

2022-03-29T06:19:55.000Z

tinynas/elastic_nn/modules/__init__.py

liuyy3364/mcunet

f53f9e20e8e912bdb111b4c32da75e71e9a59597

[ "Apache-2.0" ]

65

2021-01-18T06:06:09.000Z

2022-03-25T01:42:15.000Z

from .dynamic_layers import * from .dynamic_op import *

18.666667

29

0.785714

8

56

5.25

0.625

0.52381

0

0.142857

56

2

30

28

0.875

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

af4f1f9be162bc1f3cd196c4f0cf856e79bcf19d

7,662

py

Python

SZR/apps/groups/tests/test_tasks.py

Alek96/SZR

6c736cded0c6de88b6e4fc5a207273ec1024365b

[ "MIT" ]

1

2019-04-04T17:02:24.000Z

SZR/apps/groups/tests/test_tasks.py

Alek96/SZR

6c736cded0c6de88b6e4fc5a207273ec1024365b

[ "MIT" ]

1

2019-03-30T13:32:14.000Z

SZR/apps/groups/tests/test_tasks.py

Alek96/SZR

6c736cded0c6de88b6e4fc5a207273ec1024365b

[ "MIT" ]

null

import json from GitLabApi import mock_all_gitlab_url from GitLabApi.MockUrls import mock_all_urls_and_raise_error from GitLabApi.exceptions import GitlabGetError from core.models import GitlabUser from core.tests.test_view import LoginMethods from groups import tasks from groups.tests.models import AddMemberCreateMethods, AddSubgroupCreateMethods, AddProjectCreateMethods from httmock import HTTMock class CreateSubgroup(LoginMethods): @LoginMethods.create_user_wrapper @mock_all_gitlab_url def test_create_subgroup(self): from GitLabApi.tests.test_gitlab_api import TestGitLabGroupsApi args_dict = { 'name': 'name', 'path': 'path', 'parent_id': 1, } with HTTMock(mock_all_urls_and_raise_error): with HTTMock(TestGitLabGroupsApi().get_mock_for_create_url(args=args_dict)): self.assertTrue(tasks.create_subgroup( user_id=self.user.id, name='name', path='path', group_id=1 )) class CreateProject(LoginMethods): @LoginMethods.create_user_wrapper @mock_all_gitlab_url def test_create_project(self): from GitLabApi.tests.test_gitlab_api import TestGitLabProjectsApi args_dict = { 'name': 'name', 'path': 'path', 'namespace_id': 1, } with HTTMock(mock_all_urls_and_raise_error): with HTTMock(TestGitLabProjectsApi().get_mock_for_create_url(args=args_dict)): self.assertTrue(tasks.create_project( user_id=self.user.id, name='name', path='path', group_id=1 )) class AddOrUpdateMemberTests(LoginMethods): @LoginMethods.create_user_wrapper @mock_all_gitlab_url def test_update_user(self): from GitLabApi.tests.test_gitlab_api import TestGitLabGroupsApi, TestGitLabUsersApi, TestGitLabGroupMembersApi, \ TestGitLabGroupMemberObjApi with HTTMock(mock_all_urls_and_raise_error): with HTTMock(TestGitLabGroupsApi().get_mock_for_get_url()): with HTTMock(TestGitLabUsersApi().get_mock_for_list_url()): with HTTMock(TestGitLabGroupMembersApi().get_mock_for_get_url()): with HTTMock(TestGitLabGroupMemberObjApi().get_mock_for_save_url(args={'access_level': 10})): self.assertTrue(tasks.add_or_update_member( user_id=self.user.id, group_id=1, username='name', access_level=10 )) @LoginMethods.create_user_wrapper @mock_all_gitlab_url def test_create_user(self): from GitLabApi.tests.test_gitlab_api import TestGitLabGroupsApi, TestGitLabUsersApi, TestGitLabGroupMembersApi args_dict = { 'user_id': self.user.id, 'access_level': 10, } with HTTMock(mock_all_urls_and_raise_error): with HTTMock(TestGitLabGroupsApi().get_mock_for_get_url()): with HTTMock(TestGitLabUsersApi().get_mock_for_list_url()): with HTTMock(TestGitLabGroupMembersApi().get_mock_for_get_url(raise_error=GitlabGetError())): with HTTMock(TestGitLabGroupMembersApi().get_mock_for_create_url(args=args_dict)): self.assertTrue(tasks.add_or_update_member( user_id=self.user.id, group_id=1, username='name', access_level=10 )) class AddSubgroupTaskTests(LoginMethods): @LoginMethods.create_user_wrapper def setUp(self): self.task_model = AddSubgroupCreateMethods().create_task( owner=GitlabUser.objects.get(user_social_auth=self.user_social_auth) ) self.gitlab_group = self.task_model.gitlab_group def get_run_args(self): return json.loads(self.task_model.celery_task.kwargs) @mock_all_gitlab_url def test_gitlab_group_does_not_have_gitlab_id(self): self.gitlab_group.gitlab_id = None self.gitlab_group.save() tasks.AddSubgroupTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.assertEqual(self.task_model.status, self.task_model.FAILED) self.assertNotEqual(self.task_model.error_msg, "") @mock_all_gitlab_url def test_run_correctly(self): self.gitlab_group.gitlab_id = 2 self.gitlab_group.save() tasks.AddSubgroupTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.task_model.new_gitlab_group.refresh_from_db() self.assertEqual(self.task_model.error_msg, None) self.assertNotEqual(self.task_model.new_gitlab_group.gitlab_id, None) self.assertEqual(self.task_model.status, self.task_model.SUCCEED) class AddProjectTaskTests(LoginMethods): @LoginMethods.create_user_wrapper def setUp(self): self.task_model = AddProjectCreateMethods().create_task( owner=GitlabUser.objects.get(user_social_auth=self.user_social_auth) ) self.gitlab_group = self.task_model.gitlab_group def get_run_args(self): return json.loads(self.task_model.celery_task.kwargs) @mock_all_gitlab_url def test_gitlab_group_does_not_have_gitlab_id(self): self.gitlab_group.gitlab_id = None self.gitlab_group.save() tasks.AddProjectTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.assertEqual(self.task_model.status, self.task_model.FAILED) self.assertNotEqual(self.task_model.error_msg, "") @mock_all_gitlab_url def test_run_correctly(self): self.gitlab_group.gitlab_id = 2 self.gitlab_group.save() tasks.AddProjectTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.task_model.new_gitlab_project.refresh_from_db() self.assertEqual(self.task_model.error_msg, None) self.assertNotEqual(self.task_model.new_gitlab_project.gitlab_id, None) self.assertEqual(self.task_model.status, self.task_model.SUCCEED) class AddMemberTaskTests(LoginMethods): @LoginMethods.create_user_wrapper def setUp(self): self.task_model = AddMemberCreateMethods().create_task( owner=GitlabUser.objects.get(user_social_auth=self.user_social_auth) ) self.gitlab_group = self.task_model.gitlab_group def get_run_args(self): return json.loads(self.task_model.celery_task.kwargs) @mock_all_gitlab_url def test_gitlab_group_does_not_have_gitlab_id(self): self.gitlab_group.gitlab_id = None self.gitlab_group.save() tasks.AddMemberTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.assertEqual(self.task_model.status, self.task_model.FAILED) self.assertNotEqual(self.task_model.error_msg, "") @mock_all_gitlab_url def test_run_correctly(self): self.gitlab_group.gitlab_id = 1 self.gitlab_group.save() tasks.AddMemberTask().run(**self.get_run_args()) self.task_model.refresh_from_db() self.assertEqual(self.task_model.error_msg, None) self.assertNotEqual(self.task_model.new_gitlab_user, None) self.assertEqual(self.task_model.status, self.task_model.SUCCEED)

37.194175

121

0.663534

888

7,662

5.367117

0.113739

0.063785

0.103651

0.036928

0.844524

0.841167

0.81347

0.811162

0.80256

0.789971

0

0.002958

0.249804

7,662

205

122

37.37561

0.8262

0

0.68323

0

0.014096

0

0.118012

1

0.099379

false

0

0.080745

0.018634

0.236025

0

null

0

1

0

null

0

6

af52cb7f5173a99cc3a216bec32ed523f43d8f2e

219

py

Python

angr/engines/soot/exceptions.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

6,132

2015-08-06T23:24:47.000Z

2022-03-31T21:49:34.000Z

angr/engines/soot/exceptions.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

2,272

2015-08-10T08:40:07.000Z

2022-03-31T23:46:44.000Z

angr/engines/soot/exceptions.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

1,155

2015-08-06T23:37:39.000Z

2022-03-31T05:54:11.000Z

class BlockTerminationNotice(Exception): pass class IncorrectLocationException(Exception): pass class SootMethodNotLoadedException(Exception): pass class SootFieldNotLoadedException(Exception): pass

16.846154

46

0.799087

16

219

10.9375

0.4375

0.297143

0.308571

0

0.146119

219

12

47

18.25

0.935829

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

1

null

1

0

null

0

1

0

6

af6d63ddb4db62f035b31c6a5fd6f0572098d530

357

py

Python

synthesis/__init__.py

kunheek/style-aware-discriminator

d6ddd7c735d6c162f2a3c942d5cba1e0457f8c39

[ "MIT" ]

21

2022-03-30T06:58:50.000Z

2022-03-31T16:38:48.000Z

synthesis/__init__.py

kunheek/style-aware-discriminator

d6ddd7c735d6c162f2a3c942d5cba1e0457f8c39

[ "MIT" ]

null

synthesis/__init__.py

kunheek/style-aware-discriminator

d6ddd7c735d6c162f2a3c942d5cba1e0457f8c39

[ "MIT" ]

null

from .base_synthesizer import BaseSynthesizer from .interpolation_synthesizer import InterpolationSynthesizer from .local_translation_synthesizer import LocalTranslationSynthesizer from .prototype_synthesizer import PrototypeSynthesizer from .swap_synthesizer import SwapSynthesizer from .transplantation_synthesizer import TransplantationSynthesizer

51

71

0.89916

31

357

10.129032

0.516129

0.324841

0

0.084034

357

6

72

59.5

0.960245

0

1

0

true

0

1

0

1

0

1

null

1

0

1

0

null

0

1

0

1

0

1

0

6

afd216efcec70b183f2a5f6a0f3fa85787c79bbb

57

py

Python

main.py

theexplorerdude/Password-manager

0743c9e9e29d1034e554fd69267ae95cd5ba2317

[ "MIT" ]

null

main.py

theexplorerdude/Password-manager

0743c9e9e29d1034e554fd69267ae95cd5ba2317

[ "MIT" ]

null

main.py

theexplorerdude/Password-manager

0743c9e9e29d1034e554fd69267ae95cd5ba2317

[ "MIT" ]

null

print("its the first commit of simple password manager")

28.5

56

0.789474

9

57

5

1

0

0.140351

57

1

57

0.918367

0

0.824561

0

1

0

true

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

bb816d1f987a0a364530c5c9df802d502b830786

37

py

Python

vulnapp/crawler/models/__init__.py

Fufuhu/VulnAppSample

d6a9ab667b2b5628a649d57cd97b1979bdd986f8

[ "Apache-2.0" ]

1

2021-09-06T07:07:47.000Z

vulnapp/crawler/models/__init__.py

Fufuhu/VulnAppSample

d6a9ab667b2b5628a649d57cd97b1979bdd986f8

[ "Apache-2.0" ]

null

vulnapp/crawler/models/__init__.py

Fufuhu/VulnAppSample

d6a9ab667b2b5628a649d57cd97b1979bdd986f8

[ "Apache-2.0" ]

null

from .crawled_page import CrawledPage

37

0.891892

5

37

6.4

1

0

0.081081

37

1

37

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a59c64d642d602351d197587631217f0b410910b

207

py

Python

juon/errors/missing_dependency_error.py

joocer/seren

1563f84015b3460d766c71fbe108f17bc2b72181

[ "Apache-2.0" ]

null

juon/errors/missing_dependency_error.py

joocer/seren

1563f84015b3460d766c71fbe108f17bc2b72181

[ "Apache-2.0" ]

287

2021-05-14T21:25:26.000Z

2022-03-30T12:02:51.000Z

juon/errors/missing_dependency_error.py

joocer/juon

1563f84015b3460d766c71fbe108f17bc2b72181

[ "Apache-2.0" ]

1

2021-04-29T18:18:20.000Z

# nodoc - don't add to the documentation wiki """ This exception should be used when a lazy import fails """ from .base_exception import BaseException class MissingDependencyError(BaseException): pass

20.7

54

0.772947

27

207

5.888889

0.888889

0

0.164251

207

9

55

23

0.919075

0.478261

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

a5a488f8bbeb1fe0b5ec321de21c13f59115e9d6

40

py

Python

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_indicator.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11,750

2015-10-12T07:03:39.000Z

2022-03-31T20:43:15.000Z

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_indicator.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

2,951

2015-10-12T00:41:25.000Z

2022-03-31T22:19:26.000Z

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_indicator.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

2,623

2015-10-15T14:40:27.000Z

2022-03-28T16:05:50.000Z

from plotly.graph_objs import Indicator

20

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null

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1

0

1

0

6

36fd90f74793e87abdc3f8fbbf7152a9ff72d737

257

py

Python

catnip/forms.py

ObjectifLibre/catnip

5d89c92de0396b1e912bb498af88687dd046718d

[ "Apache-2.0" ]

2

2020-03-13T12:45:10.000Z

2020-04-01T12:04:49.000Z

catnip/forms.py

ObjectifLibre/catnip

5d89c92de0396b1e912bb498af88687dd046718d

[ "Apache-2.0" ]

1

2020-07-24T21:54:08.000Z

catnip/forms.py

ObjectifLibre/catnip

5d89c92de0396b1e912bb498af88687dd046718d

[ "Apache-2.0" ]

1

2020-05-11T19:19:12.000Z

from django import forms class AuthForm(forms.Form): domain = forms.CharField(label='Your domain', max_length=250) username = forms.CharField(label='Your name', max_length=250) password = forms.CharField(label='Your password', max_length=250)

32.125

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257

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0.485714

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0.040541

0.136187

257

7

70

36.714286

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null

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0

1

0

null

0

1

0

1

0

6

b1c430b5f856c375fc892fb7876debb5dee14d60

149

py

Python

office365/teams/schedulingGroup.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

office365/teams/schedulingGroup.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

office365/teams/schedulingGroup.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

from office365.entity import Entity class SchedulingGroup(Entity): """A logical grouping of users in a schedule (usually by role).""" pass

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149

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true

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0.666667

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null

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null

0

1

0

1

0

6

b1cc2ef42369ef8954e94c6f25717ef123a02305

29

py

Python

coincap/__init__.py

nlnsaoadc/py-coincap

d70718744593d97655c09db897fbdedf315dc432

[ "MIT" ]

null

coincap/__init__.py

nlnsaoadc/py-coincap

d70718744593d97655c09db897fbdedf315dc432

[ "MIT" ]

null

coincap/__init__.py

nlnsaoadc/py-coincap

d70718744593d97655c09db897fbdedf315dc432

[ "MIT" ]

null

from .coincap import CoinCap

14.5

28

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null

0

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0

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6

593ee71ed69d6ba6dbd4ae036c4b4272d4bf81d5

153,066

py

Python

pointCollection/CS2_wfm/data.py

tsutterley/pointCollection

04e4359e463ff8a556e0d078373578bd96390151

[ "MIT" ]

null

pointCollection/CS2_wfm/data.py

tsutterley/pointCollection

04e4359e463ff8a556e0d078373578bd96390151

[ "MIT" ]

null

pointCollection/CS2_wfm/data.py

tsutterley/pointCollection

04e4359e463ff8a556e0d078373578bd96390151

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Feb 12 2020 Class to read and manipulate CryoSat-2 waveform data Reads CryoSat Level-1b data products from baselines A, B and C Reads CryoSat Level-1b netCDF4 data products from baseline D Supported CryoSat Modes: LRM, SAR, SARin, FDM, SID, GDR INPUTS: full_filename: full path of CryoSat .DBL or .nc file PYTHON DEPENDENCIES: numpy: Scientific Computing Tools For Python http://www.numpy.org http://www.scipy.org/NumPy_for_Matlab_Users netCDF4: Python interface to the netCDF C library https://unidata.github.io/netcdf4-python/netCDF4/index.html UPDATE HISTORY: Updated 08/2020: flake8 compatible binary regular expression strings Forked 02/2020 from read_cryosat_L1b.py Updated 11/2019: empty placeholder dictionary for baseline D DSD headers Updated 09/2019: added netCDF4 read function for baseline D Updated 04/2019: USO correction signed 32 bit int Updated 10/2018: updated header read functions for python3 Updated 05/2016: using __future__ print and division functions Written 03/2016 """ from __future__ import print_function from __future__ import division import numpy as np import pointCollection as pc import netCDF4 import re import os class data(pc.data): np.seterr(invalid='ignore') def __default_field_dict__(self): """ Define the default fields that get read from the CryoSat-2 file """ field_dict = {} field_dict['Location'] = ['days_J2k','Day','Second','Micsec','USO_Corr', 'Mode_ID','SSC','Inst_config','Rec_Count','Lat','Lon','Alt','Alt_rate', 'Sat_velocity','Real_beam','Baseline','ST_ID','Roll','Pitch','Yaw','MCD'] field_dict['Data'] = ['TD', 'H_0','COR2','LAI','FAI','AGC_CH1','AGC_CH2', 'TR_gain_CH1','TR_gain_CH2','TX_Power','Doppler_range','TR_inst_range', 'R_inst_range','TR_inst_gain','R_inst_gain','Internal_phase', 'External_phase','Noise_power','Phase_slope'] field_dict['Geometry'] = ['dryTrop','wetTrop','InvBar','DAC','Iono_GIM', 'Iono_model','ocTideElv','lpeTideElv','olTideElv','seTideElv','gpTideElv', 'Surf_type','Corr_status','Corr_error'] field_dict['Waveform_20Hz'] = ['Waveform','Linear_Wfm_Multiplier', 'Power2_Wfm_Multiplier','N_avg_echoes'] field_dict['METADATA'] = ['MPH','SPH'] return field_dict def from_dbl(self, full_filename, field_dict=None, unpack=False, verbose=False): """ Read CryoSat Level-1b data from binary formats """ # file basename and file extension of input file fileBasename,fileExtension=os.path.splitext(os.path.basename(full_filename)) # CryoSat file class # OFFL (Off Line Processing/Systematic) # NRT_ (Near Real Time) # RPRO (ReProcessing) # TEST (Testing) # TIxx (Stand alone IPF1 testing) # LTA_ (Long Term Archive) regex_class = 'OFFL|NRT_|RPRO|TEST|TIxx|LTA_' # CryoSat mission products # SIR1SAR_FR: Level 1 FBR SAR Mode (Rx1 Channel) # SIR2SAR_FR: Level 1 FBR SAR Mode (Rx2 Channel) # SIR_SIN_FR: Level 1 FBR SARin Mode # SIR_LRM_1B: Level-1 Product Low Rate Mode # SIR_FDM_1B: Level-1 Product Fast Delivery Marine Mode # SIR_SAR_1B: Level-1 SAR Mode # SIR_SIN_1B: Level-1 SARin Mode # SIR1LRC11B: Level-1 CAL1 Low Rate Mode (Rx1 Channel) # SIR2LRC11B: Level-1 CAL1 Low Rate Mode (Rx2 Channel) # SIR1SAC11B: Level-1 CAL1 SAR Mode (Rx1 Channel) # SIR2SAC11B: Level-1 CAL1 SAR Mode (Rx2 Channel) # SIR_SIC11B: Level-1 CAL1 SARin Mode # SIR_SICC1B: Level-1 CAL1 SARIN Exotic Data # SIR1SAC21B: Level-1 CAL2 SAR Mode (Rx1 Channel) # SIR2SAC21B: Level-1 CAL2 SAR Mode (Rx2 Channel) # SIR1SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel) # SIR2SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel) # SIR1LRM_0M: LRM and TRK Monitoring Data from Rx 1 Channel # SIR2LRM_0M: LRM and TRK Monitoring Data from Rx 2 Channel # SIR1SAR_0M: SAR Monitoring Data from Rx 1 Channel # SIR2SAR_0M: SAR Monitoring Data from Rx 1 Channel # SIR_SIN_0M: SARIN Monitoring Data # SIR_SIC40M: CAL4 Monitoring Data regex_products = ('SIR1SAR_FR|SIR2SAR_FR|SIR_SIN_FR|SIR_LRM_1B|SIR_FDM_1B|' 'SIR_SAR_1B|SIR_SIN_1B|SIR1LRC11B|SIR2LRC11B|SIR1SAC11B|SIR2SAC11B|' 'SIR_SIC11B|SIR_SICC1B|SIR1SAC21B|SIR2SAC21B|SIR1SIC21B|SIR2SIC21B|' 'SIR1LRM_0M|SIR2LRM_0M|SIR1SAR_0M|SIR2SAR_0M|SIR_SIN_0M|SIR_SIC40M') # CRYOSAT LEVEL-1b PRODUCTS NAMING RULES # Mission Identifier # File Class # File Product # Validity Start Date and Time # Validity Stop Date and Time # Baseline Identifier # Version Number regex_pattern = r'(.*?)_({0})_({1})_(\d+T?\d+)_(\d+T?\d+)_(.*?)(\d+)' rx = re.compile(regex_pattern.format(regex_class,regex_products),re.VERBOSE) # extract file information from filename MI,CLASS,PRODUCT,START,STOP,BASELINE,VERSION=rx.findall(fileBasename).pop() # CryoSat-2 Mode record sizes i_size_timestamp = 12 n_SARIN_BC_RW = 1024 n_SARIN_RW = 512 n_SAR_BC_RW = 256 n_SAR_RW = 125 n_LRM_RW = 128 n_blocks = 20 n_BeamBehaviourParams = 50 # check baseline from file to set i_record_size and allocation function if (BASELINE == 'C'): # calculate total record sizes of each dataset group i_size_timegroup = i_size_timestamp + 4 + 2*2 + 6*4 + 3*3*4 + 3*2 + 4*4 i_size_measuregroup = 8 + 4*17 + 8 i_size_external_corr = 4*13 + 12 i_size_1Hz_LRM = i_size_timestamp + 3*4 + 8 + n_LRM_RW*2 + 2*4 + 2*2 i_size_1Hz_SAR = i_size_timestamp + 4*3 + 8 + n_SAR_RW*2 + 4 + 4 + 2 + 2 i_size_1Hz_SARIN = i_size_timestamp + 4*3 + 8 + n_SARIN_RW*2 + 4 + 4 + 2 + 2 i_size_LRM_waveform = n_LRM_RW*2 + 4 + 4 + 2 + 2 i_size_SAR_waveform = n_SAR_BC_RW*2 + 4 + 4 + 2 + 2 + n_BeamBehaviourParams*2 i_size_SARIN_waveform = n_SARIN_BC_RW*2 + 4 + 4 + 2 + 2 + n_SARIN_BC_RW*2 + \ n_SARIN_BC_RW*4 + n_BeamBehaviourParams*2 # Low-Resolution Mode Record Size i_record_size_LRM_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_LRM_waveform) + i_size_external_corr + \ i_size_1Hz_LRM # SAR Mode Record Size i_record_size_SAR_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_SAR_waveform) + i_size_external_corr + \ i_size_1Hz_SAR # SARIN Mode Record Size i_record_size_SARIN_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_SARIN_waveform) + i_size_external_corr + \ i_size_1Hz_SARIN # set read function for Baseline C read_cryosat_variables = self.cryosat_baseline_C else: # calculate total record sizes of each dataset group i_size_timegroup = i_size_timestamp + 4 + 2*2+ 6*4 + 3*3*4 + 4 i_size_measuregroup = 8 + 4*17 + 8 i_size_external_corr = 4*13 + 12 i_size_1Hz_LRM = i_size_timestamp + 3*4 + 8 + n_LRM_RW*2 + 2*4 + 2*2 i_size_1Hz_SAR = i_size_timestamp + 4*3 + 8 + n_SAR_RW*2 + 4 + 4 + 2 + 2 i_size_1Hz_SARIN = i_size_timestamp + 4*3 + 8 + n_SARIN_RW*2 + 4 + 4 + 2 + 2 i_size_LRM_waveform = n_LRM_RW*2 + 4 + 4 + 2 + 2 i_size_SAR_waveform = n_SAR_RW*2 + 4 + 4 + 2 + 2 + n_BeamBehaviourParams*2 i_size_SARIN_waveform = n_SARIN_RW*2 + 4 + 4 + 2 + 2 + n_SARIN_RW*2 + \ n_SARIN_RW*4 + n_BeamBehaviourParams*2 # Low-Resolution Mode Record Size i_record_size_LRM_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_LRM_waveform) + i_size_external_corr + \ i_size_1Hz_LRM # SAR Mode Record Size i_record_size_SAR_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_SAR_waveform) + i_size_external_corr + \ i_size_1Hz_SAR # SARIN Mode Record Size i_record_size_SARIN_L1b = n_blocks * (i_size_timegroup + \ i_size_measuregroup + i_size_SARIN_waveform) + i_size_external_corr + \ i_size_1Hz_SARIN # set read function for Baselines A and B read_cryosat_variables = self.cryosat_baseline_AB # get dataset MODE from PRODUCT portion of file name # set record sizes and DS_TYPE for read_DSD function self.MODE = re.findall('(LRM|SAR|SIN)', PRODUCT).pop() if (self.MODE == 'LRM'): i_record_size = i_record_size_LRM_L1b DS_TYPE = 'CS_L1B' elif (self.MODE == 'SAR'): i_record_size = i_record_size_SAR_L1b DS_TYPE = 'CS_L1B' elif (self.MODE == 'SIN'): i_record_size = i_record_size_SARIN_L1b DS_TYPE = 'CS_L1B' # read the input file to get file information fid = os.open(os.path.expanduser(full_filename),os.O_RDONLY) file_info = os.fstat(fid) os.close(fid) # num DSRs from SPH j_num_DSR = np.int32(file_info.st_size//i_record_size) # print file information if verbose: print(full_filename) print('{0:d} {1:d} {2:d}'.format(j_num_DSR,file_info.st_size,i_record_size)) # Check if MPH/SPH/DSD headers if (j_num_DSR*i_record_size == file_info.st_size): print('No Header on file') print('The number of DSRs is: {0:d}'.format(j_num_DSR)) else: print('Header on file') # Check if MPH/SPH/DSD headers if (j_num_DSR*i_record_size != file_info.st_size): # If there are MPH/SPH/DSD headers s_MPH_fields = self.read_MPH(full_filename) j_sph_size = np.int32(re.findall(r'[-+]?\d+',s_MPH_fields['SPH_SIZE']).pop()) s_SPH_fields = self.read_SPH(full_filename, j_sph_size) # extract information from DSD fields s_DSD_fields = self.read_DSD(full_filename, DS_TYPE=DS_TYPE) # extract DS_OFFSET j_DS_start = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['DS_OFFSET']).pop()) # extract number of DSR in the file j_num_DSR = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['NUM_DSR']).pop()) # check the record size j_DSR_size = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['DSR_SIZE']).pop()) # minimum size is start of the read plus number of records to read j_check_size = j_DS_start + (j_DSR_size*j_num_DSR) if verbose: print('The offset of the DSD is: {0:d} bytes'.format(j_DS_start)) print('The number of DSRs is {0:d}'.format(j_num_DSR)) print('The size of the DSR is {0:d}'.format(j_DSR_size)) # check if invalid file size if (j_check_size > file_info.st_size): raise IOError('File size error') # extract binary data from input CryoSat data file (skip headers) fid = open(os.path.expanduser(full_filename), 'rb') cryosat_header = fid.read(j_DS_start) # iterate through CryoSat file and fill output variables CS_L1b_mds = read_cryosat_variables(fid, j_num_DSR) # add headers to output dictionary as METADATA CS_L1b_mds['METADATA'] = {} CS_L1b_mds['METADATA']['MPH'] = s_MPH_fields CS_L1b_mds['METADATA']['SPH'] = s_SPH_fields CS_L1b_mds['METADATA']['DSD'] = s_DSD_fields # close the input CryoSat binary file fid.close() else: # If there are not MPH/SPH/DSD headers # extract binary data from input CryoSat data file fid = open(os.path.expanduser(full_filename), 'rb') # iterate through CryoSat file and fill output variables CS_L1b_mds = read_cryosat_variables(fid, j_num_DSR) # close the input CryoSat binary file fid.close() # if unpacking the units if unpack: CS_l1b_scale = self.cryosat_scaling_factors() # for each dictionary key for group in CS_l1b_scale.keys(): # for each variable for key,val in CS_L1b_mds[group].items(): # check if val is the 20Hz waveform beam variables if isinstance(val, dict): # for each waveform beam variable for k,v in val.items(): # scale variable CS_L1b_mds[group][key][k] = CS_l1b_scale[group][key][k]*v.copy() else: # scale variable CS_L1b_mds[group][key] = CS_l1b_scale[group][key]*val.copy() # calculate GPS time of CryoSat data (seconds since Jan 6, 1980 00:00:00) # from TAI time since Jan 1, 2000 00:00:00 GPS_Time = self.calc_GPS_time(CS_L1b_mds['Location']['Day'], CS_L1b_mds['Location']['Second'], CS_L1b_mds['Location']['Micsec']) # leap seconds for converting from GPS time to UTC time leap_seconds = self.count_leap_seconds(GPS_Time) # calculate dates as J2000 days (UTC) CS_L1b_mds['Location']['days_J2k'] = (GPS_Time - leap_seconds)/86400.0 - 7300.0 # parameters to extract if field_dict is None: field_dict = self.__default_field_dict__() # extract fields of interest using field dict keys for group,variables in field_dict.items(): for field in variables: if field not in self.fields: self.fields.append(field) setattr(self, field, CS_L1b_mds[group][field]) # update size and shape of input data self.__update_size_and_shape__() # return the data and header text return self def from_nc(self, full_filename, field_dict=None, unpack=False, verbose=False): """ Read CryoSat Level-1b data from netCDF4 format data """ # file basename and file extension of input file fileBasename,fileExtension=os.path.splitext(os.path.basename(full_filename)) # CryoSat file class # OFFL (Off Line Processing/Systematic) # NRT_ (Near Real Time) # RPRO (ReProcessing) # TEST (Testing) # TIxx (Stand alone IPF1 testing) # LTA_ (Long Term Archive) regex_class = 'OFFL|NRT_|RPRO|TEST|TIxx|LTA_' # CryoSat mission products # SIR1SAR_FR: Level 1 FBR SAR Mode (Rx1 Channel) # SIR2SAR_FR: Level 1 FBR SAR Mode (Rx2 Channel) # SIR_SIN_FR: Level 1 FBR SARin Mode # SIR_LRM_1B: Level-1 Product Low Rate Mode # SIR_FDM_1B: Level-1 Product Fast Delivery Marine Mode # SIR_SAR_1B: Level-1 SAR Mode # SIR_SIN_1B: Level-1 SARin Mode # SIR1LRC11B: Level-1 CAL1 Low Rate Mode (Rx1 Channel) # SIR2LRC11B: Level-1 CAL1 Low Rate Mode (Rx2 Channel) # SIR1SAC11B: Level-1 CAL1 SAR Mode (Rx1 Channel) # SIR2SAC11B: Level-1 CAL1 SAR Mode (Rx2 Channel) # SIR_SIC11B: Level-1 CAL1 SARin Mode # SIR_SICC1B: Level-1 CAL1 SARIN Exotic Data # SIR1SAC21B: Level-1 CAL2 SAR Mode (Rx1 Channel) # SIR2SAC21B: Level-1 CAL2 SAR Mode (Rx2 Channel) # SIR1SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel) # SIR2SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel) # SIR1LRM_0M: LRM and TRK Monitoring Data from Rx 1 Channel # SIR2LRM_0M: LRM and TRK Monitoring Data from Rx 2 Channel # SIR1SAR_0M: SAR Monitoring Data from Rx 1 Channel # SIR2SAR_0M: SAR Monitoring Data from Rx 1 Channel # SIR_SIN_0M: SARIN Monitoring Data # SIR_SIC40M: CAL4 Monitoring Data regex_products = ('SIR1SAR_FR|SIR2SAR_FR|SIR_SIN_FR|SIR_LRM_1B|SIR_FDM_1B|' 'SIR_SAR_1B|SIR_SIN_1B|SIR1LRC11B|SIR2LRC11B|SIR1SAC11B|SIR2SAC11B|' 'SIR_SIC11B|SIR_SICC1B|SIR1SAC21B|SIR2SAC21B|SIR1SIC21B|SIR2SIC21B|' 'SIR1LRM_0M|SIR2LRM_0M|SIR1SAR_0M|SIR2SAR_0M|SIR_SIN_0M|SIR_SIC40M') # CRYOSAT LEVEL-1b PRODUCTS NAMING RULES # Mission Identifier # File Class # File Product # Validity Start Date and Time # Validity Stop Date and Time # Baseline Identifier # Version Number regex_pattern = r'(.*?)_({0})_({1})_(\d+T?\d+)_(\d+T?\d+)_(.*?)(\d+)' rx = re.compile(regex_pattern.format(regex_class,regex_products),re.VERBOSE) # extract file information from filename MI,CLASS,PRODUCT,START,STOP,BASELINE,VERSION=rx.findall(fileBasename).pop() print(full_filename) if verbose else None # get dataset MODE from PRODUCT portion of file name self.MODE = re.findall(r'(LRM|FDM|SAR|SIN)', PRODUCT).pop() # read level-2 CryoSat-2 data from netCDF4 file CS_L1b_mds = self.cryosat_baseline_D(full_filename, unpack=unpack) # calculate GPS time of CryoSat data (seconds since Jan 6, 1980 00:00:00) # from TAI time since Jan 1, 2000 00:00:00 GPS_Time = self.calc_GPS_time(CS_L1b_mds['Location']['Day'], CS_L1b_mds['Location']['Second'], CS_L1b_mds['Location']['Micsec']) # leap seconds for converting from GPS time to UTC time leap_seconds = self.count_leap_seconds(GPS_Time) # calculate dates as J2000 days (UTC) CS_L1b_mds['Location']['days_J2k'] = (GPS_Time - leap_seconds)/86400.0 - 7300.0 # parameters to extract if field_dict is None: field_dict = self.__default_field_dict__() # extract fields of interest using field dict keys for group,variables in field_dict.items(): for field in variables: if field not in self.fields: self.fields.append(field) setattr(self, field, CS_L1b_mds[group][field]) # update size and shape of input data self.__update_size_and_shape__() # return the data and header text return self def calc_GPS_time(self, day, second, micsec): """ Calculate the GPS time (seconds since Jan 6, 1980 00:00:00) """ # TAI time is ahead of GPS by 19 seconds return (day + 7300.0)*86400.0 + second.astype('f') + micsec/1e6 - 19 def count_leap_seconds(self, GPS_Time): """ Count number of leap seconds that have passed for given GPS times """ # GPS times for leap seconds leaps = [46828800, 78364801, 109900802, 173059203, 252028804, 315187205, 346723206, 393984007, 425520008, 457056009, 504489610, 551750411, 599184012, 820108813, 914803214, 1025136015, 1119744016, 1167264017] # number of leap seconds prior to GPS_Time n_leaps = np.zeros_like(GPS_Time) for i,leap in enumerate(leaps): count = np.count_nonzero(GPS_Time >= leap) if (count > 0): i_records,i_blocks = np.nonzero(GPS_Time >= leap) n_leaps[i_records,i_blocks] += 1.0 return n_leaps def read_MPH(self, full_filename): """ Read ASCII Main Product Header (MPH) block from an ESA PDS file """ # read input data file with open(os.path.expanduser(full_filename), 'rb') as fid: file_contents = fid.read().splitlines() # Define constant values associated with PDS file formats # number of text lines in standard MPH n_MPH_lines = 41 # check that first line of header matches PRODUCT if not bool(re.match(br'PRODUCT\=\"(.*)(?=\")',file_contents[0])): raise IOError('File does not start with a valid PDS MPH') # read MPH header text s_MPH_fields = {} for i in range(n_MPH_lines): # use regular expression operators to read headers if bool(re.match(br'(.*?)\=\"(.*)(?=\")',file_contents[i])): # data fields within quotes field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop() s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(br'(.*?)\=(.*)',file_contents[i])): # data fields without quotes field,value=re.findall(br'(.*?)\=(.*)',file_contents[i]).pop() s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() # Return block name array to calling function return s_MPH_fields def read_SPH(self, full_filename, j_sph_size): """ Read ASCII Specific Product Header (SPH) block from a PDS file """ # read input data file with open(os.path.expanduser(full_filename), 'rb') as fid: file_contents = fid.read().splitlines() # Define constant values associated with PDS file formats # number of text lines in standard MPH n_MPH_lines = 41 # compile regular expression operator for reading headers rx = re.compile(br'(.*?)\=\"?(.*)',re.VERBOSE) # check first line of header matches SPH_DESCRIPTOR if not bool(re.match(br'SPH\_DESCRIPTOR\=',file_contents[n_MPH_lines+1])): raise IOError('File does not have a valid PDS DSD') # read SPH header text (no binary control characters) s_SPH_lines = [li for li in file_contents[n_MPH_lines+1:] if rx.match(li) and not re.search(br'[^\x20-\x7e]+',li)] # extract SPH header text s_SPH_fields = {} c = 0 while (c < len(s_SPH_lines)): # check if line is within DS_NAME portion of SPH header if bool(re.match(br'DS_NAME',s_SPH_lines[c])): # add dictionary for DS_NAME field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop() key = value.decode('utf-8').rstrip() s_SPH_fields[key] = {} for line in s_SPH_lines[c+1:c+7]: if bool(re.match(br'(.*?)\=\"(.*)(?=\")',line)): # data fields within quotes dsfield,dsvalue=re.findall(br'(.*?)\=\"(.*)(?=\")',line).pop() s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip() elif bool(re.match(br'(.*?)\=(.*)',line)): # data fields without quotes dsfield,dsvalue=re.findall(br'(.*?)\=(.*)',line).pop() s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip() # add 6 to counter to go to next entry c += 6 # use regular expression operators to read headers elif bool(re.match(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c])): # data fields within quotes field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop() s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(br'(.*?)\=(.*)',s_SPH_lines[c])): # data fields without quotes field,value=re.findall(br'(.*?)\=(.*)',s_SPH_lines[c]).pop() s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() # add 1 to counter to go to next line c += 1 # Return block name array to calling function return s_SPH_fields def read_DSD(self, full_filename, DS_TYPE=None): """ Read ASCII Data Set Descriptors (DSD) block from a PDS file """ # read input data file with open(os.path.expanduser(full_filename), 'rb') as fid: file_contents = fid.read().splitlines() # Define constant values associated with PDS file formats # number of text lines in standard MPH n_MPH_lines = 41 # number of text lines in a DSD header n_DSD_lines = 8 # Level-1b CryoSat DS_NAMES within files regex_patterns = [] if (DS_TYPE == 'CS_L1B'): regex_patterns.append(br'DS_NAME\="SIR_L1B_LRM[\s+]*"') regex_patterns.append(br'DS_NAME\="SIR_L1B_SAR[\s+]*"') regex_patterns.append(br'DS_NAME\="SIR_L1B_SARIN[\s+]*"') elif (DS_TYPE == 'SIR_L1B_FDM'): regex_patterns.append(br'DS_NAME\="SIR_L1B_FDM[\s+]*"') # find the DSD starting line within the SPH header c = 0 Flag = False while ((Flag is False) and (c < len(regex_patterns))): # find indice within indice = [i for i,line in enumerate(file_contents[n_MPH_lines+1:]) if re.search(regex_patterns[c],line)] if indice: Flag = True else: c+=1 # check that valid indice was found within header if not indice: raise IOError('Can not find correct DSD field') # extract s_DSD_fields info DSD_START = n_MPH_lines + indice[0] + 1 s_DSD_fields = {} for i in range(DSD_START,DSD_START+n_DSD_lines): # use regular expression operators to read headers if bool(re.match(br'(.*?)\=\"(.*)(?=\")',file_contents[i])): # data fields within quotes field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop() s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(br'(.*?)\=(.*)',file_contents[i])): # data fields without quotes field,value=re.findall(br'(.*?)\=(.*)',file_contents[i]).pop() s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() # Return block name array to calling function return s_DSD_fields def cryosat_baseline_AB(self, fid, n_records): """ Read L1b MDS variables for CryoSat Baselines A and B """ n_SARIN_RW = 512 n_SAR_RW = 128 n_LRM_RW = 128 n_blocks = 20 n_BeamBehaviourParams = 50 # Bind all the variables of the l1b_mds together into a single dictionary CS_l1b_mds = {} # CryoSat-2 Time and Orbit Group CS_l1b_mds['Location'] = {} # Time: day part CS_l1b_mds['Location']['Day'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32,fill_value=0) # Time: second part CS_l1b_mds['Location']['Second'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Time: microsecond part CS_l1b_mds['Location']['Micsec'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # USO correction factor CS_l1b_mds['Location']['USO_Corr'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Mode ID CS_l1b_mds['Location']['Mode_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16) # Source sequence counter CS_l1b_mds['Location']['SSC'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16) # Instrument configuration CS_l1b_mds['Location']['Inst_config'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Record Counter CS_l1b_mds['Location']['Rec_Count'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lat'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lon'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Location']['Alt'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s) CS_l1b_mds['Location']['Alt_rate'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s) # ITRF= International Terrestrial Reference Frame CS_l1b_mds['Location']['Sat_velocity'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Real beam direction vector. In CRF: packed units (micro-m, 1e-6 m) # CRF= CryoSat Reference Frame. CS_l1b_mds['Location']['Real_beam'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Interferometric baseline vector. In CRF: packed units (micro-m, 1e-6 m) CS_l1b_mds['Location']['Baseline'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Measurement Confidence Data Flags # Generally the MCD flags indicate problems when set # If MCD is 0 then no problems or non-nominal conditions were detected # Serious errors are indicated by setting bit 31 CS_l1b_mds['Location']['MCD'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # CryoSat-2 Measurement Group # Derived from instrument measurement parameters CS_l1b_mds['Data'] = {} # Window Delay reference (two-way) corrected for instrument delays CS_l1b_mds['Data']['TD'] = np.ma.zeros((n_records,n_blocks),dtype=np.int64) # H0 Initial Height Word from telemetry CS_l1b_mds['Data']['H_0'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # COR2 Height Rate: on-board tracker height rate over the radar cycle CS_l1b_mds['Data']['COR2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Coarse Range Word (LAI) derived from telemetry CS_l1b_mds['Data']['LAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Fine Range Word (FAI) derived from telemetry CS_l1b_mds['Data']['FAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1. # Gain calibration corrections are applied (Sum of AGC stages 1 and 2 # plus the corresponding corrections) (dB/100) CS_l1b_mds['Data']['AGC_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2. # Gain calibration corrections are applied (dB/100) CS_l1b_mds['Data']['AGC_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Transmit Power in microWatts CS_l1b_mds['Data']['TX_Power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Doppler range correction: Radial component (mm) # computed for the component of satellite velocity in the nadir direction CS_l1b_mds['Data']['Doppler_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Range Correction: transmit-receive antenna (mm) # Calibration correction to range on channel 1 computed from CAL1. CS_l1b_mds['Data']['TR_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Range Correction: receive-only antenna (mm) # Calibration correction to range on channel 2 computed from CAL1. CS_l1b_mds['Data']['R_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Gain Correction: transmit-receive antenna (dB/100) # Calibration correction to gain on channel 1 computed from CAL1 CS_l1b_mds['Data']['TR_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Gain Correction: receive-only (dB/100) # Calibration correction to gain on channel 2 computed from CAL1 CS_l1b_mds['Data']['R_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Internal Phase Correction (microradians) CS_l1b_mds['Data']['Internal_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # External Phase Correction (microradians) CS_l1b_mds['Data']['External_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Noise Power measurement (dB/100): converted from telemetry units to be # the noise floor of FBR measurement echoes. # Set to -9999.99 when the telemetry contains zero. CS_l1b_mds['Data']['Noise_power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Phase slope correction (microradians) # Computed from the CAL-4 packets during the azimuth impulse response # amplitude (SARIN only). Set from the latest available CAL-4 packet. CS_l1b_mds['Data']['Phase_slope'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) CS_l1b_mds['Data']['Spares1'] = np.ma.zeros((n_records,n_blocks,4),dtype=np.int8) # CryoSat-2 External Corrections Group CS_l1b_mds['Geometry'] = {} # Dry Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['dryTrop'] = np.ma.zeros((n_records),dtype=np.int32) # Wet Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['wetTrop'] = np.ma.zeros((n_records),dtype=np.int32) # Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['InvBar'] = np.ma.zeros((n_records),dtype=np.int32) # Delta Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['DAC'] = np.ma.zeros((n_records),dtype=np.int32) # GIM Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_GIM'] = np.ma.zeros((n_records),dtype=np.int32) # Model Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_model'] = np.ma.zeros((n_records),dtype=np.int32) # Ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['ocTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['lpeTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Ocean loading tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['olTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Solid Earth tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['seTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Geocentric Polar tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['gpTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Surface Type: enumerated key to classify surface at nadir # 0 = Open Ocean # 1 = Closed Sea # 2 = Continental Ice # 3 = Land CS_l1b_mds['Geometry']['Surf_type'] = np.ma.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Geometry']['Spare1'] = np.ma.zeros((n_records,4),dtype=np.int8) # Corrections Status Flag CS_l1b_mds['Geometry']['Corr_status'] = np.ma.zeros((n_records),dtype=np.uint32) # Correction Error Flag CS_l1b_mds['Geometry']['Corr_error'] = np.ma.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Geometry']['Spare2'] = np.ma.zeros((n_records,4),dtype=np.int8) # CryoSat-2 Average Waveforms Groups CS_l1b_mds['Waveform_1Hz'] = {} if (self.MODE == 'LRM'): # Low-Resolution Mode # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_LRM_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) elif (self.MODE == 'SAR'): # SAR Mode # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SAR_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) elif (self.MODE == 'SIN'): # SARIN Mode # Same as the LRM/SAR groups but the waveform array is 512 bins instead of # 128 and the number of echoes averaged is different. # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SARIN_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) # CryoSat-2 Waveforms Groups # Beam Behavior Parameters Beam_Behavior = {} # Standard Deviation of Gaussian fit to range integrated stack power. Beam_Behavior['SD'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Stack Center: Mean of Gaussian fit to range integrated stack power. Beam_Behavior['Center'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Stack amplitude parameter scaled in dB/100. Beam_Behavior['Amplitude'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # 3rd moment: providing the degree of asymmetry of the range integrated # stack power distribution. Beam_Behavior['Skewness'] = np.zeros((n_records,n_blocks),dtype=np.int16) # 4th moment: Measure of peakiness of range integrated stack power distribution. Beam_Behavior['Kurtosis'] = np.zeros((n_records,n_blocks),dtype=np.int16) Beam_Behavior['Spare'] = np.zeros((n_records,n_blocks,n_BeamBehaviourParams-5),dtype=np.int16) # CryoSat-2 mode specific waveforms CS_l1b_mds['Waveform_20Hz'] = {} if (self.MODE == 'LRM'): # Low-Resolution Mode # Averaged Power Echo Waveform [128] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_LRM_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) elif (self.MODE == 'SAR'): # SAR Mode # Averaged Power Echo Waveform [128] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SAR_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Beam behaviour parameters CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior elif (self.MODE == 'SIN'): # SARIN Mode # Averaged Power Echo Waveform [512] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Beam behaviour parameters CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior # Coherence [512]: packed units (1/1000) CS_l1b_mds['Waveform_20Hz']['Coherence'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.int16) # Phase Difference [512]: packed units (microradians) CS_l1b_mds['Waveform_20Hz']['Phase_diff'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.int32) # for each record in the CryoSat file for r in range(n_records): # CryoSat-2 Time and Orbit Group for b in range(n_blocks): CS_l1b_mds['Location']['Day'].data[r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Second'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Micsec'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['USO_Corr'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Mode_ID'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Location']['SSC'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Location']['Inst_config'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Rec_Count'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Lat'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Lon'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Alt'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Alt_rate'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Sat_velocity'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['Real_beam'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['Baseline'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['MCD'][r,b] = np.fromfile(fid,dtype='>u4',count=1) # CryoSat-2 Measurement Group # Derived from instrument measurement parameters for b in range(n_blocks): CS_l1b_mds['Data']['TD'][r,b] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Data']['H_0'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['COR2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['LAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['FAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['AGC_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['AGC_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_gain_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_gain_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TX_Power'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Doppler_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['R_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['R_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Internal_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['External_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Noise_power'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Phase_slope'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Spares1'][r,b,:] = np.fromfile(fid,dtype='>i1',count=4) # CryoSat-2 External Corrections Group CS_l1b_mds['Geometry']['dryTrop'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['wetTrop'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['InvBar'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['DAC'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Iono_GIM'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Iono_model'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['ocTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['lpeTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['olTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['seTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['gpTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Surf_type'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Spare1'][r,:] = np.fromfile(fid,dtype='>i1',count=4) CS_l1b_mds['Geometry']['Corr_status'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Corr_error'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Spare2'][r,:] = np.fromfile(fid,dtype='>i1',count=4) # CryoSat-2 Average Waveforms Groups if (self.MODE == 'LRM'): # Low-Resolution Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SAR'): # SAR Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SIN'): # SARIN Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) # CryoSat-2 Waveforms Groups if (self.MODE == 'LRM'): # Low-Resolution Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SAR'): # SAR Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-5)) elif (self.MODE == 'SIN'): # SARIN Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-5)) CS_l1b_mds['Waveform_20Hz']['Coherence'][r,b,:] = np.fromfile(fid,dtype='>i2',count=n_SARIN_RW) CS_l1b_mds['Waveform_20Hz']['Phase_diff'][r,b,:] = np.fromfile(fid,dtype='>i4',count=n_SARIN_RW) # set the mask from day variables mask_20Hz = CS_l1b_mds['Location']['Day'].data == CS_l1b_mds['Location']['Day'].fill_value Location_keys = [key for key in CS_l1b_mds['Location'].keys() if not re.search(r'Spare',key)] Data_keys = [key for key in CS_l1b_mds['Data'].keys() if not re.search(r'Spare',key)] Geometry_keys = [key for key in CS_l1b_mds['Geometry'].keys() if not re.search(r'Spare',key)] Wfm_1Hz_keys = [key for key in CS_l1b_mds['Waveform_1Hz'].keys() if not re.search(r'Spare',key)] Wfm_20Hz_keys = [key for key in CS_l1b_mds['Waveform_20Hz'].keys() if not re.search(r'Spare',key)] for key in Location_keys: CS_l1b_mds['Location'][key].mask = mask_20Hz.copy() for key in Data_keys: CS_l1b_mds['Data'][key].mask = mask_20Hz.copy() # return the output dictionary return CS_l1b_mds def cryosat_baseline_C(self, fid, n_records): """ Read L1b MDS variables for CryoSat Baseline C """ n_SARIN_BC_RW = 1024 n_SARIN_RW = 512 n_SAR_BC_RW = 256 n_SAR_RW = 128 n_LRM_RW = 128 n_blocks = 20 n_BeamBehaviourParams = 50 # Bind all the variables of the l1b_mds together into a single dictionary CS_l1b_mds = {} # CryoSat-2 Time and Orbit Group CS_l1b_mds['Location'] = {} # Time: day part CS_l1b_mds['Location']['Day'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32,fill_value=0) # Time: second part CS_l1b_mds['Location']['Second'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Time: microsecond part CS_l1b_mds['Location']['Micsec'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # USO correction factor CS_l1b_mds['Location']['USO_Corr'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Mode ID CS_l1b_mds['Location']['Mode_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16) # Source sequence counter CS_l1b_mds['Location']['SSC'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16) # Instrument configuration CS_l1b_mds['Location']['Inst_config'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Record Counter CS_l1b_mds['Location']['Rec_Count'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lat'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lon'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Location']['Alt'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s) CS_l1b_mds['Location']['Alt_rate'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s) # ITRF= International Terrestrial Reference Frame CS_l1b_mds['Location']['Sat_velocity'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Real beam direction vector. In CRF: packed units (micro-m/s, 1e-6 m/s) # CRF= CryoSat Reference Frame. CS_l1b_mds['Location']['Real_beam'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Interferometric baseline vector. In CRF: packed units (micro-m/s, 1e-6 m/s) CS_l1b_mds['Location']['Baseline'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32) # Star Tracker ID CS_l1b_mds['Location']['ST_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.int16) # Antenna Bench Roll Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Roll'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Antenna Bench Pitch Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Pitch'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Antenna Bench Yaw Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Yaw'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Measurement Confidence Data Flags # Generally the MCD flags indicate problems when set # If MCD is 0 then no problems or non-nominal conditions were detected # Serious errors are indicated by setting bit 31 CS_l1b_mds['Location']['MCD'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32) CS_l1b_mds['Location']['Spares'] = np.ma.zeros((n_records,n_blocks,2),dtype=np.int16) # CryoSat-2 Measurement Group # Derived from instrument measurement parameters CS_l1b_mds['Data'] = {} # Window Delay reference (two-way) corrected for instrument delays CS_l1b_mds['Data']['TD'] = np.ma.zeros((n_records,n_blocks),dtype=np.int64) # H0 Initial Height Word from telemetry CS_l1b_mds['Data']['H_0'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # COR2 Height Rate: on-board tracker height rate over the radar cycle CS_l1b_mds['Data']['COR2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Coarse Range Word (LAI) derived from telemetry CS_l1b_mds['Data']['LAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Fine Range Word (FAI) derived from telemetry CS_l1b_mds['Data']['FAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1. # Gain calibration corrections are applied (Sum of AGC stages 1 and 2 # plus the corresponding corrections) (dB/100) CS_l1b_mds['Data']['AGC_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2. # Gain calibration corrections are applied (dB/100) CS_l1b_mds['Data']['AGC_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Transmit Power in microWatts CS_l1b_mds['Data']['TX_Power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Doppler range correction: Radial component (mm) # computed for the component of satellite velocity in the nadir direction CS_l1b_mds['Data']['Doppler_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Range Correction: transmit-receive antenna (mm) # Calibration correction to range on channel 1 computed from CAL1. CS_l1b_mds['Data']['TR_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Range Correction: receive-only antenna (mm) # Calibration correction to range on channel 2 computed from CAL1. CS_l1b_mds['Data']['R_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Gain Correction: transmit-receive antenna (dB/100) # Calibration correction to gain on channel 1 computed from CAL1 CS_l1b_mds['Data']['TR_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Instrument Gain Correction: receive-only (dB/100) # Calibration correction to gain on channel 2 computed from CAL1 CS_l1b_mds['Data']['R_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Internal Phase Correction (microradians) CS_l1b_mds['Data']['Internal_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # External Phase Correction (microradians) CS_l1b_mds['Data']['External_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Noise Power measurement (dB/100) CS_l1b_mds['Data']['Noise_power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) # Phase slope correction (microradians) # Computed from the CAL-4 packets during the azimuth impulse response # amplitude (SARIN only). Set from the latest available CAL-4 packet. CS_l1b_mds['Data']['Phase_slope'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32) CS_l1b_mds['Data']['Spares1'] = np.ma.zeros((n_records,n_blocks,4),dtype=np.int8) # CryoSat-2 External Corrections Group CS_l1b_mds['Geometry'] = {} # Dry Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['dryTrop'] = np.ma.zeros((n_records),dtype=np.int32) # Wet Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['wetTrop'] = np.ma.zeros((n_records),dtype=np.int32) # Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['InvBar'] = np.ma.zeros((n_records),dtype=np.int32) # Delta Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['DAC'] = np.ma.zeros((n_records),dtype=np.int32) # GIM Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_GIM'] = np.ma.zeros((n_records),dtype=np.int32) # Model Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_model'] = np.ma.zeros((n_records),dtype=np.int32) # Ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['ocTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['lpeTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Ocean loading tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['olTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Solid Earth tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['seTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Geocentric Polar tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['gpTideElv'] = np.ma.zeros((n_records),dtype=np.int32) # Surface Type: enumerated key to classify surface at nadir # 0 = Open Ocean # 1 = Closed Sea # 2 = Continental Ice # 3 = Land CS_l1b_mds['Geometry']['Surf_type'] = np.ma.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Geometry']['Spare1'] = np.ma.zeros((n_records,4),dtype=np.int8) # Corrections Status Flag CS_l1b_mds['Geometry']['Corr_status'] = np.ma.zeros((n_records),dtype=np.uint32) # Correction Error Flag CS_l1b_mds['Geometry']['Corr_error'] = np.ma.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Geometry']['Spare2'] = np.ma.zeros((n_records,4),dtype=np.int8) # CryoSat-2 Average Waveforms Groups CS_l1b_mds['Waveform_1Hz'] = {} if (self.MODE == 'LRM'): # Low-Resolution Mode # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_LRM_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) elif (self.MODE == 'SAR'): # SAR Mode # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SAR_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) elif (self.MODE == 'SIN'): # SARIN Mode # Same as the LRM/SAR groups but the waveform array is 512 bins instead of # 128 and the number of echoes averaged is different. # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32) CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32) CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32) # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32) # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32) # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64) # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SARIN_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16) CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16) # CryoSat-2 Waveforms Groups # Beam Behavior Parameters Beam_Behavior = {} # Standard Deviation of Gaussian fit to range integrated stack power. Beam_Behavior['SD'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Stack Center: Mean of Gaussian fit to range integrated stack power. Beam_Behavior['Center'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Stack amplitude parameter scaled in dB/100. Beam_Behavior['Amplitude'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # 3rd moment: providing the degree of asymmetry of the range integrated # stack power distribution. Beam_Behavior['Skewness'] = np.zeros((n_records,n_blocks),dtype=np.int16) # 4th moment: Measure of peakiness of range integrated stack power distribution. Beam_Behavior['Kurtosis'] = np.zeros((n_records,n_blocks),dtype=np.int16) # Standard deviation as a function of boresight angle (microradians) Beam_Behavior['SD_boresight_angle'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Stack Center angle as a function of boresight angle (microradians) Beam_Behavior['Center_boresight_angle'] = np.zeros((n_records,n_blocks),dtype=np.int16) Beam_Behavior['Spare'] = np.zeros((n_records,n_blocks,n_BeamBehaviourParams-7),dtype=np.int16) # CryoSat-2 mode specific waveform variables CS_l1b_mds['Waveform_20Hz'] = {} if (self.MODE == 'LRM'): # Low-Resolution Mode # Averaged Power Echo Waveform [128] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_LRM_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) elif (self.MODE == 'SAR'): # SAR Mode # Averaged Power Echo Waveform [256] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SAR_BC_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Beam behaviour parameters CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior elif (self.MODE == 'SIN'): # SARIN Mode # Averaged Power Echo Waveform [1024] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.uint16) # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32) # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16) CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16) # Beam behaviour parameters CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior # Coherence [1024]: packed units (1/1000) CS_l1b_mds['Waveform_20Hz']['Coherence'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.int16) # Phase Difference [1024]: packed units (microradians) CS_l1b_mds['Waveform_20Hz']['Phase_diff'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.int32) # for each record in the CryoSat file for r in range(n_records): # CryoSat-2 Time and Orbit Group for b in range(n_blocks): CS_l1b_mds['Location']['Day'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Second'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Micsec'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['USO_Corr'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Mode_ID'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Location']['SSC'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Location']['Inst_config'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Rec_Count'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Lat'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Lon'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Alt'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Alt_rate'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Sat_velocity'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['Real_beam'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['Baseline'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3) CS_l1b_mds['Location']['ST_ID'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Location']['Roll'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Pitch'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['Yaw'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Location']['MCD'][r,b] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Location']['Spares'][r,b,:] = np.fromfile(fid,dtype='>i2',count=2) # CryoSat-2 Measurement Group # Derived from instrument measurement parameters for b in range(n_blocks): CS_l1b_mds['Data']['TD'][r,b] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Data']['H_0'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['COR2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['LAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['FAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['AGC_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['AGC_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_gain_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_gain_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TX_Power'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Doppler_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['R_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['TR_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['R_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Internal_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['External_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Noise_power'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Phase_slope'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Data']['Spares1'][r,b,:] = np.fromfile(fid,dtype='>i1',count=4) # CryoSat-2 External Corrections Group CS_l1b_mds['Geometry']['dryTrop'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['wetTrop'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['InvBar'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['DAC'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Iono_GIM'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Iono_model'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['ocTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['lpeTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['olTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['seTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['gpTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Geometry']['Surf_type'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Spare1'][r,:] = np.fromfile(fid,dtype='>i1',count=4) CS_l1b_mds['Geometry']['Corr_status'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Corr_error'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Geometry']['Spare2'][r,:] = np.fromfile(fid,dtype='>i1',count=4) # CryoSat-2 Average Waveforms Groups if (self.MODE == 'LRM'): # Low-Resolution Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SAR'): # SAR Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SIN'): # SARIN Mode CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1) CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1) CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_RW) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1) # CryoSat-2 Waveforms Groups if (self.MODE == 'LRM'): # Low-Resolution Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) elif (self.MODE == 'SAR'): # SAR Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_BC_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-7)) elif (self.MODE == 'SIN'): # SARIN Mode for b in range(n_blocks): CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_BC_RW) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'][r,b] = np.fromfile(fid,dtype='>u2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'][r,b] = np.fromfile(fid,dtype='>i2',count=1) CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-7)) CS_l1b_mds['Waveform_20Hz']['Coherence'][r,b,:] = np.fromfile(fid,dtype='>i2',count=n_SARIN_BC_RW) CS_l1b_mds['Waveform_20Hz']['Phase_diff'][r,b,:] = np.fromfile(fid,dtype='>i4',count=n_SARIN_BC_RW) # set the mask from day variables mask_20Hz = CS_l1b_mds['Location']['Day'].data == CS_l1b_mds['Location']['Day'].fill_value Location_keys = [key for key in CS_l1b_mds['Location'].keys() if not re.search(r'Spare',key)] Data_keys = [key for key in CS_l1b_mds['Data'].keys() if not re.search(r'Spare',key)] Geometry_keys = [key for key in CS_l1b_mds['Geometry'].keys() if not re.search(r'Spare',key)] Wfm_1Hz_keys = [key for key in CS_l1b_mds['Waveform_1Hz'].keys() if not re.search(r'Spare',key)] Wfm_20Hz_keys = [key for key in CS_l1b_mds['Waveform_20Hz'].keys() if not re.search(r'Spare',key)] for key in Location_keys: CS_l1b_mds['Location'][key].mask = mask_20Hz.copy() for key in Data_keys: CS_l1b_mds['Data'][key].mask = mask_20Hz.copy() # return the output dictionary return CS_l1b_mds def cryosat_baseline_D(self, full_filename, unpack=False): """ Read L1b MDS variables for CryoSat Baseline D (netCDF4) """ # open netCDF4 file for reading fid = netCDF4.Dataset(os.path.expanduser(full_filename),'r') # use original unscaled units unless unpack=True fid.set_auto_scale(unpack) # get dimensions ind_first_meas_20hz_01 = fid.variables['ind_first_meas_20hz_01'][:].copy() ind_meas_1hz_20_ku = fid.variables['ind_meas_1hz_20_ku'][:].copy() n_records = len(ind_first_meas_20hz_01) n_SARIN_D_RW = 1024 n_SARIN_RW = 512 n_SAR_D_RW = 256 n_SAR_RW = 128 n_LRM_RW = 128 n_blocks = 20 # Bind all the variables of the l1b_mds together into a single dictionary CS_l1b_mds = {} # CryoSat-2 Time and Orbit Group CS_l1b_mds['Location'] = {} # MDS Time CS_l1b_mds['Location']['Time'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Time'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) time_20_ku = fid.variables['time_20_ku'][:].copy() # Time: day part CS_l1b_mds['Location']['Day'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Day'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) # Time: second part CS_l1b_mds['Location']['Second'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Second'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) # Time: microsecond part CS_l1b_mds['Location']['Micsec'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Micsec'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) # USO correction factor CS_l1b_mds['Location']['USO_Corr'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['USO_Corr'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) uso_cor_20_ku = fid.variables['uso_cor_20_ku'][:].copy() # Mode ID CS_l1b_mds['Location']['Mode_ID'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Mode_ID'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_mode_op_20_ku =fid.variables['flag_instr_mode_op_20_ku'][:].copy() # Mode Flags CS_l1b_mds['Location']['Mode_flags'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Mode_flags'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_mode_flags_20_ku =fid.variables['flag_instr_mode_flags_20_ku'][:].copy() # Platform attitude control mode CS_l1b_mds['Location']['Att_control'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Att_control'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_mode_att_ctrl_20_ku =fid.variables['flag_instr_mode_att_ctrl_20_ku'][:].copy() # Instrument configuration CS_l1b_mds['Location']['Inst_config'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Inst_config'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_conf_rx_flags_20_ku = fid.variables['flag_instr_conf_rx_flags_20_ku'][:].copy() # acquisition band CS_l1b_mds['Location']['Inst_band'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Inst_band'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_conf_rx_bwdt_20_ku = fid.variables['flag_instr_conf_rx_bwdt_20_ku'][:].copy() # instrument channel CS_l1b_mds['Location']['Inst_channel'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Inst_channel'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_conf_rx_in_use_20_ku = fid.variables['flag_instr_conf_rx_in_use_20_ku'][:].copy() # tracking mode CS_l1b_mds['Location']['Tracking_mode'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Tracking_mode'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_conf_rx_trk_mode_20_ku = fid.variables['flag_instr_conf_rx_trk_mode_20_ku'][:].copy() # Source sequence counter CS_l1b_mds['Location']['SSC'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['SSC'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) seq_count_20_ku = fid.variables['seq_count_20_ku'][:].copy() # Record Counter CS_l1b_mds['Location']['Rec_Count'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Rec_Count'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) rec_count_20_ku = fid.variables['rec_count_20_ku'][:].copy() # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lat'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Lat'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) lat_20_ku = fid.variables['lat_20_ku'][:].copy() # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Lon'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Lon'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) lon_20_ku = fid.variables['lon_20_ku'][:].copy() # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Location']['Alt'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Alt'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) alt_20_ku = fid.variables['alt_20_ku'][:].copy() # Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s) CS_l1b_mds['Location']['Alt_rate'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Alt_rate'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) orb_alt_rate_20_ku = fid.variables['orb_alt_rate_20_ku'][:].copy() # Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s) # ITRF= International Terrestrial Reference Frame CS_l1b_mds['Location']['Sat_velocity'] = np.ma.zeros((n_records,n_blocks,3)) CS_l1b_mds['Location']['Sat_velocity'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) sat_vel_vec_20_ku = fid.variables['sat_vel_vec_20_ku'][:].copy() # Real beam direction vector. In CRF: packed units (micro-m/s, 1e-6 m/s) # CRF= CryoSat Reference Frame. CS_l1b_mds['Location']['Real_beam'] = np.ma.zeros((n_records,n_blocks,3)) CS_l1b_mds['Location']['Real_beam'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) beam_dir_vec_20_ku = fid.variables['beam_dir_vec_20_ku'][:].copy() # Interferometric baseline vector. In CRF: packed units (micro-m/s, 1e-6 m/s) CS_l1b_mds['Location']['Baseline'] = np.ma.zeros((n_records,n_blocks,3)) CS_l1b_mds['Location']['Baseline'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) inter_base_vec_20_ku = fid.variables['inter_base_vec_20_ku'][:].copy() # Star Tracker ID CS_l1b_mds['Location']['ST_ID'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['ST_ID'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_instr_conf_rx_str_in_use_20_ku = fid.variables['flag_instr_conf_rx_str_in_use_20_ku'][:].copy() # Antenna Bench Roll Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Roll'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Roll'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) off_nadir_roll_angle_str_20_ku = fid.variables['off_nadir_roll_angle_str_20_ku'][:].copy() # Antenna Bench Pitch Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Pitch'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Pitch'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) off_nadir_pitch_angle_str_20_ku = fid.variables['off_nadir_pitch_angle_str_20_ku'][:].copy() # Antenna Bench Yaw Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Location']['Yaw'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['Yaw'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) off_nadir_yaw_angle_str_20_ku = fid.variables['off_nadir_yaw_angle_str_20_ku'][:].copy() # Measurement Confidence Data Flags # Generally the MCD flags indicate problems when set # If MCD is 0 then no problems or non-nominal conditions were detected # Serious errors are indicated by setting bit 31 CS_l1b_mds['Location']['MCD'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Location']['MCD'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_mcd_20_ku = fid.variables['flag_mcd_20_ku'][:].copy() # CryoSat-2 Measurement Group # Derived from instrument measurement parameters CS_l1b_mds['Data'] = {} # Window Delay reference (two-way) corrected for instrument delays CS_l1b_mds['Data']['TD'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TD'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) window_del_20_ku = fid.variables['window_del_20_ku'][:].copy() # H0 Initial Height Word from telemetry CS_l1b_mds['Data']['H_0'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['H_0'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) h0_applied_20_ku = fid.variables['h0_applied_20_ku'][:].copy() # COR2 Height Rate: on-board tracker height rate over the radar cycle CS_l1b_mds['Data']['COR2'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['COR2'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) cor2_applied_20_ku = fid.variables['cor2_applied_20_ku'][:].copy() # Coarse Range Word (LAI) derived from telemetry CS_l1b_mds['Data']['LAI'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['LAI'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) h0_lai_word_20_ku = fid.variables['h0_lai_word_20_ku'][:].copy() # Fine Range Word (FAI) derived from telemetry CS_l1b_mds['Data']['FAI'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['FAI'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) h0_fai_word_20_ku = fid.variables['h0_fai_word_20_ku'][:].copy() # Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1. # Gain calibration corrections are applied (Sum of AGC stages 1 and 2 # plus the corresponding corrections) (dB/100) CS_l1b_mds['Data']['AGC_CH1'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['AGC_CH1'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) agc_ch1_20_ku = fid.variables['agc_ch1_20_ku'][:].copy() # Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2. # Gain calibration corrections are applied (dB/100) CS_l1b_mds['Data']['AGC_CH2'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['AGC_CH2'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) agc_ch2_20_ku = fid.variables['agc_ch2_20_ku'][:].copy() # Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH1'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TR_gain_CH1'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) tot_gain_ch1_20_ku = fid.variables['tot_gain_ch1_20_ku'][:].copy() # Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100) CS_l1b_mds['Data']['TR_gain_CH2'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TR_gain_CH2'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) tot_gain_ch2_20_ku = fid.variables['tot_gain_ch2_20_ku'][:].copy() # Transmit Power in microWatts CS_l1b_mds['Data']['TX_Power'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TX_Power'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) transmit_pwr_20_ku = fid.variables['transmit_pwr_20_ku'][:].copy() # Doppler range correction: Radial component (mm) # computed for the component of satellite velocity in the nadir direction CS_l1b_mds['Data']['Doppler_range'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Doppler_range'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) dop_cor_20_ku = fid.variables['dop_cor_20_ku'][:].copy() # Value of Doppler Angle for the first single look echo (1e-7 radians) CS_l1b_mds['Data']['Doppler_angle_start'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Doppler_angle_start'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) dop_angle_start_20_ku = fid.variables['dop_angle_start_20_ku'][:].copy() # Value of Doppler Angle for the last single look echo (1e-7 radians) CS_l1b_mds['Data']['Doppler_angle_stop'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Doppler_angle_stop'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) dop_angle_stop_20_ku = fid.variables['dop_angle_stop_20_ku'][:].copy() # Instrument Range Correction: transmit-receive antenna (mm) # Calibration correction to range on channel 1 computed from CAL1. CS_l1b_mds['Data']['TR_inst_range'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TR_inst_range'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_cor_range_tx_rx_20_ku = fid.variables['instr_cor_range_tx_rx_20_ku'][:].copy() # Instrument Range Correction: receive-only antenna (mm) # Calibration correction to range on channel 2 computed from CAL1. CS_l1b_mds['Data']['R_inst_range'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['R_inst_range'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_cor_range_rx_20_ku = fid.variables['instr_cor_range_rx_20_ku'][:].copy() # Instrument Gain Correction: transmit-receive antenna (dB/100) # Calibration correction to gain on channel 1 computed from CAL1 CS_l1b_mds['Data']['TR_inst_gain'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['TR_inst_gain'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_cor_gain_tx_rx_20_ku = fid.variables['instr_cor_gain_tx_rx_20_ku'][:].copy() # Instrument Gain Correction: receive-only (dB/100) # Calibration correction to gain on channel 2 computed from CAL1 CS_l1b_mds['Data']['R_inst_gain'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['R_inst_gain'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_cor_gain_rx_20_ku = fid.variables['instr_cor_gain_rx_20_ku'][:].copy() # Internal Phase Correction (microradians) CS_l1b_mds['Data']['Internal_phase'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Internal_phase'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_int_ph_cor_20_ku = fid.variables['instr_int_ph_cor_20_ku'][:].copy() # External Phase Correction (microradians) CS_l1b_mds['Data']['External_phase'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['External_phase'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) instr_ext_ph_cor_20_ku = fid.variables['instr_ext_ph_cor_20_ku'][:].copy() # Noise Power measurement (dB/100) CS_l1b_mds['Data']['Noise_power'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Noise_power'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) noise_power_20_ku = fid.variables['noise_power_20_ku'][:].copy() # Phase slope correction (microradians) # Computed from the CAL-4 packets during the azimuth impulse response # amplitude (SARIN only). Set from the latest available CAL-4 packet. CS_l1b_mds['Data']['Phase_slope'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Data']['Phase_slope'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) ph_slope_cor_20_ku = fid.variables['ph_slope_cor_20_ku'][:].copy() # CryoSat-2 External Corrections Group CS_l1b_mds['Geometry'] = {} # Data Record Time (MDSR Time Stamp) CS_l1b_mds['Geometry']['Time'] = fid.variables['time_cor_01'][:].copy() # Dry Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['dryTrop'] = fid.variables['mod_dry_tropo_cor_01'][:].copy() # Wet Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['wetTrop'] = fid.variables['mod_wet_tropo_cor_01'][:].copy() # Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['InvBar'] = fid.variables['inv_bar_cor_01'][:].copy() # Delta Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['DAC'] = fid.variables['hf_fluct_total_cor_01'][:].copy() # GIM Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_GIM'] = fid.variables['iono_cor_gim_01'][:].copy() # Model Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['Iono_model'] = fid.variables['iono_cor_01'][:].copy() # Ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['ocTideElv'] = fid.variables['ocean_tide_01'][:].copy() # Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['lpeTideElv'] = fid.variables['ocean_tide_eq_01'][:].copy() # Ocean loading tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['olTideElv'] = fid.variables['load_tide_01'][:].copy() # Solid Earth tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['seTideElv'] = fid.variables['solid_earth_tide_01'][:].copy() # Geocentric Polar tide Correction packed units (mm, 1e-3 m) CS_l1b_mds['Geometry']['gpTideElv'] = fid.variables['pole_tide_01'][:].copy() # Surface Type: enumerated key to classify surface at nadir # 0 = Open Ocean # 1 = Closed Sea # 2 = Continental Ice # 3 = Land CS_l1b_mds['Geometry']['Surf_type'] = fid.variables['surf_type_01'][:].copy() # Corrections Status Flag CS_l1b_mds['Geometry']['Corr_status'] = fid.variables['flag_cor_status_01'][:].copy() # Correction Error Flag CS_l1b_mds['Geometry']['Corr_error'] = fid.variables['flag_cor_err_01'][:].copy() # Same as the LRM/SAR groups but the waveform array is 512 bins instead of # 128 and the number of echoes averaged is different. CS_l1b_mds['Waveform_1Hz'] = {} # Data Record Time (MDSR Time Stamp) # Time (seconds since 2000-01-01) time_avg_01_ku = fid.variables['time_avg_01_ku'][:].copy() CS_l1b_mds['Waveform_1Hz']['Time'] = time_avg_01_ku.copy() # Time: day part CS_l1b_mds['Waveform_1Hz']['Day'] = np.array(time_avg_01_ku/86400.0, dtype=np.int32) # Time: second part CS_l1b_mds['Waveform_1Hz']['Second'] = np.array(time_avg_01_ku - CS_l1b_mds['Waveform_1Hz']['Day'][:]*86400.0, dtype=np.uint32) # Time: microsecond part CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.array((time_avg_01_ku - CS_l1b_mds['Waveform_1Hz']['Day'][:]*86400.0 - CS_l1b_mds['Waveform_1Hz']['Second'][:])*1e6, dtype=np.uint32) # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lat'] = fid.variables['lat_avg_01_ku'][:].copy() # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_mds['Waveform_1Hz']['Lon'] = fid.variables['lon_avg_01_ku'][:].copy() # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_mds['Waveform_1Hz']['Alt'] = fid.variables['alt_avg_01_ku'][:].copy() # Window Delay (two-way) corrected for instrument delays CS_l1b_mds['Waveform_1Hz']['TD'] = fid.variables['window_del_avg_01_ku'][:].copy() # 1 Hz Averaged Power Echo Waveform CS_l1b_mds['Waveform_1Hz']['Waveform'] = fid.variables['pwr_waveform_avg_01_ku'][:].copy() # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = fid.variables['echo_scale_factor_avg_01_ku'][:].copy() # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = fid.variables['echo_scale_pwr_avg_01_ku'][:].copy() # Number of echoes averaged CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = fid.variables['echo_numval_avg_01_ku'][:].copy() CS_l1b_mds['Waveform_1Hz']['Flags'] = fid.variables['flag_echo_avg_01_ku'][:].copy() # CryoSat-2 Waveforms Groups CS_l1b_mds['Waveform_20Hz'] = {} # Echo Scale Factor (to scale echo to watts) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) echo_scale_factor_20_ku = fid.variables['echo_scale_factor_20_ku'][:].copy() # Echo Scale Power (a power of 2) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) echo_scale_pwr_20_ku = fid.variables['echo_scale_pwr_20_ku'][:].copy() # Number of echoes averaged CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) echo_numval_20_ku = fid.variables['echo_numval_20_ku'][:].copy() # Flags for errors or information about 20Hz waveform CS_l1b_mds['Waveform_20Hz']['Flags'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Flags'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) flag_echo_20_ku = fid.variables['flag_echo_20_ku'][:].copy() # CryoSat-2 mode specific waveform variables if (self.MODE == 'LRM'): # Low-Resolution Mode # Averaged Power Echo Waveform [128] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.ma.zeros((n_records,n_blocks,n_LRM_RW)) CS_l1b_mds['Waveform_20Hz']['Waveform'].mask = np.zeros((n_records,n_blocks,n_LRM_RW),dtype=np.bool) pwr_waveform_20_ku = fid.variables['pwr_waveform_20_ku'][:].copy() elif (self.MODE == 'SAR'): # SAR Mode # Averaged Power Echo Waveform [256] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.ma.zeros((n_records,n_blocks,n_SAR_D_RW)) CS_l1b_mds['Waveform_20Hz']['Waveform'].mask = np.zeros((n_records,n_blocks,n_SAR_D_RW),dtype=np.bool) pwr_waveform_20_ku = fid.variables['pwr_waveform_20_ku'][:].copy() elif (self.MODE == 'SIN'): # SARIN Mode # Averaged Power Echo Waveform [1024] CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.ma.zeros((n_records,n_blocks,n_SARIN_D_RW)) CS_l1b_mds['Waveform_20Hz']['Waveform'].mask = np.zeros((n_records,n_blocks,n_SARIN_D_RW),dtype=np.bool) pwr_waveform_20_ku = fid.variables['pwr_waveform_20_ku'][:].copy() # Coherence [1024]: packed units (1/1000) CS_l1b_mds['Waveform_20Hz']['Coherence'] = np.ma.zeros((n_records,n_blocks,n_SARIN_D_RW)) CS_l1b_mds['Waveform_20Hz']['Coherence'].mask = np.zeros((n_records,n_blocks,n_SARIN_D_RW),dtype=np.bool) coherence_waveform_20_ku = fid.variables['coherence_waveform_20_ku'][:].copy() # Phase Difference [1024]: packed units (microradians) CS_l1b_mds['Waveform_20Hz']['Phase_diff'] = np.ma.zeros((n_records,n_blocks,n_SARIN_D_RW)) CS_l1b_mds['Waveform_20Hz']['Phase_diff'].mask = np.zeros((n_records,n_blocks,n_SARIN_D_RW),dtype=np.bool) ph_diff_waveform_20_ku = fid.variables['ph_diff_waveform_20_ku'][:].copy() # Beam Behavior Parameters if self.MODE in ('SAR','SIN'): CS_l1b_mds['Waveform_20Hz']['Beam'] = {} # Standard Deviation of Gaussian fit to range integrated stack power. CS_l1b_mds['Waveform_20Hz']['Beam']['SD'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['SD'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_std_20_ku = fid.variables['stack_std_20_ku'][:].copy() # Stack Center: Mean of Gaussian fit to range integrated stack power. CS_l1b_mds['Waveform_20Hz']['Beam']['Center'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Center'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_centre_20_ku = fid.variables['stack_centre_20_ku'][:].copy() # Stack amplitude parameter scaled in dB/100. CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_scaled_amplitude_20_ku = fid.variables['stack_scaled_amplitude_20_ku'][:].copy() # 3rd moment: providing the degree of asymmetry of the range integrated # stack power distribution. CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_skewness_20_ku = fid.variables['stack_skewness_20_ku'][:].copy() # 4th moment: Measure of peakiness of range integrated stack power distribution. CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_kurtosis_20_ku = fid.variables['stack_kurtosis_20_ku'][:].copy() # Stack peakiness computed from the range integrated power of the single look echoes CS_l1b_mds['Waveform_20Hz']['Beam']['Peakiness'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Peakiness'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_peakiness_20_ku = fid.variables['stack_peakiness_20_ku'][:].copy() # Stack residuals of Gaussian that fits the range integrated power of the single look echoes CS_l1b_mds['Waveform_20Hz']['Beam']['RMS'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['RMS'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_gaussian_fitting_residuals_20_ku = fid.variables['stack_gaussian_fitting_residuals_20_ku'][:].copy() # Standard deviation as a function of boresight angle (microradians) CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_std_angle_20_ku = fid.variables['stack_std_angle_20_ku'][:].copy() # Stack Center angle as a function of boresight angle (microradians) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_centre_angle_20_ku = fid.variables['stack_centre_angle_20_ku'][:].copy() # Stack Center angle as a function of look angle (microradians) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_look_angle'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Center_look_angle'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_centre_look_angle_20_ku = fid.variables['stack_centre_look_angle_20_ku'][:].copy() # Number of contributing beams in the stack before weighting CS_l1b_mds['Waveform_20Hz']['Beam']['Number'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Number'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_number_before_weighting_20_ku = fid.variables['stack_number_before_weighting_20_ku'][:].copy() # Number of contributing beams in the stack after weighting CS_l1b_mds['Waveform_20Hz']['Beam']['Weighted_Number'] = np.ma.zeros((n_records,n_blocks)) CS_l1b_mds['Waveform_20Hz']['Beam']['Weighted_Number'].mask = np.zeros((n_records,n_blocks),dtype=np.bool) stack_number_after_weighting_20_ku = fid.variables['stack_number_after_weighting_20_ku'][:].copy() # for each record in the CryoSat file for r in range(n_records): # index for record r idx = ind_first_meas_20hz_01[r] # number of valid blocks in record r cnt = np.count_nonzero(ind_meas_1hz_20_ku == r) # CryoSat-2 Time and Orbit Group CS_l1b_mds['Location']['Time'].data[r,:cnt] = time_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Time'].mask[r,:cnt] = False CS_l1b_mds['Location']['Day'].data[r,:cnt] = np.array(time_20_ku[idx:idx+cnt]/86400.0, dtype=np.int) CS_l1b_mds['Location']['Day'].mask[r,:cnt] = False CS_l1b_mds['Location']['Second'].data[r,:cnt] = np.array(time_20_ku[idx:idx+cnt] - CS_l1b_mds['Location']['Day'].data[r,:cnt]*86400.0, dtype=np.int) CS_l1b_mds['Location']['Second'].mask[r,:cnt] = False CS_l1b_mds['Location']['Micsec'].data[r,:cnt] = np.array((time_20_ku[idx:idx+cnt] - CS_l1b_mds['Location']['Day'].data[r,:cnt]*86400.0 - CS_l1b_mds['Location']['Second'].data[r,:cnt])*1e6, dtype=np.uint32) CS_l1b_mds['Location']['Micsec'].mask[r,:cnt] = False CS_l1b_mds['Location']['USO_Corr'].data[r,:cnt] = uso_cor_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['USO_Corr'].mask[r,:cnt] = False CS_l1b_mds['Location']['Mode_ID'].data[r,:cnt] = flag_instr_mode_op_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Mode_ID'].mask[r,:cnt] = False CS_l1b_mds['Location']['Mode_flags'].data[r,:cnt] = flag_instr_mode_flags_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Mode_flags'].mask[r,:cnt] = False CS_l1b_mds['Location']['Att_control'].data[r,:cnt] = flag_instr_mode_att_ctrl_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Att_control'].mask[r,:cnt] = False CS_l1b_mds['Location']['Inst_config'].data[r,:cnt] = flag_instr_conf_rx_flags_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Inst_config'].mask[r,:cnt] = False CS_l1b_mds['Location']['Inst_band'].data[r,:cnt] = flag_instr_conf_rx_bwdt_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Inst_band'].mask[r,:cnt] = False CS_l1b_mds['Location']['Inst_channel'].data[r,:cnt] = flag_instr_conf_rx_in_use_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Inst_channel'].mask[r,:cnt] = False CS_l1b_mds['Location']['Tracking_mode'].data[r,:cnt] = flag_instr_conf_rx_trk_mode_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Tracking_mode'].mask[r,:cnt] = False CS_l1b_mds['Location']['SSC'].data[r,:cnt] = seq_count_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['SSC'].mask[r,:cnt] = False CS_l1b_mds['Location']['Rec_Count'].data[r,:cnt] = rec_count_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Rec_Count'].mask[r,:cnt] = False CS_l1b_mds['Location']['Lat'].data[r,:cnt] = lat_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Lat'].mask[r,:cnt] = False CS_l1b_mds['Location']['Lon'].data[r,:cnt] = lon_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Lon'].mask[r,:cnt] = False CS_l1b_mds['Location']['Alt'].data[r,:cnt] = alt_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Alt'].mask[r,:cnt] = False CS_l1b_mds['Location']['Alt_rate'].data[r,:cnt] = orb_alt_rate_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Alt_rate'].mask[r,:cnt] = False CS_l1b_mds['Location']['Sat_velocity'].data[r,:cnt,:] = sat_vel_vec_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Sat_velocity'].mask[r,:cnt,:] = False CS_l1b_mds['Location']['Real_beam'].data[r,:cnt,:] = beam_dir_vec_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Real_beam'].mask[r,:cnt,:] = False CS_l1b_mds['Location']['Baseline'].data[r,:cnt,:] = inter_base_vec_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Baseline'].mask[r,:cnt,:] = False CS_l1b_mds['Location']['ST_ID'].data[r,:cnt] = flag_instr_conf_rx_str_in_use_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['ST_ID'].mask[r,:cnt] = False CS_l1b_mds['Location']['Roll'].data[r,:cnt] = off_nadir_roll_angle_str_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Roll'].mask[r,:cnt] = False CS_l1b_mds['Location']['Pitch'].data[r,:cnt] = off_nadir_pitch_angle_str_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Pitch'].mask[r,:cnt] = False CS_l1b_mds['Location']['Yaw'].data[r,:cnt] = off_nadir_yaw_angle_str_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['Yaw'].mask[r,:cnt] = False CS_l1b_mds['Location']['MCD'].data[r,:cnt] = flag_mcd_20_ku[idx:idx+cnt] CS_l1b_mds['Location']['MCD'].mask[r,:cnt] = False # CryoSat-2 Measurement Group # Derived from instrument measurement parameters CS_l1b_mds['Data']['TD'].data[r,:cnt] = window_del_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TD'].mask[r,:cnt] = False CS_l1b_mds['Data']['H_0'].data[r,:cnt] = h0_applied_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['H_0'].mask[r,:cnt] = False CS_l1b_mds['Data']['COR2'].data[r,:cnt] = cor2_applied_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['COR2'].mask[r,:cnt] = False CS_l1b_mds['Data']['LAI'].data[r,:cnt] = h0_lai_word_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['LAI'].mask[r,:cnt] = False CS_l1b_mds['Data']['FAI'].data[r,:cnt] = h0_fai_word_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['FAI'].mask[r,:cnt] = False CS_l1b_mds['Data']['AGC_CH1'].data[r,:cnt] = agc_ch1_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['AGC_CH1'].mask[r,:cnt] = False CS_l1b_mds['Data']['AGC_CH2'].data[r,:cnt] = agc_ch2_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['AGC_CH2'].mask[r,:cnt] = False CS_l1b_mds['Data']['TR_gain_CH1'].data[r,:cnt] = tot_gain_ch1_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TR_gain_CH1'].mask[r,:cnt] = False CS_l1b_mds['Data']['TR_gain_CH2'].data[r,:cnt] = tot_gain_ch2_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TR_gain_CH2'].mask[r,:cnt] = False CS_l1b_mds['Data']['TX_Power'].data[r,:cnt] = transmit_pwr_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TX_Power'].mask[r,:cnt] = False CS_l1b_mds['Data']['Doppler_range'].data[r,:cnt] = dop_cor_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Doppler_range'].mask[r,:cnt] = False CS_l1b_mds['Data']['Doppler_angle_start'].data[r,:cnt] = dop_angle_start_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Doppler_angle_start'].mask[r,:cnt] = False CS_l1b_mds['Data']['Doppler_angle_stop'].data[r,:cnt] = dop_angle_stop_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Doppler_angle_stop'].mask[r,:cnt] = False CS_l1b_mds['Data']['TR_inst_range'].data[r,:cnt] = instr_cor_range_tx_rx_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TR_inst_range'].mask[r,:cnt] = False CS_l1b_mds['Data']['R_inst_range'].data[r,:cnt] = instr_cor_range_rx_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['R_inst_range'].mask[r,:cnt] = False CS_l1b_mds['Data']['TR_inst_gain'].data[r,:cnt] = instr_cor_gain_tx_rx_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['TR_inst_gain'].mask[r,:cnt] = False CS_l1b_mds['Data']['R_inst_gain'].data[r,:cnt] = instr_cor_gain_rx_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['R_inst_gain'].mask[r,:cnt] = False CS_l1b_mds['Data']['Internal_phase'].data[r,:cnt] = instr_int_ph_cor_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Internal_phase'].mask[r,:cnt] = False CS_l1b_mds['Data']['External_phase'].data[r,:cnt] = instr_ext_ph_cor_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['External_phase'].mask[r,:cnt] = False CS_l1b_mds['Data']['Noise_power'].data[r,:cnt] = noise_power_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Noise_power'].mask[r,:cnt] = False CS_l1b_mds['Data']['Phase_slope'].data[r,:cnt] = ph_slope_cor_20_ku[idx:idx+cnt] CS_l1b_mds['Data']['Phase_slope'].mask[r,:cnt] = False # CryoSat-2 Waveforms Groups CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'].data[r,:cnt] = echo_scale_factor_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'].data[r,:cnt] = echo_scale_pwr_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'].data[r,:cnt] = echo_numval_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Flags'].data[r,:cnt] = flag_echo_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Flags'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Waveform'].data[r,:cnt,:] = pwr_waveform_20_ku[idx:idx+cnt,:] CS_l1b_mds['Waveform_20Hz']['Waveform'].mask[r,:cnt,:] = False # SARIN Mode parameters if (self.MODE == 'SIN'): CS_l1b_mds['Waveform_20Hz']['Coherence'].data[r,:cnt,:] = coherence_waveform_20_ku[idx:idx+cnt,:] CS_l1b_mds['Waveform_20Hz']['Coherence'].mask[r,:cnt,:] = False CS_l1b_mds['Waveform_20Hz']['Phase_diff'].data[r,:cnt,:] = ph_diff_waveform_20_ku[idx:idx+cnt,:] CS_l1b_mds['Waveform_20Hz']['Phase_diff'].mask[r,:cnt,:] = False # SAR/SARIN waveform beam parameters if self.MODE in ('SAR','SIN'): CS_l1b_mds['Waveform_20Hz']['Beam']['SD'].data[r,:cnt] = stack_std_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['SD'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Center'].data[r,:cnt] = stack_centre_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Center'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'].data[r,:cnt] = stack_scaled_amplitude_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'].data[r,:cnt] = stack_skewness_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'].data[r,:cnt] = stack_kurtosis_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Peakiness'].data[r,:cnt] = stack_peakiness_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Peakiness'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['RMS'].data[r,:cnt] = stack_gaussian_fitting_residuals_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['RMS'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'].data[r,:cnt] = stack_std_angle_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['SD_boresight_angle'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'].data[r,:cnt] = stack_centre_angle_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Center_boresight_angle'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Center_look_angle'].data[r,:cnt] = stack_centre_look_angle_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Center_look_angle'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Number'].data[r,:cnt] = stack_number_before_weighting_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Number'].mask[r,:cnt] = False CS_l1b_mds['Waveform_20Hz']['Beam']['Weighted_Number'].data[r,:cnt] = stack_number_after_weighting_20_ku[idx:idx+cnt] CS_l1b_mds['Waveform_20Hz']['Beam']['Weighted_Number'].mask[r,:cnt] = False # extract global attributes and assign as MPH and SPH metadata CS_l1b_mds['METADATA'] = dict(MPH={},SPH={},DSD={}) # MPH attributes CS_l1b_mds['METADATA']['MPH']['PRODUCT'] = fid.product_name CS_l1b_mds['METADATA']['MPH']['DOI'] = fid.doi CS_l1b_mds['METADATA']['MPH']['PROC_STAGE'] = fid.processing_stage CS_l1b_mds['METADATA']['MPH']['REF_DOC'] = fid.reference_document CS_l1b_mds['METADATA']['MPH']['ACQUISITION_STATION'] = fid.acquisition_station CS_l1b_mds['METADATA']['MPH']['PROC_CENTER'] = fid.processing_centre CS_l1b_mds['METADATA']['MPH']['PROC_TIME'] = fid.creation_time CS_l1b_mds['METADATA']['MPH']['SOFTWARE_VER'] = fid.software_version CS_l1b_mds['METADATA']['MPH']['SENSING_START'] = fid.sensing_start CS_l1b_mds['METADATA']['MPH']['SENSING_STOP'] = fid.sensing_stop CS_l1b_mds['METADATA']['MPH']['PHASE'] = fid.phase CS_l1b_mds['METADATA']['MPH']['CYCLE'] = fid.cycle_number CS_l1b_mds['METADATA']['MPH']['REL_ORBIT'] = fid.rel_orbit_number CS_l1b_mds['METADATA']['MPH']['ABS_ORBIT'] = fid.abs_orbit_number CS_l1b_mds['METADATA']['MPH']['STATE_VECTOR_TIME'] = fid.state_vector_time CS_l1b_mds['METADATA']['MPH']['DELTA_UT1'] = fid.delta_ut1 CS_l1b_mds['METADATA']['MPH']['X_POSITION'] = fid.x_position CS_l1b_mds['METADATA']['MPH']['Y_POSITION'] = fid.y_position CS_l1b_mds['METADATA']['MPH']['Z_POSITION'] = fid.z_position CS_l1b_mds['METADATA']['MPH']['X_VELOCITY'] = fid.x_velocity CS_l1b_mds['METADATA']['MPH']['Y_VELOCITY'] = fid.y_velocity CS_l1b_mds['METADATA']['MPH']['Z_VELOCITY'] = fid.z_velocity CS_l1b_mds['METADATA']['MPH']['VECTOR_SOURCE'] = fid.vector_source CS_l1b_mds['METADATA']['MPH']['LEAP_UTC'] = fid.leap_utc CS_l1b_mds['METADATA']['MPH']['LEAP_SIGN'] = fid.leap_sign CS_l1b_mds['METADATA']['MPH']['LEAP_ERR'] = fid.leap_err CS_l1b_mds['METADATA']['MPH']['PRODUCT_ERR'] = fid.product_err # SPH attributes CS_l1b_mds['METADATA']['SPH']['START_RECORD_TAI_TIME'] = fid.first_record_time CS_l1b_mds['METADATA']['SPH']['STOP_RECORD_TAI_TIME'] = fid.last_record_time CS_l1b_mds['METADATA']['SPH']['ABS_ORBIT_START'] = fid.abs_orbit_start CS_l1b_mds['METADATA']['SPH']['REL_TIME_ASC_NODE_START'] = fid.rel_time_acs_node_start CS_l1b_mds['METADATA']['SPH']['ABS_ORBIT_STOP'] = fid.abs_orbit_stop CS_l1b_mds['METADATA']['SPH']['REL_TIME_ASC_NODE_STOP'] = fid.rel_time_acs_node_stop CS_l1b_mds['METADATA']['SPH']['EQUATOR_CROSS_TIME_UTC'] = fid.equator_cross_time CS_l1b_mds['METADATA']['SPH']['EQUATOR_CROSS_LONG'] = fid.equator_cross_long CS_l1b_mds['METADATA']['SPH']['ASCENDING_FLAG'] = fid.ascending_flag CS_l1b_mds['METADATA']['SPH']['START_LAT'] = fid.first_record_lat CS_l1b_mds['METADATA']['SPH']['START_LONG'] = fid.first_record_lon CS_l1b_mds['METADATA']['SPH']['STOP_LAT'] = fid.last_record_lat CS_l1b_mds['METADATA']['SPH']['STOP_LONG'] = fid.last_record_lon CS_l1b_mds['METADATA']['SPH']['L0_PROC_FLAG'] = fid.l0_proc_flag CS_l1b_mds['METADATA']['SPH']['L0_PROCESSING_QUALITY'] = fid.l0_processing_quality CS_l1b_mds['METADATA']['SPH']['L0_PROC_THRESH'] = fid.l0_proc_thresh CS_l1b_mds['METADATA']['SPH']['L0_GAPS_FLAG'] = fid.l0_gaps_flag CS_l1b_mds['METADATA']['SPH']['L0_GAPS_NUM'] = fid.l0_gaps_num CS_l1b_mds['METADATA']['SPH']['INSTR_ID'] = fid.instr_id CS_l1b_mds['METADATA']['SPH']['OPEN_OCEAN_PERCENT'] = fid.open_ocean_percent CS_l1b_mds['METADATA']['SPH']['CLOSE_SEA_PERCENT'] = fid.close_sea_percent CS_l1b_mds['METADATA']['SPH']['CONTINENT_ICE_PERCENT'] = fid.continent_ice_percent CS_l1b_mds['METADATA']['SPH']['LAND_PERCENT'] = fid.land_percent CS_l1b_mds['METADATA']['SPH']['L1_PROD_STATUS'] = fid.l1b_prod_status CS_l1b_mds['METADATA']['SPH']['L1_PROC_FLAG'] = fid.l1b_proc_flag CS_l1b_mds['METADATA']['SPH']['L1_PROCESSING_QUALITY'] = fid.l1b_processing_quality CS_l1b_mds['METADATA']['SPH']['L1_PROC_THRESH'] = fid.l1b_proc_thresh CS_l1b_mds['METADATA']['SPH']['SIR_CONFIGURATION'] = fid.sir_configuration CS_l1b_mds['METADATA']['SPH']['SIR_OP_MODE'] = fid.sir_op_mode CS_l1b_mds['METADATA']['SPH']['ORBIT_FILE'] = fid.xref_orbit CS_l1b_mds['METADATA']['SPH']['PROC_CONFIG_PARAMS_FILE'] = fid.xref_pconf CS_l1b_mds['METADATA']['SPH']['CONSTANTS_FILE'] = fid.xref_constants CS_l1b_mds['METADATA']['SPH']['IPF_RA_DATABASE_FILE'] = fid.xref_siral_characterisation CS_l1b_mds['METADATA']['SPH']['DORIS_USO_DRIFT_FILE'] = fid.xref_uso CS_l1b_mds['METADATA']['SPH']['STAR_TRACKER_ATTREF_FILE'] = fid.xref_star_tracker_attref CS_l1b_mds['METADATA']['SPH']['SIRAL_LEVEL_0_FILE'] = fid.xref_siral_l0 CS_l1b_mds['METADATA']['SPH']['CALIBRATION_TYPE_1_FILE'] = fid.xref_cal1 CS_l1b_mds['METADATA']['SPH']['SIR_COMPLEX_CAL1_SARIN'] = fid.xref_cal1_sarin CS_l1b_mds['METADATA']['SPH']['CALIBRATION_TYPE_2_FILE'] = fid.xref_cal2 CS_l1b_mds['METADATA']['SPH']['SURFACE_PRESSURE_FILE'] = fid.xref_surf_pressure CS_l1b_mds['METADATA']['SPH']['MEAN_PRESSURE_FILE'] = fid.xref_mean_pressure CS_l1b_mds['METADATA']['SPH']['WET_TROPOSPHERE_FILE'] = fid.xref_wet_trop CS_l1b_mds['METADATA']['SPH']['U_WIND_FILE'] = fid.xref_u_wind CS_l1b_mds['METADATA']['SPH']['V_WIND_FILE'] = fid.xref_v_wind CS_l1b_mds['METADATA']['SPH']['METEO_GRID_DEF_FILE'] = fid.xref_meteo CS_l1b_mds['METADATA']['SPH']['S1S2_PRESSURE_00H_MAP'] = fid.xref_s1s2_pressure_00h CS_l1b_mds['METADATA']['SPH']['S1S2_PRESSURE_06H_MAP'] = fid.xref_s1s2_pressure_06h CS_l1b_mds['METADATA']['SPH']['S1S2_PRESSURE_12H_MAP'] = fid.xref_s1s2_pressure_12h CS_l1b_mds['METADATA']['SPH']['S1S2_PRESSURE_18H_MAP'] = fid.xref_s1s2_pressure_18h CS_l1b_mds['METADATA']['SPH']['S1_TIDE_AMPLITUDE_MAP'] = fid.xref_s1_tide_amplitude CS_l1b_mds['METADATA']['SPH']['S1_TIDE_PHASE_MAP'] = fid.xref_s1_tide_phase CS_l1b_mds['METADATA']['SPH']['S2_TIDE_AMPLITUDE_MAP'] = fid.xref_s2_tide_amplitude CS_l1b_mds['METADATA']['SPH']['S2_TIDE_PHASE_MAP'] = fid.xref_s2_tide_phase CS_l1b_mds['METADATA']['SPH']['GPS_IONO_MAP'] = fid.xref_gim CS_l1b_mds['METADATA']['SPH']['IONO_COEFFICENTS_FILE'] = fid.xref_iono_cor CS_l1b_mds['METADATA']['SPH']['SAI_FILE'] = fid.xref_sai CS_l1b_mds['METADATA']['SPH']['OCEAN_TIDE_FILE'] = fid.xref_ocean_tide CS_l1b_mds['METADATA']['SPH']['TIDAL_LOADING_FILE'] = fid.xref_tidal_load CS_l1b_mds['METADATA']['SPH']['EARTH_TIDE_FILE'] = fid.xref_earth_tide CS_l1b_mds['METADATA']['SPH']['POLE_TIDE_FILE'] = fid.xref_pole_location CS_l1b_mds['METADATA']['SPH']['SURFACE_TYPE_FILE'] = fid.xref_surf_type # return the output dictionary return CS_l1b_mds def cryosat_scaling_factors(self): """ Get scaling factors for converting original unpacked units in binary files """ # dictionary of scale factors for CryoSat-2 variables CS_l1b_scale = {} # CryoSat-2 Time and Orbit Group CS_l1b_scale['Location'] = {} # Time: day part CS_l1b_scale['Location']['Day'] = 1.0 # Time: second part CS_l1b_scale['Location']['Second'] = 1.0 # Time: microsecond part CS_l1b_scale['Location']['Micsec'] = 1.0 # USO correction factor CS_l1b_scale['Location']['USO_Corr'] = 1e-15 # Mode ID CS_l1b_scale['Location']['Mode_ID'] = 1 # Source sequence counter CS_l1b_scale['Location']['SSC'] = 1 # Instrument configuration CS_l1b_scale['Location']['Inst_config'] = 1 # Record Counter CS_l1b_scale['Location']['Rec_Count'] = 1 # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Location']['Lat'] = 1e-7 # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Location']['Lon'] = 1e-7 # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_scale['Location']['Alt'] = 1e-3 # Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s) CS_l1b_scale['Location']['Alt_rate'] = 1e-3 # Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s) # ITRF= International Terrestrial Reference Frame CS_l1b_scale['Location']['Sat_velocity'] = 1e-3 # Real beam direction vector. In CRF: packed units (micro-m/s, 1e-6 m/s) # CRF= CryoSat Reference Frame. CS_l1b_scale['Location']['Real_beam'] = 1e-6 # Interferometric baseline vector. In CRF: packed units (micro-m/s, 1e-6 m/s) CS_l1b_scale['Location']['Baseline'] = 1e-6 # Star Tracker ID CS_l1b_scale['Location']['ST_ID'] = 1 # Antenna Bench Roll Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Location']['Roll'] = 1e-7 # Antenna Bench Pitch Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Location']['Pitch'] = 1e-7 # Antenna Bench Yaw Angle (Derived from star trackers) # packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Location']['Yaw'] = 1e-7 # Measurement Confidence Data Flags # Generally the MCD flags indicate problems when set # If MCD is 0 then no problems or non-nominal conditions were detected # Serious errors are indicated by setting bit 31 CS_l1b_scale['Location']['MCD'] = 1 CS_l1b_scale['Location']['Spares'] = 1 # CryoSat-2 Measurement Group # Derived from instrument measurement parameters CS_l1b_scale['Data'] = {} # Window Delay reference (two-way) corrected for instrument delays CS_l1b_scale['Data']['TD'] = 1e-12 # H0 Initial Height Word from telemetry CS_l1b_scale['Data']['H_0'] = 4.88e-11 # COR2 Height Rate: on-board tracker height rate over the radar cycle CS_l1b_scale['Data']['COR2'] = 3.05e-12 # Coarse Range Word (LAI) derived from telemetry CS_l1b_scale['Data']['LAI'] = 1.25e-8 # Fine Range Word (FAI) derived from telemetry CS_l1b_scale['Data']['FAI'] = 12.5e-9/256.0 # Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1. # Gain calibration corrections are applied (Sum of AGC stages 1 and 2 # plus the corresponding corrections) (dB/100) CS_l1b_scale['Data']['AGC_CH1'] = 1e-2 # Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2. # Gain calibration corrections are applied (dB/100) CS_l1b_scale['Data']['AGC_CH2'] = 1e-2 # Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100) CS_l1b_scale['Data']['TR_gain_CH1'] = 1e-2 # Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100) CS_l1b_scale['Data']['TR_gain_CH2'] = 1e-2 # Transmit Power in microWatts CS_l1b_scale['Data']['TX_Power'] = 1e-6 # Doppler range correction: Radial component (mm) # computed for the component of satellite velocity in the nadir direction CS_l1b_scale['Data']['Doppler_range'] = 1e-3 # Instrument Range Correction: transmit-receive antenna (mm) # Calibration correction to range on channel 1 computed from CAL1. CS_l1b_scale['Data']['TR_inst_range'] = 1e-3 # Instrument Range Correction: receive-only antenna (mm) # Calibration correction to range on channel 2 computed from CAL1. CS_l1b_scale['Data']['R_inst_range'] = 1e-3 # Instrument Gain Correction: transmit-receive antenna (dB/100) # Calibration correction to gain on channel 1 computed from CAL1 CS_l1b_scale['Data']['TR_inst_gain'] = 1e-2 # Instrument Gain Correction: receive-only (dB/100) # Calibration correction to gain on channel 2 computed from CAL1 CS_l1b_scale['Data']['R_inst_gain'] = 1e-2 # Internal Phase Correction (microradians) CS_l1b_scale['Data']['Internal_phase'] = 1e-6 # External Phase Correction (microradians) CS_l1b_scale['Data']['External_phase'] = 1e-6 # Noise Power measurement (dB/100) CS_l1b_scale['Data']['Noise_power'] = 1e-2 # Phase slope correction (microradians) # Computed from the CAL-4 packets during the azimuth impulse response # amplitude (SARIN only). Set from the latest available CAL-4 packet. CS_l1b_scale['Data']['Phase_slope'] = 1e-6 CS_l1b_scale['Data']['Spares1'] = 1 # CryoSat-2 External Corrections Group CS_l1b_scale['Geometry'] = {} # Dry Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['dryTrop'] = 1e-3 # Wet Tropospheric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['wetTrop'] = 1e-3 # Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['InvBar'] = 1e-3 # Delta Inverse Barometric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['DAC'] = 1e-3 # GIM Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['Iono_GIM'] = 1e-3 # Model Ionospheric Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['Iono_model'] = 1e-3 # Ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['ocTideElv'] = 1e-3 # Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['lpeTideElv'] = 1e-3 # Ocean loading tide Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['olTideElv'] = 1e-3 # Solid Earth tide Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['seTideElv'] = 1e-3 # Geocentric Polar tide Correction packed units (mm, 1e-3 m) CS_l1b_scale['Geometry']['gpTideElv'] = 1e-3 # Surface Type: enumerated key to classify surface at nadir # 0 = Open Ocean # 1 = Closed Sea # 2 = Continental Ice # 3 = Land CS_l1b_scale['Geometry']['Surf_type'] = 1 CS_l1b_scale['Geometry']['Spare1'] = 1 # Corrections Status Flag CS_l1b_scale['Geometry']['Corr_status'] = 1 # Correction Error Flag CS_l1b_scale['Geometry']['Corr_error'] = 1 CS_l1b_scale['Geometry']['Spare2'] = 1 # CryoSat-2 Average Waveforms Groups CS_l1b_scale['Waveform_1Hz'] = {} # Data Record Time (MDSR Time Stamp) CS_l1b_scale['Waveform_1Hz']['Day'] = 1.0 CS_l1b_scale['Waveform_1Hz']['Second'] = 1.0 CS_l1b_scale['Waveform_1Hz']['Micsec'] = 1.0 # Lat: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Waveform_1Hz']['Lat'] = 1e-7 # Lon: packed units (0.1 micro-degree, 1e-7 degrees) CS_l1b_scale['Waveform_1Hz']['Lon'] = 1e-7 # Alt: packed units (mm, 1e-3 m) # Altitude of COG above reference ellipsoid (interpolated value) CS_l1b_scale['Waveform_1Hz']['Alt'] = 1e-3 # Window Delay (two-way) corrected for instrument delays CS_l1b_scale['Waveform_1Hz']['TD'] = 1e-12 # 1 Hz Averaged Power Echo Waveform CS_l1b_scale['Waveform_1Hz']['Waveform'] = 1.0 # Echo Scale Factor (to scale echo to watts) CS_l1b_scale['Waveform_1Hz']['Linear_Wfm_Multiplier'] = 1.0 # Echo Scale Power (a power of 2 to scale echo to Watts) CS_l1b_scale['Waveform_1Hz']['Power2_Wfm_Multiplier'] = 1.0 # Number of echoes averaged CS_l1b_scale['Waveform_1Hz']['N_avg_echoes'] = 1 CS_l1b_scale['Waveform_1Hz']['Flags'] = 1 # CryoSat-2 Waveforms Groups # Beam Behavior Parameters Beam_Behavior = {} # Standard Deviation of Gaussian fit to range integrated stack power. Beam_Behavior['SD'] = 1e-2 # Stack Center: Mean of Gaussian fit to range integrated stack power. Beam_Behavior['Center'] = 1e-2 # Stack amplitude parameter scaled in dB/100. Beam_Behavior['Amplitude'] = 1e-2 # 3rd moment: providing the degree of asymmetry of the range integrated # stack power distribution. Beam_Behavior['Skewness'] = 1e-2 # 4th moment: Measure of peakiness of range integrated stack power distribution. Beam_Behavior['Kurtosis'] = 1e-2 # Standard deviation as a function of boresight angle (microradians) Beam_Behavior['SD_boresight_angle'] = 1e-6 # Stack Center angle as a function of boresight angle (microradians) Beam_Behavior['Center_boresight_angle'] = 1e-6 Beam_Behavior['Spare'] = 1 # CryoSat-2 waveform variables CS_l1b_scale['Waveform_20Hz'] = {} # Averaged Power Echo Waveform CS_l1b_scale['Waveform_20Hz']['Waveform'] = 1.0 # Echo Scale Factor (to scale echo to watts) CS_l1b_scale['Waveform_20Hz']['Linear_Wfm_Multiplier'] = 1.0 # Echo Scale Power (a power of 2) CS_l1b_scale['Waveform_20Hz']['Power2_Wfm_Multiplier'] = 1.0 # Number of echoes averaged CS_l1b_scale['Waveform_20Hz']['N_avg_echoes'] = 1 CS_l1b_scale['Waveform_20Hz']['Flags'] = 1 # Beam behaviour parameters CS_l1b_scale['Waveform_20Hz']['Beam'] = Beam_Behavior # Coherence [SARIN]: packed units (1/1000) CS_l1b_scale['Waveform_20Hz']['Coherence'] = 1e-3 # Phase Difference [SARIN]: packed units (microradians) CS_l1b_scale['Waveform_20Hz']['Phase_diff'] = 1e-6 # return the scaling factors return CS_l1b_scale

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0.007057

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null

0

1

0

null

0

6

594bc5f77ca906ae0433fe140d3a32eb4d88a297

40

py

Python

Hello_World.py

Fang465/MB215Lab1

9a45c7e6fc804d541c213d1741084ba32d95b0f5

[ "MIT" ]

null

Hello_World.py

Fang465/MB215Lab1

9a45c7e6fc804d541c213d1741084ba32d95b0f5

[ "MIT" ]

null

Hello_World.py

Fang465/MB215Lab1

9a45c7e6fc804d541c213d1741084ba32d95b0f5

[ "MIT" ]

null

print("Hello, my name is Ryan Kitamura")

40

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7

40

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0

0.125

40

1

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0.857143

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0.756098

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null

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1

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6

3ca01de01e0660058ccb8475194422ab6c649039

104

py

Python

torchOnVideo/datasets/Vimeo90KTriplet/frame_interpolation/__init__.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

2

2021-03-19T08:05:06.000Z

2021-05-22T21:54:10.000Z

torchOnVideo/datasets/Vimeo90KTriplet/frame_interpolation/__init__.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

null

torchOnVideo/datasets/Vimeo90KTriplet/frame_interpolation/__init__.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

null

from .train_adacof import TrainAdaCoF from .train_cain import TrainCAIN from .test_CAIN import TestCAIN

26

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104

5.733333

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104

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3ca43b7cd44810b0c1007f18b976c480556f7152

84

py

Python

pycardano/backend/__init__.py

Blockery-io/pycardano

81749b46324346a0e3cb4808290fd565f4ed7450

[ "MIT" ]

72

2022-01-09T03:54:06.000Z

2022-03-30T22:05:44.000Z

pycardano/backend/__init__.py

Blockery-io/pycardano

81749b46324346a0e3cb4808290fd565f4ed7450

[ "MIT" ]

13

2022-02-19T13:08:11.000Z

2022-03-30T16:57:33.000Z

pycardano/backend/__init__.py

henryyuanheng-wang/pycardano

d58c53791ffef542762e6d0220d4ccd1c0950e5e

[ "MIT" ]

15

2022-02-07T23:54:51.000Z

2022-03-30T17:06:12.000Z

# flake8: noqa from .base import * from .blockfrost import * from .ogmios import *

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6

3ca803824d090f0628d75891cf8f7c180165f2a8

2,030

py

Python

Operations.py

alfredots/image-processing

6a66e75c3248bfb997ed951dace24e3d5a431f5f

[ "Apache-2.0" ]

null

Operations.py

alfredots/image-processing

6a66e75c3248bfb997ed951dace24e3d5a431f5f

[ "Apache-2.0" ]

null

Operations.py

alfredots/image-processing

6a66e75c3248bfb997ed951dace24e3d5a431f5f

[ "Apache-2.0" ]

null

import numpy as np import cv2 class Operations: @staticmethod def binarizar(img): rows, cols = img.shape for i in range(rows): for j in range(cols): if(img[i,j] < 128): img[i,j] = 0 else: img[i,j] = 255 return img @staticmethod def _or(imgOne, imgTwo): imgThree = imgOne.copy() rows, cols = imgOne.shape imgOne = Operations.binarizar(imgOne) imgTwo = Operations.binarizar(imgTwo) for i in range(rows): for j in range(cols): if(imgOne[i,j]==255 or imgTwo[i,j]==255): imgThree[i,j] = 255 else: imgThree[i,j] = 0 return imgThree @staticmethod def _and (imgOne, imgTwo): imgThree = imgOne.copy() rows, cols = imgOne.shape imgOne = Operations.binarizar(imgOne) imgTwo = Operations.binarizar(imgTwo) for i in range(rows): for j in range(cols): if(imgOne[i,j]==255 and imgTwo[i,j]==imgOne[i,j]): imgThree[i,j] = 255 else: imgThree[i,j] = 0 return imgThree @staticmethod def _xor (imgOne, imgTwo): imgThree = imgOne.copy() rows, cols = imgOne.shape imgOne = Operations.binarizar(imgOne) imgTwo = Operations.binarizar(imgTwo) for i in range(rows): for j in range(cols): if( imgTwo[i,j]!=imgOne[i,j]): imgThree[i,j] = 255 else: imgThree[i,j] = 0 return imgThree @staticmethod def _not (imgOne): imgThree = imgOne.copy() rows, cols = imgOne.shape imgOne = Operations.binarizar(imgOne) for i in range(rows): for j in range(cols): imgThree[i,j] = 255 - imgOne[i,j] return imgThree

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6

3cc3162aad3d3f093242c4bf948625131137442e

43

py

Python

packagename/core.py

ptrstn/python-starter

372e1563a84dfa8a0d5af0be52cceaf607666237

[ "Unlicense" ]

null

packagename/core.py

ptrstn/python-starter

372e1563a84dfa8a0d5af0be52cceaf607666237

[ "Unlicense" ]

4

2021-08-23T23:32:53.000Z

2022-01-24T10:41:02.000Z

packagename/core.py

ptrstn/python-starter

372e1563a84dfa8a0d5af0be52cceaf607666237

[ "Unlicense" ]

null

def do_something(): return "something"

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null

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6

3ce8085886e9bf5721e34cde26a31e4f71d5e1dd

3,632

py

Python

experiments/verification_experiments/leaf_experiment_metadata_providers.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

experiments/verification_experiments/leaf_experiment_metadata_providers.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

experiments/verification_experiments/leaf_experiment_metadata_providers.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

from enum import IntEnum, auto from typing import Dict from sources.experiments.experiment_metadata_provider_utils import FixedExperimentMetadata, ExperimentMetadataProvider class ExperimentScale(IntEnum): SMALL = auto() MEDIUM = auto() LARGE = auto() CELEBA_SCALE_EXPERIMENT_METADATA_MAP: Dict[ExperimentScale, FixedExperimentMetadata] = { ExperimentScale.SMALL: { "num_clients": None, "num_rounds": 30, "clients_per_round": 2, "batch_size": 5, "local_epochs": 10, "val_steps": 2 }, ExperimentScale.MEDIUM: { "num_clients": None, "num_rounds": 100, "clients_per_round": 2, "batch_size": 5, "local_epochs": 10, "val_steps": 2 }, ExperimentScale.LARGE: { "num_clients": None, "num_rounds": 400, "clients_per_round": 2, "batch_size": 5, "local_epochs": 20, "val_steps": 2 } } celeba_small_experiment_metadata_provider = ExperimentMetadataProvider( CELEBA_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.SMALL] ) celeba_medium_experiment_metadata_provider = ExperimentMetadataProvider( CELEBA_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.MEDIUM] ) celeba_large_experiment_metadata_provider = ExperimentMetadataProvider( CELEBA_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.LARGE] ) FEMNIST_SCALE_EXPERIMENT_METADATA_MAP: Dict[ExperimentScale, FixedExperimentMetadata] = { ExperimentScale.SMALL: { "num_clients": None, "num_rounds": 30, "clients_per_round": 2, "batch_size": 5, "local_epochs": 10, "val_steps": 2 }, ExperimentScale.MEDIUM: { "num_clients": None, "num_rounds": 100, "clients_per_round": 2, "batch_size": 5, "local_epochs": 10, "val_steps": 2 }, ExperimentScale.LARGE: { "num_clients": None, "num_rounds": 400, "clients_per_round": 3, "batch_size": 5, "local_epochs": 20, "val_steps": 2 } } femnist_small_experiment_metadata_provider = ExperimentMetadataProvider( FEMNIST_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.SMALL] ) femnist_medium_experiment_metadata_provider = ExperimentMetadataProvider( FEMNIST_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.MEDIUM] ) femnist_large_experiment_metadata_provider = ExperimentMetadataProvider( FEMNIST_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.LARGE] ) SHAKESPEARE_SCALE_EXPERIMENT_METADATA_MAP: Dict[ExperimentScale, FixedExperimentMetadata] = { ExperimentScale.SMALL: { "num_clients": None, "num_rounds": 6, "clients_per_round": 2, "batch_size": 5, "local_epochs": 2, "val_steps": 1 }, ExperimentScale.MEDIUM: { "num_clients": None, "num_rounds": 8, "clients_per_round": 2, "batch_size": 5, "local_epochs": 2, "val_steps": 1 }, ExperimentScale.LARGE: { "num_clients": None, "num_rounds": 20, "clients_per_round": 3, "batch_size": 5, "local_epochs": 1, "val_steps": 1 } } shakespeare_small_experiment_metadata_provider = ExperimentMetadataProvider( SHAKESPEARE_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.SMALL] ) shakespeare_medium_experiment_metadata_provider = ExperimentMetadataProvider( SHAKESPEARE_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.MEDIUM] ) shakespeare_large_experiment_metadata_provider = ExperimentMetadataProvider( SHAKESPEARE_SCALE_EXPERIMENT_METADATA_MAP[ExperimentScale.LARGE] )

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null

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6

3cec6e4163724eb27c47f2cce4fabae2b578c3ca

30

py

Python

dags/odoodw/utils/__init__.py

kakkurij/odoodw-test

9f50b73b2d4107c2bfde9f4aebf51e7e70f251ab

[ "MIT" ]

null

dags/odoodw/utils/__init__.py

kakkurij/odoodw-test

9f50b73b2d4107c2bfde9f4aebf51e7e70f251ab

[ "MIT" ]

null

dags/odoodw/utils/__init__.py

kakkurij/odoodw-test

9f50b73b2d4107c2bfde9f4aebf51e7e70f251ab

[ "MIT" ]

null

from .dw_setup import DWSetup

15

29

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4.8

1

0

0.133333

30

1

30

0.923077

0

1

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true

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1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a72447b1785d7014243f0659a7bd0476f7a368c8

38

py

Python

jsdb/__init__.py

talwrii/jsdb

a1134856326bee8625c4a893d595113506597b44

[ "BSD-2-Clause" ]

2

2020-08-28T19:15:11.000Z

2020-09-05T01:49:25.000Z

jsdb/__init__.py

talwrii/jsdb

a1134856326bee8625c4a893d595113506597b44

[ "BSD-2-Clause" ]

null

jsdb/__init__.py

talwrii/jsdb

a1134856326bee8625c4a893d595113506597b44

[ "BSD-2-Clause" ]

null

from .jsdb import Jsdb, DbClosedError

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1

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1

0

1

0

6

59c135a3a043b7cd21b68b6f96aa58903615e78d

37

py

Python

packtets/utils/__init__.py

maxhutch/packtets

eba7d3d354da9bef50bfdbc48e6934c4e17f165c

[ "MIT" ]

1

2017-12-13T18:24:14.000Z

packtets/utils/__init__.py

maxhutch/packtets

eba7d3d354da9bef50bfdbc48e6934c4e17f165c

[ "MIT" ]

4

2016-05-19T14:48:57.000Z

2016-05-19T19:30:54.000Z

packtets/utils/__init__.py

maxhutch/packtets

eba7d3d354da9bef50bfdbc48e6934c4e17f165c

[ "MIT" ]

null

from .io import * from .vis import *

12.333333

18

0.675676

6

37

4.166667

0.666667

0

0.216216

37

2

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18.5

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0

1

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1

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1

0

6

59c76084bdc5f9ff51731e4d8519ac275b4767f0

2,263

py

Python

src_main/devtools/bin/separate_vcprojs.py

ArcadiusGFN/SourceEngine2007

51cd6d4f0f9ed901cb9b61456eb621a50ce44f55

[ "bzip2-1.0.6" ]

25

2018-02-28T15:04:42.000Z

2021-08-16T03:49:00.000Z

tf2_src/devtools/bin/separate_vcprojs.py

Counter2828/TeamFortress2

1b81dded673d49adebf4d0958e52236ecc28a956

[ "MIT" ]

1

2019-09-20T11:06:03.000Z

tf2_src/devtools/bin/separate_vcprojs.py

Counter2828/TeamFortress2

1b81dded673d49adebf4d0958e52236ecc28a956

[ "MIT" ]

9

2019-07-31T11:58:20.000Z

2021-08-31T11:18:15.000Z

from vsdotnetxmlparser import * #print f.GetAttribute( 'VisualStudioProject\\Configurations\\Configuration\\<2>Tool\\CommandLine' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug DoD|Win32", "Release DoD|Win32"], 'cl_dll\\client_dod.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug CounterStrike|Win32", "Release CounterStrike|Win32"], 'cl_dll\\client_cs.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug HL1|Win32", "Release HL1|Win32"], 'cl_dll\\client_hl1.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug HL2|Win32", "Release HL2|Win32"], 'cl_dll\\client_hl2.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug TF2|Win32", "Release TF2|Win32"], 'cl_dll\\client_tf2.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug SDK|Win32", "Release SDK|Win32"], 'cl_dll\\client_temp_sdk.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug HL2MP|Win32", "Release HL2MP|Win32"], 'cl_dll\\client_hl2mp.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'cl_dll\\client.vcproj' ), ["Debug Episodic HL2|Win32", "Release Episodic HL2|Win32"], 'cl_dll\\client_episodic.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug DoD|Win32", "Release DoD|Win32"], 'dlls\\hl_dod.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug CounterStrike|Win32", "Release CounterStrike|Win32"], 'dlls\\hl_cs.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug HL1|Win32", "Release HL1|Win32"], 'dlls\\hl_hl1.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug HL2|Win32", "Release HL2|Win32"], 'dlls\\hl_hl2.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug TF2|Win32", "Release TF2|Win32"], 'dlls\\hl_tf2.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug SDK|Win32", "Release SDK|Win32"], 'dlls\\hl_temp_sdk.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug HL2MP|Win32", "Release HL2MP|Win32"], 'dlls\\hl_hl2mp.vcproj' ) WriteSeparateVCProj( LoadVCProj( 'dlls\\hl.vcproj' ), ["Debug Episodic HL2|Win32", "Release Episodic HL2|Win32"], 'dlls\\hl_episodic.vcproj' )

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1

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1

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null

0

1

0

6

59cc84ff386eccd17323c87cdb81cdc0fdefe19a

22,479

py

Python

scripts/ida_is_subvtable.py

RUB-SysSec/Marx

4e9dbf1e5cfaab12a6544521032ef44bd8c9dbb1

[ "MIT" ]

118

2016-12-24T00:00:07.000Z

2022-03-19T06:19:49.000Z

scripts/ida_is_subvtable.py

RUB-SysSec/Marx

4e9dbf1e5cfaab12a6544521032ef44bd8c9dbb1

[ "MIT" ]

1

2021-09-17T22:01:19.000Z

2021-09-18T16:56:38.000Z

scripts/ida_is_subvtable.py

RUB-SysSec/Marx

4e9dbf1e5cfaab12a6544521032ef44bd8c9dbb1

[ "MIT" ]

14

2016-12-24T04:34:43.000Z

2021-12-19T16:04:45.000Z

#!/usr/bin/python2 from idc import * from idaapi import * from idautils import * ''' Checks if the vtable is a possible sub-vtable. ''' vtables = [ 0x1c74170, 0x1c74150, 0x1c74d90, 0x1c74d50, 0x15bae30, 0x15bae50, 0x15bae70, 0x15bae90, 0x15baeb0, 0x15e3650, 0x15e3690, 0x15e35d0, 0x15e3610, 0x15e48d0, 0x15e3770, 0x15e37b0, 0x15e3870, 0x15e38b0, 0x15e3a50, 0x15e3a10, 0x15e4ad0, 0x15e4b30, 0x15e4a70, 0x15e4c50, 0x15e4b90, 0x15e4cb0, 0x15e4690, 0x15e4bf0, 0x15e4d10, 0x15e4a10, 0x15e46f0, 0x15e50b0, 0x15e4d70, 0x15e5030, 0x15e4eb0, 0x15e4770, 0x15e4df0, 0x15e4f10, 0x15e4fd0, 0x15e4f70, 0x15e4e50, 0x15e4910, 0x15e4950, 0x15e4990, 0x15e49d0, 0x1d1c3d0, 0x1d1c430, 0x1d17bf0, 0x1d1c490, 0x1d1c4f0, 0x1d1c590, 0x1d1c5d0, 0x1d1c8b0, 0x1c741f0, 0x1d14910, 0x1d1c8f0, 0x1d1ca50, 0x1d1ca90, 0x1d1cad0, 0x1d1c9d0, 0x1d1ca10, 0x1d1ceb0, 0x1d1cef0, 0x1d1cf30, 0x1d1d0b0, 0x1d1d0f0, 0x1d1d130, 0x1d1d070, 0x1d1e790, 0x1d1e7b0, 0x1d1e7d0, 0x1d345b0, 0x1d34570, 0x1d14410, 0x1d14450, 0x1d15270, 0x1d152b0, 0x1d1adf0, 0x1d155b0, 0x1d155f0, 0x1d190b0, 0x1d15b50, 0x1d18710, 0x1d15b70, 0x1d29c30, 0x1d29b30, 0x1d29c70, 0x1d29b70, 0x1d29bb0, 0x1d29ab0, 0x1d29bf0, 0x1d29af0, 0x1d306b0, 0x1d306f0, 0x1d30770, 0x1d30730, 0x1d30630, 0x1d30670, 0x1d30270, 0x1d30290, 0x1d346f0, 0x1d34990, 0x1d35050, 0x1d35170, 0x1d34ad0, 0x1d350b0, 0x1d34ff0, 0x1d35110, 0x1d1e750, 0x1d355f0, 0x1d35630, 0x1d3ccf0, 0x1d3cd10, 0x1c749d0, 0x1c74830, 0x1c74b70, 0x1c76690, 0x1c7a150, 0x1c77590, 0x1c7c310, 0x1c7ae10, 0x1c76090, 0x1c76390, 0x1c784b0, 0x1c7bd10, 0x1c7a490, 0x1c7b110, 0x1c7a7d0, 0x1c77290, 0x1c7ab10, 0x1c787f0, 0x1c78190, 0x1c76c90, 0x1c7ba10, 0x1c78b30, 0x1c79b30, 0x1c78e70, 0x1c7c910, 0x1c76f90, 0x1c791b0, 0x1c7b710, 0x1c75d90, 0x1c7c610, 0x1c794f0, 0x1c77b90, 0x1c77e90, 0x1c7b410, 0x1c75a90, 0x1c79810, 0x1c77890, 0x1c76990, 0x1c79e50, 0x1c7c010, 0x1d14330, 0x1c74250, 0x1d14370, 0x1d14490, 0x1d1c390, 0x1d144d0, 0x1d3b470, 0x1d36c90, 0x1d36cd0, 0x1d3ae10, 0x1d3ae50, 0x1d3add0, 0x1d3afd0, 0x1d3afb0, 0x15b54d0, 0x15b5590, 0x15b5550, 0x15b5510, 0x15caad0, 0x15cab10, 0x15cab50, 0x15caa90, 0x15d4730, 0x15d46f0, 0x15a8d70, 0x15d4e90, 0x15f80f0, 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0x1d395b0, 0x1d3a158, 0x1d39c08, 0x1d38b58, 0x1d39398, 0x1d3a8b8, 0x1d388d8, 0x1d387f0, 0x1d377f0, 0x1d39700, 0x1d39c58, 0x1d3a370, 0x1d38a58, 0x1d38a80, 0x1d37b60, 0x1d39c30, 0x1d38bd8, 0x1d38ca0, 0x1d38cd8, 0x1d37c50, 0x1d38d80, 0x1d39240, 0x1d37810, 0x1d38df8, 0x1d39278, 0x1d38ee8, 0x1d3a810, 0x1d37910, 0x1599290, 0x1d37930, 0x1d38f98, 0x1d38fc0, 0x1d38ff8, 0x1d38710, 0x1599130, 0x1c741d0, 0x1d390a0, 0x15990d8, 0x1d37c70, 0x1d39118, 0x1d38e90, 0x1d37830, 0x1d391c0, 0x1d391e8, 0x1d39218, 0x1d38c78, 0x1d3a318, 0x1d39878, 0x15992f8, 0x1d37dd8, 0x1599320, 0x1d39328, 0x1d3ac78, 0x1d39c80, 0x1d394b8, 0x1d37950, 0x1d37850, 0x1d39588, 0x1d39628, 0x1d3a570, 0x1d3a3c0, 0x1d396d8, 0x1d37970, 0x1d38db0, 0x1d39738, 0x1d38970, 0x1599390, 0x1d37b98, 0x1d38530, 0x1d39970, 0x1d39808, 0x1d39ec8, 0x1d3a278, 0x1d37a90, 0x1d37870, 0x1d39948, 0x1d39998, 0x1d39448, 0x1d379b8, 0x1d399c0, 0x1d38430, 0x1d399f8, 0x1599150, 0x1d3aa90, 0x1d37a40, 0x1d3ab38, 0x1d39ac8, 0x1d37b38, 0x1d379e0, 0x1d37bc0, 0x1d3a9f8, 0x1d37bf8, 0x1d37c20, 0x1d3a950, 0x1d378f0, 0x1d37a18, 0x1d37890, 0x1d39cb8, 0x1d37f78, 0x1d389a0, 0x1d35df0, 0x1d39ef0, 0x1d38890, 0x15992b0, 0x1d38670, 0x1d39d88, 0x1d37a70, 0x1d39df8, 0x1d3a670, 0x1d37e88, 0x1d37ed8, 0x1d39f58, 0x1d38350, 0x1d37fb8, 0x1d3abd0, 0x1d3b150, 0x1d3b4d0, 0x1d3b1b0, 0x1d3b3f0, 0x1d3b230, 0x1d3b550, 0x1d3b2b0, 0x1d3b770, 0x1d3b890, 0x1d3af10, 0x1d1c710, 0x1d1c650, 0x1d1c6b0, 0x1d3b690, 0x1d3b6f0, 0x1d3b0f0, 0x159b630, 0x159add0, 0x15ab070, 0x15ab050, 0x1d14990, 0x159ca50, 0x1d149b0, 0x15a9870, 0x159ae50, 0x15fbc70, 0x15df670, 0x159ca70, 0x15a9e90, 0x15a8d90, 0x15ded50, 0x15fc2b0, 0x159adf0, 0x1d149d0, 0x159b478, 0x15fa730, 0x1c7d1d0, 0x15a9890, 0x15fb630, 0x15fbc90, 0x159b5d0, 0x15facd0, 0x159ae70, 0x15a9fe8 ] DEBUG = True subvtable_candidates = list() basevtable_candidates = list() for vtable in vtables: ott = Qword(vtable-16) if ott != 0: subvtable_candidates.append(vtable) if DEBUG: print "0x%x - SUBVTABLE" % vtable else: basevtable_candidates.append(vtable) if DEBUG: print "0x%x - BASEVTABLE" % vtable print print "Possible base-vtables:" for vtable in basevtable_candidates: print "0x%x" % vtable print print "Possible sub-vtables:" for vtable in subvtable_candidates: print "0x%x" % vtable print

548.268293

21,759

0.813114

2,070

22,479

8.827053

0.971981

0.001532

0.001751

0.002627

0.007224

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0.095823

22,479

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21,760

548.268293

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null

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1

0

null

0

1

0

1

0

6

ab6710a479b6b16a6e9dc5c8e6504b99d346e290

119

py

Python

test-simple/calc.py

Tobils/recipe-app-api

3b0948dfd7dbefc57ae85ba8b51fcf77c5c04344

[ "MIT" ]

1

2021-02-09T04:14:18.000Z

test-simple/calc.py

Tobils/recipe-app-api

3b0948dfd7dbefc57ae85ba8b51fcf77c5c04344

[ "MIT" ]

null

test-simple/calc.py

Tobils/recipe-app-api

3b0948dfd7dbefc57ae85ba8b51fcf77c5c04344

[ "MIT" ]

null

""" add 2 number """ def add(x, y): return x + y """ substract y from x """ def substract(x, y): return y - x

14.875

26

0.529412

21

119

3

0.428571

0.095238

0.253968

0

0.011765

0.285714

119

8

27

14.875

0.729412

0.10084

0

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0.5

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0

0.5

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

abb765fee4958674e53441fedc856ec2d8b1f629

82

py

Python

project_name/serializers.py

dthorell/django-microservice

db66efbe240a465682989c729ea38b890ee614f3

[ "MIT" ]

null

project_name/serializers.py

dthorell/django-microservice

db66efbe240a465682989c729ea38b890ee614f3

[ "MIT" ]

2

2021-06-09T17:46:04.000Z

2021-06-10T18:45:20.000Z

project_name/serializers.py

dthorell/django-microservice

db66efbe240a465682989c729ea38b890ee614f3

[ "MIT" ]

null

from rest_framework import serializers # TODO: write here your model serializers

20.5

41

0.829268

11

82

6.090909

0.909091

0

0.146341

82

3

42

27.333333

0.957143

0.47561

0

0.333333

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null

0

1

0

1

0

1

0

1

0

6

abedcabcfe29e864831d0deaddc996f78c3ca286

88

py

Python

antipetros_discordbot/auxiliary_classes/aux_server_classes/__init__.py

official-antistasi-community/Antipetros_Discord_Bot

1b5c8b61c09e61cdff671e259f0478d343a50c8d

[ "MIT" ]

null

antipetros_discordbot/auxiliary_classes/aux_server_classes/__init__.py

official-antistasi-community/Antipetros_Discord_Bot

1b5c8b61c09e61cdff671e259f0478d343a50c8d

[ "MIT" ]

null

antipetros_discordbot/auxiliary_classes/aux_server_classes/__init__.py

official-antistasi-community/Antipetros_Discord_Bot

1b5c8b61c09e61cdff671e259f0478d343a50c8d

[ "MIT" ]

1

2021-02-12T01:10:51.000Z

from .server_item import * from .helper import * from .is_online_message_items import *

22

38

0.795455

13

88

5.076923

0.692308

0.30303

0

0.136364

88

3

39

29.333333

0.868421

0

1

0

true

0

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1

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0

null

0

1

0

1

0

1

0

6

f9ef1c766a0a46ece41bbb3a7a8db7b96419694c

210

py

Python

python/8kyu/generate_range_of_integers.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

3

2021-06-08T01:57:13.000Z

2021-06-26T10:52:47.000Z

python/8kyu/generate_range_of_integers.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

null

python/8kyu/generate_range_of_integers.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

2

2021-06-10T21:20:13.000Z

2021-06-30T10:13:26.000Z

"""Kata url: https://www.codewars.com/kata/55eca815d0d20962e1000106.""" from typing import List def generate_range(_min: int, _max: int, step: int) -> List[int]: return list(range(_min, _max + 1, step))

26.25

71

0.7

30

210

4.733333

0.666667

0.112676

0

0.104972

0.138095

210

7

72

30

0.679558

0.309524

0

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1

0.333333

false

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e60b5e32067fb2bda2b28e090317819a571c8cb5

184

py

Python

codewars/8kyu/doha22/kata8/alam/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

null

codewars/8kyu/doha22/kata8/alam/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

2

2019-01-22T10:53:42.000Z

2019-01-31T08:02:48.000Z

codewars/8kyu/doha22/kata8/alam/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

13

2019-01-22T10:37:42.000Z

2019-01-25T13:30:43.000Z

import unittest from alarm import set_alarm def test_set_alarm(benchmark): assert benchmark(set_alarm,(True, True)) == False assert benchmark(set_alarm,(False, True)) == True

26.285714

53

0.75

26

184

5.115385

0.423077

0.240602

0.270677

0.345865

0

0.146739

184

7

54

26.285714

0.847134

0

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false

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0

null

1

0

1

0

null

0

1

0

6

e6214603206384ef5fda0936c275e027c90d4a2d

3,622

py

Python

tests/dltranz_tests/test_distribution_target_loss.py

KirillVladimirov/pytorch-lifestream

83005b950d41de8afc11711fc955ffafb5ff7a9e

[ "Apache-2.0" ]

null

tests/dltranz_tests/test_distribution_target_loss.py

KirillVladimirov/pytorch-lifestream

83005b950d41de8afc11711fc955ffafb5ff7a9e

[ "Apache-2.0" ]

null

tests/dltranz_tests/test_distribution_target_loss.py

KirillVladimirov/pytorch-lifestream

83005b950d41de8afc11711fc955ffafb5ff7a9e

[ "Apache-2.0" ]

1

2022-02-05T15:06:48.000Z

import torch import numpy as np from dltranz.loss import DistributionTargetsLoss def test_best_loss(): eps = 1e-7 prediction = {'neg_sum': torch.tensor([[np.log(10 + 1)]]), 'neg_distribution': torch.tensor([[100., 0., 0., 0., 0., 0.]]), 'pos_sum': torch.tensor([[0]]), 'pos_distribution': torch.tensor([[0., 100., 0., 0., 0., 0.]])} label = {'neg_sum': np.array([[10]]), 'neg_distribution': np.array([[1., 0., 0., 0., 0., 0.]]), 'pos_sum': np.array([[0]]), 'pos_distribution': np.array([[0., 1., 0., 0., 0., 0.]])} loss = DistributionTargetsLoss() out = loss(prediction, label) assert abs(out.item() - 0.) < eps assert type(out) is torch.Tensor def test_loss_300(): eps = 1e-7 prediction = {'neg_sum': torch.tensor([[10]]), 'neg_distribution': torch.tensor([[100., 0., 0., 0., 0., 0.]]), 'pos_sum': torch.tensor([[0]]), 'pos_distribution': torch.tensor([[0., 100., 0., 0., 0., 0.]])} label = {'neg_sum': np.array([[0]]), 'neg_distribution': np.array([[1., 0., 0., 0., 0., 0.]]), 'pos_sum': np.array([[0]]), 'pos_distribution': np.array([[0., 1., 0., 0., 0., 0.]])} loss = DistributionTargetsLoss() out = loss(prediction, label) assert abs(out.item() - 300.) < eps assert type(out) is torch.Tensor def test_usual_loss_first(): eps = 1e-7 prediction = {'neg_sum': torch.tensor([[-1.]]), 'neg_distribution': torch.tensor([[0.1, 0.2, 0.1, 0.1, 0.3, 0.2]]), 'pos_sum': torch.tensor([[ 1.]]), 'pos_distribution': torch.tensor([[0.1, 0.2, 0.1, 0.1, 0.3, 0.2]])} label = {'neg_sum': np.array([[-1.]]), 'neg_distribution': np.array([[0.1, 0.2, 0.1, 0.1, 0.3, 0.2]]), 'pos_sum': np.array([[1.]]), 'pos_distribution': np.array([[0.1, 0.2, 0.1, 0.1, 0.3, 0.2]])} loss = DistributionTargetsLoss() out = loss(prediction, label) assert abs(out.item() - 12.138458251953125) < eps assert type(out) is torch.Tensor def test_usual_loss_second(): eps = 1e-7 prediction = {'neg_sum': torch.tensor([[-1.]]), 'neg_distribution': torch.tensor([[0.1, 0.2, 0.1, 0.1, 0.3, 0.2]]), 'pos_sum': torch.tensor([[ 1.]]), 'pos_distribution': torch.tensor([[0.3, 0.5, 0., 0.1, 0.1, 0.0]])} label = {'neg_sum': np.array([[-10.]]), 'neg_distribution': np.array([[0.5, 0.5, 0.0, 0.0, 0.0, 0.0]]), 'pos_sum': np.array([[8.]]), 'pos_distribution': np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.5]])} loss = DistributionTargetsLoss() out = loss(prediction, label) assert abs(out.item() - 38.563011169433594) < eps assert type(out) is torch.Tensor def test_one_class(): eps = 1e-7 prediction = {'neg_sum': torch.tensor([[-1.]]), 'neg_distribution': torch.tensor([[1., 0., 0., 0., 0., 0.]]), 'pos_sum': torch.tensor([[ 1.]]), 'pos_distribution': torch.tensor([[0., 1., 0., 0., 0., 0.]])} label = {'neg_sum': np.array([[-1.]]), 'neg_distribution': np.array([[1., 0., 0., 0., 0., 0.]]), 'pos_sum': np.array([[1.]]), 'pos_distribution': np.array([[0., 1., 0., 0., 0., 0.]])} loss = DistributionTargetsLoss() out = loss(prediction, label) assert abs(out.item() - 10.703149795532227) < eps assert type(out) is torch.Tensor

35.165049

85

0.503589

501

3,622

3.536926

0.097804

0.057562

0.060948

0.051919

0.895598

0.892212

0.861738

0.823928

0.762415

0

0.101438

0.270569

3,622

102

86

35.509804

0.569266

0

0.630137

0

0.127002

0

0.136986

1

0.068493

false

0

0.041096

0

0.109589

0

null

0

1

0

null

0

6

e6320f7bc2522f76b9d05fac4dac9bf5482ba3a5

330

py

Python

core/classifier/mfe_svm_classifier.py

yt7589/mgs

2faae1b69e6d4cde63afb9b2432b1bf49ebdd770

[ "Apache-2.0" ]

null

core/classifier/mfe_svm_classifier.py

yt7589/mgs

2faae1b69e6d4cde63afb9b2432b1bf49ebdd770

[ "Apache-2.0" ]

null

core/classifier/mfe_svm_classifier.py

yt7589/mgs

2faae1b69e6d4cde63afb9b2432b1bf49ebdd770

[ "Apache-2.0" ]

null

from pyAudioAnalysis import ShortTermFeatures as aSF from pyAudioAnalysis import MidTermFeatures as aMF from pyAudioAnalysis import audioBasicIO as aIO import numpy as np import plotly.graph_objs as go import plotly import sklearn.svm as sks # import SVC class MfeSvmClassifier: @staticmethod def train(): pass

27.5

52

0.793939

43

330

6.069767

0.604651

0.218391

0.287356

0

0.181818

330

12

53

27.5

0.966667

0.030303

0

1

0.090909

true

0.090909

0.636364

0

0.818182

0

null

1

0

null

0

1

0

1

0

6

e64742d7aa296f88e8ba11eeaca6dd175108b550

92

py

Python

webresume/api/tests.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-ZhimaoLin

f09753f7615c5b7c2cb6d51d51424b4f616a2241

[ "MIT" ]

null

webresume/api/tests.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-ZhimaoLin

f09753f7615c5b7c2cb6d51d51424b4f616a2241

[ "MIT" ]

4

2018-10-01T23:13:33.000Z

2020-06-05T19:10:58.000Z

webresume/api/tests.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-ZhimaoLin

f09753f7615c5b7c2cb6d51d51424b4f616a2241

[ "MIT" ]

2

2018-10-01T19:46:09.000Z

2018-10-09T00:40:18.000Z

from django.test import TestCase # Create your tests here. def test_test(): assert True

18.4

32

0.75

14

92

4.857143

0.857143

0

0.184783

92

5

33

18.4

0.906667

0.25

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

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0

1

0

null

0

1

0

1

0

1

0

6

053165b9d07b0b228d5dfaab6bcfcf22c4983f87

47

py

Python

image-stitching/src/practice.py

blackmahub/de.hs-fulda.informatik.cv.image-stitching

5ec8448322b3591b11a746d7ad99dc8e0adbcac5

[ "MIT" ]

null

image-stitching/src/practice.py

blackmahub/de.hs-fulda.informatik.cv.image-stitching

5ec8448322b3591b11a746d7ad99dc8e0adbcac5

[ "MIT" ]

null

image-stitching/src/practice.py

blackmahub/de.hs-fulda.informatik.cv.image-stitching

5ec8448322b3591b11a746d7ad99dc8e0adbcac5

[ "MIT" ]

null

print(["java", {"python": ("ML", "AI", "CV")}])

47

0.446809

6

47

3.5

1

0

0.085106

47

1

47

0.488372

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0.333333

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null

0

1

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1

0

null

0

1

0

1

0

6

0560bd42c719432ea281befd7da7e85337efdb08

27

py

Python

esinet/minimum_norm/__init__.py

LukeTheHecker/ANNDip

008fbea39d80b2bd97ca4c5510b0aefb5a766443

[ "MIT" ]

10

2021-01-27T14:13:18.000Z

2021-08-12T12:48:00.000Z

esinet/minimum_norm/__init__.py

LukeTheHecker/ANNDip

008fbea39d80b2bd97ca4c5510b0aefb5a766443

[ "MIT" ]

1

2021-12-06T12:14:17.000Z

2021-12-06T12:19:14.000Z

esinet/minimum_norm/__init__.py

LukeTheHecker/ANNDip

008fbea39d80b2bd97ca4c5510b0aefb5a766443

[ "MIT" ]

2

2021-05-18T23:54:03.000Z

2021-06-04T20:57:16.000Z

from .minimum_norm import *

27

0.814815

4

27

5.25

1

0

0.111111

27

1

27

0.875

0

1

0

true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

558f30966e1b0c96bd097eba4c0316db1c2d2a1e

53

py

Python

navtools/__init__.py

slott56/navtools

9860e4e79e3bd5d6e479755180a4d85a77502102

[ "BSD-3-Clause" ]

5

2015-11-26T01:26:43.000Z

2022-01-07T19:50:24.000Z

navtools/__init__.py

slott56/navtools

9860e4e79e3bd5d6e479755180a4d85a77502102

[ "BSD-3-Clause" ]

1

2022-01-07T05:53:09.000Z

2022-01-07T17:05:24.000Z

navtools/__init__.py

slott56/navtools

9860e4e79e3bd5d6e479755180a4d85a77502102

[ "BSD-3-Clause" ]

null

"""Navtools 2021.08.29""" __version__ = "2021.08.29"

17.666667

26

0.660377

8

53

3.875

0.625

0.387097

0.516129

0

0.333333

0.09434

53

2

27

26.5

0.3125

0.358491

0

0.357143

0

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false

0

1

0

null

1

0

1

0

1

0

1

0

null

0

6

55a47daf6dbe85fd7e6f37790b91ddf125836834

47

py

Python

libs/models/__init__.py

0h-n0/first_deep

8b4b1c3e2198774baaddac7b1045fecc95c59f0b

[ "MIT" ]

null

libs/models/__init__.py

0h-n0/first_deep

8b4b1c3e2198774baaddac7b1045fecc95c59f0b

[ "MIT" ]

null

libs/models/__init__.py

0h-n0/first_deep

8b4b1c3e2198774baaddac7b1045fecc95c59f0b

[ "MIT" ]

null

from .cnn import CNN from .rnn import RNNModel

15.666667

25

0.787234

8

47

4.625

0.625

0

0.170213

47

2

26

23.5

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e9471d26e68758672e6b24a57743d37bfcb749e0

473

py

Python

easycodef/errors/_errors.py

fiveio/easy-codef-py

97bd6831909e4d31af0ec7ed479b63fc977cd302

[ "MIT" ]

1

2019-09-17T00:47:08.000Z

easycodef/errors/_errors.py

fiveio/easy-codef-py

97bd6831909e4d31af0ec7ed479b63fc977cd302

[ "MIT" ]

null

easycodef/errors/_errors.py

fiveio/easy-codef-py

97bd6831909e4d31af0ec7ed479b63fc977cd302

[ "MIT" ]

null

class Error(Exception): """Base class""" pass class TokenGenerateError(Error): """ 토큰 생성 에러 :param: message: 에러 메세지 """ def __init__(self, message): self.message = message class ConnectedIdGenerateError(Error): """ 커넥티드 아이디 생성 에러 :param: message: 에러 메세지 """ def __init__(self, message): self.message = message class UseApiError(Error): def __init__(self, message): self.message = message

15.766667

38

0.604651

51

473

5.372549

0.372549

0.240876

0.120438

0.19708

0.583942

0.452555

0

0.281184

473

29

39

16.310345

0.805882

0.17759

0

0.545455

1

0

1

0.272727

false

0.090909

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0.636364

0

null

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

e955c5f7216626e1203ea5c95f60e47c503e989c

4,793

py

Python

tests/integrations/test_location_eligibility.py

uk-gov-mirror/alphagov.govuk-shielded-vulnerable-people-service

5b191980dec554155e9d431a514a945072032e7c

[ "MIT" ]

3

2020-08-16T19:36:26.000Z

2020-10-29T14:35:01.000Z

tests/integrations/test_location_eligibility.py

uk-gov-mirror/alphagov.govuk-shielded-vulnerable-people-service

5b191980dec554155e9d431a514a945072032e7c

[ "MIT" ]

101

2020-09-03T11:10:00.000Z

2021-10-01T03:03:46.000Z

tests/integrations/test_location_eligibility.py

alphagov-mirror/govuk-shielded-vulnerable-people-service

f9cb4ae9046fc402f0878503733a23d42546cc53

[ "MIT" ]

6

2020-07-28T09:03:20.000Z

2021-04-10T18:04:56.000Z

from unittest.mock import patch import pytest from flask import Flask from vulnerable_people_form.integrations.location_eligibility import (is_postcode_in_england, get_postcode_tier, get_uprn_tier, get_shielding_advice_by_uprn, get_shielding_advice_by_postcode) from vulnerable_people_form.form_pages.shared.constants import PostcodeTier _current_app = Flask(__name__) def test_get_uprn_tier_should_raise_err(): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": "INVALID_VALUE"}]]}), \ pytest.raises(ValueError) as exception_info: get_uprn_tier("asasas") assert "RDS procedure returned unrecognised value" in str(exception_info.value) def test_get_postcode_tier_should_raise_err(): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": "INVALID_VALUE"}]]}), \ pytest.raises(ValueError) as exception_info: get_postcode_tier("asasas") assert "RDS procedure returned unrecognised value" in str(exception_info.value) def test_is_postcode_in_england_should_raise_error(): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"stringValue": "INVALID_VALUE"}]]}), \ pytest.raises(ValueError) as exception_info: is_postcode_in_england("LSas1BA111") assert "RDS procedure returned unrecognised value" in str(exception_info.value) def test_get_shielding_advice_by_postcode_should_raise_error(): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": "INVALID_VALUE"}]]}), \ pytest.raises(ValueError) as exception_info: get_shielding_advice_by_postcode("LSas1BA111") assert "RDS procedure returned unrecognised value" in str(exception_info.value) def test_get_shielding_advice_by_uprn_should_raise_error(): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": "INVALID_VALUE"}]]}), \ pytest.raises(ValueError) as exception_info: get_shielding_advice_by_uprn("asasas") assert "RDS procedure returned unrecognised value" in str(exception_info.value) @pytest.mark.parametrize("stored_proc_return_value, expected_output", [(1, PostcodeTier.MEDIUM), (2, PostcodeTier.HIGH)]) def test_get_postcode_tier_should_return_correct_tier( stored_proc_return_value, expected_output): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": stored_proc_return_value}]]}): postcode_tier = get_postcode_tier("LS1 1BA") assert postcode_tier == expected_output @pytest.mark.parametrize("stored_proc_return_value, expected_output", [("YES", True), ("NO", False)]) def test_is_postcode_in_england_should_return_correct_eligibility_value( stored_proc_return_value, expected_output): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"stringValue": stored_proc_return_value}]]}): postcode_in_england = is_postcode_in_england("LS1 1BA") assert postcode_in_england == expected_output @pytest.mark.parametrize("stored_proc_return_value, expected_output", [(0, 0), (1, 1)]) def test_get_shielding_advice_by_uprn_should_return_correct_eligibility_value( stored_proc_return_value, expected_output): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": stored_proc_return_value}]]}): uprn_shielding = get_shielding_advice_by_uprn("10000000") assert uprn_shielding == expected_output @pytest.mark.parametrize("stored_proc_return_value, expected_output", [(0, 0), (1, 1)]) def test_get_shielding_advice_by_postcode_should_return_correct_eligibility_value( stored_proc_return_value, expected_output): with patch("vulnerable_people_form.integrations.location_eligibility.execute_sql", return_value={"records": [[{"longValue": stored_proc_return_value}]]}): postcode_shielding = get_shielding_advice_by_postcode("BB1 1TA") assert postcode_shielding == expected_output

50.989362

103

0.709159

536

4,793

5.886194

0.147388

0.073217

0.060856

0.079873

0.846276

0.794612

0.767036

0.752773

0.729636

0.711886

0

0.008318

0.197371

4,793

93

104

51.537634

0.811801

0

0.507042

0

0.264135

0.14855

0

0.126761

1

0.126761

false

0

0.070423

0

0.197183

0

null

0

1

0

null

0

6

e961df5621ca66a4e84e3da9468fa49c1caee7eb

28

py

Python

examples/NIPS/UrbanSounds8K/__init__.py

dais-ita/DeepProbCEP

22790c1672c1cce49a59d18921c710f61cdde2f2

[ "MIT" ]

6

2020-09-10T03:40:53.000Z

2021-05-26T07:30:20.000Z

examples/NIPS/UrbanSounds8K/__init__.py

dais-ita/DeepProbCEP

22790c1672c1cce49a59d18921c710f61cdde2f2

[ "MIT" ]

null

examples/NIPS/UrbanSounds8K/__init__.py

dais-ita/DeepProbCEP

22790c1672c1cce49a59d18921c710f61cdde2f2

[ "MIT" ]

1

2020-11-23T15:55:57.000Z

from .sounds_utils import *

14

27

0.785714

4

28

5.25

1

0

0.142857

28

1

28

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e9c6131de9d484c93efccdc4e9a06f8812efa730

38,322

py

Python

CouncilTag/ingest/data.py

vinvasir/engage-backend

0050d0c70c1cc0127e5bdef506ccea5b47e2db39

[ "Apache-2.0" ]

null

CouncilTag/ingest/data.py

vinvasir/engage-backend

0050d0c70c1cc0127e5bdef506ccea5b47e2db39

[ "Apache-2.0" ]

null

CouncilTag/ingest/data.py

vinvasir/engage-backend

0050d0c70c1cc0127e5bdef506ccea5b47e2db39

[ "Apache-2.0" ]

null

import requests from bs4 import BeautifulSoup import unicodedata import datetime import pytz from calendar import timegm local_tz = pytz.timezone("America/Los_Angeles") city_council_agendas_url = "https://www.smgov.net/departments/clerk/agendas.aspx" list_of_sections = [u'SPECIAL AGENDA ITEMS', u'CONSENT CALENDAR', u'STUDY SESSION', u'CONTINUED ITEMS', u'ADMINISTRATIVE PROCEEDINGS', u'ORDINANCES', u'STAFF ADMINISTRATIVE ITEMS', u'PUBLIC HEARINGS'] def agenda_date_to_epoch(date_str, year): '''Transforms scraped date to epoch time''' naive_dt = datetime.datetime.strptime( str(year) + " " + date_str.string.strip(), '%Y %B %d %I:%M %p') local_dt = local_tz.localize(naive_dt, is_dst=None) utc_dt = local_dt.astimezone(pytz.utc) utc_timetuple = utc_dt.timetuple() return timegm(utc_timetuple) def parse_query_params(params): ''' Takes the split key value pairs which are made up of ["key=value", "key=value", "key="] value may be empty except for MeetingID and ID keys Returns a dictionary with two keys "MeetingID" and "ID" ''' query = dict() for param in params: split_param = param.split("=") query[split_param[0]] = split_param[1] return query def process_information_section(body): table_body = body.find('table') if table_body is not None: table_row = table_body.find('tr') if table_row is not None: td_children = table_row.find_all('td') department = td_children[0].get_text().replace('&', 'and') sponsors = td_children[1].get_text() if sponsors == '': sponsors = None return department, sponsors def process_actions_section(body): actions = [] paragraphs = body.find_all('p') list_actions = body.find('ol') if list_actions is not None: # preferred method next = list_actions.find('li') while next is not None: if next.name == 'ol': actions[-1] += unicodedata.normalize("NFKD", next.get_text()) else: actions.append(unicodedata.normalize("NFKD", next.get_text())) next = next.next_sibling else: paragraphs = paragraphs[1:] for paragraph in paragraphs: actions.append(unicodedata.normalize("NFKD", paragraph.get_text())) if len(actions) > 0 and actions[0] == 'Staff recommends that the City Council:': actions = actions[1:] return actions def process_agenda_item(session, prefix, href): agenda_item = dict() agenda_item_url = prefix + href query = href.split('?') query_params = query[1].split("&") if query[0] != 'Detail_LegiFile.aspx': return r = session.get(agenda_item_url) agenda_item_soup = BeautifulSoup(r.text, 'html.parser') params = parse_query_params(query_params) ID = params['ID'] MeetingID = params['MeetingID'] Title = agenda_item_soup.find( 'h1', {'id': 'ContentPlaceholder1_lblLegiFileTitle'}) if Title is None: print("TITLE NONE FOR: ", agenda_item_url) Title = "SOME TITLE" return None else: Title = Title.get_text().strip() bodies = agenda_item_soup.find_all( 'div', {'class': 'LegiFileSection'}) info = agenda_item_soup.find('div', {'class': 'LegiFileInfo'}) agenda_item['Title'] = Title agenda_item['ID'] = ID agenda_item['MeetingID'] = MeetingID if info is not None: info_body = info.find('div', {'class': 'LegiFileSectionContents'}) Department, Sponsors = process_information_section(info_body) agenda_item['Department'] = Department agenda_item['Sponsors'] = Sponsors recommendations_body = agenda_item_soup.find('div', {'id': 'divItemDiscussion'}) summary_body = agenda_item_soup.find('div', {'id': 'divBody'}) if recommendations_body is not None: agenda_item['Recommendations'] = process_actions_section(recommendations_body) else: agenda_item['Recommendations'] = [] agenda_item['Body'] = [] if summary_body is not None: Body = summary_body.find_all('p') for body_element in Body: text = unicodedata.normalize("NFKD", body_element.get_text()).strip() if text != '': agenda_item['Body'].append(text) return agenda_item def process_siblings(section_begin, section_end): next = section_begin as_for_section = [] while next != section_end: links = next.find_all('a') for a in links: a_parent_prev_sibs = a.find_parent().find_previous_siblings() if len(a_parent_prev_sibs) == 2: as_for_section.append(a.get('href')) next = next.find_next_sibling() return as_for_section def scrape_agenda(agenda, sess): soup_agenda = BeautifulSoup(agenda, 'html.parser') meeting = soup_agenda.find('table', {'id': 'MeetingDetail'}) sections = meeting.find_all('td', {'class': 'Title'}) main_sections = [] processed_sections = {} agenda_items = [] for section in sections: strong = section.find('strong') if strong is not None and strong.get_text() in list_of_sections: parent_tr = section.find_parent() main_sections.append(parent_tr) for i in range(len(main_sections) - 1): processed_sections[main_sections[i].get_text().split( ". ")[1]] = process_siblings(main_sections[i], main_sections[i + 1]) for key, values in processed_sections.items(): for value in values: if value is None: continue agenda_item = process_agenda_item( sess, 'http://santamonicacityca.iqm2.com/Citizens/', value) if agenda_item is not None: agenda_items.append(agenda_item) return agenda_items def get_data(year): with requests.Session() as sess: state_encoded = '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' payload = { "__EVENTTARGET": 'ctl00$ctl00$bodyContent$mainContent$ddlYears', "__EVENTARGUMENT": '', "__LASTFOCUS": '', "__VIEWSTATE": state_encoded, "__VIEWSTATEGENERATOR": '0072609D', "__EVENTVALIDATION": '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', "ctl00_ctl00_verticalLeftBar_mainMenu_ctl00_ClientState": "", "ctl00$ctl00$bodyContent$mainContent$ddlYears": year, "ctl00$ctl00$bodyContent$mainContent$CouncilSearch$txtSearch": "" } r = sess.post(city_council_agendas_url, data=payload) soup = BeautifulSoup(r.text, 'html.parser') agendas = dict() table = soup.find('table', {'class': 'agendaTable'}) rows = table.findAll('tr') for row in rows: cells = row.findChildren('td') try: date = agenda_date_to_epoch(cells[0], year) except: date = None if date and cells[1].string == "Agenda": agenda = sess.get(cells[1].findChildren( 'a', {'href': True})[0]['href']).text if "CONSENT CALENDAR" in agenda: agendas[date] = scrape_agenda(agenda, sess) return agendas

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{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/accounts/models/users.py

Casanova-Development/cookiecutter-backend-django

ba669853b37826c699aba50f0ea3b478c21164a3

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/accounts/models/users.py

Casanova-Development/cookiecutter-backend-django

ba669853b37826c699aba50f0ea3b478c21164a3

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/accounts/models/users.py

Casanova-Development/cookiecutter-backend-django

ba669853b37826c699aba50f0ea3b478c21164a3

[ "BSD-3-Clause" ]

null

"""Users models for application Accounts.""" from django.contrib.auth.models import AbstractUser class User(AbstractUser): """Define a custom default user model.""" pass

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759359d122edb92374eac02a353263c7e3152563

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Python

halotools/empirical_models/composite_models/sfr_models/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

83

2015-01-15T14:54:16.000Z

2021-12-09T11:28:02.000Z

halotools/empirical_models/composite_models/sfr_models/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

579

2015-01-14T15:57:37.000Z

2022-01-13T18:58:44.000Z

halotools/empirical_models/composite_models/sfr_models/__init__.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

70

2015-01-14T15:15:58.000Z

2021-12-22T18:18:31.000Z

from .smhm_binary_sfr import *

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75dbef1aba6b4cc33bb33e4df08bc12d7838195c

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py

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play-1.2.4/python/Lib/site-packages/readline.py

AppSecAI-TEST/restcommander

a2523f31356938f5c7fc6d379b7678da0b1e077a

[ "Apache-2.0" ]

550

2015-01-05T16:59:00.000Z

2022-03-20T16:55:25.000Z

framework/python/Lib/site-packages/readline.py

lafayette/JBTT

94bde9d90abbb274d29ecd82e632d43a4320876e

[ "MIT" ]

15

2015-02-05T06:00:47.000Z

2018-07-07T14:34:04.000Z

framework/python/Lib/site-packages/readline.py

lafayette/JBTT

94bde9d90abbb274d29ecd82e632d43a4320876e

[ "MIT" ]

119

2015-01-08T00:48:24.000Z

2022-01-27T14:13:15.000Z

# -*- coding: UTF-8 -*- #this file is needed in site-packages to emulate readline #necessary for rlcompleter since it relies on the existance #of a readline module from pyreadline import *

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2f041e9d8dee51da93474cdd949930d98bd58053

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Python

v1/backend/src/rankings/urls.py

avgupta456/statbotics

8847cec161104ec54f4c501653cd4ec558d30379

[ "MIT" ]

14

2020-05-28T21:54:45.000Z

2022-03-17T19:39:23.000Z

v1/backend/src/rankings/urls.py

avgupta456/statbotics

8847cec161104ec54f4c501653cd4ec558d30379

[ "MIT" ]

59

2020-05-28T21:39:45.000Z

2022-03-25T23:51:39.000Z

backend/src/rankings/urls.py

statbotics/statbotics

37bb0e3730d5b3aff7b6a5ba6e78ef2eada950bc

[ "MIT" ]

1

2020-07-04T07:30:40.000Z

# type: ignore from django.conf.urls import url from django.urls import include, path from drf_yasg import openapi from drf_yasg.views import SwaggerUIRenderer, get_schema_view from rest_framework import permissions, routers from src.rankings.views import ( event_pred_views, event_views, match_views, model_views, team_event_views, team_match_views, team_views, team_year_views, year_views, ) SwaggerUIRenderer.template = "drf-yasg.html" # monkey-patching is bad :( schema_view = get_schema_view( openapi.Info( title="Statbotics.io API", default_version="v1", ), public=True, permission_classes=(permissions.AllowAny,), ) router = routers.DefaultRouter() router.register(r"_years", model_views.YearView, "year") router.register(r"_teams", model_views.TeamView, "team") router.register(r"_team_years", model_views.TeamYearView, "team_year") router.register(r"_events", model_views.EventView, "event") router.register(r"_team_events", model_views.TeamEventView, "team_event") router.register(r"_matches", model_views.MatchView, "match") router.register(r"_team_matches", model_views.TeamMatchView, "team_match") # commented out url patterns still need models urlpatterns = [ url( r"^swagger/$", schema_view.with_ui("swagger", cache_timeout=0), name="schema-swagger-ui", ), ] """TEAMS""" urlpatterns.extend( [ path("v1/team/<num>", team_views.Team), path("v1/teams", team_views.Teams), path("v1/teams/", team_views.Teams), path("v1/teams/by/<metric>", team_views.TeamsByMetric), path("v1/teams/active", team_views.TeamsActive), path("v1/teams/active/by/<metric>", team_views.TeamsActiveByMetric), path("v1/teams/country/<country>", team_views._Teams), path("v1/teams/country/<country>/by/<metric>", team_views._Teams), path("v1/teams/country/<country>/active", team_views._TeamsActive), path("v1/teams/country/<country>/active/by/<metric>", team_views._TeamsActive), path("v1/teams/country/<country>/state/<state>", team_views._Teams), path("v1/teams/country/<country>/state/<state>/by/<metric>", team_views._Teams), path( "v1/teams/country/<country>/state/<state>/active", team_views._TeamsActive ), path( "v1/teams/country/<country>/state/<state>/active/by/<metric>", team_views._TeamsActive, ), path("v1/teams/district/<district>", team_views._Teams), path("v1/teams/district/<district>/by/<metric>", team_views._Teams), path("v1/teams/district/<district>/active", team_views._TeamsActive), path( "v1/teams/district/<district>/active/by/<metric>", team_views._TeamsActive ), ] ) """TEAM EVENTS""" urlpatterns.extend( [ path("v1/team_year/team/<num>/year/<year>", team_year_views.TeamYear), path("v1/team_years/team/<num>", team_year_views.TeamYearsNum), path( "v1/team_years/team/<num>/by/<metric>", team_year_views.TeamYearsNumByMetric, ), path("v1/team_years/year/<year>", team_year_views.TeamYearsYear), path( "v1/team_years/year/<year>/by/<metric>", team_year_views.TeamYearsYearByMetric, ), path("v1/team_years/year/<year>/country/<country>", team_year_views._TeamYears), path( "v1/team_years/year/<year>/country/<country>/by/<metric>", team_year_views._TeamYears, ), path( "v1/team_years/year/<year>/country/<country>/state/<state>", team_year_views._TeamYears, ), path( "v1/team_years/year/<year>/country/<country>/state/<state>/by/<metric>", team_year_views._TeamYears, ), path( "v1/team_years/year/<year>/district/<district>", team_year_views._TeamYears ), path( "v1/team_years/year/<year>/district/<district>/by/<metric>", team_year_views._TeamYears, ), path("v1/team_years/", team_year_views.TeamYears), path("v1/team_years/page/<page>", team_year_views._TeamYears), path("v1/team_years/by/<metric>", team_year_views.TeamYearsByMetric), path("v1/team_years/by/<metric>/page/<page>", team_year_views._TeamYears), path("v1/team_years/country/<country>", team_year_views._TeamYears), path("v1/team_years/country/<country>/page/<page>", team_year_views._TeamYears), path("v1/team_years/country/<country>/by/<metric>", team_year_views._TeamYears), path( "v1/team_years/country/<country>/by/<metric>/page/<page>", team_year_views._TeamYears, ), path( "v1/team_years/country/<country>/state/<state>", team_year_views._TeamYears ), path( "v1/team_years/country/<country>/state/<state>/by/<metric>", team_year_views._TeamYears, ), path("v1/team_years/district/<district>", team_year_views._TeamYears), path( "v1/team_years/district/<district>/by/<metric>", team_year_views._TeamYears ), ] ) """TEAM EVENTS""" urlpatterns.extend( [ path("v1/team_event/team/<num>/event/<event>", team_event_views.TeamEvent), path("v1/team_events/team/<num>", team_event_views.TeamEventsNum), path( "v1/team_events/team/<num>/by/<metric>", team_event_views.TeamEventsNumByMetric, ), path("v1/team_events/team/<num>/type/<type>", team_event_views._TeamEvents), path( "v1/team_events/team/<num>/type/<type>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/team/<num>/week/<week>", team_event_views._TeamEvents), path( "v1/team_events/team/<num>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/type/<type>/week/<week>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/type/<type>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/year/<year>", team_event_views.TeamEventsYear), path("v1/team_events/year/<year>/page/<page>", team_event_views._TeamEvents), path( "v1/team_events/year/<year>/by/<metric>", team_event_views.TeamEventsYearByMetric, ), path( "v1/team_events/year/<year>/by/<metric>/page/<page>", team_event_views._TeamEvents, ), path("v1/team_events/year/<year>/type/<type>", team_event_views._TeamEvents), path( "v1/team_events/year/<year>/type/<type>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/year/<year>/week/<week>", team_event_views._TeamEvents), path( "v1/team_events/year/<year>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>/page/<page>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>/by/<metric>/page/<page>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/year/<year>/type/<type>", team_event_views._TeamEvents), path( "v1/team_events/year/<year>/type/<type>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/year/<year>/week/<week>", team_event_views._TeamEvents), path( "v1/team_events/year/<year>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/week/<week>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/year/<year>/type/<type>/week/<week>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>", team_event_views.TeamEventsNumYear ), path( "v1/team_events/team/<num>/year/<year>/by/<metric>", team_event_views.TeamEventsNumYearByMetric, ), path( "v1/team_events/team/<num>/year/<year>/type/<type>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>/type/<type>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>/week/<week>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>/type/<type>/week/<week>", team_event_views._TeamEvents, ), path( "v1/team_events/team/<num>/year/<year>/type/<type>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/event/<event>", team_event_views.TeamEventsEvent), path( "v1/team_events/event/<event>/by/<metric>", team_event_views.TeamEventsEventByMetric, ), path("v1/team_events", team_event_views.TeamEvents), path("v1/team_events/page/<page>", team_event_views._TeamEvents), path("v1/team_events/country/<country>", team_event_views._TeamEvents), path( "v1/team_events/country/<country>/page/<page>", team_event_views._TeamEvents, ), path( "v1/team_events/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/district/<district>", team_event_views._TeamEvents), path( "v1/team_events/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/type/<type>", team_event_views._TeamEvents), path("v1/team_events/type/<type>/by/<metric>", team_event_views._TeamEvents), path( "v1/team_events/type/<type>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/week/<week>", team_event_views._TeamEvents), path("v1/team_events/week/<week>/by/<metric>", team_event_views._TeamEvents), path( "v1/team_events/week/<week>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/week/<week>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/week/<week>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/week/<week>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/week/<week>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/week/<week>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), path("v1/team_events/type/<type>/week/<week>", team_event_views._TeamEvents), path( "v1/team_events/type/<type>/week/<week>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/country/<country>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/country/<country>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/country/<country>/state/<state>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/country/<country>/state/<state>/by/<metric>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/district/<district>", team_event_views._TeamEvents, ), path( "v1/team_events/type/<type>/week/<week>/district/<district>/by/<metric>", team_event_views._TeamEvents, ), ] ) """TEAM MATCHES""" urlpatterns.extend( [ path("v1/team_match/team/<num>/match/<match>", team_match_views.TeamMatch), path("v1/team_matches", team_match_views.TeamMatches), path("v1/team_matches/elims", team_match_views._TeamMatchesElim), path("v1/team_matches/page/<page>", team_match_views._TeamMatches), path("v1/team_matches/page/<page>/elims", team_match_views._TeamMatchesElim), path("v1/team_matches/team/<num>", team_match_views.TeamMatchesTeam), path("v1/team_matches/team/<num>/elims", team_match_views._TeamMatchesElim), path("v1/team_matches/year/<year>", team_match_views.TeamMatchesYear), path("v1/team_matches/year/<year>/elims", team_match_views._TeamMatchesElim), path("v1/team_matches/year/<year>/page/<page>", team_match_views._TeamMatches), path( "v1/team_matches/year/<year>/page/<page>", team_match_views._TeamMatchesElim, ), path("v1/team_matches/event/<event>", team_match_views.TeamMatchesEvent), path("v1/team_matches/event/<event>/elims", team_match_views._TeamMatchesElim), path("v1/team_matches/match/<match>", team_match_views.TeamMatchesMatch), path( "v1/team_matches/team/<num>/year/<year>", team_match_views.TeamMatchesTeamYear, ), path( "v1/team_matches/team/<num>/year/<year>/elims", team_match_views._TeamMatchesElim, ), path( "v1/team_matches/team/<num>/event/<event>", team_match_views.TeamMatchesTeamEvent, ), path( "v1/team_matches/team/<num>/event/<event>/elims", team_match_views._TeamMatchesElim, ), ] ) """YEARS""" urlpatterns.extend( [ path("v1/year/<year>", year_views.Year), path("v1/years", year_views.Years), path("v1/years/by/<metric>", year_views.YearsByMetric), ] ) """EVENTS""" urlpatterns.extend( [ path("v1/event/<event>", event_views.Event), path("v1/events", event_views.Events), path("v1/events/by/<metric>", event_views.EventsByMetric), path("v1/events/country/<country>", event_views._Events), path("v1/events/country/<country>/by/<metric>", event_views._Events), path("v1/events/country/<country>/state/<state>", event_views._Events), path( "v1/events/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path("v1/events/district/<district>", event_views._Events), path("v1/events/district/<district>/by/<metric>", event_views._Events), path("v1/events/type/<type>", event_views._Events), path("v1/events/type/<type>/by/<metric>", event_views._Events), path("v1/events/type/<type>/country/<country>", event_views._Events), path( "v1/events/type/<type>/country/<country>/by/<metric>", event_views._Events ), path( "v1/events/type/<type>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/type/<type>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path("v1/events/type/<type>/district/<district>", event_views._Events), path( "v1/events/type/<type>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/week/<week>", event_views._Events), path("v1/events/week/<week>/by/<metric>", event_views._Events), path("v1/events/week/<week>/country/<country>", event_views._Events), path( "v1/events/week/<week>/country/<country>/by/<metric>", event_views._Events ), path( "v1/events/week/<week>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/week/<week>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path("v1/events/week/<week>/district/<district>", event_views._Events), path( "v1/events/week/<week>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/type/<type>/week/<week>", event_views._Events), path("v1/events/type/<type>/week/<week>/by/<metric>", event_views._Events), path( "v1/events/type/<type>/week/<week>/country/<country>", event_views._Events ), path( "v1/events/type/<type>/week/<week>/country/<country>/by/<metric>", event_views._Events, ), path( "v1/events/type/<type>/week/<week>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/type/<type>/week/<week>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path( "v1/events/type/<type>/week/<week>/district/<district>", event_views._Events, ), path( "v1/events/type/<type>/week/<week>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/year/<year>", event_views.EventsYear), path("v1/events/year/<year>/by/<metric>", event_views.EventsYearByMetric), path("v1/events/year/<year>/country/<country>", event_views._Events), path( "v1/events/year/<year>/country/<country>/by/<metric>", event_views._Events ), path( "v1/events/year/<year>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/year/<year>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path("v1/events/year/<year>/district/<district>", event_views._Events), path( "v1/events/year/<year>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/year/<year>/type/<type>", event_views._Events), path("v1/events/year/<year>/type/<type>/by/<metric>", event_views._Events), path( "v1/events/year/<year>/type/<type>/country/<country>", event_views._Events ), path( "v1/events/year/<year>/type/<type>/country/<country>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/district/<district>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/year/<year>/week/<week>", event_views._Events), path("v1/events/year/<year>/week/<week>/by/<metric>", event_views._Events), path( "v1/events/year/<year>/week/<week>/country/<country>", event_views._Events ), path( "v1/events/year/<year>/week/<week>/country/<country>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/week/<week>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/year/<year>/week/<week>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/week/<week>/district/<district>", event_views._Events, ), path( "v1/events/year/<year>/week/<week>/district/<district>/by/<metric>", event_views._Events, ), path("v1/events/year/<year>/type/<type>/week/<week>", event_views._Events), path( "v1/events/year/<year>/type/<type>/week/<week>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/country/<country>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/country/<country>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/country/<country>/state/<state>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/country/<country>/state/<state>/by/<metric>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/district/<district>", event_views._Events, ), path( "v1/events/year/<year>/type/<type>/week/<week>/district/<district>/by/<metric>", event_views._Events, ), ] ) """MATCHES""" urlpatterns.extend( [ path("v1/match/<match>", match_views.Match), path("v1/matches", match_views.Matches), path("v1/matches/page/<page>", match_views._Matches), path("v1/matches/elims", match_views._MatchesElim), path("v1/matches/elims/page/<page>", match_views._MatchesElim), path("v1/matches/year/<year>", match_views.MatchesYear), path("v1/matches/year/<year>/elims", match_views._MatchesElim), path("v1/matches/year/<year>/page/<page>", match_views._Matches), path("v1/matches/event/<event>", match_views.MatchesEvent), path("v1/matches/event/<event>/elims", match_views._MatchesElim), ] ) """EVENT SIM""" urlpatterns.extend( [ path("v1/event_sim/event/<event>/simple", event_pred_views.QuickSim), path("v1/event_sim/event/<event>/full", event_pred_views.Sim), path( "v1/event_sim/event/<event>/full/iterations/<iterations>", event_pred_views.Sim, ), path( "v1/event_sim/event/<event>/index/<index>/simple", event_pred_views.MeanSim ), path( "v1/event_sim/event/<event>/index/<index>/full", event_pred_views.IndexSim ), path( "v1/event_sim/event/<event>/index/<index>/full/iterations/<iterations>", event_pred_views.IndexSim, ), ] ) urlpatterns.append(path("v1/", include(router.urls)))

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null

0

6

f93c37f8dbb1e504769add17add9cf3550c2f7bc

8,632

py

Python

modules/obsolete_modules/modules_skip_connection.py

ravi-0841/spect-pitch-gan

ea4b9ea8396df753e25e0b2cb210288f683d3903

[ "MIT" ]

null

modules/obsolete_modules/modules_skip_connection.py

ravi-0841/spect-pitch-gan

ea4b9ea8396df753e25e0b2cb210288f683d3903

[ "MIT" ]

null

modules/obsolete_modules/modules_skip_connection.py

ravi-0841/spect-pitch-gan

ea4b9ea8396df753e25e0b2cb210288f683d3903

[ "MIT" ]

null

import tensorflow as tf from modules.base_modules_default_init import * def sampler(input_pitch, input_mfc, final_filters=1, reuse=False, \ scope_name='sampler_generator'): # Inputs have shape [batch_size, num_features, time] input_mfc_transposed = tf.transpose(input_mfc, perm=[0, 2, 1], name='sampler_input_mfc_transposed') input_pitch_transposed = tf.transpose(input_pitch, perm=[0, 2, 1], name='sampler_input_pitch_transposed') with tf.variable_scope(scope_name) as scope: # Discriminator would be reused in CycleGAN if reuse: scope.reuse_variables() else: assert scope.reuse is False h1_mfc = conv1d_layer(inputs=input_mfc_transposed, filters=64, kernel_size=15, strides=1, activation=None, name='h1_mfc_conv') h1_mfc_gates = conv1d_layer(inputs=input_mfc_transposed, filters=64, kernel_size=15, strides=1, activation=None, name='h1_mfc_conv_gates') h1_mfc_glu = gated_linear_layer(inputs=h1_mfc, gates=h1_mfc_gates, name='h1_mfc_glu') h1_pitch = conv1d_layer(inputs=input_pitch_transposed, filters=16, kernel_size=15, strides=1, activation=None, name='h1_pitch_conv') h1_pitch_gates = conv1d_layer(inputs=input_pitch_transposed, filters=16, kernel_size=15, strides=1, activation=None, name='h1_pitch_conv_gates') h1_pitch_glu = gated_linear_layer(inputs=h1_pitch, gates=h1_pitch_gates, name='h1_pitch_glu') h1_glu = tf.concat([h1_mfc_glu, h1_pitch_glu], axis=-1, name='concat_glu') # Downsample d1 = downsample1d_block(inputs=h1_glu, filters=128, \ kernel_size=5, strides=2, \ name_prefix='downsample1d_block1_') d2 = downsample1d_block(inputs=d1, filters=256, \ kernel_size=5, strides=2, \ name_prefix='downsample1d_block2_') # Residual blocks r1 = residual1d_block(inputs=d2, filters=512, \ kernel_size=3, strides=1, \ name_prefix='residual1d_block1_') r2 = residual1d_block(inputs=r1, filters=512, \ kernel_size=3, strides=1, \ name_prefix='residual1d_block2_') # Upsample u1 = upsample1d_block(inputs=r2, filters=512, \ kernel_size=5, strides=1, \ shuffle_size=2, name_prefix='upsample1d_block1_') u2 = upsample1d_block(inputs=u1, filters=256, \ kernel_size=5, strides=1, \ shuffle_size=2, name_prefix='upsample1d_block2_') # Dropout for stochasticity u2 = tf.nn.dropout(u2, keep_prob=0.5) # Output o1 = conv1d_layer(inputs=u2, filters=final_filters, \ kernel_size=15, strides=1, \ activation=None, name='o1_conv') o2 = tf.transpose(o1, perm=[0, 2, 1], name='output_transpose') return o2 def generator(input_pitch, input_mfc, final_filters=23, reuse=False, \ scope_name='generator'): # Inputs have shape [batch_size, num_features, time] input_mfc_transposed = tf.transpose(input_mfc, perm=[0, 2, 1], name='generator_input_mfc_transposed') input_pitch_transposed = tf.transpose(input_pitch, perm=[0, 2, 1], name='generator_input_pitch_transposed') with tf.variable_scope(scope_name) as scope: # Discriminator would be reused in CycleGAN if reuse: scope.reuse_variables() else: assert scope.reuse is False h1_mfc = conv1d_layer(inputs=input_mfc_transposed, filters=32, kernel_size=15, strides=1, activation=None, name='h1_mfc_conv') h1_mfc_gates = conv1d_layer(inputs=input_mfc_transposed, filters=32, kernel_size=15, strides=1, activation=None, name='h1_mfc_conv_gates') h1_mfc_glu = gated_linear_layer(inputs=h1_mfc, gates=h1_mfc_gates, name='h1_mfc_glu') h1_pitch = conv1d_layer(inputs=input_pitch_transposed, filters=32, kernel_size=15, strides=1, activation=None, name='h1_pitch_conv') h1_pitch_gates = conv1d_layer(inputs=input_pitch_transposed, filters=32, kernel_size=15, strides=1, activation=None, name='h1_pitch_conv_gates') h1_pitch_glu = gated_linear_layer(inputs=h1_pitch, gates=h1_pitch_gates, name='h1_pitch_glu') h1_glu = tf.concat([h1_mfc_glu, h1_pitch_glu], axis=-1, name='concat_glu') # Downsample d1 = downsample1d_block(inputs=h1_glu, filters=128, kernel_size=5, strides=2, name_prefix='downsample1d_block1_') d2 = downsample1d_block(inputs=d1, filters=256, kernel_size=5, strides=2, name_prefix='downsample1d_block2_') # Residual blocks r1 = residual1d_block(inputs=d2, filters=512, kernel_size=3, strides=1, name_prefix='residual1d_block1_') r2 = residual1d_block(inputs=r1, filters=512, kernel_size=3, strides=1, name_prefix='residual1d_block2_') r3 = residual1d_block(inputs=r2, filters=512, kernel_size=3, strides=1, name_prefix='residual1d_block3_') # Upsample u1 = upsample1d_block(inputs=r3, filters=256, kernel_size=5, strides=1, shuffle_size=2, name_prefix='upsample1d_block1_') u1 = tf.add(u1, d1, name='add_downsample_1') u2 = upsample1d_block(inputs=u1, filters=128, kernel_size=5, strides=1, shuffle_size=2, name_prefix='upsample1d_block2_') u2 = tf.add(u2, h1_glu, name='add_downsample_2') # Dropout for stochasticity u2 = tf.nn.dropout(u2, keep_prob=0.5) # Output o1 = conv1d_layer(inputs=u2, filters=final_filters, \ kernel_size=15, strides=1, \ activation=None, name='o1_conv') o2 = tf.transpose(o1, perm=[0, 2, 1], name='output_transpose') return o2 def discriminator(input_mfc, input_pitch, reuse=False, scope_name='discriminator'): # input_mfc and input_pitch has shape [batch_size, num_features, time] input_mfc = tf.transpose(input_mfc, perm=[0,2,1], name='discriminator_mfc_transpose') input_pitch = tf.transpose(input_pitch, perm=[0,2,1], name='discriminator_pitch_transpose') with tf.variable_scope(scope_name) as scope: # Discriminator would be reused in CycleGAN if reuse: scope.reuse_variables() else: assert scope.reuse is False h1_mfc = conv1d_layer(inputs=input_mfc, filters=64, kernel_size=3, strides=1, activation=None, name='h1_mfc_conv') h1_mfc_gates = conv1d_layer(inputs=input_mfc, filters=64, kernel_size=3, strides=1, activation=None, name='h1_mfc_conv_gates') h1_mfc_glu = gated_linear_layer(inputs=h1_mfc, gates=h1_mfc_gates, name='h1_mfc_glu') h1_pitch = conv1d_layer(inputs=input_pitch, filters=64, kernel_size=3, strides=1, activation=None, name='h1_pitch_conv') h1_pitch_gates = conv1d_layer(inputs=input_pitch, filters=64, kernel_size=3, strides=1, activation=None, name='h1_pitch_conv_gates') h1_pitch_glu = gated_linear_layer(inputs=h1_pitch, gates=h1_pitch_gates, name='h1_pitch_glu') h1_glu = tf.concat([h1_mfc_glu, h1_pitch_glu], axis=-1, name='concat_inputs') d1 = downsample1d_block(inputs=h1_glu, filters=128, kernel_size=3, strides=2, name_prefix='downsample2d_block1_') d2 = downsample1d_block(inputs=d1, filters=256, kernel_size=3, strides=2, name_prefix='downsample2d_block2_') d3 = downsample1d_block(inputs=d2, filters=256, kernel_size=3, strides=2, name_prefix='downsample2d_block3_') # Output o1 = tf.layers.dense(inputs=d3, units=1, \ activation=tf.nn.sigmoid) return o1

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0.062934

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0.899877

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0.298424

8,632

210

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0

null

0

1

0

null

0

6

f95982d3734838b526a7c2aba805915b20f51c98

11,782

py

Python

delay_gp_funcs.py

ciiram/PyPol_II

50cf1d9f7a33d26f9d09a0fb90bb9bf9d2eee60a

[ "BSD-3-Clause" ]

2

2017-09-29T07:27:20.000Z

2019-12-11T14:56:31.000Z

delay_gp_funcs.py

ciiram/PyPol_II

50cf1d9f7a33d26f9d09a0fb90bb9bf9d2eee60a

[ "BSD-3-Clause" ]

null

delay_gp_funcs.py

ciiram/PyPol_II

50cf1d9f7a33d26f9d09a0fb90bb9bf9d2eee60a

[ "BSD-3-Clause" ]

null

# This file contains a number of useful function definitions for implementing the # delay estimation using a Gaussian process framework without convolution # # Ciira wa Maina, 2014 # Dedan Kimathi University of Technology. # Nyeri-Kenya import pylab as pb import numpy as np import scipy as sp from scipy import integrate from scipy import special from scipy.optimize import fmin_tnc import scipy.linalg import sys def rbf2(t1,t2,sigma,l): ''' RBF Kernel ''' t3= t1[:,None]-t2[None,:] return sigma*sigma*np.exp(-(1/(2.0*l*l))*t3*t3) def genCov_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs): ''' Covariance of gene segments profiles and latent function Input: D: are the delays sigma_rbf,l_rbf are the latent function parameters l: variances of the convolution kernel noise_std: noise variances ''' K=np.zeros((len(t),len(t)))#assume len(t)= len(t1) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length for i in range(0,num_seg): for j in range(i,num_seg): if i==j: K[indx[i]:indx[i+1],indx[j]:indx[j+1]]=alpha[i]*alpha[i]*rbf2(t[indx[i]:indx[i+1]],t[indx[i]:indx[i+1]],sigma_rbf,l_rbf)+noise_std[i]*noise_std[i]*np.eye(len_seg_obs[i]) else: K[indx[i]:indx[i+1],indx[j]:indx[j+1]]=alpha[i]*alpha[j]*rbf2(t[indx[i]:indx[i+1]]-D[i],t[indx[j]:indx[j+1]]-D[j],sigma_rbf,l_rbf) if i!=j: K[indx[j]:indx[j+1],indx[i]:indx[i+1]]=K[indx[i]:indx[i+1],indx[j]:indx[j+1]].T return K def genCov_l_f_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs): ''' Gradient of Covariance w.r.t l_f Input: D: are the delays sigma_rbf,l_rbf are the latent function parameters l: variances of the convolution kernel noise_std: noise variances ''' K=np.zeros((len(t),len(t)))#assume len(t)= len(t1) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length for i in range(0,num_seg): for j in range(i,num_seg): t1=t[indx[i]:indx[i+1]] t2=t[indx[j]:indx[j+1]] T_d=(t1[:,None]-t2[None,:]) if i==j: K[indx[i]:indx[i+1],indx[j]:indx[j+1]]=alpha[i]*alpha[i]*rbf2(t[indx[i]:indx[i+1]],t[indx[i]:indx[i+1]],sigma_rbf,l_rbf)*T_d*T_d*(1/(l_rbf**3)) else: K[indx[i]:indx[i+1],indx[j]:indx[j+1]]=alpha[i]*alpha[j]*rbf2(t[indx[i]:indx[i+1]]-D[i],t[indx[j]:indx[j+1]]-D[j],sigma_rbf,l_rbf)*(T_d+D[j])*(T_d+D[j])*(1/(l_rbf**3)) if i!=j: K[indx[j]:indx[j+1],indx[i]:indx[i+1]]=K[indx[i]:indx[i+1],indx[j]:indx[j+1]].T return K def genCov_alpha_i_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,seg,len_seg_obs): ''' Gradient of Covariance w.r.t alpha_i Input: D: are the delays sigma_rbf,l_rbf are the latent function parameters l: variances of the convolution kernel noise_std: noise variances ''' K=np.zeros((len(t),len(t)))#assume len(t)= len(t1) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length for j in range(0,num_seg): if seg==j: K[indx[seg]:indx[seg+1],indx[j]:indx[j+1]]=alpha[seg]*rbf2(t[indx[seg]:indx[seg+1]]-D[seg],t[indx[seg]:indx[seg+1]]-D[seg],sigma_rbf,l_rbf) else: K[indx[seg]:indx[seg+1],indx[j]:indx[j+1]]=alpha[j]*rbf2(t[indx[seg]:indx[seg+1]]-D[seg],t[indx[j]:indx[j+1]]-D[j],sigma_rbf,l_rbf) return K+K.T def genCov_D_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,seg,len_seg_obs): ''' Gradient of Covariance w.r.t D_i Input: D: are the delays sigma_rbf,l_rbf are the latent function parameters l: variances of the convolution kernel noise_std: noise variances ''' K=np.zeros((len(t),len(t)))#assume len(t)= len(t1) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length for j in range(0,num_seg): t1=t[indx[seg]:indx[seg+1]] t2=t[indx[j]:indx[j+1]] T_d=(t1[:,None]-t2[None,:]) if seg==j: K[indx[seg]:indx[seg+1],indx[j]:indx[j+1]]=np.zeros((len(t1),len(t2))) else: K[indx[seg]:indx[seg+1],indx[j]:indx[j+1]]=alpha[seg]*alpha[j]*rbf2(t1-D[seg],t2-D[j],sigma_rbf,l_rbf)*(1.0/(l_rbf*l_rbf))*(T_d-D[seg]+D[j]) K[indx[j]:indx[j+1],indx[seg]:indx[seg+1]]=alpha[seg]*alpha[j]*rbf2(t2-D[j],t1-D[seg],sigma_rbf,l_rbf)*(1.0/(l_rbf*l_rbf))*(T_d.T-D[seg]+D[j]) return K def genCov_sigma_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs): ''' Gradient of Covariance w.r.t sigma_i Input: D: are the delays sigma_rbf,l_rbf are the latent function parameters l: variances of the convolution kernel noise_std: noise variances ''' K=np.zeros((len(t),len(t)))#assume len(t)= len(t1) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length for i in range(0,num_seg): K[indx[i]:indx[i+1],indx[i]:indx[i+1]]=2.0*noise_std*np.eye(len_seg_obs[i]) return K def loglik_tied_fsf_delay(params,t,Y,num_seg,trans,a,b,diag,len_seg_obs): if trans==1: params=paramInvTrans(params,a,b) #unpack parameters sigma_rbf=1.0 l_rbf=params[0] ind=1 alpha=params[ind:ind+num_seg] #initilize Delay ind=ind+num_seg D=np.zeros(num_seg) D[1:num_seg]=params[ind:ind+num_seg-1] D[0]=0.0 #initilize noise ind=ind+num_seg-1 noise_std1=params[ind:len(params)] noise_std=np.ones(num_seg)*noise_std1 Cov=genCov_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs) if diag: Cov=blk_diag(Cov,num_seg,len(t))#need to change try: L=np.linalg.cholesky(Cov) except np.linalg.LinAlgError: return -np.inf alpha=sp.linalg.cho_solve((L,1),Y) ll=-0.5*np.dot(Y[None,:],alpha[:,None])[0,0]-np.sum(np.log(np.diag(L)))-0.5*Y.size*np.log(2*np.pi) return -ll def grad_loglik_tied_fsf_delay(params,t,Y,num_seg,trans,a,b,diag,len_seg_obs): grad=np.zeros(len(params)) if trans==1: params=paramInvTrans(params,a,b) #unpack parameters sigma_rbf=1.0 l_rbf=params[0] ind=1 alpha=params[ind:ind+num_seg] #initilize Delay ind=ind+num_seg D=np.zeros(num_seg) D[1:num_seg]=params[ind:ind+num_seg-1] D[0]=0.0 ind=ind+num_seg-1 noise_std1=params[ind:len(params)] noise_std=np.ones(num_seg)*noise_std1 Cov=genCov_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs) if diag: Cov=blk_diag(Cov,num_seg,len(t)) try: L=np.linalg.cholesky(Cov) #invCov=np.linalg.inv(Cov) except np.linalg.LinAlgError: return -np.inf alpha_cho=sp.linalg.cho_solve((L,1),Y)[:,None] #latent function parameters gK=genCov_l_f_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs) if diag: gK=blk_diag(gK,num_seg,len(t)) #grad[0]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(np.dot(invCov,gK)) grad[0]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(sp.linalg.cho_solve((L,1),gK)) #alpha ind=1 j=0 for i in range(ind,ind+num_seg): gK=genCov_alpha_i_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,j,len_seg_obs) if diag: gK=blk_diag(gK,num_seg,len(t)) #grad[i]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(np.dot(invCov,gK)) grad[i]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(sp.linalg.cho_solve((L,1),gK)) j+=1 #Delay ind=ind+num_seg j=1 for i in range(ind,ind+num_seg-1): gK=genCov_D_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,j,len_seg_obs) if diag: gK=blk_diag(gK,num_seg,len(t)) #grad[i]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(np.dot(invCov,gK)) grad[i]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(sp.linalg.cho_solve((L,1),gK)) j+=1 # noise ind=ind+num_seg-1 gK=genCov_sigma_delay(t,alpha,D,sigma_rbf,l_rbf,noise_std1,num_seg,len_seg_obs) if diag: gK=blk_diag(gK,num_seg,len(t)) #grad[ind]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(np.dot(invCov,gK)) grad[ind]=-0.5*np.dot(alpha_cho.T,np.dot(gK,alpha_cho))[0,0]+0.5*np.trace(sp.linalg.cho_solve((L,1),gK)) if trans==1: return grad*gradTrans(paramTrans(params,a,b),a,b) elif trans==0: return grad def pred_Cov_tied_fsf_delay(t_obs,t_pred,Y,params,num_seg,seg,trans,a,b,len_seg_obs): if trans==1: params=paramInvTrans(params,a,b) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length #unpack parameters sigma_rbf=1.0 l_rbf=params[0] ind=1 alpha=params[ind:ind+num_seg] #initilize Delay ind=ind+num_seg D=np.zeros(num_seg) D[1:num_seg]=params[ind:ind+num_seg-1] D[0]=0.0 ind=ind+num_seg-1 noise_std1=params[ind:len(params)] noise_std=np.ones(num_seg)*noise_std1 B=genCov_delay(t_obs,alpha,D,sigma_rbf,l_rbf,noise_std,num_seg,len_seg_obs) A=alpha[seg]*alpha[seg]*rbf2(t_pred,t_pred,sigma_rbf,l_rbf)+noise_std1**2*np.eye(t_pred.size) C=np.zeros((t_pred.size,t_obs.size)) for i in range(0,num_seg): t1=t_obs[indx[i]:indx[i+1]] CC=np.zeros((t_pred.size,t1.size)) for j in range(0,t_pred.size): for k in range(0,t1.size): if t_pred[j]==t1[k]: CC[j,k]=1.0 if i==seg: C[0:len(t_pred),indx[i]:indx[i+1]]=alpha[i]*alpha[seg]*rbf2(t_pred-D[seg],t1-D[i],sigma_rbf,l_rbf)+noise_std[seg]*noise_std[seg]*CC else: C[0:len(t_pred),indx[i]:indx[i+1]]=alpha[i]*alpha[seg]*rbf2(t_pred-D[seg],t1-D[i],sigma_rbf,l_rbf) mu=np.dot(C,np.dot(np.linalg.inv(B),Y[:,None])) Cov=A-np.dot(C,np.dot(np.linalg.inv(B),C.T))+1e-8*np.eye(t_pred.size) return {'Cov':Cov,'mu':mu} def pred_Lat_tied_fsf(t_obs,t_pred,Y,params,num_seg,trans,a,b): if trans==1: params=paramInvTrans(params,a,b) #unpack parameters sigma_rbf=1.0 l_rbf=params[0] ind=1 alpha=params[ind:ind+num_seg] #initilize Delay ind=ind+num_seg D=np.zeros(num_seg) D[1:num_seg]=params[ind:ind+num_seg-1] D[0]=0.0 ind=ind+num_seg-1 #initilize l l=params[ind:ind+num_seg] #initilize noise ind=ind+num_seg noise_std1=params[ind:len(params)] noise_std=np.ones(num_seg)*noise_std1 B=genCov(t_obs,alpha,D,sigma_rbf,l_rbf,l,noise_std,num_seg) A=rbf2(t_pred,t_pred,sigma_rbf,l_rbf) C=np.zeros((t_pred.size,t_obs.size*num_seg)) CC=np.zeros((t_pred.size,t_obs.size)) for i in range(0,t_pred.size): for j in range(0,t_obs.size): if t_pred[i]==t_obs[j]: CC[i,j]=1.0 for i in range(0,num_seg): C[0:len(t_pred),i*len(t_obs):(i+1)*len(t_obs)]=alpha[i]*cov_yiLat(t_pred,t_obs,D[i],sigma_rbf,l_rbf,l[i]) mu=np.dot(C,np.dot(np.linalg.inv(B),Y[:,None])) Cov=A-np.dot(C,np.dot(np.linalg.inv(B),C.T))+1e-8*np.eye(t_pred.size) return {'Cov':Cov,'mu':mu} def pred_Lat_tied_fsf_new(t_obs,t_pred,Y,params,num_seg,trans,a,b,len_seg_obs): if trans==1: params=paramInvTrans(params,a,b) indx=np.concatenate((np.array([0]),np.cumsum(len_seg_obs)))#allow each segment to have different length #unpack parameters sigma_rbf=1.0 l_rbf=params[0] ind=1 alpha=params[ind:ind+num_seg] #initilize Delay ind=ind+num_seg D=np.zeros(num_seg) D[1:num_seg]=params[ind:ind+num_seg-1] D[0]=0.0 ind=ind+num_seg-1 #initilize l l=params[ind:ind+num_seg] #initilize noise ind=ind+num_seg noise_std1=params[ind:len(params)] noise_std=np.ones(num_seg)*noise_std1 B=genCov_new(t_obs,alpha,D,sigma_rbf,l_rbf,l,noise_std,num_seg,len_seg_obs) A=rbf2(t_pred,t_pred,sigma_rbf,l_rbf) C=np.zeros((t_pred.size,t_obs.size)) for i in range(0,num_seg): t1=t_obs[indx[i]:indx[i+1]] C[0:len(t_pred),indx[i]:indx[i+1]]=alpha[i]*cov_yiLat(t_pred,t1,D[i],sigma_rbf,l_rbf,l[i]) mu=np.dot(C,np.dot(np.linalg.inv(B),Y[:,None])) Cov=A-np.dot(C,np.dot(np.linalg.inv(B),C.T))+1e-8*np.eye(t_pred.size) return {'Cov':Cov,'mu':mu} def paramTrans(x,a,b): return np.log((x-a)/(b-x)) def paramInvTrans(x,a,b): return a+((b-a)/(1+np.exp(-x))) def gradTrans(x,a,b): return (((b-a)*np.exp(x))/np.square(1+np.exp(x)))

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Python

AMAO/apps/Avaliacao/Questao/forms/__init__.py

arruda/amao

83648aa2c408b1450d721b3072dc9db4b53edbb8

[ "MIT" ]

2

2017-04-26T14:08:02.000Z

2017-09-01T13:10:17.000Z

AMAO/apps/Avaliacao/Questao/forms/__init__.py

arruda/amao

83648aa2c408b1450d721b3072dc9db4b53edbb8

[ "MIT" ]

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AMAO/apps/Avaliacao/Questao/forms/__init__.py

arruda/amao

83648aa2c408b1450d721b3072dc9db4b53edbb8

[ "MIT" ]

null

from consulta import * from resolucao import * from criar import * from listar import *

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Python

app/auth/__init__.py

SuYehTarn/CS651-Group8-Feedback_Forum

d1163442aea81214c4dfa8de1d353ec719bfa7ab

[ "MIT" ]

null

app/auth/__init__.py

SuYehTarn/CS651-Group8-Feedback_Forum

d1163442aea81214c4dfa8de1d353ec719bfa7ab

[ "MIT" ]

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app/auth/__init__.py

SuYehTarn/CS651-Group8-Feedback_Forum

d1163442aea81214c4dfa8de1d353ec719bfa7ab

[ "MIT" ]

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"""Module of the Auth blueprint """ from flask import Blueprint auth = Blueprint('auth', __name__) from app.auth import views

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py

Python

src/pyV3D/__init__.py

OpenMDAO/pyV3D

2baf32c489e2c91531b89e51a879ba8074ae2803

[ "Apache-2.0" ]

3

2015-05-13T23:43:56.000Z

2021-01-20T10:15:17.000Z

src/pyV3D/__init__.py

OpenMDAO/pyV3D

2baf32c489e2c91531b89e51a879ba8074ae2803

[ "Apache-2.0" ]

3

2016-10-07T08:28:20.000Z

2016-10-07T10:25:34.000Z

src/pyV3D/__init__.py

OpenMDAO/pyV3D

2baf32c489e2c91531b89e51a879ba8074ae2803

[ "Apache-2.0" ]

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2017-07-16T03:57:36.000Z

2019-10-01T23:57:45.000Z

from _pyV3D import * import handler

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