hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4f1d0ef4f45eae9b16f7e491810ae0a72016654b
| 156
|
py
|
Python
|
memorybackend/backend/firstapp/apps.py
|
HefeiTu/codechella2020
|
3e95daa46757b7a8f9b7a482f3e569cf2babc4aa
|
[
"MIT"
] | null | null | null |
memorybackend/backend/firstapp/apps.py
|
HefeiTu/codechella2020
|
3e95daa46757b7a8f9b7a482f3e569cf2babc4aa
|
[
"MIT"
] | null | null | null |
memorybackend/backend/firstapp/apps.py
|
HefeiTu/codechella2020
|
3e95daa46757b7a8f9b7a482f3e569cf2babc4aa
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.apps import AppConfig
class FirstappConfig(AppConfig):
name = 'firstapp'
| 17.333333
| 39
| 0.737179
| 18
| 156
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| 0.833333
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| 156
| 8
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| 1
| 0
|
0
| 4
|
4f6e8ab47eb4406605562c58aa0cd61255ea03ca
| 4,335
|
gyp
|
Python
|
gyp/pdfviewer.gyp
|
omapzoom/platform-external-skia
|
50bf382c46a9d2348701cb3ce822a306bd9f3812
|
[
"BSD-3-Clause"
] | null | null | null |
gyp/pdfviewer.gyp
|
omapzoom/platform-external-skia
|
50bf382c46a9d2348701cb3ce822a306bd9f3812
|
[
"BSD-3-Clause"
] | null | null | null |
gyp/pdfviewer.gyp
|
omapzoom/platform-external-skia
|
50bf382c46a9d2348701cb3ce822a306bd9f3812
|
[
"BSD-3-Clause"
] | 2
|
2019-02-04T02:15:39.000Z
|
2021-03-04T00:45:13.000Z
|
# GYP file to build pdfviewer.
#
# To build on Linux:
# ./gyp_skia pdfviewer.gyp && make pdfviewer
#
{
'includes': [
'apptype_console.gypi',
],
'targets': [
{
'target_name': 'libpdfviewer',
'type': 'static_library',
'sources': [
'../experimental/PdfViewer/SkPdfBasics.cpp',
'../experimental/PdfViewer/SkPdfFont.cpp',
'../experimental/PdfViewer/SkPdfRenderer.cpp',
'../experimental/PdfViewer/SkPdfUtils.cpp',
#'../experimental/PdfViewer/SkPdfNYI.cpp',
'../experimental/PdfViewer/SkTrackDevice.cpp',
'../experimental/PdfViewer/SkTracker.cpp',
'../experimental/PdfViewer/pdfparser/native/SkPdfObject.cpp',
'../experimental/PdfViewer/pdfparser/native/SkPdfNativeTokenizer.cpp',
'../experimental/PdfViewer/pdfparser/native/SkNativeParsedPDF.cpp',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfMapper_autogen.cpp',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfHeaders_autogen.cpp',
],
'copies': [
{
'files': [
'../experimental/PdfViewer/datatypes.py',
'../experimental/PdfViewer/generate_code.py',
],
'destination': '<(SHARED_INTERMEDIATE_DIR)',
},
],
'actions': [
{
'action_name': 'spec2def',
'inputs': [
'../experimental/PdfViewer/spec2def.py',
'../experimental/PdfViewer/PdfReference-okular-1.txt',
],
'outputs': [
'<(SHARED_INTERMEDIATE_DIR)/pdfspec_autogen.py',
],
'action': ['python', '../experimental/PdfViewer/spec2def.py', '../experimental/PdfViewer/PdfReference-okular-1.txt', '<(SHARED_INTERMEDIATE_DIR)/pdfspec_autogen.py'],
},
{
'action_name': 'generate_code',
'inputs': [
'<(SHARED_INTERMEDIATE_DIR)/datatypes.py',
'<(SHARED_INTERMEDIATE_DIR)/generate_code.py',
'<(SHARED_INTERMEDIATE_DIR)/pdfspec_autogen.py',
],
'outputs': [
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfEnums_autogen.h',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfMapper_autogen.h',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfHeaders_autogen.h',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfMapper_autogen.cpp',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen/SkPdfHeaders_autogen.cpp',
# TODO(edisonn): ok, there are many more files here, which we should list but since
# any change in the above should trigger a change here, we should be fine normally
],
'action': ['python', '<(SHARED_INTERMEDIATE_DIR)/generate_code.py', '<(SHARED_INTERMEDIATE_DIR)'],
},
],
'include_dirs': [
'../experimental/PdfViewer',
'../experimental/PdfViewer/pdfparser',
'../experimental/PdfViewer/pdfparser/native',
'<(SHARED_INTERMEDIATE_DIR)/native/autogen',
],
'dependencies': [
'skia_lib.gyp:skia_lib',
'zlib.gyp:zlib',
],
},
{
'target_name': 'pdfviewer',
'type': 'executable',
'cflags': ['-fexceptions'],
'cflags_cc': ['-fexceptions'],
'cflags!': [ '-fno-exceptions' ],
'cflags_cc!': [ '-fno-exceptions' ],
'sources': [
'../experimental/PdfViewer/pdf_viewer_main.cpp',
],
'include_dirs': [
'../experimental/PdfViewer',
'../experimental/PdfViewer/pdfparser',
'../experimental/PdfViewer/pdfparser/autogen',
'../experimental/PdfViewer/pdfparser/native',
'../experimental/PdfViewer/pdfparser/native/autogen',
],
'dependencies': [
'skia_lib.gyp:skia_lib',
'flags.gyp:flags',
'libpdfviewer',
'chop_transparency',
],
},
{
'target_name': 'chop_transparency',
'type': 'executable',
'sources': [
'../experimental/PdfViewer/chop_transparency_main.cpp',
],
'include_dirs': [
# For SkBitmapHasher.h
'../src/utils/',
],
'dependencies': [
'skia_lib.gyp:skia_lib',
'flags.gyp:flags',
],
},
],
}
# Local Variables:
# tab-width:2
# indent-tabs-mode:nil
# End:
# vim: set expandtab tabstop=2 shiftwidth=2:
| 34.404762
| 176
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| 375
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| 0.353155
| 0.330906
| 0.22411
| 0
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| 4,335
| 125
| 177
| 34.68
| 0.760025
| 0.095502
| 0
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| 0
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| 0.637225
| 0.499488
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| true
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| null | 1
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| null | 0
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| 0
|
0
| 4
|
4f94e0f27ba87a2eb7dd5da7fca63e1beaf7001f
| 17,173
|
py
|
Python
|
Tests/test_Align_nexus.py
|
lukasz-kozlowski/biopython
|
6b601cf09234e1e82cfc94ad5030389036cb6343
|
[
"BSD-3-Clause"
] | 2,856
|
2015-01-01T07:10:06.000Z
|
2022-03-31T18:17:25.000Z
|
Tests/test_Align_nexus.py
|
lukasz-kozlowski/biopython
|
6b601cf09234e1e82cfc94ad5030389036cb6343
|
[
"BSD-3-Clause"
] | 3,429
|
2015-01-05T11:11:42.000Z
|
2022-03-31T13:08:10.000Z
|
Tests/test_Align_nexus.py
|
lukasz-kozlowski/biopython
|
6b601cf09234e1e82cfc94ad5030389036cb6343
|
[
"BSD-3-Clause"
] | 1,619
|
2015-01-05T13:07:11.000Z
|
2022-03-31T19:19:52.000Z
|
# Copyright 2021 by Michiel de Hoon. All rights reserved.
#
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Tests for Bio.Align.nexus module."""
import unittest
from io import StringIO
from Bio.Align.nexus import AlignmentIterator, AlignmentWriter
try:
import numpy
except ImportError:
from Bio import MissingPythonDependencyError
raise MissingPythonDependencyError(
"Install numpy if you want to use Bio.Align.nexus."
) from None
class TestNexusReading(unittest.TestCase):
def check_reading_writing(self, path):
alignments = AlignmentIterator(path)
stream = StringIO()
writer = AlignmentWriter(stream)
n = writer.write_file(alignments)
self.assertEqual(n, 1)
alignments = AlignmentIterator(path)
alignments = list(alignments)
alignment = alignments[0]
stream.seek(0)
saved_alignments = AlignmentIterator(stream)
saved_alignments = list(saved_alignments)
self.assertEqual(len(alignments), len(saved_alignments))
saved_alignment = saved_alignments[0]
for i, (sequence, saved_sequence) in enumerate(
zip(alignment.sequences, saved_alignment.sequences)
):
self.assertEqual(sequence.id, saved_sequence.id)
self.assertEqual(sequence.seq, saved_sequence.seq)
self.assertEqual(sequence.annotations, saved_sequence.annotations)
self.assertEqual(alignment[i], saved_alignment[i])
self.assertTrue(
numpy.array_equal(alignment.coordinates, saved_alignment.coordinates)
)
def test_nexus1(self):
path = "Nexus/test_Nexus_input.nex"
with open(path) as stream:
alignments = AlignmentIterator(stream)
alignments = list(alignments)
self.assertEqual(len(alignments), 1)
alignment = alignments[0]
self.assertEqual(len(alignment), 9)
self.assertEqual(alignment.shape, (9, 46))
self.assertEqual(alignment.sequences[0].id, "t1")
self.assertEqual(alignment.sequences[1].id, "t2 the name")
self.assertEqual(alignment.sequences[2].id, "isn'that [a] strange name?")
self.assertEqual(
alignment.sequences[3].id, "one should be punished, for (that)!"
)
self.assertEqual(alignment.sequences[4].id, "t5")
self.assertEqual(alignment.sequences[5].id, "t6")
self.assertEqual(alignment.sequences[6].id, "t7")
self.assertEqual(alignment.sequences[7].id, "t8")
self.assertEqual(alignment.sequences[8].id, "t9")
self.assertEqual(alignment.sequences[0].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[1].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[2].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[3].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[4].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[5].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[6].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[7].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[8].annotations, {"molecule_type": "DNA"})
self.assertEqual(
alignment.sequences[0].seq, "ACGTcgtgtgtgctctttacgtgtgtgctcttt"
)
self.assertEqual(alignment.sequences[1].seq, "ACGcTcgtgtctttacacgtgtcttt")
self.assertEqual(alignment.sequences[2].seq, "ACcGcTcgtgtgtgctacacacgtgtgtgct")
self.assertEqual(alignment.sequences[3].seq, "ACGT")
self.assertEqual(
alignment.sequences[4].seq, "AC?GT?acgt???????????acgt????????"
)
self.assertEqual(
alignment.sequences[5].seq, "AcCaGtTc?aaaaaaaaaaacgactac?aaaaaaaaaa"
)
self.assertEqual(
alignment.sequences[6].seq, "A?CGgTgggggggggggggg???gggggggggggggggg"
)
self.assertEqual(
alignment.sequences[7].seq, "AtCtGtTtttttttttttt??ttttttttttttttttttt??"
)
self.assertEqual(
alignment.sequences[8].seq, "cccccccccccccccccccNcccccccccccccccccccccNcc"
)
self.assertTrue(
numpy.array_equal(
alignment.coordinates,
numpy.array(
[
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[
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32,
33,
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[
0,
1,
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15,
17,
17,
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23,
25,
26,
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34,
36,
36,
37,
37,
38,
],
[
0,
1,
2,
3,
3,
4,
5,
6,
7,
8,
10,
14,
15,
16,
18,
18,
19,
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20,
22,
22,
24,
25,
26,
27,
29,
33,
34,
35,
37,
37,
38,
38,
39,
],
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
11,
15,
16,
17,
19,
19,
20,
20,
21,
23,
25,
27,
28,
29,
30,
32,
36,
37,
38,
40,
40,
41,
41,
42,
],
[
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
11,
15,
16,
17,
19,
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30,
31,
33,
37,
38,
39,
41,
42,
43,
43,
44,
],
]
),
)
)
self.assertEqual(
alignment[0],
"A-C-G-Tcgtgtgtgctct-t-t------acgtgtgtgctct-t-t",
)
self.assertEqual(
alignment[1],
"A-C-GcTcgtg-----tct-t-t----acacgtg-----tct-t-t",
)
self.assertEqual(alignment[2], "A-CcGcTcgtgtgtgct--------acacacgtgtgtgct------")
self.assertEqual(alignment[3], "A-C-G-T---------------------------------------")
self.assertEqual(alignment[4], "A-C?G-T?-acgt??-???-???--??---?-acgt??-???-???")
self.assertEqual(alignment[5], "AcCaGtTc?--aaaaaaaa-a-aacgactac?--aaaaaaaa-a-a")
self.assertEqual(alignment[6], "A?C-GgTgggggggggggg-g-g??--?gggggggggggggg-g-g")
self.assertEqual(alignment[7], "AtCtGtTtttttttttttt-?-?ttttttttttttttttttt-?-?")
self.assertEqual(alignment[8], "cccccccccccccccccccNc-ccccccccccccccccccccNc-c")
self.check_reading_writing(path)
def test_nexus2(self):
path = "Nexus/codonposset.nex"
with open(path) as stream:
alignments = AlignmentIterator(stream)
alignments = list(alignments)
self.assertEqual(len(alignments), 1)
alignment = alignments[0]
self.assertEqual(len(alignment), 2)
self.assertEqual(alignment.shape, (2, 22))
self.assertEqual(alignment.sequences[0].id, "Aegotheles")
self.assertEqual(alignment.sequences[1].id, "Aerodramus")
self.assertEqual(alignment.sequences[0].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[1].annotations, {"molecule_type": "DNA"})
self.assertEqual(alignment.sequences[0].seq, "AAAAAGGCATTGTGGTGGGAAT")
self.assertEqual(alignment.sequences[1].seq, "?????????TTGTGGTGGGAAT")
self.assertTrue(
numpy.array_equal(alignment.coordinates, numpy.array([[0, 22], [0, 22]]))
)
self.assertEqual(alignment[0], "AAAAAGGCATTGTGGTGGGAAT")
self.assertEqual(alignment[1], "?????????TTGTGGTGGGAAT")
self.check_reading_writing(path)
class TestNexusBasic(unittest.TestCase):
def test_empty(self):
import io
stream = io.StringIO()
with self.assertRaisesRegex(ValueError, "Empty file."):
AlignmentIterator(stream)
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
| 35.554865
| 88
| 0.290922
| 1,009
| 17,173
| 4.904856
| 0.203171
| 0.169731
| 0.227925
| 0.220044
| 0.479289
| 0.319458
| 0.268539
| 0.258638
| 0.183876
| 0.183876
| 0
| 0.090765
| 0.633029
| 17,173
| 482
| 89
| 35.628631
| 0.694541
| 0.014732
| 0
| 0.743534
| 0
| 0
| 0.070195
| 0.049379
| 0
| 0
| 0
| 0
| 0.12931
| 1
| 0.008621
| false
| 0
| 0.015086
| 0
| 0.028017
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4faa4053b956c6df44a1ae4829bc981f9d9b63b0
| 447
|
py
|
Python
|
covid/data/__init__.py
|
caiosba/covid-19
|
2a0f43f5004e7e39bd982eaa36185859cd9db88f
|
[
"MIT"
] | null | null | null |
covid/data/__init__.py
|
caiosba/covid-19
|
2a0f43f5004e7e39bd982eaa36185859cd9db88f
|
[
"MIT"
] | null | null | null |
covid/data/__init__.py
|
caiosba/covid-19
|
2a0f43f5004e7e39bd982eaa36185859cd9db88f
|
[
"MIT"
] | null | null | null |
"""
Import data sets from various sources.
"""
from .cia_factbook import cia_factbook, age_distribution, hospital_bed_density
from .data import CONTACT_MATRIX_COUNTRIES, CONTACT_MATRIX_IDS, DATA_PATH
from .contact_matrix import contact_matrix, symmetric_contact_matrix
from .ibge import brazil_healthcare_capacity, city_id_from_name
from .mortality import covid_mortality, covid_mean_mortality
from .ibge_demographic import brazil_city_demography
| 44.7
| 78
| 0.868009
| 62
| 447
| 5.854839
| 0.5
| 0.179063
| 0.104683
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087248
| 447
| 9
| 79
| 49.666667
| 0.889706
| 0.085011
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
96cac3a574b6cfbd75d40a2580a9b4882294cc07
| 429
|
py
|
Python
|
oslib/eip/summary.py
|
fbacchella/oscmd
|
7e60f7b761a14f519b971d0cc760c949adb6fa9e
|
[
"Apache-2.0"
] | null | null | null |
oslib/eip/summary.py
|
fbacchella/oscmd
|
7e60f7b761a14f519b971d0cc760c949adb6fa9e
|
[
"Apache-2.0"
] | null | null | null |
oslib/eip/summary.py
|
fbacchella/oscmd
|
7e60f7b761a14f519b971d0cc760c949adb6fa9e
|
[
"Apache-2.0"
] | null | null | null |
from oslib.command import Command
class Summary(Command):
object = 'eip'
verb = 'summary'
def fill_parser(self, parser):
pass
def validate(self, options):
return True
def execute(self, *args, **kwargs):
count = 0
for snap in self.ec2_object.get_all():
count = count + 1
yield count
def to_str(self, value):
return "%d EIP reserved\n" % value
| 22.578947
| 46
| 0.585082
| 55
| 429
| 4.490909
| 0.672727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010204
| 0.314685
| 429
| 19
| 47
| 22.578947
| 0.829932
| 0
| 0
| 0
| 0
| 0
| 0.062791
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.266667
| false
| 0.066667
| 0.066667
| 0.133333
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
96e1c1db5e8358a8e5e4dfd16d98536c8309d25a
| 69
|
py
|
Python
|
notice/__init__.py
|
MichaelBoshell/RSCBot
|
6a77a76e7beab073bc40e8cab300b3031279298b
|
[
"MIT"
] | 12
|
2018-12-19T17:00:00.000Z
|
2021-06-10T13:27:01.000Z
|
notice/__init__.py
|
MichaelBoshell/RSCBot
|
6a77a76e7beab073bc40e8cab300b3031279298b
|
[
"MIT"
] | 37
|
2020-03-10T18:42:29.000Z
|
2021-09-29T19:36:42.000Z
|
notice/__init__.py
|
MichaelBoshell/RSCBot
|
6a77a76e7beab073bc40e8cab300b3031279298b
|
[
"MIT"
] | 14
|
2018-12-31T02:12:18.000Z
|
2021-11-13T01:49:53.000Z
|
from .notice import Notice
def setup(bot):
bot.add_cog(Notice())
| 17.25
| 26
| 0.710145
| 11
| 69
| 4.363636
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15942
| 69
| 4
| 27
| 17.25
| 0.827586
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
96e941e5f58270fb13dd6247c500d00b6f12b3fa
| 61
|
py
|
Python
|
dayliopy/__init__.py
|
MichaelCurrin/daylio-analysis-tool
|
65c3d26b172d7cc3c4ddb91f855a48d1934aa25c
|
[
"MIT"
] | 7
|
2019-04-25T10:16:29.000Z
|
2022-03-25T02:27:39.000Z
|
dayliopy/__init__.py
|
MichaelCurrin/daylio-analysis-tool
|
65c3d26b172d7cc3c4ddb91f855a48d1934aa25c
|
[
"MIT"
] | 19
|
2020-01-06T06:37:08.000Z
|
2022-02-26T08:54:15.000Z
|
dayliopy/__init__.py
|
MichaelCurrin/daylio-analysis-tool
|
65c3d26b172d7cc3c4ddb91f855a48d1934aa25c
|
[
"MIT"
] | 2
|
2021-09-28T10:20:03.000Z
|
2021-10-09T04:46:46.000Z
|
"""
Dayliopy module.
This exists to let the linter run.
"""
| 10.166667
| 34
| 0.672131
| 9
| 61
| 4.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.196721
| 61
| 5
| 35
| 12.2
| 0.836735
| 0.852459
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
96f61df6b0ea2229c326312a783d862f73d567c9
| 1,267
|
py
|
Python
|
guillotina/tests/test_oid.py
|
diefenbach/guillotina
|
a8c7247fca8294752901f643b35c5ed1c5dee76d
|
[
"BSD-2-Clause"
] | null | null | null |
guillotina/tests/test_oid.py
|
diefenbach/guillotina
|
a8c7247fca8294752901f643b35c5ed1c5dee76d
|
[
"BSD-2-Clause"
] | null | null | null |
guillotina/tests/test_oid.py
|
diefenbach/guillotina
|
a8c7247fca8294752901f643b35c5ed1c5dee76d
|
[
"BSD-2-Clause"
] | null | null | null |
from guillotina.db import oid
from guillotina.tests import utils
def test_generate_oid():
ob = utils.create_content()
assert len(oid.generate_oid(ob)) == oid.UUID_LENGTH # should just be UUID here
def test_generate_oid_with_parent():
ob = utils.create_content()
parent = ob.__parent__ = utils.create_content()
parent.__parent__ = utils.create_content()
zoid = oid.generate_oid(ob)
assert len(zoid) == (oid.UUID_LENGTH + len(oid.OID_DELIMITER) + oid.OID_SPLIT_LENGTH)
assert zoid.startswith(parent._p_oid[:oid.OID_SPLIT_LENGTH] + oid.OID_DELIMITER)
def test_generate_oid_with_parents():
parent = utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content(
parent=utils.create_content())))))))))
ob = utils.create_content(parent=parent)
zoid = oid.generate_oid(ob)
assert len(zoid) == oid.MAX_OID_LENGTH
| 38.393939
| 89
| 0.632202
| 149
| 1,267
| 5.053691
| 0.201342
| 0.219124
| 0.358566
| 0.38247
| 0.59761
| 0.414343
| 0.414343
| 0.414343
| 0.414343
| 0.318725
| 0
| 0
| 0.273086
| 1,267
| 32
| 90
| 39.59375
| 0.81759
| 0.018942
| 0
| 0.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 1
| 0.115385
| false
| 0
| 0.076923
| 0
| 0.192308
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
96f7acaf539335e696f2052f57cf6c7da070c0e0
| 7,148
|
py
|
Python
|
legacy-redirects.py
|
mk-fg/blog
|
0a7089b49504e6f7ece4e22948d6038a04246a1b
|
[
"WTFPL"
] | 1
|
2016-07-22T21:12:03.000Z
|
2016-07-22T21:12:03.000Z
|
legacy-redirects.py
|
mk-fg/blog
|
0a7089b49504e6f7ece4e22948d6038a04246a1b
|
[
"WTFPL"
] | 1
|
2017-12-01T05:29:08.000Z
|
2017-12-01T06:06:07.000Z
|
legacy-redirects.py
|
mk-fg/blog
|
0a7089b49504e6f7ece4e22948d6038a04246a1b
|
[
"WTFPL"
] | null | null | null |
# Script to redirect from long-obsolete URLs to current static-blog ones
redirects = {
"2010/11/From-Baselayout-to-Systemd-setup-on-Exherbo": "2010/11/05/from-baselayout-to-systemd-setup-on-exherbo.html",
"2010/11/Moar-free-time": "2010/11/12/moar-free-time.html",
"2010/12/Commandline-pulseaudio-mixer-tool": "2010/12/25/commandline-pulseaudio-mixer-tool.html",
"2010/12/Further-improvements-on-notification-daemon": "2010/12/09/further-improvements-for-notification-daemon.html",
"2010/12/MooseFS-usage-experiences": "2010/12/07/moosefs-usage-experiences.html",
"2010/12/oslistdir-and-oswalk-in-python-without-lists-by-the-grace-of-c-api-generator-and-recursion-custom-stack": "2010/12/15/oslistdir-and-oswalk-in-python-without-lists-by-the-grace-of-c-api-generator-and-recursion-custom-stack.html",
"2010/12/Sane-playback-for-online-streaming-video-and-via-stream-dumping": "2010/12/29/sane-playback-for-online-streaming-video-via-stream-dumping.html",
"2010/12/zcat-bzcat-lzcat-xzcat-Arrrgh-Autodetection-rocks": "2010/12/11/zcat-bzcat-lzcat-xzcat-arrrgh-autodetection-rocks.html",
"2010/1/Wheee-Ive-got-a-blog-": "2010/01/30/wheee-ive-got-a-blog.html",
"2010/2/libnotify-notification-daemon-shortcomings-and-my-solution": "2010/02/26/libnotify-notification-daemon-shortcomings-and-my-solution.html",
"2010/2/Listening-to-music-over-the-net-with-authentication-and-cache": "2010/02/17/listening-to-music-over-the-net-with-authentication-and-cache.html",
"2010/2/My-simple-ok-not-quite-backup-system": "2010/02/11/my-simple-ok-not-quite-backup-system.html",
"2010/2/My-simple-ok-not-quite-backup-system-implementation-backed-up-side": "2010/02/13/my-simple-ok-not-quite-backup-system-implementation-backed-up-side.html",
"2010/2/My-simple-ok-not-quite-backup-system-implementation-backup-host": "2010/02/14/my-simple-ok-not-quite-backup-system-implementation-backup-host.html",
"2010/2/POSIX-capabilities-for-python": "2010/02/01/posix-capabilities-for-python.html",
"2010/2/snmpd-pyagentx-or-re-discovery-of-sfnet": "2010/02/28/snmpd-pyagentx-or-re-discovery-of-sfnet.html",
"2010/3/Single-instance-daemon-or-invisible-dock": "2010/03/10/single-instance-daemon-or-invisible-dock.html",
"2010/4/Auto-away-for-pidgin": "2010/04/10/auto-away-for-pidgin.html",
"2010/4/Availability-stats-and-history-log-with-relational-database-postgresql": "2010/04/10/availability-stats-and-history-log-with-relational-database-postgresql.html",
"2010/4/Exherbo-paludis-fossil-syncer": "2010/04/25/exherbo-paludis-fossil-syncer.html",
"2010/4/LUKS-dm-crypt-rootfs-without-password-via-smartcard": "2010/04/25/luks-dm-crypt-rootfs-without-password-via-smartcard.html",
"2010/4/Thoughts-on-VCS-supporting-documentation-and-Fossil": "2010/04/17/thoughts-on-vcs-supporting-documentation-and-fossil.html",
"2010/5/Music-collection-updates-feed-via-musicbrainz-and-lastfm": "2010/05/08/music-collection-updates-feed-via-musicbrainz-and-lastfm.html",
"2010/6/Drop-in-ccrypt-replacement-for-bournal": "2010/06/13/drop-in-ccrypt-replacement-for-bournal.html",
"2010/6/Getting-rid-of-dead-bittorrent-trackers-for-rtorrent-by-scrubbing-torrent-files": "2010/06/05/getting-rid-of-dead-bittorrent-trackers-for-rtorrent-by-scrubbing-torrent-files.html",
"2010/6/No-IPSec-on-a-stick-for-me-": "2010/06/14/no-ipsec-on-a-stick-for-me.html",
"2010/8/Home-brewed-NAS-gluster-with-sensible-replication": "2010/08/15/home-brewed-nas-gluster-with-sensible-replication.html",
"2010/9/Distributed-fault-tolerant-fs-take-2-MooseFS": "2010/09/09/distributed-fault-tolerant-fs-take-2-moosefs.html",
"2010/9/Info-feeds": "2010/09/12/info-feeds.html",
"2011/10/dm-crypt-password-caching-between-dracut-and-systemd-systemd-password-agent": "2011/10/23/dm-crypt-password-caching-between-dracut-and-systemd-systemd-password-agent.html",
"2011/11/Running-stuff-like-firefox-flash-and-skype-with-apparmor": "2011/11/12/running-stuff-like-firefox-flash-and-skype-with-apparmor.html",
"2011/2/cgroups-initialization-libcgroup-and-my-ad-hoc-replacement-for-it": "2011/02/26/cgroups-initialization-libcgroup-and-my-ad-hoc-replacement-for-it.html",
"2011/2/Dashboard-for-enabled-services-in-systemd": "2011/02/27/dashboard-for-enabled-services-in-systemd.html",
"2011/3/Auto-updating-desktop-background-with-scaling-via-LQR-and-some-other-tricks": "2011/03/05/auto-updating-desktop-background-with-scaling-via-lqr-and-some-other-tricks.html",
"2011/3/Parallel-port-LED-notification-for-extra-high-system-load": "2011/03/14/parallel-port-led-notification-for-extra-high-system-load.html",
"2011/3/Selective-IPv6-AAAA-DNS-resolution": "2011/03/19/selective-ipv6-aaaa-dns-resolution.html",
"2011/4/Key-Value-storage-with-historyversioning-on-top-of-scm": "2011/04/18/key-value-storage-with-historyversioning-on-top-of-scm.html",
"2011/4/xdiskusage-like-visualization-for-any-remote-machine": "2011/04/19/xdiskusage-like-visualization-for-any-remote-machine.html",
"2011/5/Backup-of-5-million-tiny-files-and-paths": "2011/05/08/backup-of-5-million-tiny-files-and-paths.html",
"2011/5/Fossil-to-Git-export-and-mirroring": "2011/05/02/fossil-to-git-export-and-mirroring.html",
"2011/6/Using-csync2-for-security-sensitive-paths": "2011/06/12/using-csync2-for-security-sensitive-paths.html",
"2011/8/Notification-daemon-in-python": "2011/08/14/notification-daemon-in-python.html",
"2011/9/Detailed-process-memory-accounting-including-shared-and-swapped-one": "2011/09/16/detailed-process-memory-accounting-including-shared-and-swapped-one.html",
"2012/2/Late-adventures-with-time-series-data-collection-and-representation": "2012/02/28/late-adventures-with-time-series-data-collection-and-representation.html",
"2012/2/On-github-as-well-now": "2012/02/03/on-github-as-well-now.html",
"2012/2/Phasing-out-fossil-completely": "2012/02/07/phasing-out-fossil-completely.html",
"2012/6/Proper-ish-way-to-start-long-running-systemd-service-on-udev-event-device-hotplug": "2012/06/16/proper-ish-way-to-start-long-running-systemd-service-on-udev-event-device-hotplug.html",
"2012/8/A-new-toy-to-play-with-TI-Launchpad-with-MSP430-MCU": "2012/08/16/a-new-toy-to-play-with-ti-launchpad-with-msp430-mcu.html",
"2012/8/Unhosted-remoteStorage-idea": "2012/08/09/unhosted-remotestorage-idea.html",
"2012/9/Terms-of-Service-Didnt-Read": "2012/09/16/terms-of-service-didnt-read.html",
"2013/1/Migrating-configuration-settings-to-E17-enlightenment-0170-from-older-E-versions": "2013/01/16/migrating-configuration-settings-to-e17-enlightenment-0170-from-older-e-versions.html",
"2013/1/PyParsing-vs-Yapps": "2013/01/21/pyparsing-vs-yapps.html",
}
def application(env, start_response):
url = env['REQUEST_URI'].strip('/')
url_redirect = redirects.get(url)
if not url_redirect:
start_response('404 Not Found', [('Content-Type', 'text/html')])
err = f'404: Requested URL was not found: {url}'
return [f'<img alt="{err}" title="{err}" src="/misc/ie404.png">'.encode()]
url_redirect = f'/{url_redirect}'
start_response( '301 Moved Permanently',
[('Location', url_redirect), ('Content-Type', 'text/plain')] )
return [f'Redirecting to: {url_redirect}\n'.encode()]
| 100.676056
| 238
| 0.771684
| 1,160
| 7,148
| 4.746552
| 0.300862
| 0.040683
| 0.011442
| 0.014166
| 0.660915
| 0.629677
| 0.555576
| 0.42045
| 0.374682
| 0.257356
| 0
| 0.104745
| 0.033016
| 7,148
| 70
| 239
| 102.114286
| 0.69184
| 0.009793
| 0
| 0
| 0
| 0.753846
| 0.890192
| 0.857547
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015385
| false
| 0.030769
| 0
| 0
| 0.046154
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
8c01d4f64c40ebcb958cd6dc6cc678674036bee8
| 1,501
|
py
|
Python
|
website/mixins.py
|
unl-pal/paclab-www
|
9cf59bf6292da1f7bd2c2a4536def7d1323abab0
|
[
"Apache-2.0"
] | 1
|
2018-10-15T14:55:59.000Z
|
2018-10-15T14:55:59.000Z
|
website/mixins.py
|
unl-pal/paclab-www
|
9cf59bf6292da1f7bd2c2a4536def7d1323abab0
|
[
"Apache-2.0"
] | 74
|
2018-10-11T16:00:01.000Z
|
2020-09-20T10:54:03.000Z
|
website/mixins.py
|
unl-pal/paclab-www
|
9cf59bf6292da1f7bd2c2a4536def7d1323abab0
|
[
"Apache-2.0"
] | 1
|
2018-10-11T14:17:37.000Z
|
2018-10-11T14:17:37.000Z
|
from django.contrib.auth.mixins import LoginRequiredMixin
from .decorators import email_verify_warning
class EmailRequiredMixin(LoginRequiredMixin):
"""Verify that the current user has a verified email."""
permission_denied_message = ''
def dispatch(self, request, *args, **kwargs):
if not request.user.is_authenticated:
return self.handle_no_permission()
if not request.user.profile.active_email:
self.permission_denied_message = 'You must have a verified email address to view this page.'
return email_verify_warning(request)
return super().dispatch(request, *args, **kwargs)
def get_permission_denied_message(self):
return self.permission_denied_message
class DeletableReadOnlyAdminMixin(object):
"""Makes a ModelAdmin read only and disables adds/edits but allows for deletes."""
def has_add_permission(self, request):
return False
def has_delete_permission(self, request, obj=None):
return True
def has_change_permission(self, request, obj=None):
return False
def save_model(self, request, obj, form, change):
pass
def save_related(self, request, form, formsets, change):
pass
class ReadOnlyAdminMixin(DeletableReadOnlyAdminMixin):
"""Makes a ModelAdmin read only and disables adds/edits/deletes."""
def has_delete_permission(self, request, obj=None):
return False
def delete_model(self, request, obj):
pass
| 32.630435
| 104
| 0.710859
| 181
| 1,501
| 5.740331
| 0.41989
| 0.084697
| 0.067372
| 0.069297
| 0.221367
| 0.221367
| 0.221367
| 0.221367
| 0.173244
| 0
| 0
| 0
| 0.208528
| 1,501
| 45
| 105
| 33.355556
| 0.874579
| 0.125916
| 0
| 0.275862
| 0
| 0
| 0.043981
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.310345
| false
| 0.103448
| 0.068966
| 0.172414
| 0.793103
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
8c0d33b63a72c651145006b16711e900e12c3528
| 8,756
|
py
|
Python
|
config/m600/rotors.py
|
leozz37/makani
|
c94d5c2b600b98002f932e80a313a06b9285cc1b
|
[
"Apache-2.0"
] | 1,178
|
2020-09-10T17:15:42.000Z
|
2022-03-31T14:59:35.000Z
|
config/m600/rotors.py
|
leozz37/makani
|
c94d5c2b600b98002f932e80a313a06b9285cc1b
|
[
"Apache-2.0"
] | 1
|
2020-05-22T05:22:35.000Z
|
2020-05-22T05:22:35.000Z
|
config/m600/rotors.py
|
leozz37/makani
|
c94d5c2b600b98002f932e80a313a06b9285cc1b
|
[
"Apache-2.0"
] | 107
|
2020-09-10T17:29:30.000Z
|
2022-03-18T09:00:14.000Z
|
# Copyright 2020 Makani Technologies LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Rotor parameters."""
from makani.config import mconfig
from makani.control import system_types as m
import numpy as np
@mconfig.Config(deps={
'flight_plan': 'common.flight_plan',
'propellers': 'prop.propellers',
'wing_serial': 'common.wing_serial',
})
def MakeParams(params):
# Motor rotor moment-of-inertia [kg-m^2].
yasa_rotor_moment_of_inertia = 0.33
bottom_row = [m.kMotorSbo, m.kMotorSbi, m.kMotorPbi, m.kMotorPbo]
# Assign propeller versions.
propeller_versions = [None for _ in range(m.kNumMotors)]
if params['wing_serial'] == m.kWingSerial01:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] in [m.kWingSerial04Crosswind]:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] == m.kWingSerial04Hover:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] in [m.kWingSerial05Crosswind]:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] == m.kWingSerial05Hover:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] in [m.kWingSerial06Crosswind]:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] == m.kWingSerial06Hover:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] in [m.kWingSerial07Crosswind]:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSti] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
elif params['wing_serial'] == m.kWingSerial07Hover:
propeller_versions[m.kMotorSbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorSbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbi] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPbo] = m.kPropVersionRev4NegativeX
propeller_versions[m.kMotorPto] = m.kPropVersionRev4PositiveX
propeller_versions[m.kMotorPti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSti] = m.kPropVersionRev1Trimmed
propeller_versions[m.kMotorSto] = m.kPropVersionRev4PositiveX
else:
assert False, 'Unknown wing serial.'
rotors = [None for _ in range(m.kNumMotors)]
for r in range(m.kNumMotors):
rotors[r] = {
# Normal vector to the propeller plane.
'axis': [np.cos(np.deg2rad(3.0)), 0.0, np.sin(np.deg2rad(3.0))],
# Direction cosine matrix from body to rotor frame.
'dcm_b2r': {'d': [[np.cos(np.deg2rad(-3.0)), 0.0,
np.sin(np.deg2rad(-3.0))],
[0.0, 1.0, 0.0],
[-np.sin(np.deg2rad(-3.0)), 0.0,
np.cos(np.deg2rad(-3.0))]]},
# Local pressure coefficient [#] at the rotor position. The
# pressure coefficient, C_P, is related to local airspeed
# through the equation:
#
# C_P = 1 - (v / v_freestream)^2
#
# There is a significant difference in airspeeds between the top
# and bottom propellers caused by the lift of the wing. These
# pressure coefficients are derived from CFD with the slatted
# kite at 4 deg alpha (https://goo.gl/yfkJJS)
'local_pressure_coeff': 0.1448 if r in bottom_row else -0.1501,
# The rotor direction, diameter [m] and moment of inertia [kg
# m^2] are set from the corresponding propeller's information.
'version': propeller_versions[r],
'dir': params['propellers'][propeller_versions[r]]['dir'],
'D': params['propellers'][propeller_versions[r]]['D'],
'I': (yasa_rotor_moment_of_inertia +
params['propellers'][propeller_versions[r]]['I']),
}
# We check that the rotor axis is normalized. because it is used
# to determine the force-moment conversion matrix in
# rotor_control.py.
assert abs(np.linalg.norm(rotors[r]['axis']) - 1.0) < 1e-9
# Rotor positions [m].
#
# Updated on 2015-01-22 based on the COM positions given by the Mass
# and Balance spreadsheet.
rotors[m.kMotorSbo]['pos'] = [1.613, 3.639, 1.597]
rotors[m.kMotorSbi]['pos'] = [1.613, 1.213, 1.597]
rotors[m.kMotorPbi]['pos'] = [1.613, -1.213, 1.597]
rotors[m.kMotorPbo]['pos'] = [1.613, -3.639, 1.597]
rotors[m.kMotorPto]['pos'] = [1.960, -3.639, -1.216]
rotors[m.kMotorPti]['pos'] = [1.960, -1.213, -1.216]
rotors[m.kMotorSti]['pos'] = [1.960, 1.213, -1.216]
rotors[m.kMotorSto]['pos'] = [1.960, 3.639, -1.216]
return rotors
| 51.204678
| 74
| 0.74132
| 1,008
| 8,756
| 6.330357
| 0.223214
| 0.207804
| 0.203103
| 0.188685
| 0.702554
| 0.679047
| 0.663062
| 0.658047
| 0.658047
| 0.637361
| 0
| 0.034441
| 0.154408
| 8,756
| 170
| 75
| 51.505882
| 0.827391
| 0.171083
| 0
| 0.595041
| 0
| 0
| 0.042822
| 0
| 0
| 0
| 0
| 0
| 0.016529
| 1
| 0.008264
| false
| 0
| 0.024793
| 0
| 0.041322
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
8c0f2c1edf73dec86391d85813a3e91c6cb69781
| 169
|
py
|
Python
|
tests/data/expected/parser/openapi/openapi_parser_parse_any/output.py
|
languitar/datamodel-code-generator
|
ddd909746a66df5c8268d782f3ae24bee636be92
|
[
"MIT"
] | 891
|
2019-07-23T04:23:32.000Z
|
2022-03-31T13:36:33.000Z
|
tests/data/expected/parser/openapi/openapi_parser_parse_any/output.py
|
languitar/datamodel-code-generator
|
ddd909746a66df5c8268d782f3ae24bee636be92
|
[
"MIT"
] | 663
|
2019-07-23T09:50:26.000Z
|
2022-03-29T01:56:55.000Z
|
tests/data/expected/parser/openapi/openapi_parser_parse_any/output.py
|
languitar/datamodel-code-generator
|
ddd909746a66df5c8268d782f3ae24bee636be92
|
[
"MIT"
] | 108
|
2019-07-23T08:50:37.000Z
|
2022-03-09T10:50:22.000Z
|
from __future__ import annotations
from typing import Any, Optional
from pydantic import BaseModel
class Item(BaseModel):
bar: Optional[Any] = None
foo: str
| 15.363636
| 34
| 0.751479
| 22
| 169
| 5.590909
| 0.681818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195266
| 169
| 10
| 35
| 16.9
| 0.904412
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
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| 1
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| true
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8c49dda045adfaa3cfc4dcde686ceb9a3803cf6a
| 172
|
py
|
Python
|
configs/ocrnet/ocrnet_hr18_bs2x_512x1024_40k_cityscapes.py
|
openseg-group/mmsegmentation
|
23939f09d2b0bd30fc26eb7f8af974f1f5441210
|
[
"Apache-2.0"
] | 2
|
2020-07-10T12:13:56.000Z
|
2020-11-09T07:09:29.000Z
|
configs/ocrnet/ocrnet_hr18_bs2x_512x1024_40k_cityscapes.py
|
openseg-group/mmsegmentation
|
23939f09d2b0bd30fc26eb7f8af974f1f5441210
|
[
"Apache-2.0"
] | null | null | null |
configs/ocrnet/ocrnet_hr18_bs2x_512x1024_40k_cityscapes.py
|
openseg-group/mmsegmentation
|
23939f09d2b0bd30fc26eb7f8af974f1f5441210
|
[
"Apache-2.0"
] | 2
|
2020-07-28T09:12:55.000Z
|
2021-01-04T07:49:59.000Z
|
_base_ = [
'../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes_bs2x.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k_lr2x.py'
]
| 34.4
| 79
| 0.69186
| 21
| 172
| 4.952381
| 0.666667
| 0.173077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.038462
| 0.093023
| 172
| 4
| 80
| 43
| 0.628205
| 0
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| 0
| 0.790698
| 0.790698
| 0
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| 1
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
8c641d6a49a4e008488e0cb59668fa1eab08b58e
| 51
|
py
|
Python
|
Chapter 01/ch1_23.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
Chapter 01/ch1_23.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
Chapter 01/ch1_23.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
print ("Overall Average: {0:.0f}%".format(75.2876))
| 51
| 51
| 0.666667
| 8
| 51
| 4.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 0.058824
| 51
| 1
| 51
| 51
| 0.541667
| 0
| 0
| 0
| 0
| 0
| 0.480769
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
4fbbd6b64c6af7055d55d0827a37dee887ed7b4b
| 172
|
py
|
Python
|
validate.py
|
parlar/SampleSheetCreator
|
2ee43584d1922a5f97037fea91c0ddfabd7b569d
|
[
"MIT"
] | null | null | null |
validate.py
|
parlar/SampleSheetCreator
|
2ee43584d1922a5f97037fea91c0ddfabd7b569d
|
[
"MIT"
] | null | null | null |
validate.py
|
parlar/SampleSheetCreator
|
2ee43584d1922a5f97037fea91c0ddfabd7b569d
|
[
"MIT"
] | null | null | null |
#! python
# -*- coding: utf-8 -*-
#
# validate functions for all data inputs for SampleSheetCreator
#
import sys
import os
import cerberus
from ruamel.yaml import YAML
| 12.285714
| 63
| 0.726744
| 23
| 172
| 5.434783
| 0.782609
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007143
| 0.186047
| 172
| 13
| 64
| 13.230769
| 0.885714
| 0.534884
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8b438313718b1725f5f1268c87e5e0d3aaf1c286
| 60
|
py
|
Python
|
allauth_facebook/urls.py
|
fluenty/datamanager
|
97ba9d58d4527b7d61b730ea4896f09a56e6ae60
|
[
"MIT"
] | 3
|
2019-08-31T03:08:22.000Z
|
2020-04-03T13:09:20.000Z
|
allauth_facebook/urls.py
|
fluenty/datamanager
|
97ba9d58d4527b7d61b730ea4896f09a56e6ae60
|
[
"MIT"
] | 97
|
2019-04-16T07:54:38.000Z
|
2022-02-10T07:25:48.000Z
|
allauth_facebook/urls.py
|
fluenty/datamanager
|
97ba9d58d4527b7d61b730ea4896f09a56e6ae60
|
[
"MIT"
] | 14
|
2019-04-23T09:48:17.000Z
|
2021-04-13T17:48:40.000Z
|
from allauth.socialaccount.providers.facebook.urls import *
| 30
| 59
| 0.85
| 7
| 60
| 7.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066667
| 60
| 1
| 60
| 60
| 0.910714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
8c80d940772732d55b2154ba0096591e14d5bb07
| 41
|
py
|
Python
|
constructor/__init__.py
|
schrodinger/constructor
|
829f3bea6c99c0e30105b23449c8c6d0790ede8e
|
[
"BSD-3-Clause"
] | null | null | null |
constructor/__init__.py
|
schrodinger/constructor
|
829f3bea6c99c0e30105b23449c8c6d0790ede8e
|
[
"BSD-3-Clause"
] | null | null | null |
constructor/__init__.py
|
schrodinger/constructor
|
829f3bea6c99c0e30105b23449c8c6d0790ede8e
|
[
"BSD-3-Clause"
] | null | null | null |
__version__ = '2.1.1'
__version__ += 's'
| 13.666667
| 21
| 0.634146
| 6
| 41
| 3
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 0.146341
| 41
| 2
| 22
| 20.5
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0.146341
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
8c8d3e5dbb2234bd14e14b91c1a6b664adf7af19
| 714
|
py
|
Python
|
armstrong/hatband/__init__.py
|
armstrong/armstrong.hatband
|
b34027c85a8ccfe2ee37aa9348d98e143d300082
|
[
"Apache-2.0"
] | null | null | null |
armstrong/hatband/__init__.py
|
armstrong/armstrong.hatband
|
b34027c85a8ccfe2ee37aa9348d98e143d300082
|
[
"Apache-2.0"
] | 3
|
2015-05-29T05:07:09.000Z
|
2018-07-18T13:53:36.000Z
|
armstrong/hatband/__init__.py
|
armstrong/armstrong.hatband
|
b34027c85a8ccfe2ee37aa9348d98e143d300082
|
[
"Apache-2.0"
] | 2
|
2015-07-29T20:58:29.000Z
|
2015-08-07T02:59:37.000Z
|
# Make this a drop-in replacement for Django's built-in Admin
from django.contrib.admin.helpers import ACTION_CHECKBOX_NAME
from django.contrib.admin.options import HORIZONTAL, VERTICAL
# Below are overrides that we provide that are Hatband specific
from .options import ModelAdmin, StackedInline, TabularInline
from armstrong.hatband.sites import AdminSite, site
def autodiscover():
"""
TODO: document
"""
from django.contrib.admin import autodiscover as django_autodiscover
django_autodiscover()
from copy import copy
from django.contrib.admin import site as django_site
registry = copy(django_site._registry)
registry.update(site._registry)
site._registry = registry
| 32.454545
| 72
| 0.777311
| 92
| 714
| 5.934783
| 0.478261
| 0.07326
| 0.124542
| 0.161172
| 0.102564
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.161064
| 714
| 21
| 73
| 34
| 0.911519
| 0.191877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.047619
| 0
| 1
| 0.083333
| false
| 0
| 0.583333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8c99b478e032407ebf484a1cf41d3615b1240e17
| 88
|
py
|
Python
|
nbcollection/__init__.py
|
eteq/nbcollection
|
156cce71e954583886912dcc630c463084ab58dd
|
[
"BSD-3-Clause"
] | 6
|
2021-04-13T23:08:14.000Z
|
2021-11-14T03:23:20.000Z
|
nbcollection/__init__.py
|
eteq/nbcollection
|
156cce71e954583886912dcc630c463084ab58dd
|
[
"BSD-3-Clause"
] | 11
|
2020-07-08T14:10:47.000Z
|
2022-01-18T16:04:34.000Z
|
nbcollection/__init__.py
|
adrn/nbstatic
|
a1101efbf140d872a8220a6bbb3d95f29a9887f0
|
[
"BSD-3-Clause"
] | 3
|
2020-07-21T19:55:24.000Z
|
2021-09-21T15:44:26.000Z
|
from .converter import nbcollectionConverter
from .notebook import nbcollectionNotebook
| 29.333333
| 44
| 0.886364
| 8
| 88
| 9.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 88
| 2
| 45
| 44
| 0.975
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
8cb25b40149299f7dbb106cb5527368bfae802ed
| 164
|
py
|
Python
|
src/yellowdog_client/model/authentication_provider.py
|
yellowdog/yellowdog-sdk-python-public
|
da69a7d6e45c92933e34fefcaef8b5d98dcd6036
|
[
"Apache-2.0"
] | null | null | null |
src/yellowdog_client/model/authentication_provider.py
|
yellowdog/yellowdog-sdk-python-public
|
da69a7d6e45c92933e34fefcaef8b5d98dcd6036
|
[
"Apache-2.0"
] | null | null | null |
src/yellowdog_client/model/authentication_provider.py
|
yellowdog/yellowdog-sdk-python-public
|
da69a7d6e45c92933e34fefcaef8b5d98dcd6036
|
[
"Apache-2.0"
] | null | null | null |
from enum import Enum
class AuthenticationProvider(Enum):
YELLOWDOG = "YELLOWDOG"
AZURE = "AZURE"
def __str__(self) -> str:
return self.name
| 16.4
| 35
| 0.658537
| 18
| 164
| 5.777778
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 164
| 9
| 36
| 18.222222
| 0.845528
| 0
| 0
| 0
| 0
| 0
| 0.085366
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.166667
| 0.166667
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
8cf34a6caa5948acdc1a9774abee55e83fafe98e
| 10,805
|
py
|
Python
|
CONTOH_CONTOH/aoc2021/hari5.ms.py
|
jiaminglimjm/JawiPython
|
affbb34c7876498a7cc3eef2ef87d59f7cccd8b1
|
[
"0BSD"
] | null | null | null |
CONTOH_CONTOH/aoc2021/hari5.ms.py
|
jiaminglimjm/JawiPython
|
affbb34c7876498a7cc3eef2ef87d59f7cccd8b1
|
[
"0BSD"
] | null | null | null |
CONTOH_CONTOH/aoc2021/hari5.ms.py
|
jiaminglimjm/JawiPython
|
affbb34c7876498a7cc3eef2ef87d59f7cccd8b1
|
[
"0BSD"
] | null | null | null |
اينڤوت = '''599,531 -> 599,32
435,904 -> 435,489
768,714 -> 768,187
845,552 -> 596,801
167,680 -> 167,445
45,887 -> 45,346
780,295 -> 179,896
310,539 -> 602,831
535,556 -> 349,556
797,180 -> 797,62
771,406 -> 120,406
383,296 -> 383,918
689,815 -> 73,199
658,642 -> 658,333
931,104 -> 708,104
406,278 -> 406,29
315,532 -> 773,74
439,953 -> 289,953
555,162 -> 695,302
444,522 -> 444,828
460,844 -> 460,972
838,18 -> 143,713
335,785 -> 335,485
757,886 -> 757,327
266,205 -> 273,205
934,42 -> 19,957
671,622 -> 263,214
739,781 -> 739,332
848,507 -> 848,394
577,58 -> 461,174
49,905 -> 921,33
627,455 -> 205,455
106,523 -> 974,523
707,335 -> 707,313
65,214 -> 712,214
610,267 -> 610,403
47,699 -> 565,181
288,833 -> 709,833
452,59 -> 452,632
629,209 -> 125,209
535,232 -> 535,342
542,942 -> 542,753
618,905 -> 552,905
598,314 -> 976,314
350,824 -> 17,824
753,570 -> 753,617
544,302 -> 259,302
628,271 -> 628,379
856,265 -> 856,792
77,317 -> 77,122
905,420 -> 905,687
812,512 -> 812,411
844,486 -> 771,559
798,778 -> 798,215
571,160 -> 278,453
242,352 -> 227,352
958,118 -> 167,909
201,915 -> 201,564
163,583 -> 163,279
23,111 -> 23,883
248,281 -> 331,281
381,768 -> 900,768
78,988 -> 78,326
914,659 -> 247,659
532,531 -> 520,531
65,309 -> 734,978
170,923 -> 399,694
740,496 -> 196,496
832,452 -> 816,452
675,463 -> 878,463
659,852 -> 560,852
143,655 -> 227,655
334,795 -> 334,978
217,913 -> 368,913
675,33 -> 503,33
42,981 -> 811,981
458,162 -> 722,162
92,613 -> 92,542
393,584 -> 393,252
276,256 -> 725,705
752,442 -> 752,789
63,281 -> 744,281
596,845 -> 35,284
594,534 -> 964,164
337,380 -> 337,511
158,142 -> 75,225
606,47 -> 606,111
987,30 -> 62,955
192,196 -> 428,196
449,672 -> 449,77
804,151 -> 804,255
783,581 -> 287,581
860,891 -> 69,100
966,187 -> 761,392
400,742 -> 278,742
661,656 -> 592,587
787,415 -> 787,771
866,228 -> 417,228
915,385 -> 505,385
715,620 -> 715,633
615,31 -> 615,940
691,885 -> 527,885
426,705 -> 351,705
258,215 -> 258,949
480,449 -> 480,710
788,710 -> 788,67
850,90 -> 597,90
398,379 -> 18,759
248,107 -> 665,524
901,933 -> 208,240
433,424 -> 110,424
214,447 -> 389,272
468,330 -> 468,928
950,759 -> 332,759
447,541 -> 420,541
659,138 -> 604,83
821,264 -> 95,264
914,132 -> 46,132
821,604 -> 821,57
805,734 -> 85,14
806,274 -> 164,916
205,780 -> 205,133
798,472 -> 361,472
817,57 -> 127,747
172,119 -> 922,869
118,167 -> 55,167
56,548 -> 344,836
117,108 -> 940,931
530,46 -> 530,785
528,507 -> 729,708
11,986 -> 987,10
979,932 -> 76,29
863,250 -> 210,903
879,215 -> 891,215
592,219 -> 592,528
211,760 -> 211,347
21,842 -> 633,230
110,356 -> 110,254
925,606 -> 444,125
757,566 -> 757,498
702,622 -> 637,622
51,379 -> 365,379
273,906 -> 273,494
170,795 -> 929,36
159,56 -> 435,56
724,953 -> 724,735
536,748 -> 901,748
937,148 -> 937,510
963,507 -> 863,507
840,290 -> 840,221
864,154 -> 55,963
977,487 -> 685,487
863,617 -> 210,617
862,308 -> 291,879
286,477 -> 286,276
550,805 -> 550,489
964,508 -> 821,651
475,290 -> 789,290
25,882 -> 25,349
570,374 -> 604,374
354,442 -> 514,282
457,700 -> 360,700
548,889 -> 548,502
11,393 -> 11,829
60,714 -> 781,714
943,953 -> 972,924
757,386 -> 465,386
230,463 -> 27,463
815,385 -> 326,385
32,630 -> 378,976
298,853 -> 298,644
532,146 -> 23,146
958,685 -> 737,464
853,847 -> 79,73
815,590 -> 815,961
49,87 -> 751,789
55,513 -> 55,378
163,907 -> 574,907
355,168 -> 355,836
453,742 -> 674,742
273,458 -> 685,458
981,961 -> 958,984
120,59 -> 401,59
735,964 -> 395,964
277,377 -> 277,646
633,694 -> 633,707
224,376 -> 976,376
201,790 -> 293,790
950,952 -> 12,14
389,48 -> 356,48
337,424 -> 166,424
591,915 -> 591,456
205,162 -> 942,162
404,421 -> 404,748
319,983 -> 608,694
94,677 -> 94,853
873,388 -> 873,617
858,82 -> 858,890
64,503 -> 64,787
372,224 -> 50,546
531,241 -> 960,670
47,33 -> 975,961
853,52 -> 271,634
668,437 -> 668,719
162,290 -> 843,290
421,299 -> 944,822
103,983 -> 103,324
290,71 -> 290,686
209,38 -> 546,38
740,878 -> 378,878
741,795 -> 741,916
27,431 -> 445,431
795,289 -> 795,759
345,772 -> 775,772
977,480 -> 512,15
49,863 -> 49,659
223,590 -> 779,590
503,771 -> 917,771
499,289 -> 935,725
246,459 -> 246,395
860,257 -> 656,257
425,87 -> 425,603
355,378 -> 355,23
462,286 -> 462,358
181,571 -> 181,732
17,649 -> 476,649
394,321 -> 394,293
812,660 -> 515,957
21,150 -> 799,928
437,593 -> 437,372
125,495 -> 373,743
482,404 -> 482,420
283,580 -> 283,234
667,966 -> 827,806
959,961 -> 959,931
461,845 -> 206,845
299,888 -> 299,836
680,828 -> 680,855
958,977 -> 26,45
847,419 -> 290,976
892,920 -> 892,180
487,945 -> 487,445
329,570 -> 583,570
110,940 -> 989,61
475,351 -> 882,351
953,229 -> 429,229
119,125 -> 749,125
834,103 -> 212,725
978,412 -> 978,343
916,310 -> 758,310
825,761 -> 720,761
353,954 -> 353,795
422,464 -> 422,356
662,964 -> 836,790
242,873 -> 242,570
742,972 -> 797,972
698,364 -> 360,26
258,633 -> 19,872
406,649 -> 406,685
386,710 -> 925,710
347,657 -> 524,480
812,905 -> 554,647
420,505 -> 420,231
908,693 -> 908,724
130,772 -> 130,898
560,23 -> 560,987
941,831 -> 941,544
817,940 -> 132,255
515,280 -> 515,811
544,102 -> 568,102
115,612 -> 67,660
743,762 -> 743,152
246,14 -> 691,459
766,492 -> 673,492
467,179 -> 351,63
655,779 -> 655,524
314,171 -> 314,108
414,64 -> 502,64
564,239 -> 894,239
984,974 -> 56,46
201,963 -> 201,223
238,194 -> 238,832
30,652 -> 477,652
818,735 -> 582,971
225,566 -> 673,566
172,865 -> 74,865
264,101 -> 264,812
487,916 -> 979,916
879,30 -> 10,899
797,657 -> 797,136
750,642 -> 593,799
550,244 -> 418,376
158,816 -> 668,816
505,648 -> 303,648
411,688 -> 263,688
544,35 -> 771,35
545,846 -> 286,846
284,760 -> 284,929
835,401 -> 708,401
533,591 -> 545,591
866,757 -> 475,757
202,62 -> 907,767
456,655 -> 456,123
367,714 -> 225,714
359,679 -> 926,679
623,853 -> 623,865
170,120 -> 213,120
481,741 -> 481,435
928,73 -> 41,960
551,282 -> 551,265
988,986 -> 12,10
351,172 -> 791,172
49,65 -> 952,968
725,617 -> 691,617
509,159 -> 697,159
83,985 -> 83,968
206,617 -> 334,489
880,682 -> 966,768
60,896 -> 60,617
501,686 -> 49,234
801,708 -> 738,771
548,883 -> 548,33
753,162 -> 29,162
102,478 -> 102,295
115,656 -> 637,134
924,970 -> 924,963
191,340 -> 191,515
764,481 -> 523,481
97,619 -> 97,890
228,183 -> 228,624
171,867 -> 68,867
797,685 -> 167,685
510,955 -> 464,955
930,955 -> 233,258
934,572 -> 934,900
217,822 -> 797,242
868,939 -> 369,440
861,811 -> 861,36
346,617 -> 346,153
754,526 -> 754,426
482,724 -> 482,21
328,984 -> 976,984
933,895 -> 325,287
965,973 -> 232,240
502,707 -> 767,972
353,680 -> 815,218
311,210 -> 311,157
156,944 -> 928,172
615,395 -> 101,909
107,500 -> 528,921
375,42 -> 375,796
13,292 -> 818,292
613,144 -> 613,541
340,677 -> 340,406
631,655 -> 744,655
22,242 -> 723,943
705,596 -> 980,321
316,955 -> 316,515
760,279 -> 44,279
391,328 -> 391,724
917,476 -> 917,668
66,907 -> 913,60
597,260 -> 362,25
568,584 -> 568,297
375,506 -> 375,300
988,31 -> 72,947
425,342 -> 154,342
196,395 -> 899,395
904,17 -> 94,17
546,159 -> 751,159
284,557 -> 175,448
69,201 -> 697,201
130,421 -> 224,421
646,462 -> 637,453
187,638 -> 621,638
832,212 -> 416,212
614,582 -> 348,582
677,404 -> 677,709
178,122 -> 915,859
81,849 -> 223,849
717,18 -> 646,18
723,666 -> 974,666
703,234 -> 130,234
317,107 -> 106,107
207,397 -> 207,375
688,465 -> 982,171
749,201 -> 610,201
280,313 -> 827,860
773,873 -> 917,873
337,908 -> 337,155
541,427 -> 385,583
611,314 -> 131,794
966,909 -> 104,47
785,556 -> 346,556
914,645 -> 914,718
683,941 -> 657,915
919,665 -> 310,56
743,978 -> 779,978
953,925 -> 953,854
899,347 -> 705,347
46,597 -> 46,255
332,364 -> 922,954
38,987 -> 832,193
77,585 -> 77,262
155,61 -> 734,640
953,136 -> 655,136
939,730 -> 158,730
903,458 -> 393,458
50,227 -> 50,249
536,814 -> 536,242
906,694 -> 259,47
317,237 -> 853,773
828,55 -> 509,55
40,664 -> 341,965
414,820 -> 53,459
244,344 -> 272,344
191,606 -> 308,606
329,409 -> 329,960
166,863 -> 938,91
655,396 -> 291,760
634,666 -> 625,666
360,622 -> 360,550
568,473 -> 840,201
534,162 -> 534,823
583,563 -> 583,521
124,447 -> 124,79
207,559 -> 207,649
688,238 -> 26,900
173,33 -> 117,33
665,800 -> 665,86
121,515 -> 121,132
32,472 -> 32,960
513,28 -> 513,299
881,612 -> 881,415
72,71 -> 977,976
169,821 -> 111,821
603,756 -> 254,756
182,129 -> 182,824
746,670 -> 942,670
143,15 -> 72,86
108,134 -> 963,989
860,388 -> 834,362
252,811 -> 473,811
575,306 -> 575,368
686,471 -> 686,38
673,59 -> 673,861
461,949 -> 491,949
915,373 -> 330,958
933,699 -> 588,699
254,798 -> 254,498
329,865 -> 329,926
569,243 -> 659,243
762,808 -> 921,967
722,460 -> 68,460
136,470 -> 355,470
133,919 -> 56,842
87,868 -> 853,102
622,102 -> 446,102
798,494 -> 135,494
281,858 -> 281,172
141,172 -> 765,796
794,194 -> 102,886
539,983 -> 539,895
841,755 -> 841,365
695,429 -> 166,958
965,933 -> 899,933
603,699 -> 603,708
598,635 -> 844,635
288,190 -> 288,946
559,383 -> 423,383
795,332 -> 409,718
600,645 -> 478,645
831,24 -> 905,24
13,817 -> 606,224
828,878 -> 96,146
32,197 -> 32,891
84,832 -> 84,756
404,281 -> 404,781
394,441 -> 489,536
845,876 -> 589,876
833,114 -> 833,834
979,130 -> 979,238
907,189 -> 396,700
448,740 -> 714,474
145,837 -> 100,837
982,983 -> 38,39
962,506 -> 962,764
773,922 -> 975,922
892,666 -> 904,654
754,201 -> 459,496
108,829 -> 108,894
122,381 -> 122,484
683,301 -> 630,354
47,103 -> 897,953
549,880 -> 942,487
944,15 -> 44,915
713,456 -> 713,402
83,865 -> 239,865
814,585 -> 814,105
980,439 -> 685,439'''
ڤيسه = str.split
ݢاريس٢ = []
اونتوق باريس دالم ڤيسه(اينڤوت,'\n'):
مولا ,اخير = ڤيسه(باريس ,' -> ')
ݢاريس٢.append([[اينتيݢر(ک) اونتوق ک دالم ڤيسه(مولا,',')],
[اينتيݢر(ک) اونتوق ک دالم ڤيسه(اخير,',')]])
ڤاڤن = [[0 اونتوق _ دالم جولت(1000)] اونتوق __ دالم جولت(1000)]
اونتوق مولا,اخير دالم ݢاريس٢:
جک مولا[0] == اخير[0]:
اونتوق شي دالم جولت(مينيموم(مولا[1],اخير[1]) ,مکسيموم(مولا[1],اخير[1])+1):
ڤاڤن[مولا[0]][شي] += 1
جکاءين مولا[1] == اخير[1]:
اونتوق شي دالم جولت(مينيموم(مولا[0],اخير[0]) ,مکسيموم(مولا[0],اخير[0])+1):
ڤاڤن[شي][مولا[1]] += 1
جکاءين mutlak(مولا[0] - اخير[0]) == mutlak(مولا[1] - اخير[1]):
جک مولا[0] < اخير[0] دان مولا[1] < اخير[1]:
اونتوق شي دالم جولت(اخير[0] - مولا[0] + 1):
ڤاڤن[مولا[0]+شي][مولا[1]+شي] += 1
جک مولا[0] < اخير[0] دان مولا[1] > اخير[1]:
اونتوق شي دالم جولت(اخير[0] - مولا[0] + 1):
ڤاڤن [مولا[0]+شي][مولا[1]-شي] += 1
جک مولا[0] > اخير[0] دان مولا[1] < اخير[1]:
اونتوق شي دالم جولت(مولا[0] - اخير[0] + 1):
ڤاڤن[مولا[0]-شي][مولا[1]+شي] += 1
جک مولا[0] > اخير[0] دان مولا[1] > اخير[1]:
اونتوق شي دالم جولت(مولا[1] - اخير[1] + 1):
ڤاڤن[مولا[0]-شي][مولا[1]-شي] += 1
جومله = 0
اونتوق د دالم ڤاڤن:
اونتوق ن دالم د:
جک ن >= 2:
جومله += 1
چيتق(جومله)
# ans1: 7644
# ans2: 18627
# 13:46 solved :) 46 mins
#
| 19.329159
| 82
| 0.606941
| 2,241
| 10,805
| 2.925033
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0
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5092d728c7480a12da1a2fda1036ffb01fc1da99
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py
|
Python
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watoee/conf/global_settings.py
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snower/watoee
|
a64e1c3c9fefed2d3e780f6d8a2915b1bc4a7f58
|
[
"MIT"
] | 1
|
2016-12-16T04:31:29.000Z
|
2016-12-16T04:31:29.000Z
|
watoee/conf/global_settings.py
|
snower/watoee
|
a64e1c3c9fefed2d3e780f6d8a2915b1bc4a7f58
|
[
"MIT"
] | 1
|
2016-12-16T04:34:29.000Z
|
2016-12-16T04:34:29.000Z
|
watoee/conf/global_settings.py
|
snower/watoee
|
a64e1c3c9fefed2d3e780f6d8a2915b1bc4a7f58
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# 16/12/15
# create by: snower
FORMATER = "watoee.formaters.formater.Formater"
SERIALIZE = "watoee.serializes.jsonserialize.JsonSerialize"
| 27.166667
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0
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50c5cac1c95fbc3385e5615fb2447d83854040fa
| 17,034
|
py
|
Python
|
src/uarch/gpu/amd_gpu.py
|
ThatCopy/OCSysInfo
|
f23bd8520a1b6bc298e538dbeb29d7bd60398416
|
[
"MIT"
] | 6
|
2021-10-16T14:06:11.000Z
|
2022-02-12T15:12:51.000Z
|
src/uarch/gpu/amd_gpu.py
|
ThatCopy/OCSysInfo
|
f23bd8520a1b6bc298e538dbeb29d7bd60398416
|
[
"MIT"
] | 11
|
2021-10-17T22:44:12.000Z
|
2022-02-13T09:13:40.000Z
|
src/uarch/gpu/amd_gpu.py
|
ThatCopy/OCSysInfo
|
f23bd8520a1b6bc298e538dbeb29d7bd60398416
|
[
"MIT"
] | 9
|
2021-10-18T05:11:56.000Z
|
2021-11-21T03:26:02.000Z
|
amd = [
{
"Codename": "Tahiti",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6780" },
{ "Vendor": "0x1002", "Device": "0x6784" },
{ "Vendor": "0x1002", "Device": "0x6788" },
{ "Vendor": "0x1002", "Device": "0x678a" },
{ "Vendor": "0x1002", "Device": "0x6790" },
{ "Vendor": "0x1002", "Device": "0x6791" },
{ "Vendor": "0x1002", "Device": "0x6792" },
{ "Vendor": "0x1002", "Device": "0x6798" },
{ "Vendor": "0x1002", "Device": "0x6799" },
{ "Vendor": "0x1002", "Device": "0x679a" },
{ "Vendor": "0x1002", "Device": "0x679b" },
{ "Vendor": "0x1002", "Device": "0x679e" },
{ "Vendor": "0x1002", "Device": "0x679f" }
]
},
{
"Codename": "Pitcairn",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6800" },
{ "Vendor": "0x1002", "Device": "0x6801" },
{ "Vendor": "0x1002", "Device": "0x6802" },
{ "Vendor": "0x1002", "Device": "0x6806" },
{ "Vendor": "0x1002", "Device": "0x6808" },
{ "Vendor": "0x1002", "Device": "0x6809" },
{ "Vendor": "0x1002", "Device": "0x6810" },
{ "Vendor": "0x1002", "Device": "0x6811" },
{ "Vendor": "0x1002", "Device": "0x6816" },
{ "Vendor": "0x1002", "Device": "0x6817" },
{ "Vendor": "0x1002", "Device": "0x6818" },
{ "Vendor": "0x1002", "Device": "0x6819" }
]
},
{
"Codename": "Oland",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6600" },
{ "Vendor": "0x1002", "Device": "0x6601" },
{ "Vendor": "0x1002", "Device": "0x6602" },
{ "Vendor": "0x1002", "Device": "0x6603" },
{ "Vendor": "0x1002", "Device": "0x6604" },
{ "Vendor": "0x1002", "Device": "0x6605" },
{ "Vendor": "0x1002", "Device": "0x6606" },
{ "Vendor": "0x1002", "Device": "0x6607" },
{ "Vendor": "0x1002", "Device": "0x6608" },
{ "Vendor": "0x1002", "Device": "0x6610" },
{ "Vendor": "0x1002", "Device": "0x6611" },
{ "Vendor": "0x1002", "Device": "0x6613" },
{ "Vendor": "0x1002", "Device": "0x6617" },
{ "Vendor": "0x1002", "Device": "0x6620" },
{ "Vendor": "0x1002", "Device": "0x6621" },
{ "Vendor": "0x1002", "Device": "0x6623" },
{ "Vendor": "0x1002", "Device": "0x6631" }
]
},
{
"Codename": "Verde",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6820" },
{ "Vendor": "0x1002", "Device": "0x6821" },
{ "Vendor": "0x1002", "Device": "0x6822" },
{ "Vendor": "0x1002", "Device": "0x6823" },
{ "Vendor": "0x1002", "Device": "0x6824" },
{ "Vendor": "0x1002", "Device": "0x6825" },
{ "Vendor": "0x1002", "Device": "0x6826" },
{ "Vendor": "0x1002", "Device": "0x6827" },
{ "Vendor": "0x1002", "Device": "0x6828" },
{ "Vendor": "0x1002", "Device": "0x6829" },
{ "Vendor": "0x1002", "Device": "0x682a" },
{ "Vendor": "0x1002", "Device": "0x682b" },
{ "Vendor": "0x1002", "Device": "0x682c" },
{ "Vendor": "0x1002", "Device": "0x682d" },
{ "Vendor": "0x1002", "Device": "0x682f" },
{ "Vendor": "0x1002", "Device": "0x6830" },
{ "Vendor": "0x1002", "Device": "0x6831" },
{ "Vendor": "0x1002", "Device": "0x6835" },
{ "Vendor": "0x1002", "Device": "0x6837" },
{ "Vendor": "0x1002", "Device": "0x6838" },
{ "Vendor": "0x1002", "Device": "0x6839" },
{ "Vendor": "0x1002", "Device": "0x683b" },
{ "Vendor": "0x1002", "Device": "0x683d" },
{ "Vendor": "0x1002", "Device": "0x683f" }
]
},
{
"Codename": "Hainan",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6660" },
{ "Vendor": "0x1002", "Device": "0x6663" },
{ "Vendor": "0x1002", "Device": "0x6664" },
{ "Vendor": "0x1002", "Device": "0x6665" },
{ "Vendor": "0x1002", "Device": "0x6667" },
{ "Vendor": "0x1002", "Device": "0x666f" }
]
},
{
"Codename": "Kaveri",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x1304" },
{ "Vendor": "0x1002", "Device": "0x1305" },
{ "Vendor": "0x1002", "Device": "0x1306" },
{ "Vendor": "0x1002", "Device": "0x1307" },
{ "Vendor": "0x1002", "Device": "0x1309" },
{ "Vendor": "0x1002", "Device": "0x130a" },
{ "Vendor": "0x1002", "Device": "0x130b" },
{ "Vendor": "0x1002", "Device": "0x130c" },
{ "Vendor": "0x1002", "Device": "0x130d" },
{ "Vendor": "0x1002", "Device": "0x130e" },
{ "Vendor": "0x1002", "Device": "0x130f" },
{ "Vendor": "0x1002", "Device": "0x1310" },
{ "Vendor": "0x1002", "Device": "0x1311" },
{ "Vendor": "0x1002", "Device": "0x1312" },
{ "Vendor": "0x1002", "Device": "0x1313" },
{ "Vendor": "0x1002", "Device": "0x1315" },
{ "Vendor": "0x1002", "Device": "0x1316" },
{ "Vendor": "0x1002", "Device": "0x1317" },
{ "Vendor": "0x1002", "Device": "0x1318" },
{ "Vendor": "0x1002", "Device": "0x131b" },
{ "Vendor": "0x1002", "Device": "0x131c" },
{ "Vendor": "0x1002", "Device": "0x131d" }
]
},
{
"Codename": "Bonaire",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6640" },
{ "Vendor": "0x1002", "Device": "0x6641" },
{ "Vendor": "0x1002", "Device": "0x6646" },
{ "Vendor": "0x1002", "Device": "0x6647" },
{ "Vendor": "0x1002", "Device": "0x6649" },
{ "Vendor": "0x1002", "Device": "0x6650" },
{ "Vendor": "0x1002", "Device": "0x6651" },
{ "Vendor": "0x1002", "Device": "0x6658" },
{ "Vendor": "0x1002", "Device": "0x665c" },
{ "Vendor": "0x1002", "Device": "0x665d" },
{ "Vendor": "0x1002", "Device": "0x665f" }
]
},
{
"Codename": "Hawaii",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x67a0" },
{ "Vendor": "0x1002", "Device": "0x67a1" },
{ "Vendor": "0x1002", "Device": "0x67a2" },
{ "Vendor": "0x1002", "Device": "0x67a8" },
{ "Vendor": "0x1002", "Device": "0x67a9" },
{ "Vendor": "0x1002", "Device": "0x67aa" },
{ "Vendor": "0x1002", "Device": "0x67b0" },
{ "Vendor": "0x1002", "Device": "0x67b1" },
{ "Vendor": "0x1002", "Device": "0x67b8" },
{ "Vendor": "0x1002", "Device": "0x67b9" },
{ "Vendor": "0x1002", "Device": "0x67ba" },
{ "Vendor": "0x1002", "Device": "0x67be" }
]
},
{
"Codename": "Kabini",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x9830" },
{ "Vendor": "0x1002", "Device": "0x9831" },
{ "Vendor": "0x1002", "Device": "0x9832" },
{ "Vendor": "0x1002", "Device": "0x9833" },
{ "Vendor": "0x1002", "Device": "0x9834" },
{ "Vendor": "0x1002", "Device": "0x9835" },
{ "Vendor": "0x1002", "Device": "0x9836" },
{ "Vendor": "0x1002", "Device": "0x9837" },
{ "Vendor": "0x1002", "Device": "0x9838" },
{ "Vendor": "0x1002", "Device": "0x9839" },
{ "Vendor": "0x1002", "Device": "0x983a" },
{ "Vendor": "0x1002", "Device": "0x983b" },
{ "Vendor": "0x1002", "Device": "0x983c" },
{ "Vendor": "0x1002", "Device": "0x983d" },
{ "Vendor": "0x1002", "Device": "0x983e" },
{ "Vendor": "0x1002", "Device": "0x983f" }
]
},
{
"Codename": "Mullins",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x9850" },
{ "Vendor": "0x1002", "Device": "0x9851" },
{ "Vendor": "0x1002", "Device": "0x9852" },
{ "Vendor": "0x1002", "Device": "0x9853" },
{ "Vendor": "0x1002", "Device": "0x9854" },
{ "Vendor": "0x1002", "Device": "0x9855" },
{ "Vendor": "0x1002", "Device": "0x9856" },
{ "Vendor": "0x1002", "Device": "0x9857" },
{ "Vendor": "0x1002", "Device": "0x9858" },
{ "Vendor": "0x1002", "Device": "0x9859" },
{ "Vendor": "0x1002", "Device": "0x985a" },
{ "Vendor": "0x1002", "Device": "0x985b" },
{ "Vendor": "0x1002", "Device": "0x985c" },
{ "Vendor": "0x1002", "Device": "0x985d" },
{ "Vendor": "0x1002", "Device": "0x985e" },
{ "Vendor": "0x1002", "Device": "0x985f" }
]
},
{
"Codename": "Topaz",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6900" },
{ "Vendor": "0x1002", "Device": "0x6901" },
{ "Vendor": "0x1002", "Device": "0x6902" },
{ "Vendor": "0x1002", "Device": "0x6903" },
{ "Vendor": "0x1002", "Device": "0x6907" }
]
},
{
"Codename": "Tonga",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6920" },
{ "Vendor": "0x1002", "Device": "0x6921" },
{ "Vendor": "0x1002", "Device": "0x6928" },
{ "Vendor": "0x1002", "Device": "0x6929" },
{ "Vendor": "0x1002", "Device": "0x692b" },
{ "Vendor": "0x1002", "Device": "0x692f" },
{ "Vendor": "0x1002", "Device": "0x6930" },
{ "Vendor": "0x1002", "Device": "0x6938" },
{ "Vendor": "0x1002", "Device": "0x6939" }
]
},
{
"Codename": "Fiji",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7300" },
{ "Vendor": "0x1002", "Device": "0x730f" }
]
},
{
"Codename": "Carrizo",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x9870" },
{ "Vendor": "0x1002", "Device": "0x9874" },
{ "Vendor": "0x1002", "Device": "0x9875" },
{ "Vendor": "0x1002", "Device": "0x9876" },
{ "Vendor": "0x1002", "Device": "0x9877" }
]
},
{ "Codename": "Stoney", "IDs": [{ "Vendor": "0x1002", "Device": "0x98e4" }] },
{
"Codename": "Polaris 11",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x67e0" },
{ "Vendor": "0x1002", "Device": "0x67e3" },
{ "Vendor": "0x1002", "Device": "0x67e8" },
{ "Vendor": "0x1002", "Device": "0x67eb" },
{ "Vendor": "0x1002", "Device": "0x67ef" },
{ "Vendor": "0x1002", "Device": "0x67ff" },
{ "Vendor": "0x1002", "Device": "0x67e1" },
{ "Vendor": "0x1002", "Device": "0x67e7" },
{ "Vendor": "0x1002", "Device": "0x67e9" }
]
},
{
"Codename": "Polaris 10",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x67c0" },
{ "Vendor": "0x1002", "Device": "0x67c1" },
{ "Vendor": "0x1002", "Device": "0x67c2" },
{ "Vendor": "0x1002", "Device": "0x67c4" },
{ "Vendor": "0x1002", "Device": "0x67c7" },
{ "Vendor": "0x1002", "Device": "0x67d0" },
{ "Vendor": "0x1002", "Device": "0x67df" },
{ "Vendor": "0x1002", "Device": "0x67c8" },
{ "Vendor": "0x1002", "Device": "0x67c9" },
{ "Vendor": "0x1002", "Device": "0x67ca" },
{ "Vendor": "0x1002", "Device": "0x67cc" },
{ "Vendor": "0x1002", "Device": "0x67cf" },
{ "Vendor": "0x1002", "Device": "0x6fdf" }
]
},
{
"Codename": "Polaris 12",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6980" },
{ "Vendor": "0x1002", "Device": "0x6981" },
{ "Vendor": "0x1002", "Device": "0x6985" },
{ "Vendor": "0x1002", "Device": "0x6986" },
{ "Vendor": "0x1002", "Device": "0x6987" },
{ "Vendor": "0x1002", "Device": "0x6995" },
{ "Vendor": "0x1002", "Device": "0x6997" },
{ "Vendor": "0x1002", "Device": "0x699f" }
]
},
{
"Codename": "Vegam",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x694c" },
{ "Vendor": "0x1002", "Device": "0x694e" },
{ "Vendor": "0x1002", "Device": "0x694f" }
]
},
{
"Codename": "Vega 10",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x6860" },
{ "Vendor": "0x1002", "Device": "0x6861" },
{ "Vendor": "0x1002", "Device": "0x6862" },
{ "Vendor": "0x1002", "Device": "0x6863" },
{ "Vendor": "0x1002", "Device": "0x6864" },
{ "Vendor": "0x1002", "Device": "0x6867" },
{ "Vendor": "0x1002", "Device": "0x6868" },
{ "Vendor": "0x1002", "Device": "0x6869" },
{ "Vendor": "0x1002", "Device": "0x686a" },
{ "Vendor": "0x1002", "Device": "0x686b" },
{ "Vendor": "0x1002", "Device": "0x686c" },
{ "Vendor": "0x1002", "Device": "0x686d" },
{ "Vendor": "0x1002", "Device": "0x686e" },
{ "Vendor": "0x1002", "Device": "0x686f" },
{ "Vendor": "0x1002", "Device": "0x687f" }
]
},
{
"Codename": "Vega 12",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x69a0" },
{ "Vendor": "0x1002", "Device": "0x69a1" },
{ "Vendor": "0x1002", "Device": "0x69a2" },
{ "Vendor": "0x1002", "Device": "0x69a3" },
{ "Vendor": "0x1002", "Device": "0x69af" }
]
},
{
"Codename": "Vega 20",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x66a0" },
{ "Vendor": "0x1002", "Device": "0x66a1" },
{ "Vendor": "0x1002", "Device": "0x66a2" },
{ "Vendor": "0x1002", "Device": "0x66a3" },
{ "Vendor": "0x1002", "Device": "0x66a4" },
{ "Vendor": "0x1002", "Device": "0x66a7" },
{ "Vendor": "0x1002", "Device": "0x66af" }
]
},
{
"Codename": "Raven",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x15dd" },
{ "Vendor": "0x1002", "Device": "0x15d8" }
]
},
{
"Codename": "Arcturus",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x738c" },
{ "Vendor": "0x1002", "Device": "0x7388" },
{ "Vendor": "0x1002", "Device": "0x738e" },
{ "Vendor": "0x1002", "Device": "0x7390" }
]
},
{
"Codename": "Navi 10",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7310" },
{ "Vendor": "0x1002", "Device": "0x7312" },
{ "Vendor": "0x1002", "Device": "0x7318" },
{ "Vendor": "0x1002", "Device": "0x7319" },
{ "Vendor": "0x1002", "Device": "0x731a" },
{ "Vendor": "0x1002", "Device": "0x731b" },
{ "Vendor": "0x1002", "Device": "0x731e" },
{ "Vendor": "0x1002", "Device": "0x731f" }
]
},
{
"Codename": "Navi 14",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7340" },
{ "Vendor": "0x1002", "Device": "0x7341" },
{ "Vendor": "0x1002", "Device": "0x7347" },
{ "Vendor": "0x1002", "Device": "0x734f" }
]
},
{
"Codename": "Renoir",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x15e7" },
{ "Vendor": "0x1002", "Device": "0x1636" },
{ "Vendor": "0x1002", "Device": "0x1638" },
{ "Vendor": "0x1002", "Device": "0x164c" }
]
},
{
"Codename": "Navi 12",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7360" },
{ "Vendor": "0x1002", "Device": "0x7362" }
]
},
{
"Codename": "Sienna Cichlid",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x73a0" },
{ "Vendor": "0x1002", "Device": "0x73a1" },
{ "Vendor": "0x1002", "Device": "0x73a2" },
{ "Vendor": "0x1002", "Device": "0x73a3" },
{ "Vendor": "0x1002", "Device": "0x73a5" },
{ "Vendor": "0x1002", "Device": "0x73a8" },
{ "Vendor": "0x1002", "Device": "0x73a9" },
{ "Vendor": "0x1002", "Device": "0x73ab" },
{ "Vendor": "0x1002", "Device": "0x73ac" },
{ "Vendor": "0x1002", "Device": "0x73ad" },
{ "Vendor": "0x1002", "Device": "0x73ae" },
{ "Vendor": "0x1002", "Device": "0x73af" },
{ "Vendor": "0x1002", "Device": "0x73bf" }
]
},
{
"Codename": "Vangogh",
"IDs": [{ "Vendor": "0x1002", "Device": "0x163f" }]
},
{
"Codename": "Yellow Carp",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x164d" },
{ "Vendor": "0x1002", "Device": "0x1681" }
]
},
{
"Codename": "Navy Flounder",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x73c0" },
{ "Vendor": "0x1002", "Device": "0x73c1" },
{ "Vendor": "0x1002", "Device": "0x73c3" },
{ "Vendor": "0x1002", "Device": "0x73da" },
{ "Vendor": "0x1002", "Device": "0x73db" },
{ "Vendor": "0x1002", "Device": "0x73dc" },
{ "Vendor": "0x1002", "Device": "0x73dd" },
{ "Vendor": "0x1002", "Device": "0x73de" },
{ "Vendor": "0x1002", "Device": "0x73df" }
]
},
{
"Codename": "Dimgrey Cavefish",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x73e0" },
{ "Vendor": "0x1002", "Device": "0x73e1" },
{ "Vendor": "0x1002", "Device": "0x73e2" },
{ "Vendor": "0x1002", "Device": "0x73e3" },
{ "Vendor": "0x1002", "Device": "0x73e8" },
{ "Vendor": "0x1002", "Device": "0x73e9" },
{ "Vendor": "0x1002", "Device": "0x73ea" },
{ "Vendor": "0x1002", "Device": "0x73eb" },
{ "Vendor": "0x1002", "Device": "0x73ec" },
{ "Vendor": "0x1002", "Device": "0x73ed" },
{ "Vendor": "0x1002", "Device": "0x73ef" },
{ "Vendor": "0x1002", "Device": "0x73ff" }
]
},
{
"Codename": "Aldebaran",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7408" },
{ "Vendor": "0x1002", "Device": "0x740c" },
{ "Vendor": "0x1002", "Device": "0x740f" },
{ "Vendor": "0x1002", "Device": "0x7410" }
]
},
{
"Codename": "Cyan Skillfish",
"IDs": [{ "Vendor": "0x1002", "Device": "0x13fe" }]
},
{
"Codename": "Beige Goby",
"IDs": [
{ "Vendor": "0x1002", "Device": "0x7420" },
{ "Vendor": "0x1002", "Device": "0x7421" },
{ "Vendor": "0x1002", "Device": "0x7422" },
{ "Vendor": "0x1002", "Device": "0x7423" },
{ "Vendor": "0x1002", "Device": "0x743f" }
]
}
]
| 35.785714
| 80
| 0.464424
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| 17,034
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| 0.259775
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| 81
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|
0
| 4
|
50cd249da987fda060ca3038006586cdd72cce4f
| 231
|
py
|
Python
|
scapy_funcs/scapypayload_joinall.py
|
SecTraversl/Toolbox_Python_3.8
|
0ad1d92d3a12225ea60e4eef3f263aecfffd1b65
|
[
"MIT"
] | null | null | null |
scapy_funcs/scapypayload_joinall.py
|
SecTraversl/Toolbox_Python_3.8
|
0ad1d92d3a12225ea60e4eef3f263aecfffd1b65
|
[
"MIT"
] | null | null | null |
scapy_funcs/scapypayload_joinall.py
|
SecTraversl/Toolbox_Python_3.8
|
0ad1d92d3a12225ea60e4eef3f263aecfffd1b65
|
[
"MIT"
] | null | null | null |
# %%
#######################################
def scapypayload_joinall(packet_list: scapy.plist.PacketList):
allpayloads_onestring = b''.join([ p.load for p in packet_list if p.haslayer(Raw) ])
return allpayloads_onestring
| 33
| 88
| 0.619048
| 26
| 231
| 5.307692
| 0.769231
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| 0
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| 231
| 6
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| 38.5
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| 0
| 0
| 1
| 0
|
0
| 4
|
0fc1397f744ddcb10bd44fbf2ea052714a5c2d2c
| 598
|
py
|
Python
|
core/solver.py
|
ldacosta/shopping-optimization
|
469d41db064d93b80e5fdc15ffea12565f994a12
|
[
"MIT"
] | null | null | null |
core/solver.py
|
ldacosta/shopping-optimization
|
469d41db064d93b80e5fdc15ffea12565f994a12
|
[
"MIT"
] | null | null | null |
core/solver.py
|
ldacosta/shopping-optimization
|
469d41db064d93b80e5fdc15ffea12565f994a12
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Template for Solver.
Example:
Attributes:
Todo:
* Nothing for now.
.. _Google Python Style Guide:
http://google.github.io/styleguide/pyguide.html
"""
import abc
import collections
from typing import Set
from core.constraint import Constraint
Solution = collections.namedtuple('Solution', 'place items')
class Solver(abc.ABC):
def __init__(self):
pass
def recommend(self, constraints: Set[Constraint]) -> Set[Solution]:
"""Issues recommendations given constraints."""
raise RuntimeError("Abstract class, not callable.")
| 17.588235
| 71
| 0.692308
| 68
| 598
| 6.014706
| 0.705882
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| 0.185619
| 598
| 33
| 72
| 18.121212
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| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
0fc2a12423dfb22b35a565c8c76a933549fa244d
| 129
|
py
|
Python
|
igcommit/__init__.py
|
hasegeli/igcommit
|
7328c185b83c6d92f75accad339c6feab4850ea5
|
[
"MIT"
] | null | null | null |
igcommit/__init__.py
|
hasegeli/igcommit
|
7328c185b83c6d92f75accad339c6feab4850ea5
|
[
"MIT"
] | null | null | null |
igcommit/__init__.py
|
hasegeli/igcommit
|
7328c185b83c6d92f75accad339c6feab4850ea5
|
[
"MIT"
] | null | null | null |
"""igcommit - The main module
Copyright (c) 2021 InnoGames GmbH
Portions Copyright (c) 2021 Emre Hasegeli
"""
VERSION = (3, 1)
| 16.125
| 41
| 0.705426
| 18
| 129
| 5.055556
| 0.833333
| 0.21978
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| 0.09434
| 0.178295
| 129
| 7
| 42
| 18.428571
| 0.764151
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0fc82f0b7955a25fb8de9c6bdf4834f0f9925394
| 276
|
py
|
Python
|
server/books/models.py
|
zubeir68/my-library
|
a24b8daa12b7a0bc682460709606c732dc35d1aa
|
[
"MIT"
] | null | null | null |
server/books/models.py
|
zubeir68/my-library
|
a24b8daa12b7a0bc682460709606c732dc35d1aa
|
[
"MIT"
] | null | null | null |
server/books/models.py
|
zubeir68/my-library
|
a24b8daa12b7a0bc682460709606c732dc35d1aa
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
# Create your models here.
class Book(models.Model):
title = models.CharField(max_length=500)
author = models.CharField(max_length=100)
description = models.TextField()
| 25.090909
| 45
| 0.735507
| 36
| 276
| 5.444444
| 0.722222
| 0.153061
| 0.183673
| 0.244898
| 0
| 0
| 0
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| 0
| 0.030043
| 0.155797
| 276
| 11
| 46
| 25.090909
| 0.811159
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| false
| 0
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| 0
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| 1
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| null | 0
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| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
0ffdeabf459a4ffa597743af03248c15963b58ba
| 342
|
py
|
Python
|
poke/poke_model.py
|
Rozkipz/pokemonian
|
769f5ebe5c3d27aa7a608852d32c9a35e16e61aa
|
[
"MIT"
] | null | null | null |
poke/poke_model.py
|
Rozkipz/pokemonian
|
769f5ebe5c3d27aa7a608852d32c9a35e16e61aa
|
[
"MIT"
] | null | null | null |
poke/poke_model.py
|
Rozkipz/pokemonian
|
769f5ebe5c3d27aa7a608852d32c9a35e16e61aa
|
[
"MIT"
] | null | null | null |
from typing import Optional
from sqlmodel import SQLModel, Field
class pokemon(SQLModel, table=True):
id: int = Field(primary_key=True, nullable=False)
name: str = Field(nullable=False)
url: str = Field(nullable=False)
weight: Optional[int] = Field()
height: Optional[int] = Field()
speed: Optional[int] = Field()
| 24.428571
| 53
| 0.690058
| 44
| 342
| 5.340909
| 0.5
| 0.13617
| 0.204255
| 0.178723
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190058
| 342
| 13
| 54
| 26.307692
| 0.848375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.222222
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
ba260b6fb9fe82e326326b9aba40c9b846308e34
| 396
|
py
|
Python
|
topnav_capo2/scripts/camera/interface_camera.py
|
kasptom/topnav_ros_kasptom
|
9e7cd97ac0f5f22544880d71bcc91c9db4de528a
|
[
"Apache-2.0"
] | null | null | null |
topnav_capo2/scripts/camera/interface_camera.py
|
kasptom/topnav_ros_kasptom
|
9e7cd97ac0f5f22544880d71bcc91c9db4de528a
|
[
"Apache-2.0"
] | null | null | null |
topnav_capo2/scripts/camera/interface_camera.py
|
kasptom/topnav_ros_kasptom
|
9e7cd97ac0f5f22544880d71bcc91c9db4de528a
|
[
"Apache-2.0"
] | null | null | null |
from abc import ABCMeta, abstractmethod
class ICamera:
__metaclass__ = ABCMeta
@abstractmethod
def open(self):
raise NotImplementedError
@abstractmethod
def is_opened(self):
raise NotImplementedError
@abstractmethod
def close(self):
raise NotImplementedError
@abstractmethod
def get_frame(self):
raise NotImplementedError
| 18
| 39
| 0.689394
| 35
| 396
| 7.628571
| 0.514286
| 0.254682
| 0.419476
| 0.47191
| 0.505618
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.262626
| 396
| 21
| 40
| 18.857143
| 0.914384
| 0
| 0
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.266667
| false
| 0
| 0.066667
| 0
| 0.466667
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e84887c34be71f8d85217c64522cc20bb63cc1f4
| 139
|
py
|
Python
|
tools/registry.py
|
pancetta/python-hpc-performance
|
fc4c0fcd87d5a0fde78a0d6f284d1c89a31fbb03
|
[
"BSD-2-Clause"
] | 1
|
2020-10-29T06:04:43.000Z
|
2020-10-29T06:04:43.000Z
|
tools/registry.py
|
pancetta/python-performance
|
fc4c0fcd87d5a0fde78a0d6f284d1c89a31fbb03
|
[
"BSD-2-Clause"
] | null | null | null |
tools/registry.py
|
pancetta/python-performance
|
fc4c0fcd87d5a0fde78a0d6f284d1c89a31fbb03
|
[
"BSD-2-Clause"
] | null | null | null |
registry = []
def register(cls, bench_type=None, bench_params=None):
registry.append((cls, bench_type, bench_params))
return cls
| 19.857143
| 54
| 0.719424
| 19
| 139
| 5.052632
| 0.526316
| 0.166667
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.158273
| 139
| 6
| 55
| 23.166667
| 0.820513
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e86610a8c7e7e487aad3efa9b7c6c0b388eab94d
| 213
|
py
|
Python
|
aerofs/api/creds.py
|
mpillar/aerofs-python-sdk
|
c6c23869db82d5065d956b53bf26e0de8c2caff6
|
[
"MIT"
] | 1
|
2018-04-27T09:16:41.000Z
|
2018-04-27T09:16:41.000Z
|
aerofs/api/creds.py
|
mpillar/aerofs-sdk-python
|
c6c23869db82d5065d956b53bf26e0de8c2caff6
|
[
"MIT"
] | null | null | null |
aerofs/api/creds.py
|
mpillar/aerofs-sdk-python
|
c6c23869db82d5065d956b53bf26e0de8c2caff6
|
[
"MIT"
] | null | null | null |
class AppCredentials(object):
def __init__(self, client_id, client_secret, redirect_uri):
self.client_id = client_id
self.client_secret = client_secret
self.redirect_uri = redirect_uri
| 35.5
| 63
| 0.723005
| 27
| 213
| 5.222222
| 0.407407
| 0.212766
| 0.170213
| 0.255319
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.206573
| 213
| 5
| 64
| 42.6
| 0.83432
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e882e682e9a2b404fb695eea79d709fc7d7ab497
| 103
|
py
|
Python
|
wsgi.py
|
KMoszczyc/Sentiment-Predictor-Deep-L
|
86535a66d03197f61ce128b8cd10645efbc74b70
|
[
"MIT"
] | 1
|
2021-07-23T02:26:16.000Z
|
2021-07-23T02:26:16.000Z
|
wsgi.py
|
KMoszczyc/Sentiment-Predictor-Deep-L
|
86535a66d03197f61ce128b8cd10645efbc74b70
|
[
"MIT"
] | null | null | null |
wsgi.py
|
KMoszczyc/Sentiment-Predictor-Deep-L
|
86535a66d03197f61ce128b8cd10645efbc74b70
|
[
"MIT"
] | null | null | null |
from api import app
from content import train
if __name__ == "__main__":
app.run()
# train()
| 12.875
| 26
| 0.650485
| 14
| 103
| 4.214286
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.242718
| 103
| 7
| 27
| 14.714286
| 0.75641
| 0.067961
| 0
| 0
| 0
| 0
| 0.085106
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
e88ad244ddb3d77cb605fbd8bc535f158cd0d69e
| 99
|
py
|
Python
|
tests/basic/test_basic.py
|
Darless/GLockManager
|
efac37a7ef87e48a19d6aa89f1e48dd836ce6761
|
[
"Apache-2.0"
] | 2
|
2017-03-02T08:50:43.000Z
|
2017-10-30T15:38:58.000Z
|
tests/basic/test_basic.py
|
Darless/GLockManager
|
efac37a7ef87e48a19d6aa89f1e48dd836ce6761
|
[
"Apache-2.0"
] | 3
|
2017-01-03T14:36:30.000Z
|
2017-10-13T13:57:45.000Z
|
tests/basic/test_basic.py
|
Darless/GLockManager
|
efac37a7ef87e48a19d6aa89f1e48dd836ce6761
|
[
"Apache-2.0"
] | null | null | null |
import os
import subprocess
import shlex
def test_basic(utils):
utils.compile_and_run(__file__)
| 14.142857
| 33
| 0.818182
| 15
| 99
| 4.933333
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 99
| 6
| 34
| 16.5
| 0.850575
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.6
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
e8b7e753755801e59634ec61fa0e7b1476b67876
| 265
|
py
|
Python
|
examples/mixins.py
|
jeromelebleu/django-cruditor
|
da3c9be402ff87014d82ecc9c5abdf68693da4db
|
[
"MIT"
] | 10
|
2016-04-19T11:12:27.000Z
|
2020-10-09T04:12:02.000Z
|
examples/mixins.py
|
jeromelebleu/django-cruditor
|
da3c9be402ff87014d82ecc9c5abdf68693da4db
|
[
"MIT"
] | 26
|
2018-04-25T12:02:13.000Z
|
2022-02-10T15:26:07.000Z
|
examples/mixins.py
|
jeromelebleu/django-cruditor
|
da3c9be402ff87014d82ecc9c5abdf68693da4db
|
[
"MIT"
] | 3
|
2019-02-28T14:32:26.000Z
|
2020-06-08T11:06:25.000Z
|
from django.urls import reverse_lazy
class ExamplesMixin:
menu_title = 'Examples Demo'
menu_template_name = 'menu.html'
index_url = reverse_lazy('home')
logout_url = reverse_lazy('logout')
change_password_url = reverse_lazy('change-password')
| 26.5
| 57
| 0.739623
| 34
| 265
| 5.441176
| 0.617647
| 0.237838
| 0.227027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166038
| 265
| 9
| 58
| 29.444444
| 0.837104
| 0
| 0
| 0
| 0
| 0
| 0.177358
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.142857
| 0.142857
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
e8ba9a99ca69c1d3b8747e38686e40c6697547ba
| 429
|
py
|
Python
|
lang/py/cookbook/v2/source/cb2_6_5_exm_1.py
|
ch1huizong/learning
|
632267634a9fd84a5f5116de09ff1e2681a6cc85
|
[
"MIT"
] | null | null | null |
lang/py/cookbook/v2/source/cb2_6_5_exm_1.py
|
ch1huizong/learning
|
632267634a9fd84a5f5116de09ff1e2681a6cc85
|
[
"MIT"
] | null | null | null |
lang/py/cookbook/v2/source/cb2_6_5_exm_1.py
|
ch1huizong/learning
|
632267634a9fd84a5f5116de09ff1e2681a6cc85
|
[
"MIT"
] | null | null | null |
class Pricing(object):
def __init__(self, location, event):
self.location = location
self.event = event
def setlocation(self, location):
self.location = location
def getprice(self):
return self.location.getprice()
def getquantity(self):
return self.location.getquantity()
def getdiscount(self):
return self.event.getdiscount()
## and many more such methods
| 30.642857
| 42
| 0.652681
| 47
| 429
| 5.87234
| 0.382979
| 0.26087
| 0.152174
| 0.15942
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.251748
| 429
| 13
| 43
| 33
| 0.859813
| 0.060606
| 0
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.416667
| false
| 0
| 0
| 0.25
| 0.75
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
2cf9c69a58b2d8f839242403e7b68a76edb34ebd
| 25
|
py
|
Python
|
slack_token.py
|
cpieri/api_slack
|
130c88268c255fbae9feeb3f4300cb305b6dfe6c
|
[
"MIT"
] | null | null | null |
slack_token.py
|
cpieri/api_slack
|
130c88268c255fbae9feeb3f4300cb305b6dfe6c
|
[
"MIT"
] | null | null | null |
slack_token.py
|
cpieri/api_slack
|
130c88268c255fbae9feeb3f4300cb305b6dfe6c
|
[
"MIT"
] | null | null | null |
token='Your Token Slack'
| 12.5
| 24
| 0.76
| 4
| 25
| 4.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 25
| 1
| 25
| 25
| 0.863636
| 0
| 0
| 0
| 0
| 0
| 0.64
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
2cfb8a9e6c36a1c557b03fb7463c8e214fbe1449
| 92
|
py
|
Python
|
2014/09/table-state-debt-protections/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | 14
|
2015-05-08T13:41:51.000Z
|
2021-02-24T12:34:55.000Z
|
2014/09/table-state-debt-protections/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | null | null | null |
2014/09/table-state-debt-protections/graphic_config.py
|
nprapps/graphics-archive
|
97b0ef326b46a959df930f5522d325e537f7a655
|
[
"FSFAP"
] | 7
|
2015-04-04T04:45:54.000Z
|
2021-02-18T11:12:48.000Z
|
#!/usr/bin/env python
COPY_GOOGLE_DOC_KEY = '1UfWWQPek40kyjAu13zIbNkvUjUxsyDHw-xvFAZZjsLA'
| 23
| 68
| 0.836957
| 10
| 92
| 7.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05814
| 0.065217
| 92
| 3
| 69
| 30.666667
| 0.802326
| 0.217391
| 0
| 0
| 0
| 0
| 0.619718
| 0.619718
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa110332a9944ff799cafcb65060e1eecf9c1f97
| 798
|
py
|
Python
|
__init__.py
|
brando90/predicting_performance
|
a6b13cb869c04fc5415893bcf7a3fb2c6179f953
|
[
"MIT"
] | null | null | null |
__init__.py
|
brando90/predicting_performance
|
a6b13cb869c04fc5415893bcf7a3fb2c6179f953
|
[
"MIT"
] | 1
|
2021-12-09T21:46:06.000Z
|
2021-12-09T21:46:06.000Z
|
__init__.py
|
brando90/predicting_performance
|
a6b13cb869c04fc5415893bcf7a3fb2c6179f953
|
[
"MIT"
] | null | null | null |
# helps users of project/pkg from knowning the internal structure of modules
# easier to use funcs in all modules
# following line imports (i.e. similar to copying the code) from the declared packages
# https://github.com/brando90/hbf_tensorflow_code/tree/master/my_tf_proj
# from predicting_performance.data_processor import *
# from predicting_performance.model_data_gen import *
#
# from predicting_performance.metrics import *
#
# from predicting_performance.stats_collector import *
# from predicting_performance.trainer import *
# from predicting_performance.data_loader_cifar import *
#having the package name declared
#from pkg_1.module2 import *
#from pkg_1.module1 import f1 as superduperf1
# 2 options to import
# (1) from pkg_1.module1 import f1
# (2) from pkg_1 import superduperf1
| 36.272727
| 86
| 0.805764
| 116
| 798
| 5.37069
| 0.534483
| 0.134831
| 0.24077
| 0.248796
| 0.073836
| 0.073836
| 0
| 0
| 0
| 0
| 0
| 0.023022
| 0.129073
| 798
| 21
| 87
| 38
| 0.873381
| 0.949875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa2160f2037b4b8135b97b69e5ef6c88c088f987
| 43
|
py
|
Python
|
pywork/py4.py
|
infinityman8/pythonwork-uni
|
8ba7f341573f3031710d1bf4d91849508aa81bf8
|
[
"MIT"
] | null | null | null |
pywork/py4.py
|
infinityman8/pythonwork-uni
|
8ba7f341573f3031710d1bf4d91849508aa81bf8
|
[
"MIT"
] | null | null | null |
pywork/py4.py
|
infinityman8/pythonwork-uni
|
8ba7f341573f3031710d1bf4d91849508aa81bf8
|
[
"MIT"
] | null | null | null |
x=2
print(x"squared is x*x)
Xcubed=x**3
| 10.75
| 24
| 0.627907
| 11
| 43
| 2.454545
| 0.636364
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0.186047
| 43
| 3
| 25
| 14.333333
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.333333
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa32551092c62e6397340262551c19ec6352f623
| 584
|
py
|
Python
|
ch03/ch03_02/ch03_02.py
|
z2x3c4v5bz/pybook_yehnan
|
3c7f8124ac49f4abe2682f2a01835af3c3759132
|
[
"MIT"
] | null | null | null |
ch03/ch03_02/ch03_02.py
|
z2x3c4v5bz/pybook_yehnan
|
3c7f8124ac49f4abe2682f2a01835af3c3759132
|
[
"MIT"
] | null | null | null |
ch03/ch03_02/ch03_02.py
|
z2x3c4v5bz/pybook_yehnan
|
3c7f8124ac49f4abe2682f2a01835af3c3759132
|
[
"MIT"
] | null | null | null |
def change_data(x):
if x < 0 or x >255:
return None
elif 200 <= x <=255:
return int(round((x - 200) * 3 / 11.0 + 85, 0))
elif 0 <= x <=130:
return int(round(x * 6 / 13.0, 0))
else:
return int(round((x - 131) * 23 / 68.0 + 61, 0))
if __name__ == '__main__':
print('-1 => ' + str(change_data(-1)))
print('0 => ' + str(change_data(0)))
print('55 => ' + str(change_data(55)))
print('131 => ' + str(change_data(131)))
print('255 => ' + str(change_data(255)))
'''
-1 => None
0 => 0
55 => 25
131 => 61
255 => 100
'''
| 20.137931
| 56
| 0.488014
| 92
| 584
| 2.945652
| 0.358696
| 0.221402
| 0.239852
| 0.166052
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188862
| 0.292808
| 584
| 28
| 57
| 20.857143
| 0.467312
| 0
| 0
| 0
| 0
| 0
| 0.074004
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.066667
| false
| 0
| 0
| 0
| 0.333333
| 0.333333
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa40aa95de1c4f3b0c843d5644804c9f9f1af8d0
| 284
|
py
|
Python
|
src/fwinterconnect-gen/interconnect_templ.py
|
Featherweight-IP/fwinterconnect-gen
|
8626202cb8e0681de8796d4d8c2445487e028825
|
[
"Apache-2.0"
] | 3
|
2019-03-02T08:55:09.000Z
|
2022-03-01T07:20:45.000Z
|
src/fwinterconnect-gen/interconnect_templ.py
|
Featherweight-IP/fwinterconnect-gen
|
8626202cb8e0681de8796d4d8c2445487e028825
|
[
"Apache-2.0"
] | null | null | null |
src/fwinterconnect-gen/interconnect_templ.py
|
Featherweight-IP/fwinterconnect-gen
|
8626202cb8e0681de8796d4d8c2445487e028825
|
[
"Apache-2.0"
] | null | null | null |
template = """
/****************************************************************************
* ${name}.sv
****************************************************************************/
module ${name}(
${ports}
);
${wires}
${port_wire_assignments}
${interconnects}
endmodule
"""
| 15.777778
| 78
| 0.271127
| 12
| 284
| 6.25
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080986
| 284
| 17
| 79
| 16.705882
| 0.287356
| 0
| 0
| 0
| 0
| 0
| 0.936396
| 0.628975
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa4aca0cde876a0937fc8b346cf174d92ebfd1e6
| 224
|
py
|
Python
|
main/admin.py
|
Tony-MK/Healthy
|
bec2a1f917bc3d79e9315d30073ea62bdab34f3b
|
[
"Apache-2.0"
] | null | null | null |
main/admin.py
|
Tony-MK/Healthy
|
bec2a1f917bc3d79e9315d30073ea62bdab34f3b
|
[
"Apache-2.0"
] | null | null | null |
main/admin.py
|
Tony-MK/Healthy
|
bec2a1f917bc3d79e9315d30073ea62bdab34f3b
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
# Register your models here.
from .models import *
# User Types
admin.site.register(Parent);
admin.site.register(CareTaker);
#admin.site.register(Child);
admin.site.register(Gig);
| 11.2
| 32
| 0.741071
| 30
| 224
| 5.533333
| 0.533333
| 0.216867
| 0.409639
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138393
| 224
| 19
| 33
| 11.789474
| 0.860104
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
fa676c224f0392b319109e12f9d35a147cc02333
| 115
|
py
|
Python
|
imdb/__init__.py
|
iterait/cxflow-examples
|
e1c8e5a5e0cfe3abe92971748ac7f2c2a3673823
|
[
"MIT"
] | null | null | null |
imdb/__init__.py
|
iterait/cxflow-examples
|
e1c8e5a5e0cfe3abe92971748ac7f2c2a3673823
|
[
"MIT"
] | 3
|
2019-09-06T11:37:18.000Z
|
2019-09-10T11:01:07.000Z
|
imdb/__init__.py
|
iterait/emloop-examples
|
e1c8e5a5e0cfe3abe92971748ac7f2c2a3673823
|
[
"MIT"
] | null | null | null |
from .gru_net import SimpleGRU
from .imdb_prediction_hook import IMDBPredict
from .imdb_dataset import IMDBDataset
| 28.75
| 45
| 0.869565
| 16
| 115
| 6
| 0.6875
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104348
| 115
| 3
| 46
| 38.333333
| 0.932039
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
fa6dc1b30628cefd6ac995e622fe17afb9fcf10b
| 80
|
py
|
Python
|
us_holidays.py
|
hibatt/toggl_month
|
6cbf30aa2c76d09a21f582d30ca3947701f0c4f5
|
[
"MIT"
] | null | null | null |
us_holidays.py
|
hibatt/toggl_month
|
6cbf30aa2c76d09a21f582d30ca3947701f0c4f5
|
[
"MIT"
] | null | null | null |
us_holidays.py
|
hibatt/toggl_month
|
6cbf30aa2c76d09a21f582d30ca3947701f0c4f5
|
[
"MIT"
] | null | null | null |
from datetime import date
import holidays
us_holidays = holidays.UnitedStates()
| 20
| 37
| 0.8375
| 10
| 80
| 6.6
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1125
| 80
| 4
| 37
| 20
| 0.929577
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d7036bd14a694411d0ca1932e1f6d0c35b0d35d6
| 151
|
py
|
Python
|
tests/conftest.py
|
wuuuduu/django-getpaid
|
d864de53bc947e2d1ab4f2d3879a803cab1216d3
|
[
"MIT"
] | 6
|
2020-05-26T08:49:10.000Z
|
2022-01-03T17:44:19.000Z
|
tests/conftest.py
|
wuuuduu/django-getpaid
|
d864de53bc947e2d1ab4f2d3879a803cab1216d3
|
[
"MIT"
] | null | null | null |
tests/conftest.py
|
wuuuduu/django-getpaid
|
d864de53bc947e2d1ab4f2d3879a803cab1216d3
|
[
"MIT"
] | 1
|
2021-08-23T06:59:05.000Z
|
2021-08-23T06:59:05.000Z
|
from pytest_factoryboy import register
from .factories import OrderFactory, PaywallEntryFactory
register(OrderFactory)
register(PaywallEntryFactory)
| 21.571429
| 56
| 0.874172
| 14
| 151
| 9.357143
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086093
| 151
| 6
| 57
| 25.166667
| 0.949275
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d71b9a48043614e39283821a4c7ca11f90155c9a
| 276
|
py
|
Python
|
resources/routes.py
|
juliosarango/fastapi_app
|
6515d5a149a845175e6c679a26fdf72a87266e2e
|
[
"MIT"
] | null | null | null |
resources/routes.py
|
juliosarango/fastapi_app
|
6515d5a149a845175e6c679a26fdf72a87266e2e
|
[
"MIT"
] | null | null | null |
resources/routes.py
|
juliosarango/fastapi_app
|
6515d5a149a845175e6c679a26fdf72a87266e2e
|
[
"MIT"
] | null | null | null |
import imp
from fastapi import APIRouter
from resources import auth
from resources import complaint
from resources import user
api_router = APIRouter()
api_router.include_router(auth.router)
api_router.include_router(complaint.router)
api_router.include_router(user.router)
| 23
| 43
| 0.851449
| 39
| 276
| 5.846154
| 0.307692
| 0.157895
| 0.25
| 0.289474
| 0.245614
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094203
| 276
| 11
| 44
| 25.090909
| 0.912
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.555556
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d7207064a41a2108346d799b2fc78f965c0b9196
| 143
|
py
|
Python
|
search/resources.py
|
nexuszix/propm
|
8640c88d6c97a69c41e489e98c479c5eb0f81a18
|
[
"BSD-2-Clause"
] | null | null | null |
search/resources.py
|
nexuszix/propm
|
8640c88d6c97a69c41e489e98c479c5eb0f81a18
|
[
"BSD-2-Clause"
] | null | null | null |
search/resources.py
|
nexuszix/propm
|
8640c88d6c97a69c41e489e98c479c5eb0f81a18
|
[
"BSD-2-Clause"
] | null | null | null |
from import_export import resources
from .models import Land
class LandResource(resources.ModelResource):
class Meta:
model = Land
| 23.833333
| 44
| 0.762238
| 17
| 143
| 6.352941
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188811
| 143
| 6
| 45
| 23.833333
| 0.931034
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d7358f86666025f45ee48a935fdd9175a20a6ecc
| 169
|
py
|
Python
|
falkon/models/__init__.py
|
mohamad-amin/falkon
|
581c761b4a4cb7bf6a299613700db8414c419a52
|
[
"MIT"
] | 130
|
2020-06-18T08:30:30.000Z
|
2022-03-21T15:43:17.000Z
|
falkon/models/__init__.py
|
mohamad-amin/falkon
|
581c761b4a4cb7bf6a299613700db8414c419a52
|
[
"MIT"
] | 32
|
2020-06-26T09:24:45.000Z
|
2022-03-20T10:37:36.000Z
|
falkon/models/__init__.py
|
mohamad-amin/falkon
|
581c761b4a4cb7bf6a299613700db8414c419a52
|
[
"MIT"
] | 17
|
2020-07-13T17:28:02.000Z
|
2022-02-15T19:55:40.000Z
|
from .falkon import Falkon
from .logistic_falkon import LogisticFalkon
from .incore_falkon import InCoreFalkon
__all__ = ("Falkon", "LogisticFalkon", "InCoreFalkon", )
| 28.166667
| 56
| 0.798817
| 18
| 169
| 7.166667
| 0.444444
| 0.27907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112426
| 169
| 5
| 57
| 33.8
| 0.86
| 0
| 0
| 0
| 0
| 0
| 0.189349
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d74956cb14d6e8d24325d90a4bfe9feb24a08089
| 446
|
py
|
Python
|
icbd/type_analyzer/tests/imports2.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | 7
|
2015-04-06T15:17:13.000Z
|
2020-10-21T04:57:00.000Z
|
icbd/type_analyzer/tests/imports2.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | null | null | null |
icbd/type_analyzer/tests/imports2.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | 4
|
2016-05-16T17:53:08.000Z
|
2020-11-28T17:18:50.000Z
|
from import_test import dup
dup # 0 <int|module 'dup'>
from import_test import g
# packages hide modules with the same name:
g.xg # 0 module 'g' # 2 str
from import_test.f import dup1, dup2, e1, e2, xg
dup1 # 0 <int|module 'dup'>
dup2 # 0 module 'dup'
e1 # 0 module 'e'
e2 # 0 module 'e'
xg # 0 str
from import_test import a
a # 0 module 'a'
import import_test
import_test.a # 12 module 'a'
from . import sys # e 0
from .os import path # e 0
| 20.272727
| 48
| 0.686099
| 88
| 446
| 3.409091
| 0.329545
| 0.2
| 0.186667
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.059829
| 0.213004
| 446
| 21
| 49
| 21.238095
| 0.794872
| 0.414798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d75dd43cfdd3ba6022c32c843d75037567f01b99
| 184
|
py
|
Python
|
oshot/utils.py
|
daonb/okqa
|
3babf225911294dec1249472a9a3f6141fa7d6a7
|
[
"BSD-3-Clause"
] | null | null | null |
oshot/utils.py
|
daonb/okqa
|
3babf225911294dec1249472a9a3f6141fa7d6a7
|
[
"BSD-3-Clause"
] | null | null | null |
oshot/utils.py
|
daonb/okqa
|
3babf225911294dec1249472a9a3f6141fa7d6a7
|
[
"BSD-3-Clause"
] | null | null | null |
from django.conf import settings
from django.contrib.sites.models import Site
def get_root_url():
site = Site.objects.get(pk=settings.SITE_ID)
return 'http://' + site.domain
| 23
| 48
| 0.73913
| 28
| 184
| 4.75
| 0.678571
| 0.150376
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146739
| 184
| 7
| 49
| 26.285714
| 0.847134
| 0
| 0
| 0
| 0
| 0
| 0.038251
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d76d7b32bc4e9e1540d71de2c91346160a6548f8
| 167
|
py
|
Python
|
Deploying your ML Model/WebProject1/make_request.py
|
ChadderboxS/Tutorials
|
e5ff1c7cdd6c86c3253128f3c79bb9a606b1fffa
|
[
"MIT"
] | null | null | null |
Deploying your ML Model/WebProject1/make_request.py
|
ChadderboxS/Tutorials
|
e5ff1c7cdd6c86c3253128f3c79bb9a606b1fffa
|
[
"MIT"
] | null | null | null |
Deploying your ML Model/WebProject1/make_request.py
|
ChadderboxS/Tutorials
|
e5ff1c7cdd6c86c3253128f3c79bb9a606b1fffa
|
[
"MIT"
] | null | null | null |
import requests
url = 'http://localhost:3000/predict'
r = requests.post(url,json={'text': '-475 60 8 6221.92 6178.23 0.530438 0.336245 2238.601188'})
print(r.json())
| 27.833333
| 95
| 0.700599
| 29
| 167
| 4.034483
| 0.827586
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.308725
| 0.107784
| 167
| 6
| 96
| 27.833333
| 0.47651
| 0
| 0
| 0
| 0
| 0
| 0.52381
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0.25
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ad0cea5696707c7ebd4394361a9d552d9fa606fc
| 90
|
py
|
Python
|
experimental/test2.py
|
solkwonstat/stat_hami
|
9aff8d5ade5e955ba21d3e5489fe916f0444fdbb
|
[
"MIT"
] | null | null | null |
experimental/test2.py
|
solkwonstat/stat_hami
|
9aff8d5ade5e955ba21d3e5489fe916f0444fdbb
|
[
"MIT"
] | 1
|
2022-02-14T07:21:29.000Z
|
2022-02-14T07:21:29.000Z
|
experimental/test2.py
|
solkwonstat/stat_hami
|
9aff8d5ade5e955ba21d3e5489fe916f0444fdbb
|
[
"MIT"
] | null | null | null |
import time
if __name__ == "__main__":
print("Current time is %s", int(time.time()))
| 18
| 49
| 0.644444
| 13
| 90
| 3.846154
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 90
| 4
| 50
| 22.5
| 0.675676
| 0
| 0
| 0
| 0
| 0
| 0.288889
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
ad2771767de0784ed9c62d263ecb822b8c939946
| 77
|
py
|
Python
|
modules/auth/exceptions/auth_status_error.py
|
stevekineeve88/doubloon
|
4c7c9163e96877ad23663c3dd9a73ef6ccde3e22
|
[
"MIT"
] | null | null | null |
modules/auth/exceptions/auth_status_error.py
|
stevekineeve88/doubloon
|
4c7c9163e96877ad23663c3dd9a73ef6ccde3e22
|
[
"MIT"
] | 8
|
2021-01-29T15:49:17.000Z
|
2021-10-14T01:03:27.000Z
|
modules/auth/exceptions/auth_status_error.py
|
stevekineeve88/doubloon
|
4c7c9163e96877ad23663c3dd9a73ef6ccde3e22
|
[
"MIT"
] | null | null | null |
class AuthStatusError(Exception):
""" Auth status error
"""
pass
| 15.4
| 33
| 0.623377
| 7
| 77
| 6.857143
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25974
| 77
| 4
| 34
| 19.25
| 0.842105
| 0.220779
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
d13e1718e5682bf0899c40df2a52ed5761f6de06
| 209
|
py
|
Python
|
parflow/subset/data/__init__.py
|
arezaii/subsetter
|
482e90fdaeaa8fa5de7e325bdbe2f41206744524
|
[
"MIT"
] | 1
|
2020-06-16T15:57:37.000Z
|
2020-06-16T15:57:37.000Z
|
parflow/subset/data/__init__.py
|
arezaii/subsetter
|
482e90fdaeaa8fa5de7e325bdbe2f41206744524
|
[
"MIT"
] | 13
|
2020-08-21T02:24:39.000Z
|
2020-09-19T18:15:13.000Z
|
parflow/subset/data/__init__.py
|
arezaii/subsetter
|
482e90fdaeaa8fa5de7e325bdbe2f41206744524
|
[
"MIT"
] | null | null | null |
"""Data files, templates, domain definitions"""
from pathlib import Path
conus_manifest = Path(__file__).parent / 'conus_manifest.yaml'
parkinglot_template = Path(__file__).parent / 'parking_lot_template.tcl'
| 41.8
| 72
| 0.794258
| 26
| 209
| 5.884615
| 0.730769
| 0.169935
| 0.183007
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 209
| 5
| 72
| 41.8
| 0.805263
| 0.196172
| 0
| 0
| 0
| 0
| 0.263804
| 0.147239
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d146a0f435227dfa0ab6a02b9c243edc8e816a61
| 209
|
py
|
Python
|
dashboard/core/admin.py
|
Aditya-0912/recon
|
6e310cdd56caf678af54c27f2c3c97f56fb08119
|
[
"MIT"
] | 9
|
2019-11-13T18:05:51.000Z
|
2021-05-05T16:04:35.000Z
|
dashboard/core/admin.py
|
Aditya-0912/recon
|
6e310cdd56caf678af54c27f2c3c97f56fb08119
|
[
"MIT"
] | 9
|
2019-12-04T23:50:52.000Z
|
2022-02-10T12:02:50.000Z
|
dashboard/core/admin.py
|
Aditya-0912/recon
|
6e310cdd56caf678af54c27f2c3c97f56fb08119
|
[
"MIT"
] | 7
|
2020-04-19T17:34:58.000Z
|
2021-12-25T22:09:33.000Z
|
from django.contrib import admin
from .models import Employee
class EmployeeAdmin(admin.ModelAdmin):
list_display = [f.name for f in Employee._meta.fields]
admin.site.register(Employee, EmployeeAdmin)
| 20.9
| 58
| 0.789474
| 28
| 209
| 5.821429
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129187
| 209
| 9
| 59
| 23.222222
| 0.895604
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d14cab78b654642c622e7b3761cb2e6c7712db18
| 651
|
py
|
Python
|
txpipe/utils/__init__.py
|
Lhior/TXPipe
|
58fd7612326779d4c1b0e499157dddc9e3b524c0
|
[
"BSD-3-Clause"
] | 9
|
2018-03-17T02:07:52.000Z
|
2022-02-23T20:25:48.000Z
|
txpipe/utils/__init__.py
|
Lhior/TXPipe
|
58fd7612326779d4c1b0e499157dddc9e3b524c0
|
[
"BSD-3-Clause"
] | 162
|
2018-03-06T16:18:23.000Z
|
2022-03-21T18:11:37.000Z
|
txpipe/utils/__init__.py
|
Lhior/TXPipe
|
58fd7612326779d4c1b0e499157dddc9e3b524c0
|
[
"BSD-3-Clause"
] | 7
|
2018-07-26T11:49:46.000Z
|
2022-02-23T22:14:48.000Z
|
from .pixel_schemes import choose_pixelization, HealpixScheme, GnomonicPixelScheme
from .number_density_stats import SourceNumberDensityStats, LensNumberDensityStats
from .misc import array_hash, unique_list, hex_escape, rename_iterated
from .healpix import dilated_healpix_map
from .splitters import Splitter, DynamicSplitter
from .calibrators import Calibrator, NullCalibrator, MetaCalibrator, LensfitCalibrator, HSCCalibrator
from .splitters import Splitter, DynamicSplitter
from .calibration_tools import read_shear_catalog_type, band_variants, metacal_variants
from .calibration_tools import MetacalCalculator, LensfitCalculator, MeanShearInBins
| 65.1
| 101
| 0.88172
| 69
| 651
| 8.072464
| 0.666667
| 0.046679
| 0.068223
| 0.096948
| 0.165171
| 0.165171
| 0
| 0
| 0
| 0
| 0
| 0
| 0.079877
| 651
| 9
| 102
| 72.333333
| 0.929883
| 0
| 0
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
0f1d2b109ca06b249871040946a328fd531dac6a
| 182
|
py
|
Python
|
proyectos/2/RomeroVicente/VpostHorde/core/console.py
|
EnriqueGuerreroL/sistop-2019-2
|
b02fa557bb8869457fadd7961532834f265f0400
|
[
"CC-BY-4.0"
] | null | null | null |
proyectos/2/RomeroVicente/VpostHorde/core/console.py
|
EnriqueGuerreroL/sistop-2019-2
|
b02fa557bb8869457fadd7961532834f265f0400
|
[
"CC-BY-4.0"
] | null | null | null |
proyectos/2/RomeroVicente/VpostHorde/core/console.py
|
EnriqueGuerreroL/sistop-2019-2
|
b02fa557bb8869457fadd7961532834f265f0400
|
[
"CC-BY-4.0"
] | null | null | null |
import argparse
class Console:
def __init__(self):
self.parser = argparse.ArgumentParser()
def evaluar_argumentos(self):
print("evaluando argumentos")
| 20.222222
| 48
| 0.67033
| 18
| 182
| 6.5
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.241758
| 182
| 8
| 49
| 22.75
| 0.847826
| 0
| 0
| 0
| 0
| 0
| 0.10989
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0
| 0.666667
| 0.166667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0f68fee73eac52f83f099bd7ca1c279107cf2d2c
| 555
|
py
|
Python
|
fideslog/api/models/manifest_file_counts.py
|
ethyca/fideseye
|
c74bb0245724c2db77db37317226db153780860b
|
[
"Apache-2.0"
] | 1
|
2022-01-13T16:56:37.000Z
|
2022-01-13T16:56:37.000Z
|
fideslog/api/models/manifest_file_counts.py
|
ethyca/fideseye
|
c74bb0245724c2db77db37317226db153780860b
|
[
"Apache-2.0"
] | 1
|
2022-01-21T22:09:06.000Z
|
2022-01-21T22:09:06.000Z
|
fideslog/api/models/manifest_file_counts.py
|
ethyca/fideslog
|
c74bb0245724c2db77db37317226db153780860b
|
[
"Apache-2.0"
] | null | null | null |
from pydantic import BaseModel, Field
class ManifestFileCounts(BaseModel):
"""
A JSON object structure containing the counts of dataset, policy,
and system manifests currently in use.
"""
datasets: int = Field(
0,
description="The number of dataset manifests currently in use.",
)
policies: int = Field(
0,
description="The number of policy manifests currently in use.",
)
systems: int = Field(
0,
description="The number of system manifests currently in use.",
)
| 25.227273
| 72
| 0.637838
| 63
| 555
| 5.619048
| 0.47619
| 0.20339
| 0.225989
| 0.259887
| 0.426554
| 0.262712
| 0.262712
| 0
| 0
| 0
| 0
| 0.007557
| 0.284685
| 555
| 21
| 73
| 26.428571
| 0.884131
| 0.187387
| 0
| 0.214286
| 0
| 0
| 0.336427
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.071429
| 0
| 0.357143
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0f7a1b3381e871051676752b578a1a5bbfd991aa
| 245
|
py
|
Python
|
python/test_assertAndException.py
|
anbansal/TDD
|
51e26245dbe5fea9c7cb073c38eb69b49b0ff019
|
[
"MIT"
] | null | null | null |
python/test_assertAndException.py
|
anbansal/TDD
|
51e26245dbe5fea9c7cb073c38eb69b49b0ff019
|
[
"MIT"
] | 2
|
2019-09-26T13:13:36.000Z
|
2019-09-26T16:40:19.000Z
|
python/test_assertAndException.py
|
anbansal/TDD
|
51e26245dbe5fea9c7cb073c38eb69b49b0ff019
|
[
"MIT"
] | null | null | null |
from pytest import approx
from pytest import raises
def test_float():
assert (0.1 + 0.2) == approx(0.3)
def raisesValueException():
raise ValueError
def test_exception():
with raises(ValueError):
raisesValueException()
| 15.3125
| 37
| 0.693878
| 30
| 245
| 5.6
| 0.6
| 0.119048
| 0.190476
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030928
| 0.208163
| 245
| 15
| 38
| 16.333333
| 0.835052
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 1
| 0.333333
| true
| 0
| 0.222222
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0f8164785e6804637d491652223cadff7776d7b3
| 91
|
py
|
Python
|
tests/examples-bad/5.py
|
JohannesBuchner/pystrict3
|
f442a89ac6a23f4323daed8ef829d8e9e1197f90
|
[
"BSD-2-Clause"
] | 1
|
2020-06-05T08:53:26.000Z
|
2020-06-05T08:53:26.000Z
|
tests/examples-bad/5.py
|
JohannesBuchner/pystrict3
|
f442a89ac6a23f4323daed8ef829d8e9e1197f90
|
[
"BSD-2-Clause"
] | 1
|
2020-06-04T13:47:19.000Z
|
2020-06-04T13:47:57.000Z
|
tests/examples-bad/5.py
|
JohannesBuchner/pystrict3
|
f442a89ac6a23f4323daed8ef829d8e9e1197f90
|
[
"BSD-2-Clause"
] | 1
|
2020-11-07T17:02:46.000Z
|
2020-11-07T17:02:46.000Z
|
def foo(a, b):
return a * b
foo(1, 2) ## OK
foo(1) ## error: wrong number of arguments
| 15.166667
| 43
| 0.593407
| 18
| 91
| 3
| 0.722222
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 0.241758
| 91
| 5
| 44
| 18.2
| 0.73913
| 0.384615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
0f936cd5ecbce4695bcb63c09ba45b118260984b
| 284
|
py
|
Python
|
python/testData/inspections/PyDataclassInspection/fieldDefaultAndDefaultFactory.py
|
dmeybohm/intellij-community
|
7fcc441fd5902ec3d237c34ee93f5ed1faf23629
|
[
"Apache-2.0"
] | 2
|
2018-12-29T09:53:39.000Z
|
2018-12-29T09:53:42.000Z
|
python/testData/inspections/PyDataclassInspection/fieldDefaultAndDefaultFactory.py
|
tnorbye/intellij-community
|
f01cf262fc196bf4dbb99e20cd937dee3705a7b6
|
[
"Apache-2.0"
] | null | null | null |
python/testData/inspections/PyDataclassInspection/fieldDefaultAndDefaultFactory.py
|
tnorbye/intellij-community
|
f01cf262fc196bf4dbb99e20cd937dee3705a7b6
|
[
"Apache-2.0"
] | 1
|
2019-07-18T16:50:52.000Z
|
2019-07-18T16:50:52.000Z
|
import dataclasses
@dataclasses.dataclass
class E1:
a: int = dataclasses.field(default=1)
b: int = dataclasses.field(default_factory=int)
c: int = dataclasses.field<error descr="Cannot specify both 'default' and 'default_factory'">(default=1, default_factory=int)</error>
| 40.571429
| 137
| 0.75
| 38
| 284
| 5.526316
| 0.5
| 0.2
| 0.271429
| 0.247619
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012097
| 0.126761
| 284
| 7
| 137
| 40.571429
| 0.834677
| 0
| 0
| 0
| 0
| 0
| 0.178947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.166667
| null | null | 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
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| 0
| 0
| 0
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7e171e2a34f34c3de6c6379aee135a8c80b309ee
| 402
|
py
|
Python
|
cretan/__init__.py
|
AmateurECE/Cretan
|
30528c57a062b9a817852118dec2049106f7ffcf
|
[
"MIT"
] | null | null | null |
cretan/__init__.py
|
AmateurECE/Cretan
|
30528c57a062b9a817852118dec2049106f7ffcf
|
[
"MIT"
] | null | null | null |
cretan/__init__.py
|
AmateurECE/Cretan
|
30528c57a062b9a817852118dec2049106f7ffcf
|
[
"MIT"
] | null | null | null |
###############################################################################
# NAME: __init__.py
#
# AUTHOR: Ethan D. Twardy <edtwardy@mtu.edu>
#
# DESCRIPTION: Module init script
#
# CREATED: 05/30/2020
#
# LAST EDITED: 05/30/2020
###
from .Cretan import getService, Message
###############################################################################
| 25.125
| 79
| 0.345771
| 28
| 402
| 4.821429
| 0.857143
| 0.059259
| 0.118519
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04908
| 0.189055
| 402
| 15
| 80
| 26.8
| 0.365031
| 0.440299
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
7e234f0ffd1d2e37f5883481f8d041c010acdc1c
| 104
|
py
|
Python
|
accounting_tech/apps.py
|
Tim-Ilin/asup_corp_site
|
02a9573f2490ef8f31b3ba95bc351c2458d049e5
|
[
"MIT"
] | null | null | null |
accounting_tech/apps.py
|
Tim-Ilin/asup_corp_site
|
02a9573f2490ef8f31b3ba95bc351c2458d049e5
|
[
"MIT"
] | 8
|
2021-03-19T11:12:07.000Z
|
2022-03-12T00:32:27.000Z
|
accounting_tech/apps.py
|
Tim-Ilin/asup_corp_site
|
02a9573f2490ef8f31b3ba95bc351c2458d049e5
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class AccountingTechConfig(AppConfig):
name = 'accounting_tech'
| 17.333333
| 38
| 0.788462
| 11
| 104
| 7.363636
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144231
| 104
| 5
| 39
| 20.8
| 0.910112
| 0
| 0
| 0
| 0
| 0
| 0.144231
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7e2c7cd619cbd3c5cf8f8b444ff107229ceb0f89
| 4,135
|
py
|
Python
|
graphy2/flow2.py
|
samuelbaker93/graphy
|
79117d8052e126a6cb2e5c5b281c00af28ba74ab
|
[
"MIT"
] | 1
|
2020-05-20T07:48:09.000Z
|
2020-05-20T07:48:09.000Z
|
graphy2/flow2.py
|
samuelbaker93/graphy
|
79117d8052e126a6cb2e5c5b281c00af28ba74ab
|
[
"MIT"
] | 5
|
2020-05-22T08:50:52.000Z
|
2020-05-31T16:33:42.000Z
|
graphy2/flow2.py
|
samuelbaker93/graphy
|
79117d8052e126a6cb2e5c5b281c00af28ba74ab
|
[
"MIT"
] | null | null | null |
from imageObjects.ImageMaker import ImageMaker
import cv2
from graphy2.Flow import Flow
from graphy2 import default_style_sheet
# class Flow:
# def __init__(self):
# self.prisma = {}
#
# def add_to_flow(self, text, x_position, y_position):
# self.prisma[len(self.prisma.keys())] = {"x": x_position, "y": y_position, "text": text}
#
# def create_text_box(self, text):
# current_box = ImageMaker().create_text_box(text, cv2.FONT_HERSHEY_SIMPLEX, 35)
# current_box.inset_rounded_border((255, 0, 0), 5, 25, 0.1)
#
# current_box.show()
# return current_box
#
#
# def _construct_flow_columns(self):
#
# # Create columns of x's
# columns = sorted(list(set([values["x"] for values in self.prisma.values()])))
#
# column_images = {}
# for col in columns:
# # Sort on Y position within a given x
# column_values = {value["y"]: value["text"] for value in self.prisma.values() if value["x"] == col}
#
# # Create a text box for each row in a given column
# column_images[col] = {key: self.create_text_box(text)
# for key, text in zip(column_values.keys(), column_values.values())}
# return column_images
#
# @staticmethod
# def flow_bound(bound, columns, spacing=None):
# """
# We need to set the image bounds to be equal to the largest or widest column
#
# :param bound: The bound, height or width
# :param spacing: The amount of spacing of y or x
# :param columns: The dict of columns
# :return: The max bound of x or y given spacing
# """
#
# if spacing:
# return max([sum([getattr(image, bound) for image in v]) + (spacing * (len(v) - 1))
# for v in columns.values()])
# else:
# return max([max([getattr(image, bound) for image in v])
# for v in columns.values()])
#
# def _get_bounds(self, columns, key):
# return [getattr(row, key) for col in columns.values() for row in col.values()]
#
# def construct_flow(self, canvas_colour, x_spacing=1.5):
#
# columns = self._construct_flow_columns()
# widths = {key: max(self._get_bounds(columns, "width")) for key in columns.keys()}
# height = max([sum(self._get_bounds(columns, "height")) for _ in columns.keys()])
#
# # Define the canvas
# canvas = ImageMaker().create_blank(int(sum(v for v in widths.values()) * x_spacing), height)
# canvas.colour_covert()
# canvas.change_a_colour((0, 0, 0), canvas_colour)
#
# for index, image_list in zip(columns.keys(), columns.values()):
# for i, (y_placer, image) in enumerate(zip(image_list.keys(), image_list.values())):
# canvas.overlay_image(image, int(y_placer * image.height), 0 + int((index * widths[index]) * x_spacing))
#
# canvas.show()
#
# flow_obj = Flow()
#
# flow_obj.add_to_flow("UK Biobank Population: 502,507", 0, 0)
# flow_obj.add_to_flow("Born in scotland: 50,000", 1, 1)
# flow_obj.add_to_flow("Uk Biobank Population not in scotland: 450,000", 0, 1)
# flow_obj.construct_flow((255, 255, 255))
# custom_style = default_style_sheet()
# custom_style["figure_x"] = 8
# custom_style["figure_y"] = 12
#
#
# obj = Flow(r"I:\Work\Figures_and_tables\Scarlet_Long_Term\Figures", "Flow Plot Re", 20, custom_style
# )
#
# # Add a bunch of sample information
# obj.add_to_flow("UK Biobank Population", 502507)
# obj.add_to_flow("Born in Scotland", 502507, add=False)
# obj.add_to_flow("UK Biobank Population not in scotland", 502507)
# obj.add_to_flow("No Birth Coordinate", 502507, add=False)
# obj.add_to_flow("UK Biobank Population that can be geolocated", 502507)
# obj.add_to_flow("Born Before 1941", 502507, add=False)
# obj.add_to_flow("UK Biobank Population within year sample range", 502507)
# obj.add_to_flow("Missing Data", 502507, add=False)
# obj.add_to_flow("UK Biobank sample Population", 502507)
#
# # Write out the plot
# obj.construct_flow_plot(column_mod=1.05)
| 38.64486
| 121
| 0.632164
| 588
| 4,135
| 4.263605
| 0.255102
| 0.025927
| 0.046669
| 0.057439
| 0.203829
| 0.172716
| 0.161548
| 0.106103
| 0.090546
| 0.076586
| 0
| 0.038146
| 0.23289
| 4,135
| 107
| 122
| 38.64486
| 0.752207
| 0.91971
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7e3eac1aa0c6ae82283ba2721123deb33f6c9b18
| 11,061
|
py
|
Python
|
backend/django/test/core_utils/test_core_utils_annotate.py
|
rubentea16/SMART
|
b851bef46b038d80389adcca00caadb4ec88eac2
|
[
"MIT"
] | 1
|
2021-11-14T12:08:26.000Z
|
2021-11-14T12:08:26.000Z
|
backend/django/test/core_utils/test_core_utils_annotate.py
|
rubentea16/SMART
|
b851bef46b038d80389adcca00caadb4ec88eac2
|
[
"MIT"
] | 4
|
2021-03-09T17:00:12.000Z
|
2021-09-29T17:31:07.000Z
|
backend/django/test/core_utils/test_core_utils_annotate.py
|
rubentea16/SMART
|
b851bef46b038d80389adcca00caadb4ec88eac2
|
[
"MIT"
] | null | null | null |
from core.models import Data, AssignedData, Label, DataLabel, DataQueue
from core.utils.utils_annotate import (assign_datum, label_data, move_skipped_to_admin_queue,
get_assignments, unassign_datum)
from core.utils.utils_queue import fill_queue
from test.util import assert_obj_exists
from test.conftest import TEST_QUEUE_LEN
def test_assign_datum_project_queue_returns_datum(db, test_queue, test_profile, test_redis):
'''
Assign a datum from a project-wide queue (null profile ID).
'''
fill_queue(test_queue, orderby='random')
datum = assign_datum(test_profile, test_queue.project)
# Make sure we got the datum
assert isinstance(datum, Data)
def test_assign_datum_project_queue_correct_assignment(db, test_queue, test_profile, test_redis):
fill_queue(test_queue, orderby='random')
datum = assign_datum(test_profile, test_queue.project)
# Make sure the assignment is correct
assignment = AssignedData.objects.filter(data=datum)
assert len(assignment) == 1
assert assignment[0].profile == test_profile
assert assignment[0].queue == test_queue
assert assignment[0].assigned_timestamp is not None
def test_assign_datum_project_queue_pops_queues(db, test_queue, test_profile, test_redis):
fill_queue(test_queue, orderby='random')
datum = assign_datum(test_profile, test_queue.project)
# Make sure the datum was removed from queues but not set
assert test_redis.llen('queue:' + str(test_queue.pk)) == test_queue.length - 1
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
# but not from the db queue
assert test_queue.data.count() == test_queue.length
assert datum in test_queue.data.all()
def test_assign_datum_profile_queue_returns_correct_datum(db, test_profile_queue, test_profile,
test_profile_queue2, test_profile2,
test_redis):
fill_queue(test_profile_queue, orderby='random')
fill_queue(test_profile_queue2, orderby='random')
datum = assign_datum(test_profile, test_profile_queue.project)
assert isinstance(datum, Data)
def test_assign_datum_profile_queue_correct_assignment(db, test_profile_queue, test_profile,
test_profile_queue2, test_profile2,
test_redis):
fill_queue(test_profile_queue, orderby='random')
fill_queue(test_profile_queue2, orderby='random')
datum = assign_datum(test_profile, test_profile_queue.project)
assignment = AssignedData.objects.filter(data=datum)
assert len(assignment) == 1
assert assignment[0].profile == test_profile
assert assignment[0].queue == test_profile_queue
assert assignment[0].assigned_timestamp is not None
def test_assign_datum_profile_queue_pops_queues(db, test_profile_queue, test_profile,
test_profile_queue2, test_profile2, test_redis):
fill_queue(test_profile_queue, orderby='random')
fill_queue(test_profile_queue2, orderby='random')
datum = assign_datum(test_profile, test_profile_queue.project)
# Make sure the datum was removed from the correct queues but not sets
assert test_redis.llen('queue:' + str(test_profile_queue.pk)) == test_profile_queue.length - 1
assert test_redis.scard('set:' + str(test_profile_queue.pk)) == test_profile_queue.length
# ...but not the other queues
assert test_profile_queue.data.count() == test_profile_queue.length
assert datum in test_profile_queue.data.all()
assert test_redis.llen('queue:' + str(test_profile_queue2.pk)) == test_profile_queue2.length
assert test_redis.scard('set:' + str(test_profile_queue2.pk)) == test_profile_queue2.length
assert test_profile_queue2.data.count() == test_profile_queue2.length
def test_label_data(db, test_profile, test_queue, test_redis):
fill_queue(test_queue, orderby='random')
datum = assign_datum(test_profile, test_queue.project)
test_label = Label.objects.create(name='test', project=test_queue.project)
label_data(test_label, datum, test_profile, 3)
# Make sure the label was properly recorded
assert datum in test_profile.labeled_data.all()
assert_obj_exists(DataLabel, {
'data': datum,
'profile': test_profile,
'label': test_label,
'time_to_label': 3
})
# Make sure the assignment was removed
assert not AssignedData.objects.filter(profile=test_profile,
data=datum,
queue=test_queue).exists()
def test_get_assignments_no_existing_assignment_one_assignment(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assert AssignedData.objects.count() == 0
data = get_assignments(test_profile, test_project_data, 1)
assert len(data) == 1
assert isinstance(data[0], Data)
assert_obj_exists(AssignedData, {
'data': data[0],
'profile': test_profile
})
def test_get_assignments_no_existing_assignment_half_max_queue_length(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assert AssignedData.objects.count() == 0
data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN // 2)
assert len(data) == TEST_QUEUE_LEN // 2
for datum in data:
assert isinstance(datum, Data)
assert_obj_exists(AssignedData, {
'data': datum,
'profile': test_profile
})
def test_get_assignments_no_existing_assignment_max_queue_length(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assert AssignedData.objects.count() == 0
data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN)
assert len(data) == TEST_QUEUE_LEN
for datum in data:
assert isinstance(datum, Data)
assert_obj_exists(AssignedData, {
'data': datum,
'profile': test_profile
})
def test_get_assignments_no_existing_assignment_over_max_queue_length(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assert AssignedData.objects.count() == 0
data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN + 10)
assert len(data) == TEST_QUEUE_LEN
for datum in data:
assert isinstance(datum, Data)
assert_obj_exists(AssignedData, {
'data': datum,
'profile': test_profile
})
def test_get_assignments_one_existing_assignment(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assigned_datum = assign_datum(test_profile, test_project_data)
data = get_assignments(test_profile, test_project_data, 1)
assert isinstance(data[0], Data)
# We should just get the datum that was already assigned
assert data[0] == assigned_datum
def test_get_assignments_multiple_existing_assignments(db, test_profile, test_project_data, test_queue,
test_redis):
fill_queue(test_queue, orderby='random')
assigned_data = []
for i in range(5):
assigned_data.append(assign_datum(test_profile, test_project_data))
data = get_assignments(test_profile, test_project_data, 5)
assert len(data) == 5
assert len(data) == len(assigned_data)
for datum, assigned_datum in zip(data, assigned_data):
assert isinstance(datum, Data)
# We should just get the data that was already assigned
assert len(data) == len(assigned_data)
def test_unassign(db, test_profile, test_project_data, test_queue, test_redis):
fill_queue(test_queue, orderby='random')
assert test_redis.llen('queue:' + str(test_queue.pk)) == test_queue.length
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
datum = get_assignments(test_profile, test_project_data, 1)[0]
assert test_redis.llen('queue:' + str(test_queue.pk)) == (test_queue.length - 1)
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
assert AssignedData.objects.filter(
data=datum,
profile=test_profile).exists()
unassign_datum(datum, test_profile)
assert test_redis.llen('queue:' + str(test_queue.pk)) == test_queue.length
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
assert not AssignedData.objects.filter(
data=datum,
profile=test_profile).exists()
# The unassigned datum should be the next to be assigned
reassigned_datum = get_assignments(test_profile, test_project_data, 1)[0]
assert reassigned_datum == datum
def test_unassign_after_fillqueue(db, test_profile, test_project_data, test_queue, test_labels, test_redis):
fill_queue(test_queue, 'random')
assert test_redis.llen('queue:' + str(test_queue.pk)) == test_queue.length
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
data = get_assignments(test_profile, test_project_data, 10)
assert test_redis.llen('queue:' + str(test_queue.pk)) == (test_queue.length - 10)
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
test_label = test_labels[0]
for i in range(5):
label_data(test_label, data[i], test_profile, 3)
assert test_redis.llen('queue:' + str(test_queue.pk)) == (test_queue.length - 10)
assert test_redis.scard('set:' + str(test_queue.pk)) == (test_queue.length - 5)
fill_queue(test_queue, 'random')
assert test_redis.llen('queue:' + str(test_queue.pk)) == test_queue.length - 5
assert test_redis.scard('set:' + str(test_queue.pk)) == test_queue.length
def test_skip_data(db, test_profile, test_queue, test_admin_queue, test_redis):
fill_queue(test_queue, orderby='random')
project = test_queue.project
datum = assign_datum(test_profile, project)
move_skipped_to_admin_queue(datum, test_profile, project)
# Make sure the assignment was removed
assert not AssignedData.objects.filter(profile=test_profile,
data=datum,
queue=test_queue).exists()
# make sure the item was re-assigned to the admin queue
assert DataQueue.objects.filter(data=datum, queue=test_admin_queue).exists()
# make sure not in normal queue
assert not DataQueue.objects.filter(data=datum, queue=test_queue).exists()
| 39.645161
| 118
| 0.683483
| 1,430
| 11,061
| 4.968531
| 0.079021
| 0.123856
| 0.07178
| 0.058832
| 0.815482
| 0.770303
| 0.722308
| 0.687403
| 0.660239
| 0.615341
| 0
| 0.00712
| 0.225386
| 11,061
| 278
| 119
| 39.78777
| 0.822129
| 0.060212
| 0
| 0.597765
| 0
| 0
| 0.028651
| 0
| 0
| 0
| 0
| 0
| 0.374302
| 1
| 0.089385
| false
| 0
| 0.027933
| 0
| 0.117318
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7e48aa8b6d759429a6b22d9ef0a7643e06f5e7f0
| 255
|
py
|
Python
|
mediaaritmeticaexercicio.py
|
EBERTONSCHIPPNIK/Pequenos-codigospy
|
b9cc49a1cce372df2ef5217cb93766fafd9e405a
|
[
"MIT"
] | null | null | null |
mediaaritmeticaexercicio.py
|
EBERTONSCHIPPNIK/Pequenos-codigospy
|
b9cc49a1cce372df2ef5217cb93766fafd9e405a
|
[
"MIT"
] | null | null | null |
mediaaritmeticaexercicio.py
|
EBERTONSCHIPPNIK/Pequenos-codigospy
|
b9cc49a1cce372df2ef5217cb93766fafd9e405a
|
[
"MIT"
] | null | null | null |
nota1 = float(input("Digite a 1º nota: "))
nota2 = float(input("Digite a 2º nota: "))
nota3 = float(input("Digite a 3º nota: "))
nota4 = float(input("Digite a 4º nota: "))
media =(nota1+nota2+nota3+nota4)/4
print("A média aritmética é ", media)
| 28.333333
| 43
| 0.647059
| 40
| 255
| 4.125
| 0.475
| 0.242424
| 0.387879
| 0.412121
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.062201
| 0.180392
| 255
| 8
| 44
| 31.875
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0.376518
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.166667
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7e4aad374bfcda8a9206fdbb3025dddd08be1e83
| 109
|
py
|
Python
|
gunicorn_config.py
|
EmilStenstrom/json-tagger
|
a8816e6c4339c6b834c6e014d7bd6ea7b02e760b
|
[
"MIT"
] | 13
|
2015-12-07T23:05:47.000Z
|
2021-07-01T23:41:14.000Z
|
gunicorn_config.py
|
EmilStenstrom/json-tagger
|
a8816e6c4339c6b834c6e014d7bd6ea7b02e760b
|
[
"MIT"
] | 3
|
2018-10-08T10:44:54.000Z
|
2020-08-01T13:03:51.000Z
|
gunicorn_config.py
|
EmilStenstrom/json-tagger
|
a8816e6c4339c6b834c6e014d7bd6ea7b02e760b
|
[
"MIT"
] | 2
|
2020-01-26T08:17:20.000Z
|
2020-02-01T18:42:33.000Z
|
accesslog = '-'
access_log_format = \
"%(h)s %(l)s %(u)s %(t)s %(r)s %(s)s %(b)s %(f)s %(a)s [%(D)s μs]"
| 27.25
| 70
| 0.440367
| 25
| 109
| 1.84
| 0.6
| 0.086957
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192661
| 109
| 3
| 71
| 36.333333
| 0.522727
| 0
| 0
| 0
| 0
| 0.333333
| 0.59633
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7e6b1013df6677568f1575f6a7f09d0ac10c7ab5
| 2,928
|
py
|
Python
|
users/forms.py
|
RafaCucurull/Bookiernes
|
efe220a2501e20cca4e40bc4e582a4316e8718f6
|
[
"MIT"
] | null | null | null |
users/forms.py
|
RafaCucurull/Bookiernes
|
efe220a2501e20cca4e40bc4e582a4316e8718f6
|
[
"MIT"
] | 101
|
2021-03-29T16:20:09.000Z
|
2021-06-12T08:53:20.000Z
|
users/forms.py
|
RafaCucurull/Bookiernes
|
efe220a2501e20cca4e40bc4e582a4316e8718f6
|
[
"MIT"
] | null | null | null |
from django.contrib.auth.forms import UserCreationForm, UserChangeForm
from django.forms import TextInput
from .models import CustomUser
from django import forms
class CustomUserCreationForm(UserCreationForm):
nom = forms.CharField(help_text="Introdueix el seu nom i cognoms (p.ex. Carlos Ruíz Zafon)")
email = forms.CharField(help_text="Introdueix un format correcte de correu electrònic (p.ex. example@gmail.com)")
password1 = forms.CharField(
help_text=["La teva contrassenya no pot ser similar a l'altra informació personal teva.",
"La teva contrassenya ha de tenir almenys 8 caràcters.",
"La teva contrassenya no pot ser una comunment usada.",
"La teva contrassenya no pot ser completament numèrica."])
password2 = forms.CharField(help_text="Introdueix la mateixa contrassenya que anteriorment, per verificació.")
class Meta(UserCreationForm):
model = CustomUser
fields = ('nom', 'email', 'is_Editor', 'is_Escriptor', 'is_Maquetacio', 'is_IT', 'password1', 'password2')
class CustomLectorCreationForm(UserCreationForm):
nom = forms.CharField(help_text="Introdueix el seu nom i cognoms (p.ex. Carlos Ruíz Zafon)")
email = forms.CharField(help_text="Introdueix un format correcte de correu electrònic (p.ex. cruizz@escriptors.cat)")
password1 = forms.CharField(
help_text=["La teva contrassenya no pot ser similar a l'altra informació personal teva.",
"La teva contrassenya ha de tenir almenys 8 caràcters.",
"La teva contrassenya no pot ser una comunment usada.",
"La teva contrassenya no pot ser completament numèrica."])
password2 = forms.CharField(help_text="Introdueix la mateixa contrassenya que anteriorment, per verificació.")
class Meta(UserCreationForm):
model = CustomUser
fields = ('nom', 'email', 'password1', 'password2')
class CustomUserChangeForm(UserChangeForm):
class Meta:
model = CustomUser
fields = ('email',)
class ConfiguracioForm(UserChangeForm):
class Meta:
model = CustomUser
fields = ('nom', 'edat', 'sexe')
widgets = {
'nom': TextInput(attrs={
'class': "form-control",
'style': 'width: 100%;background-color: linen;font-size: 30px; margin: auto',
'placeholder': 'El seu nom...'
}),
'edat': TextInput(attrs={
'class': "form-control",
'style': 'width: 35%;background-color: linen;font-size: 30px; margin: auto',
'placeholder': 'La seva edat...'
}),
'sexe': TextInput(attrs={
'class': "form-control",
'style': 'width: 50%;background-color: linen;font-size: 30px; margin: auto',
'placeholder': 'El seu sexe...'
}),
}
| 45.75
| 121
| 0.632855
| 319
| 2,928
| 5.77116
| 0.316614
| 0.060837
| 0.078218
| 0.0956
| 0.780554
| 0.780554
| 0.732754
| 0.667572
| 0.667572
| 0.638783
| 0
| 0.010575
| 0.257172
| 2,928
| 63
| 122
| 46.47619
| 0.835862
| 0
| 0
| 0.528302
| 0
| 0
| 0.453707
| 0.029382
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.113208
| 0.075472
| 0
| 0.377358
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
0e2d407936177a6d538ec5ba1f7e9312bd975fa5
| 2,922
|
py
|
Python
|
backend/api/models/__init__.py
|
ActionAnalytics/tfrs
|
83e1805312d3f13c6a7235e99840b44f399c8fde
|
[
"Apache-2.0"
] | null | null | null |
backend/api/models/__init__.py
|
ActionAnalytics/tfrs
|
83e1805312d3f13c6a7235e99840b44f399c8fde
|
[
"Apache-2.0"
] | null | null | null |
backend/api/models/__init__.py
|
ActionAnalytics/tfrs
|
83e1805312d3f13c6a7235e99840b44f399c8fde
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
REST API Documentation for the NRS TFRS Credit Trading Application
The Transportation Fuels Reporting System is being designed to streamline compliance reporting for transportation fuel suppliers in accordance with the Renewable & Low Carbon Fuel Requirements Regulation.
OpenAPI spec version: v1
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# from __future__ import absolute_import
# import models into model package
#try:
# from . import CreditTrade
#except:
# import CreditTrade
#try:
# from . import CreditTradeHistory
#except:
# import CreditTradeHistory
#try:
# from . import CreditTradeStatus
#except:
# import CreditTradeStatus
#try:
# from . import CreditTradeType
#except:
# import CreditTradeType
#try:
# from . import CreditTradeZeroReason
#except:
# import CreditTradeZeroReason
#try:
# from . import CurrentUserViewModel
#except:
# import CurrentUserViewModel
#try:
# from . import Organization
#except:
# import Organization
#try:
# from . import OrganizationActionsType
#except:
# import OrganizationActionsType
#try:
# from . import OrganizationAttachment
#except:
# import OrganizationAttachment
#try:
# from . import OrganizationBalance
#except:
# import OrganizationBalance
#try:
# from . import OrganizationHistory
#except:
# import OrganizationHistory
#try:
# from . import OrganizationStatus
#except:
# import OrganizationStatus
#try:
# from . import Permission
#except:
# import Permission
#try:
# from . import PermissionViewModel
#except:
# import PermissionViewModel
#try:
# from . import Role
#except:
# import Role
#try:
# from . import RolePermission
#except:
# import RolePermission
#try:
# from . import RolePermissionViewModel
#except:
# import RolePermissionViewModel
#try:
# from . import RoleViewModel
#except:
# import RoleViewModel
#try:
# from . import User
#except:
# import User
#try:
# from . import UserDetailsViewModel
#except:
# import UserDetailsViewModel
#try:
# from . import UserRole
#except:
# import UserRole
#try:
# from . import UserRoleViewModel
#except:
# import UserRoleViewModel
#try:
# from . import UserViewModel
#except:
# import UserViewModel
#
from . import User
| 23.95082
| 208
| 0.701916
| 303
| 2,922
| 6.752475
| 0.39604
| 0.117302
| 0.146139
| 0.01564
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002664
| 0.229295
| 2,922
| 121
| 209
| 24.14876
| 0.905861
| 0.905886
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
0e2eaf91132dce1c850bc64279a88013387718f9
| 150
|
py
|
Python
|
automon/integrations/swift/__init__.py
|
TheShellLand/automonisaur
|
b5f304a44449b8664c93d8a8a3c3cf2d73aa0ce9
|
[
"MIT"
] | 2
|
2021-09-15T18:35:44.000Z
|
2022-01-18T05:36:54.000Z
|
automon/integrations/swift/__init__.py
|
TheShellLand/automonisaur
|
b5f304a44449b8664c93d8a8a3c3cf2d73aa0ce9
|
[
"MIT"
] | 16
|
2021-08-29T22:51:53.000Z
|
2022-03-09T16:08:19.000Z
|
automon/integrations/swift/__init__.py
|
TheShellLand/automonisaur
|
b5f304a44449b8664c93d8a8a3c3cf2d73aa0ce9
|
[
"MIT"
] | null | null | null |
from .client import SwiftClient
from .config import SwiftConfig
from .error import SwiftError_
from .iterables import SwiftItem, SwiftPage, SwiftList
| 30
| 54
| 0.84
| 18
| 150
| 6.944444
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 150
| 4
| 55
| 37.5
| 0.94697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0e3123f09813fb204abbf574124671c9d6a1bf04
| 2,060
|
py
|
Python
|
phanterpwa/tests/test_cli.py
|
PhanterJR/phanterpwa
|
6daff40845b3a853cd08d319c4ce148f8deebed7
|
[
"MIT"
] | 2
|
2019-06-06T10:37:01.000Z
|
2021-10-16T03:36:28.000Z
|
phanterpwa/tests/test_cli.py
|
PhanterJR/phanterpwa
|
6daff40845b3a853cd08d319c4ce148f8deebed7
|
[
"MIT"
] | null | null | null |
phanterpwa/tests/test_cli.py
|
PhanterJR/phanterpwa
|
6daff40845b3a853cd08d319c4ce148f8deebed7
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import unittest
import os
# from phanterpwa.interface import (
# cli
# )
# CURRENT_PATH = os.path.dirname(__file__)
# class TestCli(unittest.TestCase):
# def test_Projects(self):
# projects = cli.Projects(os.path.join(CURRENT_PATH, "test_cli_projects"))
# html_to_xmlconstructor_instance = HtmlToXmlConstructor("<html><head><meta charset=\"UTF-8\"></head><body><nav class=\"navbar\"><buttom>start</buttom></nav><main id=\"my_content\"><div class=\"row\"><div>my content</div></div></main></body></html>")
# self.assertEqual(
# html_to_xmlconstructor_instance.xmlconstructor_code(),
# sample_example_1
# )
# html_to_xmlconstructor_instance = HtmlToXmlConstructor("<div data-dict=\"i am in dict\" class=\"my_class\">content1</div><div class=\"my_class\">content2</div>")
# self.assertEqual(
# html_to_xmlconstructor_instance.xmlconstructor_code(),
# sample_example_2
# )
# html_to_xmlconstructor_instance = HtmlToXmlConstructor(sample)
# self.assertEqual(
# html_to_xmlconstructor_instance.xml(),
# sample
# )
# self.assertEqual(
# force_minify_string_content(html_to_xmlconstructor_instance).xml(),
# sample2
# )
# self.assertEqual(
# force_minify_string_content(html_to_xmlconstructor_instance).xmlconstructor_code(),
# sample3
# )
# def test1_html_to_xmlconstructor(self):
# invert = HtmlToXmlConstructor(sample_html_to_xmlconstructor)
# self.assertEqual(invert.xml(), sample_html_to_xmlconstructor)
# invert.src_attr_dict = False
# self.assertRaises(ValueError, lambda: invert.source_code())
# invert.src_attr_dict = None
# self.assertTrue(isinstance(invert.source_code(), str))
# invert.src_attr_dict = True
# self.assertTrue(isinstance(invert.source_code(), str))
if __name__ == '__main__':
unittest.main()
| 35.517241
| 258
| 0.647573
| 217
| 2,060
| 5.801843
| 0.35023
| 0.052423
| 0.174742
| 0.177919
| 0.459889
| 0.340747
| 0.292295
| 0.223987
| 0.223987
| 0.223987
| 0
| 0.005618
| 0.22233
| 2,060
| 57
| 259
| 36.140351
| 0.780275
| 0.856796
| 0
| 0
| 0
| 0
| 0.031496
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0e51b0e6d96a232b4e811166b9b0296c32ea7a0d
| 8,730
|
py
|
Python
|
test/test_logging.py
|
wilhaddenIBM/qpylib
|
6ec1c041b5e3a8d185a38f9d2d10e3e635ed285d
|
[
"Apache-2.0"
] | null | null | null |
test/test_logging.py
|
wilhaddenIBM/qpylib
|
6ec1c041b5e3a8d185a38f9d2d10e3e635ed285d
|
[
"Apache-2.0"
] | null | null | null |
test/test_logging.py
|
wilhaddenIBM/qpylib
|
6ec1c041b5e3a8d185a38f9d2d10e3e635ed285d
|
[
"Apache-2.0"
] | 1
|
2020-07-30T09:56:07.000Z
|
2020-07-30T09:56:07.000Z
|
# Copyright 2019 IBM Corporation All Rights Reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# pylint: disable=redefined-outer-name, unused-argument, invalid-name
from unittest.mock import patch
import logging
import os
import pytest
from qpylib import qpylib, log_qpylib
APP_FILE_LOG_FORMAT = '[{0}] - [APP_ID/1001][NOT:{1}] {2}'
GET_MANIFEST_JSON = 'qpylib.app_qpylib.get_root_path'
def manifest_path(manifest_file):
return os.path.join(os.path.dirname(__file__), 'manifests', manifest_file)
# This fixture avoids reading app id from the manifest.
# Setting default log level threshold is handled by separate fixtures.
@pytest.fixture(scope='module', autouse=True)
def bypass_manifest_lookup():
with patch('qpylib.app_qpylib.get_app_id') as mock_get_app_id:
mock_get_app_id.return_value = 1001
yield
@pytest.fixture(scope='function')
def info_threshold():
with patch('qpylib.log_qpylib.default_log_level') as mock_default_log_level:
mock_default_log_level.return_value = logging.INFO
yield
@pytest.fixture(scope='function')
def debug_threshold():
with patch('qpylib.log_qpylib.default_log_level') as mock_default_log_level:
mock_default_log_level.return_value = logging.DEBUG
yield
@pytest.fixture(scope='function')
def set_console_ip():
os.environ['QRADAR_CONSOLE_IP'] = '9.123.234.101'
yield
del os.environ['QRADAR_CONSOLE_IP']
@pytest.fixture(scope='function', autouse=True)
def reset_globals():
with patch('qpylib.log_qpylib.QLOGGER', 0):
with patch('qpylib.app_qpylib.Q_CACHED_MANIFEST', None):
yield
# pylint: disable=protected-access
def verify_log_file_content(log_path, expected_lines, not_expected_lines=[]): # pylint: disable=dangerous-default-value
with open(log_path) as log_file:
content = log_file.read()
for line in expected_lines:
assert APP_FILE_LOG_FORMAT.format(
line['level'], log_qpylib._map_notification_code(line['level']), line['text']) in content
for line in not_expected_lines:
assert APP_FILE_LOG_FORMAT.format(
line['level'], log_qpylib._map_notification_code(line['level']), line['text']) not in content
def test_log_without_create_raises_error():
with pytest.raises(RuntimeError, match='You cannot use log before logging has been initialised'):
qpylib.log('hello')
def test_create_without_console_ip_env_var_raises_error(info_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
with pytest.raises(KeyError, match='Environment variable QRADAR_CONSOLE_IP is not set'):
qpylib.create_log()
@patch(GET_MANIFEST_JSON, return_value=manifest_path('installed.json'))
def test_default_log_level_no_level_in_manifest(mock_manifest, set_console_ip):
assert log_qpylib.default_log_level() == logging.INFO
@patch(GET_MANIFEST_JSON, return_value=manifest_path('loglevel.json'))
def test_default_log_level_read_from_manifest(mock_manifest, set_console_ip):
assert log_qpylib.default_log_level() == logging.DEBUG
def test_all_log_levels_with_manifest_info_threshold(set_console_ip, info_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.log('hello debug', 'DEBUG')
qpylib.log('hello default info')
qpylib.log('hello info', 'INFO')
qpylib.log('hello warning', 'WARNING')
qpylib.log('hello error', 'ERROR')
qpylib.log('hello critical', 'CRITICAL')
verify_log_file_content(log_path, [
{'level': 'INFO', 'text': 'hello default info'},
{'level': 'INFO', 'text': 'hello info'},
{'level': 'WARNING', 'text': 'hello warning'},
{'level': 'ERROR', 'text': 'hello error'},
{'level': 'CRITICAL', 'text': 'hello critical'}],
not_expected_lines=[{'level': 'DEBUG', 'text': 'hello debug'}])
def test_all_log_levels_with_manifest_debug_threshold(set_console_ip, debug_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.log('hello debug', 'DEBUG')
qpylib.log('hello default info')
qpylib.log('hello info', 'INFO')
qpylib.log('hello warning', 'WARNING')
qpylib.log('hello error', 'ERROR')
qpylib.log('hello critical', 'CRITICAL')
verify_log_file_content(log_path, [
{'level': 'DEBUG', 'text': 'hello debug'},
{'level': 'INFO', 'text': 'hello default info'},
{'level': 'INFO', 'text': 'hello info'},
{'level': 'WARNING', 'text': 'hello warning'},
{'level': 'ERROR', 'text': 'hello error'},
{'level': 'CRITICAL', 'text': 'hello critical'}])
def test_all_log_levels_with_set_debug_threshold(set_console_ip, info_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.set_log_level('DEBUG')
qpylib.log('hello debug', 'DEBUG')
qpylib.log('hello default info')
qpylib.log('hello info', 'INFO')
qpylib.log('hello warning', 'WARNING')
qpylib.log('hello error', 'ERROR')
qpylib.log('hello critical', 'CRITICAL')
verify_log_file_content(log_path, [
{'level': 'DEBUG', 'text': 'hello debug'},
{'level': 'INFO', 'text': 'hello default info'},
{'level': 'INFO', 'text': 'hello info'},
{'level': 'WARNING', 'text': 'hello warning'},
{'level': 'ERROR', 'text': 'hello error'},
{'level': 'CRITICAL', 'text': 'hello critical'}])
def test_all_log_levels_with_set_warning_threshold(set_console_ip, info_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.set_log_level('WARNING')
qpylib.log('hello debug', 'DEBUG')
qpylib.log('hello default info')
qpylib.log('hello info', 'INFO')
qpylib.log('hello warning', 'WARNING')
qpylib.log('hello error', 'ERROR')
qpylib.log('hello critical', 'CRITICAL')
verify_log_file_content(log_path, [
{'level': 'WARNING', 'text': 'hello warning'},
{'level': 'ERROR', 'text': 'hello error'},
{'level': 'CRITICAL', 'text': 'hello critical'}],
not_expected_lines=[
{'level': 'DEBUG', 'text': 'hello debug'},
{'level': 'INFO', 'text': 'hello default info'},
{'level': 'INFO', 'text': 'hello info'}])
def test_log_with_bad_level_uses_info(set_console_ip, info_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.log('hello', 'BAD')
verify_log_file_content(log_path, [{'level': 'INFO', 'text': 'hello'}])
def test_set_log_level_with_bad_level_uses_info(set_console_ip, debug_threshold, tmpdir):
log_path = os.path.join(tmpdir.strpath, 'app.log')
with patch('qpylib.log_qpylib._log_file_location') as mock_log_location:
mock_log_location.return_value = log_path
qpylib.create_log()
qpylib.set_log_level('BAD')
qpylib.log('hello debug', 'DEBUG')
qpylib.log('hello default info')
qpylib.log('hello info', 'INFO')
qpylib.log('hello warning', 'WARNING')
qpylib.log('hello error', 'ERROR')
qpylib.log('hello critical', 'CRITICAL')
verify_log_file_content(log_path, [
{'level': 'INFO', 'text': 'hello default info'},
{'level': 'INFO', 'text': 'hello info'},
{'level': 'WARNING', 'text': 'hello warning'},
{'level': 'ERROR', 'text': 'hello error'},
{'level': 'CRITICAL', 'text': 'hello critical'}],
not_expected_lines=[{'level': 'DEBUG', 'text': 'hello debug'}])
| 46.43617
| 119
| 0.654181
| 1,108
| 8,730
| 4.867329
| 0.125451
| 0.083442
| 0.083071
| 0.036714
| 0.76896
| 0.743186
| 0.709624
| 0.709624
| 0.673836
| 0.673836
| 0
| 0.004045
| 0.207102
| 8,730
| 187
| 120
| 46.684492
| 0.775065
| 0.040206
| 0
| 0.654088
| 0
| 0
| 0.259529
| 0.055323
| 0
| 0
| 0
| 0
| 0.025157
| 1
| 0.106918
| false
| 0.006289
| 0.031447
| 0.006289
| 0.144654
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0e6aeb7d0a5f7d11105a3c9a6ca8aeb09de451ce
| 115
|
py
|
Python
|
KF/test.py
|
ZhouYzzz/CTT
|
385b6c7ac2e6633f72b49df7e8a599f40c50188b
|
[
"MIT"
] | 3
|
2016-12-19T12:54:38.000Z
|
2019-02-15T05:42:32.000Z
|
KF/test.py
|
ZhouYzzz/CTT
|
385b6c7ac2e6633f72b49df7e8a599f40c50188b
|
[
"MIT"
] | null | null | null |
KF/test.py
|
ZhouYzzz/CTT
|
385b6c7ac2e6633f72b49df7e8a599f40c50188b
|
[
"MIT"
] | 2
|
2018-02-07T18:30:15.000Z
|
2019-02-15T05:42:34.000Z
|
class A():
def __init__(self, ID):
self.ID = ID
self.sth = self.load()
def load(self):
print self.ID
A(5)
| 12.777778
| 24
| 0.608696
| 21
| 115
| 3.142857
| 0.47619
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011111
| 0.217391
| 115
| 9
| 25
| 12.777778
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.142857
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0e81831046470fc42a6ffa4687eeb0eb442856d6
| 23,447
|
py
|
Python
|
src/isanlp_rst/rst_tree_predictor.py
|
tchewik/isanlp_rst
|
459864b3daeeb702acf5e65543181068439ce12c
|
[
"MIT"
] | 6
|
2020-05-09T01:13:10.000Z
|
2021-02-05T01:02:40.000Z
|
src/isanlp_rst/rst_tree_predictor.py
|
tchewik/isanlp_rst
|
459864b3daeeb702acf5e65543181068439ce12c
|
[
"MIT"
] | 2
|
2019-09-26T11:32:46.000Z
|
2020-07-24T13:44:46.000Z
|
src/isanlp_rst/rst_tree_predictor.py
|
tchewik/isanlp_rst
|
459864b3daeeb702acf5e65543181068439ce12c
|
[
"MIT"
] | 3
|
2019-09-26T13:39:26.000Z
|
2021-04-12T14:34:50.000Z
|
import pandas as pd
from isanlp.annotation_rst import DiscourseUnit
from symbol_map import SYMBOL_MAP
class RSTTreePredictor:
"""
Contains classifiers and processors needed for tree building.
"""
def __init__(self, features_processor, relation_predictor_sentence, relation_predictor_text, label_predictor,
nuclearity_predictor):
self.features_processor = features_processor
self.relation_predictor_sentence = relation_predictor_sentence
self.relation_predictor_text = relation_predictor_text
self.label_predictor = label_predictor
self.nuclearity_predictor = nuclearity_predictor
if self.nuclearity_predictor:
self.nuclearities = self.nuclearity_predictor.classes_
self.genre = None
self.DEFAULT_RELATION = 'joint_NN'
self._penalty_words = ['новость :', 'культура :', 'http']
def _find_penalty_words(self, span, _penalty=0.5):
if len(span.split()) > 100:
return _penalty
for word in self._penalty_words:
if word in span.lower():
return _penalty
for word in ['.', '?', '!']:
return _penalty / 2.
return 0
class GoldTreePredictor(RSTTreePredictor):
"""
Contains classifiers and processors needed for gold tree building from corpus.
"""
def __init__(self, corpus):
"""
:param pandas.DataFrame corpus:
columns=['snippet_x', 'snippet_y', 'category_id']
rows=[all the relations pairs from corpus]
"""
RSTTreePredictor.__init__(self, None, None, None, None, None)
self.corpus = corpus
self._symbol_map = SYMBOL_MAP
for key, value in self._symbol_map.items():
self.corpus.snippet_x = self.corpus.snippet_x.replace(key, value, regex=True)
self.corpus.snippet_y = self.corpus.snippet_y.replace(key, value, regex=True)
def extract_features(self, *args):
features = pd.DataFrame({
'snippet_x': [args[0].text, ],
'snippet_y': [args[1].text, ]
})
for key, value in self._symbol_map.items():
features.snippet_x = features.snippet_x.replace(key, value, regex=True)
features.snippet_y = features.snippet_y.replace(key, value, regex=True)
return features
def initialize_features(self, *args):
features = pd.DataFrame({
'snippet_x': [args[0][i].text for i in range(len(args[0]) - 1)],
'snippet_y': [args[0][i].text for i in range(1, len(args[0]))]
})
for key, value in self._symbol_map.items():
features.snippet_x = features.snippet_x.replace(key, value, regex=True)
features.snippet_y = features.snippet_y.replace(key, value, regex=True)
return features
def predict_pair_proba(self, features, _same_sentence_bonus=0.):
def _check_snippet_pair_in_dataset(left_snippet, right_snippet):
proba = float(((self.corpus.snippet_x == left_snippet) & (self.corpus.snippet_y == right_snippet)).sum(
axis=0) != 0)
return min(1., proba)
result = features.apply(lambda row: _check_snippet_pair_in_dataset(row.snippet_x, row.snippet_y), axis=1)
return result.values.tolist()
def predict_label(self, features):
def _get_label(left_snippet, right_snippet):
joint = self.corpus[
((self.corpus.snippet_x == left_snippet) & (self.corpus.snippet_y == right_snippet))]
label = joint.category_id.map(lambda row: row.split('_')[0]) + '_' + joint.order
label = label.values
if label.size == 0:
return self.DEFAULT_RELATION
return label[0]
if type(features) == pd.Series:
result = _get_label(features.loc['snippet_x'], features.loc['snippet_y'])
return result
else:
result = features.apply(lambda row: _get_label(row.snippet_x, row.snippet_y), axis=1)
return result.values.tolist()
def predict_nuclearity(self, features):
def _get_nuclearity(left_snippet, right_snippet):
nuclearity = self.corpus[
((self.corpus.snippet_x == left_snippet) & (self.corpus.snippet_y == right_snippet))].order.values
if nuclearity.size == 0:
return '_'
if type(features) == pd.Series:
result = _get_nuclearity(features.loc['snippet_x'], features.loc['snippet_y'])
return result
else:
result = features.apply(lambda row: _get_nuclearity(row.snippet_x, row.snippet_y), axis=1)
return result.values.tolist()
class CustomTreePredictor(RSTTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
"""
def __init__(self, features_processor, relation_predictor_sentence, relation_predictor_text, label_predictor=None,
nuclearity_predictor=None):
RSTTreePredictor.__init__(self, features_processor, relation_predictor_sentence, relation_predictor_text,
label_predictor, nuclearity_predictor)
def extract_features(self, left_node: DiscourseUnit, right_node: DiscourseUnit,
annot_text, annot_tokens, annot_sentences, annot_lemma, annot_morph, annot_postag,
annot_syntax_dep_tree):
pair = pd.DataFrame({
'snippet_x': [left_node.text.strip()],
'snippet_y': [right_node.text.strip()],
})
try:
features = self.features_processor(pair, annot_text=annot_text,
annot_tokens=annot_tokens, annot_sentences=annot_sentences,
annot_postag=annot_postag, annot_morph=annot_morph,
annot_lemma=annot_lemma, annot_syntax_dep_tree=annot_syntax_dep_tree)
return features
except:
with open('errors.log', 'w+') as f:
f.write(str(pair.values))
f.write(annot_text)
return -1
def initialize_features(self, nodes,
annot_text, annot_tokens, annot_sentences, annot_lemma, annot_morph, annot_postag,
annot_syntax_dep_tree):
pairs = pd.DataFrame({
'snippet_x': [node.text.strip() for node in nodes[:-1]],
'snippet_y': [node.text.strip() for node in nodes[1:]]
})
try:
features = self.features_processor(pairs, annot_text=annot_text,
annot_tokens=annot_tokens, annot_sentences=annot_sentences,
annot_postag=annot_postag, annot_morph=annot_morph,
annot_lemma=annot_lemma, annot_syntax_dep_tree=annot_syntax_dep_tree)
return features
except IndexError:
with open('feature_extractor_errors.log', 'w+') as f:
f.write(str(pairs.values))
f.write(annot_text)
return -1
def predict_pair_proba(self, features, _same_sentence_bonus=0.5):
if type(features) == pd.DataFrame:
feat_same_sent = features[:]
feat_same_sent.snippet_x = feat_same_sent.apply(lambda row: (row.same_sentence == 1) * row.snippet_x + '',
axis=1)
feat_same_sent.snippet_y = feat_same_sent.apply(lambda row: (row.same_sentence == 1) * row.snippet_y + '',
axis=1)
probas_sentence_level = self.relation_predictor_sentence.predict_proba(feat_same_sent)
feat_not_same_sent = features[:]
feat_not_same_sent.snippet_x = feat_not_same_sent.apply(
lambda row: (row.same_sentence == 0) * row.snippet_x + '', axis=1)
feat_not_same_sent.snippet_y = feat_not_same_sent.apply(
lambda row: (row.same_sentence == 0) * row.snippet_y + '', axis=1)
probas_text_level = self.relation_predictor_text.predict_proba(feat_not_same_sent)
same_sentence_bonus = list(map(lambda value: float(value) * _same_sentence_bonus,
list(features['same_sentence'] == 1)))
return [probas_sentence_level[i][1] + same_sentence_bonus[i] + probas_text_level[i][1] for i in
range(len(probas_sentence_level))]
if type(features) == pd.Series:
if features.loc['same_sentence'] == 1:
return self.relation_predictor_sentence.predict_proba(features)[0][1] + _same_sentence_bonus
return self.relation_predictor_text.predict_proba(features)[0][1]
if type(features) == list:
return self.relation_predictor_text.predict_proba([features])[0][1]
def predict_label(self, features):
if not self.label_predictor:
return 'relation'
if type(features) == pd.DataFrame:
return self.label_predictor.predict(features)
if type(features) == pd.Series:
return self.label_predictor.predict(features.to_frame().T)[0]
def predict_nuclearity(self, features):
if not self.nuclearity_predictor:
return 'unavail'
if type(features) == pd.DataFrame:
return self.nuclearity_predictor.predict(features)
if type(features) == pd.Series:
return self.nuclearity_predictor.predict(features.to_frame().T)[0]
class NNTreePredictor(CustomTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
"""
def extract_features(self, left_node: DiscourseUnit, right_node: DiscourseUnit,
annot_text, annot_tokens, annot_sentences, annot_lemma, annot_morph, annot_postag,
annot_syntax_dep_tree):
pair = pd.DataFrame({
'snippet_x': [left_node.text.strip()],
'snippet_y': [right_node.text.strip()],
})
features = self.features_processor(pair, annot_text=annot_text,
annot_tokens=annot_tokens, annot_sentences=annot_sentences,
annot_postag=annot_postag, annot_morph=annot_morph,
annot_lemma=annot_lemma, annot_syntax_dep_tree=annot_syntax_dep_tree)
features['snippet_x'] = features['tokens_x'].map(lambda row: ' '.join(row)).values
features['snippet_y'] = features['tokens_y'].map(lambda row: ' '.join(row)).values
return features
def initialize_features(self, nodes,
annot_text, annot_tokens, annot_sentences, annot_lemma, annot_morph, annot_postag,
annot_syntax_dep_tree):
features = super().initialize_features(nodes,
annot_text, annot_tokens, annot_sentences, annot_lemma, annot_morph,
annot_postag,
annot_syntax_dep_tree)
features['snippet_x'] = features['tokens_x'].map(lambda row: ' '.join(row)).values
features['snippet_y'] = features['tokens_y'].map(lambda row: ' '.join(row)).values
return features
def predict_pair_proba(self, features, _same_sentence_bonus=0.1):
if type(features) == pd.DataFrame:
probas_text_level = self.relation_predictor_text.predict_proba_batch(
features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist())
sentence_level_map = list(map(float, list(features['same_sentence'] == 1)))
return [probas_text_level[i][1] + _same_sentence_bonus * sentence_level_map[i] for i in
range(len(probas_text_level))]
if type(features) == pd.Series:
return self.relation_predictor_text.predict_proba(features.loc['snippet_x'],
features.loc['snippet_y'])[0][1] + (
features.loc['same_sentence'] == 1) * _same_sentence_bonus
if type(features) == list:
snippet_x = [feature['snippet_x'] for feature in features]
snippet_y = [feature['snippet_y'] for feature in features]
probas = self.relation_predictor_text.predict_proba_batch(snippet_x, snippet_y)
return [proba[1] for proba in probas]
def predict_label(self, features):
result = self.DEFAULT_RELATION
if not self.label_predictor:
return result
if type(features) == pd.DataFrame:
result = self.label_predictor.predict_batch(features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist())
if type(features) == pd.Series:
result = self.label_predictor.predict(features.loc['snippet_x'],
features.loc['snippet_y'])
if type(result) == list:
return [_class_mapper.get(value) if _class_mapper.get(value) else value for value in result]
if _class_mapper.get(result):
return _class_mapper.get(result)
return result
class LargeNNTreePredictor(NNTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
"""
def predict_pair_proba(self, features, _same_sentence_bonus=1.):
if type(features) == pd.DataFrame:
probas_text_level = self.relation_predictor_text.predict_proba_batch(
features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist(),
features['same_sentence'].map(str).values.tolist(),
features['same_paragraph'].map(str).values.tolist())
sentence_level_map = list(map(float, list(features['same_sentence'] == 1)))
return [probas_text_level[i][1] + _same_sentence_bonus * sentence_level_map[i] for i in
range(len(probas_text_level))]
if type(features) == pd.Series:
return self.relation_predictor_text.predict_proba(features.loc['snippet_x'],
features.loc['snippet_y'],
str(features.loc['same_sentence'],
str(features.loc['same_paragraph'])))[0][1] + (
features.loc['same_sentence'] == 1) * _same_sentence_bonus
if type(features) == list:
snippet_x = [feature['snippet_x'] for feature in features]
snippet_y = [feature['snippet_y'] for feature in features]
same_sentence = [feature['same_sentence'].map(str) for feature in features]
same_paragraph = [feature['same_paragraph'].map(str) for feature in features]
probas = self.relation_predictor_text.predict_proba_batch(snippet_x, snippet_y, same_sentence,
same_paragraph)
sentence_level_map = list(map(float, [feature['same_sentence'] == 1 for feature in features]))
return [probas[i][1] + sentence_level_map[i] for i in range(len(probas))]
def predict_label(self, features):
result = self.DEFAULT_RELATION
if not self.label_predictor:
return result
if type(features) == pd.DataFrame:
result = self.label_predictor.predict_batch(features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist())
if type(features) == pd.Series:
result = self.label_predictor.predict(features.loc['snippet_x'],
features.loc['snippet_y'])
return result
class ContextualNNTreePredictor(NNTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
"""
def predict_pair_proba(self, features, _same_sentence_bonus=.5):
if type(features) == pd.DataFrame:
probas_text_level = self.relation_predictor_text.predict_proba_batch(
features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist(),
features['same_sentence'].map(str).values.tolist(),
features['left_context'].values.tolist(),
features['right_context'].values.tolist())
sentence_level_map = list(map(float, list(features['same_sentence'] == 1)))
return [probas_text_level[i][1] + _same_sentence_bonus * sentence_level_map[i] for i in
range(len(probas_text_level))]
if type(features) == pd.Series:
return self.relation_predictor_text.predict_proba(features.loc['snippet_x'],
features.loc['snippet_y'],
str(features.loc['same_sentence'],
features.loc['left_context'],
features.loc['right_context']))[0][1] + (
features.loc['same_sentence'] == 1) * _same_sentence_bonus
if type(features) == list:
snippet_x = [feature['snippet_x'] for feature in features]
snippet_y = [feature['snippet_y'] for feature in features]
same_sentence = [feature['same_sentence'].map(str) for feature in features]
probas = self.relation_predictor_text.predict_proba_batch(snippet_x, snippet_y, same_sentence,
left_context, right_context)
sentence_level_map = list(map(float, [feature['same_sentence'] == 1 for feature in features]))
return [probas[i][1] + sentence_level_map[i] for i in range(len(probas))]
def predict_label(self, features):
result = self.DEFAULT_RELATION
if not self.label_predictor:
return result
if type(features) == pd.DataFrame:
result = self.label_predictor.predict_batch(features['snippet_x'].values.tolist(),
features['snippet_y'].values.tolist())
if type(features) == pd.Series:
result = self.label_predictor.predict(features.loc['snippet_x'],
features.loc['snippet_y'])
return result
class EnsembleNNTreePredictor(LargeNNTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
Instead of pure allennlp classification model, as is in LargeNNTreePredictor,
predicts labels from an ensemble of allennlp and sklearn models.
"""
def predict_label(self, features):
result = self.DEFAULT_RELATION
if not self.label_predictor:
return result
if type(features) == pd.DataFrame:
result = self.label_predictor.predict_batch(snippet_x=features['snippet_x'].values.tolist(),
snippet_y=features['snippet_y'].values.tolist(),
features=features)
if type(features) == pd.Series:
result = self.label_predictor.predict(snippet_x=features.loc['snippet_x'],
snippet_y=features.loc['snippet_y'],
features=features.to_frame().T)
return result
class DoubleEnsembleNNTreePredictor(EnsembleNNTreePredictor):
"""
Contains trained classifiers and feature processors needed for tree prediction.
Instead of pure allennlp classification model, as is in LargeNNTreePredictor,
predicts labels from an ensemble of allennlp and sklearn models.
Instead of pure sklearn classification model, as is in LargeNNTreePredictor,
predicts structure from an ensemble of allennlp and sklearn models.
"""
def predict_pair_proba(self, features, _same_sentence_bonus=1.):
if type(features) == pd.DataFrame:
probas_text_level = self.relation_predictor_text.predict_proba_batch(
snippet_x=features['snippet_x'].values.tolist(),
snippet_y=features['snippet_y'].values.tolist(),
same_sentence=features['same_sentence'].map(str).values.tolist(),
same_paragraph=features['same_paragraph'].map(str).values.tolist(),
features=features)
# plus bonus for the presense in the same sentence
sentence_level_map = list(map(float, list(features['same_sentence'] == 1)))
# minus penalty for the depricated words
keywords_penalty = list(
map(float, list(features['snippet_x'].map(lambda row: self._find_penalty_words(row)))))
return [probas_text_level[i][1] + _same_sentence_bonus * sentence_level_map[i] - keywords_penalty[i] for i
in
range(len(probas_text_level))]
if type(features) == pd.Series:
return self.relation_predictor_text.predict_proba(snippet_x=features.loc['snippet_x'],
snippet_y=features.loc['snippet_y'],
same_sentence=str(features.loc['same_sentence'],
same_paragraph=str(
features.loc['same_paragraph'],
features=features)))[0][1] + (
features.loc['same_sentence'] == 1) * _same_sentence_bonus
if type(features) == list:
snippet_x = [feature['snippet_x'] for feature in features]
snippet_y = [feature['snippet_y'] for feature in features]
same_sentence = [feature['same_sentence'].map(str) for feature in features]
same_paragraph = [feature['same_paragraph'].map(str) for feature in features]
probas = self.relation_predictor_text.predict_proba_batch(
snippet_x=snippet_x,
snippet_y=snippet_y,
same_sentence=same_sentence,
same_paragraph=same_paragraph,
features=features)
sentence_level_map = list(map(float, [feature['same_sentence'] == 1 for feature in features]))
return [probas[i][1] + sentence_level_map[i] for i in range(len(probas))]
class TopDownRSTPredictor:
def __init__(self, features_processor, label_predictor):
self.features_processor = features_processor
self.label_predictor = label_predictor
| 45.264479
| 118
| 0.588945
| 2,528
| 23,447
| 5.187104
| 0.070807
| 0.036605
| 0.030962
| 0.029284
| 0.82117
| 0.768398
| 0.74163
| 0.69168
| 0.674598
| 0.670632
| 0
| 0.00523
| 0.314966
| 23,447
| 517
| 119
| 45.352031
| 0.811169
| 0.054549
| 0
| 0.582857
| 0
| 0
| 0.049177
| 0.001274
| 0
| 0
| 0
| 0
| 0
| 1
| 0.08
| false
| 0
| 0.008571
| 0
| 0.265714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0e941c73b1c42c191a08bb0f246c2463d67e13b7
| 113
|
py
|
Python
|
tests/test_get.py
|
northernSage/pytest-flask-issue-101
|
52040ba2d43c4a7b2092adcd4e07f16822da4bdf
|
[
"MIT"
] | null | null | null |
tests/test_get.py
|
northernSage/pytest-flask-issue-101
|
52040ba2d43c4a7b2092adcd4e07f16822da4bdf
|
[
"MIT"
] | null | null | null |
tests/test_get.py
|
northernSage/pytest-flask-issue-101
|
52040ba2d43c4a7b2092adcd4e07f16822da4bdf
|
[
"MIT"
] | null | null | null |
from flask import url_for
def test_get_index(client):
assert client.get(url_for('index')).status_code == 200
| 28.25
| 58
| 0.761062
| 19
| 113
| 4.263158
| 0.736842
| 0.148148
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| 0.030303
| 0.123894
| 113
| 4
| 58
| 28.25
| 0.787879
| 0
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| 0
| 0
| 0.04386
| 0
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| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| false
| 0
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| 0
| 0.666667
| 0
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| 0
| null | 0
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| 0
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| 1
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0e986f125d43edd8032d7caed9c17dbdf23c29e1
| 74,015
|
py
|
Python
|
test/python/test_onnx_backend.py
|
perfmjs/singa
|
7d220f72f15e10ff9b61bb5596e389c52ba0160c
|
[
"Apache-2.0"
] | 1
|
2020-01-03T22:35:36.000Z
|
2020-01-03T22:35:36.000Z
|
test/python/test_onnx_backend.py
|
perfmjs/singa
|
7d220f72f15e10ff9b61bb5596e389c52ba0160c
|
[
"Apache-2.0"
] | null | null | null |
test/python/test_onnx_backend.py
|
perfmjs/singa
|
7d220f72f15e10ff9b61bb5596e389c52ba0160c
|
[
"Apache-2.0"
] | null | null | null |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import unittest
from builtins import str
from singa import tensor
from singa import singa_wrap as singa
from singa import autograd
from singa import sonnx
from singa import opt
import onnx
from onnx import (defs, checker, helper, numpy_helper, mapping,
ModelProto, GraphProto, NodeProto, AttributeProto, TensorProto, OperatorSetIdProto)
from onnx.helper import make_tensor, make_tensor_value_info, make_node, make_graph
from cuda_helper import gpu_dev, cpu_dev
import numpy as np
import itertools
autograd.training = True
_default_opset_version = 10
def expect(node, inputs, outputs, name, opset_version=_default_opset_version):
onnx_node = sonnx.OnnxNode(node)
input_tensors = {}
input_labels = [x for x in onnx_node.inputs if x != ""]
# prepare input tensors
for key, val in zip(input_labels, inputs):
# very important! must be float
if not isinstance(val, np.ndarray) or len(val.shape) == 0:
val = np.array([val])
x = tensor.from_numpy(val.astype(np.float32))
x.to_device(gpu_dev)
input_tensors[key] = x
outputs_dict = sonnx.run_node(onnx_node, input_tensors, opset_version)
for out1, out2 in zip(outputs, outputs_dict.values()):
np.testing.assert_array_almost_equal(
out1, tensor.to_numpy(out2), decimal=5)
class TestPythonOnnxBackend(unittest.TestCase):
"""
This class aims to test the backend functionality of sonnx,
The most of the code is borrowed from onnx.
"""
def test_conv2d(self):
x = np.array([[[[0., 1., 2., 3., 4.], # (1, 1, 5, 5) input tensor
[5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]]]]).astype(np.float32)
W = np.array([[[[1., 1., 1.], # (1, 1, 3, 3) tensor for convolution weights
[1., 1., 1.],
[1., 1., 1.]]]]).astype(np.float32)
# Convolution with padding
node_with_padding = onnx.helper.make_node(
'Conv',
inputs=['x', 'W'],
outputs=['y'],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[1, 1, 1, 1],
)
y_with_padding = np.array([[[[12., 21., 27., 33., 24.], # (1, 1, 5, 5) output tensor
[33., 54., 63., 72., 51.],
[63., 99., 108., 117., 81.],
[93., 144., 153., 162., 111.],
[72., 111., 117., 123., 84.]]]]).astype(np.float32)
expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
name='test_basic_conv_with_padding')
# Convolution without padding
node_without_padding = onnx.helper.make_node(
'Conv',
inputs=['x', 'W'],
outputs=['y'],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[0, 0, 0, 0],
)
y_without_padding = np.array([[[[54., 63., 72.], # (1, 1, 3, 3) output tensor
[99., 108., 117.],
[144., 153., 162.]]]]).astype(np.float32)
expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
name='test_basic_conv_without_padding')
def test_conv2d_with_strides(self): # type: () -> None
x = np.array([[[[0., 1., 2., 3., 4.], # (1, 1, 7, 5) input tensor
[5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.],
[25., 26., 27., 28., 29.],
[30., 31., 32., 33., 34.]]]]).astype(np.float32)
W = np.array([[[[1., 1., 1.], # (1, 1, 3, 3) tensor for convolution weights
[1., 1., 1.],
[1., 1., 1.]]]]).astype(np.float32)
# Convolution with strides=2 and padding
node_with_padding = onnx.helper.make_node(
'Conv',
inputs=['x', 'W'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
# Default values for other attributes: dilations=[1, 1], groups=1
strides=[2, 2],
)
y_with_padding = np.array([[[[12., 27., 24.], # (1, 1, 4, 3) output tensor
[63., 108., 81.],
[123., 198., 141.],
[112., 177., 124.]]]]).astype(np.float32)
expect(node_with_padding, inputs=[x, W], outputs=[y_with_padding],
name='test_conv_with_strides_padding')
# Convolution with strides=2 and no padding
node_without_padding = onnx.helper.make_node(
'Conv',
inputs=['x', 'W'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[0, 0, 0, 0],
# Default values for other attributes: dilations=[1, 1], groups=1
strides=[2, 2],
)
y_without_padding = np.array([[[[54., 72.], # (1, 1, 3, 2) output tensor
[144., 162.],
[234., 252.]]]]).astype(np.float32)
expect(node_without_padding, inputs=[x, W], outputs=[y_without_padding],
name='test_conv_with_strides_no_padding')
# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
node_with_asymmetric_padding = onnx.helper.make_node(
'Conv',
inputs=['x', 'W'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[1, 0, 1, 0],
# Default values for other attributes: dilations=[1, 1], groups=1
strides=[2, 2],
)
y_with_asymmetric_padding = np.array([[[[21., 33.], # (1, 1, 4, 2) output tensor
[99., 117.],
[189., 207.],
[171., 183.]]]]).astype(np.float32)
expect(node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_padding],
name='test_conv_with_strides_and_asymmetric_padding')
def test_averagepool_2d_precomputed_pads(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2]
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[7, 7.5, 8, 8.5, 9],
[9.5, 10, 10.5, 11, 11.5],
[12, 12.5, 13, 13.5, 14],
[14.5, 15, 15.5, 16, 16.5],
[17, 17.5, 18, 18.5, 19]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_averagepool_2d_precomputed_pads')
def test_averagepool_2d_precomputed_strides(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[2, 2]
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[4, 6],
[14, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_averagepool_2d_precomputed_strides')
def test_averagepool_2d_precomputed_same_upper(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad='SAME_UPPER'
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[4, 5.5, 7],
[11.5, 13, 14.5],
[19, 20.5, 22]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_averagepool_2d_precomputed_same_upper')
def test_averagepool_2d_default(self): # type: () -> None
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape(
'VALID', x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, (0, 0), 'AVG')
expect(node, inputs=[x], outputs=[y],
name='test_averagepool_2d_default')
def test_averagepool_2d_pads(self): # type: () -> None
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2]
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape('VALID', np.add(
x_shape[2:], pad_shape), kernel_shape, strides)
padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
constant_values=np.nan)
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, pad_shape, 'AVG')
expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads')
def test_averagepool_2d_strides(self): # type: () -> None
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
'AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[5, 5],
strides=[3, 3]
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape = get_output_shape(
'VALID', x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, (0, 0), 'AVG')
expect(node, inputs=[x], outputs=[y],
name='test_averagepool_2d_strides')
def test_maxpool_2d_precomputed_pads(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2]
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_maxpool_2d_precomputed_pads')
def test_maxpool_with_argmax_2d_precomputed_pads(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y', 'z'],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2]
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25]]]]).astype(np.float32)
z = np.array([[[
[12, 13, 14, 14, 14],
[17, 18, 19, 19, 19],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24]]]]).astype(np.int64)
expect(node, inputs=[x], outputs=[y, z],
name='test_maxpool_with_argmax_2d_precomputed_pads')
def test_maxpool_2d_precomputed_strides(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[2, 2]
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[7, 9],
[17, 19]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_maxpool_2d_precomputed_strides')
# type: () -> None
def test_maxpool_with_argmax_2d_precomputed_strides(self):
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y', 'z'],
kernel_shape=[2, 2],
strides=[2, 2],
storage_order=1
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[7, 9],
[17, 19]]]]).astype(np.float32)
z = np.array([[[[6, 16],
[8, 18]]]]).astype(np.int64)
expect(node, inputs=[x], outputs=[
y, z], name='test_maxpool_with_argmax_2d_precomputed_strides')
def test_maxpool_2d_precomputed_same_upper(self): # type: () -> None
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad='SAME_UPPER'
)
x = np.array([[[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]]]).astype(np.float32)
y = np.array([[[[7, 9, 10],
[17, 19, 20],
[22, 24, 25]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_maxpool_2d_precomputed_same_upper')
def test_maxpool_2d_default(self): # type: () -> None
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape(
'VALID', x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, (0, 0), 'MAX')
expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_default')
def test_maxpool_2d_pads(self): # type: () -> None
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2]
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape('VALID', np.add(
x_shape[2:], pad_shape), kernel_shape, strides)
padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
constant_values=np.nan)
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, pad_shape, 'MAX')
expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_pads')
def test_maxpool_2d_strides(self): # type: () -> None
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
'MaxPool',
inputs=['x'],
outputs=['y'],
kernel_shape=[5, 5],
strides=[3, 3]
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape = get_output_shape(
'VALID', x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape,
strides, out_shape, (0, 0), 'MAX')
expect(node, inputs=[x], outputs=[y], name='test_maxpool_2d_strides')
def test_reshape(self): # type: () -> None
def reshape_reference_implementation(data, shape): # type: (np.ndarray, np.ndarray) -> np.ndarray
# replace zeros with corresponding dim size
# we need to do this because np.reshape doesn't support 0
new_shape = np.copy(shape)
zeros_index = np.where(shape == 0)
new_shape[zeros_index] = np.array(data.shape)[zeros_index]
reshaped = np.reshape(data, new_shape)
return reshaped
original_shape = [2, 3, 4]
test_cases = {
'reordered_all_dims': np.array([4, 2, 3], dtype=np.int64),
'reordered_last_dims': np.array([2, 4, 3], dtype=np.int64),
'reduced_dims': np.array([2, 12], dtype=np.int64),
'extended_dims': np.array([2, 3, 2, 2], dtype=np.int64),
'one_dim': np.array([24], dtype=np.int64),
'negative_dim': np.array([2, -1, 2], dtype=np.int64),
'negative_extended_dims': np.array([-1, 2, 3, 4], dtype=np.int64),
'zero_dim': np.array([2, 0, 4, 1], dtype=np.int64),
'zero_and_negative_dim': np.array([2, 0, 1, -1], dtype=np.int64),
}
data = np.random.random_sample(original_shape).astype(np.float32)
for test_name, shape in test_cases.items():
node = onnx.helper.make_node(
'Reshape',
inputs=['data', 'shape'],
outputs=['reshaped'],
)
reshaped = reshape_reference_implementation(data, shape)
expect(node, inputs=[data, shape], outputs=[reshaped],
name='test_reshape_' + test_name)
def test_concat(self): # type: () -> None
test_cases = {
# '1d': ([1, 2], not support 1d
# [3, 4]),
'2d': ([[1, 2], [3, 4]],
[[5, 6], [7, 8]]),
'3d': ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
[[[9, 10], [11, 12]], [[13, 14], [15, 16]]])
} # type: Dict[Text, Sequence[Any]]
for test_case, values_ in test_cases.items():
values = [np.asarray(v, dtype=np.float32) for v in values_]
for i in range(len(values[0].shape)):
in_args = ['value' + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
'Concat',
inputs=[s for s in in_args],
outputs=['output'],
axis=i
)
output = np.concatenate(values, i)
expect(node, inputs=[v for v in values], outputs=[output],
name='test_concat_' + test_case + '_axis_' + str(i))
for i in range(-len(values[0].shape), 0):
in_args = ['value' + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
'Concat',
inputs=[s for s in in_args],
outputs=['output'],
axis=i
)
output = np.concatenate(values, i)
expect(node, inputs=[v for v in values], outputs=[output],
name='test_concat_' + test_case + '_axis_negative_' + str(abs(i)))
def test_flatten(self): # type: () -> None
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(len(shape)):
node = onnx.helper.make_node(
'Flatten',
inputs=['a'],
outputs=['b'],
axis=i,
)
new_shape = (
1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b],
name='test_flatten_axis' + str(i))
def test_flatten_with_default_axis(self): # type: () -> None
node = onnx.helper.make_node(
'Flatten',
inputs=['a'],
outputs=['b'], # Default value for axis: axis=1
)
shape = (5, 4, 3, 2)
a = np.random.random_sample(shape).astype(np.float32)
new_shape = (5, 24)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b],
name='test_flatten_default_axis')
def test_flatten_negative_axis(self): # type: () -> None
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(-len(shape), 0):
node = onnx.helper.make_node(
'Flatten',
inputs=['a'],
outputs=['b'],
axis=i,
)
new_shape = (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b],
name='test_flatten_negative_axis' + str(abs(i)))
def test_add(self): # type: () -> None
node = onnx.helper.make_node(
'Add',
inputs=['x', 'y'],
outputs=['sum'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y],
name='test_add')
def test_add_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Add',
inputs=['x', 'y'],
outputs=['sum'],
)
# todo, we don't support 3d here
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y],
name='test_add_bcast')
def test_sum(self): # type: () -> None
data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([6, 9, 12]).astype(np.float32)
node = onnx.helper.make_node(
'Sum',
inputs=['data_0', 'data_1', 'data_2'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
name='test_sum_example')
node = onnx.helper.make_node(
'Sum',
inputs=['data_0'],
outputs=['result'],
)
expect(node, inputs=[data_0], outputs=[data_0],
name='test_sum_one_input')
result = np.add(data_0, data_1)
node = onnx.helper.make_node(
'Sum',
inputs=['data_0', 'data_1'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1], outputs=[result],
name='test_sum_two_inputs')
def test_relu(self): # type: () -> None
node = onnx.helper.make_node(
'Relu',
inputs=['x'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf)
expect(node, inputs=[x], outputs=[y],
name='test_relu')
def test_sigmoid(self): # type: () -> None
node = onnx.helper.make_node(
'Sigmoid',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [0.26894143, 0.5, 0.7310586]
y = 1.0 / (1.0 + np.exp(np.negative(x)))
expect(node, inputs=[x], outputs=[y],
name='test_sigmoid_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = 1.0 / (1.0 + np.exp(np.negative(x)))
expect(node, inputs=[x], outputs=[y],
name='test_sigmoid')
def test_matmul(self): # type: () -> None
node = onnx.helper.make_node(
'MatMul',
inputs=['a', 'b'],
outputs=['c'],
)
# 2d
a = np.random.randn(3, 4).astype(np.float32)
b = np.random.randn(4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c],
name='test_matmul_2d')
# todo, # 3d not support 3d
# a = np.random.randn(2, 3, 4).astype(np.float32)
# b = np.random.randn(2, 4, 3).astype(np.float32)
# c = np.matmul(a, b)
# expect(node, inputs=[a, b], outputs=[c],
# name='test_matmul_3d')
# todo, # 4d not support 4d
# a = np.random.randn(1, 2, 3, 4).astype(np.float32)
# b = np.random.randn(1, 2, 4, 3).astype(np.float32)
# c = np.matmul(a, b)
# expect(node, inputs=[a, b], outputs=[c],
# name='test_matmul_4d')
def test_cos(self): # type: () -> None
node = onnx.helper.make_node(
'Cos',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.cos(x)
expect(node, inputs=[x], outputs=[y],
name='test_cos_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.cos(x)
expect(node, inputs=[x], outputs=[y],
name='test_cos')
def test_cosh(self): # type: () -> None
node = onnx.helper.make_node(
'Cosh',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.cosh(x) # expected output [1.54308069, 1., 1.54308069]
expect(node, inputs=[x], outputs=[y],
name='test_cosh_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.cosh(x)
expect(node, inputs=[x], outputs=[y],
name='test_cosh')
def test_Sin(self): # type: () -> None
node = onnx.helper.make_node(
'Sin',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y],
name='test_sin_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y],
name='test_sin')
def test_Sinh(self): # type: () -> None
node = onnx.helper.make_node(
'Sinh',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.sinh(x) # expected output [-1.17520118, 0., 1.17520118]
expect(node, inputs=[x], outputs=[y],
name='test_sinh_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.sinh(x)
expect(node, inputs=[x], outputs=[y],
name='test_sinh')
def test_Tan(self): # type: () -> None
node = onnx.helper.make_node(
'Tan',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.tan(x)
expect(node, inputs=[x], outputs=[y],
name='test_tan_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.tan(x)
expect(node, inputs=[x], outputs=[y],
name='test_tan')
def test_Tanh(self): # type: () -> None
node = onnx.helper.make_node(
'Tanh',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.tanh(x) # expected output [-0.76159418, 0., 0.76159418]
expect(node, inputs=[x], outputs=[y],
name='test_tanh_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.tanh(x)
expect(node, inputs=[x], outputs=[y],
name='test_tanh')
def test_Acos(self): # type: () -> None
node = onnx.helper.make_node(
'Acos',
inputs=['x'],
outputs=['y'],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arccos(x)
expect(node, inputs=[x], outputs=[y],
name='test_acos_example')
x = np.random.rand(3, 4, 5).astype(np.float32)
y = np.arccos(x)
expect(node, inputs=[x], outputs=[y],
name='test_acos')
def test_Acosh(self): # type: () -> None
node = onnx.helper.make_node(
'Acosh',
inputs=['x'],
outputs=['y'],
)
x = np.array([10, np.e, 1]).astype(np.float32)
y = np.arccosh(x) # expected output [2.99322295, 1.65745449, 0.]
expect(node, inputs=[x], outputs=[y],
name='test_acosh_example')
x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
y = np.arccosh(x)
expect(node, inputs=[x], outputs=[y],
name='test_acosh')
def test_Asin(self): # type: () -> None
node = onnx.helper.make_node(
'Asin',
inputs=['x'],
outputs=['y'],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y],
name='test_asin_example')
x = np.random.rand(3, 4, 5).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y],
name='test_asin')
def test_Asinh(self): # type: () -> None
node = onnx.helper.make_node(
'Asinh',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.arcsinh(x) # expected output [-0.88137358, 0., 0.88137358]
expect(node, inputs=[x], outputs=[y],
name='test_asinh_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.arcsinh(x)
expect(node, inputs=[x], outputs=[y],
name='test_asinh')
def test_Atan(self): # type: () -> None
node = onnx.helper.make_node(
'Atan',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.arctan(x)
expect(node, inputs=[x], outputs=[y],
name='test_atan_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.arctan(x)
expect(node, inputs=[x], outputs=[y],
name='test_atan')
def test_Atanh(self): # type: () -> None
node = onnx.helper.make_node(
'Atanh',
inputs=['x'],
outputs=['y'],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arctanh(x) # expected output [-0.54930615, 0., 0.54930615]
expect(node, inputs=[x], outputs=[y],
name='test_atanh_example')
x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
y = np.arctanh(x)
expect(node, inputs=[x], outputs=[y],
name='test_atanh')
def test_selu(self): # type: () -> None
node = onnx.helper.make_node(
'Selu',
inputs=['x'],
outputs=['y'],
alpha=2.0,
gamma=3.0
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-3.79272318, 0., 3.]
y = np.clip(x, 0, np.inf) * 3.0 + \
(np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
expect(node, inputs=[x], outputs=[y],
name='test_selu_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) * 3.0 + \
(np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
expect(node, inputs=[x], outputs=[y],
name='test_selu')
def test_selu_default(self): # type: () -> None
default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
node = onnx.helper.make_node(
'Selu',
inputs=['x'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) * default_gamma + \
(np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
expect(node, inputs=[x], outputs=[y],
name='test_selu_default')
def test_elu(self): # type: () -> None
node = onnx.helper.make_node(
'Elu',
inputs=['x'],
outputs=['y'],
alpha=2.0
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-1.2642411, 0., 1.]
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y],
name='test_elu_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y],
name='test_elu')
def test_elu_default(self): # type: () -> None
default_alpha = 1.0
node = onnx.helper.make_node(
'Elu',
inputs=['x'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + \
(np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
expect(node, inputs=[x], outputs=[y],
name='test_elu_default')
def test_equal(self): # type: () -> None
node = onnx.helper.make_node(
'Equal',
inputs=['x', 'y'],
outputs=['z'],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_equal')
def test_equal_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Equal',
inputs=['x', 'y'],
outputs=['z'],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(5) * 10).astype(np.int32)
z = np.equal(x, y).astype(np.int32) # need to convert to int type
expect(node, inputs=[x, y], outputs=[z],
name='test_equal_bcast')
def test_less(self): # type: () -> None
node = onnx.helper.make_node(
'Less',
inputs=['x', 'y'],
outputs=['less'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.less(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_less')
def test_less_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Less',
inputs=['x', 'y'],
outputs=['less'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.less(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_less_bcast')
def test_sign(self): # type: () -> None
node = onnx.helper.make_node(
'Sign',
inputs=['x'],
outputs=['y'],
)
x = np.array(range(-5, 6)).astype(np.float32)
y = np.sign(x)
expect(node, inputs=[x], outputs=[y],
name='test_sign')
def test_sub(self): # type: () -> None
node = onnx.helper.make_node(
'Sub',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([3, 2, 1]).astype(np.float32)
z = x - y # expected output [-2., 0., 2.]
expect(node, inputs=[x, y], outputs=[z],
name='test_sub_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = x - y
expect(node, inputs=[x, y], outputs=[z],
name='test_sub')
def test_sub_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Sub',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = x - y
expect(node, inputs=[x, y], outputs=[z],
name='test_sub_bcast')
def test_sqrt(self): # type: () -> None
node = onnx.helper.make_node(
'Sqrt',
inputs=['x'],
outputs=['y'],
)
x = np.array([1, 4, 9]).astype(np.float32)
y = np.sqrt(x) # expected output [1., 2., 3.]
expect(node, inputs=[x], outputs=[y],
name='test_sqrt_example')
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = np.sqrt(x)
expect(node, inputs=[x], outputs=[y],
name='test_sqrt')
def test_log(self): # type: () -> None
node = onnx.helper.make_node(
'Log',
inputs=['x'],
outputs=['y'],
)
x = np.array([1, 10]).astype(np.float32)
y = np.log(x) # expected output [0., 2.30258512]
expect(node, inputs=[x], outputs=[y],
name='test_log_example')
x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
y = np.log(x)
expect(node, inputs=[x], outputs=[y],
name='test_log')
def test_greater(self): # type: () -> None
node = onnx.helper.make_node(
'Greater',
inputs=['x', 'y'],
outputs=['greater'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.greater(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_greater')
def test_greater_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Greater',
inputs=['x', 'y'],
outputs=['greater'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.greater(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_greater_bcast')
def test_hardsigmoid(self): # type: () -> None
node = onnx.helper.make_node(
'HardSigmoid',
inputs=['x'],
outputs=['y'],
alpha=0.5,
beta=0.6
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.]
expect(node, inputs=[x], outputs=[y],
name='test_hardsigmoid_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)
expect(node, inputs=[x], outputs=[y],
name='test_hardsigmoid')
def test_hardsigmoid_default(self): # type: () -> None
default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
'HardSigmoid',
inputs=['x'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y],
name='test_hardsigmoid_default')
def test_identity(self):
node = onnx.helper.make_node(
'Identity',
inputs=['x'],
outputs=['y'],
)
data = np.array([[[
[1, 2],
[3, 4],
]]], dtype=np.float32)
expect(node, inputs=[data], outputs=[data],
name='test_identity')
def test_softplus(self):
node = onnx.helper.make_node(
'Softplus',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [0.31326166, 0.69314718, 1.31326163]
y = np.log(np.exp(x) + 1)
expect(node, inputs=[x], outputs=[y],
name='test_softplus_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.log(np.exp(x) + 1)
expect(node, inputs=[x], outputs=[y],
name='test_softplus')
def test_softsign(self):
node = onnx.helper.make_node(
'Softsign',
inputs=['x'],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-0.5, 0, 0.5]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_softsign_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = x / (1 + np.abs(x))
expect(node, inputs=[x], outputs=[y],
name='test_softsign')
def test_mean(self):
data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([2, 3, 4]).astype(np.float32)
node = onnx.helper.make_node(
'Mean',
inputs=['data_0', 'data_1', 'data_2'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
name='test_mean_example')
node = onnx.helper.make_node(
'Mean',
inputs=['data_0'],
outputs=['result'],
)
expect(node, inputs=[data_0], outputs=[data_0],
name='test_mean_one_input')
result = np.divide(np.add(data_0, data_1), 2.)
node = onnx.helper.make_node(
'Mean',
inputs=['data_0', 'data_1'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1], outputs=[result],
name='test_mean_two_inputs')
def test_transpose_default(self): # type: () -> None
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
node = onnx.helper.make_node(
'Transpose',
inputs=['data'],
outputs=['transposed']
)
transposed = np.transpose(data)
expect(node, inputs=[data], outputs=[transposed],
name='test_transpose_default')
def test_transpose_all_permutations(self): # type: () -> None
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
permutations = list(itertools.permutations(np.arange(len(shape))))
for i in range(len(permutations)):
node = onnx.helper.make_node(
'Transpose',
inputs=['data'],
outputs=['transposed'],
perm=permutations[i]
)
transposed = np.transpose(data, permutations[i])
expect(node, inputs=[data], outputs=[transposed],
name='test_transpose_all_permutations_' + str(i))
def test_max(self):
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 3]).astype(np.float32)
result = np.array([3, 5, 4]).astype(np.float32)
# todo, not support 3 inputs
node = onnx.helper.make_node(
'Max',
inputs=['data_0', 'data_1', 'data_2'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
name='test_max_example')
# todo, not support 1 inputs
node = onnx.helper.make_node(
'Max',
inputs=['data_0'],
outputs=['result'],
)
expect(node, inputs=[data_0], outputs=[data_0],
name='test_max_one_input')
result = np.maximum(data_0, data_1)
node = onnx.helper.make_node(
'Max',
inputs=['data_0', 'data_1'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1], outputs=[result],
name='test_max_two_inputs')
def test_min(self):
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 0]).astype(np.float32)
result = np.array([1, 2, 0]).astype(np.float32)
node = onnx.helper.make_node(
'Min',
inputs=['data_0', 'data_1', 'data_2'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
name='test_min_example')
node = onnx.helper.make_node(
'Min',
inputs=['data_0'],
outputs=['result'],
)
expect(node, inputs=[data_0], outputs=[data_0],
name='test_min_one_input')
result = np.minimum(data_0, data_1)
node = onnx.helper.make_node(
'Min',
inputs=['data_0', 'data_1'],
outputs=['result'],
)
expect(node, inputs=[data_0, data_1], outputs=[result],
name='test_min_two_inputs')
def test_shape(self):
node = onnx.helper.make_node(
'Shape',
inputs=['x'],
outputs=['y'],
)
x = np.array([
[1, 2, 3],
[4, 5, 6],
]).astype(np.float32)
y = np.array([
2, 3,
]).astype(np.int64)
expect(node, inputs=[x], outputs=[y],
name='test_shape_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.array(x.shape).astype(np.int64)
expect(node, inputs=[x], outputs=[y],
name='test_shape')
def test_and(self): # type: () -> None
node = onnx.helper.make_node(
'And',
inputs=['x', 'y'],
outputs=['and'],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(np.bool)
y = (np.random.randn(3, 4) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and2d')
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and3d')
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and4d')
def test_and_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'And',
inputs=['x', 'y'],
outputs=['and'],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(5) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and_bcast3v1d')
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(4, 5) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and_bcast3v2d')
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(5, 6) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and_bcast4v2d')
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(4, 5, 6) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and_bcast4v3d')
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(np.bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_and_bcast4v4d')
def test_or(self):
node = onnx.helper.make_node(
'Or',
inputs=['x', 'y'],
outputs=['or'],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(np.bool)
y = (np.random.randn(3, 4) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or2d')
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or3d')
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or4d')
def test_or_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Or',
inputs=['x', 'y'],
outputs=['or'],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(5) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or_bcast3v1d')
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(4, 5) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or_bcast3v2d')
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(5, 6) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or_bcast4v2d')
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(4, 5, 6) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or_bcast4v3d')
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(np.bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_or_bcast4v4d')
def test_xor(self): # type: () -> None
node = onnx.helper.make_node(
'Xor',
inputs=['x', 'y'],
outputs=['xor'],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(np.bool)
y = (np.random.randn(3, 4) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor2d')
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor3d')
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor4d')
def test_xor_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Xor',
inputs=['x', 'y'],
outputs=['xor'],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(5) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor_bcast3v1d')
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
y = (np.random.randn(4, 5) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor_bcast3v2d')
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(5, 6) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor_bcast4v2d')
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
y = (np.random.randn(4, 5, 6) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor_bcast4v3d')
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(np.bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(np.bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_xor_bcast4v4d')
def test_not(self):
node = onnx.helper.make_node(
'Not',
inputs=['x'],
outputs=['not'],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(np.bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)],
name='test_not_2d')
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(np.bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)],
name='test_not_3d')
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(np.bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)],
name='test_not_4d')
def test_neg(self):
node = onnx.helper.make_node(
'Neg',
inputs=['x'],
outputs=['y'],
)
x = np.array([-4, 2]).astype(np.float32)
y = np.negative(x) # expected output [4., -2.],
expect(node, inputs=[x], outputs=[y],
name='test_neg_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.negative(x)
expect(node, inputs=[x], outputs=[y],
name='test_neg')
def test_reciprocal(self):
node = onnx.helper.make_node(
'Reciprocal',
inputs=['x'],
outputs=['y'],
)
x = np.array([-4, 2]).astype(np.float32)
y = np.reciprocal(x) # expected output [-0.25, 0.5],
expect(node, inputs=[x], outputs=[y],
name='test_reciprocal_example')
x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
y = np.reciprocal(x)
expect(node, inputs=[x], outputs=[y],
name='test_reciprocal')
def test_batchnorm(self): # type: () -> None
# we changed this test cases
# according to the paper https://arxiv.org/pdf/1502.03167.pdf
def _batchnorm_test_mode(x, s, bias, mean, var, momentum=0.9, epsilon=1e-5): # type: ignore
dims_x = len(x.shape)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
mean = mean.reshape(-1, *dim_ones)
var = var.reshape(-1, *dim_ones)
batch_m = x.mean(axis=(0, 2, 3), keepdims=True)
batch_v = x.var(axis=(0, 2, 3), keepdims=True)
return s * (x - batch_m) / np.sqrt(batch_v + epsilon) + bias
# input size: (1, 2, 1, 3)
x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
s = np.array([1.0, 1.5]).astype(np.float32)
bias = np.array([0, 1]).astype(np.float32)
mean = np.array([0, 3]).astype(np.float32)
var = np.array([1, 1.5]).astype(np.float32)
y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 's', 'bias', 'mean', 'var'],
outputs=['y'],
)
# output size: (1, 2, 1, 3)
expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
name='test_batchnorm_example')
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
epsilon = 1e-2
y = _batchnorm_test_mode(
x, s, bias, mean, var, epsilon).astype(np.float32)
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 's', 'bias', 'mean', 'var'],
outputs=['y'],
epsilon=epsilon,
)
# output size: (2, 3, 4, 5)
expect(node, inputs=[x, s, bias, mean, var], outputs=[y],
name='test_batchnorm_epsilon')
def test_softmax(self): # type: () -> None
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
)
x = np.array([[-1, 0, 1]]).astype(np.float32)
# expected output [[0.09003058, 0.24472848, 0.66524094]]
y = np.exp(x) / np.sum(np.exp(x), axis=1)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_example')
def test_softmax_axis(self): # type: () -> None
def softmax_2d(x): # type: (np.ndarray) -> np.ndarray
max_x = np.max(x, axis=1).reshape((-1, 1))
exp_x = np.exp(x - max_x)
return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))
x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
# expected output [[0.0320586, 0.08714432, 0.23688284, 0.64391428],
# [0.0320586, 0.08714432, 0.23688284, 0.64391428]]
y = softmax_2d(x)
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_large_number')
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
axis=0,
)
y = softmax_2d(x.reshape(1, 60)).reshape(3, 4, 5)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_axis_0')
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
axis=1,
)
y = softmax_2d(x.reshape(3, 20)).reshape(3, 4, 5)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_axis_1')
# default axis is 1
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_default_axis')
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
axis=2,
)
y = softmax_2d(x.reshape(12, 5)).reshape(3, 4, 5)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_axis_2')
node = onnx.helper.make_node(
'Softmax',
inputs=['x'],
outputs=['y'],
axis=-1,
)
y = softmax_2d(x.reshape(12, 5)).reshape(3, 4, 5)
expect(node, inputs=[x], outputs=[y],
name='test_softmax_negative_axis')
def test_div(self): # type: () -> None
node = onnx.helper.make_node(
'Div',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([3, 4]).astype(np.float32)
y = np.array([1, 2]).astype(np.float32)
z = x / y # expected output [3., 2.]
expect(node, inputs=[x, y], outputs=[z],
name='test_div_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z],
name='test_div')
def test_div_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Div',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z],
name='test_div_bcast')
def test_pow(self):
node = onnx.helper.make_node(
'Pow',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32) # todo, not exactly same
z = np.power(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z],
name='test_pow_example')
x = np.arange(24).reshape(2, 3, 4).astype(
np.float32) # todo, cannot too big here
y = np.random.randn(2, 3, 4).astype(np.float32)
z = np.power(x, y)
expect(node, inputs=[x, y], outputs=[z],
name='test_pow')
def test_pow_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Pow',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array(2).astype(np.float32)
z = np.power(x, y) # expected output [1., 4., 9.]
expect(node, inputs=[x, y], outputs=[z],
name='test_pow_bcast_scalar')
node = onnx.helper.make_node(
'Pow',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
y = np.array([1, 2, 3]).astype(np.float32)
# expected output [[1, 4, 27], [4, 25, 216]]
z = np.power(x, y).astype(np.float32)
expect(node, inputs=[x, y], outputs=[z],
name='test_pow_bcast_array')
def test_clip(self):
node = onnx.helper.make_node(
'Clip',
inputs=['x', 'min', 'max'],
outputs=['y'],
)
x = np.array([-2, 0, 2]).astype(np.float32)
min_val = np.float32(-1)
max_val = np.float32(1)
y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.]
expect(node, inputs=[x, min_val, max_val], outputs=[y],
name='test_clip_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, max_val)
expect(node, inputs=[x, min_val, max_val], outputs=[y],
name='test_clip')
node = onnx.helper.make_node(
'Clip',
inputs=['x', 'min', 'max'],
outputs=['y'],
)
min_val = np.float32(-5)
max_val = np.float32(5)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(node, inputs=[x, min_val, max_val], outputs=[y],
name='test_clip_inbounds')
x = np.array([-6, 0, 6]).astype(np.float32)
y = np.array([-5, 0, 5]).astype(np.float32)
expect(node, inputs=[x, min_val, max_val], outputs=[y],
name='test_clip_outbounds')
x = np.array([-1, 0, 6]).astype(np.float32)
y = np.array([-1, 0, 5]).astype(np.float32)
expect(node, inputs=[x, min_val, max_val], outputs=[y],
name='test_clip_splitbounds')
def test_clip_default(self): # type: () -> None
node = onnx.helper.make_node(
'Clip',
inputs=['x', 'min'],
outputs=['y'],
)
min_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, np.inf)
expect(node, inputs=[x, min_val], outputs=[y],
name='test_clip_default_min')
no_min = "" # optional input, not supplied
node = onnx.helper.make_node(
'Clip',
inputs=['x', no_min, 'max'],
outputs=['y'],
)
max_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, -np.inf, max_val)
expect(node, inputs=[x, max_val], outputs=[y],
name='test_clip_default_max')
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
'Clip',
inputs=['x', no_min, no_max],
outputs=['y'],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(node, inputs=[x], outputs=[y],
name='test_clip_default_inbounds')
def test_prelu(self):
node = onnx.helper.make_node(
'PRelu',
inputs=['x', 'slope'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y],
name='test_prelu_example')
#todo, not support prelu broadcast
def test_prelu_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'PRelu',
inputs=['x', 'slope'],
outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y],
name='test_prelu_broadcast')
def test_mul(self):
node = onnx.helper.make_node(
'Mul',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = x * y # expected output [4., 10., 18.]
expect(node, inputs=[x, y], outputs=[z],
name='test_mul_example')
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z],
name='test_mul')
def test_mul_broadcast(self): # type: () -> None
node = onnx.helper.make_node(
'Mul',
inputs=['x', 'y'],
outputs=['z'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z],
name='test_mul_bcast')
# return padding shape of conv2d or pooling
def get_pad_shape(auto_pad, # type: Text
input_spatial_shape, # type: Sequence[int]
kernel_spatial_shape, # type: Sequence[int]
strides_spatial, # type: Sequence[int]
output_spatial_shape # type: Sequence[int]
): # type: (...) -> Sequence[int]
pad_shape = [0] * len(input_spatial_shape)
if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
for i in range(len(input_spatial_shape)):
pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial[i] + \
kernel_spatial_shape[i] - input_spatial_shape[i]
elif auto_pad == 'VALID':
pass
return pad_shape
# return output shape of conv2d or pooling
def get_output_shape(auto_pad, # type: Text
input_spatial_shape, # type: Sequence[int]
kernel_spatial_shape, # type: Sequence[int]
strides_spatial # type: Sequence[int]
): # type: (...) -> Sequence[int]
out_shape = [0] * len(input_spatial_shape)
if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
for i in range(len(input_spatial_shape)):
out_shape[i] = int(
np.ceil(
float(
input_spatial_shape[i])
/ float(
strides_spatial[i])))
elif auto_pad == 'VALID':
for i in range(len(input_spatial_shape)):
out_shape[i] = int(np.ceil(float(
input_spatial_shape[i] - (kernel_spatial_shape[i] - 1)) / float(strides_spatial[i])))
return out_shape
def pool(padded, # type: np.ndarray
x_shape, # type: Sequence[int]
kernel_shape, # type: Sequence[int]
strides_shape, # type: Sequence[int]
out_shape, # type: Sequence[int]
pad_shape, # type: Sequence[int]
pooling_type, # type: Text
count_include_pad=0 # type: int
): # type: (...) -> np.ndarray
spatial_size = len(x_shape) - 2
y = np.zeros([x_shape[0], x_shape[1]] + list(out_shape))
for shape in itertools.product(range(x_shape[0]), range(x_shape[1]), *[range(int(
(x_shape[i + 2] + pad_shape[i] - kernel_shape[i]) / strides_shape[i] + 1)) for i in range(spatial_size)]):
window = padded[shape[0], shape[1]]
window_vals = np.array([window[i] for i in list(
itertools.product(
*[range(strides_shape[i] * shape[i + 2], strides_shape[i] * shape[i + 2] + kernel_shape[i]) for i in
range(spatial_size)])
)])
if pooling_type == 'AVG':
f = np.average
elif pooling_type == 'MAX':
f = np.max
else:
raise NotImplementedError(
'Pooling type {} does not support. Should be AVG, MAX'.format(pooling_type))
if count_include_pad == 1 and pooling_type == 'AVG':
y[shape] = f(window_vals)
else:
y[shape] = f(window_vals[np.where(~np.isnan(window_vals))])
return y.astype(np.float32)
if __name__ == '__main__':
unittest.main()
| 34.473684
| 118
| 0.482389
| 9,646
| 74,015
| 3.580448
| 0.052353
| 0.056287
| 0.07948
| 0.064974
| 0.795582
| 0.764456
| 0.7311
| 0.712685
| 0.674494
| 0.628891
| 0
| 0.067725
| 0.347254
| 74,015
| 2,146
| 119
| 34.489748
| 0.647134
| 0.093927
| 0
| 0.589517
| 0
| 0
| 0.065293
| 0.016508
| 0
| 0
| 0
| 0.000466
| 0.000589
| 1
| 0.055359
| false
| 0.000589
| 0.007656
| 0
| 0.067138
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7ec5a880c33141d23f12626836a070f9e6fb3272
| 1,072
|
py
|
Python
|
articulos.py
|
feedesa/MyPythonScripts
|
66f06f9d44ea6c76cfadb1a620bb468176beefe0
|
[
"MIT"
] | null | null | null |
articulos.py
|
feedesa/MyPythonScripts
|
66f06f9d44ea6c76cfadb1a620bb468176beefe0
|
[
"MIT"
] | null | null | null |
articulos.py
|
feedesa/MyPythonScripts
|
66f06f9d44ea6c76cfadb1a620bb468176beefe0
|
[
"MIT"
] | null | null | null |
import pymysql
print ( dir( pymysql) )
'''
class Articulos():
def abrir(self):
conexion=mysql.connector.connect(host="localhost",
user="root",
passwd="",
database="ejemplo1")
return conexion
def alta(self, datos):
cone=self.abrir()
cursor=cone.cursor()
sql="insert into articulos(descripcion, precio) values (%s,%s)"
cursor.execute(sql, datos)
cone.commit()
cone.close()
def consulta(self, datos):
cone=self.abrir()
cursor=cone.cursor()
sql="select descripcion, precio from articulos where codigo=%s"
cursor.execute(sql, datos)
cone.close()
return cursor.fetchall()
def recuperar_todos(self):
cone=self.abrir()
cursor=cone.cursor()
sql="select codigo, descripcion, precio from articulos"
cursor.execute(sql)
cone.close()
return cursor.fetchall()
'''
| 28.210526
| 71
| 0.525187
| 103
| 1,072
| 5.456311
| 0.417476
| 0.064057
| 0.069395
| 0.101423
| 0.412811
| 0.316726
| 0.224199
| 0.224199
| 0.145907
| 0
| 0
| 0.00146
| 0.361007
| 1,072
| 38
| 72
| 28.210526
| 0.818978
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.026316
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 4
|
7ee409c98b43e14356dab6c38af28273d01280ec
| 342
|
py
|
Python
|
tests/test_utils_ipython.py
|
rahulraj80/ipyexperiments
|
08a91ef6d7480dae90951e55754a2712de9c335b
|
[
"Apache-2.0"
] | null | null | null |
tests/test_utils_ipython.py
|
rahulraj80/ipyexperiments
|
08a91ef6d7480dae90951e55754a2712de9c335b
|
[
"Apache-2.0"
] | null | null | null |
tests/test_utils_ipython.py
|
rahulraj80/ipyexperiments
|
08a91ef6d7480dae90951e55754a2712de9c335b
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from ipyexperiments.utils.ipython import *
# at the moment just a syntax check, the test would be useless w/o ipython env
@ipython_tb_clear_frames
def do_something(): return True
def test_decorator():
assert do_something() is True, "decorator test"
def test_ctx():
with ipython_tb_clear_frames_ctx():
x = 10
| 22.8
| 78
| 0.745614
| 53
| 342
| 4.603774
| 0.660377
| 0.07377
| 0.114754
| 0.163934
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007143
| 0.181287
| 342
| 14
| 79
| 24.428571
| 0.864286
| 0.222222
| 0
| 0
| 0
| 0
| 0.05303
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 1
| 0.333333
| false
| 0
| 0.222222
| 0.111111
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
7ef8d13c454038d170cd25f7de91dc4e8f8b9261
| 308
|
py
|
Python
|
src/Cell.py
|
Konstantysz/ultimate-sudoku
|
fe69a2fbf662868af666641dc3bd4494fb6b9189
|
[
"MIT"
] | null | null | null |
src/Cell.py
|
Konstantysz/ultimate-sudoku
|
fe69a2fbf662868af666641dc3bd4494fb6b9189
|
[
"MIT"
] | 1
|
2021-10-12T17:35:17.000Z
|
2021-10-12T17:35:17.000Z
|
src/Cell.py
|
Konstantysz/ultimate-sudoku
|
fe69a2fbf662868af666641dc3bd4494fb6b9189
|
[
"MIT"
] | null | null | null |
class Cell:
# default constructor
def __init__(self, value):
self._clicked = False
self._value = value
def get_value(self):
return self._value
def get_clicked(self):
return self._clicked
def set_clicked(self, clicked):
self._clicked = clicked
| 19.25
| 35
| 0.62013
| 36
| 308
| 4.972222
| 0.361111
| 0.24581
| 0.122905
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.301948
| 308
| 15
| 36
| 20.533333
| 0.832558
| 0.061688
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.2
| 0.7
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
7d1559e66dfebcd4eb4c543636ce0bf243f51586
| 138
|
py
|
Python
|
taggit_helpers/apps.py
|
moas/django-taggit-helpers
|
963de3ab91f381343c4d3cb51e15ad09c52df22d
|
[
"BSD-3-Clause"
] | 43
|
2015-06-11T18:29:21.000Z
|
2021-11-14T06:55:14.000Z
|
taggit_helpers/apps.py
|
moas/django-taggit-helpers
|
963de3ab91f381343c4d3cb51e15ad09c52df22d
|
[
"BSD-3-Clause"
] | 4
|
2015-11-30T19:00:40.000Z
|
2020-06-27T05:25:58.000Z
|
taggit_helpers/apps.py
|
moas/django-taggit-helpers
|
963de3ab91f381343c4d3cb51e15ad09c52df22d
|
[
"BSD-3-Clause"
] | 9
|
2015-11-12T11:09:56.000Z
|
2020-12-30T14:35:40.000Z
|
from django.apps import AppConfig
class TaggitHelpersConfig(AppConfig):
name = 'taggit_helpers'
verbose_name = 'Taggit Helpers'
| 19.714286
| 37
| 0.76087
| 15
| 138
| 6.866667
| 0.733333
| 0.194175
| 0.330097
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 138
| 6
| 38
| 23
| 0.895652
| 0
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| 0.202899
| 0
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| false
| 0
| 0.25
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
7d1c433fdb231b2a1208809068d1060aff90a89c
| 636
|
py
|
Python
|
src/insert.py
|
pointThink/PyText
|
f036706cbb5d22d0c9f3d4a978ced7b2878d164a
|
[
"Unlicense"
] | null | null | null |
src/insert.py
|
pointThink/PyText
|
f036706cbb5d22d0c9f3d4a978ced7b2878d164a
|
[
"Unlicense"
] | null | null | null |
src/insert.py
|
pointThink/PyText
|
f036706cbb5d22d0c9f3d4a978ced7b2878d164a
|
[
"Unlicense"
] | null | null | null |
from tkinter import *
class Insert:
text: Text
def __init__(self, text_box):
self.text = text_box
def insert(self, symbol):
index = self.text.index('insert')
self.text.insert(INSERT, symbol)
self.text.mark_set(INSERT, index)
# Braces
def round_brace(self, event):
self.insert(')')
def square_brace(self, event):
self.insert(']')
def curly_brace(self, event):
self.insert(']')
# Quotes
def quote_double(self, event):
self.insert('\"')
def quote_single(self, event):
self.insert('\'')
| 19.875
| 42
| 0.551887
| 73
| 636
| 4.643836
| 0.328767
| 0.117994
| 0.19174
| 0.280236
| 0.294985
| 0.159292
| 0
| 0
| 0
| 0
| 0
| 0
| 0.316038
| 636
| 31
| 43
| 20.516129
| 0.77931
| 0.02044
| 0
| 0.105263
| 0
| 0
| 0.020374
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.368421
| false
| 0
| 0.052632
| 0
| 0.526316
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
7d2fe2bc3e38fef89fae2ff50cd91cee70c2206d
| 33
|
py
|
Python
|
deepmars/data/__init__.py
|
utplanets/deepmars
|
ba306aa9b25b654636b61cf952af2791b7ed0e56
|
[
"MIT"
] | 2
|
2021-08-08T03:06:58.000Z
|
2021-11-25T04:06:00.000Z
|
deepmars/data/__init__.py
|
utplanets/deepmars
|
ba306aa9b25b654636b61cf952af2791b7ed0e56
|
[
"MIT"
] | null | null | null |
deepmars/data/__init__.py
|
utplanets/deepmars
|
ba306aa9b25b654636b61cf952af2791b7ed0e56
|
[
"MIT"
] | 2
|
2020-11-23T09:38:26.000Z
|
2021-02-26T01:14:28.000Z
|
'''DeepMars data manipulation'''
| 16.5
| 32
| 0.727273
| 3
| 33
| 8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 33
| 1
| 33
| 33
| 0.8
| 0.787879
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
adac63f0d7bc76868063472d6d20d6aae380bcf7
| 33
|
py
|
Python
|
thermo-env/lib/python3.5/linecache.py
|
ndebuhr/thermoModelingAlpha
|
9e1a0f9ac4caabf386c1e3103ec69f269131a6b9
|
[
"MIT"
] | 4
|
2017-09-24T21:30:46.000Z
|
2019-06-01T13:37:44.000Z
|
thermo-env/lib/python3.5/linecache.py
|
ndebuhr/thermo-state-solver
|
9e1a0f9ac4caabf386c1e3103ec69f269131a6b9
|
[
"MIT"
] | 19
|
2020-01-28T21:41:50.000Z
|
2022-03-11T23:17:39.000Z
|
thermo-env/lib/python3.5/linecache.py
|
ndebuhr/thermo-state-solver
|
9e1a0f9ac4caabf386c1e3103ec69f269131a6b9
|
[
"MIT"
] | null | null | null |
/usr/lib64/python3.5/linecache.py
| 33
| 33
| 0.818182
| 6
| 33
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 0
| 33
| 1
| 33
| 33
| 0.69697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
adc09d5e8a4e021e2bc507bdbd2b93c498b90a1e
| 2,116
|
py
|
Python
|
tests/test_es2json_basicfunctions.py
|
slub/es2json
|
be22a193c615043693d580ff73f89adf4c0fe8f1
|
[
"Apache-2.0"
] | null | null | null |
tests/test_es2json_basicfunctions.py
|
slub/es2json
|
be22a193c615043693d580ff73f89adf4c0fe8f1
|
[
"Apache-2.0"
] | 1
|
2020-12-08T09:51:42.000Z
|
2020-12-08T09:51:42.000Z
|
tests/test_es2json_basicfunctions.py
|
slub/es2json
|
be22a193c615043693d580ff73f89adf4c0fe8f1
|
[
"Apache-2.0"
] | null | null | null |
import es2json
import uuid
def test_litter():
assert es2json.litter("foo", "bar") == ["foo", "bar"]
assert es2json.litter(["foo", "bar"], "baz") == ["foo", "bar", "baz"]
assert es2json.litter("baz", ["foo", "bar"]) == ["baz", "foo", "bar"]
assert es2json.litter(None, ["foo", "bar", "baz"]) == ["foo", "bar", "baz"]
assert es2json.litter(["foo", "foobar"], ["bar", "baz"]) == ["foo", "foobar", "bar", "baz"]
assert es2json.litter(["foo", "foobar", "bar"], ["bar", "baz"]) == ["foo", "foobar", "bar", "baz"]
def test_isint():
assert es2json.isint("2")
assert es2json.isint("2.5") is False
assert es2json.isint(2)
assert es2json.isint({"This is": "a dict"}) is False
assert es2json.isint(["this", "is", "a", "list"]) is False
def test_isfloat():
assert es2json.isfloat("2")
assert es2json.isfloat("2.5")
assert es2json.isfloat(2)
assert es2json.isfloat({"This is": "a dict"}) is False
assert es2json.isfloat(["this", "is", "a", "list"]) is False
def test_isiter():
assert es2json.isiter("2")
assert es2json.isiter("2.5")
assert es2json.isiter(2) is False
assert es2json.isiter({"This is": "a dict"})
assert es2json.isiter(["this", "is", "a", "list"])
def test_isfile():
assert es2json.isfile("tests/test_es2json_basicfunctions.py")
assert es2json.isfile("es2json/es2json.py")
assert es2json.isfile("tests/test_es2json.py_basicfunctions"+str(uuid.uuid4())) is False
def test_ArrayOrSingleValue():
assert es2json.ArrayOrSingleValue(2) == 2
assert es2json.ArrayOrSingleValue([2]) == 2
assert es2json.ArrayOrSingleValue([1, 2]) == [1, 2]
assert es2json.ArrayOrSingleValue("abc") == "abc"
assert es2json.ArrayOrSingleValue(["abc"]) == "abc"
assert es2json.ArrayOrSingleValue(["abc", "def"]) == ["abc", "def"]
assert es2json.ArrayOrSingleValue([{"foo": "bar"}]) == {"foo": "bar"}
assert es2json.ArrayOrSingleValue([{"foo": "bar"}, {"bar": "foo"}]) == [{"foo": "bar"}, {"bar": "foo"}]
assert es2json.ArrayOrSingleValue({}) is None
assert es2json.ArrayOrSingleValue([]) is None
| 38.472727
| 107
| 0.624291
| 262
| 2,116
| 5.003817
| 0.122137
| 0.337147
| 0.236461
| 0.067124
| 0.768879
| 0.620137
| 0.454615
| 0.335622
| 0.138825
| 0
| 0
| 0.033708
| 0.15879
| 2,116
| 54
| 108
| 39.185185
| 0.702809
| 0
| 0
| 0
| 0
| 0
| 0.17155
| 0.034026
| 0
| 0
| 0
| 0
| 0.809524
| 1
| 0.142857
| true
| 0
| 0.047619
| 0
| 0.190476
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
adcb24e52f7c636d16eeb1b31ddd7ed71357ee16
| 1,502
|
py
|
Python
|
algortimoGA/helper.py
|
lsbloo/GeradorHorariosUfpb
|
599db5ca382424dfc05fad039880b4717612ac44
|
[
"MIT"
] | 6
|
2020-08-04T13:12:42.000Z
|
2020-08-16T13:26:19.000Z
|
algortimoGA/helper.py
|
lsbloo/GeradorHorariosUfpb
|
599db5ca382424dfc05fad039880b4717612ac44
|
[
"MIT"
] | null | null | null |
algortimoGA/helper.py
|
lsbloo/GeradorHorariosUfpb
|
599db5ca382424dfc05fad039880b4717612ac44
|
[
"MIT"
] | null | null | null |
def helpy():
print('Olá.')
print()
print('Aqui segue as informações detalhadas sobre cada comando da ferramenta.')
print('OBS: No primeiro uso da ferramenta é necessario executar o comando 0 e 1. Uma vez importado os arquivos não sera necessário executar novamente o comando 1, lembre-se de sempre deixar o servidor rodando. ;] ')
print()
print('Comando 0: python3 initserver.py ')
print(" -> Inicia o servidor da aplicação em modo escuta, aconselhavel rodar em um bash diferente, o servidor é necessario para importação dos arquivos e execução do algoritmo genético.")
print()
print('Comando 1: python3 main.py import disciplines.csv horarios.csv salas.csv')
print(" -> Realiza a importação dos arquivos listados acima, defina a variavel de ambiente SERVER_SAVE_DIRECTORY de diretorio padrão e coloque seus arquivos lá, de acordo com os seus respectivos nomes e sequência")
print()
print('Comando 2: python3 main.py -i 100 -g 500 -m 0.10 -c 2')
print(" -> Executa o algoritmo genético, é necessario importar os arquivos e carregar as configurações")
print(" -> Configurações: (i = Número de individuos,g = Número de gerações,m = taxa de mutação,c = cruzamento)")
print(" -> Ao final da execução será exportado o melhor quadro gerado, como também o gráfico de acompanhamento de valores e um arquivo .txt com informações do tempo de execução dada as configurações.")
print()
print()
| 75.1
| 228
| 0.708389
| 214
| 1,502
| 4.962617
| 0.565421
| 0.047081
| 0.048023
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01617
| 0.21771
| 1,502
| 19
| 229
| 79.052632
| 0.88766
| 0
| 0
| 0.333333
| 0
| 0.333333
| 0.840773
| 0.013991
| 0
| 0
| 0
| 0
| 0
| 1
| 0.055556
| true
| 0
| 0.277778
| 0
| 0.333333
| 0.944444
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
adcc52421e60daf127b774cd1f1e2238a6434957
| 110
|
py
|
Python
|
hubcare/metrics/pull_request_metrics/acceptance_quality/apps.py
|
aleronupe/2019.1-hubcare-api
|
3f031eac9559a10fdcf70a88ee4c548cf93e4ac2
|
[
"MIT"
] | 7
|
2019-03-31T17:58:45.000Z
|
2020-02-29T22:44:27.000Z
|
hubcare/metrics/pull_request_metrics/acceptance_quality/apps.py
|
aleronupe/2019.1-hubcare-api
|
3f031eac9559a10fdcf70a88ee4c548cf93e4ac2
|
[
"MIT"
] | 90
|
2019-03-26T01:14:54.000Z
|
2021-06-10T21:30:25.000Z
|
hubcare/metrics/pull_request_metrics/acceptance_quality/apps.py
|
aleronupe/2019.1-hubcare-api
|
3f031eac9559a10fdcf70a88ee4c548cf93e4ac2
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class AcceptanceQualityConfig(AppConfig):
name = 'acceptance_quality'
| 18.333333
| 41
| 0.8
| 11
| 110
| 7.909091
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 110
| 5
| 42
| 22
| 0.915789
| 0
| 0
| 0
| 0
| 0
| 0.163636
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
bc0d3000d3e0ee117d4e0320b341bfa418cc7212
| 722
|
py
|
Python
|
gym/rock_base.py
|
nisarkhanatwork/rocksample_deeprl
|
ef51b0234601ccb450aaf4b9ccfc6fbdc0c4cf20
|
[
"MIT"
] | null | null | null |
gym/rock_base.py
|
nisarkhanatwork/rocksample_deeprl
|
ef51b0234601ccb450aaf4b9ccfc6fbdc0c4cf20
|
[
"MIT"
] | null | null | null |
gym/rock_base.py
|
nisarkhanatwork/rocksample_deeprl
|
ef51b0234601ccb450aaf4b9ccfc6fbdc0c4cf20
|
[
"MIT"
] | null | null | null |
from copy import deepcopy
class BaseSimulator:
def __init__(self):
self.state = None
def step(self, action):
self.state, r, t, rtup, etup = self.simulate(self.state, action)
return deepcopy(self.state), r, t, rtup, etup
def reset(self):
raise NotImplementedError
def render(self):
raise NotImplementedError
@staticmethod
def simulate(state, action):
raise NotImplementedError
@staticmethod
def rollout(state, action, use_heuristics=True):
raise NotImplementedError
@staticmethod
def tensor_shape():
raise NotImplementedError
@staticmethod
def state_to_tensor(state):
raise NotImplementedError
| 21.878788
| 72
| 0.66482
| 76
| 722
| 6.210526
| 0.407895
| 0.305085
| 0.305085
| 0.330508
| 0.080508
| 0.080508
| 0
| 0
| 0
| 0
| 0
| 0
| 0.260388
| 722
| 32
| 73
| 22.5625
| 0.883895
| 0
| 0
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.347826
| false
| 0
| 0.043478
| 0
| 0.478261
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
bc136d19625a098c7d94519c6e76aa459462e53a
| 259
|
py
|
Python
|
MontySlackBot/models.py
|
nelliesnoodles/my-first-blog
|
e552ea38891ebe005316487ae32a324659ad6367
|
[
"MIT"
] | null | null | null |
MontySlackBot/models.py
|
nelliesnoodles/my-first-blog
|
e552ea38891ebe005316487ae32a324659ad6367
|
[
"MIT"
] | 5
|
2019-12-13T17:37:55.000Z
|
2021-06-10T20:59:32.000Z
|
MontySlackBot/models.py
|
nelliesnoodles/My-Website
|
e552ea38891ebe005316487ae32a324659ad6367
|
[
"MIT"
] | null | null | null |
from django.db import models
class Team(models.Model):
name = models.CharField(max_length=200)
team_id = models.CharField(max_length=20)
bot_user_id = models.CharField(max_length=20)
bot_access_token = models.CharField(max_length=100)
| 32.375
| 55
| 0.741313
| 38
| 259
| 4.815789
| 0.526316
| 0.327869
| 0.393443
| 0.52459
| 0.338798
| 0.338798
| 0.338798
| 0
| 0
| 0
| 0
| 0.046083
| 0.162162
| 259
| 8
| 55
| 32.375
| 0.797235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
bc202bfd6fc190caaa7e2020d7d39466d85bb1a8
| 69
|
py
|
Python
|
TODO.py
|
avplugarev/NB
|
8b63c2a24fd19dab6870257dfb4095fa0d828f18
|
[
"MIT"
] | null | null | null |
TODO.py
|
avplugarev/NB
|
8b63c2a24fd19dab6870257dfb4095fa0d828f18
|
[
"MIT"
] | null | null | null |
TODO.py
|
avplugarev/NB
|
8b63c2a24fd19dab6870257dfb4095fa0d828f18
|
[
"MIT"
] | null | null | null |
"""
12 добавить в проект файл с описанием проекта и зависимостей
"""
| 17.25
| 60
| 0.73913
| 10
| 69
| 5.1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035088
| 0.173913
| 69
| 4
| 61
| 17.25
| 0.859649
| 0.869565
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
bc283eefb95db1a01cc3d9543941527a9d8b124f
| 392
|
py
|
Python
|
tests/test_leylab_pipelines.py
|
leylabmpi/leylab_pipelines
|
1c94b2ba55d877c489143a105c72c963e1e9ce51
|
[
"MIT"
] | null | null | null |
tests/test_leylab_pipelines.py
|
leylabmpi/leylab_pipelines
|
1c94b2ba55d877c489143a105c72c963e1e9ce51
|
[
"MIT"
] | null | null | null |
tests/test_leylab_pipelines.py
|
leylabmpi/leylab_pipelines
|
1c94b2ba55d877c489143a105c72c963e1e9ce51
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_leylab_pipelines
----------------------------------
Tests for `leylab_pipelines` module.
"""
import sys
import unittest
from leylab_pipelines import leylab_pipelines
class TestLeylab_pipelines(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_someting(self):
pass
| 13.517241
| 46
| 0.614796
| 43
| 392
| 5.44186
| 0.604651
| 0.25641
| 0.094017
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003215
| 0.206633
| 392
| 28
| 47
| 14
| 0.749196
| 0.34949
| 0
| 0.3
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3
| false
| 0.3
| 0.3
| 0
| 0.7
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
bc28dba8b267a1c38a202668f47a7b57c5a05fd4
| 3,355
|
py
|
Python
|
config/about/migrations/0004_auto_20210627_0451.py
|
Gregory124124/ignatovich-main
|
f9bc93a40ae954eaf785880e0aae133561d4e1cd
|
[
"MIT"
] | null | null | null |
config/about/migrations/0004_auto_20210627_0451.py
|
Gregory124124/ignatovich-main
|
f9bc93a40ae954eaf785880e0aae133561d4e1cd
|
[
"MIT"
] | null | null | null |
config/about/migrations/0004_auto_20210627_0451.py
|
Gregory124124/ignatovich-main
|
f9bc93a40ae954eaf785880e0aae133561d4e1cd
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.1.7 on 2021-06-27 01:51
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('about', '0003_auto_20210626_1346'),
]
operations = [
migrations.AlterModelOptions(
name='about',
options={'verbose_name': 'О нас', 'verbose_name_plural': 'О нас'},
),
migrations.AlterModelOptions(
name='galeryphoto',
options={'verbose_name': 'Фотография из галереи', 'verbose_name_plural': 'Фотографии из галереи'},
),
migrations.AlterField(
model_name='about',
name='about_image',
field=models.ImageField(upload_to='about/', verbose_name='Изображение библиотеки'),
),
migrations.AlterField(
model_name='about',
name='image_1',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 1'),
),
migrations.AlterField(
model_name='about',
name='image_2',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 2'),
),
migrations.AlterField(
model_name='about',
name='image_3',
field=models.ImageField(upload_to='about', verbose_name='Изображение 3'),
),
migrations.AlterField(
model_name='about',
name='image_4',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 4'),
),
migrations.AlterField(
model_name='about',
name='image_5',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 5'),
),
migrations.AlterField(
model_name='about',
name='image_6',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 6'),
),
migrations.AlterField(
model_name='about',
name='image_7',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 7'),
),
migrations.AlterField(
model_name='about',
name='image_8',
field=models.ImageField(upload_to='about/', verbose_name='Изображение 8'),
),
migrations.AlterField(
model_name='about',
name='text_1',
field=models.TextField(verbose_name='История библиотеки 1'),
),
migrations.AlterField(
model_name='about',
name='text_2',
field=models.TextField(verbose_name='История библиотеки 2'),
),
migrations.AlterField(
model_name='about',
name='text_3',
field=models.TextField(verbose_name='История библиотеки 3'),
),
migrations.AlterField(
model_name='about',
name='text_about',
field=models.TextField(verbose_name='Дополнительный текст'),
),
migrations.AlterField(
model_name='about',
name='title_about',
field=models.TextField(verbose_name='Основной текст'),
),
migrations.AlterField(
model_name='galeryphoto',
name='image',
field=models.ImageField(upload_to='galery/', verbose_name='Изображение'),
),
]
| 34.587629
| 110
| 0.565723
| 315
| 3,355
| 5.825397
| 0.190476
| 0.113896
| 0.20436
| 0.237057
| 0.758583
| 0.73733
| 0.635422
| 0.274659
| 0.274659
| 0
| 0
| 0.022855
| 0.308793
| 3,355
| 96
| 111
| 34.947917
| 0.768435
| 0.013413
| 0
| 0.533333
| 1
| 0
| 0.193773
| 0.006953
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.011111
| 0
| 0.044444
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
70c9c133059af102ad2e00e01eeb06c6bcf1782f
| 231
|
py
|
Python
|
demo_norm.py
|
Monster880/pytorch_py
|
9c5ac5974f48edb5ea3d897a1100a63d488c61d9
|
[
"MIT"
] | null | null | null |
demo_norm.py
|
Monster880/pytorch_py
|
9c5ac5974f48edb5ea3d897a1100a63d488c61d9
|
[
"MIT"
] | null | null | null |
demo_norm.py
|
Monster880/pytorch_py
|
9c5ac5974f48edb5ea3d897a1100a63d488c61d9
|
[
"MIT"
] | null | null | null |
import torch
a = torch.rand(2,3)
b = torch.rand(2,3)
print(a)
print(b)
print(torch.dist(a,b,p=1))
print(torch.dist(a,b,p=2))
print(torch.dist(a,b,p=3))
print(torch.norm(a))
print(torch.norm(a, p=2))
print(torch.norm(a, p='fro'))
| 16.5
| 29
| 0.658009
| 53
| 231
| 2.867925
| 0.245283
| 0.394737
| 0.276316
| 0.296053
| 0.546053
| 0.335526
| 0
| 0
| 0
| 0
| 0
| 0.037915
| 0.08658
| 231
| 14
| 29
| 16.5
| 0.682464
| 0
| 0
| 0
| 0
| 0
| 0.012931
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.090909
| 0
| 0.090909
| 0.727273
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
70cb18aa5b8b2321740dd2f1ef7fd56f3579e98a
| 125
|
py
|
Python
|
shrike-examples/components/count_rows/run.py
|
lynochka/azure-ml-problem-sets
|
e7e69de763444c5603e4455e35e69e917081a4cc
|
[
"MIT"
] | 3
|
2021-07-27T16:28:51.000Z
|
2021-11-15T18:29:02.000Z
|
shrike-examples/components/count_rows/run.py
|
lynochka/azure-ml-problem-sets
|
e7e69de763444c5603e4455e35e69e917081a4cc
|
[
"MIT"
] | null | null | null |
shrike-examples/components/count_rows/run.py
|
lynochka/azure-ml-problem-sets
|
e7e69de763444c5603e4455e35e69e917081a4cc
|
[
"MIT"
] | 7
|
2021-08-09T15:04:03.000Z
|
2022-03-09T05:48:56.000Z
|
"""run.py for demo component"""
import os
from contoso.count_rows_script import main
if __name__ == "__main__":
main()
| 15.625
| 42
| 0.712
| 18
| 125
| 4.388889
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168
| 125
| 7
| 43
| 17.857143
| 0.759615
| 0.2
| 0
| 0
| 0
| 0
| 0.085106
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
70d0d0bed3789ea025e1dd34d2d36423b788e40b
| 25
|
py
|
Python
|
sti/sti/sti/__init__.py
|
TheE77/chat
|
138ba2c9495cba9d72887736ad033603ddeb87ac
|
[
"MIT"
] | 1
|
2016-05-23T12:27:12.000Z
|
2016-05-23T12:27:12.000Z
|
sti/sti/sti/__init__.py
|
TheE77/chat
|
138ba2c9495cba9d72887736ad033603ddeb87ac
|
[
"MIT"
] | null | null | null |
sti/sti/sti/__init__.py
|
TheE77/chat
|
138ba2c9495cba9d72887736ad033603ddeb87ac
|
[
"MIT"
] | null | null | null |
"""
Package for sti.
"""
| 6.25
| 16
| 0.52
| 3
| 25
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 25
| 3
| 17
| 8.333333
| 0.65
| 0.64
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
70dab034ff20b0cf9225f2472c4f72d5d288417f
| 22
|
py
|
Python
|
eventsourcing/__init__.py
|
HanhThong/eventsourcing
|
99c560cca7c6e9d59855bcd82c371794e1a8bfb9
|
[
"BSD-3-Clause"
] | 39
|
2019-05-21T11:03:23.000Z
|
2022-03-22T11:40:17.000Z
|
eventsourcing/__init__.py
|
HanhThong/eventsourcing
|
99c560cca7c6e9d59855bcd82c371794e1a8bfb9
|
[
"BSD-3-Clause"
] | 749
|
2019-05-16T19:18:03.000Z
|
2022-03-31T09:03:44.000Z
|
eventsourcing/__init__.py
|
HanhThong/eventsourcing
|
99c560cca7c6e9d59855bcd82c371794e1a8bfb9
|
[
"BSD-3-Clause"
] | 19
|
2019-06-25T08:45:27.000Z
|
2022-03-08T17:35:51.000Z
|
__version__ = "8.1.0"
| 11
| 21
| 0.636364
| 4
| 22
| 2.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 0.136364
| 22
| 1
| 22
| 22
| 0.368421
| 0
| 0
| 0
| 0
| 0
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cb17a5a8d4a3e990e200192461d9e5ceeea58b5e
| 135
|
py
|
Python
|
zopeskel/templates/plone2_theme/Extensions/__init__.py
|
jean/ZopeSkel
|
c9084f30aa03c2b36411bce5c75b9b85ccbabb2f
|
[
"MIT"
] | 1
|
2021-05-31T13:51:43.000Z
|
2021-05-31T13:51:43.000Z
|
zopeskel/templates/plone2_theme/Extensions/__init__.py
|
jean/ZopeSkel
|
c9084f30aa03c2b36411bce5c75b9b85ccbabb2f
|
[
"MIT"
] | null | null | null |
zopeskel/templates/plone2_theme/Extensions/__init__.py
|
jean/ZopeSkel
|
c9084f30aa03c2b36411bce5c75b9b85ccbabb2f
|
[
"MIT"
] | null | null | null |
# this file is here to make Install.py and utils.py importable.
# keep these lines to make it non-zero size and have winzip cooperate.
| 45
| 70
| 0.77037
| 25
| 135
| 4.16
| 0.84
| 0.115385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 135
| 2
| 71
| 67.5
| 0.936937
| 0.962963
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cb1e05ded26565683d926ea596347a8b95ff657f
| 582
|
py
|
Python
|
code_all/day08/exercise01.py
|
testcg/python
|
4db4bd5d0e44af807d2df80cf8c8980b40cc03c4
|
[
"MIT"
] | null | null | null |
code_all/day08/exercise01.py
|
testcg/python
|
4db4bd5d0e44af807d2df80cf8c8980b40cc03c4
|
[
"MIT"
] | null | null | null |
code_all/day08/exercise01.py
|
testcg/python
|
4db4bd5d0e44af807d2df80cf8c8980b40cc03c4
|
[
"MIT"
] | null | null | null |
"""
练习1:请排列出2个色子可以组成的所有可能(列表)
色子(1~6) range(1,7)
色子(1~6)
练习2:请排列出3个色子可以组成的所有可能(列表)
"""
# result = []
# for x in range(1, 7):
# for y in range(1, 7):
# result.append((x , y))
result = [(x, y) for x in range(1, 7) for y in range(1, 7)]
print(result)
# result = []
# for x in range(1, 7): # 1 2
# for y in range(1, 7): # 1 2 ...
# for z in range(1, 7): # 123456 123456 ...
# result.append((x, y, z))
result = [(x, y, z) for x in range(1, 7) for y in range(1, 7) for z in range(1, 7)]
print(result)
| 26.454545
| 83
| 0.489691
| 103
| 582
| 2.76699
| 0.194175
| 0.231579
| 0.270175
| 0.315789
| 0.589474
| 0.589474
| 0.42807
| 0.273684
| 0.273684
| 0.273684
| 0
| 0.116456
| 0.321306
| 582
| 21
| 84
| 27.714286
| 0.605063
| 0.623711
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
cb4501a9cc9e26ecee82a818287a97584d0d1df8
| 31
|
py
|
Python
|
src/common/__init__.py
|
sUeharaE4/gitlab-dashboard
|
4f86cfe21e625badb82086e5ba10f4b532ebb2d1
|
[
"MIT"
] | null | null | null |
src/common/__init__.py
|
sUeharaE4/gitlab-dashboard
|
4f86cfe21e625badb82086e5ba10f4b532ebb2d1
|
[
"MIT"
] | 1
|
2022-01-02T01:31:06.000Z
|
2022-01-02T01:31:06.000Z
|
src/common/__init__.py
|
sUeharaE4/gitlab-dashboard
|
4f86cfe21e625badb82086e5ba10f4b532ebb2d1
|
[
"MIT"
] | null | null | null |
"""Provide common features."""
| 15.5
| 30
| 0.677419
| 3
| 31
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096774
| 31
| 1
| 31
| 31
| 0.75
| 0.774194
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cb6ce9a384609086ccc921f8109d76e60391ad41
| 171
|
py
|
Python
|
virtual/bin/django-admin.py
|
AhmadSAshraf/Photo-Gallery
|
1724affa92c057792a2c8b7e69b7ad0374254b12
|
[
"MIT"
] | null | null | null |
virtual/bin/django-admin.py
|
AhmadSAshraf/Photo-Gallery
|
1724affa92c057792a2c8b7e69b7ad0374254b12
|
[
"MIT"
] | 4
|
2020-06-06T00:47:45.000Z
|
2021-09-08T01:43:36.000Z
|
virtual/bin/django-admin.py
|
AhmadSAshraf/Photo-Gallery
|
1724affa92c057792a2c8b7e69b7ad0374254b12
|
[
"MIT"
] | null | null | null |
#!/media/mj/Local Disk/tutoring/ahmed/perg/virtual/bin/python3.6
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| 28.5
| 64
| 0.783626
| 24
| 171
| 5.125
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012903
| 0.093567
| 171
| 5
| 65
| 34.2
| 0.780645
| 0.368421
| 0
| 0
| 0
| 0
| 0.074766
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
cb7900a7907762eedfec031d231478ca5738e787
| 937
|
gyp
|
Python
|
test/base/base.gyp
|
junmin-zhu/crosswalk
|
6c2ab70dcdbdda99da85fa6c8f79b5371aafbb1d
|
[
"BSD-3-Clause"
] | 3
|
2018-05-13T07:02:56.000Z
|
2019-10-29T19:34:10.000Z
|
test/base/base.gyp
|
junmin-zhu/crosswalk
|
6c2ab70dcdbdda99da85fa6c8f79b5371aafbb1d
|
[
"BSD-3-Clause"
] | 1
|
2015-07-14T21:11:28.000Z
|
2015-07-14T21:11:28.000Z
|
test/base/base.gyp
|
junmin-zhu/crosswalk
|
6c2ab70dcdbdda99da85fa6c8f79b5371aafbb1d
|
[
"BSD-3-Clause"
] | 8
|
2015-06-02T21:13:45.000Z
|
2022-01-20T10:36:43.000Z
|
{
'targets': [
{
'target_name': 'xwalk_test_base',
'type': 'static_library',
'dependencies': [
# FIXME(tmpsantos): we should depend on runtime
# here but it is not really a module yet.
'../../../base/base.gyp:base',
'../../../base/base.gyp:test_support_base',
'../../../content/content.gyp:content_browser',
'../../../content/content_shell_and_tests.gyp:test_support_content'
'../../../net/net.gyp:net',
'../../../skia/skia.gyp:skia',
'../../../testing/gtest.gyp:gtest',
'../../../ui/base/ui_base.gyp:ui_base',
'../../../url/url.gyp:url_lib',
],
'sources': [
'in_process_browser_test.cc',
'in_process_browser_test.h',
'xwalk_test_launcher.cc',
'xwalk_test_suite.cc',
'xwalk_test_suite.h',
'xwalk_test_utils.cc',
'xwalk_test_utils.h',
],
},
],
}
| 30.225806
| 75
| 0.524013
| 105
| 937
| 4.380952
| 0.457143
| 0.117391
| 0.071739
| 0.086957
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.258271
| 937
| 30
| 76
| 31.233333
| 0.661871
| 0.090715
| 0
| 0.107143
| 0
| 0
| 0.636042
| 0.466431
| 0
| 0
| 0
| 0.033333
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
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