xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

3374634092151a162ca5f82b9c757fbceca8074e

966

py

Python

qq/util.py

oeg-upm/tada-qq

ad8441093e21f4d598893cc2a263b128a7782e29

[ "Apache-2.0" ]

2

2022-01-26T08:48:34.000Z

2022-01-26T08:48:37.000Z

qq/util.py

oeg-upm/tada-qq

ad8441093e21f4d598893cc2a263b128a7782e29

[ "Apache-2.0" ]

null

qq/util.py

oeg-upm/tada-qq

ad8441093e21f4d598893cc2a263b128a7782e29

[ "Apache-2.0" ]

null

def errors_mean(y_pred, y_real): """ :param y_pred: list of predicted :param y_real: list of real :return: """ if len(y_pred) != len(y_real): print("Error, unmatched number of ys") return None tot_err = 0.0 for i in range(len(y_pred)): tot_err += abs(y_pred[i]-y_real[i]) mean_tot_err = tot_err/len(y_pred) # print("total error: "+str(tot_err)) # print("mean error: "+str(mean_tot_err)) return mean_tot_err def errors_sq_mean(y_pred, y_real): """ :param y_pred: list of predicted :param y_real: list of real :return: """ if len(y_pred) != len(y_real): print("Error, unmatched number of ys") return None tot_err = 0.0 for i in range(len(y_pred)): tot_err += (y_pred[i]-y_real[i]) ** 2 mean_tot_err = tot_err/len(y_pred) # print("total error: "+str(tot_err)) # print("mean error: "+str(mean_tot_err)) return mean_tot_err

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7

688b92d43b85b20d610155bffb1e7ed6ee5fb722

140

py

Python

python/testData/inspections/AddCallSuperRepeatedOptionalParamsPassedToSuperConstructor_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/AddCallSuperRepeatedOptionalParamsPassedToSuperConstructor_after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/AddCallSuperRepeatedOptionalParamsPassedToSuperConstructor_after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class A: def __init__(self, a, b=2, c=3): self.a = a class B(A): def __init__(self, a, c): A.__init__(self, a, c)

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0.338983

0.457627

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0.021053

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0

1

0

7

cc314f24ca7786bde21cb45c84f3c4cd940469a6

935

py

Python

torchsparse/nn/functional/pooling.py

f-sky/torchsparse

65466a10c6fa54bff17c6429706b7019a2a59409

[ "MIT" ]

1

2021-03-16T02:47:56.000Z

torchsparse/nn/functional/pooling.py

f-sky/torchsparse

65466a10c6fa54bff17c6429706b7019a2a59409

[ "MIT" ]

null

torchsparse/nn/functional/pooling.py

f-sky/torchsparse

65466a10c6fa54bff17c6429706b7019a2a59409

[ "MIT" ]

null

import torch __all__ = ['global_avg_pool', 'global_max_pool'] def global_avg_pool(inputs): batch_index = inputs.C[:, -1] max_index = torch.max(batch_index).item() outputs = [] for i in range(max_index + 1): cur_inputs = torch.index_select(inputs.F, 0, torch.where(batch_index == i)[0]) cur_outputs = cur_inputs.mean(0).unsqueeze(0) outputs.append(cur_outputs) outputs = torch.cat(outputs, 0) return outputs def global_max_pool(inputs): batch_index = inputs.C[:, -1] max_index = torch.max(batch_index).item() outputs = [] for i in range(max_index + 1): cur_inputs = torch.index_select(inputs.F, 0, torch.where(batch_index == i)[0]) cur_outputs = cur_inputs.max(0)[0].unsqueeze(0) outputs.append(cur_outputs) outputs = torch.cat(outputs, 0) return outputs

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null

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7

04539ca74be12ef99bae96a07df51be6d2ca1d74

84

py

Python

app/datasets/__init__.py

ahmedassal/GAN_SASS_TF

5fda7e8ecf5c8ccc15eef47151bd2891d91737b4

[ "MIT" ]

3

2021-03-21T06:59:44.000Z

2022-03-13T12:26:04.000Z

app/datasets/__init__.py

ahmedassal/GAN_SASS_TF

5fda7e8ecf5c8ccc15eef47151bd2891d91737b4

[ "MIT" ]

null

app/datasets/__init__.py

ahmedassal/GAN_SASS_TF

5fda7e8ecf5c8ccc15eef47151bd2891d91737b4

[ "MIT" ]

null

import app.hparams as hparams import app.datasets.dataset import app.datasets.timit

21

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0.845238

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0.478873

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0.095238

84

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1

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1

0

7

f09d671aca8f25520f94a1429ea94c00340d4dba

31

py

Python

mlx90614/__init__.py

sbbean/mlx90614

c700518023c62af66e0abe4a2ee0bfda94ef45d9

[ "MIT" ]

9

2020-10-10T03:39:29.000Z

2021-12-09T08:54:56.000Z

mlx90614/__init__.py

sbbean/mlx90614

c700518023c62af66e0abe4a2ee0bfda94ef45d9

[ "MIT" ]

3

2020-10-07T09:37:17.000Z

2022-02-06T02:36:36.000Z

mlx90614/__init__.py

sbbean/mlx90614

c700518023c62af66e0abe4a2ee0bfda94ef45d9

[ "MIT" ]

4

2020-09-08T17:02:14.000Z

2021-04-27T09:59:37.000Z

from .mlx90614 import MLX90614

15.5

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0.83871

4

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6.5

0.75

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0.129032

31

1

31

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1

0

1

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7

f0a83f96a5084d479b7f8d583ddc2e985b047671

14,460

py

Python

tests/test_sorting.py

No9005/tcheasy

35dbf27ef00b4a6e17cd085eeda0868c83d354ef

[ "MIT" ]

null

tests/test_sorting.py

No9005/tcheasy

35dbf27ef00b4a6e17cd085eeda0868c83d354ef

[ "MIT" ]

null

tests/test_sorting.py

No9005/tcheasy

35dbf27ef00b4a6e17cd085eeda0868c83d354ef

[ "MIT" ]

null

""" Unittests for the parameter sorting function """ # imports import unittest from tcheasy.sort_parameters import sort_parameters # create test class class TestSortParameters(unittest.TestCase): """ methods: -------- setUp Setup method tearDown Teardown method test_only_positional Tests sorting of positionals test_only_args Tests sorting for *args test_mixed Tests sorting of mixed types """ #region 'setup & teardown' ------------------- def setUp(self) -> None: """setsUp the test class """ return super().setUp() def tearDown(self) -> None: return super().tearDown() #endregion #region 'tests' ------------------------------ def test_only_positional(self): """ Checks a function with only positional parameters. """ #region 'no defaults, no hints' """ should return only elements for positional """ # build function def example(a, b, c): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1,2,3), { 'positional':{'a':1, 'b':2, 'c':3}, 'args':[], 'kwargs':{}, 'hinting':{}, 'declared':["a","b","c"], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (use them during call), no hints' """ should return only elements for positional """ # build function def example(a, b, c=10): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1, b=2), { 'positional':{'a':1, 'b':2, 'c':10}, 'args':[], 'kwargs':{}, 'hinting':{}, 'declared':["a","b","c"], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (do not use them), no hints' """ should return only elements for positional """ # build function def example(a, b, c=10): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1, b=2, c=50), { 'positional':{'a':1, 'b':2, 'c':50}, 'args':[], 'kwargs':{}, 'hinting':{}, 'declared':["a","b","c"], 'self':{'available':False, 'value':None} }) #endregion #region 'no defaults, some hints' """ should return only elements for positional 'None' should change to the 'any'. """ # build function def example(a, b:str, c:int): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1, 2, c=3), { 'positional':{'a':1, 'b':2, 'c':3}, 'args':[], 'kwargs':{}, 'hinting':{'a':(type(None), int, float, complex, bool, str, list, tuple, dict, set, object), 'b':str, 'c':int}, 'declared':["a","b","c"], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (use them), some hints' """ should return only elements for positional 'None' should change to the 'any'. """ # build function def example(a, b:str, c:int = 1): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1, "apple")['positional'], {'a':1, 'b':"apple", 'c':1}) #endregion #region 'some defaults (do not use them), some hints' """ should return only elements for positional 'None' should change to the 'any'. """ # build function def example(a, b:str, c:int = 1): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(1, b="apple", c=15)['positional'], {'a':1, 'b':"apple", 'c':15}) #endregion #region 'for class method' # build class class TestClass: def test_method(self:int, a, b:int, c:bool = True) -> dict: # get locals loc = locals() # run sorting result = sort_parameters(self.test_method, loc, False) return result # create class case case = TestClass() # run function result = case.test_method("123", 123, False) self.assertEqual(result['positional'], {'a':"123", 'b':123, 'c':False}) self.assertEqual(result['self']['available'], True) #endregion def test_only_args(self): """ Checks a function with only *args. """ #region 'without hints' # build function def example(*args): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100,200), { 'positional':{}, 'args':(100, 200), 'kwargs':{}, 'hinting':{}, 'declared':['args'], 'self':{'available':False, 'value':None} }) #endregion #region 'with hints' """ should not add elements to hinted. """ # build function def example(*args:int): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100,200), { 'positional':{}, 'args':(100, 200), 'kwargs':{}, 'hinting':{}, 'declared':['args'], 'self':{'available':False, 'value':None} }) #endregion #region 'test class method' # build class class TestClass: def __init__(self): self.attribute = "attribute" def test_method(self, *args) -> dict: # get locals loc = locals() # run sorting result = sort_parameters(self.test_method, loc, False) return result # create class case case = TestClass() # run function result = case.test_method("123", 123, False) self.assertEqual(result['args'], ("123", 123, False)) self.assertEqual(result['self']['available'], True) #endregion def test_only_kwargs(self): """Tests a function with only **kwargs """ #region 'without hints' # build function def example(**kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(c=100,pp="something", theta=.1), { 'positional':{}, 'args':[], 'kwargs':{'c':100,'pp':"something", 'theta':.1}, 'hinting':{}, 'declared':['kwargs'], 'self':{'available':False, 'value':None} }) #endregion #region 'with hints' """ should not add elements to hinted. """ # build function def example(**kwargs:int): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(a=100,z=200), { 'positional':{}, 'args':[], 'kwargs':{'a':100, 'z':200}, 'hinting':{}, 'declared':['kwargs'], 'self':{'available':False, 'value':None} }) #endregion #region 'test class method' # build class class TestClass: def test_method(self, **kwargs) -> dict: # get locals loc = locals() # run sorting result = sort_parameters(self.test_method, loc, False) return result # create class case case = TestClass() # run function result = case.test_method(z="123", p=123, q=False, y={}) self.assertEqual(result['kwargs'], {'z':"123", 'p':123, 'q':False, 'y':{}}) self.assertTrue(result['self']['available']) #endregion def test_mixed(self): """Tests a mixed function declaration """ #region 'without defaults, without hints' # build function def example(a, b, c, *args, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100,200,300,400,k="something"), { 'positional':{'a':100, 'b':200, 'c':300}, 'args':(400,), 'kwargs':{'k':"something"}, 'hinting':{}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (not using it), without hints' # build function def example(a, b="something", c="again", *args, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100,200,300,400,k="something"), { 'positional':{'a':100, 'b':200, 'c':300}, 'args':(400,), 'kwargs':{'k':"something"}, 'hinting':{}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (using it), without hints, mixed order' # build function def example(a, b="something", c="again", *args, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100, p=13, c=15, k="something"), { 'positional':{'a':100, 'b':"something", 'c':15}, 'args':(), 'kwargs':{'k':"something", 'p':13}, 'hinting':{}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (using it), without hints, normal order' # build function def example(a, b="something", c="again", *args, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(a=100, p=13, k="something"), { 'positional':{'a':100, 'b':"something", 'c':'again'}, 'args':(), 'kwargs':{'k':"something", 'p':13}, 'hinting':{}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'some defaults (not using it), without hints, mixed order' # build function def example(a, b="something", c="again", *args, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(a=100, p=15, c="yes", b="no", k="something"), { 'positional':{'a':100, 'b':"no", 'c':'yes'}, 'args':(), 'kwargs':{'k':"something", 'p':15}, 'hinting':{}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'no defaults, with hints' # build function def example(a:int, b:None, c:dict, *args:float, **kwargs): # get locals loc = locals() # run sorting result = sort_parameters(example, loc, False) return result # call it self.assertEqual(example(100,200,400,500,500,500,Z="none"), { 'positional':{'a':100, 'b':200, 'c':400}, 'args':(500,500,500,), 'kwargs':{'Z':"none"}, 'hinting':{'a':int, 'b':None, 'c':dict}, 'declared':['a', 'b', 'c', 'kwargs', 'args'], 'self':{'available':False, 'value':None} }) #endregion #region 'test class method' # build class class TestClass: def test_method(self, a:int, *args:float, **kwargs) -> dict: # get locals loc = locals() # run sorting result = sort_parameters(self.test_method, loc, False) return result # create class case case = TestClass() # run function result = case.test_method(5, True, z="123", p=123, q=False, y={}) self.assertEqual(result['kwargs'], {'z':"123", 'p':123, 'q':False, 'y':{}}) self.assertEqual(result['args'], (True,)) self.assertEqual(result['positional'], {'a':5}) self.assertEqual(result['declared'], ["a", "kwargs", "args"]) self.assertTrue(result['self']['available']) #endregion

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f0b62e7952fd955cca10bc9a7ef72fa22f33f246

97

py

Python

purchasing/opportunities/__init__.py

hamhands/pittsburgh-purchasing-suite

a79aa77c00c95da8f0b3e2f5f7f7143d5857de35

[ "BSD-3-Clause" ]

22

2015-05-08T15:30:42.000Z

2021-04-24T20:26:32.000Z

purchasing/opportunities/__init__.py

hamhands/pittsburgh-purchasing-suite

a79aa77c00c95da8f0b3e2f5f7f7143d5857de35

[ "BSD-3-Clause" ]

516

2015-04-23T18:14:40.000Z

2017-11-08T19:27:41.000Z

purchasing/opportunities/__init__.py

CityofPittsburgh/pittsburgh-purchasing-suite

d676ed9c137e5aaa100992a798acd60ac464a2c1

[ "BSD-3-Clause" ]

10

2015-07-08T19:00:10.000Z

2021-03-15T18:56:54.000Z

# -*- coding: utf-8 -*- from .admin import blueprint as abp from .front import blueprint as fbp

19.4

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8

f0e89aa011733e051b2538631b551027de312528

11,202

py

Python

tests/test_mp7.py

iscc/iscc-sdk

329bf7611979cbb064d95902f5b29fb9b8dfc15b

[ "Apache-2.0" ]

1

2022-03-21T19:34:10.000Z

tests/test_mp7.py

iscc/iscc-sdk

329bf7611979cbb064d95902f5b29fb9b8dfc15b

[ "Apache-2.0" ]

23

2022-01-24T16:05:33.000Z

2022-03-29T10:48:04.000Z

tests/test_mp7.py

iscc/iscc-sdk

329bf7611979cbb064d95902f5b29fb9b8dfc15b

[ "Apache-2.0" ]

null

from fractions import Fraction from bitarray import bitarray from iscc_sdk import mp7 import iscc_sdk as idk def test_calc_byte_to_bit3(): assert mp7.calc_byte_to_bit3().tolist() == [ [0, 0, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 2], [0, 0, 0, 1, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 2], [0, 0, 0, 2, 0], [0, 0, 0, 2, 1], [0, 0, 0, 2, 2], [0, 0, 1, 0, 0], [0, 0, 1, 0, 1], [0, 0, 1, 0, 2], [0, 0, 1, 1, 0], [0, 0, 1, 1, 1], [0, 0, 1, 1, 2], [0, 0, 1, 2, 0], [0, 0, 1, 2, 1], [0, 0, 1, 2, 2], [0, 0, 2, 0, 0], [0, 0, 2, 0, 1], [0, 0, 2, 0, 2], [0, 0, 2, 1, 0], [0, 0, 2, 1, 1], [0, 0, 2, 1, 2], [0, 0, 2, 2, 0], [0, 0, 2, 2, 1], [0, 0, 2, 2, 2], [0, 1, 0, 0, 0], [0, 1, 0, 0, 1], [0, 1, 0, 0, 2], [0, 1, 0, 1, 0], [0, 1, 0, 1, 1], [0, 1, 0, 1, 2], [0, 1, 0, 2, 0], [0, 1, 0, 2, 1], [0, 1, 0, 2, 2], [0, 1, 1, 0, 0], [0, 1, 1, 0, 1], [0, 1, 1, 0, 2], [0, 1, 1, 1, 0], [0, 1, 1, 1, 1], [0, 1, 1, 1, 2], [0, 1, 1, 2, 0], [0, 1, 1, 2, 1], [0, 1, 1, 2, 2], [0, 1, 2, 0, 0], [0, 1, 2, 0, 1], [0, 1, 2, 0, 2], [0, 1, 2, 1, 0], [0, 1, 2, 1, 1], [0, 1, 2, 1, 2], [0, 1, 2, 2, 0], [0, 1, 2, 2, 1], [0, 1, 2, 2, 2], [0, 2, 0, 0, 0], [0, 2, 0, 0, 1], [0, 2, 0, 0, 2], [0, 2, 0, 1, 0], [0, 2, 0, 1, 1], [0, 2, 0, 1, 2], [0, 2, 0, 2, 0], [0, 2, 0, 2, 1], [0, 2, 0, 2, 2], [0, 2, 1, 0, 0], [0, 2, 1, 0, 1], [0, 2, 1, 0, 2], [0, 2, 1, 1, 0], [0, 2, 1, 1, 1], [0, 2, 1, 1, 2], [0, 2, 1, 2, 0], [0, 2, 1, 2, 1], [0, 2, 1, 2, 2], [0, 2, 2, 0, 0], [0, 2, 2, 0, 1], [0, 2, 2, 0, 2], [0, 2, 2, 1, 0], [0, 2, 2, 1, 1], [0, 2, 2, 1, 2], [0, 2, 2, 2, 0], [0, 2, 2, 2, 1], [0, 2, 2, 2, 2], [1, 0, 0, 0, 0], [1, 0, 0, 0, 1], [1, 0, 0, 0, 2], [1, 0, 0, 1, 0], [1, 0, 0, 1, 1], [1, 0, 0, 1, 2], [1, 0, 0, 2, 0], [1, 0, 0, 2, 1], [1, 0, 0, 2, 2], [1, 0, 1, 0, 0], [1, 0, 1, 0, 1], [1, 0, 1, 0, 2], [1, 0, 1, 1, 0], [1, 0, 1, 1, 1], [1, 0, 1, 1, 2], [1, 0, 1, 2, 0], [1, 0, 1, 2, 1], [1, 0, 1, 2, 2], [1, 0, 2, 0, 0], [1, 0, 2, 0, 1], [1, 0, 2, 0, 2], [1, 0, 2, 1, 0], [1, 0, 2, 1, 1], [1, 0, 2, 1, 2], [1, 0, 2, 2, 0], [1, 0, 2, 2, 1], [1, 0, 2, 2, 2], [1, 1, 0, 0, 0], [1, 1, 0, 0, 1], [1, 1, 0, 0, 2], [1, 1, 0, 1, 0], [1, 1, 0, 1, 1], [1, 1, 0, 1, 2], [1, 1, 0, 2, 0], [1, 1, 0, 2, 1], [1, 1, 0, 2, 2], [1, 1, 1, 0, 0], [1, 1, 1, 0, 1], [1, 1, 1, 0, 2], [1, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 2, 0], [1, 1, 1, 2, 1], [1, 1, 1, 2, 2], [1, 1, 2, 0, 0], [1, 1, 2, 0, 1], [1, 1, 2, 0, 2], [1, 1, 2, 1, 0], [1, 1, 2, 1, 1], [1, 1, 2, 1, 2], [1, 1, 2, 2, 0], [1, 1, 2, 2, 1], [1, 1, 2, 2, 2], [1, 2, 0, 0, 0], [1, 2, 0, 0, 1], [1, 2, 0, 0, 2], [1, 2, 0, 1, 0], [1, 2, 0, 1, 1], [1, 2, 0, 1, 2], [1, 2, 0, 2, 0], [1, 2, 0, 2, 1], [1, 2, 0, 2, 2], [1, 2, 1, 0, 0], [1, 2, 1, 0, 1], [1, 2, 1, 0, 2], [1, 2, 1, 1, 0], [1, 2, 1, 1, 1], [1, 2, 1, 1, 2], [1, 2, 1, 2, 0], [1, 2, 1, 2, 1], [1, 2, 1, 2, 2], [1, 2, 2, 0, 0], [1, 2, 2, 0, 1], [1, 2, 2, 0, 2], [1, 2, 2, 1, 0], [1, 2, 2, 1, 1], [1, 2, 2, 1, 2], [1, 2, 2, 2, 0], [1, 2, 2, 2, 1], [1, 2, 2, 2, 2], [2, 0, 0, 0, 0], [2, 0, 0, 0, 1], [2, 0, 0, 0, 2], [2, 0, 0, 1, 0], [2, 0, 0, 1, 1], [2, 0, 0, 1, 2], [2, 0, 0, 2, 0], [2, 0, 0, 2, 1], [2, 0, 0, 2, 2], [2, 0, 1, 0, 0], [2, 0, 1, 0, 1], [2, 0, 1, 0, 2], [2, 0, 1, 1, 0], [2, 0, 1, 1, 1], [2, 0, 1, 1, 2], [2, 0, 1, 2, 0], [2, 0, 1, 2, 1], [2, 0, 1, 2, 2], [2, 0, 2, 0, 0], [2, 0, 2, 0, 1], [2, 0, 2, 0, 2], [2, 0, 2, 1, 0], [2, 0, 2, 1, 1], [2, 0, 2, 1, 2], [2, 0, 2, 2, 0], [2, 0, 2, 2, 1], [2, 0, 2, 2, 2], [2, 1, 0, 0, 0], [2, 1, 0, 0, 1], [2, 1, 0, 0, 2], [2, 1, 0, 1, 0], [2, 1, 0, 1, 1], [2, 1, 0, 1, 2], [2, 1, 0, 2, 0], [2, 1, 0, 2, 1], [2, 1, 0, 2, 2], [2, 1, 1, 0, 0], [2, 1, 1, 0, 1], [2, 1, 1, 0, 2], [2, 1, 1, 1, 0], [2, 1, 1, 1, 1], [2, 1, 1, 1, 2], [2, 1, 1, 2, 0], [2, 1, 1, 2, 1], [2, 1, 1, 2, 2], [2, 1, 2, 0, 0], [2, 1, 2, 0, 1], [2, 1, 2, 0, 2], [2, 1, 2, 1, 0], [2, 1, 2, 1, 1], [2, 1, 2, 1, 2], [2, 1, 2, 2, 0], [2, 1, 2, 2, 1], [2, 1, 2, 2, 2], [2, 2, 0, 0, 0], [2, 2, 0, 0, 1], [2, 2, 0, 0, 2], [2, 2, 0, 1, 0], [2, 2, 0, 1, 1], [2, 2, 0, 1, 2], [2, 2, 0, 2, 0], [2, 2, 0, 2, 1], [2, 2, 0, 2, 2], [2, 2, 1, 0, 0], [2, 2, 1, 0, 1], [2, 2, 1, 0, 2], [2, 2, 1, 1, 0], [2, 2, 1, 1, 1], [2, 2, 1, 1, 2], [2, 2, 1, 2, 0], [2, 2, 1, 2, 1], [2, 2, 1, 2, 2], [2, 2, 2, 0, 0], [2, 2, 2, 0, 1], [2, 2, 2, 0, 2], [2, 2, 2, 1, 0], [2, 2, 2, 1, 1], [2, 2, 2, 1, 2], [2, 2, 2, 2, 0], [2, 2, 2, 2, 1], [2, 2, 2, 2, 2], [0, 0, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 2], [0, 0, 0, 1, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 2], [0, 0, 0, 2, 0], [0, 0, 0, 2, 1], [0, 0, 0, 2, 2], [0, 0, 1, 0, 0], [0, 0, 1, 0, 1], [0, 0, 1, 0, 2], [0, 0, 1, 1, 0], ] def test_pop_bits(): data = bitarray("1010101010101010") assert mp7.pop_bits(data, 4, 8) == (170, 12) def test_read_mp7_signature(mp4_file): sig = idk.video_mp7sig_extract(mp4_file) result = idk.read_mp7_signature(sig) frame = result[-1] assert isinstance(frame, mp7.Frame) assert frame.confidence == 77 assert frame.elapsed == Fraction(299, 5) assert frame.vector.tolist() == [ 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 0, 1, 1, 0, 2, 2, 1, 1, 1, 2, 1, 1, 1, 0, 2, 0, 0, 0, 2, 2, 2, 0, 2, 1, 2, 2, 2, 0, 0, 0, 1, 2, 1, 1, 1, 1, 1, 2, 0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 0, 2, 2, 2, 2, 1, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 0, 1, 0, 0, 1, 0, 1, 2, 0, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 1, 1, 1, 0, 1, 2, 2, 2, 1, 1, 0, 1, 2, 2, 2, 1, 0, 0, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 0, 1, 2, 0, 0, 2, 2, 1, 0, 2, 0, 0, 2, 1, 0, 2, 2, 2, 1, 1, 1, 1, 1, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1, 0, 1, 1, 2, 0, 1, 1, 0, 1, 1, 0, 2, 1, 1, 0, 0, 1, 2, 2, 2, 0, 0, 1, 2, 2, 0, 2, 0, 0, 0, 2, 2, 0, 2, 2, 0, 2, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 2, 2, 2, 0, 2, 1, 0, 1, 1, 2, 1, 2, 0, 1, 0, 2, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 1, 0, 0, 0, 2, 0, 2, 1, 0, 0, 2, 2, 2, 2, 2, 2, 0, 1, 2, 2, 0, 1, 1, 0, 2, 1, 0, 1, 2, 0, 1, 1, 1, 2, 2, 1, 0, 0, 2, 1, 1, 2, 0, 1, 0, 2, 1, 0, 2, 2, 2, 0, 1, 1, 1, 1, 2, 1, 2, 0, 2, 2, ]

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1

0

1

0

null

0

8

0b198aee591de64501f627df00dca996caa4cf2e

175

py

Python

alfworld/agents/eval/__init__.py

roy860328/VSGM

3ec19f9cf1401cecf45527687936b8fe4167f672

[ "MIT" ]

6

2021-05-22T15:33:42.000Z

2022-01-12T03:34:39.000Z

alfworld/agents/eval/__init__.py

roy860328/VSGM

3ec19f9cf1401cecf45527687936b8fe4167f672

[ "MIT" ]

1

2021-06-19T10:04:13.000Z

2021-06-20T03:37:23.000Z

alfworld/agents/eval/__init__.py

roy860328/VSGM

3ec19f9cf1401cecf45527687936b8fe4167f672

[ "MIT" ]

null

from agents.eval.evaluate_dagger import evaluate_dagger from agents.eval.evaluate_vision_dagger import evaluate_vision_dagger from agents.eval.evaluate_dqn import evaluate_dqn

58.333333

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175

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null

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0

1

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7

0b1cc392777e46ad5986d71666bdb67b89dc9d58

3,252

py

Python

ckanext/datapusher/tests/test_default_views.py

ziveo/ckan

f4cfe5e28789df58b2bf7e73e5989ffda00e5c5c

[ "Apache-2.0" ]

2,805

2015-01-02T18:13:15.000Z

2022-03-31T03:35:01.000Z

ckanext/datapusher/tests/test_default_views.py

ziveo/ckan

f4cfe5e28789df58b2bf7e73e5989ffda00e5c5c

[ "Apache-2.0" ]

3,801

2015-01-02T11:05:36.000Z

2022-03-31T19:24:37.000Z

ckanext/datapusher/tests/test_default_views.py

ziveo/ckan

f4cfe5e28789df58b2bf7e73e5989ffda00e5c5c

[ "Apache-2.0" ]

1,689

2015-01-02T19:46:43.000Z

2022-03-28T14:59:43.000Z

# encoding: utf-8 import datetime import pytest from ckan.tests import helpers, factories @pytest.mark.ckan_config("ckan.views.default_views", "recline_grid_view") @pytest.mark.ckan_config( "ckan.plugins", "datapusher datastore recline_grid_view" ) @pytest.mark.usefixtures("clean_db", "with_plugins") def test_datapusher_creates_default_views_on_complete(): dataset = factories.Dataset() resource = factories.Resource(package_id=dataset["id"]) # Push data directly to the DataStore for the resource to be marked as # `datastore_active=True`, so the grid view can be created data = { "resource_id": resource["id"], "fields": [{"id": "a", "type": "text"}, {"id": "b", "type": "text"}], "records": [{"a": "1", "b": "2"}], "force": True, } helpers.call_action("datastore_create", **data) # Create a task for `datapusher_hook` to update task_dict = { "entity_id": resource["id"], "entity_type": "resource", "task_type": "datapusher", "key": "datapusher", "value": '{"job_id": "my_id", "job_key":"my_key"}', "last_updated": str(datetime.datetime.now()), "state": "pending", } helpers.call_action("task_status_update", context={}, **task_dict) # Call datapusher_hook with a status of complete to trigger the # default views creation params = { "status": "complete", "metadata": {"resource_id": resource["id"]}, } helpers.call_action("datapusher_hook", context={}, **params) views = helpers.call_action("resource_view_list", id=resource["id"]) assert len(views) == 1 assert views[0]["view_type"] == "recline_grid_view" @pytest.mark.ckan_config("ckan.views.default_views", "recline_grid_view") @pytest.mark.ckan_config( "ckan.plugins", "datapusher datastore recline_grid_view" ) @pytest.mark.usefixtures("clean_db", "with_plugins") def test_datapusher_does_not_create_default_views_on_pending(): dataset = factories.Dataset() resource = factories.Resource(package_id=dataset["id"]) # Push data directly to the DataStore for the resource to be marked as # `datastore_active=True`, so the grid view can be created data = { "resource_id": resource["id"], "fields": [{"id": "a", "type": "text"}, {"id": "b", "type": "text"}], "records": [{"a": "1", "b": "2"}], "force": True, } helpers.call_action("datastore_create", **data) # Create a task for `datapusher_hook` to update task_dict = { "entity_id": resource["id"], "entity_type": "resource", "task_type": "datapusher", "key": "datapusher", "value": '{"job_id": "my_id", "job_key":"my_key"}', "last_updated": str(datetime.datetime.now()), "state": "pending", } helpers.call_action("task_status_update", context={}, **task_dict) # Call datapusher_hook with a status of complete to trigger the # default views creation params = {"status": "pending", "metadata": {"resource_id": resource["id"]}} helpers.call_action("datapusher_hook", context={}, **params) views = helpers.call_action("resource_view_list", id=resource["id"]) assert len(views) == 0

32.52

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false

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0

null

0

1

0

1

0

null

0

7

505f109474b1e15582f870886c4933191025b501

6,120

py

Python

tests/causal_refuters/test_bootstrap_refuter.py

ErikHambardzumyan/dowhy

685f64723e2a37334164dbb8da7d55f4e45975de

[ "MIT" ]

1

2020-05-30T21:04:27.000Z

tests/causal_refuters/test_bootstrap_refuter.py

ErikHambardzumyan/dowhy

685f64723e2a37334164dbb8da7d55f4e45975de

[ "MIT" ]

null

tests/causal_refuters/test_bootstrap_refuter.py

ErikHambardzumyan/dowhy

685f64723e2a37334164dbb8da7d55f4e45975de

[ "MIT" ]

1

2020-07-30T12:32:46.000Z

import pytest import numpy as np from .base import TestRefuter @pytest.mark.usefixtures("fixed_seed") class TestDataSubsetRefuter(object): ''' The first two tests are for the default behavior, in which we just bootstrap the data and obtain the estimate. ''' @pytest.mark.parametrize(["error_tolerance","estimator_method","num_samples"], [(0.05, "iv.instrumental_variable",1000)]) def test_refutation_bootstrap_refuter_continuous(self, error_tolerance, estimator_method, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter") refuter_tester.continuous_treatment_testsuite(num_samples=num_samples) # Run both @pytest.mark.parametrize(["error_tolerance", "estimator_method","num_samples"], [(0.05, "backdoor.propensity_score_matching",1000)]) def test_refutation_bootstrap_refuter_binary(self, error_tolerance, estimator_method, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter") refuter_tester.binary_treatment_testsuite(tests_to_run="atleast-one-common-cause", num_samples=num_samples) @pytest.mark.parametrize(["error_tolerance","estimator_method","num_common_causes","required_variables", "num_samples"], [(0.05, "iv.instrumental_variable",5, 3, 1000)]) def test_refutation_bootstrap_refuter_continuous_integer_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables, ) refuter_tester.continuous_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause") # Run atleast one common cause @pytest.mark.parametrize(["error_tolerance","estimator_method", "num_common_causes", "required_variables", "num_samples"], [(0.05, "iv.instrumental_variable", 5, ["W0","W1"], 1000)]) def test_refutation_bootstrap_refuter_continuous_list_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables) refuter_tester.continuous_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause") # Run atleast one common cause @pytest.mark.parametrize(["error_tolerance", "estimator_method", "num_common_causes", "required_variables", "num_samples"], [(0.1, "backdoor.propensity_score_matching", 5, 3, 5000)]) def test_refutation_bootstrap_refuter_binary_integer_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables) refuter_tester.binary_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause") @pytest.mark.parametrize(["error_tolerance", "estimator_method", "num_common_causes", "required_variables", "num_samples"], [(0.1, "backdoor.propensity_score_matching",5, ["W0", "W1"], 5000)]) def test_refutation_bootstrap_refuter_binary_list_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables) refuter_tester.binary_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause") @pytest.mark.parametrize(["error_tolerance","estimator_method", "num_common_causes", "required_variables", "num_samples"], [(0.1, "iv.instrumental_variable", 5, ["-W0","-W1"], 5000)]) def test_refutation_bootstrap_refuter_continuous_list_negative_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables) refuter_tester.continuous_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause") # Run atleast one common cause @pytest.mark.parametrize(["error_tolerance", "estimator_method", "num_common_causes", "required_variables", "num_samples"], [(0.1, "backdoor.propensity_score_matching",5, ["-W0", "-W1"], 5000)]) def test_refutation_bootstrap_refuter_binary_list_negative_argument(self, error_tolerance, estimator_method, num_common_causes, required_variables, num_samples): refuter_tester = TestRefuter(error_tolerance, estimator_method, "bootstrap_refuter", required_variables=required_variables) refuter_tester.binary_treatment_testsuite(num_samples=num_samples,num_common_causes=num_common_causes, tests_to_run="atleast-one-common-cause")

75.555556

187

0.668301

626

6,120

6.110224

0.126198

0.08366

0.144314

0.181961

0.931242

0.924967

0.906928

0.842353

0.818824

0

0.014709

0.244608

6,120

81

188

75.555556

0.812676

0.033824

0

0.609375

0

0.191658

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0

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false

0

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0

0.1875

0

null

0

1

0

null

0

7

acbcf6725dc0ba1da9d97fa878fdf8260be21451

1,832

py

Python

tests/_validation/test_size_validation.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

16

2021-04-16T02:01:29.000Z

2022-01-01T08:53:49.000Z

tests/_validation/test_size_validation.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

613

2021-03-24T03:37:38.000Z

2022-03-26T10:58:37.000Z

tests/_validation/test_size_validation.py

simon-ritchie/apyscript

c319f8ab2f1f5f7fad8d2a8b4fc06e7195476279

[ "MIT" ]

2

2021-06-20T07:32:58.000Z

2021-12-26T08:22:11.000Z

from apysc._validation import size_validation from tests import testing_helper def test_validate_size_is_int() -> None: size_validation.validate_size_is_int( size=100, err_msg='Specified width is not integer value.') testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_int, kwargs={ 'size': '100px', 'err_msg': 'Specified width is not integer value.'}) testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_int, kwargs={'size': '100px'}) def test_validate_size_is_gt_zero() -> None: size_validation.validate_size_is_gt_zero( size=1, err_msg='Specified width is less than or equal to zero.') testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_gt_zero, kwargs={'size': 0}) testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_gt_zero, kwargs={ 'size': 0, 'err_msg': 'Specified width is less than or equal to zero.'}) def test_validate_size_is_gte_zero() -> None: size_validation.validate_size_is_gte_zero(size=0) size_validation.validate_size_is_gte_zero(size=100) testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_gte_zero, kwargs={'size': -1}) testing_helper.assert_raises( expected_error_class=ValueError, func_or_method=size_validation.validate_size_is_gte_zero, kwargs={'size': -1, 'err_msg': 'Size is invalid.'})

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0.777586

0.710345

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null

0

1

0

null

0

1

0

8

acd08af32f9f9595b90a741db2d815eaa313f2ec

19,312

py

Python

multicurrency/rupee.py

fscm/multicurrency

5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91

[ "MIT" ]

2

2021-03-26T18:19:57.000Z

2021-07-27T01:15:50.000Z

multicurrency/rupee.py

fscm/multicurrency

5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91

[ "MIT" ]

null

multicurrency/rupee.py

fscm/multicurrency

5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91

[ "MIT" ]

null

# -*- coding: UTF-8 -*- # # copyright: 2020-2022, Frederico Martins # author: Frederico Martins <http://github.com/fscm> # license: SPDX-License-Identifier: MIT """Rupee currency representation(s).""" from decimal import Decimal from typing import Optional, Union from .currency import Currency class IndianRupee(Currency): """Indian Rupee currency representation. Simple usage example: >>> from multicurrency import IndianRupee >>> indian_rupee = IndianRupee( ... amount=123456.789) >>> print(indian_rupee) ₹123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ''. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '', **other) -> 'IndianRupee': """Class creator. Returns: IndianRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='INR', numeric_code='356', symbol='₹', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='₹', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class IndianRupeeBT(Currency): """Indian Rupee BT currency representation. Simple usage example: >>> from multicurrency import IndianRupeeBT >>> indian_rupee_bt = IndianRupeeBT( ... amount=123456.789) >>> print(indian_rupee_bt) ₹123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ''. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '', **other) -> 'IndianRupeeBT': """Class creator. Returns: IndianRupeeBT: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='INR', numeric_code='356', symbol='₹', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='BT₹', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class IndianRupeeIN(Currency): """Indian Rupee IN currency representation. Simple usage example: >>> from multicurrency import IndianRupeeIN >>> indian_rupee_in = IndianRupeeIN( ... amount=123456.789) >>> print(indian_rupee_in) ₹123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ''. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '', **other) -> 'IndianRupeeIN': """Class creator. Returns: IndianRupeeIN: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='INR', numeric_code='356', symbol='₹', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='IN₹', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class SriLankaRupee(Currency): """Sri Lanka Rupee currency representation. Simple usage example: >>> from multicurrency import SriLankaRupee >>> sri_lanka_rupee = SriLankaRupee( ... amount=123456.789) >>> print(sri_lanka_rupee) රු. 123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ' '. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '\u00A0', **other) -> 'SriLankaRupee': """Class creator. Returns: SriLankaRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='LKR', numeric_code='144', symbol='රු.', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='රු.', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class MauritiusRupee(Currency): """Mauritius Rupee currency representation. Simple usage example: >>> from multicurrency import MauritiusRupee >>> mauritius_rupee = MauritiusRupee( ... amount=123456.789) >>> print(mauritius_rupee) ₨ 123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ' '. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '\u00A0', **other) -> 'MauritiusRupee': """Class creator. Returns: MauritiusRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='MUR', numeric_code='480', symbol='₨', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='₨', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class NepaleseRupee(Currency): """Nepalese Rupee currency representation. Simple usage example: >>> from multicurrency import NepaleseRupee >>> nepalese_rupee = NepaleseRupee( ... amount=123456.789) >>> print(nepalese_rupee) नेरू १२३,४५६.७९ For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ' '. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '\u00A0', **other) -> 'NepaleseRupee': """Class creator. Returns: NepaleseRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='NPR', numeric_code='524', symbol='नेरू', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='नेरू', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='०१२३४५६७८९-', international=international) class PakistanRupee(Currency): """Pakistan Rupee currency representation. Simple usage example: >>> from multicurrency import PakistanRupee >>> pakistan_rupee = PakistanRupee( ... amount=123456.789) >>> print(pakistan_rupee) ₨ 123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ' '. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '\u00A0', **other) -> 'PakistanRupee': """Class creator. Returns: PakistanRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='PKR', numeric_code='586', symbol='₨', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='₨', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international) class SeychellesRupee(Currency): """Seychelles Rupee currency representation. Simple usage example: >>> from multicurrency import SeychellesRupee >>> seychelles_rupee = SeychellesRupee( ... amount=123456.789) >>> print(seychelles_rupee) ₨ 123,456.79 For more details see `multicurrency.currency.Currency` . Args: amount (Union[int, float, Decimal]): Represented value. decimal_places (int, optional): Number of decimal places for the currency representation. Defaults to 2, decimal_sign (str, optional): Decimal symbol. Defaults to '.'. grouping_places (int, optional): Number of digits for grouping. Defaults to 3, grouping_sign (str, optional): Grouping symbol. Defaults to ','. international (bool, optional): Identifies the currency using the 'currency' value instead of the 'symbol'. Defaults to False. symbol_separator (str, optional): Separation between the symbol and the value. Defaults to ' '. symbol_ahead (bool, optional): True if symbol goes ahead of the value. False otherwise. Defaults to True. """ __slots__ = [] def __new__( # pylint: disable=signature-differs,disable=unused-argument cls, amount: Union[int, float, Decimal], decimal_places: Optional[int] = 2, decimal_sign: Optional[str] = '.', grouping_places: Optional[int] = 3, grouping_sign: Optional[str] = ',', international: Optional[bool] = False, symbol_ahead: Optional[bool] = True, symbol_separator: Optional[str] = '\u00A0', **other) -> 'SeychellesRupee': """Class creator. Returns: SeychellesRupee: new opbject. """ return Currency.__new__( cls, amount=amount, alpha_code='SCR', numeric_code='690', symbol='₨', symbol_separator=symbol_separator, symbol_ahead=symbol_ahead, localized_symbol='₨', decimal_places=decimal_places, decimal_sign=decimal_sign, grouping_places=grouping_places, grouping_sign=grouping_sign, convertion='', international=international)

36.300752

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0

null

0

1

0

1

0

null

0

7

c5e5b88e1ce1523a785665937a25b4744cedc2da

199

py

Python

tp/ui/__init__.py

chinapnr/agbot

9739ce1c2198e50111629db2d1de785edd06876e

[ "MIT" ]

2

2018-06-23T06:48:46.000Z

2018-06-23T10:11:50.000Z

tp/ui/__init__.py

chinapnr/agbot

9739ce1c2198e50111629db2d1de785edd06876e

[ "MIT" ]

5

2020-01-03T09:33:02.000Z

2021-06-02T00:49:52.000Z

tp/ui/__init__.py

chinapnr/agbot

9739ce1c2198e50111629db2d1de785edd06876e

[ "MIT" ]

1

2021-07-07T07:17:27.000Z

from agbot.core.model.context import VerticalContext from .tp_ui import UiTestPoint def run(tp_conf_dict, vertical_context: VerticalContext): return UiTestPoint(tp_conf_dict, vertical_context)

28.428571

57

0.834171

27

199

5.888889

0.592593

0.075472

0.125786

0.226415

0.314465

0

0.105528

199

6

58

33.166667

0.893258

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null

0

1

0

1

0

7

a8bc33b7fe19af62c72d2b01e82f7b230434cd26

37

py

Python

src/lib/sunau.py

DTenore/skulpt

098d20acfb088d6db85535132c324b7ac2f2d212

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

src/lib/sunau.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

src/lib/sunau.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

import _sk_fail; _sk_fail._("sunau")

18.5

36

0.756757

6

37

3.833333

0.666667

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0

0.081081

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0

1

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1

0

1

0

7

7663ceebbed78990198de735e13f9f7add130be4

20,843

py

Python

archi/net.py

victor-estrade/SystGradDescent

822e7094290301ec47a99433381a8d6406798aff

[ "MIT" ]

2

2019-03-20T09:05:02.000Z

2019-03-20T15:23:44.000Z

archi/net.py

victor-estrade/SystGradDescent

822e7094290301ec47a99433381a8d6406798aff

[ "MIT" ]

null

archi/net.py

victor-estrade/SystGradDescent

822e7094290301ec47a99433381a8d6406798aff

[ "MIT" ]

null

# coding: utf-8 from __future__ import division from __future__ import print_function from __future__ import absolute_import from __future__ import unicode_literals import torch import torch.nn as nn from . import layers from .blocks import ResidualAverageBlock from .blocks import ResidualBlock from .blocks import softmax_cat class BaseArchi(nn.Module): def __init__(self, n_unit=80): super().__init__() self.name = "{}x{:d}".format(self.__class__.__name__, n_unit) """ Fix residual net initialization https://openreview.net/forum?id=H1gsz30cKX Or use batch norm ? --- Note : E = Extra input L = Linear A = Average layer S = Sum layer M = Mean operation R = Residual block AR = Average Residual block """ class F6(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.fc_in = nn.Linear(n_in, n_unit) self.fc1 = nn.Linear(n_unit, n_unit) self.fc2 = nn.Linear(n_unit, n_unit) self.fc3 = nn.Linear(n_unit, n_unit) self.fc4 = nn.Linear(n_unit, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x): x = self.fc_in(x) x = torch.relu(x) x = self.fc1(x) x = torch.relu(x) x = self.fc2(x) x = torch.relu(x) x = self.fc3(x) x = torch.relu(x) x = self.fc4(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.fc_in.reset_parameters() self.fc1.reset_parameters() self.fc2.reset_parameters() self.fc3.reset_parameters() self.fc4.reset_parameters() self.fc_out.reset_parameters() class RegNet(BaseArchi): def __init__(self, n_in=1, n_out=1): N_UNITS = 80 super().__init__(N_UNITS) self.avg1 = layers.Average(n_in, N_UNITS) self.avg2 = layers.Average(N_UNITS, N_UNITS) self.avg3 = layers.Average(N_UNITS, N_UNITS) self.fc1 = nn.Linear(N_UNITS, N_UNITS) self.fc2 = nn.Linear(N_UNITS, N_UNITS) self.fc3 = nn.Linear(N_UNITS*2, N_UNITS) self.fc_out = nn.Linear(N_UNITS, n_out) def forward(self, x, w, p=None): x = self.avg1(x, w) x = torch.relu(x) # x = self.fc1(x) # x = torch.softmax(x, 1) x = self.avg2(x, w) x = torch.relu(x) x_ = self.fc2(x) x_ = torch.softmax(x_, 1) x_ = self.avg3(x_, w) x_ = torch.relu(x_) x = torch.cat((x, x_), 1) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.fc3(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.fc1.reset_parameters() self.fc2.reset_parameters() self.fc3.reset_parameters() self.fc_out.reset_parameters() class RegNetExtra(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg1 = layers.AverageExtra(n_in, n_unit, n_extra) self.avg2 = layers.Average(n_unit, n_unit) self.avg3 = layers.Average(n_unit, n_unit) self.fc1 = nn.Linear(n_unit, n_unit) self.fc2 = nn.Linear(n_unit, n_unit) self.fc3 = nn.Linear(n_unit*2, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg1(x, w, p) x = torch.nn.functional.relu6(x) # x = self.fc1(x) # x = torch.softmax(x, 1) x = self.avg2(x, w) x = torch.nn.functional.relu6(x) x_ = self.fc2(x) x_ = torch.softmax(x_, 1) x_ = self.avg3(x_, w) x_ = torch.nn.functional.relu6(x_) x = torch.cat((x, x_), 1) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.fc3(x) x = torch.nn.functional.relu6(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.fc1.reset_parameters() self.fc2.reset_parameters() self.fc3.reset_parameters() self.fc_out.reset_parameters() class AR9R1(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p=None): x = self.avg_in(x, w) x = self.avg1(x, w) x = self.avg2(x, w) x = self.avg3(x, w) x = self.avg4(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.fc_out.reset_parameters() class AR5R5(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.res3 = ResidualBlock (n_unit, n_unit//2) self.res4 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p=None): x = self.avg_in(x, w) x = self.avg1(x, w) x = self.avg2(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res3(x) x = self.res4(x) x = layers.relu6_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.res3.reset_parameters() self.res4.reset_parameters() self.fc_out.reset_parameters() class AR5R5E(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.AverageExtra(n_in, n_unit, n_extra) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.res3 = ResidualBlock (n_unit, n_unit//2) self.res4 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w, p) x = self.avg1(x, w) x = self.avg2(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res3(x) x = self.res4(x) x = layers.relu6_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.res3.reset_parameters() self.res4.reset_parameters() self.fc_out.reset_parameters() class AR9R9E(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.AverageExtra(n_in, n_unit, n_extra) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit, n_unit//2) self.res5 = ResidualBlock (n_unit, n_unit//2) self.res6 = ResidualBlock (n_unit, n_unit//2) self.res7 = ResidualBlock (n_unit, n_unit//2) self.res8 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w, p) x = self.avg1(x, w) x = self.avg2(x, w) x = self.avg3(x, w) x = self.avg4(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res5(x) x = self.res6(x) x = self.res7(x) x = self.res8(x) x = layers.relu_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.res5.reset_parameters() self.res6.reset_parameters() self.res7.reset_parameters() self.res8.reset_parameters() self.fc_out.reset_parameters() class AR9R9(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit, n_unit//2) self.res5 = ResidualBlock (n_unit, n_unit//2) self.res6 = ResidualBlock (n_unit, n_unit//2) self.res7 = ResidualBlock (n_unit, n_unit//2) self.res8 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w) x = self.avg1(x, w) x = self.avg2(x, w) x = self.avg3(x, w) x = self.avg4(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res5(x) x = self.res6(x) x = self.res7(x) x = self.res8(x) x = layers.relu_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.res5.reset_parameters() self.res6.reset_parameters() self.res7.reset_parameters() self.res8.reset_parameters() self.fc_out.reset_parameters() class AR19R5E(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.AverageExtra(n_in, n_unit, n_extra) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit, n_unit//2) self.avg5 = ResidualAverageBlock(n_unit, n_unit//2) self.avg6 = ResidualAverageBlock(n_unit, n_unit//2) self.avg7 = ResidualAverageBlock(n_unit, n_unit//2) self.avg8 = ResidualAverageBlock(n_unit, n_unit//2) self.avg9 = ResidualAverageBlock(n_unit, n_unit//2) self.res10 = ResidualBlock (n_unit, n_unit//2) self.res11 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w, p) x = self.avg1(x, w) x = self.avg2(x, w) x = self.avg3(x, w) x = self.avg4(x, w) x = self.avg5(x, w) x = self.avg6(x, w) x = self.avg7(x, w) x = self.avg8(x, w) x = self.avg9(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res10(x) x = self.res11(x) x = layers.relu_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.avg5.reset_parameters() self.avg6.reset_parameters() self.avg7.reset_parameters() self.avg8.reset_parameters() self.avg9.reset_parameters() self.res10.reset_parameters() self.res11.reset_parameters() self.fc_out.reset_parameters() class AR19R5(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit, n_unit//2) self.avg5 = ResidualAverageBlock(n_unit, n_unit//2) self.avg6 = ResidualAverageBlock(n_unit, n_unit//2) self.avg7 = ResidualAverageBlock(n_unit, n_unit//2) self.avg8 = ResidualAverageBlock(n_unit, n_unit//2) self.avg9 = ResidualAverageBlock(n_unit, n_unit//2) self.res10 = ResidualBlock (n_unit, n_unit//2) self.res11 = ResidualBlock (n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w) x = self.avg1(x, w) x = self.avg2(x, w) x = self.avg3(x, w) x = self.avg4(x, w) x = self.avg5(x, w) x = self.avg6(x, w) x = self.avg7(x, w) x = self.avg8(x, w) x = self.avg9(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res10(x) x = self.res11(x) x = layers.relu_tanh(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.avg5.reset_parameters() self.avg6.reset_parameters() self.avg7.reset_parameters() self.avg8.reset_parameters() self.avg9.reset_parameters() self.res10.reset_parameters() self.res11.reset_parameters() self.fc_out.reset_parameters() class AF3R3(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = layers.Average(n_unit, n_unit) self.avg2 = layers.Average(n_unit, n_unit) self.fc3 = nn.Linear(n_unit, n_unit) self.fc4 = nn.Linear(n_unit, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p=None): x = self.avg_in(x, w) x = torch.relu(x) x = self.avg1(x, w) x = torch.relu(x) x = self.avg2(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = torch.relu(x) x = self.fc3(x) x = torch.relu(x) x = self.fc4(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.fc3.reset_parameters() self.fc4.reset_parameters() self.fc_out.reset_parameters() class AF3R3E(BaseArchi): def __init__(self, n_in=1, n_out=1, n_extra=0, n_unit=80): super().__init__(n_unit) self.avg_in = layers.AverageExtra(n_in, n_unit, n_extra) self.avg1 = layers.Average(n_unit, n_unit) self.avg2 = layers.Average(n_unit, n_unit) self.fc3 = nn.Linear(n_unit, n_unit) self.fc4 = nn.Linear(n_unit, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.avg_in(x, w, p) x = torch.relu(x) x = self.avg1(x, w) x = torch.relu(x) x = self.avg2(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = torch.relu(x) x = self.fc3(x) x = torch.relu(x) x = self.fc4(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.fc3.reset_parameters() self.fc4.reset_parameters() self.fc_out.reset_parameters() class F3R3(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.fc_in = nn.Linear(n_in, n_unit) self.fc1 = nn.Linear(n_unit, n_unit) self.fc2 = nn.Linear(n_unit, n_unit) self.fc3 = nn.Linear(n_unit, n_unit) self.fc4 = nn.Linear(n_unit, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p=None): x = self.fc_in(x) x = torch.relu(x) x = self.fc1(x) x = torch.relu(x) x = self.fc2(x) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = torch.relu(x) x = self.fc3(x) x = torch.relu(x) x = self.fc4(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.fc_in.reset_parameters() self.fc1.reset_parameters() self.fc2.reset_parameters() self.fc3.reset_parameters() self.fc4.reset_parameters() self.fc_out.reset_parameters() class F3R3E(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.fc_in = nn.Linear(n_in, n_unit) self.fc1 = nn.Linear(n_unit, n_unit) self.fc2 = nn.Linear(n_unit, n_unit) self.fc3 = nn.Linear(n_unit, n_unit) self.fc4 = nn.Linear(n_unit, n_unit) self.fc_out = nn.Linear(n_unit, n_out) def forward(self, x, w, p): x = self.fc_in(x) x = torch.relu(x) x = self.fc1(x) x = torch.relu(x) x = self.fc2(x) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = torch.cat((x, p), 1) x = torch.relu(x) x = self.fc3(x) x = torch.relu(x) x = self.fc4(x) x = torch.relu(x) x = self.fc_out(x) return x def reset_parameters(self): self.fc_in.reset_parameters() self.fc1.reset_parameters() self.fc2.reset_parameters() self.fc3.reset_parameters() self.fc4.reset_parameters() self.fc_out.reset_parameters() class AR5S2S2R1(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=80): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit, n_unit//2) self.avg4 = ResidualAverageBlock(n_unit+n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit+n_unit, n_out) def forward(self, x, w, p=None): x = self.avg_in(x, w) x = self.avg1(x, w) x = self.avg2(x, w) x_ = self.avg3(x, w) x = softmax_cat(x_, x) x = self.avg4(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.fc_out.reset_parameters() class AR3S2S2R3(BaseArchi): def __init__(self, n_in=1, n_out=1, n_unit=200): super().__init__(n_unit) self.avg_in = layers.Average(n_in, n_unit) self.avg1 = ResidualAverageBlock(n_unit, n_unit//2) self.avg2 = ResidualAverageBlock(n_unit, n_unit//2) self.avg3 = ResidualAverageBlock(n_unit+n_unit, n_unit//2) self.res4 = ResidualBlock (n_unit+n_unit, n_unit//2) self.fc_out = nn.Linear(n_unit+n_unit, n_out) def forward(self, x, w, p=None): x = self.avg_in(x) x = self.avg1(x, w) x_ = self.avg2(x, w) x = softmax_cat(x_, x) x = self.avg3(x, w) x = layers.torch_weighted_mean(x, w, 0, keepdim=False) x = self.res4(x) x = self.fc_out(x) return x def reset_parameters(self): self.avg_in.reset_parameters() self.avg1.reset_parameters() self.avg2.reset_parameters() self.avg3.reset_parameters() self.avg4.reset_parameters() self.fc_out.reset_parameters()

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0.943569

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0.907428

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0.284844

20,843

647

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32.214838

0.738092

0.004462

0

0.896226

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0

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4f3947cf1c8fb52e14e89525535201fe31cac395

13,847

py

Python

memsource_cli/api/translation_api.py

zerodayz/memsource-cli-client

c2574f1467539a49e6637c874e88d75c7ef789b3

[ "Apache-2.0" ]

1

2020-07-24T16:29:32.000Z

memsource_cli/api/translation_api.py

zerodayz/memsource-cli-client

c2574f1467539a49e6637c874e88d75c7ef789b3

[ "Apache-2.0" ]

null

memsource_cli/api/translation_api.py

zerodayz/memsource-cli-client

c2574f1467539a49e6637c874e88d75c7ef789b3

[ "Apache-2.0" ]

null

# coding: utf-8 """ Memsource REST API Welcome to Memsource's API documentation. To view our legacy APIs please [visit our documentation](https://wiki.memsource.com/wiki/Memsource_API) and for more information about our new APIs, [visit our blog](https://www.memsource.com/blog/2017/10/24/introducing-rest-apis-qa-with-the-memsource-api-team/). If you have any questions, please contact [Memsource Support](<mailto:support@memsource.com>). # noqa: E501 OpenAPI spec version: Latest Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from memsource_cli.api_client import ApiClient class TranslationApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def human_translate(self, project_uid, **kwargs): # noqa: E501 """Human translate (Gengo or Unbabel) # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.human_translate(project_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param HumanTranslateJobsDto body: :return: AsyncRequestWrapperDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.human_translate_with_http_info(project_uid, **kwargs) # noqa: E501 else: (data) = self.human_translate_with_http_info(project_uid, **kwargs) # noqa: E501 return data def human_translate_with_http_info(self, project_uid, **kwargs): # noqa: E501 """Human translate (Gengo or Unbabel) # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.human_translate_with_http_info(project_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param HumanTranslateJobsDto body: :return: AsyncRequestWrapperDto If the method is called asynchronously, returns the request thread. """ all_params = ['project_uid', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method human_translate" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uid' is set if ('project_uid' not in params or params['project_uid'] is None): raise ValueError("Missing the required parameter `project_uid` when calling `human_translate`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uid' in params: path_params['projectUid'] = params['project_uid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/projects/{projectUid}/jobs/humanTranslate', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AsyncRequestWrapperDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def machine_translation_job(self, project_uid, job_uid, **kwargs): # noqa: E501 """Translate using machine translation # noqa: E501 Configured machine translate settings is used # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.machine_translation_job(project_uid, job_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param str job_uid: (required) :param TranslationRequestDto body: :return: MachineTranslateResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.machine_translation_job_with_http_info(project_uid, job_uid, **kwargs) # noqa: E501 else: (data) = self.machine_translation_job_with_http_info(project_uid, job_uid, **kwargs) # noqa: E501 return data def machine_translation_job_with_http_info(self, project_uid, job_uid, **kwargs): # noqa: E501 """Translate using machine translation # noqa: E501 Configured machine translate settings is used # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.machine_translation_job_with_http_info(project_uid, job_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param str job_uid: (required) :param TranslationRequestDto body: :return: MachineTranslateResponse If the method is called asynchronously, returns the request thread. """ all_params = ['project_uid', 'job_uid', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method machine_translation_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uid' is set if ('project_uid' not in params or params['project_uid'] is None): raise ValueError("Missing the required parameter `project_uid` when calling `machine_translation_job`") # noqa: E501 # verify the required parameter 'job_uid' is set if ('job_uid' not in params or params['job_uid'] is None): raise ValueError("Missing the required parameter `job_uid` when calling `machine_translation_job`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uid' in params: path_params['projectUid'] = params['project_uid'] # noqa: E501 if 'job_uid' in params: path_params['jobUid'] = params['job_uid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/projects/{projectUid}/jobs/{jobUid}/translations/translateWithMachineTranslation', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MachineTranslateResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def pre_translate(self, project_uid, **kwargs): # noqa: E501 """Pre-translate job # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.pre_translate(project_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param PreTranslateJobsDto body: :return: AsyncRequestWrapperDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.pre_translate_with_http_info(project_uid, **kwargs) # noqa: E501 else: (data) = self.pre_translate_with_http_info(project_uid, **kwargs) # noqa: E501 return data def pre_translate_with_http_info(self, project_uid, **kwargs): # noqa: E501 """Pre-translate job # noqa: E501 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.pre_translate_with_http_info(project_uid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uid: (required) :param PreTranslateJobsDto body: :return: AsyncRequestWrapperDto If the method is called asynchronously, returns the request thread. """ all_params = ['project_uid', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method pre_translate" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uid' is set if ('project_uid' not in params or params['project_uid'] is None): raise ValueError("Missing the required parameter `project_uid` when calling `pre_translate`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uid' in params: path_params['projectUid'] = params['project_uid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/api2/v1/projects/{projectUid}/jobs/preTranslate', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AsyncRequestWrapperDto', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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7

4f93be45d400a92a989c773a2cae0f4e98838035

165

py

Python

src/auction_api/models/__init__.py

4heck/auction_backend

c9568d45f5f4873f71ae71ced72b8e5a0a02d273

[ "MIT" ]

null

src/auction_api/models/__init__.py

4heck/auction_backend

c9568d45f5f4873f71ae71ced72b8e5a0a02d273

[ "MIT" ]

null

src/auction_api/models/__init__.py

4heck/auction_backend

c9568d45f5f4873f71ae71ced72b8e5a0a02d273

[ "MIT" ]

null

from auction_api.models.user import User from auction_api.models.auction import Auction from auction_api.models.bid import Bid __all__ = ["User", "Auction", "Bid"]

27.5

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1

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7

96ce068c86f6f860bcc65a2d6132941a759a8f00

7,331

py

Python

model_scripts/pickled_app.py

beilmanmich/cap_june_2017

7bd4bc56e3d610e5919e8259d0d0cb6f2af06d9d

[ "CC-BY-3.0" ]

null

model_scripts/pickled_app.py

beilmanmich/cap_june_2017

7bd4bc56e3d610e5919e8259d0d0cb6f2af06d9d

[ "CC-BY-3.0" ]

null

model_scripts/pickled_app.py

beilmanmich/cap_june_2017

7bd4bc56e3d610e5919e8259d0d0cb6f2af06d9d

[ "CC-BY-3.0" ]

null

import flask import numpy as np import pandas as pd import pickle from sklearn.tree import DecisionTreeClassifier from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KDTree import os #---------- MODEL IN MEMORY ----------------# df = pd.DataFrame(pickle.load(open('static/dummied_recent_data.pkl', 'rb'))) essay_df = pd.DataFrame(pickle.load(open('static/short_essays.pkl', 'rb'))) X = df[[ 'school_previous_projects', 'teacher_previous_projects', 'month', 'log_price_including', 'sqrt_students_reached', 'price_per_student', 'total_state_donors', 'total_state_projects', 'state_avg_donors', 'primary_focus_subject_Applied Sciences', 'primary_focus_subject_Character Education', 'primary_focus_subject_Civics & Government', 'primary_focus_subject_College & Career Prep', 'primary_focus_subject_Community Service', 'primary_focus_subject_ESL', 'primary_focus_subject_Early Development', 'primary_focus_subject_Economics', 'primary_focus_subject_Environmental Science', 'primary_focus_subject_Extracurricular', 'primary_focus_subject_Financial Literacy', 'primary_focus_subject_Foreign Languages', 'primary_focus_subject_Gym & Fitness', 'primary_focus_subject_Health & Life Science', 'primary_focus_subject_Health & Wellness', 'primary_focus_subject_History & Geography', 'primary_focus_subject_Literacy', 'primary_focus_subject_Literature & Writing', 'primary_focus_subject_Mathematics', 'primary_focus_subject_Music', 'primary_focus_subject_Nutrition', 'primary_focus_subject_Other', 'primary_focus_subject_Parent Involvement', 'primary_focus_subject_Performing Arts', 'primary_focus_subject_Social Sciences', 'primary_focus_subject_Special Needs', 'primary_focus_subject_Team Sports', 'primary_focus_subject_Visual Arts', 'poverty_level_high poverty', 'poverty_level_highest poverty', 'poverty_level_low poverty', 'poverty_level_moderate poverty', 'grade_level_Grades 3-5', 'grade_level_Grades 6-8', 'grade_level_Grades 9-12', 'grade_level_Grades PreK-2', 'school_metro_rural', 'school_metro_suburban', 'school_metro_urban', 'resource_type_Books', 'resource_type_Other', 'resource_type_Supplies', 'resource_type_Technology', 'resource_type_Trips', 'resource_type_Visitors', 'teacher_teach_for_america', 'optional_support']] Y = df[['RESP']] Y = np.ravel(Y) PREDICTOR = LogisticRegression().fit(X, Y) #PREDICTOR = DecisionTreeClassifier(max_depth = 4, class_weight = "auto" ).fit(X, Y) lookup = df[[ '_projectid', 'school_previous_projects', 'teacher_previous_projects', 'month', 'log_price_including', 'sqrt_students_reached', 'price_per_student', 'total_state_donors', 'total_state_projects', 'state_avg_donors', 'primary_focus_subject_Applied Sciences', 'primary_focus_subject_Character Education', 'primary_focus_subject_Civics & Government', 'primary_focus_subject_College & Career Prep', 'primary_focus_subject_Community Service', 'primary_focus_subject_ESL', 'primary_focus_subject_Early Development', 'primary_focus_subject_Economics', 'primary_focus_subject_Environmental Science', 'primary_focus_subject_Extracurricular', 'primary_focus_subject_Financial Literacy', 'primary_focus_subject_Foreign Languages', 'primary_focus_subject_Gym & Fitness', 'primary_focus_subject_Health & Life Science', 'primary_focus_subject_Health & Wellness', 'primary_focus_subject_History & Geography', 'primary_focus_subject_Literacy', 'primary_focus_subject_Literature & Writing', 'primary_focus_subject_Mathematics', 'primary_focus_subject_Music', 'primary_focus_subject_Nutrition', 'primary_focus_subject_Other', 'primary_focus_subject_Parent Involvement', 'primary_focus_subject_Performing Arts', 'primary_focus_subject_Social Sciences', 'primary_focus_subject_Special Needs', 'primary_focus_subject_Team Sports', 'primary_focus_subject_Visual Arts', 'poverty_level_high poverty', 'poverty_level_highest poverty', 'poverty_level_low poverty', 'poverty_level_moderate poverty', 'grade_level_Grades 3-5', 'grade_level_Grades 6-8', 'grade_level_Grades 9-12', 'grade_level_Grades PreK-2', 'school_metro_rural', 'school_metro_suburban', 'school_metro_urban', 'resource_type_Books', 'resource_type_Other', 'resource_type_Supplies', 'resource_type_Technology', 'resource_type_Trips', 'resource_type_Visitors', 'teacher_teach_for_america', 'optional_support']] collist = ['school_previous_projects', 'teacher_previous_projects', 'month', 'log_price_including', 'sqrt_students_reached', 'price_per_student', 'total_state_donors', 'total_state_projects', 'state_avg_donors', 'primary_focus_subject_Applied Sciences', 'primary_focus_subject_Character Education', 'primary_focus_subject_Civics & Government', 'primary_focus_subject_College & Career Prep', 'primary_focus_subject_Community Service', 'primary_focus_subject_ESL', 'primary_focus_subject_Early Development', 'primary_focus_subject_Economics', 'primary_focus_subject_Environmental Science', 'primary_focus_subject_Extracurricular', 'primary_focus_subject_Financial Literacy', 'primary_focus_subject_Foreign Languages', 'primary_focus_subject_Gym & Fitness', 'primary_focus_subject_Health & Life Science', 'primary_focus_subject_Health & Wellness', 'primary_focus_subject_History & Geography', 'primary_focus_subject_Literacy', 'primary_focus_subject_Literature & Writing', 'primary_focus_subject_Mathematics', 'primary_focus_subject_Music', 'primary_focus_subject_Nutrition', 'primary_focus_subject_Other', 'primary_focus_subject_Parent Involvement', 'primary_focus_subject_Performing Arts', 'primary_focus_subject_Social Sciences', 'primary_focus_subject_Special Needs', 'primary_focus_subject_Team Sports', 'primary_focus_subject_Visual Arts', 'poverty_level_high poverty', 'poverty_level_highest poverty', 'poverty_level_low poverty', 'poverty_level_moderate poverty', 'grade_level_Grades 3-5', 'grade_level_Grades 6-8', 'grade_level_Grades 9-12', 'grade_level_Grades PreK-2', 'school_metro_rural', 'school_metro_suburban', 'school_metro_urban', 'resource_type_Books', 'resource_type_Other', 'resource_type_Supplies', 'resource_type_Technology', 'resource_type_Trips', 'resource_type_Visitors', 'teacher_teach_for_america', 'optional_support'] tree = KDTree(lookup[X], metric = "chebyshev") #---------- URLS AND WEB PAGES -------------# app = flask.Flask(__name__) @app.route("/") def viz_page(): with open("dc_prediction.html", 'r') as viz_file: return viz_file.read() @app.route("/score", methods=["POST"]) def score(): data = flask.request.json x = np.matrix(data["example"]) score = PREDICTOR.predict_proba(x) dist, ind = tree.query(data["example"], k=1) new = lookup.reset_index(drop=True) proj_id = new.ix[ind[0][0]]['_projectid'] essay_text = pd.DataFrame(essay_df['essay'].loc[essay_df['_projectid'] == proj_id]) text = essay_text['essay'].values[0] user_readability = len(text) results = {"score": score[0][1], "project_text": text, "user_readability": user_readability } return flask.jsonify(results) @app.route("/grade_essay", methods=["POST"]) def grade_essay(): data = flask.request.json essay_len = len(data["essay"]) results = {"readability": essay_len} return flask.jsonify(results)

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1

0

1

0

1

0

1

null

0

10

96dfa8bbf7d2627d47fe30d7eba05510fc81666f

128,679

py

Python

msgraph-cli-extensions/beta/sites_beta/azext_sites_beta/vendored_sdks/sites/operations/_sites_operations.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

msgraph-cli-extensions/beta/sites_beta/azext_sites_beta/vendored_sdks/sites/operations/_sites_operations.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

msgraph-cli-extensions/beta/sites_beta/azext_sites_beta/vendored_sdks/sites/operations/_sites_operations.py

thewahome/msgraph-cli

33127d9efa23a0e5f5303c93242fbdbb73348671

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar, Union T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class SitesOperations(object): """SitesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~sites.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def get_analytics( self, site_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum129"]]] expand=None, # type: Optional[List[Union[str, "models.Enum130"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphItemAnalytics" """Get analytics from sites. Get analytics from sites. :param site_id: key: id of site. :type site_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum129] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum130] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphItemAnalytics, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphItemAnalytics :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphItemAnalytics"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_analytics.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphItemAnalytics', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_analytics.metadata = {'url': '/sites/{site-id}/analytics'} # type: ignore def get_ref_analytics( self, site_id, # type: str **kwargs # type: Any ): # type: (...) -> str """Get ref of analytics from sites. Get ref of analytics from sites. :param site_id: key: id of site. :type site_id: str :keyword callable cls: A custom type or function that will be passed the direct response :return: str, or the result of cls(response) :rtype: str :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[str] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_ref_analytics.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('str', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_ref_analytics.metadata = {'url': '/sites/{site-id}/analytics/$ref'} # type: ignore def set_ref_analytics( self, site_id, # type: str body, # type: Dict[str, object] **kwargs # type: Any ): # type: (...) -> None """Update the ref of navigation property analytics in sites. Update the ref of navigation property analytics in sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property ref values. :type body: dict[str, object] :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.set_ref_analytics.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, '{object}') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) set_ref_analytics.metadata = {'url': '/sites/{site-id}/analytics/$ref'} # type: ignore def delete_ref_analytics( self, site_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete ref of navigation property analytics for sites. Delete ref of navigation property analytics for sites. :param site_id: key: id of site. :type site_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_ref_analytics.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_ref_analytics.metadata = {'url': '/sites/{site-id}/analytics/$ref'} # type: ignore def list_columns( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum131"]]] select=None, # type: Optional[List[Union[str, "models.Enum132"]]] expand=None, # type: Optional[List[str]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfColumnDefinition"] """Get columns from sites. Get columns from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum131] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum132] :param expand: Expand related entities. :type expand: list[str] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfColumnDefinition or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfColumnDefinition] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfColumnDefinition"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_columns.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfColumnDefinition', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_columns.metadata = {'url': '/sites/{site-id}/columns'} # type: ignore def create_columns( self, site_id, # type: str body, # type: "models.MicrosoftGraphColumnDefinition" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphColumnDefinition" """Create new navigation property to columns for sites. Create new navigation property to columns for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphColumnDefinition :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphColumnDefinition, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphColumnDefinition :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphColumnDefinition"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_columns.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphColumnDefinition') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphColumnDefinition', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_columns.metadata = {'url': '/sites/{site-id}/columns'} # type: ignore def get_columns( self, site_id, # type: str column_definition_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum133"]]] expand=None, # type: Optional[List[str]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphColumnDefinition" """Get columns from sites. Get columns from sites. :param site_id: key: id of site. :type site_id: str :param column_definition_id: key: id of columnDefinition. :type column_definition_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum133] :param expand: Expand related entities. :type expand: list[str] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphColumnDefinition, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphColumnDefinition :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphColumnDefinition"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_columns.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'columnDefinition-id': self._serialize.url("column_definition_id", column_definition_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphColumnDefinition', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_columns.metadata = {'url': '/sites/{site-id}/columns/{columnDefinition-id}'} # type: ignore def update_columns( self, site_id, # type: str column_definition_id, # type: str body, # type: "models.MicrosoftGraphColumnDefinition" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property columns in sites. Update the navigation property columns in sites. :param site_id: key: id of site. :type site_id: str :param column_definition_id: key: id of columnDefinition. :type column_definition_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphColumnDefinition :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_columns.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'columnDefinition-id': self._serialize.url("column_definition_id", column_definition_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphColumnDefinition') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_columns.metadata = {'url': '/sites/{site-id}/columns/{columnDefinition-id}'} # type: ignore def delete_columns( self, site_id, # type: str column_definition_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property columns for sites. Delete navigation property columns for sites. :param site_id: key: id of site. :type site_id: str :param column_definition_id: key: id of columnDefinition. :type column_definition_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_columns.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'columnDefinition-id': self._serialize.url("column_definition_id", column_definition_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_columns.metadata = {'url': '/sites/{site-id}/columns/{columnDefinition-id}'} # type: ignore def list_content_types( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum134"]]] select=None, # type: Optional[List[Union[str, "models.Enum135"]]] expand=None, # type: Optional[List[Union[str, "models.Enum136"]]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfContentType"] """Get contentTypes from sites. Get contentTypes from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum134] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum135] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum136] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfContentType or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfContentType] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfContentType"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_content_types.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfContentType', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_content_types.metadata = {'url': '/sites/{site-id}/contentTypes'} # type: ignore def create_content_types( self, site_id, # type: str body, # type: "models.MicrosoftGraphContentType" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphContentType" """Create new navigation property to contentTypes for sites. Create new navigation property to contentTypes for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphContentType :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphContentType, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphContentType :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphContentType"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_content_types.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphContentType') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphContentType', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_content_types.metadata = {'url': '/sites/{site-id}/contentTypes'} # type: ignore def get_content_types( self, site_id, # type: str content_type_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum137"]]] expand=None, # type: Optional[List[Union[str, "models.Enum138"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphContentType" """Get contentTypes from sites. Get contentTypes from sites. :param site_id: key: id of site. :type site_id: str :param content_type_id: key: id of contentType. :type content_type_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum137] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum138] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphContentType, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphContentType :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphContentType"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_content_types.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'contentType-id': self._serialize.url("content_type_id", content_type_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphContentType', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_content_types.metadata = {'url': '/sites/{site-id}/contentTypes/{contentType-id}'} # type: ignore def update_content_types( self, site_id, # type: str content_type_id, # type: str body, # type: "models.MicrosoftGraphContentType" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property contentTypes in sites. Update the navigation property contentTypes in sites. :param site_id: key: id of site. :type site_id: str :param content_type_id: key: id of contentType. :type content_type_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphContentType :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_content_types.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'contentType-id': self._serialize.url("content_type_id", content_type_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphContentType') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_content_types.metadata = {'url': '/sites/{site-id}/contentTypes/{contentType-id}'} # type: ignore def delete_content_types( self, site_id, # type: str content_type_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property contentTypes for sites. Delete navigation property contentTypes for sites. :param site_id: key: id of site. :type site_id: str :param content_type_id: key: id of contentType. :type content_type_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_content_types.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'contentType-id': self._serialize.url("content_type_id", content_type_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_content_types.metadata = {'url': '/sites/{site-id}/contentTypes/{contentType-id}'} # type: ignore def get_drive( self, site_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum142"]]] expand=None, # type: Optional[List[Union[str, "models.Enum143"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphDrive" """Get drive from sites. Get drive from sites. :param site_id: key: id of site. :type site_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum142] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum143] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphDrive, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphDrive :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphDrive"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_drive.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphDrive', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_drive.metadata = {'url': '/sites/{site-id}/drive'} # type: ignore def update_drive( self, site_id, # type: str body, # type: "models.MicrosoftGraphDrive" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property drive in sites. Update the navigation property drive in sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphDrive :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_drive.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphDrive') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_drive.metadata = {'url': '/sites/{site-id}/drive'} # type: ignore def delete_drive( self, site_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property drive for sites. Delete navigation property drive for sites. :param site_id: key: id of site. :type site_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_drive.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_drive.metadata = {'url': '/sites/{site-id}/drive'} # type: ignore def list_drives( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum144"]]] select=None, # type: Optional[List[Union[str, "models.Enum145"]]] expand=None, # type: Optional[List[Union[str, "models.Enum146"]]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfDrive"] """Get drives from sites. Get drives from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum144] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum145] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum146] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfDrive or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfDrive] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfDrive"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_drives.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfDrive', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_drives.metadata = {'url': '/sites/{site-id}/drives'} # type: ignore def create_drives( self, site_id, # type: str body, # type: "models.MicrosoftGraphDrive" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphDrive" """Create new navigation property to drives for sites. Create new navigation property to drives for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphDrive :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphDrive, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphDrive :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphDrive"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_drives.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphDrive') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphDrive', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_drives.metadata = {'url': '/sites/{site-id}/drives'} # type: ignore def get_drives( self, site_id, # type: str drive_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum147"]]] expand=None, # type: Optional[List[Union[str, "models.Enum148"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphDrive" """Get drives from sites. Get drives from sites. :param site_id: key: id of site. :type site_id: str :param drive_id: key: id of drive. :type drive_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum147] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum148] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphDrive, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphDrive :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphDrive"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_drives.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'drive-id': self._serialize.url("drive_id", drive_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphDrive', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_drives.metadata = {'url': '/sites/{site-id}/drives/{drive-id}'} # type: ignore def update_drives( self, site_id, # type: str drive_id, # type: str body, # type: "models.MicrosoftGraphDrive" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property drives in sites. Update the navigation property drives in sites. :param site_id: key: id of site. :type site_id: str :param drive_id: key: id of drive. :type drive_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphDrive :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_drives.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'drive-id': self._serialize.url("drive_id", drive_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphDrive') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_drives.metadata = {'url': '/sites/{site-id}/drives/{drive-id}'} # type: ignore def delete_drives( self, site_id, # type: str drive_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property drives for sites. Delete navigation property drives for sites. :param site_id: key: id of site. :type site_id: str :param drive_id: key: id of drive. :type drive_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_drives.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'drive-id': self._serialize.url("drive_id", drive_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_drives.metadata = {'url': '/sites/{site-id}/drives/{drive-id}'} # type: ignore def list_lists( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum149"]]] select=None, # type: Optional[List[Union[str, "models.Enum150"]]] expand=None, # type: Optional[List[Union[str, "models.Enum151"]]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfList"] """Get lists from sites. Get lists from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum149] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum150] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum151] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfList or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfList] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_lists.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfList', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_lists.metadata = {'url': '/sites/{site-id}/lists'} # type: ignore def create_lists( self, site_id, # type: str body, # type: "models.MicrosoftGraphList" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphList" """Create new navigation property to lists for sites. Create new navigation property to lists for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphList :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphList, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphList :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_lists.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphList') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_lists.metadata = {'url': '/sites/{site-id}/lists'} # type: ignore def get_lists( self, site_id, # type: str list_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum152"]]] expand=None, # type: Optional[List[Union[str, "models.Enum153"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphList" """Get lists from sites. Get lists from sites. :param site_id: key: id of site. :type site_id: str :param list_id: key: id of list. :type list_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum152] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum153] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphList, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphList :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_lists.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'list-id': self._serialize.url("list_id", list_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphList', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_lists.metadata = {'url': '/sites/{site-id}/lists/{list-id}'} # type: ignore def update_lists( self, site_id, # type: str list_id, # type: str body, # type: "models.MicrosoftGraphList" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property lists in sites. Update the navigation property lists in sites. :param site_id: key: id of site. :type site_id: str :param list_id: key: id of list. :type list_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphList :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_lists.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'list-id': self._serialize.url("list_id", list_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphList') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_lists.metadata = {'url': '/sites/{site-id}/lists/{list-id}'} # type: ignore def delete_lists( self, site_id, # type: str list_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property lists for sites. Delete navigation property lists for sites. :param site_id: key: id of site. :type site_id: str :param list_id: key: id of list. :type list_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_lists.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'list-id': self._serialize.url("list_id", list_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_lists.metadata = {'url': '/sites/{site-id}/lists/{list-id}'} # type: ignore def get_activities_by_interval( self, site_id, # type: str start_date_time, # type: str end_date_time, # type: str interval, # type: str **kwargs # type: Any ): # type: (...) -> List["models.MicrosoftGraphItemActivityStat"] """Invoke function getActivitiesByInterval. Invoke function getActivitiesByInterval. :param site_id: key: id of site. :type site_id: str :param start_date_time: :type start_date_time: str :param end_date_time: :type end_date_time: str :param interval: :type interval: str :keyword callable cls: A custom type or function that will be passed the direct response :return: list of MicrosoftGraphItemActivityStat, or the result of cls(response) :rtype: list[~sites.models.MicrosoftGraphItemActivityStat] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["models.MicrosoftGraphItemActivityStat"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_activities_by_interval.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'startDateTime': self._serialize.url("start_date_time", start_date_time, 'str'), 'endDateTime': self._serialize.url("end_date_time", end_date_time, 'str'), 'interval': self._serialize.url("interval", interval, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[MicrosoftGraphItemActivityStat]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_activities_by_interval.metadata = {'url': '/sites/{site-id}/microsoft.graph.getActivitiesByInterval(startDateTime=\'{startDateTime}\',endDateTime=\'{endDateTime}\',interval=\'{interval}\')'} # type: ignore def get_by_path( self, site_id, # type: str path, # type: str **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphSite" """Invoke function getByPath. Invoke function getByPath. :param site_id: key: id of site. :type site_id: str :param path: :type path: str :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphSite, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphSite :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphSite"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_by_path.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'path': self._serialize.url("path", path, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphSite', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_by_path.metadata = {'url': '/sites/{site-id}/microsoft.graph.getByPath(path=\'{path}\')'} # type: ignore def list_pages( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum219"]]] select=None, # type: Optional[List[Union[str, "models.Enum220"]]] expand=None, # type: Optional[List[Union[str, "models.Enum221"]]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfSitePage"] """Get pages from sites. Get pages from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum219] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum220] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum221] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfSitePage or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfSitePage] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfSitePage"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_pages.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfSitePage', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_pages.metadata = {'url': '/sites/{site-id}/pages'} # type: ignore def create_pages( self, site_id, # type: str body, # type: "models.MicrosoftGraphSitePage" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphSitePage" """Create new navigation property to pages for sites. Create new navigation property to pages for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphSitePage :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphSitePage, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphSitePage :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphSitePage"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_pages.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphSitePage') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphSitePage', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_pages.metadata = {'url': '/sites/{site-id}/pages'} # type: ignore def get_pages( self, site_id, # type: str site_page_id, # type: str select=None, # type: Optional[List[Union[str, "models.Enum222"]]] expand=None, # type: Optional[List[Union[str, "models.Enum223"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphSitePage" """Get pages from sites. Get pages from sites. :param site_id: key: id of site. :type site_id: str :param site_page_id: key: id of sitePage. :type site_page_id: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum222] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum223] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphSitePage, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphSitePage :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphSitePage"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_pages.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'sitePage-id': self._serialize.url("site_page_id", site_page_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphSitePage', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_pages.metadata = {'url': '/sites/{site-id}/pages/{sitePage-id}'} # type: ignore def update_pages( self, site_id, # type: str site_page_id, # type: str body, # type: "models.MicrosoftGraphSitePage" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property pages in sites. Update the navigation property pages in sites. :param site_id: key: id of site. :type site_id: str :param site_page_id: key: id of sitePage. :type site_page_id: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphSitePage :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_pages.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'sitePage-id': self._serialize.url("site_page_id", site_page_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphSitePage') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_pages.metadata = {'url': '/sites/{site-id}/pages/{sitePage-id}'} # type: ignore def delete_pages( self, site_id, # type: str site_page_id, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property pages for sites. Delete navigation property pages for sites. :param site_id: key: id of site. :type site_id: str :param site_page_id: key: id of sitePage. :type site_page_id: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_pages.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'sitePage-id': self._serialize.url("site_page_id", site_page_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_pages.metadata = {'url': '/sites/{site-id}/pages/{sitePage-id}'} # type: ignore def list_sites( self, site_id, # type: str orderby=None, # type: Optional[List[Union[str, "models.Enum224"]]] select=None, # type: Optional[List[Union[str, "models.Enum225"]]] expand=None, # type: Optional[List[Union[str, "models.Enum226"]]] **kwargs # type: Any ): # type: (...) -> Iterable["models.CollectionOfSite1"] """Get sites from sites. Get sites from sites. :param site_id: key: id of site. :type site_id: str :param orderby: Order items by property values. :type orderby: list[str or ~sites.models.Enum224] :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum225] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum226] :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CollectionOfSite1 or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~sites.models.CollectionOfSite1] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.CollectionOfSite1"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_sites.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if self._config.top is not None: query_parameters['$top'] = self._serialize.query("self._config.top", self._config.top, 'int', minimum=0) if self._config.skip is not None: query_parameters['$skip'] = self._serialize.query("self._config.skip", self._config.skip, 'int', minimum=0) if self._config.search is not None: query_parameters['$search'] = self._serialize.query("self._config.search", self._config.search, 'str') if self._config.filter is not None: query_parameters['$filter'] = self._serialize.query("self._config.filter", self._config.filter, 'str') if self._config.count is not None: query_parameters['$count'] = self._serialize.query("self._config.count", self._config.count, 'bool') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('CollectionOfSite1', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.odata_next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: error = self._deserialize(models.OdataError, response) map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_sites.metadata = {'url': '/sites/{site-id}/sites'} # type: ignore def create_sites( self, site_id, # type: str body, # type: "models.MicrosoftGraphSite" **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphSite" """Create new navigation property to sites for sites. Create new navigation property to sites for sites. :param site_id: key: id of site. :type site_id: str :param body: New navigation property. :type body: ~sites.models.MicrosoftGraphSite :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphSite, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphSite :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphSite"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create_sites.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphSite') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphSite', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_sites.metadata = {'url': '/sites/{site-id}/sites'} # type: ignore def get_sites( self, site_id, # type: str site_id1, # type: str select=None, # type: Optional[List[Union[str, "models.Enum227"]]] expand=None, # type: Optional[List[Union[str, "models.Enum228"]]] **kwargs # type: Any ): # type: (...) -> "models.MicrosoftGraphSite" """Get sites from sites. Get sites from sites. :param site_id: key: id of site. :type site_id: str :param site_id1: key: id of site. :type site_id1: str :param select: Select properties to be returned. :type select: list[str or ~sites.models.Enum227] :param expand: Expand related entities. :type expand: list[str or ~sites.models.Enum228] :keyword callable cls: A custom type or function that will be passed the direct response :return: MicrosoftGraphSite, or the result of cls(response) :rtype: ~sites.models.MicrosoftGraphSite :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.MicrosoftGraphSite"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.get_sites.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'site-id1': self._serialize.url("site_id1", site_id1, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if select is not None: query_parameters['$select'] = self._serialize.query("select", select, '[str]', div=',') if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, '[str]', div=',') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('MicrosoftGraphSite', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_sites.metadata = {'url': '/sites/{site-id}/sites/{site-id1}'} # type: ignore def update_sites( self, site_id, # type: str site_id1, # type: str body, # type: "models.MicrosoftGraphSite" **kwargs # type: Any ): # type: (...) -> None """Update the navigation property sites in sites. Update the navigation property sites in sites. :param site_id: key: id of site. :type site_id: str :param site_id1: key: id of site. :type site_id1: str :param body: New navigation property values. :type body: ~sites.models.MicrosoftGraphSite :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.update_sites.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'site-id1': self._serialize.url("site_id1", site_id1, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'MicrosoftGraphSite') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) update_sites.metadata = {'url': '/sites/{site-id}/sites/{site-id1}'} # type: ignore def delete_sites( self, site_id, # type: str site_id1, # type: str if_match=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Delete navigation property sites for sites. Delete navigation property sites for sites. :param site_id: key: id of site. :type site_id: str :param site_id1: key: id of site. :type site_id1: str :param if_match: ETag. :type if_match: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delete_sites.metadata['url'] # type: ignore path_format_arguments = { 'site-id': self._serialize.url("site_id", site_id, 'str'), 'site-id1': self._serialize.url("site_id1", site_id1, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] if if_match is not None: header_parameters['If-Match'] = self._serialize.header("if_match", if_match, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_sites.metadata = {'url': '/sites/{site-id}/sites/{site-id1}'} # type: ignore def add( self, body, # type: "models.PathsV2U0Z1SitesMicrosoftGraphAddPostRequestbodyContentApplicationJsonSchema" **kwargs # type: Any ): # type: (...) -> List["models.MicrosoftGraphSite"] """Invoke action add. Invoke action add. :param body: Action parameters. :type body: ~sites.models.PathsV2U0Z1SitesMicrosoftGraphAddPostRequestbodyContentApplicationJsonSchema :keyword callable cls: A custom type or function that will be passed the direct response :return: list of MicrosoftGraphSite, or the result of cls(response) :rtype: list[~sites.models.MicrosoftGraphSite] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["models.MicrosoftGraphSite"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.add.metadata['url'] # type: ignore # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'PathsV2U0Z1SitesMicrosoftGraphAddPostRequestbodyContentApplicationJsonSchema') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[MicrosoftGraphSite]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized add.metadata = {'url': '/sites/microsoft.graph.add'} # type: ignore def delta( self, **kwargs # type: Any ): # type: (...) -> List["models.MicrosoftGraphSite"] """Invoke function delta. Invoke function delta. :keyword callable cls: A custom type or function that will be passed the direct response :return: list of MicrosoftGraphSite, or the result of cls(response) :rtype: list[~sites.models.MicrosoftGraphSite] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["models.MicrosoftGraphSite"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) accept = "application/json" # Construct URL url = self.delta.metadata['url'] # type: ignore # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[MicrosoftGraphSite]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized delta.metadata = {'url': '/sites/microsoft.graph.delta()'} # type: ignore def remove( self, body, # type: "models.Paths8Behs0SitesMicrosoftGraphRemovePostRequestbodyContentApplicationJsonSchema" **kwargs # type: Any ): # type: (...) -> List["models.MicrosoftGraphSite"] """Invoke action remove. Invoke action remove. :param body: Action parameters. :type body: ~sites.models.Paths8Behs0SitesMicrosoftGraphRemovePostRequestbodyContentApplicationJsonSchema :keyword callable cls: A custom type or function that will be passed the direct response :return: list of MicrosoftGraphSite, or the result of cls(response) :rtype: list[~sites.models.MicrosoftGraphSite] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["models.MicrosoftGraphSite"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.remove.metadata['url'] # type: ignore # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(body, 'Paths8Behs0SitesMicrosoftGraphRemovePostRequestbodyContentApplicationJsonSchema') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(models.OdataError, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[MicrosoftGraphSite]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized remove.metadata = {'url': '/sites/microsoft.graph.remove'} # type: ignore

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159

py

Python

cashaccount/__init__.py

YumenoG/pycashaccount

f2a22eec729cc7b608241e1632d2ccde5fcc3bbc

[ "MIT" ]

2

2019-02-20T12:28:19.000Z

2019-02-20T12:28:22.000Z

cashaccount/__init__.py

YumenoG/pycashaccount

f2a22eec729cc7b608241e1632d2ccde5fcc3bbc

[ "MIT" ]

5

2019-01-03T19:35:17.000Z

2019-02-20T12:34:11.000Z

cashaccount/__init__.py

YumenoG/pycashaccount

f2a22eec729cc7b608241e1632d2ccde5fcc3bbc

[ "MIT" ]

1

2019-10-16T11:30:33.000Z

from .payment import KeyHashInfo, ScriptHashInfo, PaymentCodeInfo from .registration import Registration from .registration import electron_markdown, opreturn

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44,106

py

Python

venv/lib/python3.8/site-packages/azureml/_restclient/operations/run_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/azureml/_restclient/operations/run_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/azureml/_restclient/operations/run_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator 2.3.33.0 # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.pipeline import ClientRawResponse from .. import models class RunOperations(object): """RunOperations operations. :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self.config = config def get_child( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, filter=None, continuation_token=None, orderby=None, sortorder=None, top=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param filter: :type filter: str :param continuation_token: :type continuation_token: str :param orderby: :type orderby: list[str] :param sortorder: Possible values include: 'Asc', 'Desc' :type sortorder: str :param top: :type top: int :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: PaginatedRunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.PaginatedRunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_child.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if filter is not None: query_parameters['$filter'] = self._serialize.query("filter", filter, 'str') if continuation_token is not None: query_parameters['$continuationToken'] = self._serialize.query("continuation_token", continuation_token, 'str') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if sortorder is not None: query_parameters['$sortorder'] = self._serialize.query("sortorder", sortorder, 'str') if top is not None: query_parameters['$top'] = self._serialize.query("top", top, 'int') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('PaginatedRunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_child.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/children'} def get_token( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: TokenResult or ClientRawResponse if raw=true :rtype: ~_restclient.models.TokenResult or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_token.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('TokenResult', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_token.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/token'} def get_details( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDetailsDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDetailsDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_details.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDetailsDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_details.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/details'} def get( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}'} def patch( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, create_run_dto=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param create_run_dto: :type create_run_dto: ~_restclient.models.CreateRunDto :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.patch.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body if create_run_dto is not None: body_content = self._serialize.body(create_run_dto, 'CreateRunDto') else: body_content = None # Construct and send request request = self._client.patch(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized patch.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}'} def get_by_exp_id( self, subscription_id, resource_group_name, workspace_name, experiment_id, run_id, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_id: :type experiment_id: str :param run_id: :type run_id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_by_exp_id.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentId': self._serialize.url("experiment_id", experiment_id, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_by_exp_id.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experimentids/{experimentId}/runs/{runId}'} def patch_by_exp_id( self, subscription_id, resource_group_name, workspace_name, experiment_id, run_id, create_run_dto=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_id: :type experiment_id: str :param run_id: :type run_id: str :param create_run_dto: :type create_run_dto: ~_restclient.models.CreateRunDto :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.patch_by_exp_id.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentId': self._serialize.url("experiment_id", experiment_id, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body if create_run_dto is not None: body_content = self._serialize.body(create_run_dto, 'CreateRunDto') else: body_content = None # Construct and send request request = self._client.patch(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized patch_by_exp_id.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experimentids/{experimentId}/runs/{runId}'} def batch_add_or_modify( self, subscription_id, resource_group_name, workspace_name, experiment_name, request_dto, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param request_dto: :type request_dto: ~_restclient.models.BatchAddOrModifyRunRequestDto :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: BatchAddOrModifyRunResultDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.BatchAddOrModifyRunResultDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.batch_add_or_modify.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(request_dto, 'BatchAddOrModifyRunRequestDto') # Construct and send request request = self._client.patch(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('BatchAddOrModifyRunResultDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized batch_add_or_modify.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/batch/runs'} def get_by_query( self, subscription_id, resource_group_name, workspace_name, experiment_name, query_params=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param query_params: :type query_params: ~_restclient.models.QueryParamsDto :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: PaginatedRunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.PaginatedRunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_by_query.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body if query_params is not None: body_content = self._serialize.body(query_params, 'QueryParamsDto') else: body_content = None # Construct and send request request = self._client.post(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('PaginatedRunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_by_query.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs:query'} def list_by_compute( self, subscription_id, resource_group_name, workspace_name, compute_name, filter=None, continuation_token=None, top=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param compute_name: :type compute_name: str :param filter: :type filter: str :param continuation_token: :type continuation_token: str :param top: :type top: int :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: PaginatedRunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.PaginatedRunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.list_by_compute.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'computeName': self._serialize.url("compute_name", compute_name, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if filter is not None: query_parameters['$filter'] = self._serialize.query("filter", filter, 'str') if continuation_token is not None: query_parameters['$continuationToken'] = self._serialize.query("continuation_token", continuation_token, 'str') if top is not None: query_parameters['$top'] = self._serialize.query("top", top, 'int') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('PaginatedRunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized list_by_compute.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/runs'} def get_counts( self, subscription_id, resource_group_name, workspace_name, experiment_name, filter=None, continuation_token=None, orderby=None, sortorder=None, top=None, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param filter: :type filter: str :param continuation_token: :type continuation_token: str :param orderby: :type orderby: list[str] :param sortorder: Possible values include: 'Asc', 'Desc' :type sortorder: str :param top: :type top: int :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunCountsDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunCountsDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_counts.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if filter is not None: query_parameters['$filter'] = self._serialize.query("filter", filter, 'str') if continuation_token is not None: query_parameters['$continuationToken'] = self._serialize.query("continuation_token", continuation_token, 'str') if orderby is not None: query_parameters['$orderby'] = self._serialize.query("orderby", orderby, '[str]', div=',') if sortorder is not None: query_parameters['$sortorder'] = self._serialize.query("sortorder", sortorder, 'str') if top is not None: query_parameters['$top'] = self._serialize.query("top", top, 'int') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunCountsDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_counts.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runcounts'} def delete_tags( self, subscription_id, resource_group_name, workspace_name, experiment_name, run_id, tags, custom_headers=None, raw=False, **operation_config): """ :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param experiment_name: :type experiment_name: str :param run_id: :type run_id: str :param tags: :type tags: list[str] :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.delete_tags.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'experimentName': self._serialize.url("experiment_name", experiment_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(tags, '[str]') # Construct and send request request = self._client.delete(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized delete_tags.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/tags'} def get_workspace_run( self, subscription_id, resource_group_name, workspace_name, run_id, custom_headers=None, raw=False, **operation_config): """Gets the specified run within the specified workspace. Gets the specified run within the specified workspace. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param run_id: :type run_id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: RunDto or ClientRawResponse if raw=true :rtype: ~_restclient.models.RunDto or ~msrest.pipeline.ClientRawResponse :raises: :class:`ErrorResponseException<_restclient.models.ErrorResponseException>` """ # Construct URL url = self.get_workspace_run.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'runId': self._serialize.url("run_id", run_id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('RunDto', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized get_workspace_run.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/runs/{runId}'}

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1

0

null

0

7

8b193a7cb99a37671658cdc6e43a0663925262d0

113

py

Python

LnkParse3/target/printers.py

ernix/LnkParse3

ab8b2c796a501b103eb74142762e7fe9f4f1960a

[ "MIT" ]

null

LnkParse3/target/printers.py

ernix/LnkParse3

ab8b2c796a501b103eb74142762e7fe9f4f1960a

[ "MIT" ]

null

LnkParse3/target/printers.py

ernix/LnkParse3

ab8b2c796a501b103eb74142762e7fe9f4f1960a

[ "MIT" ]

null

from LnkParse3.target.lnk_target_base import LnkTargetBase class Printers(LnkTargetBase): # TODO: pass

16.142857

58

0.769912

13

113

6.538462

0.846154

0

0.010638

0.168142

113

6

59

18.833333

0.893617

0.044248

0

0.166667

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

7

8b2d02959ac26136a5b5ef98911ad974c5416e52

112

py

Python

snapx/snapx/algorithms/shortest_paths/__init__.py

ruth-ann/snap-python

fe98de7b5697b3d60eb3497893e24801ae1916f9

[ "BSD-3-Clause" ]

242

2015-01-01T08:40:28.000Z

2022-03-18T05:22:09.000Z

snapx/snapx/algorithms/shortest_paths/__init__.py

ruth-ann/snap-python

fe98de7b5697b3d60eb3497893e24801ae1916f9

[ "BSD-3-Clause" ]

99

2015-01-24T07:55:27.000Z

2021-10-30T18:20:13.000Z

snapx/snapx/algorithms/shortest_paths/__init__.py

ruth-ann/snap-python

fe98de7b5697b3d60eb3497893e24801ae1916f9

[ "BSD-3-Clause" ]

105

2015-03-03T06:45:17.000Z

2022-02-24T15:52:40.000Z

from snapx.algorithms.shortest_paths.weighted import * from snapx.algorithms.shortest_paths.unweighted import *

37.333333

56

0.857143

14

112

6.714286

0.571429

0.191489

0.404255

0.574468

0.680851

0

0.071429

112

2

57

56

0.903846

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

8c7560f03dbb3526ccb30d3de812d6405f5d7597

173,875

py

Python

CEDA/macroecon/eu.py

TerenceLiu98/CEDApy

14f21f6a8e83bd62afbf9be1465000db3c6b6dce

[ "MIT" ]

null

CEDA/macroecon/eu.py

TerenceLiu98/CEDApy

14f21f6a8e83bd62afbf9be1465000db3c6b6dce

[ "MIT" ]

null

CEDA/macroecon/eu.py

TerenceLiu98/CEDApy

14f21f6a8e83bd62afbf9be1465000db3c6b6dce

[ "MIT" ]

null

import io import os import demjson import requests import numpy as np import pandas as pd from fake_useragent import UserAgent from pandas.core.frame import DataFrame from pandas.core.reshape.merge import merge # Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205 url = { "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?", "eurostat": "http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/", "ecb": "https://sdw-wsrest.ecb.europa.eu/service/data/", "OECD": "https://stats.oecd.org/sdmx-json/data/DP_LIVE/" } def merge_data(data_1: pd.DataFrame, data_2: pd.DataFrame, col_name: str): data = pd.merge_asof(data_1, data_2, on=col_name) return data def National_Account(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=NAEXCP04EZQ189S,NAEXCP02EZQ189S,NAEXCP01EZQ189S,NAEXCP06EZQ189S,NAEXCP07EZQ189S,NAEXCP03EZQ189S,NAGIGP01EZQ661S,NAEXKP06EZQ659S,NAEXKP04EZQ659S,NAEXKP01EZQ652S,NAEXKP07EZQ652S,NAEXKP03EZQ659S&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1996-01-01,1996-01-01,1995-01-01,1995-01-01,1996-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'NAEXCP04EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Gross Fixed Capital Formation for the Euro Area", 'NAEXCP02EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Private Final Consumption Expenditure for the Euro Area", 'NAEXCP01EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Total Gross Domestic Product for the Euro Area", 'NAEXCP06EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Exports of Goods and Services for the Euro Area", 'NAEXCP07EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Less Imports of Goods and Services for the Euro Area", 'NAEXCP03EZQ189S': "Gross Domestic Product by Expenditure in Current Prices: Government Final Consumption Expenditure for the Euro Area", 'NAGIGP01EZQ661S': "Gross Domestic Product Deflator for the Euro Area", 'NAEXKP06EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Exports of Goods and Services for the Euro Area", 'NAEXKP04EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Gross Fixed Capital Formation for the Euro Area", 'NAEXKP01EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for the Euro Area", 'NAEXKP07EZQ652S': "Gross Domestic Product by Expenditure in Constant Prices: Less: Imports of Goods and Services for the Euro Area", 'NAEXKP03EZQ659S': "Gross Domestic Product by Expenditure in Constant Prices: Government Final Consumption Expenditure for the Euro Area"} description = "National Accounts, Quarterly, Seasonally, Adjusted" return df, name_list, description def International_Trade(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=XTEXVA01EZQ188S,XTIMVA01EZQ188S,EA19XTNTVA01STSAQ&scale=left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2017-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2017-04-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'XTEXVA01EZQ188S': "Exports: Value Goods for the Euro Area", 'XTIMVA01EZQ188SS': "Imports: Value Goods for the Euro Area", 'EA19XTNTVA01STSAQ': "International Trade: Net trade: Value (goods): Total for the Euro Area"} description = "International Trade, Quarterly, Seasonally Adjusted" return df, name_list, description def Balance_of_Payments_BPM6(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19B6BLTT02STSAQ,EA19B6DBSE02STSAQ,EA19B6DBSE03STSAQ,EA19B6CRSE03STSAQ,EA19B6CRSE02STSAQ&scale=left,left,left,left,left&cosd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01&coed=2020-10-01,2020-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1999-01-01,1999-01-01,1999-01-01,1999-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19B6BLTT02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area", 'EA19B6DBSE02STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Current account for the Euro Area", 'EA19B6DBSE03STSAQ': "Balance of payments BPM6: Current account Debits: Services: Total Debits as % of Goods and Services for the Euro Area", 'EA19B6CRSE03STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Goods and Services for Euro Area", 'EA19B6CRSE02STSAQ': "Balance of payments BPM6: Current account Credits: Services: Total Credits as % of Current account for Euro Area"} description = "Balanced of payments BPM6, Quarterly, Seasonally Adjusted" return df, name_list, description def Leading_Indicators_OECD(startdate = "1950-01", enddate = "2021-05"): # CLI tmp_url = url["OECD"] + "EA19.CLI.AMPLITUD.LTRENDIDX.M/OECD" ua = UserAgent(verify_ssl=False) request_params = { "contentType": "csv", "detail": "code", "separator": "comma", "csv-lang": "en", "startPeriod": "{}".format(startdate), "endPeriod": "{}".format(enddate) } request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, params = request_params, headers=request_header) data_text = r.content df_cli = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]] df_cli.columns = ["Date", "EU_OECD_CLI"] df_cli["Date"] = pd.to_datetime(df_cli["Date"], format = "%Y-%m") df_cli["EU_OECD_CLI"] = df_cli["EU_OECD_CLI"].astype(float) #BCI tmp_url = url["OECD"] + "EA19.BCI.AMPLITUD.LTRENDIDX.M/OECD" ua = UserAgent(verify_ssl=False) request_params = { "contentType": "csv", "detail": "code", "separator": "comma", "csv-lang": "en", "startPeriod": "{}".format(startdate), "endPeriod": "{}".format(enddate) } request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, params = request_params, headers=request_header) data_text = r.content df_bci = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]] df_bci.columns = ["Date", "EU_OECD_BCI"] df_bci["Date"] = pd.to_datetime(df_bci["Date"], format = "%Y-%m") df_bci["EU_OECD_BCI"] = df_bci["EU_OECD_BCI"].astype(float) # CCI tmp_url = url["OECD"] + "EA19.CCI.AMPLITUD.LTRENDIDX.M/OECD" ua = UserAgent(verify_ssl=False) request_params = { "contentType": "csv", "detail": "code", "separator": "comma", "csv-lang": "en", "startPeriod": "{}".format(startdate), "endPeriod": "{}".format(enddate) } request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, params = request_params, headers=request_header) data_text = r.content df_cci = pd.read_csv(io.StringIO(data_text.decode('utf-8')))[["TIME", "Value"]] df_cci.columns = ["Date", "EU_OECD_CCI"] df_cci["Date"] = pd.to_datetime(df_cci["Date"], format = "%Y-%m") df_cci["EU_OECD_CCI"] = df_cci["EU_OECD_CCI"].astype(float) df = pd.merge_asof(df_cli, df_bci, on = "Date") df = pd.merge_asof(df, df_cci, on = "Date") return df def Monetary_Aggregates_Monthly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19MABMM301GYSAM,EA19MANMM101IXOBSAM&scale=left,left&cosd=1971-01-01,1970-01-01&coed=2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Monthly,Monthly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1970-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = {'EA19MABMM301GYSAM': "Monetary aggregates and their components: Broad money and components: M3: M3 for the Euro Area", 'EA19MANMM101IXOBSAM': "Monetary aggregates and their components: Narrow money and components: M1 and components: M1 for the Euro Area"} description = "Monetary aggregates and their components, Monthly, Seasonally Adjusted" return df, name_list, description def Monetary_Aggregates_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MABMM301EZQ189S,MANMM101EZQ189S&scale=left,left&cosd=1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1970-01-01,1970-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'MABMM301EZQ189S': "M3 for the Euro Area", 'MANMM101EZQ189S': "M1 for the Euro Area" } description = "Monetary aggregates and their components, Quarterly, Seasonally Adjusted" return df, name_list, description def Currency_Conversion_Quarterly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCEUSP02EZQ655N,CCUSMA02EZQ618N,CCUSSP01EZQ650N,CCRETT02EZQ661N,CCRETT01EZQ661N&scale=left,left,left,left,left&cosd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1999-01-01,1979-01-01,1999-01-01,1970-01-01,1970-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'CCEUSP02EZQ655N': "National Currency to Euro Spot Exchange Rate for the Euro Area", 'CCUSMA02EZQ618N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area", 'CCUSSP01EZQ650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area", 'CCRETT02EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Unit Labor Cost for the Euro Area", 'CCRETT01EZQ661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area"} description = "Currency Conversions, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Currency_Conversion_Monthly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CCRETT01EZM661N,CCUSMA02EZM659N,CCUSSP01EZM650N,CCEUSP02EZM655N&scale=left,left,left,left&cosd=1970-01-01,1991-01-01,1999-01-01,1999-01-01&coed=2021-04-01,2021-04-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1991-01-01,1999-01-01,1999-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'CCRETT01EZM661N': "Real Effective Exchange Rates Based on Manufacturing Consumer Price Index for the Euro Area", 'CCUSMA02EZM659N': "National Currency to US Dollar Exchange Rate: Average of Daily Rates for the Euro Area", 'CCUSSP01EZM650N': "US Dollar to National Currency Spot Exchange Rate for the Euro Area", 'CCEUSP02EZM655N': "National Currency to Euro Spot Exchange Rate for the Euro Area"} description = "Currency Conversions, Monthly, Not Seasonally Adjusted" return df, name_list, description def Interest_Rates_Quarterly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZQ156N,IR3TIB01EZQ156N,IRSTCI01EZQ156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'IRLTLT01EZQ156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area", 'IR3TIB01EZQ156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area", 'IRSTCI01EZQ156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"} description = "Interest Rates, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Interest_Rates_Monthly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=IRLTLT01EZM156N,IR3TIB01EZM156N,IRSTCI01EZM156N&scale=left,left,left&cosd=1970-01-01,1994-01-01,1994-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1970-01-01,1994-01-01,1994-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'IRLTLT01EZM156N': "Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the Euro Area", 'IR3TIB01EZM156N': "3-Month or 90-day Rates and Yields: Interbank Rates for the Euro Area", 'IRSTCI01EZM156N': "Immediate Rates: Less than 24 Hours: Call Money/Interbank Rate for the Euro Area"} description = "Interest Rates, Monthly, Not Seasonally Adjusted" return df, name_list, description def Share_Prices_Quarterly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZQ661N&scale=left&cosd=1987-01-01&coed=2021-01-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Quarterly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1987-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'SPASTT01EZQ661N': "Total Share Prices for All Shares for the Euro Area"} description = "Share Prices, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Share_Prices_Monthly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SPASTT01EZM661N&scale=left&cosd=1986-12-01&coed=2021-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2021-06-07&revision_date=2021-06-07&nd=1986-12-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'SPASTT01EZM661N': "Total Share Prices for All Shares for the Euro Area"} description = "Share Prices, Monthly, Not Seasonally Adjusted" return df, name_list, description def CPI_Monthly(startdate="1970-01-01", enddate="2021-01-01"): """ """ tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CPHPTT01EZM661N,EA19CPHP0401IXOBM,EA19CPHP0403IXOBM,EA19CPHP0404IXOBM,EA19CPHP0405IXOBM,EA19CPHP0500IXOBM,EA19CPHP0600IXOBM,EA19CPHP0700IXOBM,EA19CPHP0702IXOBM,EA19CPHP0800IXOBM,EA19CPHP0900IXOBM,CPHPEN01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01,1996-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { "CPHPTT01EZM661N": "CPI:Harmonized Prices: Total All Items for the Euro Area", "EA19CPHP0401IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Actual rentals for housing for the Euro Area", "EA19CPHP0403IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance & repairs of the dwellings for the Euro Area", "EA19CPHP0404IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Water supply and miscellaneous services relating to the dwelling for the Euro Area", "EA19CPHP0405IXOBM": "CPI:Harmonised_Price:Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels for the Euro Area", "EA19CPHP0500IXOBM": "CPI:Harmonised_Price:Furnishings, household equip. and routine household maintenance (COICOP 05): Total for the Euro Area ", "EA19CPHP0600IXOBM": "CPI:Harmonised_Price:Health (COICOP 06): Total for the Euro Area", "EA19CPHP0700IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Total for the Euro Area", "EA19CPHP0702IXOBM": "CPI:Harmonised_Price:Transport (COICOP 07): Fuels and lubricants for personal transport equipment for the Euro Area", "EA19CPHP0800IXOBM": "CPI:Harmonised_Price:Communication (COICOP 08): Total for the Euro Area", "EA19CPHP0900IXOBM": "CPI:Harmonised_Price:Recreation and culture (COICOP 09): Total for the Euro Area", "CPHPEN01EZM661N": "CPI:Harmonized Prices: Total Energy for the Euro Area"} description = "Consumer Price Index, Monthly, Not Seasonally Adjusted" return df, name_list, description def CPI_Quarterly(): """ """ tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19CPALTT01GYQ,EA19CPGRLE01GYQ,EA19CPGREN01GYQ,EA19CPHP0401IXOBQ&scale=left,left,left,left&cosd=1991-01-01,1997-01-01,1997-01-01,1996-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b&link_values=false,false,false,false&line_style=solid,solid,solid,solid&mark_type=none,none,none,none&mw=3,3,3,3&lw=2,2,2,2&ost=-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999&mma=0,0,0,0&fml=a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg&fgst=lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4&transformation=lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1991-01-01,1997-01-01,1997-01-01,1996-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19CPALTT01GYQ': "CPI:All items:Total:Total for the Euro Area", 'EA19CPGRLE01GYQ': "CPI:OECD Groups:All items non-food non-energy:Total for the Euro Area", 'EA19CPGREN01GYQ': "CPI:OECD Groups:Energy (Fuel, electricity & gasoline):Total for the Euro Area", 'EA19CPHP0401IXOBQ': "CPI:Harmonised prices:Housing, water, electricity, gas and other fuels (COICOP 04):Actual rentals for housing for the Euro Area"} description = "Consumer Price Index, Quarterly, Not Seasonally Adjusted" return df, name_list, description def PPI_Monthly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAMP02EZM659N,PIEAMP01EZM661N,PIEATI01EZM661N,PIEATI02EZM661N,PITGND02EZM661N,PITGND01EZM661N,PITGIG01EZM661N,PITGIG02EZM661N,PIEAFD02EZM661N,PITGCG02EZM661N,PITGCG01EZM661N,PITGCD01EZM661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01&coed=2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01,2021-03-01,2021-03-01,2021-03-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1996-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'PIEAMP02EZM659N': "Producer Prices Index: Economic Activities: Domestic Manufacturing for the Euro Area", "PIEAMP01EZM661N": "Producer Prices Index: Economic Activities: Total Manufacturing for the Euro Area", "PIEATI01EZM661N": "Producer Prices Index: Economic Activities: Total Industrial Activities for the Euro Area", "PIEATI02EZM661N": "Producer Prices Index: Economic Activities: Domestic Industrial Activities for the Euro Area", "PITGND02EZM661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area", "PITGND01EZM661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area", "PITGIG01EZM661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area", "PITGIG02EZM661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area", "PIEAFD02EZM661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area", "PITGCG02EZM661N": "Producer Prices Index: Domestic Consumer Goods for the Euro Area", "PITGCG01EZM661N": "Producer Prices Index: Total Consumer Goods for the Euro Area", "PITGCD01EZM661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area"} description = "Producer Prices Index, Monthly, Not Seasonally Adjusted" return df, name_list, description def PPI_Quarterly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PIEAFD01EZQ661N,PIEAEN02EZQ661N,PIEAEN01EZQ661N,PITGND02EZQ661N,PITGND01EZQ661N,PITGIG01EZQ661N,PITGIG02EZQ661N,PIEAFD02EZQ661N,PITGCD02EZQ661N,PITGCD01EZQ661N,PITGVG01EZQ661N,PITGVG02EZQ661N&scale=left,left,left,left,left,left,left,left,left,left,left,left&cosd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01&coed=2020-10-01,2021-01-01,2020-10-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01,2021-01-01,2021-01-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92,%2391e8e1,%238d4653,%238085e8&link_values=false,false,false,false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9,10,11,12&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=2000-01-01,2000-01-01,2000-01-01,1995-01-01,2000-01-01,2000-01-01,1995-01-01,1995-01-01,2000-01-01,2000-01-01,2000-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'PIEAFD01EZQ661N': "Producer Prices Index: Economic Activities: Total Manufacture of Food Products for the Euro Area", "PIEAEN02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Energy for the Euro Area", "PIEAEN01EZQ661N": "Producer Prices Index: Economic Activities: Total Energy for the Euro Area", "PITGND02EZQ661N": "Producer Prices Index: Domestic Nondurable Consumer Goods for the Euro Area", "PITGND01EZQ661N": "Producer Prices Index: Total Nondurable Consumer Goods for the Euro Area", "PITGIG01EZQ661N": "Producer Prices Index: Total Intermediate Goods for the Euro Area", "PITGIG02EZQ661N": "Producer Prices Index: Domestic Intermediate Goods for the Euro Area", "PIEAFD02EZQ661N": "Producer Prices Index: Economic Activities: Domestic Manufacture of Food Products for the Euro Area", "PITGCD02EZQ661N": "Producer Prices Index: Domestic Durable Consumer Goods for the Euro Area", "PITGCD01EZQ661N": "Producer Prices Index: Total Durable Consumer Goods for the Euro Area", "PITGVG01EZQ661N": "Producer Prices Index: Investments Goods: Total for the Euro Area", "PITGVG02EZQ661N": "Producer Prices Index: Domestic Investments Goods for the Euro Area"} description = "Producer Prices Index, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Business_Tendency_Surveys_Construction(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BCBUTE02STSAM,BCOBLV02EZM460S,BCEMFT02EZM460S,BCCICP02EZM460S,BCSPFT02EZM460S&scale=left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae&link_values=false,false,false,false,false&line_style=solid,solid,solid,solid,solid&mark_type=none,none,none,none,none&mw=3,3,3,3,3&lw=2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999&mma=0,0,0,0,0&fml=a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5&transformation=lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19BCBUTE02STSAM': "Business tendency surveys (construction): Business situation - Activity: Tendency: National indicator for the Euro Area", 'BCOBLV02EZM460S': "Business Tendency Surveys for Construction: Order Books: Level: European Commission Indicator for the Euro Area", 'BCEMFT02EZM460S': "Business Tendency Surveys for Construction: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", 'BCCICP02EZM460S': "Business Tendency Surveys for Construction: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", 'BCSPFT02EZM460S': "Business Tendency Surveys for Construction: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area"} description = "Business tendency surveys (construction), Monthly, Seasonally Adjusted" return df, name_list, description def Business_Tendency_Surveys_Services(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BVBUTE02STSAM,BVCICP02EZM460S,BVEMTE02EZM460S,BVEMFT02EZM460S,BVDEFT02EZM460S,BVDETE02EZM460S&scale=left,left,left,left,left,left&cosd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-04-01,1995-04-01,1995-04-01,1996-10-01,1995-04-01,1995-04-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19BVBUTE02STSAM': "Business tendency surveys (services): Business situation - Activity: Tendency: National indicator for Euro Area", 'BVCICP02EZM460S': "Business Tendency Surveys for Services: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", 'BVEMTE02EZM460S': "Business Tendency Surveys for Services: Employment: Tendency: European Commission Indicator for the Euro Area", 'BVEMFT02EZM460S': "Business Tendency Surveys for Services: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", 'BVDEFT02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Future Tendency: European Commission Indicator for the Euro Area", 'BVDETE02EZM460S': "Business Tendency Surveys for Services: Demand Evolution: Tendency: European Commission Indicator for the Euro Area"} description = "Business tendency surveys (services), Monthly, Seasonally Adjusted" return df, name_list, description def Business_Tendency_Surveys_Manufacturing_Quarterly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSCURT02EZQ160S,BSOITE02EZQ460S&scale=left,left&cosd=1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'BSCURT02EZQ160S': "Business Tendency Surveys for Manufacturing: Capacity Utilization: Rate of Capacity Utilization: European Commission and National Indicators for the Euro Area", 'BSOITE02EZQ460S': "Business Tendency Surveys for Manufacturing: Orders Inflow: Tendency: European Commission Indicator for the Euro Area"} description = "Business tendency surveys (manufacturing), Quarterly, Seasonally Adjusted" return df, name_list, description def Business_Tendency_Surveys_Manufacturing_Monthly(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=BSSPFT02EZM460S,BSOBLV02EZM460S,BSEMFT02EZM460S,BSFGLV02EZM460S,BSXRLV02EZM086S,BSCICP02EZM460S,BSPRTE02EZM460S,BSPRFT02EZM460S&scale=left,left,left,left,left,left,left,left&cosd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'BSSPFT02EZM460S': "Business Tendency Surveys for Manufacturing: Selling Prices: Future Tendency: European Commission Indicator for the Euro Area", 'BSOBLV02EZM460S': "Business Tendency Surveys for Manufacturing: Order Books: Level: European Commission and National Indicators for the Euro Area", 'BSEMFT02EZM460S': "Business Tendency Surveys for Manufacturing: Employment: Future Tendency: European Commission and National Indicators for the Euro Area", 'BSFGLV02EZM460S': "Business Tendency Surveys for Manufacturing: Finished Goods Stocks: Level: European Commission and National Indicators for the Euro Area", 'BSXRLV02EZM086S': "Business Tendency Surveys for Manufacturing: Export Order Books or Demand: Level: European Commission Indicator for the Euro Area", 'BSCICP02EZM460S': "Business Tendency Surveys for Manufacturing: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", 'BSPRTE02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Tendency: European Commission and National Indicators for the Euro Area", 'BSPRFT02EZM460S': "Business Tendency Surveys for Manufacturing: Production: Future Tendency: European Commission and National Indicators for the Euro Area"} description = "Business tendency surveys (manufacturing), Monthly, Seasonally Adjusted" return df, name_list, description def Business_Tendency_Surveys_Retail_Trade(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19BREMFT02STSAM,EA19BRODFT02STSAM,EA19BRVSLV02STSAM,EA19BRCICP02STSAM,EA19BRBUFT02STSAM,EA19BRBUTE02STSAM&scale=left,left,left,left,left,left&cosd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&transformation=lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-04-01,1985-02-01,1985-01-01,1985-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19BREMFT02STSAM': "Business tendency surveys (retail trade): Employment: Future tendency: National indicator for the Euro Area", 'EA19BRODFT02STSAM': "Business tendency surveys (retail trade): Order intentions or Demand: Future tendency: National indicator for the Euro Area", 'EA19BRVSLV02STSAM': "Business tendency surveys (retail trade): Volume of stocks: Level: National indicator for the Euro Area", 'EA19BRCICP02STSAM': "Business tendency surveys (retail trade): Confidence indicators: Composite indicators: National indicator for the Euro Area", 'EA19BRBUFT02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Future tendency: National indicator for Euro Area", 'EA19BRBUTE02STSAM': "Business tendency surveys (retail trade): Business situation - Activity: Tendency: National indicator for Euro Area"} description = "Business tendency surveys (retail trade), Monthly, Seasonally Adjusted" return df, name_list, description def Labor_Compensation_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LCEAMN01EZQ661S': "Hourly Earnings: Manufacturing for the Euro Area", 'LCEAPR01EZQ661S': "Hourly Earnings: Private Sector for the Euro Area" } description = "Labor Compensation, Quarterly, Seasonally Adjusted" return df, name_list, description def Labor_Compensation_Quarterly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LCEAMN01EZQ661S,LCEAPR01EZQ661S&scale=left,left&cosd=1971-01-01,1996-01-01&coed=2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643&link_values=false,false&line_style=solid,solid&mark_type=none,none&mw=3,3&lw=2,2&ost=-99999,-99999&oet=99999,99999&mma=0,0&fml=a,a&fq=Quarterly,Quarterly&fam=avg,avg&fgst=lin,lin&fgsnd=2020-02-01,2020-02-01&line_index=1,2&transformation=lin,lin&vintage_date=2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07&nd=1971-01-01,1996-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LCEAMN01EZQ661N': "Hourly Earnings: Manufacturing for the Euro Area", 'LCEAPR01EZQ661N': "Hourly Earnings: Private Sector for the Euro Area" } description = "Labor Compensation, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Unit_Labor_costs(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=ULQECU01EZQ661S,ULQEUL01EZQ659S,ULQELP01EZQ661S&scale=left,left,left&cosd=1995-01-01,1996-01-01,1995-01-01&coed=2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Quarterly,Quarterly,Quarterly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1996-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'ULQECU01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Compensation per Unit of Labor Input for the Euro Area", 'ULQEUL01EZQ659S': "Early Estimate of Quarterly ULC Indicators: Total for the Euro Area", 'ULQELP01EZQ661S': "Early Estimate of Quarterly ULC Indicators: Total Labor Productivity for the Euro Area"} description = "Unit Labor Costs, Quarterly, Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Rates_Quarterly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24TTEZQ156N,LRHU24FEEZQ156N,LRHU24MAEZQ156N,LRHUADMAEZQ156N,LRHUADTTEZQ156N,LRHUADFEEZQ156N,LRHUTTFEEZQ156N,LRHUTTTTEZQ156N,LRHUTTMAEZQ156N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1993-01-01,1993-01-01,1993-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LRHU24TTEZQ156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LRHU24FEEZQ156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LRHU24MAEZQ156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LRHUADMAEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LRHUADTTEZQ156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LRHUADFEEZQ156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LRHUTTFEEZQ156N': "Harmonized Unemployment: Total: Females for the Euro Area", 'LRHUTTTTEZQ156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", 'LRHUTTMAEZQ156N': "Harmonized Unemployment: Total: Males for the Euro Area"} description = "Labor Force Survey - quarterly rates, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Rates_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHU24MAEZQ156S,LRHU24TTEZQ156S,LRHU24FEEZQ156S,LRHUADFEEZQ156S,LRHUADMAEZQ156S,LRHUADTTEZQ156S,LRHUTTTTEZQ156S,LRHUTTMAEZQ156S,LRHUTTFEEZQ156S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01&coed=2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1990-07-01,1990-07-01,1990-07-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LRHU24MAEZQ156S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LRHU24TTEZQ156S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LRHU24FEEZQ156S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LRHUADFEEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LRHUADMAEZQ156S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LRHUADTTEZQ156S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LRHUTTTTEZQ156S': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", 'LRHUTTMAEZQ156S': "Harmonized Unemployment: Total: Males for the Euro Area", 'LRHUTTFEEZQ156S': "Harmonized Unemployment: Total: Females for the Euro Area"} description = "Labor Force Survey - quarterly rates, Quarterly, Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Rates_Monthly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LRHUTTFEEZM156N': "Harmonized Unemployment: Total: Females for the Euro Area", 'LRHUTTMAEZM156N': "Harmonized Unemployment: Total: Males for the Euro Area", 'LRHUTTTTEZM156N': "Harmonized Unemployment Rate: Total: All Persons for the Euro Area", 'LRHUADTTEZM156N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LRHUADMAEZM156N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LRHUADFEEZM156N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LRHU24FEEZM156N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LRHU24MAEZM156N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LRHU24TTEZM156N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area"} description = "Labor Force Survey - quarterly rates, Monthly, Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Level_Quarterly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LFHU24FEEZQ647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LFHU24TTEZQ647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LFHU24MAEZQ647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LFHUADTTEZQ647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LFHUADMAEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LFHUADFEEZQ647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LFHUTTMAEZQ647N': "Total Harmonized Unemployment: Males for the Euro Area", 'LFHUTTFEEZQ647N': "Total Harmonized Unemployment: Females for the Euro Area", 'LFHUTTTTEZQ647N': "Total Harmonized Unemployment: All Persons for the Euro Area"} description = "Labor Force Survey - quarterly levels, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Level_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LRHUTTFEEZM156N,LRHUTTMAEZM156N,LRHUTTTTEZM156N,LRHUADTTEZM156N,LRHUADMAEZM156N,LRHUADFEEZM156N,LRHU24FEEZM156N,LRHU24MAEZM156N,LRHU24TTEZM156N&scale=left,left,left,left,left,left,left,left,left&cosd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1993-01-01,1993-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LFHU24TTEZQ647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LFHU24MAEZQ647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LFHU24FEEZQ647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LFHUTTFEEZQ647S': "Total Harmonized Unemployment: Females for the Euro Area", 'LFHUTTTTEZQ647S': "Total Harmonized Unemployment: All Persons for the Euro Area", 'LFHUTTMAEZQ647S': "Total Harmonized Unemployment: Males for the Euro Area", 'LFHUADMAEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LFHUADFEEZQ647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LFHUADTTEZQ647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area"} description = "Labor Force Survey - quarterly levels, Quarterly, Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Level_Monthly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24FEEZM647S,LFHU24TTEZM647S,LFHU24MAEZM647S,LFHUADFEEZM647S,LFHUADTTEZM647S,LFHUADMAEZM647S,LFHUTTTTEZM647S,LFHUTTMAEZM647S,LFHUTTFEEZM647S&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LFHU24FEEZM647S': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LFHU24TTEZM647S': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LFHU24MAEZM647S': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LFHUADFEEZM647S': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LFHUADTTEZM647S': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LFHUADMAEZM647S': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LFHUTTTTEZM647S': "Total Harmonized Unemployment: All Persons for the Euro Area", 'LFHUTTMAEZM647S': "Total Harmonized Unemployment: Males for the Euro Area", 'LFHUTTFEEZM647S': "Total Harmonized Unemployment: Females for the Euro Area"} description = "Labor Force Survey - quarterly levels, Monthly, Seasonally Adjusted" return df, name_list, description def Labor_Force_Survey_Level_Monthly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=LFHU24MAEZM647N,LFHU24FEEZM647N,LFHU24TTEZM647N,LFHUADMAEZM647N,LFHUADFEEZM647N,LFHUADTTEZM647N,LFHUTTFEEZM647N,LFHUTTTTEZM647N,LFHUTTMAEZM647N&scale=left,left,left,left,left,left,left,left,left&cosd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01&coed=2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01,2021-03-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c,%23b5ca92&link_values=false,false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7,8,9&transformation=lin,lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'LFHU24MAEZM647N': "Harmonized Unemployment: Aged 15-24: Males for the Euro Area", 'LFHU24FEEZM647N': "Harmonized Unemployment: Aged 15-24: Females for the Euro Area", 'LFHU24TTEZM647N': "Harmonized Unemployment: Aged 15-24: All Persons for the Euro Area", 'LFHUADMAEZM647N': "Harmonized Unemployment: Aged 25 and Over: Males for the Euro Area", 'LFHUADFEEZM647N': "Harmonized Unemployment: Aged 25 and Over: Females for the Euro Area", 'LFHUADTTEZM647N': "Harmonized Unemployment: Aged 25 and Over: All Persons for the Euro Area", 'LFHUTTFEEZM647N': "Total Harmonized Unemployment: Females for the Euro Area", 'LFHUTTTTEZM647N': "Total Harmonized Unemployment: All Persons for the Euro Area", 'LFHUTTMAEZM647N': "Total Harmonized Unemployment: Males for the Euro Area"} description = "Labor Force Survey - quarterly levels, Monthly, Not Seasonally Adjusted" return df, name_list, description def Production_Monthly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRINTO01GYSAM,EA19PRMNCG03IXOBSAM,EA19PRMNCG02IXOBSAM,EA19PRMNVG01IXOBSAM,EA19PRMNTO01IXOBSAM,EA19PRMNIG01IXOBSAM,EA19PRCNTO01IXOBSAM&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2021-02-01,2017-12-01,2018-12-01,2018-12-01,2021-02-01,2018-12-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2017-12-01,2018-12-01,2018-12-01,2020-02-01,2018-12-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19PRINTO01GYSAM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area", 'EA19PRMNCG03IXOBSAM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area", 'EA19PRMNCG02IXOBSAM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area", 'EA19PRMNVG01IXOBSAM': "Production: Manufacturing: Investment goods: Total for the Euro Area", 'EA19PRMNTO01IXOBSAM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area", 'EA19PRMNIG01IXOBSAM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area", 'EA19PRCNTO01IXOBSAM': "Production: Construction: Total construction: Total for the Euro Area"} description = "Production, Monthly, Seasonally Adjusted" return df, name_list, description def Production_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRINTO01EZQ659S,PRMNVG01EZQ661S,PRMNCG02EZQ661S,PRMNCG03EZQ661S,PRMNTO01EZQ661S,PRMNIG01EZQ661S,PRCNTO01EZQ661S&scale=left,left,left,left,left,left,left&cosd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2017-10-01,2020-10-01,2018-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2017-10-01,2020-02-01,2018-10-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1976-07-01,1985-01-01,1990-01-01,1985-01-01,1980-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'PRINTO01EZQ659S': "Total Industry Production Excluding Construction for the Euro Area", 'PRMNVG01EZQ661S': "Total Production of Investment Goods for Manufacturing for the Euro Area", 'PRMNCG02EZQ661S': "Production of Durable Consumer Goods for Manufacturing for the Euro Area", 'PRMNCG03EZQ661S': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area", 'PRMNTO01EZQ661S': "Total Manufacturing Production for the Euro Area", 'PRMNIG01EZQ661S': "Total Production of Intermediate Goods for Manufacturing for the Euro Area", 'PRCNTO01EZQ661S': "Total Construction for the Euro Area"} description = "Production, Monthly, Not Seasonally Adjusted" return df, name_list, description def Production_Monthly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19PRMNIG01IXOBM,EA19PRMNTO01IXOBM,EA19PRMNCG02IXOBM,EA19PRMNCG03IXOBM,EA19PRMNVG01IXOBM,EA19PRCNTO01IXOBM,EA19PRINTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1980-01-01,1990-01-01,1985-01-01,1985-01-01,1985-01-01,1980-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19PRMNIG01IXOBM': "Production: Manufacturing: Intermediate goods: Total for the Euro Area", 'EA19PRMNTO01IXOBM': "Production: Manufacturing: Total manufacturing: Total manufacturing for the Euro Area", 'EA19PRMNCG02IXOBM': "Production: Manufacturing: Consumer goods: Durable goods for the Euro Area", 'EA19PRMNCG03IXOBM': "Production: Manufacturing: Consumer goods: Non durable goods for the Euro Area", 'EA19PRMNVG01IXOBM': "Production: Manufacturing: Investment goods: Total for the Euro Area", 'EA19PRCNTO01IXOBM': "Production: Construction: Total construction: Total for the Euro Area", 'EA19PRINTO01IXOBM': "Production: Industry: Total industry: Total industry excluding construction for the Euro Area"} description = "Production, Monthly, Not Seasonally Adjusted" return df, name_list, description def Production_Quarterly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=PRMNCG03EZQ661N,PRMNCG02EZQ661N,PRMNVG01EZQ661N,PRMNIG01EZQ661N,PRMNTO01EZQ661N,PRINTO01EZQ661N,PRCNTO01EZQ661N&scale=left,left,left,left,left,left,left&cosd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01&coed=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2020-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1985-01-01,1990-01-01,1985-01-01,1985-01-01,1980-01-01,1980-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'PRMNCG03EZQ661N': "Production of Nondurable Consumer Goods for Manufacturing for the Euro Area", 'PRMNCG02EZQ661N': "Production of Durable Consumer Goods for Manufacturing for the Euro Area", 'PRMNVG01EZQ661N': "Total Production of Investment Goods for Manufacturing for the Euro Area", 'PRMNIG01EZQ661N': "Total Production of Intermediate Goods for Manufacturing for the Euro Area", 'PRMNTO01EZQ661N': "Total Manufacturing Production for the Euro Area", 'PRINTO01EZQ661N': "Total Industry Production Excluding Construction for the Euro Area", 'PRCNTO01EZQ661N': "Total Construction for the Euro Area"} description = "Production, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Sales_Monthly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNTO02IXOBSAM,EA19SLMNIG02IXOBSAM,EA19SLMNCD02IXOBSAM,EA19SLMNCN02IXOBSAM,EA19SLMNVG02IXOBSAM,EA19SLRTTO01IXOBSAM,EA19SLRTTO02IXOBSAM,EA19SLRTCR03IXOBSAM&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01&coed=2021-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2021-02-01,2021-02-01,2018-12-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-12-01,2018-12-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2018-12-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1990-01-01,1993-01-01,1995-01-01,1980-01-01,1995-01-01,1995-01-01,1970-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19SLMNTO02IXOBSAM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area", 'EA19SLMNIG02IXOBSAM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area", 'EA19SLMNCD02IXOBSAM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area", 'EA19SLMNCN02IXOBSAM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area", 'EA19SLMNVG02IXOBSAM': "Sales: Manufacturing: Investment goods: Value for the Euro Area", 'EA19SLRTTO01IXOBSAM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area", 'EA19SLRTTO02IXOBSAM': "Sales: Retail trade: Total retail trade: Value for the Euro Area", 'EA19SLRTCR03IXOBSAM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area"} description = "Sales, Monthly, Seasonally Adjusted" return df, name_list, description def Sales_Quarterly_Adj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNTO02EZQ661S,SLMNVG02EZQ661S,SLMNCD02EZQ661S,SLMNCN02EZQ661S,SLMNIG02EZQ661S,SLRTTO02EZQ661S,SLRTTO01EZQ659S,SLRTCR03EZQ661S&scale=left,left,left,left,left,left,left,left&cosd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01&coed=2020-10-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2018-10-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd,%23a47d7c&link_values=false,false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin,lin&fgsnd=2020-02-01,2018-10-01,2018-10-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2018-10-01&line_index=1,2,3,4,5,6,7,8&transformation=lin,lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1980-01-01,1980-01-01,1993-01-01,1995-01-01,1990-01-01,1995-01-01,1996-01-01,1970-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'SLMNTO02EZQ661S': "Sales Value of Total Manufactured Goods for the Euro Area", 'SLMNVG02EZQ661S': "Sales Value of Manufactured Investment Goods for the Euro Area", 'SLMNCD02EZQ661S': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area", 'SLMNCN02EZQ661S': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area", 'SLMNIG02EZQ661S': "Sales Value of Manufactured Intermediate Goods for the Euro Area", 'SLRTTO02EZQ661S': "Value of Total Retail Trade sales for the Euro Areaa", 'SLRTTO01EZQ659S': "Volume of Total Retail Trade sales for the Euro Area", 'SLRTCR03EZQ661S': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"} description = "Sales, Quarterly, Seasonally Adjusted" return df, name_list, description def Sales_Monthly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=EA19SLMNIG02IXOBM,EA19SLRTTO02IXOBM,EA19SLMNCD02IXOBM,EA19SLMNCN02IXOBM,EA19SLMNTO02IXOBM,EA19SLRTCR03IXOBM,EA19SLRTTO01IXOBM&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01&coed=2018-12-01,2021-02-01,2018-12-01,2018-12-01,2021-02-01,2021-03-01,2021-02-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Monthly,Monthly,Monthly,Monthly,Monthly,Monthly,Monthly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-12-01,2020-02-01,2018-12-01,2018-12-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1995-01-01,1993-01-01,1995-01-01,1980-01-01,1985-01-01,1995-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'EA19SLMNIG02IXOBM': "Sales: Manufacturing: Intermediate goods: Value for the Euro Area", 'EA19SLRTTO02IXOBM': "Sales: Retail trade: Total retail trade: Value for the Euro Area", 'EA19SLMNCD02IXOBM': "Sales: Manufacturing: Consumer goods durable: Value for the Euro Area", 'EA19SLMNCN02IXOBM': "Sales: Manufacturing: Consumer goods non durable: Value for the Euro Area", 'EA19SLMNTO02IXOBM': "Sales: Manufacturing: Total manufacturing: Value for the Euro Area", 'EA19SLRTCR03IXOBM': "Sales: Retail trade: Car registration: Passenger cars for the Euro Area", 'EA19SLRTTO01IXOBM': "Sales: Retail trade: Total retail trade: Volume for the Euro Area"} description = "Sales, Monthly, Not Seasonally Adjusted" return df, name_list, description def Sales_Quarterly_NAdj(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=SLMNIG02EZQ661N,SLMNTO02EZQ661N,SLMNCD02EZQ661N,SLMNCN02EZQ661N,SLRTTO01EZQ661N,SLRTTO02EZQ661N,SLRTCR03EZQ661N&scale=left,left,left,left,left,left,left&cosd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01&coed=2018-10-01,2020-10-01,2018-10-01,2018-10-01,2020-10-01,2020-10-01,2021-01-01&line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d,%2392a8cd&link_values=false,false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid,solid&mark_type=none,none,none,none,none,none,none&mw=3,3,3,3,3,3,3&lw=2,2,2,2,2,2,2&ost=-99999,-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999,99999&mma=0,0,0,0,0,0,0&fml=a,a,a,a,a,a,a&fq=Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly,Quarterly&fam=avg,avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin,lin&fgsnd=2018-10-01,2020-02-01,2018-10-01,2018-10-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6,7&transformation=lin,lin,lin,lin,lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07,2021-06-07&nd=1990-01-01,1980-01-01,1993-01-01,1995-01-01,1995-01-01,1995-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'SLMNIG02EZQ661N': "Sales Value of Manufactured Intermediate Goods for the Euro Area", 'SLMNTO02EZQ661N': "Sales Value of Total Manufactured Goods for the Euro Area", 'SLMNCD02EZQ661N': "Sales Value of Manufactured Durable Consumer Goods for the Euro Area", 'SLMNCN02EZQ661N': "Sales Value of Manufactured Nondurable Consumer Goods for the Euro Area", 'SLRTTO01EZQ661N': "Volume of Total Retail Trade sales for the Euro Area", 'SLRTTO02EZQ661N': "Value of Total Retail Trade sales for the Euro Area", 'SLRTCR03EZQ661N': "Retail Trade Sales: Passenger Car Registrations for the Euro Area"} description = "Sales, Quarterly, Not Seasonally Adjusted" return df, name_list, description def Consumer_Opinion_Survey(): tmp_url = url["fred_econ"] + "bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=748&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=CSCICP02EZM460S,CSESFT02EZM460S,CSINFT02EZM460S&scale=left,left,left&cosd=1973-01-01,1985-01-01,1985-01-01&coed=2021-04-01,2021-04-01,2021-04-01&line_color=%234572a7,%23aa4643,%2389a54e&link_values=false,false,false&line_style=solid,solid,solid&mark_type=none,none,none&mw=3,3,3&lw=2,2,2&ost=-99999,-99999,-99999&oet=99999,99999,99999&mma=0,0,0&fml=a,a,a&fq=Monthly,Monthly,Monthly&fam=avg,avg,avg&fgst=lin,lin,lin&fgsnd=2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3&transformation=lin,lin,lin&vintage_date=2021-06-07,2021-06-07,2021-06-07&revision_date=2021-06-07,2021-06-07,2021-06-07&nd=1973-01-01,1985-01-01,1985-01-01" ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random} r = requests.get(tmp_url, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d") #df = df[list(df.columns[1:])].replace(".", np.nan).astype(float) name_list = { 'CSCICP02EZM460S': "Consumer Opinion Surveys: Confidence Indicators: Composite Indicators: European Commission and National Indicators for the Euro Area", 'CSESFT02EZM460S': "Consumer Opinion Surveys: Economic Situation: Future Tendency: European Commission Indicator for the Euro Area", 'CSINFT02EZM460S': "Consumer Opinion Surveys: Consumer Prices: Future Tendency of Inflation: European Commission and National Indicators for the Euro Area"} description = "Consumer opinion surveys, Monthly, Seasonally Adjusted" return df, name_list, description def EU_EPU_Monthly(): df = pd.read_excel("https://www.policyuncertainty.com/media/Europe_Policy_Uncertainty_Data.xlsx")[:-1] df['Date']=pd.to_datetime(df['Year'].apply(str).str.cat(df['Month'].apply(int).apply(str),sep='-'), format='%Y-%m') df = df[["Date", "European_News_Index", "Germany_News_Index", "Italy_News_Index", "UK_News_Index", "France_News_Index"]] return df class ecb_data(object): def __init__(self, url=url["ecb"]): self.url = url def codebook(self): return "please follow the ECB's codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101" def get_data(self, datacode="ICP", key="M.U2.N.000000.4.ANR", startdate="2000-01-01", enddate="2020-01-01"): """ """ tmp_url = self.url + "{}/".format(datacode) + "{}".format(key) ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'} request_params = { "startPeriod": "{}".format(startdate), "endPeriod": "{}".format(enddate) } r = requests.get( tmp_url, params=request_params, headers=request_header) data_text = r.content df = pd.read_csv(io.StringIO(data_text.decode('utf-8'))) return df class eurostat_data(object): def __init__(self, url=url["eurostat"]): self.url = url def codebook(self): return "please follow the EuroStat's codebook: \nhttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic" def get_data(self, datasetcode="nama_10_gdp", precision="1", unit="CP_MEUR", na_item="B1GQ", time="2020"): """ """ tmp_url = self.url + "{}".format(datasetcode) ua = UserAgent(verify_ssl=False) request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'} request_params = { "precision": "{}".format(precision), "unit": "{}".format(unit), "na_item": "{}".format(na_item), "time": "{}".format(time) } r = requests.get( tmp_url, params=request_params, headers=request_header) data_text = r.text data_json = demjson.decode(data_text) value = data_json['value'] abb = data_json['dimension']['geo']['category']['index'] abb = {abb[k]: k for k in abb} geo = data_json['dimension']['geo']['category']['label'] geo_list = [abb[int(k)] for k in list(value.keys())] geo = [geo[k] for k in geo_list] df = pd.DataFrame( {"Geo": geo, "{}".format(na_item): list(value.values())}) return df def QtoM(data:pd.Series): date = pd.PeriodIndex(data.str.replace(r'(Q\d)_(\d+)', r'20\2-\1'), freq='Q').strftime('%Y-%m-%d') return date # EU - Main Economic Indicator ecb = ecb_data() eurostat = eurostat_data() eu_columns_list = { "Gross Domestic Product", "Private Finance Consumption", "Government final consumption", "Gross fixed capital formation", "Changes in inventories and acquisition less disposals of valuables", "Exports of goods and services", "Imports of goods and services" } # https://www.ecb.europa.eu/stats/ecb_statistics/key_euro_area_indicators/html/index.en.html startdate, enddate = "2010-01-01", "2021-01-01" daterange = pd.DataFrame({"Date": pd.date_range(start=startdate, end=enddate, freq="MS")}) class real_sector(): def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange): self.startdate = startdate self.enddate = enddate self.daterange = daterange class current_price_gdp_by_expenditure_category(real_sector): ## National Account (current price) def __init__(self): super(current_price_gdp_by_expenditure_category, self).__init__() pass def gdp(self): """ * Title: Gross domestic product at market prices * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gdp = ecb.get_data(datacode="MNA", key="Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gdp.columns = ["Date", "EU_GDP"] eu_gdp["Date"] = pd.to_datetime(QtoM(eu_gdp["Date"]), format="%Y-%m-%d") eu_gdp = pd.merge_asof(self.daterange, eu_gdp, on="Date", direction="nearest") return eu_gdp def pfc(self): """ * Title: Private final consumption * URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_pfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pfc.columns = ["Date", "EU_PFC"] eu_pfc["Date"] = pd.to_datetime(QtoM(eu_pfc["Date"]), format="%Y-%m-%d") eu_pfc = pd.merge_asof(self.daterange, eu_pfc, on="Date", direction="nearest") return eu_pfc def gfc(self): """ * Title: Government final consumption * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gfc.columns = ["Date", "EU_GFC"] eu_gfc["Date"] = pd.to_datetime(QtoM(eu_gfc["Date"]), format="%Y-%m-%d") eu_gfc = pd.merge_asof(daterange, self.eu_gfc, on="Date", direction="nearest") return eu_gfc def gfcf(self): """ * Title: Gross fixed capital formation * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gfcf = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gfcf.columns = ["Date", "EU_GFCF"] eu_gfcf["Date"] = pd.to_datetime(QtoM(eu_gfcf["Date"]), format="%Y-%m-%d") eu_gfcf = pd.merge_asof(self.daterange, eu_gfcf, on="Date", direction="nearest") return eu_gfcf def cia(self): """ * Title: Changes in inventories and acquisition less disposals of valuables * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P5M.N1MG._T._Z.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_cia = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P5M.N1MG._T._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cia.columns = ["Date", "EU_CIA"] eu_cia["Date"] = pd.to_datetime(QtoM(eu_cia["Date"]), format="%Y-%m-%d") eu_cia = pd.merge_asof(self.daterange, eu_cia, on="Date", direction="nearest") return eu_cia def export(self): """ * Title: Exports of goods and services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.EUR.V.NN * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_export = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_export.columns = ["Date", "EU_EXPORT"] eu_export["Date"] = pd.to_datetime(QtoM(eu_export["Date"]), format="%Y-%m-%d") eu_export = pd.merge_asof(self.daterange, eu_export, on="Date", direction="nearest") def import_(self): """ * Title: Imports of goods and services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.EUR.V.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_import = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.EUR.V.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_import.columns = ["Date", "EU_IMPORT"] eu_import["Date"] = pd.to_datetime(QtoM(eu_import["Date"]), format="%Y-%m-%d") eu_import = pd.merge_asof(self.daterange, eu_import, on="Date", direction="nearest") return eu_import class volume_gdp_by_expenditure_category_in_previous_year_price(real_sector): ## National Account (volume in previous year price) ## National Account (current price) def __init__(self): super(volume_gdp_by_expenditure_category_in_previous_year_price, self).__init__() pass def gdp(self): """ * Title: Gross domestic product at market prices * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gdp = ecb.get_data(datacode="MNA", key="Q.Y.I8.W2.S1.S1.B.B1GQ._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gdp.columns = ["Date", "EU_GDP"] eu_gdp["Date"] = pd.to_datetime(QtoM(eu_gdp["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gdp = pd.merge_asof(self.daterange, eu_gdp, on="Date", direction="nearest") return eu_gdp def pfc(self): """ * Title: Private final consumption * URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_pfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1M.S1.D.P31._Z._Z._T.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pfc.columns = ["Date", "EU_PFC"] eu_pfc["Date"] = pd.to_datetime(QtoM(eu_pfc["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pfc = pd.merge_asof(self.daterange, eu_pfc, on="Date", direction="nearest") return eu_pfc def gfc(self): """ * Title: Government final consumption * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gfc = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S13.S1.D.P3._Z._Z._T.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gfc.columns = ["Date", "EU_GFC"] eu_gfc["Date"] = pd.to_datetime(QtoM(eu_gfc["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gfc = pd.merge_asof(self.daterange, eu_gfc, on="Date", direction="nearest") return eu_gfc def gfcf(self): """ * Title: Gross fixed capital formation * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=MNA.Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gfcf = ecb.get_data(datacode="MNA", key="Q.Y.I8.W0.S1.S1.D.P51G.N11G._T._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gfcf.columns = ["Date", "EU_GFCF"] eu_gfcf["Date"] = pd.to_datetime(QtoM(eu_gfcf["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gfcf = pd.merge_asof(self.daterange, eu_gfcf, on="Date", direction="nearest") return eu_gfcf def export(self): """ * Title: Exports of goods and services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_export = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.D.P6._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_export.columns = ["Date", "EU_EXPORT"] eu_export["Date"] = pd.to_datetime(QtoM(eu_export["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_export = pd.merge_asof(self.daterange, eu_export, on="Date", direction="nearest") return eu_gfcf def import_(self): """ * Title: Imports of goods and services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.IX.LR.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_import = ecb.get_data(datacode="MNA", key="Q.Y.I8.W1.S1.S1.C.P7._Z._Z._Z.IX.LR.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_import.columns = ["Date", "EU_IMPORT"] eu_import["Date"] = pd.to_datetime(QtoM(eu_import["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_import = pd.merge_asof(self.daterange, eu_import, on="Date", direction="nearest") return eu_import def industrial_production(self): """ * Title: Industrial production for the euro area * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=132.STS.M.I8.Y.PROD.NS0020.4.000 * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_industrial_production = ecb.get_data(datacode="STS", key="M.I8.Y.PROD.NS0020.4.000", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_industrial_production.columns = ["Date", "EU_INDUSTRIAL_PRODUCTION"] eu_industrial_production["Date"] = pd.to_datetime(QtoM(eu_industrial_production["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_industrial_production = pd.merge_asof(self.daterange, eu_industrial_production, on="Date", direction="nearest") return eu_industrial_production def employment(self): """ * Title: Employment (in thousands of persons) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=ENA.Q.Y.I8.W2.S1.S1._Z.EMP._Z._T._Z.PS._Z.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_employment = ecb.get_data(datacode="ENA", key="Q.Y.I8.W2.S1.S1._Z.EMP._Z._T._Z.PS._Z.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_employment.columns = ["Date", "EU_EMPLOYMENT"] eu_employment["Date"] = pd.to_datetime(QtoM(eu_employment["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_employment = pd.merge_asof(self.daterange, eu_employment, on="Date", direction="nearest") return eu_employment def unemployment(self): """ * Title: Unemployment (in thousands of persons) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LFSI.M.I8.S.UNEMPL.TOTAL0.15_74.T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_unemployment = ecb.get_data(datacode="LFSI", key="M.I8.S.UNEMPL.TOTAL0.15_74.T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_unemployment.columns = ["Date", "EU_UNEMPLOYMENT"] eu_unemployment["Date"] = pd.to_datetime(eu_unemployment["Date"], format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) return eu_unemployment def unemployment_rate(self): """ * Title: Unemployment Rate * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LFSI.M.I8.S.UNEHRT.TOTAL0.15_74.T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_unemployment_rate = ecb.get_data(datacode="LFSI", key="M.I8.S.UNEHRT.TOTAL0.15_74.T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_unemployment_rate.columns = ["Date", "EU_UNEMPLOYMENT_RATE"] eu_unemployment_rate["Date"] = pd.to_datetime(eu_unemployment_rate["Date"], format="%Y-%m-%d") return eu_unemployment_rate def labour_cost_index(self): """ * Title: Labour cost index * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=LCI.Q.I8.Y.LCI_T.BTN * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_labour_cost_index = ecb.get_data(datacode="LCI", key="Q.I8.Y.LCI_T.BTN", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_labour_cost_index.columns = ["Date", "EU_LABOUR_COST_INDEX"] eu_labour_cost_index["Date"] = pd.to_datetime(QtoM(eu_labour_cost_index["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_labour_cost_index = pd.merge_asof(self.daterange, eu_labour_cost_index, on="Date", direction="nearest") return eu_labour_cost_index def hicp(self): """ * Title: HICP - Overall index * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=ICP.M.U2.N.000000.4.INX * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_hicp = ecb.get_data(datacode="ICP", key="M.U2.N.000000.4.INX", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_hicp.columns = ["Date", "EU_HICP"] eu_hicp["Date"] = pd.to_datetime(eu_hicp["Date"], format="%Y-%m-%d") return eu_hicp def ppi(self): """ * Title:Industrial producer prices (excl. construction) for the euro area [PPI] * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=STS.M.I8.N.PRIN.NS0020.4.000 * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ppi = ecb.get_data(datacode="STS", key="M.I8.N.PRIN.NS0020.4.000", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ppi.columns = ["Date", "EU_PPI"] eu_ppi["Date"] = pd.to_datetime(eu_ppi["Date"], format="%Y-%m-%d") return eu_ppi class fiscal_sector(): def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange): self.startdate = startdate self.enddate = enddate self.daterange = daterange class general_government_operation(fiscal_sector): ## National Account (current price) def __init__(self): super(general_government_operation, self).__init__() pass def revenue(self): """ * Title: Government total revenue (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.P.C.OTR._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gtr = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.P.C.OTR._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gtr.columns = ["Date", "EU_GOVERNMENT_TOTAL_REVENUE"] eu_gtr["Date"] = pd.to_datetime(QtoM(eu_gtr["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gtr = pd.merge_asof(self.daterange, eu_gtr, on="Date", direction="nearest") return eu_gtr def expenditure(self): """ * Title: Government total expenditure (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.P.D.OTE._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gte = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.P.D.OTE._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gte.columns = ["Date", "EU_GOVERNMENT_TOTAL_EXPENDITURE"] eu_gte["Date"] = pd.to_datetime(QtoM(eu_gte["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gte = pd.merge_asof(self.daterange, eu_gte, on="Date", direction="nearest") return eu_gte def interest_expenditure(self): """ * Title: Government interest expenditure (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.C.D.D41._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gie = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.C.D.D41._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gie.columns = ["Date", "EU_GOVERNMENT_INTEREST_EXPENDITURE"] eu_gie["Date"] = pd.to_datetime(QtoM(eu_gie["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gie = pd.merge_asof(self.daterange, eu_gie, on="Date", direction="nearest") return eu_gie def investment_expenditure(self): """ * Title: Government investment expenditure (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1.N.D.P51G._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_gie = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1.N.D.P51G._Z._Z._T.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_gie.columns = ["Date", "EU_GOVERNMENT_INVESTMENT_EXPENDITURE"] eu_gie["Date"] = pd.to_datetime(QtoM(eu_gie["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_gie = pd.merge_asof(self.daterange, eu_gie, on="Date", direction="nearest") return eu_gie def balance(self): """ * Title: Government deficit(-) or surplus(+) (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.Q.N.I8.W0.S13.S1._Z.B.B9._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_balance = ecb.get_data(datacode="GFS", key="Q.N.I8.W0.S13.S1._Z.B.B9._Z._Z._Z.XDC_R_B1GQ_CY._Z.S.V.CY._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_balance.columns = ["Date", "EU_GOVERNMENT_BALANCE"] eu_balance["Date"] = pd.to_datetime(QtoM(eu_balance["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_balance = pd.merge_asof(self.daterange, eu_balance, on="Date", direction="nearest") return eu_balance class general_government_debt(fiscal_sector): ## National Account (current price) def __init__(self): super(general_government_debt, self).__init__() pass def gross_outstanding_debt_total(self): """ * Title: Government debt (consolidated) (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_TOTAL"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_in_euro(self): """ * Title: Government debt denominated in national currency and euro (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.EUR.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.EUR.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_IN_EURO"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_no_euro(self): """ * Title: Government debt denominated in currencies other than national currency and euro (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.XNC.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ.XNC.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NO_EURO"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_resident(self): """ * Title: Government debt held by residents (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_RESIDENTS"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_mfi(self): """ * Title: Government debt held by monetary financial institutions (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S12K.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S12K.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_MFI"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_non_mfi(self): """ * Title: Government debt held by financial institutions other than monetary financial institutions (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S12P.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S12P.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_MFI"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_non_fin_sector(self): """ * Title: Government debt held by the non-financial sectors (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W2.S13.S1U.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W2.S13.S1U.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_FIN_SECTOR"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od def gross_outstanding_debt_non_resident(self): """ * Title: Government debt held by non-residents (as % of GDP) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=325.GFS.A.N.I8.W1.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Yearly """ eu_od = ecb.get_data(datacode="GFS", key="A.N.I8.W1.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ._T.F.V.N._T", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_od.columns = ["Date", "EU_GOVERNMENT_OUT_STANDING_DEBT_NON_RESIDENT"] eu_od["Date"] = pd.to_datetime(eu_od["Date"], format="%Y") + pd.tseries.offsets.MonthBegin(-1) eu_od = pd.merge_asof(self.daterange, eu_od, on="Date", direction="nearest") return eu_od class financial_sector(): def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange): self.startdate = startdate self.enddate = enddate self.daterange = daterange class analytical_accounts_of_the_banking_sector(financial_sector): ## National Account (current price) def __init__(self): super(analytical_accounts_of_the_banking_sector, self).__init__() pass def monetary_aggrate_m1(self): """ * Title: Monetary aggregate M1 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M10.X.1.U2.2300.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_m1 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M30.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_m1.columns = ["Date", "EU_MONETARY_AGGRATE_M3"] eu_m1["Date"] = pd.to_datetime(eu_m1["Date"], format="%Y-%m-%d") return eu_m1 def monetary_aggrate_m2(self): """ * Title: Monetary aggregate M2 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M10.X.1.U2.2300.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_m2 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M20.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_m2.columns = ["Date", "EU_MONETARY_AGGRATE_M3"] eu_m2["Date"] = pd.to_datetime(eu_m2["Date"], format="%Y-%m-%d") return eu_m2 def monetary_aggrate_m3(self): """ * Title: Monetary aggregate M3 vis-a-vis euro area non-MFI excl. central gov. reported by MFI & central gov. & post office giro Inst. in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.V.M30.X.1.U2.2300.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_m3 = ecb.get_data(datacode="BSI", key="M.U2.Y.V.M30.X.1.U2.2300.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_m3.columns = ["Date", "EU_MONETARY_AGGRATE_M3"] eu_m3["Date"] = pd.to_datetime(eu_m3["Date"], format="%Y-%m-%d") return eu_m3 def domestic_credit(self): """ * Title: Total loans and securities vis-a-vis euro area non-MFI reported by MFI in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dc.columns = ["Date", "EU_DOMESTIC_CREDIT"] eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d") return eu_dc def credit_general_government(self): """ * Title: Total loans and securities vis-a-vis euro area General Government reported by MFI in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2100.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2100.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dc.columns = ["Date", "EU_GOVERNMENT_CREDIT"] eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d") return eu_dc def credit_general_other_resident(self): """ * Title: Total loans and securities vis-a-vis euro area non-MFI excl. general gov. reported by MFI in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AT2.A.1.U2.2200.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_dc = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AT2.A.1.U2.2200.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dc.columns = ["Date", "EU_OTHER_RESIDENT_CREDIT"] eu_dc["Date"] = pd.to_datetime(eu_dc["Date"], format="%Y-%m-%d") return eu_dc def external_assets(self): """ * Title: External assets reported by MFI in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.AXG.A.1.U4.0000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ea = ecb.get_data(datacode="BSI", key="M.U2.Y.U.AXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ea.columns = ["Date", "EU_EXTERNAL_ASSETS"] eu_ea["Date"] = pd.to_datetime(eu_ea["Date"], format="%Y-%m-%d") return eu_ea def external_liabilities(self): """ * Title: External liabilities reported by MFI in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.Y.U.LXG.A.1.U4.0000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_el = ecb.get_data(datacode="BSI", key="M.U2.Y.U.LXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_el.columns = ["Date", "EU_EXTERNAL_LIABILITIES"] eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d") return eu_el class analytical_accounts_of_the_central_banks(financial_sector): ## National Account (current price) def __init__(self): super(analytical_accounts_of_the_banking_sector, self).__init__() pass def currency_in_circulation(self): """ * Title: Currency in circulation reported by Eurosystem in the euro area (stock) * URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.L10.X.1.Z5.0000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.L10.X.1.Z5.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cc.columns = ["Date", "EU_CURRENCY_IN_CIRCULATION"] eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d") return eu_cc def deposits_at_eurosystem_mfi(self): """ * Title: Deposit liabilities vis-a-vis euro area MFI reported by Eurosystem in the euro area (stock) * URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.L20.A.1.U2.1000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.L20.A.1.U2.1000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cc.columns = ["Date", "EU_DEPOSITS_AT_EUROSYSTEM_MFI"] eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d") return eu_cc def credit(self): """ * Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock) * URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cc.columns = ["Date", "EU_CREDIT"] eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d") return eu_cc def credit_to_general_governemnt(self): """ * Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock) * URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2100.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2100.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cc.columns = ["Date", "EU_CREDIT_TO_GENERAL_GOVERNMENT"] eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d") return eu_cc def credit_to_other_resident_sector(self): """ * Title: Total loans and securities vis-a-vis euro area non-MFI reported by Eurosystem in the euro area (stock) * URL:https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=117.BSI.M.U2.N.C.AT2.A.1.U2.2200.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_cc = ecb.get_data(datacode="BSI", key="M.U2.N.C.AT2.A.1.U2.2200.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_cc.columns = ["Date", "EU_CREDIT_TO_OTHER_RESIDENT"] eu_cc["Date"] = pd.to_datetime(eu_cc["Date"], format="%Y-%m-%d") return eu_cc def external_assets(self): """ * Title: External assets reported by Eurosystem in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.N.C.AXG.A.1.U4.0000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ea = ecb.get_data(datacode="BSI", key="M.U2.N.C.AXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ea.columns = ["Date", "EU_EXTERNAL_ASSETS"] eu_ea["Date"] = pd.to_datetime(eu_ea["Date"], format="%Y-%m-%d") return eu_ea def external_liabilities(self): """ * Title: External liabilities reported by Eurosystem in the euro area (stock) * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=BSI.M.U2.N.C.LXG.A.1.U4.0000.Z01.E * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_el = ecb.get_data(datacode="BSI", key="M.U2.N.C.LXG.A.1.U4.0000.Z01.E", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_el.columns = ["Date", "EU_EXTERNAL_LIABILITIES"] eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d") return eu_el class interest_rate(financial_sector): ## National Account (current price) def __init__(self): super(interest_rate, self).__init__() pass def one_year_interbank(self): """ * Title: Euribor 1-year - Historical close, average of observations through period * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=143.FM.M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_el = ecb.get_data(datacode="FM", key="M.U2.EUR.RT.MM.EURIBOR1YD_.HSTA", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_el.columns = ["Date", "EU_ONE_YEAR_INTERBANK_RATE"] eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d") return eu_el def ten_year_government_banchmar_bond_yild(self): """ * Title: Euro area 10-year Government Benchmark bond yield - Yield * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=143.FM.M.U2.EUR.4F.BB.U2_10Y.YLD * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_el = ecb.get_data(datacode="FM", key="M.U2.EUR.4F.BB.U2_10Y.YLD", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_el.columns = ["Date", "EU_TEN_YEART_BOND_RATE"] eu_el["Date"] = pd.to_datetime(eu_el["Date"], format="%Y-%m-%d") return eu_el class external_sector(): def __init__(self, startdate=startdate, enddate=enddate, daterange=daterange): self.startdate = startdate self.enddate = enddate self.daterange = daterange class balance_of_payments(external_sector): ## National Account (current price) def __init__(self): super(balance_of_payments, self).__init__() pass def net_current_account(self): """ * Title: Current account * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.CA._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ca = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.CA._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ca.columns = ["Date", "EU_CURRENT_ACCOUNT"] eu_ca["Date"] = pd.to_datetime(eu_ca["Date"], format="%Y-%m-%d") return eu_ca def exports_goods(self): """ * Title: Goods * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.C.G._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_eg = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.C.G._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_eg.columns = ["Date", "EU_EXPORT_GOODS"] eu_eg["Date"] = pd.to_datetime(eu_eg["Date"], format="%Y-%m-%d") return eu_ca def exports_services(self): """ * Title: Services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.C.S._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_eg = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.C.S._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_es.columns = ["Date", "EU_EXPORT_SERVICES"] eu_es["Date"] = pd.to_datetime(eu_es["Date"], format="%Y-%m-%d") return eu_es def imports_goods(self): """ * Title: Goods * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.D.G._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ig = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.D.G._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ig.columns = ["Date", "EU_IMPORT_GOODS"] eu_ig["Date"] = pd.to_datetime(eu_ig["Date"], format="%Y-%m-%d") return eu_ca def import_services(self): """ * Title: Services * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.D.S._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_is = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.D.S._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_is.columns = ["Date", "EU_IMPORT_SERVICES"] eu_is["Date"] = pd.to_datetime(eu_is["Date"], format="%Y-%m-%d") return eu_is def net_primary_income(self): """ * Title: Primary income * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.IN1._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_npi = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.IN1._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_npi.columns = ["Date", "EU_NET_PRIMARY_INCOME"] eu_npi["Date"] = pd.to_datetime(eu_npi["Date"], format="%Y-%m-%d") return eu_npi def net_secondary_income(self): """ * Title: Secondary income * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.Y.I8.W1.S1.S1.T.B.IN2._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_nsi = ecb.get_data(datacode="BP6", key="M.Y.I8.W1.S1.S1.T.B.IN2._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_nsi.columns = ["Date", "EU_NET_SECONDARY_INCOME"] eu_nsi["Date"] = pd.to_datetime(eu_nsi["Date"], format="%Y-%m-%d") return eu_nsi def net_capital_account(self): """ * Title: Capitial account * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.B.KA._Z._Z._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_nca = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.B.KA._Z._Z._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_nca.columns = ["Date", "EU_NET_CAPTIAL_ACCOUNT"] eu_nca["Date"] = pd.to_datetime(eu_nca["Date"], format="%Y-%m-%d") return eu_nca def net_financial_account(self): """ * Title: Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA._T.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA._T.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_nfa.columns = ["Date", "EU_NET_FINANCIAL_ACCOUNT"] eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d") return eu_nfa def direct_investment(self): """ * Title: Direct Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.D.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_di = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_di.columns = ["Date", "EU_DIRECT_INVESTMENT"] eu_di["Date"] = pd.to_datetime(eu_di["Date"], format="%Y-%m-%d") eu_di["EU_DIRECT_INVESTMENT_ASSETS"], eu_di["EU_DIRECT_INVESTMENT_LIABILITIES"] = eu_dia["OBS_VALUE"], eu_dil["OBS_VALUE"] return eu_di def porfolio_investment(self): """ * Title: Portfolio Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.P.F._Z.EUR._T.M.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_pi = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.P.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pi.columns = ["Date", "EU_PORFOLIO_INVESTMENT"] eu_pi["Date"] = pd.to_datetime(eu_pi["Date"], format="%Y-%m-%d") eu_pi["EU_PORFOLIO_INVESTMENT_ASSETS"], eu_pi["EU_PORFOLIO_INVESTMENT_LIABILITIES"] = eu_pia["OBS_VALUE"], eu_pil["OBS_VALUE"] return eu_pi def other_investment(self): """ * Title: Other Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.O.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_pi = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pia = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.A.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pil = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.L.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pi.columns = ["Date", "EU_OTHER_INVESTMENT"] eu_pi["Date"] = pd.to_datetime(eu_pi["Date"], format="%Y-%m-%d") eu_pi["EU_OTHER_INVESTMENT_ASSETS"], eu_pi["EU_OTHER_INVESTMENT_LIABILITIES"] = eu_pia["OBS_VALUE"], eu_pil["OBS_VALUE"] return eu_pi def financial_derivatives(self): """ * Title: Financial Derivatives and Employee Stock Options, Financial derivatives and employee stock options * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S1.S1.T.N.FA.F.F7.T.EUR._T.T.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S1.S1.T.N.FA.F.F7.T.EUR._T.T.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_nfa.columns = ["Date", "EU_FINANCIAL_DERIVATIVES"] eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d") return eu_nfa def reserve_assets(self): """ * Title: Reserve Assets, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.M.N.I8.W1.S121.S1.T.A.FA.R.F._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_nfa = ecb.get_data(datacode="BP6", key="M.N.I8.W1.S121.S1.T.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_nfa.columns = ["Date", "EU_RESERVE_ASSETS"] eu_nfa["Date"] = pd.to_datetime(eu_nfa["Date"], format="%Y-%m-%d") return eu_nfa class international_reserves_and_foreign_currency_liquidity(external_sector): ## National Account (current price) def __init__(self): super(balance_of_payments, self).__init__() pass def official_reserve_assets(self): """ * Title: Official reserve assets * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_ora = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ora.columns = ["Date", "EU_OFFICIAL_RESERVE_ASSETS"] eu_ora["Date"] = pd.to_datetime(eu_ora["Date"], format="%Y-%m-%d") return eu_ora def monetary_gold(self): """ * Title: Monetary gold * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.F11._Z.EUR.XAU.M.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_mg = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.F11._Z.EUR.XAU.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_mg.columns = ["Date", "EU_MONETARY_GOLD"] eu_mg["Date"] = pd.to_datetime(eu_mg["Date"], format="%Y-%m-%d") return eu_mg def imf_reserve_position(self): """ * Title: Reserve position in the IMF * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.1C.S121.S121.LE.A.FA.R.FK._Z.EUR.XDR.M.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.1C.S121.S121.LE.A.FA.R.FK._Z.EUR.XDR.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_img_rp.columns = ["Date", "EU_IMF_RESERVE_POSITION"] eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d") return eu_img_rp def sdr(self): """ * Title: Reserve position in the IMF * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1N.LE.A.FA.R.F12.T.EUR.XDR.M.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1N.LE.A.FA.R.F12.T.EUR.XDR.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_img_rp.columns = ["Date", "EU_SDR"] eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d") return eu_img_rp def other_reserve_assets(self): """ * Title: Other reserve assets * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W1.S121.S1.LE.A.FA.R.FR2._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W1.S121.S1.LE.A.FA.R.FR2._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_img_rp.columns = ["Date", "EU_OTHER_RESERVE_ASSETS"] eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d") return eu_img_rp def other_foreign_currency_assets(self): """ * Title: Other foreign currency assets (not included in reserve assets) * URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W0.S121.S1.LE.A.FA.RT.F._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W0.S121.S1.LE.A.FA.RT.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_img_rp.columns = ["Date", "EU_FOREIGN_CURRENCY_ASSETS"] eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d") return eu_img_rp def predeterminated_short_term_net_drains_on_foreign_currency_assets(self): """ * Title: Not applicable, Total financial assets/liabilities * URL: http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=340.RA6.M.N.U2.W0.S121.S1.FP.FN._Z.RT.F.TS.EUR.X1.N.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_img_rp = ecb.get_data(datacode="RA6", key="M.N.U2.W0.S121.S1.FP.FN._Z.RT.F.TS.EUR.X1.N.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_img_rp.columns = ["Date", "EU_TOTAL_FINANCIAL_ASSETS"] eu_img_rp["Date"] = pd.to_datetime(eu_img_rp["Date"], format="%Y-%m-%d") return eu_img_rp class merchandise_trade(external_sector): ## National Account (current price) def __init__(self): super(merchandise_trade, self).__init__() pass def merchandise_trade(self): """ * Title: Total trade, Value (Community concept) (Export/Import) * URL1: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=133.TRD.M.I8.Y.X.TTT.J8.4.VAL * URL2: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=133.TRD.M.I8.Y.M.TTT.J8.4.VAL * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Monthly """ eu_mte = ecb.get_data(datacode="TRD", key="M.I8.Y.X.TTT.J8.4.VAL", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_mti = ecb.get_data(datacode="TRD", key="M.I8.Y.M.TTT.J8.4.VAL", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_mte.columns = ["Date", "EU_MERCHANDISE_TRADE_EXPORT"] eu_mte["Date"] = pd.to_datetime(eu_mte["Date"], format="%Y-%m-%d") eu_mte["EU_MERCHANDISE_TRADE_IMPORT"] = eu_mti["OBS_VALUE"] return eu_mte class international_investment_position(external_sector): ## National Account (current price) def __init__(self): super(international_investment_position, self).__init__() pass def total_net_international_investment_position(self): """ * Title: Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA._T.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_tniip = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA._T.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_tniip.columns = ["Date", "EU_TOTAL_FINANCIAL_ASSETS"] eu_tniip["Date"] = pd.to_datetime(QtoM(eu_tniip["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) return eu_tniip def direct_investment(self): """ * Title: Direct Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.D.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_di = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_dil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.D.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_di.columns = ["Date", "EU_DIRECT_INVESTMENT"] eu_dia.columns = ["Date", "EU_DIRECT_INVESTMENT_ASSETS"] eu_dil.columns = ["Date", "EU_DIRECT_INVESTMENT_LIABILITIES"] eu_di["Date"] = pd.to_datetime(QtoM(eu_di["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_dia["Date"] = pd.to_datetime(QtoM(eu_dia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_dil["Date"] = pd.to_datetime(QtoM(eu_dil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_di = pd.merge_asof(eu_di, eu_dia, on="Date") eu_di = pd.merge_asof(eu_di, eu_dil, on="Date") return eu_di def portfolio_investment(self): """ * Title: Direct Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.P.F._Z.EUR._T.M.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_pi = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.P.F._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.P.F5._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.P.F5._Z.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pida = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.P.F3.T.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pidl = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.P.F3.T.EUR._T.M.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_pi.columns = ["Date", "EU_PORTFOLIO_INVESTMENT"] eu_pia.columns = ["Date", "EU_EQUITY_AND_INVESTMENT_FUND_ASSETS"] eu_pil.columns = ["Date", "EU_EQUITY_AND_INVESTMENT_FUND_LIABILITIES"] eu_pida.columns = ["Date", "EU_DEBT_SECURITIES_aSSETS"] eu_pidl.columns = ["Date", "EU_DEBT_SECURITIES_LIABILITIES"] eu_pi["Date"] = pd.to_datetime(QtoM(eu_pi["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pia["Date"] = pd.to_datetime(QtoM(eu_pia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pil["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pida["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pidl["Date"] = pd.to_datetime(QtoM(eu_pil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_pi = pd.merge_asof(eu_pi, eu_pia, on="Date") eu_pi = pd.merge_asof(eu_pi, eu_pil, on="Date") eu_pi = pd.merge_asof(eu_pi, eu_pida, on="Date") eu_pi = pd.merge_asof(eu_pi, eu_pidl, on="Date") return eu_pi def other_investment(self): """ * Title: Other Investment, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S1.S1.LE.N.FA.O.F._Z.EUR._T._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_oi = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.N.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_oia = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.A.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_oil = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA.O.F._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_oi.columns = ["Date", "EU_DIRECT_INVESTMENT"] eu_oia.columns = ["Date", "EU_DIRECT_INVESTMENT_ASSETS"] eu_oil.columns = ["Date", "EU_DIRECT_INVESTMENT_LIABILITIES"] eu_oi["Date"] = pd.to_datetime(QtoM(eu_oi["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_oia["Date"] = pd.to_datetime(QtoM(eu_oia["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_oil["Date"] = pd.to_datetime(QtoM(eu_oil["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) eu_oi = pd.merge_asof(eu_oi, eu_oia, on="Date") eu_oi = pd.merge_asof(eu_oi, eu_oil, on="Date") return eu_di def reserve_assetsc(self): """ * Title: Reserve Assets, Total financial assets/liabilities * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_ra = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ra.columns = ["Date", "EU_OTHER_INVESTMENT_ASSETS"] eu_ra["Date"] = pd.to_datetime(QtoM(eu_ra["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) return eu_ra def gross_external_debt(self): """ * Title: Gross external debt * URL: https://sdw.ecb.europa.eu/quickview.do?SERIES_KEY=338.BP6.Q.N.I8.W1.S121.S1.LE.A.FA.R.F._Z.EUR.X1._X.N * Reference area: Euro area 19 (fixed composition) as of 1 January 2015 (I8) * Frequency: Quarterly """ eu_ged = ecb.get_data(datacode="BP6", key="Q.N.I8.W1.S1.S1.LE.L.FA._T.FGED._Z.EUR._T._X.N", startdate=self.startdate, enddate=self.enddate)[["TIME_PERIOD", "OBS_VALUE"]] eu_ged.columns = ["Date", "EU_GROSS_EXTERNAL_DEBT"] eu_ged["Date"] = pd.to_datetime(QtoM(eu_ged["Date"]), format="%Y-%m") + pd.tseries.offsets.MonthBegin(-1) return eu_ged if __name__ == "__main__": data, name_list = CPI_monthly()

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py

Python

reveal_graph_embedding/quality/conductance.py

MKLab-ITI/reveal-graph-embedding

72d4af794536f97b8ede06c0f27f261ea85d8c4b

[ "Apache-2.0" ]

31

2015-07-14T16:21:25.000Z

2021-06-30T14:10:44.000Z

reveal_graph_embedding/quality/conductance.py

MKLab-ITI/reveal-graph-embedding

72d4af794536f97b8ede06c0f27f261ea85d8c4b

[ "Apache-2.0" ]

null

reveal_graph_embedding/quality/conductance.py

MKLab-ITI/reveal-graph-embedding

72d4af794536f97b8ede06c0f27f261ea85d8c4b

[ "Apache-2.0" ]

11

2016-08-21T03:07:20.000Z

2020-03-07T03:17:05.000Z

__author__ = 'Georgios Rizos (georgerizos@iti.gr)' import numpy as np def conductance(adjacency_matrix, node_array): number_of_nodes = adjacency_matrix.shape[0] node_array_bar = np.setdiff1d(np.arange(number_of_nodes), node_array) submatrix = adjacency_matrix[np.ix_(node_array, node_array)] submatrix_bar = adjacency_matrix[np.ix_(node_array_bar, node_array_bar)] submatrix_volume = submatrix.getnnz() # TODO: If empty? submatrix_bar_volume = submatrix_bar.getnnz() # TODO: If empty? matrix_volume = adjacency_matrix.getnnz() cut_volume = (matrix_volume - submatrix_volume - submatrix_bar_volume)/2 try: cut_conductance = cut_volume/min(submatrix_volume, submatrix_bar_volume) except ZeroDivisionError: cut_conductance = np.Inf return cut_conductance def conductance_and_clustering_coefficient(adjacency_matrix, node_array, seed_node): number_of_nodes = adjacency_matrix.shape[0] node_array_bar = np.setdiff1d(np.arange(number_of_nodes), node_array) submatrix = adjacency_matrix[np.ix_(node_array, node_array)] submatrix_bar = adjacency_matrix[np.ix_(node_array_bar, node_array_bar)] submatrix_volume = submatrix.getnnz() # TODO: If empty? submatrix_bar_volume = submatrix_bar.getnnz() # TODO: If empty? matrix_volume = adjacency_matrix.getnnz() cut_volume = (matrix_volume - submatrix_volume - submatrix_bar_volume)/2 cut_conductance = cut_volume/min(submatrix_volume, submatrix_bar_volume) new_node_array = np.setdiff1d(node_array, seed_node) clustering_coefficient = adjacency_matrix[np.ix_(new_node_array, new_node_array)] clustering_coefficient = clustering_coefficient.getnnz()/(new_node_array.size*new_node_array.size) return cut_conductance, clustering_coefficient def fast_conductance(array_of_arrays, node_array, matrix_volume): submatrix_volume = 0 cut_volume = 0 for node in node_array: neighbors = array_of_arrays[node] degree = neighbors.size common = np.intersect1d(node_array, neighbors).size submatrix_volume += common cut_volume += degree - common submatrix_bar_volume = matrix_volume - submatrix_volume - 2*cut_volume try: cut_conductance = cut_volume/min(submatrix_volume, submatrix_bar_volume) except ZeroDivisionError: cut_conductance = np.Inf return cut_conductance, cut_volume, submatrix_volume def incremental_conductance(array_of_arrays, node_array, new_node, cut_volume, submatrix_volume, matrix_volume): # TODO: What if I have ones in the diagonal? neighbors = array_of_arrays[new_node] degree = neighbors.size common = np.intersect1d(node_array, neighbors).size submatrix_volume += common cut_volume += degree - common submatrix_bar_volume = matrix_volume - submatrix_volume - 2*cut_volume try: cut_conductance = cut_volume/min(submatrix_volume, submatrix_bar_volume) except ZeroDivisionError: cut_conductance = np.Inf return cut_conductance, cut_volume, submatrix_volume def decremental_conductance(array_of_arrays, node_array, new_node, cut_volume, submatrix_volume, matrix_volume): # TODO: What if I have ones in the diagonal? neighbors = array_of_arrays[new_node] degree = neighbors.size common = np.intersect1d(node_array, neighbors).size submatrix_volume -= common cut_volume += common - degree submatrix_bar_volume = matrix_volume - submatrix_volume - 2*cut_volume try: cut_conductance = cut_volume/min(submatrix_volume, submatrix_bar_volume) except ZeroDivisionError: cut_conductance = np.Inf return cut_conductance, cut_volume, submatrix_volume

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1

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null

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7

8ced7434cb879444a7aa7c69a5a7671e68b30702

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py

Python

simpleui/__init__.py

skyformat99/simpleui-1

84e68ffe27f261ed1fbc889430e61fdd9add7657

[ "MIT" ]

null

simpleui/__init__.py

skyformat99/simpleui-1

84e68ffe27f261ed1fbc889430e61fdd9add7657

[ "MIT" ]

null

simpleui/__init__.py

skyformat99/simpleui-1

84e68ffe27f261ed1fbc889430e61fdd9add7657

[ "MIT" ]

1

2019-08-27T18:05:36.000Z

def get_version(): return '2.8'

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null

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0

1

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7

0fe72a6430b726017aba6aca0045eab7c5759123

142

py

Python

pylogger/utils/idgenerator.py

agSant01/pylogger

99a5d08b0f486c43dc4936cd89474e21a86f377a

[ "MIT" ]

null

pylogger/utils/idgenerator.py

agSant01/pylogger

99a5d08b0f486c43dc4936cd89474e21a86f377a

[ "MIT" ]

null

pylogger/utils/idgenerator.py

agSant01/pylogger

99a5d08b0f486c43dc4936cd89474e21a86f377a

[ "MIT" ]

null

import string import random def id_generator(): return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))

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1

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7

ba0a080b0cf1de1be674bf37d6678cbbececade9

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py

Python

mahjonggscoring/rules/test/test_melded_kong.py

kajiki/py-mahjongg-scoring

b23202de018a8206a4be5594247faa0754e7e54e

[ "MIT" ]

null

mahjonggscoring/rules/test/test_melded_kong.py

kajiki/py-mahjongg-scoring

b23202de018a8206a4be5594247faa0754e7e54e

[ "MIT" ]

null

mahjonggscoring/rules/test/test_melded_kong.py

kajiki/py-mahjongg-scoring

b23202de018a8206a4be5594247faa0754e7e54e

[ "MIT" ]

null

import unittest2 from mahjonggscoring.rules import MeldedKong from mahjonggscoring import Hand class TestMeldedKongPartial(unittest2.TestCase): def setUp(self): data = [["6/", "6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data, {"concealed": [False, True, False, False, False]}) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() def test_passed(self): self.assertTrue(self.passed) def test_points(self): self.assertEqual(self.examination.points, 1) class TestMeldedKongExplicit(unittest2.TestCase): def setUp(self): data = [["6/", "6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data, {"concealed": False}) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() def test_passed(self): self.assertTrue(self.passed) def test_points(self): self.assertEqual(self.examination.points, 1) class TestMeldedKongImplicit(unittest2.TestCase): def setUp(self): data = [["6/", "6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() def test_passed(self): self.assertTrue(self.passed) def test_points(self): self.assertEqual(self.examination.points, 1) class TestNotMeldedKong(unittest2.TestCase): def test_not_kong(self): data = [["6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() self.assertFalse(self.passed) def test_not_melded(self): data = [["6/", "6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data, {"concealed": [True, False, False, False, False]}) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() self.assertFalse(self.passed) def test_two_melded(self): data = [["6/", "6/", "6/", "6/"], ["2/", "3/", "4/"], ["F", "F", "F", "F"], ["2/", "3/", "4/"], ["8/", "8/"]] hand = Hand(data, {"concealed": [False, True, False, True, False]}) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() self.assertFalse(self.passed) def test_special_hand(self): data = [["5/", "5/", "3/", "3/", "4/", "4/", "8/", "8/", "6/", "6/", "7/", "7/", "5/", "5/"]] hand = Hand(data) self.examination = MeldedKong(hand) self.passed = self.examination.evaluate() self.assertFalse(self.passed) if __name__ == '__main__': unittest2.main()

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null

0

1

0

1

0

8

ba0b71fea3955a06b0fb30ee870bffb2695154ef

5,223

py

Python

picture/tests.py

ENAIKA/picture

4a5f81a6fc3f56b0322ed753af0e601cc0ec8149

[ "Unlicense" ]

null

picture/tests.py

ENAIKA/picture

4a5f81a6fc3f56b0322ed753af0e601cc0ec8149

[ "Unlicense" ]

8

2021-03-19T04:10:34.000Z

2021-09-22T19:06:00.000Z

picture/tests.py

ENAIKA/picture

4a5f81a6fc3f56b0322ed753af0e601cc0ec8149

[ "Unlicense" ]

null

from django.test import TestCase from .models import PhotoImage,Category,Location import pyperclip # Create your tests here. class LocationTestClass(TestCase): # Set up method def setUp(self): #creating a new location and saving it self.new_location=Location(location_name="Mozambique") self.new_location.save_location() def tearDown(self): Location.objects.all().delete() # Testing instance def test_instance(self): self.assertTrue(isinstance(self.new_location,Location)) # Testing Save Method def test_save_method(self): self.new_location.save_location() location =Location.objects.all() self.assertTrue(len(location) > 0) # Testing delete Method def test_delete_method(self): self.new_location.delete_location() location =Location.objects.all() self.assertTrue(len(location) == 0) # Testing update Method def test_update_location_method(self): self.new_location.update_location(name="Mozambique",field="location_name", value="TestMozambique") photo =Location.objects.all() self.assertTrue(len(photo) ==1) class CategoryTestClass(TestCase): # Set up method def setUp(self): #creating a new category and saving it self.new_category=Category(title="test") self.new_category.save_category() def tearDown(self): Category.objects.all().delete() # Testing instance def test_instance(self): self.assertTrue(isinstance(self.new_category,Category)) # Testing Save Method def test_save_method(self): self.new_category.save_category() category =Category.objects.all() self.assertTrue(len(category) > 0) # Testing delete Method def test_delete_method(self): self.new_category.delete_category() category =Category.objects.all() self.assertTrue(len(category) == 0) # Testing update Method def test_update_location_method(self): self.new_category.update_category(name="test",field="title", value="TestCategory") category =Category.objects.all() self.assertTrue(len(category) ==1) class PhotoImageTestClass(TestCase): # Set up method def setUp(self): #creating a new location and saving it self.new_location=Location(location_name="Mozambique") self.new_location.save_location() #creating a new category and saving it self.new_category=Category(title="test") self.new_category.save_category() #creating a new photo and saving it self.photo= PhotoImage(name = 'test1',description="MozambiqueTest",category=self.new_category) self.photo.save_photo() self.photo.location.add(self.new_location) def tearDown(self): Category.objects.all().delete() Location.objects.all().delete() PhotoImage.objects.all().delete() # Testing instance def test_instance(self): self.assertTrue(isinstance(self.photo,PhotoImage)) # Testing Save Method def test_save_method(self): self.photo.save_photo() photo =PhotoImage.objects.all() self.assertTrue(len(photo) > 0) # Testing delete Method def test_delete_method(self): self.photo.delete_photo() photo =PhotoImage.objects.all() self.assertTrue(len(photo) == 0) # Testing update Method def test_update_category_method(self): self.photo.update_photo(name="test1",field="description", value="TestMozambique") photo =PhotoImage.objects.all() self.assertTrue(len(photo) ==1) # Testing search by category Method def test_search_by_category_method(self): self.photo.search_by_category(category="test") photo =PhotoImage.objects.all() self.assertTrue(len(photo) ==1) # Testing search by id Method def test_get_image_by_id_method(self): self.photo.get_image_by_id(id=1) photo =PhotoImage.objects.all() self.assertTrue(len(photo) ==1) class CopyTest(TestCase): # Set up method def setUp(self): #creating a new photo and saving it self.new_location=Location(location_name="Mozambique") self.new_location.save_location() #creating a new category and saving it self.new_category=Category(title="test") self.new_category.save_category() #creating a new photo and saving it self.photo= PhotoImage(name = 'test1',image="imageurl",description="MozambiqueTest",category=self.new_category) self.photo.save_photo() self.photo.location.add(self.new_location) def tearDown(self): Category.objects.all().delete() Location.objects.all().delete() PhotoImage.objects.all().delete() # Testing instance def test_instance(self): self.assertTrue(isinstance(self.photo,PhotoImage)) # Testing Save Method def test_copy_method(self): new_copy=self.photo.copy_photo("imageurl") self.assertTrue(new_copy==self.photo.image.url)

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null

0

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7

ba0c1b6262ce1cb0869cfc7cde679c3191f17e85

5,024

py

Python

rodnet/utils/visualization/demo.py

zhengzangw/RODNet

eca5f2bd1f3051c2b823d279532ddafa71b009c1

[ "MIT" ]

109

2020-11-13T11:58:41.000Z

2022-03-29T06:46:09.000Z

rodnet/utils/visualization/demo.py

yh-luo/RODNet

969cad6f08b8957b26bc16f86ac4e835d1294050

[ "MIT" ]

46

2021-01-13T08:53:12.000Z

2022-03-31T02:51:16.000Z

rodnet/utils/visualization/demo.py

yh-luo/RODNet

969cad6f08b8957b26bc16f86ac4e835d1294050

[ "MIT" ]

43

2021-01-07T05:09:36.000Z

2022-03-20T11:13:58.000Z

import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg from rodnet.core.object_class import get_class_name from .fig_configs import fig, fp, symbols def visualize_train_img_old(fig_name, input_radar, output_confmap, confmap_gt): fig = plt.figure(figsize=(8, 4)) img = input_radar fig.add_subplot(1, 3, 1) plt.imshow(img, vmin=0, vmax=1, origin='lower', aspect='auto') img = output_confmap fig.add_subplot(1, 3, 2) plt.imshow(img, vmin=0, vmax=1, origin='lower', aspect='auto') img = confmap_gt fig.add_subplot(1, 3, 3) plt.imshow(img, vmin=0, vmax=1, origin='lower', aspect='auto') plt.savefig(fig_name) plt.close(fig) def visualize_train_img(fig_name, img_path, input_radar, output_confmap, confmap_gt): fig = plt.figure(figsize=(8, 8)) img_data = mpimg.imread(img_path) fig.add_subplot(2, 2, 1) plt.imshow(img_data.astype(np.uint8)) fig.add_subplot(2, 2, 2) plt.imshow(input_radar, origin='lower', aspect='auto') fig.add_subplot(2, 2, 3) output_confmap = np.transpose(output_confmap, (1, 2, 0)) output_confmap[output_confmap < 0] = 0 plt.imshow(output_confmap, vmin=0, vmax=1, origin='lower', aspect='auto') fig.add_subplot(2, 2, 4) confmap_gt = np.transpose(confmap_gt, (1, 2, 0)) plt.imshow(confmap_gt, vmin=0, vmax=1, origin='lower', aspect='auto') plt.savefig(fig_name) plt.close(fig) def visualize_test_img(fig_name, img_path, input_radar, confmap_pred, confmap_gt, res_final, dataset, viz=False, sybl=False): max_dets, _ = res_final.shape classes = dataset.object_cfg.classes img_data = mpimg.imread(img_path) if img_data.shape[0] > 864: img_data = img_data[:img_data.shape[0] // 5 * 4, :, :] fig.add_subplot(2, 2, 1) plt.imshow(img_data.astype(np.uint8)) plt.axis('off') plt.title("Image") fig.add_subplot(2, 2, 2) plt.imshow(input_radar, origin='lower', aspect='auto') plt.axis('off') plt.title("RA Heatmap") fig.add_subplot(2, 2, 3) confmap_pred = np.transpose(confmap_pred, (1, 2, 0)) confmap_pred[confmap_pred < 0] = 0 confmap_pred[confmap_pred > 1] = 1 plt.imshow(confmap_pred, vmin=0, vmax=1, origin='lower', aspect='auto') for d in range(max_dets): cla_id = int(res_final[d, 0]) if cla_id == -1: continue row_id = res_final[d, 1] col_id = res_final[d, 2] conf = res_final[d, 3] conf = 1.0 if conf > 1 else conf cla_str = get_class_name(cla_id, classes) if sybl: text = symbols[cla_str] plt.text(col_id, row_id + 3, text, fontproperties=fp, color='white', size=20, ha="center") else: plt.scatter(col_id, row_id, s=10, c='white') text = cla_str + '\n%.2f' % conf plt.text(col_id + 5, row_id, text, color='white', fontsize=10) plt.axis('off') plt.title("RODNet Detection") fig.add_subplot(2, 2, 4) confmap_gt = np.transpose(confmap_gt, (1, 2, 0)) plt.imshow(confmap_gt, vmin=0, vmax=1, origin='lower', aspect='auto') plt.axis('off') plt.title("Ground Truth") plt.savefig(fig_name) if viz: plt.pause(0.1) plt.clf() def visualize_test_img_wo_gt(fig_name, img_path, input_radar, confmap_pred, res_final, dataset, viz=False, sybl=False): max_dets, _ = res_final.shape classes = dataset.object_cfg.classes fig.set_size_inches(12, 4) img_data = mpimg.imread(img_path) if img_data.shape[0] > 864: img_data = img_data[:img_data.shape[0] // 5 * 4, :, :] fig.add_subplot(1, 3, 1) plt.imshow(img_data.astype(np.uint8)) plt.axis('off') plt.title("RGB Image") fig.add_subplot(1, 3, 2) input_radar[input_radar < 0] = 0 input_radar[input_radar > 1] = 1 plt.imshow(input_radar, vmin=0, vmax=1, origin='lower', aspect='auto') plt.axis('off') plt.title("RF Image") fig.add_subplot(1, 3, 3) confmap_pred = np.transpose(confmap_pred, (1, 2, 0)) confmap_pred[confmap_pred < 0] = 0 confmap_pred[confmap_pred > 1] = 1 plt.imshow(confmap_pred, vmin=0, vmax=1, origin='lower', aspect='auto') for d in range(max_dets): cla_id = int(res_final[d, 0]) if cla_id == -1: continue row_id = res_final[d, 1] col_id = res_final[d, 2] conf = res_final[d, 3] conf = 1.0 if conf > 1 else conf cla_str = get_class_name(cla_id, classes) if sybl: text = symbols[cla_str] plt.text(col_id - 3, row_id + 2, text, fontproperties=fp, color='white', size=20) else: plt.scatter(col_id, row_id, s=10, c='white') text = cla_str + '\n%.2f' % conf plt.text(col_id + 5, row_id, text, color='white', fontsize=10) plt.axis('off') plt.title("RODNet Detections") plt.savefig(fig_name) if viz: plt.pause(0.1) plt.clf()

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null

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7

e8ac38cbeb3fcd5576ff820f4b114c8e541ed403

128,507

py

Python

librespot/proto/Metadata_pb2.py

forslund/librespot-python

7a340b1b20889e1afae47aa0f433a0893f4290f1

[ "Apache-2.0" ]

64

2021-02-24T06:46:34.000Z

2022-03-29T11:33:46.000Z

librespot/proto/Metadata_pb2.py

forslund/librespot-python

7a340b1b20889e1afae47aa0f433a0893f4290f1

[ "Apache-2.0" ]

16

2021-04-24T12:25:30.000Z

2022-02-19T00:02:44.000Z

librespot/proto/Metadata_pb2.py

forslund/librespot-python

7a340b1b20889e1afae47aa0f433a0893f4290f1

[ "Apache-2.0" ]

22

2021-04-05T23:57:14.000Z

2022-03-10T04:45:08.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: metadata.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='metadata.proto', package='spotify.metadata.proto', syntax='proto2', serialized_options=b'\n\024com.spotify.metadataB\010MetadataH\002', create_key=_descriptor._internal_create_key, serialized_pb= b'\n\x0emetadata.proto\x12\x16spotify.metadata.proto\"\x8a\x07\n\x06\x41rtist\x12\x0b\n\x03gid\x18\x01 \x01(\x0c\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x12\n\npopularity\x18\x03 \x01(\x11\x12\x34\n\ttop_track\x18\x04 \x03(\x0b\x32!.spotify.metadata.proto.TopTracks\x12\x37\n\x0b\x61lbum_group\x18\x05 \x03(\x0b\x32\".spotify.metadata.proto.AlbumGroup\x12\x38\n\x0csingle_group\x18\x06 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\x01(\tH\x00\"U\n\tCatalogue\x12\x06\n\x02\x41\x44\x10\x00\x12\x10\n\x0cSUBSCRIPTION\x10\x01\x12\x11\n\rCATALOGUE_ALL\x10\x02\x12\x0b\n\x07SHUFFLE\x10\x03\x12\x0e\n\nCOMMERCIAL\x10\x04\"\x15\n\x04Type\x12\r\n\tSTREAMING\x10\x00\x42\x15\n\x13\x63ountry_restriction\"R\n\x0c\x41vailability\x12\x15\n\rcatalogue_str\x18\x01 \x03(\t\x12+\n\x05start\x18\x02 \x01(\x0b\x32\x1c.spotify.metadata.proto.Date\"\x9e\x01\n\nSalePeriod\x12\x38\n\x0brestriction\x18\x01 \x03(\x0b\x32#.spotify.metadata.proto.Restriction\x12+\n\x05start\x18\x02 \x01(\x0b\x32\x1c.spotify.metadata.proto.Date\x12)\n\x03\x65nd\x18\x03 \x01(\x0b\x32\x1c.spotify.metadata.proto.Date\"&\n\nExternalId\x12\x0c\n\x04type\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\t\"\x89\x02\n\tAudioFile\x12\x0f\n\x07\x66ile_id\x18\x01 \x01(\x0c\x12\x38\n\x06\x66ormat\x18\x02 \x01(\x0e\x32(.spotify.metadata.proto.AudioFile.Format\"\xb0\x01\n\x06\x46ormat\x12\x11\n\rOGG_VORBIS_96\x10\x00\x12\x12\n\x0eOGG_VORBIS_160\x10\x01\x12\x12\n\x0eOGG_VORBIS_320\x10\x02\x12\x0b\n\x07MP3_256\x10\x03\x12\x0b\n\x07MP3_320\x10\x04\x12\x0b\n\x07MP3_160\x10\x05\x12\n\n\x06MP3_96\x10\x06\x12\x0f\n\x0bMP3_160_ENC\x10\x07\x12\n\n\x06\x41\x41\x43_24\x10\x08\x12\n\n\x06\x41\x41\x43_48\x10\t\x12\x0f\n\x0b\x41\x41\x43_24_NORM\x10\x10\"\x1c\n\tVideoFile\x12\x0f\n\x07\x66ile_id\x18\x01 \x01(\x0c\x42\"\n\x14\x63om.spotify.metadataB\x08MetadataH\x02' ) _ALBUM_TYPE = _descriptor.EnumDescriptor( name='Type', full_name='spotify.metadata.proto.Album.Type', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='ALBUM', index=0, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='SINGLE', index=1, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='COMPILATION', index=2, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='EP', index=3, number=4, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='AUDIOBOOK', index=4, number=5, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='PODCAST', index=5, number=6, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1742, serialized_end=1824, ) _sym_db.RegisterEnumDescriptor(_ALBUM_TYPE) _SHOW_MEDIATYPE = _descriptor.EnumDescriptor( name='MediaType', full_name='spotify.metadata.proto.Show.MediaType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='MIXED', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='AUDIO', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='VIDEO', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=3152, serialized_end=3196, ) _sym_db.RegisterEnumDescriptor(_SHOW_MEDIATYPE) _SHOW_CONSUMPTIONORDER = _descriptor.EnumDescriptor( name='ConsumptionOrder', full_name='spotify.metadata.proto.Show.ConsumptionOrder', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='SEQUENTIAL', index=0, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='EPISODIC', index=1, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='RECENT', index=2, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=3198, serialized_end=3258, ) _sym_db.RegisterEnumDescriptor(_SHOW_CONSUMPTIONORDER) _EPISODE_EPISODETYPE = _descriptor.EnumDescriptor( name='EpisodeType', full_name='spotify.metadata.proto.Episode.EpisodeType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='FULL', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='TRAILER', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='BONUS', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=4103, serialized_end=4150, ) _sym_db.RegisterEnumDescriptor(_EPISODE_EPISODETYPE) _IMAGE_SIZE = _descriptor.EnumDescriptor( name='Size', full_name='spotify.metadata.proto.Image.Size', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='DEFAULT', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='SMALL', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='LARGE', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='XLARGE', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=4574, serialized_end=4627, ) _sym_db.RegisterEnumDescriptor(_IMAGE_SIZE) _COPYRIGHT_TYPE = _descriptor.EnumDescriptor( name='Type', full_name='spotify.metadata.proto.Copyright.Type', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='P', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='C', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=4991, serialized_end=5011, ) _sym_db.RegisterEnumDescriptor(_COPYRIGHT_TYPE) _RESTRICTION_CATALOGUE = _descriptor.EnumDescriptor( name='Catalogue', full_name='spotify.metadata.proto.Restriction.Catalogue', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='AD', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='SUBSCRIPTION', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='CATALOGUE_ALL', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='SHUFFLE', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='COMMERCIAL', index=4, number=4, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=5234, serialized_end=5319, ) _sym_db.RegisterEnumDescriptor(_RESTRICTION_CATALOGUE) _RESTRICTION_TYPE = _descriptor.EnumDescriptor( name='Type', full_name='spotify.metadata.proto.Restriction.Type', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='STREAMING', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=5321, serialized_end=5342, ) _sym_db.RegisterEnumDescriptor(_RESTRICTION_TYPE) _AUDIOFILE_FORMAT = _descriptor.EnumDescriptor( name='Format', full_name='spotify.metadata.proto.AudioFile.Format', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='OGG_VORBIS_96', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='OGG_VORBIS_160', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='OGG_VORBIS_320', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='MP3_256', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='MP3_320', index=4, number=4, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='MP3_160', index=5, number=5, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='MP3_96', index=6, number=6, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='MP3_160_ENC', index=7, number=7, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='AAC_24', index=8, number=8, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='AAC_48', index=9, number=9, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='AAC_24_NORM', index=10, number=16, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=5742, serialized_end=5918, ) _sym_db.RegisterEnumDescriptor(_AUDIOFILE_FORMAT) _ARTIST = _descriptor.Descriptor( name='Artist', full_name='spotify.metadata.proto.Artist', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='gid', full_name='spotify.metadata.proto.Artist.gid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Artist.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='popularity', full_name='spotify.metadata.proto.Artist.popularity', index=2, number=3, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='top_track', full_name='spotify.metadata.proto.Artist.top_track', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='album_group', full_name='spotify.metadata.proto.Artist.album_group', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='single_group', full_name='spotify.metadata.proto.Artist.single_group', index=5, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='compilation_group', full_name='spotify.metadata.proto.Artist.compilation_group', index=6, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='appears_on_group', full_name='spotify.metadata.proto.Artist.appears_on_group', index=7, number=8, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='genre', full_name='spotify.metadata.proto.Artist.genre', index=8, number=9, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='external_id', full_name='spotify.metadata.proto.Artist.external_id', index=9, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='portrait', full_name='spotify.metadata.proto.Artist.portrait', index=10, number=11, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='biography', full_name='spotify.metadata.proto.Artist.biography', index=11, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='activity_period', full_name='spotify.metadata.proto.Artist.activity_period', index=12, number=13, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.Artist.restriction', index=13, number=14, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='related', full_name='spotify.metadata.proto.Artist.related', index=14, number=15, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='is_portrait_album_cover', full_name='spotify.metadata.proto.Artist.is_portrait_album_cover', index=15, number=16, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='portrait_group', full_name='spotify.metadata.proto.Artist.portrait_group', index=16, number=17, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sale_period', full_name='spotify.metadata.proto.Artist.sale_period', index=17, number=18, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='availability', full_name='spotify.metadata.proto.Artist.availability', index=18, number=20, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=43, serialized_end=949, ) _ALBUM = _descriptor.Descriptor( name='Album', full_name='spotify.metadata.proto.Album', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='gid', full_name='spotify.metadata.proto.Album.gid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Album.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='artist', full_name='spotify.metadata.proto.Album.artist', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='spotify.metadata.proto.Album.type', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=1, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='label', full_name='spotify.metadata.proto.Album.label', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='date', full_name='spotify.metadata.proto.Album.date', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='popularity', full_name='spotify.metadata.proto.Album.popularity', index=6, number=7, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='genre', full_name='spotify.metadata.proto.Album.genre', index=7, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cover', full_name='spotify.metadata.proto.Album.cover', index=8, number=9, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='external_id', full_name='spotify.metadata.proto.Album.external_id', index=9, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='disc', full_name='spotify.metadata.proto.Album.disc', index=10, number=11, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='review', full_name='spotify.metadata.proto.Album.review', index=11, number=12, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='copyright', full_name='spotify.metadata.proto.Album.copyright', index=12, number=13, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.Album.restriction', index=13, number=14, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='related', full_name='spotify.metadata.proto.Album.related', index=14, number=15, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sale_period', full_name='spotify.metadata.proto.Album.sale_period', index=15, number=16, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cover_group', full_name='spotify.metadata.proto.Album.cover_group', index=16, number=17, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='original_title', full_name='spotify.metadata.proto.Album.original_title', index=17, number=18, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='version_title', full_name='spotify.metadata.proto.Album.version_title', index=18, number=19, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type_str', full_name='spotify.metadata.proto.Album.type_str', index=19, number=20, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='availability', full_name='spotify.metadata.proto.Album.availability', index=20, number=23, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _ALBUM_TYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=952, serialized_end=1824, ) _TRACK = _descriptor.Descriptor( name='Track', full_name='spotify.metadata.proto.Track', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='gid', full_name='spotify.metadata.proto.Track.gid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Track.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='album', full_name='spotify.metadata.proto.Track.album', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='artist', full_name='spotify.metadata.proto.Track.artist', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='number', full_name='spotify.metadata.proto.Track.number', index=4, number=5, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='disc_number', full_name='spotify.metadata.proto.Track.disc_number', index=5, number=6, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='duration', full_name='spotify.metadata.proto.Track.duration', index=6, number=7, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='popularity', full_name='spotify.metadata.proto.Track.popularity', index=7, number=8, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='explicit', full_name='spotify.metadata.proto.Track.explicit', index=8, number=9, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='external_id', full_name='spotify.metadata.proto.Track.external_id', index=9, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.Track.restriction', index=10, number=11, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='file', full_name='spotify.metadata.proto.Track.file', index=11, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='alternative', full_name='spotify.metadata.proto.Track.alternative', index=12, number=13, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sale_period', full_name='spotify.metadata.proto.Track.sale_period', index=13, number=14, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='preview', full_name='spotify.metadata.proto.Track.preview', index=14, number=15, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tags', full_name='spotify.metadata.proto.Track.tags', index=15, number=16, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='earliest_live_timestamp', full_name='spotify.metadata.proto.Track.earliest_live_timestamp', index=16, number=17, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='has_lyrics', full_name='spotify.metadata.proto.Track.has_lyrics', index=17, number=18, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='availability', full_name='spotify.metadata.proto.Track.availability', index=18, number=19, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='licensor', full_name='spotify.metadata.proto.Track.licensor', index=19, number=21, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=1827, serialized_end=2552, ) _SHOW = _descriptor.Descriptor( name='Show', full_name='spotify.metadata.proto.Show', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='gid', full_name='spotify.metadata.proto.Show.gid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Show.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='description', full_name='spotify.metadata.proto.Show.description', index=2, number=64, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='deprecated_popularity', full_name='spotify.metadata.proto.Show.deprecated_popularity', index=3, number=65, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\030\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='publisher', full_name='spotify.metadata.proto.Show.publisher', index=4, number=66, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='language', full_name='spotify.metadata.proto.Show.language', index=5, number=67, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='explicit', full_name='spotify.metadata.proto.Show.explicit', index=6, number=68, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cover_image', full_name='spotify.metadata.proto.Show.cover_image', index=7, number=69, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='episode', full_name='spotify.metadata.proto.Show.episode', index=8, number=70, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='copyright', full_name='spotify.metadata.proto.Show.copyright', index=9, number=71, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.Show.restriction', index=10, number=72, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='keyword', full_name='spotify.metadata.proto.Show.keyword', index=11, number=73, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='media_type', full_name='spotify.metadata.proto.Show.media_type', index=12, number=74, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='consumption_order', full_name='spotify.metadata.proto.Show.consumption_order', index=13, number=75, type=14, cpp_type=8, label=1, has_default_value=False, default_value=1, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='availability', full_name='spotify.metadata.proto.Show.availability', index=14, number=78, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='trailer_uri', full_name='spotify.metadata.proto.Show.trailer_uri', index=15, number=83, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _SHOW_MEDIATYPE, _SHOW_CONSUMPTIONORDER, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=2555, serialized_end=3258, ) _EPISODE = _descriptor.Descriptor( name='Episode', full_name='spotify.metadata.proto.Episode', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='gid', full_name='spotify.metadata.proto.Episode.gid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Episode.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='duration', full_name='spotify.metadata.proto.Episode.duration', index=2, number=7, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='audio', full_name='spotify.metadata.proto.Episode.audio', index=3, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='description', full_name='spotify.metadata.proto.Episode.description', index=4, number=64, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='number', full_name='spotify.metadata.proto.Episode.number', index=5, number=65, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='publish_time', full_name='spotify.metadata.proto.Episode.publish_time', index=6, number=66, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='deprecated_popularity', full_name='spotify.metadata.proto.Episode.deprecated_popularity', index=7, number=67, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\030\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cover_image', full_name='spotify.metadata.proto.Episode.cover_image', index=8, number=68, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='language', full_name='spotify.metadata.proto.Episode.language', index=9, number=69, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='explicit', full_name='spotify.metadata.proto.Episode.explicit', index=10, number=70, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='show', full_name='spotify.metadata.proto.Episode.show', index=11, number=71, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='video', full_name='spotify.metadata.proto.Episode.video', index=12, number=72, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='video_preview', full_name='spotify.metadata.proto.Episode.video_preview', index=13, number=73, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='audio_preview', full_name='spotify.metadata.proto.Episode.audio_preview', index=14, number=74, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.Episode.restriction', index=15, number=75, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='freeze_frame', full_name='spotify.metadata.proto.Episode.freeze_frame', index=16, number=76, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='keyword', full_name='spotify.metadata.proto.Episode.keyword', index=17, number=77, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='allow_background_playback', full_name= 'spotify.metadata.proto.Episode.allow_background_playback', index=18, number=81, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='availability', full_name='spotify.metadata.proto.Episode.availability', index=19, number=82, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='external_url', full_name='spotify.metadata.proto.Episode.external_url', index=20, number=83, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='spotify.metadata.proto.Episode.type', index=21, number=87, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _EPISODE_EPISODETYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=3261, serialized_end=4150, ) _LICENSOR = _descriptor.Descriptor( name='Licensor', full_name='spotify.metadata.proto.Licensor', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='uuid', full_name='spotify.metadata.proto.Licensor.uuid', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4152, serialized_end=4176, ) _TOPTRACKS = _descriptor.Descriptor( name='TopTracks', full_name='spotify.metadata.proto.TopTracks', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='country', full_name='spotify.metadata.proto.TopTracks.country', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='track', full_name='spotify.metadata.proto.TopTracks.track', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4178, serialized_end=4252, ) _ACTIVITYPERIOD = _descriptor.Descriptor( name='ActivityPeriod', full_name='spotify.metadata.proto.ActivityPeriod', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='start_year', full_name='spotify.metadata.proto.ActivityPeriod.start_year', index=0, number=1, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='end_year', full_name='spotify.metadata.proto.ActivityPeriod.end_year', index=1, number=2, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='decade', full_name='spotify.metadata.proto.ActivityPeriod.decade', index=2, number=3, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4254, serialized_end=4324, ) _ALBUMGROUP = _descriptor.Descriptor( name='AlbumGroup', full_name='spotify.metadata.proto.AlbumGroup', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='album', full_name='spotify.metadata.proto.AlbumGroup.album', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4326, serialized_end=4384, ) _DATE = _descriptor.Descriptor( name='Date', full_name='spotify.metadata.proto.Date', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='year', full_name='spotify.metadata.proto.Date.year', index=0, number=1, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='month', full_name='spotify.metadata.proto.Date.month', index=1, number=2, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='day', full_name='spotify.metadata.proto.Date.day', index=2, number=3, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='hour', full_name='spotify.metadata.proto.Date.hour', index=3, number=4, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='minute', full_name='spotify.metadata.proto.Date.minute', index=4, number=5, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4386, serialized_end=4464, ) _IMAGE = _descriptor.Descriptor( name='Image', full_name='spotify.metadata.proto.Image', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='file_id', full_name='spotify.metadata.proto.Image.file_id', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='size', full_name='spotify.metadata.proto.Image.size', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='width', full_name='spotify.metadata.proto.Image.width', index=2, number=3, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='height', full_name='spotify.metadata.proto.Image.height', index=3, number=4, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _IMAGE_SIZE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4467, serialized_end=4627, ) _IMAGEGROUP = _descriptor.Descriptor( name='ImageGroup', full_name='spotify.metadata.proto.ImageGroup', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='image', full_name='spotify.metadata.proto.ImageGroup.image', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4629, serialized_end=4687, ) _BIOGRAPHY = _descriptor.Descriptor( name='Biography', full_name='spotify.metadata.proto.Biography', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='text', full_name='spotify.metadata.proto.Biography.text', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='portrait', full_name='spotify.metadata.proto.Biography.portrait', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='portrait_group', full_name='spotify.metadata.proto.Biography.portrait_group', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4690, serialized_end=4824, ) _DISC = _descriptor.Descriptor( name='Disc', full_name='spotify.metadata.proto.Disc', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='number', full_name='spotify.metadata.proto.Disc.number', index=0, number=1, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spotify.metadata.proto.Disc.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='track', full_name='spotify.metadata.proto.Disc.track', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4826, serialized_end=4908, ) _COPYRIGHT = _descriptor.Descriptor( name='Copyright', full_name='spotify.metadata.proto.Copyright', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='type', full_name='spotify.metadata.proto.Copyright.type', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='text', full_name='spotify.metadata.proto.Copyright.text', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _COPYRIGHT_TYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4910, serialized_end=5011, ) _RESTRICTION = _descriptor.Descriptor( name='Restriction', full_name='spotify.metadata.proto.Restriction', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='catalogue', full_name='spotify.metadata.proto.Restriction.catalogue', index=0, number=1, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='spotify.metadata.proto.Restriction.type', index=1, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='catalogue_str', full_name='spotify.metadata.proto.Restriction.catalogue_str', index=2, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='countries_allowed', full_name='spotify.metadata.proto.Restriction.countries_allowed', index=3, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='countries_forbidden', full_name='spotify.metadata.proto.Restriction.countries_forbidden', index=4, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _RESTRICTION_CATALOGUE, _RESTRICTION_TYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='country_restriction', full_name='spotify.metadata.proto.Restriction.country_restriction', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=5014, serialized_end=5365, ) _AVAILABILITY = _descriptor.Descriptor( name='Availability', full_name='spotify.metadata.proto.Availability', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='catalogue_str', full_name='spotify.metadata.proto.Availability.catalogue_str', index=0, number=1, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='start', full_name='spotify.metadata.proto.Availability.start', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=5367, serialized_end=5449, ) _SALEPERIOD = _descriptor.Descriptor( name='SalePeriod', full_name='spotify.metadata.proto.SalePeriod', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='restriction', full_name='spotify.metadata.proto.SalePeriod.restriction', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='start', full_name='spotify.metadata.proto.SalePeriod.start', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='end', full_name='spotify.metadata.proto.SalePeriod.end', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=5452, serialized_end=5610, ) _EXTERNALID = _descriptor.Descriptor( name='ExternalId', full_name='spotify.metadata.proto.ExternalId', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='type', full_name='spotify.metadata.proto.ExternalId.type', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='id', full_name='spotify.metadata.proto.ExternalId.id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=5612, serialized_end=5650, ) _AUDIOFILE = _descriptor.Descriptor( name='AudioFile', full_name='spotify.metadata.proto.AudioFile', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='file_id', full_name='spotify.metadata.proto.AudioFile.file_id', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='format', full_name='spotify.metadata.proto.AudioFile.format', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[ _AUDIOFILE_FORMAT, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=5653, serialized_end=5918, ) _VIDEOFILE = _descriptor.Descriptor( name='VideoFile', full_name='spotify.metadata.proto.VideoFile', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='file_id', full_name='spotify.metadata.proto.VideoFile.file_id', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=5920, serialized_end=5948, ) _ARTIST.fields_by_name['top_track'].message_type = _TOPTRACKS _ARTIST.fields_by_name['album_group'].message_type = _ALBUMGROUP _ARTIST.fields_by_name['single_group'].message_type = _ALBUMGROUP _ARTIST.fields_by_name['compilation_group'].message_type = _ALBUMGROUP _ARTIST.fields_by_name['appears_on_group'].message_type = _ALBUMGROUP _ARTIST.fields_by_name['external_id'].message_type = _EXTERNALID _ARTIST.fields_by_name['portrait'].message_type = _IMAGE _ARTIST.fields_by_name['biography'].message_type = _BIOGRAPHY _ARTIST.fields_by_name['activity_period'].message_type = _ACTIVITYPERIOD _ARTIST.fields_by_name['restriction'].message_type = _RESTRICTION _ARTIST.fields_by_name['related'].message_type = _ARTIST _ARTIST.fields_by_name['portrait_group'].message_type = _IMAGEGROUP _ARTIST.fields_by_name['sale_period'].message_type = _SALEPERIOD _ARTIST.fields_by_name['availability'].message_type = _AVAILABILITY _ALBUM.fields_by_name['artist'].message_type = _ARTIST _ALBUM.fields_by_name['type'].enum_type = _ALBUM_TYPE _ALBUM.fields_by_name['date'].message_type = _DATE _ALBUM.fields_by_name['cover'].message_type = _IMAGE _ALBUM.fields_by_name['external_id'].message_type = _EXTERNALID _ALBUM.fields_by_name['disc'].message_type = _DISC _ALBUM.fields_by_name['copyright'].message_type = _COPYRIGHT _ALBUM.fields_by_name['restriction'].message_type = _RESTRICTION _ALBUM.fields_by_name['related'].message_type = _ALBUM _ALBUM.fields_by_name['sale_period'].message_type = _SALEPERIOD _ALBUM.fields_by_name['cover_group'].message_type = _IMAGEGROUP _ALBUM.fields_by_name['availability'].message_type = _AVAILABILITY _ALBUM_TYPE.containing_type = _ALBUM _TRACK.fields_by_name['album'].message_type = _ALBUM _TRACK.fields_by_name['artist'].message_type = _ARTIST _TRACK.fields_by_name['external_id'].message_type = _EXTERNALID _TRACK.fields_by_name['restriction'].message_type = _RESTRICTION _TRACK.fields_by_name['file'].message_type = _AUDIOFILE _TRACK.fields_by_name['alternative'].message_type = _TRACK _TRACK.fields_by_name['sale_period'].message_type = _SALEPERIOD _TRACK.fields_by_name['preview'].message_type = _AUDIOFILE _TRACK.fields_by_name['availability'].message_type = _AVAILABILITY _TRACK.fields_by_name['licensor'].message_type = _LICENSOR _SHOW.fields_by_name['cover_image'].message_type = _IMAGEGROUP _SHOW.fields_by_name['episode'].message_type = _EPISODE _SHOW.fields_by_name['copyright'].message_type = _COPYRIGHT _SHOW.fields_by_name['restriction'].message_type = _RESTRICTION _SHOW.fields_by_name['media_type'].enum_type = _SHOW_MEDIATYPE _SHOW.fields_by_name['consumption_order'].enum_type = _SHOW_CONSUMPTIONORDER _SHOW.fields_by_name['availability'].message_type = _AVAILABILITY _SHOW_MEDIATYPE.containing_type = _SHOW _SHOW_CONSUMPTIONORDER.containing_type = _SHOW _EPISODE.fields_by_name['audio'].message_type = _AUDIOFILE _EPISODE.fields_by_name['publish_time'].message_type = _DATE _EPISODE.fields_by_name['cover_image'].message_type = _IMAGEGROUP _EPISODE.fields_by_name['show'].message_type = _SHOW _EPISODE.fields_by_name['video'].message_type = _VIDEOFILE _EPISODE.fields_by_name['video_preview'].message_type = _VIDEOFILE _EPISODE.fields_by_name['audio_preview'].message_type = _AUDIOFILE _EPISODE.fields_by_name['restriction'].message_type = _RESTRICTION _EPISODE.fields_by_name['freeze_frame'].message_type = _IMAGEGROUP _EPISODE.fields_by_name['availability'].message_type = _AVAILABILITY _EPISODE.fields_by_name['type'].enum_type = _EPISODE_EPISODETYPE _EPISODE_EPISODETYPE.containing_type = _EPISODE _TOPTRACKS.fields_by_name['track'].message_type = _TRACK _ALBUMGROUP.fields_by_name['album'].message_type = _ALBUM _IMAGE.fields_by_name['size'].enum_type = _IMAGE_SIZE _IMAGE_SIZE.containing_type = _IMAGE _IMAGEGROUP.fields_by_name['image'].message_type = _IMAGE _BIOGRAPHY.fields_by_name['portrait'].message_type = _IMAGE _BIOGRAPHY.fields_by_name['portrait_group'].message_type = _IMAGEGROUP _DISC.fields_by_name['track'].message_type = _TRACK _COPYRIGHT.fields_by_name['type'].enum_type = _COPYRIGHT_TYPE _COPYRIGHT_TYPE.containing_type = _COPYRIGHT _RESTRICTION.fields_by_name['catalogue'].enum_type = _RESTRICTION_CATALOGUE _RESTRICTION.fields_by_name['type'].enum_type = _RESTRICTION_TYPE _RESTRICTION_CATALOGUE.containing_type = _RESTRICTION _RESTRICTION_TYPE.containing_type = _RESTRICTION _RESTRICTION.oneofs_by_name['country_restriction'].fields.append( _RESTRICTION.fields_by_name['countries_allowed']) _RESTRICTION.fields_by_name[ 'countries_allowed'].containing_oneof = _RESTRICTION.oneofs_by_name[ 'country_restriction'] _RESTRICTION.oneofs_by_name['country_restriction'].fields.append( _RESTRICTION.fields_by_name['countries_forbidden']) _RESTRICTION.fields_by_name[ 'countries_forbidden'].containing_oneof = _RESTRICTION.oneofs_by_name[ 'country_restriction'] _AVAILABILITY.fields_by_name['start'].message_type = _DATE _SALEPERIOD.fields_by_name['restriction'].message_type = _RESTRICTION _SALEPERIOD.fields_by_name['start'].message_type = _DATE _SALEPERIOD.fields_by_name['end'].message_type = _DATE _AUDIOFILE.fields_by_name['format'].enum_type = _AUDIOFILE_FORMAT _AUDIOFILE_FORMAT.containing_type = _AUDIOFILE DESCRIPTOR.message_types_by_name['Artist'] = _ARTIST DESCRIPTOR.message_types_by_name['Album'] = _ALBUM DESCRIPTOR.message_types_by_name['Track'] = _TRACK DESCRIPTOR.message_types_by_name['Show'] = _SHOW DESCRIPTOR.message_types_by_name['Episode'] = _EPISODE DESCRIPTOR.message_types_by_name['Licensor'] = _LICENSOR DESCRIPTOR.message_types_by_name['TopTracks'] = _TOPTRACKS DESCRIPTOR.message_types_by_name['ActivityPeriod'] = _ACTIVITYPERIOD DESCRIPTOR.message_types_by_name['AlbumGroup'] = _ALBUMGROUP DESCRIPTOR.message_types_by_name['Date'] = _DATE DESCRIPTOR.message_types_by_name['Image'] = _IMAGE DESCRIPTOR.message_types_by_name['ImageGroup'] = _IMAGEGROUP DESCRIPTOR.message_types_by_name['Biography'] = _BIOGRAPHY DESCRIPTOR.message_types_by_name['Disc'] = _DISC DESCRIPTOR.message_types_by_name['Copyright'] = _COPYRIGHT DESCRIPTOR.message_types_by_name['Restriction'] = _RESTRICTION DESCRIPTOR.message_types_by_name['Availability'] = _AVAILABILITY DESCRIPTOR.message_types_by_name['SalePeriod'] = _SALEPERIOD DESCRIPTOR.message_types_by_name['ExternalId'] = _EXTERNALID DESCRIPTOR.message_types_by_name['AudioFile'] = _AUDIOFILE DESCRIPTOR.message_types_by_name['VideoFile'] = _VIDEOFILE _sym_db.RegisterFileDescriptor(DESCRIPTOR) Artist = _reflection.GeneratedProtocolMessageType( 'Artist', (_message.Message, ), { 'DESCRIPTOR': _ARTIST, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Artist) }) _sym_db.RegisterMessage(Artist) Album = _reflection.GeneratedProtocolMessageType( 'Album', (_message.Message, ), { 'DESCRIPTOR': _ALBUM, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Album) }) _sym_db.RegisterMessage(Album) Track = _reflection.GeneratedProtocolMessageType( 'Track', (_message.Message, ), { 'DESCRIPTOR': _TRACK, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Track) }) _sym_db.RegisterMessage(Track) Show = _reflection.GeneratedProtocolMessageType( 'Show', (_message.Message, ), { 'DESCRIPTOR': _SHOW, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Show) }) _sym_db.RegisterMessage(Show) Episode = _reflection.GeneratedProtocolMessageType( 'Episode', (_message.Message, ), { 'DESCRIPTOR': _EPISODE, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Episode) }) _sym_db.RegisterMessage(Episode) Licensor = _reflection.GeneratedProtocolMessageType( 'Licensor', (_message.Message, ), { 'DESCRIPTOR': _LICENSOR, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Licensor) }) _sym_db.RegisterMessage(Licensor) TopTracks = _reflection.GeneratedProtocolMessageType( 'TopTracks', (_message.Message, ), { 'DESCRIPTOR': _TOPTRACKS, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.TopTracks) }) _sym_db.RegisterMessage(TopTracks) ActivityPeriod = _reflection.GeneratedProtocolMessageType( 'ActivityPeriod', (_message.Message, ), { 'DESCRIPTOR': _ACTIVITYPERIOD, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.ActivityPeriod) }) _sym_db.RegisterMessage(ActivityPeriod) AlbumGroup = _reflection.GeneratedProtocolMessageType( 'AlbumGroup', (_message.Message, ), { 'DESCRIPTOR': _ALBUMGROUP, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.AlbumGroup) }) _sym_db.RegisterMessage(AlbumGroup) Date = _reflection.GeneratedProtocolMessageType( 'Date', (_message.Message, ), { 'DESCRIPTOR': _DATE, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Date) }) _sym_db.RegisterMessage(Date) Image = _reflection.GeneratedProtocolMessageType( 'Image', (_message.Message, ), { 'DESCRIPTOR': _IMAGE, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Image) }) _sym_db.RegisterMessage(Image) ImageGroup = _reflection.GeneratedProtocolMessageType( 'ImageGroup', (_message.Message, ), { 'DESCRIPTOR': _IMAGEGROUP, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.ImageGroup) }) _sym_db.RegisterMessage(ImageGroup) Biography = _reflection.GeneratedProtocolMessageType( 'Biography', (_message.Message, ), { 'DESCRIPTOR': _BIOGRAPHY, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Biography) }) _sym_db.RegisterMessage(Biography) Disc = _reflection.GeneratedProtocolMessageType( 'Disc', (_message.Message, ), { 'DESCRIPTOR': _DISC, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Disc) }) _sym_db.RegisterMessage(Disc) Copyright = _reflection.GeneratedProtocolMessageType( 'Copyright', (_message.Message, ), { 'DESCRIPTOR': _COPYRIGHT, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Copyright) }) _sym_db.RegisterMessage(Copyright) Restriction = _reflection.GeneratedProtocolMessageType( 'Restriction', (_message.Message, ), { 'DESCRIPTOR': _RESTRICTION, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Restriction) }) _sym_db.RegisterMessage(Restriction) Availability = _reflection.GeneratedProtocolMessageType( 'Availability', (_message.Message, ), { 'DESCRIPTOR': _AVAILABILITY, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.Availability) }) _sym_db.RegisterMessage(Availability) SalePeriod = _reflection.GeneratedProtocolMessageType( 'SalePeriod', (_message.Message, ), { 'DESCRIPTOR': _SALEPERIOD, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.SalePeriod) }) _sym_db.RegisterMessage(SalePeriod) ExternalId = _reflection.GeneratedProtocolMessageType( 'ExternalId', (_message.Message, ), { 'DESCRIPTOR': _EXTERNALID, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.ExternalId) }) _sym_db.RegisterMessage(ExternalId) AudioFile = _reflection.GeneratedProtocolMessageType( 'AudioFile', (_message.Message, ), { 'DESCRIPTOR': _AUDIOFILE, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.AudioFile) }) _sym_db.RegisterMessage(AudioFile) VideoFile = _reflection.GeneratedProtocolMessageType( 'VideoFile', (_message.Message, ), { 'DESCRIPTOR': _VIDEOFILE, '__module__': 'metadata_pb2' # @@protoc_insertion_point(class_scope:spotify.metadata.proto.VideoFile) }) _sym_db.RegisterMessage(VideoFile) DESCRIPTOR._options = None _SHOW.fields_by_name['deprecated_popularity']._options = None _EPISODE.fields_by_name['deprecated_popularity']._options = None # @@protoc_insertion_point(module_scope)

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128,507

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128,507

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null

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e8c10c4f512a8859ad0d6347eb53b0188b0c7e0b

123

py

Python

src/omlt/neuralnet/layers/__init__.py

fracek/OMLT

b0ccafda34d1ea2b1187186081ed50f17c10ba7f

[ "BSD-3-Clause" ]

115

2021-11-04T03:15:35.000Z

2022-03-28T19:05:55.000Z

src/omlt/neuralnet/layers/__init__.py

fracek/OMLT

b0ccafda34d1ea2b1187186081ed50f17c10ba7f

[ "BSD-3-Clause" ]

56

2021-11-03T13:59:41.000Z

2022-03-21T14:01:52.000Z

src/omlt/neuralnet/layers/__init__.py

fracek/OMLT

b0ccafda34d1ea2b1187186081ed50f17c10ba7f

[ "BSD-3-Clause" ]

17

2021-11-04T03:15:23.000Z

2022-03-24T02:24:15.000Z

from .full_space import full_space_dense_layer, full_space_conv_layer from .reduced_space import reduced_space_dense_layer

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fa71eeac3d674093c4c774373dd96dc7070f8a46

22,469

py

Python

python/powermeter_api/api/recent_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

python/powermeter_api/api/recent_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

python/powermeter_api/api/recent_api.py

thracesystems/powermeter-api

7bdab034ff916ee49e986de88f157bd044e981c1

[ "Apache-2.0" ]

null

# coding: utf-8 """ PowerMeter API API # noqa: E501 The version of the OpenAPI document: 2021.4.1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from powermeter_api.api_client import ApiClient from powermeter_api.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class RecentApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def recent_dashboard_list(self, **kwargs): # noqa: E501 """recent_dashboard_list # noqa: E501 Get list of recent design dashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_dashboard_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[DashboardAccess] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.recent_dashboard_list_with_http_info(**kwargs) # noqa: E501 def recent_dashboard_list_with_http_info(self, **kwargs): # noqa: E501 """recent_dashboard_list # noqa: E501 Get list of recent design dashboards # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_dashboard_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[DashboardAccess], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method recent_dashboard_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['Basic'] # noqa: E501 return self.api_client.call_api( '/recent/dashboard/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[DashboardAccess]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def recent_design_list(self, **kwargs): # noqa: E501 """recent_design_list # noqa: E501 Get list of recent designs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_design_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[DesignAccess] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.recent_design_list_with_http_info(**kwargs) # noqa: E501 def recent_design_list_with_http_info(self, **kwargs): # noqa: E501 """recent_design_list # noqa: E501 Get list of recent designs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_design_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[DesignAccess], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method recent_design_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['Basic'] # noqa: E501 return self.api_client.call_api( '/recent/design/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[DesignAccess]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def recent_project_list(self, **kwargs): # noqa: E501 """recent_project_list # noqa: E501 Get list of recent projects # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_project_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[ProjectAccess] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.recent_project_list_with_http_info(**kwargs) # noqa: E501 def recent_project_list_with_http_info(self, **kwargs): # noqa: E501 """recent_project_list # noqa: E501 Get list of recent projects # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_project_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[ProjectAccess], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method recent_project_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['Basic'] # noqa: E501 return self.api_client.call_api( '/recent/project/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ProjectAccess]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def recent_scenario_list(self, **kwargs): # noqa: E501 """recent_scenario_list # noqa: E501 Get list of recent project scenarios # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_scenario_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[ScenarioAccess] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.recent_scenario_list_with_http_info(**kwargs) # noqa: E501 def recent_scenario_list_with_http_info(self, **kwargs): # noqa: E501 """recent_scenario_list # noqa: E501 Get list of recent project scenarios # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_scenario_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[ScenarioAccess], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method recent_scenario_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['Basic'] # noqa: E501 return self.api_client.call_api( '/recent/scenario/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ScenarioAccess]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def recent_simulation_list(self, **kwargs): # noqa: E501 """recent_simulation_list # noqa: E501 Get list of recent sims # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_simulation_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[SimAccess] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.recent_simulation_list_with_http_info(**kwargs) # noqa: E501 def recent_simulation_list_with_http_info(self, **kwargs): # noqa: E501 """recent_simulation_list # noqa: E501 Get list of recent sims # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.recent_simulation_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[SimAccess], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method recent_simulation_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['Basic'] # noqa: E501 return self.api_client.call_api( '/recent/simulation/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[SimAccess]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

39.909414

96

0.580044

2,398

22,469

5.173478

0.071726

0.036112

0.045139

0.036273

0.933661

0.925601

0.903434

0

0.013205

0.349504

22,469

562

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0.835591

0.480573

0

0.731225

1

0

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0.039083

0

1

0.043478

false

0

0.019763

0

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0

null

0

1

0

1

0

null

0

8

d744d61c248f582a0e3f83b3d3b43d79fb1c17ac

223

py

Python

nmigen/vendor/xilinx.py

psumesh/nmigen

7d611b8fc1d9e58853ff268ec38ff8f4131a9774

[ "BSD-2-Clause" ]

528

2020-01-28T18:21:00.000Z

2021-12-09T06:27:51.000Z

nmigen/vendor/xilinx.py

psumesh/nmigen

7d611b8fc1d9e58853ff268ec38ff8f4131a9774

[ "BSD-2-Clause" ]

360

2020-01-28T18:34:30.000Z

2021-12-10T08:03:32.000Z

nmigen/vendor/xilinx.py

psumesh/nmigen

7d611b8fc1d9e58853ff268ec38ff8f4131a9774

[ "BSD-2-Clause" ]

100

2020-02-06T21:55:46.000Z

2021-11-25T19:20:44.000Z

from amaranth.vendor.xilinx import * from amaranth.vendor.xilinx import __all__ import warnings warnings.warn("instead of nmigen.vendor.xilinx, use amaranth.vendor.xilinx", DeprecationWarning, stacklevel=2)

27.875

76

0.7713

27

223

6.222222

0.555556

0.285714

0.357143

0.285714

0.357143

0

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0.147982

223

7

77

31.857143

0.878947

0

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0.192825

0

1

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true

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0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

d7584cfd8836d2082bc285338a1155104c8d7013

35,879

py

Python

backends/tests/integration/core/models_tests.py

sltk/crmint

bc8417bc4ed225faa5caa88daca48f1f12f2ac94

[ "Apache-2.0" ]

null

backends/tests/integration/core/models_tests.py

sltk/crmint

bc8417bc4ed225faa5caa88daca48f1f12f2ac94

[ "Apache-2.0" ]

null

backends/tests/integration/core/models_tests.py

sltk/crmint

bc8417bc4ed225faa5caa88daca48f1f12f2ac94

[ "Apache-2.0" ]

null

# Copyright 2018 Google Inc # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from google.appengine.api import taskqueue from google.appengine.ext import testbed import mock from core import cache from core import models from tests import utils class TestPipelineWithJobs(utils.ModelTestCase): def setUp(self): super(TestPipelineWithJobs, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_taskqueue_stub() self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() def tearDown(self): super(TestPipelineWithJobs, self).tearDown() self.testbed.deactivate() def test_start_fails_without_jobs(self): pipeline = models.Pipeline.create() self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, False) self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) def test_start_fails_if_already_running(self): pipeline = models.Pipeline.create() pipeline.status = models.Pipeline.STATUS.RUNNING pipeline.save() self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) result = pipeline.start() self.assertEqual(result, False) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_start_succeeds_with_one_job_idle(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, True) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_start_fails_with_one_job_running(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job1.status = models.Job.STATUS.RUNNING job1.save() self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, False) self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) def test_start_succeeds_with_one_job_succeeded(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job1.status = models.Job.STATUS.SUCCEEDED job1.save() self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, True) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_start_succeeds_with_one_job_failed(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job1.status = models.Job.STATUS.FAILED job1.save() self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, True) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) @mock.patch('core.cloud_logging.logger') def test_start_fails_with_one_job_not_getting_ready(self, patched_logger): patched_logger.log_struct.__name__ = 'foo' pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) models.Param.create( job_id=job1.id, name='field1', type='number', value='{% ABC %}') # initialize with a non-boolean value self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.start() self.assertEqual(result, False) self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) def test_stop_fails_if_not_running(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.IDLE) self.assertEqual(pipeline.status, models.Pipeline.STATUS.IDLE) result = pipeline.stop() self.assertEqual(result, False) def test_stop_succeeds_and_stop_all_jobs(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.RUNNING) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.RUNNING) self.assertEqual(len(pipeline.jobs.all()), 3) self.assertEqual(pipeline.jobs[0].get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(pipeline.jobs[1].get_status(), models.Job.STATUS.RUNNING) self.assertEqual(pipeline.jobs[2].get_status(), models.Job.STATUS.RUNNING) result = pipeline.stop() self.assertTrue(result) self.assertEqual(pipeline.jobs[0].get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(pipeline.jobs[1].get_status(), models.Job.STATUS.FAILED) self.assertEqual(pipeline.jobs[2].get_status(), models.Job.STATUS.FAILED) def test_stop_succeeds_if_all_jobs_succeeded(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) self.assertEqual(len(pipeline.jobs.all()), 3) result = pipeline.stop() self.assertTrue(result) self.assertEqual(pipeline.jobs[0].get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(pipeline.jobs[1].get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(pipeline.jobs[2].get_status(), models.Job.STATUS.SUCCEEDED) def test_start_single_job_succeeds(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.IDLE) job1 = models.Job.create(pipeline_id=pipeline.id) result = pipeline.start_single_job(job1) self.assertTrue(result) self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_start_single_job_fails_if_running(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) job1 = models.Job.create(pipeline_id=pipeline.id) result = pipeline.start_single_job(job1) self.assertFalse(result) self.assertEqual(job1.get_status(), models.Job.STATUS.IDLE) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_job_finished_succeeds(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) result = pipeline.job_finished() self.assertTrue(result) self.assertEqual(pipeline.status, models.Pipeline.STATUS.SUCCEEDED) def test_job_finished_fails_if_one_remains(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.RUNNING) result = pipeline.job_finished() self.assertFalse(result) self.assertEqual(pipeline.status, models.Pipeline.STATUS.RUNNING) def test_job_finished_fails_if_mix_succeeded_and_failed(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) job1 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) job2 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.FAILED) models.StartCondition.create(job_id=job2.id, preceding_job_id=None) result = pipeline.job_finished() self.assertTrue(result) self.assertEqual(pipeline.status, models.Pipeline.STATUS.FAILED) def test_pipeline_success_with_failed_condition_fulfilled(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) job1 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) job2 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.FAILED) job3 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.SUCCEEDED) models.StartCondition.create( job_id=job3.id, preceding_job_id=job2.id, condition=models.StartCondition.CONDITION.FAIL) result = pipeline.job_finished() self.assertTrue(result) self.assertEqual(pipeline.status, models.Pipeline.STATUS.SUCCEEDED) def test_successfully_cancel_tasks_on_failure_without_conditions(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertIsNotNone(task1) self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job1._enqueued_task_count(), 1) task2 = job2.start() self.assertIsNotNone(task2) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2._enqueued_task_count(), 1) job2.task_failed(task2.name) self.assertEqual(job2.get_status(), models.Job.STATUS.FAILED) # It should trigger the end of the pipeline by itself self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job1._enqueued_task_count(), 0) self.assertEqual(job2._enqueued_task_count(), 0) self.assertEqual(pipeline.status, models.Pipeline.STATUS.FAILED) class TestPipelineDestroy(utils.ModelTestCase): def setUp(self): super(TestPipelineDestroy, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() def tearDown(self): super(TestPipelineDestroy, self).tearDown() self.testbed.deactivate() def test_destroy_succeeds(self): pipeline = models.Pipeline.create() pipeline.destroy() self.assertIsNone(models.Pipeline.find(pipeline.id)) def test_destroy_deletes_all_schedules(self): pipeline = models.Pipeline.create() sc1 = models.Schedule.create(pipeline_id=pipeline.id) self.assertIsNotNone(models.Schedule.find(sc1.id)) pipeline.destroy() self.assertIsNone(models.Schedule.find(sc1.id)) def test_destroy_deletes_all_jobs(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id, name='j1') self.assertIsNotNone(models.Job.find(job1.id)) pipeline.destroy() self.assertIsNone(models.Job.find(job1.id)) def test_destroy_deletes_all_params(self): pipeline = models.Pipeline.create() param1 = models.Param.create( pipeline_id=pipeline.id, name='p1', type='string') self.assertIsNotNone(models.Param.find(param1.id)) pipeline.destroy() self.assertIsNone(models.Param.find(param1.id)) class TestPipelineImport(utils.ModelTestCase): def setUp(self): super(TestPipelineImport, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_taskqueue_stub() self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() def tearDown(self): super(TestPipelineImport, self).tearDown() self.testbed.deactivate() def test_import_data_succeeds(self): pipeline = models.Pipeline.create() job1 = models.Job.create() job2 = models.Job.create() data = { 'params': [ {'name': 'p1', 'type': 'string', 'value': 'foo'}, {'name': 'p2', 'type': 'string', 'value': 'bar'}, ], 'schedules': [ {'id': None, 'cron': 'NEW1'}, {'id': None, 'cron': 'NEW2'}, ], 'jobs': [ {'id': job1.id, 'name': 'j1', 'hash_start_conditions': []}, {'id': job2.id, 'name': 'j2', 'hash_start_conditions': []}, ] } pipeline.import_data(data) self.assertEqual(len(pipeline.params.all()), 2) self.assertEqual(pipeline.params[0].name, 'p1') self.assertEqual(pipeline.params[0].value, 'foo') self.assertEqual(pipeline.params[1].name, 'p2') self.assertEqual(pipeline.params[1].value, 'bar') self.assertEqual(len(pipeline.jobs.all()), 2) self.assertEqual(pipeline.jobs[0].name, 'j1') self.assertEqual(pipeline.jobs[1].name, 'j2') class TestJobStartedStatus(utils.ModelTestCase): def setUp(self): super(TestJobStartedStatus, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() self.testbed.init_taskqueue_stub() def tearDown(self): super(TestJobStartedStatus, self).tearDown() self.testbed.deactivate() def test_succeeds_status_running(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.status, models.Job.STATUS.WAITING) self.assertTrue(job.start()) self.assertEqual(job.status, models.Job.STATUS.RUNNING) class TestJobDestroy(utils.ModelTestCase): def setUp(self): super(TestJobDestroy, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() def tearDown(self): super(TestJobDestroy, self).tearDown() self.testbed.deactivate() def test_destroy_succeeds(self): job = models.Job.create() job.destroy() self.assertIsNone(models.Job.find(job.id)) def test_destroy_deletes_all_starting_conditions(self): job1 = models.Job.create() job2 = models.Job.create() sc1 = models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id) self.assertIsNotNone(models.StartCondition.find(sc1.id)) job2.destroy() self.assertIsNone(models.StartCondition.find(sc1.id)) def test_destroy_deletes_preceding_starting_conditions(self): job1 = models.Job.create() job2 = models.Job.create() sc1 = models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id) self.assertIsNotNone(models.StartCondition.find(sc1.id)) job1.destroy() self.assertIsNone(models.StartCondition.find(sc1.id)) def test_destroy_deletes_all_params(self): job = models.Job.create() param1 = models.Param.create( job_id=job.id, name='p1', type='string') self.assertIsNotNone(models.Param.find(param1.id)) job.destroy() self.assertIsNone(models.Param.find(param1.id)) class TestStartConditionWithJobs(utils.ModelTestCase): def setUp(self): super(TestStartConditionWithJobs, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() def tearDown(self): super(TestStartConditionWithJobs, self).tearDown() self.testbed.deactivate() def test_value_succeeds(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id, name='job1') job2 = models.Job.create(pipeline_id=pipeline.id, name='job2') sc1 = models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertEqual(sc1.value, '%s,success' % job1.id) def test_preceding_job_name_succeeds(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id, name='job1') job2 = models.Job.create(pipeline_id=pipeline.id, name='job2') sc1 = models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertEqual(sc1.preceding_job_name, 'job1') class TestJobStartConditions(utils.ModelTestCase): def setUp(self): super(TestJobStartConditions, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() self.testbed.init_taskqueue_stub() def tearDown(self): super(TestJobStartConditions, self).tearDown() self.testbed.deactivate() def test_create_start_conditions_succeeds(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.IDLE) job2 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.IDLE) job3 = models.Job.create(pipeline_id=pipeline.id, status=models.Job.STATUS.IDLE) arg_start_conditions = [ {'preceding_job_id': job1.id, 'condition': models.StartCondition.CONDITION.SUCCESS}, {'preceding_job_id': job2.id, 'condition': models.StartCondition.CONDITION.SUCCESS}, ] job3.assign_start_conditions(arg_start_conditions) self.assertEqual(len(job3.start_conditions), 2) def test_update_start_conditions_succeeds(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) job3 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job3.id, preceding_job_id=job2.id, condition=models.StartCondition.CONDITION.FAIL) arg_start_conditions = [ { 'preceding_job_id': job1.id, 'condition': models.StartCondition.CONDITION.SUCCESS}, { 'preceding_job_id': job2.id, 'condition': models.StartCondition.CONDITION.SUCCESS}, ] self.assertEqual(len(job3.start_conditions), 1) self.assertEqual(job3.start_conditions[0].condition, models.StartCondition.CONDITION.FAIL) job3.assign_start_conditions(arg_start_conditions) self.assertEqual(len(job3.start_conditions), 2) self.assertEqual(job3.start_conditions[0].condition, models.StartCondition.CONDITION.SUCCESS) self.assertEqual(job3.start_conditions[1].condition, models.StartCondition.CONDITION.SUCCESS) def test_fails_if_running(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.get_status(), models.Job.STATUS.WAITING) task1 = job.start() self.assertIsNotNone(task1) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) task2 = job.start() self.assertIsNone(task2) def test_succeeds_if_waiting_without_start_conditions(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.get_status(), models.Job.STATUS.WAITING) task = job.start() self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) self.assertIsNotNone(task) def test_succeeds_with_start_condition_fulfill_success_with_succeeded(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition='success') self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_succeeded(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) def test_fails_with_start_condition_unfulfill_success_with_failed(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_failed(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job2.get_status(), models.Job.STATUS.FAILED) def test_succeeds_with_start_condition_fulfill_fail_with_failed(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.FAIL) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_failed(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) self.assertNotEqual(pipeline.status, models.Pipeline.STATUS.FAILED) def test_fails_with_start_condition_unfulfill_fail_with_succeeded(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition='fail') self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_succeeded(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(job2.get_status(), models.Job.STATUS.FAILED) def test_succeeds_with_start_condition_fulfill_whatever_with_failed(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.WHATEVER) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_failed(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) def test_succeeds_with_start_condition_fulfill_whatever_with_succeeded(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.WHATEVER) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) job1.task_succeeded(task1.name) self.assertEqual(job1.get_status(), models.Job.STATUS.SUCCEEDED) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) def test_fails_with_start_condition_unfulfill_whatever_with_running(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.WHATEVER) self.assertTrue(pipeline.get_ready()) task1 = job1.start() self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) task2 = job2.start() self.assertIsNone(task2) self.assertEqual(job1.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) class TestJobStopConditions(utils.ModelTestCase): def setUp(self): super(TestJobStopConditions, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() self.testbed.init_taskqueue_stub() def tearDown(self): super(TestJobStopConditions, self).tearDown() self.testbed.deactivate() def test_stop_fails_with_idle(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) self.assertEqual(job1.get_status(), models.Job.STATUS.IDLE) result = job1.stop() self.assertFalse(result) self.assertEqual(job1.get_status(), models.Job.STATUS.IDLE) def test_stop_reset_to_idle(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) result = job1.stop() self.assertTrue(result) self.assertEqual(job1.status, models.Job.STATUS.IDLE) self.assertEqual(job1.get_status(), models.Job.STATUS.IDLE) def test_stop_succeeds_with_running(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) task1 = job1.start() self.assertIsNotNone(task1) self.assertTrue(job1.stop()) self.assertEqual(job1.get_status(), models.Job.STATUS.STOPPING) def test_stop_succeeds_with_outdated_tasks(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) self.assertTrue(pipeline.get_ready()) task1 = job1.start() self.assertIsNotNone(task1) taskqueue.Queue().delete_tasks([taskqueue.Task(name=task1.name)]) self.assertTrue(job1.stop()) self.assertEqual(job1.get_status(), models.Job.STATUS.STOPPING) class TestJobStartWithDependentJobs(utils.ModelTestCase): def setUp(self): super(TestJobStartWithDependentJobs, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() self.testbed.init_taskqueue_stub() def tearDown(self): super(TestJobStartWithDependentJobs, self).tearDown() self.testbed.deactivate() def test_start_fails_with_dependent_jobs_and_expecting_success(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) job3 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.SUCCESS) models.StartCondition.create( job_id=job3.id, preceding_job_id=job2.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job3.get_status(), models.Job.STATUS.WAITING) task = job1.start() self.assertIsNotNone(task) job1.task_failed(task.name) self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job2.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job3.get_status(), models.Job.STATUS.FAILED) def test_start_fails_with_dependent_jobs_and_expecting_fail(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) job3 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.FAIL) models.StartCondition.create( job_id=job3.id, preceding_job_id=job2.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job3.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() self.assertIsNotNone(task1) job1.task_succeeded(task1.name) task2 = job2.start() self.assertIsNone(task2) self.assertEqual(job2.get_status(), models.Job.STATUS.FAILED) self.assertEqual(job3.get_status(), models.Job.STATUS.FAILED) def test_dependent_job_starts_after_multiple_workers_finish_with_fail(self): pipeline = models.Pipeline.create() job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) job3 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.FAIL) models.StartCondition.create( job_id=job3.id, preceding_job_id=job2.id, condition=models.StartCondition.CONDITION.SUCCESS) self.assertTrue(pipeline.get_ready()) self.assertEqual(job1.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job2.get_status(), models.Job.STATUS.WAITING) self.assertEqual(job3.get_status(), models.Job.STATUS.WAITING) task1 = job1.start() task2 = job1.enqueue(job1.worker_class, {}) self.assertIsNotNone(task1) job1.task_succeeded(task1.name) job1.task_failed(task2.name) self.assertEqual(job1.get_status(), models.Job.STATUS.FAILED) task3 = job2.start() self.assertIsNone(task3) self.assertEqual(job2.get_status(), models.Job.STATUS.RUNNING) self.assertEqual(job3.get_status(), models.Job.STATUS.WAITING) class TestJobStartingMultipleTasks(utils.ModelTestCase): def setUp(self): super(TestJobStartingMultipleTasks, self).setUp() self.testbed = testbed.Testbed() self.testbed.activate() # Activate which service we want to stub self.testbed.init_memcache_stub() self.testbed.init_app_identity_stub() self.testbed.init_taskqueue_stub() def tearDown(self): super(TestJobStartingMultipleTasks, self).tearDown() self.testbed.deactivate() def test_succeeds_completing_tasks_in_series(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) worker_params = dict([(p.name, p.val) for p in job.params]) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.get_status(), models.Job.STATUS.WAITING) task1 = job.start() self.assertIsNotNone(task1) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) task2 = job.enqueue(job.worker_class, worker_params) self.assertIsNotNone(task2) job.task_succeeded(task1.name) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) job.task_succeeded(task2.name) self.assertEqual(job.get_status(), models.Job.STATUS.SUCCEEDED) def test_pipeline_fails_second_task_succeeded_fail_start_condition_fail(self): pipeline = models.Pipeline.create(status=models.Pipeline.STATUS.RUNNING) job1 = models.Job.create(pipeline_id=pipeline.id) job2 = models.Job.create(pipeline_id=pipeline.id) job3 = models.Job.create(pipeline_id=pipeline.id) models.StartCondition.create( job_id=job2.id, preceding_job_id=job1.id, condition=models.StartCondition.CONDITION.SUCCESS) pipeline.get_ready() task1 = job1.start() job1.task_failed(task1.name) self.assertTrue(job1.get_status(), models.Job.STATUS.FAILED) self.assertTrue(job2.get_status(), models.Job.STATUS.STOPPING) self.assertEqual(pipeline.status, models.Pipeline.STATUS.FAILED) def test_succeeds_completing_tasks_in_parallel(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) worker_params = dict([(p.name, p.val) for p in job.params]) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.get_status(), models.Job.STATUS.WAITING) task1 = job.start() self.assertIsNotNone(task1) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) task2 = job.enqueue(job.worker_class, worker_params) task3 = job.enqueue(job.worker_class, worker_params) self.assertIsNotNone(task2) self.assertIsNotNone(task3) job.task_succeeded(task1.name) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) job.task_succeeded(task3.name) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) job.task_succeeded(task2.name) self.assertEqual(job.get_status(), models.Job.STATUS.SUCCEEDED) def test_succeeds_completing_tasks_with_multiple_memcache_clients(self): pipeline = models.Pipeline.create() job = models.Job.create(pipeline_id=pipeline.id) worker_params = dict([(p.name, p.val) for p in job.params]) self.assertTrue(pipeline.get_ready()) self.assertEqual(job.get_status(), models.Job.STATUS.WAITING) task1 = job.start() self.assertIsNotNone(task1) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) # Simulates that the task will complete from another process/machine. cache.clear_memcache_client() job = models.Job.find(job.id) # refresh the job entity task2 = job.enqueue(job.worker_class, worker_params) task3 = job.enqueue(job.worker_class, worker_params) self.assertIsNotNone(task2) self.assertIsNotNone(task3) job.task_succeeded(task1.name) # Simulates that the task will complete from another process/machine. cache.clear_memcache_client() job = models.Job.find(job.id) # refresh the job entity self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) job.task_succeeded(task3.name) self.assertEqual(job.get_status(), models.Job.STATUS.RUNNING) # Simulates that the task will complete from another process/machine. cache.clear_memcache_client() job = models.Job.find(job.id) # refresh the job entity job.task_succeeded(task2.name) self.assertEqual(job.get_status(), models.Job.STATUS.SUCCEEDED)

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null

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null

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d784168dac88b85eacedf7bc81a50fb3bf6ae2ca

196

py

Python

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/decoders/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/decoders/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

3

2021-03-31T20:15:40.000Z

2022-02-09T23:50:46.000Z

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/decoders/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright Huawei Noah's Ark Lab. from noahnmt.decoders import attention_decoder from noahnmt.decoders import beam_search_decoder from noahnmt.decoders import transformer_decoder

28

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null

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1

0

8

ad06a083b65ff952a6f645b56ae1d8a2a566a546

42

py

Python

tests/test_psdm_qs_cli.py

teddyrendahl/psdm_qs_cli

3b693932f64daa948319f24441a326920a7a7f08

[ "MIT" ]

null

tests/test_psdm_qs_cli.py

teddyrendahl/psdm_qs_cli

3b693932f64daa948319f24441a326920a7a7f08

[ "MIT" ]

null

tests/test_psdm_qs_cli.py

teddyrendahl/psdm_qs_cli

3b693932f64daa948319f24441a326920a7a7f08

[ "MIT" ]

null

def test_import(): import psdm_qs_cli

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null

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1

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7

ad1562186d9b68e5f46211db9be82f2787739647

17,161

py

Python

src/scripts/data_processing/combine_ts_fresh_features.py

arnabbiswas1/k_tab_aug_muticlass_rmse_logloss_weightedf1_stratified_tsfresh_cesium

13db3cb9d0b2f25181ccf4b1316e12425abfc276

[ "Apache-2.0" ]

null

src/scripts/data_processing/combine_ts_fresh_features.py

arnabbiswas1/k_tab_aug_muticlass_rmse_logloss_weightedf1_stratified_tsfresh_cesium

13db3cb9d0b2f25181ccf4b1316e12425abfc276

[ "Apache-2.0" ]

null

src/scripts/data_processing/combine_ts_fresh_features.py

arnabbiswas1/k_tab_aug_muticlass_rmse_logloss_weightedf1_stratified_tsfresh_cesium

13db3cb9d0b2f25181ccf4b1316e12425abfc276

[ "Apache-2.0" ]

null

""" Script to combine the features generated for tsfresh and write the combined DF back to disk """ from src import common import pandas as pd import src.config.constants as constants def select_features(logger, df, features_to_drop): logger.info(f"Shape of the features {df.shape}") df = df.drop(features_to_drop, axis=1) logger.info(f"Shape of the features after dropping {df.shape}") return df def load_data(logger, name, features_to_drop): df = pd.read_parquet(f"{constants.FEATURES_DATA_DIR}/cast/{name}_cast.parquet") logger.info(f"Shape of {name} before droipping {df.shape}") df = select_features(df, features_to_drop) logger.info(f"Shape of {name} after droipping {df.shape}") return df def combine_features(logger): name = "mixed_1_set" features_to_drop = [ "loan__has_duplicate_min", "loan__length", "loan__sample_entropy", ] df_mixed_1_set = load_data(logger, name, features_to_drop) name = "symmetry_large_std_quantile_set" features_to_drop = features_to_drop = [ "loan__symmetry_looking__r_0.0", "loan__symmetry_looking__r_0.1", "loan__symmetry_looking__r_0.15000000000000002", "loan__symmetry_looking__r_0.2", "loan__symmetry_looking__r_0.25", "loan__symmetry_looking__r_0.30000000000000004", "loan__symmetry_looking__r_0.35000000000000003", "loan__symmetry_looking__r_0.4", "loan__symmetry_looking__r_0.45", "loan__symmetry_looking__r_0.5", "loan__symmetry_looking__r_0.55", "loan__symmetry_looking__r_0.6000000000000001", "loan__symmetry_looking__r_0.65", "loan__symmetry_looking__r_0.7000000000000001", "loan__symmetry_looking__r_0.75", "loan__symmetry_looking__r_0.8", "loan__symmetry_looking__r_0.8500000000000001", "loan__symmetry_looking__r_0.9", "loan__symmetry_looking__r_0.9500000000000001", "loan__large_standard_deviation__r_0.05", "loan__large_standard_deviation__r_0.1", "loan__large_standard_deviation__r_0.15000000000000002", "loan__large_standard_deviation__r_0.30000000000000004", "loan__large_standard_deviation__r_0.35000000000000003", "loan__large_standard_deviation__r_0.4", "loan__large_standard_deviation__r_0.45", "loan__large_standard_deviation__r_0.5", "loan__large_standard_deviation__r_0.55", "loan__large_standard_deviation__r_0.6000000000000001", "loan__large_standard_deviation__r_0.65", "loan__large_standard_deviation__r_0.7000000000000001", "loan__large_standard_deviation__r_0.75", "loan__large_standard_deviation__r_0.8", "loan__large_standard_deviation__r_0.8500000000000001", "loan__large_standard_deviation__r_0.9", "loan__large_standard_deviation__r_0.9500000000000001", ] df_sym = load_data(logger, name, features_to_drop) name = "acf_pacf_set" features_to_drop = ["loan__partial_autocorrelation__lag_0"] df_acf_pacf_set = load_data(logger, name, features_to_drop) name = "cwt_coeff_set" features_to_drop = [] df_cwt_coeff_set = load_data(logger, name, features_to_drop) name = "change_quantile_set" features_to_drop = [] df_change_quantile_set = load_data(logger, name, features_to_drop) name = "liner_agg_linear_set" features_to_drop = [ "loan__agg_linear_trend__attr_stderr__chunk_len_50__f_agg_max", "loan__agg_linear_trend__attr_stderr__chunk_len_50__f_agg_min", "loan__agg_linear_trend__attr_stderr__chunk_len_50__f_agg_mean", "loan__agg_linear_trend__attr_stderr__chunk_len_50__f_agg_var", ] df_liner_agg_linear_set = load_data(logger, name, features_to_drop) name = "mixed_2_set" features_to_drop = [ "loan__count_above__t_0", "loan__query_similarity_count__query_None__threshold_00", "loan__matrix_profile__feature_min__threshold_098", "loan__matrix_profile__feature_max__threshold_098", "loan__matrix_profile__feature_mean__threshold_098", "loan__matrix_profile__feature_median__threshold_098", "loan__matrix_profile__feature_25__threshold_098", "loan__matrix_profile__feature_75__threshold_098", ] df_mixed_2_set = load_data(logger, name, features_to_drop) name = "mixed_3_set" features_to_drop = [] df_mixed_3_set = load_data(logger, name, features_to_drop) name = "mixed_4_set" features_to_drop = [ "loan__value_count__value_minus1", "loan__range_count__max_0__min_10000000000000", "loan__range_count__max_10000000000000__min_0", "loan__number_crossing_m__m_minus1", "loan__ratio_beyond_r_sigma__r_5", "loan__ratio_beyond_r_sigma__r_6", "loan__ratio_beyond_r_sigma__r_7", "loan__ratio_beyond_r_sigma__r_10", ] df_mixed_4_set = load_data(logger, name, features_to_drop) name = "fft_real_set" features_to_drop = [ "loan__fft_coefficient__attr_real__coeff_51", "loan__fft_coefficient__attr_real__coeff_52", "loan__fft_coefficient__attr_real__coeff_53", "loan__fft_coefficient__attr_real__coeff_54", "loan__fft_coefficient__attr_real__coeff_55", "loan__fft_coefficient__attr_real__coeff_56", "loan__fft_coefficient__attr_real__coeff_57", "loan__fft_coefficient__attr_real__coeff_58", "loan__fft_coefficient__attr_real__coeff_59", "loan__fft_coefficient__attr_real__coeff_60", "loan__fft_coefficient__attr_real__coeff_61", "loan__fft_coefficient__attr_real__coeff_62", "loan__fft_coefficient__attr_real__coeff_63", "loan__fft_coefficient__attr_real__coeff_64", "loan__fft_coefficient__attr_real__coeff_65", "loan__fft_coefficient__attr_real__coeff_66", "loan__fft_coefficient__attr_real__coeff_67", "loan__fft_coefficient__attr_real__coeff_68", "loan__fft_coefficient__attr_real__coeff_69", "loan__fft_coefficient__attr_real__coeff_70", "loan__fft_coefficient__attr_real__coeff_71", "loan__fft_coefficient__attr_real__coeff_72", "loan__fft_coefficient__attr_real__coeff_73", "loan__fft_coefficient__attr_real__coeff_74", "loan__fft_coefficient__attr_real__coeff_75", "loan__fft_coefficient__attr_real__coeff_76", "loan__fft_coefficient__attr_real__coeff_77", "loan__fft_coefficient__attr_real__coeff_78", "loan__fft_coefficient__attr_real__coeff_79", "loan__fft_coefficient__attr_real__coeff_80", "loan__fft_coefficient__attr_real__coeff_81", "loan__fft_coefficient__attr_real__coeff_82", "loan__fft_coefficient__attr_real__coeff_83", "loan__fft_coefficient__attr_real__coeff_84", "loan__fft_coefficient__attr_real__coeff_85", "loan__fft_coefficient__attr_real__coeff_86", "loan__fft_coefficient__attr_real__coeff_87", "loan__fft_coefficient__attr_real__coeff_88", "loan__fft_coefficient__attr_real__coeff_89", "loan__fft_coefficient__attr_real__coeff_90", "loan__fft_coefficient__attr_real__coeff_91", "loan__fft_coefficient__attr_real__coeff_92", "loan__fft_coefficient__attr_real__coeff_93", "loan__fft_coefficient__attr_real__coeff_94", "loan__fft_coefficient__attr_real__coeff_95", "loan__fft_coefficient__attr_real__coeff_96", "loan__fft_coefficient__attr_real__coeff_97", "loan__fft_coefficient__attr_real__coeff_98", "loan__fft_coefficient__attr_real__coeff_99", ] df_fft_real_set = load_data(logger, name, features_to_drop) name = "fft_imag_set" features_to_drop = [ "loan__fft_coefficient__attr_imag__coeff_0", "loan__fft_coefficient__attr_imag__coeff_50", "loan__fft_coefficient__attr_imag__coeff_51", "loan__fft_coefficient__attr_imag__coeff_52", "loan__fft_coefficient__attr_imag__coeff_53", "loan__fft_coefficient__attr_imag__coeff_54", "loan__fft_coefficient__attr_imag__coeff_55", "loan__fft_coefficient__attr_imag__coeff_56", "loan__fft_coefficient__attr_imag__coeff_57", "loan__fft_coefficient__attr_imag__coeff_58", "loan__fft_coefficient__attr_imag__coeff_59", "loan__fft_coefficient__attr_imag__coeff_60", "loan__fft_coefficient__attr_imag__coeff_61", "loan__fft_coefficient__attr_imag__coeff_62", "loan__fft_coefficient__attr_imag__coeff_63", "loan__fft_coefficient__attr_imag__coeff_64", "loan__fft_coefficient__attr_imag__coeff_65", "loan__fft_coefficient__attr_imag__coeff_66", "loan__fft_coefficient__attr_imag__coeff_67", "loan__fft_coefficient__attr_imag__coeff_68", "loan__fft_coefficient__attr_imag__coeff_69", "loan__fft_coefficient__attr_imag__coeff_70", "loan__fft_coefficient__attr_imag__coeff_71", "loan__fft_coefficient__attr_imag__coeff_72", "loan__fft_coefficient__attr_imag__coeff_73", "loan__fft_coefficient__attr_imag__coeff_74", "loan__fft_coefficient__attr_imag__coeff_75", "loan__fft_coefficient__attr_imag__coeff_76", "loan__fft_coefficient__attr_imag__coeff_77", "loan__fft_coefficient__attr_imag__coeff_78", "loan__fft_coefficient__attr_imag__coeff_79", "loan__fft_coefficient__attr_imag__coeff_80", "loan__fft_coefficient__attr_imag__coeff_81", "loan__fft_coefficient__attr_imag__coeff_82", "loan__fft_coefficient__attr_imag__coeff_83", "loan__fft_coefficient__attr_imag__coeff_84", "loan__fft_coefficient__attr_imag__coeff_85", "loan__fft_coefficient__attr_imag__coeff_86", "loan__fft_coefficient__attr_imag__coeff_87", "loan__fft_coefficient__attr_imag__coeff_88", "loan__fft_coefficient__attr_imag__coeff_89", "loan__fft_coefficient__attr_imag__coeff_90", "loan__fft_coefficient__attr_imag__coeff_91", "loan__fft_coefficient__attr_imag__coeff_92", "loan__fft_coefficient__attr_imag__coeff_93", "loan__fft_coefficient__attr_imag__coeff_94", "loan__fft_coefficient__attr_imag__coeff_95", "loan__fft_coefficient__attr_imag__coeff_96", "loan__fft_coefficient__attr_imag__coeff_97", "loan__fft_coefficient__attr_imag__coeff_98", "loan__fft_coefficient__attr_imag__coeff_99", ] df_fft_imag_set = load_data(logger, name, features_to_drop) name = "fft_abs_set" features_to_drop = [ "loan__fft_coefficient__attr_abs__coeff_51", "loan__fft_coefficient__attr_abs__coeff_52", "loan__fft_coefficient__attr_abs__coeff_53", "loan__fft_coefficient__attr_abs__coeff_54", "loan__fft_coefficient__attr_abs__coeff_55", "loan__fft_coefficient__attr_abs__coeff_56", "loan__fft_coefficient__attr_abs__coeff_57", "loan__fft_coefficient__attr_abs__coeff_58", "loan__fft_coefficient__attr_abs__coeff_59", "loan__fft_coefficient__attr_abs__coeff_60", "loan__fft_coefficient__attr_abs__coeff_61", "loan__fft_coefficient__attr_abs__coeff_62", "loan__fft_coefficient__attr_abs__coeff_63", "loan__fft_coefficient__attr_abs__coeff_64", "loan__fft_coefficient__attr_abs__coeff_65", "loan__fft_coefficient__attr_abs__coeff_66", "loan__fft_coefficient__attr_abs__coeff_67", "loan__fft_coefficient__attr_abs__coeff_68", "loan__fft_coefficient__attr_abs__coeff_69", "loan__fft_coefficient__attr_abs__coeff_70", "loan__fft_coefficient__attr_abs__coeff_71", "loan__fft_coefficient__attr_abs__coeff_72", "loan__fft_coefficient__attr_abs__coeff_73", "loan__fft_coefficient__attr_abs__coeff_74", "loan__fft_coefficient__attr_abs__coeff_75", "loan__fft_coefficient__attr_abs__coeff_76", "loan__fft_coefficient__attr_abs__coeff_77", "loan__fft_coefficient__attr_abs__coeff_78", "loan__fft_coefficient__attr_abs__coeff_79", "loan__fft_coefficient__attr_abs__coeff_80", "loan__fft_coefficient__attr_abs__coeff_81", "loan__fft_coefficient__attr_abs__coeff_82", "loan__fft_coefficient__attr_abs__coeff_83", "loan__fft_coefficient__attr_abs__coeff_84", "loan__fft_coefficient__attr_abs__coeff_85", "loan__fft_coefficient__attr_abs__coeff_86", "loan__fft_coefficient__attr_abs__coeff_87", "loan__fft_coefficient__attr_abs__coeff_88", "loan__fft_coefficient__attr_abs__coeff_89", "loan__fft_coefficient__attr_abs__coeff_90", "loan__fft_coefficient__attr_abs__coeff_91", "loan__fft_coefficient__attr_abs__coeff_92", "loan__fft_coefficient__attr_abs__coeff_93", "loan__fft_coefficient__attr_abs__coeff_94", "loan__fft_coefficient__attr_abs__coeff_95", "loan__fft_coefficient__attr_abs__coeff_96", "loan__fft_coefficient__attr_abs__coeff_97", "loan__fft_coefficient__attr_abs__coeff_98", "loan__fft_coefficient__attr_abs__coeff_99", ] df_fft_abs_set = load_data(logger, name, features_to_drop) name = "fft_angle_set" features_to_drop = [ "loan__fft_coefficient__attr_angle__coeff_0", "loan__fft_coefficient__attr_angle__coeff_51", "loan__fft_coefficient__attr_angle__coeff_52", "loan__fft_coefficient__attr_angle__coeff_53", "loan__fft_coefficient__attr_angle__coeff_54", "loan__fft_coefficient__attr_angle__coeff_55", "loan__fft_coefficient__attr_angle__coeff_56", "loan__fft_coefficient__attr_angle__coeff_57", "loan__fft_coefficient__attr_angle__coeff_58", "loan__fft_coefficient__attr_angle__coeff_59", "loan__fft_coefficient__attr_angle__coeff_60", "loan__fft_coefficient__attr_angle__coeff_61", "loan__fft_coefficient__attr_angle__coeff_62", "loan__fft_coefficient__attr_angle__coeff_63", "loan__fft_coefficient__attr_angle__coeff_64", "loan__fft_coefficient__attr_angle__coeff_65", "loan__fft_coefficient__attr_angle__coeff_66", "loan__fft_coefficient__attr_angle__coeff_67", "loan__fft_coefficient__attr_angle__coeff_68", "loan__fft_coefficient__attr_angle__coeff_69", "loan__fft_coefficient__attr_angle__coeff_70", "loan__fft_coefficient__attr_angle__coeff_71", "loan__fft_coefficient__attr_angle__coeff_72", "loan__fft_coefficient__attr_angle__coeff_73", "loan__fft_coefficient__attr_angle__coeff_74", "loan__fft_coefficient__attr_angle__coeff_75", "loan__fft_coefficient__attr_angle__coeff_76", "loan__fft_coefficient__attr_angle__coeff_77", "loan__fft_coefficient__attr_angle__coeff_78", "loan__fft_coefficient__attr_angle__coeff_79", "loan__fft_coefficient__attr_angle__coeff_80", "loan__fft_coefficient__attr_angle__coeff_81", "loan__fft_coefficient__attr_angle__coeff_82", "loan__fft_coefficient__attr_angle__coeff_83", "loan__fft_coefficient__attr_angle__coeff_84", "loan__fft_coefficient__attr_angle__coeff_85", "loan__fft_coefficient__attr_angle__coeff_86", "loan__fft_coefficient__attr_angle__coeff_87", "loan__fft_coefficient__attr_angle__coeff_88", "loan__fft_coefficient__attr_angle__coeff_89", "loan__fft_coefficient__attr_angle__coeff_90", "loan__fft_coefficient__attr_angle__coeff_91", "loan__fft_coefficient__attr_angle__coeff_92", "loan__fft_coefficient__attr_angle__coeff_93", "loan__fft_coefficient__attr_angle__coeff_94", "loan__fft_coefficient__attr_angle__coeff_95", "loan__fft_coefficient__attr_angle__coeff_96", "loan__fft_coefficient__attr_angle__coeff_97", "loan__fft_coefficient__attr_angle__coeff_98", "loan__fft_coefficient__attr_angle__coeff_99", ] df_fft_angle_set = load_data(logger, name, features_to_drop) dfs = [ df_acf_pacf_set, df_change_quantile_set, df_cwt_coeff_set, df_fft_abs_set, df_fft_angle_set, df_fft_imag_set, df_fft_real_set, df_liner_agg_linear_set, df_mixed_1_set, df_mixed_2_set, df_mixed_3_set, df_mixed_4_set, df_sym, ] result_df = pd.concat(dfs, axis=1) logger.info(f"Shape of the combined Data Frame {result_df.shape}") return result_df if __name__ == "__main__": # Create a Stream only logger logger = common.get_logger("generate_features") logger.info("Starting to generate features") results_df = combine_features(logger=logger) logger.info( f"Writing the combined parquet to {constants.FEATURES_DATA_DIR}/cast/tsfresh_f_merged.parquet" ) results_df.to_parquet( f"{constants.FEATURES_DATA_DIR}/cast/tsfresh_f_merged.parquet", index=True )

44.924084

102

0.757823

2,290

17,161

4.611354

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0.339205

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0.101231

0.074811

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0.177379

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0

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0

1

0

1

null

0

7

ad5e794854817e95b07ec1b47f801079caf75efc

36

py

Python

tests/sample.py

VoshVolk/public_python

8480f8220531534268f42449c66ebbfd3011bf6e

[ "Apache-2.0" ]

1

2021-11-08T08:09:29.000Z

tests/sample.py

VoshVolk/public_python

8480f8220531534268f42449c66ebbfd3011bf6e

[ "Apache-2.0" ]

null

tests/sample.py

VoshVolk/public_python

8480f8220531534268f42449c66ebbfd3011bf6e

[ "Apache-2.0" ]

null

def first_entry(): return "a"

7.2

18

0.583333

5

36

4

1

0

0.277778

36

5

19

7.2

0.769231

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0.029412

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0.5

true

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1

0

null

0

1

0

1

0

7

ad73b98e5937ee7641afdacd13e0f2d6675bd2e5

2,291

py

Python

pynncml/metrics/regression.py

haihabi/PyNNcml

808892da798913928fbc219cbb5f9e41156d9d49

[ "MIT" ]

4

2020-06-28T22:52:19.000Z

2021-10-31T10:19:51.000Z

pynncml/metrics/regression.py

haihabi/PyNNcml

808892da798913928fbc219cbb5f9e41156d9d49

[ "MIT" ]

null

pynncml/metrics/regression.py

haihabi/PyNNcml

808892da798913928fbc219cbb5f9e41156d9d49

[ "MIT" ]

4

2020-06-28T22:52:24.000Z

2021-11-04T00:28:42.000Z

import numpy as np def mse(input_array: np.ndarray, reference_array: np.ndarray) -> float: r""" The mse function compute the mean square error of predication array. .. math:: mse=\frac{1}{N}\sum_i^N (p_i-r_i)^2 where mse is the mean square error measurement, p is the predication array, r is the reference array. Note:reference array shape must be equal to input array shape :param input_array: A numpy array of any shape :param reference_array: A numpy array of any shape :return: a floating point number that represent the mean square error measurement """ return float(np.mean(np.power(input_array - reference_array, 2))) def nmse(input_array: np.ndarray, reference_array: np.ndarray, epsilon: float = 0.00001) -> float: r""" The nmse function compute the normalized mean square error of predication array. .. math:: nmse=\frac{1}{N}\sum_i^N \frac{(p_i-r_i)^2}{r_i^2+\epsilon} where nmse is the normalized mean square error measurement, p is the predication array, r is the reference array and epsilon is a floating point number fo numeric stability. Note:reference array shape must be equal to input array shape :param input_array: A numpy array of any shape :param reference_array: A numpy array of any shape :param epsilon: a floating point number fo numric stabiliy :return: a floating point number that represent the normalized mean square error measurement """ return float(np.mean(np.power(input_array - reference_array, 2) / (epsilon + np.power(reference_array, 2)))) def rmse(input_array: np.ndarray, reference_array: np.ndarray) -> float: r""" The rmse function compute the mean square error of predication array. .. math:: mse=\sqrt{\frac{1}{N}\sum_i^N (p_i-r_i)^2} where mse is the mean square error measurement, p is the predication array, r is the reference array. Note:reference array shape must be equal to input array shape :param input_array: A numpy array of any shape :param reference_array: A numpy array of any shape :return: a floating point number that represent the mean square error measurement """ return float(np.sqrt(np.mean(np.power(input_array - reference_array, 2))))

44.057692

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0.165746

0.140263

0.084534

0.067627

0.872887

0.834064

0.812774

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0.710081

0

0.009377

0.208643

2,291

51

117

44.921569

0.871484

0.700131

0

1

0.3

false

0

0.1

0

0.7

0

null

0

1

0

null

0

1

0

1

0

8

d159bb5b3249c9ecab80c7d7f8d8e48ce50482d4

28,733

py

Python

sdk/python/pulumi_github/repository_pull_request.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

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

20

2020-04-27T15:05:01.000Z

2022-02-08T00:28:32.000Z

sdk/python/pulumi_github/repository_pull_request.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

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

103

2020-05-01T17:36:32.000Z

2022-03-31T15:26:35.000Z

sdk/python/pulumi_github/repository_pull_request.py

pulumi/pulumi-github

303ed7a28cbfe6ba1db75b3b365dcfa0b00e6e91

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

4

2020-06-24T19:15:02.000Z

2021-11-26T08:05:46.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['RepositoryPullRequestArgs', 'RepositoryPullRequest'] @pulumi.input_type class RepositoryPullRequestArgs: def __init__(__self__, *, base_ref: pulumi.Input[str], base_repository: pulumi.Input[str], head_ref: pulumi.Input[str], title: pulumi.Input[str], body: Optional[pulumi.Input[str]] = None, maintainer_can_modify: Optional[pulumi.Input[bool]] = None, owner: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a RepositoryPullRequest resource. :param pulumi.Input[str] base_ref: Name of the branch serving as the base of the Pull Request. :param pulumi.Input[str] base_repository: Name of the base repository to retrieve the Pull Requests from. :param pulumi.Input[str] head_ref: Name of the branch serving as the head of the Pull Request. :param pulumi.Input[str] title: The title of the Pull Request. :param pulumi.Input[str] body: Body of the Pull Request. :param pulumi.Input[bool] maintainer_can_modify: Controls whether the base repository maintainers can modify the Pull Request. Default: false. :param pulumi.Input[str] owner: Owner of the repository. If not provided, the provider's default owner is used. """ pulumi.set(__self__, "base_ref", base_ref) pulumi.set(__self__, "base_repository", base_repository) pulumi.set(__self__, "head_ref", head_ref) pulumi.set(__self__, "title", title) if body is not None: pulumi.set(__self__, "body", body) if maintainer_can_modify is not None: pulumi.set(__self__, "maintainer_can_modify", maintainer_can_modify) if owner is not None: pulumi.set(__self__, "owner", owner) @property @pulumi.getter(name="baseRef") def base_ref(self) -> pulumi.Input[str]: """ Name of the branch serving as the base of the Pull Request. """ return pulumi.get(self, "base_ref") @base_ref.setter def base_ref(self, value: pulumi.Input[str]): pulumi.set(self, "base_ref", value) @property @pulumi.getter(name="baseRepository") def base_repository(self) -> pulumi.Input[str]: """ Name of the base repository to retrieve the Pull Requests from. """ return pulumi.get(self, "base_repository") @base_repository.setter def base_repository(self, value: pulumi.Input[str]): pulumi.set(self, "base_repository", value) @property @pulumi.getter(name="headRef") def head_ref(self) -> pulumi.Input[str]: """ Name of the branch serving as the head of the Pull Request. """ return pulumi.get(self, "head_ref") @head_ref.setter def head_ref(self, value: pulumi.Input[str]): pulumi.set(self, "head_ref", value) @property @pulumi.getter def title(self) -> pulumi.Input[str]: """ The title of the Pull Request. """ return pulumi.get(self, "title") @title.setter def title(self, value: pulumi.Input[str]): pulumi.set(self, "title", value) @property @pulumi.getter def body(self) -> Optional[pulumi.Input[str]]: """ Body of the Pull Request. """ return pulumi.get(self, "body") @body.setter def body(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "body", value) @property @pulumi.getter(name="maintainerCanModify") def maintainer_can_modify(self) -> Optional[pulumi.Input[bool]]: """ Controls whether the base repository maintainers can modify the Pull Request. Default: false. """ return pulumi.get(self, "maintainer_can_modify") @maintainer_can_modify.setter def maintainer_can_modify(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "maintainer_can_modify", value) @property @pulumi.getter def owner(self) -> Optional[pulumi.Input[str]]: """ Owner of the repository. If not provided, the provider's default owner is used. """ return pulumi.get(self, "owner") @owner.setter def owner(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "owner", value) @pulumi.input_type class _RepositoryPullRequestState: def __init__(__self__, *, base_ref: Optional[pulumi.Input[str]] = None, base_repository: Optional[pulumi.Input[str]] = None, base_sha: Optional[pulumi.Input[str]] = None, body: Optional[pulumi.Input[str]] = None, draft: Optional[pulumi.Input[bool]] = None, head_ref: Optional[pulumi.Input[str]] = None, head_sha: Optional[pulumi.Input[str]] = None, labels: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, maintainer_can_modify: Optional[pulumi.Input[bool]] = None, number: Optional[pulumi.Input[int]] = None, opened_at: Optional[pulumi.Input[int]] = None, opened_by: Optional[pulumi.Input[str]] = None, owner: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, title: Optional[pulumi.Input[str]] = None, updated_at: Optional[pulumi.Input[int]] = None): """ Input properties used for looking up and filtering RepositoryPullRequest resources. :param pulumi.Input[str] base_ref: Name of the branch serving as the base of the Pull Request. :param pulumi.Input[str] base_repository: Name of the base repository to retrieve the Pull Requests from. :param pulumi.Input[str] base_sha: Head commit SHA of the Pull Request base. :param pulumi.Input[str] body: Body of the Pull Request. :param pulumi.Input[bool] draft: Indicates Whether this Pull Request is a draft. :param pulumi.Input[str] head_ref: Name of the branch serving as the head of the Pull Request. :param pulumi.Input[str] head_sha: Head commit SHA of the Pull Request head. :param pulumi.Input[Sequence[pulumi.Input[str]]] labels: List of label names set on the Pull Request. :param pulumi.Input[bool] maintainer_can_modify: Controls whether the base repository maintainers can modify the Pull Request. Default: false. :param pulumi.Input[int] number: The number of the Pull Request within the repository. :param pulumi.Input[int] opened_at: Unix timestamp indicating the Pull Request creation time. :param pulumi.Input[str] opened_by: GitHub login of the user who opened the Pull Request. :param pulumi.Input[str] owner: Owner of the repository. If not provided, the provider's default owner is used. :param pulumi.Input[str] state: the current Pull Request state - can be "open", "closed" or "merged". :param pulumi.Input[str] title: The title of the Pull Request. :param pulumi.Input[int] updated_at: The timestamp of the last Pull Request update. """ if base_ref is not None: pulumi.set(__self__, "base_ref", base_ref) if base_repository is not None: pulumi.set(__self__, "base_repository", base_repository) if base_sha is not None: pulumi.set(__self__, "base_sha", base_sha) if body is not None: pulumi.set(__self__, "body", body) if draft is not None: pulumi.set(__self__, "draft", draft) if head_ref is not None: pulumi.set(__self__, "head_ref", head_ref) if head_sha is not None: pulumi.set(__self__, "head_sha", head_sha) if labels is not None: pulumi.set(__self__, "labels", labels) if maintainer_can_modify is not None: pulumi.set(__self__, "maintainer_can_modify", maintainer_can_modify) if number is not None: pulumi.set(__self__, "number", number) if opened_at is not None: pulumi.set(__self__, "opened_at", opened_at) if opened_by is not None: pulumi.set(__self__, "opened_by", opened_by) if owner is not None: pulumi.set(__self__, "owner", owner) if state is not None: pulumi.set(__self__, "state", state) if title is not None: pulumi.set(__self__, "title", title) if updated_at is not None: pulumi.set(__self__, "updated_at", updated_at) @property @pulumi.getter(name="baseRef") def base_ref(self) -> Optional[pulumi.Input[str]]: """ Name of the branch serving as the base of the Pull Request. """ return pulumi.get(self, "base_ref") @base_ref.setter def base_ref(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "base_ref", value) @property @pulumi.getter(name="baseRepository") def base_repository(self) -> Optional[pulumi.Input[str]]: """ Name of the base repository to retrieve the Pull Requests from. """ return pulumi.get(self, "base_repository") @base_repository.setter def base_repository(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "base_repository", value) @property @pulumi.getter(name="baseSha") def base_sha(self) -> Optional[pulumi.Input[str]]: """ Head commit SHA of the Pull Request base. """ return pulumi.get(self, "base_sha") @base_sha.setter def base_sha(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "base_sha", value) @property @pulumi.getter def body(self) -> Optional[pulumi.Input[str]]: """ Body of the Pull Request. """ return pulumi.get(self, "body") @body.setter def body(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "body", value) @property @pulumi.getter def draft(self) -> Optional[pulumi.Input[bool]]: """ Indicates Whether this Pull Request is a draft. """ return pulumi.get(self, "draft") @draft.setter def draft(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "draft", value) @property @pulumi.getter(name="headRef") def head_ref(self) -> Optional[pulumi.Input[str]]: """ Name of the branch serving as the head of the Pull Request. """ return pulumi.get(self, "head_ref") @head_ref.setter def head_ref(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "head_ref", value) @property @pulumi.getter(name="headSha") def head_sha(self) -> Optional[pulumi.Input[str]]: """ Head commit SHA of the Pull Request head. """ return pulumi.get(self, "head_sha") @head_sha.setter def head_sha(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "head_sha", value) @property @pulumi.getter def labels(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ List of label names set on the Pull Request. """ return pulumi.get(self, "labels") @labels.setter def labels(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "labels", value) @property @pulumi.getter(name="maintainerCanModify") def maintainer_can_modify(self) -> Optional[pulumi.Input[bool]]: """ Controls whether the base repository maintainers can modify the Pull Request. Default: false. """ return pulumi.get(self, "maintainer_can_modify") @maintainer_can_modify.setter def maintainer_can_modify(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "maintainer_can_modify", value) @property @pulumi.getter def number(self) -> Optional[pulumi.Input[int]]: """ The number of the Pull Request within the repository. """ return pulumi.get(self, "number") @number.setter def number(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "number", value) @property @pulumi.getter(name="openedAt") def opened_at(self) -> Optional[pulumi.Input[int]]: """ Unix timestamp indicating the Pull Request creation time. """ return pulumi.get(self, "opened_at") @opened_at.setter def opened_at(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "opened_at", value) @property @pulumi.getter(name="openedBy") def opened_by(self) -> Optional[pulumi.Input[str]]: """ GitHub login of the user who opened the Pull Request. """ return pulumi.get(self, "opened_by") @opened_by.setter def opened_by(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "opened_by", value) @property @pulumi.getter def owner(self) -> Optional[pulumi.Input[str]]: """ Owner of the repository. If not provided, the provider's default owner is used. """ return pulumi.get(self, "owner") @owner.setter def owner(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "owner", value) @property @pulumi.getter def state(self) -> Optional[pulumi.Input[str]]: """ the current Pull Request state - can be "open", "closed" or "merged". """ return pulumi.get(self, "state") @state.setter def state(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "state", value) @property @pulumi.getter def title(self) -> Optional[pulumi.Input[str]]: """ The title of the Pull Request. """ return pulumi.get(self, "title") @title.setter def title(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "title", value) @property @pulumi.getter(name="updatedAt") def updated_at(self) -> Optional[pulumi.Input[int]]: """ The timestamp of the last Pull Request update. """ return pulumi.get(self, "updated_at") @updated_at.setter def updated_at(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "updated_at", value) class RepositoryPullRequest(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, base_ref: Optional[pulumi.Input[str]] = None, base_repository: Optional[pulumi.Input[str]] = None, body: Optional[pulumi.Input[str]] = None, head_ref: Optional[pulumi.Input[str]] = None, maintainer_can_modify: Optional[pulumi.Input[bool]] = None, owner: Optional[pulumi.Input[str]] = None, title: Optional[pulumi.Input[str]] = None, __props__=None): """ This resource allows you to create and manage PullRequests for repositories within your GitHub organization or personal account. ## Example Usage ```python import pulumi import pulumi_github as github example = github.RepositoryPullRequest("example", base_ref="main", base_repository="example-repository", body="This will change everything", head_ref="feature-branch", title="My newest feature") ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] base_ref: Name of the branch serving as the base of the Pull Request. :param pulumi.Input[str] base_repository: Name of the base repository to retrieve the Pull Requests from. :param pulumi.Input[str] body: Body of the Pull Request. :param pulumi.Input[str] head_ref: Name of the branch serving as the head of the Pull Request. :param pulumi.Input[bool] maintainer_can_modify: Controls whether the base repository maintainers can modify the Pull Request. Default: false. :param pulumi.Input[str] owner: Owner of the repository. If not provided, the provider's default owner is used. :param pulumi.Input[str] title: The title of the Pull Request. """ ... @overload def __init__(__self__, resource_name: str, args: RepositoryPullRequestArgs, opts: Optional[pulumi.ResourceOptions] = None): """ This resource allows you to create and manage PullRequests for repositories within your GitHub organization or personal account. ## Example Usage ```python import pulumi import pulumi_github as github example = github.RepositoryPullRequest("example", base_ref="main", base_repository="example-repository", body="This will change everything", head_ref="feature-branch", title="My newest feature") ``` :param str resource_name: The name of the resource. :param RepositoryPullRequestArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RepositoryPullRequestArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, base_ref: Optional[pulumi.Input[str]] = None, base_repository: Optional[pulumi.Input[str]] = None, body: Optional[pulumi.Input[str]] = None, head_ref: Optional[pulumi.Input[str]] = None, maintainer_can_modify: Optional[pulumi.Input[bool]] = None, owner: Optional[pulumi.Input[str]] = None, title: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RepositoryPullRequestArgs.__new__(RepositoryPullRequestArgs) if base_ref is None and not opts.urn: raise TypeError("Missing required property 'base_ref'") __props__.__dict__["base_ref"] = base_ref if base_repository is None and not opts.urn: raise TypeError("Missing required property 'base_repository'") __props__.__dict__["base_repository"] = base_repository __props__.__dict__["body"] = body if head_ref is None and not opts.urn: raise TypeError("Missing required property 'head_ref'") __props__.__dict__["head_ref"] = head_ref __props__.__dict__["maintainer_can_modify"] = maintainer_can_modify __props__.__dict__["owner"] = owner if title is None and not opts.urn: raise TypeError("Missing required property 'title'") __props__.__dict__["title"] = title __props__.__dict__["base_sha"] = None __props__.__dict__["draft"] = None __props__.__dict__["head_sha"] = None __props__.__dict__["labels"] = None __props__.__dict__["number"] = None __props__.__dict__["opened_at"] = None __props__.__dict__["opened_by"] = None __props__.__dict__["state"] = None __props__.__dict__["updated_at"] = None super(RepositoryPullRequest, __self__).__init__( 'github:index/repositoryPullRequest:RepositoryPullRequest', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, base_ref: Optional[pulumi.Input[str]] = None, base_repository: Optional[pulumi.Input[str]] = None, base_sha: Optional[pulumi.Input[str]] = None, body: Optional[pulumi.Input[str]] = None, draft: Optional[pulumi.Input[bool]] = None, head_ref: Optional[pulumi.Input[str]] = None, head_sha: Optional[pulumi.Input[str]] = None, labels: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, maintainer_can_modify: Optional[pulumi.Input[bool]] = None, number: Optional[pulumi.Input[int]] = None, opened_at: Optional[pulumi.Input[int]] = None, opened_by: Optional[pulumi.Input[str]] = None, owner: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, title: Optional[pulumi.Input[str]] = None, updated_at: Optional[pulumi.Input[int]] = None) -> 'RepositoryPullRequest': """ Get an existing RepositoryPullRequest resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] base_ref: Name of the branch serving as the base of the Pull Request. :param pulumi.Input[str] base_repository: Name of the base repository to retrieve the Pull Requests from. :param pulumi.Input[str] base_sha: Head commit SHA of the Pull Request base. :param pulumi.Input[str] body: Body of the Pull Request. :param pulumi.Input[bool] draft: Indicates Whether this Pull Request is a draft. :param pulumi.Input[str] head_ref: Name of the branch serving as the head of the Pull Request. :param pulumi.Input[str] head_sha: Head commit SHA of the Pull Request head. :param pulumi.Input[Sequence[pulumi.Input[str]]] labels: List of label names set on the Pull Request. :param pulumi.Input[bool] maintainer_can_modify: Controls whether the base repository maintainers can modify the Pull Request. Default: false. :param pulumi.Input[int] number: The number of the Pull Request within the repository. :param pulumi.Input[int] opened_at: Unix timestamp indicating the Pull Request creation time. :param pulumi.Input[str] opened_by: GitHub login of the user who opened the Pull Request. :param pulumi.Input[str] owner: Owner of the repository. If not provided, the provider's default owner is used. :param pulumi.Input[str] state: the current Pull Request state - can be "open", "closed" or "merged". :param pulumi.Input[str] title: The title of the Pull Request. :param pulumi.Input[int] updated_at: The timestamp of the last Pull Request update. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _RepositoryPullRequestState.__new__(_RepositoryPullRequestState) __props__.__dict__["base_ref"] = base_ref __props__.__dict__["base_repository"] = base_repository __props__.__dict__["base_sha"] = base_sha __props__.__dict__["body"] = body __props__.__dict__["draft"] = draft __props__.__dict__["head_ref"] = head_ref __props__.__dict__["head_sha"] = head_sha __props__.__dict__["labels"] = labels __props__.__dict__["maintainer_can_modify"] = maintainer_can_modify __props__.__dict__["number"] = number __props__.__dict__["opened_at"] = opened_at __props__.__dict__["opened_by"] = opened_by __props__.__dict__["owner"] = owner __props__.__dict__["state"] = state __props__.__dict__["title"] = title __props__.__dict__["updated_at"] = updated_at return RepositoryPullRequest(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="baseRef") def base_ref(self) -> pulumi.Output[str]: """ Name of the branch serving as the base of the Pull Request. """ return pulumi.get(self, "base_ref") @property @pulumi.getter(name="baseRepository") def base_repository(self) -> pulumi.Output[str]: """ Name of the base repository to retrieve the Pull Requests from. """ return pulumi.get(self, "base_repository") @property @pulumi.getter(name="baseSha") def base_sha(self) -> pulumi.Output[str]: """ Head commit SHA of the Pull Request base. """ return pulumi.get(self, "base_sha") @property @pulumi.getter def body(self) -> pulumi.Output[Optional[str]]: """ Body of the Pull Request. """ return pulumi.get(self, "body") @property @pulumi.getter def draft(self) -> pulumi.Output[bool]: """ Indicates Whether this Pull Request is a draft. """ return pulumi.get(self, "draft") @property @pulumi.getter(name="headRef") def head_ref(self) -> pulumi.Output[str]: """ Name of the branch serving as the head of the Pull Request. """ return pulumi.get(self, "head_ref") @property @pulumi.getter(name="headSha") def head_sha(self) -> pulumi.Output[str]: """ Head commit SHA of the Pull Request head. """ return pulumi.get(self, "head_sha") @property @pulumi.getter def labels(self) -> pulumi.Output[Sequence[str]]: """ List of label names set on the Pull Request. """ return pulumi.get(self, "labels") @property @pulumi.getter(name="maintainerCanModify") def maintainer_can_modify(self) -> pulumi.Output[Optional[bool]]: """ Controls whether the base repository maintainers can modify the Pull Request. Default: false. """ return pulumi.get(self, "maintainer_can_modify") @property @pulumi.getter def number(self) -> pulumi.Output[int]: """ The number of the Pull Request within the repository. """ return pulumi.get(self, "number") @property @pulumi.getter(name="openedAt") def opened_at(self) -> pulumi.Output[int]: """ Unix timestamp indicating the Pull Request creation time. """ return pulumi.get(self, "opened_at") @property @pulumi.getter(name="openedBy") def opened_by(self) -> pulumi.Output[str]: """ GitHub login of the user who opened the Pull Request. """ return pulumi.get(self, "opened_by") @property @pulumi.getter def owner(self) -> pulumi.Output[Optional[str]]: """ Owner of the repository. If not provided, the provider's default owner is used. """ return pulumi.get(self, "owner") @property @pulumi.getter def state(self) -> pulumi.Output[str]: """ the current Pull Request state - can be "open", "closed" or "merged". """ return pulumi.get(self, "state") @property @pulumi.getter def title(self) -> pulumi.Output[str]: """ The title of the Pull Request. """ return pulumi.get(self, "title") @property @pulumi.getter(name="updatedAt") def updated_at(self) -> pulumi.Output[int]: """ The timestamp of the last Pull Request update. """ return pulumi.get(self, "updated_at")

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0f671ec5ae5d7110499cfacf5a341c8c1013e3ad

26,956

py

Python

ats/tests/tests_views.py

dictoss/active-task-summary

1febb3b9e9e2a4e83a555cecfab374eb5eeaa816

[ "BSD-2-Clause" ]

2

2016-11-11T00:07:42.000Z

2017-04-23T10:58:43.000Z

ats/tests/tests_views.py

dictoss/active-task-summary

1febb3b9e9e2a4e83a555cecfab374eb5eeaa816

[ "BSD-2-Clause" ]

43

2015-04-02T13:06:37.000Z

2022-02-19T14:30:57.000Z

ats/tests/tests_views.py

dictoss/active-task-summary

1febb3b9e9e2a4e83a555cecfab374eb5eeaa816

[ "BSD-2-Clause" ]

null

from django.test import TestCase, Client, RequestFactory from django.urls import reverse from django.contrib.auth.models import User from django.db import IntegrityError import datetime from datetime import timedelta import pytz from ..views import ( format_totaltime, format_hours_float, get_projects_in_date, error404, error500, index, login_view, logout_view) from ..models import ( Job, Task, Project, ProjectWorker, UsedTaskTime) class AtsTestClient(Client): pass class AtsViewTestCase(TestCase): pass class AtsViewFuncTestClient(TestCase): fixtures = ['test_views.json'] def test_get_projects_in_date(self): _ret = None _user = User.objects.get(username='testuser100') _ret = get_projects_in_date(_user, '2014-01-30') self.assertIsNotNone(_ret) class Ats404ViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = '' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_404(self): _response = self.client.get('/ats/zzz/') self.assertEqual(_response.status_code, 404) _request = self.factory.get('/ats/zzz/') _responsev = error404(_request) self.assertEqual(_responsev.status_code, 404) self.assertTrue(_responsev.content.find(b'404 NOT FOUND')) class Ats500ViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:error_internal' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_500(self): try: _response = self.client.get(reverse(self.view_name)) except Exception as e: pass else: self.fail() class IndexViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:index' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_index(self): _response = self.client.get(reverse('ats:top')) self.assertEqual(_response.status_code, 302) _request = self.factory.get(reverse('ats:top')) _responsev = index(_request) self.assertEqual(_responsev.status_code, 302) class LoginViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = '' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_login_success(self): self.client.logout() # if not login _response = self.client.get(reverse('ats:login_view')) self.assertEqual(_response.status_code, 200) # login _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse('ats:login_view')) self.assertRedirects(_response, expected_url=reverse('ats:top'), status_code=302, target_status_code=200) # logout _response = self.client.get(reverse('ats:logout_view')) self.assertRedirects(_response, expected_url=reverse('ats:login_view'), status_code=302, target_status_code=200) def test_login_success_has_next(self): self.client.logout() # login has next. _url = reverse('ats:login_view') _response = self.client.post( '%s?next=%s' % (_url, reverse('ats:regist')), {'username': self.user.username, 'password': self._password}) self.assertRedirects(_response, expected_url=reverse('ats:regist'), status_code=302, target_status_code=200) # logout _response = self.client.get(reverse('ats:logout_view')) self.assertRedirects(_response, expected_url=reverse('ats:login_view'), status_code=302, target_status_code=200) def test_loginform(self): # success login _response = self.client.post( reverse('ats:login_view'), {'username': self.user.username, 'password': self._password}) self.assertRedirects(_response, expected_url=reverse('ats:top'), status_code=302, target_status_code=200) # wrong password _response = self.client.post( reverse('ats:login_view'), {'username': self.user.username, 'password': 'dummypass'}) self.assertEqual(_response.status_code, 200) # wrong user and password _response = self.client.post( reverse('ats:login_view'), {'username': 'dummyuser', 'password': 'dummypass'}) self.assertEqual(_response.status_code, 200) def test_login_fail_password_miss(self): self.client.logout() # if not login _response = self.client.get(reverse('ats:login_view')) self.assertEqual(_response.status_code, 200) _result = self.client.login(username=self.user.username, password="dummypass") self.assertFalse(_result) _response = self.client.get(reverse('ats:login_view')) self.assertEqual(_response.status_code, 200) def test_login_fail(self): self.client.logout() # if not login _response = self.client.get(reverse('ats:login_view')) self.assertEqual(_response.status_code, 200) _result = self.client.login(username="dummyuser", password="12345678") self.assertFalse(_result) _response = self.client.get(reverse('ats:login_view')) self.assertEqual(_response.status_code, 200) class TopViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:top' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_top(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) class QueryViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:query' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_query(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) class ManageViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:manage' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_manage(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) class ManageChpasswdViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:manage_chpasswd' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_get(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_post_success(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.post( reverse(self.view_name), { 'old_password': self._password, 'new_password1': 'qwertyuiop', 'new_password2': 'qwertyuiop', }) self.assertEqual(_response.status_code, 200) def test_post_error(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) # missing old password _response = self.client.post( reverse(self.view_name), { 'old_password': '12345678', 'new_password1': 'qwertyuiop', 'new_password2': 'qwertyuiop', }) self.assertEqual(_response.status_code, 200) # difference new password. _response = self.client.post( reverse(self.view_name), { 'old_password': self._password, 'new_password1': 'qwertyuiop', 'new_password2': 'qwertyuiop@', }) self.assertEqual(_response.status_code, 200) class SummaryProjectViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:summary_p' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_get(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_post(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'projectlist': 1, 'is_show_taskdetail': '0', }) self.assertEqual(_response.status_code, 200) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'projectlist': 1, 'is_show_taskdetail': '1', }) self.assertEqual(_response.status_code, 200) def test_post_with_data(self): _result = self.client.login(username='testuser100', password='password') self.assertTrue(_result) # insert data _project = Project.objects.get(pk=1) _job = Job.objects.get(pk=1) _task = list(Task.objects.filter(job=_job).order_by('id'))[0] _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-02-25', tasktime='04:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'projectlist': _project.id, 'is_show_taskdetail': '0', }) self.assertEqual(_response.status_code, 200) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'projectlist': _project.id, 'is_show_taskdetail': '1', }) self.assertEqual(_response.status_code, 200) class SummaryJobViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:summary_j' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_get(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_post(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'joblist': 1, }) self.assertEqual(_response.status_code, 200) def test_post_with_data(self): _result = self.client.login(username='testuser100', password='password') self.assertTrue(_result) # insert data _project = Project.objects.get(pk=1) _job1 = Job.objects.get(pk=1) _job2 = Job.objects.get(pk=2) _task = list(Task.objects.filter(job=_job1).order_by('id'))[0] _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-02-25', tasktime='04:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'joblist': [_job1.id, _job2.id], }) self.assertEqual(_response.status_code, 200) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'joblist': [_job1.id, _job2.id], }) self.assertEqual(_response.status_code, 200) class SummaryUserViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:summary_u' _password = 'passpass' def setUp(self): self.factory = RequestFactory() self.user = User.objects.create_user( 'testuser1', 'testuser1@example.com', self._password) def tearDown(self): pass def test_get(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_post(self): _result = self.client.login(username=self.user.username, password=self._password) self.assertTrue(_result) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'userlist': 2, }) self.assertEqual(_response.status_code, 200) def test_post_with_data(self): _user_id_list = [] # inser data 1st user. _result = self.client.login(username='testuser100', password='password') self.assertTrue(_result) _user_id_list.append(self.user.id) _project1 = Project.objects.get(pk=1) _project2 = Project.objects.get(pk=2) _job = Job.objects.get(pk=1) _task = list(Task.objects.filter(job=_job).order_by('id'))[0] _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-02-25', tasktime='04:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-02-25', tasktime='04:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) self.client.logout() # inser data 2nd user. _result = self.client.login(username='testuser200', password='password') self.assertTrue(_result) _user_id_list.append(self.user.id) _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-02-25', tasktime='01:00:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project1, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-01-25', tasktime='02:15:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-02-25', tasktime='01:00:00' ) _obj = UsedTaskTime.objects.create( user=self.user, project=_project2, task=_task, taskdate='2014-03-25', tasktime='08:30:00' ) # self.client.logout() # post _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'userlist': _user_id_list, }) self.assertEqual(_response.status_code, 200) _response = self.client.post( reverse(self.view_name), { 'from_date': '2014-01-01', 'to_date': '2014-03-31', 'userlist': _user_id_list, }) self.assertEqual(_response.status_code, 200) class RegistViewTestCase(AtsViewTestCase): fixtures = ['test_views.json'] client_class = AtsTestClient view_name = 'ats:regist' username = 'testuser100' password = 'password' def setUp(self): self.factory = RequestFactory() def tearDown(self): pass def test_get_unassign_user(self): _user = User.objects.create_user( 'testuser1', 'testuser1@example.com', 'passpass') _result = self.client.login(username=_user.username, password='passpass') self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_get(self): _result = self.client.login(username=self.username, password=self.password) self.assertTrue(_result) _response = self.client.get(reverse(self.view_name)) self.assertEqual(_response.status_code, 200) def test_get_dateselect(self): _result = self.client.login(username=self.username, password=self.password) self.assertTrue(_result) _response = self.client.get( reverse(self.view_name), { 'submit_type': 'dateselect', 'regist_date': '2014-01-30', 'projectlist': 1, }) self.assertEqual(_response.status_code, 200) def test_unsupported_method(self): _result = self.client.login(username=self.username, password=self.password) self.assertTrue(_result) _response = self.client.put( reverse(self.view_name), {}) self.assertEqual(_response.status_code, 200) def test_post_regist_nocheck(self): _result = self.client.login(username=self.username, password=self.password) self.assertTrue(_result) _response = self.client.post( reverse(self.view_name), { 'submit_type': 'regist', 'regist_date': '2014-01-30', 'project_id': 1, 'registcheck': [], 'uttid': [], 'tasktime_hour': [], 'tasktime_min': [], }) self.assertEqual(_response.status_code, 200) def test_post_regist(self): _user = User.objects.get(username=self.username) _result = self.client.login(username=self.username, password=self.password) self.assertTrue(_result) _project_id = 1 _pjw_qs = ProjectWorker.objects.filter( user=_user, project__pk=_project_id).order_by('id') _datalist = [] for pjw in _pjw_qs: _job = Job.objects.get(pk=pjw.job.id) _task_qs = Task.objects.filter( job=_job).order_by('id') for t in _task_qs: # generate post data. _data = { 'registcheck': 'p%s_t%s' % (pjw.project.id, t.id), 'uttid': 'p%s_t%s' % (pjw.project.id, t.id), 'tasktime_hour': 2, 'tasktime_min': 15, } _datalist.append(_data) # regist (add) _response = self.client.post( reverse(self.view_name), { 'submit_type': 'regist', 'regist_date': '2014-01-30', 'project_id': _project_id, 'registcheck': [o['registcheck'] for o in _datalist], 'uttid': [o['uttid'] for o in _datalist], 'tasktime_hour': [o['tasktime_hour'] for o in _datalist], 'tasktime_min': [o['tasktime_min'] for o in _datalist], }) # regist (update) for d in _datalist: d['tasktime_min'] = 30 _response = self.client.post( reverse(self.view_name), { 'submit_type': 'regist', 'regist_date': '2014-01-30', 'project_id': _project_id, 'registcheck': [o['registcheck'] for o in _datalist], 'uttid': [o['uttid'] for o in _datalist], 'tasktime_hour': [o['tasktime_hour'] for o in _datalist], 'tasktime_min': [o['tasktime_min'] for o in _datalist], }) # regist (delete) for d in _datalist: d['tasktime_hour'] = 0 d['tasktime_min'] = 0 _response = self.client.post( reverse(self.view_name), { 'submit_type': 'regist', 'regist_date': '2014-01-30', 'project_id': _project_id, 'registcheck': [o['registcheck'] for o in _datalist], 'uttid': [o['uttid'] for o in _datalist], 'tasktime_hour': [o['tasktime_hour'] for o in _datalist], 'tasktime_min': [o['tasktime_min'] for o in _datalist], }) self.assertEqual(_response.status_code, 200)

30.458757

73

0.549414

2,681

26,956

5.293174

0.075345

0.052146

0.054542

0.071524

0.866394

0.847368

0.825382

0.820943

0.8115

0.803256

0

0.041585

0.33896

26,956

884

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30.493213

0.754812

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null

0

1

0

8

0f6b71be95be7ce75420c4b6a729f4804369a4e7

949

py

Python

UrsinaShaderBuilder/ExtraData/extra_models.py

Werxzy/UrsinaShaderBuilder

1ee13e36d3787a73e84f6b4bf9b51c51ba75c79b

[ "MIT" ]

3

2022-03-01T00:01:30.000Z

2022-03-13T16:05:06.000Z

UrsinaShaderBuilder/ExtraData/extra_models.py

Werxzy/UrsinaShaderBuilder

1ee13e36d3787a73e84f6b4bf9b51c51ba75c79b

[ "MIT" ]

null

UrsinaShaderBuilder/ExtraData/extra_models.py

Werxzy/UrsinaShaderBuilder

1ee13e36d3787a73e84f6b4bf9b51c51ba75c79b

[ "MIT" ]

null

from ursina import Vec3 # mode = 'ngon' x_vert = [Vec3(0,1,0), Vec3(-1,2,0), Vec3(-2,1,0), Vec3(-1,0,0), Vec3(-2,-1,0), Vec3(-1,-2,0), Vec3(0,-1,0), Vec3(1,-2,0), Vec3(2,-1,0), Vec3(1,0,0), Vec3(2,1,0), Vec3(1,2,0)] check_vert = [Vec3(-0.5,0,0), Vec3(-1.5,1,0), Vec3(-2.5,0,0), Vec3(-0.5,-2,0), Vec3(2.5,1,0), Vec3(1.5,2,0)] down_arrow_vert = [Vec3(0,0,0), Vec3(-1,1,0), Vec3(-2,0,0), Vec3(0,-2,0), Vec3(2,0,0), Vec3(1,1,0)] right_arrow_vert = [Vec3(0,0,0), Vec3(-1,-1,0), Vec3(0,-2,0), Vec3(2,0,0), Vec3(0,2,0), Vec3(-1,1,0)] up_arrow_vert = [Vec3(0,0,0), Vec3(1,-1,0), Vec3(2,0,0), Vec3(0,2,0), Vec3(-2,0,0), Vec3(-1,-1,0)] left_arrow_vert = [Vec3(0,0,0), Vec3(1,1,0), Vec3(0,2,0), Vec3(-2,0,0), Vec3(0,-2,0), Vec3(1,-1,0)] # mode = 'triangle' scale_arrow_vert = [Vec3(-2,2,0), Vec3(-2,-1,0), Vec3(1,2,0), Vec3(1,0,0), Vec3(0,1,0), Vec3(-1,0,0), Vec3(1,0,0), Vec3(-1,0,0), Vec3(0,-1,0), Vec3(2,-2,0), Vec3(2,1,0), Vec3(-1,-2,0)]

67.785714

175

0.542677

249

949

2.02008

0.080321

0.467197

0.262425

0.139165

0.739563

0

0.262045

0.103267

949

14

176

67.785714

0.329025

0.032666

0

1

0

false

0

0.090909

0

0.090909

0

null

1

0

1

0

1

0

1

0

null

0

9

0f6e4a410cf57c465a5c2b2cb63e53b5f1a4f640

31,494

py

Python

read_xml_all/calcul_matrix_je_le_qui_dans_de_192_matrix_good_compare_1.py

daniel20162016/my-first

f9554dd476302b26e8a296393025f150922f349c

[ "MIT" ]

null

read_xml_all/calcul_matrix_je_le_qui_dans_de_192_matrix_good_compare_1.py

daniel20162016/my-first

f9554dd476302b26e8a296393025f150922f349c

[ "MIT" ]

null

read_xml_all/calcul_matrix_je_le_qui_dans_de_192_matrix_good_compare_1.py

daniel20162016/my-first

f9554dd476302b26e8a296393025f150922f349c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Oct 31 15:45:22 2016 @author: wang """ #from matplotlib import pylab as plt #from numpy import fft, fromstring, int16, linspace #import wave from read_wav_xml_good_1 import* from matrix_24_2 import* from max_matrix_norm import* import numpy as np # open a wave file filename = 'francois_filon_pure_3.wav' filename_1 ='francois_filon_pure_3.xml' word ='je' word_2='le' word_3='qui' word_4='dans' word_5='de' #============================================================================== # this is the parti for the 'je' start #============================================================================== wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word) XJ_1 =wave_signal_float t_step=1920; t_entre_step=1440; t_du_1_1 = int(word_start_point[0]); t_du_1_2 = int(word_end_point[0]); t_du_2_1 = int(word_start_point[1]); t_du_2_2 = int(word_end_point[1]); t_du_3_1 = int(word_start_point[2]); t_du_3_2 = int(word_end_point[2]); t_du_4_1 = int(word_start_point[3]); t_du_4_2 = int(word_end_point[3]); t_du_5_1 = int(word_start_point[4]); t_du_5_2 = int(word_end_point[4]); fs=framerate #XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2]; #length_XJ_du_1 = int(word_length_point[0]+1); #x1,y1,z1=matrix_24_2(XJ_du_1,fs) #x1=max_matrix_norm(x1) #============================================================================== # this part is to calcul the first matrix #============================================================================== XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_1 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_1[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_1[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the second matrix #============================================================================== for k in range (1,2): t_start=t_du_2_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_2 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_2[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_2[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 3 matrix #============================================================================== for k in range (1,2): t_start=t_du_3_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_3 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_3[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_3[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 4 matrix #============================================================================== for k in range (1,2): t_start=t_du_4_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_4 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_4[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_4[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 5 matrix #============================================================================== for k in range (1,2): t_start=t_du_5_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_5 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_5[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_5[24*i+j]=x1_all[j] je_compare_1 = matrix_all_step_new_1 je_compare_2 = matrix_all_step_new_2 je_compare_3 = matrix_all_step_new_3 je_compare_4 = matrix_all_step_new_4 je_compare_5 = matrix_all_step_new_5 #============================================================================== # # this is the parti for the 'je' end #============================================================================== #np.savez('je_le_qui_dans_de_192_matrix.npz',matrix_all_step_new_1,matrix_all_step_new_2,matrix_all_step_new_3,matrix_all_step_new_4,matrix_all_step_new_5) #============================================================================== # this is the parti for the 'le' start #============================================================================== wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word_2) XJ_1 =wave_signal_float t_step=1920; t_entre_step=1440; t_du_1_1 = int(word_start_point[0]); t_du_1_2 = int(word_end_point[0]); t_du_2_1 = int(word_start_point[1]); t_du_2_2 = int(word_end_point[1]); t_du_3_1 = int(word_start_point[2]); t_du_3_2 = int(word_end_point[2]); t_du_4_1 = int(word_start_point[3]); t_du_4_2 = int(word_end_point[3]); t_du_5_1 = int(word_start_point[4]); t_du_5_2 = int(word_end_point[4]); fs=framerate #XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2]; #length_XJ_du_1 = int(word_length_point[0]+1); #x1,y1,z1=matrix_24_2(XJ_du_1,fs) #x1=max_matrix_norm(x1) #============================================================================== # this part is to calcul the first matrix #============================================================================== XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_1 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_1[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_1[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the second matrix #============================================================================== for k in range (1,2): t_start=t_du_2_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_2 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_2[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_2[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 3 matrix #============================================================================== for k in range (1,2): t_start=t_du_3_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_3 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_3[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_3[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 4 matrix #============================================================================== for k in range (1,2): t_start=t_du_4_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_4 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_4[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_4[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 5 matrix #============================================================================== for k in range (1,2): t_start=t_du_5_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_5 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_5[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_5[24*i+j]=x1_all[j] le_compare_1 = matrix_all_step_new_1 le_compare_2 = matrix_all_step_new_2 le_compare_3 = matrix_all_step_new_3 le_compare_4 = matrix_all_step_new_4 le_compare_5 = matrix_all_step_new_5 #============================================================================== # # this is the parti for the 'le' end #============================================================================== #============================================================================== # this is the parti for the 'qui' start #============================================================================== wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word_3) XJ_1 =wave_signal_float t_step=1920; t_entre_step=1440; t_du_1_1 = int(word_start_point[0]); t_du_1_2 = int(word_end_point[0]); t_du_2_1 = int(word_start_point[1]); t_du_2_2 = int(word_end_point[1]); t_du_3_1 = int(word_start_point[2]); t_du_3_2 = int(word_end_point[2]); t_du_4_1 = int(word_start_point[3]); t_du_4_2 = int(word_end_point[3]); t_du_5_1 = int(word_start_point[4]); t_du_5_2 = int(word_end_point[4]); fs=framerate #XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2]; #length_XJ_du_1 = int(word_length_point[0]+1); #x1,y1,z1=matrix_24_2(XJ_du_1,fs) #x1=max_matrix_norm(x1) #============================================================================== # this part is to calcul the first matrix #============================================================================== XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_1 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_1[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_1[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the second matrix #============================================================================== for k in range (1,2): t_start=t_du_2_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_2 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_2[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_2[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 3 matrix #============================================================================== for k in range (1,2): t_start=t_du_3_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_3 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_3[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_3[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 4 matrix #============================================================================== for k in range (1,2): t_start=t_du_4_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_4 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_4[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_4[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 5 matrix #============================================================================== for k in range (1,2): t_start=t_du_5_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_5 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_5[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_5[24*i+j]=x1_all[j] qui_compare_1 = matrix_all_step_new_1 qui_compare_2 = matrix_all_step_new_2 qui_compare_3 = matrix_all_step_new_3 qui_compare_4 = matrix_all_step_new_4 qui_compare_5 = matrix_all_step_new_5 #============================================================================== # this is the parti for the 'dans' start #============================================================================== wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word_4) XJ_1 =wave_signal_float t_step=1920; t_entre_step=1440; t_du_1_1 = int(word_start_point[0]); t_du_1_2 = int(word_end_point[0]); t_du_2_1 = int(word_start_point[1]); t_du_2_2 = int(word_end_point[1]); t_du_3_1 = int(word_start_point[2]); t_du_3_2 = int(word_end_point[2]); t_du_4_1 = int(word_start_point[3]); t_du_4_2 = int(word_end_point[3]); t_du_5_1 = int(word_start_point[4]); t_du_5_2 = int(word_end_point[4]); fs=framerate #XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2]; #length_XJ_du_1 = int(word_length_point[0]+1); #x1,y1,z1=matrix_24_2(XJ_du_1,fs) #x1=max_matrix_norm(x1) #============================================================================== # this part is to calcul the first matrix #============================================================================== XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_1 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_1[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_1[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the second matrix #============================================================================== for k in range (1,2): t_start=t_du_2_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_2 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_2[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_2[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 3 matrix #============================================================================== for k in range (1,2): t_start=t_du_3_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_3 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_3[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_3[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 4 matrix #============================================================================== for k in range (1,2): t_start=t_du_4_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_4 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_4[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_4[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 5 matrix #============================================================================== for k in range (1,2): t_start=t_du_5_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_5 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_5[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_5[24*i+j]=x1_all[j] dans_compare_1 = matrix_all_step_new_1 dans_compare_2 = matrix_all_step_new_2 dans_compare_3 = matrix_all_step_new_3 dans_compare_4 = matrix_all_step_new_4 dans_compare_5 = matrix_all_step_new_5 #============================================================================== # this is the parti for the 'de' start #============================================================================== wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word_5) XJ_1 =wave_signal_float t_step=1920; t_entre_step=1440; t_du_1_1 = int(word_start_point[0]); t_du_1_2 = int(word_end_point[0]); t_du_2_1 = int(word_start_point[1]); t_du_2_2 = int(word_end_point[1]); t_du_3_1 = int(word_start_point[2]); t_du_3_2 = int(word_end_point[2]); t_du_4_1 = int(word_start_point[3]); t_du_4_2 = int(word_end_point[3]); t_du_5_1 = int(word_start_point[4]); t_du_5_2 = int(word_end_point[4]); fs=framerate #XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2]; #length_XJ_du_1 = int(word_length_point[0]+1); #x1,y1,z1=matrix_24_2(XJ_du_1,fs) #x1=max_matrix_norm(x1) #============================================================================== # this part is to calcul the first matrix #============================================================================== XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_1 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_1[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_1[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the second matrix #============================================================================== for k in range (1,2): t_start=t_du_2_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_2 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_2[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_2[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 3 matrix #============================================================================== for k in range (1,2): t_start=t_du_3_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_3 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_3[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_3[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 4 matrix #============================================================================== for k in range (1,2): t_start=t_du_4_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_4 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_4[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_4[24*i+j]=x1_all[j] #============================================================================== # this part is to calcul the 5 matrix #============================================================================== for k in range (1,2): t_start=t_du_5_1 XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)]; x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs) x1_1=max_matrix_norm(x1_1) matrix_all_step_new_5 = np.zeros([192]) for i in range(0,24): matrix_all_step_new_5[i]=x1_1[i] #============================================================================== # the other colonne is the all fft #============================================================================== for i in range(1,8): # print i XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )]; x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs) x1_all=max_matrix_norm(x1_all) for j in range(0,24): matrix_all_step_new_5[24*i+j]=x1_all[j] de_compare_1 = matrix_all_step_new_1 de_compare_2 = matrix_all_step_new_2 de_compare_3 = matrix_all_step_new_3 de_compare_4 = matrix_all_step_new_4 de_compare_5 = matrix_all_step_new_5 print 'finish_part_2' #============================================================================== # # this is the parti for the 'le' end #============================================================================== np.savez('je_le_qui_dans_de_192_matrix_compare.npz',je_compare_1,je_compare_2,je_compare_3,je_compare_4,je_compare_5,le_compare_1,le_compare_2,le_compare_3,le_compare_4,le_compare_5,qui_compare_1,qui_compare_2,qui_compare_3,qui_compare_4,qui_compare_5,dans_compare_1,dans_compare_2,dans_compare_3,dans_compare_4,dans_compare_5,de_compare_1,de_compare_2,de_compare_3,de_compare_4,de_compare_5)

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null

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9

0f885f32400bdb20dc67b77fc5021d99ed6b859f

84

py

Python

frameworks/NODE/__init__.py

termit209/automlbenchmark

07046564a5fac762a9beae8e77a9a672170873c7

[ "MIT" ]

null

frameworks/NODE/__init__.py

termit209/automlbenchmark

07046564a5fac762a9beae8e77a9a672170873c7

[ "MIT" ]

1

2021-02-04T11:57:14.000Z

2021-02-04T11:59:54.000Z

frameworks/NODE/__init__.py

termit209/automlbenchmark

07046564a5fac762a9beae8e77a9a672170873c7

[ "MIT" ]

2

2021-02-04T12:00:49.000Z

2021-02-04T12:26:23.000Z

def run(*args, **kwargs): from .exec import run return run(*args, **kwargs)

21

31

0.619048

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8

0e291545e3b5c24d932d94c62743c850a5df89f3

1,927

py

Python

recipes-python_st/imx6sx-webserver/addwebserver/Flask_orion/app/xml_parser.py

Stefan12321/meta-python_st

8ff187963bfd15dbfd6a6c74ae913935be3e6cce

[ "MIT" ]

null

recipes-python_st/imx6sx-webserver/addwebserver/Flask_orion/app/xml_parser.py

Stefan12321/meta-python_st

8ff187963bfd15dbfd6a6c74ae913935be3e6cce

[ "MIT" ]

null

recipes-python_st/imx6sx-webserver/addwebserver/Flask_orion/app/xml_parser.py

Stefan12321/meta-python_st

8ff187963bfd15dbfd6a6c74ae913935be3e6cce

[ "MIT" ]

1

2022-02-08T08:35:31.000Z

import xml.etree.cElementTree as ET def xml_parser_read(path='/files_61850/KALINA.CID'): tree = ET.parse(path) root = tree.getroot() pref = '{http://www.iec.ch/61850/2003/SCL}' ip = root.findall( '{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP"]'.format(pref=pref)) mask = root.findall( '{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP-SUBNET"]'.format(pref=pref)) gate = root.findall( '{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP-GATEWAY"]'.format(pref=pref)) ied1 = root.findall('{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP'.format(pref=pref)) ied2 = root.findall('{pref}IED'.format(pref=pref)) # print(ip[0].text, mask[0].text, gate[0].text) # ip[0].text = "192.168.2.13" return ip[0].text, mask[0].text, gate[0].text, ied2[0].attrib.get('name') def xml_parser_write(ip, mask, gate, ied, path='/files_61850/KALINA.CID'): tree = ET.parse(path) root = tree.getroot() pref = '{http://www.iec.ch/61850/2003/SCL}' ip_path = root.findall('{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP"]'.format(pref=pref)) mask_path = root.findall('{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP-SUBNET"]'.format(pref=pref)) gate_path = root.findall('{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP/{pref}Address/{pref}P[@type="IP-GATEWAY"]'.format(pref=pref)) ied1_path = root.findall('{pref}Communication/{pref}SubNetwork/{pref}ConnectedAP'.format(pref=pref)) ied2_path = root.findall('{pref}IED'.format(pref=pref)) ip_path[0].text = ip mask_path[0].text = mask gate_path[0].text = gate ied1_path[0].attrib = {'apName': 'S1', 'iedName': ied} ied2_path[0].attrib = {'name': ied} tree.write(path)

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0e41c7c27bf635315f4fb9ff9b27bbf64aeaed81

4,951

py

Python

usaspending_api/disaster/tests/integration/test_disaster_award_amount.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

1

2020-08-14T04:14:32.000Z

usaspending_api/disaster/tests/integration/test_disaster_award_amount.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

null

usaspending_api/disaster/tests/integration/test_disaster_award_amount.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

null

import pytest from rest_framework import status url = "/api/v2/disaster/award/amount/" @pytest.mark.django_db def test_award_amount_success(client, monkeypatch, generic_account_data, unlinked_faba_account_data, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) helpers.reset_dabs_cache() resp = helpers.post_for_amount_endpoint(client, url, ["L"], ["A", "09", "10"]) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 1 assert resp.data["outlay"] == 222 assert resp.data["obligation"] == 200 resp = helpers.post_for_amount_endpoint(client, url, ["N", "O"], ["A", "07", "08"]) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 2 assert resp.data["outlay"] == 334 assert resp.data["obligation"] == 4 resp = helpers.post_for_amount_endpoint(client, url, ["9"], ["B"]) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 0 assert resp.data["outlay"] == 0 assert resp.data["obligation"] == 0 @pytest.mark.django_db def test_award_amount_no_award_type_success( client, monkeypatch, generic_account_data, unlinked_faba_account_data, helpers ): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) resp = helpers.post_for_amount_endpoint(client, url, ["N"], None) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 4 assert resp.data["outlay"] == 10890108.00 assert resp.data["obligation"] == 1088898.00 resp = helpers.post_for_amount_endpoint(client, url, ["L", "M", "N", "O", "P"], None) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 7 assert resp.data["outlay"] == 10890997.00 assert resp.data["obligation"] == 1089204.00 @pytest.mark.django_db def test_award_amount_on_sum_non_zero_toa(client, monkeypatch, multiple_file_c_to_same_award, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) helpers.reset_dabs_cache() resp = helpers.post_for_amount_endpoint(client, url, ["M"], None) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 1 assert resp.data["outlay"] == 0.0 assert resp.data["obligation"] == 14.0 @pytest.mark.django_db def test_award_amount_on_sum_non_zero_outlay(client, monkeypatch, multiple_outlay_file_c_to_same_award, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) helpers.reset_dabs_cache() resp = helpers.post_for_amount_endpoint(client, url, ["M"], None) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 1 assert resp.data["outlay"] == 14.0 assert resp.data["obligation"] == 0.0 @pytest.mark.django_db def test_award_amount_on_sum_zero_toa(client, monkeypatch, multiple_file_c_to_same_award_that_cancel_out, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) helpers.reset_dabs_cache() resp = helpers.post_for_amount_endpoint(client, url, ["M"], None) assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 0 assert resp.data["outlay"] == 0.0 assert resp.data["obligation"] == 0.0 @pytest.mark.django_db def test_award_amount_invalid_defc(client, monkeypatch, generic_account_data, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) resp = helpers.post_for_amount_endpoint(client, url, ["ZZ"], ["A", "09", "10"]) assert resp.status_code == status.HTTP_400_BAD_REQUEST assert resp.data["detail"] == "Field 'filter|def_codes' is outside valid values ['9', 'A', 'L', 'M', 'N', 'O', 'P']" @pytest.mark.django_db def test_award_amount_exclusive_filters(client, generic_account_data, helpers): resp = helpers.post_for_amount_endpoint( client, url, ["M"], award_type_codes=["A", "09", "10"], award_type="procurement" ) assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY @pytest.mark.django_db def test_award_amount_bad_award_type_value(client, helpers): resp = helpers.post_for_amount_endpoint(client, url, ["ZZ"], award_type="financial") assert resp.status_code == status.HTTP_400_BAD_REQUEST @pytest.mark.django_db def test_award_type_filters(client, monkeypatch, generic_account_data, unlinked_faba_account_data, helpers): helpers.patch_datetime_now(monkeypatch, 2022, 12, 31) helpers.reset_dabs_cache() resp = helpers.post_for_amount_endpoint(client, url, ["L"], award_type="procurement") assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 2 assert resp.data["outlay"] == 777 assert resp.data["obligation"] == 205 resp = helpers.post_for_amount_endpoint(client, url, ["N", "O"], award_type="assistance") assert resp.status_code == status.HTTP_200_OK assert resp.data["award_count"] == 3 assert resp.data["outlay"] == 1222 assert resp.data["obligation"] == 12

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0.802381

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4,951

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null

0

1

0

null

0

1

0

7

0e43b04c38d9bdc7a00d6636f52199eb19ef6669

2,592

py

Python

src/utils/batch_cpu_insert.py

Metroynome/robo

78c389decce98d0d1e4e4e02ccbfcba7b465209c

[ "MIT" ]

null

src/utils/batch_cpu_insert.py

Metroynome/robo

78c389decce98d0d1e4e4e02ccbfcba7b465209c

[ "MIT" ]

null

src/utils/batch_cpu_insert.py

Metroynome/robo

78c389decce98d0d1e4e4e02ccbfcba7b465209c

[ "MIT" ]

null

import sqlite3 import random n_cpus = 999 db_file = "../../logs/database.db" db_file = "file:" + db_file + "?mode=" + "rwc" print("Using DB: {}".format(db_file)) # This will raise an error if it can't connect conn = sqlite3.connect(db_file, uri=True, check_same_thread=False) default_stats = '00C0A84400C0A84400C0A84400C0A8440000AF430000AF430000AF430000AF430000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000' default_ladderstatswide = '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000' def generate_session_key() -> bytes: new_session_key = ''.join(random.choice('0123456789ABCDEF') for i in range(16)) + '\0' new_session_key = new_session_key.encode() return new_session_key def _create_new_user(username: str, encrypted_password: str, session_key: bytes): c = conn.cursor() insert_command = """INSERT INTO users (account_type, username, password, session_key, stats, ladderstatswide) values(?,?,?,?,?,?); """ account_type = 2 stats = default_stats ladderstatswide = default_ladderstatswide c.execute(insert_command, [account_type, username, encrypted_password, session_key.decode(), stats, ladderstatswide]) conn.commit() c.close() print(f"Created new user: {username}") for i in range(1, n_cpus): _create_new_user(f"CPU-{str(i).zfill(3)}", "", generate_session_key())

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0.109954

2,592

38

830

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1

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false

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null

1

0

1

0

7

7ef0ddd65f9d900e2aa0d797937d9c580d6ad255

33

py

Python

server/blueprints/admin_home/__init__.py

prakashsellathurai/fashion-Image-Recommender

e0f481133f19e3b8f7f45bf6fc97e39ea866d050

[ "MIT" ]

2

2021-07-31T14:01:01.000Z

2021-08-01T11:09:57.000Z

server/blueprints/admin_home/__init__.py

prakashsellathurai/fashion-Image-Recommender

e0f481133f19e3b8f7f45bf6fc97e39ea866d050

[ "MIT" ]

4

2021-04-30T21:36:10.000Z

2021-11-10T19:58:49.000Z

server/blueprints/admin_home/__init__.py

prakashsellathurai/fashion-Image-Recommender

e0f481133f19e3b8f7f45bf6fc97e39ea866d050

[ "MIT" ]

null

from .blueprint import admin_home

33

0.878788

5

33

5.6

1

0

0.090909

33

1

33

0.933333

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null

0

1

0

1

0

1

0

7

7efdb5e041e5ac7a51dad9560ec80c85ac2503ba

190

py

Python

test/integration/samples_in/two_liner.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

487

2019-06-10T17:44:56.000Z

2022-03-26T01:28:19.000Z

test/integration/samples_in/two_liner.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

118

2019-07-03T12:26:39.000Z

2022-03-06T22:40:17.000Z

test/integration/samples_in/two_liner.py

Inveracity/flynt

b975b6f61893d5db1114d68fbb5d212c4e11aeb8

[ "MIT" ]

25

2019-07-10T08:39:58.000Z

2022-03-03T14:44:15.000Z

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Python

tests/test_packages/test_connections/test_http_server/test_http_server.py

lrahmani/agents-aea

9bd1d51530fc21bf41b5adea031cda19a94b048b

[ "Apache-2.0" ]

null

tests/test_packages/test_connections/test_http_server/test_http_server.py

lrahmani/agents-aea

9bd1d51530fc21bf41b5adea031cda19a94b048b

[ "Apache-2.0" ]

null

tests/test_packages/test_connections/test_http_server/test_http_server.py

lrahmani/agents-aea

9bd1d51530fc21bf41b5adea031cda19a94b048b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2018-2019 Fetch.AI Limited # # 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. # # ------------------------------------------------------------------------------ """This module contains the tests of the gym connection module.""" import asyncio import concurrent.futures import functools import http.client import logging import os from threading import Thread from typing import Dict, Tuple, cast import pytest from aea.configurations.base import PublicId from aea.mail.base import Envelope from packages.fetchai.connections.http_server.connection import HTTPServerConnection from packages.fetchai.protocols.http.message import HttpMessage from packages.fetchai.protocols.http.serialization import HttpSerializer from ....conftest import ( ROOT_DIR, get_host, get_unused_tcp_port, ) logger = logging.getLogger(__name__) @pytest.mark.asyncio class TestHTTPServerConnectionConnectDisconnect: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class and test connect.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) assert cls.http_connection.channel.is_stopped cls.http_connection.channel.connect() assert not cls.http_connection.channel.is_stopped @pytest.mark.asyncio async def test_http_connection_disconnect_channel(self): """Test the disconnect.""" self.http_connection.channel.disconnect() assert self.http_connection.channel.is_stopped @pytest.mark.asyncio class TestHTTPServerConnectionSend: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) loop = asyncio.get_event_loop() value = loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected @pytest.mark.asyncio async def test_send_connection_drop(self): """Test send connection error.""" client_id = "to_key" message = HttpMessage( performative=HttpMessage.Performative.RESPONSE, dialogue_reference=("", ""), target=1, message_id=2, headers="", version="", status_code=200, status_text="Success", bodyy=b"", ) envelope = Envelope( to=client_id, sender="from_key", protocol_id=self.protocol_id, message=HttpSerializer().encode(message), ) await self.http_connection.send(envelope) # we expect the envelope to be dropped assert ( self.http_connection.channel.dispatch_ready_envelopes.get(client_id) is None ) @pytest.mark.asyncio async def test_send_connection_send(self): """Test send connection error.""" client_id = "to_key" message = HttpMessage( performative=HttpMessage.Performative.RESPONSE, dialogue_reference=("", ""), target=1, message_id=2, headers="", version="", status_code=200, status_text="Success", bodyy=b"", ) envelope = Envelope( to=client_id, sender="from_key", protocol_id=self.protocol_id, message=HttpSerializer().encode(message), ) self.http_connection.channel.pending_request_ids.add("to_key") await self.http_connection.send(envelope) assert ( self.http_connection.channel.dispatch_ready_envelopes.get(client_id) == envelope ) assert self.http_connection.channel.pending_request_ids == set() # clean up: self.http_connection.channel.dispatch_ready_envelopes = ( {} ) # type: Dict[str, Envelope] @classmethod def teardown_class(cls): """Teardown the class.""" loop = asyncio.get_event_loop() value = loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionGET404: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() # cls.loop.set_debug(enabled=True) cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_get_404(self): """Test send post request w/ 404 response.""" def request_response_cycle(host, port) -> Tuple[int, str, bytes]: conn = http.client.HTTPConnection(host, port) conn.request("GET", "/") response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port) -> Tuple[int, str, bytes]: executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result response_status_code, response_status_text, response_body = await client_thread( self.host, self.port ) assert ( response_status_code == 404 and response_status_text == "Request Not Found" and response_body == b"" ) @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionGET408: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() # cls.loop.set_debug(enabled=True) cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_get_408(self): """Test send get request w/ 408 response.""" def request_response_cycle(host, port) -> Tuple[int, str, bytes]: conn = http.client.HTTPConnection(host, port) conn.request("GET", "/pets") response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port) -> Tuple[int, str, bytes]: executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result async def agent_processing(http_connection, address) -> bool: # we block here to give it some time for the envelope to make it to the queue await asyncio.sleep(8) envelope = await http_connection.receive() is_exiting_correctly = ( envelope is not None and envelope.to == address and len(http_connection.channel.timed_out_request_ids) == 1 ) return is_exiting_correctly client_task = asyncio.ensure_future(client_thread(self.host, self.port)) agent_task = asyncio.ensure_future( agent_processing(self.http_connection, self.address) ) await asyncio.gather(client_task, agent_task) response_status_code, response_status_text, response_body = client_task.result() is_exiting_correctly = agent_task.result() assert ( response_status_code == 408 and response_status_text == "Request Timeout" and response_body == b"" ) assert is_exiting_correctly @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionGET200: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() # cls.loop.set_debug(enabled=True) cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_get_200(self): """Test send get request w/ 200 response.""" def request_response_cycle(host, port) -> Tuple[int, str, bytes]: conn = http.client.HTTPConnection(host, port) conn.request("GET", "/pets") response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port) -> Tuple[int, str, bytes]: executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result async def agent_processing(http_connection) -> bool: # we block here to give it some time for the envelope to make it to the queue await asyncio.sleep(1) envelope = await http_connection.receive() if envelope is not None: incoming_message = cast( HttpMessage, HttpSerializer().decode(envelope.message) ) message = HttpMessage( performative=HttpMessage.Performative.RESPONSE, dialogue_reference=("", ""), target=incoming_message.message_id, message_id=incoming_message.message_id + 1, version=incoming_message.version, headers=incoming_message.headers, status_code=200, status_text="Success", bodyy=b"Response body", ) response_envelope = Envelope( to=envelope.sender, sender=envelope.to, protocol_id=envelope.protocol_id, context=envelope.context, message=HttpSerializer().encode(message), ) await http_connection.send(response_envelope) is_exiting_correctly = True else: is_exiting_correctly = False return is_exiting_correctly client_task = asyncio.ensure_future(client_thread(self.host, self.port)) agent_task = asyncio.ensure_future(agent_processing(self.http_connection)) await asyncio.gather(client_task, agent_task) response_status_code, response_status_text, response_body = client_task.result() is_exiting_correctly = agent_task.result() assert ( response_status_code == 200 and response_status_text == "Success" and response_body == b"Response body" ) assert is_exiting_correctly @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionPOST404: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_post_404(self): """Test send post request w/ 404 response.""" def request_response_cycle(host, port): conn = http.client.HTTPConnection(host, port) body = "some body" conn.request("POST", "/", body) response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port): executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result response_status_code, response_status_text, response_body = await client_thread( self.host, self.port ) assert ( response_status_code == 404 and response_status_text == "Request Not Found" and response_body == b"" ) @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionPOST408: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_post_408(self): """Test send post request w/ 408 response.""" def request_response_cycle(host, port): conn = http.client.HTTPConnection(host, port) body = "some body" conn.request("POST", "/pets", body) response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port): executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result async def agent_processing(http_connection, address) -> bool: # we block here to give it some time for the envelope to make it to the queue await asyncio.sleep(8) envelope = await http_connection.receive() is_exiting_correctly = ( envelope is not None and envelope.to == address and len(http_connection.channel.timed_out_request_ids) == 1 ) return is_exiting_correctly client_task = asyncio.ensure_future(client_thread(self.host, self.port)) agent_task = asyncio.ensure_future( agent_processing(self.http_connection, self.address) ) await asyncio.gather(client_task, agent_task) response_status_code, response_status_text, response_body = client_task.result() is_exiting_correctly = agent_task.result() assert ( response_status_code == 408 and response_status_text == "Request Timeout" and response_body == b"" ) assert is_exiting_correctly @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None @pytest.mark.asyncio class TestHTTPServerConnectionPOST201: """Test the packages/fetchai/connection/http/connection.py.""" @classmethod def setup_class(cls): """Initialise the class.""" cls.address = "my_key" cls.host = get_host() cls.port = get_unused_tcp_port() cls.api_spec_path = os.path.join(ROOT_DIR, "tests", "data", "petstore_sim.yaml") cls.connection_id = PublicId("fetchai", "http_server", "0.1.0") cls.protocol_id = PublicId("fetchai", "http", "0.1.0") cls.http_connection = HTTPServerConnection( address=cls.address, host=cls.host, port=cls.port, api_spec_path=cls.api_spec_path, connection_id=cls.connection_id, restricted_to_protocols=set([cls.protocol_id]), ) cls.loop = asyncio.new_event_loop() cls.http_connection.loop = cls.loop value = cls.loop.run_until_complete(cls.http_connection.connect()) assert value is None assert cls.http_connection.connection_status.is_connected assert not cls.http_connection.channel.is_stopped cls.t = Thread(target=cls.loop.run_forever) cls.t.start() @pytest.mark.asyncio async def test_post_201(self): """Test send post request w/ 201 response.""" def request_response_cycle(host, port) -> Tuple[int, str, bytes]: conn = http.client.HTTPConnection(host, port) conn.request("POST", "/pets") response = conn.getresponse() return response.status, response.reason, response.read() async def client_thread(host, port) -> Tuple[int, str, bytes]: executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) loop = asyncio.get_event_loop() result = await loop.run_in_executor( executor, functools.partial(request_response_cycle, host=host, port=port), ) return result async def agent_processing(http_connection) -> bool: # we block here to give it some time for the envelope to make it to the queue await asyncio.sleep(1) envelope = await http_connection.receive() if envelope is not None: incoming_message = cast( HttpMessage, HttpSerializer().decode(envelope.message) ) message = HttpMessage( performative=HttpMessage.Performative.RESPONSE, dialogue_reference=("", ""), target=incoming_message.message_id, message_id=incoming_message.message_id + 1, version=incoming_message.version, headers=incoming_message.headers, status_code=201, status_text="Created", bodyy=b"Response body", ) response_envelope = Envelope( to=envelope.sender, sender=envelope.to, protocol_id=envelope.protocol_id, context=envelope.context, message=HttpSerializer().encode(message), ) await http_connection.send(response_envelope) is_exiting_correctly = True else: is_exiting_correctly = False return is_exiting_correctly client_task = asyncio.ensure_future(client_thread(self.host, self.port)) agent_task = asyncio.ensure_future(agent_processing(self.http_connection)) await asyncio.gather(client_task, agent_task) response_status_code, response_status_text, response_body = client_task.result() is_exiting_correctly = agent_task.result() assert ( response_status_code == 201 and response_status_text == "Created" and response_body == b"Response body" ) assert is_exiting_correctly @classmethod def teardown_class(cls): """Teardown the class.""" cls.loop.call_soon_threadsafe(cls.loop.stop) cls.t.join() value = cls.loop.run_until_complete(cls.http_connection.disconnect()) assert value is None

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Python

align_rudder/envs/utils.py

ml-jku/align-rudder

26cf4b62a713e180063cefc2921981484ebb9165

[ "MIT" ]

12

2020-09-30T08:15:44.000Z

2021-12-22T03:36:33.000Z

align_rudder/envs/utils.py

ml-jku/align-rudder

26cf4b62a713e180063cefc2921981484ebb9165

[ "MIT" ]

null

align_rudder/envs/utils.py

ml-jku/align-rudder

26cf4b62a713e180063cefc2921981484ebb9165

[ "MIT" ]

1

2020-12-09T21:33:28.000Z

import numpy as np class Utils: @staticmethod def to_one_hot(a, len_): b = np.zeros((a.size, len_)) b[np.arange(a.size), a] = 1 return b @staticmethod def to_one_hot_obs(obs, len_): return np.array([Utils.to_one_hot(np.array(x), len_) for x in obs]).reshape(-1, len_ * 2) @staticmethod def to_one_hot_flatten(a, len_): b = np.zeros((a.shape[0], len_ * len_)) def to_idx(el): return el[0] * el[1] + el[1] idx = np.apply_along_axis(to_idx, axis=1, arr=a) b[np.arange(a.shape[0]), idx] = 1 return b @staticmethod def to_one_hot_flatten_obs(obs, len_): return Utils.to_one_hot_flatten(np.array(obs), len_) class UtilsRooms: @staticmethod def to_one_hot(a, len_, rooms): b = np.zeros((1, len_ * 2 + rooms)) b[0, [a[0], a[1] + len_, a[2] + len_ * 2]] = 1 return b @staticmethod def to_one_hot_obs(obs, len_, rooms): return np.array([UtilsRooms.to_one_hot(obs, len_, rooms)]) @staticmethod def to_one_hot_flatten(a, len_): b = np.zeros((a.shape[0], len_ * len_)) def to_idx(el): return el[0] * el[1] + el[1] idx = np.apply_along_axis(to_idx, axis=1, arr=a) b[np.arange(a.shape[0]), idx] = 1 return b @staticmethod def to_one_hot_flatten_obs(obs, len_): return Utils.to_one_hot_flatten(np.array(obs), len_)

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1

0

7

cbb2b26da682b7d50be415dbdc32907ea48f0164

8,021

py

Python

knrm.py

YuanyuanQi/CGTR

363d96842a23016aaad5254074b10ecea91bf475

[ "MIT" ]

null

knrm.py

YuanyuanQi/CGTR

363d96842a23016aaad5254074b10ecea91bf475

[ "MIT" ]

null

knrm.py

YuanyuanQi/CGTR

363d96842a23016aaad5254074b10ecea91bf475

[ "MIT" ]

null

import os '''os.environ["CUDA_VISIBLE_DEVICES"] = "1,0" for item in [1,2,3,4,5]: os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.stem.sw.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm/base-clean-stem-sw/f{2}".format(item,item,item)) os.system("python seq_train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs.150new --valid_run data/robust/f{1}.valid.run.150new --model_out_dir models/robust/seq-vbert-cedr-knrm/width-27-1-res150/f{2}".format(item,item,item)) print ('DONE',item)#''' '''os.environ["CUDA_VISIBLE_DEVICES"] = "0,1" for item in [3]: os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp800.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm/textrank-stp800-doc512-lr0.001/f{2} --lr 0.001".format(item,item,item)) print ('DONE',item) os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp800.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm/textrank-stp800-doc512-lr0.0001/f{2} --lr 0.0001 --epoch 150".format(item,item,item)) print ('DONE',item)#''' #document.textrank '''# os.environ["CUDA_VISIBLE_DEVICES"] = "1,0" # for item in [3]: # os.system("python train.py --model cedr_conv_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp1000.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-12kn-256hd-knrm-float16/textrank-stp1000-doc1000-lr0.00002/f{2} --lr 0.00002 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp1000.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-12kn-256hd-knrm-float16/textrank-stp1000-doc1000-lr0.00003/f{2} --lr 0.00003 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp1000.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-12kn-256hd-knrm-float16/textrank-stp1000-doc1000-lr0.00004/f{2} --lr 0.00004 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_knrm --datafiles data/robust/queries.tsv data/robust/documents.textrank.stp1000.Maxlen1759.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-12kn-256hd-knrm-float16/textrank-stp1000-doc1000-lr0.00005/f{2} --lr 0.00005 --epoch 150".format(item,item,item)) # print ('DONE',item)#''' #robust04 document '''# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1" # for item in [3]: # os.system("python train.py --model bert_knrm_bk --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/fine-tuning/bert-knrm-orig-float16/orig-doc1200-lr0.00005/f{2} --lr 0.00005 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm-float16/orig-doc800-lr0.00001/f{2} --lr 0.00001 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_gcn_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-gcn-knrm-float16/orig-doc1600-lr0.0001-lastlayer-bn/f{2} --lr 0.00001 --epoch 200".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm-float16/orig-doc1200-lr0.001-13layers/f{2} --lr 0.001 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-knrm-float16/orig-doc1200-lr0.00001-13layers/f{2} --lr 0.00001 --epoch 250".format(item,item,item)) # os.system("python train.py --model cedr_gan_sim --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-gan-sim-float16/orig-doc600-lr0.0001/f{2} --lr 0.0001 --epoch 150".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_gan_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-gan-knrm-float16/orig-doc600-lr0.000001/f{2} --lr 0.000001 --epoch 150".format(item,item,item)) # print ('DONE',item)#''' # os.system("python train.py --model cedr_conv_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv13-knrm-float16/orig-doc1200-lr0.00005/f{2} --lr 0.00005 --epoch 250".format(item,item,item)) # print ('DONE',item) # os.system("python train.py --model cedr_conv_gcn_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/cedr-conv-gcn-knrm-float16-test/orig-doc1200-lr0.00005-lastlayer/f{2} --lr 0.00005 --epoch 250".format(item,item,item)) # print ('DONE',item) #robust04 # os.environ["CUDA_VISIBLE_DEVICES"] = "1,0" # for item in [3]: # os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/fine-tuning/bert-knrm-float16/orig-doc1200-lr0.001-lastlayer-f1/f{2} --lr 0.001 --epoch 150".format(item,item,item)) # print ('DONE',item) os.environ["CUDA_VISIBLE_DEVICES"] = "0,1" for item in [3]: os.system("python train.py --model bert_knrm --datafiles data/robust/queries.tsv data/robust/documents.tsv --qrels data/robust/qrels --train_pairs data/robust/f{0}.train.pairs --valid_run data/robust/f{1}.valid.run --model_out_dir models/robust/fine-tuning/bert-knrm-float16/orig-doc1200-lr0.01-lastlayer-f1/f{2} --lr 0.01 --epoch 150".format(item,item,item)) print ('DONE',item)

104.168831

412

0.734322

1,333

8,021

4.327832

0.070518

0.164673

0.072456

0.08563

0.943491

0.934651

0.920784

0.91281

0.899289

0.892529

0

0.070664

0.091385

8,021

76

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105.539474

0.720911

0.158584

0

0.2

0.719758

0.368952

0

1

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false

0

0.2

0

0.2

0

null

0

1

0

1

0

1

0

null

0

8

cbb8dc80c2308a98f5d5197aadea80711edf32b6

338

py

Python

linear_algebra_by_j_borwnlee/ch_07/vector_arithmetic.py

pavelexpertov/scribbles

50ebcd6a686fd32be20d401563db7cc87781a428

[ "MIT" ]

null

linear_algebra_by_j_borwnlee/ch_07/vector_arithmetic.py

pavelexpertov/scribbles

50ebcd6a686fd32be20d401563db7cc87781a428

[ "MIT" ]

null

linear_algebra_by_j_borwnlee/ch_07/vector_arithmetic.py

pavelexpertov/scribbles

50ebcd6a686fd32be20d401563db7cc87781a428

[ "MIT" ]

null

def add(l1, l2): return [x + y for x, y in zip(l1, l2)] def minus(l1, l2): return [x - y for x, y in zip(l1, l2)] def multiply(l1, l2): return [x * y for x, y in zip(l1, l2)] def divide(l1, l2): return [x / y for x, y in zip(l1, l2)] def dot_product(l1, l2): products = multiply(l1, l2) return sum(products)

18.777778

42

0.571006

68

338

2.823529

0.25

0.208333

0.260417

0.229167

0.604167

0

0.080972

0.269231

338

17

43

19.882353

0.696356

0

1

0.454545

false

0

0.363636

0.909091

0

null

1

0

1

0

null

0

1

0

1

0

7

1dff73c3014c6d29cac64be101db79118171969a

915

py

Python

colour_hdri/tonemapping/global_operators/__init__.py

colour-science/colour-hdri

3a97c4ad8bc328e2fffabf84ac8b56d795dbeb82

[ "BSD-3-Clause" ]

92

2015-09-19T22:11:15.000Z

2022-03-13T06:37:53.000Z

colour_hdri/tonemapping/global_operators/__init__.py

colour-science/colour-hdri

3a97c4ad8bc328e2fffabf84ac8b56d795dbeb82

[ "BSD-3-Clause" ]

24

2017-05-25T08:55:10.000Z

2022-03-30T18:26:43.000Z

colour_hdri/tonemapping/global_operators/__init__.py

colour-science/colour-hdri

3a97c4ad8bc328e2fffabf84ac8b56d795dbeb82

[ "BSD-3-Clause" ]

9

2016-01-18T17:29:51.000Z

2020-11-12T12:54:18.000Z

# -*- coding: utf-8 -*- from .operators import ( tonemapping_operator_simple, tonemapping_operator_normalisation, tonemapping_operator_gamma, tonemapping_operator_logarithmic, tonemapping_operator_exponential, tonemapping_operator_logarithmic_mapping, tonemapping_operator_exponentiation_mapping, tonemapping_operator_Schlick1994, tonemapping_operator_Tumblin1999, tonemapping_operator_Reinhard2004, tonemapping_operator_filmic) __all__ = [ 'tonemapping_operator_simple', 'tonemapping_operator_normalisation', 'tonemapping_operator_gamma', 'tonemapping_operator_logarithmic', 'tonemapping_operator_exponential', 'tonemapping_operator_logarithmic_mapping', 'tonemapping_operator_exponentiation_mapping', 'tonemapping_operator_Schlick1994', 'tonemapping_operator_Tumblin1999', 'tonemapping_operator_Reinhard2004', 'tonemapping_operator_filmic', ]

38.125

79

0.821858

77

915

9.090909

0.25974

0.597143

0.171429

0.102857

0.954286

0

0.030864

0.114754

915

23

80

39.782609

0.833333

0.022951

0

0.401345

0

1

0

false

0

0.05

0

0.05

0

null

1

0

1

0

1

null

0

8

3804a5675d3a303137a1b9189e165facab2709ca

34,388

py

Python

genomics_data_index/test/integration/storage/io/mutation/test_VariationFile.py

apetkau/genomics-data-index

d0cc119fd57b8cbd701affb1c84450cf7832fa01

[ "Apache-2.0" ]

12

2021-05-03T20:56:05.000Z

2022-01-04T14:52:19.000Z

genomics_data_index/test/integration/storage/io/mutation/test_VariationFile.py

apetkau/genomics-data-index

d0cc119fd57b8cbd701affb1c84450cf7832fa01

[ "Apache-2.0" ]

30

2021-04-26T23:03:40.000Z

2022-02-25T18:41:14.000Z

genomics_data_index/test/integration/storage/io/mutation/test_VariationFile.py

apetkau/genomics-data-index

d0cc119fd57b8cbd701affb1c84450cf7832fa01

[ "Apache-2.0" ]

null

import tempfile from pathlib import Path import pytest from genomics_data_index.storage.MaskedGenomicRegions import MaskedGenomicRegions from genomics_data_index.storage.io.mutation.SequenceFile import SequenceFile from genomics_data_index.storage.io.mutation.VariationFile import VariationFile from genomics_data_index.storage.io.mutation.VcfSnpEffAnnotationParser import VcfSnpEffAnnotationParser from genomics_data_index.test.integration import data_dir, regular_vcf_dir, variation_dir, reference_file, consensus_dir from genomics_data_index.test.integration import extra_snippy_dir from genomics_data_index.test.integration import reference_file_5000_snpeff, snpeff_vcf_file from genomics_data_index.test.integration import snpeff_sample_vcfs, snpeff_sarscov2_vcfs from genomics_data_index.test.integration.storage.io import read_vcf_df @pytest.fixture def snpeff_parser() -> VcfSnpEffAnnotationParser: return VcfSnpEffAnnotationParser() def test_write(): sample_vcf = data_dir / 'SampleA' / 'snps.vcf.gz' with tempfile.TemporaryDirectory() as out_dir: out_file = Path(out_dir) / 'out.vcf.gz' assert not out_file.exists() file, index = VariationFile(sample_vcf).write(out_file) assert out_file.exists() assert file == out_file assert index.exists() assert str(file) + '.csi' == str(index) df = read_vcf_df(out_file) assert 'SNP' == df[df['POS'] == 293]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 302]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 324]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 374]['TYPE'].tolist()[0] assert 'OTHER' == df[df['POS'] == 461]['TYPE'].tolist()[0] assert 'SNP' == df[df['POS'] == 506]['TYPE'].tolist()[0] def test_write_2(): sample_vcf = data_dir / 'SampleC' / 'snps.vcf.gz' with tempfile.TemporaryDirectory() as out_dir: out_file = Path(out_dir) / 'out.vcf.gz' assert not out_file.exists() file, index = VariationFile(sample_vcf).write(out_file) assert out_file.exists() assert file == out_file assert index.exists() assert str(file) + '.csi' == str(index) df = read_vcf_df(out_file) assert 'INDEL' == df[df['POS'] == 347]['TYPE'].tolist()[0] assert 'SNP' == df[df['POS'] == 619]['TYPE'].tolist()[0] assert 'OTHER' == df[df['POS'] == 1984]['TYPE'].tolist()[0] def test_write_missing_type_tag(): sample_vcf = regular_vcf_dir / 'SampleA.vcf.gz' with tempfile.TemporaryDirectory() as out_dir: out_file = Path(out_dir) / 'out.vcf.gz' assert not out_file.exists() VariationFile(sample_vcf).write(out_file) assert out_file.exists() df = read_vcf_df(out_file) assert 'SNP' == df[df['POS'] == 293]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 302]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 324]['TYPE'].tolist()[0] assert 'INDEL' == df[df['POS'] == 374]['TYPE'].tolist()[0] assert 'OTHER' == df[df['POS'] == 461]['TYPE'].tolist()[0] assert 'SNP' == df[df['POS'] == 506]['TYPE'].tolist()[0] def test_write_2_missing_type_tag(): sample_vcf = regular_vcf_dir / 'SampleC.vcf.gz' with tempfile.TemporaryDirectory() as out_dir: out_file = Path(out_dir) / 'out.vcf.gz' assert not out_file.exists() VariationFile(sample_vcf).write(out_file) assert out_file.exists() df = read_vcf_df(out_file) assert 'INDEL' == df[df['POS'] == 347]['TYPE'].tolist()[0] assert 'SNP' == df[df['POS'] == 619]['TYPE'].tolist()[0] assert 'OTHER' == df[df['POS'] == 1984]['TYPE'].tolist()[0] def test_write_bcf(): sample_vcf = data_dir / 'SampleA' / 'snps.vcf.gz' with tempfile.TemporaryDirectory() as out_dir: out_file = Path(out_dir) / 'out.bcf' assert not out_file.exists() VariationFile(sample_vcf).write(out_file) assert out_file.exists() def test_consensus_no_mask(): sample_bcf = variation_dir / 'SampleA.bcf' expected_consensus_file = consensus_dir / 'SampleA-consensus-nomask.fasta.gz' name, expected_consensus_records = SequenceFile(expected_consensus_file).parse_sequence_file() expected_consensus_record = expected_consensus_records[0] seq_records = VariationFile(sample_bcf).consensus(reference_file=reference_file) assert 1 == len(seq_records) actual_seq_record = seq_records[0] assert 5180 == len(actual_seq_record) assert expected_consensus_record.id == actual_seq_record.id assert expected_consensus_record.seq == actual_seq_record.seq def test_consensus_empty_mask(): sample_bcf = variation_dir / 'SampleA.bcf' empty_mask = MaskedGenomicRegions.empty_mask() expected_consensus_file = consensus_dir / 'SampleA-consensus-nomask.fasta.gz' name, expected_consensus_records = SequenceFile(expected_consensus_file).parse_sequence_file() expected_consensus_record = expected_consensus_records[0] with tempfile.TemporaryDirectory() as out_dir: mask_file = Path(out_dir) / 'mask.bed.gz' empty_mask.write(mask_file) seq_records = VariationFile(sample_bcf).consensus(reference_file=reference_file, mask_file=mask_file) assert 1 == len(seq_records) actual_seq_record = seq_records[0] assert 5180 == len(actual_seq_record) assert expected_consensus_record.id == actual_seq_record.id assert expected_consensus_record.seq == actual_seq_record.seq def test_consensus_mask(): sample_bcf = variation_dir / 'SampleA.bcf' sample_mask_file = variation_dir / 'SampleA.bed.gz' expected_consensus_file = consensus_dir / 'SampleA-consensus-withmask.fasta.gz' name, expected_consensus_records = SequenceFile(expected_consensus_file).parse_sequence_file() expected_consensus_record = expected_consensus_records[0] seq_records = VariationFile(sample_bcf).consensus(reference_file=reference_file, mask_file=sample_mask_file) assert 1 == len(seq_records) actual_seq_record = seq_records[0] assert 5180 == len(actual_seq_record) assert expected_consensus_record.id == actual_seq_record.id assert expected_consensus_record.seq == actual_seq_record.seq def test_consensus_mask_over_mutation(): sample_bcf = variation_dir / 'SampleA.bcf' sample_mask_file = variation_dir / 'SampleA-mask-over-mutation.bed.gz' expected_consensus_file = consensus_dir / 'SampleA-consensus-withmask-over-mutation.fasta.gz' name, expected_consensus_records = SequenceFile(expected_consensus_file).parse_sequence_file() expected_consensus_record = expected_consensus_records[0] seq_records = VariationFile(sample_bcf).consensus(reference_file=reference_file, mask_file=sample_mask_file) assert 1 == len(seq_records) actual_seq_record = seq_records[0] assert 5180 == len(actual_seq_record) assert expected_consensus_record.id == actual_seq_record.id assert expected_consensus_record.seq == actual_seq_record.seq def test_union_all_files(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, include_expression='TYPE="SNP"') assert 60 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 190)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 5061)]['COUNT'].values[0] assert 2 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 4975)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 2076)]['COUNT'].values[0] def test_union_one_file(): sample_bcf = variation_dir / 'SampleA.bcf' union_df = VariationFile.union_all_files([sample_bcf], include_expression='TYPE="SNP"') assert 26 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 293)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 4929)]['COUNT'].values[0] def test_union_batch_size_1(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, include_expression='TYPE="SNP"', batch_size=1) assert 60 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 190)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 5061)]['COUNT'].values[0] assert 2 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 4975)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 2076)]['COUNT'].values[0] def test_union_batch_size_2_all_data(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, batch_size=2) assert 112 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 190)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 5061)]['COUNT'].values[0] assert 2 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 4975)]['COUNT'].values[0] assert 1 == union_df[(union_df['CHROM'] == 'reference') & (union_df['POS'] == 2076)]['COUNT'].values[0] def test_union_many_files_batch_size_2_more_data(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz', extra_snippy_dir / 'SampleB2.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB3.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB4.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5-different-allele.fill-tags.vcf.gz', ] union_df = VariationFile.union_all_files(variant_files, batch_size=2) assert 115 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 6 == union_df[union_df['ID'] == 'reference:190:A:G']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:5061:G:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4975:T:C']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4975:T:CAT']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:2076:A:T']['COUNT'].values[0] assert 2 == union_df[union_df['ID'] == 'reference:1483:AAAGAGGGGCTGCTGGAGCCG:A']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:1640:C:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4693:C:CGA']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4693:C:G']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:883:CACATG:C']['COUNT'].values[0] assert 5 == union_df[union_df['ID'] == 'reference:349:AAGT:A']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:349:AAGT:T']['COUNT'].values[0] def test_union_many_files_batch_size_2_with_empty_vcf(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz', extra_snippy_dir / 'SampleB2.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB3.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB4.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5-different-allele.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB-empty.snps.fill-tags.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, batch_size=2) print(union_df) assert 115 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 6 == union_df[union_df['ID'] == 'reference:190:A:G']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:5061:G:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4975:T:C']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4975:T:CAT']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:2076:A:T']['COUNT'].values[0] assert 2 == union_df[union_df['ID'] == 'reference:1483:AAAGAGGGGCTGCTGGAGCCG:A']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:1640:C:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4693:C:CGA']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4693:C:G']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:883:CACATG:C']['COUNT'].values[0] assert 5 == union_df[union_df['ID'] == 'reference:349:AAGT:A']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:349:AAGT:T']['COUNT'].values[0] def test_union_many_files_batch_size_odd_cores_3(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz', extra_snippy_dir / 'SampleB2.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB3.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB4.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5-different-allele.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB-empty.snps.fill-tags.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, ncores=3, batch_size=3) print(union_df) assert 115 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 6 == union_df[union_df['ID'] == 'reference:190:A:G']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:5061:G:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4975:T:C']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4975:T:CAT']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:2076:A:T']['COUNT'].values[0] assert 2 == union_df[union_df['ID'] == 'reference:1483:AAAGAGGGGCTGCTGGAGCCG:A']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:1640:C:A']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4693:C:CGA']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4693:C:G']['COUNT'].values[0] assert 3 == union_df[union_df['ID'] == 'reference:883:CACATG:C']['COUNT'].values[0] assert 5 == union_df[union_df['ID'] == 'reference:349:AAGT:A']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:349:AAGT:T']['COUNT'].values[0] def test_union_many_files_ambiguous(): # List like this to guarantee a specific order variant_files = [ data_dir / 'SampleA' / 'snps.vcf.gz', data_dir / 'SampleB' / 'snps.vcf.gz', data_dir / 'SampleC' / 'snps.vcf.gz', extra_snippy_dir / 'SampleB2.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB3.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB4.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5.snps.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5-different-allele.fill-tags.vcf.gz', extra_snippy_dir / 'SampleB5-different-allele-ambiguous.vcf.gz', ] union_df = VariationFile.union_all_files(variant_files) print(union_df) assert 119 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() assert 6 == union_df[union_df['ID'] == 'reference:190:A:G']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:190:A:N']['COUNT'].values[0] assert 7 == union_df[union_df['ID'] == 'reference:5061:G:A']['COUNT'].values[0] assert 2 == union_df[union_df['ID'] == 'reference:1483:AAAGAGGGGCTGCTGGAGCCG:A']['COUNT'].values[0] assert 5 == union_df[union_df['ID'] == 'reference:349:AAGT:A']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:349:AAGT:T']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:349:ANGT:T']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4693:C:CGA']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4693:C:G']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4693:N:G']['COUNT'].values[0] assert 6 == union_df[union_df['ID'] == 'reference:4975:T:C']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4975:T:CAT']['COUNT'].values[0] assert 1 == union_df[union_df['ID'] == 'reference:4975:T:CNT']['COUNT'].values[0] def test_union_many_files_batch_size_2_single_empty_vcf(): # List like this to guarantee a specific order variant_files = [ extra_snippy_dir / 'SampleB-empty.snps.fill-tags.vcf.gz' ] union_df = VariationFile.union_all_files(variant_files, batch_size=2) print(union_df) assert 0 == len(union_df) assert ['ID', 'CHROM', 'POS', 'REF', 'ALT', 'COUNT'] == union_df.columns.tolist() def test_read_features(snpeff_parser): vcf_file = data_dir / 'SampleA' / 'snps.vcf.gz' df = VariationFile(vcf_file).read_features('SampleA', snpeff_parser=snpeff_parser) assert 46 == len(df), 'Data fram has incorrect length' assert {'snps.vcf.gz'} == set(df['FILE'].tolist()), 'Incorrect filename' assert {'SampleA'} == set(df['SAMPLE'].tolist()), 'Incorrect sample name' v = df[df['POS'] == 461] assert 'AAAT' == v['REF'].values[0], 'Incorrect reference' assert 'G' == v['ALT'].values[0], 'Incorrect alt' v = df[df['POS'] == 1048] assert 'C' == v['REF'].values[0], 'Incorrect reference' assert 'G' == v['ALT'].values[0], 'Incorrect alt' v = df[df['POS'] == 1253] assert 'T' == v['REF'].values[0], 'Incorrect reference' assert 'TAA' == v['ALT'].values[0], 'Incorrect alt' v = df[df['POS'] == 3656] assert 'CATT' == v['REF'].values[0], 'Incorrect reference' assert 'C' == v['ALT'].values[0], 'Incorrect alt' def test_read_features_snpeff(snpeff_parser): sample_10_014 = VariationFile( snpeff_sample_vcfs['SH10-014']).read_features('SH10-014', snpeff_parser=snpeff_parser).sort_values('POS') sample_14_001 = VariationFile( snpeff_sample_vcfs['SH14-001']).read_features('SH14-001', snpeff_parser=snpeff_parser).sort_values('POS') sample_14_014 = VariationFile( snpeff_sample_vcfs['SH14-014']).read_features('SH14-014', snpeff_parser=snpeff_parser).sort_values('POS') assert 139 == len(sample_10_014) assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list(sample_10_014.columns) # snv/snp sample_10_014_varA = sample_10_014[sample_10_014['POS'] == 140658] assert 1 == len(sample_10_014_varA) assert ['SH10-014', 'NC_011083', 140658, 'C', 'A', 'snp', 'SH10-014.vcf.gz', 'NC_011083:140658:C:A', 'A', 'missense_variant', 'MODERATE', 'murF', 'SEHA_RS01180', 'transcript', 'protein_coding', 'c.497C>A', 'p.Ala166Glu'] == sample_10_014_varA[ sample_10_014_varA['ANN.Annotation'] == 'missense_variant'].iloc[0].tolist() # del sample_10_014_varB = sample_10_014[sample_10_014['POS'] == 1125996] assert 1 == len(sample_10_014_varB) assert ['SH10-014', 'NC_011083', 1125996, 'CG', 'C', 'del', 'SH10-014.vcf.gz', 'NC_011083:1125996:CG:C', 'C', 'frameshift_variant', 'HIGH', 'SEHA_RS05995', 'SEHA_RS05995', 'transcript', 'protein_coding', 'c.418delG', 'p.Glu140fs'] == sample_10_014_varB[ sample_10_014_varB['ANN.Annotation'] == 'frameshift_variant'].iloc[0].tolist() # ins sample_10_014_varC = sample_10_014[sample_10_014['POS'] == 1246085] assert 1 == len(sample_10_014_varC) assert ['SH10-014', 'NC_011083', 1246085, 'C', 'CG', 'ins', 'SH10-014.vcf.gz', 'NC_011083:1246085:C:CG', 'CG', 'frameshift_variant', 'HIGH', 'mdtG', 'SEHA_RS06605', 'transcript', 'protein_coding', 'c.722dupC', 'p.Leu242fs'] == sample_10_014_varC[ sample_10_014_varC['ANN.Annotation'] == 'frameshift_variant'].iloc[0].tolist() # complex sample_10_014_varD = sample_10_014[sample_10_014['POS'] == 3535121] assert 1 == len(sample_10_014_varD) assert ['SH10-014', 'NC_011083', 3535121, 'CGCGA', 'TGTGG', 'complex', 'SH10-014.vcf.gz', 'NC_011083:3535121:CGCGA:TGTGG', 'TGTGG', 'missense_variant', 'MODERATE', 'oadA', 'SEHA_RS17780', 'transcript', 'protein_coding', 'c.1119_1123delTCGCGinsCCACA', 'p.ArgAla374HisThr'] == sample_10_014_varD[ sample_10_014_varD['ANN.Annotation'] == 'missense_variant'].iloc[0].tolist() assert 115 == len(sample_14_001) assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list(sample_14_001.columns) sample_14_001_var = sample_14_001[sample_14_001['POS'] == 140658] assert 1 == len(sample_14_001_var) assert ['SH14-001', 'NC_011083', 140658, 'C', 'A', 'snp', 'SH14-001.vcf.gz', 'NC_011083:140658:C:A', 'A', 'missense_variant', 'MODERATE', 'murF', 'SEHA_RS01180', 'transcript', 'protein_coding', 'c.497C>A', 'p.Ala166Glu'] == sample_14_001_var[ sample_14_001_var['ANN.Annotation'] == 'missense_variant'].iloc[0].tolist() assert 107 == len(sample_14_014) assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list(sample_14_014.columns) sample_14_014_var = sample_14_014[sample_14_014['POS'] == 298472] assert 1 == len(sample_14_014_var) assert ['SH14-014', 'NC_011083', 298472, 'A', 'C', 'snp', 'SH14-014.vcf.gz', 'NC_011083:298472:A:C', 'C', 'intergenic_region', 'MODIFIER', 'SEHA_RS01880-SEHA_RS01885', 'SEHA_RS01880-SEHA_RS01885', 'intergenic_region', 'n.298472A>C'] == sample_14_014_var[ sample_14_014_var['ANN.Annotation'] == 'intergenic_region'].drop( ['ANN.Transcript_BioType', 'ANN.HGVS.p'], axis='columns').iloc[0].tolist() assert {True} == set(sample_14_014_var[sample_14_014_var['ANN.Annotation'] == 'intergenic_region'] \ [['ANN.Transcript_BioType', 'ANN.HGVS.p']].iloc[0].isna().tolist()) def test_read_features_snpeff_sars_cov_2(snpeff_parser): sample_sarscov2_1 = VariationFile( snpeff_sarscov2_vcfs['USA/CA-CDPH-3000143037/2021'] ).read_features('USA/CA-CDPH-3000143037/2021', snpeff_parser=snpeff_parser).sort_values('POS') assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list(sample_sarscov2_1.columns) assert 31 == len(sample_sarscov2_1) sample_sarscov2_1['ANN.Annotation'] = sample_sarscov2_1['ANN.Annotation'].astype(str) sample_sarscov2_1['ANN.Annotation_Impact'] = sample_sarscov2_1['ANN.Annotation_Impact'].astype(str) # ORF1ab (ORF1a region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 3948] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 3948, 'A', 'G', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:3948:A:G', 'G', 'missense_variant', 'MODERATE', 'ORF1ab', 'GU280_gp01', 'transcript', 'protein_coding', 'c.3683A>G', 'p.D1228G'] == sample_sarscov2_1_var.iloc[0].tolist() # ORF1ab (ORF1a region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 3037] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 3037, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:3037:C:T', 'T', 'synonymous_variant', 'LOW', 'ORF1ab', 'GU280_gp01', 'transcript', 'protein_coding', 'c.2772C>T', 'p.F924F'] == sample_sarscov2_1_var.iloc[0].tolist() # ORF1ab (ORF1b region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 19220] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 19220, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:19220:C:T', 'T', 'missense_variant', 'MODERATE', 'ORF1ab', 'GU280_gp01', 'transcript', 'protein_coding', 'c.18956C>T', 'p.A6319V'] == sample_sarscov2_1_var.iloc[0].tolist() # S sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 22917] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 22917, 'T', 'G', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:22917:T:G', 'G', 'missense_variant', 'MODERATE', 'S', 'GU280_gp02', 'transcript', 'protein_coding', 'c.1355T>G', 'p.L452R'] == sample_sarscov2_1_var.iloc[0].tolist() # ORF7b sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 27874] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 27874, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:27874:C:T', 'T', 'missense_variant', 'MODERATE', 'ORF7b', 'GU280_gp08', 'transcript', 'protein_coding', 'c.119C>T', 'p.T40I'] == sample_sarscov2_1_var.iloc[0].tolist() # intergenic sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 210].fillna('<NA>') assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 210, 'G', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.vcf.gz', 'NC_045512.2:210:G:T', 'T', 'intergenic_region', 'MODIFIER', 'CHR_START-ORF1ab', 'CHR_START-GU280_gp01', 'intergenic_region', '<NA>', 'n.210G>T', '<NA>'] == sample_sarscov2_1_var.iloc[0].tolist() def test_read_features_sars_cov_2_no_snpeff_annotation(snpeff_parser): sample_sarscov2_1 = VariationFile( snpeff_sarscov2_vcfs['USA/CA-CDPH-3000143037/2021.noann'] ).read_features('USA/CA-CDPH-3000143037/2021', snpeff_parser=snpeff_parser).sort_values('POS') ann_columns = ['ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list(sample_sarscov2_1.columns) assert 31 == len(sample_sarscov2_1) # ORF1ab (ORF1a region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 3948] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 3948, 'A', 'G', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:3948:A:G'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) # ORF1ab (ORF1a region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 3037] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 3037, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:3037:C:T'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) # ORF1ab (ORF1b region) sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 19220] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 19220, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:19220:C:T'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) # S sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 22917] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 22917, 'T', 'G', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:22917:T:G'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) # ORF7b sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 27874] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 27874, 'C', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:27874:C:T'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) # intergenic sample_sarscov2_1_var = sample_sarscov2_1[sample_sarscov2_1['POS'] == 210] assert 1 == len(sample_sarscov2_1_var) assert ['USA/CA-CDPH-3000143037/2021', 'NC_045512.2', 210, 'G', 'T', 'SNP', 'USA__CA-CDPH-3000143037__2021.noann.vcf.gz', 'NC_045512.2:210:G:T'] == sample_sarscov2_1_var.drop( ann_columns, axis='columns').iloc[0].tolist() assert {True} == set(sample_sarscov2_1_var[ann_columns].iloc[0].isna().tolist()) def test_annotate(snpeff_parser): with tempfile.TemporaryDirectory() as out_dir: database_dir = Path(out_dir) output_vcf_file = database_dir / 'output.vcf.gz' variation_file = VariationFile(snpeff_vcf_file) snpeff_database = SequenceFile(reference_file_5000_snpeff).create_snpeff_database(database_dir) annotated_variation_file = variation_file.annotate(snpeff_database=snpeff_database, annotated_vcf=output_vcf_file) assert output_vcf_file == annotated_variation_file.file # Verify VCF annotation contents vcf_annotation_df = annotated_variation_file.read_features('SampleA', snpeff_parser=snpeff_parser).sort_values( 'POS') assert 2 == len(vcf_annotation_df) assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list( vcf_annotation_df.columns) assert ['NC_011083.1:195:C:G', 'NC_011083.1:207:C:G'] == vcf_annotation_df['VARIANT_ID'].tolist() assert ['SNP', 'SNP'] == vcf_annotation_df['TYPE'].tolist() assert ['missense_variant', 'synonymous_variant'] == vcf_annotation_df['ANN.Annotation'].tolist() assert ['SEHA_RS00560', 'SEHA_RS00560'] == vcf_annotation_df['ANN.Gene_ID'].tolist() assert ['thrL', 'thrL'] == vcf_annotation_df['ANN.Gene_Name'].tolist() assert ['c.6C>G', 'c.18C>G'] == vcf_annotation_df['ANN.HGVS.c'].tolist() assert ['p.N2K', 'p.T6T'] == vcf_annotation_df['ANN.HGVS.p'].tolist() # Original file should still exist and be unannotated vcf_no_annotation_df = variation_file.read_features('SampleA', snpeff_parser=snpeff_parser).sort_values('POS') assert 2 == len(vcf_no_annotation_df) assert ['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID', 'ANN.Allele', 'ANN.Annotation', 'ANN.Annotation_Impact', 'ANN.Gene_Name', 'ANN.Gene_ID', 'ANN.Feature_Type', 'ANN.Transcript_BioType', 'ANN.HGVS.c', 'ANN.HGVS.p'] == list( vcf_no_annotation_df.columns) assert ['NC_011083.1:195:C:G', 'NC_011083.1:207:C:G'] == vcf_no_annotation_df['VARIANT_ID'].tolist() assert ['SNP', 'SNP'] == vcf_no_annotation_df['TYPE'].tolist() assert all(vcf_no_annotation_df['ANN.Annotation'].isna()) assert all(vcf_no_annotation_df['ANN.Gene_ID'].isna()) assert all(vcf_no_annotation_df['ANN.HGVS.c'].isna()) assert all(vcf_no_annotation_df['ANN.HGVS.p'].isna())

52.340944

120

0.656043

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null

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1

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null

0

1

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7

380877c8ff4a71bf178da3cd1d696808024ca34b

128

py

Python

anvil/sub_rig_templates/biped_leg.py

AndresMWeber/Anvil

9cd202183ac998983c2bf6e55cc46bbc0ca1a78e

[ "Apache-2.0" ]

3

2019-11-22T04:38:06.000Z

2022-01-19T08:27:18.000Z

anvil/sub_rig_templates/biped_leg.py

AndresMWeber/Anvil

9cd202183ac998983c2bf6e55cc46bbc0ca1a78e

[ "Apache-2.0" ]

28

2018-02-01T20:39:42.000Z

2018-04-26T17:25:23.000Z

anvil/sub_rig_templates/biped_leg.py

AndresMWeber/Anvil

9cd202183ac998983c2bf6e55cc46bbc0ca1a78e

[ "Apache-2.0" ]

1

2018-03-11T06:47:26.000Z

from limb import Limb class BipedLeg(Limb): BUILT_IN_META_DATA = Limb.BUILT_IN_META_DATA.merge({'name': 'leg'}, new=True)

21.333333

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0.734375

21

128

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1

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null

0

1

0

1

0

7

380e954db5d10829faf1e4c91d126ec2e099c3b3

40,282

py

Python

sdk/python/pulumi_aws_native/dynamodb/_inputs.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/dynamodb/_inputs.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/dynamodb/_inputs.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'GlobalTableAttributeDefinitionArgs', 'GlobalTableCapacityAutoScalingSettingsArgs', 'GlobalTableContributorInsightsSpecificationArgs', 'GlobalTableGlobalSecondaryIndexArgs', 'GlobalTableKeySchemaArgs', 'GlobalTableLocalSecondaryIndexArgs', 'GlobalTablePointInTimeRecoverySpecificationArgs', 'GlobalTableProjectionArgs', 'GlobalTableReadProvisionedThroughputSettingsArgs', 'GlobalTableReplicaGlobalSecondaryIndexSpecificationArgs', 'GlobalTableReplicaSSESpecificationArgs', 'GlobalTableReplicaSpecificationArgs', 'GlobalTableSSESpecificationArgs', 'GlobalTableStreamSpecificationArgs', 'GlobalTableTagArgs', 'GlobalTableTargetTrackingScalingPolicyConfigurationArgs', 'GlobalTableTimeToLiveSpecificationArgs', 'GlobalTableWriteProvisionedThroughputSettingsArgs', 'TableAttributeDefinitionArgs', 'TableContributorInsightsSpecificationArgs', 'TableGlobalSecondaryIndexArgs', 'TableKeySchemaArgs', 'TableKinesisStreamSpecificationArgs', 'TableLocalSecondaryIndexArgs', 'TablePointInTimeRecoverySpecificationArgs', 'TableProjectionArgs', 'TableProvisionedThroughputArgs', 'TableSSESpecificationArgs', 'TableStreamSpecificationArgs', 'TableTagArgs', 'TableTimeToLiveSpecificationArgs', ] @pulumi.input_type class GlobalTableAttributeDefinitionArgs: def __init__(__self__, *, attribute_name: pulumi.Input[str], attribute_type: pulumi.Input[str]): pulumi.set(__self__, "attribute_name", attribute_name) pulumi.set(__self__, "attribute_type", attribute_type) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_name", value) @property @pulumi.getter(name="attributeType") def attribute_type(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_type") @attribute_type.setter def attribute_type(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_type", value) @pulumi.input_type class GlobalTableCapacityAutoScalingSettingsArgs: def __init__(__self__, *, max_capacity: pulumi.Input[int], min_capacity: pulumi.Input[int], target_tracking_scaling_policy_configuration: pulumi.Input['GlobalTableTargetTrackingScalingPolicyConfigurationArgs'], seed_capacity: Optional[pulumi.Input[int]] = None): pulumi.set(__self__, "max_capacity", max_capacity) pulumi.set(__self__, "min_capacity", min_capacity) pulumi.set(__self__, "target_tracking_scaling_policy_configuration", target_tracking_scaling_policy_configuration) if seed_capacity is not None: pulumi.set(__self__, "seed_capacity", seed_capacity) @property @pulumi.getter(name="maxCapacity") def max_capacity(self) -> pulumi.Input[int]: return pulumi.get(self, "max_capacity") @max_capacity.setter def max_capacity(self, value: pulumi.Input[int]): pulumi.set(self, "max_capacity", value) @property @pulumi.getter(name="minCapacity") def min_capacity(self) -> pulumi.Input[int]: return pulumi.get(self, "min_capacity") @min_capacity.setter def min_capacity(self, value: pulumi.Input[int]): pulumi.set(self, "min_capacity", value) @property @pulumi.getter(name="targetTrackingScalingPolicyConfiguration") def target_tracking_scaling_policy_configuration(self) -> pulumi.Input['GlobalTableTargetTrackingScalingPolicyConfigurationArgs']: return pulumi.get(self, "target_tracking_scaling_policy_configuration") @target_tracking_scaling_policy_configuration.setter def target_tracking_scaling_policy_configuration(self, value: pulumi.Input['GlobalTableTargetTrackingScalingPolicyConfigurationArgs']): pulumi.set(self, "target_tracking_scaling_policy_configuration", value) @property @pulumi.getter(name="seedCapacity") def seed_capacity(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "seed_capacity") @seed_capacity.setter def seed_capacity(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "seed_capacity", value) @pulumi.input_type class GlobalTableContributorInsightsSpecificationArgs: def __init__(__self__, *, enabled: pulumi.Input[bool]): pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value) @pulumi.input_type class GlobalTableGlobalSecondaryIndexArgs: def __init__(__self__, *, index_name: pulumi.Input[str], key_schema: pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]], projection: pulumi.Input['GlobalTableProjectionArgs'], write_provisioned_throughput_settings: Optional[pulumi.Input['GlobalTableWriteProvisionedThroughputSettingsArgs']] = None): pulumi.set(__self__, "index_name", index_name) pulumi.set(__self__, "key_schema", key_schema) pulumi.set(__self__, "projection", projection) if write_provisioned_throughput_settings is not None: pulumi.set(__self__, "write_provisioned_throughput_settings", write_provisioned_throughput_settings) @property @pulumi.getter(name="indexName") def index_name(self) -> pulumi.Input[str]: return pulumi.get(self, "index_name") @index_name.setter def index_name(self, value: pulumi.Input[str]): pulumi.set(self, "index_name", value) @property @pulumi.getter(name="keySchema") def key_schema(self) -> pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]]: return pulumi.get(self, "key_schema") @key_schema.setter def key_schema(self, value: pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]]): pulumi.set(self, "key_schema", value) @property @pulumi.getter def projection(self) -> pulumi.Input['GlobalTableProjectionArgs']: return pulumi.get(self, "projection") @projection.setter def projection(self, value: pulumi.Input['GlobalTableProjectionArgs']): pulumi.set(self, "projection", value) @property @pulumi.getter(name="writeProvisionedThroughputSettings") def write_provisioned_throughput_settings(self) -> Optional[pulumi.Input['GlobalTableWriteProvisionedThroughputSettingsArgs']]: return pulumi.get(self, "write_provisioned_throughput_settings") @write_provisioned_throughput_settings.setter def write_provisioned_throughput_settings(self, value: Optional[pulumi.Input['GlobalTableWriteProvisionedThroughputSettingsArgs']]): pulumi.set(self, "write_provisioned_throughput_settings", value) @pulumi.input_type class GlobalTableKeySchemaArgs: def __init__(__self__, *, attribute_name: pulumi.Input[str], key_type: pulumi.Input[str]): pulumi.set(__self__, "attribute_name", attribute_name) pulumi.set(__self__, "key_type", key_type) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_name", value) @property @pulumi.getter(name="keyType") def key_type(self) -> pulumi.Input[str]: return pulumi.get(self, "key_type") @key_type.setter def key_type(self, value: pulumi.Input[str]): pulumi.set(self, "key_type", value) @pulumi.input_type class GlobalTableLocalSecondaryIndexArgs: def __init__(__self__, *, index_name: pulumi.Input[str], key_schema: pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]], projection: pulumi.Input['GlobalTableProjectionArgs']): pulumi.set(__self__, "index_name", index_name) pulumi.set(__self__, "key_schema", key_schema) pulumi.set(__self__, "projection", projection) @property @pulumi.getter(name="indexName") def index_name(self) -> pulumi.Input[str]: return pulumi.get(self, "index_name") @index_name.setter def index_name(self, value: pulumi.Input[str]): pulumi.set(self, "index_name", value) @property @pulumi.getter(name="keySchema") def key_schema(self) -> pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]]: return pulumi.get(self, "key_schema") @key_schema.setter def key_schema(self, value: pulumi.Input[Sequence[pulumi.Input['GlobalTableKeySchemaArgs']]]): pulumi.set(self, "key_schema", value) @property @pulumi.getter def projection(self) -> pulumi.Input['GlobalTableProjectionArgs']: return pulumi.get(self, "projection") @projection.setter def projection(self, value: pulumi.Input['GlobalTableProjectionArgs']): pulumi.set(self, "projection", value) @pulumi.input_type class GlobalTablePointInTimeRecoverySpecificationArgs: def __init__(__self__, *, point_in_time_recovery_enabled: Optional[pulumi.Input[bool]] = None): if point_in_time_recovery_enabled is not None: pulumi.set(__self__, "point_in_time_recovery_enabled", point_in_time_recovery_enabled) @property @pulumi.getter(name="pointInTimeRecoveryEnabled") def point_in_time_recovery_enabled(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "point_in_time_recovery_enabled") @point_in_time_recovery_enabled.setter def point_in_time_recovery_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "point_in_time_recovery_enabled", value) @pulumi.input_type class GlobalTableProjectionArgs: def __init__(__self__, *, non_key_attributes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, projection_type: Optional[pulumi.Input[str]] = None): if non_key_attributes is not None: pulumi.set(__self__, "non_key_attributes", non_key_attributes) if projection_type is not None: pulumi.set(__self__, "projection_type", projection_type) @property @pulumi.getter(name="nonKeyAttributes") def non_key_attributes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "non_key_attributes") @non_key_attributes.setter def non_key_attributes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "non_key_attributes", value) @property @pulumi.getter(name="projectionType") def projection_type(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "projection_type") @projection_type.setter def projection_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "projection_type", value) @pulumi.input_type class GlobalTableReadProvisionedThroughputSettingsArgs: def __init__(__self__, *, read_capacity_auto_scaling_settings: Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']] = None, read_capacity_units: Optional[pulumi.Input[int]] = None): if read_capacity_auto_scaling_settings is not None: pulumi.set(__self__, "read_capacity_auto_scaling_settings", read_capacity_auto_scaling_settings) if read_capacity_units is not None: pulumi.set(__self__, "read_capacity_units", read_capacity_units) @property @pulumi.getter(name="readCapacityAutoScalingSettings") def read_capacity_auto_scaling_settings(self) -> Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']]: return pulumi.get(self, "read_capacity_auto_scaling_settings") @read_capacity_auto_scaling_settings.setter def read_capacity_auto_scaling_settings(self, value: Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']]): pulumi.set(self, "read_capacity_auto_scaling_settings", value) @property @pulumi.getter(name="readCapacityUnits") def read_capacity_units(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "read_capacity_units") @read_capacity_units.setter def read_capacity_units(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "read_capacity_units", value) @pulumi.input_type class GlobalTableReplicaGlobalSecondaryIndexSpecificationArgs: def __init__(__self__, *, index_name: pulumi.Input[str], contributor_insights_specification: Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']] = None, read_provisioned_throughput_settings: Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']] = None): pulumi.set(__self__, "index_name", index_name) if contributor_insights_specification is not None: pulumi.set(__self__, "contributor_insights_specification", contributor_insights_specification) if read_provisioned_throughput_settings is not None: pulumi.set(__self__, "read_provisioned_throughput_settings", read_provisioned_throughput_settings) @property @pulumi.getter(name="indexName") def index_name(self) -> pulumi.Input[str]: return pulumi.get(self, "index_name") @index_name.setter def index_name(self, value: pulumi.Input[str]): pulumi.set(self, "index_name", value) @property @pulumi.getter(name="contributorInsightsSpecification") def contributor_insights_specification(self) -> Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']]: return pulumi.get(self, "contributor_insights_specification") @contributor_insights_specification.setter def contributor_insights_specification(self, value: Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']]): pulumi.set(self, "contributor_insights_specification", value) @property @pulumi.getter(name="readProvisionedThroughputSettings") def read_provisioned_throughput_settings(self) -> Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']]: return pulumi.get(self, "read_provisioned_throughput_settings") @read_provisioned_throughput_settings.setter def read_provisioned_throughput_settings(self, value: Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']]): pulumi.set(self, "read_provisioned_throughput_settings", value) @pulumi.input_type class GlobalTableReplicaSSESpecificationArgs: def __init__(__self__, *, k_ms_master_key_id: pulumi.Input[str]): pulumi.set(__self__, "k_ms_master_key_id", k_ms_master_key_id) @property @pulumi.getter(name="kMSMasterKeyId") def k_ms_master_key_id(self) -> pulumi.Input[str]: return pulumi.get(self, "k_ms_master_key_id") @k_ms_master_key_id.setter def k_ms_master_key_id(self, value: pulumi.Input[str]): pulumi.set(self, "k_ms_master_key_id", value) @pulumi.input_type class GlobalTableReplicaSpecificationArgs: def __init__(__self__, *, region: pulumi.Input[str], contributor_insights_specification: Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']] = None, global_secondary_indexes: Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableReplicaGlobalSecondaryIndexSpecificationArgs']]]] = None, point_in_time_recovery_specification: Optional[pulumi.Input['GlobalTablePointInTimeRecoverySpecificationArgs']] = None, read_provisioned_throughput_settings: Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']] = None, s_se_specification: Optional[pulumi.Input['GlobalTableReplicaSSESpecificationArgs']] = None, table_class: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableTagArgs']]]] = None): pulumi.set(__self__, "region", region) if contributor_insights_specification is not None: pulumi.set(__self__, "contributor_insights_specification", contributor_insights_specification) if global_secondary_indexes is not None: pulumi.set(__self__, "global_secondary_indexes", global_secondary_indexes) if point_in_time_recovery_specification is not None: pulumi.set(__self__, "point_in_time_recovery_specification", point_in_time_recovery_specification) if read_provisioned_throughput_settings is not None: pulumi.set(__self__, "read_provisioned_throughput_settings", read_provisioned_throughput_settings) if s_se_specification is not None: pulumi.set(__self__, "s_se_specification", s_se_specification) if table_class is not None: pulumi.set(__self__, "table_class", table_class) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def region(self) -> pulumi.Input[str]: return pulumi.get(self, "region") @region.setter def region(self, value: pulumi.Input[str]): pulumi.set(self, "region", value) @property @pulumi.getter(name="contributorInsightsSpecification") def contributor_insights_specification(self) -> Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']]: return pulumi.get(self, "contributor_insights_specification") @contributor_insights_specification.setter def contributor_insights_specification(self, value: Optional[pulumi.Input['GlobalTableContributorInsightsSpecificationArgs']]): pulumi.set(self, "contributor_insights_specification", value) @property @pulumi.getter(name="globalSecondaryIndexes") def global_secondary_indexes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableReplicaGlobalSecondaryIndexSpecificationArgs']]]]: return pulumi.get(self, "global_secondary_indexes") @global_secondary_indexes.setter def global_secondary_indexes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableReplicaGlobalSecondaryIndexSpecificationArgs']]]]): pulumi.set(self, "global_secondary_indexes", value) @property @pulumi.getter(name="pointInTimeRecoverySpecification") def point_in_time_recovery_specification(self) -> Optional[pulumi.Input['GlobalTablePointInTimeRecoverySpecificationArgs']]: return pulumi.get(self, "point_in_time_recovery_specification") @point_in_time_recovery_specification.setter def point_in_time_recovery_specification(self, value: Optional[pulumi.Input['GlobalTablePointInTimeRecoverySpecificationArgs']]): pulumi.set(self, "point_in_time_recovery_specification", value) @property @pulumi.getter(name="readProvisionedThroughputSettings") def read_provisioned_throughput_settings(self) -> Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']]: return pulumi.get(self, "read_provisioned_throughput_settings") @read_provisioned_throughput_settings.setter def read_provisioned_throughput_settings(self, value: Optional[pulumi.Input['GlobalTableReadProvisionedThroughputSettingsArgs']]): pulumi.set(self, "read_provisioned_throughput_settings", value) @property @pulumi.getter(name="sSESpecification") def s_se_specification(self) -> Optional[pulumi.Input['GlobalTableReplicaSSESpecificationArgs']]: return pulumi.get(self, "s_se_specification") @s_se_specification.setter def s_se_specification(self, value: Optional[pulumi.Input['GlobalTableReplicaSSESpecificationArgs']]): pulumi.set(self, "s_se_specification", value) @property @pulumi.getter(name="tableClass") def table_class(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "table_class") @table_class.setter def table_class(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "table_class", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableTagArgs']]]]: return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GlobalTableTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class GlobalTableSSESpecificationArgs: def __init__(__self__, *, s_se_enabled: pulumi.Input[bool], s_se_type: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "s_se_enabled", s_se_enabled) if s_se_type is not None: pulumi.set(__self__, "s_se_type", s_se_type) @property @pulumi.getter(name="sSEEnabled") def s_se_enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "s_se_enabled") @s_se_enabled.setter def s_se_enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "s_se_enabled", value) @property @pulumi.getter(name="sSEType") def s_se_type(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "s_se_type") @s_se_type.setter def s_se_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "s_se_type", value) @pulumi.input_type class GlobalTableStreamSpecificationArgs: def __init__(__self__, *, stream_view_type: pulumi.Input[str]): pulumi.set(__self__, "stream_view_type", stream_view_type) @property @pulumi.getter(name="streamViewType") def stream_view_type(self) -> pulumi.Input[str]: return pulumi.get(self, "stream_view_type") @stream_view_type.setter def stream_view_type(self, value: pulumi.Input[str]): pulumi.set(self, "stream_view_type", value) @pulumi.input_type class GlobalTableTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class GlobalTableTargetTrackingScalingPolicyConfigurationArgs: def __init__(__self__, *, target_value: pulumi.Input[float], disable_scale_in: Optional[pulumi.Input[bool]] = None, scale_in_cooldown: Optional[pulumi.Input[int]] = None, scale_out_cooldown: Optional[pulumi.Input[int]] = None): pulumi.set(__self__, "target_value", target_value) if disable_scale_in is not None: pulumi.set(__self__, "disable_scale_in", disable_scale_in) if scale_in_cooldown is not None: pulumi.set(__self__, "scale_in_cooldown", scale_in_cooldown) if scale_out_cooldown is not None: pulumi.set(__self__, "scale_out_cooldown", scale_out_cooldown) @property @pulumi.getter(name="targetValue") def target_value(self) -> pulumi.Input[float]: return pulumi.get(self, "target_value") @target_value.setter def target_value(self, value: pulumi.Input[float]): pulumi.set(self, "target_value", value) @property @pulumi.getter(name="disableScaleIn") def disable_scale_in(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "disable_scale_in") @disable_scale_in.setter def disable_scale_in(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "disable_scale_in", value) @property @pulumi.getter(name="scaleInCooldown") def scale_in_cooldown(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "scale_in_cooldown") @scale_in_cooldown.setter def scale_in_cooldown(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "scale_in_cooldown", value) @property @pulumi.getter(name="scaleOutCooldown") def scale_out_cooldown(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "scale_out_cooldown") @scale_out_cooldown.setter def scale_out_cooldown(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "scale_out_cooldown", value) @pulumi.input_type class GlobalTableTimeToLiveSpecificationArgs: def __init__(__self__, *, enabled: pulumi.Input[bool], attribute_name: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "enabled", enabled) if attribute_name is not None: pulumi.set(__self__, "attribute_name", attribute_name) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "attribute_name", value) @pulumi.input_type class GlobalTableWriteProvisionedThroughputSettingsArgs: def __init__(__self__, *, write_capacity_auto_scaling_settings: Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']] = None): if write_capacity_auto_scaling_settings is not None: pulumi.set(__self__, "write_capacity_auto_scaling_settings", write_capacity_auto_scaling_settings) @property @pulumi.getter(name="writeCapacityAutoScalingSettings") def write_capacity_auto_scaling_settings(self) -> Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']]: return pulumi.get(self, "write_capacity_auto_scaling_settings") @write_capacity_auto_scaling_settings.setter def write_capacity_auto_scaling_settings(self, value: Optional[pulumi.Input['GlobalTableCapacityAutoScalingSettingsArgs']]): pulumi.set(self, "write_capacity_auto_scaling_settings", value) @pulumi.input_type class TableAttributeDefinitionArgs: def __init__(__self__, *, attribute_name: pulumi.Input[str], attribute_type: pulumi.Input[str]): pulumi.set(__self__, "attribute_name", attribute_name) pulumi.set(__self__, "attribute_type", attribute_type) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_name", value) @property @pulumi.getter(name="attributeType") def attribute_type(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_type") @attribute_type.setter def attribute_type(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_type", value) @pulumi.input_type class TableContributorInsightsSpecificationArgs: def __init__(__self__, *, enabled: pulumi.Input[bool]): pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value) @pulumi.input_type class TableGlobalSecondaryIndexArgs: def __init__(__self__, *, index_name: pulumi.Input[str], key_schema: pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]], projection: pulumi.Input['TableProjectionArgs'], contributor_insights_specification: Optional[pulumi.Input['TableContributorInsightsSpecificationArgs']] = None, provisioned_throughput: Optional[pulumi.Input['TableProvisionedThroughputArgs']] = None): pulumi.set(__self__, "index_name", index_name) pulumi.set(__self__, "key_schema", key_schema) pulumi.set(__self__, "projection", projection) if contributor_insights_specification is not None: pulumi.set(__self__, "contributor_insights_specification", contributor_insights_specification) if provisioned_throughput is not None: pulumi.set(__self__, "provisioned_throughput", provisioned_throughput) @property @pulumi.getter(name="indexName") def index_name(self) -> pulumi.Input[str]: return pulumi.get(self, "index_name") @index_name.setter def index_name(self, value: pulumi.Input[str]): pulumi.set(self, "index_name", value) @property @pulumi.getter(name="keySchema") def key_schema(self) -> pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]]: return pulumi.get(self, "key_schema") @key_schema.setter def key_schema(self, value: pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]]): pulumi.set(self, "key_schema", value) @property @pulumi.getter def projection(self) -> pulumi.Input['TableProjectionArgs']: return pulumi.get(self, "projection") @projection.setter def projection(self, value: pulumi.Input['TableProjectionArgs']): pulumi.set(self, "projection", value) @property @pulumi.getter(name="contributorInsightsSpecification") def contributor_insights_specification(self) -> Optional[pulumi.Input['TableContributorInsightsSpecificationArgs']]: return pulumi.get(self, "contributor_insights_specification") @contributor_insights_specification.setter def contributor_insights_specification(self, value: Optional[pulumi.Input['TableContributorInsightsSpecificationArgs']]): pulumi.set(self, "contributor_insights_specification", value) @property @pulumi.getter(name="provisionedThroughput") def provisioned_throughput(self) -> Optional[pulumi.Input['TableProvisionedThroughputArgs']]: return pulumi.get(self, "provisioned_throughput") @provisioned_throughput.setter def provisioned_throughput(self, value: Optional[pulumi.Input['TableProvisionedThroughputArgs']]): pulumi.set(self, "provisioned_throughput", value) @pulumi.input_type class TableKeySchemaArgs: def __init__(__self__, *, attribute_name: pulumi.Input[str], key_type: pulumi.Input[str]): pulumi.set(__self__, "attribute_name", attribute_name) pulumi.set(__self__, "key_type", key_type) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_name", value) @property @pulumi.getter(name="keyType") def key_type(self) -> pulumi.Input[str]: return pulumi.get(self, "key_type") @key_type.setter def key_type(self, value: pulumi.Input[str]): pulumi.set(self, "key_type", value) @pulumi.input_type class TableKinesisStreamSpecificationArgs: def __init__(__self__, *, stream_arn: pulumi.Input[str]): pulumi.set(__self__, "stream_arn", stream_arn) @property @pulumi.getter(name="streamArn") def stream_arn(self) -> pulumi.Input[str]: return pulumi.get(self, "stream_arn") @stream_arn.setter def stream_arn(self, value: pulumi.Input[str]): pulumi.set(self, "stream_arn", value) @pulumi.input_type class TableLocalSecondaryIndexArgs: def __init__(__self__, *, index_name: pulumi.Input[str], key_schema: pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]], projection: pulumi.Input['TableProjectionArgs']): pulumi.set(__self__, "index_name", index_name) pulumi.set(__self__, "key_schema", key_schema) pulumi.set(__self__, "projection", projection) @property @pulumi.getter(name="indexName") def index_name(self) -> pulumi.Input[str]: return pulumi.get(self, "index_name") @index_name.setter def index_name(self, value: pulumi.Input[str]): pulumi.set(self, "index_name", value) @property @pulumi.getter(name="keySchema") def key_schema(self) -> pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]]: return pulumi.get(self, "key_schema") @key_schema.setter def key_schema(self, value: pulumi.Input[Sequence[pulumi.Input['TableKeySchemaArgs']]]): pulumi.set(self, "key_schema", value) @property @pulumi.getter def projection(self) -> pulumi.Input['TableProjectionArgs']: return pulumi.get(self, "projection") @projection.setter def projection(self, value: pulumi.Input['TableProjectionArgs']): pulumi.set(self, "projection", value) @pulumi.input_type class TablePointInTimeRecoverySpecificationArgs: def __init__(__self__, *, point_in_time_recovery_enabled: Optional[pulumi.Input[bool]] = None): if point_in_time_recovery_enabled is not None: pulumi.set(__self__, "point_in_time_recovery_enabled", point_in_time_recovery_enabled) @property @pulumi.getter(name="pointInTimeRecoveryEnabled") def point_in_time_recovery_enabled(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "point_in_time_recovery_enabled") @point_in_time_recovery_enabled.setter def point_in_time_recovery_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "point_in_time_recovery_enabled", value) @pulumi.input_type class TableProjectionArgs: def __init__(__self__, *, non_key_attributes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, projection_type: Optional[pulumi.Input[str]] = None): if non_key_attributes is not None: pulumi.set(__self__, "non_key_attributes", non_key_attributes) if projection_type is not None: pulumi.set(__self__, "projection_type", projection_type) @property @pulumi.getter(name="nonKeyAttributes") def non_key_attributes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "non_key_attributes") @non_key_attributes.setter def non_key_attributes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "non_key_attributes", value) @property @pulumi.getter(name="projectionType") def projection_type(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "projection_type") @projection_type.setter def projection_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "projection_type", value) @pulumi.input_type class TableProvisionedThroughputArgs: def __init__(__self__, *, read_capacity_units: pulumi.Input[int], write_capacity_units: pulumi.Input[int]): pulumi.set(__self__, "read_capacity_units", read_capacity_units) pulumi.set(__self__, "write_capacity_units", write_capacity_units) @property @pulumi.getter(name="readCapacityUnits") def read_capacity_units(self) -> pulumi.Input[int]: return pulumi.get(self, "read_capacity_units") @read_capacity_units.setter def read_capacity_units(self, value: pulumi.Input[int]): pulumi.set(self, "read_capacity_units", value) @property @pulumi.getter(name="writeCapacityUnits") def write_capacity_units(self) -> pulumi.Input[int]: return pulumi.get(self, "write_capacity_units") @write_capacity_units.setter def write_capacity_units(self, value: pulumi.Input[int]): pulumi.set(self, "write_capacity_units", value) @pulumi.input_type class TableSSESpecificationArgs: def __init__(__self__, *, s_se_enabled: pulumi.Input[bool], k_ms_master_key_id: Optional[pulumi.Input[str]] = None, s_se_type: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "s_se_enabled", s_se_enabled) if k_ms_master_key_id is not None: pulumi.set(__self__, "k_ms_master_key_id", k_ms_master_key_id) if s_se_type is not None: pulumi.set(__self__, "s_se_type", s_se_type) @property @pulumi.getter(name="sSEEnabled") def s_se_enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "s_se_enabled") @s_se_enabled.setter def s_se_enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "s_se_enabled", value) @property @pulumi.getter(name="kMSMasterKeyId") def k_ms_master_key_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "k_ms_master_key_id") @k_ms_master_key_id.setter def k_ms_master_key_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "k_ms_master_key_id", value) @property @pulumi.getter(name="sSEType") def s_se_type(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "s_se_type") @s_se_type.setter def s_se_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "s_se_type", value) @pulumi.input_type class TableStreamSpecificationArgs: def __init__(__self__, *, stream_view_type: pulumi.Input[str]): pulumi.set(__self__, "stream_view_type", stream_view_type) @property @pulumi.getter(name="streamViewType") def stream_view_type(self) -> pulumi.Input[str]: return pulumi.get(self, "stream_view_type") @stream_view_type.setter def stream_view_type(self, value: pulumi.Input[str]): pulumi.set(self, "stream_view_type", value) @pulumi.input_type class TableTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class TableTimeToLiveSpecificationArgs: def __init__(__self__, *, attribute_name: pulumi.Input[str], enabled: pulumi.Input[bool]): pulumi.set(__self__, "attribute_name", attribute_name) pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter(name="attributeName") def attribute_name(self) -> pulumi.Input[str]: return pulumi.get(self, "attribute_name") @attribute_name.setter def attribute_name(self, value: pulumi.Input[str]): pulumi.set(self, "attribute_name", value) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value)

39.453477

156

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0.069144

0.050528

0.828618

0.775356

0.748541

0.70311

0.672342

0.62536

0

0.00003

0.18013

40,282

1,020

157

39.492157

0.819748

0.003997

0

0.668317

1

0

0.194516

0.121625

0

1

0.216584

false

0

0.006188

0.089109

0.350248

0

null

0

1

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1

0

1

0

null

0

1

0

7

384a9e349e88c305a8a8ae1fc6d200c52d5864b0

1,395

py

Python

nautobot_ssot/migrations/0002_performance_metrics.py

smk4664/nautobot-plugin-ssot

7020d0d1910a09c408885bae9f9324fb91977928

[ "Apache-2.0" ]

9

2021-07-28T19:21:22.000Z

2022-02-16T10:00:36.000Z

nautobot_ssot/migrations/0002_performance_metrics.py

smk4664/nautobot-plugin-ssot

7020d0d1910a09c408885bae9f9324fb91977928

[ "Apache-2.0" ]

15

2021-11-10T07:18:59.000Z

2022-03-28T05:39:55.000Z

nautobot_ssot/migrations/0002_performance_metrics.py

smk4664/nautobot-plugin-ssot

7020d0d1910a09c408885bae9f9324fb91977928

[ "Apache-2.0" ]

6

2021-09-22T15:38:11.000Z

2022-03-15T14:46:14.000Z

# Generated by Django 3.1.13 on 2021-12-13 14:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("nautobot_ssot", "0001_initial"), ] operations = [ migrations.AddField( model_name="sync", name="diff_time", field=models.DurationField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="load_time_source", field=models.DurationField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="load_time_target", field=models.DurationField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="memory_peak", field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="memory_size", field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="memory_usage", field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name="sync", name="sync_time", field=models.DurationField(blank=True, null=True), ), ]

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0.551254

135

1,395

5.562963

0.318519

0.08522

0.214381

0.251664

0.76032

0.713715

0.648469

0

0.02139

0.329749

1,395

48

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29.0625

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false

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1

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null

0

7

698eaa6bca50ad74cb6cd234f3e80d030358b056

88

py

Python

03_Estrutura_de_Repeticao/06_imprime_vinte_numeros.py

gabrieldcpadilha/ListaDeExercicios-PythonBrasil

a92d477468bde5eac8987a26ea79af2ffeb6ad81

[ "MIT" ]

null

03_Estrutura_de_Repeticao/06_imprime_vinte_numeros.py

gabrieldcpadilha/ListaDeExercicios-PythonBrasil

a92d477468bde5eac8987a26ea79af2ffeb6ad81

[ "MIT" ]

10

2020-08-19T04:31:52.000Z

2020-09-21T22:48:29.000Z

03_Estrutura_de_Repeticao/06_imprime_vinte_numeros.py

gabrieldcpadilha/ListaDeExercicios-PythonBrasil

a92d477468bde5eac8987a26ea79af2ffeb6ad81

[ "MIT" ]

null

for n in range(1, 21): print(n) for n in range(1, 21): print(f"{n}", end=', ')

14.666667

27

0.5

18

88

2.444444

0.5

0.181818

0.272727

0.5

0.863636

0

0.092308

0.261364

88

5

28

17.6

0.584615

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0.5

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0.056818

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1

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false

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0

null

0

1

0

1

0

null

0

1

0

8

69bf2c35ee78c064112da892785606d9cec29fd2

146

py

Python

panoptes/analysis/axes/all.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

2

2017-07-24T05:11:59.000Z

2017-08-27T19:17:42.000Z

panoptes/analysis/axes/all.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

null

panoptes/analysis/axes/all.py

oberlin/panoptes

67d451ea4ffc58c23b5f347bfa5609fa7f853b45

[ "BSD-3-Clause" ]

null

from panoptes.analysis.axes.x import applications, days, hours, workstations from panoptes.analysis.axes.y import app_use, avg_session, sessions

36.5

76

0.828767

21

146

5.666667

0.761905

0.201681

0.336134

0.403361

0

0.09589

146

3

77

48.666667

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1

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1

0

null

0

1

0

1

0

7

69c5e31059156644a6fde78ba8bc910362beb4de

41

py

Python

to_classification.py

SkirOwen/aie

22c64983d53f193e70b045003cc872970ab56804

[ "MIT" ]

2

2021-03-24T04:02:32.000Z

2021-03-25T16:29:37.000Z

to_classification.py

SkirOwen/aie

22c64983d53f193e70b045003cc872970ab56804

[ "MIT" ]

2

2021-03-25T16:00:12.000Z

2021-04-01T00:40:26.000Z

to_classification.py

SkirOwen/aie

22c64983d53f193e70b045003cc872970ab56804

[ "MIT" ]

null

import numpy as np def n2a(): pass

6.833333

18

0.609756

7

41

3.571429

1

0

0.035714

0.317073

41

5

19

8.2

0.857143

0

1

0.333333

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

7

385e6bb9993041b8a045764572d82b7438e2b123

130

py

Python

src/test/test_todolist.py

eu-snehagupta/todolist

c476f35930cb58c182b8e771a347883a8623910f

[ "MIT" ]

null

src/test/test_todolist.py

eu-snehagupta/todolist

c476f35930cb58c182b8e771a347883a8623910f

[ "MIT" ]

7

2021-06-07T14:24:19.000Z

2021-06-14T14:05:13.000Z

src/test/test_todolist.py

eu-snehagupta/todolist

c476f35930cb58c182b8e771a347883a8623910f

[ "MIT" ]

null

import pytest from src.main import todolist def get_list_when_empty(): # tasklist = todolist.Todolist.get_list() pass

13

45

0.730769

18

130

5.055556

0.722222

0.153846

0

0.192308

130

9

46

14.444444

0.866667

0.3

0

0.111111

0

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0.25

true

0.25

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0.75

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null

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null

0

1

0

1

0

1

0

8

387a3d7f19c4aa2fd99e4f3c0db420c386b34b01

30,622

py

Python

faceplate_emulator/host/vwradio/tests/test_radios.py

mullets/vwradio

ac5e95579ac16389e59e8c1a45b41b5e0933cad4

[ "BSD-3-Clause" ]

45

2018-06-16T07:52:05.000Z

2022-03-08T15:55:02.000Z

faceplate_emulator/host/vwradio/tests/test_radios.py

matt2005/vwradio

ac5e95579ac16389e59e8c1a45b41b5e0933cad4

[ "BSD-3-Clause" ]

1

2018-03-28T17:13:53.000Z

2019-03-02T19:06:34.000Z

faceplate_emulator/host/vwradio/tests/test_radios.py

matt2005/vwradio

ac5e95579ac16389e59e8c1a45b41b5e0933cad4

[ "BSD-3-Clause" ]

8

2018-06-15T09:22:44.000Z

2021-09-23T20:16:54.000Z

import unittest from vwradio.radios import Radio from vwradio.constants import OperationModes, DisplayModes, TunerBands class TestRadio(unittest.TestCase): def test_safe_mode(self): values = ( # Premium 4 (b" 0000 ", 0, 0, OperationModes.SAFE_ENTRY), (b"1 1234 ", 1234, 1, OperationModes.SAFE_ENTRY), (b"2 5678 ", 5678, 2, OperationModes.SAFE_ENTRY), (b"9 9999 ", 9999, 9, OperationModes.SAFE_ENTRY), (b" NO CODE", 0, 0, OperationModes.SAFE_NO_CODE), # Premium 5 (b" 0000 ", 0, 0, OperationModes.SAFE_ENTRY), (b"1 1234 ", 1234, 1, OperationModes.SAFE_ENTRY), (b"2 5678 ", 5678, 2, OperationModes.SAFE_ENTRY), (b"9 9999 ", 9999, 9, OperationModes.SAFE_ENTRY), # Premium 4 and 5 (b" SAFE ", 1000, 0, OperationModes.SAFE_LOCKED), (b"1 SAFE ", 1000, 1, OperationModes.SAFE_LOCKED), (b"2 SAFE ", 1000, 2, OperationModes.SAFE_LOCKED), (b"9 SAFE ", 1000, 9, OperationModes.SAFE_LOCKED), ) for display, safe_code, safe_tries, mode in values: radio = Radio() radio.parse(display) self.assertEqual(radio.safe_code, safe_code) self.assertEqual(radio.safe_tries, safe_tries) self.assertEqual(radio.operation_mode, mode) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_initial(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(b" INITIAL") self.assertEqual(radio.operation_mode, OperationModes.INITIALIZING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_monsoon_premium_5(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(b" MONSOON") self.assertEqual(radio.operation_mode, OperationModes.MONSOON) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_diag(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(b" DIAG ") self.assertEqual(radio.operation_mode, OperationModes.DIAGNOSTICS) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_sound_volume(self): displays = ( b"AM MIN ", b"AM MAX ", b"FM1 MIN ", b"FM1 MAX ", b"FM2 MIN ", b"FM2 MAX ", b"CD MIN ", b"CD MAX ", b"TAP MIN ", b"TAP MAX ", ) for display in displays: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_VOLUME) def test_sound_balance(self): values = ( (b"BAL LEFT 9", -9), (b"BAL LEFT 1", -1), (b"BAL CENTER ", 0), (b"BAL RIGHT 1", 1), (b"BAL RIGHT 9", 9), ) for display, balance in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_BALANCE) self.assertEqual(radio.sound_balance, balance) def test_sound_fade(self): values = ( (b"FADEREAR 9", -9), (b"FADEREAR 1", -1), (b"FADECENTER ", 0), (b"FADEFRONT 1", 1), (b"FADEFRONT 9", 9), ) for display, fade in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_FADE) self.assertEqual(radio.sound_fade, fade) def test_sound_bass(self): values = ( (b"BASS - 9 ", -9), (b"BASS - 1 ", -1), (b"BASS 0 ", 0), (b"BASS + 1 ", 1), (b"BASS + 9 ", 9), ) for display, bass in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_BASS) self.assertEqual(radio.sound_bass, bass) def test_sound_treble(self): values = ( (b"TREB - 9 ", -9), (b"TREB - 1 ", -1), (b"TREB 0 ", 0), (b"TREB + 1 ", 1), (b"TREB + 9 ", 9), ) for display, treble in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_TREBLE) self.assertEqual(radio.sound_treble, treble) def test_sound_midrange_premium_5(self): values = ( (b"MID - 9 ", -9), (b"MID - 1 ", -1), (b"MID 0 ", 0), (b"MID + 1 ", 1), (b"MID + 9 ", 9), ) for display, mid in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(display) self.assertEqual(radio.sound_midrange, mid) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.ADJUSTING_SOUND_MIDRANGE) def test_set_option_on_vol(self): values = ( (b"SET ONVOL 0", 0), (b"SET ONVOL 1", 1), (b"SET ONVOL02", 2), (b"SET ONVOL13", 13), (b"SET ONVOL63", 63), (b"SET ONVOL99", 99), ) for display, on_vol in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.option_on_vol, on_vol) self.assertEqual(radio.operation_mode, OperationModes.SETTING_ON_VOL) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_set_option_cd_mix(self): values = ( (b"SET CD MIX1", 1), (b"SET CD MIX6", 6), ) for display, cd_mix in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.option_cd_mix, cd_mix) self.assertEqual(radio.operation_mode, OperationModes.SETTING_CD_MIX) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_set_option_tape_skip(self): values = ( (b"TAPE SKIP N", 0), (b"TAPE SKIP Y", 1), ) for display, tape_skip in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.option_tape_skip, tape_skip) self.assertEqual(radio.operation_mode, OperationModes.SETTING_TAPE_SKIP) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_test_fern(self): values = ( (b"FERN OFF ", 0), (b"FERN ON ", 1), ) for display, fern in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.test_fern, fern) self.assertEqual(radio.operation_mode, OperationModes.TESTING_FERN) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_test_rad(self): values = ( (b"RAD 3CP T7 ", b"3CP T7 "), # Premium 4 (b"RAD DE2 ", b" DE2 "), # Premium 5 (b"RAD 0123456", b"0123456"), ) for display, rad in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.test_rad, rad) self.assertEqual(radio.operation_mode, OperationModes.TESTING_RAD) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_test_ver(self): values = ( (b"VER 0702 ", b" 0702 "), # Premium 4 (b"VersA99CZ23", b"A99CZ23"), # Premium 5 (b"VER ABCDEFG", b"ABCDEFG"), ) for display, ver in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.test_ver, ver) self.assertEqual(radio.operation_mode, OperationModes.TESTING_VER) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_test_signal_premium5(self): values = ( (b" 5300 2 6 F", 530, 0x026F), # AM 530 KHz (b"17100 1 2 3", 1710, 0x0123), # AM 1710 KHz (b" 8770 5 3 0", 877, 0x0530), # FM 87.7 MHz (b"10770 6 4 0", 1077, 0x0640), # FM 107.7 MHz (b"1077A B C D", 1077, 0xABCD), (b"1077E F 1 2", 1077, 0xEF12), (b"10770 0 0 0", 1077, 0x0000), (b"1077F F F F", 1077, 0xFFFF), ) for display, freq, strength in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.ADJUSTING_SOUND_VOLUME # parse display radio.parse(display) self.assertEqual(radio.test_signal_freq, freq) self.assertEqual(radio.test_signal_strength, strength) self.assertEqual(radio.operation_mode, OperationModes.TESTING_SIGNAL) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_playing(self): values = ( (b"CD 1 TR 01 ", 1, 1), (b"CD 6 TR 99 ", 6, 99), ) for display, disc, track in values: radio = Radio() radio.parse(display) self.assertEqual(radio.cd_disc, disc) self.assertEqual(radio.cd_track, track) self.assertEqual(radio.operation_mode, OperationModes.CD_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_cue_rev_pos(self): values = ( (b"CUE 000 ", OperationModes.CD_CUE, 5, (0*60)+0), (b"CUE 001 ", OperationModes.CD_CUE, 5, (0*60)+1), (b"CUE 012 ", OperationModes.CD_CUE, 5, (0*60)+12), (b"CUE 123 ", OperationModes.CD_CUE, 5, (1*60)+23), (b"CUE 1234 ", OperationModes.CD_CUE, 5, (12*60)+34), (b"CUE 9999 ", OperationModes.CD_CUE, 5, (99*60)+99), (b"CUE -002 ", OperationModes.CD_CUE, 5, 0), (b"CUE -1234 ", OperationModes.CD_CUE, 5, 0), (b"REV 000 ", OperationModes.CD_REV, 5, (0*60)+0), (b"REV 001 ", OperationModes.CD_REV, 5, (0*60)+1), (b"REV 012 ", OperationModes.CD_REV, 5, (0*60)+12), (b"REV 123 ", OperationModes.CD_REV, 5, (1*60)+23), (b"REV 1234 ", OperationModes.CD_REV, 5, (12*60)+34), (b"REV 9999 ", OperationModes.CD_REV, 5, (99*60)+99), (b"REV -002 ", OperationModes.CD_REV, 5, 0), (b"REV -1234 ", OperationModes.CD_REV, 5, 0), (b"CD 2 000 ", OperationModes.CD_PLAYING, 2, (0*60)+0), (b"CD 2 001 ", OperationModes.CD_PLAYING, 2, (0*60)+1), (b"CD 2 012 ", OperationModes.CD_PLAYING, 2, (0*60)+12), (b"CD 2 123 ", OperationModes.CD_PLAYING, 2, (1*60)+23), (b"CD 2 1234 ", OperationModes.CD_PLAYING, 2, (12*60)+34), (b"CD 2 9999 ", OperationModes.CD_PLAYING, 2, (99*60)+99), (b"CD 2 -002 ", OperationModes.CD_PLAYING, 2, 0), (b"CD 2-1234 ", OperationModes.CD_PLAYING, 2, 0), ) for display, operation_mode, cd_disc, cd_track_pos in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 12 # parse display radio.parse(display) self.assertEqual(radio.cd_disc, cd_disc) self.assertEqual(radio.cd_track, 12) self.assertEqual(radio.cd_track_pos, cd_track_pos) self.assertEqual(radio.operation_mode, operation_mode) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_scanning(self): values = ( (b"SCANCD1TR04", 1, 4), (b"SCANCD3TR15", 3, 15), ) for display, disc, track in values: radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 12 radio.cd_track_pos = 42 # parse display radio.parse(display) self.assertEqual(radio.cd_disc, disc) self.assertEqual(radio.cd_track, track) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_check_magazine(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 1 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(b"CHK MAGAZIN") self.assertEqual(radio.cd_disc, 0) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_CHECK_MAGAZINE) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_cdx_no_cd(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 1 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(b"CD 2 NO CD ") # space in "CD 2" self.assertEqual(radio.cd_disc, 2) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_CDX_NO_CD) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_cdx_cd_err(self): displays = ( b"CD1 CD ERR ", # Premium 4 b"CD 1CD ERR ", # Premium 5 ) for display in displays: radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(display) self.assertEqual(radio.cd_disc, 1) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_CDX_CD_ERR) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_no_disc(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(b" NO DISC") self.assertEqual(radio.cd_disc, 0) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_NO_DISC) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_no_changer(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(b"NO CHANGER") self.assertEqual(radio.cd_disc, 0) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_NO_CHANGER) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_cd_no_magazine(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.CD_PLAYING radio.cd_disc = 5 radio.cd_track = 3 radio.cd_track_pos = 99 # parse display radio.parse(b"NO MAGAZIN") self.assertEqual(radio.cd_disc, 0) self.assertEqual(radio.cd_track, 0) self.assertEqual(radio.cd_track_pos, 0) self.assertEqual(radio.operation_mode, OperationModes.CD_NO_MAGAZINE) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_load_premium_5(self): radio = Radio() # set up known values radio.tape_side = 1 # parse display radio.parse(b"TAPE LOAD ") self.assertEqual(radio.tape_side, 0) self.assertEqual(radio.operation_mode, OperationModes.TAPE_LOAD) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_metal_premium_5(self): radio = Radio() # set up known values radio.tape_side = 1 radio.operation_mode = OperationModes.TAPE_PLAYING # parse display radio.parse(b"TAPE METAL ") self.assertEqual(radio.tape_side, 1) self.assertEqual(radio.operation_mode, OperationModes.TAPE_METAL) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_bls(self): radio = Radio() # set up known values radio.tape_side = 2 radio.operation_mode = OperationModes.TAPE_PLAYING # parse display radio.parse(b"TAPE BLS ") self.assertEqual(radio.tape_side, 2) self.assertEqual(radio.operation_mode, OperationModes.TAPE_BLS) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_play_a(self): radio = Radio() # set up known values radio.tape_side = 2 radio.operation_mode = OperationModes.TUNER_PLAYING # parse display radio.parse(b"TAPE PLAY A") self.assertEqual(radio.tape_side, 1) self.assertEqual(radio.operation_mode, OperationModes.TAPE_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_play_b(self): radio = Radio() # set up known values radio.tape_side = 1 radio.operation_mode = OperationModes.TUNER_PLAYING # parse display radio.parse(b"TAPE PLAY B") self.assertEqual(radio.tape_side, 2) self.assertEqual(radio.operation_mode, OperationModes.TAPE_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_scan_a(self): radio = Radio() # set up known values radio.tape_side = 2 radio.operation_mode = OperationModes.TUNER_PLAYING # parse display radio.parse(b"TAPE SCAN A") self.assertEqual(radio.tape_side, 1) self.assertEqual(radio.operation_mode, OperationModes.TAPE_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_scan_b(self): radio = Radio() # set up known values radio.tape_side = 1 radio.operation_mode = OperationModes.TUNER_PLAYING # parse display radio.parse(b"TAPE SCAN B") self.assertEqual(radio.tape_side, 2) self.assertEqual(radio.operation_mode, OperationModes.TAPE_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_ff(self): radio = Radio() # set up known values radio.tape_side = 1 # parse display radio.parse(b"TAPE FF ") self.assertEqual(radio.tape_side, 1) self.assertEqual(radio.operation_mode, OperationModes.TAPE_FF) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_mss_ff(self): radio = Radio() # set up known values radio.tape_side = 2 # parse display radio.parse(b"TAPEMSS FF ") self.assertEqual(radio.tape_side, 2) self.assertEqual(radio.operation_mode, OperationModes.TAPE_MSS_FF) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_rew(self): radio = Radio() # set up known values radio.tape_side = 1 # parse display radio.parse(b"TAPE REW ") self.assertEqual(radio.tape_side, 1) self.assertEqual(radio.operation_mode, OperationModes.TAPE_REW) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_mss_rew(self): radio = Radio() # set up known values radio.tape_side = 2 # parse display radio.parse(b"TAPEMSS REW") self.assertEqual(radio.tape_side, 2) self.assertEqual(radio.operation_mode, OperationModes.TAPE_MSS_REW) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_error(self): radio = Radio() # set up known values radio.tape_side = 1 # parse display radio.parse(b"TAPE ERROR ") self.assertEqual(radio.tape_side, 0) self.assertEqual(radio.operation_mode, OperationModes.TAPE_ERROR) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tape_no_tape(self): radio = Radio() # set up known values radio.tape_side = 1 # parse display radio.parse(b" NO TAPE") self.assertEqual(radio.tape_side, 0) self.assertEqual(radio.operation_mode, OperationModes.TAPE_NO_TAPE) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_fm_scan_off(self): values = ( (b"FM1 887MHz", 887, TunerBands.FM1, 0), (b"FM1 887MHZ", 887, TunerBands.FM1, 0), (b"FM1 1023MHZ", 1023, TunerBands.FM1, 0), (b"FM11 915MHZ", 915, TunerBands.FM1, 1), (b"FM161079MHZ", 1079, TunerBands.FM1, 6), (b"FM2 887MHZ", 887, TunerBands.FM2, 0), (b"FM2 1023MHZ", 1023, TunerBands.FM2, 0), (b"FM21 915MHZ", 915, TunerBands.FM2, 1), (b"FM261079MHZ", 1079, TunerBands.FM2, 6), ) for display, freq, band, preset in values: radio = Radio() radio.parse(display) self.assertEqual(radio.tuner_band, band) self.assertEqual(radio.tuner_freq, freq) self.assertEqual(radio.tuner_preset, preset) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_fm_scan_on_fm1_band(self): radio = Radio() # set up known values radio.tuner_band = TunerBands.FM1 radio.tuner_freq = 915 radio.tuner_preset = 1 # parse display radio.parse(b"SCAN 879MHZ") self.assertEqual(radio.tuner_freq, 879) self.assertEqual(radio.tuner_preset, 0) self.assertEqual(radio.tuner_band, TunerBands.FM1) self.assertEqual(radio.operation_mode, OperationModes.TUNER_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_fm_scan_on_fm2_band(self): radio = Radio() # set up known values radio.tuner_band = TunerBands.FM2 radio.tuner_freq = 915 radio.tuner_preset = 1 # parse display radio.parse(b"SCAN1035MHZ") self.assertEqual(radio.tuner_freq, 1035) self.assertEqual(radio.tuner_preset, 0) self.assertEqual(radio.tuner_band, TunerBands.FM2) self.assertEqual(radio.operation_mode, OperationModes.TUNER_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_fm_scan_on_unknown_band_sets_fm1(self): radio = Radio() self.assertEqual(radio.tuner_band, TunerBands.UNKNOWN) radio.parse(b"SCAN 879MHZ") self.assertEqual(radio.tuner_freq, 879) self.assertEqual(radio.tuner_preset, 0) self.assertEqual(radio.tuner_band, TunerBands.FM1) self.assertEqual(radio.operation_mode, OperationModes.TUNER_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_am_scan_off(self): values = ( (b"AM 670kHz", 670, 0), (b"AM 670KHZ", 670, 0), (b"AM 1540KHZ", 1540, 0), (b"AM 1 670KHZ", 670, 1), (b"AM 61540KHZ", 1540, 6), ) for display, freq, preset in values: radio = Radio() radio.parse(display) self.assertEqual(radio.tuner_freq, freq) self.assertEqual(radio.tuner_preset, preset) self.assertEqual(radio.tuner_band, TunerBands.AM) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_tuner_am_scan_on(self): values = ( (b"SCAN 530kHz", 530), (b"SCAN1710KHZ", 1710), ) for display, freq in values: radio = Radio() radio.tuner_band = TunerBands.AM radio.parse(display) self.assertEqual(radio.tuner_freq, freq) self.assertEqual(radio.tuner_band, TunerBands.AM) self.assertEqual(radio.tuner_preset, 0) self.assertEqual(radio.operation_mode, OperationModes.TUNER_SCANNING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION) def test_ignores_blank(self): radio = Radio() # set up known values radio.operation_mode = OperationModes.TUNER_PLAYING radio.display_mode = DisplayModes.SHOWING_OPERATION # parse display radio.parse(b" " * 11) self.assertEqual(radio.operation_mode, OperationModes.TUNER_PLAYING) self.assertEqual(radio.display_mode, DisplayModes.SHOWING_OPERATION)

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3882604e53e6064936b2cdae0370ecfe7ea5fcbe

21,333

py

Python

util/data/gen/GameAssembly.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/GameAssembly.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/GameAssembly.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'CloseZStream', 'address': '0x7ffaa6a3f0f0'}, {'type': 'function', 'name': 'CreateZStream', 'address': '0x7ffaa6a3f1a0'}, {'type': 'function', 'name': 'DllCanUnloadNow', 'address': '0x7ffaa6a1f2d0'}, {'type': 'function', 'name': 'DllGetActivationFactory', 'address': '0x7ffaa6a1f2e0'}, {'type': 'function', 'name': 'Flush', 'address': '0x7ffaa6a3f2c0'}, {'type': 'function', 'name': 'ReadZStream', 'address': '0x7ffaa6a3f300'}, {'type': 'function', 'name': 'UnityPalGetLocalTimeZoneData', 'address': '0x7ffaa69d50c0'}, {'type': 'function', 'name': 'UnityPalGetTimeZoneDataForID', 'address': '0x7ffaa69d50d0'}, {'type': 'function', 'name': 'UnityPalTimeZoneInfoGetTimeZoneIDs', 'address': '0x7ffaa69b1030'}, {'type': 'function', 'name': 'UseUnityPalForTimeZoneInformation', 'address': '0x7ffaa69b1030'}, {'type': 'function', 'name': 'WriteZStream', 'address': '0x7ffaa6a3f3c0'}, {'type': 'function', 'name': 'il2cpp_add_internal_call', 'address': '0x7ffaa69c8180'}, {'type': 'function', 'name': 'il2cpp_alloc', 'address': '0x7ffaa69c8190'}, {'type': 'function', 'name': 'il2cpp_allocation_granularity', 'address': '0x7ffaa69605a0'}, {'type': 'function', 'name': 'il2cpp_array_class_get', 'address': '0x7ffaa69c81a0'}, {'type': 'function', 'name': 'il2cpp_array_element_size', 'address': '0x7ffaa69c81b0'}, {'type': 'function', 'name': 'il2cpp_array_get_byte_length', 'address': '0x7ffaa69c81c0'}, {'type': 'function', 'name': 'il2cpp_array_length', 'address': '0x7ffaa69c81d0'}, {'type': 'function', 'name': 'il2cpp_array_new', 'address': '0x7ffaa69c81e0'}, {'type': 'function', 'name': 'il2cpp_array_new_full', 'address': '0x7ffaa69c81f0'}, {'type': 'function', 'name': 'il2cpp_array_new_specific', 'address': '0x7ffaa69c8200'}, {'type': 'function', 'name': 'il2cpp_array_object_header_size', 'address': '0x7ffaa69c8210'}, {'type': 'function', 'name': 'il2cpp_assembly_get_image', 'address': '0x7ffaa69c8220'}, {'type': 'function', 'name': 'il2cpp_bounded_array_class_get', 'address': '0x7ffaa69c8230'}, {'type': 'function', 'name': 'il2cpp_capture_memory_snapshot', 'address': '0x7ffaa69c8240'}, {'type': 'function', 'name': 'il2cpp_class_array_element_size', 'address': '0x7ffaa69c8250'}, {'type': 'function', 'name': 'il2cpp_class_enum_basetype', 'address': '0x7ffaa69c8260'}, {'type': 'function', 'name': 'il2cpp_class_for_each', 'address': '0x7ffaa69c8270'}, {'type': 'function', 'name': 'il2cpp_class_from_il2cpp_type', 'address': '0x7ffaa69c8280'}, {'type': 'function', 'name': 'il2cpp_class_from_name', 'address': '0x7ffaa69c8290'}, {'type': 'function', 'name': 'il2cpp_class_from_system_type', 'address': '0x7ffaa69c82a0'}, {'type': 'function', 'name': 'il2cpp_class_from_type', 'address': '0x7ffaa69c8280'}, {'type': 'function', 'name': 'il2cpp_class_get_assemblyname', 'address': '0x7ffaa69c82b0'}, {'type': 'function', 'name': 'il2cpp_class_get_bitmap', 'address': '0x7ffaa69c82c0'}, {'type': 'function', 'name': 'il2cpp_class_get_bitmap_size', 'address': '0x7ffaa69c82e0'}, {'type': 'function', 'name': 'il2cpp_class_get_data_size', 'address': '0x7ffaa69c82f0'}, {'type': 'function', 'name': 'il2cpp_class_get_declaring_type', 'address': '0x7ffaa69c8300'}, {'type': 'function', 'name': 'il2cpp_class_get_element_class', 'address': '0x7ffaa69c8310'}, {'type': 'function', 'name': 'il2cpp_class_get_events', 'address': '0x7ffaa69c8320'}, {'type': 'function', 'name': 'il2cpp_class_get_field_from_name', 'address': '0x7ffaa69c8330'}, {'type': 'function', 'name': 'il2cpp_class_get_fields', 'address': '0x7ffaa69c8340'}, {'type': 'function', 'name': 'il2cpp_class_get_flags', 'address': '0x7ffaa69c8350'}, {'type': 'function', 'name': 'il2cpp_class_get_image', 'address': '0x7ffaa69c8220'}, {'type': 'function', 'name': 'il2cpp_class_get_interfaces', 'address': '0x7ffaa69c8360'}, {'type': 'function', 'name': 'il2cpp_class_get_method_from_name', 'address': '0x7ffaa69c8370'}, {'type': 'function', 'name': 'il2cpp_class_get_methods', 'address': '0x7ffaa69c8380'}, {'type': 'function', 'name': 'il2cpp_class_get_name', 'address': '0x7ffaa69c8390'}, {'type': 'function', 'name': 'il2cpp_class_get_namespace', 'address': '0x7ffaa69c83a0'}, {'type': 'function', 'name': 'il2cpp_class_get_nested_types', 'address': '0x7ffaa69c83b0'}, {'type': 'function', 'name': 'il2cpp_class_get_parent', 'address': '0x7ffaa69c83c0'}, {'type': 'function', 'name': 'il2cpp_class_get_properties', 'address': '0x7ffaa69c83d0'}, {'type': 'function', 'name': 'il2cpp_class_get_property_from_name', 'address': '0x7ffaa69c83e0'}, {'type': 'function', 'name': 'il2cpp_class_get_rank', 'address': '0x7ffaa69c83f0'}, {'type': 'function', 'name': 'il2cpp_class_get_static_field_data', 'address': '0x7ffaa68f3de0'}, {'type': 'function', 'name': 'il2cpp_class_get_type', 'address': '0x7ffaa69c8400'}, {'type': 'function', 'name': 'il2cpp_class_get_type_token', 'address': '0x7ffaa69c8410'}, {'type': 'function', 'name': 'il2cpp_class_get_userdata_offset', 'address': '0x7ffaa69c8420'}, {'type': 'function', 'name': 'il2cpp_class_has_attribute', 'address': '0x7ffaa69c8430'}, {'type': 'function', 'name': 'il2cpp_class_has_parent', 'address': '0x7ffaa69c8440'}, {'type': 'function', 'name': 'il2cpp_class_has_references', 'address': '0x7ffaa69c8450'}, {'type': 'function', 'name': 'il2cpp_class_instance_size', 'address': '0x7ffaa69c8460'}, {'type': 'function', 'name': 'il2cpp_class_is_abstract', 'address': '0x7ffaa69c8470'}, {'type': 'function', 'name': 'il2cpp_class_is_assignable_from', 'address': '0x7ffaa69c8480'}, {'type': 'function', 'name': 'il2cpp_class_is_blittable', 'address': '0x7ffaa69c8490'}, {'type': 'function', 'name': 'il2cpp_class_is_enum', 'address': '0x7ffaa69c84a0'}, {'type': 'function', 'name': 'il2cpp_class_is_generic', 'address': '0x7ffaa69c84b0'}, {'type': 'function', 'name': 'il2cpp_class_is_inflated', 'address': '0x7ffaa69c84c0'}, {'type': 'function', 'name': 'il2cpp_class_is_interface', 'address': '0x7ffaa69c84d0'}, {'type': 'function', 'name': 'il2cpp_class_is_subclass_of', 'address': '0x7ffaa69c84e0'}, {'type': 'function', 'name': 'il2cpp_class_is_valuetype', 'address': '0x7ffaa69c84f0'}, {'type': 'function', 'name': 'il2cpp_class_num_fields', 'address': '0x7ffaa69c8500'}, {'type': 'function', 'name': 'il2cpp_class_set_userdata', 'address': '0x7ffaa69c8510'}, {'type': 'function', 'name': 'il2cpp_class_value_size', 'address': '0x7ffaa69c8520'}, {'type': 'function', 'name': 'il2cpp_current_thread_get_frame_at', 'address': '0x7ffaa69c8530'}, {'type': 'function', 'name': 'il2cpp_current_thread_get_stack_depth', 'address': '0x7ffaa69c8540'}, {'type': 'function', 'name': 'il2cpp_current_thread_get_top_frame', 'address': '0x7ffaa69c8560'}, {'type': 'function', 'name': 'il2cpp_current_thread_walk_frame_stack', 'address': '0x7ffaa69c8570'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_construct', 'address': '0x7ffaa69c8580'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_free', 'address': '0x7ffaa68f49c0'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_from_class', 'address': '0x7ffaa69c8590'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_from_method', 'address': '0x7ffaa69c85b0'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_get_attr', 'address': '0x7ffaa69c85d0'}, {'type': 'function', 'name': 'il2cpp_custom_attrs_has_attr', 'address': '0x7ffaa69c85e0'}, {'type': 'function', 'name': 'il2cpp_debug_get_method_info', 'address': '0x7ffaa69c85f0'}, {'type': 'function', 'name': 'il2cpp_debugger_set_agent_options', 'address': '0x7ffaa68f49c0'}, {'type': 'function', 'name': 'il2cpp_domain_assembly_open', 'address': '0x7ffaa69c8600'}, {'type': 'function', 'name': 'il2cpp_domain_get', 'address': '0x7ffaa69c8610'}, {'type': 'function', 'name': 'il2cpp_domain_get_assemblies', 'address': '0x7ffaa69c8620'}, {'type': 'function', 'name': 'il2cpp_exception_from_name_msg', 'address': '0x7ffaa69c8650'}, {'type': 'function', 'name': 'il2cpp_field_get_flags', 'address': '0x7ffaa69c8660'}, {'type': 'function', 'name': 'il2cpp_field_get_name', 'address': '0x7ffaa69c8220'}, {'type': 'function', 'name': 'il2cpp_field_get_offset', 'address': '0x7ffaa69c8670'}, {'type': 'function', 'name': 'il2cpp_field_get_parent', 'address': '0x7ffaa69c8390'}, {'type': 'function', 'name': 'il2cpp_field_get_type', 'address': '0x7ffaa69c8680'}, {'type': 'function', 'name': 'il2cpp_field_get_value', 'address': '0x7ffaa69c8690'}, {'type': 'function', 'name': 'il2cpp_field_get_value_object', 'address': '0x7ffaa69c86a0'}, {'type': 'function', 'name': 'il2cpp_field_has_attribute', 'address': '0x7ffaa69c86b0'}, {'type': 'function', 'name': 'il2cpp_field_is_literal', 'address': '0x7ffaa69c86c0'}, {'type': 'function', 'name': 'il2cpp_field_set_value', 'address': '0x7ffaa69c86d0'}, {'type': 'function', 'name': 'il2cpp_field_set_value_object', 'address': '0x7ffaa69c86e0'}, {'type': 'function', 'name': 'il2cpp_field_static_get_value', 'address': '0x7ffaa69c86f0'}, {'type': 'function', 'name': 'il2cpp_field_static_set_value', 'address': '0x7ffaa69c8700'}, {'type': 'function', 'name': 'il2cpp_format_exception', 'address': '0x7ffaa69c8710'}, {'type': 'function', 'name': 'il2cpp_format_stack_trace', 'address': '0x7ffaa69c87a0'}, {'type': 'function', 'name': 'il2cpp_free', 'address': '0x7ffaa69c8830'}, {'type': 'function', 'name': 'il2cpp_free_captured_memory_snapshot', 'address': '0x7ffaa69c8840'}, {'type': 'function', 'name': 'il2cpp_gc_collect', 'address': '0x7ffaa69c8850'}, {'type': 'function', 'name': 'il2cpp_gc_collect_a_little', 'address': '0x7ffaa69c8860'}, {'type': 'function', 'name': 'il2cpp_gc_disable', 'address': '0x7ffaa69c8870'}, {'type': 'function', 'name': 'il2cpp_gc_enable', 'address': '0x7ffaa69c8880'}, {'type': 'function', 'name': 'il2cpp_gc_foreach_heap', 'address': '0x7ffaa69c8890'}, {'type': 'function', 'name': 'il2cpp_gc_get_heap_size', 'address': '0x7ffaa69c88c0'}, {'type': 'function', 'name': 'il2cpp_gc_get_max_time_slice_ns', 'address': '0x7ffaa69c88d0'}, {'type': 'function', 'name': 'il2cpp_gc_get_used_size', 'address': '0x7ffaa69c88e0'}, {'type': 'function', 'name': 'il2cpp_gc_has_strict_wbarriers', 'address': '0x7ffaa6958400'}, {'type': 'function', 'name': 'il2cpp_gc_is_disabled', 'address': '0x7ffaa69c88f0'}, {'type': 'function', 'name': 'il2cpp_gc_is_incremental', 'address': '0x7ffaa69c8900'}, {'type': 'function', 'name': 'il2cpp_gc_set_external_allocation_tracker', 'address': '0x7ffaa68f49c0'}, {'type': 'function', 'name': 'il2cpp_gc_set_external_wbarrier_tracker', 'address': '0x7ffaa68f49c0'}, {'type': 'function', 'name': 'il2cpp_gc_set_max_time_slice_ns', 'address': '0x7ffaa69c8910'}, {'type': 'function', 'name': 'il2cpp_gc_wbarrier_set_field', 'address': '0x7ffaa69c8920'}, {'type': 'function', 'name': 'il2cpp_gchandle_foreach_get_target', 'address': '0x7ffaa69c8930'}, {'type': 'function', 'name': 'il2cpp_gchandle_free', 'address': 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'function', 'name': 'il2cpp_init', 'address': '0x7ffaa69c89f0'}, {'type': 'function', 'name': 'il2cpp_init_utf16', 'address': '0x7ffaa69c8a20'}, {'type': 'function', 'name': 'il2cpp_is_debugger_attached', 'address': '0x7ffaa69c8ab0'}, {'type': 'function', 'name': 'il2cpp_is_vm_thread', 'address': '0x7ffaa69c8ac0'}, {'type': 'function', 'name': 'il2cpp_method_get_class', 'address': '0x7ffaa69c83a0'}, {'type': 'function', 'name': 'il2cpp_method_get_declaring_type', 'address': '0x7ffaa69c83a0'}, {'type': 'function', 'name': 'il2cpp_method_get_flags', 'address': '0x7ffaa69c8ad0'}, {'type': 'function', 'name': 'il2cpp_method_get_from_reflection', 'address': '0x7ffaa69c8390'}, {'type': 'function', 'name': 'il2cpp_method_get_name', 'address': '0x7ffaa69c8390'}, {'type': 'function', 'name': 'il2cpp_method_get_object', 'address': '0x7ffaa69c8b00'}, {'type': 'function', 'name': 'il2cpp_method_get_param', 'address': '0x7ffaa69c8b10'}, {'type': 'function', 'name': 'il2cpp_method_get_param_count', 'address': '0x7ffaa69c8b20'}, {'type': 'function', 'name': 'il2cpp_method_get_param_name', 'address': '0x7ffaa69c8b30'}, {'type': 'function', 'name': 'il2cpp_method_get_return_type', 'address': '0x7ffaa69c8b40'}, {'type': 'function', 'name': 'il2cpp_method_get_token', 'address': '0x7ffaa69c8b50'}, {'type': 'function', 'name': 'il2cpp_method_has_attribute', 'address': '0x7ffaa69c8b60'}, {'type': 'function', 'name': 'il2cpp_method_is_generic', 'address': '0x7ffaa69c8b70'}, {'type': 'function', 'name': 'il2cpp_method_is_inflated', 'address': '0x7ffaa69c8b80'}, {'type': 'function', 'name': 'il2cpp_method_is_instance', 'address': '0x7ffaa69c8b90'}, {'type': 'function', 'name': 'il2cpp_monitor_enter', 'address': '0x7ffaa69c8ba0'}, {'type': 'function', 'name': 'il2cpp_monitor_exit', 'address': '0x7ffaa69c8bb0'}, {'type': 'function', 'name': 'il2cpp_monitor_pulse', 'address': '0x7ffaa69c8bc0'}, {'type': 'function', 'name': 'il2cpp_monitor_pulse_all', 'address': '0x7ffaa69c8bd0'}, {'type': 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Python

seqgra/learner/tensorflow/keraslearner.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

null

seqgra/learner/tensorflow/keraslearner.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

null

seqgra/learner/tensorflow/keraslearner.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

2

2021-06-14T20:27:40.000Z

2021-06-14T20:29:29.000Z

"""MIT - CSAIL - Gifford Lab - seqgra TensorFlow Keras learners @author: Konstantin Krismer """ from distutils.util import strtobool from typing import Any, List, Optional import numpy as np import tensorflow as tf from seqgra import ModelSize from seqgra.learner import DNAMultiClassClassificationLearner from seqgra.learner import DNAMultiLabelClassificationLearner from seqgra.learner import ProteinMultiClassClassificationLearner from seqgra.learner import ProteinMultiLabelClassificationLearner from seqgra.learner.tensorflow import KerasHelper from seqgra.model import ModelDefinition class KerasDNAMultiClassClassificationLearner( DNAMultiClassClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) KerasHelper.init_tf_memory_policy() gpus = tf.config.list_physical_devices("GPU") self.use_cuda: bool = tf.test.is_built_with_gpu_support() and \ len(gpus) > 0 and gpu_id != -1 if self.use_cuda: tf.config.set_visible_devices(gpus[gpu_id], "GPU") self.device_label: str = "/GPU:" + str(gpu_id) else: self.device_label: str = "/CPU:0" self._check_task_loss_compatibility() def _check_task_loss_compatibility(self) -> None: if "loss" in self.definition.loss_hyperparameters: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if not loss in KerasHelper.MULTI_CLASS_CLASSIFICATION_LOSSES: self.logger.warning("loss function '%s' is incompatible with " "multi-class classification models", loss) def _get_output_layer_activation_function(self) -> Optional[str]: if "from_logits" in self.definition.loss_hyperparameters and \ "loss" in self.definition.loss_hyperparameters: from_logits: bool = bool(strtobool( self.definition.loss_hyperparameters["from_logits"])) if from_logits: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if loss == "categoricalcrossentropy" or \ loss == "sparsecategoricalcrossentropy": return "softmax" elif loss == "binarycrossentropy": self.logger.warning("activation function 'sigmoid' is " "incompatible with multi-class " "classification models") return "sigmoid" return None def create_model(self) -> None: KerasHelper.create_model(self) def print_model_summary(self): KerasHelper.print_model_summary(self) def set_seed(self) -> None: KerasHelper.set_seed(self) def _train_model(self, file_name_train: Optional[str] = None, file_name_val: Optional[str] = None, x_train: Optional[List[str]] = None, y_train: Optional[List[str]] = None, x_val: Optional[List[str]] = None, y_val: Optional[List[str]] = None) -> None: if x_train is not None and y_train is not None: training_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_train), self.encode_y(y_train))) elif file_name_train is not None: seq_len: int = self.get_sequence_length(file_name_train) def train_generator(): return self.dataset_generator(file_name_train) training_dataset = tf.data.Dataset.from_generator( train_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_train or x_train, y_train") if x_val is not None and y_val is not None: validation_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_val), self.encode_y(y_val))) elif file_name_val is not None: seq_len: int = self.get_sequence_length(file_name_val) def val_generator(): return self.dataset_generator(file_name_val) validation_dataset = tf.data.Dataset.from_generator( val_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_val or x_val, y_val") KerasHelper.train_model(self, training_dataset, validation_dataset, self.silent) def evaluate_model(self, file_name: Optional[str] = None, x: Optional[List[str]] = None, y: Optional[List[str]] = None): if x is not None and y is not None: dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x), self.encode_y(y))) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name or x, y") return KerasHelper.evaluate_model(self, dataset) def predict(self, file_name: Optional[str] = None, x: Optional[Any] = None, encode: bool = True): if x is not None: if encode: x = self.encode_x(x) dataset = tf.data.Dataset.from_tensor_slices((x)) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]))) else: raise Exception("specify either file_name or x") return KerasHelper.predict(self, dataset) def save_model(self, file_name: Optional[str] = None) -> None: KerasHelper.save_model(self, file_name) def write_session_info(self) -> None: KerasHelper.write_session_info(self) def load_model(self, file_name: Optional[str] = None) -> None: KerasHelper.load_model(self, file_name) def get_num_params(self) -> ModelSize: return KerasHelper.get_num_params(self) def encode_x(self, x: List[str]): encoded_x = super().encode_x(x) if self.definition.input_encoding == "2D": # from (N, W, C) to (N, H, W, C) encoded_x = np.expand_dims(encoded_x, axis=1) return encoded_x def decode_x(self, x): if self.definition.input_encoding == "2D": # from (N, H, W, C) to (N, W, C) x = np.squeeze(x, axis=1) return super().decode_x(x) class KerasDNAMultiLabelClassificationLearner( DNAMultiLabelClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) KerasHelper.init_tf_memory_policy() gpus = tf.config.list_physical_devices("GPU") self.use_cuda: bool = tf.test.is_built_with_gpu_support() and \ len(gpus) > 0 and gpu_id != -1 if self.use_cuda: tf.config.set_visible_devices(gpus[gpu_id], "GPU") self.device_label: str = "/GPU:" + str(gpu_id) else: self.device_label: str = "/CPU:0" self._check_task_loss_compatibility() def _check_task_loss_compatibility(self) -> None: if "loss" in self.definition.loss_hyperparameters: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if not loss in KerasHelper.MULTI_LABEL_CLASSIFICATION_LOSSES: self.logger.warning("loss function '%s' is incompatible with " "multi-label classification models", loss) def _get_output_layer_activation_function(self) -> Optional[str]: if "from_logits" in self.definition.loss_hyperparameters and \ "loss" in self.definition.loss_hyperparameters: from_logits: bool = bool(strtobool( self.definition.loss_hyperparameters["from_logits"])) if from_logits: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if loss == "categoricalcrossentropy" or \ loss == "sparsecategoricalcrossentropy": self.logger.warning("activation function 'sofmax' is " "incompatible with multi-label " "classification models") return "softmax" elif loss == "binarycrossentropy": return "sigmoid" return None def create_model(self) -> None: KerasHelper.create_model(self) def print_model_summary(self): KerasHelper.print_model_summary(self) def set_seed(self) -> None: KerasHelper.set_seed(self) def _train_model(self, file_name_train: Optional[str] = None, file_name_val: Optional[str] = None, x_train: Optional[List[str]] = None, y_train: Optional[List[str]] = None, x_val: Optional[List[str]] = None, y_val: Optional[List[str]] = None) -> None: if x_train is not None and y_train is not None: training_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_train), self.encode_y(y_train))) elif file_name_train is not None: seq_len: int = self.get_sequence_length(file_name_train) def train_generator(): return self.dataset_generator(file_name_train) training_dataset = tf.data.Dataset.from_generator( train_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_train or x_train, y_train") if x_val is not None and y_val is not None: validation_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_val), self.encode_y(y_val))) elif file_name_val is not None: seq_len: int = self.get_sequence_length(file_name_val) def val_generator(): return self.dataset_generator(file_name_val) validation_dataset = tf.data.Dataset.from_generator( val_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_val or x_val, y_val") KerasHelper.train_model(self, training_dataset, validation_dataset, self.silent) def evaluate_model(self, file_name: Optional[str] = None, x: Optional[List[str]] = None, y: Optional[List[str]] = None): if x is not None and y is not None: dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x), self.encode_y(y))) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name or x, y") return KerasHelper.evaluate_model(self, dataset) def predict(self, file_name: Optional[str] = None, x: Optional[Any] = None, encode: bool = True): if x is not None: if encode: x = self.encode_x(x) dataset = tf.data.Dataset.from_tensor_slices((x)) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]))) else: raise Exception("specify either file_name or x") return KerasHelper.predict(self, dataset) def save_model(self, file_name: Optional[str] = None) -> None: KerasHelper.save_model(self, file_name) def write_session_info(self) -> None: KerasHelper.write_session_info(self) def load_model(self, file_name: Optional[str] = None) -> None: KerasHelper.load_model(self, file_name) def get_num_params(self) -> ModelSize: return KerasHelper.get_num_params(self) def encode_x(self, x: List[str]): encoded_x = super().encode_x(x) if self.definition.input_encoding == "2D": # from (N, W, C) to (N, H, W, C) encoded_x = np.expand_dims(encoded_x, axis=1) return encoded_x def decode_x(self, x): if self.definition.input_encoding == "2D": # from (N, H, W, C) to (N, W, C) x = np.squeeze(x, axis=1) return super().decode_x(x) class KerasProteinMultiClassClassificationLearner( ProteinMultiClassClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) KerasHelper.init_tf_memory_policy() gpus = tf.config.list_physical_devices("GPU") self.use_cuda: bool = tf.test.is_built_with_gpu_support() and \ len(gpus) > 0 and gpu_id != -1 if self.use_cuda: tf.config.set_visible_devices(gpus[gpu_id], "GPU") self.device_label: str = "/GPU:" + str(gpu_id) else: self.device_label: str = "/CPU:0" self._check_task_loss_compatibility() def _check_task_loss_compatibility(self) -> None: if "loss" in self.definition.loss_hyperparameters: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if not loss in KerasHelper.MULTI_CLASS_CLASSIFICATION_LOSSES: self.logger.warning("loss function '%s' is incompatible with " "multi-class classification models", loss) def _get_output_layer_activation_function(self) -> Optional[str]: if "from_logits" in self.definition.loss_hyperparameters and \ "loss" in self.definition.loss_hyperparameters: from_logits: bool = bool(strtobool( self.definition.loss_hyperparameters["from_logits"])) if from_logits: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if loss == "categoricalcrossentropy" or \ loss == "sparsecategoricalcrossentropy": return "softmax" elif loss == "binarycrossentropy": self.logger.warning("activation function 'sigmoid' is " "incompatible with multi-class " "classification models") return "sigmoid" return None def create_model(self) -> None: KerasHelper.create_model(self) def print_model_summary(self): KerasHelper.print_model_summary(self) def set_seed(self) -> None: KerasHelper.set_seed(self) def _train_model(self, file_name_train: Optional[str] = None, file_name_val: Optional[str] = None, x_train: Optional[List[str]] = None, y_train: Optional[List[str]] = None, x_val: Optional[List[str]] = None, y_val: Optional[List[str]] = None) -> None: if x_train is not None and y_train is not None: training_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_train), self.encode_y(y_train))) elif file_name_train is not None: seq_len: int = self.get_sequence_length(file_name_train) def train_generator(): return self.dataset_generator(file_name_train) training_dataset = tf.data.Dataset.from_generator( train_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_train or x_train, y_train") if x_val is not None and y_val is not None: validation_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_val), self.encode_y(y_val))) elif file_name_val is not None: seq_len: int = self.get_sequence_length(file_name_val) def val_generator(): return self.dataset_generator(file_name_val) validation_dataset = tf.data.Dataset.from_generator( val_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_val or x_val, y_val") KerasHelper.train_model(self, training_dataset, validation_dataset, self.silent) def evaluate_model(self, file_name: Optional[str] = None, x: Optional[List[str]] = None, y: Optional[List[str]] = None): if x is not None and y is not None: dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x), self.encode_y(y))) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name or x, y") return KerasHelper.evaluate_model(self, dataset) def predict(self, file_name: Optional[str] = None, x: Optional[Any] = None, encode: bool = True): if x is not None: if encode: x = self.encode_x(x) dataset = tf.data.Dataset.from_tensor_slices((x)) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]))) else: raise Exception("specify either file_name or x") return KerasHelper.predict(self, dataset) def save_model(self, file_name: Optional[str] = None) -> None: KerasHelper.save_model(self, file_name) def write_session_info(self) -> None: KerasHelper.write_session_info(self) def load_model(self, file_name: Optional[str] = None) -> None: KerasHelper.load_model(self, file_name) def get_num_params(self) -> ModelSize: return KerasHelper.get_num_params(self) def encode_x(self, x: List[str]): encoded_x = super().encode_x(x) if self.definition.input_encoding == "2D": # from (N, W, C) to (N, H, W, C) encoded_x = np.expand_dims(encoded_x, axis=1) return encoded_x def decode_x(self, x): if self.definition.input_encoding == "2D": # from (N, H, W, C) to (N, W, C) x = np.squeeze(x, axis=1) return super().decode_x(x) class KerasProteinMultiLabelClassificationLearner( ProteinMultiLabelClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) KerasHelper.init_tf_memory_policy() gpus = tf.config.list_physical_devices("GPU") self.use_cuda: bool = tf.test.is_built_with_gpu_support() and \ len(gpus) > 0 and gpu_id != -1 if self.use_cuda: tf.config.set_visible_devices(gpus[gpu_id], "GPU") self.device_label: str = "/GPU:" + str(gpu_id) else: self.device_label: str = "/CPU:0" self._check_task_loss_compatibility() def _check_task_loss_compatibility(self) -> None: if "loss" in self.definition.loss_hyperparameters: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if not loss in KerasHelper.MULTI_LABEL_CLASSIFICATION_LOSSES: self.logger.warning("loss function '%s' is incompatible with " "multi-label classification models", loss) def _get_output_layer_activation_function(self) -> Optional[str]: if "from_logits" in self.definition.loss_hyperparameters and \ "loss" in self.definition.loss_hyperparameters: from_logits: bool = bool(strtobool( self.definition.loss_hyperparameters["from_logits"])) if from_logits: loss: str = self.definition.loss_hyperparameters["loss"] loss = loss.lower().replace("_", "").strip() if loss == "categoricalcrossentropy" or \ loss == "sparsecategoricalcrossentropy": self.logger.warning("activation function 'softmax' is " "incompatible with multi-label " "classification models") return "softmax" elif loss == "binarycrossentropy": return "sigmoid" return None def create_model(self) -> None: KerasHelper.create_model(self) def print_model_summary(self): KerasHelper.print_model_summary(self) def set_seed(self) -> None: KerasHelper.set_seed(self) def _train_model(self, file_name_train: Optional[str] = None, file_name_val: Optional[str] = None, x_train: Optional[List[str]] = None, y_train: Optional[List[str]] = None, x_val: Optional[List[str]] = None, y_val: Optional[List[str]] = None) -> None: if x_train is not None and y_train is not None: training_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_train), self.encode_y(y_train))) elif file_name_train is not None: seq_len: int = self.get_sequence_length(file_name_train) def train_generator(): return self.dataset_generator(file_name_train) training_dataset = tf.data.Dataset.from_generator( train_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_train or x_train, y_train") if x_val is not None and y_val is not None: validation_dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x_val), self.encode_y(y_val))) elif file_name_val is not None: seq_len: int = self.get_sequence_length(file_name_val) def val_generator(): return self.dataset_generator(file_name_val) validation_dataset = tf.data.Dataset.from_generator( val_generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name_val or x_val, y_val") KerasHelper.train_model(self, training_dataset, validation_dataset, self.silent) def evaluate_model(self, file_name: Optional[str] = None, x: Optional[List[str]] = None, y: Optional[List[str]] = None): if x is not None and y is not None: dataset = tf.data.Dataset.from_tensor_slices( (self.encode_x(x), self.encode_y(y))) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64, tf.bool), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]), tf.TensorShape([len(self.definition.labels)]))) else: raise Exception("specify either file_name or x, y") return KerasHelper.evaluate_model(self, dataset) def predict(self, file_name: Optional[str] = None, x: Optional[Any] = None, encode: bool = True): if x is not None: if encode: x = self.encode_x(x) dataset = tf.data.Dataset.from_tensor_slices((x)) elif file_name is not None: seq_len: int = self.get_sequence_length(file_name) def generator(): return self.dataset_generator(file_name) dataset = tf.data.Dataset.from_generator( generator, (tf.float64), output_shapes=(tf.TensorShape([seq_len, self.alphabet_size]))) else: raise Exception("specify either file_name or x") return KerasHelper.predict(self, dataset) def save_model(self, file_name: Optional[str] = None) -> None: KerasHelper.save_model(self, file_name) def write_session_info(self) -> None: KerasHelper.write_session_info(self) def load_model(self, file_name: Optional[str] = None) -> None: KerasHelper.load_model(self, file_name) def get_num_params(self) -> ModelSize: return KerasHelper.get_num_params(self) def encode_x(self, x: List[str]): encoded_x = super().encode_x(x) if self.definition.input_encoding == "2D": # from (N, W, C) to (N, H, W, C) encoded_x = np.expand_dims(encoded_x, axis=1) return encoded_x def decode_x(self, x): if self.definition.input_encoding == "2D": # from (N, H, W, C) to (N, W, C) x = np.squeeze(x, axis=1) return super().decode_x(x)

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0.038958

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29,150

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null

0

1

0

1

0

null

0

7

c7f9549e95823f0cbe72ff61ca903d6a0d6c6820

162

py

Python

src/utils/general_validation.py

BoaVaga/boavaga_server

7d25a68832d3b9f4f5666d0a3d55c99025498511

[ "MIT" ]

null

src/utils/general_validation.py

BoaVaga/boavaga_server

7d25a68832d3b9f4f5666d0a3d55c99025498511

[ "MIT" ]

null

src/utils/general_validation.py

BoaVaga/boavaga_server

7d25a68832d3b9f4f5666d0a3d55c99025498511

[ "MIT" ]

null

import re _REGEX_VALIDATE_TEL = re.compile(r'^\+?[0-9]{1,19}$') def validate_telefone(tel: str) -> bool: return _REGEX_VALIDATE_TEL.match(tel) is not None

20.25

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0.703704

27

162

3.962963

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0.242991

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0

0.035714

0.135802

162

7

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1

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null

0

1

0

1

0

7

2a009883358ef4f7b8a269b3e2fa367f463e7834

77

py

Python

themata/__init__.py

Thecarisma/themata

09a8ce670479ea4e9b5a26457f5cb290728f604a

[ "CC0-1.0" ]

2

2020-04-27T10:14:54.000Z

2020-04-28T01:24:59.000Z

themata/__init__.py

Thecarisma/themata

09a8ce670479ea4e9b5a26457f5cb290728f604a

[ "CC0-1.0" ]

28

2020-05-16T19:50:54.000Z

2021-12-02T07:38:03.000Z

themata/__init__.py

Thecarisma/themata

09a8ce670479ea4e9b5a26457f5cb290728f604a

[ "CC0-1.0" ]

null

import os def get_html_theme_path(): return os.path.dirname(__file__)

19.25

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77

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0.168831

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19.25

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1

0

null

0

1

0

1

0

7

aa7bd4f0e5ded6bb26301c1f01d5a48f4f082f1f

137

py

Python

database/seed/users.py

eddycheong/skeleton-flask-sqlalchemy

117e4cac0bf4d912f9546e2aeac77bccc2b7e3c0

[ "MIT" ]

null

database/seed/users.py

eddycheong/skeleton-flask-sqlalchemy

117e4cac0bf4d912f9546e2aeac77bccc2b7e3c0

[ "MIT" ]

null

database/seed/users.py

eddycheong/skeleton-flask-sqlalchemy

117e4cac0bf4d912f9546e2aeac77bccc2b7e3c0

[ "MIT" ]

null

from database.model import User def seed_users(): return [ User(name="seed_user_1"), User(name="seed_user_2"), ]

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1

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7

aab9141ed7548918316b11d21e1c62648a531143

9,349

py

Python

tasks/LM/configs.py

omri123/rotational-unit-of-memory

e796c841e1e837df09497ba77c3bc285db47d02d

[ "MIT" ]

82

2019-04-18T19:32:03.000Z

2022-03-19T00:50:56.000Z

tasks/LM/configs.py

omri123/rotational-unit-of-memory

e796c841e1e837df09497ba77c3bc285db47d02d

[ "MIT" ]

4

2019-04-22T11:58:43.000Z

2020-05-31T01:43:03.000Z

tasks/LM/configs.py

omri123/rotational-unit-of-memory

e796c841e1e837df09497ba77c3bc285db47d02d

[ "MIT" ]

26

2019-04-22T11:21:42.000Z

2021-11-29T06:01:10.000Z

def get_config(model): if model == 'ptb_fs_rum_test': return ptb_fs_rum_test_config() if model == 'ptb_fs_rum': return ptb_fs_rum_config() elif model == 'ptb_fs_goru': return ptb_fs_goru_config() elif model == 'ptb_fs_eunn': return ptb_fs_eunn_config() elif model == 'ptb_lstm_single': return ptb_lstm_single_config() elif model == 'ptb_rum_single': return ptb_rum_single_config() elif model == 'ptb_rum_single_U': return ptb_rum_single_U_config() elif model == 'ptb_rum_single_tanh': return ptb_rum_single_tanh_config() elif model == 'ptb_rum_single_sigmoid': return ptb_rum_single_sigmoid_config() elif model == 'ptb_rum_single_softsign': return ptb_rum_single_softsign_config() elif model == 'ptb_rum_single_1500': return ptb_rum_single_1500_config() elif model == 'ptb': return ptb_config() elif model == 'enwik_rum': return enwik_rum_config() elif model == 'enwik': return enwik_config() else: raise ValueError("Invalid model: %s", model) class enwik_config(object): """Enwik8 config.""" cell = "fs-lstm" init_scale = 0.01 learning_rate = 0.001 max_grad_norm = 1.0 num_layers = 2 num_steps = 100 cell_size = 1200 hyper_size = 1500 embed_size = 256 max_epoch = 35 max_max_epoch = max_epoch keep_prob = 0.75 zoneout_h = 0.95 zoneout_c = 0.7 lr_decay = 0.1 batch_size = 128 vocab_size = 205 fast_layers = 4 dataset = 'enwik8' activation = None class enwik_rum_config(object): """Enwik8 config.""" cell = "fs-rum" init_scale = 0.01 learning_rate = 0.001 max_grad_norm = 1.0 num_layers = 2 num_steps = 100 cell_size = 1200 hyper_size = 2000 embed_size = 256 max_epoch = 60 max_max_epoch = max_epoch keep_prob = 0.75 zoneout_h = 0.95 zoneout_c = 0.7 lr_decay = 0.1 batch_size = 128 vocab_size = 205 fast_layers = 4 T_norm = 1.0 use_zoneout = True use_layer_norm = True activation = "tanh" dataset = 'enwik8' class ptb_lstm_single_config(object): """PTB config.""" cell = "lstm" num_steps = 150 learning_rate = 0.002 T_norm = 1.0 num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1000 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True dataset = 'ptb' class ptb_rum_single_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = 1.0 num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1000 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "relu" update_gate = True dataset = 'ptb' class ptb_rum_single_U_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = 1.0 num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1400 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "relu" update_gate = False dataset = 'ptb' class ptb_rum_single_tanh_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = None num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1000 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "tanh" update_gate = True dataset = 'ptb' class ptb_rum_single_sigmoid_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = None num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1000 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "sigmoid" update_gate = True dataset = 'ptb' class ptb_rum_single_softsign_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = None num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1000 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "softsign" update_gate = True dataset = 'ptb' class ptb_rum_single_1500_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = 1.0 num_layers = 1 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1500 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True activation = "relu" update_gate = True dataset = 'ptb' class ptb_config(object): """PTB config.""" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 700 hyper_size = 400 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 dataset = 'ptb' class ptb_fs_rum_test_config(object): """PTB config.""" cell = "fs-rum" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 200 hyper_size = 200 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 T_norm = 1.0 use_zoneout = True use_layer_norm = True dataset = 'ptb' class ptb_fs_rum_config(object): """PTB config.""" cell = "fs-rum" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 700 hyper_size = 1000 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 T_norm = 1.0 use_zoneout = True use_layer_norm = True dataset = 'ptb' class ptb_fs_goru_config(object): """PTB config.""" cell = "fs-goru" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 700 hyper_size = 800 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 T_norm = 1.0 use_zoneout = True use_layer_norm = True dataset = 'ptb' class ptb_fs_eunn_config(object): """PTB config.""" cell = "fs-eunn" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 700 hyper_size = 2000 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 T_norm = 1.0 use_zoneout = True use_layer_norm = True dataset = 'ptb' class ptb_fs_goru_config(object): """PTB config.""" cell = "fs-goru" init_scale = 0.01 learning_rate = 0.002 max_grad_norm = 1.0 num_layers = 2 num_steps = 150 cell_size = 700 hyper_size = 800 embed_size = 128 max_epoch = 200 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 zoneout_c = 0.5 lr_decay = 0.1 batch_size = 128 vocab_size = 50 fast_layers = 2 T_norm = 1.0 use_zoneout = True use_layer_norm = True dataset = 'ptb' class ptb_rum_double_config(object): """PTB config.""" cell = "rum" num_steps = 150 learning_rate = 0.002 T_norm = 0.3 num_layers = 2 init_scale = 0.01 max_grad_norm = 1.0 cell_size = 1500 embed_size = 128 max_epoch = 100 max_max_epoch = max_epoch keep_prob = 0.65 zoneout_h = 0.9 lr_decay = 0.1 batch_size = 128 vocab_size = 50 use_layer_norm = True use_zoneout = True dataset = 'ptb'

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py

Python

pysit/modeling/frequency_modeling.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

null

pysit/modeling/frequency_modeling.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

null

pysit/modeling/frequency_modeling.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

1

2020-06-13T07:13:07.000Z

import itertools from pysit.util.derivatives import build_derivative_matrix, build_permutation_matrix, build_heterogenous_matrices from pysit.solvers.model_parameter import * import sys import copy import numpy as np from numpy.random import uniform __all__ = ['FrequencyModeling'] __docformat__ = "restructuredtext en" class FrequencyModeling(object): # read only class description @property def solver_type(self): return "frequency" @property def modeling_type(self): return "frequency" def __init__(self, solver): """Constructor for the FrequencyInversion class. Parameters ---------- solver : pysit wave solver object A wave solver that inherits from pysit.solvers.WaveSolverBase """ if self.solver_type == solver.supports['equation_dynamics']: self.solver = solver else: raise TypeError("Argument 'solver' type {1} does not match modeling solver type {0}.".format( self.solver_type, solver.supports['equation_dynamics'])) def forward_model(self, shot, m0, frequencies, return_parameters=[]): """Applies the forward model to the model for the given solver. Parameters ---------- shot : pysit.Shot Gives the source signal approximation for the right hand side. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'wavefield', 'simdata', 'simdata_time', 'dWaveOp'} Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * uhat is used to generically refer to the DFT of u that is needed to compute the imaging condition. Forward model computes: For constant density: -m*(omega**2)*u - lap u = f, where m = 1.0/c**2 For variable density: -m1*(omega**2)*u - div(m2 grad)u = f, where m1=1.0/kappa, m2=1.0/rho, and C = (kappa/rho)**0.5 """ # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh d = solver.domain source = shot.sources # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Setup data storage for the forward modeled data if 'simdata' in return_parameters: simdata = dict() # Storage for the derivative of the propagation operator with respect to the model \frac{d\script{L}}{dm} if 'dWaveOp' in return_parameters: dWaveOp = dict() # Initialize the DFT components uhats = dict() # Step k = 0 # p_0 is a zero array because if we assume the input signal is causal # and we assume that the initial system (i.e., p_(-2) and p_(-1)) is # uniformly zero, then the leapfrog scheme would compute that p_0 = 0 as # well. ukm1 is needed to compute the temporal derivative. solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for nu in frequencies: rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) result = solver.solve(solver_data, rhs, nu) uhat = solver_data.k.primary_wavefield # Save the unpadded wavefield if 'wavefield' in return_parameters: uhats[nu] = mesh.unpad_array(uhat, copy=True) # Record the data at t_k if 'simdata' in return_parameters: simdata[nu] = shot.receivers.sample_data_from_array(mesh.unpad_array(uhat)) # Save the derivative if 'dWaveOp' in return_parameters: dWaveOp[nu] = solver.compute_dWaveOp('frequency', uhat, nu) retval = dict() if 'dWaveOp' in return_parameters: retval['dWaveOp'] = dWaveOp if 'simdata' in return_parameters: retval['simdata'] = simdata if 'wavefield' in return_parameters: retval['wavefield'] = uhats return retval def forward_model_list(self, shot_list, m0, frequencies, return_parameters=[], **kwargs): """Applies the forward model to the model for the given solver and severals shots Parameters ---------- shot_list : list of pysit.Shot Gives the source signal approximation for the right hand side. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'wavefield', 'simdata', 'simdata_time', 'dWaveOp'} Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * uhat is used to generically refer to the DFT of u that is needed to compute the imaging condition. """ # importing the Petsc libraries for the multiple rhs solve try: import petsc4py petsc4py.init(sys.argv) from petsc4py import PETSc from pysit.util.wrappers.petsc import PetscWrapper except ImportError: raise ImportError('petsc4py is not installed, please install it and try again') flag = 'petsc' in kwargs if flag == 0: petsc = None else: petsc = kwargs['petsc'] # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh d = solver.domain # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Setup data storage for the forward modeled data if 'simdata' in return_parameters: Simdata = dict() # Storage for the derivative of the propagation operator with respect to the model \frac{d\script{L}}{dm} if 'dWaveOp' in return_parameters: DWaveOp = dict() # Initialize the DFT components # Uhats is a dictionnary of dictionnary Uhats = dict() # Step k = 0 # p_0 is a zero array because if we assume the input signal is causal # and we assume that the initial system (i.e., p_(-2) and p_(-1)) is # uniformly zero, then the leapfrog scheme would compute that p_0 = 0 as # well. ukm1 is needed to compute the temporal derivative. solver_data_list = list() for i in range(len(shot_list)): solver_data = solver.SolverData() solver_data_list.append(solver_data) Uhats[i] = dict() if 'simdata' in return_parameters: Simdata[i] = dict() if 'dWaveOp' in return_parameters: DWaveOp[i] = dict() rhs_list = list() for nu in frequencies: del rhs_list[:] rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for i in range(len(shot_list)): source = shot_list[i].sources rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) rhs_list.append(rhs.data.copy()) if petsc is True: result = solver.solve_petsc(solver_data_list, rhs_list, nu, **kwargs) else: for i in range(len(shot_list)): result = solver.solve(solver_data_list[i], rhs_list[i], nu) for i in range(len(shot_list)): uhat = solver_data_list[i].k.primary_wavefield # Save the unpadded wavefield if 'wavefield' in return_parameters: Uhats[i][nu] = mesh.unpad_array(uhat, copy=True) # Record the data at t_k if 'simdata' in return_parameters: Simdata[i][nu] = shot_list[i].receivers.sample_data_from_array( mesh.unpad_array(uhat)) # Save the derivative if 'dWaveOp' in return_parameters: DWaveOp[i][nu] = solver.compute_dWaveOp('frequency', uhat, nu) retval = dict() if 'dWaveOp' in return_parameters: retval['dWaveOp'] = DWaveOp if 'simdata' in return_parameters: retval['simdata'] = Simdata if 'wavefield' in return_parameters: retval['wavefield'] = Uhats return retval def migrate_shot(self, shot, m0, operand_simdata, frequencies, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, dWaveOp=None, adjointfield=None, dWaveOpAdj=None, wavefield=None): """Performs migration on a single shot. Parameters ---------- shot : pysit.Shot Shot for which to compute migration. operand_simdata : ndarray Operand, i.e., b in F*b. This data is in TIME to properly compute the adjoint. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. utt : list Imaging condition components from the forward model for each receiver in the shot. qs : list Optional return list allowing us to retrieve the adjoint field as desired. """ # If the imaging component has not already been computed, compute it. prep_rp = list() if dWaveOp is None: prep_rp.append('dWaveOp') dWaveOp = dict() if len(prep_rp) > 0: retval = self.forward_model(shot, m0, frequencies, return_parameters=prep_rp) if 'dWaveOp' in prep_rp: dWaveOp = retval['dWaveOp'] rp = ['imaging_condition'] if adjointfield is not None: rp.append('adjointfield') if dWaveOpAdj is not None: rp.append('dWaveOpAdj') rv = self.adjoint_model(shot, m0, operand_simdata, frequencies, operand_dWaveOpAdj=operand_dWaveOpAdj, operand_model=operand_model, frequency_weights=frequency_weights, return_parameters=rp, dWaveOp=dWaveOp, wavefield=wavefield) # If the adjoint field is desired as output. for nu in frequencies: if adjointfield is not None: adjointfield[nu] = rv['adjointfield'][nu] if dWaveOpAdj is not None: dWaveOpAdj[nu] = rv['dWaveOpAdj'][nu] # Get the imaging condition part from the result, this is the migrated image. ic = rv['imaging_condition'] return ic def migrate_shot_list(self, shots_list, m0, operand_simdata, frequencies, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, dWaveOp=None, adjointfield=None, dWaveOpAdj=None, wavefield=None, **kwargs): """Performs migration a list of shot shot. Parameters ---------- shots_list : list of pysit.Shot Shot for which to compute migration. operand_simdata : ndarray Operand, i.e., b in F*b. This data is in TIME to properly compute the adjoint. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. utt : list Imaging condition components from the forward model for each receiver in the shot. qs : list Optional return list allowing us to retrieve the adjoint field as desired. """ # If the imaging component has not already been computed, compute it. prep_rp = list() if dWaveOp is None: prep_rp.append('dWaveOp') dWaveOp = dict() if len(prep_rp) > 0: retval = self.forward_model_list(shots_list, m0, frequencies, return_parameters=prep_rp) if 'dWaveOp' in prep_rp: dWaveOp = retval['dWaveOp'] rp = ['imaging_condition'] if adjointfield is not None: rp.append('adjointfield') if dWaveOpAdj is not None: rp.append('dWaveOpAdj') rv = self.adjoint_model_list(shots_list, m0, operand_simdata, frequencies, operand_dWaveOpAdj=operand_dWaveOpAdj, operand_model=operand_model, frequency_weights=frequency_weights, return_parameters=rp, dWaveOp=dWaveOp, wavefield=wavefield, **kwargs) # If the adjoint field is desired as output. for nu in frequencies: if adjointfield is not None: adjointfield[nu] = rv['adjointfield'][nu] if dWaveOpAdj is not None: dWaveOpAdj[nu] = rv['dWaveOpAdj'][nu] # Get the imaging condition part from the result, this is the migrated image. ic = rv['imaging_condition'] return ic def migrate_shots_extend(self, shots_list, m0, operand_simdata, frequencies, max_sub_offset, h, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, dWaveOp=None, adjointfield=None, dWaveOpAdj=None, wavefield=None, **kwargs): """Performs migration a list of shot shot. Parameters ---------- shots_list : list of pysit.Shot Shot for which to compute migration. m0 : background model, solver.ModelParameters (should be velocity [km/s]) operand_simdata : ndarray Operand, i.e., b in F*b. This data is in TIME to properly compute the adjoint. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. max_sub_offset : maximum subsurface offset for extended modeling h : subsurface offset interval utt : list Imaging condition components from the forward model for each receiver in the shot. qs : list Optional return list allowing us to retrieve the adjoint field as desired. output: ic: the extended migrated images """ flag = 'petsc' in kwargs if flag == 0: petsc = None else: petsc = kwargs['petsc'] # If the imaging component has not already been computed, compute it. prep_rp = list() if dWaveOp is None: prep_rp.append('dWaveOp') dWaveOp = dict() if len(prep_rp) > 0: retval = self.forward_model_list( shots_list, m0, frequencies, return_parameters=prep_rp) if 'dWaveOp' in prep_rp: dWaveOp = retval['dWaveOp'] rp = ['imaging_condition'] if adjointfield is not None: rp.append('adjointfield') if dWaveOpAdj is not None: rp.append('dWaveOpAdj') rv = self.adjoint_model_extend(shots_list, m0, operand_simdata, frequencies, max_sub_offset, h, operand_dWaveOpAdj=operand_dWaveOpAdj, operand_model=operand_model, frequency_weights=frequency_weights, return_parameters=rp, dWaveOp=dWaveOp, wavefield=wavefield) # rv = self.adjoint_model_extend(shots_list, m0, operand_simdata, frequencies, max_sub_offset, h, return_parameters=rp) # If the adjoint field is desired as output. for nu in frequencies: if adjointfield is not None: adjointfield[nu] = rv['adjointfield'][nu] if dWaveOpAdj is not None: dWaveOpAdj[nu] = rv['dWaveOpAdj'][nu] # Get the imaging condition part from the result, this is the migrated image. ic = rv['imaging_condition'] return ic def adjoint_model(self, shot, m0, operand_simdata, frequencies, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, return_parameters=[], dWaveOp=None, wavefield=None): """Solves for the adjoint field in frequency. For constant density: -m*(omega**2)*q - lap q = resid, where m = 1.0/c**2 For variable density: -m1*(omega**2)*q - div(m2 grad)q = resid, where m1=1.0/kappa, m2=1.0/rho, and C = (kappa/rho)**0.5 Parameters ---------- shot : pysit.Shot Gives the receiver model for the right hand side. operand : ndarray Right hand side, usually the residual. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'q', 'qhat', 'ic'} dWaveOp : ndarray Imaging component from the forward model (in frequency). Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * q is the adjoint field. * qhat is the DFT of oq at the specified frequencies * ic is the imaging component. Because this function computes many of the things required to compute the imaging condition, there is an option to compute the imaging condition as we go. This should be used to save computational effort. If the imaging condition is to be computed, the optional argument utt must be present. Imaging Condition for variable density has terms: ic.m1 = omegas**2 * conj(u) * q ic.m2 = conj(grad(u)) dot grad(q), summed over all shots and frequencies. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver solver.model_parameters = m0 mesh = solver.mesh d = solver.domain source = shot.sources # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] qhats = dict() if 'dWaveOpAdj' in return_parameters: dWaveOpAdj = dict() # If we are computing the imaging condition, ensure that all of the parts are there. if dWaveOp is None and 'imaging_condition' in return_parameters: raise ValueError('To compute imaging condition, forward component must be specified.') if 'imaging_condition' in return_parameters: ic = solver.model_parameters.perturbation(dtype=np.complex) if frequency_weights is None: frequency_weights = itertools.repeat(1.0) freq_weights = {nu: weight for nu, weight in zip(frequencies, frequency_weights)} # if we are dealing with variable density, we need to collect the gradient operators, D1 and D2. (note: D2 is the negative adjoint of the leftmost gradient used in our heterogenous laplacian) if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): print("WARNING: Ian's operators are still used here even though the solver has changed. Gradient may be incorrect. These routines need to be updated.") deltas = [mesh.x.delta, mesh.z.delta] sh = mesh.shape(include_bc=True, as_grid=True) D1, D2 = build_heterogenous_matrices(sh, deltas) if operand_model is not None: operand_model = operand_model.with_padding() # Time-reversed wave solver solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for nu in frequencies: # If we are dealing with variable density, we will need these values computed for the imagining condition in terms of m2. if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): uhat = wavefield[nu] uhat = mesh.pad_array(uhat) D1u, D2u = np.conj(D1[0]*uhat), np.conj(D2[0]*uhat) # Need the conj. of grad (uhat) # Compute the rhs array. rhs_ = mesh.pad_array(shot.receivers.extend_data_to_array( data=operand_simdata[nu])) # for primary adjoint equation if (operand_dWaveOpAdj is not None) and (operand_model is not None): dWaveOpAdj_nu = operand_dWaveOpAdj[nu] rhs_ += reshape(operand_model*dWaveOpAdj_nu.reshape(operand_model.shape), rhs_.shape) # for secondary adjoint equation rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) np.conj(rhs.data, rhs.data) result = solver.solve(solver_data, rhs, nu) vhat = solver_data.k.primary_wavefield # Compute the conjugate in place. # After this operation, vhats _is_ conjugated, so its value does not # match the mathematics. This is done to save computation, as computing # the conjufation in place requires no further allocation. vhats should # not be used beyond this point, so it is assigned to None. qhat = np.conj(vhat, vhat) if 'adjointfield' in return_parameters: qhats[nu] = mesh.unpad_array(qhat, copy=True) if 'dWaveOpAdj' in return_parameters: dWaveOpAdj[nu] = solver.compute_dWaveOp('frequency', qhat, nu) # If the imaging component needs to be computed, do it if 'imaging_condition' in return_parameters: weight = freq_weights[nu] # if we are dealing with variable density, we compute 2 parts to the imagaing condition seperatly. Otherwise, if it is just constant density- we compute only 1. if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): ic.rho -= weight*((D1u)*(D1[1]*qhat)+(D2u)*(D2[1]*qhat)) ic.kappa -= weight*qhat*np.conj(dWaveOp[nu]) else: # note, no dnu here because the nus are not generally the complete set, so dnu makes little sense, otherwise dnu = 1./(nsteps*dt) ic -= weight*qhat*np.conj(dWaveOp[nu]) retval = dict() if 'adjointfield' in return_parameters: retval['adjointfield'] = qhats if 'dWaveOpAdj' in return_parameters: retval['dWaveOpAdj'] = dWaveOpAdj # If the imaging component needs to be computed, do it if 'imaging_condition' in return_parameters: # retval['imaging_condition'] = ic.without_padding() # Comment out by Zhilong, simply cutting the pml can not produce a correct gradient # In order to make the gradient correct, you should add all the weights in the pml to the last layer of the computational grid if m0.padded is True: retval['imaging_condition'] = ic else: if solver.inv_padding_mode is 'add': retval['imaging_condition'] = ic.add_padding() else: retval['imaging_condition'] = ic.without_padding() return retval def adjoint_model_list(self, shots_list, m0, operand_simdata, frequencies, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, return_parameters=[], dWaveOp=None, wavefield=None, **kwargs): """Solves for the adjoint field in frequency. For constant density: -m*(omega**2)*q - lap q = resid, where m = 1.0/c**2 For variable density: -m1*(omega**2)*q - div(m2 grad)q = resid, where m1=1.0/kappa, m2=1.0/rho, and C = (kappa/rho)**0.5 Parameters ---------- shot : pysit.Shot Gives the receiver model for the right hand side. operand : ndarray Right hand side, usually the residual. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'q', 'qhat', 'ic'} dWaveOp : ndarray Imaging component from the forward model (in frequency). Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * q is the adjoint field. * qhat is the DFT of oq at the specified frequencies * ic is the imaging component. Because this function computes many of the things required to compute the imaging condition, there is an option to compute the imaging condition as we go. This should be used to save computational effort. If the imaging condition is to be computed, the optional argument utt must be present. Imaging Condition for variable density has terms: ic.m1 = omegas**2 * conj(u) * q ic.m2 = conj(grad(u)) dot grad(q), summed over all shots and frequencies. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver solver.model_parameters = m0 mesh = solver.mesh d = solver.domain # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] Qhats = dict() if 'dWaveOpAdj' in return_parameters: DWaveOpAdj = dict() # If we are computing the imaging condition, ensure that all of the parts are there. if dWaveOp is None and 'imaging_condition' in return_parameters: raise ValueError('To compute imaging condition, forward component must be specified.') if 'imaging_condition' in return_parameters: Ic = dict() if frequency_weights is None: frequency_weights = itertools.repeat(1.0) freq_weights = {nu: weight for nu, weight in zip(frequencies, frequency_weights)} if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): deltas = [mesh.x.delta, mesh.z.delta] sh = mesh.shape(include_bc=True, as_grid=True) D1, D2 = build_heterogenous_matrices(sh, deltas) solver_data_list = list() # initialisation for the muliple rhs resolution for i in range(len(shots_list)): solver_data = solver.SolverData() solver_data_list.append(solver_data) Qhats[i] = dict() if 'imaging_condition' in return_parameters: Ic[i] = solver.model_parameters.perturbation(dtype=np.complex) if 'dWaveOpAdj' in return_parameters: DWaveOpAdj[i] = dict() rhs_list = list() if operand_model is not None: operand_model = operand_model.with_padding() for nu in frequencies: del rhs_list[:] for i in range(len(shots_list)): rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) rhs_ = mesh.pad_array(shots_list[i].receivers.extend_data_to_array( data=operand_simdata[i][nu])) if (operand_dWaveOpAdj is not None) and (operand_model is not None): dWaveOpAdj_nu = operand_dWaveOpAdj[nu] # for secondary adjoint equation rhs_ += reshape(operand_model * dWaveOpAdj_nu.reshape(operand_model.shape), rhs_.shape) rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) np.conj(rhs.data, rhs.data) rhs_list.append(rhs.data.copy()) result = solver.solve_petsc(solver_data_list, rhs_list, nu, **kwargs) for i in range(len(shots_list)): # If we are dealing with variable density, we will need these values computed for the imagining condition in terms of m2. if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): uhat = wavefield[i][nu] uhat = mesh.pad_array(uhat) # Need the conj. of grad (uhat) D1u, D2u = np.conj(D1[0]*uhat), np.conj(D2[0]*uhat) vhat = solver_data_list[i].k.primary_wavefield qhat = np.conj(vhat, vhat) if 'adjointfield' in return_parameters: Qhats[i][nu] = mesh.unpad_array(qhat, copy=True) if 'dWaveOpAdj' in return_parameters: DWaveOpAdj[i][nu] = solver.compute_dWaveOp('frequency', qhat, nu) if 'imaging_condition' in return_parameters: weight = freq_weights[nu] if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): Ic[i].rho -= weight*((D1u)*(D1[1]*qhat)+(D2u)*(D2[1]*qhat)) Ic[i].kappa -= weight*qhat*np.conj(dWaveOp[i][nu]) else: # note, no dnu here because the nus are not generally the complete set, so dnu makes little sense, otherwise dnu = 1./(nsteps*dt) Ic[i] -= weight*qhat*np.conj(dWaveOp[i][nu]) # If the imaging component needs to be computed, do it retval = dict() if 'adjointfield' in return_parameters: retval['adjointfield'] = Qhats if 'dWaveOpAdj' in return_parameters: retval['dWaveOpAdj'] = DWaveOpAdj for i in range(len(Ic)): Ic[i] = Ic[i].without_padding() # If the imaging component needs to be computed, do it if 'imaging_condition' in return_parameters: retval['imaging_condition'] = Ic return retval def adjoint_model_extend(self, shots_list, m0, operand_simdata, frequencies, max_sub_offset, h, operand_dWaveOpAdj=None, operand_model=None, frequency_weights=None, return_parameters=[], dWaveOp=None, wavefield=None, **kwargs): """Solves for the extend adjoint modeling in frequency. For constant density: -m*(omega**2)*q - lap q = resid, where m = 1.0/c**2 For variable density: -m1*(omega**2)*q - div(m2 grad)q = resid, where m1=1.0/kappa, m2=1.0/rho, and C = (kappa/rho)**0.5 Parameters ---------- shot : pysit.Shot Gives the receiver model for the right hand side. operand : ndarray Right hand side, usually the residual. frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'q', 'qhat', 'ic'} dWaveOp : ndarray Imaging component from the forward model (in frequency). Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * q is the adjoint field. * qhat is the DFT of oq at the specified frequencies * ic is the imaging component. Because this function computes many of the things required to compute the imaging condition, there is an option to compute the imaging condition as we go. This should be used to save computational effort. If the imaging condition is to be computed, the optional argument utt must be present. Imaging Condition for variable density has terms: ic.m1 = omegas**2 * conj(u) * q ic.m2 = conj(grad(u)) dot grad(q), summed over all shots and frequencies. """ flag = 'petsc' in kwargs if flag == 0: petsc = None else: petsc = kwargs['petsc'] # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver solver.model_parameters = m0 mesh = solver.mesh d = solver.domain nh = 2*int(max_sub_offset / h) + 1 # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] Qhats = dict() if 'dWaveOpAdj' in return_parameters: DWaveOpAdj = dict() # If we are computing the imaging condition, ensure that all of the parts are there. if dWaveOp is None and 'imaging_condition' in return_parameters: raise ValueError( 'To compute imaging condition, forward component must be specified.') if 'imaging_condition' in return_parameters: kwargs = {'dtype': 'complex'} Ic = ExtendedModelingParameter2D(mesh, max_sub_offset, h, **kwargs) Ic_data_tmp = np.zeros(Ic.sh_data, dtype='complex') if frequency_weights is None: frequency_weights = itertools.repeat(1.0) freq_weights = {nu: weight for nu, weight in zip( frequencies, frequency_weights)} sh_sub = Ic.sh_sub dof_sub = Ic.dof_sub # Create a fake mesh structrue to perform intermediate padding and unpadding operators mesh_ih = copy.deepcopy(mesh) if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): deltas = [mesh.x.delta, mesh.z.delta] sh = mesh.shape(include_bc=True, as_grid=True) D1, D2 = build_heterogenous_matrices(sh, deltas) solver_data_list = list() # initialisation for the muliple rhs resolution for i in range(len(shots_list)): solver_data = solver.SolverData() solver_data_list.append(solver_data) Qhats[i] = dict() # if 'imaging_condition' in return_parameters: if 'dWaveOpAdj' in return_parameters: DWaveOpAdj[i] = dict() rhs_list = list() if operand_model is not None: operand_model = operand_model.with_padding() for nu in frequencies: del rhs_list[:] for i in range(len(shots_list)): rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) rhs_ = mesh.pad_array(shots_list[i].receivers.extend_data_to_array(data=operand_simdata[i][nu])) if (operand_dWaveOpAdj is not None) and (operand_model is not None): dWaveOpAdj_nu = operand_dWaveOpAdj[nu] # for secondary adjoint equation rhs_ += np.reshape(operand_model * dWaveOpAdj_nu.reshape(operand_model.shape), rhs_.shape) rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) np.conj(rhs.data, rhs.data) rhs_list.append(rhs.data.copy()) if petsc is True: result = solver.solve_petsc(solver_data_list, rhs_list, nu, **kwargs) else: for i in range(len(shots_list)): result = solver.solve(solver_data_list[i], rhs_list[i], nu, **kwargs) for i in range(len(shots_list)): # If we are dealing with variable density, we will need these values computed for the imagining condition in terms of m2. if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): uhat = wavefield[i][nu] uhat = mesh.pad_array(uhat) # Need the conj. of grad (uhat) D1u, D2u = np.conj(D1[0]*uhat), np.conj(D2[0]*uhat) vhat = solver_data_list[i].k.primary_wavefield qhat = np.conj(vhat, vhat) if 'adjointfield' in return_parameters: Qhats[i][nu] = mesh.unpad_array(qhat, copy=True) if 'dWaveOpAdj' in return_parameters: DWaveOpAdj[i][nu] = solver.compute_dWaveOp('frequency', qhat, nu) if 'imaging_condition' in return_parameters: weight = freq_weights[nu] # The extended imaging for variational density is not implemented yet if hasattr(m0, 'kappa') and hasattr(m0, 'rho'): Ic[i].rho -= weight * \ ((D1u)*(D1[1]*qhat)+(D2u)*(D2[1]*qhat)) Ic[i].kappa -= weight*qhat*np.conj(dWaveOp[i][nu]) else: # note, no dnu here because the nus are not generally the complete set, so dnu makes little sense, otherwise dnu = 1./(nsteps*dt) for ih in range(0, nh): n_bcx_extend_u_ih = Ic.n_bcx_extend_u[ih, :] n_bcx_extend_v_ih = Ic.n_bcx_extend_v[ih, :] mesh._shapes[(False, False)] = (dof_sub, 1) mesh._shapes[(False, True)] = sh_sub mesh.parameters[0].lbc._n = n_bcx_extend_u_ih[0] mesh.parameters[0].rbc._n = n_bcx_extend_u_ih[1] u_tmp = mesh.unpad_array(np.conj(dWaveOp[i][nu])) mesh.parameters[0].lbc._n = n_bcx_extend_v_ih[0] mesh.parameters[0].rbc._n = n_bcx_extend_v_ih[1] v_tmp = mesh.unpad_array(qhat) mesh._shapes[(False, False)] = mesh_ih._shapes[(False, False)] mesh._shapes[(False, True)] = mesh_ih._shapes[(False, True)] mesh.parameters[0].lbc._n = mesh_ih.parameters[0]['lbc'].n mesh.parameters[0].rbc._n = mesh_ih.parameters[0]['rbc'].n Ic_data_tmp[:, ih] = (weight*v_tmp*u_tmp).reshape((-1,)) Ic.data -= Ic_data_tmp # If the imaging component needs to be computed, do it retval = dict() if 'adjointfield' in return_parameters: retval['adjointfield'] = Qhats if 'dWaveOpAdj' in return_parameters: retval['dWaveOpAdj'] = DWaveOpAdj # for i in range(len(Ic)): # Ic[i] = Ic[i].without_padding() # Ic.data = np.real(Ic.data) # If the imaging component needs to be computed, do it if 'imaging_condition' in return_parameters: retval['imaging_condition'] = Ic return retval def linear_forward_model(self, shot, m0, m1, frequencies, return_parameters=[], dWaveOp0=None): """Applies the forward model to the model for the given solver. Parameters ---------- shot : pysit.Shot Gives the source signal approximation for the right hand side. m1 : solver.ModelParameters frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'dWaveOp0', 'wavefield1', 'dWaveOp1', 'simdata', 'simdata_time'}, optional Values to return. Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u1 is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * u1tt is used to generically refer to the derivative of u1 that is needed to compute the imaging condition. * If u0tt is not specified, it may be computed on the fly at potentially high expense. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh d = solver.domain source = shot.sources # added the padding_mode by Zhilong, still needs to discuss which padding mode to use m1_padded = m1.with_padding(padding_mode='edge') # m1_padded = m1.with_padding(padding_mode=None) # Storage for the field u1hats = dict() # Setup data storage for the forward modeled data if 'simdata' in return_parameters: simdata = dict() # Storage for the time derivatives of p if 'dWaveOp0' in return_parameters: dWaveOp0ret = dict() # Storage for the time derivatives of p if 'dWaveOp1' in return_parameters: dWaveOp1 = dict() if dWaveOp0 is None: solver_data_u0 = solver.SolverData() solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for nu in frequencies: if dWaveOp0 is None: rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) solver.solve(solver_data_u0, rhs, nu) u0hat = solver_data_u0.k.primary_wavefield dWaveOp0_nu = solver.compute_dWaveOp('frequency', u0hat, nu) else: dWaveOp0_nu = dWaveOp0[nu] if 'dWaveOp0' in return_parameters: dWaveOp0ret[nu] = dWaveOp0_nu rhs_ = m1_padded*(-1*dWaveOp0_nu) # make the rhs vector the correct length rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) solver.solve(solver_data, rhs, nu) u1hat = solver_data.k.primary_wavefield # Store the wavefield if 'wavefield1' in return_parameters: u1hats[nu] = mesh.unpad_array(u1hat, copy=True) # Compute the derivative if 'dWaveOp1' in return_parameters: dWaveOp1[nu] = solver.compute_dWaveOp('frequency', u1hat, nu) # Extract the data if 'simdata' in return_parameters: simdata[nu] = shot.receivers.sample_data_from_array(mesh.unpad_array(u1hat)) retval = dict() if 'dWaveOp0' in return_parameters: retval['dWaveOp0'] = dWaveOp0ret if 'wavefield1' in return_parameters: retval['wavefield1'] = u1hats if 'dWaveOp1' in return_parameters: retval['dWaveOp1'] = dWaveOp1 if 'simdata' in return_parameters: retval['simdata'] = simdata return retval def linear_forward_model_extend(self, shots, m0, m1_extend, frequencies, max_sub_offset, h, return_parameters=[], DWaveOp0In=None): """Applies the extended linear forward model to the model for the given solver. Parameters ---------- shots : a list of pysit.Shot Gives the source signal approximation for the right hand side. m0 : background model, solver.ModelParameters m1_extend : extended model perturbation, it is a structure of ExtendedModelingParameter2D max_offset: maximum subsurface offset for extended modeling h : subsurface offest interval frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'dWaveOp0', 'wavefield1', 'dWaveOp1', 'simdata', 'simdata_time'}, optional Values to return. Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u1 is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * u1tt is used to generically refer to the derivative of u1 that is needed to compute the imaging condition. * If u0tt is not specified, it may be computed on the fly at potentially high expense. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh d = solver.domain nh = 2*int(max_sub_offset / h) + 1 # # added the padding_mode by Zhilong, still needs to discuss which padding mode to use # m1_padded = m1.with_padding(padding_mode='edge') # Storage for the field u1hats = dict() # Setup data storage for the forward modeled data if 'simdata' in return_parameters: Simdata = dict() # Storage for the time derivatives of p if 'dWaveOp0' in return_parameters: DWaveOp0ret = dict() # Storage for the time derivatives of p if 'dWaveOp1' in return_parameters: DWaveOp1 = dict() if 'wavefield1' in return_parameters: U1hats = dict() if DWaveOp0In is None: solver_data_u0 = solver.SolverData() solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) rhslin = solver.WavefieldVector(mesh, dtype=solver.dtype) sh_sub = m1_extend.sh_sub dof_sub = m1_extend.dof_sub # Create a fake mesh structrue to perform intermediate padding and unpadding operators mesh_ih = copy.deepcopy(mesh) for i in range(len(shots)): shot = shots[i] source = shot.sources if 'simdata' in return_parameters: Simdata[i] = dict() if 'dWaveOp0' in return_parameters: DWaveOp0ret[i] = dict() if 'dWaveOp1' in return_parameters: DWaveOp1[i] = dict() if 'wavefield1' in return_parameters: U1hats[i] = dict() for nu in frequencies: # m1_padded = m1.with_padding(padding_mode='edge') if DWaveOp0In is None: rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) solver.solve(solver_data_u0, rhs, nu) u0hat = solver_data_u0.k.primary_wavefield dWaveOp0_nu = solver.compute_dWaveOp('frequency', u0hat, nu) else: dWaveOp0_nu = DWaveOp0In[i][nu] if 'dWaveOp0' in return_parameters: DWaveOp0ret[i][nu] = dWaveOp0_nu rhslin.data = 0.0 for ih in range(0, nh): m1_ih = m1_extend.data[:, ih] m1_ih = m1_ih.reshape((m1_ih.size, 1)) n_bcx_extend_u_ih = m1_extend.n_bcx_extend_u[ih, :] n_bcx_extend_v_ih = m1_extend.n_bcx_extend_v[ih, :] mesh._shapes[(False, False)] = (dof_sub, 1) mesh._shapes[(False, True)] = sh_sub mesh.parameters[0].lbc._n = n_bcx_extend_u_ih[0] mesh.parameters[0].rbc._n = n_bcx_extend_u_ih[1] rhs_ = m1_ih * (mesh.unpad_array(-1*dWaveOp0_nu)) mesh.parameters[0].lbc._n = n_bcx_extend_v_ih[0] mesh.parameters[0].rbc._n = n_bcx_extend_v_ih[1] rhs_ = mesh.pad_array(rhs_) mesh._shapes[(False, False)] = mesh_ih._shapes[(False, False)] mesh._shapes[(False, True)] = mesh_ih._shapes[(False, True)] mesh.parameters[0].lbc._n = mesh_ih.parameters[0]['lbc'].n mesh.parameters[0].rbc._n = mesh_ih.parameters[0]['rbc'].n # rhs_ = m1_padded*(-1*dWaveOp0_nu) # make the rhs vector the correct length rhs_tmp = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) rhslin.data = rhslin.data + rhs_tmp.data solver.solve(solver_data, rhslin, nu) u1hat = solver_data.k.primary_wavefield # Store the wavefield if 'wavefield1' in return_parameters: U1hats[i][nu] = mesh.unpad_array(u1hat, copy=True) # Compute the derivative if 'dWaveOp1' in return_parameters: DWaveOp1[i][nu] = solver.compute_dWaveOp('frequency', u1hat, nu) # Extract the data if 'simdata' in return_parameters: Simdata[i][nu] = shot.receivers.sample_data_from_array(mesh.unpad_array(u1hat)) retval = dict() if 'dWaveOp0' in return_parameters: retval['dWaveOp0'] = DWaveOp0ret if 'wavefield1' in return_parameters: retval['wavefield1'] = U1hats if 'dWaveOp1' in return_parameters: retval['dWaveOp1'] = DWaveOp1 if 'simdata' in return_parameters: retval['simdata'] = Simdata return retval def linear_forward_model_kappa(self, shot, m0, m1, frequencies, return_parameters=[], dWaveOp0=None, wavefield=None): """Applies the forward model to the model for the given solver in terms of a pertubation of kappa Parameters ---------- shot : pysit.Shot Gives the source signal approximation for the right hand side. m1 : solver.ModelParameters frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'dWaveOp0', 'wavefield1', 'dWaveOp1', 'simdata', 'simdata_time'}, optional Values to return. Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u1 is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * u1tt is used to generically refer to the derivative of u1 that is needed to compute the imaging condition. * If u0tt is not specified, it may be computed on the fly at potentially high expense. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh d = solver.domain source = shot.sources model_1 = 1.0/m1.kappa model_1 = mesh.pad_array(model_1) # Storage for the field u1hats = dict() # Setup data storage for the forward modeled data if 'simdata' in return_parameters: simdata = dict() # Storage for the time derivatives of p if 'dWaveOp0' in return_parameters: dWaveOp0ret = dict() # Storage for the time derivatives of p if 'dWaveOp1' in return_parameters: dWaveOp1 = dict() if dWaveOp0 is None: solver_data_u0 = solver.SolverData() solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for nu in frequencies: if dWaveOp0 is None: rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) solver.solve(solver_data_u0, rhs, nu) u0hat = solver_data_u0.k.primary_wavefield dWaveOp0_nu = solver.compute_dWaveOp('frequency', u0hat, nu) else: dWaveOp0_nu = dWaveOp0[nu] if 'dWaveOp0' in return_parameters: dWaveOp0ret[nu] = dWaveOp0_nu rhs_ = model_1*(-1*dWaveOp0_nu) # make the rhs vector the correct length rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) solver.solve(solver_data, rhs, nu) u1hat = solver_data.k.primary_wavefield # Store the wavefield if 'wavefield1' in return_parameters: u1hats[nu] = mesh.unpad_array(u1hat, copy=True) # Compute the derivative if 'dWaveOp1' in return_parameters: dWaveOp1[nu] = solver.compute_dWaveOp('frequency', u1hat, nu) # Extract the data if 'simdata' in return_parameters: simdata[nu] = shot.receivers.sample_data_from_array(mesh.unpad_array(u1hat)) retval = dict() if 'dWaveOp0' in return_parameters: retval['dWaveOp0'] = dWaveOp0ret if 'wavefield1' in return_parameters: retval['wavefield1'] = u1hats if 'dWaveOp1' in return_parameters: retval['dWaveOp1'] = dWaveOp1 if 'simdata' in return_parameters: retval['simdata'] = simdata return retval def linear_forward_model_rho(self, shot, m0, m1, frequencies, return_parameters=[], dWaveOp0=None, wavefield=None): """Applies the forward model to the model for the given solver in terms of a pertubation of rho Parameters ---------- shot : pysit.Shot Gives the source signal approximation for the right hand side. m1 : solver.ModelParameters frequencies : list of 2-tuples 2-tuple, first element is the frequency to use, second element the weight. return_parameters : list of {'dWaveOp0', 'wavefield1', 'dWaveOp1', 'simdata', 'simdata_time'}, optional Values to return. Returns ------- retval : dict Dictionary whose keys are return_parameters that contains the specified data. Notes ----- * u1 is used as the target field universally. It could be velocity potential, it could be displacement, it could be pressure. * u1tt is used to generically refer to the derivative of u1 that is needed to compute the imaging condition. * If u0tt is not specified, it may be computed on the fly at potentially high expense. """ # Sanitize the input if not np.iterable(frequencies): frequencies = [frequencies] # Local references solver = self.solver # this updates dt and the number of steps so that is appropriate for the current model solver.model_parameters = m0 mesh = solver.mesh sh = mesh.shape(include_bc=True, as_grid=True) d = solver.domain source = shot.sources model_2 = 1.0/m1.rho model_2 = mesh.pad_array(model_2) rp = dict() rp['laplacian'] = True print("WARNING: Ian's operators are still used here even though the solver has changed. These tests need to be updated.") Lap = build_heterogenous_matrices( sh, [mesh.x.delta, mesh.z.delta], model_2.reshape(-1,), rp=rp) # Storage for the field u1hats = dict() # Setup data storage for the forward modeled data if 'simdata' in return_parameters: simdata = dict() # Storage for the time derivatives of p if 'dWaveOp0' in return_parameters: dWaveOp0ret = dict() # Storage for the time derivatives of p if 'dWaveOp1' in return_parameters: dWaveOp1 = dict() if dWaveOp0 is None: solver_data_u0 = solver.SolverData() solver_data = solver.SolverData() rhs = solver.WavefieldVector(mesh, dtype=solver.dtype) for nu in frequencies: u0_hat = wavefield[nu] u0_hat = mesh.pad_array(u0_hat) if dWaveOp0 is None: rhs = solver.build_rhs(mesh.pad_array(source.f(nu=nu)), rhs_wavefieldvector=rhs) solver.solve(solver_data_u0, rhs, nu) u0hat = solver_data_u0.k.primary_wavefield dWaveOp0_nu = solver.compute_dWaveOp('frequency', u0hat, nu) else: dWaveOp0_nu = dWaveOp0[nu] if 'dWaveOp0' in return_parameters: dWaveOp0ret[nu] = dWaveOp0_nu rhs_ = Lap*u0_hat # make the rhs vector the correct length rhs = solver.build_rhs(rhs_, rhs_wavefieldvector=rhs) solver.solve(solver_data, rhs, nu) u1hat = solver_data.k.primary_wavefield # Store the wavefield if 'wavefield1' in return_parameters: u1hats[nu] = mesh.unpad_array(u1hat, copy=True) # Compute the derivative if 'dWaveOp1' in return_parameters: dWaveOp1[nu] = solver.compute_dWaveOp('frequency', u1hat, nu) # Extract the data if 'simdata' in return_parameters: simdata[nu] = shot.receivers.sample_data_from_array(mesh.unpad_array(u1hat)) retval = dict() if 'dWaveOp0' in return_parameters: retval['dWaveOp0'] = dWaveOp0ret if 'wavefield1' in return_parameters: retval['wavefield1'] = u1hats if 'dWaveOp1' in return_parameters: retval['dWaveOp1'] = dWaveOp1 if 'simdata' in return_parameters: retval['simdata'] = simdata return retval def adjoint_test_kappa(): # if __name__ == '__main__': # from pysit import * import numpy as np import matplotlib.pyplot as plt from numpy.random import uniform from pysit import PML, Dirichlet, RectangularDomain, CartesianMesh, PointSource, ReceiverSet, Shot, ConstantDensityAcousticWave, VariableDensityHelmholtz, generate_seismic_data, PointReceiver, RickerWavelet from pysit.gallery.horizontal_reflector import horizontal_reflector # Define Domain bc = PML(0.3, 100, ftype='quadratic') # bc = Dirichlet() x_config = (0.1, 1.0, bc, bc) z_config = (0.1, 0.8, bc, bc) d = RectangularDomain(x_config, z_config) m = CartesianMesh(d, 70, 90) # Generate true wave speed # (M = C^-2 - C0^-2) C, C0, m, d = horizontal_reflector(m) w = 1.3 M = [w*C, C/w] M0 = [C0, C0] # Set up shots Nshots = 1 shots = [] xmin = d.x.lbound xmax = d.x.rbound nx = m.x.n zmin = d.z.lbound zmax = d.z.rbound point_approx = 'delta' for i in range(Nshots): # Define source location and type source = PointSource(m, (.188888, 0.18888), RickerWavelet(10.0), approximation=point_approx) # Define set of receivers zpos = zmin + (1./9.)*zmax xpos = np.linspace(xmin, xmax, nx) receivers = ReceiverSet(m, [PointReceiver(m, (x, zpos)) for x in xpos]) # Create and store the shot shot = Shot(source, receivers) shots.append(shot) # Define and configure the wave solver # trange=(0.,3.0) freqs = [3.0, 5.0, 7.0] solver = VariableDensityHelmholtz(m, model_parameters={'kappa': M[0], 'rho': M[1]}, spatial_shifted_differences=False, spatial_accuracy_order=2) # Generate synthetic Seismic data print('Generating data...') base_model = solver.ModelParameters(m, {'kappa': M[0], 'rho': M[1]}) generate_seismic_data(shots, solver, base_model, frequencies=freqs) tools = FrequencyModeling(solver) m0 = solver.ModelParameters(m, {'kappa': M[0], 'rho': M[1]}) np.random.seed(0) m1 = m0.perturbation() v = uniform(0.5, 1.5, len(m0.kappa)).reshape((len(m0.kappa), 1)) # v is pertubation of model 1/kappa. (which we have declared as m1). Thus, kappa is 1/v. m1.kappa += 1.0/v # freqs = np.linspace(3,20,20) fwdret = tools.forward_model(shot, m0, freqs, ['wavefield', 'dWaveOp', 'simdata']) data = fwdret['simdata'] dWaveOp0 = fwdret['dWaveOp'] u0hat = fwdret['wavefield'] # data -= shot.receivers.interpolate_data(solver.ts()) # data *= -1 # for nu in freqs: # data[nu] += np.random.rand(*data[nu].shape) linfwdret = tools.linear_forward_model_kappa(shot, m0, m1, freqs, ['simdata', 'wavefield1']) lindata = linfwdret['simdata'] u1hat = linfwdret['wavefield1'][freqs[0]] adjret = tools.adjoint_model(shot, m0, data, freqs, return_parameters=[ 'imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0, wavefield=u0hat) qhat = adjret['adjointfield'][freqs[0]] adjmodel = adjret['imaging_condition'].kappa # adjret2 = tools.adjoint_model(shot, m0, lindata_time, freqs, return_parameters=['imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) ## qhat = adjret['adjointfield'][freqs[0]] # adjmodel2 = adjret2['imaging_condition'].view(np.ndarray) temp_data_prod = 0.0 for nu in freqs: temp_data_prod += np.dot(lindata[nu].reshape(data[nu].shape).T, np.conj(data[nu])) print("data space: ", temp_data_prod.squeeze()) print("model space: ", np.dot(v.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas)) print("their diff: ", np.dot(v.T, np.conj(adjmodel)).squeeze() * np.prod(m.deltas) - temp_data_prod.squeeze()) def adjoint_test_rho(): # if __name__ == '__main__': # from pysit import * import numpy as np import matplotlib.pyplot as plt from numpy.random import uniform from pysit import PML, Dirichlet, RectangularDomain, CartesianMesh, PointSource, ReceiverSet, Shot, ConstantDensityAcousticWave, VariableDensityHelmholtz, generate_seismic_data, PointReceiver, RickerWavelet from pysit.gallery.horizontal_reflector import horizontal_reflector # Define Domain bc = PML(0.3, 100, ftype='quadratic') # bc = Dirichlet() x_config = (0.1, 1.0, bc, bc) z_config = (0.1, 0.8, bc, bc) d = RectangularDomain(x_config, z_config) m = CartesianMesh(d, 70, 90) # Generate true wave speed # (M = C^-2 - C0^-2) C, C0, m, d = horizontal_reflector(m) w = 1.3 M = [w*C, C/w] M0 = [C0, C0] # Set up shots Nshots = 1 shots = [] xmin = d.x.lbound xmax = d.x.rbound nx = m.x.n zmin = d.z.lbound zmax = d.z.rbound point_approx = 'delta' for i in range(Nshots): # Define source location and type source = PointSource(m, (.188888, 0.18888), RickerWavelet(10.0), approximation=point_approx) # Define set of receivers zpos = zmin + (1./9.)*zmax xpos = np.linspace(xmin, xmax, nx) receivers = ReceiverSet(m, [PointReceiver(m, (x, zpos)) for x in xpos]) # Create and store the shot shot = Shot(source, receivers) shots.append(shot) # Define and configure the wave solver # trange=(0.,3.0) freqs = [3.0, 5.0, 7.0] solver = VariableDensityHelmholtz(m, model_parameters={'kappa': M[0], 'rho': M[1]}, spatial_shifted_differences=False, spatial_accuracy_order=2) # Generate synthetic Seismic data print('Generating data...') base_model = solver.ModelParameters(m, {'kappa': M[0], 'rho': M[1]}) generate_seismic_data(shots, solver, base_model, frequencies=freqs) tools = FrequencyModeling(solver) m0 = solver.ModelParameters(m, {'kappa': M[0], 'rho': M[1]}) np.random.seed(0) m1 = m0.perturbation() # v is pertubation of model 1/rho. (which we have declared as m1). Thus, rho is 1/v. v = uniform(0.5, 1.5, len(m0.rho)).reshape((len(m0.rho), 1)) m1.rho += 1.0/v # freqs = np.linspace(3,20,20) fwdret = tools.forward_model(shot, m0, freqs, ['wavefield', 'dWaveOp', 'simdata']) data = fwdret['simdata'] dWaveOp0 = fwdret['dWaveOp'] u0hat = fwdret['wavefield'] # data -= shot.receivers.interpolate_data(solver.ts()) # data *= -1 # for nu in freqs: # data[nu] += np.random.rand(*data[nu].shape) linfwdret = tools.linear_forward_model_rho( shot, m0, m1, freqs, ['simdata', 'wavefield1'], wavefield=u0hat) lindata = linfwdret['simdata'] #u1hat = linfwdret['wavefield1'][freqs[0]] adjret = tools.adjoint_model(shot, m0, data, freqs, return_parameters=[ 'imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0, wavefield=u0hat) qhat = adjret['adjointfield'][freqs[0]] adjmodel = adjret['imaging_condition'].rho # adjret2 = tools.adjoint_model(shot, m0, lindata_time, freqs, return_parameters=['imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) ## qhat = adjret['adjointfield'][freqs[0]] # adjmodel2 = adjret2['imaging_condition'].view(np.ndarray) temp_data_prod = 0.0 for nu in freqs: temp_data_prod += np.dot(lindata[nu].reshape(data[nu].shape).T, np.conj(data[nu])) print("data space: ", temp_data_prod.squeeze()) print("model space: ", np.dot(v.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas)) print("their diff: ", np.dot(v.T, np.conj(adjmodel)).squeeze() * np.prod(m.deltas) - temp_data_prod.squeeze()) def adjoint_test(): # if __name__ == '__main__': # from pysit import * import numpy as np import matplotlib.pyplot as plt from pysit import PML, Dirichlet, RectangularDomain, CartesianMesh, PointSource, ReceiverSet, Shot, ConstantDensityAcousticWave, ConstantDensityHelmholtz, generate_seismic_data, PointReceiver, RickerWavelet from pysit.gallery import horizontal_reflector # Define Domain bc = PML(0.3, 100, ftype='quadratic') # bc = Dirichlet() x_config = (0.1, 1.0, bc, bc) z_config = (0.1, 0.8, bc, bc) d = RectangularDomain(x_config, z_config) m = CartesianMesh(d, 90, 70) # Generate true wave speed # (M = C^-2 - C0^-2) # C0, C = horizontal_reflector(m) C0, C, m, d = horizontal_reflector(m) # Set up shots Nshots = 1 shots = [] xmin = d.x.lbound xmax = d.x.rbound nx = m.x.n zmin = d.z.lbound zmax = d.z.rbound point_approx = 'delta' for i in range(Nshots): # Define source location and type source = PointSource(m, (.188888, 0.18888), RickerWavelet(10.0), approximation=point_approx) # Define set of receivers zpos = zmin + (1./9.)*zmax xpos = np.linspace(xmin, xmax, nx) receivers = ReceiverSet(m, [PointReceiver(m, (x, zpos)) for x in xpos]) # Create and store the shot shot = Shot(source, receivers) shots.append(shot) # Define and configure the wave solver trange = (0., 3.0) solver = ConstantDensityAcousticWave(m, formulation='scalar', model_parameters={'C': C}, spatial_accuracy_order=4, trange=trange, time_accuracy_order=6) # Generate synthetic Seismic data print('Generating data...') base_model = solver.ModelParameters(m, {'C': C}) generate_seismic_data(shots, solver, base_model) solver_frequency = ConstantDensityHelmholtz(m, model_parameters={'C': C0}, spatial_shifted_differences=True, spatial_accuracy_order=4) tools = FrequencyModeling(solver_frequency) m0 = solver_frequency.ModelParameters(m, {'C': C0}) np.random.seed(0) m1 = m0.perturbation() # m1 += M m1 += np.random.rand(*m1.data.shape) # m1 += np.ones(m1.data.shape) # freqs = [10.0, 10.5, 10.123334145252] freqs = [10.0] # freqs = np.linspace(3,20,20) fwdret = tools.forward_model(shot, m0, freqs, ['wavefield', 'dWaveOp', 'simdata']) data = fwdret['simdata'] dWaveOp0 = fwdret['dWaveOp'] u0hat = fwdret['wavefield'][freqs[0]] # data -= shot.receivers.interpolate_data(solver.ts()) # data *= -1 # for nu in freqs: # data[nu] += np.random.rand(*data[nu].shape) linfwdret = tools.linear_forward_model(shot, m0, m1, freqs, ['simdata', 'wavefield1']) lindata = linfwdret['simdata'] u1hat = linfwdret['wavefield1'][freqs[0]] adjret = tools.adjoint_model(shot, m0, data, freqs, return_parameters=[ 'imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) qhat = adjret['adjointfield'][freqs[0]] adjmodel = adjret['imaging_condition'].data # adjret2 = tools.adjoint_model(shot, m0, lindata_time, freqs, return_parameters=['imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) ## qhat = adjret['adjointfield'][freqs[0]] # adjmodel2 = adjret2['imaging_condition'].view(np.ndarray) m1 = m1.data temp_data_prod = 0.0 for nu in freqs: temp_data_prod += np.dot(lindata[nu].reshape(data[nu].shape).T, np.conj(data[nu])) print(temp_data_prod.squeeze()) print(np.dot(m1.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas)) print(np.dot(m1.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas) - temp_data_prod.squeeze()) # temp_data_prod = 0.0 # for nu in freqs: # temp_data_prod += np.dot(lindata[nu].reshape(dhat[nu].shape), np.conj(lindata[nu].reshape(dhat[nu].shape))) # # print temp_data_prod # print np.dot(m1.T, np.conj(adjmodel2)).squeeze()*np.prod(d.deltas) # plt.figure() # plt.subplot(2,3,1) # display_on_grid(np.real(u0hat), d) # plt.title(r're(${\hat u_0}$)') # plt.subplot(2,3,4) # display_on_grid(np.imag(u0hat), d) # plt.title(r'im(${\hat u_0}$)') # plt.subplot(2,3,2) # display_on_grid(np.real(qhat), d) # plt.title(r're(${\hat q}$)') # plt.subplot(2,3,5) # display_on_grid(np.imag(qhat), d) # plt.title(r'im(${\hat q}$)') # plt.subplot(2,3,3) # display_on_grid(np.real(u1hat), d) # plt.title(r're(${\hat u_1}$)') # plt.subplot(2,3,6) # display_on_grid(np.imag(u1hat), d) # plt.title(r'im(${\hat u_1}$)') # plt.show() # plt.figure() # plt.subplot(2,3,1) # display_on_grid(np.real(u0hat), d) # plt.title(r're(${\hat u_0}$)') # plt.subplot(2,3,4) # display_on_grid(np.imag(u0hat), d) # plt.title(r'im(${\hat u_0}$)') # plt.subplot(2,3,2) # display_on_grid(np.real(qhat), d) # plt.title(r're(${\hat q}$)') # plt.subplot(2,3,5) # display_on_grid(np.imag(qhat), d) # plt.title(r'im(${\hat q}$)') # plt.subplot(2,3,3) # display_on_grid(np.real(adjmodel), d) # plt.title(r're(${m_1}$)') # plt.subplot(2,3,6) # display_on_grid(np.imag(adjmodel), d) # plt.title(r'im(${m_1}$)') # plt.show() def extended_modeling_test(): # if __name__ == '__main__': # from pysit import * import numpy as np import matplotlib as mpl mpl.use('TkAgg') import matplotlib.pyplot as plt from pysit import PML, Dirichlet, RectangularDomain, CartesianMesh, PointSource, ReceiverSet, Shot, ConstantDensityAcousticWave, ConstantDensityHelmholtz, generate_seismic_data, PointReceiver, RickerWavelet from pysit.gallery import horizontal_reflector # Define Domain bc = PML(0.3, 100, ftype='quadratic') # bc = Dirichlet() x_config = (0.1, 1.0, bc, bc) z_config = (0.1, 0.8, bc, bc) d = RectangularDomain(x_config, z_config) m = CartesianMesh(d, 91, 71) # Generate true wave speed # (M = C^-2 - C0^-2) C0, C, m, d = horizontal_reflector(m) max_sub_offset = 0.1 h = 0.01 m1_extend = ExtendedModelingParameter2D(m, max_sub_offset, h) # Set up shots Nshots = 2 shots = [] xmin = d.x.lbound xmax = d.x.rbound nx = m.x.n zmin = d.z.lbound zmax = d.z.rbound point_approx = 'delta' for i in range(Nshots): # Define source location and type source = PointSource(m, (.188888, 0.18888), RickerWavelet(10.0), approximation=point_approx) # Define set of receivers zpos = zmin + (1./9.)*zmax xpos = np.linspace(xmin, xmax, nx) receivers = ReceiverSet(m, [PointReceiver(m, (x, zpos)) for x in xpos]) # Create and store the shot shot = Shot(source, receivers) shots.append(shot) # Define and configure the wave solver trange = (0., 3.0) solver = ConstantDensityAcousticWave(m, formulation='scalar', model_parameters={'C': C}, spatial_accuracy_order=4, trange=trange, time_accuracy_order=6) # Generate synthetic Seismic data print('Generating data...') base_model = solver.ModelParameters(m, {'C': C}) generate_seismic_data(shots, solver, base_model) solver_frequency = ConstantDensityHelmholtz(m, model_parameters={'C': C0}, spatial_shifted_differences=True, spatial_accuracy_order=4) tools = FrequencyModeling(solver_frequency) m0 = solver_frequency.ModelParameters(m, {'C': C0}) # m1_extend.setter(np.random.rand(m1_extend.sh_data[0], m1_extend.sh_data[1])) m1_extend.setter(np.zeros(m1_extend.sh_data)) d_m = solver.ModelParameters(m, {'C': C}) m1 = m0.perturbation() dmtmp = d_m.data dmtmp[np.where(dmtmp <= 2)] = 0 sh_true = m1.mesh._shapes[(False, True)] dmtmp = np.reshape(dmtmp, sh_true) sh_cut = m1_extend.sh_sub dmtmp = dmtmp[0:sh_cut[0], :] dmtmp = np.ones(dmtmp.shape) dmtmp[:, 40] = 1.0 dmtmp = dmtmp.reshape(-1) m1_extend.data[:, (m1_extend.sh_data[1]-1)//2] = dmtmp freqs = [10.0] fwdret = tools.forward_model_list(shots, m0, freqs, ['simdata']) datas = fwdret['simdata'] # datas=[] # datas.append(data) # np.random.seed(0) # # m1 = m0.perturbation() # # m1 += M # m1 += np.random.rand(*m1.data.shape) # freqs = np.linspace(3,20,20) linfwdret = tools.linear_forward_model_extend(shots, m0, m1_extend, freqs, max_sub_offset, h, ['simdata']) lindatas = linfwdret['simdata'] lindatas2 = [] for i in range(len(shots)): print(i) shot_i= [shots[i]] linfwdret = tools.linear_forward_model_extend(shot_i, m0, m1_extend, freqs, max_sub_offset, h, ['simdata']) lindatas2.append(linfwdret['simdata'][0]) # lindatas = [] # lindatas.append(lindata) m1 = m0.perturbation() # m1 += M # m1 += np.random.rand(*m1.data.shape) Ic = tools.migrate_shots_extend(shots, m0, datas, freqs, max_sub_offset, h, return_parameters=['imaging_condition'] ) # linfwdret2 = tools.linear_forward_model_extend(shots, m0, Ic, freqs, max_sub_offset, h, ['simdata']) # lindatas2 = linfwdret2['simdata'] # Ic2 = tools.migrate_shots_extend(shots, m0, lindatas2, # freqs, max_sub_offset, h, # return_parameters=['imaging_condition'] # ) a = 0.0 for i in range(len(shots)): for key in lindatas[i]: a += np.dot(np.conj(lindatas[i][key]).reshape(-1), datas[i][key].reshape(-1)) print(['Data inner product =', a]) Ic_data1 = m1_extend.data.reshape(-1) Ic_data2 = Ic.data.reshape(-1) b = np.dot(np.conj(Ic_data1), Ic_data2).squeeze()*np.prod(m.deltas) print(['Model inner produc =', b]) # m1 += np.ones(m1.data.shape) # linfwdret = tools.linear_forward_model(shot, m0, m1, freqs, ['simdata', 'wavefield1']) # lindatas_no1 = linfwdret['simdata'] # c = np.dot(np.conj(lindatas_no1[key]).reshape(-1),lindatas_no1[key].reshape(-1)) # adjret = tools.migrate_shot(shots[0], m0, lindatas_no1, freqs) # linfwdret = tools.linear_forward_model(shot, m0, adjret, freqs, ['simdata', 'wavefield1']) # lindatas_no2 = linfwdret['simdata'] # adjret2 = tools.migrate_shot(shots[0], m0, lindatas_no2, freqs) a = 1 # fwdret = tools.forward_model(shot, m0, freqs, ['wavefield', 'dWaveOp', 'simdata']) # data = fwdret['simdata'] # dWaveOp0 = fwdret['dWaveOp'] # u0hat = fwdret['wavefield'][freqs[0]] # # # data -= shot.receivers.interpolate_data(solver.ts()) # # data *= -1 # # # for nu in freqs: # # data[nu] += np.random.rand(*data[nu].shape) # # linfwdret = tools.linear_forward_model(shot, m0, m1, freqs, ['simdata', 'wavefield1']) # lindata = linfwdret['simdata'] # u1hat = linfwdret['wavefield1'][freqs[0]] # # adjret = tools.adjoint_model(shot, m0, data, freqs, return_parameters=[ # 'imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) # qhat = adjret['adjointfield'][freqs[0]] # adjmodel = adjret['imaging_condition'].data # # # adjret2 = tools.adjoint_model(shot, m0, lindata_time, freqs, return_parameters=['imaging_condition', 'adjointfield'], dWaveOp=dWaveOp0) # ## qhat = adjret['adjointfield'][freqs[0]] # # adjmodel2 = adjret2['imaging_condition'].view(np.ndarray) # # m1 = m1.data # # temp_data_prod = 0.0 # for nu in freqs: # temp_data_prod += np.dot(lindata[nu].reshape(data[nu].shape).T, np.conj(data[nu])) # # print(temp_data_prod.squeeze()) # print(np.dot(m1.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas)) # print(np.dot(m1.T, np.conj(adjmodel)).squeeze()*np.prod(m.deltas) - temp_data_prod.squeeze()) if __name__ == '__main__': print("testing extended modeling") extended_modeling_test() print("testing constant density") adjoint_test() print("testing pertubation of rho:") adjoint_test_rho() print("testing pertubation of kappa:") adjoint_test_kappa()

37.81919

210

0.595255

9,600

78,437

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1

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null

0

7

6331a05f39c0de1583407780f0d9ac4609563f18

11,735

py

Python

wolf/flows/macow.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

75

2020-03-31T22:21:04.000Z

2022-03-20T10:58:17.000Z

wolf/flows/macow.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

3

2021-02-03T07:07:14.000Z

2022-03-08T20:58:43.000Z

wolf/flows/macow.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

10

2020-04-27T05:31:44.000Z

2021-11-21T14:11:16.000Z

__author__ = 'max' from overrides import overrides from typing import Dict, Tuple import torch import torch.nn as nn from wolf.flows.flow import Flow from wolf.flows.normalization import ActNorm2dFlow from wolf.flows.permutation import Conv1x1Flow from wolf.flows.couplings import NICE2d, MaskedConvFlow from wolf.flows.multiscale_architecture import MultiScaleArchitecture class MaCowUnit(Flow): """ A Unit of Flows with an MCF(A), MCF(B), an Conv1x1, followd by an ActNorm and an activation. """ def __init__(self, in_channels, kernel_size, h_channels=0, inverse=False, transform='affine', alpha=1.0, h_type=None, activation='relu'): super(MaCowUnit, self).__init__(inverse) self.conv1 = MaskedConvFlow(in_channels, (kernel_size[0], kernel_size[1]), order='A', h_channels=h_channels, transform=transform, alpha=alpha, h_type=h_type, activation=activation) self.conv2 = MaskedConvFlow(in_channels, (kernel_size[0], kernel_size[1]), order='B', h_channels=h_channels, transform=transform, alpha=alpha, h_type=h_type, activation=activation) self.actnorm1 = ActNorm2dFlow(in_channels, inverse=inverse) self.conv3 = MaskedConvFlow(in_channels, (kernel_size[1], kernel_size[0]), order='C', h_channels=h_channels, transform=transform, alpha=alpha, h_type=h_type, activation=activation) self.conv4 = MaskedConvFlow(in_channels, (kernel_size[1], kernel_size[0]), order='D', h_channels=h_channels, transform=transform, alpha=alpha, h_type=h_type, activation=activation) self.actnorm2 = ActNorm2dFlow(in_channels, inverse=inverse) @overrides def forward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: # MCF1 out, logdet_accum = self.conv1.forward(input, h=h) # MCF2 out, logdet = self.conv2.forward(out, h=h) logdet_accum = logdet_accum + logdet # ActNorm1 out, logdet = self.actnorm1.forward(out) logdet_accum = logdet_accum + logdet # MCF3 out, logdet = self.conv3.forward(out, h=h) logdet_accum = logdet_accum + logdet # MCF4 out, logdet = self.conv4.forward(out, h=h) logdet_accum = logdet_accum + logdet # ActNorm2 out, logdet = self.actnorm2.forward(out) logdet_accum = logdet_accum + logdet return out, logdet_accum def backward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: # ActNorm2 out, logdet_accum = self.actnorm2.backward(input) # MCF4 out, logdet = self.conv4.backward(out, h=h) logdet_accum = logdet_accum + logdet # MCF3 out, logdet = self.conv3.backward(out, h=h) logdet_accum = logdet_accum + logdet # ActNorm1 out, logdet = self.actnorm1.backward(out) logdet_accum = logdet_accum + logdet # MCF2 out, logdet = self.conv2.backward(out, h=h) logdet_accum = logdet_accum + logdet # MCF1 out, logdet = self.conv1.backward(out, h=h) logdet_accum = logdet_accum + logdet return out, logdet_accum @overrides def init(self, data, h=None, init_scale=1.0) -> Tuple[torch.Tensor, torch.Tensor]: # MCF1 out, logdet_accum = self.conv1.init(data, h=h, init_scale=init_scale) # MCF2 out, logdet = self.conv2.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet # ActNorm1 out, logdet = self.actnorm1.init(out, init_scale=init_scale) logdet_accum = logdet_accum + logdet # MCF3 out, logdet = self.conv3.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet # MCF4 out, logdet = self.conv4.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet # ActNorm2 out, logdet = self.actnorm2.init(out, init_scale=init_scale) logdet_accum = logdet_accum + logdet return out, logdet_accum class MaCowStep(Flow): """ A step of Macow Flows """ def __init__(self, in_channels, kernel_size, hidden_channels, h_channels, inverse=False, transform='affine', alpha=1.0, coupling_type='conv', h_type=None, activation='relu', normalize=None, num_groups=None, **kwargs): super(MaCowStep, self).__init__(inverse) num_units = 2 self.actnorm1 = ActNorm2dFlow(in_channels, inverse=inverse) self.conv1x1 = Conv1x1Flow(in_channels, inverse=inverse) units = [MaCowUnit(in_channels, kernel_size, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, h_type=h_type, activation=activation) for _ in range(num_units)] self.units1 = nn.ModuleList(units) self.coupling1_up = NICE2d(in_channels, hidden_channels=hidden_channels, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, type=coupling_type, h_type=h_type, split_type='continuous', order='up', activation=activation, normalize=normalize, num_groups=num_groups) self.coupling1_dn = NICE2d(in_channels, hidden_channels=hidden_channels, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, type=coupling_type, h_type=h_type, split_type='continuous', order='down', activation=activation, normalize=normalize, num_groups=num_groups) self.actnorm2 = ActNorm2dFlow(in_channels, inverse=inverse) units = [MaCowUnit(in_channels, kernel_size, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, h_type=h_type, activation=activation) for _ in range(num_units)] self.units2 = nn.ModuleList(units) self.coupling2_up = NICE2d(in_channels, hidden_channels=hidden_channels, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, type=coupling_type, h_type=h_type, split_type='skip', order='up', activation=activation, normalize=normalize, num_groups=num_groups) self.coupling2_dn = NICE2d(in_channels, hidden_channels=hidden_channels, h_channels=h_channels, transform=transform, alpha=alpha, inverse=inverse, type=coupling_type, h_type=h_type, split_type='skip', order='down', activation=activation, normalize=normalize, num_groups=num_groups) def sync(self): self.conv1x1.sync() @overrides def forward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: # part1 out, logdet_accum = self.actnorm1.forward(input) out, logdet = self.conv1x1.forward(out) logdet_accum = logdet_accum + logdet for unit in self.units1: out, logdet = unit.forward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.coupling1_up.forward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.coupling1_dn.forward(out, h=h) logdet_accum = logdet_accum + logdet # part 2 out, logdet = self.actnorm2.forward(out) logdet_accum = logdet_accum + logdet for unit in self.units2: out, logdet = unit.forward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.coupling2_up.forward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.coupling2_dn.forward(out, h=h) logdet_accum = logdet_accum + logdet return out, logdet_accum @overrides def backward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: # part 2 out, logdet_accum = self.coupling2_dn.backward(input, h=h) out, logdet = self.coupling2_up.backward(out, h=h) logdet_accum = logdet_accum + logdet for unit in reversed(self.units2): out, logdet = unit.backward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.actnorm2.backward(out) logdet_accum = logdet_accum + logdet # part1 out, logdet = self.coupling1_dn.backward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.coupling1_up.backward(out, h=h) logdet_accum = logdet_accum + logdet for unit in reversed(self.units1): out, logdet = unit.backward(out, h=h) logdet_accum = logdet_accum + logdet out, logdet = self.conv1x1.backward(out) logdet_accum = logdet_accum + logdet out, logdet = self.actnorm1.backward(out) logdet_accum = logdet_accum + logdet return out, logdet_accum @overrides def init(self, data, h=None, init_scale=1.0) -> Tuple[torch.Tensor, torch.Tensor]: out, logdet_accum = self.actnorm1.init(data, init_scale=init_scale) out, logdet = self.conv1x1.init(out, init_scale=init_scale) logdet_accum = logdet_accum + logdet for unit in self.units1: out, logdet = unit.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet out, logdet = self.coupling1_up.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet out, logdet = self.coupling1_dn.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet # part 2 out, logdet = self.actnorm2.init(out, init_scale=init_scale) logdet_accum = logdet_accum + logdet for unit in self.units2: out, logdet = unit.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet out, logdet = self.coupling2_up.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet out, logdet = self.coupling2_dn.init(out, h=h, init_scale=init_scale) logdet_accum = logdet_accum + logdet return out, logdet_accum class MaCow(MultiScaleArchitecture): """ MaCow model in paper https://arxiv.org/pdf/1902.04208.pdf """ def __init__(self, levels, num_steps, in_channels, factors, hidden_channels, h_channels=0, inverse=False, transform='affine', prior_transform='affine', alpha=1.0, kernel_size=(2, 3), coupling_type='conv', h_type=None, activation='relu', normalize=None, num_groups=None): assert len(kernel_size) == 2, 'kernel size should contain two numbers' super(MaCow, self).__init__(MaCowStep, levels, num_steps, in_channels, factors, hidden_channels, h_channels=h_channels, inverse=inverse, transform=transform, prior_transform=prior_transform, alpha=alpha, kernel_size=kernel_size, coupling_type=coupling_type, h_type=h_type, activation=activation, normalize=normalize, num_groups=num_groups) @classmethod def from_params(cls, params: Dict) -> "MaCow": return MaCow(**params) MaCow.register('macow')

46.752988

108

0.625735

1,413

11,735

4.985846

0.094126

0.140525

0.188219

0.121789

0.817459

0.782967

0.77885

0.733428

0.707026

0.692122

0

0.016304

0.278739

11,735

250

109

46.94

0.816044

0.027269

0

0.535135

0

0.012259

0

0.005405

1

0.059459

false

0

0.048649

0.005405

0.162162

0

null

0

1

0

1

0

null

0

7

2d6b232504f384dc58e0fde159c4c49dd8bd23d1

30,667

py

Python

unopartylib/test/utxolocks_test.py

terhnt/unoparty-lib

f23bcfc91ac109dc8c248210229c253be7d405ed

[ "MIT" ]

null

unopartylib/test/utxolocks_test.py

terhnt/unoparty-lib

f23bcfc91ac109dc8c248210229c253be7d405ed

[ "MIT" ]

null

unopartylib/test/utxolocks_test.py

terhnt/unoparty-lib

f23bcfc91ac109dc8c248210229c253be7d405ed

[ "MIT" ]

null

#! /usr/bin/python3 import pytest import binascii from io import BytesIO import bitcoin import tempfile from unopartylib.test import conftest # this is require near the top to do setup of the test suite from unopartylib.test.util_test import CURR_DIR from unopartylib.lib import (transaction) from unopartylib.lib.messages import send FIXTURE_SQL_FILE = CURR_DIR + '/fixtures/scenarios/parseblock_unittest_fixture.sql' FIXTURE_DB = tempfile.gettempdir() + '/fixtures.parseblock_unittest_fixture.db' FIXTURE_OPTIONS = { 'utxo_locks_max_addresses': 2000 } def construct_tx(db, source, destination, disable_utxo_locks=False, custom_inputs=None): tx_info = send.compose(db, source, destination, 'XUP', 1) return transaction.construct(db, tx_info, disable_utxo_locks=disable_utxo_locks, custom_inputs=custom_inputs) def test_utxolocks(server_db): transaction.initialise() # reset UTXO_LOCKS """it shouldn't use the same UTXO""" tx1hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz") tx2hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz") tx1f = BytesIO(binascii.unhexlify(tx1hex)) tx1 = bitcoin.core.CTransaction.stream_deserialize(tx1f) tx2f = BytesIO(binascii.unhexlify(tx2hex)) tx2 = bitcoin.core.CTransaction.stream_deserialize(tx2f) assert (tx1.vin[0].prevout.hash, tx1.vin[0].prevout.n) != (tx2.vin[0].prevout.hash, tx2.vin[0].prevout.n) def test_utxolocks_custom_input(server_db): transaction.initialise() # reset UTXO_LOCKS """it should use the same UTXO""" custom_inputs = [{ 'txid': '2c3416c8742fa71caea929e6cdf10e02fc10dd39d8b5bd36a71498f3173ed0bd', 'txhex': 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'amount': 0.41700000, 'vout': 0, 'confirmations': 11, 'scriptPubKey': '76a91478a7602aa3bd71b1a5e777d15a1f8718d50d658388ac', 'address': 'Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz' }] tx1hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", custom_inputs=custom_inputs) tx2hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", custom_inputs=custom_inputs) tx1f = BytesIO(binascii.unhexlify(tx1hex)) tx1 = bitcoin.core.CTransaction.stream_deserialize(tx1f) tx2f = BytesIO(binascii.unhexlify(tx2hex)) tx2 = bitcoin.core.CTransaction.stream_deserialize(tx2f) assert (tx1.vin[0].prevout.hash, tx1.vin[0].prevout.n) == (tx2.vin[0].prevout.hash, tx2.vin[0].prevout.n) def test_disable_utxolocks(server_db): transaction.initialise() # reset UTXO_LOCKS """with `disable_utxo_locks=True` it should use the same UTXO""" tx1hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", disable_utxo_locks=True) tx2hex = construct_tx(server_db, "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", "Ukn3L4dgG13R3dSdxLvAAJizeiaW7cyUFz", disable_utxo_locks=True) tx1f = BytesIO(binascii.unhexlify(tx1hex)) tx1 = bitcoin.core.CTransaction.stream_deserialize(tx1f) tx2f = BytesIO(binascii.unhexlify(tx2hex)) tx2 = bitcoin.core.CTransaction.stream_deserialize(tx2f) assert (tx1.vin[0].prevout.hash, tx1.vin[0].prevout.n) == (tx2.vin[0].prevout.hash, tx2.vin[0].prevout.n)

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2dba028f15a26355a03fde0270efee77b963fbf2

739

py

Python

chap3/3-6.py

StewedChickenwithStats/Answers-to-Python-Crash-Course

9ffbe02abba5d111f702d920db7932303daf59d4

[ "MIT" ]

1

2022-02-21T07:05:48.000Z

chap3/3-6.py

StewedChickenwithStats/Answers-to-Python-Crash-Course

9ffbe02abba5d111f702d920db7932303daf59d4

[ "MIT" ]

null

chap3/3-6.py

StewedChickenwithStats/Answers-to-Python-Crash-Course

9ffbe02abba5d111f702d920db7932303daf59d4

[ "MIT" ]

null

people=['mom','dad','sister'] print("Now there will be a larger dining-table.") people.insert(0,'brother') people.insert(2,'friend') people.append('teacher') print("Dear "+people[0]+", I'd like to invite you to have dinner with me on Friday at my home.") print("Dear "+people[1]+", I'd like to invite you to have dinner with me on Friday at my home.") print("Dear "+people[2]+", I'd like to invite you to have dinner with me on Friday at my home.") print("Dear "+people[3]+", I'd like to invite you to have dinner with me on Friday at my home.") print("Dear "+people[4]+", I'd like to invite you to have dinner with me on Friday at my home.") print("Dear "+people[5]+", I'd like to invite you to have dinner with me on Friday at my home.")

67.181818

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144

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3.576389

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0.104854

0.174757

0.093204

0.739806

0

0.012759

0.151556

739

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97

67.181818

0.808612

0

0.697297

0

1

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false

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0.636364

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null

0

1

0

7

931d107109b34c70d04ad0ab51c5390610f0e179

153

py

Python

llvm/utils/lit/lit/LitFormats.py

clairechingching/ScaffCC

737ae90f85d9fe79819d66219747d27efa4fa5b9

[ "BSD-2-Clause" ]

2,151

2020-04-18T07:31:17.000Z

2022-03-31T08:39:18.000Z

llvm/utils/lit/lit/LitFormats.py

clairechingching/ScaffCC

737ae90f85d9fe79819d66219747d27efa4fa5b9

[ "BSD-2-Clause" ]

395

2020-04-18T08:22:18.000Z

2021-12-08T13:04:49.000Z

llvm/utils/lit/lit/LitFormats.py

clairechingching/ScaffCC

737ae90f85d9fe79819d66219747d27efa4fa5b9

[ "BSD-2-Clause" ]

338

2020-04-18T08:03:10.000Z

2022-03-29T12:33:22.000Z

from TestFormats import FileBasedTest from TestFormats import GoogleTest, ShTest, TclTest from TestFormats import SyntaxCheckTest, OneCommandPerFileTest

38.25

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153

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0.098039

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true

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null

0

1

0

1

0

1

0

7

93245be5d708a2647fdae572c9b153da1c48df99

4,783

py

Python

tests/python/test_ringbuf.py

yzhao1012/bcc

15340c44b98d8ee97a6dce775de614fd268cee13

[ "Apache-2.0" ]

7

2020-08-24T01:56:06.000Z

2022-02-26T15:49:44.000Z

tests/python/test_ringbuf.py

yzhao1012/bcc

15340c44b98d8ee97a6dce775de614fd268cee13

[ "Apache-2.0" ]

9

2021-07-29T21:15:28.000Z

2022-02-16T18:17:49.000Z

tests/python/test_ringbuf.py

yzhao1012/bcc

15340c44b98d8ee97a6dce775de614fd268cee13

[ "Apache-2.0" ]

8

2019-01-25T21:48:34.000Z

2022-03-15T16:21:50.000Z

#!/usr/bin/env python # Copyright (c) PLUMgrid, Inc. # Licensed under the Apache License, Version 2.0 (the "License") from bcc import BPF import os import ctypes as ct import random import time import subprocess from unittest import main, TestCase, skipUnless from utils import kernel_version_ge class TestRingbuf(TestCase): @skipUnless(kernel_version_ge(5,8), "requires kernel >= 5.8") def test_ringbuf_output(self): self.counter = 0 class Data(ct.Structure): _fields_ = [("ts", ct.c_ulonglong)] def cb(ctx, data, size): self.assertEqual(size, ct.sizeof(Data)) event = ct.cast(data, ct.POINTER(Data)).contents self.counter += 1 text = """ BPF_RINGBUF_OUTPUT(events, 8); struct data_t { u64 ts; }; int do_sys_nanosleep(void *ctx) { struct data_t data = {bpf_ktime_get_ns()}; events.ringbuf_output(&data, sizeof(data), 0); return 0; } """ b = BPF(text=text) b.attach_kprobe(event=b.get_syscall_fnname("nanosleep"), fn_name="do_sys_nanosleep") b.attach_kprobe(event=b.get_syscall_fnname("clock_nanosleep"), fn_name="do_sys_nanosleep") b["events"].open_ring_buffer(cb) subprocess.call(['sleep', '0.1']) b.ring_buffer_poll() self.assertGreater(self.counter, 0) b.cleanup() @skipUnless(kernel_version_ge(5,8), "requires kernel >= 5.8") def test_ringbuf_consume(self): self.counter = 0 class Data(ct.Structure): _fields_ = [("ts", ct.c_ulonglong)] def cb(ctx, data, size): self.assertEqual(size, ct.sizeof(Data)) event = ct.cast(data, ct.POINTER(Data)).contents self.counter += 1 text = """ BPF_RINGBUF_OUTPUT(events, 8); struct data_t { u64 ts; }; int do_sys_nanosleep(void *ctx) { struct data_t data = {bpf_ktime_get_ns()}; events.ringbuf_output(&data, sizeof(data), 0); return 0; } """ b = BPF(text=text) b.attach_kprobe(event=b.get_syscall_fnname("nanosleep"), fn_name="do_sys_nanosleep") b.attach_kprobe(event=b.get_syscall_fnname("clock_nanosleep"), fn_name="do_sys_nanosleep") b["events"].open_ring_buffer(cb) subprocess.call(['sleep', '0.1']) b.ring_buffer_consume() self.assertGreater(self.counter, 0) b.cleanup() @skipUnless(kernel_version_ge(5,8), "requires kernel >= 5.8") def test_ringbuf_submit(self): self.counter = 0 class Data(ct.Structure): _fields_ = [("ts", ct.c_ulonglong)] def cb(ctx, data, size): self.assertEqual(size, ct.sizeof(Data)) event = ct.cast(data, ct.POINTER(Data)).contents self.counter += 1 text = """ BPF_RINGBUF_OUTPUT(events, 8); struct data_t { u64 ts; }; int do_sys_nanosleep(void *ctx) { struct data_t *data = events.ringbuf_reserve(sizeof(struct data_t)); if (!data) return 1; data->ts = bpf_ktime_get_ns(); events.ringbuf_submit(data, 0); return 0; } """ b = BPF(text=text) b.attach_kprobe(event=b.get_syscall_fnname("nanosleep"), fn_name="do_sys_nanosleep") b.attach_kprobe(event=b.get_syscall_fnname("clock_nanosleep"), fn_name="do_sys_nanosleep") b["events"].open_ring_buffer(cb) subprocess.call(['sleep', '0.1']) b.ring_buffer_poll() self.assertGreater(self.counter, 0) b.cleanup() @skipUnless(kernel_version_ge(5,8), "requires kernel >= 5.8") def test_ringbuf_discard(self): self.counter = 0 class Data(ct.Structure): _fields_ = [("ts", ct.c_ulonglong)] def cb(ctx, data, size): self.assertEqual(size, ct.sizeof(Data)) event = ct.cast(data, ct.POINTER(Data)).contents self.counter += 1 text = """ BPF_RINGBUF_OUTPUT(events, 8); struct data_t { u64 ts; }; int do_sys_nanosleep(void *ctx) { struct data_t *data = events.ringbuf_reserve(sizeof(struct data_t)); if (!data) return 1; data->ts = bpf_ktime_get_ns(); events.ringbuf_discard(data, 0); return 0; } """ b = BPF(text=text) b.attach_kprobe(event=b.get_syscall_fnname("nanosleep"), fn_name="do_sys_nanosleep") b.attach_kprobe(event=b.get_syscall_fnname("clock_nanosleep"), fn_name="do_sys_nanosleep") b["events"].open_ring_buffer(cb) subprocess.call(['sleep', '0.1']) b.ring_buffer_poll() self.assertEqual(self.counter, 0) b.cleanup() if __name__ == "__main__": main()

30.081761

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4.354067

0.151515

0.048352

0.061538

0.052747

0.88022

0.872894

0

0.017041

0.263851

4,783

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0

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null

0

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null

0

7

93453eddb88a7ed666a9350a363bc531cfe4670a

69,932

py

Python

infoblox_netmri/api/broker/v3_8_0/device_environment_monitor_broker.py

infobloxopen/infoblox_netmri

aa1c744df7e439dbe163bb9edd165e4e85a9771b

[ "Apache-2.0" ]

12

2016-02-19T12:37:54.000Z

2022-03-04T20:11:08.000Z

infoblox_netmri/api/broker/v3_8_0/device_environment_monitor_broker.py

azinfoblox/infoblox-netmri

02372c5231e2677ab6299cb659a73c9a41b4b0f4

[ "Apache-2.0" ]

18

2015-11-12T18:37:00.000Z

2021-05-19T07:59:55.000Z

infoblox_netmri/api/broker/v3_8_0/device_environment_monitor_broker.py

azinfoblox/infoblox-netmri

02372c5231e2677ab6299cb659a73c9a41b4b0f4

[ "Apache-2.0" ]

18

2016-01-07T12:04:34.000Z

2022-03-31T11:05:41.000Z

from ..broker import Broker class DeviceEnvironmentMonitorBroker(Broker): controller = "device_environment_monitors" def index(self, **kwargs): """Lists the available device environment monitors. Any of the inputs listed may be be used to narrow the list; other inputs will be ignored. Of the various ways to query lists, using this method is most efficient. **Inputs** | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Array of Integer | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DeviceID: The internal NetMRI identifier for the device from which device environment information was collected. :type DeviceID: Integer | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DeviceID: The internal NetMRI identifier for the device from which device environment information was collected. :type DeviceID: Array of Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param DeviceGroupID: The internal NetMRI identifier of the device groups to which to limit the results. :type DeviceGroupID: Array of Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param timestamp: The data returned will represent the device environment monitors as of this date and time. If omitted, the result will indicate the most recently collected data. :type timestamp: DateTime | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param methods: A list of device environment monitor methods. The listed methods will be called on each device environment monitor returned and included in the output. Available methods are: device. :type methods: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param include: A list of associated object types to include in the output. The listed associations will be returned as outputs named according to the association name (see outputs below). Available includes are: device. :type include: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 0 :param start: The record number to return in the selected page of data. It will always appear, although it may not be the first record. See the :limit for more information. :type start: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 1000 :param limit: The size of the page of data, that is, the maximum number of records returned. The limit size will be used to break the data up into pages and the first page with the start record will be returned. So if you have 100 records and use a :limit of 10 and a :start of 10, you will get records 10-19. The maximum limit is 10000. :type limit: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` DevEnvMonID :param sort: The data field(s) to use for sorting the output. Default is DevEnvMonID. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. :type sort: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` asc :param dir: The direction(s) in which to sort the data. Default is 'asc'. Valid values are 'asc' and 'desc'. :type dir: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param select: The list of attributes to return for each DeviceEnvironmentMonitor. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. If empty or omitted, all attributes will be returned. :type select: Array | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_field: The field name for NIOS GOTO that is used for locating a row position of records. :type goto_field: String | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_value: The value of goto_field for NIOS GOTO that is used for locating a row position of records. :type goto_value: String **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return device_environment_monitors: An array of the DeviceEnvironmentMonitor objects that match the specified input criteria. :rtype device_environment_monitors: Array of DeviceEnvironmentMonitor """ return self.api_list_request(self._get_method_fullname("index"), kwargs) def show(self, **kwargs): """Shows the details for the specified device environment monitor. **Inputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` True | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param methods: A list of device environment monitor methods. The listed methods will be called on each device environment monitor returned and included in the output. Available methods are: device. :type methods: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param include: A list of associated object types to include in the output. The listed associations will be returned as outputs named according to the association name (see outputs below). Available includes are: device. :type include: Array of String **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return device_environment_monitor: The device environment monitor identified by the specified DevEnvMonID. :rtype device_environment_monitor: DeviceEnvironmentMonitor """ return self.api_request(self._get_method_fullname("show"), kwargs) def search(self, **kwargs): """Lists the available device environment monitors matching the input criteria. This method provides a more flexible search interface than the index method, but searching using this method is more demanding on the system and will not perform to the same level as the index method. The input fields listed below will be used as in the index method, to filter the result, along with the optional query string and XML filter described below. **Inputs** | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DataSourceID: The internal NetMRI identifier for the collector NetMRI that collected this data record. :type DataSourceID: Integer | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DataSourceID: The internal NetMRI identifier for the collector NetMRI that collected this data record. :type DataSourceID: Array of Integer | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonChangedCols: The fields that changed between this revision of the record and the previous revision. :type DevEnvMonChangedCols: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonChangedCols: The fields that changed between this revision of the record and the previous revision. :type DevEnvMonChangedCols: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonDescr: The NetMRI-determined description of the device environment monitor. :type DevEnvMonDescr: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonDescr: The NetMRI-determined description of the device environment monitor. :type DevEnvMonDescr: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonEndTime: The ending effective time of this record, or empty if still in effect. :type DevEnvMonEndTime: DateTime | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonEndTime: The ending effective time of this record, or empty if still in effect. :type DevEnvMonEndTime: Array of DateTime | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonHighShutdown: The high value of the shut down process in the device environment monitor. :type DevEnvMonHighShutdown: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonHighShutdown: The high value of the shut down process in the device environment monitor. :type DevEnvMonHighShutdown: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonHighWarnVal: The high value of the warning message in the device environment monitor. :type DevEnvMonHighWarnVal: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonHighWarnVal: The high value of the warning message in the device environment monitor. :type DevEnvMonHighWarnVal: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Array of Integer | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonIndex: The index of the device in the device environment. :type DevEnvMonIndex: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonIndex: The index of the device in the device environment. :type DevEnvMonIndex: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonLowShutdown: The low value of the shut down process in the device environment monitor. :type DevEnvMonLowShutdown: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonLowShutdown: The low value of the shut down process in the device environment monitor. :type DevEnvMonLowShutdown: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonLowWarnVal: The low value of the warning message in the device environment monitor. :type DevEnvMonLowWarnVal: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonLowWarnVal: The low value of the warning message in the device environment monitor. :type DevEnvMonLowWarnVal: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonMeasure: The measure of the device environment monitor. :type DevEnvMonMeasure: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonMeasure: The measure of the device environment monitor. :type DevEnvMonMeasure: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonStartTime: The starting effective time of this record. :type DevEnvMonStartTime: DateTime | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonStartTime: The starting effective time of this record. :type DevEnvMonStartTime: Array of DateTime | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonState: The current state of the device in the device environment monitor. :type DevEnvMonState: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonState: The current state of the device in the device environment monitor. :type DevEnvMonState: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonStatus: The status of the device in the Device Environment Monitor. :type DevEnvMonStatus: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonStatus: The status of the device in the Device Environment Monitor. :type DevEnvMonStatus: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonStatusAlert: The alert status of the device environment monitor. :type DevEnvMonStatusAlert: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonStatusAlert: The alert status of the device environment monitor. :type DevEnvMonStatusAlert: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonStatusMessage: The status message of the device environment monitor. :type DevEnvMonStatusMessage: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonStatusMessage: The status message of the device environment monitor. :type DevEnvMonStatusMessage: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonTimestamp: The date and time this record was collected or calculated. :type DevEnvMonTimestamp: DateTime | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonTimestamp: The date and time this record was collected or calculated. :type DevEnvMonTimestamp: Array of DateTime | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DevEnvMonType: The NetMRI-determined monitor type of Device Environment. :type DevEnvMonType: String | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DevEnvMonType: The NetMRI-determined monitor type of Device Environment. :type DevEnvMonType: Array of String | ``api version min:`` 2.4 | ``api version max:`` 2.4 | ``required:`` False | ``default:`` None :param DeviceID: The internal NetMRI identifier for the device from which device environment information was collected. :type DeviceID: Integer | ``api version min:`` 2.5 | ``api version max:`` None | ``required:`` False | ``default:`` None :param DeviceID: The internal NetMRI identifier for the device from which device environment information was collected. :type DeviceID: Array of Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param DeviceGroupID: The internal NetMRI identifier of the device groups to which to limit the results. :type DeviceGroupID: Array of Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param timestamp: The data returned will represent the device environment monitors as of this date and time. If omitted, the result will indicate the most recently collected data. :type timestamp: DateTime | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param methods: A list of device environment monitor methods. The listed methods will be called on each device environment monitor returned and included in the output. Available methods are: device. :type methods: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param include: A list of associated object types to include in the output. The listed associations will be returned as outputs named according to the association name (see outputs below). Available includes are: device. :type include: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 0 :param start: The record number to return in the selected page of data. It will always appear, although it may not be the first record. See the :limit for more information. :type start: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 1000 :param limit: The size of the page of data, that is, the maximum number of records returned. The limit size will be used to break the data up into pages and the first page with the start record will be returned. So if you have 100 records and use a :limit of 10 and a :start of 10, you will get records 10-19. The maximum limit is 10000. :type limit: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` DevEnvMonID :param sort: The data field(s) to use for sorting the output. Default is DevEnvMonID. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. :type sort: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` asc :param dir: The direction(s) in which to sort the data. Default is 'asc'. Valid values are 'asc' and 'desc'. :type dir: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param select: The list of attributes to return for each DeviceEnvironmentMonitor. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. If empty or omitted, all attributes will be returned. :type select: Array | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_field: The field name for NIOS GOTO that is used for locating a row position of records. :type goto_field: String | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_value: The value of goto_field for NIOS GOTO that is used for locating a row position of records. :type goto_value: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param query: This value will be matched against device environment monitors, looking to see if one or more of the listed attributes contain the passed value. You may also surround the value with '/' and '/' to perform a regular expression search rather than a containment operation. Any record that matches will be returned. The attributes searched are: DataSourceID, DevEnvMonChangedCols, DevEnvMonDescr, DevEnvMonEndTime, DevEnvMonHighShutdown, DevEnvMonHighWarnVal, DevEnvMonID, DevEnvMonIndex, DevEnvMonLowShutdown, DevEnvMonLowWarnVal, DevEnvMonMeasure, DevEnvMonStartTime, DevEnvMonState, DevEnvMonStatus, DevEnvMonStatusAlert, DevEnvMonStatusMessage, DevEnvMonTimestamp, DevEnvMonType, DeviceID. :type query: String | ``api version min:`` 2.3 | ``api version max:`` None | ``required:`` False | ``default:`` None :param xml_filter: A SetFilter XML structure to further refine the search. The SetFilter will be applied AFTER any search query or field values, but before any limit options. The limit and pagination will be enforced after the filter. Remind that this kind of filter may be costly and inefficient if not associated with a database filtering. :type xml_filter: String **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return device_environment_monitors: An array of the DeviceEnvironmentMonitor objects that match the specified input criteria. :rtype device_environment_monitors: Array of DeviceEnvironmentMonitor """ return self.api_list_request(self._get_method_fullname("search"), kwargs) def find(self, **kwargs): """Lists the available device environment monitors matching the input specification. This provides the most flexible search specification of all the query mechanisms, enabling searching using comparison operations other than equality. However, it is more complex to use and will not perform as efficiently as the index or search methods. In the input descriptions below, 'field names' refers to the following fields: DataSourceID, DevEnvMonChangedCols, DevEnvMonDescr, DevEnvMonEndTime, DevEnvMonHighShutdown, DevEnvMonHighWarnVal, DevEnvMonID, DevEnvMonIndex, DevEnvMonLowShutdown, DevEnvMonLowWarnVal, DevEnvMonMeasure, DevEnvMonStartTime, DevEnvMonState, DevEnvMonStatus, DevEnvMonStatusAlert, DevEnvMonStatusMessage, DevEnvMonTimestamp, DevEnvMonType, DeviceID. **Inputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DataSourceID: The operator to apply to the field DataSourceID. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DataSourceID: The internal NetMRI identifier for the collector NetMRI that collected this data record. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DataSourceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DataSourceID: If op_DataSourceID is specified, the field named in this input will be compared to the value in DataSourceID using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DataSourceID must be specified if op_DataSourceID is specified. :type val_f_DataSourceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DataSourceID: If op_DataSourceID is specified, this value will be compared to the value in DataSourceID using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DataSourceID must be specified if op_DataSourceID is specified. :type val_c_DataSourceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonChangedCols: The operator to apply to the field DevEnvMonChangedCols. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonChangedCols: The fields that changed between this revision of the record and the previous revision. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonChangedCols: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonChangedCols: If op_DevEnvMonChangedCols is specified, the field named in this input will be compared to the value in DevEnvMonChangedCols using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonChangedCols must be specified if op_DevEnvMonChangedCols is specified. :type val_f_DevEnvMonChangedCols: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonChangedCols: If op_DevEnvMonChangedCols is specified, this value will be compared to the value in DevEnvMonChangedCols using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonChangedCols must be specified if op_DevEnvMonChangedCols is specified. :type val_c_DevEnvMonChangedCols: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonDescr: The operator to apply to the field DevEnvMonDescr. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonDescr: The NetMRI-determined description of the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonDescr: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonDescr: If op_DevEnvMonDescr is specified, the field named in this input will be compared to the value in DevEnvMonDescr using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonDescr must be specified if op_DevEnvMonDescr is specified. :type val_f_DevEnvMonDescr: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonDescr: If op_DevEnvMonDescr is specified, this value will be compared to the value in DevEnvMonDescr using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonDescr must be specified if op_DevEnvMonDescr is specified. :type val_c_DevEnvMonDescr: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonEndTime: The operator to apply to the field DevEnvMonEndTime. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonEndTime: The ending effective time of this record, or empty if still in effect. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonEndTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonEndTime: If op_DevEnvMonEndTime is specified, the field named in this input will be compared to the value in DevEnvMonEndTime using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonEndTime must be specified if op_DevEnvMonEndTime is specified. :type val_f_DevEnvMonEndTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonEndTime: If op_DevEnvMonEndTime is specified, this value will be compared to the value in DevEnvMonEndTime using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonEndTime must be specified if op_DevEnvMonEndTime is specified. :type val_c_DevEnvMonEndTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonHighShutdown: The operator to apply to the field DevEnvMonHighShutdown. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonHighShutdown: The high value of the shut down process in the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonHighShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonHighShutdown: If op_DevEnvMonHighShutdown is specified, the field named in this input will be compared to the value in DevEnvMonHighShutdown using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonHighShutdown must be specified if op_DevEnvMonHighShutdown is specified. :type val_f_DevEnvMonHighShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonHighShutdown: If op_DevEnvMonHighShutdown is specified, this value will be compared to the value in DevEnvMonHighShutdown using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonHighShutdown must be specified if op_DevEnvMonHighShutdown is specified. :type val_c_DevEnvMonHighShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonHighWarnVal: The operator to apply to the field DevEnvMonHighWarnVal. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonHighWarnVal: The high value of the warning message in the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonHighWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonHighWarnVal: If op_DevEnvMonHighWarnVal is specified, the field named in this input will be compared to the value in DevEnvMonHighWarnVal using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonHighWarnVal must be specified if op_DevEnvMonHighWarnVal is specified. :type val_f_DevEnvMonHighWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonHighWarnVal: If op_DevEnvMonHighWarnVal is specified, this value will be compared to the value in DevEnvMonHighWarnVal using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonHighWarnVal must be specified if op_DevEnvMonHighWarnVal is specified. :type val_c_DevEnvMonHighWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonID: The operator to apply to the field DevEnvMonID. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonID: The internal NetMRI identifier of Device Environment. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonID: If op_DevEnvMonID is specified, the field named in this input will be compared to the value in DevEnvMonID using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonID must be specified if op_DevEnvMonID is specified. :type val_f_DevEnvMonID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonID: If op_DevEnvMonID is specified, this value will be compared to the value in DevEnvMonID using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonID must be specified if op_DevEnvMonID is specified. :type val_c_DevEnvMonID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonIndex: The operator to apply to the field DevEnvMonIndex. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonIndex: The index of the device in the device environment. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonIndex: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonIndex: If op_DevEnvMonIndex is specified, the field named in this input will be compared to the value in DevEnvMonIndex using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonIndex must be specified if op_DevEnvMonIndex is specified. :type val_f_DevEnvMonIndex: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonIndex: If op_DevEnvMonIndex is specified, this value will be compared to the value in DevEnvMonIndex using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonIndex must be specified if op_DevEnvMonIndex is specified. :type val_c_DevEnvMonIndex: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonLowShutdown: The operator to apply to the field DevEnvMonLowShutdown. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonLowShutdown: The low value of the shut down process in the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonLowShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonLowShutdown: If op_DevEnvMonLowShutdown is specified, the field named in this input will be compared to the value in DevEnvMonLowShutdown using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonLowShutdown must be specified if op_DevEnvMonLowShutdown is specified. :type val_f_DevEnvMonLowShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonLowShutdown: If op_DevEnvMonLowShutdown is specified, this value will be compared to the value in DevEnvMonLowShutdown using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonLowShutdown must be specified if op_DevEnvMonLowShutdown is specified. :type val_c_DevEnvMonLowShutdown: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonLowWarnVal: The operator to apply to the field DevEnvMonLowWarnVal. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonLowWarnVal: The low value of the warning message in the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonLowWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonLowWarnVal: If op_DevEnvMonLowWarnVal is specified, the field named in this input will be compared to the value in DevEnvMonLowWarnVal using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonLowWarnVal must be specified if op_DevEnvMonLowWarnVal is specified. :type val_f_DevEnvMonLowWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonLowWarnVal: If op_DevEnvMonLowWarnVal is specified, this value will be compared to the value in DevEnvMonLowWarnVal using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonLowWarnVal must be specified if op_DevEnvMonLowWarnVal is specified. :type val_c_DevEnvMonLowWarnVal: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonMeasure: The operator to apply to the field DevEnvMonMeasure. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonMeasure: The measure of the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonMeasure: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonMeasure: If op_DevEnvMonMeasure is specified, the field named in this input will be compared to the value in DevEnvMonMeasure using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonMeasure must be specified if op_DevEnvMonMeasure is specified. :type val_f_DevEnvMonMeasure: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonMeasure: If op_DevEnvMonMeasure is specified, this value will be compared to the value in DevEnvMonMeasure using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonMeasure must be specified if op_DevEnvMonMeasure is specified. :type val_c_DevEnvMonMeasure: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonStartTime: The operator to apply to the field DevEnvMonStartTime. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonStartTime: The starting effective time of this record. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonStartTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonStartTime: If op_DevEnvMonStartTime is specified, the field named in this input will be compared to the value in DevEnvMonStartTime using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonStartTime must be specified if op_DevEnvMonStartTime is specified. :type val_f_DevEnvMonStartTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonStartTime: If op_DevEnvMonStartTime is specified, this value will be compared to the value in DevEnvMonStartTime using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonStartTime must be specified if op_DevEnvMonStartTime is specified. :type val_c_DevEnvMonStartTime: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonState: The operator to apply to the field DevEnvMonState. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonState: The current state of the device in the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonState: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonState: If op_DevEnvMonState is specified, the field named in this input will be compared to the value in DevEnvMonState using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonState must be specified if op_DevEnvMonState is specified. :type val_f_DevEnvMonState: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonState: If op_DevEnvMonState is specified, this value will be compared to the value in DevEnvMonState using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonState must be specified if op_DevEnvMonState is specified. :type val_c_DevEnvMonState: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonStatus: The operator to apply to the field DevEnvMonStatus. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonStatus: The status of the device in the Device Environment Monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonStatus: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonStatus: If op_DevEnvMonStatus is specified, the field named in this input will be compared to the value in DevEnvMonStatus using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonStatus must be specified if op_DevEnvMonStatus is specified. :type val_f_DevEnvMonStatus: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonStatus: If op_DevEnvMonStatus is specified, this value will be compared to the value in DevEnvMonStatus using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonStatus must be specified if op_DevEnvMonStatus is specified. :type val_c_DevEnvMonStatus: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonStatusAlert: The operator to apply to the field DevEnvMonStatusAlert. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonStatusAlert: The alert status of the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonStatusAlert: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonStatusAlert: If op_DevEnvMonStatusAlert is specified, the field named in this input will be compared to the value in DevEnvMonStatusAlert using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonStatusAlert must be specified if op_DevEnvMonStatusAlert is specified. :type val_f_DevEnvMonStatusAlert: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonStatusAlert: If op_DevEnvMonStatusAlert is specified, this value will be compared to the value in DevEnvMonStatusAlert using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonStatusAlert must be specified if op_DevEnvMonStatusAlert is specified. :type val_c_DevEnvMonStatusAlert: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonStatusMessage: The operator to apply to the field DevEnvMonStatusMessage. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonStatusMessage: The status message of the device environment monitor. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonStatusMessage: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonStatusMessage: If op_DevEnvMonStatusMessage is specified, the field named in this input will be compared to the value in DevEnvMonStatusMessage using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonStatusMessage must be specified if op_DevEnvMonStatusMessage is specified. :type val_f_DevEnvMonStatusMessage: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonStatusMessage: If op_DevEnvMonStatusMessage is specified, this value will be compared to the value in DevEnvMonStatusMessage using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonStatusMessage must be specified if op_DevEnvMonStatusMessage is specified. :type val_c_DevEnvMonStatusMessage: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonTimestamp: The operator to apply to the field DevEnvMonTimestamp. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonTimestamp: The date and time this record was collected or calculated. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonTimestamp: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonTimestamp: If op_DevEnvMonTimestamp is specified, the field named in this input will be compared to the value in DevEnvMonTimestamp using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonTimestamp must be specified if op_DevEnvMonTimestamp is specified. :type val_f_DevEnvMonTimestamp: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonTimestamp: If op_DevEnvMonTimestamp is specified, this value will be compared to the value in DevEnvMonTimestamp using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonTimestamp must be specified if op_DevEnvMonTimestamp is specified. :type val_c_DevEnvMonTimestamp: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DevEnvMonType: The operator to apply to the field DevEnvMonType. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DevEnvMonType: The NetMRI-determined monitor type of Device Environment. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DevEnvMonType: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DevEnvMonType: If op_DevEnvMonType is specified, the field named in this input will be compared to the value in DevEnvMonType using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DevEnvMonType must be specified if op_DevEnvMonType is specified. :type val_f_DevEnvMonType: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DevEnvMonType: If op_DevEnvMonType is specified, this value will be compared to the value in DevEnvMonType using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DevEnvMonType must be specified if op_DevEnvMonType is specified. :type val_c_DevEnvMonType: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param op_DeviceID: The operator to apply to the field DeviceID. Valid values are: =, <>, rlike, not rlike, >, >=, <, <=, like, not like, is null, is not null, between. DeviceID: The internal NetMRI identifier for the device from which device environment information was collected. For the between operator the value will be treated as an Array if comma delimited string is passed, and it must contain an even number of values. :type op_DeviceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_f_DeviceID: If op_DeviceID is specified, the field named in this input will be compared to the value in DeviceID using the specified operator. That is, the value in this input will be treated as another field name, rather than a constant value. Either this field or val_c_DeviceID must be specified if op_DeviceID is specified. :type val_f_DeviceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param val_c_DeviceID: If op_DeviceID is specified, this value will be compared to the value in DeviceID using the specified operator. The value in this input will be treated as an explicit constant value. Either this field or val_f_DeviceID must be specified if op_DeviceID is specified. :type val_c_DeviceID: String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param DeviceGroupID: The internal NetMRI identifier of the device groups to which to limit the results. :type DeviceGroupID: Array of Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param timestamp: The data returned will represent the device environment monitors as of this date and time. If omitted, the result will indicate the most recently collected data. :type timestamp: DateTime | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param methods: A list of device environment monitor methods. The listed methods will be called on each device environment monitor returned and included in the output. Available methods are: device. :type methods: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param include: A list of associated object types to include in the output. The listed associations will be returned as outputs named according to the association name (see outputs below). Available includes are: device. :type include: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 0 :param start: The record number to return in the selected page of data. It will always appear, although it may not be the first record. See the :limit for more information. :type start: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` 1000 :param limit: The size of the page of data, that is, the maximum number of records returned. The limit size will be used to break the data up into pages and the first page with the start record will be returned. So if you have 100 records and use a :limit of 10 and a :start of 10, you will get records 10-19. The maximum limit is 10000. :type limit: Integer | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` DevEnvMonID :param sort: The data field(s) to use for sorting the output. Default is DevEnvMonID. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. :type sort: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` asc :param dir: The direction(s) in which to sort the data. Default is 'asc'. Valid values are 'asc' and 'desc'. :type dir: Array of String | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :param select: The list of attributes to return for each DeviceEnvironmentMonitor. Valid values are DevEnvMonID, DeviceID, DataSourceID, DevEnvMonStartTime, DevEnvMonEndTime, DevEnvMonTimestamp, DevEnvMonChangedCols, DevEnvMonIndex, DevEnvMonType, DevEnvMonDescr, DevEnvMonState, DevEnvMonStatus, DevEnvMonMeasure, DevEnvMonLowWarnVal, DevEnvMonLowShutdown, DevEnvMonHighWarnVal, DevEnvMonHighShutdown, DevEnvMonStatusMessage, DevEnvMonStatusAlert. If empty or omitted, all attributes will be returned. :type select: Array | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_field: The field name for NIOS GOTO that is used for locating a row position of records. :type goto_field: String | ``api version min:`` 2.8 | ``api version max:`` None | ``required:`` False | ``default:`` None :param goto_value: The value of goto_field for NIOS GOTO that is used for locating a row position of records. :type goto_value: String | ``api version min:`` 2.3 | ``api version max:`` None | ``required:`` False | ``default:`` None :param xml_filter: A SetFilter XML structure to further refine the search. The SetFilter will be applied AFTER any search query or field values, but before any limit options. The limit and pagination will be enforced after the filter. Remind that this kind of filter may be costly and inefficient if not associated with a database filtering. :type xml_filter: String **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return device_environment_monitors: An array of the DeviceEnvironmentMonitor objects that match the specified input criteria. :rtype device_environment_monitors: Array of DeviceEnvironmentMonitor """ return self.api_list_request(self._get_method_fullname("find"), kwargs) def data_source(self, **kwargs): """The collector NetMRI that collected this data record. **Inputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` True | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return : The collector NetMRI that collected this data record. :rtype : DataSource """ return self.api_request(self._get_method_fullname("data_source"), kwargs) def device(self, **kwargs): """The device from which this data was collected. **Inputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` True | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return : The device from which this data was collected. :rtype : Device """ return self.api_request(self._get_method_fullname("device"), kwargs) def infradevice(self, **kwargs): """The device from which this data was collected. **Inputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` True | ``default:`` None :param DevEnvMonID: The internal NetMRI identifier of Device Environment. :type DevEnvMonID: Integer **Outputs** | ``api version min:`` None | ``api version max:`` None | ``required:`` False | ``default:`` None :return : The device from which this data was collected. :rtype : InfraDevice """ return self.api_request(self._get_method_fullname("infradevice"), kwargs)

55.194949

773

0.62598

8,124

69,932

5.337765

0.037543

0.068259

0.044369

0.073056

0.95148

0.950604

0.918412

0.906143

0.896573

0.893921

0

0.004155

0.294457

69,932

1,266

774

55.238547

0.874726

0.829177

0

0.074899

0.027328

0

1

0.411765

false

0

0.058824

0

1

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null

0

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0

1

0

null

0

1

0

1

0

9

fa79e9dcf40af0a83a9acd7897863e5ff55fe079

1,169

py

Python

python/hetu/gpu_links/SamMaxLink.py

codecaution/Hetu

e278732c2fe3554c8d576585f5bcbf79ade31b68

[ "Apache-2.0" ]

null

python/hetu/gpu_links/SamMaxLink.py

codecaution/Hetu

e278732c2fe3554c8d576585f5bcbf79ade31b68

[ "Apache-2.0" ]

null

python/hetu/gpu_links/SamMaxLink.py

codecaution/Hetu

e278732c2fe3554c8d576585f5bcbf79ade31b68

[ "Apache-2.0" ]

3

2021-11-29T13:47:48.000Z

2022-03-03T02:00:43.000Z

from __future__ import absolute_import import ctypes from .._base import _LIB from .. import ndarray as _nd def sammax_link(in_mat, top1_group, topk_indice, out_mat, num_local_gpus, stream=None): assert isinstance(in_mat, _nd.NDArray) assert isinstance(top1_group, _nd.NDArray) assert isinstance(topk_indice, _nd.NDArray) assert isinstance(out_mat, _nd.NDArray) _LIB.DLGpuSamMax( in_mat.handle, top1_group.handle, topk_indice.handle, out_mat.handle, ctypes.c_int(num_local_gpus), stream.handle if stream else None) def sammax_grad_link(output_grad, in_mat, top1_group, topk_indice, out_mat, num_local_gpus, stream=None): assert isinstance(output_grad, _nd.NDArray) assert isinstance(in_mat, _nd.NDArray) assert isinstance(top1_group, _nd.NDArray) assert isinstance(topk_indice, _nd.NDArray) assert isinstance(out_mat, _nd.NDArray) _LIB.DLGpuSamMaxGrad( output_grad.handle, in_mat.handle, top1_group.handle, topk_indice.handle, out_mat.handle, ctypes.c_int(num_local_gpus), stream.handle if stream else None)

46.76

162

0.71343

160

1,169

4.84375

0.225

0.185806

0.135484

0.225806

0.766452

0

0.006472

0.207015

1,169

24

163

48.708333

0.829558

0

0.421053

0

0.473684

1

0.105263

false

0

0.210526

0

0.315789

0

null

0

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0

1

0

null

0

1

0

7

35707622aa43bdf303238809cc191637a145ceca

4,056

py

Python

chord_rec/models/seq2seq/Seq2Seq.py

TianxueHu/ChordSymbolRec

d64a5be4f4914e6f682cb6d4079d7ba8a6fc2eac

[ "Unlicense", "MIT" ]

null

chord_rec/models/seq2seq/Seq2Seq.py

TianxueHu/ChordSymbolRec

d64a5be4f4914e6f682cb6d4079d7ba8a6fc2eac

[ "Unlicense", "MIT" ]

null

chord_rec/models/seq2seq/Seq2Seq.py

TianxueHu/ChordSymbolRec

d64a5be4f4914e6f682cb6d4079d7ba8a6fc2eac

[ "Unlicense", "MIT" ]

null

import random import torch import torch.nn as nn import torch.optim as optim class BaseSeq2Seq(nn.Module): """ The Sequence to Sequence model. """ def __init__(self, encoder, decoder, device): super().__init__() self.device = device self.encoder = encoder.to(device) self.decoder = decoder.to(device) assert self.encoder.encoder_hidden_size == self.decoder.decoder_hidden_size, \ "Hidden dimensions of encoder and decoder must be equal!" assert self.encoder.n_layers == self.decoder.n_layers, \ "Encoder and decoder must have equal number of layers!" def forward(self, source, target, out_seq_len = None, teacher_forcing = True, start_idx = None): """ The forward pass of the Seq2Seq model. Args: source (tensor): sequences in source language of shape (batch_size, seq_len, input_size) out_seq_len (int): the maximum length of the output sequence. If None, the length is determined by the input sequences. """ batch_size = source.shape[0] if out_seq_len is None: seq_len = source.shape[1] if start_idx is None: start_idx = 0 outputs = torch.full((batch_size, seq_len, self.decoder.output_size), start_idx, dtype = torch.float).to(self.device) # problem??? # outputs = torch.zeros(batch_size, seq_len, self.decoder.output_size).to(self.device) encoder_outputs, hidden = self.encoder(source) # first input to the decoder is the <sos> token input = target[:,0].unsqueeze(1) # input = source[:,0] for t in range(1, seq_len): output, hidden = self.decoder(input, hidden) outputs[:,t,:] = output # input = output.max(1)[1].unsqueeze(1) if teacher_forcing: input = target[:,t].unsqueeze(1) else: input = output.max(1)[1].unsqueeze(1) # print(outputs) return outputs class AttnSeq2Seq(nn.Module): """ The Sequence to Sequence model. """ def __init__(self, encoder, decoder, device): super().__init__() self.device = device self.encoder = encoder.to(device) self.decoder = decoder.to(device) assert self.encoder.encoder_hidden_size == self.decoder.decoder_hidden_size, \ "Hidden dimensions of encoder and decoder must be equal!" assert self.encoder.n_layers == self.decoder.n_layers, \ "Encoder and decoder must have equal number of layers!" def forward(self, source, target, out_seq_len = None, teacher_forcing = True, start_idx = None): """ The forward pass of the Seq2Seq model. Args: source (tensor): sequences in source language of shape (batch_size, seq_len, input_size) out_seq_len (int): the maximum length of the output sequence. If None, the length is determined by the input sequences. """ batch_size = source.shape[0] if out_seq_len is None: seq_len = source.shape[1] if start_idx is None: start_idx = 0 outputs = torch.full((batch_size, seq_len, self.decoder.output_size), start_idx, dtype = torch.float).to(self.device) # problem??? # outputs = torch.zeros(batch_size, seq_len, self.decoder.output_size).to(self.device) encoder_outputs, hidden = self.encoder(source) # first input to the decoder is the <sos> token input = target[:,0].unsqueeze(1) # input = source[:,0] for t in range(1, seq_len): output, hidden, attn= self.decoder(input, hidden, encoder_outputs) outputs[:,t,:] = output # input = output.max(1)[1].unsqueeze(1) if teacher_forcing: input = target[:,t].unsqueeze(1) else: input = output.max(1)[1].unsqueeze(1) # print(outputs) return outputs

36.872727

138

0.605523

517

4,056

4.597679

0.162476

0.040387

0.022718

0.037863

0.93395

0

0.01115

0.292406

4,056

109

139

37.211009

0.817073

0.251479

0

0.862069

0

0.074227

0

0.068966

1

0.068966

false

0

0.068966

0

0.206897

0

null

0

1

0

1

0

null

0

7

358a5d02775541f8be8951352bd6bbea60ce714e

121

py

Python

ktrain/text/ner/__init__.py

Niekvdplas/ktrain

808a212a9b8ebddd4e2d75eaca2e54a7ea990b4e

[ "Apache-2.0" ]

null

ktrain/text/ner/__init__.py

Niekvdplas/ktrain

808a212a9b8ebddd4e2d75eaca2e54a7ea990b4e

[ "Apache-2.0" ]

null

ktrain/text/ner/__init__.py

Niekvdplas/ktrain

808a212a9b8ebddd4e2d75eaca2e54a7ea990b4e

[ "Apache-2.0" ]

null

from .data import entities_from_conll2003, entities_from_gmb from .models import print_sequence_taggers, sequence_tagger

40.333333

60

0.884298

17

121

5.882353

0.647059

0.24

0

0.036036

0.082645

121

2

61

60.5

0.864865

0

1

0

true

0

1

0

1

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

35bf17164497091c3ba52580d494ce85ce0fef06

3,486

py

Python

crossword/tests/test_grid_undo_redo.py

philhanna/crossword

db05c8301bba8b1b5f31e059d2bba2c734b0d0c0

[ "MIT" ]

1

2020-06-30T06:22:31.000Z

crossword/tests/test_grid_undo_redo.py

philhanna/crossword

db05c8301bba8b1b5f31e059d2bba2c734b0d0c0

[ "MIT" ]

145

2020-06-02T17:33:18.000Z

2020-08-25T03:25:40.000Z

crossword/tests/test_grid_undo_redo.py

philhanna/crossword

db05c8301bba8b1b5f31e059d2bba2c734b0d0c0

[ "MIT" ]

null

from unittest import TestCase from crossword import Grid class TestGridUndoRedo(TestCase): def test_undo_with_empty_stack(self): grid = Grid(5) grid.undo() self.assertListEqual([], grid.undo_stack) self.assertListEqual([], grid.redo_stack) pass def test_redo_with_empty_stack(self): grid = Grid(5) grid.redo() self.assertListEqual([], grid.undo_stack) self.assertListEqual([], grid.redo_stack) pass def test_add__remove_black_cell(self): grid = Grid(5) grid.add_black_cell(1, 2) self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual([(1, 2)], grid.undo_stack) self.assertEqual([], grid.redo_stack) grid.remove_black_cell(1, 2) self.assertEqual(False, grid.is_black_cell(1, 2)) self.assertEqual(False, grid.is_black_cell(5, 4)) self.assertEqual([(1, 2), (1, 2)], grid.undo_stack) self.assertEqual([], grid.redo_stack) def test_add_undo(self): grid = Grid(5) grid.add_black_cell(1, 2) self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual([(1, 2)], grid.undo_stack) self.assertEqual([], grid.redo_stack) grid.undo() self.assertEqual(False, grid.is_black_cell(1, 2)) self.assertEqual(False, grid.is_black_cell(5, 4)) self.assertEqual([], grid.undo_stack) self.assertEqual([(1, 2)], grid.redo_stack) def test_add__add_undo_redo(self): grid = Grid(5) grid.add_black_cell(1, 2) grid.add_black_cell(3, 4) self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual(True, grid.is_black_cell(3, 4)) self.assertEqual(True, grid.is_black_cell(3, 2)) self.assertEqual([(1, 2), (3, 4)], grid.undo_stack) self.assertEqual([], grid.redo_stack) grid.undo() self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual(False, grid.is_black_cell(3, 4)) self.assertEqual(False, grid.is_black_cell(3, 2)) self.assertEqual([(1, 2)], grid.undo_stack) self.assertEqual([(3, 4)], grid.redo_stack) grid.redo() self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual(True, grid.is_black_cell(3, 4)) self.assertEqual(True, grid.is_black_cell(3, 2)) self.assertEqual([(1, 2), (3, 4)], grid.undo_stack) self.assertEqual([], grid.redo_stack) grid.undo() self.assertEqual(True, grid.is_black_cell(1, 2)) self.assertEqual(True, grid.is_black_cell(5, 4)) self.assertEqual(False, grid.is_black_cell(3, 4)) self.assertEqual(False, grid.is_black_cell(3, 2)) self.assertEqual([(1, 2)], grid.undo_stack) self.assertEqual([(3, 4)], grid.redo_stack) grid.undo() self.assertEqual(False, grid.is_black_cell(1, 2)) self.assertEqual(False, grid.is_black_cell(5, 4)) self.assertEqual(False, grid.is_black_cell(3, 4)) self.assertEqual(False, grid.is_black_cell(3, 2)) self.assertEqual([], grid.undo_stack) self.assertEqual([(3, 4), (1, 2)], grid.redo_stack)

38.733333

59

0.631096

497

3,486

4.209256

0.062374

0.329828

0.147228

0.200765

0.92782

0.919694

0.90392

0.876195

0.846558

0

0.037394

0.225186

3,486

89

60

39.168539

0.737134

0

0.831169

0

0.649351

1

0.064935

false

0.025974

0

0.103896

0

null

1

0

1

0

1

0

null

0

1

0

10

ea34631c3060db2ad16aa2888d898da8e44a3078

3,601

py

Python

jangl_webleads_inbound/verticals/health_insurance.py

jangl-platform/jangl-webleads-inbound

7ba7734c0614c946f52af23829b7c61ba4fa9460

[ "MIT" ]

1

2021-01-14T21:55:27.000Z

jangl_webleads_inbound/verticals/health_insurance.py

jangl-platform/jangl-webleads-inbound

7ba7734c0614c946f52af23829b7c61ba4fa9460

[ "MIT" ]

1

2021-06-10T21:22:36.000Z

jangl_webleads_inbound/verticals/health_insurance.py

jangl-platform/jangl-webleads-inbound

7ba7734c0614c946f52af23829b7c61ba4fa9460

[ "MIT" ]

null

from rest_framework import serializers allow_blank = {'default': '', 'initial': '', 'allow_blank': True} allow_null = {'default': None, 'initial': None, 'allow_null': True} empty_list = {'default': [], 'initial': [], 'many': True} class RelativeSerializer(serializers.Serializer): height = serializers.IntegerField(**allow_null) weight = serializers.IntegerField(**allow_null) birth_date = serializers.DateField(**allow_null) gender = serializers.CharField(max_length=1, **allow_blank) student = serializers.NullBooleanField(required=False) tobacco = serializers.NullBooleanField(required=False) medical_condition = serializers.CharField(max_length=200, **allow_blank) class CurrentPolicySerializer(serializers.Serializer): insurance_company = serializers.CharField(max_length=50, **allow_blank) expiration_date = serializers.DateField(**allow_null) insured_since = serializers.DateField(**allow_null) class PingDataSerializer(serializers.Serializer): height = serializers.IntegerField(max_value=100, **allow_null) weight = serializers.IntegerField(**allow_null) birth_date = serializers.DateField(**allow_null) gender = serializers.CharField(max_length=1, **allow_blank) student = serializers.NullBooleanField(required=False) tobacco = serializers.NullBooleanField(required=False) bmi = serializers.IntegerField(**allow_null) medical_condition = serializers.CharField(max_length=200, **allow_blank) currently_employed = serializers.NullBooleanField(required=False) number_in_household = serializers.IntegerField(**allow_null) household_income = serializers.IntegerField(**allow_null) hospitalized = serializers.NullBooleanField(required=False) ongoing_medical_treatment = serializers.NullBooleanField(required=False) previously_denied = serializers.NullBooleanField(required=False) prescriptions = serializers.NullBooleanField(required=False) prescription_description = serializers.CharField(max_length=255, **allow_blank) qualifying_life_condition = serializers.CharField(max_length=255, **allow_blank) spouse = RelativeSerializer(**allow_null) children = RelativeSerializer(**empty_list) current_policy = CurrentPolicySerializer(**allow_null) class PostDataSerializer(serializers.Serializer): height = serializers.IntegerField(max_value=100, **allow_null) weight = serializers.IntegerField(**allow_null) birth_date = serializers.DateField(**allow_null) gender = serializers.CharField(max_length=1, **allow_blank) student = serializers.NullBooleanField(required=False) tobacco = serializers.NullBooleanField(required=False) bmi = serializers.IntegerField(**allow_null) medical_condition = serializers.CharField(max_length=200, **allow_blank) currently_employed = serializers.NullBooleanField(required=False) number_in_household = serializers.IntegerField(**allow_null) household_income = serializers.IntegerField(**allow_null) hospitalized = serializers.NullBooleanField(required=False) ongoing_medical_treatment = serializers.NullBooleanField(required=False) previously_denied = serializers.NullBooleanField(required=False) prescriptions = serializers.NullBooleanField(required=False) prescription_description = serializers.CharField(max_length=255, **allow_blank) qualifying_life_condition = serializers.CharField(max_length=255, **allow_blank) spouse = RelativeSerializer(**allow_null) children = RelativeSerializer(**empty_list) current_policy = CurrentPolicySerializer(**allow_null)

51.442857

84

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ea7fa36bd4b3d1d04f92039a39c92c6c8c915a72

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Python

hovercraft/tests/test_position.py

royaso/hovercraft

2ca3e8cfd00f5e28077d37bf142e1efd55a63df3

[ "MIT" ]

null

hovercraft/tests/test_position.py

royaso/hovercraft

2ca3e8cfd00f5e28077d37bf142e1efd55a63df3

[ "MIT" ]

null

hovercraft/tests/test_position.py

royaso/hovercraft

2ca3e8cfd00f5e28077d37bf142e1efd55a63df3

[ "MIT" ]

null

import os import unittest from pkg_resources import resource_string from lxml import etree from hovercraft.parse import rst2xml, SlideMaker from hovercraft.position import gather_positions, calculate_positions, position_slides TEST_DATA = os.path.join(os.path.split(__file__)[0], 'test_data') def make_tree(file_name): """Loads reStructuredText, outputs an lxml tree""" rst = resource_string(__name__, os.path.join('test_data', file_name)) xml = rst2xml(rst) return SlideMaker(etree.fromstring(xml)).walk() class GatherTests(unittest.TestCase): """Tests that position information is correctly parsed""" def test_gathering(self): tree = make_tree('positioning.rst') positions = list(gather_positions(tree)) self.assertEqual(positions, [ {'data-x': 'r0', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': '1', 'is_path': False}, {'data-x': 'r1600', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': False}, {'data-x': 'r1600', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True, 'path': 'm 100 100 l 200 0 l 0 200'}, {'data-x': 'r1600', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True}, {'data-x': 'r1600', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True}, {'data-x': '0', 'data-y': '0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': False}, {'data-x': 'r0', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': '90', 'data-scale': 'r0', 'is_path': False}, {'data-x': 'r0', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': False}, {'data-x': 'r0', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True, 'path': 'm 100 100 l 200 0 l 0 200'}, {'data-x': 'r0', 'data-y': 'r0', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True}, {'data-x': 'r0', 'data-y': 'r0', 'data-z': '1000', 'data-rotate-x': '180', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': True}, {'data-x': '3000', 'data-y': '1000', 'data-z': 'r0', 'data-rotate-x': 'r0', 'data-rotate-y': 'r0', 'data-rotate-z': 'r0', 'data-scale': 'r0', 'is_path': False}, ]) class CalculateTests(unittest.TestCase): """Tests that positions are correctly calculated""" def test_square(self): # Slides, positioned in a square positions = [ {'data-x': '0', 'data-y': '0'}, {'data-x': 'r1200', 'data-y': '0'}, {'data-x': 'r1200', 'data-y': '0'}, {'data-x': 'r1200', 'data-y': '0'}, {'data-x': 'r0', 'data-y': 'r-1000'}, {'data-x': 'r0', 'data-y': 'r-1000'}, {'data-x': 'r0', 'data-y': 'r-1000'}, {'data-x': 'r-1200', 'data-y': 'r0'}, {'data-x': 'r-1200', 'data-y': 'r0'}, {'data-x': 'r-1200', 'data-y': 'r0'}, {'data-x': 'r0', 'data-y': 'r1000'}, {'data-x': 'r0', 'data-y': 'r1000'}, ] positions = list(calculate_positions(positions)) self.assertEqual(positions, [ {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1200, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 2400, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3600, 'data-y': -1000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3600, 'data-y': -2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3600, 'data-y': -3000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 2400, 'data-y': -3000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1200, 'data-y': -3000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 0, 'data-y': -3000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 0, 'data-y': -2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 0, 'data-y': -1000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, ]) def test_relative_positioning(self): # Relative positioning is probably the most useful positioning. # It allows you to insert or remove a slide, and everything adjusts. positions = [ # The first two slides are just default positons {'data-x': 'r0', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0'}, # Then suddenly we move vertically! {'data-x': 'r0', 'data-y': 'r1000'}, # Continue the same way one slide. {'data-x': 'r0', 'data-y': 'r1000'}, # Stand still {'data-x': 'r0', 'data-y': 'r0'}, # Stand still again! {'data-x': 'r0', 'data-y': 'r0'}, # Move a little bit {'data-x': 'r-40', 'data-y': 'r-200'}, # Go back to normal movement to the right {'data-x': 'r1600', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0'}, # Absolute movement back to start! {'data-x': '0', 'data-y': '0'}, # Absolute movement to a center for end (with zoomout for example) {'data-x': '3000', 'data-y': '1000'}, ] positions = list(calculate_positions(positions)) self.assertEqual(positions, [ {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 1000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1560, 'data-y': 1800, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3160, 'data-y': 1800, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 4760, 'data-y': 1800, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 6360, 'data-y': 1800, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3000, 'data-y': 1000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, ]) def test_absolute_path(self): # Position slides along a path positions = [ {'data-x': 'r0', 'data-y': 'r0', 'path': 'M 100 100 L 300 100 L 300 300', 'is_path': True}, {'is_path': True}, {'is_path': True}, {'is_path': True}, {'is_path': True}, ] positions = list(calculate_positions(positions)) self.assertEqual(positions, [ {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 2000, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 4000, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 44.99999999999999, 'data-scale': 1}, {'data-x': 4000, 'data-y': 2000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, {'data-x': 4000, 'data-y': 4000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, ]) def test_relative_path(self): positions = [ {'data-x': 'r0', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0', 'is_path': True, 'path': 'm 100 100 l 200 0 l 0 200', }, {'data-x': 'r0', 'data-y': 'r0', 'is_path': True}, {'data-x': 'r0', 'data-y': 'r0', 'is_path': True}, {'data-x': 'r1600', 'data-y': 'r0'}, {'data-x': 'r0', 'data-y': 'r2400'}, ] positions = list(calculate_positions(positions)) self.assertEqual(positions, [ {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3200, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, # This point is exactly on a 90 degree angle. Therefore, # it's angle is calculated as 45 degrees, it being the # average. {'data-x': 5600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 44.99999999999999, 'data-scale': 1}, {'data-x': 5600, 'data-y': 2400, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, {'data-x': 7200, 'data-y': 2400, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, {'data-x': 7200, 'data-y': 4800, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, ]) def test_complex_path(self): positions = [ {'data-x': 'r0', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0'}, {'data-x': 'r1600', 'data-y': 'r0', 'path': 'm 100 100 l 200 0 l 0 200', 'is_path': True}, {'is_path': True}, {'is_path': True}, # Note that we don't change the rotation, so it stays at 90, here. {'data-x': '0', 'data-y': '0'}, # No new x and y, previous was absolute: Stay still! {}, {'data-x': 'r0', 'data-y': 'r0', 'path': 'm 100 100 l 200 0 l 0 200', 'is_path': True}, {'is_path': True}, {'is_path': True}, {'data-x': '3000', 'data-y': '1000', 'data-rotate-z': '0'}, ] positions = list(calculate_positions(positions)) self.assertEqual(positions, [ {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 1600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 3200, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 5600, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 44.99999999999999, 'data-scale': 1}, {'data-x': 5600, 'data-y': 2400, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, # Note that we don't change the rotation, so it stays at 90, here. {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, # No settings, still same place and rotation. {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, # We start a path, but x and y are r0, so no movement. # However, the rotation will come from the path, so it resets to 0. {'data-x': 0, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, {'data-x': 2400, 'data-y': 0, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 44.99999999999999, 'data-scale': 1}, {'data-x': 2400, 'data-y': 2400, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 90.0, 'data-scale': 1}, {'data-x': 3000, 'data-y': 1000, 'data-z': 0, 'data-rotate-x': 0, 'data-rotate-y': 0, 'data-rotate-z': 0, 'data-scale': 1}, ]) class PositionTest(unittest.TestCase): def test_complete(self): tree = make_tree('positioning.rst') # Position the slides: position_slides(tree) # Get all slide position data: positions = [] for step in tree.findall('step'): pos = {} for key in step.attrib: if key.startswith('data-'): pos[key] = step.attrib[key] positions.append(pos) self.assertEqual(positions, [ {'data-x': '0', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '0', 'data-scale': '1'}, {'data-x': '1600', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '0', 'data-scale': '1'}, # Because of the path, we now get an explicit rotation: {'data-x': '3200', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '0', 'data-scale': '1'}, {'data-x': '5600', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '44.99999999999999', 'data-scale': '1'}, {'data-x': '5600', 'data-y': '2400', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '90.0', 'data-scale': '1'}, # Rotation carries over from last part of path. {'data-x': '0', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '90.0', 'data-scale': '1'}, # No position change {'data-x': '0', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '90', 'data-scale': '1'}, # No change at all. {'data-x': '0', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '90', 'data-scale': '1'}, # Path starts, rotation comes from path: {'data-x': '0', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '0', 'data-scale': '1'}, {'data-x': '2400', 'data-y': '0', 'data-z': '0', 'data-rotate-x': '0', 'data-rotate-y': '0', 'data-rotate-z': '44.99999999999999', 'data-scale': '1'}, # Explicit rotate-x and z, automatic position including rotate-z from path. {'data-x': '2400', 'data-y': '2400', 'data-z': '1000', 'data-rotate-x': '180', 'data-rotate-y': '0', 'data-rotate-z': '90.0', 'data-scale': '1'}, # Explicit x and y, all other carry over from last slide. {'data-x': '3000', 'data-y': '1000', 'data-z': '1000', 'data-rotate-x': '180', 'data-rotate-y': '0', 'data-rotate-z': '90.0', 'data-scale': '1'}, ]) if __name__ == '__main__': unittest.main()

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576265c72506f0b10ca6c1c578362d47e884e43d

1,418

py

Python

rest/service/restapi.py

estuaryoss/estuary-agent

b8b4264a616be21c86458da75cf29d13a8fb263d

[ "Apache-2.0" ]

null

rest/service/restapi.py

estuaryoss/estuary-agent

b8b4264a616be21c86458da75cf29d13a8fb263d

[ "Apache-2.0" ]

null

rest/service/restapi.py

estuaryoss/estuary-agent

b8b4264a616be21c86458da75cf29d13a8fb263d

[ "Apache-2.0" ]

null

import requests class RestApi: def __init__(self, connection): """ REST API Service usually used for self calls """ self.conn = connection self.__timeout = 5 if not self.conn.get('timeout') else self.conn.get('timeout') def post(self, data, headers): url_format = f"{self.conn.get('protocol')}://{self.conn.get('ip')}:{self.conn.get('port')}{self.conn.get('endpoint')}" return requests.post(url_format, headers=headers, data=data, timeout=self.__timeout, verify=self.conn.get('cert')) def put(self, data, headers): url_format = f"{self.conn.get('protocol')}://{self.conn.get('ip')}:{self.conn.get('port')}{self.conn.get('endpoint')}" return requests.put(url_format, headers=headers, data=data, timeout=self.__timeout, verify=self.conn.get('cert')) def delete(self, headers): url_format = f"{self.conn.get('protocol')}://{self.conn.get('ip')}:{self.conn.get('port')}{self.conn.get('endpoint')}" return requests.delete(url_format, headers=headers, timeout=self.__timeout, verify=self.conn.get('cert')) def get(self, headers): url_format = f"{self.conn.get('protocol')}://{self.conn.get('ip')}:{self.conn.get('port')}{self.conn.get('endpoint')}" return requests.get(url_format, headers=headers, timeout=self.__timeout, verify=self.conn.get('cert'))

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10

aa2e9a7bae656c1aadea77715a5fbfdfe3c7a26f

18,797

py

Python

components/plots.py

koukyo1994/streamlit-audio

f9dd8e86b332e4425d964b59642e0b5eaf8e0c0a

[ "MIT" ]

5

2020-09-18T05:35:52.000Z

2022-02-28T02:29:15.000Z

components/plots.py

koukyo1994/streamlit-audio

f9dd8e86b332e4425d964b59642e0b5eaf8e0c0a

[ "MIT" ]

null

components/plots.py

koukyo1994/streamlit-audio

f9dd8e86b332e4425d964b59642e0b5eaf8e0c0a

[ "MIT" ]

1

2021-01-15T01:49:35.000Z

import librosa import librosa.display as display import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np import pandas as pd import streamlit as st def waveplot(y: np.ndarray, sr: int, processed=None, tp: pd.DataFrame=None, fp: pd.DataFrame=None): plot_wave = st.checkbox("Waveplot") if plot_wave: st.sidebar.markdown("#### Waveplot settings") start_second = st.sidebar.number_input( "start second", min_value=0, max_value=len(y) // sr, value=0, step=1, key="waveplot_start") end_second = st.sidebar.number_input( "end second", min_value=0, max_value=len(y) // sr, value=len(y) // sr, step=1, key="waveplot_end") start_index = start_second * sr if end_second == len(y) // sr: end_index = len(y) else: end_index = end_second * sr fig = plt.figure(figsize=(12, 4)) plt.grid(True) display.waveplot(y[start_index:end_index], sr=sr, alpha=0.5) if processed is not None: display.waveplot( processed[start_index:end_index], sr=sr, alpha=0.5, color="red") if tp is not None and len(tp) > 0: for _, row in tp.iterrows(): plt.axvspan(row["t_min"], row["t_max"], color="g", alpha=0.5, label=str(row["species_id"])) if fp is not None and len(fp) > 0: for _, row in fp.iterrows(): plt.axvspan(row["t_min"], row["t_max"], color="r", alpha=0.5, label=str(row["species_id"])) plt.legend() st.pyplot(fig) def waveplot_with_annotation(y: np.ndarray, sr: int, annotation: pd.DataFrame, filename: str, processed=None): plot_wave = st.checkbox("Waveplot") if filename.endswith(".mp3"): filename = filename.replace(".mp3", ".wav") events = annotation.query(f"filename == '{filename}'") colors = [ "#bf6565", "#ac7ceb", "#e3e176", "#f081e1", "#e8cb6b", "#25b4db", "#fa787e", "#a9f274", "#1d7335", "#797fb3" ] if plot_wave: st.sidebar.markdown("#### Waveplot settings") start_second = st.sidebar.number_input( "start second", min_value=0, max_value=len(y) // sr, value=0, step=1, key="waveplot_start") end_second = st.sidebar.number_input( "end second", min_value=0, max_value=len(y) // sr, value=len(y) // sr, step=1, key="waveplot_end") start_index = start_second * sr if end_second == len(y) // sr: end_index = len(y) end_second = len(y) / sr else: end_index = end_second * sr events_in_period = events.query( f"onset >= {start_second} & offset <= {end_second}") uniq_labels = events_in_period["ebird_code"].unique().tolist() fig = plt.figure(figsize=(12, 4)) plt.grid(True) display.waveplot(y[start_index:end_index], sr=sr, alpha=0.5) used_color = [] # type: ignore for i, event in events_in_period.iterrows(): onset = event.onset offset = event.offset color = colors[uniq_labels.index(event.ebird_code)] if color not in used_color: label = event.ebird_code used_color.append(color) else: label = "_" + event.ebird_code plt.axvspan(onset, offset, facecolor=color, alpha=0.5, label=label) plt.legend() if processed is not None: display.waveplot( processed[start_index:end_index], sr=sr, alpha=0.5, color="red") st.pyplot(fig) @st.cache def melspectrogram(y: np.ndarray, params: dict, log=True): melspec = librosa.feature.melspectrogram(y=y, **params) if log: melspec = librosa.power_to_db(melspec) return melspec @st.cache def spectrogram(y: np.ndarray, params: dict, log=True): spec = librosa.stft(y, **params) if log: spec = librosa.power_to_db(spec) return spec def specshow_with_annotation(y: np.ndarray, sr: int, annotation: pd.DataFrame, filename: str, y_processed=None): plot_spectrogram = st.checkbox("Spectrogram plot") if filename.endswith(".mp3"): filename = filename.replace(".mp3", ".wav") events = annotation.query(f"filename == '{filename}'") colors = [ "#bf6565", "#ac7ceb", "#e3e176", "#f081e1", "#e8cb6b", "#25b4db", "#fa787e", "#a9f274", "#1d7335", "#797fb3" ] if plot_spectrogram: st.sidebar.markdown("#### Spectrogram plot settings") start_second = st.sidebar.number_input( "start second", min_value=0, max_value=len(y) // sr, value=0, step=1, key="specshow_start") end_second = st.sidebar.number_input( "end second", min_value=0, max_value=len(y) // sr, value=len(y) // sr, step=1, key="specshow_end") start_index = start_second * sr if end_second == len(y) // sr: end_index = len(y) else: end_index = end_second * sr y_plot = y[start_index:end_index] if y_processed is not None: y_plot_processed = y_processed[start_index:end_index] events_in_period = events.query( f"onset >= {start_second} & offset <= {end_second}") uniq_labels = events_in_period["ebird_code"].unique().tolist() st.sidebar.markdown("##### (Mel)spectrogram parameters") mel = st.sidebar.checkbox("Mel scale", value=True) n_fft = st.sidebar.number_input( "n_fft", min_value=64, max_value=8192, value=1024, step=64) hop_length = st.sidebar.number_input( "hop_length", min_value=1, max_value=2048, value=320, step=10) if mel: n_mels = st.sidebar.number_input( "n_mels", min_value=1, max_value=512, value=64, step=16) fmin = st.sidebar.number_input( "fmin", min_value=1, max_value=8192, value=20, step=100) fmax = st.sidebar.number_input( "fmax", min_value=4000, max_value=44100, value=14000, step=100) log = st.sidebar.checkbox("apply log", value=True) if mel: melspec_params = { "n_fft": n_fft, "hop_length": hop_length, "n_mels": n_mels, "fmin": fmin, "fmax": fmax, "sr": sr } else: spec_params = { "n_fft": n_fft, "hop_length": hop_length } if st.button("Show melspectrogram"): with st.spinner("Calculating melspectrogram"): if mel: spec = melspectrogram(y_plot, melspec_params, log) else: spec = spectrogram(y_plot, spec_params, log) if y_processed is not None: if mel: spec_processed = melspectrogram(y_plot_processed, melspec_params, log) else: spec_processed = spectrogram(y_plot_processed, spec_params, log) height, width = spec.shape st.write(f"{height} x {width} matrix") if y_processed is not None: with st.spinner("Plotting"): fig = plt.figure(figsize=(12, 8)) ax1 = fig.add_subplot(2, 1, 1) if mel: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax, ax=ax1) else: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear", ax=ax1) used_color = [] # type: ignore for i, event in events_in_period.iterrows(): onset = event.onset offset = event.offset color = colors[uniq_labels.index(event.ebird_code)] if color not in used_color: label = event.ebird_code used_color.append(color) else: label = "_" + event.ebird_code ax1.axvspan( onset, offset, facecolor=color, alpha=0.5, label=label) ax1.legend() ax2 = fig.add_subplot(2, 1, 2) if mel: display.specshow( spec_processed, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax, ax=ax2) else: display.specshow( spec_processed, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear", ax=ax2) else: with st.spinner("Plotting"): fig = plt.figure(figsize=(12, 4)) if mel: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax) plt.colorbar() else: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear") plt.colorbar() used_color = [] # type: ignore for i, event in events_in_period.iterrows(): onset = event.onset offset = event.offset color = colors[uniq_labels.index(event.ebird_code)] if color not in used_color: label = event.ebird_code used_color.append(color) else: label = "_" + event.ebird_code plt.axvspan( onset, offset, facecolor=color, alpha=0.5, label=label) plt.legend() st.pyplot(fig) def specshow(y: np.ndarray, sr: int, y_processed=None, tp: pd.DataFrame=None, fp: pd.DataFrame=None): plot_spectrogram = st.checkbox("Spectrogram plot") if plot_spectrogram: st.sidebar.markdown("#### Spectrogram plot settings") start_second = st.sidebar.number_input( "start second", min_value=0, max_value=len(y) // sr, value=0, step=1, key="specshow_start") end_second = st.sidebar.number_input( "end second", min_value=0, max_value=len(y) // sr, value=len(y) // sr, step=1, key="specshow_end") start_index = start_second * sr if end_second == len(y) // sr: end_index = len(y) else: end_index = end_second * sr y_plot = y[start_index:end_index] if y_processed is not None: y_plot_processed = y_processed[start_index:end_index] st.sidebar.markdown("##### (Mel)spectrogram parameters") mel = st.sidebar.checkbox("Mel scale", value=True) n_fft = st.sidebar.number_input( "n_fft", min_value=64, max_value=8192, value=1024, step=64) hop_length = st.sidebar.number_input( "hop_length", min_value=1, max_value=2048, value=320, step=10) if mel: n_mels = st.sidebar.number_input( "n_mels", min_value=1, max_value=512, value=64, step=16) fmin = st.sidebar.number_input( "fmin", min_value=1, max_value=8192, value=20, step=100) fmax = st.sidebar.number_input( "fmax", min_value=4000, max_value=44100, value=14000, step=100) log = st.sidebar.checkbox("apply log", value=True) if mel: melspec_params = { "n_fft": n_fft, "hop_length": hop_length, "n_mels": n_mels, "fmin": fmin, "fmax": fmax, "sr": sr } else: spec_params = { "n_fft": n_fft, "hop_length": hop_length } if st.button("Show melspectrogram"): with st.spinner("Calculating melspectrogram"): if mel: spec = melspectrogram(y_plot, melspec_params, log) else: spec = spectrogram(y_plot, spec_params, log) if y_processed is not None: if mel: spec_processed = melspectrogram(y_plot_processed, melspec_params, log) else: spec_processed = spectrogram(y_plot_processed, spec_params, log) height, width = spec.shape st.write(f"{height} x {width} matrix") if y_processed is not None: with st.spinner("Plotting"): fig = plt.figure(figsize=(12, 8)) ax1 = fig.add_subplot(2, 1, 1) if mel: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax, ax=ax1) else: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear", ax=ax1) ax2 = fig.add_subplot(2, 1, 2) if mel: display.specshow( spec_processed, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax, ax=ax2) else: display.specshow( spec_processed, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear", ax=ax2) else: with st.spinner("Plotting"): fig = plt.figure(figsize=(12, 4)) ax = plt.axes() if mel: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="mel", fmin=fmin, fmax=fmax) plt.colorbar() else: display.specshow( spec, sr=sr, hop_length=hop_length, x_axis="time", y_axis="linear") plt.colorbar() if tp is not None and len(tp) > 0: for _, row in tp.iterrows(): rect = patches.Rectangle( (row["t_min"], row["f_min"]), row["t_max"] - row["t_min"], row["f_max"] - row["f_min"], linewidth=1, edgecolor="g", facecolor="g", alpha=0.5, label="tp") ax.add_patch(rect) if fp is not None and len(fp) > 0: for _, row in fp.iterrows(): rect = patches.Rectangle( (row["t_min"], row["f_min"]), row["t_max"] - row["t_min"], row["f_max"] - row["f_min"], linewidth=1, edgecolor="r", facecolor="r", alpha=0.5, label="fp") ax.add_patch(rect) st.pyplot(fig)

37.973737

107

0.419854

1,809

18,797

4.180763

0.098397

0.04284

0.0357

0.0476

0.933756

0.924104

0.916171

0.902155

0.884437

0

0.028651

0.483801

18,797

494

108

38.050607

0.750799

0.002022

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1

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false

0

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0.032895

0

null

0

1

0

1

0

null

0

7

104e4c1bac71e2a3cfbdadaf2cd745ca07fae398

1,800

py

Python

traversal_tests.py

Sorrop/py-graph-algorithms

e688fba3dd8aa44bcd3a608625fdf71649b83920

[ "MIT" ]

5

2017-01-31T11:09:55.000Z

2022-03-16T15:38:54.000Z

traversal_tests.py

Sorrop/py-graph-algorithms

e688fba3dd8aa44bcd3a608625fdf71649b83920

[ "MIT" ]

null

traversal_tests.py

Sorrop/py-graph-algorithms

e688fba3dd8aa44bcd3a608625fdf71649b83920

[ "MIT" ]

6

2020-09-09T23:58:57.000Z

2021-10-16T18:49:02.000Z

import graph from depth_first_search import depth_first_search from breadth_first_search import breadth_first_search edges = [(0, 1), (0, 2), (0, 3), (1, 4), (1, 5), (2, 6), (2, 7), (3, 8), (3, 9), (4, 10), (4, 11)] G, _ = graph.create_graph(edges) start_vertex = G.get_vertex(0) breadth = breadth_first_search(G) breadth(G, start_vertex) depth = depth_first_search(G) depth(G, start_vertex) print('Undirected Case.') print(edges) print(' ') print('==============================') print('Breadth First traversal of G') for edge in breadth.breadth_traversal: print((edge.endPoints()[0].element(), edge.endPoints()[1].element())) print('==============================') print('Depth First traversal of G') for edge in depth.depth_traversal: print((edge.endPoints()[0].element(), edge.endPoints()[1].element())) print(' ') print('==============================') print('==============================') print(' ') edges = [('a', 'b'), ('c', 'a'), ('c', 'b'), ('d', 'c'), ('d', 'e'), ('b', 'e')] G, _ = graph.create_graph(edges, True) start_vertex = G.get_vertex('a') breadth = breadth_first_search(G) breadth(G, start_vertex) depth = depth_first_search(G) depth(G, start_vertex) print('Directed Case.') print(edges) print(' ') print('==============================') print('Breadth First traversal of G') for edge in breadth.breadth_traversal: print((edge.endPoints()[0].element(), edge.endPoints()[1].element())) print('==============================') print('Depth First traversal of G') for edge in depth.depth_traversal: print((edge.endPoints()[0].element(), edge.endPoints()[1].element()))

33.333333

74

0.535

217

1,800

4.290323

0.193548

0.09667

0.068743

0.07304

0.807734

0.71536

0

0.022822

0.196667

1,800

53

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33.962264

0.621024

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0.711111

0

0.255867

0.103034

0

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false

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1

0

1

0

null

0

1

0

8

10a4806044f0d753cfe47dd7983b3682eff504b2

97,171

py

Python

functions.py

FatinaBasmadji/space-robot-planar-case

c1cacba7ef2c2509a282577147cd90421f471360

[ "MIT" ]

null

functions.py

FatinaBasmadji/space-robot-planar-case

c1cacba7ef2c2509a282577147cd90421f471360

[ "MIT" ]

null

functions.py

FatinaBasmadji/space-robot-planar-case

c1cacba7ef2c2509a282577147cd90421f471360

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[ ]: import numpy as np from math import cos, sin # In[ ]: # Space robot parameters pi = 3.14159 p1 = 0.3696 # manipulator mounting point in x axis p2 = 0.001279 # manipulator mounting point in y axis a1 = 0.1805 # center of mass of the first link a2 = 0.2000 # center of mass of the second link a3 = 0.1029 # center of mass of the third link L1 = 0.4488 # the length of the first link L2 = 0.4499 # the length of the second link L3 = 0.3545 # the length of the third link m0 = 64.859 # the mass of the satellite m1 = 2.9130 # the mass of the first kinematic pair m2 = 2.6460 # the mass of the second kinematic pair m3 = 1.6990 # the mass of the third kinematic pair I0 = 2.6952 # Satellite moment of inertia I1 = 0.091391 # Moment of inertia of the first kinematic pair I2 = 0.081375 # Moment of inertia of the second kinematic pair I3 = 0.021904 # Moment of inertia of the third kinematic pair d = 0.3536 # Distance from base geometry center to each corner # In[ ]: # Hyper parameters dt = 0.01 nstep = 500 # In[ ]: def kinematics(state): EE = np.zeros(3) Jacobian = np.zeros((3,6)) EE[0] = state[0] + p1*cos(state[2]) - p2*sin(state[2]) + L1*cos(state[2]+state[3]) + L2*cos(state[2]+state[3]+state[4]) + L3*cos(state[2]+state[3]+state[4]+state[5]); EE[1] = state[1] + p1*sin(state[2]) + p2*cos(state[2]) + L1*sin(state[2]+state[3]) + L2*sin(state[2]+state[3]+state[4]) + L3*sin(state[2]+state[3]+state[4]+state[5]); EE[2] = state[2] + state[3] + state[4] + state[5]; Jacobian[0][0] = 1; Jacobian[0][1] = 0; Jacobian[0][2] = - L3*sin(state[2]+state[3]+state[4]+state[5]) - L1*sin(state[2]+state[3]) - p2*cos(state[2]) - p1*sin(state[2]) - L2*sin(state[2]+state[3]+state[4]); Jacobian[0][3] = - L3*sin(state[2]+state[3]+state[4]+state[5]) - L1*sin(state[2]+state[3]) - L2*sin(state[2]+state[3]+state[4]); Jacobian[0][4] = - L3*sin(state[2]+state[3]+state[4]+state[5]) - L2*sin(state[2]+state[3]+state[4]); Jacobian[0][5] = - L3*sin(state[2]+state[3]+state[4]+state[5]); Jacobian[1][0] = 0; Jacobian[1][1] = 1; Jacobian[1][2] = L3*cos(state[2]+state[3]+state[4]+state[5]) + L1*cos(state[2]+state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2]+state[3]+state[4]); Jacobian[1][3] = L3*cos(state[2]+state[3]+state[4]+state[5]) + L1*cos(state[2]+state[3]) + L2*cos(state[2]+state[3]+state[4]); Jacobian[1][4] = L3*cos(state[2]+state[3]+state[4]+state[5]) + L2*cos(state[2]+state[3]+state[4]); Jacobian[1][5] = L3*cos(state[2]+state[3]+state[4]+state[5]); Jacobian[2][0] = 0; Jacobian[2][1] = 0; Jacobian[2][2] = 1; Jacobian[2][3] = 1; Jacobian[2][4] = 1; Jacobian[2][5] = 1; return EE, Jacobian # In[ ]: def dynamics(state): J0v = np.zeros((2,6)) J0w = np.zeros((1,6)) J1v = np.zeros((2,6)) J1w = np.zeros((1,6)) J2v = np.zeros((2,6)) J2w = np.zeros((1,6)) J3v = np.zeros((2,6)) J3w = np.zeros((1,6)) C = np.zeros((6,1)) J0v = [[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0]] J0vt = np.transpose(J0v) J0w = [[0, 0, 1, 0, 0, 0]] J0wt = np.transpose(J0w) var1 = - p1*sin(state[2]) -p2*cos(state[2]) - a1*sin(state[2]+state[3]) var2 = -a1*sin(state[2]+state[3]) var3 = -p2*sin(state[2]) + p1*cos(state[2])+a1*cos(state[3]+state[2]) var4 = a1*cos(state[3]+state[2]) J1v = [[1, 0, var1, var2, 0, 0],[0, 1, var3, var4, 0, 0]]; J1vt = np.transpose(J1v) J1w = [[0, 0, 1, 1, 0, 0]] J1wt = np.transpose(J1w) var5 = -p2*cos(state[2]) - L1*sin(state[3]+state[2]) - p1*sin(state[2]) - a2*sin(state[3]+state[4]+state[2]); var6 = -L1*sin(state[3]+state[2]) - a2*sin(state[3]+state[4]+state[2]); var7 = -a2*sin(state[3]+state[4]+state[2]); var8 = -p2*sin(state[2]) + a2*cos(state[3]+state[4]+state[2]) + L1*cos(state[3]+state[2])+p1*cos(state[2]); var9 = a2*cos(state[3]+state[4]+state[2]) + L1*cos(state[3]+state[2]); var10 = a2*cos(state[3]+state[4]+state[2]); J2v = [[1, 0, var5, var6, var7, 0],[0, 1, var8, var9, var10, 0]] J2vt = np.transpose(J2v) J2w = [[0, 0, 1, 1, 1, 0]] J2wt = np.transpose(J2w) var11 = -p2*cos(state[2]) - L1*sin(state[3]+state[2]) - p1*sin(state[2]) - L2*sin(state[3]+state[4]+state[2]) -a3*sin(state[2]+state[3]+state[4]+state[5]); var12 = -L1*sin(state[3]+state[2]) - L2*sin(state[3]+state[4]+state[2]) - a3*sin(state[3]+state[4]+state[2]+state[5]); var13 = -L2*sin(state[3]+state[4]+state[2]) - a3*sin(state[3]+state[4]+state[2]+state[5]); var14 = -a3*sin(state[3]+state[4]+state[2]+state[5]); var15 = -p2*sin(state[2]) + L2*cos(state[3]+state[4]+state[2]) + L1*cos(state[3]+state[2])+p1*cos(state[2]) + a3*cos(state[2]+state[3]+state[4]+state[5]); var16 = L2*cos(state[3]+state[4]+state[2]) + L1*cos(state[3]+state[2]) + a3*cos(state[2]+state[3]+state[4]+state[5]); var17 = L2*cos(state[3]+state[4]+state[2]) + a3*cos(state[2]+state[3]+state[4]+state[5]); var18 = a3*cos(state[2]+state[3]+state[4]+state[5]); J3v = [[1, 0, var11, var12, var13, var14],[0, 1, var15, var16, var17, var18]] J3vt = np.transpose(J3v) J3w = [[0, 0, 1, 1, 1, 1]] J3wt = np.transpose(J3w) Tv = np.matmul(m0*J0vt,J0v) + np.matmul(m1*J1vt,J1v) + np.matmul(m2*J2vt,J2v) + np.matmul(m3*J3vt,J3v) Tw = J0wt*I0*J0w + J1wt*I1*J1w + J2wt*I2*J2w + J3wt*I3*J3w; M = Tv+Tw; C[0][0] = - state[10]*(state[8]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[9]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[10]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + a3*m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])) - state[8]*(state[8]*(m2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]))) + state[10]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[9]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + a3*m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])) - state[9]*(state[10]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[8]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + state[9]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + a3*m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])) - state[11]*(a3*m3*state[8]*cos(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[9]*cos(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[10]*cos(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])); C[1][0] = - state[11]*(a3*m3*state[8]*sin(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[9]*sin(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[10]*sin(state[2] + state[3] + state[4] + state[5]) + a3*m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])) - state[8]*(state[9]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3])) + state[10]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[8]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + m2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) + m1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]))) + a3*m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])) - state[9]*(state[8]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3])) + state[9]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3])) + state[10]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + a3*m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])) - state[10]*(state[8]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[9]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[10]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + a3*m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])); C[2][0] = (state[9]*(state[6]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + state[7]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3]))))/2 - state[8]*(state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + state[10]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4]))) - state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4]))*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m1*cos(state[2] + state[3])*a1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2])) - m1*sin(state[2] + state[3])*a1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2])) + a1*m1*cos(state[2] + state[3])*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1) - a1*m1*sin(state[2] + state[3])*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1))) - state[6]*(state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[8]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m1*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1) + m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2)) + m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])*a3) - state[7]*(state[8]*(m2*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m1*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1)) + state[9]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])*a3) + (state[11]*(a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 - state[11]*(state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[10]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + state[9]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1))) - state[9]*(state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))) - state[9]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4]))*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + a1*m1*cos(state[2] + state[3])*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1) - a1*m1*sin(state[2] + state[3])*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1) + a1*m1*cos(state[2] + state[3])*sin(state[2] + state[3])*a1 - a1*m1*sin(state[2] + state[3])*cos(state[2] + state[3])*a1) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1))) + (state[10]*(state[6]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4]))))/2 - state[10]*(state[9]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) + state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])))) + (state[8]*(state[6]*(m2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]))) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + m2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) + m1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2])))))/2 + (state[6]*(state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[8]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m1*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1) + m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2)) + state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 + (state[7]*(state[9]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + state[8]*(m2*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m1*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1)) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2; C[3][0] = (state[10]*(state[6]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[8]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1))))/2 - state[11]*(state[10]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3)) - state[10]*(state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) + state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1))) - (state[8]*(state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) - state[6]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + state[8]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4]))*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m1*cos(state[2] + state[3])*a1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2])) - m1*sin(state[2] + state[3])*a1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2])) + a1*m1*cos(state[2] + state[3])*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1) - a1*m1*sin(state[2] + state[3])*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1)) + state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m1*cos(state[2] + state[3])*a1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2])) - m1*sin(state[2] + state[3])*a1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2])) - a1*m1*cos(state[2] + state[3])*sin(state[2] + state[3])*a1 + a1*m1*sin(state[2] + state[3])*cos(state[2] + state[3])*a1) - state[7]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3])) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])))))/2 + (state[6]*(state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[8]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 + (state[7]*(state[8]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[9]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 + (state[11]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 - state[9]*(state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1))) - state[6]*(state[8]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) + m1*cos(state[2] + state[3])*a1) + state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])*a3) - state[7]*(state[8]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[9]*(m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m1*sin(state[2] + state[3])*a1) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])*a3) - state[8]*(state[10]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4]))) - state[9]*(m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m1*cos(state[2] + state[3])*a1*(a1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2])) - m1*sin(state[2] + state[3])*a1*(a1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2])) - a1*m1*cos(state[2] + state[3])*sin(state[2] + state[3])*a1 + a1*m1*sin(state[2] + state[3])*cos(state[2] + state[3])*a1) + state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])))) + (state[9]*(state[6]*(m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + a1*m1*cos(state[2] + state[3])) + state[7]*(m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a1*m1*sin(state[2] + state[3])) - state[8]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4]))*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m2*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1)*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - m2*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + a1*m1*cos(state[2] + state[3])*(cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*a1) - a1*m1*sin(state[2] + state[3])*(cos(state[2])*p1 - sin(state[2])*p2 + cos(state[2] + state[3])*a1) + a1*m1*cos(state[2] + state[3])*sin(state[2] + state[3])*a1 - a1*m1*sin(state[2] + state[3])*cos(state[2] + state[3])*a1)))/2; C[4][0] = (state[8]*(state[6]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) - state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) + state[8]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4]))) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) - state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + state[9]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])))))/2 - state[7]*(state[8]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + state[9]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])*a3) + state[9]*(state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) - state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2))) - state[11]**2*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + (state[9]*(state[6]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[9]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1)) - state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) - state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))) + state[8]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1))))/2 - state[6]*(state[8]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[9]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])*a3) + (state[7]*(state[8]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + state[9]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m2*sin(state[2] + state[3] + state[4])*a2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 + state[8]*(state[9]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + a2*cos(state[2] + state[3] + state[4])) - m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + a2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4]))) - state[11]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + state[10]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2)*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) - m2*sin(state[2] + state[3] + state[4])*a2*(L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + a2*cos(state[2] + state[3] + state[4])) + m2*cos(state[2] + state[3] + state[4])*a2*(L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + a2*sin(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2)) + (state[10]*(state[6]*(m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a2*m2*cos(state[2] + state[3] + state[4])) + state[7]*(m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a2*m2*sin(state[2] + state[3] + state[4])) + state[9]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2] + state[3])*L1) - a2*m2*cos(state[2] + state[3] + state[4])*(sin(state[2] + state[3] + state[4])*a2 + sin(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2) + state[8]*(m3*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - m3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4]))*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a2*m2*sin(state[2] + state[3] + state[4])*(cos(state[2] + state[3] + state[4])*a2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a2*m2*cos(state[2] + state[3] + state[4])*(cos(state[2])*p2 + sin(state[2] + state[3] + state[4])*a2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + a2*m2*cos(state[2] + state[3] + state[4])*sin(state[2] + state[3] + state[4])*a2 - a2*m2*sin(state[2] + state[3] + state[4])*cos(state[2] + state[3] + state[4])*a2)))/2 + (state[11]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + state[9]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 + (state[6]*(state[8]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[9]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + state[10]*(m3*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) + m2*cos(state[2] + state[3] + state[4])*a2) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 - state[10]*state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)); C[5][0] = (state[10]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4]))) + state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[10]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + state[9]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1)) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 - state[9]*(state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) - state[10]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])))) + (state[6]*(m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[8] + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[9] + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[10] + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 + state[8]*(state[10]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) - state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[9]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) - m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) + m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])))) + (state[7]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[8] + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[9] + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[10] + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*state[11]))/2 - state[6]*(m3*state[8]*cos(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[9]*cos(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[10]*cos(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[11]*cos(state[2] + state[3] + state[4] + state[5])*a3) + (state[8]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[10]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + state[9]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + p1*cos(state[2]) - p2*sin(state[2]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + p2*cos(state[2]) + p1*sin(state[2]) + L2*sin(state[2] + state[3] + state[4]))) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 - state[7]*(m3*state[8]*sin(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[9]*sin(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[10]*sin(state[2] + state[3] + state[4] + state[5])*a3 + m3*state[11]*sin(state[2] + state[3] + state[4] + state[5])*a3) + (state[11]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[10]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[9]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 + (state[9]*(state[8]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2])*p1 + cos(state[2] + state[3])*L1 - sin(state[2])*p2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + cos(state[2])*p2 + sin(state[2])*p1 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))) + state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3) + state[10]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2)) + state[9]*(a3*m3*sin(state[2] + state[3] + state[4] + state[5])*(cos(state[2] + state[3] + state[4] + state[5])*a3 + cos(state[2] + state[3] + state[4])*L2 + cos(state[2] + state[3])*L1) - a3*m3*cos(state[2] + state[3] + state[4] + state[5])*(sin(state[2] + state[3] + state[4] + state[5])*a3 + sin(state[2] + state[3] + state[4])*L2 + sin(state[2] + state[3])*L1) + m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L1*cos(state[2] + state[3]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L1*sin(state[2] + state[3]) + L2*sin(state[2] + state[3] + state[4]))) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*state[6] + a3*m3*sin(state[2] + state[3] + state[4] + state[5])*state[7]))/2 - state[10]*state[11]*(m3*sin(state[2] + state[3] + state[4] + state[5])*a3*(a3*cos(state[2] + state[3] + state[4] + state[5]) + L2*cos(state[2] + state[3] + state[4])) - m3*cos(state[2] + state[3] + state[4] + state[5])*a3*(a3*sin(state[2] + state[3] + state[4] + state[5]) + L2*sin(state[2] + state[3] + state[4])) + a3*m3*cos(state[2] + state[3] + state[4] + state[5])*sin(state[2] + state[3] + state[4] + state[5])*a3 - a3*m3*sin(state[2] + state[3] + state[4] + state[5])*cos(state[2] + state[3] + state[4] + state[5])*a3); return M, C # In[ ]: def distance(ini_pos, fin_pos): dist = (ini_pos[0]-fin_pos[0])**2 + (ini_pos[1]-fin_pos[1])**2 dist = np.sqrt(dist) return dist # In[ ]: def step(state, action, ee_final): action = action.numpy() x = state old_x = x old_EE, old_Jacobian = kinematics(x) q = np.array([[state[0]], [state[1]], [state[2]], [state[3]], [state[4]], [state[5]]]) M, C = dynamics(x) rsT = [[-state[1], state[0]]] Ms_11 = M[0:2,0:2] Ms_12 = M[0:2,2] Ms_12 = Ms_12.reshape((2, 1)) MsT_12 = np.transpose(Ms_12) Ms_22 = M[2,2] H1[0:2,0:2] = Ms_11 H1[0:2,2:3] = Ms_12 H1[2:3,0:2] = MsT_12 + np.matmul(rsT,Ms_11) H1[2,2] = Ms_22 + np.matmul(rsT,Ms_12) H1inv = np.linalg.inv(H1) Msm_11 = M[0:2,3:6] Msm_21 = M[2:3,3:6] H2[0:2,0:3] = Msm_11 H2[2:3,0:3] = Msm_21 + np.matmul(rsT,Msm_11) teta_prim = [[action[0]], [action[1]], [action[2]]] sat_vel = np.matmul(np.matmul(-H1inv,H2),teta_prim) dq = np.vstack([sat_vel,teta_prim]) k1 = dt*dq k2 = dt*(dq + 0.5*k1) k3 = dt*(dq + 0.5*k2) k4 = dt*(dq + k3) q = q + (k1 + 2*k2 + 2*k3 + k4)/6 new_x = [q[0], q[1], q[2], q[3], q[4], q[5], dq[0], dq[1], dq[2], dq[3], dq[4], dq[5]] new_EE, Jacobian = kinematics(new_x) ini_pos = [ee_final[0], ee_final[1], ee_final[2]] fin_pos = [new_EE[0], new_EE[1], new_EE[2]] done = 0 reward = -distance(ini_pos, fin_pos) if abs(ee_final_x-new_EE[0])<0.001 and abs(ee_final_y-new_EE[1])<0.001: done = 1 reward = 0 new_x = np.array([q[0], q[1], q[2], q[3], q[4], q[5], dq[0], dq[1], dq[2], dq[3], dq[4], dq[5]]) new_x = new_x.reshape(12,) return new_x, new_EE, done, reward

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null

1

0

1

0

null

0

11

52b2893d801640e5dcf82ab0cfc28d541ace2709

128

py

Python

scCloud/scCloud/annotate_cluster/__init__.py

broadinstitute/scRNA-Seq

03aafb92274a97f4d634ac9e42f0e0feca91ed98

[ "BSD-3-Clause" ]

12

2019-04-08T11:39:33.000Z

2022-02-22T02:50:27.000Z

scCloud/scCloud/annotate_cluster/__init__.py

broadinstitute/scRNA-Seq

03aafb92274a97f4d634ac9e42f0e0feca91ed98

[ "BSD-3-Clause" ]

null

scCloud/scCloud/annotate_cluster/__init__.py

broadinstitute/scRNA-Seq

03aafb92274a97f4d634ac9e42f0e0feca91ed98

[ "BSD-3-Clause" ]

3

2019-03-06T20:44:33.000Z

2020-02-17T13:43:46.000Z

from .annotate_cluster import annotate_clusters from .run_annotate_cluster import run_annotate_cluster, annotate_anndata_object

42.666667

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0

1

0

1

0

7

52b2f348984654cc44d1955932c22bfab70fad0f

20,091

py

Python

menu/functions.py

WillRazorFace/InstaMax

4ebb5ee5ad88c2e2a2283bcd13e264cc99513627

[ "Apache-2.0" ]

null

menu/functions.py

WillRazorFace/InstaMax

4ebb5ee5ad88c2e2a2283bcd13e264cc99513627

[ "Apache-2.0" ]

null

menu/functions.py

WillRazorFace/InstaMax

4ebb5ee5ad88c2e2a2283bcd13e264cc99513627

[ "Apache-2.0" ]

null

from core.instabot import Bot from os import system, path from .constants import CLEAR_CONSOLE_COMMAND, OPTIONS_FILE, DRIVER_MENU from time import sleep def configure() -> tuple: driver_options = {'1': 'chrome', '2': 'firefox', '3': 'safari'} system(CLEAR_CONSOLE_COMMAND) print('Enter your Instagram account username: @', end='') username = input() print('Enter your Instagram account password: ', end='') password = input() system(CLEAR_CONSOLE_COMMAND) while True: print('Select your driver model') print(DRIVER_MENU) driver = input('>>> ') try: driver = driver_options[driver] system(CLEAR_CONSOLE_COMMAND) while True: print('Enter the path to your driver (/example/driver/path/driver.exe): ', end='') driver_path = input('') if path.isfile(driver_path): break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid path, file does not exist\n') continue break except KeyError: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue with open(OPTIONS_FILE, 'w') as file: file.write(username + '\n' + password + '\n' + driver + '\n' + driver_path) return username, password, driver, driver_path def follow_suggested(bot_instance: Bot) -> int: system(CLEAR_CONSOLE_COMMAND) while True: print('How many users do you want to follow? (numbers only) ', end='') quantity = input() try: quantity = int(quantity) while True: ignore = [] print("Are there any accounts you don't want to follow? (Y/N) ", end='') dont_follow = input() if dont_follow == 'Y' or dont_follow == 'y': system(CLEAR_CONSOLE_COMMAND) while True: print('[1] - Get accounts from a file (one user per line)\n[2] - Insert accounts one by one\n') accounts_input = input('>>> ') if accounts_input == '1': system(CLEAR_CONSOLE_COMMAND) while True: print('Enter the path to the file (example/path/to/the/file): ', end='') file_path = input() if path.isfile(file_path): with open(file_path, 'r') as file: for account in file.readlines(): ignore.append(account.strip()) break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid path\n') continue break elif accounts_input == '2': while True: system(CLEAR_CONSOLE_COMMAND) print(f'{len(ignore)} accounts to not follow\n') print('Enter the account username (type "exit" to stop): ', end='') username = input() if username == 'exit': break ignore.append(username) continue break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue elif dont_follow == 'N' or dont_follow == 'n': break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue break break except ValueError: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue system(CLEAR_CONSOLE_COMMAND) print('Following') followed = bot_instance.follow_suggested(quantity, ignore) system(CLEAR_CONSOLE_COMMAND) print(f'{followed} users followed. Press anything to return to the menu.', end='') input() def like_feed(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) while True: print('How many posts from feed do you want to like? (numbers only) ', end='') quantity = input() try: quantity = int(quantity) except ValueError: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue print('Do you want to comment on posts that are liked? (Y/N) (This will delay the process in trying to avoid Instagram comment blocking. You can change comments in the "comments.txt" file) ', end='') comment = input() if comment == 'Y' or comment == 'y': comment = True elif comment == 'N' or comment == 'n': comment = False else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue break system(CLEAR_CONSOLE_COMMAND) print(f'Liking posts from your feed') liked_posts = bot_instance.like_feed_posts(quantity, comment) system(CLEAR_CONSOLE_COMMAND) print(f'{liked_posts} posts liked. Press anything to return to the menu.', end='') input() def like_posts(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) while True: print('How many posts do you want to like? (numbers only) ', end='') quantity = input() try: quantity = int(quantity) print('Enter the posts hashtag: #', end='') hashtag = input() except ValueError: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue print('Do you want to comment on posts that are liked? (Y/N) (This will delay the process in trying to avoid Instagram comment blocking. You can change comments in the "comments.txt" file) ', end='') comment = input() if comment == 'Y' or comment == 'y': comment = True elif comment == 'N' or comment == 'n': comment = False else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue break system(CLEAR_CONSOLE_COMMAND) print(f'Liking posts from #{hashtag}') liked_posts = bot_instance.like_posts_by_hashtag(hashtag, quantity, comment) system(CLEAR_CONSOLE_COMMAND) print(f'{liked_posts} posts liked. Press anything to return to the menu.', end='') input() def get_followers(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) all = False while True: print('Enter the instagram account username: @', end='') account = input() print('How many followers you want to get? (numbers only) (type "all" to get all followers) ', end='') quantity = input() try: quantity = int(quantity) except ValueError: if quantity == 'all' or quantity == 'All' or quantity == 'ALL': all = True else: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue break system(CLEAR_CONSOLE_COMMAND) print(f'Searching for followers of {account}') followers = [follower for follower in bot_instance.get_followers(account, quantity, all)] while True: system(CLEAR_CONSOLE_COMMAND) for follower in followers: print(follower) print(f'\n{len(followers)} followers found on @{account}. Do you want to save this information into a file? (Y/N) ', end='') save = input() if save == 'Y' or save == 'y': while True: print('\nEnter the path for the file to be saved: ', end='') path = input() try: with open(path, 'w') as file: for follower in followers: file.write(follower + '\n') except FileNotFoundError: system(CLEAR_CONSOLE_COMMAND) print('Invalid path') continue break print(f'Information saved in {path}. Press anything to return to menu.', end='') input() break elif save == 'N' or save == 'n': print('\nNo information saved. Press anything to return to menu.', end='') input() break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option') continue def search_follower(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) print('Enter the account to be searched: @', end='') search_account = input() print('Enter the account to be found: @', end='') account = input() system(CLEAR_CONSOLE_COMMAND) print(f'Searching for @{account} in @{search_account} list of followers') is_follower = bot_instance.search_follower(search_account, account) if is_follower: print(f'Found. @{account} is following @{search_account}. Press anything to return to menu.', end='') input() else: print(f'Not found. @{account} is not following @{search_account}. Press anything to return to menu.', end='') input() def get_following(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) all = False while True: print('Enter the instagram account username: @', end='') account = input() print('How many following users you want to get? (numbers only) (type "all" to get all following users) ', end='') quantity = input() try: quantity = int(quantity) except ValueError: if quantity == 'all' or quantity == 'All' or quantity == 'ALL': all = True else: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue break system(CLEAR_CONSOLE_COMMAND) print(f'Searching for following users in @{account}') following = [user for user in bot_instance.get_following(account, quantity, all)] while True: system(CLEAR_CONSOLE_COMMAND) for user in following: print(user) print(f'\n{len(following)} following users found on @{account}. Do you want to save this information into a file? (Y/N) ', end='') save = input() if save == 'Y' or save == 'y': while True: print('\nEnter the path for the file to be saved: ', end='') path = input() try: with open(path, 'w') as file: for user in following: file.write(user + '\n') except FileNotFoundError: system(CLEAR_CONSOLE_COMMAND) print('Invalid path') continue break print(f'Information saved in {path}. Press anything to return to menu.', end='') input() break elif save == 'N' or save == 'n': print('\nNo information saved. Press anything to return to menu.', end='') input() break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option') continue def search_following(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) print('Enter the account to be searched: @', end='') search_account = input() print('Enter the account to be found: @', end='') account = input() system(CLEAR_CONSOLE_COMMAND) print(f'Searching for @{account} in @{search_account} list of following users') is_following = bot_instance.search_following(search_account, account) if is_following: print(f'Found. @{account} is followed by @{search_account}. Press anything to return to menu.', end='') input() else: print(f'Not found. @{account} is not followed by @{search_account}. Press anything to return to menu.', end='') input() def search_not_followers(bot_instance: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) print('Searching for not followers') not_followers = bot_instance.search_not_followers() while True: system(CLEAR_CONSOLE_COMMAND) for user in not_followers: print(user) print(f'\n{len(not_followers)} not followers found on your account. Do you want to save this information into a file? (Y/N) ', end='') save = input() if save == 'Y' or save == 'y': while True: print('\nEnter the path for the file to be saved: ', end='') path = input() try: with open(path, 'w') as file: for not_follower in not_followers: file.write(not_follower + '\n') except FileNotFoundError: system(CLEAR_CONSOLE_COMMAND) print('Invalid path') continue break print(f'Information saved in {path}. Press anything to return to menu.', end='') input() break elif save == 'N' or save == 'n': print('\nNo information saved. Press anything to return to menu.', end='') input() break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option') continue def unfollow_not_followers(bot_instace: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) while True: ignore = [] print("Are there any accounts you don't want to unfollow? (Y/N) ", end='') dont_unfollow = input() if dont_unfollow == 'Y' or dont_unfollow == 'y': system(CLEAR_CONSOLE_COMMAND) while True: print('[1] - Get accounts from a file (one user per line)\n[2] - Insert accounts one by one\n') accounts_input = input('>>> ') if accounts_input == '1': system(CLEAR_CONSOLE_COMMAND) while True: print('Enter the path to the file (example/path/to/the/file): ', end='') file_path = input() if path.isfile(file_path): with open(file_path, 'r') as file: for account in file.readlines(): ignore.append(account.strip()) break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid path\n') continue break elif accounts_input == '2': while True: system(CLEAR_CONSOLE_COMMAND) print(f'{len(ignore)} accounts to not follow\n') print('Enter the account username (type "exit" to stop): ', end='') username = input() if username == 'exit': break ignore.append(username) continue break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue elif dont_unfollow == 'N' or dont_unfollow == 'n': break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option') continue break system(CLEAR_CONSOLE_COMMAND) print('Unfollowing not followers') unfollowed = bot_instace.unfollow_not_followers(ignore) system(CLEAR_CONSOLE_COMMAND) print(f'{unfollowed} not followers unfollowed. Press anything to return to menu.', end='') input() def unfollow(bot_instace: Bot) -> None: system(CLEAR_CONSOLE_COMMAND) all = False while True: print('How many following users you want to unfollow? (numbers only) (type "all" to unfollow all users except those you specify) ', end='') quantity = input() try: quantity = int(quantity) except ValueError: if quantity == 'all' or quantity == 'All' or quantity == 'ALL': all = True else: system(CLEAR_CONSOLE_COMMAND) print('Invalid quantity\n') continue break while True: ignore = [] print("Are there any accounts you don't want to unfollow? (Y/N) ", end='') dont_unfollow = input() if dont_unfollow == 'Y' or dont_unfollow == 'y': system(CLEAR_CONSOLE_COMMAND) while True: print('[1] - Get accounts from a file (one user per line)\n[2] - Insert accounts one by one\n') accounts_input = input('>>> ') if accounts_input == '1': system(CLEAR_CONSOLE_COMMAND) while True: print('Enter the path to the file (example/path/to/the/file): ', end='') file_path = input() if path.isfile(file_path): with open(file_path, 'r') as file: for account in file.readlines(): ignore.append(account.strip()) break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid path\n') continue break elif accounts_input == '2': while True: system(CLEAR_CONSOLE_COMMAND) print(f'{len(ignore)} accounts to not follow\n') print('Enter the account username (type "exit" to stop): ', end='') username = input() if username == 'exit': break ignore.append(username) continue break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option\n') continue elif dont_unfollow == 'N' or dont_unfollow == 'n': break else: system(CLEAR_CONSOLE_COMMAND) print('Invalid option') continue break system(CLEAR_CONSOLE_COMMAND) print('Unfollowing') unfollowed = bot_instace.unfollow(quantity, ignore, all) system(CLEAR_CONSOLE_COMMAND) print(f'{unfollowed} unfollowed users. Press anything to return to menu.', end='') input()

35.309315

207

0.498034

2,016

20,091

4.843254

0.078373

0.079885

0.126485

0.163867

0.834904

0.807251

0.799775

0.782978

0.769562

0.731872

0

0.00127

0.412075

20,091

568

208

35.371479

0.825347

0

0.818386

0

0.024664

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1

0.024664

false

0.008969

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0.035874

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null

0

1

0

1

0

null

0

8

52e2c41544b3b3fc6a8b85c1092c82b5d2630833

11,234

py

Python

test/trace_processor/python/api_unittest.py

jumeder/perfetto

df3ae5e6f975204d2f35aeed61cbbd0746151d8e

[ "Apache-2.0" ]

1

2021-01-18T09:36:54.000Z

test/trace_processor/python/api_unittest.py

jumeder/perfetto

df3ae5e6f975204d2f35aeed61cbbd0746151d8e

[ "Apache-2.0" ]

8

2020-12-04T22:03:54.000Z

2021-11-08T01:29:31.000Z

test/trace_processor/python/api_unittest.py

jumeder/perfetto

df3ae5e6f975204d2f35aeed61cbbd0746151d8e

[ "Apache-2.0" ]

3

2019-02-10T12:40:29.000Z

2022-01-24T09:16:29.000Z

#!/usr/bin/env python3 # Copyright (C) 2020 The Android Open Source Project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from trace_processor.api import TraceProcessor, TraceProcessorException from trace_processor.protos import ProtoFactory class TestQueryResultIterator(unittest.TestCase): # The numbers input into cells correspond the the CellType enum values # defined under trace_processor.proto CELL_VARINT = ProtoFactory().CellsBatch().CELL_VARINT CELL_STRING = ProtoFactory().CellsBatch().CELL_STRING CELL_INVALID = ProtoFactory().CellsBatch().CELL_INVALID def test_one_batch(self): int_values = [100, 200] str_values = ['bar1', 'bar2'] batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend(int_values) batch.string_cells = "\0".join(str_values) + "\0" batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) for num, row in enumerate(qr_iterator): self.assertEqual(row.foo_id, str_values[num]) self.assertEqual(row.foo_num, int_values[num]) def test_many_batches(self): int_values = [100, 200, 300, 400] str_values = ['bar1', 'bar2', 'bar3', 'bar4'] batch_1 = ProtoFactory().CellsBatch() batch_1.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch_1.varint_cells.extend(int_values[:2]) batch_1.string_cells = "\0".join(str_values[:2]) + "\0" batch_1.is_last_batch = False batch_2 = ProtoFactory().CellsBatch() batch_2.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch_2.varint_cells.extend(int_values[2:]) batch_2.string_cells = "\0".join(str_values[2:]) + "\0" batch_2.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch_1, batch_2]) for num, row in enumerate(qr_iterator): self.assertEqual(row.foo_id, str_values[num]) self.assertEqual(row.foo_num, int_values[num]) def test_empty_batch(self): batch = ProtoFactory().CellsBatch() batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator([], [batch]) for num, row in enumerate(qr_iterator): self.assertIsNone(row.foo_id) self.assertIsNone(row.foo_num) def test_invalid_batch(self): batch = ProtoFactory().CellsBatch() qr_iterator = TraceProcessor.QueryResultIterator([], [batch]) # Since the batch isn't defined as the last batch, the QueryResultsIterator # expects another batch and thus raises IndexError as no next batch exists. with self.assertRaises(IndexError): for row in qr_iterator: pass def test_incorrect_cells_batch(self): str_values = ['bar1', 'bar2'] batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch.string_cells = "\0".join(str_values) + "\0" batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) # The batch specifies there ought to be 2 cells of type VARINT and 2 cells # of type STRING, but there are no string cells defined in the batch. Thus # an IndexError occurs as it tries to access the empty string cells list. with self.assertRaises(IndexError): for row in qr_iterator: pass def test_incorrect_columns_batch(self): batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend([100, 200]) batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator( ['foo_id', 'foo_num', 'foo_dur', 'foo_ms'], [batch]) # It's always the case that the number of cells is a multiple of the number # of columns. However, here this is clearly not the case, so when the # iterator tries to access the cell for the third column, it raises an # IndexError due to having exhausted the cells list. with self.assertRaises(IndexError): for row in qr_iterator: pass def test_invalid_cell_type(self): batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_INVALID, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend([100, 200]) batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) # In this batch we declare the columns types to be CELL_INVALID, # CELL_VARINT but that doesn't match the data which are both ints* # so we should raise a TraceProcessorException. with self.assertRaises(TraceProcessorException): for row in qr_iterator: pass def test_one_batch_as_pandas(self): int_values = [100, 200] str_values = ['bar1', 'bar2'] batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend(int_values) batch.string_cells = "\0".join(str_values) + "\0" batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) qr_df = qr_iterator.as_pandas_dataframe() for num, row in qr_df.iterrows(): self.assertEqual(row['foo_id'], str_values[num]) self.assertEqual(row['foo_num'], int_values[num]) def test_many_batches_as_pandas(self): int_values = [100, 200, 300, 400] str_values = ['bar1', 'bar2', 'bar3', 'bar4'] batch_1 = ProtoFactory().CellsBatch() batch_1.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch_1.varint_cells.extend(int_values[:2]) batch_1.string_cells = "\0".join(str_values[:2]) + "\0" batch_1.is_last_batch = False batch_2 = ProtoFactory().CellsBatch() batch_2.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch_2.varint_cells.extend(int_values[2:]) batch_2.string_cells = "\0".join(str_values[2:]) + "\0" batch_2.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch_1, batch_2]) qr_df = qr_iterator.as_pandas_dataframe() for num, row in qr_df.iterrows(): self.assertEqual(row['foo_id'], str_values[num]) self.assertEqual(row['foo_num'], int_values[num]) def test_empty_batch_as_pandas(self): batch = ProtoFactory().CellsBatch() batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator([], [batch]) qr_df = qr_iterator.as_pandas_dataframe() for num, row in qr_df.iterrows(): self.assertEqual(row['foo_id'], str_values[num]) self.assertEqual(row['foo_num'], int_values[num]) def test_invalid_batch_as_pandas(self): batch = ProtoFactory().CellsBatch() qr_iterator = TraceProcessor.QueryResultIterator([], [batch]) # Since the batch isn't defined as the last batch, the QueryResultsIterator # expects another batch and thus raises IndexError as no next batch exists. with self.assertRaises(IndexError): qr_df = qr_iterator.as_pandas_dataframe() def test_incorrect_cells_batch_as_pandas(self): str_values = ['bar1', 'bar2'] batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_STRING, TestQueryResultIterator.CELL_VARINT ]) batch.string_cells = "\0".join(str_values) + "\0" batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) # The batch specifies there ought to be 2 cells of type VARINT and 2 cells # of type STRING, but there are no string cells defined in the batch. Thus # an IndexError occurs as it tries to access the empty string cells list. with self.assertRaises(IndexError): qr_df = qr_iterator.as_pandas_dataframe() def test_incorrect_columns_batch_as_pandas(self): batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_VARINT, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend([100, 200]) batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator( ['foo_id', 'foo_num', 'foo_dur', 'foo_ms'], [batch]) # It's always the case that the number of cells is a multiple of the number # of columns. However, here this is clearly not the case, so when the # iterator tries to access the cell for the third column, it raises an # IndexError due to having exhausted the cells list. with self.assertRaises(IndexError): qr_df = qr_iterator.as_pandas_dataframe() def test_invalid_cell_type_as_pandas(self): batch = ProtoFactory().CellsBatch() batch.cells.extend([ TestQueryResultIterator.CELL_INVALID, TestQueryResultIterator.CELL_VARINT ]) batch.varint_cells.extend([100, 200]) batch.is_last_batch = True qr_iterator = TraceProcessor.QueryResultIterator(['foo_id', 'foo_num'], [batch]) # In this batch we declare the columns types to be CELL_INVALID, # CELL_VARINT but that doesn't match the data which are both ints* # so we should raise a TraceProcessorException. with self.assertRaises(TraceProcessorException): qr_df = qr_iterator.as_pandas_dataframe()

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py

Python

sdk/python/pulumi_vault/aws/auth_backend_client.py

pulumi/pulumi-vault

1682875f4a5d7d508f36e166529ad2b8aec34090

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

10

2019-10-07T17:44:18.000Z

2022-03-30T20:46:33.000Z

sdk/python/pulumi_vault/aws/auth_backend_client.py

pulumi/pulumi-vault

1682875f4a5d7d508f36e166529ad2b8aec34090

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

79

2019-10-11T18:13:07.000Z

2022-03-31T21:09:41.000Z

sdk/python/pulumi_vault/aws/auth_backend_client.py

pulumi/pulumi-vault

1682875f4a5d7d508f36e166529ad2b8aec34090

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

2

2019-10-28T10:08:40.000Z

2020-03-17T14:20:55.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = ['AuthBackendClientArgs', 'AuthBackendClient'] @pulumi.input_type class AuthBackendClientArgs: def __init__(__self__, *, access_key: Optional[pulumi.Input[str]] = None, backend: Optional[pulumi.Input[str]] = None, ec2_endpoint: Optional[pulumi.Input[str]] = None, iam_endpoint: Optional[pulumi.Input[str]] = None, iam_server_id_header_value: Optional[pulumi.Input[str]] = None, secret_key: Optional[pulumi.Input[str]] = None, sts_endpoint: Optional[pulumi.Input[str]] = None, sts_region: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a AuthBackendClient resource. :param pulumi.Input[str] access_key: The AWS access key that Vault should use for the auth backend. :param pulumi.Input[str] backend: The path the AWS auth backend being configured was mounted at. Defaults to `aws`. :param pulumi.Input[str] ec2_endpoint: Override the URL Vault uses when making EC2 API calls. :param pulumi.Input[str] iam_endpoint: Override the URL Vault uses when making IAM API calls. :param pulumi.Input[str] iam_server_id_header_value: The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. :param pulumi.Input[str] secret_key: The AWS secret key that Vault should use for the auth backend. :param pulumi.Input[str] sts_endpoint: Override the URL Vault uses when making STS API calls. :param pulumi.Input[str] sts_region: Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ if access_key is not None: pulumi.set(__self__, "access_key", access_key) if backend is not None: pulumi.set(__self__, "backend", backend) if ec2_endpoint is not None: pulumi.set(__self__, "ec2_endpoint", ec2_endpoint) if iam_endpoint is not None: pulumi.set(__self__, "iam_endpoint", iam_endpoint) if iam_server_id_header_value is not None: pulumi.set(__self__, "iam_server_id_header_value", iam_server_id_header_value) if secret_key is not None: pulumi.set(__self__, "secret_key", secret_key) if sts_endpoint is not None: pulumi.set(__self__, "sts_endpoint", sts_endpoint) if sts_region is not None: pulumi.set(__self__, "sts_region", sts_region) @property @pulumi.getter(name="accessKey") def access_key(self) -> Optional[pulumi.Input[str]]: """ The AWS access key that Vault should use for the auth backend. """ return pulumi.get(self, "access_key") @access_key.setter def access_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "access_key", value) @property @pulumi.getter def backend(self) -> Optional[pulumi.Input[str]]: """ The path the AWS auth backend being configured was mounted at. Defaults to `aws`. """ return pulumi.get(self, "backend") @backend.setter def backend(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "backend", value) @property @pulumi.getter(name="ec2Endpoint") def ec2_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making EC2 API calls. """ return pulumi.get(self, "ec2_endpoint") @ec2_endpoint.setter def ec2_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ec2_endpoint", value) @property @pulumi.getter(name="iamEndpoint") def iam_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making IAM API calls. """ return pulumi.get(self, "iam_endpoint") @iam_endpoint.setter def iam_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "iam_endpoint", value) @property @pulumi.getter(name="iamServerIdHeaderValue") def iam_server_id_header_value(self) -> Optional[pulumi.Input[str]]: """ The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. """ return pulumi.get(self, "iam_server_id_header_value") @iam_server_id_header_value.setter def iam_server_id_header_value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "iam_server_id_header_value", value) @property @pulumi.getter(name="secretKey") def secret_key(self) -> Optional[pulumi.Input[str]]: """ The AWS secret key that Vault should use for the auth backend. """ return pulumi.get(self, "secret_key") @secret_key.setter def secret_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "secret_key", value) @property @pulumi.getter(name="stsEndpoint") def sts_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making STS API calls. """ return pulumi.get(self, "sts_endpoint") @sts_endpoint.setter def sts_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sts_endpoint", value) @property @pulumi.getter(name="stsRegion") def sts_region(self) -> Optional[pulumi.Input[str]]: """ Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ return pulumi.get(self, "sts_region") @sts_region.setter def sts_region(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sts_region", value) @pulumi.input_type class _AuthBackendClientState: def __init__(__self__, *, access_key: Optional[pulumi.Input[str]] = None, backend: Optional[pulumi.Input[str]] = None, ec2_endpoint: Optional[pulumi.Input[str]] = None, iam_endpoint: Optional[pulumi.Input[str]] = None, iam_server_id_header_value: Optional[pulumi.Input[str]] = None, secret_key: Optional[pulumi.Input[str]] = None, sts_endpoint: Optional[pulumi.Input[str]] = None, sts_region: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering AuthBackendClient resources. :param pulumi.Input[str] access_key: The AWS access key that Vault should use for the auth backend. :param pulumi.Input[str] backend: The path the AWS auth backend being configured was mounted at. Defaults to `aws`. :param pulumi.Input[str] ec2_endpoint: Override the URL Vault uses when making EC2 API calls. :param pulumi.Input[str] iam_endpoint: Override the URL Vault uses when making IAM API calls. :param pulumi.Input[str] iam_server_id_header_value: The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. :param pulumi.Input[str] secret_key: The AWS secret key that Vault should use for the auth backend. :param pulumi.Input[str] sts_endpoint: Override the URL Vault uses when making STS API calls. :param pulumi.Input[str] sts_region: Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ if access_key is not None: pulumi.set(__self__, "access_key", access_key) if backend is not None: pulumi.set(__self__, "backend", backend) if ec2_endpoint is not None: pulumi.set(__self__, "ec2_endpoint", ec2_endpoint) if iam_endpoint is not None: pulumi.set(__self__, "iam_endpoint", iam_endpoint) if iam_server_id_header_value is not None: pulumi.set(__self__, "iam_server_id_header_value", iam_server_id_header_value) if secret_key is not None: pulumi.set(__self__, "secret_key", secret_key) if sts_endpoint is not None: pulumi.set(__self__, "sts_endpoint", sts_endpoint) if sts_region is not None: pulumi.set(__self__, "sts_region", sts_region) @property @pulumi.getter(name="accessKey") def access_key(self) -> Optional[pulumi.Input[str]]: """ The AWS access key that Vault should use for the auth backend. """ return pulumi.get(self, "access_key") @access_key.setter def access_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "access_key", value) @property @pulumi.getter def backend(self) -> Optional[pulumi.Input[str]]: """ The path the AWS auth backend being configured was mounted at. Defaults to `aws`. """ return pulumi.get(self, "backend") @backend.setter def backend(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "backend", value) @property @pulumi.getter(name="ec2Endpoint") def ec2_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making EC2 API calls. """ return pulumi.get(self, "ec2_endpoint") @ec2_endpoint.setter def ec2_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ec2_endpoint", value) @property @pulumi.getter(name="iamEndpoint") def iam_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making IAM API calls. """ return pulumi.get(self, "iam_endpoint") @iam_endpoint.setter def iam_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "iam_endpoint", value) @property @pulumi.getter(name="iamServerIdHeaderValue") def iam_server_id_header_value(self) -> Optional[pulumi.Input[str]]: """ The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. """ return pulumi.get(self, "iam_server_id_header_value") @iam_server_id_header_value.setter def iam_server_id_header_value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "iam_server_id_header_value", value) @property @pulumi.getter(name="secretKey") def secret_key(self) -> Optional[pulumi.Input[str]]: """ The AWS secret key that Vault should use for the auth backend. """ return pulumi.get(self, "secret_key") @secret_key.setter def secret_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "secret_key", value) @property @pulumi.getter(name="stsEndpoint") def sts_endpoint(self) -> Optional[pulumi.Input[str]]: """ Override the URL Vault uses when making STS API calls. """ return pulumi.get(self, "sts_endpoint") @sts_endpoint.setter def sts_endpoint(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sts_endpoint", value) @property @pulumi.getter(name="stsRegion") def sts_region(self) -> Optional[pulumi.Input[str]]: """ Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ return pulumi.get(self, "sts_region") @sts_region.setter def sts_region(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sts_region", value) class AuthBackendClient(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, access_key: Optional[pulumi.Input[str]] = None, backend: Optional[pulumi.Input[str]] = None, ec2_endpoint: Optional[pulumi.Input[str]] = None, iam_endpoint: Optional[pulumi.Input[str]] = None, iam_server_id_header_value: Optional[pulumi.Input[str]] = None, secret_key: Optional[pulumi.Input[str]] = None, sts_endpoint: Optional[pulumi.Input[str]] = None, sts_region: Optional[pulumi.Input[str]] = None, __props__=None): """ ## Import AWS auth backend clients can be imported using `auth/`, the `backend` path, and `/config/client` e.g. ```sh $ pulumi import vault:aws/authBackendClient:AuthBackendClient example auth/aws/config/client ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] access_key: The AWS access key that Vault should use for the auth backend. :param pulumi.Input[str] backend: The path the AWS auth backend being configured was mounted at. Defaults to `aws`. :param pulumi.Input[str] ec2_endpoint: Override the URL Vault uses when making EC2 API calls. :param pulumi.Input[str] iam_endpoint: Override the URL Vault uses when making IAM API calls. :param pulumi.Input[str] iam_server_id_header_value: The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. :param pulumi.Input[str] secret_key: The AWS secret key that Vault should use for the auth backend. :param pulumi.Input[str] sts_endpoint: Override the URL Vault uses when making STS API calls. :param pulumi.Input[str] sts_region: Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ ... @overload def __init__(__self__, resource_name: str, args: Optional[AuthBackendClientArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ ## Import AWS auth backend clients can be imported using `auth/`, the `backend` path, and `/config/client` e.g. ```sh $ pulumi import vault:aws/authBackendClient:AuthBackendClient example auth/aws/config/client ``` :param str resource_name: The name of the resource. :param AuthBackendClientArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(AuthBackendClientArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, access_key: Optional[pulumi.Input[str]] = None, backend: Optional[pulumi.Input[str]] = None, ec2_endpoint: Optional[pulumi.Input[str]] = None, iam_endpoint: Optional[pulumi.Input[str]] = None, iam_server_id_header_value: Optional[pulumi.Input[str]] = None, secret_key: Optional[pulumi.Input[str]] = None, sts_endpoint: Optional[pulumi.Input[str]] = None, sts_region: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = AuthBackendClientArgs.__new__(AuthBackendClientArgs) __props__.__dict__["access_key"] = access_key __props__.__dict__["backend"] = backend __props__.__dict__["ec2_endpoint"] = ec2_endpoint __props__.__dict__["iam_endpoint"] = iam_endpoint __props__.__dict__["iam_server_id_header_value"] = iam_server_id_header_value __props__.__dict__["secret_key"] = secret_key __props__.__dict__["sts_endpoint"] = sts_endpoint __props__.__dict__["sts_region"] = sts_region super(AuthBackendClient, __self__).__init__( 'vault:aws/authBackendClient:AuthBackendClient', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, access_key: Optional[pulumi.Input[str]] = None, backend: Optional[pulumi.Input[str]] = None, ec2_endpoint: Optional[pulumi.Input[str]] = None, iam_endpoint: Optional[pulumi.Input[str]] = None, iam_server_id_header_value: Optional[pulumi.Input[str]] = None, secret_key: Optional[pulumi.Input[str]] = None, sts_endpoint: Optional[pulumi.Input[str]] = None, sts_region: Optional[pulumi.Input[str]] = None) -> 'AuthBackendClient': """ Get an existing AuthBackendClient resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] access_key: The AWS access key that Vault should use for the auth backend. :param pulumi.Input[str] backend: The path the AWS auth backend being configured was mounted at. Defaults to `aws`. :param pulumi.Input[str] ec2_endpoint: Override the URL Vault uses when making EC2 API calls. :param pulumi.Input[str] iam_endpoint: Override the URL Vault uses when making IAM API calls. :param pulumi.Input[str] iam_server_id_header_value: The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. :param pulumi.Input[str] secret_key: The AWS secret key that Vault should use for the auth backend. :param pulumi.Input[str] sts_endpoint: Override the URL Vault uses when making STS API calls. :param pulumi.Input[str] sts_region: Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _AuthBackendClientState.__new__(_AuthBackendClientState) __props__.__dict__["access_key"] = access_key __props__.__dict__["backend"] = backend __props__.__dict__["ec2_endpoint"] = ec2_endpoint __props__.__dict__["iam_endpoint"] = iam_endpoint __props__.__dict__["iam_server_id_header_value"] = iam_server_id_header_value __props__.__dict__["secret_key"] = secret_key __props__.__dict__["sts_endpoint"] = sts_endpoint __props__.__dict__["sts_region"] = sts_region return AuthBackendClient(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="accessKey") def access_key(self) -> pulumi.Output[Optional[str]]: """ The AWS access key that Vault should use for the auth backend. """ return pulumi.get(self, "access_key") @property @pulumi.getter def backend(self) -> pulumi.Output[Optional[str]]: """ The path the AWS auth backend being configured was mounted at. Defaults to `aws`. """ return pulumi.get(self, "backend") @property @pulumi.getter(name="ec2Endpoint") def ec2_endpoint(self) -> pulumi.Output[Optional[str]]: """ Override the URL Vault uses when making EC2 API calls. """ return pulumi.get(self, "ec2_endpoint") @property @pulumi.getter(name="iamEndpoint") def iam_endpoint(self) -> pulumi.Output[Optional[str]]: """ Override the URL Vault uses when making IAM API calls. """ return pulumi.get(self, "iam_endpoint") @property @pulumi.getter(name="iamServerIdHeaderValue") def iam_server_id_header_value(self) -> pulumi.Output[Optional[str]]: """ The value to require in the `X-Vault-AWS-IAM-Server-ID` header as part of `GetCallerIdentity` requests that are used in the IAM auth method. """ return pulumi.get(self, "iam_server_id_header_value") @property @pulumi.getter(name="secretKey") def secret_key(self) -> pulumi.Output[Optional[str]]: """ The AWS secret key that Vault should use for the auth backend. """ return pulumi.get(self, "secret_key") @property @pulumi.getter(name="stsEndpoint") def sts_endpoint(self) -> pulumi.Output[Optional[str]]: """ Override the URL Vault uses when making STS API calls. """ return pulumi.get(self, "sts_endpoint") @property @pulumi.getter(name="stsRegion") def sts_region(self) -> pulumi.Output[Optional[str]]: """ Override the default region when making STS API calls. The `sts_endpoint` argument must be set when using `sts_region`. """ return pulumi.get(self, "sts_region")

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0.836204

0

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135

41.932849

0.825454

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null

0

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0

1

0

null

0

8

eac974d88a94a374d3a16673bf0be5690fccfc38

1,050

py

Python

dt_stripe/managers.py

itsnamgyu/api-demo

ddf726928bd7f1021143c4dbb530e3017a3edda9

[ "MIT" ]

1

2019-06-02T08:20:38.000Z

dt_stripe/managers.py

itsnamgyu/api-demo

ddf726928bd7f1021143c4dbb530e3017a3edda9

[ "MIT" ]

12

2019-07-21T18:40:35.000Z

2019-10-05T19:57:49.000Z

dt_stripe/managers.py

itsnamgyu/django-template

20f64974e0dda69cf8dcf0dac9e0a309f200fb61

[ "MIT" ]

null

from django.apps import apps from django.db import models class ServiceManager(models.Manager): def get_queryset(self): return super().get_queryset().filter(product_type="service") class GoodManager(models.Manager): def get_queryset(self): return super().get_queryset().filter(product_type="good") class PlanManager(models.Manager): def get_queryset(self): return super().get_queryset() def subscribed_by(self, customer): Subscription = apps.get_model("dt_stripe", "Subscription") return self.get_queryset().filter( subscriptions__in=Subscription.objects.filter( customer=customer, status=Subscription.SUBSCRIPTION_ACTIVE ) ) def not_subscribed_by(self, customer): Subscription = apps.get_model("dt_stripe", "Subscription") return self.get_queryset().exclude( subscriptions__in=Subscription.objects.filter( customer=customer, status=Subscription.SUBSCRIPTION_ACTIVE ) )

30.882353

74

0.67619

111

1,050

6.189189

0.324324

0.128093

0.069869

0.082969

0.80786

0

0.222857

1,050

33

75

31.818182

0.841912

0

0.36

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0

1

0.2

false

0

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0.12

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0

null

0

1

0

null

0

1

0

8

d8134620d1d4e2ae84dd1fde2b57220b82314f3a

120

py

Python

discord/state.py

kuzaku-developers/disnake

61cc1ad4c2bafd39726a1447c85f7e469e41af10

[ "MIT" ]

null

discord/state.py

kuzaku-developers/disnake

61cc1ad4c2bafd39726a1447c85f7e469e41af10

[ "MIT" ]

null

discord/state.py

kuzaku-developers/disnake

61cc1ad4c2bafd39726a1447c85f7e469e41af10

[ "MIT" ]

null

from disnake.state import * from disnake.state import __dict__ as __original_dict__ locals().update(__original_dict__)

24

55

0.833333

16

120

5.375

0.5625

0.255814

0.372093

0.511628

0

0.1

120

4

56

30

0.796296

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

dc5c8d18d6f732ec5d82ba0578235665dd684ccd

25,337

py

Python

util/data/gen/CRYPT32.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/CRYPT32.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/CRYPT32.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'CertAddCRLContextToStore', 'address': '0x7ffb3be3ff70'}, {'type': 'function', 'name': 'CertAddCRLLinkToStore', 'address': '0x7ffb3be3fff0'}, {'type': 'function', 'name': 'CertAddCTLContextToStore', 'address': '0x7ffb3be3ff70'}, {'type': 'function', 'name': 'CertAddCTLLinkToStore', 'address': '0x7ffb3be3fff0'}, {'type': 'function', 'name': 'CertAddCertificateContextToStore', 'address': '0x7ffb3bdf2610'}, {'type': 'function', 'name': 'CertAddCertificateLinkToStore', 'address': '0x7ffb3be3fff0'}, {'type': 'function', 'name': 'CertAddEncodedCRLToStore', 'address': '0x7ffb3be0a730'}, {'type': 'function', 'name': 'CertAddEncodedCTLToStore', 'address': '0x7ffb3bdc9830'}, {'type': 'function', 'name': 'CertAddEncodedCertificateToStore', 'address': '0x7ffb3bddad60'}, {'type': 'function', 'name': 'CertAddEncodedCertificateToSystemStoreA', 'address': '0x7ffb3be41060'}, {'type': 'function', 'name': 'CertAddEncodedCertificateToSystemStoreW', 'address': '0x7ffb3be410e0'}, {'type': 'function', 'name': 'CertAddEnhancedKeyUsageIdentifier', 'address': '0x7ffb3be41800'}, {'type': 'function', 'name': 'CertAddRefServerOcspResponse', 'address': '0x7ffb3be431e0'}, {'type': 'function', 'name': 'CertAddRefServerOcspResponseContext', 'address': '0x7ffb3be43200'}, {'type': 'function', 'name': 'CertAddSerializedElementToStore', 'address': '0x7ffb3bdd1430'}, {'type': 'function', 'name': 'CertAddStoreToCollection', 'address': '0x7ffb3bdf1830'}, {'type': 'function', 'name': 'CertAlgIdToOID', 'address': '0x7ffb3be43a10'}, {'type': 'function', 'name': 'CertCloseServerOcspResponse', 'address': '0x7ffb3be43220'}, {'type': 'function', 'name': 'CertCloseStore', 'address': '0x7ffb3bddc9a0'}, {'type': 'function', 'name': 'CertCompareCertificate', 'address': '0x7ffb3be08340'}, {'type': 'function', 'name': 'CertCompareCertificateName', 'address': '0x7ffb3bdf2b40'}, {'type': 'function', 'name': 'CertCompareIntegerBlob', 'address': '0x7ffb3be43a50'}, {'type': 'function', 'name': 'CertComparePublicKeyInfo', 'address': '0x7ffb3bde2a90'}, {'type': 'function', 'name': 'CertControlStore', 'address': '0x7ffb3bdcd6c0'}, {'type': 'function', 'name': 'CertCreateCRLContext', 'address': '0x7ffb3be0a6f0'}, {'type': 'function', 'name': 'CertCreateCTLContext', 'address': '0x7ffb3bdc97f0'}, {'type': 'function', 'name': 'CertCreateCTLEntryFromCertificateContextProperties', 'address': '0x7ffb3be40050'}, {'type': 'function', 'name': 'CertCreateCertificateChainEngine', 'address': '0x7ffb3be90830'}, {'type': 'function', 'name': 'CertCreateCertificateContext', 'address': '0x7ffb3be06c40'}, {'type': 'function', 'name': 'CertCreateContext', 'address': '0x7ffb3bdced90'}, {'type': 'function', 'name': 'CertCreateSelfSignCertificate', 'address': '0x7ffb3be44020'}, {'type': 'function', 'name': 'CertDeleteCRLFromStore', 'address': '0x7ffb3be40590'}, {'type': 'function', 'name': 'CertDeleteCTLFromStore', 'address': '0x7ffb3be40590'}, {'type': 'function', 'name': 'CertDeleteCertificateFromStore', 'address': '0x7ffb3be40590'}, {'type': 'function', 'name': 'CertDuplicateCRLContext', 'address': '0x7ffb3bdf9eb0'}, {'type': 'function', 'name': 'CertDuplicateCTLContext', 'address': '0x7ffb3bdf9eb0'}, {'type': 'function', 'name': 'CertDuplicateCertificateChain', 'address': '0x7ffb3be0b0c0'}, {'type': 'function', 'name': 'CertDuplicateCertificateContext', 'address': '0x7ffb3bdd2b90'}, {'type': 'function', 'name': 'CertDuplicateStore', 'address': '0x7ffb3be0afe0'}, {'type': 'function', 'name': 'CertEnumCRLContextProperties', 'address': '0x7ffb3be405b0'}, {'type': 'function', 'name': 'CertEnumCRLsInStore', 'address': '0x7ffb3bdc65c0'}, {'type': 'function', 'name': 'CertEnumCTLContextProperties', 'address': '0x7ffb3be405b0'}, {'type': 'function', 'name': 'CertEnumCTLsInStore', 'address': '0x7ffb3bdf4380'}, {'type': 'function', 'name': 'CertEnumCertificateContextProperties', 'address': '0x7ffb3be405b0'}, {'type': 'function', 'name': 'CertEnumCertificatesInStore', 'address': '0x7ffb3bdce1f0'}, {'type': 'function', 'name': 'CertEnumPhysicalStore', 'address': '0x7ffb3be48d50'}, {'type': 'function', 'name': 'CertEnumSubjectInSortedCTL', 'address': '0x7ffb3be405d0'}, {'type': 'function', 'name': 'CertEnumSystemStore', 'address': '0x7ffb3be48d70'}, {'type': 'function', 'name': 'CertEnumSystemStoreLocation', 'address': '0x7ffb3be49100'}, {'type': 'function', 'name': 'CertFindAttribute', 'address': '0x7ffb3bdf8af0'}, {'type': 'function', 'name': 'CertFindCRLInStore', 'address': '0x7ffb3be06d20'}, {'type': 'function', 'name': 'CertFindCTLInStore', 'address': '0x7ffb3be40740'}, {'type': 'function', 'name': 'CertFindCertificateInCRL', 'address': '0x7ffb3be0bec0'}, {'type': 'function', 'name': 'CertFindCertificateInStore', 'address': '0x7ffb3bdcf140'}, {'type': 'function', 'name': 'CertFindChainInStore', 'address': '0x7ffb3be49e80'}, {'type': 'function', 'name': 'CertFindExtension', 'address': '0x7ffb3bdf39d0'}, {'type': 'function', 'name': 'CertFindRDNAttr', 'address': '0x7ffb3be43a70'}, {'type': 'function', 'name': 'CertFindSubjectInCTL', 'address': '0x7ffb3bdf9650'}, {'type': 'function', 'name': 'CertFindSubjectInSortedCTL', 'address': '0x7ffb3bdecb30'}, {'type': 'function', 'name': 'CertFreeCRLContext', 'address': '0x7ffb3be0b740'}, {'type': 'function', 'name': 'CertFreeCTLContext', 'address': '0x7ffb3be0b740'}, {'type': 'function', 'name': 'CertFreeCertificateChain', 'address': '0x7ffb3bdf8ef0'}, {'type': 'function', 'name': 'CertFreeCertificateChainEngine', 'address': '0x7ffb3be90840'}, {'type': 'function', 'name': 'CertFreeCertificateChainList', 'address': '0x7ffb3be4a250'}, {'type': 'function', 'name': 'CertFreeCertificateContext', 'address': '0x7ffb3bdd9260'}, {'type': 'function', 'name': 'CertFreeServerOcspResponseContext', 'address': '0x7ffb3be43290'}, {'type': 'function', 'name': 'CertGetCRLContextProperty', 'address': '0x7ffb3bde09d0'}, {'type': 'function', 'name': 'CertGetCRLFromStore', 'address': '0x7ffb3be407a0'}, {'type': 'function', 'name': 'CertGetCTLContextProperty', 'address': '0x7ffb3bde09d0'}, {'type': 'function', 'name': 'CertGetCertificateChain', 'address': '0x7ffb3bdd4c80'}, {'type': 'function', 'name': 'CertGetCertificateContextProperty', 'address': '0x7ffb3bde09d0'}, {'type': 'function', 'name': 'CertGetEnhancedKeyUsage', 'address': '0x7ffb3bdf3d40'}, {'type': 'function', 'name': 'CertGetIntendedKeyUsage', 'address': '0x7ffb3bdf43e0'}, {'type': 'function', 'name': 'CertGetIssuerCertificateFromStore', 'address': '0x7ffb3be40890'}, {'type': 'function', 'name': 'CertGetNameStringA', 'address': '0x7ffb3be4ae00'}, {'type': 'function', 'name': 'CertGetNameStringW', 'address': '0x7ffb3bdf2d50'}, {'type': 'function', 'name': 'CertGetPublicKeyLength', 'address': '0x7ffb3bdf4c50'}, {'type': 'function', 'name': 'CertGetServerOcspResponseContext', 'address': '0x7ffb3be432e0'}, {'type': 'function', 'name': 'CertGetStoreProperty', 'address': '0x7ffb3be40990'}, {'type': 'function', 'name': 'CertGetSubjectCertificateFromStore', 'address': '0x7ffb3be0aaa0'}, {'type': 'function', 'name': 'CertGetValidUsages', 'address': '0x7ffb3bdf33f0'}, {'type': 'function', 'name': 'CertIsRDNAttrsInCertificateName', 'address': '0x7ffb3be43ae0'}, {'type': 'function', 'name': 'CertIsStrongHashToSign', 'address': '0x7ffb3be4cee0'}, {'type': 'function', 'name': 'CertIsValidCRLForCertificate', 'address': '0x7ffb3bdc7b40'}, {'type': 'function', 'name': 'CertIsWeakHash', 'address': '0x7ffb3bdd6c20'}, {'type': 'function', 'name': 'CertNameToStrA', 'address': '0x7ffb3be0db70'}, {'type': 'function', 'name': 'CertNameToStrW', 'address': '0x7ffb3be091b0'}, {'type': 'function', 'name': 'CertOIDToAlgId', 'address': '0x7ffb3bdf9830'}, {'type': 'function', 'name': 'CertOpenServerOcspResponse', 'address': '0x7ffb3be433c0'}, {'type': 'function', 'name': 'CertOpenStore', 'address': '0x7ffb3bde86b0'}, {'type': 'function', 'name': 'CertOpenSystemStoreA', 'address': '0x7ffb3be41160'}, {'type': 'function', 'name': 'CertOpenSystemStoreW', 'address': '0x7ffb3be411e0'}, {'type': 'function', 'name': 'CertRDNValueToStrA', 'address': '0x7ffb3be4aec0'}, {'type': 'function', 'name': 'CertRDNValueToStrW', 'address': '0x7ffb3be4af90'}, {'type': 'function', 'name': 'CertRegisterPhysicalStore', 'address': '0x7ffb3be49200'}, {'type': 'function', 'name': 'CertRegisterSystemStore', 'address': '0x7ffb3be494c0'}, {'type': 'function', 'name': 'CertRemoveEnhancedKeyUsageIdentifier', 'address': '0x7ffb3be419d0'}, {'type': 'function', 'name': 'CertRemoveStoreFromCollection', 'address': '0x7ffb3be0a370'}, {'type': 'function', 'name': 'CertResyncCertificateChainEngine', 'address': '0x7ffb3be90880'}, {'type': 'function', 'name': 'CertRetrieveLogoOrBiometricInfo', 'address': '0x7ffb3be4d240'}, {'type': 'function', 'name': 'CertSaveStore', 'address': '0x7ffb3be08a50'}, {'type': 'function', 'name': 'CertSelectCertificateChains', 'address': '0x7ffb3be4a270'}, {'type': 'function', 'name': 'CertSerializeCRLStoreElement', 'address': '0x7ffb3be076b0'}, {'type': 'function', 'name': 'CertSerializeCTLStoreElement', 'address': '0x7ffb3be076b0'}, {'type': 'function', 'name': 'CertSerializeCertificateStoreElement', 'address': '0x7ffb3be076b0'}, {'type': 'function', 'name': 'CertSetCRLContextProperty', 'address': '0x7ffb3be0a6c0'}, {'type': 'function', 'name': 'CertSetCTLContextProperty', 'address': '0x7ffb3be0a6c0'}, {'type': 'function', 'name': 'CertSetCertificateContextPropertiesFromCTLEntry', 'address': '0x7ffb3bdc8080'}, {'type': 'function', 'name': 'CertSetCertificateContextProperty', 'address': '0x7ffb3be0a6c0'}, {'type': 'function', 'name': 'CertSetEnhancedKeyUsage', 'address': '0x7ffb3be41ad0'}, {'type': 'function', 'name': 'CertSetStoreProperty', 'address': '0x7ffb3be40b90'}, {'type': 'function', 'name': 'CertStrToNameA', 'address': '0x7ffb3be4b140'}, {'type': 'function', 'name': 'CertStrToNameW', 'address': '0x7ffb3bdc66c0'}, {'type': 'function', 'name': 'CertUnregisterPhysicalStore', 'address': '0x7ffb3be495d0'}, {'type': 'function', 'name': 'CertUnregisterSystemStore', 'address': '0x7ffb3be496f0'}, {'type': 'function', 'name': 'CertVerifyCRLRevocation', 'address': '0x7ffb3be43d00'}, {'type': 'function', 'name': 'CertVerifyCRLTimeValidity', 'address': '0x7ffb3be43d90'}, {'type': 'function', 'name': 'CertVerifyCTLUsage', 'address': '0x7ffb3be4da60'}, {'type': 'function', 'name': 'CertVerifyCertificateChainPolicy', 'address': '0x7ffb3bde4aa0'}, {'type': 'function', 'name': 'CertVerifyRevocation', 'address': '0x7ffb3bdf4790'}, {'type': 'function', 'name': 'CertVerifySubjectCertificateContext', 'address': '0x7ffb3be40c10'}, {'type': 'function', 'name': 'CertVerifyTimeValidity', 'address': '0x7ffb3bdf0c40'}, {'type': 'function', 'name': 'CertVerifyValidityNesting', 'address': '0x7ffb3be43e50'}, {'type': 'function', 'name': 'CryptAcquireCertificatePrivateKey', 'address': '0x7ffb3be0e510'}, {'type': 'function', 'name': 'CryptBinaryToStringA', 'address': '0x7ffb3bdd0b80'}, {'type': 'function', 'name': 'CryptBinaryToStringW', 'address': '0x7ffb3bdd0a40'}, {'type': 'function', 'name': 'CryptCloseAsyncHandle', 'address': '0x7ffb3be4ec60'}, {'type': 'function', 'name': 'CryptCreateAsyncHandle', 'address': '0x7ffb3be4ec90'}, {'type': 'function', 'name': 'CryptCreateKeyIdentifierFromCSP', 'address': '0x7ffb3be4c3e0'}, {'type': 'function', 'name': 'CryptDecodeMessage', 'address': '0x7ffb3be50690'}, {'type': 'function', 'name': 'CryptDecodeObject', 'address': '0x7ffb3bde2e90'}, {'type': 'function', 'name': 'CryptDecodeObjectEx', 'address': '0x7ffb3bde2ed0'}, {'type': 'function', 'name': 'CryptDecryptAndVerifyMessageSignature', 'address': '0x7ffb3be50730'}, {'type': 'function', 'name': 'CryptDecryptMessage', 'address': '0x7ffb3be50960'}, {'type': 'function', 'name': 'CryptEncodeObject', 'address': '0x7ffb3be0b490'}, {'type': 'function', 'name': 'CryptEncodeObjectEx', 'address': '0x7ffb3bde4360'}, {'type': 'function', 'name': 'CryptEncryptMessage', 'address': '0x7ffb3be509d0'}, {'type': 'function', 'name': 'CryptEnumKeyIdentifierProperties', 'address': '0x7ffb3be40c60'}, {'type': 'function', 'name': 'CryptEnumOIDFunction', 'address': '0x7ffb3bdeefd0'}, {'type': 'function', 'name': 'CryptEnumOIDInfo', 'address': '0x7ffb3be5f9e0'}, {'type': 'function', 'name': 'CryptExportPKCS8', 'address': '0x7ffb3be9b7d0'}, {'type': 'function', 'name': 'CryptExportPublicKeyInfo', 'address': '0x7ffb3be4c470'}, {'type': 'function', 'name': 'CryptExportPublicKeyInfoEx', 'address': '0x7ffb3be0e8b0'}, {'type': 'function', 'name': 'CryptExportPublicKeyInfoFromBCryptKeyHandle', 'address': '0x7ffb3be4c4b0'}, {'type': 'function', 'name': 'CryptFindCertificateKeyProvInfo', 'address': '0x7ffb3be43eb0'}, {'type': 'function', 'name': 'CryptFindLocalizedName', 'address': '0x7ffb3be5fae0'}, {'type': 'function', 'name': 'CryptFindOIDInfo', 'address': '0x7ffb3bde2690'}, {'type': 'function', 'name': 'CryptFormatObject', 'address': '0x7ffb3be6b2c0'}, {'type': 'function', 'name': 'CryptFreeOIDFunctionAddress', 'address': '0x7ffb3bdf4ef0'}, {'type': 'function', 'name': 'CryptGetAsyncParam', 'address': '0x7ffb3be4ecc0'}, {'type': 'function', 'name': 'CryptGetDefaultOIDDllList', 'address': '0x7ffb3bdf5270'}, {'type': 'function', 'name': 'CryptGetDefaultOIDFunctionAddress', 'address': '0x7ffb3bdf5320'}, {'type': 'function', 'name': 'CryptGetKeyIdentifierProperty', 'address': '0x7ffb3be40d60'}, {'type': 'function', 'name': 'CryptGetMessageCertificates', 'address': '0x7ffb3be50ab0'}, {'type': 'function', 'name': 'CryptGetMessageSignerCount', 'address': '0x7ffb3be50af0'}, {'type': 'function', 'name': 'CryptGetOIDFunctionAddress', 'address': '0x7ffb3bde4790'}, {'type': 'function', 'name': 'CryptGetOIDFunctionValue', 'address': '0x7ffb3be5dd80'}, {'type': 'function', 'name': 'CryptHashCertificate', 'address': '0x7ffb3bdd1c20'}, {'type': 'function', 'name': 'CryptHashCertificate2', 'address': '0x7ffb3bddb070'}, {'type': 'function', 'name': 'CryptHashMessage', 'address': '0x7ffb3be50ba0'}, {'type': 'function', 'name': 'CryptHashPublicKeyInfo', 'address': '0x7ffb3bdc3d20'}, {'type': 'function', 'name': 'CryptHashToBeSigned', 'address': '0x7ffb3bde1ff0'}, {'type': 'function', 'name': 'CryptImportPKCS8', 'address': '0x7ffb3be9bbd0'}, {'type': 'function', 'name': 'CryptImportPublicKeyInfo', 'address': '0x7ffb3bdc6470'}, {'type': 'function', 'name': 'CryptImportPublicKeyInfoEx', 'address': '0x7ffb3bdf4d60'}, {'type': 'function', 'name': 'CryptImportPublicKeyInfoEx2', 'address': '0x7ffb3bde3400'}, {'type': 'function', 'name': 'CryptInitOIDFunctionSet', 'address': '0x7ffb3be00d80'}, {'type': 'function', 'name': 'CryptInstallDefaultContext', 'address': '0x7ffb3be712d0'}, {'type': 'function', 'name': 'CryptInstallOIDFunctionAddress', 'address': '0x7ffb3be00d10'}, {'type': 'function', 'name': 'CryptLoadSip', 'address': '0x7ffb3beb00f0'}, {'type': 'function', 'name': 'CryptMemAlloc', 'address': '0x7ffb3be065f0'}, {'type': 'function', 'name': 'CryptMemFree', 'address': '0x7ffb3bde1d10'}, {'type': 'function', 'name': 'CryptMemRealloc', 'address': '0x7ffb3be71760'}, {'type': 'function', 'name': 'CryptMsgCalculateEncodedLength', 'address': '0x7ffb3beb20b0'}, {'type': 'function', 'name': 'CryptMsgClose', 'address': '0x7ffb3bde1980'}, {'type': 'function', 'name': 'CryptMsgControl', 'address': '0x7ffb3be02540'}, {'type': 'function', 'name': 'CryptMsgCountersign', 'address': '0x7ffb3bebce40'}, {'type': 'function', 'name': 'CryptMsgCountersignEncoded', 'address': '0x7ffb3bebcfe0'}, {'type': 'function', 'name': 'CryptMsgDuplicate', 'address': '0x7ffb3be0cb60'}, {'type': 'function', 'name': 'CryptMsgEncodeAndSignCTL', 'address': '0x7ffb3bebd7c0'}, {'type': 'function', 'name': 'CryptMsgGetAndVerifySigner', 'address': '0x7ffb3bdc92b0'}, {'type': 'function', 'name': 'CryptMsgGetParam', 'address': '0x7ffb3bdd5270'}, {'type': 'function', 'name': 'CryptMsgOpenToDecode', 'address': '0x7ffb3bdf5060'}, {'type': 'function', 'name': 'CryptMsgOpenToEncode', 'address': '0x7ffb3bebd380'}, {'type': 'function', 'name': 'CryptMsgSignCTL', 'address': '0x7ffb3bebd8c0'}, {'type': 'function', 'name': 'CryptMsgUpdate', 'address': '0x7ffb3bde8c10'}, {'type': 'function', 'name': 'CryptMsgVerifyCountersignatureEncoded', 'address': '0x7ffb3bebd490'}, {'type': 'function', 'name': 'CryptMsgVerifyCountersignatureEncodedEx', 'address': '0x7ffb3bebd4f0'}, {'type': 'function', 'name': 'CryptObjectLocatorFree', 'address': '0x7ffb3be97f70'}, {'type': 'function', 'name': 'CryptObjectLocatorGet', 'address': '0x7ffb3be97fc0'}, {'type': 'function', 'name': 'CryptObjectLocatorGetContent', 'address': '0x7ffb3be98000'}, {'type': 'function', 'name': 'CryptObjectLocatorGetUpdated', 'address': '0x7ffb3be980c0'}, {'type': 'function', 'name': 'CryptObjectLocatorInitialize', 'address': '0x7ffb3be98110'}, {'type': 'function', 'name': 'CryptObjectLocatorIsChanged', 'address': '0x7ffb3be98490'}, {'type': 'function', 'name': 'CryptObjectLocatorRelease', 'address': '0x7ffb3be984c0'}, {'type': 'function', 'name': 'CryptProtectData', 'address': '0x7ffb3be0c5f0'}, {'type': 'function', 'name': 'CryptQueryObject', 'address': '0x7ffb3bdc1e00'}, {'type': 'function', 'name': 'CryptRegisterDefaultOIDFunction', 'address': '0x7ffb3be5de80'}, {'type': 'function', 'name': 'CryptRegisterOIDFunction', 'address': '0x7ffb3be5e1a0'}, {'type': 'function', 'name': 'CryptRegisterOIDInfo', 'address': '0x7ffb3be5fb90'}, {'type': 'function', 'name': 'CryptRetrieveTimeStamp', 'address': '0x7ffb3beb07e0'}, {'type': 'function', 'name': 'CryptSIPAddProvider', 'address': '0x7ffb3beb01a0'}, {'type': 'function', 'name': 'CryptSIPCreateIndirectData', 'address': '0x7ffb3bdf5730'}, {'type': 'function', 'name': 'CryptSIPGetCaps', 'address': '0x7ffb3bdf6440'}, {'type': 'function', 'name': 'CryptSIPGetSealedDigest', 'address': '0x7ffb3beb0530'}, {'type': 'function', 'name': 'CryptSIPGetSignedDataMsg', 'address': '0x7ffb3be0c180'}, {'type': 'function', 'name': 'CryptSIPLoad', 'address': '0x7ffb3bdf46b0'}, {'type': 'function', 'name': 'CryptSIPPutSignedDataMsg', 'address': '0x7ffb3beb0620'}, {'type': 'function', 'name': 'CryptSIPRemoveProvider', 'address': '0x7ffb3beb0380'}, {'type': 'function', 'name': 'CryptSIPRemoveSignedDataMsg', 'address': '0x7ffb3beb0710'}, {'type': 'function', 'name': 'CryptSIPRetrieveSubjectGuid', 'address': '0x7ffb3be06fd0'}, {'type': 'function', 'name': 'CryptSIPRetrieveSubjectGuidForCatalogFile', 'address': '0x7ffb3be07340'}, {'type': 'function', 'name': 'CryptSIPVerifyIndirectData', 'address': '0x7ffb3be0e140'}, {'type': 'function', 'name': 'CryptSetAsyncParam', 'address': '0x7ffb3be4ec60'}, {'type': 'function', 'name': 'CryptSetKeyIdentifierProperty', 'address': '0x7ffb3be40e80'}, {'type': 'function', 'name': 'CryptSetOIDFunctionValue', 'address': '0x7ffb3be5e2a0'}, {'type': 'function', 'name': 'CryptSignAndEncodeCertificate', 'address': '0x7ffb3bdd1970'}, {'type': 'function', 'name': 'CryptSignAndEncryptMessage', 'address': '0x7ffb3be50e10'}, {'type': 'function', 'name': 'CryptSignCertificate', 'address': '0x7ffb3bdd1b10'}, {'type': 'function', 'name': 'CryptSignMessage', 'address': '0x7ffb3be50f70'}, {'type': 'function', 'name': 'CryptSignMessageWithKey', 'address': '0x7ffb3be51080'}, {'type': 'function', 'name': 'CryptStringToBinaryA', 'address': '0x7ffb3bdfb610'}, {'type': 'function', 'name': 'CryptStringToBinaryW', 'address': '0x7ffb3bdfb470'}, {'type': 'function', 'name': 'CryptUninstallDefaultContext', 'address': '0x7ffb3be714a0'}, {'type': 'function', 'name': 'CryptUnprotectData', 'address': '0x7ffb3be05440'}, {'type': 'function', 'name': 'CryptUnregisterDefaultOIDFunction', 'address': '0x7ffb3be5e3b0'}, {'type': 'function', 'name': 'CryptUnregisterOIDFunction', 'address': '0x7ffb3be5e670'}, {'type': 'function', 'name': 'CryptUnregisterOIDInfo', 'address': '0x7ffb3be5fd90'}, {'type': 'function', 'name': 'CryptVerifyCertificateSignature', 'address': '0x7ffb3be43fd0'}, {'type': 'function', 'name': 'CryptVerifyCertificateSignatureEx', 'address': '0x7ffb3bdd8230'}, {'type': 'function', 'name': 'CryptVerifyDetachedMessageHash', 'address': '0x7ffb3be51310'}, {'type': 'function', 'name': 'CryptVerifyDetachedMessageSignature', 'address': '0x7ffb3be51370'}, {'type': 'function', 'name': 'CryptVerifyMessageHash', 'address': '0x7ffb3be513f0'}, {'type': 'function', 'name': 'CryptVerifyMessageSignature', 'address': '0x7ffb3be51440'}, {'type': 'function', 'name': 'CryptVerifyMessageSignatureWithKey', 'address': '0x7ffb3be514c0'}, {'type': 'function', 'name': 'CryptVerifyTimeStampSignature', 'address': '0x7ffb3bdc4750'}, {'type': 'function', 'name': 'I_CertChainEngineIsDisallowedCertificate', 'address': '0x7ffb3bdf9b20'}, {'type': 'function', 'name': 'I_CertDiagControl', 'address': '0x7ffb3bdcbec0'}, {'type': 'function', 'name': 'I_CertFinishSslHandshake', 'address': '0x7ffb3bdf7a70'}, {'type': 'function', 'name': 'I_CertProcessSslHandshake', 'address': '0x7ffb3bdf7c20'}, {'type': 'function', 'name': 'I_CertProtectFunction', 'address': '0x7ffb3bdcdbc0'}, {'type': 'function', 'name': 'I_CertSrvProtectFunction', 'address': '0x7ffb3bdfae40'}, {'type': 'function', 'name': 'I_CertSyncStore', 'address': '0x7ffb3be41040'}, {'type': 'function', 'name': 'I_CertUpdateStore', 'address': '0x7ffb3bdc63a0'}, {'type': 'function', 'name': 'I_CertWnfEnableFlushCache', 'address': '0x7ffb3be10260'}, {'type': 'function', 'name': 'I_CryptAddRefLruEntry', 'address': '0x7ffb3be0aa90'}, {'type': 'function', 'name': 'I_CryptAddSmartCardCertToStore', 'address': '0x7ffb3be75260'}, {'type': 'function', 'name': 'I_CryptAllocTls', 'address': '0x7ffb3bdf9fe0'}, {'type': 'function', 'name': 'I_CryptAllocTlsEx', 'address': '0x7ffb3bdfa6e0'}, {'type': 'function', 'name': 'I_CryptCreateLruCache', 'address': '0x7ffb3bdd8030'}, {'type': 'function', 'name': 'I_CryptCreateLruEntry', 'address': '0x7ffb3bdd9920'}, {'type': 'function', 'name': 'I_CryptDetachTls', 'address': '0x7ffb3be06870'}, {'type': 'function', 'name': 'I_CryptDisableLruOfEntries', 'address': '0x7ffb3be91ae0'}, {'type': 'function', 'name': 'I_CryptEnableLruOfEntries', 'address': '0x7ffb3be91b30'}, {'type': 'function', 'name': 'I_CryptEnumMatchingLruEntries', 'address': '0x7ffb3bdc3270'}, {'type': 'function', 'name': 'I_CryptFindLruEntry', 'address': '0x7ffb3bdda0a0'}, {'type': 'function', 'name': 'I_CryptFindLruEntryData', 'address': '0x7ffb3be91ba0'}, {'type': 'function', 'name': 'I_CryptFindSmartCardCertInStore', 'address': '0x7ffb3be75380'}, {'type': 'function', 'name': 'I_CryptFlushLruCache', 'address': '0x7ffb3bdd2270'}, {'type': 'function', 'name': 'I_CryptFreeLruCache', 'address': '0x7ffb3be0cd60'}, {'type': 'function', 'name': 'I_CryptFreeTls', 'address': '0x7ffb3bdfa7b0'}, {'type': 'function', 'name': 'I_CryptGetAsn1Decoder', 'address': '0x7ffb3bde7450'}, {'type': 'function', 'name': 'I_CryptGetAsn1Encoder', 'address': '0x7ffb3bdec490'}, {'type': 'function', 'name': 'I_CryptGetDefaultCryptProv', 'address': '0x7ffb3bdf5180'}, {'type': 'function', 'name': 'I_CryptGetDefaultCryptProvForEncrypt', 'address': '0x7ffb3be71620'}, {'type': 'function', 'name': 'I_CryptGetFileVersion', 'address': '0x7ffb3be2f550'}, {'type': 'function', 'name': 'I_CryptGetLruEntryData', 'address': '0x7ffb3bdd7150'}, {'type': 'function', 'name': 'I_CryptGetLruEntryIdentifier', 'address': '0x7ffb3be91bd0'}, {'type': 'function', 'name': 'I_CryptGetOssGlobal', 'address': '0x7ffb3be755f0'}, {'type': 'function', 'name': 'I_CryptGetTls', 'address': '0x7ffb3bde8660'}, {'type': 'function', 'name': 'I_CryptInsertLruEntry', 'address': '0x7ffb3bdd97e0'}, {'type': 'function', 'name': 'I_CryptInstallAsn1Module', 'address': '0x7ffb3bdf9ff0'}, {'type': 'function', 'name': 'I_CryptInstallOssGlobal', 'address': '0x7ffb3be755f0'}, {'type': 'function', 'name': 'I_CryptReadTrustedPublisherDWORDValueFromRegistry', 'address': '0x7ffb3bdefe00'}, {'type': 'function', 'name': 'I_CryptRegisterSmartCardStore', 'address': '0x7ffb3be4ec60'}, {'type': 'function', 'name': 'I_CryptReleaseLruEntry', 'address': '0x7ffb3bdd9760'}, {'type': 'function', 'name': 'I_CryptRemoveLruEntry', 'address': '0x7ffb3bdc6320'}, {'type': 'function', 'name': 'I_CryptSetTls', 'address': '0x7ffb3bdeca00'}, {'type': 'function', 'name': 'I_CryptTouchLruEntry', 'address': '0x7ffb3bdc6050'}, {'type': 'function', 'name': 'I_CryptUninstallAsn1Module', 'address': '0x7ffb3bdfaf80'}, {'type': 'function', 'name': 'I_CryptUninstallOssGlobal', 'address': '0x7ffb3be755f0'}, {'type': 'function', 'name': 'I_CryptUnregisterSmartCardStore', 'address': '0x7ffb3be4ec60'}, {'type': 'function', 'name': 'I_CryptWalkAllLruCacheEntries', 'address': '0x7ffb3be06c90'}, {'type': 'function', 'name': 'I_PFXDecrypt', 'address': '0x7ffb3be9d940'}, {'type': 'function', 'name': 'I_PFXHMAC', 'address': '0x7ffb3be9dd60'}, {'type': 'function', 'name': 'I_PFXImportCertStoreEx', 'address': '0x7ffb3be99860'}, {'type': 'function', 'name': 'PFXExportCertStore', 'address': '0x7ffb3be9a320'}, {'type': 'function', 'name': 'PFXExportCertStore2', 'address': '0x7ffb3be9a480'}, {'type': 'function', 'name': 'PFXExportCertStoreEx', 'address': '0x7ffb3be9a4a0'}, {'type': 'function', 'name': 'PFXImportCertStore', 'address': '0x7ffb3be9a730'}, {'type': 'function', 'name': 'PFXIsPFXBlob', 'address': '0x7ffb3be9a760'}, {'type': 'function', 'name': 'PFXVerifyPassword', 'address': '0x7ffb3be9a7f0'}]

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7

f4a1080d5af6bc7222a6f5cdd899b208f6afdf45

3,434

py

Python

python/tests/test_cg.py

Dareen/pubnub-python

713e98b53f6623a8abca2cee8a47fd92ceb7a75b

[ "MIT" ]

null

python/tests/test_cg.py

Dareen/pubnub-python

713e98b53f6623a8abca2cee8a47fd92ceb7a75b

[ "MIT" ]

null

python/tests/test_cg.py

Dareen/pubnub-python

713e98b53f6623a8abca2cee8a47fd92ceb7a75b

[ "MIT" ]

1

2019-09-10T04:07:35.000Z

from pubnub import Pubnub import time import random pubnub = Pubnub("demo","demo") pubnub.set_u(True) def rand_str(s): return str(s) + '-' + str(random.randint(1, 100000000000)) def test_1(): channel = rand_str('channel') channel2 = rand_str('channel') channel_group = rand_str('group') channel_group2 = rand_str('group') namespace = rand_str('ns') resp = pubnub.channel_group_add_channel(channel_group=namespace + ':' + channel_group, channel=channel) assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_add_channel(channel_group=namespace + ':' + channel_group, channel=channel2) assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_add_channel(channel_group=namespace + ':' + channel_group2, channel=channel) assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_add_channel(channel_group=namespace + ':' + channel_group2, channel=channel2) assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_list_channels(channel_group=namespace + ':' + channel_group) assert channel in resp['payload']['channels'] assert channel2 in resp['payload']['channels'] assert len(resp['payload']['channels']) == 2 resp = pubnub.channel_group_remove_channel(channel_group=namespace + ':' + channel_group, channel=channel2) print resp assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_list_channels(channel_group=namespace + ':' + channel_group) print resp assert channel in resp['payload']['channels'] assert len(resp['payload']['channels']) == 1 resp = pubnub.channel_group_list_channels(channel_group=namespace + ':' + channel_group2) assert channel in resp['payload']['channels'] assert channel2 in resp['payload']['channels'] assert len(resp['payload']['channels']) == 2 resp = pubnub.channel_group_remove_channel(channel_group=namespace + ':' + channel_group2, channel=channel2) print resp assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_list_channels(channel_group=namespace + ':' + channel_group2) print resp assert channel in resp['payload']['channels'] assert len(resp['payload']['channels']) == 1 resp = pubnub.channel_group_list_groups(namespace=namespace) assert channel_group in resp['payload']['groups'] assert channel_group2 in resp['payload']['groups'] assert len(resp['payload']['groups']) == 2 resp = pubnub.channel_group_remove_group(channel_group=namespace + ':' + channel_group2) print resp assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_list_groups(namespace=namespace) assert channel_group in resp['payload']['groups'] assert len(resp['payload']['groups']) == 1 resp = pubnub.channel_group_list_namespaces() assert namespace in resp['payload']['namespaces'] resp = pubnub.channel_group_remove_namespace(namespace=namespace) print resp assert resp['status'] == 200 assert resp['message'] == 'OK' assert resp['error'] == False resp = pubnub.channel_group_list_namespaces() assert namespace not in resp['payload']['namespaces']

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0

null

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null

0

1

0

8

f4a68715c22c7dd76533253c3f022db9360c134f

107

py

Python

car_core/scripts/car_core/common/__init__.py

vstucar/vstucar

46ba2aebed1d9bd76a66db06af2bd8a4384b403d

[ "MIT" ]

null

car_core/scripts/car_core/common/__init__.py

vstucar/vstucar

46ba2aebed1d9bd76a66db06af2bd8a4384b403d

[ "MIT" ]

null

car_core/scripts/car_core/common/__init__.py

vstucar/vstucar

46ba2aebed1d9bd76a66db06af2bd8a4384b403d

[ "MIT" ]

null

from . import geom_helpers, msgs_helpers, rviz_helpers __all__ = [geom_helpers, msgs_helpers, rviz_helpers]

53.5

54

0.831776

15

107

5.266667

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0.379747

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0.835443

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53.5

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null

0

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8

f4c73420df0ffa6198882fd80815c6501c2d5a11

104

py

Python

serializers/templates/__init__.py

d0cd/vectre-x86-disasm

098a2a67c8d588d9960150a6eda538f42694548b

[ "MIT" ]

null

serializers/templates/__init__.py

d0cd/vectre-x86-disasm

098a2a67c8d588d9960150a6eda538f42694548b

[ "MIT" ]

null

serializers/templates/__init__.py

d0cd/vectre-x86-disasm

098a2a67c8d588d9960150a6eda538f42694548b

[ "MIT" ]

null

from .prog_def_template import * from .platform_def_template import * from .inst_def_template import *

20.8

36

0.817308

15

104

5.266667

0.466667

0.417722

0.64557

0.531646

0

0.125

104

4

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0

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1

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0

null

0

1

0

1

0

1

0

8

f4f1f45161e522773f69331441d95190bb58bd7c

2,893

py

Python

pyaz/afd/secret/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/afd/secret/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/afd/secret/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

''' Manage secrets within the specified profile. ''' from ... pyaz_utils import _call_az def show(profile_name, resource_group, secret_name): ''' Required Parameters: - profile_name -- Name of the CDN profile which is unique within the resource group. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - secret_name -- Name of the secret. ''' return _call_az("az afd secret show", locals()) def delete(profile_name, resource_group, secret_name, yes=None): ''' Required Parameters: - profile_name -- Name of the CDN profile which is unique within the resource group. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - secret_name -- Name of the secret. Optional Parameters: - yes -- Do not prompt for confirmation. ''' return _call_az("az afd secret delete", locals()) def list(profile_name, resource_group): ''' Required Parameters: - profile_name -- Name of the CDN profile which is unique within the resource group. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az afd secret list", locals()) def create(profile_name, resource_group, secret_name, secret_source, secret_version=None, use_latest_version=None): ''' Creates a new secret within the specified profile. Required Parameters: - profile_name -- Name of the CDN profile which is unique within the resource group. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - secret_name -- Name of the secret. - secret_source -- ID of the Azure key vault certificate. Optional Parameters: - secret_version -- Version of the certificate to be used. - use_latest_version -- Whether to use the latest version for the certificate. ''' return _call_az("az afd secret create", locals()) def update(profile_name, resource_group, secret_name, secret_source=None, secret_version=None, use_latest_version=None): ''' Update an existing secret within the specified profile. Required Parameters: - profile_name -- Name of the CDN profile which is unique within the resource group. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - secret_name -- Name of the secret. Optional Parameters: - secret_source -- ID of the Azure key vault certificate. - secret_version -- Version of the certificate to be used. - use_latest_version -- Whether to use the latest version for the certificate. ''' return _call_az("az afd secret update", locals())

37.571429

128

0.711718

393

2,893

5.094148

0.16285

0.12987

0.044955

0.058442

0.865634

0.797203

0.76024

0.714286

0.671329

0

0.204978

2,893

76

129

38.065789

0.870435

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0

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0

1

0.454545

false

0

0.090909

0

1

0

null

0

1

0

null

0

1

0

1

0

8

7625226fe17a880b6f098a1fe26757979e4659cb

1,975

py

Python

tests/test_pykdebugparser.py

matan1008/pykdebugparser

e219c2434d012b935ee25f75571647aaed1a4dda

[ "MIT" ]

10

2021-06-17T14:07:38.000Z

2021-12-09T18:33:48.000Z

tests/test_pykdebugparser.py

matan1008/pykdebugparser

e219c2434d012b935ee25f75571647aaed1a4dda

[ "MIT" ]

null

tests/test_pykdebugparser.py

matan1008/pykdebugparser

e219c2434d012b935ee25f75571647aaed1a4dda

[ "MIT" ]

3

2021-06-22T13:01:59.000Z

2021-06-27T03:35:04.000Z

from io import BytesIO from pykdebugparser.kd_buf_parser import RAW_VERSION2_BYTES from pykdebugparser.kevent import Kevent from pykdebugparser.pykdebugparser import PyKdebugParser def test_kevents(): events_buf = RAW_VERSION2_BYTES + b'\x00' * 0x11c events_buf += (b'\xa50\x147_\x06\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\xc6\x01\x00\x00\x00\x00\x00\x00y\xd8\t\x00\x00\x00\x00' b'\x00*\x03\x0c\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00') parser = PyKdebugParser() events = list(parser.kevents(BytesIO(events_buf))) assert events == [ Kevent(timestamp=7006015729829, data=(b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\xc6\x01\x00\x00\x00\x00\x00\x00'), values=(0, 0, 0, 454), tid=645241, debugid=67896106, eventid=67896104, func_qualifier=2) ] def test_kevents_filter_tid(): events_buf = RAW_VERSION2_BYTES + b'\x00' * 0x11c events_buf += (b'\xa50\x147_\x06\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\xc6\x01\x00\x00\x00\x00\x00\x00y\xd8\t\x00\x00\x00\x00' b'\x00*\x03\x0c\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00') parser = PyKdebugParser() parser.filter_tid = 645241 events = list(parser.kevents(BytesIO(events_buf))) assert events == [ Kevent(timestamp=7006015729829, data=(b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\xc6\x01\x00\x00\x00\x00\x00\x00'), values=(0, 0, 0, 454), tid=645241, debugid=67896106, eventid=67896104, func_qualifier=2) ] parser.filter_tid = 3 events = list(parser.kevents(BytesIO(events_buf))) assert events == []

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