xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

f391d9ff35976d65033711cd8a502817c935c662

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py

Python

Section 8/voice_changer.py

PacktPublishing/Learning-Python-v-

30fb28dfaaa18815f1b4c0b683e8839da223b195

[ "MIT" ]

1

2021-10-05T19:45:43.000Z

Section 8/voice_changer.py

PacktPublishing/Learning-Python-v-

30fb28dfaaa18815f1b4c0b683e8839da223b195

[ "MIT" ]

null

Section 8/voice_changer.py

PacktPublishing/Learning-Python-v-

30fb28dfaaa18815f1b4c0b683e8839da223b195

[ "MIT" ]

2

2020-09-25T19:56:46.000Z

2021-09-02T11:14:28.000Z

from sound_conversion import rectomp3 from sound_conversion.rectowma import rec2wma print rectomp3.rec2mp3() print rec2wma()

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Python

coaddit/tests/test_rasterize.py

beckermr/python-r3d

c7dc6f7c53398387ad729248677351a2775016d7

[ "BSD-3-Clause" ]

null

coaddit/tests/test_rasterize.py

beckermr/python-r3d

c7dc6f7c53398387ad729248677351a2775016d7

[ "BSD-3-Clause" ]

5

2019-04-23T11:11:32.000Z

2019-04-25T13:08:35.000Z

coaddit/tests/test_rasterize.py

beckermr/python-r3d

c7dc6f7c53398387ad729248677351a2775016d7

[ "BSD-3-Clause" ]

1

2019-04-23T10:51:38.000Z

import numpy as np import pytest from coaddit.rasterize import rasterize_poly @pytest.mark.parametrize('off', [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]) def test_smoke(off): # convention here is that last dimension is x then y verts = np.zeros((4, 2)) + 0.5 + off verts[1, 0] = 1.5 + off verts[2, 0] = 1.5 + off verts[2, 1] = 1.5 + off verts[3, 1] = 1.5 + off verts[:, 0] += 2 arr, start_inds = rasterize_poly(verts, 1) assert start_inds[0] == off + 2 assert start_inds[1] == off assert np.all(arr == 0.25), arr @pytest.mark.parametrize('off', [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]) def test_shift(off): # convention here is that last dimension is x then y verts = np.zeros((4, 2)) + off verts[0, 1] += 0.25 verts[0, 0] += 0.3 verts[1, 1] += 1.25 verts[1, 0] += 0.3 verts[2, 1] += 1.25 verts[2, 0] += 1.3 verts[3, 1] += 0.25 verts[3, 0] += 1.3 verts[:, 0] += 2 area = np.array([ [0.75*0.7, 0.25*0.7], [0.75*0.3, 0.25*0.3]]) arr, start_inds = rasterize_poly(verts, 1) assert start_inds[0] == off + 2 assert start_inds[1] == off assert arr.shape[0] == 2 assert arr.shape[1] == 2 assert np.allclose(arr, area) @pytest.mark.parametrize('off', [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]) def test_shift_dims(off): # convention here is that last dimension is x then y verts = np.zeros((4, 2)) + off verts[0, 1] += 0.25 verts[0, 0] += 0.3 verts[1, 1] += 2.25 verts[1, 0] += 0.3 verts[2, 1] += 2.25 verts[2, 0] += 1.3 verts[3, 1] += 0.25 verts[3, 0] += 1.3 verts[:, 0] += 2 area = np.array([ [0.75*0.7, 1 * 0.7, 0.25*0.7], [0.75*0.3, 1 * 0.3, 0.25*0.3]]) area /= np.sum(area) arr, start_inds = rasterize_poly(verts, 1) assert start_inds[0] == off + 2 assert start_inds[1] == off assert arr.shape[0] == 2 assert arr.shape[1] == 3 assert np.allclose(arr, area)

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py

Python

dask_gdf/tests/test_join.py

jrhemstad/dask_gdf

c49933d72ce39ca720662b5d5124072e63087a62

[ "Apache-2.0" ]

null

dask_gdf/tests/test_join.py

jrhemstad/dask_gdf

c49933d72ce39ca720662b5d5124072e63087a62

[ "Apache-2.0" ]

null

dask_gdf/tests/test_join.py

jrhemstad/dask_gdf

c49933d72ce39ca720662b5d5124072e63087a62

[ "Apache-2.0" ]

null

import pytest import numpy as np import pygdf as gd import dask_gdf as dgd from functools import partial param_nrows = [5, 10, 100, 400] @pytest.mark.parametrize('left_nrows', param_nrows) @pytest.mark.parametrize('right_nrows', param_nrows) @pytest.mark.parametrize('left_nkeys', [4, 5]) @pytest.mark.parametrize('right_nkeys', [4, 5]) def test_join_inner(left_nrows, right_nrows, left_nkeys, right_nkeys): chunksize = 50 np.random.seed(0) # PyGDF left = gd.DataFrame({'x': np.random.randint(0, left_nkeys, size=left_nrows), 'a': np.arange(left_nrows)}.items()) right = gd.DataFrame({'x': np.random.randint(0, right_nkeys, size=right_nrows), 'a': 1000 * np.arange(right_nrows)}.items()) expect = left.set_index('x').join(right.set_index('x'), how='inner', sort=True, lsuffix='l', rsuffix='r') expect = expect.to_pandas() # Dask GDf left = dgd.from_pygdf(left, chunksize=chunksize) right = dgd.from_pygdf(right, chunksize=chunksize) joined = left.set_index('x').join(right.set_index('x'), how='inner', lsuffix='l', rsuffix='r') got = joined.compute().to_pandas() # Check index np.testing.assert_array_equal(expect.index.values, got.index.values) # Check rows in each groups expect_rows = {} got_rows = {} def gather(df, grows): grows[df['index'].values[0]] = (set(df.al), set(df.ar)) expect.reset_index().groupby('index')\ .apply(partial(gather, grows=expect_rows)) expect.reset_index().groupby('index')\ .apply(partial(gather, grows=got_rows)) assert got_rows == expect_rows @pytest.mark.parametrize('left_nrows', param_nrows) @pytest.mark.parametrize('right_nrows', param_nrows) @pytest.mark.parametrize('left_nkeys', [4, 5]) @pytest.mark.parametrize('right_nkeys', [4, 5]) @pytest.mark.parametrize('how', ['left', 'right']) def test_join_left(left_nrows, right_nrows, left_nkeys, right_nkeys, how): chunksize = 50 np.random.seed(0) # PyGDF left = gd.DataFrame({'x': np.random.randint(0, left_nkeys, size=left_nrows), 'a': np.arange(left_nrows, dtype=np.float64)}.items()) right = gd.DataFrame({'x': np.random.randint(0, right_nkeys, size=right_nrows), 'a': 1000 * np.arange(right_nrows, dtype=np.float64)}.items()) expect = left.set_index('x').join(right.set_index('x'), how=how, sort=True, lsuffix='l', rsuffix='r') expect = expect.to_pandas() # Dask GDf left = dgd.from_pygdf(left, chunksize=chunksize) right = dgd.from_pygdf(right, chunksize=chunksize) joined = left.set_index('x').join(right.set_index('x'), how=how, lsuffix='l', rsuffix='r') got = joined.compute().to_pandas() # Check index np.testing.assert_array_equal(expect.index.values, got.index.values) # Check rows in each groups expect_rows = {} got_rows = {} def gather(df, grows): cola = np.sort(np.asarray(df.al)) colb = np.sort(np.asarray(df.ar)) grows[df['index'].values[0]] = (cola, colb) expect.reset_index().groupby('index')\ .apply(partial(gather, grows=expect_rows)) expect.reset_index().groupby('index')\ .apply(partial(gather, grows=got_rows)) for k in expect_rows: np.testing.assert_array_equal(expect_rows[k][0], got_rows[k][0]) np.testing.assert_array_equal(expect_rows[k][1], got_rows[k][1])

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py

Python

vectorflow/optimizer/__init__.py

dongrenguang/VectorFlow

1e08a0cff6e0a282b03356d58cf4bab66339f922

[ "MIT" ]

null

vectorflow/optimizer/__init__.py

dongrenguang/VectorFlow

1e08a0cff6e0a282b03356d58cf4bab66339f922

[ "MIT" ]

null

vectorflow/optimizer/__init__.py

dongrenguang/VectorFlow

1e08a0cff6e0a282b03356d58cf4bab66339f922

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .optimizer import * from .gradient_descent import * from .momentum import * from .ada_grad import * from .rms_prop import * from .adam import *

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py

Python

airship_convert/render/__init__.py

eliasah/airship-convert

80b414513f7268dcb98e30902cbb992055ce5ccc

[ "MIT" ]

2

2016-06-19T20:36:38.000Z

2016-06-20T06:08:19.000Z

airship_convert/render/__init__.py

eliasah/airship-convert

80b414513f7268dcb98e30902cbb992055ce5ccc

[ "MIT" ]

2

2016-08-05T10:01:10.000Z

2016-08-10T14:00:40.000Z

airship_convert/render/__init__.py

eliasah/airship-convert

80b414513f7268dcb98e30902cbb992055ce5ccc

[ "MIT" ]

1

2016-08-05T09:05:12.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function, division, absolute_import from airship_convert.render.document import render_doc from airship_convert.render.source import render_source

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py

Python

tests/test_bibliometric.py

robertatakenaka/processing

138389b9d44df92daddeb1107fd78ae7849c0b66

[ "BSD-2-Clause" ]

2

2016-08-10T13:33:53.000Z

2019-03-16T04:31:35.000Z

tests/test_bibliometric.py

DalavanCloud/processing

629b50b45ba7a176651cd3bfcdb441dab6fddfcc

[ "BSD-2-Clause" ]

18

2015-05-25T14:15:18.000Z

2021-12-13T19:50:55.000Z

tests/test_bibliometric.py

DalavanCloud/processing

629b50b45ba7a176651cd3bfcdb441dab6fddfcc

[ "BSD-2-Clause" ]

5

2015-05-21T19:31:05.000Z

2019-03-16T04:31:42.000Z

import unittest from bibliometric import citedby_journal class TestBibliometric(unittest.TestCase): def test_compute_citations(self): query_result = { "took": 1857, "hits": { "max_score": 0.0, "hits": [], "total": 195 }, "aggregations": { "publication_year": { "sum_other_doc_count": 0, "buckets": [ { "reference_publication_year": { "sum_other_doc_count": 0, "buckets": [ { "doc_count": 1, "key": "2012" }, { "doc_count": 2, "key": "2011" }, { "doc_count": 1, "key": "2010" }, { "doc_count": 2, "key": "2008" }, { "doc_count": 3, "key": "2007" }, { "doc_count": 2, "key": "2005" }, { "doc_count": 1, "key": "2003" }, { "doc_count": 1, "key": "2001" }, { "doc_count": 1, "key": "1998" }, { "doc_count": 1, "key": "1997" }, { "doc_count": 1, "key": "1993" }, { "doc_count": 1, "key": "1990" }, { "doc_count": 3, "key": "1988" }, { "doc_count": 1, "key": "1986" }, { "doc_count": 2, "key": "1980" }, { "doc_count": 1, "key": "1979" }, { "doc_count": 1, "key": "1973" } ], "doc_count_error_upper_bound": 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"doc_count": 1, "key": "1997" }, { "reference_publication_year": { "sum_other_doc_count": 0, "buckets": [ { "doc_count": 1, "key": "1972" } ], "doc_count_error_upper_bound": 0 }, "doc_count": 1, "key": "2002" }, { "reference_publication_year": { "sum_other_doc_count": 0, "buckets": [ { "doc_count": 1, "key": "1992" } ], "doc_count_error_upper_bound": 0 }, "doc_count": 1, "key": "2003" } ], "doc_count_error_upper_bound": 0 } }, "timed_out": False, "_shards": { "failed": 0, "total": 5, "successful": 5 } } result = citedby_journal.compute_citations(query_result) self.assertEqual([('2012', ('2012', 1)), ('2012', ('2011', 2)), ('2012', ('2010', 1)), ('2012', ('2008', 2)), ('2012', ('2007', 3)), ('2012', ('2005', 2)), ('2012', ('2003', 1)), ('2012', ('2001', 1)), ('2012', ('1998', 1)), ('2012', ('1997', 1)), ('2012', ('1993', 1)), ('2012', ('1990', 1)), ('2012', ('1988', 3)), ('2012', ('1986', 1)), ('2012', ('1980', 2)), ('2012', ('1979', 1)), ('2012', ('1973', 1)), ('2015', ('2013', 1)), ('2015', ('2012', 3)), ('2015', ('2006', 1)), ('2015', ('2005', 1)), ('2015', ('2004', 1)), ('2015', ('2002', 1)), ('2015', ('1998', 1)), ('2015', ('1996', 2)), ('2015', ('1995', 1)), ('2015', ('1993', 2)), ('2015', ('1992', 5)), ('2015', ('1989', 1)), ('2015', ('1988', 1)), ('2015', ('1981', 1)), ('2015', ('1979', 1)), ('2013', ('2011', 2)), ('2013', ('2009', 2)), ('2013', ('2008', 1)), ('2013', ('2006', 1)), ('2013', ('2005', 1)), ('2013', ('2000', 2)), ('2013', ('1997', 2)), ('2013', ('1994', 1)), ('2013', ('1993', 1)), ('2013', ('1988', 1)), ('2013', ('1986', 1)), ('2013', ('1984', 1)), ('2013', ('1981', 1)), ('2013', ('1980', 1)), ('2013', ('1974', 2)), ('2014', ('2012', 2)), ('2014', ('2010', 3)), ('2014', ('2009', 1)), ('2014', ('2008', 1)), ('2014', ('2006', 1)), ('2014', ('2002', 2)), ('2014', ('1989', 1)), ('2014', ('1988', 2)), ('2014', ('1984', 3)), ('2014', ('1980', 2)), ('2014', ('1972', 1)), ('2016', ('2013', 2)), ('2016', ('2012', 1)), ('2016', ('2011', 3)), ('2016', ('2010', 1)), ('2016', ('2009', 2)), ('2016', ('2005', 3)), ('2016', ('2004', 1)), ('2016', ('2003', 1)), ('2016', ('1998', 1)), ('2016', ('1997', 1)), ('2016', ('1995', 1)), ('2016', ('1959', 1)), ('2007', ('1996', 1)), ('2007', ('1994', 1)), ('2007', ('1990', 2)), ('2007', ('1989', 2)), ('2007', ('1988', 1)), ('2007', ('1985', 1)), ('2007', ('1984', 1)), ('2007', ('1980', 1)), ('2007', ('1975', 1)), ('2007', ('1970', 1)), ('2007', ('1968', 1)), ('2011', ('2008', 1)), ('2011', ('2006', 1)), ('2011', ('2005', 1)), ('2011', ('2002', 1)), ('2011', ('2001', 2)), ('2011', ('1994', 1)), ('2011', ('1988', 1)), ('2011', ('1985', 1)), ('2011', ('1984', 1)), ('2011', ('1982', 1)), ('2011', ('1974', 2)), ('2010', ('2006', 1)), ('2010', ('2003', 1)), ('2010', ('2001', 1)), ('2010', ('2000', 1)), ('2010', ('1998', 1)), ('2010', ('1997', 1)), ('2010', ('1995', 1)), ('2010', ('1993', 1)), ('2010', ('1991', 1)), ('2010', ('1989', 1)), ('2010', ('1984', 1)), ('2010', ('1980', 1)), ('2009', ('2008', 1)), ('2009', ('2007', 2)), ('2009', ('2005', 1)), ('2009', ('2004', 1)), ('2009', ('2002', 1)), ('2009', ('1998', 1)), ('2009', ('1990', 2)), ('2009', ('1989', 1)), ('2009', ('1968', 1)), ('1999', ('1989', 1)), ('1999', ('1988', 1)), ('1999', ('1986', 2)), ('1999', ('1975', 2)), ('1999', ('1974', 2)), ('1998', ('1994', 1)), ('1998', ('1991', 2)), ('1998', ('1989', 1)), ('1998', ('1986', 1)), ('1998', ('1979', 1)), ('2006', ('2003', 1)), ('2006', ('1996', 1)), ('2006', ('1995', 1)), ('2006', ('1994', 1)), ('2006', ('1991', 1)), ('2000', ('1999', 1)), ('2000', ('1990', 1)), ('2000', ('1986', 1)), ('2000', ('1974', 1)), ('2008', ('2004', 1)), ('2008', ('2003', 1)), ('2008', ('1994', 1)), ('2008', ('1981', 1)), ('2008', ('1964', 1)), ('2004', ('1995', 1)), ('2004', ('1992', 1)), ('2004', ('1983', 1)), ('2004', ('1971', 1)), ('2001', ('1989', 1)), ('2001', ('1979', 1)), ('2005', ('1996', 1)), ('2005', ('1989', 1)), ('1997', ('1994', 1)), ('2002', ('1972', 1)), ('2003', ('1992', 1))], 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50,896

py

Python

pirates/leveleditor/worldData/port_royal_area_jungle_a_1.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

81

2018-04-08T18:14:24.000Z

2022-01-11T07:22:15.000Z

pirates/leveleditor/worldData/port_royal_area_jungle_a_1.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

4

2018-09-13T20:41:22.000Z

2022-01-08T06:57:00.000Z

pirates/leveleditor/worldData/port_royal_area_jungle_a_1.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

26

2018-05-26T12:49:27.000Z

2021-09-11T09:11:59.000Z

from pandac.PandaModules import Point3, VBase3, Vec4, Vec3 objectStruct = {'Interact Links': [['1176150400.0dxschafe', '1176151296.0dxschafe', 'Bi-directional'], ['1176149760.0dxschafe', '1176151040.0dxschafe0', 'Bi-directional'], ['1186437632.0dxschafe0', '1176151168.0dxschafe0', 'Bi-directional'], ['1190848640.0dxschafe', '1176150912.0dxschafe', 'Bi-directional'], ['1170568896.56sdnaik', '1176151040.0dxschafe1', 'Bi-directional'], ['1176151424.0dxschafe', '1189818368.0dxschafe1', 'Bi-directional'], ['1176151040.0dxschafe', '1189818496.0dxschafe', 'Bi-directional']],'Objects': {'1169592956.59sdnaik': {'Type': 'Island Game Area','Name': 'port_royal_area_jungle_a_1','File': '','AdditionalData': ['JungleAreaA'],'Environment': 'Jungle','Footstep Sound': 'Sand','Instanced': True,'Minimap': True,'Minimap Prefix': 'minimap','Objects': {'1165004689.08sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_1','Hpr': VBase3(-81.0, 0.0, 0.0),'Pos': Point3(36.719, 255.714, 7.06),'Scale': VBase3(1.0, 1.0, 1.0)},'1165004689.08sdnaik0': {'Type': 'Locator Node','Name': 'portal_interior_2','Hpr': VBase3(142.379, 0.0, 0.0),'Pos': Point3(837.183, 5.167, 52.393),'Scale': VBase3(1.0, 1.0, 1.0)},'1165004689.09sdnaik': {'Type': 'Locator Node','Name': 'portal_interior_3','Hpr': VBase3(-79.736, 0.0, 0.0),'Pos': Point3(380.725, 407.485, 61.219),'Scale': VBase3(1.0, 1.0, 1.0)},'1169864696.7sdnaik': {'Type': 'Movement Node','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '3','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(733.749, 1.651, 50.962),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Low Skeleton','Team': '1','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1169864772.36sdnaik': {'Type': 'Movement Node','Hpr': Point3(0.0, 0.0, 0.0),'Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(620.991, 29.674, 52.211),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.65, 0, 0, 1),'Model': 'models/misc/smiley'}},'1169864776.48sdnaik': {'Type': 'Movement Node','Hpr': Point3(0.0, 0.0, 0.0),'Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(597.214, 91.4, 53.221),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.65, 0, 0, 1),'Model': 'models/misc/smiley'}},'1170568896.56sdnaik': {'Type': 'Spawn Node','Aggro Radius': '23.7952','AnimSet': 'default','Hpr': VBase3(-103.415, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(31.895, 92.81, 4.309),'PoseAnim': '','PoseFrame': '','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Gator T2','Start State': 'Patrol','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1170568957.41sdnaik': {'Type': 'Spawn Node','Aggro Radius': '43.3735','AnimSet': 'default','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(-6.53, 41.535, -3.0),'PoseAnim': '','PoseFrame': '','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Gator T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0.0, 0.0, 0.65, 1.0),'Model': 'models/misc/smiley'}},'1176148480.0dxschafe0': {'Type': 'Cemetary','DisableCollision': True,'Holiday': '','Hpr': VBase3(154.974, 1.297, 0.15),'Pos': Point3(589.824, -56.073, 57.693),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/crypt1'}},'1176148864.0dxschafe': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(78.046, 1.755, 3.812),'Pos': Point3(493.344, -44.369, 68.356),'Scale': VBase3(1.283, 1.283, 1.283),'Visual': {'Color': (0.45, 0.5600000023841858, 0.5, 1.0),'Model': 'models/props/crypt1'}},'1176148864.0dxschafe0': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(160.357, 1.624, -5.263),'Pos': Point3(543.854, -28.695, 63.444),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.68, 0.78, 0.699999988079071, 1.0),'Model': 'models/props/crypt2'}},'1176148992.0dxschafe': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(75.365, 4.04, 1.779),'Pos': Point3(503.033, 1.363, 67.429),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.84, 0.84, 0.8899999856948853, 1.0),'Model': 'models/props/crypt1'}},'1176148992.0dxschafe0': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(77.998, -0.824, -1.069),'Pos': Point3(499.149, -21.763, 68.374),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.68, 0.78, 0.699999988079071, 1.0),'Model': 'models/props/crypt2'}},'1176149120.0dxschafe': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(0.0, 0.0, 0.829),'Pos': Point3(525.913, -44.581, 64.968),'Scale': VBase3(0.859, 0.859, 0.859),'Visual': {'Color': (0.54, 0.61, 0.54, 1.0),'Model': 'models/props/crypt2'}},'1176149248.0dxschafe': {'Type': 'Cemetary','DisableCollision': True,'Holiday': '','Hpr': VBase3(3.103, 11.818, 1.32),'Pos': Point3(575.616, -47.032, 59.663),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_a'}},'1176149248.0dxschafe1': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(-2.017, 17.936, 6.525),'Pos': Point3(532.038, -23.401, 64.861),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_c'}},'1176149248.0dxschafe2': {'Type': 'Cemetary','DisableCollision': False,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(481.68, 20.009, 68.876),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_a'}},'1176149248.0dxschafe3': {'Type': 'Cemetary','DisableCollision': False,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(475.017, 30.925, 68.573),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_a'}},'1176149248.0dxschafe4': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(0.0, 0.0, 0.0),'Pos': Point3(484.846, 43.778, 68.439),'Scale': VBase3(1.353, 0.829, 1.353),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_c'}},'1176149248.0dxschafe5': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(30.81, -0.178, 4.357),'Pos': Point3(514.043, -52.091, 66.93),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_d'}},'1176149376.0dxschafe': {'Type': 'Cemetary','DisableCollision': False,'Holiday': '','Hpr': VBase3(-2.674, 8.699, 5.439),'Pos': Point3(559.009, -37.012, 63.264),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_d'}},'1176149376.0dxschafe0': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(179.925, 2.392, -3.647),'Pos': Point3(492.727, 31.86, 68.929),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_d'}},'1176149376.0dxschafe1': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(-1.972, -2.512, 3.565),'Pos': Point3(483.233, 0.806, 68.502),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_d'}},'1176149376.0dxschafe2': {'Type': 'Cemetary','DisableCollision': False,'Holiday': '','Hpr': VBase3(179.925, 2.392, -3.647),'Pos': Point3(578.936, -31.555, 59.109),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_d'}},'1176149376.0dxschafe3': {'Type': 'Cemetary','DisableCollision': False,'Holiday': '','Hpr': VBase3(179.925, 2.392, -3.647),'Pos': Point3(607.105, -40.696, 55.137),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_b'}},'1176149376.0dxschafe4': {'Type': 'Cemetary','DisableCollision': False,'Hpr': VBase3(168.194, 3.084, -3.084),'Pos': Point3(503.077, 18.34, 67.33),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/pir_m_prp_cem_headstones_b'}},'1176149760.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'barrel_hide','Hpr': VBase3(-24.419, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(501.158, -29.033, 68.153),'PoseAnim': '','PoseFrame': '','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176149760.0dxschafe1': {'Type': 'Spawn Node','Aggro Radius': '5.1205','AnimSet': 'attack_sword_thrust','Hpr': VBase3(-103.082, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(558.375, -5.971, 61.411),'PoseAnim': '','PoseFrame': '','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T2','Start State': 'Idle','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176150400.0dxschafe': {'Type': 'Spawn Node','Aggro Radius': '28.6145','AnimSet': 'default','Hpr': VBase3(88.48, 0.0, 0.0),'Min Population': '1','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(289.982, 128.808, 51.564),'PoseAnim': '','PoseFrame': '','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Skel T1','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}},'1176150912.0dxschafe': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(637.963, 183.674, 51.985),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': 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1b40e7ccef4ac996be60c1cf953ddaab56b2673f

709

py

Python

cmsplugin_blocks/choices_helpers.py

emencia/cmsplugin-blocks

7ec99afd542948aef5d9069bd001729f5c14bded

[ "MIT" ]

1

2019-04-14T01:30:37.000Z

cmsplugin_blocks/choices_helpers.py

emencia/cmsplugin-blocks

7ec99afd542948aef5d9069bd001729f5c14bded

[ "MIT" ]

16

2018-02-19T11:13:15.000Z

2022-02-05T00:10:41.000Z

cmsplugin_blocks/choices_helpers.py

emencia/cmsplugin-blocks

7ec99afd542948aef5d9069bd001729f5c14bded

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.conf import settings def get_album_template_choices(): return settings.BLOCKS_ALBUM_TEMPLATES def get_album_default_template(): return settings.BLOCKS_ALBUM_TEMPLATES[0][0] def get_card_template_choices(): return settings.BLOCKS_CARD_TEMPLATES def get_card_default_template(): return settings.BLOCKS_CARD_TEMPLATES[0][0] def get_hero_template_choices(): return settings.BLOCKS_HERO_TEMPLATES def get_hero_default_template(): return settings.BLOCKS_HERO_TEMPLATES[0][0] def get_slider_template_choices(): return settings.BLOCKS_SLIDER_TEMPLATES def get_slider_default_template(): return settings.BLOCKS_SLIDER_TEMPLATES[0][0]

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Python

tests/test_single_recipe_retrival.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

null

tests/test_single_recipe_retrival.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

41

2017-11-07T00:39:02.000Z

2019-10-21T15:09:58.000Z

tests/test_single_recipe_retrival.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

3

2017-11-18T16:03:34.000Z

2017-12-20T19:49:59.000Z

# tests/test_single_recipe_retrival.py import unittest import json import uuid import time from api import db from api.models import User, RecipeCategory, Recipe from tests.register_login import RegisterLogin class TestRetriveSingleRecipeBlueprint(RegisterLogin): def test_get_single_recipe_in_category(self): """ Test for getting single recipe in category """ response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) # registered user login rep_login = self.login_user("pwalukagga@gmail.com", "telnetcmd123") # valid token headers=dict( Authorization='Bearer ' + json.loads( rep_login.data.decode() )['auth_token'] ) category = RecipeCategory( name="Breakfast", description="How to make breakfast", user_id=1 ) category.save() response = self.create_category("LunchBuffe", "How to make lunch buffe", headers) recipe = Recipe( name="Rolex for Lunch", cat_id=2, user_id=1, ingredients="oil, Onions, Tomatoes", description="How to make breakfast rolex" ) recipe.save() response = self.create_recipe_in_category(2, "Chicken Lunch Buffe", "oil, Onions,Tomatoes", "Fresh chicken", "Mix and boil", headers ) response = self.client.get('/recipe_category/2/recipes/1', headers=headers) self.assertEqual(response.status_code, 200) self.assertIn('Rolex for Lunch', str(response.data)) self.assertNotIn('Mix and boil', str(response.data)) # get recipe not yet in database response = self.client.get('/recipe_category/2/recipes/4', headers=headers) self.assertEqual(response.status_code, 404) self.assertIn('Recipe not found', str(response.data)) # get recipe in category not yet in database response = self.client.get('/recipe_category/3/recipes/1', headers=headers) self.assertEqual(response.status_code, 404) self.assertIn('Category not found in database', str(response.data)) def test_get_single_recipe_in_category_catid_not_number(self): """ Test for getting single recipe in category cat_id and recipe_id not number """ response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) # registered user login rep_login = self.login_user("pwalukagga@gmail.com", "telnetcmd123") # valid token headers=dict( Authorization='Bearer ' + json.loads( rep_login.data.decode() )['auth_token'] ) category = RecipeCategory( name="Breakfast", description="How to make breakfast", user_id=1 ) category.save() response = self.create_category("LunchBuffe", "How to make lunch buffe", headers) recipe = Recipe( name="Rolex for Lunch", cat_id=2, user_id=1, ingredients="oil, Onions, Tomatoes", description="How to make breakfast rolex" ) recipe.save() response = self.create_recipe_in_category(2, "Chicken Lunch Buffe", "oil, Onions,Tomatoes", "Fresh chicken", "Mix and boil", headers ) response = self.client.get('/recipe_category/a/recipes/1', headers=headers) self.assertEqual(response.status_code, 400) self.assertIn('Category ID must be an integer', str(response.data)) self.assertIn('fail', str(response.data)) # recipe id not number response = self.client.get('/recipe_category/2/recipes/a', headers=headers) self.assertEqual(response.status_code, 400) self.assertIn('Recipe ID must be an integer', str(response.data)) self.assertIn('fail', str(response.data)) def test_recipe_crud_when_not_logged_in(self): """ Test for recipe crud when not logged in """ response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) headers=dict(Authorization='Bearer ') category = RecipeCategory( name="Breakfast", description="How to make breakfast", user_id=1 ) category.save() response = self.create_category("LunchBuffe", "How to make lunch buffe", headers) self.assertEqual(response.status_code, 401) self.assertIn('Token is missing', str(response.data)) recipe = Recipe( name="Rolex for breakfast", cat_id=1, user_id=1, ingredients="oil, Onions, Tomatoes", description="How to make breakfast rolex" ) recipe.save() response = self.create_recipe_in_category(2, "Chicken Lunch Buffe", "oil, Onions,Tomatoes", "Fresh chicken", "Mix and boil", headers ) response = self.client.delete('/recipe_category/2/recipes/2', headers=headers) self.assertEqual(response.status_code, 401) self.assertIn('Token is missing', str(response.data)) # delete recipe not yet in database response = self.client.delete('/recipe_category/2/recipes/4', headers=headers) self.assertEqual(response.status_code, 401) self.assertIn('Token is missing', str(response.data)) # delete recipe in category not yet in database response = self.client.delete('/recipe_category/3/recipes/1', headers=headers) self.assertEqual(response.status_code, 401) self.assertIn('Token is missing', str(response.data)) if __name__ == '__main__': unittest.main()

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null

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6

1bb7421f83c6ca8271b910e80ff1d08d59669bf0

182

py

Python

mypy_fails/wrong_signature_forward_ref.py

brentyi/overrides

59af886a60236a9a71b60c982bf41dfc6419231c

[ "Apache-2.0" ]

197

2015-05-23T13:51:47.000Z

2022-03-25T07:14:00.000Z

mypy_fails/wrong_signature_forward_ref.py

brentyi/overrides

59af886a60236a9a71b60c982bf41dfc6419231c

[ "Apache-2.0" ]

78

2015-05-25T20:00:22.000Z

2022-03-21T21:50:24.000Z

mypy_fails/wrong_signature_forward_ref.py

brentyi/overrides

59af886a60236a9a71b60c982bf41dfc6419231c

[ "Apache-2.0" ]

33

2015-05-28T14:14:38.000Z

2021-04-29T08:01:45.000Z

from overrides import overrides class Parent: def metoda(self) -> None: pass class Child(Parent): @overrides def metoda(self) -> 'Child': return self

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0

6

9417edc36eeecdfeca76e1e0a3159145ea7ca4d8

203

py

Python

src/onqg/dataset/__init__.py

WING-NUS/RL-for-Question-Generation

745b8f823df9bcf4cc422c97a83ce096ac9c5e35

[ "MIT" ]

1

2021-01-05T05:30:00.000Z

src/onqg/dataset/__init__.py

MrSchnappi/RL-for-Question-Generation

d1966a47ef28c076902189469508194f659c5270

[ "MIT" ]

null

src/onqg/dataset/__init__.py

MrSchnappi/RL-for-Question-Generation

d1966a47ef28c076902189469508194f659c5270

[ "MIT" ]

1

2021-03-23T16:34:06.000Z

import onqg.dataset.Constants as Constants from onqg.dataset.Dataset import Dataset from onqg.dataset.Vocab import Vocab from onqg.dataset.data_processor import preprocess_batch, preprocess_rl_batch

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null

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1

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6

946ee4dd07e6728e6862a56a239dd374528ce685

106

py

Python

office365/directory/device.py

andrewcchoi/Office365-REST-Python-Client

43db12ae532c804c75a3a34f7b0d7d79e30fdac3

[ "MIT" ]

null

office365/directory/device.py

andrewcchoi/Office365-REST-Python-Client

43db12ae532c804c75a3a34f7b0d7d79e30fdac3

[ "MIT" ]

null

office365/directory/device.py

andrewcchoi/Office365-REST-Python-Client

43db12ae532c804c75a3a34f7b0d7d79e30fdac3

[ "MIT" ]

null

from office365.directory.directoryObject import DirectoryObject class Device(DirectoryObject): pass

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6

94751860482cd133c9a15b1e7297409aa5febcea

56

py

Python

rdf_io/protocols/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

rdf_io/protocols/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

rdf_io/protocols/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

from api import * from rdf4j import * from ldp import *

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0.214286

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true

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1

0

null

0

1

0

1

0

1

0

6

847697dcb34d1eac713f0cf5327111b9d11705f5

29

py

Python

quantifiedcode/plugins/example/backend/models/__init__.py

marcinguy/quantifiedcode

cafc8b99d56a5e51820421af5d77be8b736ab03d

[ "BSD-3-Clause" ]

138

2022-02-02T15:38:29.000Z

2022-03-30T21:23:33.000Z

quantifiedcode/plugins/example/backend/models/__init__.py

bbbfkl/scanmycode-ce

786ae9a83a0839b70ac773a673a3ac69a0484ee4

[ "BSD-3-Clause" ]

14

2016-12-21T11:26:48.000Z

2022-03-02T10:32:24.000Z

quantifiedcode/plugins/example/backend/models/__init__.py

bbbfkl/scanmycode-ce

786ae9a83a0839b70ac773a673a3ac69a0484ee4

[ "BSD-3-Clause" ]

26

2017-08-01T10:00:16.000Z

2022-02-06T15:31:55.000Z

from .example import Example

14.5

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1

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null

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1

0

1

0

1

0

6

ca2b1b4b90c259d14c7721bc59b97bdb2d0ae7a1

92

py

Python

webmap/graph/__init__.py

rtruxal/webmap-webapp

4f068dc7d1dde72c2e19151a37194dc2cbc52b1a

[ "MIT" ]

1

2019-04-28T21:17:16.000Z

webmap/graph/__init__.py

rtruxal/webmap-webapp

4f068dc7d1dde72c2e19151a37194dc2cbc52b1a

[ "MIT" ]

1

2019-04-30T00:49:56.000Z

webmap/graph/__init__.py

rtruxal/webmap-webapp

4f068dc7d1dde72c2e19151a37194dc2cbc52b1a

[ "MIT" ]

null

from .flask_interface import URLNode, IPNode, POINTSAT from .graphql_interface import schema

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null

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1

0

1

0

1

0

6

ca33149657c414ae6bea5e610d29e642d3299526

31

py

Python

pavlidis/__init__.py

chunglabmit/pavlidis

987302f28dd9101c1d74c31cea8cee31b5d39771

[ "MIT" ]

3

2019-01-12T13:13:33.000Z

2020-05-29T06:49:51.000Z

pavlidis/__init__.py

chunglabmit/pavlidis

987302f28dd9101c1d74c31cea8cee31b5d39771

[ "MIT" ]

1

2018-04-20T17:40:28.000Z

2018-04-20T18:13:10.000Z

pavlidis/__init__.py

chunglabmit/pavlidis

987302f28dd9101c1d74c31cea8cee31b5d39771

[ "MIT" ]

null

from .pavlidis import pavlidis

15.5

30

0.83871

4

31

6.5

0.75

0

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31

1

31

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true

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null

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null

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1

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1

0

1

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6

ca39c14aa6087ed49829ad2df0bb647fff1716dc

34

py

Python

custom/__init__.py

274869388/debug_dataloader

6785250509189bf3ff2562a7cffcc9215d0bbe6f

[ "Apache-2.0" ]

null

custom/__init__.py

274869388/debug_dataloader

6785250509189bf3ff2562a7cffcc9215d0bbe6f

[ "Apache-2.0" ]

null

custom/__init__.py

274869388/debug_dataloader

6785250509189bf3ff2562a7cffcc9215d0bbe6f

[ "Apache-2.0" ]

null

from .aws_client import AWSBackend

34

0.882353

5

34

5.8

1

0

0.088235

34

1

34

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ca70e1caf721c375676f04ea88c23196c4ccdc50

24

py

Python

project/__init__.py

morganzwest/bugtracker

073a81fbfb4acc8a46a1942df51a4617adec3d58

[ "MIT" ]

null

project/__init__.py

morganzwest/bugtracker

073a81fbfb4acc8a46a1942df51a4617adec3d58

[ "MIT" ]

null

project/__init__.py

morganzwest/bugtracker

073a81fbfb4acc8a46a1942df51a4617adec3d58

[ "MIT" ]

null

from connection import *

24

0.833333

3

24

6.666667

1

0

0.125

24

1

24

0.952381

0

1

0

true

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1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ca995a61e08a6505c1561ac249dd7e0ee0004f44

217

py

Python

code/hiking/devops/__init__.py

david-liu/hiking

a031ba66472809d2a01201fea9bdd5f12fcc19de

[ "Apache-2.0" ]

null

code/hiking/devops/__init__.py

david-liu/hiking

a031ba66472809d2a01201fea9bdd5f12fcc19de

[ "Apache-2.0" ]

1

2018-11-07T08:33:17.000Z

code/hiking/devops/__init__.py

david-liu/hiking

a031ba66472809d2a01201fea9bdd5f12fcc19de

[ "Apache-2.0" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function from hiking.devops.cmdline import * from hiking.devops import command_parser as command_parameters_parser

27.125

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29

217

5.931034

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0

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217

7

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true

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null

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null

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6

04936401df545155b827a0b9b46c1ed413724c7d

28,952

py

Python

scripts/Objects.py

AlfonsoXIII/chess_manager

bb0800c4992604a9c69c32ac91e65e97507ea1b0

[ "MIT" ]

1

2021-09-25T22:48:09.000Z

scripts/Objects.py

AlfonsoXIII/chess_manager

bb0800c4992604a9c69c32ac91e65e97507ea1b0

[ "MIT" ]

null

scripts/Objects.py

AlfonsoXIII/chess_manager

bb0800c4992604a9c69c32ac91e65e97507ea1b0

[ "MIT" ]

null

#Llibreries importades import pygame from PIL import Image from copy import deepcopy import numpy as np #Classe per a la construcció de la peça Peó class Pawn(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, fliped, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite self.image = self.image.resize(size, resample=Image.BILINEAR , box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.fliped = fliped self.pos = pos #Posició de la peça self.id = "P" #ID de la peça self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Movement(self, board): #Funció que retorna una llista amb les caselles jugables per a la peça en concret mv = [] k = (-1 if self.colour == 0 else 1) if self.fliped == True: k = k*(-1) if self.pos[0]+k <= 7: if board[self.pos[0]+k][self.pos[1]] == "": mv.append((self.pos[0]+k, self.pos[1])) if self.pos[0] == (1 if k == 1 else 6) and board[self.pos[0]+(k*2)][self.pos[1]] == "": mv.append((self.pos[0]+(k*2), self.pos[1])) if self.pos[1]+1 <= 7 and board[self.pos[0]+k][self.pos[1]+1] != "" and board[self.pos[0]+k][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+k, self.pos[1]+1)) if self.pos[1]-1 <= 7 and board[self.pos[0]+k][self.pos[1]-1] != "" and board[self.pos[0]+k][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+k, self.pos[1]-1)) return mv #Classe per a la construcció de la peça Dama class Queen(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite self.image = self.image.resize(size, resample=Image.BILINEAR, box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.pos = pos #Posició de la peça self.id = "Q" #ID de la peça self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Movement(self, board): #Funció que retorna una llista amb les caselles jugables per a la peça en concret mv = [] for x in range(1, 8): if (self.pos[0]+x <= 7 and self.pos[1]+x <= 7) and board[self.pos[0]+x][self.pos[1]+x] == "": mv.append((self.pos[0]+x, self.pos[1]+x)) else: if (self.pos[0]+x <= 7 and self.pos[1]+x <= 7) and board[self.pos[0]+x][self.pos[1]+x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+x, self.pos[1]+x)) break for x in range(1, 8): if (self.pos[0]+x <= 7 and self.pos[1]-x >= 0) and board[self.pos[0]+x][self.pos[1]-x] == "": mv.append((self.pos[0]+x, self.pos[1]-x)) else: if (self.pos[0]+x <= 7 and self.pos[1]-x >= 0) and board[self.pos[0]+x][self.pos[1]-x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+x, self.pos[1]-x)) break for x in range(1, 8): if (self.pos[0]-x >= 0 and self.pos[1]-x >= 0) and board[self.pos[0]-x][self.pos[1]-x] == "": mv.append((self.pos[0]-x, self.pos[1]-x)) else: if (self.pos[0]-x >= 0 and self.pos[1]-x >= 0) and board[self.pos[0]-x][self.pos[1]-x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]-x, self.pos[1]-x)) break for x in range(1, 8): if (self.pos[0]-x >= 0 and self.pos[1]+x <= 7) and board[self.pos[0]-x][self.pos[1]+x] == "": mv.append((self.pos[0]-x, self.pos[1]+x)) else: if (self.pos[0]-x >= 0 and self.pos[1]+x <= 7) and board[self.pos[0]-x][self.pos[1]+x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]-x, self.pos[1]+x)) break for x in range(self.pos[0]+1, 8): if board[x][self.pos[1]] == "": mv.append((x, self.pos[1])) else: if board[x][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((x, self.pos[1])) break for x in range(self.pos[0]-1, -1, -1): if board[x][self.pos[1]] == "": mv.append((x, self.pos[1])) else: if board[x][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((x, self.pos[1])) break for x in range(self.pos[1]+1, 8): if board[self.pos[0]][x] == "": mv.append((self.pos[0], x)) else: if board[self.pos[0]][x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0], x)) break for x in range(self.pos[1]-1, -1, -1): if board[self.pos[0]][x] == "": mv.append((self.pos[0], x)) else: if board[self.pos[0]][x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0], x)) break return mv #Classe per a la construcció de la peça Cavall class Knight(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite self.image = self.image.resize(size, resample=Image.BILINEAR, box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.pos = pos #Posició de la peça self.id = "N" #ID de la peça self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Movement(self, board): #Función que retorna las casillas disponibles para el movimiento de la pieza mv = [] if self.pos[0]+2 <= 7 and self.pos[1]+1 <= 7: if board[self.pos[0]+2][self.pos[1]+1] == "" or (board[self.pos[0]+2][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]+2, self.pos[1]+1)) if self.pos[0]+2 <= 7 and self.pos[1]-1 >= 0: if board[self.pos[0]+2][self.pos[1]-1] == "" or (board[self.pos[0]+2][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]+2, self.pos[1]-1)) if self.pos[0]-2 >= 0 and self.pos[1]+1 <= 7: if board[self.pos[0]-2][self.pos[1]+1] == "" or (board[self.pos[0]-2][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]-2, self.pos[1]+1)) if self.pos[0]-2 >= 0 and self.pos[1]-1 >= 0: if board[self.pos[0]-2][self.pos[1]-1] == "" or (board[self.pos[0]-2][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]-2, self.pos[1]-1)) if self.pos[0]+1 <= 7 and self.pos[1]+2 <= 7: if board[self.pos[0]+1][self.pos[1]+2] == "" or (board[self.pos[0]+1][self.pos[1]+2].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]+1, self.pos[1]+2)) if self.pos[0]+1 <= 7 and self.pos[1]-2 >= 0: if board[self.pos[0]+1][self.pos[1]-2] == "" or (board[self.pos[0]+1][self.pos[1]-2].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]+1, self.pos[1]-2)) if self.pos[0]-1 >= 0 and self.pos[1]+2 <= 7: if board[self.pos[0]-1][self.pos[1]+2] == "" or (board[self.pos[0]-1][self.pos[1]+2].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]-1, self.pos[1]+2)) if self.pos[0]-1 >= 0 and self.pos[1]-2 >= 0: if board[self.pos[0]-1][self.pos[1]-2] == "" or (board[self.pos[0]-1][self.pos[1]-2].isupper() != board[self.pos[0]][self.pos[1]].isupper()): mv.append((self.pos[0]-1, self.pos[1]-2)) return mv #Classe per a la construcció de la peça Àlfil class Bishop(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite self.image = self.image.resize(size, resample=Image.BILINEAR, box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.pos = pos #Posició de la peça self.id = "B" #ID de la peça self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Movement(self, board): #Función que retorna las casillas disponibles para el movimiento de la pieza mv = [] for x in range(1, 8): if (self.pos[0]+x <= 7 and self.pos[1]+x <= 7) and board[self.pos[0]+x][self.pos[1]+x] == "": mv.append((self.pos[0]+x, self.pos[1]+x)) else: if (self.pos[0]+x <= 7 and self.pos[1]+x <= 7) and board[self.pos[0]+x][self.pos[1]+x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+x, self.pos[1]+x)) break for x in range(1, 8): if (self.pos[0]+x <= 7 and self.pos[1]-x >= 0) and board[self.pos[0]+x][self.pos[1]-x] == "": mv.append((self.pos[0]+x, self.pos[1]-x)) else: if (self.pos[0]+x <= 7 and self.pos[1]-x >= 0) and board[self.pos[0]+x][self.pos[1]-x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]+x, self.pos[1]-x)) break for x in range(1, 8): if (self.pos[0]-x >= 0 and self.pos[1]-x >= 0) and board[self.pos[0]-x][self.pos[1]-x] == "": mv.append((self.pos[0]-x, self.pos[1]-x)) else: if (self.pos[0]-x >= 0 and self.pos[1]-x >= 0) and board[self.pos[0]-x][self.pos[1]-x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]-x, self.pos[1]-x)) break for x in range(1, 8): if (self.pos[0]-x >= 0 and self.pos[1]+x <= 7) and board[self.pos[0]-x][self.pos[1]+x] == "": mv.append((self.pos[0]-x, self.pos[1]+x)) else: if (self.pos[0]-x >= 0 and self.pos[1]+x <= 7) and board[self.pos[0]-x][self.pos[1]+x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0]-x, self.pos[1]+x)) break return mv #Classe per a la construcció de la peça Torre class Rock(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite self.image = self.image.resize(size, resample=Image.BILINEAR, box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.pos = pos #Posició de la peça self.id = "R" #ID de la peça self.h_moved = False #Atribut per a emmagatzemar si s'ha mogut self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Movement(self, board): #Función que retorna las casillas disponibles para el movimiento de la pieza mv = [] for x in range(self.pos[0]+1, 8): if board[x][self.pos[1]] == "": mv.append((x, self.pos[1])) else: if board[x][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((x, self.pos[1])) break for x in range(self.pos[0]-1, -1, -1): if board[x][self.pos[1]] == "": mv.append((x, self.pos[1])) else: if board[x][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((x, self.pos[1])) break for x in range(self.pos[1]+1, 8): if board[self.pos[0]][x] == "": mv.append((self.pos[0], x)) else: if board[self.pos[0]][x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0], x)) break for x in range(self.pos[1]-1, -1, -1): if board[self.pos[0]][x] == "": mv.append((self.pos[0], x)) else: if board[self.pos[0]][x].isupper() != board[self.pos[0]][self.pos[1]].isupper(): mv.append((self.pos[0], x)) break return mv #Classe per a la construcció de la peça Rei class King(pygame.sprite.Sprite): def __init__(self, sprite, colour, pos, fliped, size): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.colour = colour #Color de peça #Selecció i escalatge de l'imatge per a composar el seu sprite self.image = sprite.resize(size, resample=Image.BILINEAR, box=None) self.image = pygame.image.fromstring(self.image.tobytes(), self.image.size, self.image.mode) self.pos = pos #Posició de la peça self.id = "K" #ID de la peça self.h_moved = False #Atribut per a emmagatzemar si s'ha mogut self.fliped = fliped #Atribut per a emmagatzemar si el taulell està rotat self.rect = self.image.get_rect() #Posició del collider del sprite en funció de les dimensions de la seva imatge def Check(self, board, pos): local_id = board[pos[0]][pos[1]] modificadores_diagonales = [(-1 , -1), (1, 1), (-1, 1), (1, -1)] modificadores_lineales = [(1, 0), (0, 1), (-1, 0), (0, -1)] agressiveKnight = 0 for x in range(1, 8): temp_vectorlist_1 = deepcopy(modificadores_diagonales) for y in range(0, len(modificadores_diagonales)): temp_vect = np.array(pos)+(x*np.array(modificadores_diagonales[y])) if 0 <= temp_vect[0] <= 7 and 0 <= temp_vect[1] <= 7 and (board[temp_vect[0]][temp_vect[1]].isupper() != board[pos[0]][pos[1]].isupper() or board[temp_vect[0]][temp_vect[1]] == ""): if board[temp_vect[0]][temp_vect[1]] == ("p" if local_id.isupper() else "P"): if x == 1 and (modificadores_diagonales[y] == ((-1, -1) if local_id.islower() else (-1, 1)) or modificadores_diagonales[y] == ((1, 1) if local_id.islower() else (1, -1))): #print("Gate: 1 (DIAGONAL_1)") return False else: temp_vectorlist_1.remove(modificadores_diagonales[y]) break if (0 < x <= 2) and board[temp_vect[0]][temp_vect[1]] == ("n" if local_id.isupper() else "N"): agressiveKnight += 1 temp_vectorlist_1.remove(modificadores_diagonales[y]) break if x == 1 and board[temp_vect[0]][temp_vect[1]] == ("k" if local_id.isupper() else "K"): #print("Gate: 1 (DIAGONAL_2)") return False if board[temp_vect[0]][temp_vect[1]] == ("q" if local_id.isupper() else "Q") or board[temp_vect[0]][temp_vect[1]] == ("b" if local_id.isupper() else "B"): #print("Gate: 1 (DIAGONAL)") return False else: temp_vectorlist_1.remove(modificadores_diagonales[y]) modificadores_diagonales = deepcopy(temp_vectorlist_1) temp_vectorlist_2 = deepcopy(modificadores_lineales) for y in range(0, len(modificadores_lineales)): temp_vect = np.array(pos)+(x*np.array(modificadores_lineales[y])) if 0 <= temp_vect[0] <= 7 and 0 <= temp_vect[1] <= 7 and (board[temp_vect[0]][temp_vect[1]].isupper() != board[pos[0]][pos[1]].isupper() or board[temp_vect[0]][temp_vect[1]] == ""): if board[temp_vect[0]][temp_vect[1]] == ("p" if local_id.isupper() else "P"): if x == 1 and 0 <= temp_vect[0]+(modificadores_lineales[y])[0] <= 7 and 0 <= temp_vect[1]+(modificadores_lineales[y])[1] <= 7: if board[temp_vect[0]+(modificadores_lineales[y])[0]][temp_vect[1]+(modificadores_lineales[y])[1]] == ("n" if local_id.isupper() else "N"): agressiveKnight += 1 temp_vectorlist_2.remove(modificadores_lineales[y]) break else: temp_vectorlist_2.remove(modificadores_lineales[y]) break if (x != 0 and x <= 2) and board[temp_vect[0]][temp_vect[1]] == ("n" if local_id.isupper() else "N"): agressiveKnight += 1 temp_vectorlist_2.remove(modificadores_lineales[y]) break if x == 1 and board[temp_vect[0]][temp_vect[1]] == ("k" if local_id.isupper() else "K"): #print("Gate: 2 (LINEAL_1)") return False if board[temp_vect[0]][temp_vect[1]] == ("q" if local_id.isupper() else "Q") or board[temp_vect[0]][temp_vect[1]] == ("r" if local_id.isupper() else "R"): #print("Gate: 2 (LINEAL)") return False else: temp_vectorlist_2.remove(modificadores_lineales[y]) modificadores_lineales = deepcopy(temp_vectorlist_2) local_knights = 0 for a in range(0, 8): for b in range(0, 8): if board[b][a] == ("n" if local_id.isupper() else "N") and pos[0]-2 <= b <= pos[0]+2 and pos[1]-2 <= a <= pos[1]+2: local_knights += 1 if local_knights != agressiveKnight: #print("Gate: 2 (HORSE)") return False #print("Gate: 3 (No check)") return True def Castling(self, board, h_moved): local_castling = (False, False) k = (1 if self.fliped == False else -1) if h_moved == False and 0 <= self.pos[1]+(2*k) <= 7: f_board_1 = deepcopy(board) f_board_1[self.pos[0]][self.pos[1]+(1*k)] = "K" if self.colour == 0 else "k" f_board_1[self.pos[0]][self.pos[1]] = "" f_board_2 = deepcopy(board) f_board_2[self.pos[0]][self.pos[1]+(2*k)] = "K" if self.colour == 0 else "k" f_board_2[self.pos[0]][self.pos[1]] = "" if (board[self.pos[0]][self.pos[1]+(1*k)] == "" and King.Check(self, f_board_1, (self.pos[0], self.pos[1]+(1*k)))) and (board[self.pos[0]][self.pos[1]+(2*k)] == "" and King.Check(self, f_board_2, (self.pos[0], self.pos[1]+(2*k)))) and board[self.pos[0]][self.pos[1]+(3*k)] == ("R" if board[self.pos[0]][self.pos[1]].isupper() else "r"): local_castling = (True, local_castling[1]) if h_moved == False and 0 <= self.pos[1]+(-3*k) <= 7: f_board = deepcopy(board) f_board[self.pos[0]][self.pos[1]-1*k] = "K" if self.colour == 0 else "k" f_board[self.pos[0]][self.pos[1]] = "" f_board_1 = deepcopy(board) f_board_1[self.pos[0]][self.pos[1]-2*k] = ("K" if self.colour == 0 else "k") f_board_1[self.pos[0]][self.pos[1]] = "" f_board_2 = deepcopy(board) f_board_2[self.pos[0]][self.pos[1]-3*k] = "K" if self.colour == 0 else "k" f_board_2[self.pos[0]][self.pos[1]] = "" if (board[self.pos[0]][self.pos[1]-1*k] == "" and King.Check(self, f_board, (self.pos[0], self.pos[1]-1*k))) and (board[self.pos[0]][self.pos[1]-2*k] == "" and King.Check(self, f_board_1, (self.pos[0], self.pos[1]-2*k))) and (board[self.pos[0]][self.pos[1]-3*k] == "" and King.Check(self, f_board_2, (self.pos[0], self.pos[1]-3*k))) and board[self.pos[0]][self.pos[1]-4*k] == ("R" if board[self.pos[0]][self.pos[1]].isupper() else "r"): local_castling = (local_castling[0], True) return local_castling def Movement(self, board): #Función que retorna las casillas disponibles para el movimiento de la pieza mv = [] k = 1 if self.colour == 1 else -1 if self.pos[0]+1 <= 7 and (board[self.pos[0]+1][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]+1][self.pos[1]] == ""): mv.append((self.pos[0]+1, self.pos[1])) if self.pos[0]-1 >= 0 and (board[self.pos[0]-1][self.pos[1]].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]-1][self.pos[1]] == ""): mv.append((self.pos[0]-1, self.pos[1])) if self.pos[1]+1 <= 7 and (board[self.pos[0]][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]][self.pos[1]+1] == ""): mv.append((self.pos[0], self.pos[1]+1)) if self.pos[1]-1 >= 0 and (board[self.pos[0]][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]][self.pos[1]-1] == ""): mv.append((self.pos[0], self.pos[1]-1)) if self.pos[0]+1 <= 7 and self.pos[1]+1 <= 7 and (board[self.pos[0]+1][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]+1][self.pos[1]+1] == ""): mv.append((self.pos[0]+1, self.pos[1]+1)) if self.pos[0]+1 <= 7 and self.pos[1]-1 >= 0 and (board[self.pos[0]+1][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]+1][self.pos[1]-1] == ""): mv.append((self.pos[0]+1, self.pos[1]-1)) if self.pos[0]-1 >= 0 and self.pos[1]+1 <= 7 and (board[self.pos[0]-1][self.pos[1]+1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]-1][self.pos[1]+1] == ""): mv.append((self.pos[0]-1, self.pos[1]+1)) if self.pos[0]-1 >= 0 and self.pos[1]-1 >= 0 and (board[self.pos[0]-1][self.pos[1]-1].isupper() != board[self.pos[0]][self.pos[1]].isupper() or board[self.pos[0]-1][self.pos[1]-1] == ""): mv.append((self.pos[0]-1, self.pos[1]-1)) return mv class Menu(pygame.sprite.Sprite): def __init__(self): super().__init__() self.status = [] self.im = 0 images = Image.open("images/manu_pressed.png") self.image1 = images.crop((0, 146.5, 150, 293)) self.image1 = self.image1.resize((70, 70), resample=Image.BILINEAR, box=None) self.image1 = pygame.image.fromstring(self.image1.tobytes(), self.image1.size, self.image1.mode) self.status.append(self.image1) self.image2 = images.crop((0, 0, 150, 144)) self.image2 = self.image2.resize((70, 70), resample=Image.BILINEAR, box=None) self.image2 = pygame.image.fromstring(self.image2.tobytes(), self.image2.size, self.image2.mode) self.status.append(self.image2) self.image = self.status[self.im] self.k = 2 self.rect = self.image.get_rect() def Update(self): self.im = 1 if self.im == 0 else 0 self.image = self.status[self.im] class Render_Image(pygame.sprite.Sprite): def __init__(self, image_path, image_size, k): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.status = [] #Llista per a recollir les dues imatges del botó (encés/apagat) self.im = 0 #Posició (encés/apagat) del botó image = Image.open(image_path) #Obrim l'imatge amb el mòdul PIL #Selecció i escalatge de l'imatge 1 self.image1 = image.resize(image_size, resample=Image.BILINEAR, box=None) self.image1 = pygame.image.fromstring(self.image1.tobytes(), self.image1.size, self.image1.mode) self.status.append(self.image1) self.image = self.image1 #Determinem la imatge en funció del seu estatus o posició self.id = k #ID del botó self.rect = self.image.get_rect() #Posició del collider del botó #Classe per a la construcció d'un botó class Button(pygame.sprite.Sprite): def __init__(self, image_object, image1_crop, image2_crop, image_size, k): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.status = [] #Llista per a recollir les dues imatges del botó (encés/apagat) self.im = 0 #Posició (encés/apagat) del botó image = image_object #Selecció i escalatge de l'imatge 1 self.image1 = image.crop(image1_crop) self.image1 = self.image1.resize(image_size, resample=Image.BILINEAR, box=None) self.image1 = pygame.image.fromstring(self.image1.tobytes(), self.image1.size, self.image1.mode) self.status.append(self.image1) #Selecció i escalatge de l'imatge 1 self.image2 = image.crop(image2_crop) self.image2 = self.image2.resize(image_size, resample=Image.BILINEAR, box=None) self.image2 = pygame.image.fromstring(self.image2.tobytes(), self.image2.size, self.image2.mode) self.status.append(self.image2) self.image = self.status[self.im] #Determinem la imatge en funció del seu estatus o posició self.id = k #ID del botó self.rect = self.image.get_rect() #Posició del collider del botó def Update(self): #Funció per a actualitzar la posició de la imatge (encés/apagat) self.im = 1 if self.im == 0 else 0 self.image = self.status[self.im] class Arrow_Button(pygame.sprite.Sprite): def __init__(self, sprite, image1_crop, image_size, k, proportion): super().__init__() #Herència dels atributs de la classe Sprite de pygame #Atributs de classe self.status = {} #Llista per a recollir les dues imatges del botó (encés/apagat) self.im = 0 #Posició (encés/apagat) del botó image = sprite temp_list = [] for a in range(0, 21): #Selecció i escalatge de l'imatge 1 self.image1 = image.crop(image1_crop) self.image1 = self.image1.resize(image_size, resample=Image.BILINEAR, box=None) self.image1 = self.image1.rotate(a*9, expand=False) self.image1 = pygame.image.fromstring(self.image1.tobytes(), self.image1.size, self.image1.mode) temp_list.append(self.image1) self.status["0"] = temp_list self.image = (self.status["0"])[self.im] #Determinem la imatge en funció del seu estatus o posició self.id = k #ID del botó self.rect = self.image.get_rect() #Posició del collider del botó #self.rect.center = (pos[0]+(image_size[0]/2), pos[1]+(image_size[1]/2)) def Update(self, direction): #Funció per a actualitzar la posició de la imatge (encés/apagat) if 0 <= self.im <= 20: self.im += (direction) self.image = self.status["0"][self.im]

49.575342

448

0.544971

4,442

28,952

3.492121

0.044575

0.184116

0.104177

0.090511

0.90575

0.891632

0.88409

0.863847

0.848182

0.831227

0

0.044599

0.282088

28,952

584

449

49.575342

0.701708

0.129525

0

0.655779

0

0.002788

0.000916

0

1

0.052764

false

0

0.01005

0

0.123116

0

null

0

1

0

null

0

6

049c052d7ab09e1a334a611e68038b1db9c3d7c9

136

py

Python

venv/src/home/views.py

AkashSDas/Bloare

20d0f56252346b4891c3cb62acaf8d20e0d3a7b7

[ "MIT" ]

null

venv/src/home/views.py

AkashSDas/Bloare

20d0f56252346b4891c3cb62acaf8d20e0d3a7b7

[ "MIT" ]

15

2021-04-08T19:53:30.000Z

2022-03-12T00:50:04.000Z

venv/src/home/views.py

AkashSDas/Bloare

20d0f56252346b4891c3cb62acaf8d20e0d3a7b7

[ "MIT" ]

null

from django.shortcuts import render def home_view(request, *args, **kwargs): return render(request, 'home/landing-page.html', {})

22.666667

56

0.720588

18

136

5.388889

0.833333

0

0.132353

136

5

57

27.2

0.822034

0

0.161765

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b6c4525f028a6ad78a4a2f7cb88388bb9ea7d9f3

8,317

py

Python

tests/adapters/repository/sqlalchemy_repo/sqlite/test_transactions.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

tests/adapters/repository/sqlalchemy_repo/sqlite/test_transactions.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

tests/adapters/repository/sqlalchemy_repo/sqlite/test_transactions.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

# Standard Library Imports import random import string # Protean import pytest from protean.core.exceptions import ObjectNotFoundError from protean.core.unit_of_work import UnitOfWork # Local/Relative Imports from .elements import Person, PersonRepository @pytest.mark.sqlite class TestTransactions: @pytest.fixture(autouse=True) def register_elements(self, test_domain): test_domain.register(Person) test_domain.register(PersonRepository, aggregate_cls=Person) def random_name(self): return "".join(random.choices(string.ascii_uppercase + string.digits, k=15)) def persisted_person(self, test_domain): repo = test_domain.repository_for(Person) person = Person(first_name=self.random_name(), last_name=self.random_name()) repo.add(person) return person def test_new_objects_are_committed_as_part_of_one_transaction(self, test_domain): # Add a Person the database repo = test_domain.repository_for(Person) repo.add(self.persisted_person(test_domain)) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session with UnitOfWork(): repo = test_domain.repository_for(Person) person2 = Person(first_name="Jane", last_name="Doe") repo.add(person2) # Test that the underlying database is untouched assert len(person_dao.outside_uow().query.all().items) == 1 assert len(person_dao.query.all().items) == 2 def test_updated_objects_are_committed_as_part_of_one_transaction( self, test_domain ): # Add a Person the database repo = test_domain.repository_for(Person) person = Person(first_name="John", last_name="Doe") repo.add(person) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session with UnitOfWork(): repo = test_domain.repository_for(Person) persisted_person = repo.get(person.id) persisted_person.last_name = "Dane" repo.add(persisted_person) # Test that the underlying database is untouched assert person_dao.outside_uow().find_by(id=person.id).last_name == "Doe" assert person_dao.get(person.id).last_name == "Dane" def test_deleted_objects_are_committed_as_part_of_one_transaction( self, test_domain ): # Add a Person the database repo = test_domain.repository_for(Person) person_to_be_added = self.persisted_person(test_domain) repo.add(person_to_be_added) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session with UnitOfWork(): repo = test_domain.repository_for(Person) persisted_person = repo.get(person_to_be_added.id) repo.remove(persisted_person) # Test that the underlying database is untouched assert len(person_dao.outside_uow().query.all().items) == 1 assert len(person_dao.query.all().items) == 0 def test_changed_objects_are_committed_as_part_of_one_transaction( self, test_domain ): # Add a Person the database repo = test_domain.repository_for(Person) person_to_be_updated = self.persisted_person(test_domain) person_to_be_deleted = self.persisted_person(test_domain) repo.add(person_to_be_updated) repo.add(person_to_be_deleted) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session with UnitOfWork(): repo_with_uow = test_domain.repository_for(Person) # Create a new person object to be added person_to_be_added = Person(first_name="John", last_name="Doe") repo_with_uow.add(person_to_be_added) # Update an existing Person record person_to_be_updated.last_name = "FooBar" repo_with_uow.add(person_to_be_updated) # Remove an existing Person record repo_with_uow.remove(person_to_be_deleted) # Test that the underlying database is untouched assert len(person_dao.query.all().items) == 2 assert ( person_dao.outside_uow().get(person_to_be_updated.id).last_name != "FooBar" ) assert person_dao.get(person_to_be_deleted.id) is not None assert len(person_dao.query.all().items) == 2 assert person_dao.get(person_to_be_added.id) is not None assert person_dao.get(person_to_be_updated.id).last_name == "FooBar" with pytest.raises(ObjectNotFoundError): person_dao.get(person_to_be_deleted.id) def test_changed_objects_are_committed_as_part_of_one_transaction_on_explict_commit( self, test_domain ): # Add a Person the database repo = test_domain.repository_for(Person) person_to_be_updated = self.persisted_person(test_domain) person_to_be_deleted = self.persisted_person(test_domain) repo.add(person_to_be_updated) repo.add(person_to_be_deleted) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session uow = UnitOfWork() uow.start() repo_with_uow = test_domain.repository_for(Person) # Create a new person object to be added person_to_be_added = Person(first_name="John", last_name="Doe") repo_with_uow.add(person_to_be_added) # Update an existing Person record person_to_be_updated.last_name = "FooBar" repo_with_uow.add(person_to_be_updated) # Remove an existing Person record repo_with_uow.remove(person_to_be_deleted) # Test that the underlying database is untouched assert len(person_dao.query.all().items) == 2 assert ( person_dao.outside_uow().get(person_to_be_updated.id).last_name != "FooBar" ) assert person_dao.get(person_to_be_deleted.id) is not None uow.commit() assert uow.in_progress is False assert len(person_dao.query.all().items) == 2 assert person_dao.get(person_to_be_added.id) is not None assert person_dao.get(person_to_be_updated.id).last_name == "FooBar" with pytest.raises(ObjectNotFoundError): person_dao.get(person_to_be_deleted.id) def test_all_changes_are_discarded_on_rollback(self, test_domain): repo = test_domain.repository_for(Person) person_to_be_updated = self.persisted_person(test_domain) person_to_be_deleted = self.persisted_person(test_domain) repo.add(person_to_be_updated) repo.add(person_to_be_deleted) person_dao = test_domain.get_dao(Person) # Initiate a UnitOfWork Session uow = UnitOfWork() uow.start() repo_with_uow = test_domain.repository_for(Person) # Create a new person object to be added person_to_be_added = Person(first_name="John", last_name="Doe") repo_with_uow.add(person_to_be_added) # Update an existing Person record person_to_be_updated.last_name = "FooBar" repo_with_uow.add(person_to_be_updated) # Remove an existing Person record repo_with_uow.remove(person_to_be_deleted) # Test that the underlying database is untouched assert len(person_dao.query.all().items) == 2 assert ( person_dao.outside_uow().get(person_to_be_updated.id).last_name != "FooBar" ) assert person_dao.get(person_to_be_deleted.id) is not None uow.rollback() assert uow.in_progress is False assert len(person_dao.query.all().items) == 2 assert person_dao.get(person_to_be_updated.id).last_name != "FooBar" assert person_dao.get(person_to_be_deleted.id) is not None def test_session_is_destroyed_after_commit(self, test_domain): uow = UnitOfWork() uow.start() uow.commit() assert uow._sessions == {} assert uow.in_progress is False def test_session_is_destroyed_after_rollback(self, test_domain): uow = UnitOfWork() uow.start() uow.rollback() assert uow._sessions == {} assert uow.in_progress is False

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py

Python

aoc/common/__init__.py

klittlepage/aoc2020

7135ac08263480a8cc9d6536d7caeb26bf85ae4f

[ "MIT" ]

null

aoc/common/__init__.py

klittlepage/aoc2020

7135ac08263480a8cc9d6536d7caeb26bf85ae4f

[ "MIT" ]

null

aoc/common/__init__.py

klittlepage/aoc2020

7135ac08263480a8cc9d6536d7caeb26bf85ae4f

[ "MIT" ]

null

from aoc.common.helpers import read_chunked

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py

Python

custom-outset/pkgroot/usr/local/outset/boot-every/boot-every_example.py

flammable/outset

c1b21b7c9814b6c0cf868c09c1dbcc743e2d1f7d

[ "Apache-2.0" ]

533

2015-01-02T05:41:55.000Z

2022-03-30T22:34:57.000Z

custom-outset/pkgroot/usr/local/outset/boot-every/boot-every_example.py

flammable/outset

c1b21b7c9814b6c0cf868c09c1dbcc743e2d1f7d

[ "Apache-2.0" ]

80

2015-02-16T11:52:31.000Z

2022-01-21T01:52:46.000Z

custom-outset/pkgroot/usr/local/outset/boot-every/boot-every_example.py

flammable/outset

c1b21b7c9814b6c0cf868c09c1dbcc743e2d1f7d

[ "Apache-2.0" ]

95

2015-02-10T21:12:39.000Z

2022-03-25T10:00:34.000Z

#!/usr/bin/python # Replace this script with your scripts, profiles, and/or packages # which you want to run at every boot. print("These scripts will run at every boot.")

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6d617e44072a8df7827e8485dba93ffcfedb28b6

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py

Python

bittrex_signalr/__init__.py

r3bers/custom-bittrex-signalr

ffc5fcd56ac9ead4db305036f01a50b192bd9003

[ "MIT" ]

null

bittrex_signalr/__init__.py

r3bers/custom-bittrex-signalr

ffc5fcd56ac9ead4db305036f01a50b192bd9003

[ "MIT" ]

null

bittrex_signalr/__init__.py

r3bers/custom-bittrex-signalr

ffc5fcd56ac9ead4db305036f01a50b192bd9003

[ "MIT" ]

null

from bittrex_signalr import _logger from bittrex_signalr.websocket_client import BittrexSocket from bittrex_signalr.constants import BittrexMethods

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6d665d9abaae97abcc886109fc28f27bd3c0947a

335

py

Python

allure-nose2/test/with_mp/test_pm.py

bhumikapaharia/allure-python

b571b9bfc80af6f0431062ee83425e62d90163e4

[ "Apache-2.0" ]

558

2015-03-14T18:26:56.000Z

2022-02-21T00:09:49.000Z

allure-nose2/test/with_mp/test_pm.py

bhumikapaharia/allure-python

b571b9bfc80af6f0431062ee83425e62d90163e4

[ "Apache-2.0" ]

448

2015-01-09T10:00:47.000Z

2022-03-24T15:25:02.000Z

allure-nose2/test/with_mp/test_pm.py

bhumikapaharia/allure-python

b571b9bfc80af6f0431062ee83425e62d90163e4

[ "Apache-2.0" ]

244

2015-01-26T08:03:11.000Z

2022-03-07T17:06:30.000Z

# Todo test mp from test.example_runner import run_docstring_example def test_func_fullname(): """ >>> def test_func_fullname_example1(): ... pass >>> def test_func_fullname_example2(): ... pass >>> def test_func_fullname_example3(): ... pass """ allure_report = run_docstring_example()

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118

py

Python

deepnade/buml/NADE/__init__.py

vlimant/NADE

e2446c73250a99979c8710a8acbb14823a54bce0

[ "BSD-3-Clause" ]

43

2017-06-19T21:19:55.000Z

2022-02-06T01:21:48.000Z

deepnade/buml/NADE/__init__.py

vlimant/NADE

e2446c73250a99979c8710a8acbb14823a54bce0

[ "BSD-3-Clause" ]

1

2017-08-29T14:09:49.000Z

2017-09-08T12:34:19.000Z

deepnade/buml/NADE/__init__.py

vlimant/NADE

e2446c73250a99979c8710a8acbb14823a54bce0

[ "BSD-3-Clause" ]

12

2017-09-12T07:56:13.000Z

2021-09-19T19:11:41.000Z

from BernoulliNADE import * from MoGNADE import * from OrderlessBernoulliNADE import * from OrderlessMoGNADE import *

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797

py

Python

src/Card.py

codavex/Cards

7dba1c219c7188f176f152c2671096e16accbc66

[ "MIT" ]

null

src/Card.py

codavex/Cards

7dba1c219c7188f176f152c2671096e16accbc66

[ "MIT" ]

null

src/Card.py

codavex/Cards

7dba1c219c7188f176f152c2671096e16accbc66

[ "MIT" ]

null

class Card: def __init__(self, rank, suit): self._rank = rank self._suit = suit def __repr__(self): return "%s%s" % (repr(self._rank), self._suit.name) def __str__(self): return "%s of %s" % (str(self._rank), self._suit.value) def __eq__(self, other): return self._rank == other._rank def __ne__(self, other): return self._rank != other._rank def __lt__(self, other): return self._rank < other._rank def __le__(self, other): return self._rank <= other._rank def __gt__(self, other): return self._rank > other._rank def __ge__(self, other): return self._rank >= other._rank def getRank(self): return self._rank def getSuit(self): return self._suit

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eddf9244d02922ceac3322d9a53346f7976639a1

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py

Python

neurora/ctrdm_corr.py

ZitongLu1996/NeuroRA

4e72f5b37ff308a4a068107b35f7555df6b7df0d

[ "MIT" ]

110

2019-04-30T03:52:48.000Z

2022-03-19T08:23:38.000Z

neurora/ctrdm_corr.py

ZitongLu1996/NeuroRA

4e72f5b37ff308a4a068107b35f7555df6b7df0d

[ "MIT" ]

2

2020-07-23T14:31:30.000Z

2022-01-14T08:30:00.000Z

neurora/ctrdm_corr.py

ZitongLu1996/NeuroRA

4e72f5b37ff308a4a068107b35f7555df6b7df0d

[ "MIT" ]

20

2020-03-02T11:58:30.000Z

2021-12-31T08:29:53.000Z

# -*- coding: utf-8 -*- ' a module for calculating the Similarity/Correlation Coefficient between two Cross-temporal RDMs ' __author__ = 'Zitong Lu' import numpy as np from scipy.stats import spearmanr from scipy.stats import pearsonr from scipy.stats import kendalltau from neurora.stuff import permutation_corr ' a function for calculating the Spearman correlation coefficient between two CTRDMs ' def ctrdm_correlation_spearman(CTRDM1, CTRDM2): """ Calculate the similarity based on Spearman Correlation Coefficient between two CTRDMs Parameters ---------- CTRDM1 : array [n_conditions, n_conditions] The CTRDM 1. The shape of CTRDM1 must be [n_cons, n_cons]. n_cons represent the number of conidtions. CTRDM2 : array [n_conditions, n_conditions] The CTRDM 2. The shape of CTRDM2 must be [n_cons, n_cons]. n_cons represent the number of conidtions. Returns ------- corr : array [r, p]. The Spearman Correlation result. The shape of corr is [2], including a r-value and a p-value. """ # get number of conditions n_cons = np.shape(CTRDM1)[0] # calculate the number of value above the diagonal in RDM n = n_cons * (n_cons - 1) # initialize two vectors to store the values above the diagnal of two RDMs v1 = np.zeros([n], dtype=np.float64) v2 = np.zeros([n], dtype=np.float64) # assignment nn = 0 for i in range(n_cons): for j in range(n_cons): if i != j: v1[nn] = CTRDM1[i, j] v2[nn] = CTRDM2[i, j] nn = nn + 1 # calculate the Spearman Correlation corr = np.array(spearmanr(v1, v2)) return corr ' a function for calculating the similarity based on Pearson Correlation Coefficient between two CTRDMs ' def ctrdm_correlation_pearson(CTRDM1, CTRDM2): """ Calculate the similarity based on Pearson Correlation Coefficient between two CTRDMs Parameters ---------- CTRDM1 : array [n_conditions, n_conditions] The CTRDM 1. The shape of CTRDM1 must be [n_cons, n_cons]. n_cons represent the number of conidtions. CTRDM2 : array [n_conditions, n_conditions] The CTRDM 2. The shape of CTRDM2 must be [n_cons, n_cons]. n_cons represent the number of conidtions. Returns ------- corr : array [r, p]. The Pearson Correlation result. The shape of corr is [2], including a r-value and a p-value. """ # get number of conditions n_cons = np.shape(CTRDM1)[0] # calculate the number of value above the diagonal in RDM n = n_cons * (n_cons - 1) # initialize two vectors to store the values above the diagnal of two RDMs v1 = np.zeros([n], dtype=np.float64) v2 = np.zeros([n], dtype=np.float64) # assignment nn = 0 for i in range(n_cons): for j in range(n_cons): if i != j: v1[nn] = CTRDM1[i, j] v2[nn] = CTRDM2[i, j] nn = nn + 1 # calculate the Pearson Correlation corr = np.array(pearsonr(v1, v2)) return corr ' a function for calculating the similarity based on Kendalls tau Correlation Coefficient between two CTRDMs ' def ctrdm_correlation_kendall(CTRDM1, CTRDM2): """ Calculate the similarity based on Kendalls tau Correlation Coefficient between two CTRDMs Parameters ---------- CTRDM1 : array [n_conditions, n_conditions] The CTRDM 1. The shape of CTRDM1 must be [n_cons, n_cons]. n_cons represent the number of conidtions. CTRDM2 : array [n_conditions, n_conditions] The CTRDM 2. The shape of CTRDM2 must be [n_cons, n_cons]. n_cons represent the number of conidtions. Returns ------- corr : array [r, p]. The Kendalls tau Correlation result. The shape of corr is [2], including a r-value and a p-value. """ # get number of conditions n_cons = np.shape(CTRDM1)[0] # calculate the number of value above the diagonal in RDM n = n_cons * (n_cons - 1) # initialize two vectors to store the values above the diagnal of two RDMs v1 = np.zeros([n], dtype=np.float64) v2 = np.zeros([n], dtype=np.float64) # assignment nn = 0 for i in range(n_cons): for j in range(n_cons): if i != j: v1[nn] = CTRDM1[i, j] v2[nn] = CTRDM2[i, j] nn = nn + 1 # calculate the Kendalls tau Correlation corr = np.array(kendalltau(v1, v2)) return corr def ctrdm_similarity(CTRDM1, CTRDM2): """ Calculate the similarity based on Cosine Similarity between two CTRDMs Parameters ---------- CTRDM1 : array [n_conditions, n_conditions] The CTRDM 1. The shape of CTRDM1 must be [n_cons, n_cons]. n_cons represent the number of conidtions. CTRDM2 : array [n_conditions, n_conditions] The CTRDM 2. The shape of CTRDM2 must be [n_cons, n_cons]. n_cons represent the number of conidtions. Returns ------- similarity : float The Cosine Similarity result. """ # get number of conditions n_cons = np.shape(CTRDM1)[0] # calculate the number of value above the diagonal in RDM n = n_cons * (n_cons - 1) # initialize two vectors to store the values above the diagnal of two RDMs v1 = np.zeros([n], dtype=np.float64) v2 = np.zeros([n], dtype=np.float64) # assignment nn = 0 for i in range(n_cons): for j in range(n_cons): if i != j: v1[nn] = CTRDM1[i, j] v2[nn] = CTRDM2[i, j] nn = nn + 1 # calculate the Cosine Similarity V1 = np.mat(v1) V2 = np.mat(v2) num = float(V1 * V2.T) denom = np.linalg.norm(V1) * np.linalg.norm(V2) cos = num / denom similarity = 0.5 + 0.5 * cos return similarity ' a function for calculating the similarity based on Euclidean Distance between two CTRDMs ' def ctrdm_distance(CTRDM1, CTRDM2): """ Calculate the similarity based on Euclidean Distance between two CTRDMs Parameters ---------- CTRDM1 : array [n_conditions, n_conditions] The CTRDM 1. The shape of CTRDM1 must be [n_cons, n_cons]. n_cons represent the number of conidtions. CTRDM2 : array [n_conditions, n_conditions] The CTRDM 2. The shape of CTRDM2 must be [n_cons, n_cons]. n_cons represent the number of conidtions. Returns ------- dist : float. The Euclidean Distance result. """ # get number of conditions n_cons = np.shape(CTRDM1)[0] # calculate the number of value above the diagonal in RDM n = n_cons * (n_cons - 1) # initialize two vectors to store the values above the diagnal of two RDMs v1 = np.zeros([n], dtype=np.float64) v2 = np.zeros([n], dtype=np.float64) # assignment nn = 0 for i in range(n_cons): for j in range(n_cons): if i != j: v1[nn] = CTRDM1[i, j] v2[nn] = CTRDM2[i, j] nn = nn + 1 # calculate the Euclidean Distance dist = np.linalg.norm(v1 - v2) return dist

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edf937fa1fee8c19ee67f57144045b7f63a92424

44

py

Python

dist/python/tests/__init__.py

natir/ssik

873cdc5b2e3c6b3f03191e26506ccb5e6d4b4d89

[ "MIT" ]

1

2019-02-07T10:23:18.000Z

dist/python/tests/__init__.py

natir/pcon

d198a8d8e4469bc39b5bedde95e4b71a1f95ef81

[ "MIT" ]

1

2019-02-01T17:02:45.000Z

2019-02-08T21:03:30.000Z

dist/python/tests/__init__.py

natir/ssik

873cdc5b2e3c6b3f03191e26506ccb5e6d4b4d89

[ "MIT" ]

1

2019-11-04T09:17:59.000Z

import sys sys.path.pop(0) print(sys.path)

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6

edfd85b132ad1d9089aec68bee79b6df772c2acd

43

py

Python

plexiglass/detectors/__init__.py

jkartzman/plexiglass

257e3305e31f032c26300b0a9c78260ccd251cd6

[ "MIT" ]

null

plexiglass/detectors/__init__.py

jkartzman/plexiglass

257e3305e31f032c26300b0a9c78260ccd251cd6

[ "MIT" ]

null

plexiglass/detectors/__init__.py

jkartzman/plexiglass

257e3305e31f032c26300b0a9c78260ccd251cd6

[ "MIT" ]

null

from .mesonet import MesoNet, MesoInception

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107,496

py

Python

sdk/python/pulumi_spotinst/azure/_inputs.py

pulumi/pulumi-spotinst

75592d6293d63f6cec703722f2e02ff1fb1cca44

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

4

2019-12-21T20:50:43.000Z

2021-12-01T20:57:38.000Z

sdk/python/pulumi_spotinst/azure/_inputs.py

pulumi/pulumi-spotinst

75592d6293d63f6cec703722f2e02ff1fb1cca44

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

103

2019-12-09T22:03:16.000Z

2022-03-30T17:07:34.000Z

sdk/python/pulumi_spotinst/azure/_inputs.py

pulumi/pulumi-spotinst

75592d6293d63f6cec703722f2e02ff1fb1cca44

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'ElastigroupHealthCheckArgs', 'ElastigroupImageArgs', 'ElastigroupImageCustomArgs', 'ElastigroupImageMarketplaceArgs', 'ElastigroupIntegrationKubernetesArgs', 'ElastigroupIntegrationMultaiRuntimeArgs', 'ElastigroupLoadBalancerArgs', 'ElastigroupLoginArgs', 'ElastigroupManagedServiceIdentityArgs', 'ElastigroupNetworkArgs', 'ElastigroupNetworkAdditionalIpConfigArgs', 'ElastigroupScalingDownPolicyArgs', 'ElastigroupScalingDownPolicyDimensionArgs', 'ElastigroupScalingUpPolicyArgs', 'ElastigroupScalingUpPolicyDimensionArgs', 'ElastigroupScheduledTaskArgs', 'ElastigroupStrategyArgs', 'ElastigroupUpdatePolicyArgs', 'ElastigroupUpdatePolicyRollConfigArgs', 'OceanAutoscalerArgs', 'OceanAutoscalerAutoscaleDownArgs', 'OceanAutoscalerAutoscaleHeadroomArgs', 'OceanAutoscalerAutoscaleHeadroomAutomaticArgs', 'OceanAutoscalerResourceLimitsArgs', 'OceanExtensionArgs', 'OceanHealthArgs', 'OceanImageArgs', 'OceanImageMarketplaceArgs', 'OceanLoadBalancerArgs', 'OceanManagedServiceIdentityArgs', 'OceanNetworkArgs', 'OceanNetworkNetworkInterfaceArgs', 'OceanNetworkNetworkInterfaceAdditionalIpConfigArgs', 'OceanNetworkNetworkInterfaceSecurityGroupArgs', 'OceanOsDiskArgs', 'OceanStrategyArgs', 'OceanTagArgs', 'OceanVirtualNodeGroupAutoscaleArgs', 'OceanVirtualNodeGroupAutoscaleAutoscaleHeadroomArgs', 'OceanVirtualNodeGroupLabelArgs', 'OceanVirtualNodeGroupLaunchSpecificationArgs', 'OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs', 'OceanVirtualNodeGroupLaunchSpecificationTagArgs', 'OceanVirtualNodeGroupResourceLimitArgs', 'OceanVirtualNodeGroupTaintArgs', 'OceanVmSizeArgs', ] @pulumi.input_type class ElastigroupHealthCheckArgs: def __init__(__self__, *, health_check_type: pulumi.Input[str], auto_healing: Optional[pulumi.Input[bool]] = None, grace_period: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[str] health_check_type: Sets the health check type to use. Valid values: `"INSTANCE_STATE"`, `"NONE"`. :param pulumi.Input[bool] auto_healing: Enable auto-healing of unhealthy VMs. :param pulumi.Input[int] grace_period: Sets the grace period for new instances to become healthy. """ pulumi.set(__self__, "health_check_type", health_check_type) if auto_healing is not None: pulumi.set(__self__, "auto_healing", auto_healing) if grace_period is not None: pulumi.set(__self__, "grace_period", grace_period) @property @pulumi.getter(name="healthCheckType") def health_check_type(self) -> pulumi.Input[str]: """ Sets the health check type to use. Valid values: `"INSTANCE_STATE"`, `"NONE"`. """ return pulumi.get(self, "health_check_type") @health_check_type.setter def health_check_type(self, value: pulumi.Input[str]): pulumi.set(self, "health_check_type", value) @property @pulumi.getter(name="autoHealing") def auto_healing(self) -> Optional[pulumi.Input[bool]]: """ Enable auto-healing of unhealthy VMs. """ return pulumi.get(self, "auto_healing") @auto_healing.setter def auto_healing(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "auto_healing", value) @property @pulumi.getter(name="gracePeriod") def grace_period(self) -> Optional[pulumi.Input[int]]: """ Sets the grace period for new instances to become healthy. """ return pulumi.get(self, "grace_period") @grace_period.setter def grace_period(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "grace_period", value) @pulumi.input_type class ElastigroupImageArgs: def __init__(__self__, *, customs: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageCustomArgs']]]] = None, marketplaces: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageMarketplaceArgs']]]] = None): if customs is not None: pulumi.set(__self__, "customs", customs) if marketplaces is not None: pulumi.set(__self__, "marketplaces", marketplaces) @property @pulumi.getter def customs(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageCustomArgs']]]]: return pulumi.get(self, "customs") @customs.setter def customs(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageCustomArgs']]]]): pulumi.set(self, "customs", value) @property @pulumi.getter def marketplaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageMarketplaceArgs']]]]: return pulumi.get(self, "marketplaces") @marketplaces.setter def marketplaces(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupImageMarketplaceArgs']]]]): pulumi.set(self, "marketplaces", value) @pulumi.input_type class ElastigroupImageCustomArgs: def __init__(__self__, *, image_name: pulumi.Input[str], resource_group_name: pulumi.Input[str]): """ :param pulumi.Input[str] image_name: Name of the custom image. Required if resource_group_name is specified. :param pulumi.Input[str] resource_group_name: Vnet Resource Group Name. """ pulumi.set(__self__, "image_name", image_name) pulumi.set(__self__, "resource_group_name", resource_group_name) @property @pulumi.getter(name="imageName") def image_name(self) -> pulumi.Input[str]: """ Name of the custom image. Required if resource_group_name is specified. """ return pulumi.get(self, "image_name") @image_name.setter def image_name(self, value: pulumi.Input[str]): pulumi.set(self, "image_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ Vnet Resource Group Name. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @pulumi.input_type class ElastigroupImageMarketplaceArgs: def __init__(__self__, *, offer: pulumi.Input[str], publisher: pulumi.Input[str], sku: pulumi.Input[str]): """ :param pulumi.Input[str] offer: Name of the image to use. Required if publisher is specified. :param pulumi.Input[str] publisher: Image publisher. Required if resource_group_name is not specified. :param pulumi.Input[str] sku: Image's Stock Keeping Unit, which is the specific version of the image. Required if publisher is specified. """ pulumi.set(__self__, "offer", offer) pulumi.set(__self__, "publisher", publisher) pulumi.set(__self__, "sku", sku) @property @pulumi.getter def offer(self) -> pulumi.Input[str]: """ Name of the image to use. Required if publisher is specified. """ return pulumi.get(self, "offer") @offer.setter def offer(self, value: pulumi.Input[str]): pulumi.set(self, "offer", value) @property @pulumi.getter def publisher(self) -> pulumi.Input[str]: """ Image publisher. Required if resource_group_name is not specified. """ return pulumi.get(self, "publisher") @publisher.setter def publisher(self, value: pulumi.Input[str]): pulumi.set(self, "publisher", value) @property @pulumi.getter def sku(self) -> pulumi.Input[str]: """ Image's Stock Keeping Unit, which is the specific version of the image. Required if publisher is specified. """ return pulumi.get(self, "sku") @sku.setter def sku(self, value: pulumi.Input[str]): pulumi.set(self, "sku", value) @pulumi.input_type class ElastigroupIntegrationKubernetesArgs: def __init__(__self__, *, cluster_identifier: pulumi.Input[str]): """ :param pulumi.Input[str] cluster_identifier: The cluster ID. """ pulumi.set(__self__, "cluster_identifier", cluster_identifier) @property @pulumi.getter(name="clusterIdentifier") def cluster_identifier(self) -> pulumi.Input[str]: """ The cluster ID. """ return pulumi.get(self, "cluster_identifier") @cluster_identifier.setter def cluster_identifier(self, value: pulumi.Input[str]): pulumi.set(self, "cluster_identifier", value) @pulumi.input_type class ElastigroupIntegrationMultaiRuntimeArgs: def __init__(__self__, *, deployment_id: pulumi.Input[str]): """ :param pulumi.Input[str] deployment_id: The deployment id you want to get """ pulumi.set(__self__, "deployment_id", deployment_id) @property @pulumi.getter(name="deploymentId") def deployment_id(self) -> pulumi.Input[str]: """ The deployment id you want to get """ return pulumi.get(self, "deployment_id") @deployment_id.setter def deployment_id(self, value: pulumi.Input[str]): pulumi.set(self, "deployment_id", value) @pulumi.input_type class ElastigroupLoadBalancerArgs: def __init__(__self__, *, type: pulumi.Input[str], auto_weight: Optional[pulumi.Input[bool]] = None, balancer_id: Optional[pulumi.Input[str]] = None, target_set_id: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] type: The resource type. Valid values: CLASSIC, TARGET_GROUP, MULTAI_TARGET_SET. :param pulumi.Input[str] balancer_id: The balancer ID. :param pulumi.Input[str] target_set_id: The scale set ID associated with the load balancer. """ pulumi.set(__self__, "type", type) if auto_weight is not None: pulumi.set(__self__, "auto_weight", auto_weight) if balancer_id is not None: pulumi.set(__self__, "balancer_id", balancer_id) if target_set_id is not None: pulumi.set(__self__, "target_set_id", target_set_id) @property @pulumi.getter def type(self) -> pulumi.Input[str]: """ The resource type. Valid values: CLASSIC, TARGET_GROUP, MULTAI_TARGET_SET. """ return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @property @pulumi.getter(name="autoWeight") def auto_weight(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "auto_weight") @auto_weight.setter def auto_weight(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "auto_weight", value) @property @pulumi.getter(name="balancerId") def balancer_id(self) -> Optional[pulumi.Input[str]]: """ The balancer ID. """ return pulumi.get(self, "balancer_id") @balancer_id.setter def balancer_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "balancer_id", value) @property @pulumi.getter(name="targetSetId") def target_set_id(self) -> Optional[pulumi.Input[str]]: """ The scale set ID associated with the load balancer. """ return pulumi.get(self, "target_set_id") @target_set_id.setter def target_set_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "target_set_id", value) @pulumi.input_type class ElastigroupLoginArgs: def __init__(__self__, *, user_name: pulumi.Input[str], password: Optional[pulumi.Input[str]] = None, ssh_public_key: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] user_name: Set admin access for accessing your VMs. :param pulumi.Input[str] password: Password for admin access to Windows VMs. Required for Windows product types. :param pulumi.Input[str] ssh_public_key: SSH for admin access to Linux VMs. Required for Linux product types. """ pulumi.set(__self__, "user_name", user_name) if password is not None: pulumi.set(__self__, "password", password) if ssh_public_key is not None: pulumi.set(__self__, "ssh_public_key", ssh_public_key) @property @pulumi.getter(name="userName") def user_name(self) -> pulumi.Input[str]: """ Set admin access for accessing your VMs. """ return pulumi.get(self, "user_name") @user_name.setter def user_name(self, value: pulumi.Input[str]): pulumi.set(self, "user_name", value) @property @pulumi.getter def password(self) -> Optional[pulumi.Input[str]]: """ Password for admin access to Windows VMs. Required for Windows product types. """ return pulumi.get(self, "password") @password.setter def password(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "password", value) @property @pulumi.getter(name="sshPublicKey") def ssh_public_key(self) -> Optional[pulumi.Input[str]]: """ SSH for admin access to Linux VMs. Required for Linux product types. """ return pulumi.get(self, "ssh_public_key") @ssh_public_key.setter def ssh_public_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ssh_public_key", value) @pulumi.input_type class ElastigroupManagedServiceIdentityArgs: def __init__(__self__, *, name: pulumi.Input[str], resource_group_name: pulumi.Input[str]): """ :param pulumi.Input[str] name: The dimension name. :param pulumi.Input[str] resource_group_name: Vnet Resource Group Name. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "resource_group_name", resource_group_name) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The dimension name. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ Vnet Resource Group Name. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @pulumi.input_type class ElastigroupNetworkArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], subnet_name: pulumi.Input[str], virtual_network_name: pulumi.Input[str], additional_ip_configs: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupNetworkAdditionalIpConfigArgs']]]] = None, assign_public_ip: Optional[pulumi.Input[bool]] = None): """ :param pulumi.Input[str] resource_group_name: Vnet Resource Group Name. :param pulumi.Input[str] subnet_name: ID of subnet. :param pulumi.Input[str] virtual_network_name: Name of Vnet. :param pulumi.Input[Sequence[pulumi.Input['ElastigroupNetworkAdditionalIpConfigArgs']]] additional_ip_configs: Array of additional IP configuration objects. """ pulumi.set(__self__, "resource_group_name", resource_group_name) pulumi.set(__self__, "subnet_name", subnet_name) pulumi.set(__self__, "virtual_network_name", virtual_network_name) if additional_ip_configs is not None: pulumi.set(__self__, "additional_ip_configs", additional_ip_configs) if assign_public_ip is not None: pulumi.set(__self__, "assign_public_ip", assign_public_ip) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ Vnet Resource Group Name. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="subnetName") def subnet_name(self) -> pulumi.Input[str]: """ ID of subnet. """ return pulumi.get(self, "subnet_name") @subnet_name.setter def subnet_name(self, value: pulumi.Input[str]): pulumi.set(self, "subnet_name", value) @property @pulumi.getter(name="virtualNetworkName") def virtual_network_name(self) -> pulumi.Input[str]: """ Name of Vnet. """ return pulumi.get(self, "virtual_network_name") @virtual_network_name.setter def virtual_network_name(self, value: pulumi.Input[str]): pulumi.set(self, "virtual_network_name", value) @property @pulumi.getter(name="additionalIpConfigs") def additional_ip_configs(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupNetworkAdditionalIpConfigArgs']]]]: """ Array of additional IP configuration objects. """ return pulumi.get(self, "additional_ip_configs") @additional_ip_configs.setter def additional_ip_configs(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupNetworkAdditionalIpConfigArgs']]]]): pulumi.set(self, "additional_ip_configs", value) @property @pulumi.getter(name="assignPublicIp") def assign_public_ip(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "assign_public_ip") @assign_public_ip.setter def assign_public_ip(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "assign_public_ip", value) @pulumi.input_type class ElastigroupNetworkAdditionalIpConfigArgs: def __init__(__self__, *, name: pulumi.Input[str], private_ip_version: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: The dimension name. :param pulumi.Input[str] private_ip_version: Available from Azure Api-Version 2017-03-30 onwards, it represents whether the specific ipconfiguration is IPv4 or IPv6. Valid values: `IPv4`, `IPv6`. """ pulumi.set(__self__, "name", name) if private_ip_version is not None: pulumi.set(__self__, "private_ip_version", private_ip_version) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The dimension name. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter(name="privateIpVersion") def private_ip_version(self) -> Optional[pulumi.Input[str]]: """ Available from Azure Api-Version 2017-03-30 onwards, it represents whether the specific ipconfiguration is IPv4 or IPv6. Valid values: `IPv4`, `IPv6`. """ return pulumi.get(self, "private_ip_version") @private_ip_version.setter def private_ip_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_ip_version", value) @pulumi.input_type class ElastigroupScalingDownPolicyArgs: def __init__(__self__, *, metric_name: pulumi.Input[str], namespace: pulumi.Input[str], policy_name: pulumi.Input[str], threshold: pulumi.Input[float], action_type: Optional[pulumi.Input[str]] = None, adjustment: Optional[pulumi.Input[str]] = None, cooldown: Optional[pulumi.Input[int]] = None, dimensions: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingDownPolicyDimensionArgs']]]] = None, evaluation_periods: Optional[pulumi.Input[int]] = None, max_target_capacity: Optional[pulumi.Input[str]] = None, maximum: Optional[pulumi.Input[str]] = None, min_target_capacity: Optional[pulumi.Input[str]] = None, minimum: Optional[pulumi.Input[str]] = None, operator: Optional[pulumi.Input[str]] = None, period: Optional[pulumi.Input[int]] = None, statistic: Optional[pulumi.Input[str]] = None, target: Optional[pulumi.Input[str]] = None, unit: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] metric_name: Metric to monitor by Azure metric display name. :param pulumi.Input[str] namespace: The namespace for the alarm's associated metric. Valid values: :param pulumi.Input[str] policy_name: The name of the policy. :param pulumi.Input[float] threshold: The value against which the specified statistic is compared. :param pulumi.Input[str] action_type: The type of action to perform for scaling. Valid values: `"adjustment"`, `"percentageAdjustment"`, `"setMaxTarget"`, `"setMinTarget"`, `"updateCapacity"`. :param pulumi.Input[str] adjustment: The number of instances to add/remove to/from the target capacity when scale is needed. :param pulumi.Input[int] cooldown: The amount of time, in seconds, after a scaling activity completes and before the next scaling activity can start. If this parameter is not specified, the default cooldown period for the group applies. :param pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingDownPolicyDimensionArgs']]] dimensions: A list of dimensions describing qualities of the metric. Required when `namespace` is defined AND not `"Microsoft.Compute"`. :param pulumi.Input[int] evaluation_periods: The number of periods over which data is compared to the specified threshold. :param pulumi.Input[str] max_target_capacity: . The number of the desired target (and maximum) capacity :param pulumi.Input[str] maximum: The maximal number of instances to have in the group. :param pulumi.Input[str] min_target_capacity: . The number of the desired target (and minimum) capacity :param pulumi.Input[str] minimum: The minimal number of instances to have in the group. :param pulumi.Input[str] operator: The operator to use in order to determine if the scaling policy is applicable. Valid values: `"gt"`, `"gte"`, `"lt"`, `"lte"`. :param pulumi.Input[int] period: The granularity, in seconds, of the returned datapoints. Period must be at least 60 seconds and must be a multiple of 60. :param pulumi.Input[str] statistic: The metric statistics to return. Valid values: `average`. :param pulumi.Input[str] target: The target number of instances to have in the group. :param pulumi.Input[str] unit: The unit for the alarm's associated metric. Valid values: `"percent`, `"seconds"`, `"microseconds"`, `"milliseconds"`, `"bytes"`, `"kilobytes"`, `"megabytes"`, `"gigabytes"`, `"terabytes"`, `"bits"`, `"kilobits"`, `"megabits"`, `"gigabits"`, `"terabits"`, `"count"`, `"bytes/second"`, `"kilobytes/second"`, `"megabytes/second"`, `"gigabytes/second"`, `"terabytes/second"`, `"bits/second"`, `"kilobits/second"`, `"megabits/second"`, `"gigabits/second"`, `"terabits/second"`, `"count/second"`, `"none"`. """ pulumi.set(__self__, "metric_name", metric_name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "policy_name", policy_name) pulumi.set(__self__, "threshold", threshold) if action_type is not None: pulumi.set(__self__, "action_type", action_type) if adjustment is not None: pulumi.set(__self__, "adjustment", adjustment) if cooldown is not None: pulumi.set(__self__, "cooldown", cooldown) if dimensions is not None: pulumi.set(__self__, "dimensions", dimensions) if evaluation_periods is not None: pulumi.set(__self__, "evaluation_periods", evaluation_periods) if max_target_capacity is not None: pulumi.set(__self__, "max_target_capacity", max_target_capacity) if maximum is not None: pulumi.set(__self__, "maximum", maximum) if min_target_capacity is not None: pulumi.set(__self__, "min_target_capacity", min_target_capacity) if minimum is not None: pulumi.set(__self__, "minimum", minimum) if operator is not None: pulumi.set(__self__, "operator", operator) if period is not None: pulumi.set(__self__, "period", period) if statistic is not None: pulumi.set(__self__, "statistic", statistic) if target is not None: pulumi.set(__self__, "target", target) if unit is not None: pulumi.set(__self__, "unit", unit) @property @pulumi.getter(name="metricName") def metric_name(self) -> pulumi.Input[str]: """ Metric to monitor by Azure metric display name. """ return pulumi.get(self, "metric_name") @metric_name.setter def metric_name(self, value: pulumi.Input[str]): pulumi.set(self, "metric_name", value) @property @pulumi.getter def namespace(self) -> pulumi.Input[str]: """ The namespace for the alarm's associated metric. Valid values: """ return pulumi.get(self, "namespace") @namespace.setter def namespace(self, value: pulumi.Input[str]): pulumi.set(self, "namespace", value) @property @pulumi.getter(name="policyName") def policy_name(self) -> pulumi.Input[str]: """ The name of the policy. """ return pulumi.get(self, "policy_name") @policy_name.setter def policy_name(self, value: pulumi.Input[str]): pulumi.set(self, "policy_name", value) @property @pulumi.getter def threshold(self) -> pulumi.Input[float]: """ The value against which the specified statistic is compared. """ return pulumi.get(self, "threshold") @threshold.setter def threshold(self, value: pulumi.Input[float]): pulumi.set(self, "threshold", value) @property @pulumi.getter(name="actionType") def action_type(self) -> Optional[pulumi.Input[str]]: """ The type of action to perform for scaling. Valid values: `"adjustment"`, `"percentageAdjustment"`, `"setMaxTarget"`, `"setMinTarget"`, `"updateCapacity"`. """ return pulumi.get(self, "action_type") @action_type.setter def action_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "action_type", value) @property @pulumi.getter def adjustment(self) -> Optional[pulumi.Input[str]]: """ The number of instances to add/remove to/from the target capacity when scale is needed. """ return pulumi.get(self, "adjustment") @adjustment.setter def adjustment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "adjustment", value) @property @pulumi.getter def cooldown(self) -> Optional[pulumi.Input[int]]: """ The amount of time, in seconds, after a scaling activity completes and before the next scaling activity can start. If this parameter is not specified, the default cooldown period for the group applies. """ return pulumi.get(self, "cooldown") @cooldown.setter def cooldown(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "cooldown", value) @property @pulumi.getter def dimensions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingDownPolicyDimensionArgs']]]]: """ A list of dimensions describing qualities of the metric. Required when `namespace` is defined AND not `"Microsoft.Compute"`. """ return pulumi.get(self, "dimensions") @dimensions.setter def dimensions(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingDownPolicyDimensionArgs']]]]): pulumi.set(self, "dimensions", value) @property @pulumi.getter(name="evaluationPeriods") def evaluation_periods(self) -> Optional[pulumi.Input[int]]: """ The number of periods over which data is compared to the specified threshold. """ return pulumi.get(self, "evaluation_periods") @evaluation_periods.setter def evaluation_periods(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "evaluation_periods", value) @property @pulumi.getter(name="maxTargetCapacity") def max_target_capacity(self) -> Optional[pulumi.Input[str]]: """ . The number of the desired target (and maximum) capacity """ return pulumi.get(self, "max_target_capacity") @max_target_capacity.setter def max_target_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_target_capacity", value) @property @pulumi.getter def maximum(self) -> Optional[pulumi.Input[str]]: """ The maximal number of instances to have in the group. """ return pulumi.get(self, "maximum") @maximum.setter def maximum(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "maximum", value) @property @pulumi.getter(name="minTargetCapacity") def min_target_capacity(self) -> Optional[pulumi.Input[str]]: """ . The number of the desired target (and minimum) capacity """ return pulumi.get(self, "min_target_capacity") @min_target_capacity.setter def min_target_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "min_target_capacity", value) @property @pulumi.getter def minimum(self) -> Optional[pulumi.Input[str]]: """ The minimal number of instances to have in the group. """ return pulumi.get(self, "minimum") @minimum.setter def minimum(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "minimum", value) @property @pulumi.getter def operator(self) -> Optional[pulumi.Input[str]]: """ The operator to use in order to determine if the scaling policy is applicable. Valid values: `"gt"`, `"gte"`, `"lt"`, `"lte"`. """ return pulumi.get(self, "operator") @operator.setter def operator(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "operator", value) @property @pulumi.getter def period(self) -> Optional[pulumi.Input[int]]: """ The granularity, in seconds, of the returned datapoints. Period must be at least 60 seconds and must be a multiple of 60. """ return pulumi.get(self, "period") @period.setter def period(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "period", value) @property @pulumi.getter def statistic(self) -> Optional[pulumi.Input[str]]: """ The metric statistics to return. Valid values: `average`. """ return pulumi.get(self, "statistic") @statistic.setter def statistic(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "statistic", value) @property @pulumi.getter def target(self) -> Optional[pulumi.Input[str]]: """ The target number of instances to have in the group. """ return pulumi.get(self, "target") @target.setter def target(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "target", value) @property @pulumi.getter def unit(self) -> Optional[pulumi.Input[str]]: """ The unit for the alarm's associated metric. Valid values: `"percent`, `"seconds"`, `"microseconds"`, `"milliseconds"`, `"bytes"`, `"kilobytes"`, `"megabytes"`, `"gigabytes"`, `"terabytes"`, `"bits"`, `"kilobits"`, `"megabits"`, `"gigabits"`, `"terabits"`, `"count"`, `"bytes/second"`, `"kilobytes/second"`, `"megabytes/second"`, `"gigabytes/second"`, `"terabytes/second"`, `"bits/second"`, `"kilobits/second"`, `"megabits/second"`, `"gigabits/second"`, `"terabits/second"`, `"count/second"`, `"none"`. """ return pulumi.get(self, "unit") @unit.setter def unit(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "unit", value) @pulumi.input_type class ElastigroupScalingDownPolicyDimensionArgs: def __init__(__self__, *, name: pulumi.Input[str], value: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: The dimension name. :param pulumi.Input[str] value: The dimension value. """ pulumi.set(__self__, "name", name) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The dimension name. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ The dimension value. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class ElastigroupScalingUpPolicyArgs: def __init__(__self__, *, metric_name: pulumi.Input[str], namespace: pulumi.Input[str], policy_name: pulumi.Input[str], threshold: pulumi.Input[float], action_type: Optional[pulumi.Input[str]] = None, adjustment: Optional[pulumi.Input[str]] = None, cooldown: Optional[pulumi.Input[int]] = None, dimensions: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingUpPolicyDimensionArgs']]]] = None, evaluation_periods: Optional[pulumi.Input[int]] = None, max_target_capacity: Optional[pulumi.Input[str]] = None, maximum: Optional[pulumi.Input[str]] = None, min_target_capacity: Optional[pulumi.Input[str]] = None, minimum: Optional[pulumi.Input[str]] = None, operator: Optional[pulumi.Input[str]] = None, period: Optional[pulumi.Input[int]] = None, statistic: Optional[pulumi.Input[str]] = None, target: Optional[pulumi.Input[str]] = None, unit: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] metric_name: Metric to monitor by Azure metric display name. :param pulumi.Input[str] namespace: The namespace for the alarm's associated metric. Valid values: :param pulumi.Input[str] policy_name: The name of the policy. :param pulumi.Input[float] threshold: The value against which the specified statistic is compared. :param pulumi.Input[str] action_type: The type of action to perform for scaling. Valid values: `"adjustment"`, `"percentageAdjustment"`, `"setMaxTarget"`, `"setMinTarget"`, `"updateCapacity"`. :param pulumi.Input[str] adjustment: The number of instances to add/remove to/from the target capacity when scale is needed. :param pulumi.Input[int] cooldown: The amount of time, in seconds, after a scaling activity completes and before the next scaling activity can start. If this parameter is not specified, the default cooldown period for the group applies. :param pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingUpPolicyDimensionArgs']]] dimensions: A list of dimensions describing qualities of the metric. Required when `namespace` is defined AND not `"Microsoft.Compute"`. :param pulumi.Input[int] evaluation_periods: The number of periods over which data is compared to the specified threshold. :param pulumi.Input[str] max_target_capacity: . The number of the desired target (and maximum) capacity :param pulumi.Input[str] maximum: The maximal number of instances to have in the group. :param pulumi.Input[str] min_target_capacity: . The number of the desired target (and minimum) capacity :param pulumi.Input[str] minimum: The minimal number of instances to have in the group. :param pulumi.Input[str] operator: The operator to use in order to determine if the scaling policy is applicable. Valid values: `"gt"`, `"gte"`, `"lt"`, `"lte"`. :param pulumi.Input[int] period: The granularity, in seconds, of the returned datapoints. Period must be at least 60 seconds and must be a multiple of 60. :param pulumi.Input[str] statistic: The metric statistics to return. Valid values: `average`. :param pulumi.Input[str] target: The target number of instances to have in the group. :param pulumi.Input[str] unit: The unit for the alarm's associated metric. Valid values: `"percent`, `"seconds"`, `"microseconds"`, `"milliseconds"`, `"bytes"`, `"kilobytes"`, `"megabytes"`, `"gigabytes"`, `"terabytes"`, `"bits"`, `"kilobits"`, `"megabits"`, `"gigabits"`, `"terabits"`, `"count"`, `"bytes/second"`, `"kilobytes/second"`, `"megabytes/second"`, `"gigabytes/second"`, `"terabytes/second"`, `"bits/second"`, `"kilobits/second"`, `"megabits/second"`, `"gigabits/second"`, `"terabits/second"`, `"count/second"`, `"none"`. """ pulumi.set(__self__, "metric_name", metric_name) pulumi.set(__self__, "namespace", namespace) pulumi.set(__self__, "policy_name", policy_name) pulumi.set(__self__, "threshold", threshold) if action_type is not None: pulumi.set(__self__, "action_type", action_type) if adjustment is not None: pulumi.set(__self__, "adjustment", adjustment) if cooldown is not None: pulumi.set(__self__, "cooldown", cooldown) if dimensions is not None: pulumi.set(__self__, "dimensions", dimensions) if evaluation_periods is not None: pulumi.set(__self__, "evaluation_periods", evaluation_periods) if max_target_capacity is not None: pulumi.set(__self__, "max_target_capacity", max_target_capacity) if maximum is not None: pulumi.set(__self__, "maximum", maximum) if min_target_capacity is not None: pulumi.set(__self__, "min_target_capacity", min_target_capacity) if minimum is not None: pulumi.set(__self__, "minimum", minimum) if operator is not None: pulumi.set(__self__, "operator", operator) if period is not None: pulumi.set(__self__, "period", period) if statistic is not None: pulumi.set(__self__, "statistic", statistic) if target is not None: pulumi.set(__self__, "target", target) if unit is not None: pulumi.set(__self__, "unit", unit) @property @pulumi.getter(name="metricName") def metric_name(self) -> pulumi.Input[str]: """ Metric to monitor by Azure metric display name. """ return pulumi.get(self, "metric_name") @metric_name.setter def metric_name(self, value: pulumi.Input[str]): pulumi.set(self, "metric_name", value) @property @pulumi.getter def namespace(self) -> pulumi.Input[str]: """ The namespace for the alarm's associated metric. Valid values: """ return pulumi.get(self, "namespace") @namespace.setter def namespace(self, value: pulumi.Input[str]): pulumi.set(self, "namespace", value) @property @pulumi.getter(name="policyName") def policy_name(self) -> pulumi.Input[str]: """ The name of the policy. """ return pulumi.get(self, "policy_name") @policy_name.setter def policy_name(self, value: pulumi.Input[str]): pulumi.set(self, "policy_name", value) @property @pulumi.getter def threshold(self) -> pulumi.Input[float]: """ The value against which the specified statistic is compared. """ return pulumi.get(self, "threshold") @threshold.setter def threshold(self, value: pulumi.Input[float]): pulumi.set(self, "threshold", value) @property @pulumi.getter(name="actionType") def action_type(self) -> Optional[pulumi.Input[str]]: """ The type of action to perform for scaling. Valid values: `"adjustment"`, `"percentageAdjustment"`, `"setMaxTarget"`, `"setMinTarget"`, `"updateCapacity"`. """ return pulumi.get(self, "action_type") @action_type.setter def action_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "action_type", value) @property @pulumi.getter def adjustment(self) -> Optional[pulumi.Input[str]]: """ The number of instances to add/remove to/from the target capacity when scale is needed. """ return pulumi.get(self, "adjustment") @adjustment.setter def adjustment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "adjustment", value) @property @pulumi.getter def cooldown(self) -> Optional[pulumi.Input[int]]: """ The amount of time, in seconds, after a scaling activity completes and before the next scaling activity can start. If this parameter is not specified, the default cooldown period for the group applies. """ return pulumi.get(self, "cooldown") @cooldown.setter def cooldown(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "cooldown", value) @property @pulumi.getter def dimensions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingUpPolicyDimensionArgs']]]]: """ A list of dimensions describing qualities of the metric. Required when `namespace` is defined AND not `"Microsoft.Compute"`. """ return pulumi.get(self, "dimensions") @dimensions.setter def dimensions(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ElastigroupScalingUpPolicyDimensionArgs']]]]): pulumi.set(self, "dimensions", value) @property @pulumi.getter(name="evaluationPeriods") def evaluation_periods(self) -> Optional[pulumi.Input[int]]: """ The number of periods over which data is compared to the specified threshold. """ return pulumi.get(self, "evaluation_periods") @evaluation_periods.setter def evaluation_periods(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "evaluation_periods", value) @property @pulumi.getter(name="maxTargetCapacity") def max_target_capacity(self) -> Optional[pulumi.Input[str]]: """ . The number of the desired target (and maximum) capacity """ return pulumi.get(self, "max_target_capacity") @max_target_capacity.setter def max_target_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "max_target_capacity", value) @property @pulumi.getter def maximum(self) -> Optional[pulumi.Input[str]]: """ The maximal number of instances to have in the group. """ return pulumi.get(self, "maximum") @maximum.setter def maximum(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "maximum", value) @property @pulumi.getter(name="minTargetCapacity") def min_target_capacity(self) -> Optional[pulumi.Input[str]]: """ . The number of the desired target (and minimum) capacity """ return pulumi.get(self, "min_target_capacity") @min_target_capacity.setter def min_target_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "min_target_capacity", value) @property @pulumi.getter def minimum(self) -> Optional[pulumi.Input[str]]: """ The minimal number of instances to have in the group. """ return pulumi.get(self, "minimum") @minimum.setter def minimum(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "minimum", value) @property @pulumi.getter def operator(self) -> Optional[pulumi.Input[str]]: """ The operator to use in order to determine if the scaling policy is applicable. Valid values: `"gt"`, `"gte"`, `"lt"`, `"lte"`. """ return pulumi.get(self, "operator") @operator.setter def operator(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "operator", value) @property @pulumi.getter def period(self) -> Optional[pulumi.Input[int]]: """ The granularity, in seconds, of the returned datapoints. Period must be at least 60 seconds and must be a multiple of 60. """ return pulumi.get(self, "period") @period.setter def period(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "period", value) @property @pulumi.getter def statistic(self) -> Optional[pulumi.Input[str]]: """ The metric statistics to return. Valid values: `average`. """ return pulumi.get(self, "statistic") @statistic.setter def statistic(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "statistic", value) @property @pulumi.getter def target(self) -> Optional[pulumi.Input[str]]: """ The target number of instances to have in the group. """ return pulumi.get(self, "target") @target.setter def target(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "target", value) @property @pulumi.getter def unit(self) -> Optional[pulumi.Input[str]]: """ The unit for the alarm's associated metric. Valid values: `"percent`, `"seconds"`, `"microseconds"`, `"milliseconds"`, `"bytes"`, `"kilobytes"`, `"megabytes"`, `"gigabytes"`, `"terabytes"`, `"bits"`, `"kilobits"`, `"megabits"`, `"gigabits"`, `"terabits"`, `"count"`, `"bytes/second"`, `"kilobytes/second"`, `"megabytes/second"`, `"gigabytes/second"`, `"terabytes/second"`, `"bits/second"`, `"kilobits/second"`, `"megabits/second"`, `"gigabits/second"`, `"terabits/second"`, `"count/second"`, `"none"`. """ return pulumi.get(self, "unit") @unit.setter def unit(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "unit", value) @pulumi.input_type class ElastigroupScalingUpPolicyDimensionArgs: def __init__(__self__, *, name: pulumi.Input[str], value: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: The dimension name. :param pulumi.Input[str] value: The dimension value. """ pulumi.set(__self__, "name", name) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The dimension name. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ The dimension value. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class ElastigroupScheduledTaskArgs: def __init__(__self__, *, cron_expression: pulumi.Input[str], task_type: pulumi.Input[str], adjustment: Optional[pulumi.Input[str]] = None, adjustment_percentage: Optional[pulumi.Input[str]] = None, batch_size_percentage: Optional[pulumi.Input[str]] = None, grace_period: Optional[pulumi.Input[str]] = None, is_enabled: Optional[pulumi.Input[bool]] = None, scale_max_capacity: Optional[pulumi.Input[str]] = None, scale_min_capacity: Optional[pulumi.Input[str]] = None, scale_target_capacity: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] cron_expression: A valid cron expression (`* * * * *`). The cron is running in UTC time zone and is in Unix cron format Cron Expression Validator Script. :param pulumi.Input[str] task_type: The task type to run. Valid Values: `backup_ami`, `scale`, `scaleUp`, `roll`, `statefulUpdateCapacity`, `statefulRecycle`. :param pulumi.Input[str] adjustment: The number of instances to add/remove to/from the target capacity when scale is needed. :param pulumi.Input[str] adjustment_percentage: The percent of instances to add/remove to/from the target capacity when scale is needed. :param pulumi.Input[str] batch_size_percentage: Sets the percentage of the instances to deploy in each batch. :param pulumi.Input[str] grace_period: Sets the grace period for new instances to become healthy. :param pulumi.Input[bool] is_enabled: Describes whether the task is enabled. When true the task should run when false it should not run. :param pulumi.Input[str] scale_max_capacity: The max capacity of the group. Required when ‘task_type' is ‘scale'. :param pulumi.Input[str] scale_min_capacity: The min capacity of the group. Should be used when choosing ‘task_type' of ‘scale'. :param pulumi.Input[str] scale_target_capacity: The target capacity of the group. Should be used when choosing ‘task_type' of ‘scale'. """ pulumi.set(__self__, "cron_expression", cron_expression) pulumi.set(__self__, "task_type", task_type) if adjustment is not None: pulumi.set(__self__, "adjustment", adjustment) if adjustment_percentage is not None: pulumi.set(__self__, "adjustment_percentage", adjustment_percentage) if batch_size_percentage is not None: pulumi.set(__self__, "batch_size_percentage", batch_size_percentage) if grace_period is not None: pulumi.set(__self__, "grace_period", grace_period) if is_enabled is not None: pulumi.set(__self__, "is_enabled", is_enabled) if scale_max_capacity is not None: pulumi.set(__self__, "scale_max_capacity", scale_max_capacity) if scale_min_capacity is not None: pulumi.set(__self__, "scale_min_capacity", scale_min_capacity) if scale_target_capacity is not None: pulumi.set(__self__, "scale_target_capacity", scale_target_capacity) @property @pulumi.getter(name="cronExpression") def cron_expression(self) -> pulumi.Input[str]: """ A valid cron expression (`* * * * *`). The cron is running in UTC time zone and is in Unix cron format Cron Expression Validator Script. """ return pulumi.get(self, "cron_expression") @cron_expression.setter def cron_expression(self, value: pulumi.Input[str]): pulumi.set(self, "cron_expression", value) @property @pulumi.getter(name="taskType") def task_type(self) -> pulumi.Input[str]: """ The task type to run. Valid Values: `backup_ami`, `scale`, `scaleUp`, `roll`, `statefulUpdateCapacity`, `statefulRecycle`. """ return pulumi.get(self, "task_type") @task_type.setter def task_type(self, value: pulumi.Input[str]): pulumi.set(self, "task_type", value) @property @pulumi.getter def adjustment(self) -> Optional[pulumi.Input[str]]: """ The number of instances to add/remove to/from the target capacity when scale is needed. """ return pulumi.get(self, "adjustment") @adjustment.setter def adjustment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "adjustment", value) @property @pulumi.getter(name="adjustmentPercentage") def adjustment_percentage(self) -> Optional[pulumi.Input[str]]: """ The percent of instances to add/remove to/from the target capacity when scale is needed. """ return pulumi.get(self, "adjustment_percentage") @adjustment_percentage.setter def adjustment_percentage(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "adjustment_percentage", value) @property @pulumi.getter(name="batchSizePercentage") def batch_size_percentage(self) -> Optional[pulumi.Input[str]]: """ Sets the percentage of the instances to deploy in each batch. """ return pulumi.get(self, "batch_size_percentage") @batch_size_percentage.setter def batch_size_percentage(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "batch_size_percentage", value) @property @pulumi.getter(name="gracePeriod") def grace_period(self) -> Optional[pulumi.Input[str]]: """ Sets the grace period for new instances to become healthy. """ return pulumi.get(self, "grace_period") @grace_period.setter def grace_period(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "grace_period", value) @property @pulumi.getter(name="isEnabled") def is_enabled(self) -> Optional[pulumi.Input[bool]]: """ Describes whether the task is enabled. When true the task should run when false it should not run. """ return pulumi.get(self, "is_enabled") @is_enabled.setter def is_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_enabled", value) @property @pulumi.getter(name="scaleMaxCapacity") def scale_max_capacity(self) -> Optional[pulumi.Input[str]]: """ The max capacity of the group. Required when ‘task_type' is ‘scale'. """ return pulumi.get(self, "scale_max_capacity") @scale_max_capacity.setter def scale_max_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_max_capacity", value) @property @pulumi.getter(name="scaleMinCapacity") def scale_min_capacity(self) -> Optional[pulumi.Input[str]]: """ The min capacity of the group. Should be used when choosing ‘task_type' of ‘scale'. """ return pulumi.get(self, "scale_min_capacity") @scale_min_capacity.setter def scale_min_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_min_capacity", value) @property @pulumi.getter(name="scaleTargetCapacity") def scale_target_capacity(self) -> Optional[pulumi.Input[str]]: """ The target capacity of the group. Should be used when choosing ‘task_type' of ‘scale'. """ return pulumi.get(self, "scale_target_capacity") @scale_target_capacity.setter def scale_target_capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "scale_target_capacity", value) @pulumi.input_type class ElastigroupStrategyArgs: def __init__(__self__, *, draining_timeout: Optional[pulumi.Input[int]] = None, low_priority_percentage: Optional[pulumi.Input[int]] = None, od_count: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] draining_timeout: Time (seconds) to allow the instance to be drained from incoming TCP connections and detached from MLB before terminating it during a scale-down operation. :param pulumi.Input[int] low_priority_percentage: Percentage of Low Priority instances to maintain. Required if `od_count` is not specified. :param pulumi.Input[int] od_count: Number of On-Demand instances to maintain. Required if low_priority_percentage is not specified. """ if draining_timeout is not None: pulumi.set(__self__, "draining_timeout", draining_timeout) if low_priority_percentage is not None: pulumi.set(__self__, "low_priority_percentage", low_priority_percentage) if od_count is not None: pulumi.set(__self__, "od_count", od_count) @property @pulumi.getter(name="drainingTimeout") def draining_timeout(self) -> Optional[pulumi.Input[int]]: """ Time (seconds) to allow the instance to be drained from incoming TCP connections and detached from MLB before terminating it during a scale-down operation. """ return pulumi.get(self, "draining_timeout") @draining_timeout.setter def draining_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "draining_timeout", value) @property @pulumi.getter(name="lowPriorityPercentage") def low_priority_percentage(self) -> Optional[pulumi.Input[int]]: """ Percentage of Low Priority instances to maintain. Required if `od_count` is not specified. """ return pulumi.get(self, "low_priority_percentage") @low_priority_percentage.setter def low_priority_percentage(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "low_priority_percentage", value) @property @pulumi.getter(name="odCount") def od_count(self) -> Optional[pulumi.Input[int]]: """ Number of On-Demand instances to maintain. Required if low_priority_percentage is not specified. """ return pulumi.get(self, "od_count") @od_count.setter def od_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "od_count", value) @pulumi.input_type class ElastigroupUpdatePolicyArgs: def __init__(__self__, *, should_roll: pulumi.Input[bool], roll_config: Optional[pulumi.Input['ElastigroupUpdatePolicyRollConfigArgs']] = None): """ :param pulumi.Input[bool] should_roll: Sets the enablement of the roll option. :param pulumi.Input['ElastigroupUpdatePolicyRollConfigArgs'] roll_config: While used, you can control whether the group should perform a deployment after an update to the configuration. """ pulumi.set(__self__, "should_roll", should_roll) if roll_config is not None: pulumi.set(__self__, "roll_config", roll_config) @property @pulumi.getter(name="shouldRoll") def should_roll(self) -> pulumi.Input[bool]: """ Sets the enablement of the roll option. """ return pulumi.get(self, "should_roll") @should_roll.setter def should_roll(self, value: pulumi.Input[bool]): pulumi.set(self, "should_roll", value) @property @pulumi.getter(name="rollConfig") def roll_config(self) -> Optional[pulumi.Input['ElastigroupUpdatePolicyRollConfigArgs']]: """ While used, you can control whether the group should perform a deployment after an update to the configuration. """ return pulumi.get(self, "roll_config") @roll_config.setter def roll_config(self, value: Optional[pulumi.Input['ElastigroupUpdatePolicyRollConfigArgs']]): pulumi.set(self, "roll_config", value) @pulumi.input_type class ElastigroupUpdatePolicyRollConfigArgs: def __init__(__self__, *, batch_size_percentage: pulumi.Input[int], grace_period: Optional[pulumi.Input[int]] = None, health_check_type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[int] batch_size_percentage: Sets the percentage of the instances to deploy in each batch. :param pulumi.Input[int] grace_period: Sets the grace period for new instances to become healthy. :param pulumi.Input[str] health_check_type: Sets the health check type to use. Valid values: `"INSTANCE_STATE"`, `"NONE"`. """ pulumi.set(__self__, "batch_size_percentage", batch_size_percentage) if grace_period is not None: pulumi.set(__self__, "grace_period", grace_period) if health_check_type is not None: pulumi.set(__self__, "health_check_type", health_check_type) @property @pulumi.getter(name="batchSizePercentage") def batch_size_percentage(self) -> pulumi.Input[int]: """ Sets the percentage of the instances to deploy in each batch. """ return pulumi.get(self, "batch_size_percentage") @batch_size_percentage.setter def batch_size_percentage(self, value: pulumi.Input[int]): pulumi.set(self, "batch_size_percentage", value) @property @pulumi.getter(name="gracePeriod") def grace_period(self) -> Optional[pulumi.Input[int]]: """ Sets the grace period for new instances to become healthy. """ return pulumi.get(self, "grace_period") @grace_period.setter def grace_period(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "grace_period", value) @property @pulumi.getter(name="healthCheckType") def health_check_type(self) -> Optional[pulumi.Input[str]]: """ Sets the health check type to use. Valid values: `"INSTANCE_STATE"`, `"NONE"`. """ return pulumi.get(self, "health_check_type") @health_check_type.setter def health_check_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "health_check_type", value) @pulumi.input_type class OceanAutoscalerArgs: def __init__(__self__, *, autoscale_down: Optional[pulumi.Input['OceanAutoscalerAutoscaleDownArgs']] = None, autoscale_headroom: Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomArgs']] = None, autoscale_is_enabled: Optional[pulumi.Input[bool]] = None, resource_limits: Optional[pulumi.Input['OceanAutoscalerResourceLimitsArgs']] = None): """ :param pulumi.Input['OceanAutoscalerAutoscaleDownArgs'] autoscale_down: Auto Scaling scale down operations. :param pulumi.Input['OceanAutoscalerAutoscaleHeadroomArgs'] autoscale_headroom: Spare Resource Capacity Management feature enables fast assignment of Pods without having to wait for new resources to be launched. :param pulumi.Input[bool] autoscale_is_enabled: Enable the Ocean Kubernetes Autoscaler. :param pulumi.Input['OceanAutoscalerResourceLimitsArgs'] resource_limits: Optionally set upper and lower bounds on the resource usage of the cluster. """ if autoscale_down is not None: pulumi.set(__self__, "autoscale_down", autoscale_down) if autoscale_headroom is not None: pulumi.set(__self__, "autoscale_headroom", autoscale_headroom) if autoscale_is_enabled is not None: pulumi.set(__self__, "autoscale_is_enabled", autoscale_is_enabled) if resource_limits is not None: pulumi.set(__self__, "resource_limits", resource_limits) @property @pulumi.getter(name="autoscaleDown") def autoscale_down(self) -> Optional[pulumi.Input['OceanAutoscalerAutoscaleDownArgs']]: """ Auto Scaling scale down operations. """ return pulumi.get(self, "autoscale_down") @autoscale_down.setter def autoscale_down(self, value: Optional[pulumi.Input['OceanAutoscalerAutoscaleDownArgs']]): pulumi.set(self, "autoscale_down", value) @property @pulumi.getter(name="autoscaleHeadroom") def autoscale_headroom(self) -> Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomArgs']]: """ Spare Resource Capacity Management feature enables fast assignment of Pods without having to wait for new resources to be launched. """ return pulumi.get(self, "autoscale_headroom") @autoscale_headroom.setter def autoscale_headroom(self, value: Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomArgs']]): pulumi.set(self, "autoscale_headroom", value) @property @pulumi.getter(name="autoscaleIsEnabled") def autoscale_is_enabled(self) -> Optional[pulumi.Input[bool]]: """ Enable the Ocean Kubernetes Autoscaler. """ return pulumi.get(self, "autoscale_is_enabled") @autoscale_is_enabled.setter def autoscale_is_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "autoscale_is_enabled", value) @property @pulumi.getter(name="resourceLimits") def resource_limits(self) -> Optional[pulumi.Input['OceanAutoscalerResourceLimitsArgs']]: """ Optionally set upper and lower bounds on the resource usage of the cluster. """ return pulumi.get(self, "resource_limits") @resource_limits.setter def resource_limits(self, value: Optional[pulumi.Input['OceanAutoscalerResourceLimitsArgs']]): pulumi.set(self, "resource_limits", value) @pulumi.input_type class OceanAutoscalerAutoscaleDownArgs: def __init__(__self__, *, max_scale_down_percentage: Optional[pulumi.Input[float]] = None): """ :param pulumi.Input[float] max_scale_down_percentage: Would represent the maximum % to scale-down. """ if max_scale_down_percentage is not None: pulumi.set(__self__, "max_scale_down_percentage", max_scale_down_percentage) @property @pulumi.getter(name="maxScaleDownPercentage") def max_scale_down_percentage(self) -> Optional[pulumi.Input[float]]: """ Would represent the maximum % to scale-down. """ return pulumi.get(self, "max_scale_down_percentage") @max_scale_down_percentage.setter def max_scale_down_percentage(self, value: Optional[pulumi.Input[float]]): pulumi.set(self, "max_scale_down_percentage", value) @pulumi.input_type class OceanAutoscalerAutoscaleHeadroomArgs: def __init__(__self__, *, automatic: Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomAutomaticArgs']] = None): """ :param pulumi.Input['OceanAutoscalerAutoscaleHeadroomAutomaticArgs'] automatic: Automatic headroom configuration. """ if automatic is not None: pulumi.set(__self__, "automatic", automatic) @property @pulumi.getter def automatic(self) -> Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomAutomaticArgs']]: """ Automatic headroom configuration. """ return pulumi.get(self, "automatic") @automatic.setter def automatic(self, value: Optional[pulumi.Input['OceanAutoscalerAutoscaleHeadroomAutomaticArgs']]): pulumi.set(self, "automatic", value) @pulumi.input_type class OceanAutoscalerAutoscaleHeadroomAutomaticArgs: def __init__(__self__, *, is_enabled: Optional[pulumi.Input[bool]] = None, percentage: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[bool] is_enabled: Enable automatic headroom. When set to `true`, Ocean configures and optimizes headroom automatically. :param pulumi.Input[int] percentage: Optionally set a number between 0-100 to control the percentage of total cluster resources dedicated to headroom. Relevant when `isEnabled` is toggled on. """ if is_enabled is not None: pulumi.set(__self__, "is_enabled", is_enabled) if percentage is not None: pulumi.set(__self__, "percentage", percentage) @property @pulumi.getter(name="isEnabled") def is_enabled(self) -> Optional[pulumi.Input[bool]]: """ Enable automatic headroom. When set to `true`, Ocean configures and optimizes headroom automatically. """ return pulumi.get(self, "is_enabled") @is_enabled.setter def is_enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_enabled", value) @property @pulumi.getter def percentage(self) -> Optional[pulumi.Input[int]]: """ Optionally set a number between 0-100 to control the percentage of total cluster resources dedicated to headroom. Relevant when `isEnabled` is toggled on. """ return pulumi.get(self, "percentage") @percentage.setter def percentage(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "percentage", value) @pulumi.input_type class OceanAutoscalerResourceLimitsArgs: def __init__(__self__, *, max_memory_gib: Optional[pulumi.Input[int]] = None, max_vcpu: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] max_memory_gib: The maximum memory in GiB units that can be allocated to the cluster. :param pulumi.Input[int] max_vcpu: The maximum cpu in vCpu units that can be allocated to the cluster. """ if max_memory_gib is not None: pulumi.set(__self__, "max_memory_gib", max_memory_gib) if max_vcpu is not None: pulumi.set(__self__, "max_vcpu", max_vcpu) @property @pulumi.getter(name="maxMemoryGib") def max_memory_gib(self) -> Optional[pulumi.Input[int]]: """ The maximum memory in GiB units that can be allocated to the cluster. """ return pulumi.get(self, "max_memory_gib") @max_memory_gib.setter def max_memory_gib(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_memory_gib", value) @property @pulumi.getter(name="maxVcpu") def max_vcpu(self) -> Optional[pulumi.Input[int]]: """ The maximum cpu in vCpu units that can be allocated to the cluster. """ return pulumi.get(self, "max_vcpu") @max_vcpu.setter def max_vcpu(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_vcpu", value) @pulumi.input_type class OceanExtensionArgs: def __init__(__self__, *, api_version: Optional[pulumi.Input[str]] = None, minor_version_auto_upgrade: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, publisher: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] api_version: API version of the extension. :param pulumi.Input[bool] minor_version_auto_upgrade: Toggles whether auto upgrades are allowed. :param pulumi.Input[str] name: Name of the Load Balancer. :param pulumi.Input[str] publisher: Image publisher. :param pulumi.Input[str] type: The type of load balancer. Supported value: `loadBalancer` """ if api_version is not None: pulumi.set(__self__, "api_version", api_version) if minor_version_auto_upgrade is not None: pulumi.set(__self__, "minor_version_auto_upgrade", minor_version_auto_upgrade) if name is not None: pulumi.set(__self__, "name", name) if publisher is not None: pulumi.set(__self__, "publisher", publisher) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[pulumi.Input[str]]: """ API version of the extension. """ return pulumi.get(self, "api_version") @api_version.setter def api_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "api_version", value) @property @pulumi.getter(name="minorVersionAutoUpgrade") def minor_version_auto_upgrade(self) -> Optional[pulumi.Input[bool]]: """ Toggles whether auto upgrades are allowed. """ return pulumi.get(self, "minor_version_auto_upgrade") @minor_version_auto_upgrade.setter def minor_version_auto_upgrade(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "minor_version_auto_upgrade", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the Load Balancer. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def publisher(self) -> Optional[pulumi.Input[str]]: """ Image publisher. """ return pulumi.get(self, "publisher") @publisher.setter def publisher(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "publisher", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The type of load balancer. Supported value: `loadBalancer` """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class OceanHealthArgs: def __init__(__self__, *, grace_period: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] grace_period: The amount of time to wait, in seconds, from the moment the instance has launched before monitoring its health checks. """ if grace_period is not None: pulumi.set(__self__, "grace_period", grace_period) @property @pulumi.getter(name="gracePeriod") def grace_period(self) -> Optional[pulumi.Input[int]]: """ The amount of time to wait, in seconds, from the moment the instance has launched before monitoring its health checks. """ return pulumi.get(self, "grace_period") @grace_period.setter def grace_period(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "grace_period", value) @pulumi.input_type class OceanImageArgs: def __init__(__self__, *, marketplaces: Optional[pulumi.Input[Sequence[pulumi.Input['OceanImageMarketplaceArgs']]]] = None): """ :param pulumi.Input[Sequence[pulumi.Input['OceanImageMarketplaceArgs']]] marketplaces: Select an image from Azure's Marketplace image catalogue. """ if marketplaces is not None: pulumi.set(__self__, "marketplaces", marketplaces) @property @pulumi.getter def marketplaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanImageMarketplaceArgs']]]]: """ Select an image from Azure's Marketplace image catalogue. """ return pulumi.get(self, "marketplaces") @marketplaces.setter def marketplaces(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanImageMarketplaceArgs']]]]): pulumi.set(self, "marketplaces", value) @pulumi.input_type class OceanImageMarketplaceArgs: def __init__(__self__, *, offer: Optional[pulumi.Input[str]] = None, publisher: Optional[pulumi.Input[str]] = None, sku: Optional[pulumi.Input[str]] = None, version: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] offer: Image name. :param pulumi.Input[str] publisher: Image publisher. :param pulumi.Input[str] sku: Image Stock Keeping Unit (which is the specific version of the image). :param pulumi.Input[str] version: Image version. """ if offer is not None: pulumi.set(__self__, "offer", offer) if publisher is not None: pulumi.set(__self__, "publisher", publisher) if sku is not None: pulumi.set(__self__, "sku", sku) if version is not None: pulumi.set(__self__, "version", version) @property @pulumi.getter def offer(self) -> Optional[pulumi.Input[str]]: """ Image name. """ return pulumi.get(self, "offer") @offer.setter def offer(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "offer", value) @property @pulumi.getter def publisher(self) -> Optional[pulumi.Input[str]]: """ Image publisher. """ return pulumi.get(self, "publisher") @publisher.setter def publisher(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "publisher", value) @property @pulumi.getter def sku(self) -> Optional[pulumi.Input[str]]: """ Image Stock Keeping Unit (which is the specific version of the image). """ return pulumi.get(self, "sku") @sku.setter def sku(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sku", value) @property @pulumi.getter def version(self) -> Optional[pulumi.Input[str]]: """ Image version. """ return pulumi.get(self, "version") @version.setter def version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "version", value) @pulumi.input_type class OceanLoadBalancerArgs: def __init__(__self__, *, backend_pool_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, load_balancer_sku: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[Sequence[pulumi.Input[str]]] backend_pool_names: Names of the Backend Pools to register the Cluster VMs to. Each Backend Pool is a separate load balancer. :param pulumi.Input[str] load_balancer_sku: Supported values: `Standard`, `Basic`. :param pulumi.Input[str] name: Name of the Load Balancer. :param pulumi.Input[str] resource_group_name: The Resource Group name of the Load Balancer. :param pulumi.Input[str] type: The type of load balancer. Supported value: `loadBalancer` """ if backend_pool_names is not None: pulumi.set(__self__, "backend_pool_names", backend_pool_names) if load_balancer_sku is not None: pulumi.set(__self__, "load_balancer_sku", load_balancer_sku) if name is not None: pulumi.set(__self__, "name", name) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="backendPoolNames") def backend_pool_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Names of the Backend Pools to register the Cluster VMs to. Each Backend Pool is a separate load balancer. """ return pulumi.get(self, "backend_pool_names") @backend_pool_names.setter def backend_pool_names(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "backend_pool_names", value) @property @pulumi.getter(name="loadBalancerSku") def load_balancer_sku(self) -> Optional[pulumi.Input[str]]: """ Supported values: `Standard`, `Basic`. """ return pulumi.get(self, "load_balancer_sku") @load_balancer_sku.setter def load_balancer_sku(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "load_balancer_sku", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the Load Balancer. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[pulumi.Input[str]]: """ The Resource Group name of the Load Balancer. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The type of load balancer. Supported value: `loadBalancer` """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class OceanManagedServiceIdentityArgs: def __init__(__self__, *, name: pulumi.Input[str], resource_group_name: pulumi.Input[str]): """ :param pulumi.Input[str] name: Name of the Load Balancer. :param pulumi.Input[str] resource_group_name: The Resource Group name of the Load Balancer. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "resource_group_name", resource_group_name) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ Name of the Load Balancer. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The Resource Group name of the Load Balancer. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @pulumi.input_type class OceanNetworkArgs: def __init__(__self__, *, network_interfaces: Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceArgs']]]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, virtual_network_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceArgs']]] network_interfaces: A list of virtual network interfaces. The publicIpSku must be identical between all the network interfaces. One network interface must be set as the primary. :param pulumi.Input[str] resource_group_name: The Resource Group name of the Load Balancer. :param pulumi.Input[str] virtual_network_name: Virtual network. """ if network_interfaces is not None: pulumi.set(__self__, "network_interfaces", network_interfaces) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if virtual_network_name is not None: pulumi.set(__self__, "virtual_network_name", virtual_network_name) @property @pulumi.getter(name="networkInterfaces") def network_interfaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceArgs']]]]: """ A list of virtual network interfaces. The publicIpSku must be identical between all the network interfaces. One network interface must be set as the primary. """ return pulumi.get(self, "network_interfaces") @network_interfaces.setter def network_interfaces(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceArgs']]]]): pulumi.set(self, "network_interfaces", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[pulumi.Input[str]]: """ The Resource Group name of the Load Balancer. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="virtualNetworkName") def virtual_network_name(self) -> Optional[pulumi.Input[str]]: """ Virtual network. """ return pulumi.get(self, "virtual_network_name") @virtual_network_name.setter def virtual_network_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "virtual_network_name", value) @pulumi.input_type class OceanNetworkNetworkInterfaceArgs: def __init__(__self__, *, additional_ip_configs: Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceAdditionalIpConfigArgs']]]] = None, assign_public_ip: Optional[pulumi.Input[bool]] = None, is_primary: Optional[pulumi.Input[bool]] = None, security_group: Optional[pulumi.Input['OceanNetworkNetworkInterfaceSecurityGroupArgs']] = None, subnet_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceAdditionalIpConfigArgs']]] additional_ip_configs: Additional configuration of network interface. The name fields between all the `additional_ip_config` must be unique. :param pulumi.Input[bool] assign_public_ip: Assign public IP. :param pulumi.Input[bool] is_primary: Defines whether the network interface is primary or not. :param pulumi.Input[str] subnet_name: Subnet name. """ if additional_ip_configs is not None: pulumi.set(__self__, "additional_ip_configs", additional_ip_configs) if assign_public_ip is not None: pulumi.set(__self__, "assign_public_ip", assign_public_ip) if is_primary is not None: pulumi.set(__self__, "is_primary", is_primary) if security_group is not None: pulumi.set(__self__, "security_group", security_group) if subnet_name is not None: pulumi.set(__self__, "subnet_name", subnet_name) @property @pulumi.getter(name="additionalIpConfigs") def additional_ip_configs(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceAdditionalIpConfigArgs']]]]: """ Additional configuration of network interface. The name fields between all the `additional_ip_config` must be unique. """ return pulumi.get(self, "additional_ip_configs") @additional_ip_configs.setter def additional_ip_configs(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanNetworkNetworkInterfaceAdditionalIpConfigArgs']]]]): pulumi.set(self, "additional_ip_configs", value) @property @pulumi.getter(name="assignPublicIp") def assign_public_ip(self) -> Optional[pulumi.Input[bool]]: """ Assign public IP. """ return pulumi.get(self, "assign_public_ip") @assign_public_ip.setter def assign_public_ip(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "assign_public_ip", value) @property @pulumi.getter(name="isPrimary") def is_primary(self) -> Optional[pulumi.Input[bool]]: """ Defines whether the network interface is primary or not. """ return pulumi.get(self, "is_primary") @is_primary.setter def is_primary(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_primary", value) @property @pulumi.getter(name="securityGroup") def security_group(self) -> Optional[pulumi.Input['OceanNetworkNetworkInterfaceSecurityGroupArgs']]: return pulumi.get(self, "security_group") @security_group.setter def security_group(self, value: Optional[pulumi.Input['OceanNetworkNetworkInterfaceSecurityGroupArgs']]): pulumi.set(self, "security_group", value) @property @pulumi.getter(name="subnetName") def subnet_name(self) -> Optional[pulumi.Input[str]]: """ Subnet name. """ return pulumi.get(self, "subnet_name") @subnet_name.setter def subnet_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "subnet_name", value) @pulumi.input_type class OceanNetworkNetworkInterfaceAdditionalIpConfigArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None, private_ip_version: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: Name of the Load Balancer. :param pulumi.Input[str] private_ip_version: Supported values: `IPv4`, `IPv6`. """ if name is not None: pulumi.set(__self__, "name", name) if private_ip_version is not None: pulumi.set(__self__, "private_ip_version", private_ip_version) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the Load Balancer. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="privateIpVersion") def private_ip_version(self) -> Optional[pulumi.Input[str]]: """ Supported values: `IPv4`, `IPv6`. """ return pulumi.get(self, "private_ip_version") @private_ip_version.setter def private_ip_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_ip_version", value) @pulumi.input_type class OceanNetworkNetworkInterfaceSecurityGroupArgs: def __init__(__self__, *, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: Name of the Load Balancer. :param pulumi.Input[str] resource_group_name: The Resource Group name of the Load Balancer. """ if name is not None: pulumi.set(__self__, "name", name) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the Load Balancer. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[pulumi.Input[str]]: """ The Resource Group name of the Load Balancer. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_group_name", value) @pulumi.input_type class OceanOsDiskArgs: def __init__(__self__, *, size_gb: pulumi.Input[int], type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[int] size_gb: The size of the OS disk in GB. :param pulumi.Input[str] type: The type of load balancer. Supported value: `loadBalancer` """ pulumi.set(__self__, "size_gb", size_gb) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="sizeGb") def size_gb(self) -> pulumi.Input[int]: """ The size of the OS disk in GB. """ return pulumi.get(self, "size_gb") @size_gb.setter def size_gb(self, value: pulumi.Input[int]): pulumi.set(self, "size_gb", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The type of load balancer. Supported value: `loadBalancer` """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class OceanStrategyArgs: def __init__(__self__, *, fallback_to_ondemand: Optional[pulumi.Input[bool]] = None, spot_percentage: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[bool] fallback_to_ondemand: If no spot instance markets are available, enable Ocean to launch on-demand instances instead. :param pulumi.Input[int] spot_percentage: Percentage of Spot VMs to maintain. """ if fallback_to_ondemand is not None: pulumi.set(__self__, "fallback_to_ondemand", fallback_to_ondemand) if spot_percentage is not None: pulumi.set(__self__, "spot_percentage", spot_percentage) @property @pulumi.getter(name="fallbackToOndemand") def fallback_to_ondemand(self) -> Optional[pulumi.Input[bool]]: """ If no spot instance markets are available, enable Ocean to launch on-demand instances instead. """ return pulumi.get(self, "fallback_to_ondemand") @fallback_to_ondemand.setter def fallback_to_ondemand(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "fallback_to_ondemand", value) @property @pulumi.getter(name="spotPercentage") def spot_percentage(self) -> Optional[pulumi.Input[int]]: """ Percentage of Spot VMs to maintain. """ return pulumi.get(self, "spot_percentage") @spot_percentage.setter def spot_percentage(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "spot_percentage", value) @pulumi.input_type class OceanTagArgs: def __init__(__self__, *, key: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] key: Tag key. :param pulumi.Input[str] value: Tag value. """ if key is not None: pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> Optional[pulumi.Input[str]]: """ Tag key. """ return pulumi.get(self, "key") @key.setter def key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ Tag value. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class OceanVirtualNodeGroupAutoscaleArgs: def __init__(__self__, *, autoscale_headroom: Optional[pulumi.Input['OceanVirtualNodeGroupAutoscaleAutoscaleHeadroomArgs']] = None): if autoscale_headroom is not None: pulumi.set(__self__, "autoscale_headroom", autoscale_headroom) @property @pulumi.getter(name="autoscaleHeadroom") def autoscale_headroom(self) -> Optional[pulumi.Input['OceanVirtualNodeGroupAutoscaleAutoscaleHeadroomArgs']]: return pulumi.get(self, "autoscale_headroom") @autoscale_headroom.setter def autoscale_headroom(self, value: Optional[pulumi.Input['OceanVirtualNodeGroupAutoscaleAutoscaleHeadroomArgs']]): pulumi.set(self, "autoscale_headroom", value) @pulumi.input_type class OceanVirtualNodeGroupAutoscaleAutoscaleHeadroomArgs: def __init__(__self__, *, num_of_units: pulumi.Input[int], cpu_per_unit: Optional[pulumi.Input[int]] = None, gpu_per_unit: Optional[pulumi.Input[int]] = None, memory_per_unit: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] num_of_units: The number of headroom units to maintain, where each unit has the defined CPU, memory and GPU. :param pulumi.Input[int] cpu_per_unit: Configure the number of CPUs to allocate for the headroom. CPUs are denoted in millicores, where 1000 millicores = 1 vCPU. :param pulumi.Input[int] gpu_per_unit: How many GPU cores should be allocated for headroom unit. :param pulumi.Input[int] memory_per_unit: Configure the amount of memory (MiB) to allocate the headroom. """ pulumi.set(__self__, "num_of_units", num_of_units) if cpu_per_unit is not None: pulumi.set(__self__, "cpu_per_unit", cpu_per_unit) if gpu_per_unit is not None: pulumi.set(__self__, "gpu_per_unit", gpu_per_unit) if memory_per_unit is not None: pulumi.set(__self__, "memory_per_unit", memory_per_unit) @property @pulumi.getter(name="numOfUnits") def num_of_units(self) -> pulumi.Input[int]: """ The number of headroom units to maintain, where each unit has the defined CPU, memory and GPU. """ return pulumi.get(self, "num_of_units") @num_of_units.setter def num_of_units(self, value: pulumi.Input[int]): pulumi.set(self, "num_of_units", value) @property @pulumi.getter(name="cpuPerUnit") def cpu_per_unit(self) -> Optional[pulumi.Input[int]]: """ Configure the number of CPUs to allocate for the headroom. CPUs are denoted in millicores, where 1000 millicores = 1 vCPU. """ return pulumi.get(self, "cpu_per_unit") @cpu_per_unit.setter def cpu_per_unit(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "cpu_per_unit", value) @property @pulumi.getter(name="gpuPerUnit") def gpu_per_unit(self) -> Optional[pulumi.Input[int]]: """ How many GPU cores should be allocated for headroom unit. """ return pulumi.get(self, "gpu_per_unit") @gpu_per_unit.setter def gpu_per_unit(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "gpu_per_unit", value) @property @pulumi.getter(name="memoryPerUnit") def memory_per_unit(self) -> Optional[pulumi.Input[int]]: """ Configure the amount of memory (MiB) to allocate the headroom. """ return pulumi.get(self, "memory_per_unit") @memory_per_unit.setter def memory_per_unit(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "memory_per_unit", value) @pulumi.input_type class OceanVirtualNodeGroupLabelArgs: def __init__(__self__, *, key: pulumi.Input[str], value: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] key: Tag Key for Vms in the cluster. :param pulumi.Input[str] value: Tag Value for VMs in the cluster. """ pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: """ Tag Key for Vms in the cluster. """ 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) -> Optional[pulumi.Input[str]]: """ Tag Value for VMs in the cluster. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class OceanVirtualNodeGroupLaunchSpecificationArgs: def __init__(__self__, *, os_disk: Optional[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs']] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationTagArgs']]]] = None): """ :param pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs'] os_disk: Specify OS disk specification other than default. :param pulumi.Input[Sequence[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationTagArgs']]] tags: Additional key-value pairs to be used to tag the VMs in the virtual node group. """ if os_disk is not None: pulumi.set(__self__, "os_disk", os_disk) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="osDisk") def os_disk(self) -> Optional[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs']]: """ Specify OS disk specification other than default. """ return pulumi.get(self, "os_disk") @os_disk.setter def os_disk(self, value: Optional[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs']]): pulumi.set(self, "os_disk", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationTagArgs']]]]: """ Additional key-value pairs to be used to tag the VMs in the virtual node group. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanVirtualNodeGroupLaunchSpecificationTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class OceanVirtualNodeGroupLaunchSpecificationOsDiskArgs: def __init__(__self__, *, size_gb: pulumi.Input[int], type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[int] size_gb: The size of the OS disk in GB, Required if dataDisks is specified. :param pulumi.Input[str] type: The type of the OS disk. Valid values: `"Standard_LRS"`, `"Premium_LRS"`, `"StandardSSD_LRS"`. """ pulumi.set(__self__, "size_gb", size_gb) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="sizeGb") def size_gb(self) -> pulumi.Input[int]: """ The size of the OS disk in GB, Required if dataDisks is specified. """ return pulumi.get(self, "size_gb") @size_gb.setter def size_gb(self, value: pulumi.Input[int]): pulumi.set(self, "size_gb", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The type of the OS disk. Valid values: `"Standard_LRS"`, `"Premium_LRS"`, `"StandardSSD_LRS"`. """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class OceanVirtualNodeGroupLaunchSpecificationTagArgs: def __init__(__self__, *, key: Optional[pulumi.Input[str]] = None, value: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] key: Tag Key for Vms in the cluster. :param pulumi.Input[str] value: Tag Value for VMs in the cluster. """ if key is not None: pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> Optional[pulumi.Input[str]]: """ Tag Key for Vms in the cluster. """ return pulumi.get(self, "key") @key.setter def key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> Optional[pulumi.Input[str]]: """ Tag Value for VMs in the cluster. """ return pulumi.get(self, "value") @value.setter def value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "value", value) @pulumi.input_type class OceanVirtualNodeGroupResourceLimitArgs: def __init__(__self__, *, max_instance_count: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] max_instance_count: Option to set a maximum number of instances per virtual node group. If set, value must be greater than or equal to 0. """ if max_instance_count is not None: pulumi.set(__self__, "max_instance_count", max_instance_count) @property @pulumi.getter(name="maxInstanceCount") def max_instance_count(self) -> Optional[pulumi.Input[int]]: """ Option to set a maximum number of instances per virtual node group. If set, value must be greater than or equal to 0. """ return pulumi.get(self, "max_instance_count") @max_instance_count.setter def max_instance_count(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "max_instance_count", value) @pulumi.input_type class OceanVirtualNodeGroupTaintArgs: def __init__(__self__, *, effect: pulumi.Input[str], key: pulumi.Input[str], value: pulumi.Input[str]): """ :param pulumi.Input[str] effect: The effect of the taint. Valid values: `"NoSchedule"`, `"PreferNoSchedule"`, `"NoExecute"`, `"PreferNoExecute"`. :param pulumi.Input[str] key: Tag Key for Vms in the cluster. :param pulumi.Input[str] value: Tag Value for VMs in the cluster. """ pulumi.set(__self__, "effect", effect) pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def effect(self) -> pulumi.Input[str]: """ The effect of the taint. Valid values: `"NoSchedule"`, `"PreferNoSchedule"`, `"NoExecute"`, `"PreferNoExecute"`. """ return pulumi.get(self, "effect") @effect.setter def effect(self, value: pulumi.Input[str]): pulumi.set(self, "effect", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: """ Tag Key for Vms in the cluster. """ 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]: """ Tag Value for VMs in the cluster. """ return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class OceanVmSizeArgs: def __init__(__self__, *, whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None): """ :param pulumi.Input[Sequence[pulumi.Input[str]]] whitelists: VM types allowed in the Ocean cluster. """ if whitelists is not None: pulumi.set(__self__, "whitelists", whitelists) @property @pulumi.getter def whitelists(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ VM types allowed in the Ocean cluster. """ return pulumi.get(self, "whitelists") @whitelists.setter def whitelists(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "whitelists", value)

39.59337

540

0.652443

12,422

107,496

5.461198

0.043149

0.110585

0.078421

0.059346

0.85868

0.795368

0.749245

0.679992

0.665414

0.638468

0

0.000787

0.231311

107,496

2,714

541

39.607959

0.820201

0.24986

0

0.633172

1

0

0.127602

0.051134

0

1

0.209443

false

0.004843

0.003027

0.003632

0.331114

0

null

0

1

0

1

0

1

0

null

0

1

0

6

b644fe9fa763e62ec78da8f9d37fd190ef7c108f

23

py

Python

femb/backbones/networks/__init__.py

jonasgrebe/pt-femb-face-embeddings

8f055a59293d75ad60d4b0a92f86ee6f3f07e950

[ "MIT" ]

16

2021-04-16T14:57:08.000Z

2022-02-23T08:09:39.000Z

femb/backbones/networks/__init__.py

jonasgrebe/pt-femb-face-embeddings

8f055a59293d75ad60d4b0a92f86ee6f3f07e950

[ "MIT" ]

1

2022-01-05T14:10:16.000Z

2022-01-06T08:13:13.000Z

femb/backbones/networks/__init__.py

jonasgrebe/pt-femb-face-embeddings

8f055a59293d75ad60d4b0a92f86ee6f3f07e950

[ "MIT" ]

3

2021-04-16T13:41:25.000Z

2022-02-23T08:09:42.000Z

from .iresnet import *

11.5

22

0.73913

3

23

5.666667

1

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23

1

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0

1

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true

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b65dbf1a6a3da6387d778343cbcbd38fb3bc9384

16,564

py

Python

issue_tracker/tracker/tests.py

BrnoPCmaniak/example-django-issue-tracker

1ae51eb3bf1fa532dbdcf336b97bd41e4f9d40fc

[ "MIT" ]

null

issue_tracker/tracker/tests.py

BrnoPCmaniak/example-django-issue-tracker

1ae51eb3bf1fa532dbdcf336b97bd41e4f9d40fc

[ "MIT" ]

null

issue_tracker/tracker/tests.py

BrnoPCmaniak/example-django-issue-tracker

1ae51eb3bf1fa532dbdcf336b97bd41e4f9d40fc

[ "MIT" ]

null

from django.contrib.auth.models import User from django.core.exceptions import ObjectDoesNotExist, ValidationError from django.test import Client, TestCase from tracker.models import ISSUE_ASSIGNED, ISSUE_CANCELED, ISSUE_CREATED, ISSUE_DONE, Issue, IssueCategory class ModelTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a") self.test_user_2 = User.objects.create(username="user_b") def test_assign(self): """Test that when user is assigned the state changes too.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") self.assertEqual(issue.state, ISSUE_CREATED) self.assertIsNone(issue.solver) issue.solver = self.test_user_2 issue.save() self.assertEqual(issue.state, ISSUE_ASSIGNED) self.assertEqual(issue.solver, self.test_user_2) self.assertIsNotNone(issue.assigned_at) def test_done(self): """Test that when state is done the duration is calculated.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.", solver=self.test_user_2) self.assertEqual(issue.state, ISSUE_ASSIGNED) self.assertEqual(issue.solver, self.test_user_2) self.assertIsNotNone(issue.assigned_at) issue.state = ISSUE_DONE issue.save() self.assertEqual(issue.state, ISSUE_DONE) self.assertIsNotNone(issue.completed_in) def test_done_without_assigned(self): """Test that issue can be marked as done without having to be assigned first.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") self.assertEqual(issue.state, ISSUE_CREATED) self.assertIsNone(issue.solver) self.assertIsNone(issue.assigned_at) issue.state = ISSUE_DONE issue.save() self.assertEqual(issue.state, ISSUE_DONE) self.assertIsNotNone(issue.completed_in) def test_clean_assigned(self): """Test that when state is marked as assigned issue have to have solver.""" issue = Issue(name="Test", created_by=self.test_user_1, description="Test description.", state=ISSUE_ASSIGNED, solver=None) self.assertRaises(ValidationError, issue.full_clean) class EditTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") self.category = IssueCategory.objects.create(name="Test") self.client = Client() self.client.force_login(self.test_user_1) def test_name(self): """Test changing name via API.""" new_value = "Test change" response = self.client.post("/issue/edit/%d/" % self.issue.pk, {"name": "name", "value": new_value}) self.assertEqual(response.status_code, 200) self.assertEqual(Issue.objects.get(pk=self.issue.pk).name, new_value) def test_description(self): """Test changing description via API.""" new_value = "Test longer description." response = self.client.post("/issue/edit/%d/" % self.issue.pk, {"name": "description", "value": new_value}) self.assertEqual(response.status_code, 200) self.assertEqual(Issue.objects.get(pk=self.issue.pk).description, new_value) def test_category(self): """Test changing category via API.""" new_value = self.category.pk response = self.client.post("/issue/edit/%d/" % self.issue.pk, {"name": "category", "value": new_value}) self.assertEqual(response.status_code, 200) self.assertEqual(Issue.objects.get(pk=self.issue.pk).category_id, new_value) def test_solver(self): """Test assigning solver via API.""" new_value = self.test_user_2.pk response = self.client.post("/issue/edit/%d/" % self.issue.pk, {"name": "solver", "value": new_value}) self.assertEqual(response.status_code, 200) issue = Issue.objects.get(pk=self.issue.pk) self.assertEqual(issue.solver_id, new_value) self.assertEqual(issue.state, ISSUE_ASSIGNED) def test_permission_denied(self): """Test change without permission won't do anything.""" c = Client() c.force_login(self.test_user_2) i = Issue.objects.get(pk=self.issue.pk) response = c.post("/issue/edit/%d/" % self.issue.pk, {"name": "name", "value": "XX"}) self.assertEqual(response.status_code, 403) self.assertEqual(Issue.objects.get(pk=i.pk).name, i.name) class UserSelectViewTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.client = Client() self.client.force_login(self.test_user_1) def test_username_search(self): """Test searching for users by their username.""" u = User.objects.create(username="user_c") response = self.client.post("/users/", {"q": u.username}) self.assertEqual(response.status_code, 200) self.assertEqual('[{"ID": %d, "Name": "%s", "Username": "%s"}]' % (u.pk, u.username, u.username), response.content.decode('ascii')) def test_first_name_search(self): """Test searching for users by their first name.""" u = User.objects.create(username="user_d", first_name="John", last_name="Smith") response = self.client.post("/users/", {"q": u.first_name}) self.assertEqual(response.status_code, 200) self.assertEqual('[{"ID": %d, "Name": "%s", "Username": "%s"}]' % (u.pk, u.get_full_name(), u.username), response.content.decode('ascii')) def test_last_name_search(self): """Test searching for users by their last name.""" u = User.objects.create(username="user_e", first_name="John", last_name="Smith") response = self.client.post("/users/", {"q": u.last_name}) self.assertEqual(response.status_code, 200) self.assertEqual('[{"ID": %d, "Name": "%s", "Username": "%s"}]' % (u.pk, u.get_full_name(), u.username), response.content.decode('ascii')) def test_permission_denied(self): """Test that users without permission can't search anyone.""" c = Client() c.force_login(self.test_user_2) u = User.objects.create(username="user_f") response = c.post("/users/", {"q": "user_f"}) self.assertEqual(response.status_code, 302) class DeleteViewTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.client = Client() self.client.force_login(self.test_user_1) def test_delete(self): """Test deletion of an Issue.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = self.client.get("/issue/delete/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertRaises(ObjectDoesNotExist, Issue.objects.get, pk=issue.pk) def test_permission_denied(self): """When user doesn't have permission do nothing.""" c = Client() c.force_login(self.test_user_2) issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = c.get("/issue/delete/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).name, issue.name) class IssueDoneTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.test_user_3 = User.objects.create(username="user_c") self.client_1 = Client() self.client_1.force_login(self.test_user_1) self.client_2 = Client() self.client_2.force_login(self.test_user_2) def test_superuser_done_assigned(self): """Test marking issue as done as superuser while previously been marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = self.client_1.get("/issue/done/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_DONE) def test_solver_done_assigned(self): """Test marking issue as done as solver while previously been marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = self.client_2.get("/issue/done/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_DONE) def test_superuser_done_unassigned(self): """Test marking issue as done as superuser while not been previously marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = self.client_1.get("/issue/done/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_DONE) def test_permission_denied_assigned(self): """When user doesn't haver permission do nothing.""" c = Client() c.force_login(self.test_user_3) issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = c.get("/issue/done/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertNotEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_DONE) def test_permission_denied_unassigned(self): """When user doesn't haver permission do nothing.""" c = Client() c.force_login(self.test_user_3) issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = c.get("/issue/done/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertNotEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_DONE) class IssueCancelTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.test_user_3 = User.objects.create(username="user_c") self.client_1 = Client() self.client_1.force_login(self.test_user_1) self.client_2 = Client() self.client_2.force_login(self.test_user_2) def test_superuser_cancel_assigned(self): """Test marking issue as canceled as superuser while previously been marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = self.client_1.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) def test_solver_cancel_assigned(self): """Test marking issue as canceled as solver while previously been marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = self.client_2.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) def test_superuser_cancel_unassigned(self): """Test marking issue as canceled as superuser while not been previously marked as assigned.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = self.client_1.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) def test_permission_denied_assigned(self): """When user doesn't haver permission do nothing.""" c = Client() c.force_login(self.test_user_3) issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = c.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertNotEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) def test_permission_denied_unassigned(self): """When user doesn't have permission do nothing.""" c = Client() c.force_login(self.test_user_3) issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.") response = c.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertNotEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) def test_superuser_cancel_done(self): """When user try to mark done issue as canceled do nothing.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, description="Test description.", state=ISSUE_DONE) response = self.client_1.get("/issue/cancel/%d/" % issue.pk) self.assertEqual(response.status_code, 302) self.assertNotEqual(Issue.objects.get(pk=issue.pk).state, ISSUE_CANCELED) class UnassignedTestCase(TestCase): def setUp(self): self.test_user_1 = User.objects.create(username="user_a", is_superuser=True) self.test_user_2 = User.objects.create(username="user_b") self.client = Client() self.client.force_login(self.test_user_1) def test_correct_state(self): """Test unassigning solver.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") response = self.client.get("/issue/unassign/%d/" % issue.pk) self.assertEqual(response.status_code, 302) new_issue = Issue.objects.get(pk=issue.pk) self.assertEqual(new_issue.state, ISSUE_CREATED) self.assertIsNone(new_issue.solver) self.assertIsNone(new_issue.assigned_at) def test_done_state(self): """Test that solver can't be removed when issue was marked as done.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") issue.state = ISSUE_DONE issue.save() response = self.client.get("/issue/unassign/%d/" % issue.pk) self.assertEqual(response.status_code, 302) new_issue = Issue.objects.get(pk=issue.pk) self.assertEqual(new_issue.state, ISSUE_DONE) self.assertIsNotNone(new_issue.solver) self.assertIsNotNone(new_issue.assigned_at) def test_permission_denied(self): """When user doesn't haver permission do nothing.""" issue = Issue.objects.create(name="Test", created_by=self.test_user_1, solver=self.test_user_2, description="Test description.") c = Client() c.force_login(self.test_user_2) response = c.get("/issue/unassign/%d/" % issue.pk) self.assertEqual(response.status_code, 302) new_issue = Issue.objects.get(pk=issue.pk) self.assertEqual(new_issue.state, ISSUE_ASSIGNED) self.assertIsNotNone(new_issue.solver) self.assertIsNotNone(new_issue.assigned_at)

44.888889

116

0.661193

2,153

16,564

4.911287

0.068277

0.067335

0.076036

0.041801

0.858426

0.847267

0.80471

0.789389

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0.732079

0

0.012006

0.210517

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368

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45.01087

0.796589

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0

0.187251

0

null

0

1

0

null

0

6

b6966c647cad21330c65797757d98c9f314d8102

257

py

Python

chef.py

samnalkande/basic-program-by-python

1f7eabe2cab18b694f6649e33dccb26eba08feae

[ "MIT" ]

null

chef.py

samnalkande/basic-program-by-python

1f7eabe2cab18b694f6649e33dccb26eba08feae

[ "MIT" ]

null

chef.py

samnalkande/basic-program-by-python

1f7eabe2cab18b694f6649e33dccb26eba08feae

[ "MIT" ]

3

2020-10-04T15:56:25.000Z

2021-10-01T11:31:44.000Z

class chef: def make_chicken(self): print("The chef makes a chicken Quickly") def make_salad(self): print("The chef makes a salad Quickly") def make_spacial_dish(self): print("The chef makes bbq ribs Quickly")

25.7

50

0.63035

36

257

4.388889

0.444444

0.132911

0.227848

0.303797

0.411392

0.278481

0

0.284047

257

9

51

28.555556

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1

0.428571

false

0

0.571429

0.428571

0

null

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1

0

1

0

null

0

1

0

1

0

6

1e35565dbda567f7b13341f8807cb81e2000089e

84

py

Python

gcd/inference/beam_search/__init__.py

CogComp/gcd

ab8736346b383b2fc9fbe57274b70ed1cd1b9422

[ "Apache-2.0" ]

3

2021-05-23T23:48:40.000Z

2021-11-09T06:56:39.000Z

gcd/inference/beam_search/__init__.py

danieldeutsch/gcd

fdf1a0a8628272fca8dd5c9ce182d16428b1ad38

[ "Apache-2.0" ]

null

gcd/inference/beam_search/__init__.py

danieldeutsch/gcd

fdf1a0a8628272fca8dd5c9ce182d16428b1ad38

[ "Apache-2.0" ]

1

2021-11-27T16:38:20.000Z

from gcd.inference.beam_search.constrained_beam_search import ConstrainedBeamSearch

42

83

0.916667

10

84

7.4

0.8

0.27027

0

0.047619

84

1

84

0.925

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true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

1e3b7613572d86510499f9718cc7cca6d65506ab

9,624

py

Python

a10sdk/core/ip/ip_nat.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

16

2015-05-20T07:26:30.000Z

2021-01-23T11:56:57.000Z

a10sdk/core/ip/ip_nat.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

6

2015-03-24T22:07:11.000Z

2017-03-28T21:31:18.000Z

a10sdk/core/ip/ip_nat.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

23

2015-03-29T15:43:01.000Z

2021-06-02T17:12:01.000Z

from a10sdk.common.A10BaseClass import A10BaseClass class RangeListList(A10BaseClass): """This class does not support CRUD Operations please use parent. :param uuid: {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"} :param global_start_ipv6_addr: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Global Start IPv6 Address of this list", "format": "ipv6-address-plen"} :param v4_vrid: {"description": "VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"} :param global_netmaskv4: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Mask for this Address range", "format": "ipv4-netmask"} :param local_start_ipv6_addr: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Local Start IPv6 Address of this list", "format": "ipv6-address-plen"} :param local_netmaskv4: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Mask for this Address range", "format": "ipv4-netmask"} :param local_start_ipv4_addr: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Local Start IPv4 Address of this list", "format": "ipv4-address"} :param global_start_ipv4_addr: {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Global Start IPv4 Address of this list", "format": "ipv4-address"} :param v6_vrid: {"description": "VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"} :param v4_count: {"description": "Number of addresses to be translated in this range", "format": "number", "optional": true, "maximum": 200000, "minimum": 1, "modify-not-allowed": 1, "type": "number"} :param v6_count: {"description": "Number of addresses to be translated in this range", "format": "number", "optional": true, "maximum": 200000, "minimum": 1, "modify-not-allowed": 1, "type": "number"} :param name: {"description": "Name for this Static List", "format": "string", "minLength": 1, "optional": false, "maxLength": 63, "type": "string"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.b_key = "range-list-list" self.DeviceProxy = "" self.uuid = "" self.global_start_ipv6_addr = "" self.v4_vrid = "" self.global_netmaskv4 = "" self.local_start_ipv6_addr = "" self.local_netmaskv4 = "" self.local_start_ipv4_addr = "" self.global_start_ipv4_addr = "" self.v6_vrid = "" self.v4_count = "" self.v6_count = "" self.name = "" for keys, value in kwargs.items(): setattr(self,keys, value) class Nat(A10BaseClass): """Class Description:: Configure NAT. Class nat supports CRUD Operations and inherits from `common/A10BaseClass`. This class is the `"PARENT"` class for this module.` :param range_list_list: {"minItems": 1, "items": {"type": "range-list"}, "uniqueItems": true, "array": [{"required": ["name"], "properties": {"uuid": {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"}, "global-start-ipv6-addr": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Global Start IPv6 Address of this list", "format": "ipv6-address-plen"}, "v4-vrid": {"description": "VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "global-netmaskv4": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Mask for this Address range", "format": "ipv4-netmask"}, "local-start-ipv6-addr": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Local Start IPv6 Address of this list", "format": "ipv6-address-plen"}, "local-netmaskv4": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Mask for this Address range", "format": "ipv4-netmask"}, "local-start-ipv4-addr": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Local Start IPv4 Address of this list", "format": "ipv4-address"}, "global-start-ipv4-addr": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Global Start IPv4 Address of this list", "format": "ipv4-address"}, "v6-vrid": {"description": "VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "v4-count": {"description": "Number of addresses to be translated in this range", "format": "number", "optional": true, "maximum": 200000, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "v6-count": {"description": "Number of addresses to be translated in this range", "format": "number", "optional": true, "maximum": 200000, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "name": {"description": "Name for this Static List", "format": "string", "minLength": 1, "optional": false, "maxLength": 63, "type": "string"}}}], "type": "array", "$ref": "/axapi/v3/ip/nat/range-list/{name}"} :param pool_group_list: {"minItems": 1, "items": {"type": "pool-group"}, "uniqueItems": true, "array": [{"required": ["pool-group-name"], "properties": {"member-list": {"minItems": 1, "items": {"type": "member"}, "uniqueItems": true, "array": [{"required": ["pool-name"], "properties": {"uuid": {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"}, "pool-name": {"description": "Specify NAT pool name", "format": "string-rlx", "minLength": 1, "optional": false, "maxLength": 63, "type": "string"}}}], "type": "array", "$ref": "/axapi/v3/ip/nat/pool-group/{pool-group-name}/member/{pool-name}"}, "pool-group-name": {"description": "Specify pool group name", "format": "string-rlx", "minLength": 1, "optional": false, "maxLength": 63, "type": "string"}, "vrid": {"description": "Specify VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "uuid": {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"}}}], "type": "array", "$ref": "/axapi/v3/ip/nat/pool-group/{pool-group-name}"} :param pool_list: {"minItems": 1, "items": {"type": "pool"}, "uniqueItems": true, "array": [{"required": ["pool-name"], "properties": {"use-if-ip": {"description": "Use Interface IP", "format": "flag", "default": 0, "optional": true, "plat-pos-list": ["soft-ax"], "not": "start-address", "type": "number"}, "uuid": {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"}, "start-address": {"description": "Configure start IP address of NAT pool", "format": "ipv4-address", "type": "string", "modify-not-allowed": 1, "not": "use-if-ip", "optional": true}, "vrid": {"description": "Configure VRRP-A vrid (Specify ha VRRP-A vrid)", "format": "number", "optional": true, "maximum": 31, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "netmask": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Configure mask for pool", "format": "ipv4-netmask-brief"}, "end-address": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Configure end IP address of NAT pool", "format": "ipv4-address"}, "ip-rr": {"description": "Use IP address round-robin behavior", "format": "flag", "default": 0, "type": "number", "modify-not-allowed": 1, "optional": true}, "ethernet": {"optional": true, "plat-pos-list": ["soft-ax"], "type": "number", "description": "Ethernet interface", "format": "interface"}, "scaleout-device-id": {"description": "Configure Scaleout device id to which this NAT pool is to be bound (Specify Scaleout device id)", "format": "number", "optional": true, "maximum": 64, "minimum": 1, "modify-not-allowed": 1, "type": "number"}, "gateway": {"optional": true, "modify-not-allowed": 1, "type": "string", "description": "Configure gateway IP", "format": "ipv4-address"}, "pool-name": {"description": "Specify pool name or pool group", "format": "string-rlx", "minLength": 1, "optional": false, "maxLength": 63, "type": "string"}}}], "type": "array", "$ref": "/axapi/v3/ip/nat/pool/{pool-name}"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` URL for this object:: `https://<Hostname|Ip address>//axapi/v3/ip/nat`. """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.required=[] self.b_key = "nat" self.a10_url="/axapi/v3/ip/nat" self.DeviceProxy = "" self.range_list_list = [] self.alg = {} self.pool_group_list = [] self.nat_global = {} self.template = {} self.translation = {} self.icmp = {} self.inside = {} self.pool_list = [] for keys, value in kwargs.items(): setattr(self,keys, value)

106.933333

2,269

0.640067

1,219

9,624

5.002461

0.114848

0.068875

0.086586

0.091997

0.75287

0.735323

0.72204

0.699738

0.671696

0

0.023423

0.148275

9,624

89

2,270

108.134831

0.720508

0.854738

0

0.263158

0

0.027006

0

1

0.052632

false

0

0.026316

0

0.131579

0

null

0

1

0

1

0

1

0

1

0

null

0

6

1e68526d43e2092453e905ad532f141b784793ea

174

py

Python

images/pannotator/p_procariota/prueba2.py

ezequieljsosa/sndg-bio

5f709b5b572564ec1dfa40d090eca9a34295743e

[ "MIT" ]

null

images/pannotator/p_procariota/prueba2.py

ezequieljsosa/sndg-bio

5f709b5b572564ec1dfa40d090eca9a34295743e

[ "MIT" ]

null

images/pannotator/p_procariota/prueba2.py

ezequieljsosa/sndg-bio

5f709b5b572564ec1dfa40d090eca9a34295743e

[ "MIT" ]

1

2020-09-01T15:57:54.000Z

#!/usr/bin/python hits = [] print len(hits) if len(hits)==0: print "sorete" hits.append("hola") print len(hits) if len(hits)!=0: print "soretedos" else: print "cagada"

11.6

19

0.655172

28

174

4.071429

0.5

0.245614

0.210526

0.245614

0.473684

0

0.013514

0.149425

174

14

20

12.428571

0.756757

0.091954

0

0.2

0

0.159236

0

null

0

null

0.5

1

0

null

1

0

null

0

1

0

1

0

6

1eba0afbcf489e0ed3ce0a02802db552768a4884

61

py

Python

JDjango/api/djangotools/views/__init__.py

JIYANG-PLUS/JDjango

57cbb13b2b4c07f34d546c0c637c22f60c1e692a

[ "MIT" ]

3

2020-12-28T05:09:02.000Z

2021-06-23T10:02:03.000Z

JDjango/api/djangotools/views/__init__.py

JIYANG-PLUS/JDjango

57cbb13b2b4c07f34d546c0c637c22f60c1e692a

[ "MIT" ]

null

JDjango/api/djangotools/views/__init__.py

JIYANG-PLUS/JDjango

57cbb13b2b4c07f34d546c0c637c22f60c1e692a

[ "MIT" ]

null

from .gets import * from .sets import * from .judge import *

15.25

20

0.704918

9

61

4.777778

0.555556

0.465116

0

0.196721

61

3

21

20.333333

0.877551

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

1ec984be7323e88fe785516fd8e9a49347855543

3,390

py

Python

wod/characters/migrations/0017_auto_20150424_1354.py

wlansu/wod

7a91747c7e25f9304c42ef6418612d3b391f4662

[ "MIT" ]

null

wod/characters/migrations/0017_auto_20150424_1354.py

wlansu/wod

7a91747c7e25f9304c42ef6418612d3b391f4662

[ "MIT" ]

null

wod/characters/migrations/0017_auto_20150424_1354.py

wlansu/wod

7a91747c7e25f9304c42ef6418612d3b391f4662

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('characters', '0016_auto_20150424_1352'), ] operations = [ migrations.AlterField( model_name='magecharacter', name='death', field=models.IntegerField(default=0, null=True, verbose_name='Death', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='fate', field=models.IntegerField(default=0, null=True, verbose_name='Fate', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='forces', field=models.IntegerField(default=0, null=True, verbose_name='Forces', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='life', field=models.IntegerField(default=0, null=True, verbose_name='Life', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='matter', field=models.IntegerField(default=0, null=True, verbose_name='Matter', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='mind', field=models.IntegerField(default=0, null=True, verbose_name='Mind', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='prime', field=models.IntegerField(default=0, null=True, verbose_name='Prime', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='space', field=models.IntegerField(default=0, null=True, verbose_name='Space', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='spirit', field=models.IntegerField(default=0, null=True, verbose_name='Spirit', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), migrations.AlterField( model_name='magecharacter', name='time', field=models.IntegerField(default=0, null=True, verbose_name='Time', blank=True, choices=[(0, b'0'), (1, b'1'), (2, b'2'), (3, b'3'), (4, b'4'), (5, b'5')]), preserve_default=True, ), ]

45.2

171

0.526844

435

3,390

4.018391

0.117241

0.114416

0.143021

0.165904

0.852403

0.826087

0.540046

0

0.059155

0.266962

3,390

74

172

45.810811

0.644266

0.006195

0

0.588235

0

0.095337

0.006831

0

1

0

false

0

0.029412

0

0.073529

0

null

0

1

0

null

0

6

948e8065a482214b7a97b001b999cf189f956a05

198

py

Python

api/admin.py

crypticleopard/IIPEDIA

33717ccd2ff0f809dcb1953880769f423f64474c

[ "MIT" ]

1

2021-05-01T19:20:32.000Z

api/admin.py

crypticleopard/IIPEDIA

33717ccd2ff0f809dcb1953880769f423f64474c

[ "MIT" ]

null

api/admin.py

crypticleopard/IIPEDIA

33717ccd2ff0f809dcb1953880769f423f64474c

[ "MIT" ]

null

from django.contrib import admin from .models import Book,Teacher,Review,Community admin.site.register(Book) admin.site.register(Teacher) admin.site.register(Review) admin.site.register(Community)

24.75

49

0.828283

28

198

5.857143

0.428571

0.219512

0.414634

0

0.065657

198

7

50

28.285714

0.886486

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

94cfd0cef295f1a6fc8025675657ba1930f81775

9,271

py

Python

tests/test_basic_api.py

talavis/dds_web

576b5e36e981182cc7f3440c96bf7d1a038dfaef

[ "BSD-3-Clause" ]

3

2021-06-18T09:38:28.000Z

2022-02-28T19:37:54.000Z

tests/test_basic_api.py

talavis/dds_web

576b5e36e981182cc7f3440c96bf7d1a038dfaef

[ "BSD-3-Clause" ]

610

2021-05-12T08:33:31.000Z

2022-03-31T14:55:05.000Z

tests/test_basic_api.py

MatthiasZepper/dds_web

28c297f15017eaf17328b607ba242c9587c24eb9

[ "BSD-3-Clause" ]

12

2021-05-19T10:33:45.000Z

2022-03-16T10:23:27.000Z

# IMPORTS ################################################################################ IMPORTS # # Standard library import flask import http import datetime # Installed from jwcrypto import jwk, jws # Own import tests import dds_web from dds_web.api.user import encrypted_jwt_token, jwt_token # TESTS #################################################################################### TESTS # def test_auth_check_statuscode_401_missing_info(client): """ Test that the auth endpoint returns: Status code: 401/UNAUTHORIZED Message: Missing or incorrect credentials """ # No params, no auth response = client.get(tests.DDSEndpoint.TOKEN) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Missing or incorrect credentials" in response_json.get("message") def test_auth_incorrect_username_check_statuscode_401_incorrect_info(client): """Test that the auth endpoint returns Status code: 401/UNAUTHORIZED Message: Missing or incorrect credentials """ response = client.get( tests.DDSEndpoint.TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["nouser"]).as_tuple() ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Missing or incorrect credentials" == response_json.get("message") def test_auth_incorrect_username_and_password_check_statuscode_401_incorrect_info(client): """Test that the auth endpoint returns Status code: 401/UNAUTHORIZED Message: Missing or incorrect credentials """ response = client.get( tests.DDSEndpoint.TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["nopassword"]).as_tuple(), ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Missing or incorrect credentials" == response_json.get("message") def test_auth_incorrect_password_check_statuscode_401_incorrect_info(client): """Test that the auth endpoint returns Status code: 401/UNAUTHORIZED Message: Missing or incorrect credentials """ response = client.get( tests.DDSEndpoint.TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["wronguser"]).as_tuple() ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Missing or incorrect credentials" == response_json.get("message") def test_auth_correctauth_check_statuscode_200_correct_info(client): """Test that the auth endpoint returns Status code: 200/OK Token: includes the authenticated username """ response = client.get( tests.DDSEndpoint.TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["researchuser"]).as_tuple(), ) assert response.status_code == http.HTTPStatus.OK response_json = response.json assert response_json.get("token") jwstoken = jws.JWS() jwstoken.deserialize(response_json.get("token")) jwstoken.verify(jwk.JWK.from_password(flask.current_app.config.get("SECRET_KEY"))) # extracting JWS token payload before verification will raise a `InvalidJWSOperation` error payload = jws.json_decode(jwstoken.payload) assert ( payload.get("sub") == tests.UserAuth(tests.USER_CREDENTIALS["researchuser"]).as_tuple()[0] ) def test_auth_incorrect_token_without_periods(client): """Test that a malformatted token returns unauthorized""" # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": "Bearer " + "madeuptoken"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Invalid token" == response_json.get("message") def test_auth_incorrect_token_with_periods(client): """Test that a made up token returns unauthorized""" # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": "Bearer made.up.token"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Invalid token" == response_json.get("message") def test_auth_expired_signed_token(client): """Test that an signed expired token returns unauthorized""" token = dds_web.api.user.jwt_token("researchuser", expires_in=datetime.timedelta(hours=-2)) # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": f"Bearer {token}"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Expired token" == response_json.get("message") def test_auth_token_wrong_secret_key_signed_token(client): """Test that an encrypted token signed with the wrong key returns unauthorized""" old_secret = flask.current_app.config.get("SECRET_KEY") flask.current_app.config["SECRET_KEY"] = "XX" * 16 token = dds_web.api.user.jwt_token("researchuser", expires_in=datetime.timedelta(hours=-2)) # reset secret key flask.current_app.config["SECRET_KEY"] = old_secret # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": f"Bearer {token}"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Invalid token" == response_json.get("message") def test_auth_with_token(client): """Test that token can be used for authentication""" response = client.get( tests.DDSEndpoint.TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["researchuser"]).as_tuple(), ) assert response.status_code == http.HTTPStatus.OK response_json = response.json assert response_json.get("token") # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": "Bearer " + response_json.get("token")}, ) assert response.status_code == http.HTTPStatus.OK response_json = response.json assert response_json.get("public") # ENCRYPTED TOKEN ################################################################ ENCRYPTED TOKEN # def test_auth_expired_encrypted_token(client): """Test that an encrypted expired token returns unauthorized""" token = dds_web.api.user.encrypted_jwt_token( "researchuser", None, expires_in=datetime.timedelta(hours=-2) ) # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": f"Bearer {token}"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Expired token" == response_json.get("message") def test_auth_token_wrong_secret_key_encrypted_token(client): """Test that an encrypted token signed with the wrong key returns unauthorized""" old_secret = flask.current_app.config.get("SECRET_KEY") flask.current_app.config["SECRET_KEY"] = "XX" * 16 token = dds_web.api.user.encrypted_jwt_token( "researchuser", None, expires_in=datetime.timedelta(hours=-2) ) # reset secret key flask.current_app.config["SECRET_KEY"] = old_secret # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": f"Bearer {token}"}, ) assert response.status_code == http.HTTPStatus.UNAUTHORIZED response_json = response.json assert response_json.get("message") assert "Invalid token" == response_json.get("message") def test_auth_with_encrypted_token(client): """Test that token can be used for authentication""" response = client.get( tests.DDSEndpoint.ENCRYPTED_TOKEN, auth=tests.UserAuth(tests.USER_CREDENTIALS["researchuser"]).as_tuple(), ) assert response.status_code == http.HTTPStatus.OK response_json = response.json assert response_json.get("token") # Fetch the project public key as an example response = client.get( tests.DDSEndpoint.PROJ_PUBLIC, query_string={"project": "public_project_id"}, headers={"Authorization": "Bearer " + response_json.get("token")}, ) assert response.status_code == http.HTTPStatus.OK response_json = response.json assert response_json.get("public")

36.5

100

0.700572

1,102

9,271

5.692378

0.112523

0.110952

0.066954

0.070142

0.898932

0.879962

0.86944

0.856528

0.846804

0.827674

0

0.005087

0.17312

9,271

253

101

36.644269

0.813201

0.169777

0

0.679245

0

0.135375

0

0.257862

1

0.081761

false

0.025157

0.044025

0

0.125786

0

null

0

1

0

null

0

6

94d4e1faf553d8369a5bbe76fbbdcd4eba168e89

27

py

Python

netbox_netdisco/core/notification/__init__.py

mksoska/netbox-plugin-netdisco

7c1324f075b47ddd9adfbdf7e19d5afef09f22fd

[ "MIT" ]

1

2021-11-11T11:50:14.000Z

netbox_netdisco/core/notification/__init__.py

mksoska/netbox-plugin-netdisco

7c1324f075b47ddd9adfbdf7e19d5afef09f22fd

[ "MIT" ]

null

netbox_netdisco/core/notification/__init__.py

mksoska/netbox-plugin-netdisco

7c1324f075b47ddd9adfbdf7e19d5afef09f22fd

[ "MIT" ]

null

from .icinga2 import Icinga

27

0.851852

4

27

5.75

1

0

0.041667

0.111111

27

1

27

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bf60084f863e48637b70138c2bbd2ab84c595adf

188

py

Python

src/clearskies_aws/contexts/__init__.py

cmancone/clearskies-aws

6dc8714127c67553c7c161ec82756680b848113b

[ "MIT" ]

null

src/clearskies_aws/contexts/__init__.py

cmancone/clearskies-aws

6dc8714127c67553c7c161ec82756680b848113b

[ "MIT" ]

null

src/clearskies_aws/contexts/__init__.py

cmancone/clearskies-aws

6dc8714127c67553c7c161ec82756680b848113b

[ "MIT" ]

null

from .lambda_api_gateway import lambda_api_gateway from .lambda_elb import lambda_elb from .lambda_http_gateway import lambda_http_gateway from .lambda_invocation import lambda_invocation

37.6

52

0.893617

28

188

5.571429

0.285714

0.25641

0.205128

0

0.085106

188

4

53

47

0.906977

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

78493fa74bf8864381ba00f7ad6b063a332e8722

45,566

py

Python

render.py

Perlkonig/avabur-clan-stats

66deb49f60e31ff33af7462fa11d36ceee3a4286

[ "MIT" ]

null

render.py

Perlkonig/avabur-clan-stats

66deb49f60e31ff33af7462fa11d36ceee3a4286

[ "MIT" ]

5

2018-05-10T19:25:17.000Z

2018-07-29T18:31:38.000Z

render.py

Perlkonig/avabur-clan-stats

66deb49f60e31ff33af7462fa11d36ceee3a4286

[ "MIT" ]

2

2018-05-11T01:38:48.000Z

2020-09-09T12:27:35.000Z

#!/usr/local/bin/python3 import json import sqlite3 import csv import os import math import numpy def level2xp(lvl): #up to level 111 xptiers = [20000, 121678, 350851, 745429, 1339951, 2167182, 3258923, 4646498, 6361081, 8433930, 10896556, 13780871, 17119294, 20944855, 25291273, 30193033, 35685457, 41804763, 48588126, 56073734, 64300844, 73309830, 83142239, 93840842, 105449680, 118014118, 131580895, 146198170, 161915581, 178784289, 196857031, 216188176, 236833776, 258851620, 282301291, 307244220, 333743747, 361865174, 391675830, 423245132, 456644644, 491948143, 529231687, 568573676, 610054927, 653758741, 699770974, 748180144, 799077355, 852556671, 908714903, 967651833, 1029470270, 1094276137, 1162178557, 1233289942, 1307726087, 1385606263, 1467053316, 1552193763, 1641157895, 1734079883, 1831097884, 1932354148, 2037995137, 2148171633, 2263038863, 2382000000, 2507000000, 2637406405, 2772681975, 2913495393, 3060031195, 3212479279, 3371035044, 3535899544, 3707279636, 3885388140, 4070443993, 4262672421, 4462305102, 4669580337, 4884743233, 5108045881, 5339747538, 5580114827, 5829421923, 6087950759, 6355991231, 6633841407, 6921807744, 7220205308, 7529358005, 7849598808, 8181270001, 8524723420, 8880320706, 9248433560, 9629444009, 10023744672, 10431739043, 10853841770, 11290478946, 11742088411, 12209120054, 12692036127, 13191311569, 13707434329, 14240905711, 14792240714, 15361968389] assert(len(xptiers) == 111) whole = math.floor(lvl) decimal = lvl - whole # print("Level: {}, Whole: {}, Decimal: {}".format(lvl, whole, decimal)) xp = 0 for i in range(whole): # print("\tAdding xp for lvl {}".format(i+1)) xp += xptiers[i] xp += int(round(xptiers[whole] * decimal)) return xp def calcDeltas(lst): if (lst == None) or (len(lst) == 0): return None elif len(lst) == 1: return [0] else: ret = [] for i in range(1, len(lst)): ret.append(lst[i] - lst[i-1]) assert len(ret) == len(lst)-1 return ret def buildData(dates, deltas): ret = [] for i in range(len(deltas)): if len(dates) > len(deltas): ret.append((dates[i+1], deltas[i])) else: ret.append((dates[i], deltas[i])) return ret def trimOutliers(lst, percent, ceil=True): lst = sorted(lst) count = len(lst) * percent if ceil: count = math.ceil(count) else: count = math.floor(count) if (count*2 >= len(lst)): return(lst) else: return lst[count:len(lst)-count] def trimDataByStd(lst, maxdev): flat = [x[1] for x in lst] std = numpy.std(flat) if std == 0: return lst mean = numpy.mean(flat) # print("Mean: {}, Std: {}".format(mean, std)) result = list() for x in lst: if abs(x[1] - mean) / std > maxdev: result.append((x[0], None)) else: result.append(x) return result def calcMedian(lst): lst = sorted(lst) if (len(lst) % 2 == 1): return lst[(len(lst)-1)//2] else: val1 = lst[int(math.floor((len(lst)-1)/2))] val2 = lst[int(math.ceil((len(lst)-1)/2))] return (val1 + val2) / 2 #Load settings with open('/home/protected/avabur/settings.json') as j: settings = json.load(j) clandays = 0 if 'clandays' in settings: clandays = int(settings['clandays']) actiondays = 0 if 'actiondays' in settings: actiondays = int(settings['actiondays']) byslicedays = 0 if 'byslicedays' in settings: byslicedays = int(settings['byslicedays']) leveldays = 0 if 'leveldays' in settings: leveldays = int(settings['leveldays']) leveldays_maxlvls = 0 if 'leveldays_maxlvls' in settings: leveldays_maxlvls = int(settings['leveldays_maxlvls']) allclans = ["Us"] for rival in settings['rivals']: allclans.append(rival['name']) #Load/Initialize database try: conn = sqlite3.connect(settings['dbfile']) except sqlite3.DatabaseError as e: raise sqlite3.DatabaseError(repr(e)) c = conn.cursor() #xp gained xpdata = dict() if (clandays > 0): c.execute("SELECT datestamp, xp, level FROM clan WHERE level IS NOT NULL AND (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT datestamp, xp, level FROM clan WHERE level IS NOT NULL ORDER BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] xps = [(level2xp(x[2]) + x[1]) for x in recs] xpdeltas = calcDeltas(xps) for entry in buildData(dates, xpdeltas): xpdata[entry[0]] = {'Us': entry[1]} for rival in settings['rivals']: if (clandays > 0): c.execute("SELECT datestamp, xp, level FROM rivalclans WHERE clanid=? AND (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (rival['id'], clandays)) else: c.execute("SELECT datestamp, xp, level FROM rivalclans WHERE clanid=? ORDER BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] xps = [(level2xp(x[2]) + x[1]) for x in recs] xpdeltas = calcDeltas(xps) for entry in buildData(dates, xpdeltas): xpdata[entry[0]][rival['name']] = entry[1] with open(os.path.join(settings['csvdir'], 'clan_xp.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in xpdata.keys(): row = [date] for clan in allclans: if clan in xpdata[date]: row.append(xpdata[date][clan]) else: row.append(None) csvw.writerow(row) #total actions if (clandays > 0): c.execute("SELECT datestamp, sum(totalacts), count() FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) GROUP BY datestamp ORDER BY datestamp", (clandays,)) else: c.execute("SELECT datestamp, sum(totalacts), count() FROM members GROUP BY datestamp ORDER BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] totals = [x[1] for x in recs] counts = [x[2] for x in recs] deltas = calcDeltas(totals) avgs = [round(deltas[i] / counts[i]) for i in range(len(deltas))] # avgs = [round(totals[i] / counts[i]) for i in range(len(totals))] ## Try to trim really wide swings actions_total_whatiswide = 500000 actions_average_whatiswide = 50000 if 'actions_total_whatiswide' in settings: actions_total_whatiswide = settings['actions_total_whatiswide'] if 'actions_average_whatiswide' in settings: actions_average_whatiswide = settings['actions_average_whatiswide'] dd = calcDeltas(deltas) for i in range(len(dd)): if abs(dd[i]) > actions_total_whatiswide: deltas[i+1] = None dd = calcDeltas(avgs) for i in range(len(dd)): if abs(dd[i]) > actions_average_whatiswide: avgs[i+1] = None totaldata = buildData(dates, deltas) avgdata = buildData(dates, avgs) with open(os.path.join(settings['csvdir'], 'clan_actions_total.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date", "Total Actions"]) for row in totaldata: csvw.writerow(row) with open(os.path.join(settings['csvdir'], 'clan_actions_avg.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date", "Average Actions"]) for row in avgdata: csvw.writerow(row) #aggregate donations (other than xp) if (clandays > 0): c.execute("SELECT datestamp, d_crystals, d_platinum, d_gold, d_food, d_wood, d_iron, d_stone FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) GROUP BY datestamp ORDER BY datestamp", (clandays,)) else: c.execute("SELECT datestamp, d_crystals, d_platinum, d_gold, d_food, d_wood, d_iron, d_stone FROM members GROUP BY datestamp ORDER BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] plat = [x[2] for x in recs] gold = [x[3] for x in recs] plat = calcDeltas(plat) ## Try to trim really wide swings for i in range(len(plat)): if plat[i] < 0: plat[i] = None gold = calcDeltas(gold) ## Try to trim really wide swings for i in range(len(gold)): if gold[i] < 0: gold[i] = None platdata = buildData(dates, plat) golddata = buildData(dates, gold) with open(os.path.join(settings['csvdir'], 'clan_donations_plat.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date", "Platinum"]) for row in platdata: csvw.writerow(row) with open(os.path.join(settings['csvdir'], 'clan_donations_gold.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date", "Gold"]) for row in golddata: csvw.writerow(row) #per-user total actions ## First get maxdate c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Now get their total action data rawdata = dict() for u in usernames: rawdata[u] = list() if (clandays > 0): c.execute("SELECT datestamp, totalacts FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, totalacts FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1])) ## Now turn that into deltas for each user deltadata = dict() for u in usernames: dates = [x[0] for x in rawdata[u]] counts = [x[1] for x in rawdata[u]] deltas = calcDeltas(counts) deltadata[u] = buildData(dates, deltas) ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. csvout = [] csvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in deltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) csvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_actions.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in csvout: csvw.writerow(row) #per-user base stats ## First get list of all users c.execute("SELECT DISTINCT(username) FROM members ORDER BY username COLLATE NOCASE") usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] ## Now get their total action data rawdata = dict() for u in usernames: rawdata[u] = list() if (clandays > 0): c.execute("SELECT datestamp, stats FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, stats FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1])) ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. csvout = [] csvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for stat in rawdata[u]: if (stat[0] == d): found = True row.append(stat[1]) break if not found: row.append(None) csvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_stats.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in csvout: csvw.writerow(row) #per-user xp donations ## Get latest date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of all current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Now get their xp donation data rawdata = dict() for u in usernames: rawdata[u] = list() if (clandays > 0): c.execute("SELECT datestamp, d_xp FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, d_xp FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1])) ## Now turn that into deltas for each user deltadata = dict() for u in usernames: dates = [x[0] for x in rawdata[u]] counts = [x[1] for x in rawdata[u]] deltas = calcDeltas(counts) deltadata[u] = buildData(dates, deltas) xpdates = list() xpdeltas = list() ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. csvout = [] csvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] xpdates.append(d) node = 0 ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in deltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) node += delta[1] break if not found: row.append(None) xpdeltas.append(node) csvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_xpdonated.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in csvout: csvw.writerow(row) #per-user gold donations ## Get latest date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of all current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Now get their xp donation data rawdata = dict() for u in usernames: rawdata[u] = list() if (clandays > 0): c.execute("SELECT datestamp, d_gold FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, d_gold FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1])) ## Now turn that into deltas for each user deltadata = dict() for u in usernames: dates = [x[0] for x in rawdata[u]] counts = [x[1] for x in rawdata[u]] deltas = calcDeltas(counts) deltadata[u] = buildData(dates, deltas) ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. csvout = [] csvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in deltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) csvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_golddonated.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in csvout: csvw.writerow(row) #per-user plat donations ## Get latest date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of all current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Now get their plat donation data rawdata = dict() for u in usernames: rawdata[u] = list() if (clandays > 0): c.execute("SELECT datestamp, d_platinum FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, d_platinum FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1])) ## Now turn that into deltas for each user deltadata = dict() for u in usernames: dates = [x[0] for x in rawdata[u]] counts = [x[1] for x in rawdata[u]] deltas = calcDeltas(counts) deltadata[u] = buildData(dates, deltas) ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. csvout = [] csvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in deltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) csvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_platdonated.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in csvout: csvw.writerow(row) #activity status c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] c.execute("SELECT username, (STRFTIME('%s', 'now') - lastactive) AS inactive FROM members WHERE datestamp=? AND inactive>= 86400 ORDER BY inactive", [maxdate]) recs = c.fetchall() recs = [(x[0], math.floor(x[1]/86400)) for x in recs] with open(os.path.join(settings['csvdir'], 'individual_lastactive.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Member", "Time Inactive"]) for row in recs: csvw.writerow(row) #per-user average actions ## Get max date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of current users c.execute("SELECT DISTINCT(username) FROM members where datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Now get their total action data actions_outliers_percent = 0.1 if 'actions_outliers_percent' in settings: actions_outliers_percent = settings['actions_outliers_percent'] avgacts = list() for u in usernames: totals = [] if (actiondays > 0): c.execute("SELECT totalacts FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", [u, actiondays]) else: c.execute("SELECT totalacts FROM members WHERE username=? ORDER BY datestamp", [u]) for row in c: totals.append(row[0]) deltas = calcDeltas(totals) deltas = trimOutliers(deltas, actions_outliers_percent) avg = round(sum(deltas) / len(deltas)) avgacts.append((u, avg)) ## sort by average avgacts = sorted(avgacts, key=lambda x: x[1]) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_avgacts.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Member","Average Actions"]) for row in avgacts: csvw.writerow(row) #per-user median actions ## Get max date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of current users c.execute("SELECT DISTINCT(username) FROM members where datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Now get their total action data medacts = list() for u in usernames: totals = [] if (actiondays > 0): c.execute("SELECT totalacts FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", [u, actiondays]) else: c.execute("SELECT totalacts FROM members WHERE username=? ORDER BY datestamp", [u]) for row in c: totals.append(row[0]) deltas = calcDeltas(totals) deltas = trimOutliers(deltas, actions_outliers_percent) median = round(calcMedian(deltas)) medacts.append((u, median)) ## sort by average medacts = sorted(medacts, key=lambda x: x[1]) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_medacts.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Member","Median Actions"]) for row in medacts: csvw.writerow(row) # Treasury status (single graph) with open(os.path.join(settings['csvdir'], 'clan_treasury.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date","Crystals", "Platinum", "Gold", "Food", "Wood", "Iron", "Stone"]) if (clandays > 0): c.execute("SELECT datestamp, crystals, platinum, gold, food, wood, iron, stone FROM clan WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT datestamp, crystals, platinum, gold, food, wood, iron, stone FROM clan ORDER BY datestamp") for row in c: csvw.writerow(row) # Battler/harvest ratio ## Get max date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of current users c.execute("SELECT DISTINCT(username) FROM members where datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get battle/harvest data treedata = list() if (actiondays > 0): c.execute("SELECT username, ((max(kills)-min(kills))+(max(deaths)-min(deaths))) AS battles, ( (max(harvests)-min(harvests))+(max(craftingacts)-min(craftingacts))+(max(carvingacts)-min(carvingacts)) ) AS harvests FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) GROUP BY username", [actiondays]) else: c.execute("SELECT username, ((max(kills)-min(kills))+(max(deaths)-min(deaths))) AS battles, ( (max(harvests)-min(harvests))+(max(craftingacts)-min(craftingacts))+(max(carvingacts)-min(carvingacts)) ) AS harvests FROM members GROUP BY username") for row in c: if row[0] in usernames: total = row[1] + row[2] ratio = 0 if (total > 0): ratio = round(row[1] / total, 2) treedata.append((row[0], ratio)) treedata = sorted(treedata, key=lambda x: (x[1], x[0].lower())) with open(os.path.join(settings['csvdir'], 'individual_ratios.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Member", "Ratio"]) for row in treedata: csvw.writerow(row) #Rankings table ranks = {'data': []} for row in c.execute("SELECT username, skill, rank, level FROM ranks WHERE rank<=100"): ranks['data'].append(row) with open(os.path.join(settings['csvdir'], 'ranks.json'), 'w', newline='') as csvfile: json.dump(ranks, csvfile) #Nearest clans # lvldata = list() # xpdata = list() # if (clandays > 0): # c.execute("SELECT datestamp, ours, above, below FROM nearestclans WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) # else: # c.execute("SELECT datestamp, ours, above, below FROM nearestclans ORDER BY datestamp") # for row in c: # lvlnode = [row[0]] # xpnode = [row[0]] # if row[2] is not None: # lvlnode.append(abs(row[1] - row[2])) # xpnode.append(abs(level2xp(row[1]) - level2xp(row[2]))) # else: # lvlnode.append(None) # xpnode.append(None) # if row[3] is not None: # lvlnode.append(abs(row[1] - row[3])) # xpnode.append(abs(level2xp(row[1]) - level2xp(row[3]))) # else: # lvlnode.append(None) # xpnode.append(None) # lvldata.append(lvlnode) # xpdata.append(xpnode) # with open(os.path.join(settings['csvdir'], 'clan_nearest_lvl.csv'), 'w', newline='') as csvfile: # csvw = csv.writer(csvfile, dialect=csv.excel) # csvw.writerow(["Date","Above", "Below"]) # for row in lvldata: # csvw.writerow(row) # with open(os.path.join(settings['csvdir'], 'clan_nearest_xp.csv'), 'w', newline='') as csvfile: # csvw = csv.writer(csvfile, dialect=csv.excel) # csvw.writerow(["Date","Above", "Below"]) # for row in xpdata: # csvw.writerow(row) #Kill-to-death ratio ## First get maxdate c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (clandays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (clandays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Now get their total action data rawkills = dict() rawdeaths = dict() for u in usernames: rawkills[u] = list() rawdeaths[u] = list() if (clandays > 0): c.execute("SELECT datestamp, kills, deaths FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, clandays]) else: c.execute("SELECT datestamp, kills, deaths FROM members WHERE username=?", [u]) for row in c: rawkills[u].append((row[0], row[1])) rawdeaths[u].append((row[0], row[2])) ## Now turn that into deltas for each user kdeltadata = dict() ddeltadata = dict() kdratio = dict() kdpercent = dict() for u in usernames: kdates = [x[0] for x in rawkills[u]] kcounts = [x[1] for x in rawkills[u]] ddates = [x[0] for x in rawdeaths[u]] dcounts = [x[1] for x in rawdeaths[u]] kdeltas = calcDeltas(kcounts) ddeltas = calcDeltas(dcounts) avgnode = list() kdpnode = list() for i in range(len(kdeltas)): avg = None kdp = None if ddeltas[i] > 0: avg = kdeltas[i] / ddeltas[i] avgnode.append(avg) if (kdeltas[i] + ddeltas[i]) > 0: kdp = kdeltas[i] / (kdeltas[i] + ddeltas[i]) kdpnode.append(kdp) kdeltadata[u] = buildData(kdates, kdeltas) ddeltadata[u] = buildData(ddates, ddeltas) kdratio[u] = buildData(kdates, avgnode) kdpercent[u] = buildData(kdates, kdpnode) ## Now convert that into a format suitable for CSV output (rows are dates, users are columns) ## This uses a number nested loops. It's not the most efficient, but it's good enough. kcsvout = [] kcsvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in kdeltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) kcsvout.append(row) dcsvout = [] dcsvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in ddeltadata[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) dcsvout.append(row) kdcsvout = [] kdcsvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in kdratio[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) kdcsvout.append(row) kdpcsvout = [] kdpcsvout.append(['Date'] + usernames) ### This gives us the row structure for d in alldates: row = [d] ### This loop ensures the correct order for u in usernames: ### Look at each delta entry for the given user and see if it matches the date. found = False for delta in kdpercent[u]: if (delta[0] == d): found = True row.append(delta[1]) break if not found: row.append(None) kdpcsvout.append(row) ## Print it! with open(os.path.join(settings['csvdir'], 'individual_kills.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in kcsvout: csvw.writerow(row) with open(os.path.join(settings['csvdir'], 'individual_deaths.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in dcsvout: csvw.writerow(row) with open(os.path.join(settings['csvdir'], 'individual_kdratio.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in kdcsvout: csvw.writerow(row) with open(os.path.join(settings['csvdir'], 'individual_kdpercent.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) for row in kdpcsvout: csvw.writerow(row) #xp donations by 10-level slice ## Get latest date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of all current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Get list of distinct dates if (byslicedays > 0): c.execute("SELECT DISTINCT(datestamp) FROM members WHERE (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", (byslicedays,)) else: c.execute("SELECT DISTINCT(datestamp) FROM members ORDER BY datestamp") alldates = [x[0] for x in c.fetchall()] alldates.pop(0) ## Collect xp and level data rawdata = dict() for u in usernames: rawdata[u] = list() if (byslicedays > 0): c.execute("SELECT datestamp, d_xp, level FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?)", [u, byslicedays]) else: c.execute("SELECT datestamp, d_xp, level FROM members WHERE username=?", [u]) for row in c: rawdata[u].append((row[0], row[1], row[2])) ## Now turn that into deltas for each slice deltadata = dict() for u in usernames: dates = [x[0] for x in rawdata[u]] counts = [x[1] for x in rawdata[u]] levels = [x[2] for x in rawdata[u]] deltadata[u] = (max(levels), max(counts) - min(counts)) levels = [x[0] for x in deltadata.values()] minlevel = int((min(levels) // 10) * 10) maxlevel = int(((max(levels) // 10) + 1) * 10) width = 10 xpsum = sum([x[1] for x in deltadata.values()]) slices = dict() for base in range(minlevel, maxlevel, width): users = [x for x in deltadata.keys() if ( (deltadata[x][0] >= base) and (deltadata[x][0] < base+width) )] slicesum = sum([deltadata[x][1] for x in users]) slicepc = round((slicesum / xpsum) * 10000) / 100 sliceavgabs = 0 if (len(users) > 0): sliceavgabs = slicesum / len(users) sliceavgpc = round((sliceavgabs / xpsum) * 10000) / 100 slices[base] = (sorted(users), slicepc, sliceavgpc) ## Print it! # with open(os.path.join(settings['csvdir'], 'xpdonations_byslice.csv'), 'w', newline='') as csvfile: # csvw = csv.writer(csvfile, dialect=csv.excel) # csvw.writerow(["Slice","% xp donations"]) # for base in sorted(slices.keys()): # csvw.writerow(("{}--{} ({} members)".format(base, base+9, len(slices[base][0])), slices[base][1])) ### JSON version with open(os.path.join(settings['csvdir'], 'xpdonations_byslice.json'), 'w', newline='') as jsonfile: jsonfile.write(json.dumps(slices)) # #Days per level # ## Get latest date # c.execute("SELECT MAX(datestamp) FROM members") # maxdate = c.fetchone()[0] # ## Get list of all current members # c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) # usernames = [x[0] for x in c.fetchall()] # ## Collect xp and level data # data = list() # for u in usernames: # lvldays = dict() # if (leveldays > 0): # c.execute("SELECT datestamp, level FROM members WHERE username=? AND (julianday('now') - julianday(datestamp) <= ?) ORDER BY datestamp", [u, leveldays]) # else: # c.execute("SELECT datestamp, level FROM members WHERE username=? ORDER BY datestamp", [u]) # for row in c: # level = row[1] # if (level in lvldays): # lvldays[level] += 1 # else: # lvldays[level] = 1 # avg = 0 # if (len(lvldays) > 0): # avg = sum(lvldays.values()) / len(lvldays) # data.append((u, avg)) # ## Print it! # with open(os.path.join(settings['csvdir'], 'avg_days_in_level.csv'), 'w', newline='') as csvfile: # csvw = csv.writer(csvfile, dialect=csv.excel) # csvw.writerow(["Member","Average days in level"]) # for row in data: # csvw.writerow(row) #Days per level ## Get latest date c.execute("SELECT MAX(datestamp) FROM members") maxdate = c.fetchone()[0] ## Get list of all current members c.execute("SELECT DISTINCT(username) FROM members WHERE datestamp=? ORDER BY username COLLATE NOCASE", [maxdate]) usernames = [x[0] for x in c.fetchall()] ## Collect xp and level data data = list() for u in usernames: c.execute("SELECT COUNT(datestamp), level FROM members WHERE username=? GROUP BY level ORDER BY username COLLATE NOCASE;", [u]) days = c.fetchall() #remove current level because it's not complete yet days.pop() #if shorter or equal to max levels, cut off the first element if ( (len(days) > 0) and ( (leveldays_maxlvls == 0) or (len(days) <= leveldays_maxlvls) ) ): days.pop(0) #now trim to length while (len(days) > leveldays_maxlvls): days.pop(0) lvldays = [x[0] for x in days] avg = 0 if (len(lvldays) > 0): avg = sum(lvldays) / len(lvldays) data.append((u, avg, len(days))) ## Print it! with open(os.path.join(settings['csvdir'], 'avg_days_in_level_full_levels.json'), 'w', newline='') as csvfile: csvfile.write(json.dumps(data)) # csvw = csv.writer(csvfile, dialect=csv.excel) # csvw.writerow(["Member","Average days in level"]) # for row in data: # csvw.writerow(row) # RIVAL SUMMARY GRAPHS ## Total XP summary = dict() c.execute("SELECT datestamp, level, xp FROM clan WHERE level IS NOT NULL ORDER BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] xp = [(level2xp(x[1]) + x[2]) for x in recs] for entry in buildData(dates, xp): summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, level, xp FROM rivalclans WHERE clanid=? ORDER BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] xp = [(level2xp(x[1]) + x[2]) for x in recs] for entry in buildData(dates, xp): if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_totalxp.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Total actions summary = dict() maxdev = 2.0 c.execute("SELECT datestamp, SUM(totalacts) FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, SUM(totalacts) FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_actions.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Total levels summary = dict() c.execute("SELECT datestamp, SUM(level) FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] lvls = [x[1] for x in recs] data = buildData(dates, lvls) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, SUM(level) FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] lvls = [x[1] for x in recs] data = buildData(dates, lvls) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_levels.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Kills summary = dict() maxdev = 1.0 c.execute("SELECT datestamp, SUM(kills) FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, SUM(kills) FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_kills.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Deaths summary = dict() maxdev = 1.0 c.execute("SELECT datestamp, SUM(deaths) FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, SUM(deaths) FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_deaths.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Harvests summary = dict() maxdev = 1.0 c.execute("SELECT datestamp, SUM(harvests) FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, SUM(harvests) FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_harvests.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) ## Crafts/Carves summary = dict() maxdev = 1.0 c.execute("SELECT datestamp, (SUM(craftingacts) + SUM(carvingacts)) AS harvests FROM members GROUP BY datestamp") recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: summary[entry[0]] = {"Us": entry[1]} for rival in settings['rivals']: c.execute("SELECT datestamp, (SUM(craftingacts) + SUM(carvingacts)) AS harvests FROM rivals WHERE clanid=? GROUP BY datestamp", (rival['id'],)) recs = c.fetchall() dates = [x[0] for x in recs] acts = [x[1] for x in recs] actdeltas = calcDeltas(acts) data = trimDataByStd(buildData(dates, actdeltas), maxdev) for entry in data: if entry[0] in summary: summary[entry[0]][rival['name']] = entry[1] else: summary[entry[0]] = {rival['name']: entry[1]} with open(os.path.join(settings['csvdir'], 'rivals_crafts.csv'), 'w', newline='') as csvfile: csvw = csv.writer(csvfile, dialect=csv.excel) csvw.writerow(["Date"] + allclans) for date in summary.keys(): row = [date] for clan in allclans: if clan in summary[date]: row.append(summary[date][clan]) else: row.append(None) csvw.writerow(row) c.close() conn.close()

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0.738353

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0

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false

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786b5a28083873009be21437d1a5f009343e7cfd

27,090

py

Python

tests/test_dataflow/test_dataset/test_playground.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

null

tests/test_dataflow/test_dataset/test_playground.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

null

tests/test_dataflow/test_dataset/test_playground.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

2

2021-02-03T12:37:53.000Z

2022-03-09T03:48:12.000Z

import pytest import numpy as np from plums.commons.path import Path from plums.commons.data import Taxonomy, Label, TileCollection from plums.dataflow.io import dump, RGB, BGR, Tile from plums.dataflow.io.tile._backend import Image from plums.dataflow.dataset.playground import PlaygroundDataset, TaxonomyReader, TileDriver, AnnotationDriver @pytest.fixture() def reference_image(): return np.array(Image.load(Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg')) def test_taxonomy_reader(tmp_path): # Prepare taxonomy taxonomy_file = tmp_path / 'taxonomy.json' dump({'some_label': {'child': {}, 'other': {'nested': {}}}, 'root': {}}, taxonomy_file) nested = Label('nested') other = Label('other', children=(nested, )) child = Label('child') some_label = Label('some_label', children=(child, other)) root = Label('root') taxonomy_reference = Taxonomy(some_label, root) # Assert equal reader = TaxonomyReader() assert reader(tmp_path) == taxonomy_reference def test_annotation_driver(tmp_path, json_feature_collection): # noqa: R701 annotation_path = tmp_path / 'annotation.json' annotation_path.write_text(json_feature_collection) # +-> Error driver = AnnotationDriver() with pytest.raises(ValueError, match='More than one annotation file was provided'): _ = driver((annotation_path, annotation_path), group='value') # +-> Base driver = AnnotationDriver() annotation = driver((annotation_path, ), group='value') assert len(annotation.record_collection) == 1 assert annotation.record_collection[0].labels == ('tag', 'class') assert annotation.record_collection[0].confidence is None assert annotation.record_collection[0].dataset_id == 'f16fff43-2535-4e34-afec-6404dcdcd545' assert annotation.record_collection[0].zone_id == '10187fa3-30df-4eb4-a1e9-6b1dcdc79951' assert annotation.record_collection[0].id == '6e73eff2-06f3-11ea-976a-b2cdca212bc0' assert \ annotation.mask_collection['zone_footprint'].coordinates == [[[0, 0], [0, 256], [256, 256], [256, 0], [0, 0]]] assert (annotation_path, ) not in driver._memcache # +--> Reopen assert driver((annotation_path, ), group='value') is not annotation # +-> Confidence driver = AnnotationDriver(confidence_key='surface') annotation = driver((annotation_path, ), group='value') assert len(annotation.record_collection) == 1 assert annotation.record_collection[0].labels == ('tag', 'class') assert annotation.record_collection[0].confidence - 64.2146176930851 <= 1e-4 assert annotation.record_collection[0].dataset_id == 'f16fff43-2535-4e34-afec-6404dcdcd545' assert annotation.record_collection[0].zone_id == '10187fa3-30df-4eb4-a1e9-6b1dcdc79951' assert annotation.record_collection[0].id == '6e73eff2-06f3-11ea-976a-b2cdca212bc0' assert \ annotation.mask_collection['zone_footprint'].coordinates == [[[0, 0], [0, 256], [256, 256], [256, 0], [0, 0]]] # +-> Id driver = AnnotationDriver(record_id_key='owner_id') annotation = driver((annotation_path, ), group='value') assert len(annotation.record_collection) == 1 assert annotation.record_collection[0].labels == ('tag', 'class') assert annotation.record_collection[0].confidence is None assert annotation.record_collection[0].dataset_id == 'f16fff43-2535-4e34-afec-6404dcdcd545' assert annotation.record_collection[0].zone_id == '10187fa3-30df-4eb4-a1e9-6b1dcdc79951' assert annotation.record_collection[0].id == '35e370a9-6b76-4ac6-a3d5-1eeb983c3dc7' assert \ annotation.mask_collection['zone_footprint'].coordinates == [[[0, 0], [0, 256], [256, 256], [256, 0], [0, 0]]] # +-> Cache driver = AnnotationDriver(cache=True) annotation = driver((annotation_path, ), group='value') assert len(annotation.record_collection) == 1 assert annotation.record_collection[0].labels == ('tag', 'class') assert annotation.record_collection[0].confidence is None assert annotation.record_collection[0].dataset_id == 'f16fff43-2535-4e34-afec-6404dcdcd545' assert annotation.record_collection[0].zone_id == '10187fa3-30df-4eb4-a1e9-6b1dcdc79951' assert annotation.record_collection[0].id == '6e73eff2-06f3-11ea-976a-b2cdca212bc0' assert \ annotation.mask_collection['zone_footprint'].coordinates == [[[0, 0], [0, 256], [256, 256], [256, 0], [0, 0]]] assert driver._memcache[(annotation_path, )] is annotation # +--> Reopen assert driver((annotation_path, ), group='value') is annotation def test_tile_driver(reference_image): # noqa: R701 # +-> Base driver = TileDriver(fetch_ordering=False) tiles = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') assert isinstance(tiles, TileCollection) assert len(tiles) == 3 assert all(isinstance(tile, Tile) for tile in tiles.values()) assert all(name == 'tile_{}'.format(i) for i, name in enumerate(tiles.keys())) assert all(tile.ptype == RGB for tile in tiles.values()) assert all(tile.dtype == np.uint8 for tile in tiles.values()) assert all(np.array_equal(reference_image, tile) for tile in tiles.values()) # +-> PType driver = TileDriver(ptype=BGR, fetch_ordering=False) tiles = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') assert isinstance(tiles, TileCollection) assert len(tiles) == 3 assert all(isinstance(tile, Tile) for tile in tiles.values()) assert all(name == 'tile_{}'.format(i) for i, name in enumerate(tiles.keys())) assert all(tile.ptype == BGR for tile in tiles.values()) assert all(tile.dtype == np.uint8 for tile in tiles.values()) assert all(np.array_equal(reference_image, tile.astype(ptype=RGB)) for tile in tiles.values()) # +-> DType driver = TileDriver(dtype=np.float64, fetch_ordering=False) tiles = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') assert isinstance(tiles, TileCollection) assert len(tiles) == 3 assert all(isinstance(tile, Tile) for tile in tiles.values()) assert all(name == 'tile_{}'.format(i) for i, name in enumerate(tiles.keys())) assert all(tile.ptype == RGB for tile in tiles.values()) assert all(tile.dtype == np.float64 for tile in tiles.values()) assert all(np.array_equal(reference_image, tile.astype(dtype=np.uint8)) for tile in tiles.values()) # +-> Names with pytest.raises(ValueError, match='The number of tiles is incompatible with the provided number'): driver = TileDriver('not', 'enough', fetch_ordering=False) _ = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') with pytest.raises(ValueError, match='The number of tiles is incompatible with the provided number'): driver = TileDriver('too', 'many', 'names', 'provided', fetch_ordering=False) _ = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') names = ['some', 'tile', 'set'] driver = TileDriver(*names, fetch_ordering=False) tiles = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') assert isinstance(tiles, TileCollection) assert len(tiles) == 3 assert all(isinstance(tile, Tile) for tile in tiles.values()) assert all(name == names[i] for i, name in enumerate(tiles.keys())) assert all(tile.ptype == RGB for tile in tiles.values()) assert all(tile.dtype == np.uint8 for tile in tiles.values()) assert all(np.array_equal(reference_image, tile.astype(dtype=np.uint8)) for tile in tiles.values()) # +-> All driver = TileDriver(*names, ptype=BGR, dtype=np.float64, fetch_ordering=False) tiles = driver((Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg', Path(__file__)[:-1] / '..' / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), group='value') assert isinstance(tiles, TileCollection) assert len(tiles) == 3 assert all(isinstance(tile, Tile) for tile in tiles.values()) assert all(name == names[i] for i, name in enumerate(tiles.keys())) assert all(tile.ptype == BGR for tile in tiles.values()) assert all(tile.dtype == np.float64 for tile in tiles.values()) assert all(np.array_equal(reference_image, tile.astype(ptype=RGB, dtype=np.uint8)) for tile in tiles.values()) def test_base(playground_tree, reference_image): root, paths = playground_tree dataset = PlaygroundDataset(root, use_taxonomy=False) assert len(dataset) == 5 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8', '92eb5ffee6ae2fec3ad71c777531578b') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[3] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') assert dataset._group_index[4] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') assert len(dataset[0].tiles) == 2 assert len(dataset[1].tiles) == 1 assert len(dataset[2].tiles) == 1 assert len(dataset[3].tiles) == 1 assert len(dataset[4].tiles) == 2 assert np.array_equal(reference_image, dataset[0].tiles.iloc[0]) # Test ordering assert tuple(tile.image_id for tile in dataset[0].tiles.values()) == ("4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed", "5562b632-72c3-4c21-b24e-e0536d8b20c8") assert tuple(tile.image_id for tile in dataset[4].tiles.values()) == ("f9525e3bfbd081cd545261b3b5414eb88f689005", "75ad128196254e711ef7c9b129d1c59153098b18") assert len(dataset[0].annotation.record_collection) == 1 assert dataset[0].annotation.record_collection[0].labels == ('tag', 'class') assert dataset[0].annotation.record_collection[0].dataset_id == 'f16fff43-2535-4e34-afec-6404dcdcd545' assert dataset[0].annotation.record_collection[0].zone_id == '10187fa3-30df-4eb4-a1e9-6b1dcdc79951' assert dataset[0].annotation.record_collection[0].id == '6e73eff2-06f3-11ea-976a-b2cdca212bc0' assert dataset[0].annotation.mask_collection['zone_footprint'].coordinates \ == [[[0, 0], [0, 256], [256, 256], [256, 0], [0, 0]]] def test_select_exclude(playground_tree): # noqa: R701 root, paths = playground_tree # Dataset: # +-> Select: dataset = PlaygroundDataset(root, use_taxonomy=False, select_datasets=('63d0da07-0a4b-4ffd-844f-af75c02288e0', )) assert len(dataset) == 3 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') # +-> Exclude: dataset = PlaygroundDataset(root, use_taxonomy=False, exclude_datasets=('1af6c4c5-278d-40ae-9e32-dc8192f8402a', )) assert len(dataset) == 3 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') # +-> Both: with pytest.raises(ValueError, match='Invalid dataset: No matches where found between tiles and annotation'): _ = PlaygroundDataset(root, use_taxonomy=False, select_datasets=('63d0da07-0a4b-4ffd-844f-af75c02288e0', ), exclude_datasets=('63d0da07-0a4b-4ffd-844f-af75c02288e0', )) # Zone: # +-> Select: dataset = PlaygroundDataset(root, use_taxonomy=False, select_zones=('b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8')) assert len(dataset) == 3 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8', '92eb5ffee6ae2fec3ad71c777531578b') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') # +-> Exclude: dataset = PlaygroundDataset(root, use_taxonomy=False, exclude_zones=('b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8')) assert len(dataset) == 2 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') # +-> Both: dataset = PlaygroundDataset(root, use_taxonomy=False, select_zones=('b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8'), exclude_zones=('c3e8b68b-f862-41bd-848c-6e2df28e4dd8', )) assert len(dataset) == 2 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') # Image: # +-> Select: dataset = PlaygroundDataset(root, use_taxonomy=False, select_images=('S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548', 'f9525e3bfbd081cd545261b3b5414eb88f689005')) assert len(dataset) == 2 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') assert len(dataset[0].tiles) == 1 assert len(dataset[1].tiles) == 1 # +-> Exclude: dataset = PlaygroundDataset(root, use_taxonomy=False, exclude_images=('S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548', 'f9525e3bfbd081cd545261b3b5414eb88f689005')) assert len(dataset) == 4 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', 'c3e8b68b-f862-41bd-848c-6e2df28e4dd8', '92eb5ffee6ae2fec3ad71c777531578b') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[3] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') assert len(dataset[0].tiles) == 2 assert len(dataset[1].tiles) == 1 assert len(dataset[2].tiles) == 1 assert len(dataset[3].tiles) == 1 # +-> Both: dataset = PlaygroundDataset(root, use_taxonomy=False, select_images=('S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548', 'f9525e3bfbd081cd545261b3b5414eb88f689005', '75ad128196254e711ef7c9b129d1c59153098b18'), exclude_images=('S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548', '75ad128196254e711ef7c9b129d1c59153098b18', )) assert len(dataset) == 1 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') assert len(dataset[0].tiles) == 1 # Tile: # +-> Select: dataset = PlaygroundDataset(root, use_taxonomy=False, select_tiles=('4a8a08f09d37b73795649038408b5f33', '0cc175b9c0f1b6a831c399e269772661', '7c47df1097b349278c052e93e1d1903a')) assert len(dataset) == 3 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') # +-> Exclude: dataset = PlaygroundDataset(root, use_taxonomy=False, exclude_tiles=('92eb5ffee6ae2fec3ad71c777531578b', )) assert len(dataset) == 4 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') assert dataset._group_index[2] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '453e41d218e071ccfb2d1c99ce23906a') assert dataset._group_index[3] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') # +-> Both: dataset = PlaygroundDataset(root, use_taxonomy=False, select_tiles=('4a8a08f09d37b73795649038408b5f33', '0cc175b9c0f1b6a831c399e269772661', '7c47df1097b349278c052e93e1d1903a'), exclude_tiles=('7c47df1097b349278c052e93e1d1903a', )) assert len(dataset) == 2 assert dataset._group_index[0] == ('1af6c4c5-278d-40ae-9e32-dc8192f8402a', '2411dbb6-e7bf-41fd-8898-83325a9c6e5a', '4a8a08f09d37b73795649038408b5f33') assert dataset._group_index[1] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7', '0cc175b9c0f1b6a831c399e269772661') def test_select_exclude_composition(playground_tree): root, paths = playground_tree dataset = PlaygroundDataset(root, use_taxonomy=False, exclude_datasets=('1af6c4c5-278d-40ae-9e32-dc8192f8402a', ), select_zones=('fa719db8-31e9-49d1-9344-d4608ef6417e', ), exclude_images=('f9525e3bfbd081cd545261b3b5414eb88f689005', )) assert len(dataset) == 1 assert dataset._group_index[0] == ('63d0da07-0a4b-4ffd-844f-af75c02288e0', 'fa719db8-31e9-49d1-9344-d4608ef6417e', '7c47df1097b349278c052e93e1d1903a') assert len(dataset[0].tiles) == 1 def test_pass_taxonomy(playground_tree): root, paths = playground_tree dataset = PlaygroundDataset(root, use_taxonomy=True) with pytest.raises(ValueError): _ = dataset[0] def test_taxonomy_conflict_raise(playground_tree_conflict): root, paths = playground_tree_conflict with pytest.raises(ValueError, match='Some datasets have mismatching taxonomies'): _ = PlaygroundDataset(root, use_taxonomy=True) def test_taxonomy_conflict_warn(playground_tree_conflict): root, paths = playground_tree_conflict with pytest.warns(UserWarning, match='Some datasets have mismatching taxonomies'): _ = PlaygroundDataset(root, use_taxonomy=False) def test_fetch_ordering_missing_image(playground_tree_summary_missing_image): root, paths = playground_tree_summary_missing_image dataset = PlaygroundDataset(root, use_taxonomy=False) with pytest.raises(ValueError, match='Invalid dataset: Some images seem to be missing from the summaries'): _ = dataset[5] dataset = PlaygroundDataset(root, use_taxonomy=False, tile_driver=TileDriver(fetch_ordering=False)) assert isinstance(dataset[5].tiles.iloc[0], Tile) def test_fetch_ordering_missing_zone(playground_tree_summary_missing_zone): root, paths = playground_tree_summary_missing_zone dataset = PlaygroundDataset(root, use_taxonomy=False) with pytest.raises(ValueError, match='Invalid dataset: Some zones or datasets seem to be ' 'missing from the summaries'): _ = dataset[1] dataset = PlaygroundDataset(root, use_taxonomy=False, tile_driver=TileDriver(fetch_ordering=False)) assert isinstance(dataset[1].tiles.iloc[0], Tile) def test_fetch_ordering_missing_dataset(playground_tree_summary_missing_dataset): root, paths = playground_tree_summary_missing_dataset dataset = PlaygroundDataset(root, use_taxonomy=False) with pytest.raises(ValueError, match='Invalid dataset: Some zones or datasets seem to be ' 'missing from the summaries'): _ = dataset[0] dataset = PlaygroundDataset(root, use_taxonomy=False, tile_driver=TileDriver(fetch_ordering=False)) assert isinstance(dataset[0].tiles.iloc[0], Tile) def test_fetch_ordering_missing_summaries(playground_tree_summary_missing_summaries): root, paths = playground_tree_summary_missing_summaries dataset = PlaygroundDataset(root, use_taxonomy=False) with pytest.raises(FileNotFoundError, match='Invalid dataset: No file summaries could be found'): _ = dataset[0] dataset = PlaygroundDataset(root, use_taxonomy=False, tile_driver=TileDriver(fetch_ordering=False)) assert isinstance(dataset[0].tiles.iloc[0], Tile)

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null

0

1

0

null

0

6

7873368a0ed09fce4544a2b27786ebeec2bd5f3f

67

py

Python

smsframework/providers/forward/__init__.py

JoshBBOXX/py-smsframework

4f3d812711f5e2e037dc80c4014c815fe2d68a0b

[ "BSD-2-Clause" ]

14

2015-08-20T23:26:51.000Z

2022-02-17T17:41:35.000Z

smsframework/providers/forward/__init__.py

JoshBBOXX/py-smsframework

4f3d812711f5e2e037dc80c4014c815fe2d68a0b

[ "BSD-2-Clause" ]

2

2015-08-20T20:46:25.000Z

2020-05-30T14:05:57.000Z

smsframework/providers/forward/__init__.py

JoshBBOXX/py-smsframework

4f3d812711f5e2e037dc80c4014c815fe2d68a0b

[ "BSD-2-Clause" ]

6

2015-06-15T16:10:59.000Z

2020-01-24T23:07:48.000Z

from .provider import ForwardClientProvider, ForwardServerProvider

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0

1

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1

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6

152eac24b88ebd07da6d0484ea512d57a6244683

2,350

py

Python

Cryptography/Mini RSA/main.py

YanickT/picoCTF2021

7b3fcdf9d8375d4428cf1fa2fdf4b981bee7d69f

[ "MIT" ]

null

Cryptography/Mini RSA/main.py

YanickT/picoCTF2021

7b3fcdf9d8375d4428cf1fa2fdf4b981bee7d69f

[ "MIT" ]

null

Cryptography/Mini RSA/main.py

YanickT/picoCTF2021

7b3fcdf9d8375d4428cf1fa2fdf4b981bee7d69f

[ "MIT" ]

null

from decimal import * N = Decimal(1615765684321463054078226051959887884233678317734892901740763321135213636796075462401950274602405095138589898087428337758445013281488966866073355710771864671726991918706558071231266976427184673800225254531695928541272546385146495736420261815693810544589811104967829354461491178200126099661909654163542661541699404839644035177445092988952614918424317082380174383819025585076206641993479326576180793544321194357018916215113009742654408597083724508169216182008449693917227497813165444372201517541788989925461711067825681947947471001390843774746442699739386923285801022685451221261010798837646928092277556198145662924691803032880040492762442561497760689933601781401617086600593482127465655390841361154025890679757514060456103104199255917164678161972735858939464790960448345988941481499050248673128656508055285037090026439683847266536283160142071643015434813473463469733112182328678706702116054036618277506997666534567846763938692335069955755244438415377933440029498378955355877502743215305768814857864433151287) e = Decimal(3) c = Decimal(1220012318588871886132524757898884422174534558055593713309088304910273991073554732659977133980685370899257850121970812405700793710546674062154237544840177616746805668666317481140872605653768484867292138139949076102907399831998827567645230986345455915692863094364797526497302082734955903755050638155202890599808154558034707767377524500302754459807923331810585173010977657982069888996945830789092526932364658459034145456505057469113036134559745659079236466119515004648189278227777550415021840140147319061470183840214034417917161940379351273394212022847037696265532968684592354941479799473941357715953204487236888712642494877545201005807776354854390358015733495331101077851132489983665939643188064986446883595239842621440918456201787168234988410659153219277329426230136499096098072681939491840913961290536851217677043565743644469862992310241563891464225935615676242084658617931225618537173689559419607688905143683603007487996422560430269750305079282818976557285786253025774883158125978164878245223052992502106) getcontext().prec = 800 i = 0 while True: m = pow(c + i * N, 1 / e) hex_m = hex(int(m))[2:] flag = "".join([chr(int(hex_m[i:i+2], 16)) for i in range(0, len(hex_m), 2)]) if "picoCTF" in flag: print(i) print(flag) break i += 1

123.684211

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0.930213

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2,350

35.209677

0.548387

0.005497

0

0.9004

0.042979

2,350

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0

1

0

1

0

null

1

0

6

1551c0de9790ca96cb8227a8e99eaa5f1d2e8c8b

2,954

py

Python

tests/unit/util/test_video.py

zerofox-oss/deepstar

fe0fe12317975104fa6ff6c058d141f11e6e951d

[ "BSD-3-Clause-Clear" ]

44

2019-08-09T16:14:27.000Z

2022-02-10T06:54:35.000Z

tests/unit/util/test_video.py

zerofox-oss/deepstar

fe0fe12317975104fa6ff6c058d141f11e6e951d

[ "BSD-3-Clause-Clear" ]

2

2020-09-26T00:05:52.000Z

2021-03-22T13:27:36.000Z

tests/unit/util/test_video.py

zerofox-oss/deepstar

fe0fe12317975104fa6ff6c058d141f11e6e951d

[ "BSD-3-Clause-Clear" ]

14

2019-08-19T16:47:32.000Z

2022-03-04T03:57:27.000Z

import os import unittest import cv2 from deepstar.util.tempdir import tempdir from deepstar.util.video import create_one_video_file_from_one_image_file, \ create_one_video_file_from_many_image_files class TestVideo(unittest.TestCase): """ This class tests the video module. """ def test_create_one_video_file_from_one_image_file(self): image_0001 = os.path.dirname(os.path.realpath(__file__)) + '/../../support/image_0001.jpg' # noqa with tempdir() as tempdir_: video_path = os.path.join(tempdir_, 'video.mp4') ret = create_one_video_file_from_one_image_file(image_0001, video_path) self.assertTrue(ret) vc = cv2.VideoCapture(video_path) try: self.assertTrue(vc.isOpened()) self.assertEqual(vc.get(cv2.CAP_PROP_FRAME_COUNT), 1) finally: vc.release() def test_create_one_video_file_from_one_image_file_frame_count(self): image_0001 = os.path.dirname(os.path.realpath(__file__)) + '/../../support/image_0001.jpg' # noqa with tempdir() as tempdir_: video_path = os.path.join(tempdir_, 'video.mp4') ret = create_one_video_file_from_one_image_file(image_0001, video_path, frame_count=5) self.assertTrue(ret) vc = cv2.VideoCapture(video_path) try: self.assertTrue(vc.isOpened()) self.assertEqual(vc.get(cv2.CAP_PROP_FRAME_COUNT), 5) finally: vc.release() def test_create_one_video_file_from_one_image_file_fails_to_open_image(self): # noqa image_0001 = 'test' with tempdir() as tempdir_: video_path = os.path.join(tempdir_, 'video.mp4') ret = create_one_video_file_from_one_image_file(image_0001, video_path, frame_count=5) self.assertFalse(ret) def test_create_one_video_file_from_many_image_files(self): image_0001 = os.path.dirname(os.path.realpath(__file__)) + '/../../support/image_0001.jpg' # noqa with tempdir() as tempdir_: video_path = os.path.join(tempdir_, 'video.mp4') def image_paths(): for _ in range(0, 5): yield image_0001 ret = create_one_video_file_from_many_image_files(image_paths, video_path) # noqa self.assertTrue(ret) vc = cv2.VideoCapture(video_path) try: self.assertTrue(vc.isOpened()) self.assertEqual(vc.get(cv2.CAP_PROP_FRAME_COUNT), 5) finally: vc.release()

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0.721897

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2,954

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34.348837

0.764889

0.020311

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0.666667

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0.175439

1

0.087719

false

0

0.087719

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0.192982

0

null

0

1

0

null

0

6

ec7a30c305681af1a5daa3ed554c0e92947c7973

124

py

Python

site/filters.py

qnub/qnub.github.io

b2bacc8e7c92d0fdffe7f870a1b7c31f24f68553

[ "MIT" ]

null

site/filters.py

qnub/qnub.github.io

b2bacc8e7c92d0fdffe7f870a1b7c31f24f68553

[ "MIT" ]

null

site/filters.py

qnub/qnub.github.io

b2bacc8e7c92d0fdffe7f870a1b7c31f24f68553

[ "MIT" ]

null

# -*- coding: utf-8 -*- def cat_name(cat): from publishconf import CATEGORY_MAP return CATEGORY_MAP.get(cat, '')

15.5

40

0.653226

17

124

4.588235

0.764706

0.282051

0

0.010101

0.201613

124

7

41

17.714286

0.777778

0.169355

0

1

0.333333

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0

0.333333

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ec8707d87b3b7f71792cf9591b9348f7a3122714

264

py

Python

lists_mutation/append_method_adding_list_plus_operator.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

lists_mutation/append_method_adding_list_plus_operator.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

lists_mutation/append_method_adding_list_plus_operator.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

countries = ['United States', 'Canada', 'Japan'] print(countries) print(len(countries)) countries.append('Japan') print(countries) print(len(countries)) countries.append('Iraq') print(countries) print(len(countries)) countries.append('Bolivia') print(countries)

18.857143

48

0.757576

31

264

6.451613

0.322581

0.28

0.285

0.33

0.74

0.51

0

0.068182

264

14

49

18.857143

0.813008

0

0.636364

0

0.150943

0

1

0

false

0

0.636364

0

null

1

0

1

0

null

0

1

0

6

ecb2521b149f1740ae7786fab3341375af08a11f

31

py

Python

Using Python to Interact with the Operating System/WEEK 3/Practice Quiz/Solutions.py

manavnarang/Google-IT-Automation-with-Python-Professional-Certificate

ce982870f07cba8200947eda97764fcf8c7dc441

[ "MIT" ]

42

2020-04-28T09:06:21.000Z

2022-01-09T01:01:55.000Z

Using Python to Interact with the Operating System/WEEK 3/Practice Quiz/Solutions.py

vaquarkhan/Google-IT-Automation-with-Python-Professional-Certificate

d87dffe924de218f73d61d27689798646824ed6c

[ "MIT" ]

null

Using Python to Interact with the Operating System/WEEK 3/Practice Quiz/Solutions.py

vaquarkhan/Google-IT-Automation-with-Python-Professional-Certificate

d87dffe924de218f73d61d27689798646824ed6c

[ "MIT" ]

52

2020-05-12T05:29:46.000Z

2022-01-26T21:24:08.000Z

print("Check the files out!!")

15.5

30

0.677419

5

31

4.2

1

0

0.129032

31

1

31

0.777778

0

0.677419

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

ecb4a66cfbbf89326eb6bace06b8e3acb86acd7e

128

py

Python

run.py

ivanleoncz/taxi-tracker

506b94845024b9e32d6b43a36efda6cd50f3c227

[ "MIT" ]

null

run.py

ivanleoncz/taxi-tracker

506b94845024b9e32d6b43a36efda6cd50f3c227

[ "MIT" ]

null

run.py

ivanleoncz/taxi-tracker

506b94845024b9e32d6b43a36efda6cd50f3c227

[ "MIT" ]

null

from app import app if __name__ == "__main__": app.run(ssl_context=('app/ssl/taxi-driver.crt', 'app/ssl/taxi-driver.key'))

25.6

79

0.695313

21

128

3.809524

0.619048

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ecde8dc9ac88c1b5347eb79191538aff4f84b27e

106

py

Python

gaiadet/models/detectors/__init__.py

zengming16/GAIA-det

cac6b5601d63aeaa3882cea2256dcb2539fecb34

[ "Apache-2.0" ]

149

2021-06-21T06:18:16.000Z

2022-03-23T08:55:23.000Z

gaiadet/models/detectors/__init__.py

zengming16/GAIA-det

cac6b5601d63aeaa3882cea2256dcb2539fecb34

[ "Apache-2.0" ]

7

2021-07-11T07:52:58.000Z

2022-03-30T11:41:39.000Z

gaiadet/models/detectors/__init__.py

zengming16/GAIA-det

cac6b5601d63aeaa3882cea2256dcb2539fecb34

[ "Apache-2.0" ]

13

2021-06-29T06:06:13.000Z

2022-02-28T01:31:17.000Z

from .dynamic_two_stage import DynamicTwoStageDetector from .dynamic_faster_rcnn import DynamicFasterRCNN

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ece88b42c1d0635990d34619d47e4d7f99a8309a

4,830

py

Python

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/20. Exercise - Testing/03. Hero_Skeleton/hero/test/test_hero.py

kzborisov/SoftUni

ccb2b8850adc79bfb2652a45124c3ff11183412e

[ "MIT" ]

1

2021-02-07T07:51:12.000Z

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/20. Exercise - Testing/03. Hero_Skeleton/hero/test/test_hero.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/20. Exercise - Testing/03. Hero_Skeleton/hero/test/test_hero.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

import unittest from project.hero import Hero class TestHero(unittest.TestCase): username = "Hero" level = 10 health = 100 damage = 10 def test_hero_initialization(self): hero = Hero(self.username, self.level, self.health, self.damage) self.assertEqual(self.username, hero.username) self.assertEqual(self.level, hero.level) self.assertEqual(self.health, hero.health) self.assertEqual(self.damage, hero.damage) def test_battle__when_username_is_the_same__expect_exception(self): hero = Hero(self.username, self.level, self.health, self.damage) enemy = Hero(self.username, self.level, self.health, self.damage) expected = "You cannot fight yourself" with self.assertRaises(Exception) as context: hero.battle(enemy) self.assertEqual(expected, str(context.exception)) def test_battle__when_hero_health_is_negative__expected_exception(self): hero = Hero(self.username, self.level, -1, self.damage) enemy = Hero("enemy", self.level, self.health, self.damage) expected = "Your health is lower than or equal to 0. You need to rest" with self.assertRaises(Exception) as context: hero.battle(enemy) self.assertEqual(expected, str(context.exception)) def test_battle__when_hero_health_is_zero__expected_exception(self): hero = Hero(self.username, self.level, 0, self.damage) enemy = Hero("enemy", self.level, self.health, self.damage) expected = "Your health is lower than or equal to 0. You need to rest" with self.assertRaises(Exception) as context: hero.battle(enemy) self.assertEqual(expected, str(context.exception)) def test_battle__when_enemy_health_is_zero__expected_exception(self): enemy_name = "enemy" hero = Hero(self.username, self.level, self.health, self.damage) enemy = Hero(enemy_name, self.level, 0, self.damage) expected = f"You cannot fight {enemy_name}. He needs to rest" with self.assertRaises(Exception) as context: hero.battle(enemy) self.assertEqual(expected, str(context.exception)) def test_battle__when_enemy_health_is_negative__expected_exception(self): enemy_name = "enemy" hero = Hero(self.username, self.level, self.health, self.damage) enemy = Hero(enemy_name, self.level, -1, self.damage) expected = f"You cannot fight {enemy_name}. He needs to rest" with self.assertRaises(Exception) as context: hero.battle(enemy) self.assertEqual(expected, str(context.exception)) def test_battle__when_hero_and_enemy_health_is_zero__expected_draw(self): hero = Hero(self.username, self.level, self.health, self.damage) enemy = Hero("enemy", self.level, self.health, self.damage) damage = self.damage * self.level expected = f"Draw" actual = hero.battle(enemy) self.assertEqual(self.health - damage, hero.health) self.assertEqual(self.health - damage, enemy.health) self.assertEqual(expected, actual) def test_battle__when_hero_and_enemy_health_is_negative__expected_draw(self): hero = Hero(self.username, self.level, self.health, 50) enemy = Hero("enemy", self.level, self.health, 50) damage = hero.damage * self.level expected = f"Draw" actual = hero.battle(enemy) self.assertEqual(self.health - damage, hero.health) self.assertEqual(self.health - damage, enemy.health) self.assertEqual(expected, actual) def test_battle__when_enemy_health_is_negative__expected_win(self): hero = Hero(self.username, 10, 1000, 20) enemy = Hero("enemy", 10, 100, 50) expected = f"You win" actual = hero.battle(enemy) self.assertEqual(expected, actual) self.assertEqual(11, hero.level) self.assertEqual(1000 - 10 * 50 + 5, hero.health) self.assertEqual(20 + 5, hero.damage) def test_battle__when_enemy_health_is_more_than_zero__expected_win(self): hero = Hero(self.username, 10, 1000, 50) enemy = Hero("enemy", 10, 1000, 10) expected = f"You lose" actual = hero.battle(enemy) self.assertEqual(expected, actual) self.assertEqual(11, enemy.level) self.assertEqual(1000 - 10 * 50 + 5, enemy.health) self.assertEqual(10 + 5, enemy.damage) def test_str(self): hero = Hero(self.username, self.level, self.health, self.damage) expected = f"Hero {self.username}: {self.level} lvl\n" \ f"Health: {self.health}\n" \ f"Damage: {self.damage}\n" self.assertEqual(expected, str(hero)) if __name__ == "__main__": unittest.main()

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01da8beb8a0adce4f66ae2d1d0eda4eea3560615

153

py

Python

boa3_test/test_sc/interop_test/stdlib/MemorySearchTooFewArguments.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/interop_test/stdlib/MemorySearchTooFewArguments.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/interop_test/stdlib/MemorySearchTooFewArguments.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from typing import Union from boa3.builtin.interop.stdlib import memory_search def main(mem: Union[bytes, str]) -> int: return memory_search(mem)

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bf0f51af6c7bf440ab56f594cf1fadb41ea85af6

41

py

Python

vilmedic/__init__.py

jbdel/vilmedic

17d462a540a2632811cc2a78edd2861800a33b07

[ "MIT" ]

15

2021-07-24T10:41:07.000Z

2022-03-27T14:40:47.000Z

vilmedic/__init__.py

jbdel/vilmedic

17d462a540a2632811cc2a78edd2861800a33b07

[ "MIT" ]

null

vilmedic/__init__.py

jbdel/vilmedic

17d462a540a2632811cc2a78edd2861800a33b07

[ "MIT" ]

2

2022-02-22T17:37:22.000Z

2022-03-20T12:55:40.000Z

from .zoo.modeling_auto import AutoModel

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6

bd91d53934122a96828fca7265633c50201170f5

16,094

py

Python

mailslurp_client/api/bulk_actions_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

6

2020-04-30T07:47:42.000Z

2022-03-24T20:58:58.000Z

mailslurp_client/api/bulk_actions_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

1

2020-09-20T19:58:21.000Z

2020-11-29T16:49:19.000Z

mailslurp_client/api/bulk_actions_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

1

2019-08-09T14:55:50.000Z

# coding: utf-8 """ MailSlurp API MailSlurp is an API for sending and receiving emails from dynamically allocated email addresses. It's designed for developers and QA teams to test applications, process inbound emails, send templated notifications, attachments, and more. ## Resources - [Homepage](https://www.mailslurp.com) - Get an [API KEY](https://app.mailslurp.com/sign-up/) - Generated [SDK Clients](https://www.mailslurp.com/docs/) - [Examples](https://github.com/mailslurp/examples) repository # noqa: E501 The version of the OpenAPI document: 6.5.2 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 mailslurp_client.api_client import ApiClient from mailslurp_client.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class BulkActionsControllerApi(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 bulk_create_inboxes(self, count, **kwargs): # noqa: E501 """Bulk create Inboxes (email addresses) # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_inboxes(count, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int count: Number of inboxes to be created in bulk (required) :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[Inbox] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.bulk_create_inboxes_with_http_info(count, **kwargs) # noqa: E501 def bulk_create_inboxes_with_http_info(self, count, **kwargs): # noqa: E501 """Bulk create Inboxes (email addresses) # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_inboxes_with_http_info(count, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int count: Number of inboxes to be created in bulk (required) :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[Inbox], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'count' ] 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 bulk_create_inboxes" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'count' is set if self.api_client.client_side_validation and ('count' not in local_var_params or # noqa: E501 local_var_params['count'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `count` when calling `bulk_create_inboxes`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'count' in local_var_params and local_var_params['count'] is not None: # noqa: E501 query_params.append(('count', local_var_params['count'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/bulk/inboxes', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Inbox]', # 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 bulk_delete_inboxes(self, ids, **kwargs): # noqa: E501 """Bulk Delete Inboxes # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_inboxes(ids, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param list[str] ids: ids (required) :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: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.bulk_delete_inboxes_with_http_info(ids, **kwargs) # noqa: E501 def bulk_delete_inboxes_with_http_info(self, ids, **kwargs): # noqa: E501 """Bulk Delete Inboxes # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_inboxes_with_http_info(ids, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param list[str] ids: ids (required) :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: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'ids' ] 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 bulk_delete_inboxes" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'ids' is set if self.api_client.client_side_validation and ('ids' not in local_var_params or # noqa: E501 local_var_params['ids'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `ids` when calling `bulk_delete_inboxes`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'ids' in local_var_params: body_params = local_var_params['ids'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/bulk/inboxes', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # 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 bulk_send_emails(self, bulk_send_email_options, **kwargs): # noqa: E501 """Bulk Send Emails # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_send_emails(bulk_send_email_options, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param BulkSendEmailOptions bulk_send_email_options: bulkSendEmailOptions (required) :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: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.bulk_send_emails_with_http_info(bulk_send_email_options, **kwargs) # noqa: E501 def bulk_send_emails_with_http_info(self, bulk_send_email_options, **kwargs): # noqa: E501 """Bulk Send Emails # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_send_emails_with_http_info(bulk_send_email_options, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param BulkSendEmailOptions bulk_send_email_options: bulkSendEmailOptions (required) :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: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'bulk_send_email_options' ] 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 bulk_send_emails" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'bulk_send_email_options' is set if self.api_client.client_side_validation and ('bulk_send_email_options' not in local_var_params or # noqa: E501 local_var_params['bulk_send_email_options'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `bulk_send_email_options` when calling `bulk_send_emails`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'bulk_send_email_options' in local_var_params: body_params = local_var_params['bulk_send_email_options'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/bulk/send', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # 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)

43.032086

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0.816925

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0

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0.332919

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null

0

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null

0

6

bdb56b6180b74a7055be2545f59a1f748cad4a24

178

py

Python

practice/pid_line.py

jarzab3/smart_city_mdx

957ecfc35414d2833f2112bf3d6e0d0e366b119a

[ "MIT" ]

1

2019-01-22T17:19:22.000Z

practice/pid_line.py

jarzab3/smart_city_mdx

957ecfc35414d2833f2112bf3d6e0d0e366b119a

[ "MIT" ]

null

practice/pid_line.py

jarzab3/smart_city_mdx

957ecfc35414d2833f2112bf3d6e0d0e366b119a

[ "MIT" ]

null

from python_asip_client.mirto_robot import MirtoRobot from python_asip_client.tcp_mirto_robot import TCPMirtoRobot from python_asip_client.serial_mirto_robot import SerialBoard

35.6

61

0.910112

26

178

5.807692

0.461538

0.198676

0.278146

0.397351

0

0.073034

178

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0

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true

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null

0

1

0

1

0

6

bdd7f9d5598309beefb2b3ddec22c7a99e68657c

200

py

Python

PythonComparisonOperators/comparison_operator.py

theprogrammingthinker/Python-practice

fef11a7fbd5082a0614b01f88a13ea29d68860bf

[ "Unlicense" ]

1

2017-05-02T10:28:36.000Z

PythonComparisonOperators/comparison_operator.py

theprogrammingthinker/Python-practice

fef11a7fbd5082a0614b01f88a13ea29d68860bf

[ "Unlicense" ]

null

PythonComparisonOperators/comparison_operator.py

theprogrammingthinker/Python-practice

fef11a7fbd5082a0614b01f88a13ea29d68860bf

[ "Unlicense" ]

null

print(2 == 2) print(1 == 0) print(1 == 1.0) # not equal print(2 != 1) print(2 != 1) print(2 > 1) print(2 > 4) print(2 < 4) print(2 < 1) print(2 >= 2) print(2 >= 1) print(2 <= 2) print(2 <= 10)

9.090909

15

0.505

42

200

2.404762

0.190476

0.653465

0.346535

0.594059

0.683168

0.60396

0

0.18543

0.245

200

21

16

9.52381

0.483444

0.045

0

0.153846

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

bddc2c96592e5cf51fcffd7182f4e247f21435b7

237

py

Python

parser/team05/proyecto/Retorno.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

35

2020-12-07T03:11:43.000Z

2021-04-15T17:38:16.000Z

parser/team05/proyecto/Retorno.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

47

2020-12-09T01:29:09.000Z

2021-01-13T05:37:50.000Z

parser/team05/proyecto/Retorno.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

556

2020-12-07T03:13:31.000Z

2021-06-17T17:41:10.000Z

class Retorno: def __init__(self, instruccion, nodo): self._instruccion = instruccion self._nodo = nodo def getInstruccion(self): return self._instruccion def getNodo(self): return self._nodo

23.7

42

0.649789

25

237

5.84

0.4

0.308219

0.191781

0

0.274262

237

10

43

23.7

0.848837

0

1

0.375

false

0

0.25

0.75

0

1

0

null

1

0

null

0

1

0

1

0

6

da03f5cf64ee459331054f81550f36350ec81a87

40

py

Python

python/testData/refactoring/rename/renamePackageUpdatesFirstFormImports/before/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/rename/renamePackageUpdatesFirstFormImports/before/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/rename/renamePackageUpdatesFirstFormImports/before/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import f<caret>oo.m1 print(foo.m1.f())

10

20

0.675

9

40

3

0.777778

0

0.055556

0.1

40

3

21

13.333333

0.694444

0

null

0

0.5

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

da66ceb9984ff8608ffc522df8f5da9f1c70a466

17,584

py

Python

misc/leaderFollower.py

danalex97/nfsTorrent

1364d920aca0c1b656cd52ab1107e35801fae83f

[ "MIT" ]

1

2019-03-12T12:34:13.000Z

misc/leaderFollower.py

wade-welles/CacheTorrent

1364d920aca0c1b656cd52ab1107e35801fae83f

[ "MIT" ]

38

2018-04-11T08:47:07.000Z

2018-06-20T17:51:11.000Z

misc/leaderFollower.py

wade-welles/CacheTorrent

1364d920aca0c1b656cd52ab1107e35801fae83f

[ "MIT" ]

1

2019-03-12T12:34:10.000Z

leader = [0,1357131,1357131,1360131,1360131,1360131,1360131,1361631,1361631,1361631,1361631,1361631,1361631,1361631,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1363131,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1364631,1365131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1366131,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1367631,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1369131,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1370631,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1372131,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1373631,1375131,1375131,1375131,1375131,1375131,1375131,1375131,1375131,1375131,1375131,1376631,1376631,1376631,1376631,1376631,1376631,1376631,1376631,1376631,1376631,1378131,1378131,1378131,1378131,1379631,1381131,1381131,1381131,1381131,1384131,1384131,1384131,1385631,1385631,1385631,1387131,1387131,1388631,1390131,1390131,1391631,1393131,1396131,1397631,1399131,1403631] follower = [0,1393131,1393131,1417131,1420131,1420131,1429131,1433631,1435131,1436631,1441131,1442631,1444131,1454631,1466631,1466631,1469631,1472631,1474131,1474131,1480131,1486131,1487631,1490631,1493631,1498131,1499631,1499631,1501131,1510131,1514631,1514631,1516131,1517631,1517631,1519131,1520631,1523631,1523631,1523631,1523631,1523631,1525131,1525131,1526631,1528131,1528131,1529631,1529631,1531131,1531131,1532631,1532631,1532631,1534131,1535631,1540131,1540131,1544631,1547631,1547631,1547631,1549131,1550631,1556631,1559631,1559631,1561131,1562631,1562631,1562631,1564131,1564131,1564131,1565631,1565631,1565631,1565631,1573131,1573131,1574631,1576131,1577631,1579131,1580631,1580631,1580631,1582131,1582131,1582131,1583631,1583631,1585131,1586631,1588131,1588131,1588131,1589631,1591131,1591131,1594131,1594131,1594131,1595631,1595631,1595631,1597131,1598631,1601631,1603131,1603131,1604631,1604631,1604631,1607631,1609131,1610631,1612131,1616631,1618131,1618131,1619631,1621131,1622631,1624131,1625631,1625631,1625631,1625631,1627131,1627131,1628631,1628631,1628631,1630131,1631631,1631631,1634631,1634631,1634631,1636131,1636131,1636131,1637631,1637631,1637631,1637631,1639131,1640631,1642131,1643631,1643631,1643631,1643631,1645131,1646631,1648131,1649631,1649631,1649631,1649631,1649631,1651131,1651131,1652631,1654131,1657131,1657131,1657131,1658631,1658631,1658631,1660131,1660131,1661631,1663131,1666131,1666131,1667631,1669131,1670631,1670631,1670631,1672131,1672131,1673631,1675131,1676631,1676631,1678131,1678131,1678131,1678131,1678131,1678131,1679631,1681131,1681131,1682631,1684131,1688631,1690131,1690131,1691631,1693131,1693131,1696131,1697631,1697631,1697631,1699131,1699131,1699131,1700631,1700631,1700631,1702131,1702131,1702131,1702131,1703631,1705131,1706631,1706631,1706631,1706631,1708131,1708131,1709631,1711131,1711131,1715631,1720131,1720131,1721631,1721631,1721631,1721631,1726131,1726131,1726131,1730631,1732131,1735131,1735131,1735131,1736631,1738131,1738131,1739631,1742631,1742631,1742631,1748631,1750131,1753131,1756131,1756131,1760631,1760631,1766631,1768131,1771131,1772631,1772631,1774131,1774131,1777131,1780131,1783131,1784631,1787631,1787631,1789131,1790631,1795131,1796631,1796631,1798131,1798131,1798131,1799631,1799631,1799631,1801131,1804131,1807131,1808631,1808631,1808631,1810131,1810131,1810131,1813131,1813131,1817631,1823631,1823631,1823631,1825131,1826631,1828131,1828131,1831131,1834131,1834131,1834131,1835631,1841631,1849131,1855131,1856631,1859631,1864131,1867131,1871631,1871631,1876131,1877631,1885131,1889631,1889631,1891131,1897131,1900131,1901631,1904631,1907631,1910631,1913631,1913631,1915131,1915131,1916631,1918131,1919631,1924131,1928631,1930131,1931631,1934631,1936131,1936131,1955631,1955631,1967631,1967631,1973631,1973631,1982631,1987131,1991631,2023131,2035131,2056131] leader2 = [0,1240667,1240667,1242167,1242167,1242167,1242167,1242167,1242167,1242167,1242667,1242667,1242667,1242667,1242667,1242667,1242667,1243667,1243667,1243667,1243667,1243667,1243667,1243667,1243667,1243667,1243667,1244167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251667,1251667,1252667,1252667,1252667,1252667,1252798,1254167,1255667,1258667,1260167,1260167] follower2 = [0,1242167,1243667,1244167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1245167,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1246667,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1248167,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249667,1249878,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1250167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1251167,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1252667,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1254167,1255667,1255667,1255667,1255667,1255667,1255667,1255667,1257167,1257167,1257167,1257167,1257167,1257167,1260167,1263167,1266167,1267667,1269167,1270667,1270667,1272167,1272167,1273667,1273667,1275167,1275167,1278167,1279667,1281167,1285667,1285667,1287167,1299167,1303667,1306667,1308167,1314167,1320167,1321691,1321699,1341167,1347167,1347167,1348682,1359167,1362167,1365167,1375667,1441679] leader3 = [0,584232,608232,648732,651732,825732,825732,840732,848232,851232,851232,858732,861732,866232,866232,870732,872232,876732,879732,884232,884232,884232,887232,888732,888732,891732,891732,893232,893232,894732,896232,897732,897732,897732,900732,900732,900732,900732,902232,903732,903732,903732,905232,905232,905232,909732,911232,912732,912732,912732,912732,912732,914232,914232,914232,915732,915732,915732,915732,917232,917232,917232,917232,917232,918732,920232,920232,921732,921732,921732,923232,923232,923232,923232,924732,924732,924732,924732,926232,926232,926232,926232,927732,927732,927732,927732,927732,929232,929232,929232,929232,929232,930732,930732,930732,930732,932232,933732,935232,935232,935232,935232,936732,936732,936732,938232,938232,938232,938232,938232,939732,939732,939732,939732,939732,941232,941232,941232,942732,942732,942732,942732,942732,942732,942732,942732,944232,945732,945732,945732,945732,945732,945732,945732,947232,947232,947232,947232,947232,947232,947232,947232,948732,948732,948732,948732,948732,948732,950232,950232,950232,950232,950232,950232,950298,951732,951732,951732,951732,951732,951732,951732,951732,953232,953232,953232,953232,953232,953232,954732,954732,954732,954732,954732,954732,954732,956232,956232,956232,956232,956232,956232,957732,957732,957732,957732,959232,959232,959232,959232,959232,959232,959232,959232,959232,960732,960732,960732,960732,960732,960732,962232,962232,962232,962232,962232,962232,962232,962232,963732,963732,963732,963732,963732,963732,963732,963732,963732,963732,965232,965232,965232,965232,965232,965232,965232,965232,965232,966732,966732,966732,966732,966732,968232,968232,968232,968232,968232,968232,968232,969732,969732,969732,969732,971232,971232,971232,971232,971232,971232,971232,971232,971232,972732,972732,972732,972732,972732,974232,974232,974232,974232,975732,975732,975732,975732,975732,977232,977232,977232,977232,978732,978732,978732,978732,978732,980232,980232,980232,980232,980232,980232,980232,980232,980232,980232,980232,980232,981732,981732,981732,981732,981732,981732,983232,983232,983232,983232,984732,984732,984732,986232,986232,986232,986232,986232,986232,986232,986232,987732,987732,987732,989232,989232,989232,990732,990732,992232,992232,993732,993732,993732,993732,995232,995232,995232,995232,995232,996732,996732,999732,999732,1001232,1002732,1004232,1004232,1005732,1005732,1007232,1007232,1008732,1010232,1011732,1014732,1014732,1016232,1019232] follower3 = [0,1110732,1119732,1119732,1122732,1136232,1139232,1139232,1142232,1143732,1145232,1145232,1146732,1152732,1152732,1154232,1154232,1157232,1157232,1158732,1158732,1163232,1172232,1173732,1175232,1176732,1181232,1181232,1181232,1184232,1184232,1187232,1190232,1191732,1193232,1194732,1194732,1196232,1196232,1200732,1200732,1200732,1203732,1203732,1206732,1206732,1206732,1206732,1208232,1209732,1209732,1211232,1211232,1211232,1211232,1212732,1212732,1214232,1214232,1214232,1214232,1214232,1214232,1215732,1215732,1215732,1215732,1217232,1217232,1217232,1217232,1217232,1218732,1218732,1218732,1218732,1220232,1220232,1221732,1221732,1224732,1224732,1224732,1224732,1226232,1227732,1227732,1227732,1227732,1229232,1229232,1229232,1229232,1230732,1232232,1233732,1233732,1233732,1233732,1238232,1238232,1238232,1239732,1241232,1241232,1242732,1242732,1242732,1242732,1242732,1244232,1245732,1245732,1247232,1247232,1248732,1248732,1250232,1250232,1250232,1250232,1250232,1250232,1251732,1251732,1251732,1251732,1253232,1254732,1254732,1254732,1257732,1257732,1257732,1259232,1259232,1259232,1259232,1259232,1260732,1260732,1262232,1262232,1262232,1263732,1263732,1265232,1265232,1268232,1268232,1268232,1268232,1268232,1269732,1269732,1269732,1271232,1272732,1274232,1274232,1274232,1275732,1275732,1275732,1277232,1277232,1278732,1278732,1278732,1278732,1278732,1278732,1281732,1281732,1284732,1286232,1286232,1287732,1287732,1289232,1290732,1290732,1290732,1292232,1292232,1296732,1296732,1296732,1298232,1299732,1299732,1299732,1299732,1299732,1299732,1299732,1301232,1301232,1302732,1302732,1304232,1305732,1305732,1305732,1305732,1307232,1307232,1308732,1311732,1311732,1313232,1314732,1316232,1316232,1317732,1317732,1317732,1319232,1319232,1319232,1320732,1320732,1320732,1320732,1322232,1322232,1322232,1323732,1326732,1326732,1326732,1326732,1328232,1329732,1329732,1331232,1331232,1332732,1332732,1332732,1334232,1334232,1335732,1335732,1337232,1338732,1338732,1338732,1340232,1340232,1340232,1341732,1343232,1343232,1343232,1343232,1344732,1344732,1346232,1346232,1347732,1349232,1349232,1350732,1353732,1355232,1356732,1359732,1359732,1361232,1361232,1362732,1364232,1364232,1364232,1364232,1365732,1365732,1367232,1367232,1368732,1368732,1371732,1374732,1376232,1376232,1376232,1377732,1377732,1379232,1379232,1380732,1380732,1382232,1382232,1383732,1388232,1391232,1392732,1392732,1395732,1395732,1398732,1400232,1400232,1401732,1401732,1401732,1403232,1404732,1404732,1404732,1406232,1406232,1407732,1409232,1409232,1410732,1412232,1413732,1415232,1415232,1415232,1421232,1421232,1424232,1424232,1425732,1427232,1427232,1427232,1431732,1437732,1439232,1439275,1442232,1452732,1461732,1466232,1466232,1470732,1472232,1473732,1475232,1475232,1490263,1511232,1514232,1517232,1520232,1526232,1553232,1562232,1625232] def sparse(y): return list(y[:-10:10]) def hmm(x): return [float(i) / float(len(x)) * 100 for i in range(len(x))] from plot import * plots = [ LeaderPlot() .plot(sparse(leader)[1:], sparse(hmm(leader))[1:], '+', fillstyle='none', color='black', label="Leader CDF") .plot(sparse(follower)[1:], sparse(hmm(follower))[1:], '^', fillstyle='none', color='black', label="Follower CDF") .save("plots/leaderFollower.png"), # Asym LeaderPlot() .plot(sparse(leader2)[1:], sparse(hmm(leader2))[1:], '+', fillstyle='none', color='black', label="Leader CDF") .plot(sparse(follower2)[1:], sparse(hmm(follower2))[1:], '^', fillstyle='none', color='black', label="Follower CDF") .save("plots/leaderFollower2.png"), # Bigseed LeaderPlot() .plot(sparse(leader3)[1:], sparse(hmm(leader3))[1:], '+', fillstyle='none', color='black', label="Leader CDF") .plot(sparse(follower3)[1:], sparse(hmm(follower3))[1:], '^', fillstyle='none', color='black', label="Follower CDF") .save("plots/leaderFollower3.png"), ]

517.176471

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0.85629

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null

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da6c637f1b37355f05c71e72b2be500df154bdec

323

py

Python

skywalker/__init__.py

dgerosa/plottingstuff

9c52d289cddc01b00da6689fca4b20635da028d5

[ "MIT" ]

2

2018-08-17T00:59:50.000Z

2020-03-27T21:09:46.000Z

skywalker/__init__.py

dgerosa/plottingstuff

9c52d289cddc01b00da6689fca4b20635da028d5

[ "MIT" ]

null

skywalker/__init__.py

dgerosa/plottingstuff

9c52d289cddc01b00da6689fca4b20635da028d5

[ "MIT" ]

null

from .skywalker import __name__ from .skywalker import __version__ from .skywalker import __description__ from .skywalker import __license__ from .skywalker import __author__ from .skywalker import __author_email__ from .skywalker import __url__ from .skywalker import __doc__ from .skywalker import * from .test import *

26.916667

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0.835913

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323

6.076923

0.307692

0.493671

0.721519

0.21097

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323

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29.363636

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0

null

0

1

0

1

0

1

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6

16fdca397b9c6435ec02f333e8439d7be2deddba

27

py

Python

src/schnetpack/md/parsers/__init__.py

sxie22/schnetpack

a421e7c121c7bdb2838fb30f887812110ecfa3c6

[ "MIT" ]

null

src/schnetpack/md/parsers/__init__.py

sxie22/schnetpack

a421e7c121c7bdb2838fb30f887812110ecfa3c6

[ "MIT" ]

null

src/schnetpack/md/parsers/__init__.py

sxie22/schnetpack

a421e7c121c7bdb2838fb30f887812110ecfa3c6

[ "MIT" ]

1

2022-02-10T17:39:11.000Z

from .orca_parser import *

13.5

26

0.777778

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0

1

0

6

e5306a81bdfaa94441b4fb79275af84e6f3a5407

9,070

py

Python

gmm_fit3.py

stlucas44/direct_gmm

b5523d835f21a35089cfd6975caf2a6f07c43b78

[ "MIT" ]

13

2019-04-14T21:09:08.000Z

2020-04-08T22:10:24.000Z

gmm_fit3.py

stlucas44/direct_gmm

b5523d835f21a35089cfd6975caf2a6f07c43b78

[ "MIT" ]

null

gmm_fit3.py

stlucas44/direct_gmm

b5523d835f21a35089cfd6975caf2a6f07c43b78

[ "MIT" ]

4

2019-04-14T00:58:08.000Z

2020-11-16T17:21:47.000Z

import numpy as np from scipy.stats import multivariate_normal as mvn_pdf import matplotlib.pyplot as plt from cluster import MiniBatchKMeans from mixture import GaussianMixture import pymesh from scipy.special import logsumexp mesh0 = pymesh.load_mesh("bunny/bun_zipper_res4.ply") #mesh3 = pymesh.load_mesh("bunny/bun_zipper_res4_pds.ply") #mesh4 = pymesh.load_mesh("bunny/bun_zipper_res4_25k_pds.ply") mesh4 = pymesh.load_mesh("bunny/bun_zipper_res4_sds.ply") def get_centroids(mesh): # obtain a vertex for each face index face_vert = mesh.vertices[mesh.faces.reshape(-1),:].reshape((mesh.faces.shape[0],3,-1)) # face_vert is size (faces,3(one for each vert), 3(one for each dimension)) centroids = face_vert.sum(1)/3.0 ABAC = face_vert[:,1:3,:] - face_vert[:,0:1,:] areas = np.linalg.norm(np.cross(ABAC[:,0,:],ABAC[:,1,:]),axis=1)/2.0 return centroids, areas com,a = get_centroids(mesh0) face_vert = mesh0.vertices[mesh0.faces.reshape(-1),:].reshape((mesh0.faces.shape[0],3,-1)) #gm3 = GaussianMixture(100,init_params='kmeans'); gm3.set_triangles(face_vert); gm3.fit(com); gm3.set_triangles(None) gm3 = GaussianMixture(1,init_params='kmeans',tol=1e-4,max_iter=100); gm3.fit(mesh4.vertices) def tri_loss(gmm,faces_and_verts): centroids = face_vert.mean(1) ABAC = face_vert[:,1:3,:] - face_vert[:,0:1,:] areas = np.linalg.norm(np.cross(ABAC[:,0,:],ABAC[:,1,:]),axis=1)/2.0 #areas = areas/areas.sum() total = 0.0 #for idx, face in enumerate(faces_and_verts): #face is 3 faces with 3d locs #center = face.mean(0) #centr2 = centroids[idx,:] A = faces_and_verts[:,0,:] B = faces_and_verts[:,1,:] C = faces_and_verts[:,2,:] #m = center.reshape((-1,1)) #thing = np.zeros(gmm.weights_.shape) thing = np.zeros((faces_and_verts.shape[0],gmm.weights_.shape[0])) i = 0 #things = weights = np.zeros(thing.shape) for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): weights[:,i] = mvn_pdf.pdf(centroids,mu,s) #print(mvn_pdf.pdf(points,mu,s).shape,weights.shape) i+=1 row_sums = weights.sum(axis=1) #print(row_sums.shape) weights = weights / row_sums[:, np.newaxis] i=0 for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = (centroids - mu) res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si)*dev).sum(1) t2 = (A.dot(si)*A + B.dot(si)*B + C.dot(si)*C - 3*centroids.dot(si)*centroids).sum(1) #print("T1\t",t1.sum(),t1.min(),t1.max(),t1.mean()) #print("T2\t",t2.sum(),t2.min(),t2.max(),t2.mean()) res -= 0.5 * (t1 + (1.0/12.0) * t2) total += ((res + np.log(pi))).sum() thing[:,i] = ((res+ np.log(pi))) i+=1 #total += thing.sum()*#areas[idx]#logsumexp(thing)*areas[idx] return logsumexp(thing,axis=1).mean()#.sum()/areas.sum()#.mean()#/points.shape[0] #return total/areas.sum()#faces_and_verts.shape[0] def tri_loss_lb(gmm,faces_and_verts): centroids = face_vert.mean(1) ABAC = face_vert[:,1:3,:] - face_vert[:,0:1,:] areas = np.linalg.norm(np.cross(ABAC[:,0,:],ABAC[:,1,:]),axis=1)/2.0 #areas = areas/areas.sum() total = 0.0 #for idx, face in enumerate(faces_and_verts): #face is 3 faces with 3d locs #center = face.mean(0) #centr2 = centroids[idx,:] A = faces_and_verts[:,0,:] B = faces_and_verts[:,1,:] C = faces_and_verts[:,2,:] #m = center.reshape((-1,1)) #thing = np.zeros(gmm.weights_.shape) thing = np.zeros((faces_and_verts.shape[0],gmm.weights_.shape[0])) i = 0 #things = weights = np.zeros(thing.shape) for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): weights[:,i] = mvn_pdf.pdf(centroids,mu,s) #print(mvn_pdf.pdf(points,mu,s).shape,weights.shape) i+=1 row_sums = weights.sum(axis=1) #print(row_sums.shape) weights = weights / row_sums[:, np.newaxis] i=0 for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = (centroids - mu) res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si)*dev).sum(1) t2 = (A.dot(si)*A + B.dot(si)*B + C.dot(si)*C - 3*centroids.dot(si)*centroids).sum(1) #print("T1\t",t1.sum(),t1.min(),t1.max(),t1.mean()) #print("T2\t",t2.sum(),t2.min(),t2.max(),t2.mean()) res -= 0.5 * (t1 + (1.0/12.0) * t2) total += ((res + np.log(pi))).sum() thing[:,i] = ((res+ np.log(pi)))*areas#/areas.mean() i+=1 #total += thing.sum()*#areas[idx]#logsumexp(thing)*areas[idx] #thing = thing*weights return np.sum(thing,axis=1).sum()/areas.sum()#.sum()/areas.sum()#.mean()#/points.shape[0] #return total/areas.sum()#faces_and_verts.shape[0] def pt_loss(gmm,points): total = 0.0 #for p in points: thing = np.zeros((points.shape[0],gmm.weights_.shape[0])) i = 0 #things = for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = points-mu res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si) * dev).sum(1) res -= 0.5 * t1 #total += (res + np.log(pi)).sum() thing[:,i] = (res + np.log(pi)) i+=1 #total += thing.sum()#logsumexp(thing) return logsumexp(thing,axis=1).mean()#logsumexp(thing,axis=1).mean()#/points.shape[0] def pt_loss_lb(gmm,points): total = 0.0 #for p in points: thing = np.zeros((points.shape[0],gmm.weights_.shape[0])) i = 0 #things = weights = np.zeros(thing.shape) for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): weights[:,i] = mvn_pdf.pdf(points,mu,s) #print(mvn_pdf.pdf(points,mu,s).shape,weights.shape) i+=1 row_sums = weights.sum(axis=1) #print(row_sums.shape) weights = weights / row_sums[:, np.newaxis] i=0 for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = points-mu res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si) * dev).sum(1) res -= 0.5 * t1 #total += (res + np.log(pi)).sum() thing[:,i] = (res + np.log(pi)) i+=1 #total += thing.sum()#logsumexp(thing) #thing = thing*weights return thing.sum(axis=1).mean()#logsumexp(thing,axis=1).mean()#/points.shape[0] def com_loss(gmm,points,areas): total = 0.0 #for p in points: thing = np.zeros((points.shape[0],gmm.weights_.shape[0])) i = 0 #things = for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = points-mu res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si) * dev).sum(1) res -= 0.5 * t1 #total += (res + np.log(pi)).sum() thing[:,i] = (res + np.log(pi))*(areas/areas.mean()) i+=1 #total += thing.sum()#logsumexp(thing) return logsumexp(thing,axis=1).mean()#/points.shape[0] def com_loss_lb(gmm,points,areas): total = 0.0 #for p in points: thing = np.zeros((points.shape[0],gmm.weights_.shape[0])) i = 0 #things = for mu, s, si, pi in zip(gmm.means_,gmm.covariances_,gmm.precisions_,gmm.weights_): res = 0.0 dev = points-mu res = 0.0 res -= 0.5 * np.log(2*np.pi) *3 res -= 0.5 * np.log(np.linalg.det(s)) t1 = (dev.dot(si) * dev).sum(1) res -= 0.5 * t1 #total += (res + np.log(pi)).sum() thing[:,i] = (res + np.log(pi))*(areas/areas.mean()) i+=1 #total += thing.sum()#logsumexp(thing) return np.sum(thing,axis=1).mean()#/points.shape[0] if True: tl = tri_loss_lb cl = com_loss_lb pl = pt_loss_lb print("OMG") else: tl = tri_loss cl = com_loss pl = pt_loss print("tri\t",tl(gm3,face_vert),'\t',0) print("mpt\t",pl(gm3,com),'\t',0) print('com\t',cl(gm3,com,a),'\t',0) #print("ptLB\t",pt_loss_lb(gm3,com)) #print("spt\t",gm3.score(com)) #print("sp\t",gm3._estimate_weighted_log_prob(com).sum()) for pn in np.logspace(1,np.log10(mesh4.vertices.shape[0]*.95),10): scores = [] for itern in range(10): ptsn = np.random.choice(range(mesh4.vertices.shape[0]),int(pn),replace=False) scores.append(pl(gm3,mesh4.vertices[ptsn,:])) #scores.append(gm3._estimate_weighted_log_prob(mesh4.vertices[ptsn,:]).sum()/pn) scores = np.array(scores) print(ptsn.shape[0],'\t',scores.mean(),'\t',scores.std()) #print(" ",gm3.score(mesh4.vertices)) #print(" ",gm3._estimate_weighted_log_prob(mesh4.vertices).sum()/mesh4.vertices.shape[0])

36.28

117

0.593054

1,493

9,070

3.496986

0.108506

0.022984

0.017238

0.016089

0.767861

0.755602

0.743919

0.711741

0

0.043496

0.209151

9,070

249

118

36.425703

0.684372

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0

null

0

1

0

1

0

null

0

6

e54a7586196132392254687a371512fbba6370e8

143

py

Python

sanrr/__init__.py

ddfabbro/SANRR

aa5b71b1e8ac1e0471828922ff50e098d550a157

[ "MIT" ]

1

2019-01-18T02:53:12.000Z

sanrr/__init__.py

ddfabbro/SANRR

aa5b71b1e8ac1e0471828922ff50e098d550a157

[ "MIT" ]

null

sanrr/__init__.py

ddfabbro/SANRR

aa5b71b1e8ac1e0471828922ff50e098d550a157

[ "MIT" ]

null

from sanrr.download_data import create_lfw_db, create_fei_db, save_files from sanrr.metamodel import MyKriging from sanrr.register import SANRR

47.666667

72

0.874126

23

143

5.173913

0.608696

0.226891

0

0.090909

143

3

73

47.666667

0.915385

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e54fa83884441021ce4f8d5b0576c772667a1c58

1,066

py

Python

models/datasets.py

Jerrypiglet/Total3DUnderstanding

655d00a988c839af3b73f8ab890c3f70c1500147

[ "MIT" ]

288

2020-06-27T16:13:35.000Z

2022-03-31T12:47:42.000Z

models/datasets.py

Jerrypiglet/Total3DUnderstanding

655d00a988c839af3b73f8ab890c3f70c1500147

[ "MIT" ]

38

2020-07-03T09:19:24.000Z

2022-03-17T12:32:56.000Z

models/datasets.py

Jerrypiglet/Total3DUnderstanding

655d00a988c839af3b73f8ab890c3f70c1500147

[ "MIT" ]

40

2020-06-28T03:21:01.000Z

2022-03-29T10:17:20.000Z

# Base data of networks # author: ynie # date: Feb, 2020 import os from torch.utils.data import Dataset import json class SUNRGBD(Dataset): def __init__(self, config, mode): ''' initiate SUNRGBD dataset for data loading :param config: config file :param mode: train/val/test mode ''' self.config = config self.mode = mode split_file = os.path.join(config['data']['split'], mode + '.json') with open(split_file) as file: self.split = json.load(file) def __len__(self): return len(self.split) class PIX3D(Dataset): def __init__(self, config, mode): ''' initiate PIX3D dataset for data loading :param config: config file :param mode: train/val/test mode ''' self.config = config self.mode = mode split_file = os.path.join(config['data']['split'], mode + '.json') with open(split_file) as file: self.split = json.load(file) def __len__(self): return len(self.split)

27.333333

74

0.592871

135

1,066

4.533333

0.303704

0.065359

0.045752

0.058824

0.800654

0.683007

0

0.007979

0.294559

1,066

39

75

27.333333

0.805851

0.237336

0

0.761905

0

0.038199

0

1

0.190476

false

0

0.142857

0.095238

0.52381

0

null

0

1

0

1

0

1

0

null

0

1

0

6

e56dfe0402cd897bebabb77a08d06f2ce191dcfa

43

py

Python

HelloWorld.py

debajyoti-iitkgp/Hello-World

fb77b47681a635c71ccee0e0d2d269102941c337

[ "MIT" ]

null

HelloWorld.py

debajyoti-iitkgp/Hello-World

fb77b47681a635c71ccee0e0d2d269102941c337

[ "MIT" ]

null

HelloWorld.py

debajyoti-iitkgp/Hello-World

fb77b47681a635c71ccee0e0d2d269102941c337

[ "MIT" ]

null

#printing hello world print('Hello World')

14.333333

21

0.767442

6

43

5.5

0.666667

0.606061

0

0.116279

43

2

22

21.5

0.868421

0.465116

0

0.5

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e5bf7fa6d8b7753273fe876864ab8a7931dc8d74

44

py

Python

dpmhm/datasets/xjtu/__init__.py

yanncalec/dpmhm

0a242bc8add0ba1463bb2b63b2c15abb80b83fa7

[ "MIT" ]

null

dpmhm/datasets/xjtu/__init__.py

yanncalec/dpmhm

0a242bc8add0ba1463bb2b63b2c15abb80b83fa7

[ "MIT" ]

null

dpmhm/datasets/xjtu/__init__.py

yanncalec/dpmhm

0a242bc8add0ba1463bb2b63b2c15abb80b83fa7

[ "MIT" ]

null

"""xjtu dataset.""" from .xjtu import Xjtu

11

22

0.659091

6

44

4.833333

0.666667

0

0.159091

44

3

23

14.666667

0.783784

0.295455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e5d4339e9f2cad35d466847ae465cbfce99f5150

19,448

py

Python

telaPrincipal.py

jhontavares/NOVO-SISTEMA-ERP

a44f9ff77b6ff3fa57d326847e8e81a797064170

[ "MIT" ]

null

telaPrincipal.py

jhontavares/NOVO-SISTEMA-ERP

a44f9ff77b6ff3fa57d326847e8e81a797064170

[ "MIT" ]

null

telaPrincipal.py

jhontavares/NOVO-SISTEMA-ERP

a44f9ff77b6ff3fa57d326847e8e81a797064170

[ "MIT" ]

null

from tkinter import ttk from tkinter import * import sqlite3 class Product(object): #db_name = 'database.db' def __init__(self, window): self.wind = window self.wind.title("NOME DO SISTEMA") self.wind.geometry("803x500+150+120") self.wind.configure(bg='#FFFF00') self.frame_02 = Frame(self.wind, width=799, height=26, bg='#000000', bd=2, relief='groove') self.frame_02.place(x=2, y=1) self.lbl_01_02 = Label(self.frame_02, text='Usuário', font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_01_02.place(x=2, y=1) self.lbl_01_02 = Label(self.frame_02, text='NOME DA EMPRESA', font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_01_02.place(x=300, y=1) self.lbl_01_02 = Label(self.frame_02, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_01_02.place(x=550, y=1) self.frame_01 = Frame(self.wind, width=799, height=39, bg='#000000', bd=2, relief='groove') self.frame_01.place(x=2, y=40) self.lbl_01_01 = Label(self.frame_01, text="HOME", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_01_01.place(x=370, y=8) # Botões modulares do sistema self.frame_03 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_03.place(x=2, y=100) self.btn_03 = Button(self.frame_03, text='Administrativo', font='Georgia 15 bold', width=13, bg='pink', height=5, command=self.administrativo) self.btn_03.pack() self.frame_04 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_04.place(x=203, y=100) self.btn_04 = Button(self.frame_04, text='Comercial', font='Georgia 15 bold', width=13, height=5, command=self.comercial) self.btn_04.pack() self.frame_05 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_05.place(x=404, y=100) self.btn_05 = Button(self.frame_05, text='Financeiro', font='Georgia 15 bold', width=13, height=5, command=self.financeiro) self.btn_05.pack() self.frame_06 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_06.place(x=605, y=100) self.btn_06 = Button(self.frame_06, text='Logística', font='Georgia 15 bold', width=13, height=5, command=self.logistica) self.btn_06.pack() self.frame_07 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_07.place(x=2, y=248) self.btn_07 = Button(self.frame_07, text='Transporte', font='Georgia 15 bold', width=13, height=5, command=self.transporte) self.btn_07.pack() self.frame_08 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_08.place(x=203, y=248) self.btn_08 = Button(self.frame_08, text='Fiscal', font='Georgia 15 bold', width=13, height=5) self.btn_08.pack() self.frame_09 = Frame(self.wind, width=200, height=150, bg='Chocolate', bd=2, relief='groove') self.frame_09.place(x=404, y=248) self.btn_09 = Button(self.frame_09, text='Contábil', font='Georgia 15 bold', width=13, height=5) self.btn_09.pack() self.frame_10 = Frame(self.wind, width=200, height=150, bg='#4F4F4F', bd=2, relief='groove') self.frame_10.place(x=605, y=248) self.btn_10 = Button(self.frame_10, text='Pessoal', font='Georgia 15 bold', width=13, height=5) self.btn_10.pack() # Botão de Configuração self.frame_11 = Frame(self.wind, width=130, height=50, bg='#4F4F4F', bd=2, relief='groove') self.frame_11.place(x=25, y=433) self.btn_11 = Button(self.frame_11, text='Configuração', width=18, height=2) self.btn_11.pack() # Botão de fechar sistema self.frame_12 = Frame(self.wind, width=130, height=50, bg='#4F4F4F', bd=2, relief='groove') self.frame_12.place(x=166, y=433) self.btn_12 = Button(self.frame_12, text='Fechar', width=18, height=2) self.btn_12.pack() # Telas de Módulos def administrativo(self): #self.botaocadastro = PhotoImage('./img/cadastro.gif') self.tela_administrativo = Toplevel() self.tela_administrativo.title('NOME DO SISTEMA') self.tela_administrativo.geometry("803x500+190+120") self.tela_administrativo.configure(bg='#FFFF00') self.frame_adm_01 = Frame(self.tela_administrativo, width=799, height=26, bg='#000000', bd=2, relief='flat') self.frame_adm_01.place(x=2, y=2) self.lbl_m1_01 = Label(self.frame_adm_01, text="Usuário", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_01.place(x=2, y=1) self.lbl_m1_03 = Label(self.frame_adm_01, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_03.place(x=600, y=1) self.frame_adm_02 = Frame(self.tela_administrativo, width=799, height=39, bg='#000000', bd=2, relief='flat') self.frame_adm_02.place(x=2, y=44) self.lbl_m1_04 = Label(self.frame_adm_02, text="ADMINISTRATIVO", font='Georgia 16 bold', bg='#000000', fg='white') self.lbl_m1_04.place(x=291, y=2) # Frame Cadastro self.frame_adm_03 = Frame(self.tela_administrativo, width=190, height=300, bg='black') self.frame_adm_03.place(x=20, y=106) self.lbl_m1_05 = Label(self.frame_adm_03, text='Cadastros', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_05.place(x=2, y=2) self.btn_cad_fin_01 = Button(self.frame_adm_03, width=18, height=14, bg='green') self.btn_cad_fin_01.place(x=1, y=33) self.frame_adm_04 = Frame(self.tela_administrativo, width=190, height=300) self.frame_adm_04.place(x=305, y=106) self.lbl_m1_06 = Label(self.frame_adm_04, text='Movimentação', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_06.place(x=2, y=2) self.frame_adm_05 = Frame(self.tela_administrativo, width=190, height=300) self.frame_adm_05.place(x=592, y=106) self.lbl_m1_07 = Label(self.frame_adm_05, text='Visualização', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_07.place(x=2, y=2) self.frame_adm_06 = Frame(self.tela_administrativo, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_adm_06.place(x=25, y=437) self.btn_m1_01 = Button(self.frame_adm_06, text='Configuração', width=18, height=2) self.btn_m1_01.pack() self.frame_adm_07 = Frame(self.tela_administrativo, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_adm_07.place(x=166, y=437) self.btn_m1_02 = Button(self.frame_adm_07, text='Manual', width=18, height=2) self.btn_m1_02.pack() self.frame_adm_08 = Frame(self.tela_administrativo, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_adm_08.place(x=640, y=437) self.btn_m1_03 = Button(self.frame_adm_08, text='Fechar', width=18, height=2) self.btn_m1_03.pack() self.tela_administrativo.mainloop() def comercial(self): self.tela_Comercial = Toplevel() self.tela_Comercial.title('NOME DO SISTEMA') self.tela_Comercial.geometry("803x500+190+120") self.tela_Comercial.configure(bg='#FFFF00') self.frame_com_01 = Frame(self.tela_Comercial, width=799, height=26, bg='#000000', bd=2, relief='flat') self.frame_com_01.place(x=2, y=2) self.lbl_m1_01 = Label(self.frame_com_01, text="Usuário", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_01.place(x=2, y=1) self.lbl_m1_02 = Label(self.frame_com_01, text="NOME DA EMPRESA", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_02.place(x=300, y=1) self.lbl_m1_03 = Label(self.frame_com_01, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_03.place(x=600, y=1) self.frame_com_02 = Frame(self.tela_Comercial, width=799, height=39, bg='#000000', bd=2, relief='flat') self.frame_com_02.place(x=2, y=44) self.lbl_m1_04 = Label(self.frame_com_02, text="COMERCIAL", font='Georgia 16 bold', bg='#000000', fg='white') self.lbl_m1_04.place(x=291, y=2) self.frame_com_03 = Frame(self.tela_Comercial, width=190, height=300) self.frame_com_03.place(x=20, y=106) self.lbl_m1_05 = Label(self.frame_com_03, text='Cadastros', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_05.place(x=2, y=2) self.frame_com_04 = Frame(self.tela_Comercial, width=190, height=300) self.frame_com_04.place(x=305, y=106) self.lbl_m1_06 = Label(self.frame_com_04, text='Movimentação', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_06.place(x=2, y=2) self.frame_com_05 = Frame(self.tela_Comercial, width=190, height=300) self.frame_com_05.place(x=592, y=106) self.lbl_m1_07 = Label(self.frame_com_05, text='Visualização', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_07.place(x=2, y=2) self.frame_com_06 = Frame(self.tela_Comercial, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_com_06.place(x=25, y=437) self.btn_m1_01 = Button(self.frame_com_06, text='Configuração', width=18, height=2) self.btn_m1_01.pack() self.frame_com_07 = Frame(self.tela_Comercial, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_com_07.place(x=166, y=437) self.btn_m1_02 = Button(self.frame_com_07, text='Manual', width=18, height=2) self.btn_m1_02.pack() self.frame_com_08 = Frame(self.tela_Comercial, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_com_08.place(x=640, y=437) self.btn_m1_03 = Button(self.frame_com_08, text='Fechar', width=18, height=2) self.btn_m1_03.pack() self.tela_Comercial.mainloop() # Tela módulo Financeiro def financeiro(self): self.tela_Financeiro = Toplevel() self.tela_Financeiro.title('NOME DO SISTEMA') self.tela_Financeiro.geometry("803x500+190+120") self.tela_Financeiro.configure(bg='#FFFF00') self.frame_fin_01 = Frame(self.tela_Financeiro, width=799, height=26, bg='#000000', bd=2, relief='flat') self.frame_fin_01.place(x=2, y=2) self.lbl_m1_01 = Label(self.frame_fin_01, text="Usuário", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_01.place(x=2, y=1) self.lbl_m1_02 = Label(self.frame_fin_01, text="NOME DA EMPRESA", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_02.place(x=300, y=1) self.lbl_m1_03 = Label(self.frame_fin_01, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_03.place(x=600, y=1) self.frame_fin_02 = Frame(self.tela_Financeiro, width=799, height=39, bg='#000000', bd=2, relief='flat') self.frame_fin_02.place(x=2, y=44) self.lbl_m1_04 = Label(self.frame_fin_02, text="COMERCIAL", font='Georgia 16 bold', bg='#000000', fg='white') self.lbl_m1_04.place(x=291, y=2) self.frame_fin_03 = Frame(self.tela_Financeiro, width=190, height=300) self.frame_fin_03.place(x=20, y=106) self.lbl_m1_05 = Label(self.frame_fin_03, text='Cadastros',bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_05.place(x=2, y=2) self.frame_fin_04 = Frame(self.tela_Financeiro, width=190, height=300) self.frame_fin_04.place(x=305, y=106) self.lbl_m1_06 = Label(self.frame_fin_04, text='Movimentação', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_06.place(x=2, y=2) self.frame_fin_05 = Frame(self.tela_Financeiro, width=190, height=300) self.frame_fin_05.place(x=592, y=106) self.lbl_m1_07 = Label(self.frame_fin_05, text='Visualização', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_07.place(x=2, y=2) self.frame_fin_06 = Frame(self.tela_Financeiro, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_fin_06.place(x=25, y=437) self.btn_m1_01 = Button(self.frame_fin_06, text='Configuração', width=18, height=2) self.btn_m1_01.pack() self.frame_fin_07 = Frame(self.tela_Financeiro, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_fin_07.place(x=166, y=437) self.btn_m1_02 = Button(self.frame_fin_07, text='Manual', width=18, height=2) self.btn_m1_02.pack() self.frame_fin_08 = Frame(self.tela_Financeiro, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_fin_08.place(x=640, y=437) self.btn_m1_03 = Button(self.frame_fin_08, text='Fechar', width=18, height=2) self.btn_m1_03.pack() self.tela_Financeiro.mainloop() # Tela módulo Logística def logistica(self): self.tela_Logistica = Toplevel() self.tela_Logistica.title('NOME DO SISTEMA') self.tela_Logistica.geometry("803x500+190+120") self.tela_Logistica.configure(bg='#FFFF00') self.frame_log_01 = Frame(self.tela_Logistica, width=799, height=26, bg='#000000', bd=2, relief='flat') self.frame_log_01.place(x=2, y=2) self.lbl_m1_01 = Label(self.frame_log_01, text="Usuário", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_01.place(x=2, y=1) self.lbl_m1_02 = Label(self.frame_log_01, text="NOME DA EMPRESA", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_02.place(x=300, y=1) self.lbl_m1_03 = Label(self.frame_log_01, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_03.place(x=600, y=1) self.frame_log_02 = Frame(self.tela_Logistica, width=799, height=39, bg='#000000', bd=2, relief='flat') self.frame_log_02.place(x=2, y=44) self.lbl_m1_04 = Label(self.frame_log_02, text="LOGÍSTICA", font='Georgia 16 bold', bg='#000000', fg='white') self.lbl_m1_04.place(x=291, y=2) self.frame_log_03 = Frame(self.tela_Logistica, width=190, height=300) self.frame_log_03.place(x=20, y=106) self.lbl_m1_05 = Label(self.frame_log_03, text='Cadastros',bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_05.place(x=2, y=2) self.frame_log_04 = Frame(self.tela_Logistica, width=190, height=300) self.frame_log_04.place(x=305, y=106) self.lbl_m1_06 = Label(self.frame_log_04, text='Movimentação', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_06.place(x=2, y=2) self.frame_log_05 = Frame(self.tela_Logistica, width=190, height=300) self.frame_log_05.place(x=592, y=106) self.lbl_m1_07 = Label(self.frame_log_05, text='Visualização', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_07.place(x=2, y=2) self.frame_log_06 = Frame(self.tela_Logistica, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_log_06.place(x=25, y=437) self.btn_m1_01 = Button(self.frame_log_06, text='Configuração', width=18, height=2) self.btn_m1_01.pack() self.frame_log_07 = Frame(self.tela_Logistica, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_log_07.place(x=166, y=437) self.btn_m1_02 = Button(self.frame_log_07, text='Manual', width=18, height=2) self.btn_m1_02.pack() self.frame_log_08 = Frame(self.tela_Logistica, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_log_08.place(x=640, y=437) self.btn_m1_03 = Button(self.frame_log_08, text='Fechar', width=18, height=2) self.btn_m1_03.pack() self.tela_Logistica.mainloop() # Tela módulo Logística def transporte(self): self.tela_Transporte = Toplevel() self.tela_Transporte.title('NOME DO SISTEMA') self.tela_Transporte.geometry("803x500+190+120") self.tela_Transporte.configure(bg='#FFFF00') self.frame_tra_01 = Frame(self.tela_Transporte, width=799, height=26, bg='#000000', bd=2, relief='flat') self.frame_tra_01.place(x=2, y=2) self.lbl_m1_01 = Label(self.frame_tra_01, text="Usuário", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_01.place(x=2, y=1) self.lbl_m1_02 = Label(self.frame_tra_01, text="NOME DA EMPRESA", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_02.place(x=300, y=1) self.lbl_m1_03 = Label(self.frame_tra_01, text="'Dia' de 'Mês' de 'Ano'", font='Georgia 12 bold', bg='#000000', fg='white') self.lbl_m1_03.place(x=600, y=1) self.frame_tra_02 = Frame(self.tela_Transporte, width=799, height=39, bg='#000000', bd=2, relief='flat') self.frame_tra_02.place(x=2, y=44) self.lbl_m1_04 = Label(self.frame_tra_02, text="TRANSPORTE", font='Georgia 16 bold', bg='#000000', fg='white') self.lbl_m1_04.place(x=291, y=2) self.frame_tra_03 = Frame(self.tela_Transporte, width=190, height=300) self.frame_tra_03.place(x=20, y=106) self.lbl_m1_05 = Label(self.frame_tra_03, text='Cadastros', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_05.place(x=2, y=2) self.frame_tra_04 = Frame(self.tela_Transporte, width=190, height=300) self.frame_tra_04.place(x=305, y=106) self.lbl_m1_06 = Label(self.frame_tra_04, text='Movimentação', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_06.place(x=2, y=2) self.frame_tra_05 = Frame(self.tela_Transporte, width=190, height=300) self.frame_tra_05.place(x=592, y=106) self.lbl_m1_07 = Label(self.frame_tra_05, text='Visualização', bg='blue', width=15, font='Georgia 14 bold', fg='white') self.lbl_m1_07.place(x=2, y=2) self.frame_tra_06 = Frame(self.tela_Transporte, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_tra_06.place(x=25, y=437) self.btn_m1_01 = Button(self.frame_tra_06, text='Configuração', width=18, height=2) self.btn_m1_01.pack() self.frame_tra_07 = Frame(self.tela_Transporte, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_tra_07.place(x=166, y=437) self.btn_m1_02 = Button(self.frame_tra_07, text='Manual', width=18, height=2) self.btn_m1_02.pack() self.frame_tra_08 = Frame(self.tela_Transporte, width=130, height=50, bg='#000000', bd=2, relief='groove') self.frame_tra_08.place(x=640, y=437) self.btn_m1_03 = Button(self.frame_tra_08, text='Fechar', width=18, height=2) self.btn_m1_03.pack() self.tela_Transporte.mainloop() if __name__ == '__main__': window = Tk() application = Product(window) window.mainloop()

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null

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e5da2c1c0ca3a26c2885a057cffe52eb642e5443

103

py

Python

finrl_meta/env_execution_optimizing/order_execution_qlib/trade/network/__init__.py

eitin-infant/FinRL-Meta

4c94011e58425796e7e2e5c1bf848afd65c828d6

[ "MIT" ]

214

2021-11-08T17:06:11.000Z

2022-03-31T18:29:48.000Z

finrl_meta/env_execution_optimizing/order_execution_qlib/trade/network/__init__.py

eitin-infant/FinRL-Meta

4c94011e58425796e7e2e5c1bf848afd65c828d6

[ "MIT" ]

51

2021-11-14T19:11:02.000Z

2022-03-30T20:23:08.000Z

finrl_meta/env_execution_optimizing/order_execution_qlib/trade/network/__init__.py

eitin-infant/FinRL-Meta

4c94011e58425796e7e2e5c1bf848afd65c828d6

[ "MIT" ]

110

2021-11-03T07:41:40.000Z

2022-03-31T03:23:38.000Z

from .ppo import * from .qmodel import * from .teacher import * from .util import * from .opd import *

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224

py

Python

jupyter_matlab_vnc_proxy/resources/matlab_launcher.py

mathworks/jupyter-matlab-vnc-proxy

0a946b3007c450e7fa79c5069174f87a8b3f6b09

[ "BSD-2-Clause" ]

10

2020-12-21T17:58:06.000Z

2022-01-06T20:41:54.000Z

jupyter_matlab_vnc_proxy/resources/matlab_launcher.py

mathworks/jupyter-matlab-vnc-proxy

0a946b3007c450e7fa79c5069174f87a8b3f6b09

[ "BSD-2-Clause" ]

null

jupyter_matlab_vnc_proxy/resources/matlab_launcher.py

mathworks/jupyter-matlab-vnc-proxy

0a946b3007c450e7fa79c5069174f87a8b3f6b09

[ "BSD-2-Clause" ]

3

2020-12-15T11:13:15.000Z

2021-09-13T14:42:23.000Z

# Copyright 2020 The MathWorks, Inc. from os import environ import subprocess if "MLM_LICENSE_FILE" in environ: subprocess.check_call("matlab") else : subprocess.check_call(["matlab", "-desktop", "-licmode", "online"])

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6

e5ee0279145e8728df8b605801d61fbf95cd9f9a

4,079

py

Python

models/AUG.py

badeok0716/FINAL-PROJECT-AI-LECTURE

5bbf5f58f8f8caa7a22898c5f809ce2327395c9b

[ "MIT" ]

null

models/AUG.py

badeok0716/FINAL-PROJECT-AI-LECTURE

5bbf5f58f8f8caa7a22898c5f809ce2327395c9b

[ "MIT" ]

null

models/AUG.py

badeok0716/FINAL-PROJECT-AI-LECTURE

5bbf5f58f8f8caa7a22898c5f809ce2327395c9b

[ "MIT" ]

null

import torch import torch.nn.functional as F import random def AUGMENT(x, aug='', diff=False): if aug != 'noaug': for p in policy.split('_'): for f in AUGMENT_FNS[p]: x = f(x, diff=diff) x = x.contiguous() return x def mask(x, prob=0.1,diff=False): if diff: batch, seq_len, vocab = x.shape mask = torch.ones(batch, seq_len, dtype=x.dtype, device=x.device) mask_tokens = torch.zeros(batch, seq_len, vocab, dtype=x.dtype, device=x.device) num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. for bidx in range(batch): idx_mask = random.sample(idx_list, num_mask) for midx in idx_mask: mask[bidx][midx] = 0 mask_tokens[bidx][midx][4658] = 1 x = x * mask.unsqueeze(-1) + mask_tokens * (1- mask.unsqueeze(-1)) else: if x.requires_grad: print("x has requires grad. something wrong!") x = x.detach() batch, seq_len = x.shape num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. for bidx in range(batch): idx_mask = random.sample(idx_list, num_mask) for midx in idx_mask: x[bidx][midx] = 4658 return x def rand(x, prob=0.1,diff=False): if diff: batch, seq_len, vocab = x.shape mask = torch.ones(batch, seq_len, dtype=x.dtype, device=x.device) mask_tokens = torch.zeros(batch, seq_len, vocab, dtype=x.dtype, device=x.device) num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. token_list = [int(i) for i in range(1,4658)] for bidx in range(batch): idx_mask = random.sample(idx_list, num_mask) for midx in idx_mask: mask[bidx][midx] = 0 mask_tokens[bidx][midx][random.choice(token_list)] = 1 x = x * mask.unsqueeze(-1) + mask_tokens * (1- mask.unsqueeze(-1)) else: if x.requires_grad: print("x has requires grad. something wrong!") x = x.detach() batch, seq_len = x.shape num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. token_list = [int(i) for i in range(1,4658)] for bidx in range(batch): idx_mask = random.sample(idx_list, num_mask) for midx in idx_mask: x[bidx][midx] = random.choice(token_list) return x def swap(x, diff=False): if diff: x_detach = x.detach() batch, seq_len, vocab = x.shape mask = torch.ones(batch, seq_len, dtype=x.dtype, device=x.device) mask_tokens = torch.zeros(batch, seq_len, vocab, dtype=x.dtype, device=x.device) num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. token_list = [int(i) for i in range(1,4658)] for bidx in range(batch): i1, i2 = random.sample(idx_list, 2) mask[bidx][i1] = 0 mask[bidx][i2] = 0 mask_tokens[bidx][i1] = x_detach[bidx][i1] mask_tokens[bidx][i2] = x_detach[bidx][i2] x = x * mask.unsqueeze(-1) + mask_tokens * (1- mask.unsqueeze(-1)) else: if x.requires_grad: print("x has requires grad. something wrong!") x = x.detach() batch, seq_len = x.shape num_mask = int(seq_len * prob) idx_list = [int(i) for i in range(1,seq_len)] # prevent initial token from masking. token_list = [int(i) for i in range(1,4658)] for bidx in range(batch): i1, i2 = random.sample(idx_list, 2) tmp = x[bidx][i1] x[bidx][i1] = x[bidx][i2] x[bidx][i2] = tmp AUGMENT_FNS = { 'mask': [mask], 'rand': [rand], 'swap' : [swap], }

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0.049261

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0.835647

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0.307183

4,079

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40.386139

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null

0

6

f9242b68f7491d47f1097ea7c5163bb5f86b9c0e

146

py

Python

lpipe/testing/__init__.py

anton-chekanov/lpipe

acc2c18150584e2e330eb0fbce889ea0ec77cd62

[ "Apache-2.0" ]

null

lpipe/testing/__init__.py

anton-chekanov/lpipe

acc2c18150584e2e330eb0fbce889ea0ec77cd62

[ "Apache-2.0" ]

null

lpipe/testing/__init__.py

anton-chekanov/lpipe

acc2c18150584e2e330eb0fbce889ea0ec77cd62

[ "Apache-2.0" ]

null

from .awslambda import * from .dynamodb import * from .kinesis import * from .s3 import * from .sqs import * from .utils import * # flake8: noqa

16.222222

24

0.712329

20

146

5.2

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0.016949

0.191781

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6

00851f92a820c7ae1ca517ae7c493628404cd0e4

24

py

Python

icoshift3/__init__.py

Sour-Smelno/icoshift_py3

1a4b6947bfa61fb66682e04372d92865bd517637

[ "BSD-2-Clause-FreeBSD" ]

13

2015-02-03T22:22:41.000Z

2022-02-09T10:21:26.000Z

icoshift3/__init__.py

Sour-Smelno/icoshift_py3

1a4b6947bfa61fb66682e04372d92865bd517637

[ "BSD-2-Clause-FreeBSD" ]

2

2016-02-05T12:07:17.000Z

2020-12-02T15:41:41.000Z

icoshift3/__init__.py

Sour-Smelno/icoshift_py3

1a4b6947bfa61fb66682e04372d92865bd517637

[ "BSD-2-Clause-FreeBSD" ]

10

2016-09-12T16:19:12.000Z

2021-09-03T06:35:20.000Z

from .icoshift import *

12

23

0.75

3

24

6

1

0

0.166667

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1

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0.9

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true

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1

0

6

9703d54c13447bc8a11679c3c592a213bcd6eec3

28

py

Python

Charm/models/__init__.py

TanyaAdams1/Charm

cc6dd64d01f8cb4cf0eb92dadefcb7575d75ec9d

[ "BSD-3-Clause" ]

17

2018-04-23T20:17:58.000Z

2021-04-12T19:28:40.000Z

Charm/models/__init__.py

TanyaAdams1/Charm

cc6dd64d01f8cb4cf0eb92dadefcb7575d75ec9d

[ "BSD-3-Clause" ]

52

2019-08-29T00:39:11.000Z

2021-01-02T22:49:41.000Z

Charm/models/__init__.py

TanyaAdams1/Charm

cc6dd64d01f8cb4cf0eb92dadefcb7575d75ec9d

[ "BSD-3-Clause" ]

3

2018-04-19T19:24:38.000Z

2020-11-06T00:33:53.000Z

from .distributions import *

28

0.821429

3

28

7.666667

1

0

0.107143

28

1

28

0.92

0

1

0

true

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

6

972a40940661deb5fcf836f6e6848b59739928c9

33

py

Python

GameLauncher.py

YEETER1234321/PyhtonGame

2ab1796d089bbeb2077ddff3f2aea6d8d4d22968

[ "MIT" ]

null

GameLauncher.py

YEETER1234321/PyhtonGame

2ab1796d089bbeb2077ddff3f2aea6d8d4d22968

[ "MIT" ]

null

GameLauncher.py

YEETER1234321/PyhtonGame

2ab1796d089bbeb2077ddff3f2aea6d8d4d22968

[ "MIT" ]

null

import game from sys import exit

11

20

0.818182

6

33

4.5

0.833333

0

0.181818

33

2

21

16.5

1

0

1

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true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

975c7734da90e07557e858e066a9a86562e855d1

234

py

Python

emojigg/errors.py

NextChai/emojigg

0583d789b3ea1ff26d49275901107cb6529eff16

[ "MIT" ]

null

emojigg/errors.py

NextChai/emojigg

0583d789b3ea1ff26d49275901107cb6529eff16

[ "MIT" ]

null

emojigg/errors.py

NextChai/emojigg

0583d789b3ea1ff26d49275901107cb6529eff16

[ "MIT" ]

null

class NotImplemented(Exception): def __init__(self, message, *args): self.message = message self.args = args def __str__(self) -> str: return self.message class WrongType(Exception): pass

21.272727

39

0.619658

25

234

5.48

0.48

0.240876

0

0.286325

234

11

40

21.272727

0.820359

0

1

0.25

false

0.125

0

0.125

0.625

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

976246577d663752a224d60d76f4b1655877f09a

70

py

Python

server/app/utils/__init__.py

WagnerJM/sentinel

d978ebac1b2ede79f6bdd3c48167a278acf46654

[ "MIT" ]

null

server/app/utils/__init__.py

WagnerJM/sentinel

d978ebac1b2ede79f6bdd3c48167a278acf46654

[ "MIT" ]

null

server/app/utils/__init__.py

WagnerJM/sentinel

d978ebac1b2ede79f6bdd3c48167a278acf46654

[ "MIT" ]

null

from uuid import UUID def str2uuid(string): return UUID(string)

11.666667

23

0.728571

10

70

5.1

0.7

0

0.017857

0.2

70

5

24

14

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0

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false

0

0.333333

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1

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null

0

1

0

null

0

1

0

1

0

6

9765e82f5ab2d7b995d95f9ab82525eae4a7554f

274

py

Python

Codewars/5kyu/simple-css-selector-comparison/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/5kyu/simple-css-selector-comparison/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/5kyu/simple-css-selector-comparison/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 2.7.6 Test.describe('Do some testing') Test.assert_equals(compare('body p', 'div'), 'body p') Test.assert_equals(compare('.class', '#id'), '#id') Test.assert_equals(compare('div.big', '.small'), 'div.big') Test.assert_equals(compare('.big', '.small'), '.small')

34.25

59

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274

4.341463

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0.359551

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0

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274

7

60

39.142857

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1

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null

0

1

0

1

0

6

979027af717f3e116840e7ebf4fb0d584a31cd8c

28,881

py

Python

models/Unet.py

hemanths933/Segmentation_Unet

701585b31df7e4159e2fdbe56aaca99d9a4a8ea9

[ "MIT" ]

null

models/Unet.py

hemanths933/Segmentation_Unet

701585b31df7e4159e2fdbe56aaca99d9a4a8ea9

[ "MIT" ]

null

models/Unet.py

hemanths933/Segmentation_Unet

701585b31df7e4159e2fdbe56aaca99d9a4a8ea9

[ "MIT" ]

null

# -*- coding: utf-8 -*- import tensorflow as tf import numpy as np from models.Model import Model from losses.Pixelwise_weighted_loss import Pixelwise_weighted_loss from metrics.IOU import IOU from tensorflow.python.keras import layers from tensorflow.python.keras import losses from tensorflow.python.keras import models from tensorflow.python.keras import backend as K import math class Unet(Model): def __init__(self): print("unet init") Model.__init__(self) self.learning_rate = tf.train.exponential_decay(0.0001, tf.Variable(0, trainable=False), 10, 0.8, staircase=True) self.loss = Pixelwise_weighted_loss().compute_loss self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate) self.metric = IOU() def crop(self,tensor, reference): # print(tensor.shape.as_list()[1]-reference.shape.as_list()[1]) if ((tensor.shape.as_list()[1] - reference.shape.as_list()[1]) % 2 == 0): offset_x = (tensor.shape.as_list()[1] - reference.shape.as_list()[1]) // 2 else: offset_x = ((tensor.shape.as_list()[1] - reference.shape.as_list()[1]) // 2) + 1 # print(tensor.shape.as_list()[2]-reference.shape.as_list()[2]) if ((tensor.shape.as_list()[2] - reference.shape.as_list()[2]) % 2 == 0): offset_y = (tensor.shape.as_list()[2] - reference.shape.as_list()[2]) // 2 else: offset_y = ((tensor.shape.as_list()[2] - reference.shape.as_list()[2]) // 2) + 1 offset = [0, offset_x, offset_y, 0] # print("offset is ",offset) cropped_tensor = tf.slice(tensor, offset, [-1, reference.shape.as_list()[1], reference.shape.as_list()[2], -1]) return cropped_tensor def concat(self,tensor,reference): cropped = self.crop(tensor,reference) return tf.concat([cropped,reference],axis=-1) def network(self,images,reuse = tf.AUTO_REUSE): with tf.variable_scope("weights", reuse=reuse): input_layer = tf.reshape(images,[-1,572,572,3]) print(input_layer) W1 = tf.get_variable(name='W1',shape=[3,3,3,64],initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv1 = tf.nn.conv2d(input_layer,W1,padding="VALID",strides=[1,1,1,1]) conv1 = tf.nn.relu(conv1) #conv1 = tf.layers.conv2d(inputs=input_layer,filters=W1,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv1) W2 = tf.get_variable(name='W2', shape=[3, 3, 64, 64], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv2 = tf.nn.conv2d(conv1,W2,padding="VALID",strides=[1,1,1,1]) conv2 = tf.nn.relu(conv2) #conv2 = tf.layers.conv2d(inputs=conv1,filters=W2,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_4' print(conv2) pool1 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) print(pool1) W3 = tf.get_variable(name='W3', shape=[3, 3, 64, 128], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv3 = tf.nn.conv2d(pool1,W3,padding="VALID",strides=[1,1,1,1]) conv3 = tf.nn.relu(conv3) #conv3 = tf.layers.conv2d(inputs=pool1,filters=W3,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv3) W4 = tf.get_variable(name='W4', shape=[3, 3, 128, 128], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv4 = tf.nn.conv2d(conv3,W4,padding="VALID",strides=[1,1,1,1]) conv4 = tf.nn.relu(conv4) #conv4 = tf.layers.conv2d(inputs=conv3,filters=W4,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_3' print(conv4) pool2 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2) print(pool2) W5 = tf.get_variable(name='W5', shape=[3, 3, 128, 256], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv5 = tf.nn.conv2d(pool2,W5,padding="VALID",strides=[1,1,1,1]) conv5 = tf.nn.relu(conv5) #conv5 = tf.layers.conv2d(inputs=pool2,filters=W5,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv5) W6 = tf.get_variable(name='W6', shape=[3, 3, 256, 256], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv6 = tf.nn.conv2d(conv5,W6,padding="VALID",strides=[1,1,1,1]) conv6 = tf.nn.relu(conv6) #conv6 = tf.layers.conv2d(inputs=conv5,filters=W6,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_2' print(conv6) pool3 = tf.layers.max_pooling2d(inputs=conv6, pool_size=[2, 2], strides=2) print(pool3) W7 = tf.get_variable(name='W7', shape=[3, 3, 256, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv7 = tf.nn.conv2d(pool3,W7,padding="VALID",strides=[1,1,1,1]) conv7 = tf.nn.relu(conv7) #conv7 = tf.layers.conv2d(inputs=pool3,filters=W7,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv7) W8 = tf.get_variable(name='W8', shape=[3, 3, 512, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv8 = tf.nn.conv2d(conv7,W8,padding="VALID",strides=[1,1,1,1]) conv8 = tf.nn.relu(conv8) #conv8 = tf.layers.conv2d(inputs=conv7,filters=W8,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_1' print(conv8) pool4 = tf.layers.max_pooling2d(inputs=conv8, pool_size=[2, 2], strides=2,name = 'p4') print(pool4) W9 = tf.get_variable(name='W9', shape=[3, 3, 512, 1024], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv9 = tf.nn.conv2d(pool4,W9,padding="VALID",strides=[1,1,1,1]) conv9 = tf.nn.relu(conv9) #conv9 = tf.layers.conv2d(inputs=pool4,filters=W9,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv9) W10 = tf.get_variable(name='W10', shape=[3, 3, 1024, 1024], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv10 = tf.nn.conv2d(conv9,W10,padding="VALID",strides=[1,1,1,1]) conv10 = tf.nn.relu(conv10) #conv10 = tf.layers.conv2d(inputs=conv9,filters=W10,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv10) W11 = tf.get_variable(name='W11', shape=[2, 2, 512, 1024], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) deconv1 = tf.nn.conv2d_transpose(conv10,W11,strides = [1,2,2,1],padding='VALID',output_shape=[1,56,56,512]) deconv1 = tf.nn.relu(deconv1) #deconv1 = tf.layers.conv2d_transpose(inputs=conv10,filters=W11,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv1) concat1 = self.concat(conv8,deconv1) print("concat1:",concat1) W12 = tf.get_variable(name='W12', shape=[3, 3, 1024, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv11 = tf.nn.conv2d(concat1,W12,padding="VALID",strides=[1,1,1,1]) conv11 = tf.nn.relu(conv11) #conv11 = tf.layers.conv2d(inputs=concat1,filters=W12,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv11) W13 = tf.get_variable(name='W13', shape=[3, 3, 512, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv12 = tf.nn.conv2d(conv11,W13,padding="VALID",strides=[1,1,1,1]) conv12 = tf.nn.relu(conv12) #conv12 = tf.layers.conv2d(inputs=conv11,filters=W13,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv12) W14 = tf.get_variable(name='W14', shape=[2, 2, 256, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) deconv2 = tf.nn.conv2d_transpose(conv12,W14,strides = [1,2,2,1],padding='VALID',output_shape=[1,104,104,256]) deconv2 = tf.nn.relu(deconv2) #deconv2 = tf.layers.conv2d_transpose(inputs=conv12,filters=W14,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv2) concat2 = self.concat(conv6,deconv2) print("concat2:",concat2) W15 = tf.get_variable(name='W15', shape=[3, 3, 512, 256], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv13 = tf.nn.conv2d(concat2,W15,padding="VALID",strides=[1,1,1,1]) conv13 = tf.nn.relu(conv13) #conv13 = tf.layers.conv2d(inputs=concat2,filters=W15,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv13) W16 = tf.get_variable(name='W16', shape=[3, 3, 256, 256], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv14 = tf.nn.conv2d(conv13,W16,padding="VALID",strides=[1,1,1,1]) conv14 = tf.nn.relu(conv14) #conv14 = tf.layers.conv2d(inputs=conv13,filters=W16,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv14) W17 = tf.get_variable(name='W17', shape=[2, 2, 128, 256], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) deconv3 = tf.nn.conv2d_transpose(conv14,W17,strides = [1,2,2,1],padding='VALID',output_shape=[1,200,200,128]) deconv3 = tf.nn.relu(deconv3) #deconv3 = tf.layers.conv2d_transpose(inputs=conv14,filters=W17,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv3) concat3 = self.concat(conv4,deconv3) print("concat3:",concat3) W18 = tf.get_variable(name='W18', shape=[3, 3, 256, 128], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv15 = tf.nn.conv2d(concat3,W18,padding="VALID",strides=[1,1,1,1]) conv15 = tf.nn.relu(conv15) #conv15 = tf.layers.conv2d(inputs=concat3,filters=W18,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv15) W19 = tf.get_variable(name='W19', shape=[3, 3, 128, 128], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv16 = tf.nn.conv2d(conv15,W19,padding="VALID",strides=[1,1,1,1]) conv16 = tf.nn.relu(conv16) #conv16 = tf.layers.conv2d(inputs=conv15,filters=W19,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv16) W20 = tf.get_variable(name='W20', shape=[2, 2, 64, 128], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) deconv4 = tf.nn.conv2d_transpose(conv16,W20,strides = [1,2,2,1],padding='VALID',output_shape=[1,392,392,64]) deconv4 = tf.nn.relu(deconv4) #deconv4 = tf.layers.conv2d_transpose(inputs=conv16,filters=W20,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv4) concat4 = self.concat(conv2,deconv4) print("concat4:",concat4) W21 = tf.get_variable(name='W21', shape=[3, 3, 128, 64], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv17 = tf.nn.conv2d(concat4,W21,padding="VALID",strides=[1,1,1,1]) conv17 = tf.nn.relu(conv17) #conv17 = tf.layers.conv2d(inputs=concat4,filters=W21,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv17) W22 = tf.get_variable(name='W22', shape=[3, 3, 64, 64], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv18 = tf.nn.conv2d(conv17,W22,padding="VALID",strides=[1,1,1,1]) conv18 = tf.nn.relu(conv18) #conv18 = tf.layers.conv2d(inputs=conv17,filters=W22,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv18) W23 = tf.get_variable(name='W23', shape=[1, 1, 64, 1], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) output = tf.nn.conv2d(conv18,W23,padding="VALID",strides=[1,1,1,1]) #output = tf.layers.conv2d(inputs=conv18,filters=W23,kernel_size=[1, 1],padding="valid",activation=tf.nn.sigmoid) print(output) return output def network_visualize(self,images,reuse=tf.AUTO_REUSE): with tf.variable_scope("weights", reuse=reuse): summarylist = [] input_layer = tf.reshape(images,[-1,572,572,3]) input_image_summary = tf.summary.image('input_image_summary',tf.reshape(input_layer,[input_layer.shape[3],input_layer.shape[1],input_layer.shape[2],3]),1) summarylist.append(input_image_summary) print(input_layer) W1 = tf.get_variable(name='W1',shape=[3,3,3,64],initializer=tf.contrib.layers.xavier_initializer()) conv1 = tf.nn.conv2d(input_layer,W1,padding="VALID",strides=[1,1,1,1]) conv1 = tf.nn.relu(conv1) #conv1 = tf.layers.conv2d(inputs=input_layer,filters=W1,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) conv1_summary = tf.summary.image('conv1_summary',tf.reshape(conv1,[conv1.shape[3],conv1.shape[1],conv1.shape[2],1]),64) #summarylist.append(conv1_summary) print(conv1) W2 = tf.get_variable(name='W2', shape=[3, 3, 64, 64], initializer=tf.contrib.layers.xavier_initializer()) conv2 = tf.nn.conv2d(conv1,W2,padding="VALID",strides=[1,1,1,1]) conv2 = tf.nn.relu(conv2) conv2_summary = tf.summary.image('conv2_summary',tf.reshape(conv2,[conv2.shape[3],conv2.shape[1],conv2.shape[2],1]),64) #summarylist.append(conv2_summary) #conv2 = tf.layers.conv2d(inputs=conv1,filters=W2,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_4' print(conv2) pool1 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) pool1_summary = tf.summary.image('pool1_summary',tf.reshape(pool1,[pool1.shape[3],pool1.shape[1],pool1.shape[2],1]),64) #summarylist.append(pool1_summary) print(pool1) W3 = tf.get_variable(name='W3', shape=[3, 3, 64, 128], initializer=tf.contrib.layers.xavier_initializer()) conv3 = tf.nn.conv2d(pool1,W3,padding="VALID",strides=[1,1,1,1]) conv3 = tf.nn.relu(conv3) conv3_summary = tf.summary.image('conv3_summary',tf.reshape(conv3,[conv3.shape[3],conv3.shape[1],conv3.shape[2],1]),128) #summarylist.append(conv3_summary) #conv3 = tf.layers.conv2d(inputs=pool1,filters=W3,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv3) W4 = tf.get_variable(name='W4', shape=[3, 3, 128, 128], initializer=tf.contrib.layers.xavier_initializer()) conv4 = tf.nn.conv2d(conv3,W4,padding="VALID",strides=[1,1,1,1]) conv4 = tf.nn.relu(conv4) conv4_summary = tf.summary.image('conv4_summary',tf.reshape(conv4,[conv4.shape[3],conv4.shape[1],conv4.shape[2],1]),128) #summarylist.append(conv4_summary) #conv4 = tf.layers.conv2d(inputs=conv3,filters=W4,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_3' print(conv4) pool2 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2) pool2_summary = tf.summary.image('pool2_summary',tf.reshape(pool2,[pool2.shape[3],pool2.shape[1],pool2.shape[2],1]),128) #summarylist.append(pool2_summary) print(pool2) W5 = tf.get_variable(name='W5', shape=[3, 3, 128, 256], initializer=tf.contrib.layers.xavier_initializer()) conv5 = tf.nn.conv2d(pool2,W5,padding="VALID",strides=[1,1,1,1]) conv5 = tf.nn.relu(conv5) conv5_summary = tf.summary.image('conv5_summary',tf.reshape(conv5,[conv5.shape[3],conv5.shape[1],conv5.shape[2],1]),256) #summarylist.append(conv5_summary) #conv5 = tf.layers.conv2d(inputs=pool2,filters=W5,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv5) W6 = tf.get_variable(name='W6', shape=[3, 3, 256, 256], initializer=tf.contrib.layers.xavier_initializer()) conv6 = tf.nn.conv2d(conv5,W6,padding="VALID",strides=[1,1,1,1]) conv6 = tf.nn.relu(conv6) conv6_summary = tf.summary.image('conv6_summary',tf.reshape(conv6,[conv6.shape[3],conv6.shape[1],conv6.shape[2],1]),256) #summarylist.append(conv6_summary) #conv6 = tf.layers.conv2d(inputs=conv5,filters=W6,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_2' print(conv6) pool3 = tf.layers.max_pooling2d(inputs=conv6, pool_size=[2, 2], strides=2) pool3_summary = tf.summary.image('pool3_summary',tf.reshape(pool3,[pool3.shape[3],pool3.shape[1],pool3.shape[2],1]),256) #summarylist.append(pool3_summary) print(pool3) W7 = tf.get_variable(name='W7', shape=[3, 3, 256, 512], initializer=tf.contrib.layers.xavier_initializer()) conv7 = tf.nn.conv2d(pool3,W7,padding="VALID",strides=[1,1,1,1]) conv7 = tf.nn.relu(conv7) conv7_summary = tf.summary.image('conv7_summary',tf.reshape(conv7,[conv7.shape[3],conv7.shape[1],conv7.shape[2],1]),512) #summarylist.append(conv7_summary) #conv7 = tf.layers.conv2d(inputs=pool3,filters=W7,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv7) W8 = tf.get_variable(name='W8', shape=[3, 3, 512, 512], initializer=tf.contrib.layers.xavier_initializer()) conv8 = tf.nn.conv2d(conv7,W8,padding="VALID",strides=[1,1,1,1]) conv8 = tf.nn.relu(conv8) conv8_summary = tf.summary.image('conv8_summary',tf.reshape(conv8,[conv8.shape[3],conv8.shape[1],conv8.shape[2],1]),512) #summarylist.append(conv8_summary) #conv8 = tf.layers.conv2d(inputs=conv7,filters=W8,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)#,name = 'conv_merge_1' print(conv8) pool4 = tf.layers.max_pooling2d(inputs=conv8, pool_size=[2, 2], strides=2,name = 'p4') pool4_summary = tf.summary.image('pool4_summary',tf.reshape(pool4,[pool4.shape[3],pool4.shape[1],pool4.shape[2],1]),512) #summarylist.append(pool4_summary) print(pool4) W9 = tf.get_variable(name='W9', shape=[3, 3, 512, 1024], initializer=tf.contrib.layers.xavier_initializer()) conv9 = tf.nn.conv2d(pool4,W9,padding="VALID",strides=[1,1,1,1]) conv9 = tf.nn.relu(conv9) conv9_summary = tf.summary.image('conv9_summary',tf.reshape(conv9,[conv9.shape[3],conv9.shape[1],conv9.shape[2],1]),1024) #summarylist.append(conv9_summary) #conv9 = tf.layers.conv2d(inputs=pool4,filters=W9,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv9) W10 = tf.get_variable(name='W10', shape=[3, 3, 1024, 1024], initializer=tf.contrib.layers.xavier_initializer()) conv10 = tf.nn.conv2d(conv9,W10,padding="VALID",strides=[1,1,1,1]) conv10 = tf.nn.relu(conv10) conv10_summary =tf.summary.image('conv10_summary',tf.reshape(conv10,[conv10.shape[3],conv10.shape[1],conv10.shape[2],1]),1024) #summarylist.append(conv10_summary) #conv10 = tf.layers.conv2d(inputs=conv9,filters=W10,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv10) W11 = tf.get_variable(name='W11', shape=[2, 2, 512, 1024], initializer=tf.contrib.layers.xavier_initializer()) deconv1 = tf.nn.conv2d_transpose(conv10,W11,strides = [1,2,2,1],padding='VALID',output_shape=[1,56,56,512]) deconv1 = tf.nn.relu(deconv1) #deconv1 = tf.layers.conv2d_transpose(inputs=conv10,filters=W11,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv1) concat1 = self.concat(conv8,deconv1) print("concat1:",concat1) W12 = tf.get_variable(name='W12', shape=[3, 3, 1024, 512], initializer=tf.contrib.layers.xavier_initializer()) conv11 = tf.nn.conv2d(concat1,W12,padding="VALID",strides=[1,1,1,1]) conv11 = tf.nn.relu(conv11) #conv11 = tf.layers.conv2d(inputs=concat1,filters=W12,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv11) W13 = tf.get_variable(name='W13', shape=[3, 3, 512, 512], initializer=tf.initializers.random_normal(mean=0,stddev=math.sqrt(2/576))) conv12 = tf.nn.conv2d(conv11,W13,padding="VALID",strides=[1,1,1,1]) conv12 = tf.nn.relu(conv12) conv12_summary = tf.summary.image('conv12_summary',tf.reshape(conv12,[conv12.shape[3],conv12.shape[1],conv12.shape[2],1]),512) #summarylist.append(conv12_summary) #conv12 = tf.layers.conv2d(inputs=conv11,filters=W13,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv12) W14 = tf.get_variable(name='W14', shape=[2, 2, 256, 512], initializer=tf.contrib.layers.xavier_initializer()) deconv2 = tf.nn.conv2d_transpose(conv12,W14,strides = [1,2,2,1],padding='VALID',output_shape=[1,104,104,256]) deconv2 = tf.nn.relu(deconv2) #deconv2 = tf.layers.conv2d_transpose(inputs=conv12,filters=W14,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv2) concat2 = self.concat(conv6,deconv2) print("concat2:",concat2) W15 = tf.get_variable(name='W15', shape=[3, 3, 512, 256], initializer=tf.contrib.layers.xavier_initializer()) conv13 = tf.nn.conv2d(concat2,W15,padding="VALID",strides=[1,1,1,1]) conv13 = tf.nn.relu(conv13) #conv13 = tf.layers.conv2d(inputs=concat2,filters=W15,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv13) W16 = tf.get_variable(name='W16', shape=[3, 3, 256, 256], initializer=tf.contrib.layers.xavier_initializer()) conv14 = tf.nn.conv2d(conv13,W16,padding="VALID",strides=[1,1,1,1]) conv14 = tf.nn.relu(conv14) conv14_summary = tf.summary.image('conv14_summary',tf.reshape(conv14,[conv14.shape[3],conv14.shape[1],conv14.shape[2],1]),512) #summarylist.append(conv14_summary) #conv14 = tf.layers.conv2d(inputs=conv13,filters=W16,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv14) W17 = tf.get_variable(name='W17', shape=[2, 2, 128, 256], initializer=tf.contrib.layers.xavier_initializer()) deconv3 = tf.nn.conv2d_transpose(conv14,W17,strides = [1,2,2,1],padding='VALID',output_shape=[1,200,200,128]) deconv3 = tf.nn.relu(deconv3) #deconv3 = tf.layers.conv2d_transpose(inputs=conv14,filters=W17,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv3) concat3 = self.concat(conv4,deconv3) print("concat3:",concat3) W18 = tf.get_variable(name='W18', shape=[3, 3, 256, 128], initializer=tf.contrib.layers.xavier_initializer()) conv15 = tf.nn.conv2d(concat3,W18,padding="VALID",strides=[1,1,1,1]) conv15 = tf.nn.relu(conv15) #conv15 = tf.layers.conv2d(inputs=concat3,filters=W18,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv15) W19 = tf.get_variable(name='W19', shape=[3, 3, 128, 128], initializer=tf.contrib.layers.xavier_initializer()) conv16 = tf.nn.conv2d(conv15,W19,padding="VALID",strides=[1,1,1,1]) conv16 = tf.nn.relu(conv16) conv16_summary = tf.summary.image('conv16_summary',tf.reshape(conv16,[conv16.shape[3],conv16.shape[1],conv16.shape[2],1]),512) #summarylist.append(conv16_summary) #conv16 = tf.layers.conv2d(inputs=conv15,filters=W19,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv16) W20 = tf.get_variable(name='W20', shape=[2, 2, 64, 128], initializer=tf.contrib.layers.xavier_initializer()) deconv4 = tf.nn.conv2d_transpose(conv16,W20,strides = [1,2,2,1],padding='VALID',output_shape=[1,392,392,64]) deconv4 = tf.nn.relu(deconv4) #deconv4 = tf.layers.conv2d_transpose(inputs=conv16,filters=W20,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu) print(deconv4) concat4 = self.concat(conv2,deconv4) print("concat4:",concat4) W21 = tf.get_variable(name='W21', shape=[3, 3, 128, 64], initializer=tf.contrib.layers.xavier_initializer()) conv17 = tf.nn.conv2d(concat4,W21,padding="VALID",strides=[1,1,1,1]) conv17 = tf.nn.relu(conv17) #conv17 = tf.layers.conv2d(inputs=concat4,filters=W21,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv17) W22 = tf.get_variable(name='W22', shape=[3, 3, 64, 64], initializer=tf.contrib.layers.xavier_initializer()) conv18 = tf.nn.conv2d(conv17,W22,padding="VALID",strides=[1,1,1,1]) conv18 = tf.nn.relu(conv18) #conv18 = tf.layers.conv2d(inputs=conv17,filters=W22,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu) print(conv18) W23 = tf.get_variable(name='W23', shape=[1, 1, 64, 1], initializer=tf.contrib.layers.xavier_initializer()) output = tf.nn.conv2d(conv18,W23,padding="VALID",strides=[1,1,1,1]) output_image_summary = tf.summary.image('output_image_summary',tf.reshape(output,[output.shape[3],output.shape[1],output.shape[2],1]),512) summarylist.append(output_image_summary) #output = tf.layers.conv2d(inputs=conv18,filters=W23,kernel_size=[1, 1],padding="valid",activation=tf.nn.sigmoid) print(output) return output,summarylist def network_keras(self): input_layer = layers.Input(shape=[572,572,3]) conv1 = layers.Conv2D(filters=64,kernel_size=[3, 3],padding="valid",activation=tf.nn.relu)(input_layer) conv2 = layers.Conv2D(filters=64, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv1) pool1 = layers.MaxPool2D(pool_size=[2, 2], strides=2)(conv2) conv3 = layers.Conv2D(filters=128, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(pool1) conv4 = layers.Conv2D(filters=128, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv3) pool2 = layers.MaxPool2D(pool_size=[2, 2], strides=2)(conv4) conv5 = layers.Conv2D(filters=256, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(pool2) conv6 = layers.Conv2D(filters=256, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv5) pool3 = layers.MaxPool2D(pool_size=[2, 2], strides=2)(conv6) conv7 = layers.Conv2D(filters=512, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(pool3) conv8 = layers.Conv2D(filters=512, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv7) pool4 = layers.MaxPool2D(pool_size=[2, 2], strides=2)(conv8) conv9 = layers.Conv2D(filters=1024, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(pool4) conv10 = layers.Conv2D(filters=1024, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv9) deconv1 = layers.Conv2DTranspose(filters=512,kernel_size=[2, 2],strides = (2,2),padding='valid',activation=tf.nn.relu)(conv10) concat1 = layers.Lambda(self.concat,arguments={'reference':deconv1})(conv8) #concat1 = self.concat(conv8, deconv1) conv11 = layers.Conv2D(filters=512, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(concat1) conv12 = layers.Conv2D(filters=512, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv11) deconv2 = layers.Conv2DTranspose(filters=256, kernel_size=[2, 2], strides=(2, 2), padding='valid',activation=tf.nn.relu)(conv12) concat2 = layers.Lambda(self.concat,arguments={'reference':deconv2})(conv6) #concat2 = self.concat(conv6, deconv2) conv13 = layers.Conv2D(filters=256, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(concat2) conv14 = layers.Conv2D(filters=256, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv13) deconv3 = layers.Conv2DTranspose(filters=128, kernel_size=[2, 2], strides=(2, 2), padding='valid',activation=tf.nn.relu)(conv14) concat3 = layers.Lambda(self.concat,arguments={'reference':deconv3})(conv4) #concat3 = self.concat(conv4, deconv3) conv15 = layers.Conv2D(filters=128, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(concat3) conv16 = layers.Conv2D(filters=128, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv15) deconv4 = layers.Conv2DTranspose(filters=64, kernel_size=[2, 2], strides=(2, 2), padding='valid', activation=tf.nn.relu)(conv16) concat4 = layers.Lambda(self.concat,arguments={'reference':deconv4})(conv2) #concat4 = self.concat(conv2, deconv4) conv17 = layers.Conv2D(filters=64, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(concat4) conv18 = layers.Conv2D(filters=64, kernel_size=[3, 3], padding="valid", activation=tf.nn.relu)(conv17) output_layer = layers.Conv2D(filters=1, kernel_size=[1, 1], padding="valid", activation=tf.nn.sigmoid)(conv18) model = models.Model(inputs = input_layer,outputs=output_layer) return model if __name__=='__main__': net = Unet() net.train()

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8ae5d9b404e6f6d284da150f6a3c364d8b9363b2

25

py

Python

quad_sim_python/disp/__init__.py

ricardodeazambuja/quad_sim_python

f4afe76399b5325cd9136158b3f52c2b4d5170e5

[ "MIT" ]

null

quad_sim_python/disp/__init__.py

ricardodeazambuja/quad_sim_python

f4afe76399b5325cd9136158b3f52c2b4d5170e5

[ "MIT" ]

null

quad_sim_python/disp/__init__.py

ricardodeazambuja/quad_sim_python

f4afe76399b5325cd9136158b3f52c2b4d5170e5

[ "MIT" ]

null

from .animation import *

12.5

24

0.76

3

25

6.333333

1

0

0.16

25

1

25

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0

1

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null

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1

0

6

c10ac39259a258dc16c7bb3248ebfc8c16d818e3

679

py

Python

dataset/script/cafe24product_check_prd_img_cnt.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

dataset/script/cafe24product_check_prd_img_cnt.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

dataset/script/cafe24product_check_prd_img_cnt.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

import os, glob # image_path = "D:/data/fashion/image_retrieval/cafe24product/dataset_train" # # prd_list = glob.glob(os.path.join(image_path, "*")) # # cnt_list = {} # for prd_dir in prd_list: # cnt = len(glob.glob(os.path.join(prd_dir, "*.jpg"))) # if cnt not in cnt_list: # cnt_list[cnt] = 0 # cnt_list[cnt] += 1 # print(cnt_list) image_path = "D:/data/fashion/image_retrieval/cafe24product/dataset_test/query" prd_list = glob.glob(os.path.join(image_path, "*")) cnt_list = {} for prd_dir in prd_list: cnt = len(glob.glob(os.path.join(prd_dir, "*.jpg"))) if cnt not in cnt_list: cnt_list[cnt] = 0 cnt_list[cnt] += 1 print(cnt_list)

26.115385

79

0.659794

112

679

3.767857

0.25

0.165877

0.14218

0.132701

0.938389

0.677725

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0.173785

679

25

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27.16

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0.198864

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false

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0.1

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null

0

1

0

null

0

6

c10d9d9d88e837fec3f75950a8fbc28ddee287ce

61

py

Python

cirrus/cli/components/functions/__init__.py

kave/cirrus-geo

cc10a6df55c5e124d87663e9c32b53b871216a62

[ "Apache-2.0" ]

null

cirrus/cli/components/functions/__init__.py

kave/cirrus-geo

cc10a6df55c5e124d87663e9c32b53b871216a62

[ "Apache-2.0" ]

null

cirrus/cli/components/functions/__init__.py

kave/cirrus-geo

cc10a6df55c5e124d87663e9c32b53b871216a62

[ "Apache-2.0" ]

null

from ..base import Lambda class Function(Lambda): pass

10.166667

25

0.704918

8

61

5.375

0.875

0

0.213115

61

5

26

12.2

0.895833

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

c1822f1ade741e1f10bb047c3bc3c4d0149796d2

225

py

Python

tcellmatch/models/layers/__init__.py

theislab/tcellmatch

ddd344e44147f97f35d6a4e7c3c7677981fd177e

[ "BSD-3-Clause" ]

25

2019-08-14T22:39:40.000Z

2022-03-02T15:42:35.000Z

tcellmatch/models/layers/__init__.py

theislab/tcellmatch

ddd344e44147f97f35d6a4e7c3c7677981fd177e

[ "BSD-3-Clause" ]

2

2021-07-13T23:40:14.000Z

2021-12-18T10:08:37.000Z

tcellmatch/models/layers/__init__.py

theislab/tcellmatch

ddd344e44147f97f35d6a4e7c3c7677981fd177e

[ "BSD-3-Clause" ]

4

2020-02-21T20:43:41.000Z

2022-03-21T14:38:58.000Z

from .layer_aa_embedding import LayerAaEmbedding from .layer_attention import LayerMultiheadSelfAttention from .layer_conv import LayerConv from .layer_inception import LayerInception from .layer_stack import build_layer_set

37.5

56

0.888889

28

225

6.857143

0.535714

0.234375

0

0.088889

225

5

57

45

0.936585

0

1

0

true

0

1

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1

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1

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null

1

0

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0

null

0

1

0

1

0

1

0

6

c1c3d352319f759c0de58f3e0858ba58fa2b29e6

44

py

Python

applications/losses/__init__.py

khoehlein/fV-SRN

601f3e952b090df92e875c233c2c9ca646523948

[ "MIT" ]

null

applications/losses/__init__.py

khoehlein/fV-SRN

601f3e952b090df92e875c233c2c9ca646523948

[ "MIT" ]

null

applications/losses/__init__.py

khoehlein/fV-SRN

601f3e952b090df92e875c233c2c9ca646523948

[ "MIT" ]

null

from .lossbuilder import LossBuilder

8.8

37

0.727273

4

44

8

0.75

0

0.25

44

4

38

11

0.969697

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a9f7c005913eb9de5f4cb0b01703a241f9cae891

2,296

py

Python

epytope/Data/pssms/smm/mat/B_44_03_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smm/mat/B_44_03_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smm/mat/B_44_03_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_44_03_9 = {0: {'A': -0.284, 'C': -0.093, 'E': -0.17, 'D': 0.057, 'G': 0.046, 'F': 0.151, 'I': 0.154, 'H': 0.093, 'K': 0.176, 'M': -0.47, 'L': 0.155, 'N': -0.047, 'Q': -0.105, 'P': 0.332, 'S': -0.244, 'R': 0.117, 'T': 0.193, 'W': -0.004, 'V': 0.127, 'Y': -0.185}, 1: {'A': 0.137, 'C': 0.0, 'E': -1.644, 'D': -0.154, 'G': 0.142, 'F': 0.001, 'I': -0.003, 'H': 0.054, 'K': 0.252, 'M': -0.164, 'L': 0.057, 'N': -0.072, 'Q': -0.196, 'P': 0.36, 'S': 0.056, 'R': 0.296, 'T': 0.19, 'W': 0.0, 'V': 0.322, 'Y': 0.368}, 2: {'A': -0.072, 'C': 0.147, 'E': 0.237, 'D': 0.709, 'G': 0.145, 'F': -0.191, 'I': -0.38, 'H': -0.017, 'K': 0.602, 'M': -0.549, 'L': -0.14, 'N': -0.435, 'Q': 0.199, 'P': 0.768, 'S': -0.086, 'R': 0.19, 'T': -0.112, 'W': -0.563, 'V': -0.231, 'Y': -0.221}, 3: {'A': -0.034, 'C': -0.017, 'E': 0.0, 'D': -0.054, 'G': 0.008, 'F': 0.016, 'I': -0.038, 'H': -0.03, 'K': 0.104, 'M': 0.038, 'L': 0.009, 'N': -0.005, 'Q': -0.075, 'P': 0.081, 'S': -0.044, 'R': 0.018, 'T': -0.012, 'W': 0.008, 'V': -0.005, 'Y': 0.032}, 4: {'A': -0.156, 'C': -0.057, 'E': 0.086, 'D': 0.105, 'G': 0.17, 'F': -0.109, 'I': -0.063, 'H': 0.029, 'K': 0.237, 'M': -0.052, 'L': -0.215, 'N': 0.002, 'Q': 0.194, 'P': 0.065, 'S': -0.109, 'R': 0.199, 'T': 0.087, 'W': -0.208, 'V': -0.106, 'Y': -0.097}, 5: {'A': -0.317, 'C': 0.042, 'E': 0.242, 'D': 0.089, 'G': -0.119, 'F': -0.181, 'I': -0.094, 'H': -0.048, 'K': 0.11, 'M': 0.061, 'L': 0.007, 'N': 0.0, 'Q': -0.001, 'P': 0.154, 'S': 0.02, 'R': 0.151, 'T': -0.088, 'W': 0.053, 'V': -0.079, 'Y': -0.001}, 6: {'A': -0.01, 'C': 0.002, 'E': 0.157, 'D': 0.169, 'G': 0.224, 'F': -0.123, 'I': -0.039, 'H': -0.075, 'K': -0.075, 'M': 0.072, 'L': -0.291, 'N': 0.136, 'Q': -0.055, 'P': 0.011, 'S': 0.036, 'R': -0.093, 'T': -0.015, 'W': -0.147, 'V': 0.104, 'Y': 0.011}, 7: {'A': -0.017, 'C': 0.004, 'E': 0.005, 'D': 0.015, 'G': -0.001, 'F': -0.006, 'I': 0.035, 'H': -0.009, 'K': -0.02, 'M': 0.013, 'L': -0.001, 'N': 0.008, 'Q': 0.022, 'P': 0.029, 'S': 0.001, 'R': -0.01, 'T': -0.005, 'W': -0.046, 'V': 0.004, 'Y': -0.023}, 8: {'A': 0.288, 'C': -0.137, 'E': 0.48, 'D': 0.408, 'G': 0.251, 'F': -0.662, 'I': -0.359, 'H': 0.124, 'K': 0.294, 'M': -0.221, 'L': -0.183, 'N': 0.361, 'Q': 0.65, 'P': 0.0, 'S': 0.174, 'R': 0.217, 'T': 0.553, 'W': -1.301, 'V': -0.02, 'Y': -0.915}, -1: {'con': 4.85249}}

2,296

0.392422

557

2,296

1.612208

0.290844

0.020045

0

0.371295

0.162456

2,296

1

2,296

0.095684

0

0.079669

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

e706236965078c9cd5120168bce51646c37b19b8

46

py

Python

zendesk/__init__.py

optixx/zendesk

7a4439f1c5b46913acad6b3153266d52f011c11e

[ "MIT" ]

31

2015-01-02T01:44:18.000Z

2021-06-10T16:29:54.000Z

zendesk/__init__.py

optixx/zendesk

7a4439f1c5b46913acad6b3153266d52f011c11e

[ "MIT" ]

1

2015-04-08T07:54:50.000Z

2015-04-09T14:29:38.000Z

zendesk/__init__.py

optixx/zendesk

7a4439f1c5b46913acad6b3153266d52f011c11e

[ "MIT" ]

23

2015-01-12T23:42:34.000Z

2021-09-08T11:20:12.000Z

from zendesk import * from endpoints import *

15.333333

23

0.782609

6

46

6

0.666667

0

0.173913

46

2

24

23

0.947368

0

1

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true

0

1

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1

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null

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null

0

1

0

1

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1

0

6

e795665bdb6d9a6a25571990aa478dec4d964eb5

11,650

py

Python

lsq_quantizer/utils/lsq_network.py

yashbhalgat/QualcommAI-MicroNet-submission-MixNet

ea43bb6b08f2fd00a51742d62795f90fa386741f

[ "MIT" ]

29

2019-11-07T02:52:03.000Z

2022-02-19T03:45:22.000Z

lsq_quantizer/utils/lsq_network.py

yashbhalgat/QualcommAI-MicroNet-submission-nanoWRN

8e4a2a253e68dc67eed91a5d3bb764afda5f32c8

[ "MIT" ]

1

2022-03-23T12:01:54.000Z

2022-03-23T12:16:19.000Z

lsq_quantizer/utils/lsq_network.py

yashbhalgat/QualcommAI-MicroNet-submission-nanoWRN

8e4a2a253e68dc67eed91a5d3bb764afda5f32c8

[ "MIT" ]

7

2019-12-18T02:05:35.000Z

2021-02-03T03:44:50.000Z

import math import torch import torch.nn as nn from .lsq_module import Conv2d from .lsq_module import Linear from .lsq_module import LsqActivation def _make_layer(block, in_channels, planes, nblocks, stride=1, constr_activation=None): layers = list() downsample = stride != 1 or in_channels != planes * block.expansion layers.append(block(in_channels, planes, stride, downsample, constr_activation)) in_channels = planes * block.expansion for i in range(1, nblocks): layers.append(block(in_channels, planes, constr_activation=constr_activation)) return nn.Sequential(*layers), planes * block.expansion class _Identity(nn.Module): def forward(self, x): return x class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channels, planes, stride=1, downsample=False, constr_activation=None): super(BasicBlock, self).__init__() self.quan_activation = constr_activation is not None self.conv1 = Conv2d(in_channels, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(num_features=planes) self.activation1 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.conv2 = Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(num_features=planes) self.activation2 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.downsample = None if downsample: conv = Conv2d(in_channels, planes, kernel_size=1, stride=stride, padding=0, bias=False) bn = nn.BatchNorm2d(num_features=planes) self.downsample = nn.Sequential(*[conv, bn]) def forward(self, x): residual = x if self.downsample is None else self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.activation1(out) out = self.conv2(out) out = self.bn2(out) out += residual out = self.activation2(out) return out class PreActivationBlock(nn.Module): expansion = 1 def __init__(self, in_channels, planes, stride=1, downsample=False, constr_activation=None): super(PreActivationBlock, self).__init__() self.quan_activation = constr_activation is not None self.bn1 = nn.BatchNorm2d(num_features=in_channels) self.activation1 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.conv1 = Conv2d(in_channels, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(num_features=planes) self.activation2 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.conv2 = Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.downsample = None if downsample: bn = nn.BatchNorm2d(num_features=in_channels) activation = LsqActivation(constr_activation) if self.quan_activation else _Identity() conv = Conv2d(in_channels, planes, kernel_size=1, stride=stride, padding=0, bias=False) self.downsample = nn.Sequential(*[bn, activation, conv]) def forward(self, x): residual = x if self.downsample is None else self.downsample(x) out = self.bn1(x) out = self.activation1(out) out = self.conv1(out) out = self.bn2(out) out = self.activation2(out) out = self.conv2(out) out += residual return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_channels, planes, stride=1, downsample=None, constr_activation=None): super(Bottleneck, self).__init__() self.quan_activation = constr_activation is not None self.conv1 = Conv2d(in_channels, planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.activation1 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.conv2 = Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.activation2 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.conv3 = Conv2d(planes, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.activation3 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) self.downsample = None if downsample: conv = Conv2d(in_channels, planes * 4, kernel_size=1, stride=stride, padding=0, bias=False) bn = nn.BatchNorm2d(num_features=planes * 4) self.downsample = nn.Sequential(*[conv, bn]) def forward(self, x): residual = x if self.downsample is None else self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.activation1(out) out = self.conv2(out) out = self.bn2(out) out = self.activation2(out) out = self.conv3(out) out = self.bn3(out) out += residual out = self.activation3(out) return out class Resnet20(nn.Module): def __init__(self, block, quan_first=False, quan_last=False, constr_activation=None): super(Resnet20, self).__init__() self.quan_first = quan_first self.quan_last = quan_last self.quan_activation = constr_activation is not None if quan_first: self.first_act = LsqActivation(constr_activation) if self.quan_activation else _Identity() self.conv1 = Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1, bias=False) else: self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(num_features=16) self.activation1 = LsqActivation(constr_activation) if self.quan_activation else nn.ReLU(inplace=True) in_channels = 16 self.layer1, in_channels = _make_layer(block, in_channels, planes=16, nblocks=3, stride=1, constr_activation=constr_activation) self.layer2, in_channels = _make_layer(block, in_channels, planes=32, nblocks=3, stride=2, constr_activation=constr_activation) self.layer3, in_channels = _make_layer(block, in_channels, planes=64, nblocks=3, stride=2, constr_activation=constr_activation) self.avgpool = nn.AvgPool2d(kernel_size=8, stride=1) if quan_last: self.last_act = LsqActivation(constr_activation) if self.quan_activation else _Identity() self.fc = Linear(in_features=64, out_features=100, bias=True) else: self.fc = nn.Linear(in_features=64, out_features=100, bias=True) self._init_weight() def _init_weight(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, x): if self.quan_first: x = self.first_act(x) out = self.conv1(x) out = self.bn1(out) out = self.activation1(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.avgpool(out) out = torch.flatten(out, start_dim=1) if self.quan_last: out = self.last_act(out) out = self.fc(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, quan_first=False, quan_last=False, constr_activation=None): super(ResNet, self).__init__() self.quan_first = quan_first self.quan_last = quan_last self.quan_activation = constr_activation is not None self.constr_activation = constr_activation if self.quan_first: self.first_act = LsqActivation(constr_activation) if self.quan_activation else _Identity() self.conv1 = Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) else: self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) if self.quan_activation: self.activation1 = LsqActivation(constr_activation) else: self.activation1 = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) in_channels = 64 self.layer1, in_channels = _make_layer(block, in_channels, planes=64, nblocks=layers[0], stride=1, constr_activation=constr_activation) self.layer2, in_channels = _make_layer(block, in_channels, planes=128, nblocks=layers[1], stride=2, constr_activation=constr_activation) self.layer3, in_channels = _make_layer(block, in_channels, planes=256, nblocks=layers[2], stride=2, constr_activation=constr_activation) self.layer4, in_channels = _make_layer(block, in_channels, planes=512, nblocks=layers[3], stride=2, constr_activation=constr_activation) self.avgpool = nn.AvgPool2d(7, stride=1) if self.quan_last: self.last_act = LsqActivation(constr_activation) if self.quan_activation else _Identity() self.fc = Linear(512 * block.expansion, num_classes) else: self.fc = nn.Linear(512 * block.expansion, num_classes) self._init_weight() def _init_weight(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, x): if self.quan_first: x = self.first_act(x) x = self.conv1(x) x = self.bn1(x) x = self.activation1(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) if self.quan_last: x = self.last_act(x) x = self.fc(x) return x def resnet18(quan_first=False, quan_last=False, constr_activation=None, preactivation=False): block = PreActivationBlock if preactivation else BasicBlock model = ResNet(block, [2, 2, 2, 2], quan_first=quan_first, quan_last=quan_last, constr_activation=constr_activation) return model def resnet20(quan_first=False, quan_last=False, constr_activation=None, preactivation=False): block = PreActivationBlock if preactivation else BasicBlock model = Resnet20(block, quan_first, quan_last, constr_activation) return model def resnet50(quan_first=False, quan_last=False, constr_activation=None, preactivation=False): block = Bottleneck model = ResNet(block, [3, 4, 6, 3], quan_first=quan_first, quan_last=quan_last, constr_activation=constr_activation) return model

42.518248

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0.643605

1,497

11,650

4.829659

0.082164

0.115076

0.046473

0.0426

0.818257

0.770263

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0.71065

0.705118

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0

0.030516

0.254592

11,650

273

121

42.673993

0.80205

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0.026906

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0.188341

0

null

0

1

0

null

0

6

99c3e437189981d2d550be0a20ae67512eeb4d68

29

py

Python

Exercicios/ex021.py

Dobravoski/Exercicios-Python

e7169e1ee6954a7bc9216063845611107a13759f

[ "MIT" ]

null

Exercicios/ex021.py

Dobravoski/Exercicios-Python

e7169e1ee6954a7bc9216063845611107a13759f

[ "MIT" ]

null

Exercicios/ex021.py

Dobravoski/Exercicios-Python

e7169e1ee6954a7bc9216063845611107a13759f

[ "MIT" ]

null

print('IMPOSSIVEL DE FAZER')

14.5

28

0.758621

4

29

5.5

1

0

0.103448

29

1

29

0.846154

0

0.655172

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

99f01c363bd879b385c9ff64f058d5a220657e01

45

py

Python

docker/confluent-kafka/verify.py

arnarayanan/dockerfiles

32a6299c3d22276b1df41dca5a0001246bdfd9d2

[ "MIT" ]

48

2018-12-12T12:18:09.000Z

2022-03-05T02:23:42.000Z

docker/confluent-kafka/verify.py

arnarayanan/dockerfiles

32a6299c3d22276b1df41dca5a0001246bdfd9d2

[ "MIT" ]

7,201

2018-12-24T17:14:17.000Z

2022-03-31T13:39:12.000Z

docker/confluent-kafka/verify.py

HeyLaurelTestOrg/dockerfiles

7cadb7a10c1307bfdcdb93ef6e890b56ccb1223a

[ "MIT" ]

94

2018-12-17T10:59:21.000Z

2022-03-29T12:59:30.000Z

import confluent_kafka print("all is good")

11.25

22

0.777778

7

45

4.857143

1

0

0.133333

45

3

23

15

0.871795

0

0.244444

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8201978a45387ba39226b2d97aaed78eb0c4d793

78

py

Python

beginner/chapter_1/exam_1_7.py

Bokji24Dev/CodeStudy

4c0fc852e6f472d082e9836c59ad22d229f74d87

[ "MIT" ]

null

beginner/chapter_1/exam_1_7.py

Bokji24Dev/CodeStudy

4c0fc852e6f472d082e9836c59ad22d229f74d87

[ "MIT" ]

null

beginner/chapter_1/exam_1_7.py

Bokji24Dev/CodeStudy

4c0fc852e6f472d082e9836c59ad22d229f74d87

[ "MIT" ]

null

# -*- coding:utf-8 -*- count = 1 print(count) count = count + 1 print(count)

11.142857

22

0.602564

12

78

3.916667

0.5

0.255319

0.468085

0.680851

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0.047619

0.192308

78

6

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1

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null

0

1

0

6

821fc14587204d69dad0bfd037e28ee443e2e51d

22

py

Python

datadict/jupyter/__init__.py

177arc/pandas-datadict

743d76ea8b71e9b94de83c44d008e6f80ddd232b

[ "MIT" ]

2

2019-10-21T19:32:54.000Z

2020-12-19T06:14:58.000Z

datadict/jupyter/__init__.py

177arc/pandas-datadict

743d76ea8b71e9b94de83c44d008e6f80ddd232b

[ "MIT" ]

6

2019-11-03T17:46:45.000Z

2021-01-03T17:11:45.000Z

datadict/jupyter/__init__.py

177arc/pandas-datadict

743d76ea8b71e9b94de83c44d008e6f80ddd232b

[ "MIT" ]

null

from .jupyter import *

22

0.772727

3

22

5.666667

1

0

0.136364

22

1

22

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

822a353a5d78275fd1d3cc2da0423ae308cfe110

136

py

Python

app/views.py

Kalenai/timestamp-microservice

326677ba55a51aa9cc173707154a3d3d5d688182

[ "MIT" ]

1

2018-05-10T14:04:58.000Z

app/views.py

Kalenai/timestamp-microservice

326677ba55a51aa9cc173707154a3d3d5d688182

[ "MIT" ]

null

app/views.py

Kalenai/timestamp-microservice

326677ba55a51aa9cc173707154a3d3d5d688182

[ "MIT" ]

null

from flask import Flask, render_template from app import app @app.route('/') def homepage(): return render_template('index.html')

17

40

0.735294

19

136

5.157895

0.631579

0.285714

0

0.147059

136

7

41

19.428571

0.844828

0

0.080882

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

413537614acd8bd0b26bcfca50df834bd1a40a4d

42

py

Python

yelp_reviews_scraper/__init__.py

meta-scraper/yelp-reviews-scraper-python

0e0a200380b4c28bac10feb4b315b634e1926ce1

[ "MIT" ]

null

yelp_reviews_scraper/__init__.py

meta-scraper/yelp-reviews-scraper-python

0e0a200380b4c28bac10feb4b315b634e1926ce1

[ "MIT" ]

null

yelp_reviews_scraper/__init__.py

meta-scraper/yelp-reviews-scraper-python

0e0a200380b4c28bac10feb4b315b634e1926ce1

[ "MIT" ]

null

from yelp_reviews_scraper.client import *

21

41

0.857143

6

42

5.666667

1

0

0.095238

42

1

42

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

417e201e8f73f5074d3a83d93de75fbf1c42b019

216

py

Python

colossalai/zero/shard_utils/__init__.py

oikosohn/ColossalAI

fc5101f24c9a2ad8e7e16cb81e1ef7646a1061fd

[ "Apache-2.0" ]

null

colossalai/zero/shard_utils/__init__.py

oikosohn/ColossalAI

fc5101f24c9a2ad8e7e16cb81e1ef7646a1061fd

[ "Apache-2.0" ]

null

colossalai/zero/shard_utils/__init__.py

oikosohn/ColossalAI

fc5101f24c9a2ad8e7e16cb81e1ef7646a1061fd

[ "Apache-2.0" ]

null

from colossalai.zero.shard_utils.base_shard_strategy import BaseShardStrategy from colossalai.zero.shard_utils.tensor_shard_strategy import TensorShardStrategy __all__ = ['BaseShardStrategy', 'TensorShardStrategy']

43.2

81

0.875

23

216

7.782609

0.521739

0.156425

0.201117

0.256983

0.312849

0

0.060185

216

4

82

54

0.881773

0

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0

1

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false

0

0.666667

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0.666667

0

1

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null

0

1

0

1

0

null

0

1

0

1

0

6

41820c5ea72b589f23aa10f3bcc6dcc1843969f1

4,665

py

Python

algorithms/agglomerate.py

matinraayai/ibex

7792d1299a04da360faa1cd8a16a4c5a3990b48c

[ "MIT" ]

3

2018-08-10T21:11:09.000Z

2019-07-26T13:47:24.000Z

algorithms/agglomerate.py

matinraayai/ibex

7792d1299a04da360faa1cd8a16a4c5a3990b48c

[ "MIT" ]

null

algorithms/agglomerate.py

matinraayai/ibex

7792d1299a04da360faa1cd8a16a4c5a3990b48c

[ "MIT" ]

6

2018-03-05T20:14:11.000Z

2020-07-23T18:39:16.000Z

from ibex.transforms import seg2seg, seg2gold from ibex.data_structures import UnionFind from ibex.utilities import dataIO import numpy as np import os import struct def Agglomerate(prefix, model_prefix, threshold=0.5): # read the segmentation data segmentation = dataIO.ReadSegmentationData(prefix) # get the multicut filename (with graph weights) multicut_filename = 'multicut/{}-{}.graph'.format(model_prefix, prefix) # get the maximum segmentation value max_value = np.amax(segmentation) + 1 # create union find data structure union_find = [UnionFind.UnionFindElement(iv) for iv in range(max_value)] # read in all of the labels and merge the result with open(multicut_filename, 'rb') as fd: # read the number of vertices and edges nvertices, nedges, = struct.unpack('QQ', fd.read(16)) # read in all of the edges for ie in range(nedges): # read in both labels label_one, label_two, = struct.unpack('QQ', fd.read(16)) # skip over the reduced labels fd.read(16) # read in the edge weight edge_weight, = struct.unpack('d', fd.read(8)) # merge label one and label two in the union find data structure if (edge_weight > threshold): UnionFind.Union(union_find[label_one], union_find[label_two]) # create a mapping mapping = np.zeros(max_value, dtype=np.int64) # update the segmentation for iv in range(max_value): label = UnionFind.Find(union_find[iv]).label mapping[iv] = label # update the labels agglomerated_segmentation = seg2seg.MapLabels(segmentation, mapping) gold_filename = 'gold/{}_gold.h5'.format(prefix) # TODO fix this code temporary filename agglomeration_filename = 'multicut/{}-agglomerate.h5'.format(prefix) # temporary - write h5 file dataIO.WriteH5File(agglomerated_segmentation, agglomeration_filename, 'stack') import time start_time = time.time() print 'Agglomeration - {}:'.format(threshold) # create the command line command = '~/software/PixelPred2Seg/comparestacks --stack1 {} --stackbase {} --dilate1 1 --dilatebase 1 --relabel1 --relabelbase --filtersize 100 --anisotropic'.format(agglomeration_filename, gold_filename) # execute the command os.system(command) print time.time() - start_time def MergeGroundTruth(prefix, model_prefix): # read the segmentation data segmentation = dataIO.ReadSegmentationData(prefix) # get the multicut filename (with graph weights) multicut_filename = 'multicut/{}-{}.graph'.format(model_prefix, prefix) # read the gold data gold = dataIO.ReadGoldData(prefix) # read in the segmentation to gold mapping mapping = seg2gold.Mapping(segmentation, gold) # get the maximum segmentation value max_value = np.amax(segmentation) + 1 # create union find data structure union_find = [UnionFind.UnionFindElement(iv) for iv in range(max_value)] # read in all of the labels with open(multicut_filename, 'rb') as fd: # read the number of vertices and edges nvertices, nedges, = struct.unpack('QQ', fd.read(16)) # read in all of the edges for ie in range(nedges): # read in the two labels label_one, label_two, = struct.unpack('QQ', fd.read(16)) # skip over the reduced labels and edge weight fd.read(24) # if the labels are the same and the mapping is non zero if mapping[label_one] == mapping[label_two] and mapping[label_one]: UnionFind.Union(union_find[label_one], union_find[label_two]) # create a mapping mapping = np.zeros(max_value, dtype=np.int64) # update the segmentation for iv in range(max_value): label = UnionFind.Find(union_find[iv]).label mapping[iv] = label merged_segmentation = seg2seg.MapLabels(segmentation, mapping) gold_filename = 'gold/{}_gold.h5'.format(prefix) # TODO fix this code temporary filename truth_filename = 'multicut/{}-truth.h5'.format(prefix) # temporary write h5 file dataIO.WriteH5File(merged_segmentation, truth_filename, 'stack') import time start_time = time.time() print 'Ground truth: ' # create the command line command = '~/software/PixelPred2Seg/comparestacks --stack1 {} --stackbase {} --dilate1 1 --dilatebase 1 --relabel1 --relabelbase --filtersize 100 --anisotropic'.format(truth_filename, gold_filename) # execute the command os.system(command) print time.time() - start_time

33.561151

210

0.676313

586

4,665

5.284983

0.220137

0.031966

0.012916

0.015499

0.740071

0.735551

0.673555

0

0.014461

0.229153

4,665

138

211

33.804348

0.846774

0.240086

0

0.590164

0

0.032787

0.133409

0.029076

0

0.007246

0

null

0

0.131148

null

0.065574

0

null

0

1

0

null

0

1

0

6

ec58099db927f4e615178d7c8c63f98d21999aa1

2,960

py

Python

src/pattern/sorting/sort_patterns.py

leodenault/led-ws2801

1003dd25824fbbb96564145ee3c7c46711c9cd1a

[ "MIT" ]

null

src/pattern/sorting/sort_patterns.py

leodenault/led-ws2801

1003dd25824fbbb96564145ee3c7c46711c9cd1a

[ "MIT" ]

null

src/pattern/sorting/sort_patterns.py

leodenault/led-ws2801

1003dd25824fbbb96564145ee3c7c46711c9cd1a

[ "MIT" ]

null

from colour import led_colour from pattern.pattern_chain import PatternChain from pattern.sorting.bubble_sort_pattern import BubbleSortPattern from pattern.sorting.colour_distributor import ColourDistributor from pattern.sorting.merge_sort_pattern import MergeSortPattern from pattern.sorting.sort_celebration import SortCelebration def compare(colour1, colour2, colours): return colours.index(colour1) < colours.index(colour2) def create_bubble_sort_pattern( num_leds, distribution_duration, sort_step_duration, num_celebration_flashes, celebration_flash_duration, colour_palette): """Creates a pattern instance that sorts colours using the bubble sort algorithm. :param num_leds: the number of LEDs on the device. :param distribution_duration: the time it should take to distribute all colours across the LED strip. :param sort_step_duration: the amount of time a single step takes to execute. :param num_celebration_flashes: the number of times the LEDs should flash in celebration. :param celebration_flash_duration: the amount of time, in seconds, it should take for a single flash to occur. :param colour_palette: the palette of colours which will be used to randomly distribute colours onto the strip. """ strip_data = [led_colour.BLACK] * num_leds return PatternChain([ lambda: ColourDistributor( num_leds, distribution_duration, colour_palette, strip_data), lambda: BubbleSortPattern(colour_palette, strip_data, sort_step_duration), lambda: SortCelebration( num_celebration_flashes, celebration_flash_duration, strip_data), ]) def create_merge_sort_pattern( num_leds, distribution_duration, sort_step_duration, num_celebration_flashes, celebration_flash_duration, colour_palette): """Creates a pattern instance that sorts colours using the merge sort algorithm. :param num_leds: the number of LEDs on the device. :param distribution_duration: the time it should take to distribute all colours across the LED strip. :param sort_step_duration: the amount of time a single step takes to execute. :param num_celebration_flashes: the number of times the LEDs should flash in celebration. :param celebration_flash_duration: the amount of time, in seconds, it should take for a single flash to occur. :param colour_palette: the palette of colours which will be used to randomly distribute colours onto the strip. """ strip_data = [led_colour.BLACK] * num_leds return PatternChain([ lambda: ColourDistributor( num_leds, distribution_duration, colour_palette, strip_data), lambda: MergeSortPattern(num_leds, sort_step_duration, colour_palette, strip_data), lambda: SortCelebration( num_celebration_flashes, celebration_flash_duration, strip_data), ])

36.097561

83

0.749324

377

2,960

5.66313

0.193634

0.029508

0.044965

0.050585

0.769087

0.755035

0

0.001691

0.200676

2,960

81

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0.900676

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0

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1

0.068182

false

0

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0.272727

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null

0

1

0

1

0

null

0

6

6b9d3a76779d1715fda723b8fbcdc4ef91b87cd8

22,402

py

Python

src/model/model.py

theLongLab/phx-nn

81e0f87faa82d6995b37095815655224cb5bf438

[ "MIT" ]

null

src/model/model.py

theLongLab/phx-nn

81e0f87faa82d6995b37095815655224cb5bf438

[ "MIT" ]

null

src/model/model.py

theLongLab/phx-nn

81e0f87faa82d6995b37095815655224cb5bf438

[ "MIT" ]

null

# src/model/model.py from collections import OrderedDict from pathlib import Path import pickle from typing import ( Any, Dict, List, Mapping, MutableMapping, Optional, NoReturn, Sequence, Tuple, Union, ) from adabound import AdaBound from optuna import Trial import pytorch_lightning as pl from sklearn.base import BaseEstimator import torch import torch.nn as nn import torch.nn.functional as F from torch.optim.optimizer import Optimizer from torch.optim.lr_scheduler import StepLR from torch.utils.data import DataLoader from xgboost import XGBRegressor from src.base import BaseDataLoader, BaseModel from src.data_loader import SumStatDataLoader from src.model.loss import phx_param_map GPU_COUNT: int = torch.cuda.device_count() HEADS: Tuple[str, ...] = ( "num_gap_window", "inpool_gap_supp_min", "allpool_gap_supp_min", "l1_region_size_min", "l1_region_size_max", "l2_region_size_min", "l2_region_size_max", "l34_region_mis_tol", "l56_region_mis_tol", "l78_region_mis_tol", "est_indv_perpool", "aem_max_l", "bfs_mis_tol", "aem_conv_cutoff", "aem_zero_cutoff", "aem_regional_crosspool_freq_cutoff", "aem_regional_hapsetsize_min", "aem_regional_hapsetsize_max", "regr_one_vec_weight", "regr_hap_vc_weight", "regr_hap_11_weight", "regr_regional_hapsetsize_max", "regr_gamma_min", "regr_n_gamma", "regr_mis_tol", "regr_coverage_weight", "regr_distance_max_weight", "regr_max_regions", ) INPUT_SIZE: int = 40 class HParamNet(BaseModel): def __init__(self, trial: Trial, hparam_space: Mapping[str, Sequence]) -> None: super().__init__() self.layers: List[nn.Module] = [] self.batchnorms: List[nn.Module] = [] self.dropouts: List[nn.Module] = [] # Order of PHX params for inner model input. self.heads: Tuple[str, ...] = HEADS final_output_dim: int = self.__optimize_layers(trial=trial, hparam_space=hparam_space) self._batch_size: int = trial.suggest_categorical("batch_size", hparam_space["batch_size"]) self._lr: float = trial.suggest_uniform("lr", hparam_space["lr"][0], hparam_space["lr"][1]) self._final_lr: float = trial.suggest_uniform( "final_lr", hparam_space["final_lr"][0], hparam_space["final_lr"][1] ) self.__build_model(final_output_dim) def __optimize_layers(self, trial: Trial, hparam_space: Mapping[str, Sequence]) -> int: # Optimize the number of layers, hidden units in each layer and dropout rate. n_layers: int = trial.suggest_int( "n_layers", hparam_space["n_layers"][0], hparam_space["n_layers"][1] ) input_dropout_rate: float = trial.suggest_loguniform( "input_dropout_rate", hparam_space["dropout_rate"][0], 0.2 ) dropout_rate: float = trial.suggest_loguniform( "dropout_rate", input_dropout_rate, hparam_space["dropout_rate"][1] ) input_dim: int = INPUT_SIZE # number of input summary statistics i: int for i in range(n_layers): output_dim: int = trial.suggest_int( "n_units_l{}".format(i), hparam_space["n_units_l"][0], hparam_space["n_units_l"][1], ) self.layers.append(nn.Linear(input_dim, output_dim)) if i != n_layers - 1: self.batchnorms.append(nn.BatchNorm1d(num_features=output_dim)) self.dropouts.append(nn.Dropout(dropout_rate)) # Input layer needs a lower dropout rate. if i == 0: self.dropouts[-1] = nn.Dropout(input_dropout_rate) input_dim = output_dim return output_dim def __build_model(self, final_output_dim: int) -> None: # Assign layers as class variables (PyTorch requirement). layer: nn.Module for i, layer in enumerate(self.layers): layer_name: str = "fc{}".format(i) setattr(self, layer_name, layer) nn.init.xavier_normal_(getattr(self, layer_name).weight) # init weight # Assign batchnorm actions as class variables (Pytorch requirement) batchnorm: nn.Module for i, batchnorm in enumerate(self.batchnorms): setattr(self, "bn{}".format(i), batchnorm) # Assign dropout actions as class variables (PyTorch requirement). dropout: nn.Module for i, dropout in enumerate(self.dropouts): setattr(self, "dropout{}".format(i), dropout) # Assign heads as class variables (PyTorch requirement). head: str for i, head in enumerate(self.heads): setattr(self, head, nn.Linear(final_output_dim, 1)) nn.init.xavier_normal_(getattr(self, head).weight) def forward(self, X): x: torch.Tensor = X for layer, batchnorm, dropout in zip(self.layers[:-1], self.batchnorms, self.dropouts): x = F.leaky_relu(layer(x)) x = batchnorm(x) x = dropout(x) for layer in self.layers[-1:]: x = F.leaky_relu(layer(x)) # Clamp values of PHX parameters at each head. num_gap_window: torch.Tensor = torch.clamp(torch.round(self.num_gap_window(x)).int(), 1, 5) inpool_gap_supp_min: torch.Tensor = torch.sigmoid(self.inpool_gap_supp_min(x)) allpool_gap_supp_min: torch.Tensor = torch.sigmoid(self.allpool_gap_supp_min(x)) l1_region_size_min: torch.Tensor = torch.clamp( torch.round(self.l1_region_size_min(x)).int(), 3, 13 ) l1_region_size_max: torch.Tensor = torch.clamp( torch.round(self.l1_region_size_max(x)).int(), max(l1_region_size_min).item() + 1, 16 ) l2_region_size_min: torch.Tensor = l1_region_size_min l2_region_size_max: torch.Tensor = l1_region_size_max l34_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l34_region_mis_tol(x)).int(), 0, 5 ) l56_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l56_region_mis_tol(x)).int(), 1, 6 ) l78_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l78_region_mis_tol(x)).int(), 2, 7 ) est_indv_perpool: torch.Tensor = torch.clamp( torch.round(self.est_indv_perpool(x)).int(), 1000, 1000000 ) aem_max_l: torch.Tensor = torch.clamp(torch.round(self.aem_max_l(x)).int(), 0, 6) tens: torch.Tensor for tens in aem_max_l: tens += 1 if tens.item() % 2 == 0 else 0 bfs_mis_tol: torch.Tensor = torch.clamp(torch.round(self.bfs_mis_tol(x)).int(), 0, 10) aem_conv_cutoff: torch.Tensor = torch.clamp(self.aem_conv_cutoff(x), 0, 1e-4) aem_zero_cutoff: torch.Tensor = torch.clamp(self.aem_zero_cutoff(x), 0, 1e-6) aem_regional_crosspool_freq_cutoff: torch.Tensor = torch.clamp( self.aem_regional_crosspool_freq_cutoff(x), 0, 0.05 ) aem_regional_hapsetsize_min: torch.Tensor = torch.clamp( torch.round(self.aem_regional_hapsetsize_min(x)).int(), 1, 10 ) aem_regional_hapsetsize_max: torch.Tensor = torch.clamp( torch.round(self.aem_regional_hapsetsize_max(x)).int(), max(11, max(aem_regional_hapsetsize_min).item() + 4), 100, ) regr_one_vec_weight: torch.Tensor = torch.clamp(self.regr_one_vec_weight(x), 1, 10) regr_hap_vc_weight: torch.Tensor = torch.clamp(self.regr_hap_vc_weight(x), 1, 10) regr_hap_11_weight: torch.Tensor = torch.clamp(self.regr_hap_11_weight(x), 1, 10) regr_regional_hapsetsize_max: torch.Tensor = torch.clamp( torch.round(self.regr_regional_hapsetsize_max(x)).int(), 11, 100 ) regr_gamma_min: torch.Tensor = torch.sigmoid(self.regr_gamma_min(x)) / 4 regr_n_gamma: torch.Tensor = torch.clamp(torch.round(self.regr_n_gamma(x)).int(), 2, 10) regr_mis_tol: torch.Tensor = torch.clamp(torch.round(self.regr_mis_tol(x)).int(), 8, 20) regr_coverage_weight: torch.Tensor = torch.clamp(self.regr_coverage_weight(x), 0.5, 2.5) regr_distance_max_weight: torch.Tensor = torch.clamp(self.regr_distance_max_weight(x), 1, 5) regr_max_regions: torch.Tensor = torch.clamp( torch.round(self.regr_max_regions(x)).int(), 2, 3 ) output: List[torch.Tensor] = [] for head in self.heads: output.append(eval(head).float()) return tuple(output) class LightningHParamNet(pl.LightningModule): def __init__( self, est: nn.Module, cvtrain_data: torch.Tensor, cvval_data: torch.Tensor, loss_fn: BaseEstimator, shuffle: bool = False, validation_split: Union[float, int] = 0.0, num_workers: int = 0, ) -> None: super().__init__() # Avoid overriding `LightningModule` attributes (e.g. self.model) self._model: nn.Module = est self.loss_fn: XGBRegressor = loss_fn self._dataloader_args: Dict[str, Any] = { "data": cvtrain_data, "batch_size": self._model._batch_size, "shuffle": shuffle, "validation_split": validation_split, "num_workers": num_workers, } self._cvval_data: torch.Tensor = cvval_data def forward(self, X: torch.Tensor) -> Tuple[torch.Tensor, ...]: return self._model(X) def training_step(self, batch: Tuple, batch_idx: int) -> Dict[str, torch.Tensor]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) return {"loss": loss} def validation_step(self, batch: Tuple, batch_idx: int) -> Dict[str, torch.Tensor]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) val_loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) return {"val_loss": val_loss} def validation_end(self, outputs: Sequence[Mapping]) -> Dict[str, Dict[str, float]]: x: Dict[str, torch.Tensor] mean_val_loss: Union[Any, torch.Tensor] = sum( # Union Any to make mypy happy x["val_loss"].clone().detach() for x in outputs ) / len(outputs) return {"log": {"mean_val_loss": mean_val_loss.item()}} def test_step(self, batch: Tuple, batch_idx: int) -> Dict[str, torch.Tensor]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) test_loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) return {"test_loss": test_loss} def test_end(self, outputs: Sequence[Mapping]) -> Dict[str, Dict[str, float]]: x: Dict[str, torch.Tensor] mean_test_loss: Union[Any, torch.Tensor] = sum( # Union Any to make mypy mappy x["test_loss"].clone().detach() for x in outputs ) / len(outputs) return {"log": {"mean_test_loss": mean_test_loss.item()}} def configure_optimizers(self) -> Tuple[List[Optimizer], List[StepLR]]: self.opt: Optimizer = AdaBound( self._model.parameters(), lr=self._model._lr, final_lr=self._model._final_lr ) self.lr_sch: StepLR = StepLR(self.opt, 50) return [self.opt], [self.lr_sch] def on_epoch_end(self) -> None: self.lr_sch.step() @pl.data_loader def train_dataloader(self) -> DataLoader: self._train_dataloader: BaseDataLoader = SumStatDataLoader(**self._dataloader_args) return self._train_dataloader @pl.data_loader def val_dataloader(self) -> Optional[DataLoader]: return self._train_dataloader.split_validation() @pl.data_loader def test_dataloader(self) -> DataLoader: return SumStatDataLoader( data=self._cvval_data, batch_size=512, shuffle=False, validation_split=0.0, num_workers=4 * GPU_COUNT, ) class PoolHapXNet(pl.LightningModule): def __init__( self, model_hparams: Mapping[str, Any], train_data: torch.Tensor, val_data: torch.Tensor, loss_fn: BaseEstimator, shuffle: bool = False, validation_split: Union[float, int] = 0.0, num_workers: int = 0, ) -> None: super().__init__() self.layers: List[nn.Module] = [] self.batchnorms: List[nn.Module] = [] self.dropouts: List[nn.Module] = [] self.heads: Tuple[str, ...] = HEADS final_output_dim: int = self.__set_layers(model_hparams) self.__build_model(final_output_dim) self.loss_fn: XGBRegressor = loss_fn self._dataloader_args: Dict[str, Any] = { "data": train_data, "batch_size": model_hparams["batch_size"], "shuffle": shuffle, "validation_split": validation_split, "num_workers": num_workers, # "test_set": True, } self._lr: float = model_hparams["lr"] self._final_lr: float = model_hparams["final_lr"] self._val_data: torch.Tensor = val_data def __set_layers(self, model_hparams: Mapping[str, Any]) -> int: # Optimize the number of layers, hidden units in each layer and dropout rate. n_layers: int = model_hparams["n_layers"] input_dropout_rate: float = model_hparams["input_dropout_rate"] dropout_rate: float = model_hparams["dropout_rate"] input_dim: int = INPUT_SIZE # number of input summary statistics i: int for i in range(n_layers): output_dim: int = model_hparams["n_units_l{}".format(i)] self.layers.append(nn.Linear(input_dim, output_dim)) if i != n_layers - 1: self.batchnorms.append(nn.BatchNorm1d(num_features=output_dim)) self.dropouts.append(nn.Dropout(dropout_rate)) # Input layer needs a lower dropout rate. if i == 0: self.dropouts[-1] = nn.Dropout(input_dropout_rate) input_dim = output_dim return output_dim def __build_model(self, final_output_dim: int) -> None: # Assign layers as class variables (PyTorch requirement). layer: nn.Module for i, layer in enumerate(self.layers): layer_name: str = "fc{}".format(i) setattr(self, layer_name, layer) nn.init.xavier_normal_(getattr(self, layer_name).weight) # init weight # Assign batchnorm actions as class variables (Pytorch requirement) batchnorm: nn.Module for i, batchnorm in enumerate(self.batchnorms): setattr(self, "bn{}".format(i), batchnorm) # Assign dropout actions as class variables (PyTorch requirement). dropout: nn.Module for i, dropout in enumerate(self.dropouts): setattr(self, "dropout{}".format(i), dropout) # Assign heads as class variables (PyTorch requirement). head: str for i, head in enumerate(self.heads): setattr(self, head, nn.Linear(final_output_dim, 1)) nn.init.xavier_normal_(getattr(self, head).weight) def forward(self, X): x: torch.Tensor = X for layer, batchnorm, dropout in zip(self.layers[:-1], self.batchnorms, self.dropouts): x = F.leaky_relu(layer(x)) x = batchnorm(x) x = dropout(x) for layer in self.layers[-1:]: x = F.leaky_relu(layer(x)) # Clamp values of PHX parameters at each head. num_gap_window: torch.Tensor = torch.clamp(torch.round(self.num_gap_window(x)).int(), 1, 5) inpool_gap_supp_min: torch.Tensor = torch.sigmoid(self.inpool_gap_supp_min(x)) allpool_gap_supp_min: torch.Tensor = torch.sigmoid(self.allpool_gap_supp_min(x)) l1_region_size_min: torch.Tensor = torch.clamp( torch.round(self.l1_region_size_min(x)).int(), 3, 13 ) l1_region_size_max: torch.Tensor = torch.clamp( torch.round(self.l1_region_size_max(x)).int(), max(l1_region_size_min).item() + 1, 16 ) l2_region_size_min: torch.Tensor = l1_region_size_min l2_region_size_max: torch.Tensor = l1_region_size_max l34_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l34_region_mis_tol(x)).int(), 0, 5 ) l56_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l56_region_mis_tol(x)).int(), 1, 6 ) l78_region_mis_tol: torch.Tensor = torch.clamp( torch.round(self.l78_region_mis_tol(x)).int(), 2, 7 ) est_indv_perpool: torch.Tensor = torch.clamp( torch.round(self.est_indv_perpool(x)).int(), 1000, 1000000 ) aem_max_l: torch.Tensor = torch.clamp(torch.round(self.aem_max_l(x)).int(), 0, 6) tens: torch.Tensor for tens in aem_max_l: tens += 1 if tens.item() % 2 == 0 else 0 bfs_mis_tol: torch.Tensor = torch.clamp(torch.round(self.bfs_mis_tol(x)).int(), 0, 10) aem_conv_cutoff: torch.Tensor = torch.clamp(self.aem_conv_cutoff(x), 0, 1e-4) aem_zero_cutoff: torch.Tensor = torch.clamp(self.aem_zero_cutoff(x), 0, 1e-6) aem_regional_crosspool_freq_cutoff: torch.Tensor = torch.clamp( self.aem_regional_crosspool_freq_cutoff(x), 0, 0.05 ) aem_regional_hapsetsize_min: torch.Tensor = torch.clamp( torch.round(self.aem_regional_hapsetsize_min(x)).int(), 1, 10 ) aem_regional_hapsetsize_max: torch.Tensor = torch.clamp( torch.round(self.aem_regional_hapsetsize_max(x)).int(), max(11, max(aem_regional_hapsetsize_min).item() + 4), 100, ) regr_one_vec_weight: torch.Tensor = torch.clamp(self.regr_one_vec_weight(x), 1, 10) regr_hap_vc_weight: torch.Tensor = torch.clamp(self.regr_hap_vc_weight(x), 1, 10) regr_hap_11_weight: torch.Tensor = torch.clamp(self.regr_hap_11_weight(x), 1, 10) regr_regional_hapsetsize_max: torch.Tensor = torch.clamp( torch.round(self.regr_regional_hapsetsize_max(x)).int(), 11, 100 ) regr_gamma_min: torch.Tensor = torch.sigmoid(self.regr_gamma_min(x)) / 4 regr_n_gamma: torch.Tensor = torch.clamp(torch.round(self.regr_n_gamma(x)).int(), 2, 10) regr_mis_tol: torch.Tensor = torch.clamp(torch.round(self.regr_mis_tol(x)).int(), 8, 20) regr_coverage_weight: torch.Tensor = torch.clamp(self.regr_coverage_weight(x), 0.5, 2.5) regr_distance_max_weight: torch.Tensor = torch.clamp(self.regr_distance_max_weight(x), 1, 5) regr_max_regions: torch.Tensor = torch.clamp( torch.round(self.regr_max_regions(x)).int(), 2, 3 ) output: List[torch.Tensor] = [] for head in self.heads: output.append(eval(head).float()) return output def training_step( self, batch: Tuple, batch_idx: int ) -> Dict[str, Union[Dict[str, torch.Tensor], torch.Tensor]]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) tqdm_dict: Dict[str, torch.Tensor] = {"train_loss": loss} log_output: OrderedDict = OrderedDict( {"loss": loss, "progress_bar": tqdm_dict, "log": tqdm_dict} ) return log_output def validation_step(self, batch: Tuple, batch_idx: int) -> Dict[str, torch.Tensor]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) val_loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) return {"val_loss": val_loss} def validation_end(self, outputs: Sequence[Mapping]) -> Dict[str, Dict[str, float]]: x: Dict[str, torch.Tensor] mean_val_loss: Union[Any, torch.Tensor] = sum( # Union Any to make mypy happy x["val_loss"].clone().detach() for x in outputs ) / len(outputs) tqdm_dict: Dict[str, float] = {"mean_val_loss": mean_val_loss.item()} return {"progress_bar": tqdm_dict, "log": {"mean_val_loss": mean_val_loss.item()}} def test_step(self, batch: Tuple, batch_idx: int) -> Dict[str, torch.Tensor]: X: torch.Tensor y: torch.Tensor X, y = batch output: Tuple[torch.Tensor, ...] = self.forward(X) test_loss: torch.Tensor = phx_param_map(output=output, gbtree=self.loss_fn) return {"test_loss": test_loss} def test_end(self, outputs: Sequence[Mapping]) -> Dict[str, Dict[str, float]]: x: Dict[str, torch.Tensor] mean_test_loss: Union[Any, torch.Tensor] = sum( # Union Any to make mypy happy x["test_loss"].clone().detach() for x in outputs ) / len(outputs) tqdm_dict: Dict[str, float] = {"mean_test_loss": mean_test_loss.item()} print("\n\n==\nMean Test Loss: {}\n==\n\n".format(mean_test_loss.item())) return {"progress_bar": tqdm_dict, "log": {"mean_test_loss": mean_test_loss.item()}} def configure_optimizers(self) -> Tuple[List[Optimizer], List[StepLR]]: self.opt: Optimizer = AdaBound(self.parameters(), lr=self._lr, final_lr=self._final_lr) self.lr_sch: StepLR = StepLR(self.opt, 50) return [self.opt], [self.lr_sch] def on_epoch_end(self) -> None: self.lr_sch.step() @pl.data_loader def train_dataloader(self) -> DataLoader: self._train_dataloader: BaseDataLoader = SumStatDataLoader(**self._dataloader_args) return self._train_dataloader @pl.data_loader def val_dataloader(self) -> Optional[DataLoader]: return self._train_dataloader.split_validation() @pl.data_loader def test_dataloader(self) -> DataLoader: return SumStatDataLoader( data=self._val_data, batch_size=512, shuffle=False, validation_split=0.0, num_workers=4 * GPU_COUNT, # 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