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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
97f121027e62941f27d940447c4f471f8f19dbcb
| 64
|
py
|
Python
|
test/pytest.py
|
kagesaburo27/py2assignment
|
1a91eb7a7610f157f4f0de371af048cee2ee389c
|
[
"Unlicense"
] | null | null | null |
test/pytest.py
|
kagesaburo27/py2assignment
|
1a91eb7a7610f157f4f0de371af048cee2ee389c
|
[
"Unlicense"
] | null | null | null |
test/pytest.py
|
kagesaburo27/py2assignment
|
1a91eb7a7610f157f4f0de371af048cee2ee389c
|
[
"Unlicense"
] | null | null | null |
import sys
sys.path.append('.')
from src.WebScrapping import *
| 21.333333
| 30
| 0.734375
| 9
| 64
| 5.222222
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 64
| 3
| 30
| 21.333333
| 0.839286
| 0
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| 0
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| 0.015873
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| 1
| 0
| true
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| 0.666667
| 0
| 0.666667
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| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3f17f129964c7a234d847fccf57a839f941310b0
| 119
|
py
|
Python
|
scipyplot/plot/utils/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 4
|
2016-12-02T20:24:54.000Z
|
2020-07-26T20:54:03.000Z
|
scipyplot/plot/utils/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 6
|
2017-01-17T01:22:14.000Z
|
2021-06-10T09:26:12.000Z
|
scipyplot/plot/utils/__init__.py
|
robertocalandra/scipyplot
|
c2f2ffe00207fbb5b78732981d572c031660ed74
|
[
"MIT"
] | 1
|
2021-04-22T08:46:22.000Z
|
2021-04-22T08:46:22.000Z
|
from __future__ import absolute_import
from .niceFigure import niceFigure
from .interactivePlot import interactivePlot
| 29.75
| 44
| 0.882353
| 13
| 119
| 7.692308
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10084
| 119
| 4
| 44
| 29.75
| 0.934579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3f187a1c882b7516f053cb91b3faf1020edf5490
| 39
|
py
|
Python
|
n3jet/general/fks/__init__.py
|
JosephPB/n3jet
|
f097c5e829b5f86fc0ce9007c5fa76ea229dfc56
|
[
"MIT"
] | 3
|
2020-06-03T13:50:59.000Z
|
2021-12-01T08:21:34.000Z
|
n3jet/general/fks/__init__.py
|
JosephPB/n3jet
|
f097c5e829b5f86fc0ce9007c5fa76ea229dfc56
|
[
"MIT"
] | 2
|
2021-08-25T16:15:38.000Z
|
2022-02-10T03:36:12.000Z
|
n3jet/general/fks/__init__.py
|
JosephPB/n3jet
|
f097c5e829b5f86fc0ce9007c5fa76ea229dfc56
|
[
"MIT"
] | 1
|
2020-06-12T15:00:56.000Z
|
2020-06-12T15:00:56.000Z
|
from .fks_model_run import FKSModelRun
| 19.5
| 38
| 0.871795
| 6
| 39
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 39
| 1
| 39
| 39
| 0.914286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3f1c33310114889f778232d2ba2c85675d7ef194
| 36
|
py
|
Python
|
tests/__init__.py
|
steveYeah/PyBomb
|
5f545144d3b690a5ac031ea45242d05f4890d65f
|
[
"MIT"
] | 7
|
2016-12-07T14:48:30.000Z
|
2020-10-01T15:42:01.000Z
|
tests/__init__.py
|
steveYeah/PyBomb
|
5f545144d3b690a5ac031ea45242d05f4890d65f
|
[
"MIT"
] | 21
|
2015-08-25T22:18:33.000Z
|
2021-09-16T21:52:34.000Z
|
tests/__init__.py
|
steveYeah/PyBomb
|
5f545144d3b690a5ac031ea45242d05f4890d65f
|
[
"MIT"
] | 6
|
2017-04-30T16:44:17.000Z
|
2020-10-01T19:17:49.000Z
|
"""Tests for the PyBomb library."""
| 18
| 35
| 0.666667
| 5
| 36
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138889
| 36
| 1
| 36
| 36
| 0.774194
| 0.805556
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3f2c19ad459fdc4c032ef2641298b176c4bf9a28
| 167
|
py
|
Python
|
savecode/threeyears/idownserver/taskdispatcher/strategy/stgunits/__init__.py
|
Octoberr/swm0920
|
8f05a6b91fc205960edd57f9076facec04f49a1a
|
[
"Apache-2.0"
] | 2
|
2019-05-19T11:54:26.000Z
|
2019-05-19T12:03:49.000Z
|
savecode/threeyears/idownserver/taskdispatcher/strategy/stgunits/__init__.py
|
Octoberr/swm0920
|
8f05a6b91fc205960edd57f9076facec04f49a1a
|
[
"Apache-2.0"
] | 1
|
2020-11-27T07:55:15.000Z
|
2020-11-27T07:55:15.000Z
|
savecode/threeyears/idownserver/taskdispatcher/strategy/stgunits/__init__.py
|
Octoberr/swm0920
|
8f05a6b91fc205960edd57f9076facec04f49a1a
|
[
"Apache-2.0"
] | 2
|
2021-09-06T18:06:12.000Z
|
2021-12-31T07:44:43.000Z
|
"""task dispatch strategy"""
# -*- coding:utf-8 -*-
from .stgbandwidth import StgBandWidth
from .stgtasklen import StgTaskLen
from .strategybase import StrategyBase
| 20.875
| 38
| 0.766467
| 18
| 167
| 7.111111
| 0.611111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006849
| 0.125749
| 167
| 7
| 39
| 23.857143
| 0.869863
| 0.263473
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3f52d8553048ca6bbd8369e2a67dc63cb0a50acb
| 48
|
py
|
Python
|
Curso_Python_3_UDEMY/pacotes/pacote_v2.py
|
DanilooSilva/Cursos_de_Python
|
8f167a4c6e16f01601e23b6f107578aa1454472d
|
[
"MIT"
] | null | null | null |
Curso_Python_3_UDEMY/pacotes/pacote_v2.py
|
DanilooSilva/Cursos_de_Python
|
8f167a4c6e16f01601e23b6f107578aa1454472d
|
[
"MIT"
] | null | null | null |
Curso_Python_3_UDEMY/pacotes/pacote_v2.py
|
DanilooSilva/Cursos_de_Python
|
8f167a4c6e16f01601e23b6f107578aa1454472d
|
[
"MIT"
] | null | null | null |
from pacotes.pacote1.modulo2 import main
main()
| 16
| 40
| 0.8125
| 7
| 48
| 5.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046512
| 0.104167
| 48
| 3
| 41
| 16
| 0.860465
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3f67d4cb363eaf724a09702f82e9a1659c9def8a
| 240
|
py
|
Python
|
somenlp/distant_supervision/__init__.py
|
dave-s477/SoMeNLP
|
79007a78348ce34610a8cccbabbe8e571039a8db
|
[
"MIT"
] | null | null | null |
somenlp/distant_supervision/__init__.py
|
dave-s477/SoMeNLP
|
79007a78348ce34610a8cccbabbe8e571039a8db
|
[
"MIT"
] | 3
|
2022-02-07T11:56:37.000Z
|
2022-02-08T10:04:45.000Z
|
somenlp/distant_supervision/__init__.py
|
BeTKH/SoMeNLP
|
0f22931b20f7c2f21c255410984257f0e3d225f6
|
[
"MIT"
] | 1
|
2022-01-26T22:01:55.000Z
|
2022-01-26T22:01:55.000Z
|
from .packages import load_package_names
from .perform_wiki_data_queries import query_wikidata
from .gen_sequences import generate_triplets
from .gen_wiktionary_dict import load_wiktionary
from .combine_info import merge_results, load_dicts
| 48
| 53
| 0.891667
| 35
| 240
| 5.714286
| 0.657143
| 0.1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 240
| 5
| 54
| 48
| 0.909091
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
| 0
| true
| 0
| 1
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
58b54ed1405ab84888ededa9c40a81b19735e16c
| 3,179
|
py
|
Python
|
kubernetes/test/test_apps_v1beta1_api.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
kubernetes/test/test_apps_v1beta1_api.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
kubernetes/test/test_apps_v1beta1_api.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.5.0-beta.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
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 os
import sys
import unittest
import kubernetes.client
from kubernetes.client.rest import ApiException
from kubernetes.client.apis.apps_v1beta1_api import AppsV1beta1Api
class TestAppsV1beta1Api(unittest.TestCase):
""" AppsV1beta1Api unit test stubs """
def setUp(self):
self.api = kubernetes.client.apis.apps_v1beta1_api.AppsV1beta1Api()
def tearDown(self):
pass
def test_create_namespaced_stateful_set(self):
"""
Test case for create_namespaced_stateful_set
"""
pass
def test_delete_collection_namespaced_stateful_set(self):
"""
Test case for delete_collection_namespaced_stateful_set
"""
pass
def test_delete_namespaced_stateful_set(self):
"""
Test case for delete_namespaced_stateful_set
"""
pass
def test_get_api_resources(self):
"""
Test case for get_api_resources
"""
pass
def test_list_namespaced_stateful_set(self):
"""
Test case for list_namespaced_stateful_set
"""
pass
def test_list_stateful_set_for_all_namespaces(self):
"""
Test case for list_stateful_set_for_all_namespaces
"""
pass
def test_patch_namespaced_stateful_set(self):
"""
Test case for patch_namespaced_stateful_set
"""
pass
def test_patch_namespaced_stateful_set_status(self):
"""
Test case for patch_namespaced_stateful_set_status
"""
pass
def test_read_namespaced_stateful_set(self):
"""
Test case for read_namespaced_stateful_set
"""
pass
def test_read_namespaced_stateful_set_status(self):
"""
Test case for read_namespaced_stateful_set_status
"""
pass
def test_replace_namespaced_stateful_set(self):
"""
Test case for replace_namespaced_stateful_set
"""
pass
def test_replace_namespaced_stateful_set_status(self):
"""
Test case for replace_namespaced_stateful_set_status
"""
pass
if __name__ == '__main__':
unittest.main()
| 22.076389
| 105
| 0.64643
| 370
| 3,179
| 5.254054
| 0.335135
| 0.124486
| 0.216049
| 0.092593
| 0.541667
| 0.516461
| 0.416667
| 0.212449
| 0
| 0
| 0
| 0.009296
| 0.289399
| 3,179
| 143
| 106
| 22.230769
| 0.851262
| 0.423718
| 0
| 0.342105
| 1
| 0
| 0.005857
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.368421
| false
| 0.342105
| 0.184211
| 0
| 0.578947
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
58c6a9afb1930a9e5f071f5274af8fcb5b949270
| 145
|
py
|
Python
|
playground/MINE/__init__.py
|
jizongFox/deep-clustering-toolbox
|
0721cbbb278af027409ed4c115ccc743b6daed1b
|
[
"MIT"
] | 34
|
2019-08-05T03:48:36.000Z
|
2022-03-29T03:04:51.000Z
|
playground/MINE/__init__.py
|
jizongFox/deep-clustering-toolbox
|
0721cbbb278af027409ed4c115ccc743b6daed1b
|
[
"MIT"
] | 10
|
2019-05-03T21:02:50.000Z
|
2021-12-23T08:01:30.000Z
|
playground/MINE/__init__.py
|
ETS-Research-Repositories/deep-clustering-toolbox
|
0721cbbb278af027409ed4c115ccc743b6daed1b
|
[
"MIT"
] | 5
|
2019-09-29T07:56:03.000Z
|
2021-04-22T12:08:50.000Z
|
"""
This folder is modified from https://github.com/sungyubkim/MINE-Mutual-Information-Neural-Estimation-
from mutual information estimation
"""
| 29
| 101
| 0.8
| 18
| 145
| 6.444444
| 0.777778
| 0.293103
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082759
| 145
| 4
| 102
| 36.25
| 0.87218
| 0.937931
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 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
| 5
|
58d5d6a904362a570a9252ed0c10507b4052fbcd
| 110
|
py
|
Python
|
src/genregraph/__init__.py
|
sabbirahm3d/music-genre-nw
|
708f2908bf2153ee97203d63a5c5ee7708b71dc7
|
[
"MIT"
] | null | null | null |
src/genregraph/__init__.py
|
sabbirahm3d/music-genre-nw
|
708f2908bf2153ee97203d63a5c5ee7708b71dc7
|
[
"MIT"
] | null | null | null |
src/genregraph/__init__.py
|
sabbirahm3d/music-genre-nw
|
708f2908bf2153ee97203d63a5c5ee7708b71dc7
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from .graph import NetworkGraph
from .draw import GraphPlotter
| 18.333333
| 31
| 0.709091
| 15
| 110
| 5.2
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010638
| 0.145455
| 110
| 5
| 32
| 22
| 0.819149
| 0.381818
| 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
| 1
| 0
|
0
| 5
|
58f2caef423c15908caf91e80aaf62f09d7a70c4
| 119
|
py
|
Python
|
test/input/083.py
|
EliRibble/pyfmt
|
e84a5531a7c06703eddd9dbc2072b0c8deae8c57
|
[
"MIT"
] | null | null | null |
test/input/083.py
|
EliRibble/pyfmt
|
e84a5531a7c06703eddd9dbc2072b0c8deae8c57
|
[
"MIT"
] | null | null | null |
test/input/083.py
|
EliRibble/pyfmt
|
e84a5531a7c06703eddd9dbc2072b0c8deae8c57
|
[
"MIT"
] | null | null | null |
if False:
print("Really, really false.")
elif False:
print("nested")
else:
if "seems rediculous":
print("it is.")
| 14.875
| 31
| 0.663866
| 17
| 119
| 4.647059
| 0.647059
| 0.253165
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.159664
| 119
| 7
| 32
| 17
| 0.79
| 0
| 0
| 0
| 0
| 0
| 0.411765
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.428571
| 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
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
4502b22023ca50cdce35772b2fe5e42e6293b0f0
| 90
|
py
|
Python
|
helper.py
|
gvevance/engineers-tools
|
944bf95aa7b62ded522727038a88d4b8232a7d1d
|
[
"MIT"
] | null | null | null |
helper.py
|
gvevance/engineers-tools
|
944bf95aa7b62ded522727038a88d4b8232a7d1d
|
[
"MIT"
] | null | null | null |
helper.py
|
gvevance/engineers-tools
|
944bf95aa7b62ded522727038a88d4b8232a7d1d
|
[
"MIT"
] | null | null | null |
''' Contains helper functions. '''
def valid_expression(expression_str) :
return True
| 22.5
| 38
| 0.733333
| 10
| 90
| 6.4
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155556
| 90
| 4
| 39
| 22.5
| 0.842105
| 0.288889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
188e2c06eb08f781f620b75cea47f13179b4e4f3
| 71,091
|
py
|
Python
|
appengine/monorail/api/v3/api_proto/projects_prpc_pb2.py
|
xswz8015/infra
|
f956b78ce4c39cc76acdda47601b86794ae0c1ba
|
[
"BSD-3-Clause"
] | null | null | null |
appengine/monorail/api/v3/api_proto/projects_prpc_pb2.py
|
xswz8015/infra
|
f956b78ce4c39cc76acdda47601b86794ae0c1ba
|
[
"BSD-3-Clause"
] | 7
|
2022-02-15T01:11:37.000Z
|
2022-03-02T12:46:13.000Z
|
appengine/monorail/api/v3/api_proto/projects_prpc_pb2.py
|
NDevTK/chromium-infra
|
d38e088e158d81f7f2065a38aa1ea1894f735ec4
|
[
"BSD-3-Clause"
] | null | null | null |
# Generated by the pRPC protocol buffer compiler plugin. DO NOT EDIT!
# source: api/v3/api_proto/projects.proto
import base64
import zlib
from google.protobuf import descriptor_pb2
# Includes description of the api/v3/api_proto/projects.proto and all of its transitive
# dependencies. Includes source code info.
FILE_DESCRIPTOR_SET = descriptor_pb2.FileDescriptorSet()
FILE_DESCRIPTOR_SET.ParseFromString(zlib.decompress(base64.b64decode(
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_INDEX = {
f.name: {
'descriptor': f,
'services': {s.name: s for s in f.service},
}
for f in FILE_DESCRIPTOR_SET.file
}
ProjectsServiceDescription = {
'file_descriptor_set': FILE_DESCRIPTOR_SET,
'file_descriptor': _INDEX[u'api/v3/api_proto/projects.proto']['descriptor'],
'service_descriptor': _INDEX[u'api/v3/api_proto/projects.proto']['services'][u'Projects'],
}
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0
| 5
|
189757a8b85f64d8f7eb7db6b8f9e7ecff189370
| 1,082
|
py
|
Python
|
setup.py
|
tstringer/azure-ansible-base
|
87acb5d236caa01cda48f58b2d39a6a4db047998
|
[
"MIT"
] | null | null | null |
setup.py
|
tstringer/azure-ansible-base
|
87acb5d236caa01cda48f58b2d39a6a4db047998
|
[
"MIT"
] | null | null | null |
setup.py
|
tstringer/azure-ansible-base
|
87acb5d236caa01cda48f58b2d39a6a4db047998
|
[
"MIT"
] | null | null | null |
from setuptools import setup
setup(
name='azure-ansible-base',
version='1.0.0',
description='Azure SDK dependencies for Ansible Azure modules',
author='Thomas Stringer',
author_email='me@trstringer.com',
url='https://github.com/tstringer/azure-ansible-base',
keywords=['azure'],
classifiers=[
'Development Status :: 4 - Beta',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'License :: OSI Approved :: MIT License'
],
install_requires=[
'azure-mgmt-compute~=2.0.0',
'azure-mgmt-network~=1.3.0',
'azure-mgmt-storage~=1.2.0',
'azure-mgmt-resource~=1.1.0',
'azure-storage~=0.35.1',
'azure-cli-core~=2.0.12',
'msrestazure~=0.4.11'
]
)
| 32.787879
| 67
| 0.580407
| 126
| 1,082
| 4.968254
| 0.460317
| 0.242812
| 0.319489
| 0.207668
| 0.086262
| 0
| 0
| 0
| 0
| 0
| 0
| 0.049322
| 0.250462
| 1,082
| 32
| 68
| 33.8125
| 0.722565
| 0
| 0
| 0
| 0
| 0
| 0.620148
| 0.133087
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.032258
| 0
| 0.032258
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
7a088a32682ebbac18eabd4bb87813c394434f87
| 3,413
|
py
|
Python
|
crux/exceptions.py
|
anshul-patel-infostretch/crux-python
|
e112a09ff61049b2396cf68f947c7583f67488c6
|
[
"MIT"
] | null | null | null |
crux/exceptions.py
|
anshul-patel-infostretch/crux-python
|
e112a09ff61049b2396cf68f947c7583f67488c6
|
[
"MIT"
] | 2
|
2019-07-30T14:37:01.000Z
|
2019-09-09T06:12:23.000Z
|
crux/exceptions.py
|
anshul-patel-infostretch/crux-python
|
e112a09ff61049b2396cf68f947c7583f67488c6
|
[
"MIT"
] | null | null | null |
"""Module contains the set of crux-python's exceptions."""
from typing import Dict, Union # noqa: F401
import requests # pylint: disable=unused-import
class CruxAPIError(Exception):
"""Exception which should be raised when the API response expects an error."""
def __init__(self, message):
# type: (Dict[str, Union[str, int]]) -> None
"""
Args:
message (str): Human readable string describing the exception.
Attributes:
message (str): Human readable string describing the exception.
status_code (int): Exception error code.
"""
super(CruxAPIError, self).__init__(message)
self.status_code = message["statusCode"]
self.message = message
def __str__(self):
return "{message}".format(message=self.message)
class CruxClientError(Exception):
"""Exception which should be raised when the client SDK expects an error."""
def __init__(self, message):
# type: (str) -> None
"""
Args:
message (str): Human readable string describing the exception.
Attributes:
message (str): Human readable string describing the exception.
"""
super(CruxClientError, self).__init__(message)
self.message = message
def __str__(self):
return "{message}".format(message=self.message)
class CruxClientHTTPError(CruxClientError):
"""Exception should be raised when SDK expects any HTTP related errors."""
def __init__(self, message, response):
# type: (str, requests.Response) -> None
"""
Args:
message (str): Human readable string describing the exception.
response (requests.Response): Response object.
Attributes:
message (str): Human readable string describing the exception.
response (requests.Response): Response object.
"""
super(CruxClientHTTPError, self).__init__(message)
self.message = message
self.response = response
def __str__(self):
return "{message}".format(message=self.message)
class CruxClientTooManyRedirects(CruxClientError):
"""Exception should be raised when SDK gets too many redirects."""
def __str__(self):
return "{message}".format(message=self.message)
class CruxClientConnectionError(CruxClientError):
"""Exception should be raised when SDK expects any connection related errors."""
def __str__(self):
return "{message}".format(message=self.message)
class CruxClientTimeout(CruxClientError):
"""Exception should be raised when SDK expects any timeout related errors."""
def __str__(self):
return "{message}".format(message=self.message)
class CruxResourceNotFoundError(CruxAPIError):
"""Exception which should be raised when Crux Resource is not found."""
def __init__(self, message):
# type: (Dict[str, Union[str, int]]) -> None
"""
Args:
message (str): Human readable string describing the exception.
Attributes:
message (str): Human readable string describing the exception.
status_code (int): Exception error code.
"""
super(CruxResourceNotFoundError, self).__init__(message)
self.message = message
def __str__(self):
return "{message}".format(message=self.message)
| 31.311927
| 84
| 0.650161
| 357
| 3,413
| 6.039216
| 0.212885
| 0.076531
| 0.083488
| 0.085343
| 0.737477
| 0.737477
| 0.707328
| 0.686456
| 0.622449
| 0.54128
| 0
| 0.001169
| 0.248169
| 3,413
| 108
| 85
| 31.601852
| 0.839049
| 0.448286
| 0
| 0.567568
| 0
| 0
| 0.045145
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.297297
| false
| 0
| 0.054054
| 0.189189
| 0.72973
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 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
| 5
|
e1bcc563966f4b13bba25ef6ea2c8145cb26157a
| 124
|
py
|
Python
|
stachrl/agents/__init__.py
|
christophstach/reinforcement-learning-klutzy-back-phoebe
|
7fdf557f51ea29038a193fbfc6b63261e5fe4685
|
[
"MIT"
] | null | null | null |
stachrl/agents/__init__.py
|
christophstach/reinforcement-learning-klutzy-back-phoebe
|
7fdf557f51ea29038a193fbfc6b63261e5fe4685
|
[
"MIT"
] | null | null | null |
stachrl/agents/__init__.py
|
christophstach/reinforcement-learning-klutzy-back-phoebe
|
7fdf557f51ea29038a193fbfc6b63261e5fe4685
|
[
"MIT"
] | null | null | null |
from .dqn import DQNAgent
from .ddqn import DDQNAgent
from .qlearning import QLearningAgent
from .random import RandomAgent
| 24.8
| 37
| 0.83871
| 16
| 124
| 6.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 124
| 4
| 38
| 31
| 0.962963
| 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
| 1
| 0
|
0
| 5
|
e1bf6c52917c9abd550c942ea45a966a3338fdd8
| 5,286
|
py
|
Python
|
Problem5[Medium]/main.py
|
Philipnah/DailyCodingProblem
|
da0f1fe6cb124ba719e0a94622db68645f8e23a7
|
[
"MIT"
] | null | null | null |
Problem5[Medium]/main.py
|
Philipnah/DailyCodingProblem
|
da0f1fe6cb124ba719e0a94622db68645f8e23a7
|
[
"MIT"
] | null | null | null |
Problem5[Medium]/main.py
|
Philipnah/DailyCodingProblem
|
da0f1fe6cb124ba719e0a94622db68645f8e23a7
|
[
"MIT"
] | null | null | null |
# Given an integer n and a list of integers l,
# write a function that randomly generates a number from 0 to n-1
# that isn't in l (uniform).
from random import randint
import time
l = [568, 824, 249, 39, 550, 191, 387, 854, 888, 783, 564, 58, 926, 446, 486, 994, 93, 182, 870, 52, 82, 62, 503, 150, 821, 974, 528, 637, 386, 416, 701, 454, 327, 709, 801, 166, 985, 311, 336, 255, 367, 61, 128, 864, 385, 518, 199, 401, 210, 776, 245, 886, 427, 770, 84, 346, 176, 313, 742, 437, 140, 282, 811, 634, 308, 356, 906, 159, 916, 556, 660, 753, 194, 181, 816, 830, 696, 291, 462, 101, 493, 422, 707, 693, 399, 572, 397, 251, 904, 643, 370, 885, 880, 803, 979, 991, 625, 237, 45, 699, 99, 576, 788, 566, 50, 582, 402, 421, 211, 835, 768, 88, 104, 261, 716, 428, 895, 592, 855, 802, 380, 959, 619, 903, 918, 635, 481, 600, 850, 606, 558, 429, 417, 443, 729, 515, 175, 47, 555, 465, 542, 977, 354, 966, 103, 819, 86, 533, 319, 160, 595, 335, 836, 992, 321, 917, 187, 461, 762, 587, 127, 525, 124, 981, 563, 81, 306, 929, 612, 377, 202, 723, 522, 975, 441, 677, 364, 418, 207, 29, 843, 298, 54, 314, 694, 350, 607, 507, 787, 246, 665, 765, 954, 289, 559, 825, 131, 332, 268, 197, 472, 700, 233, 841, 683, 611, 447, 650, 396, 514, 78, 580, 689, 391, 853, 248, 603, 840, 540, 24, 69, 121, 220, 881, 813, 59, 732, 177, 494,
91, 284, 871, 169, 822, 309, 860, 504, 842, 466, 526, 20, 363, 809, 931, 338, 506, 791, 545, 388, 247, 815, 164, 695, 353, 657, 279, 107, 204, 747, 357, 597, 622, 995, 119, 982, 517, 455, 924, 896, 964, 366, 190, 440, 659, 789, 897, 948, 329, 38, 738, 728, 591, 351, 484, 523, 567, 409, 973, 184, 512, 361, 960, 37, 499, 482, 690, 805, 46, 767, 764, 941, 905, 483, 28, 927, 83, 667, 312, 673, 274, 303, 393, 92, 744, 996, 315, 294, 109, 848, 874, 136, 516, 359, 456, 392, 57, 852, 120, 937, 679, 691, 839, 118, 340, 498, 898, 490, 769, 266, 986, 980, 760, 814, 537, 325, 432, 943, 208, 911, 934, 902, 196, 749, 658, 778, 144, 912, 276, 666, 478, 793, 110, 780, 944, 11, 541,
662, 333, 438, 439, 940, 847, 70, 857, 244, 44, 557, 283, 331, 795, 431, 987, 766, 106, 818, 955, 849, 575, 952, 102, 891, 90, 799, 859, 258, 27, 375, 618, 535, 761, 957, 773, 55, 965, 373, 877, 468, 846, 252, 502, 111, 961,
838, 147, 170, 845, 371, 669, 259, 337, 348, 900, 277, 676, 423, 914, 613, 923, 23, 112, 463, 579, 882, 143, 640, 680, 569, 342, 823, 42, 406, 950, 935, 990, 326, 227, 963, 479, 130, 152, 583, 998, 457, 14, 360, 212, 403, 300, 287, 534, 323, 653, 616, 43, 752, 604, 115, 894, 63, 492, 702, 901, 301, 33, 939, 686, 726, 628, 305, 322, 302, 280, 225, 500, 543, 873, 64, 157, 741, 318, 654, 495, 599, 997, 213, 339, 988, 408, 670, 630, 149, 953, 273, 178, 240, 316, 349, 812, 485, 413, 763, 740, 685, 687, 138, 475, 216, 65, 866, 863, 668, 962, 682, 718, 95, 909, 381, 415, 527, 442, 195, 477, 449, 205, 831, 560, 407, 703, 94, 395, 593, 872, 458, 549, 254, 12, 334, 922, 231, 782, 735, 390, 389, 330, 508, 275, 829, 794, 796, 757, 584, 139, 219, 452, 257, 206, 296, 203, 501, 817, 875, 678, 651, 790, 214, 122, 450, 464, 215, 241, 708, 40, 777, 530, 108, 797, 820, 398, 754, 51, 256, 126, 163, 531, 155, 317, 85, 188, 297, 460, 620, 798, 167, 644, 968, 453, 884, 532, 565, 932, 851, 234, 972, 983, 161, 156, 16, 861, 117, 473, 221, 858, 148, 80, 800, 174, 471, 310, 641, 285, 519, 775, 236, 746, 430, 772, 664, 172, 883, 146, 200, 807, 521, 509, 862, 426, 889, 946, 589, 10, 704, 578, 123, 928, 828, 253, 949, 76, 292, 269, 833, 624, 505, 497, 379, 487, 969, 376, 451, 806, 967, 970, 681, 706, 444, 68, 79, 608, 942, 30, 137, 609, 750, 71, 675, 724, 671, 930, 907, 804, 281, 153, 18, 412, 21, 774, 684, 692, 384, 265, 638, 224, 198, 134, 158, 602, 629, 989, 77, 165, 368, 544, 223, 424, 605, 304, 925, 727, 520, 915, 743, 239, 358, 719, 436, 627, 35, 142, 711, 295, 868, 435, 378, 299, 759, 547, 586, 271, 656, 74, 562, 362, 730, 758, 425, 125, 610, 785, 32, 341, 722, 179, 217, 41, 552, 771, 25, 36, 145, 382, 601, 639, 956, 645, 879, 491, 739, 113, 827, 936, 574, 976, 372, 646, 647, 971, 49, 781, 725, 993, 596, 154, 293, 590, 553, 784, 910, 623, 180, 585, 193, 617, 588, 75, 201, 921, 636, 984, 538, 230, 414, 561, 688, 893, 48, 621, 546, 655, 344, 243, 135, 13, 844, 290, 288, 410, 865, 15, 513, 736, 470, 162, 717, 445, 663, 129, 834, 632, 168, 53, 235, 890, 189, 262, 571, 328, 649, 347, 324, 581, 878, 598, 714, 411, 222, 272, 365, 672, 89, 524, 22, 734, 270, 469, 697, 933, 186, 737, 345, 548, 355, 577, 133, 652, 19, 394, 405, 999, 698, 536,
947, 594, 756, 173, 642, 87, 96, 554, 228, 474, 615, 97, 892, 419, 705, 67, 420, 551, 876, 320, 631, 278, 132, 286, 56, 614, 480, 748, 260, 114, 238, 192, 511, 715, 476, 920, 867, 745, 218, 733, 951, 352, 510, 151, 529, 755,
792, 488, 720, 242, 229, 105, 945, 116, 573, 808, 633, 141, 570, 66, 713, 648, 73, 60, 34, 369, 264, 383, 919, 232, 810, 721, 404, 958, 751, 832, 826, 307, 899, 869, 938, 400, 98, 183, 31, 712, 374, 489, 26, 786, 209, 263, 433, 913, 710, 72, 17, 887, 908, 856, 496, 171, 731, 267, 661, 837, 100, 343, 674, 626]
n = 1000
def function(num, lis):
i = 0
while True:
generated = randint(0, num - 1)
i += 1
if generated not in lis:
print(i)
return generated
start = time.time()
var = function(n, l)
end = time.time()
print(var)
print("Time consumed in working: ", end - start)
| 151.028571
| 2,250
| 0.598184
| 1,061
| 5,286
| 2.980207
| 0.96607
| 0.00506
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.682415
| 0.207151
| 5,286
| 35
| 2,251
| 151.028571
| 0.072059
| 0.025918
| 0
| 0
| 1
| 0
| 0.005052
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.045455
| false
| 0
| 0.090909
| 0
| 0.181818
| 0.136364
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e1c971725eea044ff9153d50ceda8695a878d176
| 16
|
py
|
Python
|
hola.py
|
jarangobu/demo-clase
|
aab6f6ac92629bb0ccdedf7dd7dc8025a7ac086a
|
[
"MIT"
] | null | null | null |
hola.py
|
jarangobu/demo-clase
|
aab6f6ac92629bb0ccdedf7dd7dc8025a7ac086a
|
[
"MIT"
] | null | null | null |
hola.py
|
jarangobu/demo-clase
|
aab6f6ac92629bb0ccdedf7dd7dc8025a7ac086a
|
[
"MIT"
] | null | null | null |
print ("Hola")
| 5.333333
| 14
| 0.5625
| 2
| 16
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1875
| 16
| 2
| 15
| 8
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0.266667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 1
|
0
| 5
|
bed0f2bd6ef807f6a6c930fbf1ba214c06278c3e
| 215
|
py
|
Python
|
challenges/buffer-overflow/compile.py
|
mcfx/trivm
|
5b77ea157c562cfbfe87f7e7d256fb9702f8ceec
|
[
"MIT"
] | 6
|
2022-02-21T15:49:52.000Z
|
2022-02-23T07:16:02.000Z
|
challenges/buffer-overflow/compile.py
|
mcfx/trivm
|
5b77ea157c562cfbfe87f7e7d256fb9702f8ceec
|
[
"MIT"
] | null | null | null |
challenges/buffer-overflow/compile.py
|
mcfx/trivm
|
5b77ea157c562cfbfe87f7e7d256fb9702f8ceec
|
[
"MIT"
] | null | null | null |
import os
os.chdir('../../sim')
assert(os.system('python compile.py ../chals/pwn/pwn.c ../chals/pwn/pwn.S') == 0)
assert(os.system('python asm.py ../chals/pwn/pwn.S ../chals/pwn/pwn') == 0)
os.chdir('../chals/pwn')
| 35.833333
| 81
| 0.632558
| 38
| 215
| 3.578947
| 0.394737
| 0.294118
| 0.323529
| 0.294118
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01005
| 0.074419
| 215
| 5
| 82
| 43
| 0.673367
| 0
| 0
| 0
| 0
| 0
| 0.581395
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0
| true
| 0
| 0.2
| 0
| 0.2
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
83069e0772a339761e2f0dcf68a2a31203519414
| 138
|
py
|
Python
|
lojavirtual/main/admin.py
|
ErickMBS/dj-seller
|
ebaff4e05f65e3b6bccf127549492460b9e73a08
|
[
"MIT"
] | null | null | null |
lojavirtual/main/admin.py
|
ErickMBS/dj-seller
|
ebaff4e05f65e3b6bccf127549492460b9e73a08
|
[
"MIT"
] | null | null | null |
lojavirtual/main/admin.py
|
ErickMBS/dj-seller
|
ebaff4e05f65e3b6bccf127549492460b9e73a08
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from main.models import Categoria, Produto
admin.site.register(Categoria)
admin.site.register(Produto)
| 19.714286
| 42
| 0.826087
| 19
| 138
| 6
| 0.578947
| 0.157895
| 0.298246
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094203
| 138
| 6
| 43
| 23
| 0.912
| 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
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
832ddc59fbed3873f362aee43ebe27c9d5984deb
| 279
|
py
|
Python
|
tfrecord/__init__.py
|
gsgoncalves/tfrecord
|
b5d0bddf0cbe14e6aea9a1585d186e36a847248a
|
[
"MIT"
] | 662
|
2019-11-26T04:57:30.000Z
|
2022-03-31T21:07:43.000Z
|
tfrecord/__init__.py
|
gsgoncalves/tfrecord
|
b5d0bddf0cbe14e6aea9a1585d186e36a847248a
|
[
"MIT"
] | 67
|
2019-11-26T21:16:05.000Z
|
2022-01-29T02:52:30.000Z
|
tfrecord/__init__.py
|
gsgoncalves/tfrecord
|
b5d0bddf0cbe14e6aea9a1585d186e36a847248a
|
[
"MIT"
] | 85
|
2019-11-26T06:20:12.000Z
|
2022-03-15T03:15:44.000Z
|
from tfrecord import tools
from tfrecord import torch
from tfrecord import example_pb2
from tfrecord import iterator_utils
from tfrecord import reader
from tfrecord import writer
from tfrecord.iterator_utils import *
from tfrecord.reader import *
from tfrecord.writer import *
| 23.25
| 37
| 0.842294
| 39
| 279
| 5.948718
| 0.282051
| 0.465517
| 0.465517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004149
| 0.136201
| 279
| 11
| 38
| 25.363636
| 0.958506
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 5
|
8333eb9de50ced162e54f6f7295d0ecd5e9e213e
| 59
|
py
|
Python
|
wmf/__init__.py
|
maribelacosta/wikiwho
|
5c53f129b018541aad0cc63be5e03a862e6183a1
|
[
"MIT"
] | 17
|
2015-01-04T15:17:15.000Z
|
2019-09-17T15:38:43.000Z
|
wmf/__init__.py
|
maribelacosta/wikiwho
|
5c53f129b018541aad0cc63be5e03a862e6183a1
|
[
"MIT"
] | 5
|
2015-06-03T09:07:40.000Z
|
2017-03-31T16:36:13.000Z
|
wmf/__init__.py
|
maribelacosta/wikiwho
|
5c53f129b018541aad0cc63be5e03a862e6183a1
|
[
"MIT"
] | 10
|
2015-02-11T11:50:11.000Z
|
2021-07-28T02:17:16.000Z
|
from __future__ import absolute_import
from .util import *
| 19.666667
| 38
| 0.830508
| 8
| 59
| 5.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 2
| 39
| 29.5
| 0.862745
| 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
| 1
| 0
|
0
| 5
|
83430f9bad858a863e9117a0c3af3e7791135f87
| 150
|
py
|
Python
|
udemy/02_walkthrough/00_hello_world.py
|
inderpal2406/python
|
7bd7d03a6b3cd09ff16a4447ff495a2393a87a33
|
[
"MIT"
] | null | null | null |
udemy/02_walkthrough/00_hello_world.py
|
inderpal2406/python
|
7bd7d03a6b3cd09ff16a4447ff495a2393a87a33
|
[
"MIT"
] | null | null | null |
udemy/02_walkthrough/00_hello_world.py
|
inderpal2406/python
|
7bd7d03a6b3cd09ff16a4447ff495a2393a87a33
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
print("Hello Inderpal Singh!")
print("Welcome to python scripting.")
print("Learning python opens new doors of opportunity.")
| 25
| 56
| 0.753333
| 21
| 150
| 5.380952
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007519
| 0.113333
| 150
| 5
| 57
| 30
| 0.842105
| 0.14
| 0
| 0
| 0
| 0
| 0.75
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
55cbcef9999313a85e73b92aecb91a53c38b40f2
| 84
|
py
|
Python
|
GuiBuilder/BUILDER/ProjectTemplate/Tabs/FrameTab/__init__.py
|
lon3wolf/MyPyBuilder
|
5823ae9e5fcd2d745a70425c37a3aaa432c0389d
|
[
"MIT"
] | 237
|
2019-09-28T03:21:19.000Z
|
2022-03-19T22:41:04.000Z
|
GuiBuilder/BUILDER/ProjectTemplate/Tabs/FrameTab/__init__.py
|
pr1malHunter/MyPyBuilder
|
5823ae9e5fcd2d745a70425c37a3aaa432c0389d
|
[
"MIT"
] | 3
|
2019-09-29T08:35:53.000Z
|
2020-09-13T18:00:07.000Z
|
GuiBuilder/BUILDER/ProjectTemplate/Tabs/FrameTab/__init__.py
|
pr1malHunter/MyPyBuilder
|
5823ae9e5fcd2d745a70425c37a3aaa432c0389d
|
[
"MIT"
] | 29
|
2019-09-28T13:52:45.000Z
|
2022-01-06T01:25:45.000Z
|
from GuiBuilder.BUILDER.ProjectTemplate.Tabs.FrameTab.FrameTabBuild import FrameTab
| 42
| 83
| 0.892857
| 9
| 84
| 8.333333
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.047619
| 84
| 1
| 84
| 84
| 0.9375
| 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
| 1
| 0
|
0
| 5
|
360afe905bae45261fd053fb02ab17b11d192ee4
| 108
|
py
|
Python
|
section_7/ex 01.py
|
thiagofreitascarneiro/Python-avancado-Geek-University
|
861b742ad6b30955fcbe63274b8cf8afc6ca028f
|
[
"MIT"
] | null | null | null |
section_7/ex 01.py
|
thiagofreitascarneiro/Python-avancado-Geek-University
|
861b742ad6b30955fcbe63274b8cf8afc6ca028f
|
[
"MIT"
] | null | null | null |
section_7/ex 01.py
|
thiagofreitascarneiro/Python-avancado-Geek-University
|
861b742ad6b30955fcbe63274b8cf8afc6ca028f
|
[
"MIT"
] | null | null | null |
a = [1, 0, 5, -2, -5, 7]
soma = a[0] + a[1] + a[5]
print(soma)
a[4] = 100
print(a)
for v in a:
print(v)
| 13.5
| 25
| 0.462963
| 27
| 108
| 1.851852
| 0.481481
| 0.08
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164557
| 0.268519
| 108
| 7
| 26
| 15.428571
| 0.468354
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.428571
| 1
| 0
| 1
| 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
| 1
|
0
| 5
|
3631fd94224c00253b431036f46d9ac8578ab36e
| 80
|
py
|
Python
|
torch/utils/data/__init__.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/utils/data/__init__.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
torch/utils/data/__init__.py
|
UmaTaru/run
|
be29e4d41a4de3dee27cd6796801bfe51382d294
|
[
"MIT"
] | null | null | null |
from .dataset import Dataset, TensorDataset
from .dataloader import DataLoader
| 20
| 43
| 0.8375
| 9
| 80
| 7.444444
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 80
| 3
| 44
| 26.666667
| 0.957143
| 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
| 1
| 0
|
0
| 5
|
366a95b0ceb1c5f04a0b58aa466ac8463cf43031
| 1,054
|
py
|
Python
|
src/comodash_api_client_lowlevel/test/test_query_result_result_set_meta_data.py
|
ComotionLabs/dash-sdk
|
8ab532dd58cbcb85969bb84503678cd54b3b2bfe
|
[
"Apache-2.0"
] | 1
|
2021-06-19T18:44:31.000Z
|
2021-06-19T18:44:31.000Z
|
src/comodash_api_client_lowlevel/test/test_query_result_result_set_meta_data.py
|
ComotionLabs/dash-sdk
|
8ab532dd58cbcb85969bb84503678cd54b3b2bfe
|
[
"Apache-2.0"
] | null | null | null |
src/comodash_api_client_lowlevel/test/test_query_result_result_set_meta_data.py
|
ComotionLabs/dash-sdk
|
8ab532dd58cbcb85969bb84503678cd54b3b2bfe
|
[
"Apache-2.0"
] | 3
|
2021-06-25T14:50:50.000Z
|
2021-09-16T13:00:29.000Z
|
"""
Comotion Dash API
Comotion Dash API # noqa: E501
The version of the OpenAPI document: 2.0
Generated by: https://openapi-generator.tech
"""
import sys
import unittest
import comodash_api_client_lowlevel
from comodash_api_client_lowlevel.model.query_result_result_set_meta_data_column_info import QueryResultResultSetMetaDataColumnInfo
globals()['QueryResultResultSetMetaDataColumnInfo'] = QueryResultResultSetMetaDataColumnInfo
from comodash_api_client_lowlevel.model.query_result_result_set_meta_data import QueryResultResultSetMetaData
class TestQueryResultResultSetMetaData(unittest.TestCase):
"""QueryResultResultSetMetaData unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testQueryResultResultSetMetaData(self):
"""Test QueryResultResultSetMetaData"""
# FIXME: construct object with mandatory attributes with example values
# model = QueryResultResultSetMetaData() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| 27.736842
| 131
| 0.76945
| 104
| 1,054
| 7.519231
| 0.548077
| 0.042199
| 0.065217
| 0.095908
| 0.158568
| 0.158568
| 0.158568
| 0.158568
| 0.158568
| 0.158568
| 0
| 0.009101
| 0.166034
| 1,054
| 37
| 132
| 28.486486
| 0.880546
| 0.321632
| 0
| 0.2
| 1
| 0
| 0.067747
| 0.055965
| 0
| 0
| 0
| 0.027027
| 0
| 1
| 0.2
| false
| 0.2
| 0.333333
| 0
| 0.6
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
367c3a19a0dc2771dcf7788a2c4ffb1ce8233260
| 65
|
py
|
Python
|
egvparser/parser/__init__.py
|
jaganadhg/egvsemicon
|
e51cf320bcf1a3730a0cf8283d5455c517ce96ec
|
[
"Apache-2.0"
] | null | null | null |
egvparser/parser/__init__.py
|
jaganadhg/egvsemicon
|
e51cf320bcf1a3730a0cf8283d5455c517ce96ec
|
[
"Apache-2.0"
] | null | null | null |
egvparser/parser/__init__.py
|
jaganadhg/egvsemicon
|
e51cf320bcf1a3730a0cf8283d5455c517ce96ec
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
from .egienvcparser import egienvec_parser
| 21.666667
| 42
| 0.815385
| 9
| 65
| 5.777778
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092308
| 65
| 3
| 42
| 21.666667
| 0.881356
| 0.307692
| 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
| 1
| 0
|
0
| 5
|
36d3dcd4ca9486f56935ed22f034e63ae870b812
| 85
|
py
|
Python
|
errors/custom_errors.py
|
gera9/PyProject
|
236048ec6668ad63841e414123162c8397867891
|
[
"MIT"
] | null | null | null |
errors/custom_errors.py
|
gera9/PyProject
|
236048ec6668ad63841e414123162c8397867891
|
[
"MIT"
] | null | null | null |
errors/custom_errors.py
|
gera9/PyProject
|
236048ec6668ad63841e414123162c8397867891
|
[
"MIT"
] | null | null | null |
class ArtistNotFound(Exception):
pass
class AlbumNotFound(Exception):
pass
| 12.142857
| 32
| 0.741176
| 8
| 85
| 7.875
| 0.625
| 0.412698
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188235
| 85
| 6
| 33
| 14.166667
| 0.913043
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
36e6d69ad64c2621ac63311b8672e1391f03f389
| 45
|
py
|
Python
|
AesOracle/__init__.py
|
lesterpotter/AesOracle
|
8414a305117fe90b2fdc66c467c0042438a50846
|
[
"MIT"
] | 2
|
2019-12-20T01:04:18.000Z
|
2020-03-10T07:33:05.000Z
|
AesOracle/__init__.py
|
lesterpotter/AesOracle
|
8414a305117fe90b2fdc66c467c0042438a50846
|
[
"MIT"
] | null | null | null |
AesOracle/__init__.py
|
lesterpotter/AesOracle
|
8414a305117fe90b2fdc66c467c0042438a50846
|
[
"MIT"
] | null | null | null |
from .AesOracle import PaddingOracleCracker
| 15
| 43
| 0.866667
| 4
| 45
| 9.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 45
| 2
| 44
| 22.5
| 0.975
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
36f513b745c827acb29634be8c7dc7651c1a16d0
| 133
|
py
|
Python
|
python/geospark/utils/__init__.py
|
Maxar-Corp/GeoSpark
|
6248c6773dc88bf3354ea9b223f16ceb064e7627
|
[
"Apache-2.0",
"MIT"
] | 7
|
2019-10-10T05:47:37.000Z
|
2020-09-08T06:37:03.000Z
|
python/geospark/utils/__init__.py
|
mayankkt9/GeoSpark
|
618da90413f7d86c59def92ba765fbd6d9d49761
|
[
"Apache-2.0",
"MIT"
] | 3
|
2019-12-16T16:49:57.000Z
|
2021-08-23T20:43:32.000Z
|
python/geospark/utils/__init__.py
|
mayankkt9/GeoSpark
|
618da90413f7d86c59def92ba765fbd6d9d49761
|
[
"Apache-2.0",
"MIT"
] | 3
|
2019-10-17T16:10:41.000Z
|
2022-01-24T12:56:21.000Z
|
from .serde import KryoSerializer
from .serde import GeoSparkKryoRegistrator
__all__ = ["KryoSerializer", "GeoSparkKryoRegistrator"]
| 33.25
| 55
| 0.834586
| 11
| 133
| 9.727273
| 0.545455
| 0.168224
| 0.280374
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090226
| 133
| 4
| 55
| 33.25
| 0.884298
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| 0.276119
| 0.171642
| 0
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| 0
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| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
7fd0919b786474614f1ab482faa88567aad7d4f9
| 70
|
py
|
Python
|
syft/frameworks/__init__.py
|
racinger/PySyft
|
10ac42e198e112b266bae3e913dff79ca40b6f89
|
[
"Apache-2.0"
] | 1
|
2019-07-03T07:23:38.000Z
|
2019-07-03T07:23:38.000Z
|
syft/frameworks/__init__.py
|
racinger/PySyft
|
10ac42e198e112b266bae3e913dff79ca40b6f89
|
[
"Apache-2.0"
] | 1
|
2019-06-05T14:19:07.000Z
|
2019-06-05T14:19:07.000Z
|
syft/frameworks/__init__.py
|
abmazhr/PySyft
|
dc0ba29df83de3bd0cf59956b433b418a9731baa
|
[
"Apache-2.0"
] | 1
|
2019-07-06T06:28:49.000Z
|
2019-07-06T06:28:49.000Z
|
from . import keras
from . import torch
__all__ = ["keras", "torch"]
| 14
| 28
| 0.671429
| 9
| 70
| 4.777778
| 0.555556
| 0.465116
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.185714
| 70
| 4
| 29
| 17.5
| 0.754386
| 0
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| 0.142857
| 0
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| false
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| 0
| 1
| 0
|
0
| 5
|
7fed3aea04d1c779abaff79bc4c209020a5429c3
| 48
|
py
|
Python
|
torchforce/relabelers/__init__.py
|
brandontrabucco/torchforce
|
00e2dc1df2440842d630a4674c6fb427098b4a98
|
[
"MIT"
] | 5
|
2019-07-14T07:34:32.000Z
|
2019-08-16T17:41:13.000Z
|
torchforce/relabelers/__init__.py
|
brandontrabucco/torchforce
|
00e2dc1df2440842d630a4674c6fb427098b4a98
|
[
"MIT"
] | 7
|
2019-08-16T21:48:26.000Z
|
2022-02-09T23:30:47.000Z
|
torchforce/relabelers/__init__.py
|
brandontrabucco/torchforce
|
00e2dc1df2440842d630a4674c6fb427098b4a98
|
[
"MIT"
] | null | null | null |
"""Author: Brandon Trabucco, Copyright 2019"""
| 24
| 47
| 0.708333
| 5
| 48
| 6.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 0.125
| 48
| 1
| 48
| 48
| 0.714286
| 0.833333
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
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| null | 0
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| null | 0
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| 0
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| 0
| 0
| 0
|
0
| 5
|
7fef09c69011436108bd6f0cc3fa5b51357c4853
| 13,311
|
py
|
Python
|
Gan/car/carnet/models.py
|
eranbTAU/Closing-the-Reality-Gap-for-a-Multi-Agent-System-Using-GAN
|
3df5f8ba1069ce3f16f1ab743da9cbdd3bddd43c
|
[
"MIT"
] | null | null | null |
Gan/car/carnet/models.py
|
eranbTAU/Closing-the-Reality-Gap-for-a-Multi-Agent-System-Using-GAN
|
3df5f8ba1069ce3f16f1ab743da9cbdd3bddd43c
|
[
"MIT"
] | null | null | null |
Gan/car/carnet/models.py
|
eranbTAU/Closing-the-Reality-Gap-for-a-Multi-Agent-System-Using-GAN
|
3df5f8ba1069ce3f16f1ab743da9cbdd3bddd43c
|
[
"MIT"
] | 1
|
2022-02-22T11:06:40.000Z
|
2022-02-22T11:06:40.000Z
|
import torch
import torch.nn as nn
from torch.nn import init
def make_mlp(dim_list, activation='relu', batch_norm=True, dropout=0):
layers = []
for dim_in, dim_out in zip(dim_list[:-1], dim_list[1:]):
layers.append(nn.Linear(dim_in, dim_out))
if batch_norm:
layers.append(nn.BatchNorm1d(dim_out))
if activation == 'relu':
layers.append(nn.ReLU())
elif activation == 'leakyrelu':
layers.append(nn.LeakyReLU())
if dropout > 0:
layers.append(nn.Dropout(p=dropout))
return nn.Sequential(*layers)
def init_weights(net, init_type='xavier', gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
init.normal_(m.weight.data, 1.0, gain)
init.constant_(m.bias.data, 0.0)
net.apply(init_func)
class FNet(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super(FNet, self).__init__()
self.linear_1 = nn.Linear(input_dim, 320)
self.linear_2 = nn.Linear(320, 256)
self.linear_3 = nn.Linear(256, 128)
self.linear_4 = nn.Linear(128, 64)
self.linear_5 = nn.Linear(64, output_dim)
self.activation = nn.ReLU()
self.Sigmoid = nn.Sigmoid()
self.bn1 = nn.BatchNorm1d(num_features=320)
self.bn2 = nn.BatchNorm1d(num_features=256)
self.bn3 = nn.BatchNorm1d(num_features=128)
self.bn4 = nn.BatchNorm1d(num_features=64)
def forward(self, x):
x = self.Sigmoid(self.bn1(self.linear_1(x)))
x = self.Sigmoid(self.bn2(self.linear_2(x)))
x = self.Sigmoid(self.bn3(self.linear_3(x)))
x = self.Sigmoid(self.bn4(self.linear_4(x)))
x = self.activation(self.linear_5(x))
return x
class FNetSkip(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super(FNetSkip, self).__init__()
self.linear_1 = nn.Linear(input_dim, 320)
self.linear_2 = nn.Linear(320, 8)
self.linear_3 = nn.Linear(8, 512)
self.linear_4 = nn.Linear(512, 256)
self.linear_5 = nn.Linear(256, 16)
self.linear_6 = nn.Linear(16, 128)
self.linear_7 = nn.Linear(128, 64)
self.linear_8 = nn.Linear(64, output_dim)
self.activation = nn.Tanh()
self.bn1 = nn.BatchNorm1d(num_features=320)
self.bn2 = nn.BatchNorm1d(num_features=8)
self.bn3 = nn.BatchNorm1d(num_features=512)
self.bn4 = nn.BatchNorm1d(num_features=256)
self.bn5 = nn.BatchNorm1d(num_features=16)
self.Sigmoid = nn.Sigmoid()
self.Dropout = nn.Dropout(0.2)
def forward(self, x):
x1 = self.activation(self.bn1(self.linear_1(x)))
# x1_d = self.Dropout(x1)
x2 = self.activation(self.bn2(self.linear_2(x1)))
# x2_d = self.Dropout(x2)
x3 = self.activation(self.bn3(self.linear_3(x2)))
# x3_d = self.Dropout(x3)
x4 = self.activation(self.bn4(self.linear_4(x3)))
# x4_4 = self.Dropout(x4)
x5 = self.activation(self.bn5(self.linear_5(x4)))
# x5 = x5 + x5
x6 = self.activation(self.linear_6(x5))
# x6 = x6 + x6
x7 = self.activation(self.linear_7(x6))
# x7 = x7 + x6
x8 = self.Sigmoid(self.linear_8(x7))
return x8
class ConFNet(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super(ConFNet, self).__init__()
self.cnn1 = nn.Conv1d(1, 8, kernel_size=1, stride=1, padding=1)
self.cnn2 = nn.Conv1d(8, 64, kernel_size=1, stride=1, padding=1)
self.rnn = nn.RNN(input_size=6, hidden_size=5, num_layers=3, batch_first=True)
self.linear_1 = nn.Linear(320, 8)
self.linear_2 = nn.Linear(8, 512)
self.linear_3 = nn.Linear(512, 256)
self.linear_4 = nn.Linear(256, 16)
self.linear_5 = nn.Linear(16, 128)
self.linear_6 = nn.Linear(128, 64)
self.linear_7 = nn.Linear(64, 64)
self.linear_8 = nn.Linear(64, output_dim)
self.activation = nn.Tanh()
self.bn1 = nn.BatchNorm1d(num_features=8)
self.bn2 = nn.BatchNorm1d(num_features=512)
self.bn3 = nn.BatchNorm1d(num_features=256)
self.bn4 = nn.BatchNorm1d(num_features=16)
self.bn5 = nn.BatchNorm1d(num_features=128)
self.bnc = nn.BatchNorm1d(num_features=8)
self.bnc2 = nn.BatchNorm1d(num_features=64)
self.Dropout = nn.Dropout(0.2)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
self.Sigmoid = nn.Sigmoid()
def forward(self, x):
x_c = self.LKR(self.bnc(self.cnn1(x)))
x_c2 = self.LKR(self.bnc2(self.cnn2(x_c)))
outRNN, h_h = self.rnn(x_c2)
x_flatten = outRNN.reshape(-1, 64*5)
x1 = self.activation(self.bn1(self.linear_1(x_flatten)))
x1_d = self.Dropout(x1)
x2 = self.activation(self.bn2(self.linear_2(x1_d)))
x2_d = self.Dropout(x2)
# x2_d = x2_d + x1_d
x3 = self.activation(self.bn3(self.linear_3(x2_d)))
x3_d = self.Dropout(x3)
# x3_d = x3_d + x2_d
x4 = self.activation(self.bn4(self.linear_4(x3_d)))
x4_4 = self.Dropout(x4)
x5 = self.activation(self.bn5(self.linear_5(x4_4)))
x6 = self.activation(self.linear_6(x5))
x7 = self.activation(self.linear_7(x6))
x7 = x7 + x6
x8 = self.Sigmoid(self.linear_8(x7))
return x8
class ConFNet4(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super(ConFNet4, self).__init__()
self.cnn1 = nn.Conv1d(1, 8, kernel_size=1, stride=1, padding=1)
self.cnn2 = nn.Conv1d(8, 64, kernel_size=1, stride=1, padding=1)
self.rnn = nn.LSTM(input_size=6, hidden_size=5, num_layers=1, batch_first=True)
self.linear_1 = nn.Linear(320, 128)
self.linear_2 = nn.Linear(128, 64)
self.linear_3 = nn.Linear(64, 256)
self.linear_4 = nn.Linear(256, 16)
self.linear_5 = nn.Linear(16, 256)
self.linear_6 = nn.Linear(256, 64)
self.linear_7 = nn.Linear(64, 32)
self.linear_8 = nn.Linear(32, output_dim)
self.activation = nn.Tanh()
self.bn1 = nn.BatchNorm1d(num_features=128)
self.bn2 = nn.BatchNorm1d(num_features=64)
self.bn3 = nn.BatchNorm1d(num_features=256)
self.bn4 = nn.BatchNorm1d(num_features=16)
self.bn5 = nn.BatchNorm1d(num_features=256)
self.bnc = nn.BatchNorm1d(num_features=8)
self.bnc2 = nn.BatchNorm1d(num_features=64)
self.Dropout = nn.Dropout(0.1)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
self.Sigmoid = nn.Sigmoid()
def forward(self, x):
x_c = self.LKR(self.bnc(self.cnn1(x)))
x_c2 = self.LKR(self.bnc2(self.cnn2(x_c)))
outRNN, h_h = self.rnn(x_c2)
x_flatten = outRNN.reshape(-1, 64*5)
x1 = self.activation(self.bn1(self.linear_1(x_flatten)))
x1_d = self.Dropout(x1)
x2 = self.activation(self.bn2(self.linear_2(x1_d)))
x2_d = self.Dropout(x2)
# x2_d = x2_d + x1_d
x3 = self.activation(self.bn3(self.linear_3(x2_d)))
x3_d = self.Dropout(x3)
# x3_d = x3_d + x2_d
x4 = self.activation(self.bn4(self.linear_4(x3_d)))
x4_4 = self.Dropout(x4)
# x4_4 = x4_4 + x3_d
x5 = self.activation(self.bn5(self.linear_5(x4_4)))
x5 = x3_d + x5
x6 = self.activation(self.linear_6(x5))
x6 = x6 + x2_d
x7 = self.activation(self.linear_7(x6))
# x7 = x7 + x6
x8 = self.Sigmoid(self.linear_8(x7))
return x8
class CNN(nn.Module):
def __init__(self, input_dim, output_dim):
super(CNN, self).__init__()
self.cnn1 = nn.Conv1d(1, 8, kernel_size=1, stride=1, padding=1)
self.cnn2 = nn.Conv1d(8, 64, kernel_size=1, stride=1, padding=1)
self.cnn3 = nn.Conv1d(64, 96, kernel_size=1, stride=1, padding=1)
self.cnn4 = nn.Conv1d(96, 32, kernel_size=1, stride=1, padding=1)
self.fc1 = nn.Linear(320, 120) # 5*5 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, output_dim)
self.bn1 = nn.BatchNorm1d(num_features=8)
self.bn2 = nn.BatchNorm1d(num_features=64)
self.bn3 = nn.BatchNorm1d(num_features=96)
self.bn4 = nn.BatchNorm1d(num_features=32)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
def forward(self, x):
x_c = self.LKR(self.bn1(self.cnn1(x)))
x_c2 = self.LKR(self.bn2(self.cnn2(x_c)))
x_c3 = self.LKR(self.bn3(self.cnn3(x_c2)))
x_c4 = self.LKR(self.bn4(self.cnn4(x_c3)))
x_flatten = x_c4.view(-1, 32 * 10)
x_c5 = self.LKR(self.fc1(x_flatten))
x_c6 = self.LKR(self.fc2(x_c5))
x_c7 = self.LKR(self.fc3(x_c6))
return x_c7
class LSTM(nn.Module):
def __init__(self, input_dim, output_dim):
super(LSTM, self).__init__()
self.cnn1 = nn.Conv1d(1, 8, kernel_size=1, stride=1, padding=1)
self.cnn2 = nn.Conv1d(8, 64, kernel_size=1, stride=1, padding=1)
self.cnn3 = nn.Conv1d(64, 96, kernel_size=1, stride=1, padding=1)
self.cnn4 = nn.Conv1d(96, 32, kernel_size=1, stride=1, padding=1)
self.fc1 = nn.Linear(320, 120) # 5*5 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, output_dim)
self.bn1 = nn.BatchNorm1d(num_features=8)
self.bn2 = nn.BatchNorm1d(num_features=64)
self.bn3 = nn.BatchNorm1d(num_features=96)
self.bn4 = nn.BatchNorm1d(num_features=32)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
def forward(self, x):
x_c = self.LKR(self.bn1(self.cnn1(x)))
x_c2 = self.LKR(self.bn2(self.cnn2(x_c)))
x_c3 = self.LKR(self.bn3(self.cnn3(x_c2)))
x_c4 = self.LKR(self.bn4(self.cnn4(x_c3)))
x_flatten = x_c4.view(-1, 32 * 10)
x_c5 = self.LKR(self.fc1(x_flatten))
x_c6 = self.LKR(self.fc2(x_c5))
x_c7 = self.LKR(self.fc3(x_c6))
return x_c7
class LSTM(nn.Module):
def __init__(self, input_dim, output_dim):
super(LSTM, self).__init__()
self.num_layers = 1 # number of layers
self.input_size = input_dim # input size
self.hidden_size = 2 # hidden state
self.rnn = nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size,
num_layers=self.num_layers, batch_first=True)
self.fc1 = nn.Linear(2, 32)
self.fc2 = nn.Linear(32, 80)
self.fc3 = nn.Linear(80, 16)
self.fc4 = nn.Linear(16, output_dim)
self.bn1 = nn.BatchNorm1d(num_features=32)
self.bn2 = nn.BatchNorm1d(num_features=80)
self.bn3 = nn.BatchNorm1d(num_features=16)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
def forward(self, x):
outRNN, h_h = self.rnn(x)
x_flatten = outRNN.reshape(-1, 1*2)
x_f1 = self.LKR(self.bn1(self.fc1(x_flatten)))
x_f2 = self.LKR(self.bn2(self.fc2(x_f1)))
x_f3 = self.LKR(self.bn3(self.fc3(x_f2)))
x_f4 = self.LKR(self.fc4(x_f3))
return x_f4
class GRU(nn.Module):
def __init__(self, input_dim, output_dim):
super(GRU, self).__init__()
self.num_layers = 1 # number of layers
self.input_size = input_dim # input size
self.hidden_size = 1 # hidden state
self.rnn = nn.GRU(input_size=self.input_size, hidden_size=self.hidden_size,
num_layers=self.num_layers, batch_first=True)
self.fc1 = nn.Linear(1, 64)
self.fc2 = nn.Linear(64, 128)
self.fc3 = nn.Linear(128, 32)
self.fc4 = nn.Linear(32, output_dim)
self.bn1 = nn.BatchNorm1d(num_features=64)
self.bn2 = nn.BatchNorm1d(num_features=128)
self.bn3 = nn.BatchNorm1d(num_features=32)
self.LKR = nn.LeakyReLU(negative_slope=0.01)
def forward(self, x):
outRNN, h_h = self.rnn(x)
x_flatten = outRNN.reshape(-1, 1*1)
x_f1 = self.LKR(self.bn1(self.fc1(x_flatten)))
x_f2 = self.LKR(self.bn2(self.fc2(x_f1)))
x_f3 = self.LKR(self.bn3(self.fc3(x_f2)))
x_f4 = self.LKR(self.fc4(x_f3))
return x_f4
| 41.990536
| 103
| 0.59124
| 2,002
| 13,311
| 3.743257
| 0.082418
| 0.077395
| 0.078997
| 0.118495
| 0.854017
| 0.802108
| 0.721777
| 0.705631
| 0.682279
| 0.658393
| 0
| 0.083394
| 0.272106
| 13,311
| 317
| 104
| 41.990536
| 0.690061
| 0.028097
| 0
| 0.567766
| 0
| 0
| 0.010635
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.069597
| false
| 0
| 0.010989
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
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| 0
| 0
|
0
| 5
|
3d39ffb1b1501890e48273ad4c23e744fe2689c2
| 111
|
py
|
Python
|
Testes/lista compacta(programador).py
|
Raiane-nepomuceno/Python
|
acf8bd0436c717614fe7fd4f62e9fa2e432c386a
|
[
"MIT"
] | null | null | null |
Testes/lista compacta(programador).py
|
Raiane-nepomuceno/Python
|
acf8bd0436c717614fe7fd4f62e9fa2e432c386a
|
[
"MIT"
] | null | null | null |
Testes/lista compacta(programador).py
|
Raiane-nepomuceno/Python
|
acf8bd0436c717614fe7fd4f62e9fa2e432c386a
|
[
"MIT"
] | null | null | null |
minha_lista = [1,2,3,4,5]
sua_lista = [item ** 2 for item in minha_lista]
print(minha_lista)
print(sua_lista)
| 18.5
| 47
| 0.720721
| 22
| 111
| 3.409091
| 0.545455
| 0.4
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 0.135135
| 111
| 5
| 48
| 22.2
| 0.71875
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| false
| 0
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| 0.5
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
3d3f19a1233abb83ecc7abfd4799391cb3cb486a
| 129
|
py
|
Python
|
rScraper/__init__.py
|
rolba/rScraper
|
1554a51ea6ee0bc87b9dafefa0be07f8b4f90034
|
[
"MIT"
] | null | null | null |
rScraper/__init__.py
|
rolba/rScraper
|
1554a51ea6ee0bc87b9dafefa0be07f8b4f90034
|
[
"MIT"
] | 1
|
2020-01-08T21:49:03.000Z
|
2020-01-08T21:49:03.000Z
|
rScraper/__init__.py
|
rolba/rScraper
|
1554a51ea6ee0bc87b9dafefa0be07f8b4f90034
|
[
"MIT"
] | null | null | null |
from .rScraperGoogle import ScraperGoogle
from .rScraperYoutube import ScraperYoutube
from .rScraperWraper import ScraperWraper
| 25.8
| 43
| 0.875969
| 12
| 129
| 9.416667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.100775
| 129
| 4
| 44
| 32.25
| 0.974138
| 0
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| null | 0
| 0
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| 0
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| 1
| 0
| 1
| 0
|
0
| 5
|
1839689506e4da7417c3a8689488281e62e97a85
| 3,110
|
py
|
Python
|
back/infolica/alembic/versions/20210909_13fcb71e77c1.py
|
maltaesousa/infolica
|
9b510b706daba8f8a04434d281c1f8730651f25f
|
[
"MIT"
] | null | null | null |
back/infolica/alembic/versions/20210909_13fcb71e77c1.py
|
maltaesousa/infolica
|
9b510b706daba8f8a04434d281c1f8730651f25f
|
[
"MIT"
] | 327
|
2019-10-29T13:35:25.000Z
|
2022-03-03T10:01:46.000Z
|
back/infolica/alembic/versions/20210909_13fcb71e77c1.py
|
maltaesousa/infolica
|
9b510b706daba8f8a04434d281c1f8730651f25f
|
[
"MIT"
] | 5
|
2019-11-07T15:49:05.000Z
|
2021-03-08T08:59:56.000Z
|
"""update ctrl_geom
Revision ID: 13fcb71e77c1
Revises: 5a8069c68433
Create Date: 2021-09-09 17:05:14.208648
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '13fcb71e77c1'
down_revision = '5a8069c68433'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('controle_geometre', sa.Column('check_48', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_49', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_50', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_51', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_52', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_53', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_54', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_55', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_56', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_57', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_58', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_59', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_60', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_61', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_62', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_63', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_64', sa.Boolean(), nullable=True))
op.add_column('controle_geometre', sa.Column('check_65', sa.Boolean(), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('controle_geometre', 'check_65')
op.drop_column('controle_geometre', 'check_64')
op.drop_column('controle_geometre', 'check_63')
op.drop_column('controle_geometre', 'check_62')
op.drop_column('controle_geometre', 'check_61')
op.drop_column('controle_geometre', 'check_60')
op.drop_column('controle_geometre', 'check_59')
op.drop_column('controle_geometre', 'check_58')
op.drop_column('controle_geometre', 'check_57')
op.drop_column('controle_geometre', 'check_56')
op.drop_column('controle_geometre', 'check_55')
op.drop_column('controle_geometre', 'check_54')
op.drop_column('controle_geometre', 'check_53')
op.drop_column('controle_geometre', 'check_52')
op.drop_column('controle_geometre', 'check_51')
op.drop_column('controle_geometre', 'check_50')
op.drop_column('controle_geometre', 'check_49')
op.drop_column('controle_geometre', 'check_48')
# ### end Alembic commands ###
| 50.983607
| 90
| 0.722508
| 425
| 3,110
| 5.023529
| 0.16
| 0.236066
| 0.37096
| 0.160187
| 0.822014
| 0.822014
| 0.544731
| 0.544731
| 0.485714
| 0.485714
| 0
| 0.045652
| 0.11254
| 3,110
| 60
| 91
| 51.833333
| 0.727899
| 0.09582
| 0
| 0
| 0
| 0
| 0.332973
| 0
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| 1
| 0.045455
| false
| 0
| 0.045455
| 0
| 0.090909
| 0
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| 0
| null | 1
| 1
| 1
| 1
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|
0
| 5
|
186a2a3a4ff08ac6210cc6ab0f8b9c26d2453ae5
| 182,587
|
py
|
Python
|
public/pagekite.py
|
P-Shakibafar/ddaudio-web
|
c454818a7eb86b1e7cb839fb12d8d5b6e8928c62
|
[
"MIT"
] | null | null | null |
public/pagekite.py
|
P-Shakibafar/ddaudio-web
|
c454818a7eb86b1e7cb839fb12d8d5b6e8928c62
|
[
"MIT"
] | null | null | null |
public/pagekite.py
|
P-Shakibafar/ddaudio-web
|
c454818a7eb86b1e7cb839fb12d8d5b6e8928c62
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# vim: set fileencoding=utf-8 :
# WARNING: This file is a combination of multiple Python files.
# The source code lives here: http://pagekite.org/
#
# This file is part of pagekite.py (version 0.5.9.3)
# Copyright 2010-2012, the Beanstalks Project ehf. and Bjarni Runar Einarsson
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
##[ Combined with Breeder: http://pagekite.net/wiki/Floss/PyBreeder/ ]#########
import base64, imp, os, sys, StringIO, zlib
__FILES = {}
__os_path_exists = os.path.exists
__os_path_getsize = os.path.getsize
__builtin_open = open
def __comb_open(filename, *args, **kwargs):
if filename in __FILES:
return StringIO.StringIO(__FILES[filename])
else:
return __builtin_open(filename, *args, **kwargs)
def __comb_exists(filename, *args, **kwargs):
if filename in __FILES:
return True
else:
return __os_path_exists(filename, *args, **kwargs)
def __comb_getsize(filename, *args, **kwargs):
if filename in __FILES:
return len(__FILES[filename])
else:
return __os_path_getsize(filename, *args, **kwargs)
if 'b64decode' in dir(base64):
__b64d = base64.b64decode
else:
__b64d = base64.decodestring
open = __comb_open
os.path.exists = __comb_exists
os.path.getsize = __comb_getsize
sys.path[0:0] = ['.SELF/']
###############################################################################
__FILES[".SELF/sockschain/__init__.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["sockschain"] = imp.new_module("sockschain")
m.__file__ = "sockschain/__init__.py"
m.open = __comb_open
exec __FILES[".SELF/sockschain/__init__.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/__init__.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite"] = imp.new_module("pagekite")
m.__file__ = "pagekite/__init__.py"
m.open = __comb_open
exec __FILES[".SELF/pagekite/__init__.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/common.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.common"] = imp.new_module("pagekite.common")
m.__file__ = "pagekite/common.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("common", m)
exec __FILES[".SELF/pagekite/common.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/compat.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.compat"] = imp.new_module("pagekite.compat")
m.__file__ = "pagekite/compat.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("compat", m)
exec __FILES[".SELF/pagekite/compat.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/logging.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.logging"] = imp.new_module("pagekite.logging")
m.__file__ = "pagekite/logging.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("logging", m)
exec __FILES[".SELF/pagekite/logging.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/manual.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.manual"] = imp.new_module("pagekite.manual")
m.__file__ = "pagekite/manual.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("manual", m)
exec __FILES[".SELF/pagekite/manual.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/__init__.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto"] = imp.new_module("pagekite.proto")
m.__file__ = "pagekite/proto/__init__.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("proto", m)
exec __FILES[".SELF/pagekite/proto/__init__.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/proto.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto.proto"] = imp.new_module("pagekite.proto.proto")
m.__file__ = "pagekite/proto/proto.py"
m.open = __comb_open
sys.modules["pagekite.proto"].__setattr__("proto", m)
exec __FILES[".SELF/pagekite/proto/proto.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/parsers.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto.parsers"] = imp.new_module("pagekite.proto.parsers")
m.__file__ = "pagekite/proto/parsers.py"
m.open = __comb_open
sys.modules["pagekite.proto"].__setattr__("parsers", m)
exec __FILES[".SELF/pagekite/proto/parsers.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/selectables.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto.selectables"] = imp.new_module("pagekite.proto.selectables")
m.__file__ = "pagekite/proto/selectables.py"
m.open = __comb_open
sys.modules["pagekite.proto"].__setattr__("selectables", m)
exec __FILES[".SELF/pagekite/proto/selectables.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/filters.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto.filters"] = imp.new_module("pagekite.proto.filters")
m.__file__ = "pagekite/proto/filters.py"
m.open = __comb_open
sys.modules["pagekite.proto"].__setattr__("filters", m)
exec __FILES[".SELF/pagekite/proto/filters.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/proto/conns.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.proto.conns"] = imp.new_module("pagekite.proto.conns")
m.__file__ = "pagekite/proto/conns.py"
m.open = __comb_open
sys.modules["pagekite.proto"].__setattr__("conns", m)
exec __FILES[".SELF/pagekite/proto/conns.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/ui/__init__.py"] = zlib.decompress(__b64d("""\
eNoDAAAAAAE=
"""))
m = sys.modules["pagekite.ui"] = imp.new_module("pagekite.ui")
m.__file__ = "pagekite/ui/__init__.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("ui", m)
exec __FILES[".SELF/pagekite/ui/__init__.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/ui/nullui.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.ui.nullui"] = imp.new_module("pagekite.ui.nullui")
m.__file__ = "pagekite/ui/nullui.py"
m.open = __comb_open
sys.modules["pagekite.ui"].__setattr__("nullui", m)
exec __FILES[".SELF/pagekite/ui/nullui.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/ui/basic.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.ui.basic"] = imp.new_module("pagekite.ui.basic")
m.__file__ = "pagekite/ui/basic.py"
m.open = __comb_open
sys.modules["pagekite.ui"].__setattr__("basic", m)
exec __FILES[".SELF/pagekite/ui/basic.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/ui/remote.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.ui.remote"] = imp.new_module("pagekite.ui.remote")
m.__file__ = "pagekite/ui/remote.py"
m.open = __comb_open
sys.modules["pagekite.ui"].__setattr__("remote", m)
exec __FILES[".SELF/pagekite/ui/remote.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/yamond.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.yamond"] = imp.new_module("pagekite.yamond")
m.__file__ = "pagekite/yamond.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("yamond", m)
exec __FILES[".SELF/pagekite/yamond.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/httpd.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.httpd"] = imp.new_module("pagekite.httpd")
m.__file__ = "pagekite/httpd.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("httpd", m)
exec __FILES[".SELF/pagekite/httpd.py"] in m.__dict__
###############################################################################
__FILES[".SELF/pagekite/pk.py"] = zlib.decompress(__b64d("""\
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"""))
m = sys.modules["pagekite.pk"] = imp.new_module("pagekite.pk")
m.__file__ = "pagekite/pk.py"
m.open = __comb_open
sys.modules["pagekite"].__setattr__("pk", m)
exec __FILES[".SELF/pagekite/pk.py"] in m.__dict__
###############################################################################
#!/usr/bin/env python
"""
This is the pagekite.py Main() function.
"""
##############################################################################
LICENSE = """\
This file is part of pagekite.py.
Copyright 2010-2017, the Beanstalks Project ehf. and Bjarni Runar Einarsson
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU Affero General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option) any
later version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see: <http://www.gnu.org/licenses/>
"""
##############################################################################
import sys
from pagekite import pk
from pagekite import httpd
if __name__ == "__main__":
if hasattr(sys.stdout, 'isatty') and sys.stdout.isatty():
import pagekite.ui.basic
uiclass = pagekite.ui.basic.BasicUi
else:
import pagekite.ui.nullui
uiclass = pagekite.ui.nullui.NullUi
pk.Main(pk.PageKite, pk.Configure,
uiclass=uiclass,
http_handler=httpd.UiRequestHandler,
http_server=httpd.UiHttpServer)
##############################################################################
CERTS="""\
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StartCom Ltd.
=============
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StartCom Certification Authority
================================
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Letsencrypt X1, X3, X4
======================
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ARYiaHR0cDovL2Nwcy5yb290LXgxLmxldHNlbmNyeXB0Lm9yZzA8BgNVHR8ENTAz
MDGgL6AthitodHRwOi8vY3JsLmlkZW50cnVzdC5jb20vRFNUUk9PVENBWDNDUkwu
Y3JsMB0GA1UdDgQWBBTFsatOTLHNZDCTfsGEmQWr5gPiJTANBgkqhkiG9w0BAQsF
AAOCAQEANlaeSdstfAtqFN3jdRZJFjx9X+Ob3PIDlekPYQ1OQ1Uw43rE1FUj7hUw
g2MJKfs9b7M0WoQg7C20nJY/ajsg7pWhUG3J6rlkDTfVY9faeWi0qsPYXE6BpBDr
5BrW/Xv8yT8U2BiEAmNggWq8dmFl82fghmLzHBM8X8NZ3ZwA1fGePA53AP5IoD+0
ArpW8Ik1sSuQBjZ8oQLfN+G8OoY7MNRopyLyQQCNy4aWfE+xYnoVoa5+yr+aPiX0
7YQrY/cKawAn7QB4PyF5//IKSAVs7mAuB68wbMdE3FKfOHfJ24W4z/bIJTrTY8Y5
Sr4AUhtzf8oVDrHZYWRrP4joIcOu/Q==
-----END CERTIFICATE-----
"""
#EOF#
| 73.122547
| 92
| 0.935894
| 8,080
| 182,587
| 21.083911
| 0.820421
| 0.002008
| 0.003592
| 0.002231
| 0.047576
| 0.0421
| 0.039775
| 0.038355
| 0.035108
| 0.033089
| 0
| 0.144683
| 0.017608
| 182,587
| 2,496
| 93
| 73.151843
| 0.805064
| 0.004798
| 0
| 0.056785
| 0
| 0.000835
| 0.972544
| 0.94709
| 0
| 1
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| null | null | 0
| 0.002505
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| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
43ff6c5faac4720d9a1a1a3117fda6a1c65da272
| 183
|
py
|
Python
|
src/libs/custom_exceptions.py
|
rushan7750/LotteryBot
|
b6730fbc28bd63da8ef0065053b1246e6c31238a
|
[
"MIT"
] | 1
|
2021-08-29T13:20:47.000Z
|
2021-08-29T13:20:47.000Z
|
src/libs/custom_exceptions.py
|
rushan7750/LotteryBot
|
b6730fbc28bd63da8ef0065053b1246e6c31238a
|
[
"MIT"
] | null | null | null |
src/libs/custom_exceptions.py
|
rushan7750/LotteryBot
|
b6730fbc28bd63da8ef0065053b1246e6c31238a
|
[
"MIT"
] | null | null | null |
class UserAlreadyEnteredError(Exception):
pass
class NoUsersEnteredError(Exception):
pass
class WrongTimeFormatError(Exception):
pass
#custom error just for readability
| 18.3
| 41
| 0.79235
| 17
| 183
| 8.529412
| 0.647059
| 0.268966
| 0.248276
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0.153005
| 183
| 10
| 42
| 18.3
| 0.935484
| 0.180328
| 0
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| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
a1388175d1b3b165e7ae060954d17a24aa029693
| 90
|
py
|
Python
|
project_e/dealers/admin.py
|
ElectricFleming/project-e
|
cf05d2a835a09555e3dba5813d635d329684a71c
|
[
"bzip2-1.0.6"
] | null | null | null |
project_e/dealers/admin.py
|
ElectricFleming/project-e
|
cf05d2a835a09555e3dba5813d635d329684a71c
|
[
"bzip2-1.0.6"
] | 3
|
2020-01-30T03:47:26.000Z
|
2021-05-11T00:58:08.000Z
|
project_e/dealers/admin.py
|
effortless-electric/project-e
|
ae4e8415204319999ee2ecac248e2504ec1fff63
|
[
"bzip2-1.0.6"
] | 1
|
2019-12-27T22:45:45.000Z
|
2019-12-27T22:45:45.000Z
|
from django.contrib import admin
from .models import Dealer
admin.site.register(Dealer)
| 15
| 32
| 0.811111
| 13
| 90
| 5.615385
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.122222
| 90
| 5
| 33
| 18
| 0.924051
| 0
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| 0
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| 0
| true
| 0
| 0.666667
| 0
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| 0
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| null | 0
| 0
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| 0
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| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a1a46471c319b36605cf9389895043c8ce73dc45
| 127
|
py
|
Python
|
Aula 08/ex021.py
|
alaanlimaa/Python_CVM1-2-3
|
6d9a9bd693580fd1679a1d0b23afd26841b962a6
|
[
"MIT"
] | null | null | null |
Aula 08/ex021.py
|
alaanlimaa/Python_CVM1-2-3
|
6d9a9bd693580fd1679a1d0b23afd26841b962a6
|
[
"MIT"
] | null | null | null |
Aula 08/ex021.py
|
alaanlimaa/Python_CVM1-2-3
|
6d9a9bd693580fd1679a1d0b23afd26841b962a6
|
[
"MIT"
] | null | null | null |
import pygame
pygame.init()
pygame.mixer.music.load('ex021.mp3')
pygame.mixer.music.play()
input('Digite algo para parar....')
| 21.166667
| 36
| 0.740157
| 19
| 127
| 4.947368
| 0.736842
| 0.234043
| 0.340426
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033898
| 0.070866
| 127
| 5
| 37
| 25.4
| 0.762712
| 0
| 0
| 0
| 0
| 0
| 0.275591
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.2
| 0
| 0.2
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a1b18cda00be075216679a9638e8148a9a8bb92e
| 169
|
py
|
Python
|
DQM/DTMonitorClient/python/dtCertificationSummary_cfi.py
|
Purva-Chaudhari/cmssw
|
32e5cbfe54c4d809d60022586cf200b7c3020bcf
|
[
"Apache-2.0"
] | 852
|
2015-01-11T21:03:51.000Z
|
2022-03-25T21:14:00.000Z
|
DQM/DTMonitorClient/python/dtCertificationSummary_cfi.py
|
Purva-Chaudhari/cmssw
|
32e5cbfe54c4d809d60022586cf200b7c3020bcf
|
[
"Apache-2.0"
] | 30,371
|
2015-01-02T00:14:40.000Z
|
2022-03-31T23:26:05.000Z
|
DQM/DTMonitorClient/python/dtCertificationSummary_cfi.py
|
Purva-Chaudhari/cmssw
|
32e5cbfe54c4d809d60022586cf200b7c3020bcf
|
[
"Apache-2.0"
] | 3,240
|
2015-01-02T05:53:18.000Z
|
2022-03-31T17:24:21.000Z
|
import FWCore.ParameterSet.Config as cms
from DQMServices.Core.DQMEDHarvester import DQMEDHarvester
dtCertificationSummary = DQMEDHarvester('DTCertificationSummary')
| 24.142857
| 65
| 0.863905
| 15
| 169
| 9.733333
| 0.733333
| 0.493151
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08284
| 169
| 6
| 66
| 28.166667
| 0.941935
| 0
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| 0.131737
| 0.131737
| 0
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| 1
| 0
| false
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| 0.666667
| 0
| 0.666667
| 0
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| null | 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a1e62736e6b8b75cd115cfd970a66419c883e632
| 129
|
py
|
Python
|
adventofcode/__init__.py
|
vonNiklasson/adventofcode-library
|
53c3cc30d4879e0dde56c63a2064d27b86f40deb
|
[
"MIT"
] | null | null | null |
adventofcode/__init__.py
|
vonNiklasson/adventofcode-library
|
53c3cc30d4879e0dde56c63a2064d27b86f40deb
|
[
"MIT"
] | null | null | null |
adventofcode/__init__.py
|
vonNiklasson/adventofcode-library
|
53c3cc30d4879e0dde56c63a2064d27b86f40deb
|
[
"MIT"
] | null | null | null |
from . import exceptions
from ._version import __version__
from .adventofcode import AdventOfCode
from .question import Question
| 25.8
| 38
| 0.844961
| 15
| 129
| 6.933333
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.124031
| 129
| 4
| 39
| 32.25
| 0.920354
| 0
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| true
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b806c53f897e965b7239790cbc4fa9ad7950b31f
| 2,105
|
py
|
Python
|
resources/dot_PyCharm/system/python_stubs/-762174762/PySide/QtXml/QXmlReader.py
|
basepipe/developer_onboarding
|
05b6a776f8974c89517868131b201f11c6c2a5ad
|
[
"MIT"
] | 1
|
2020-04-20T02:27:20.000Z
|
2020-04-20T02:27:20.000Z
|
resources/dot_PyCharm/system/python_stubs/cache/d1acfdaecbc43dfcba0c1287ba0e29c0ef0e2d37695269b2505d76ec531b8b76/PySide/QtXml/QXmlReader.py
|
basepipe/developer_onboarding
|
05b6a776f8974c89517868131b201f11c6c2a5ad
|
[
"MIT"
] | null | null | null |
resources/dot_PyCharm/system/python_stubs/cache/d1acfdaecbc43dfcba0c1287ba0e29c0ef0e2d37695269b2505d76ec531b8b76/PySide/QtXml/QXmlReader.py
|
basepipe/developer_onboarding
|
05b6a776f8974c89517868131b201f11c6c2a5ad
|
[
"MIT"
] | null | null | null |
# encoding: utf-8
# module PySide.QtXml
# from C:\Python27\lib\site-packages\PySide\QtXml.pyd
# by generator 1.147
# no doc
# imports
import Shiboken as __Shiboken
class QXmlReader(__Shiboken.Object):
# no doc
def contentHandler(self, *args, **kwargs): # real signature unknown
pass
def declHandler(self, *args, **kwargs): # real signature unknown
pass
def DTDHandler(self, *args, **kwargs): # real signature unknown
pass
def entityResolver(self, *args, **kwargs): # real signature unknown
pass
def errorHandler(self, *args, **kwargs): # real signature unknown
pass
def feature(self, *args, **kwargs): # real signature unknown
pass
def hasFeature(self, *args, **kwargs): # real signature unknown
pass
def hasProperty(self, *args, **kwargs): # real signature unknown
pass
def lexicalHandler(self, *args, **kwargs): # real signature unknown
pass
def parse(self, *args, **kwargs): # real signature unknown
pass
def property(self, *args, **kwargs): # real signature unknown
pass
def setContentHandler(self, *args, **kwargs): # real signature unknown
pass
def setDeclHandler(self, *args, **kwargs): # real signature unknown
pass
def setDTDHandler(self, *args, **kwargs): # real signature unknown
pass
def setEntityResolver(self, *args, **kwargs): # real signature unknown
pass
def setErrorHandler(self, *args, **kwargs): # real signature unknown
pass
def setFeature(self, *args, **kwargs): # real signature unknown
pass
def setLexicalHandler(self, *args, **kwargs): # real signature unknown
pass
def setProperty(self, *args, **kwargs): # real signature unknown
pass
def __init__(self, *args, **kwargs): # real signature unknown
pass
@staticmethod # known case of __new__
def __new__(S, *more): # real signature unknown; restored from __doc__
""" T.__new__(S, ...) -> a new object with type S, a subtype of T """
pass
| 26.64557
| 77
| 0.638955
| 241
| 2,105
| 5.481328
| 0.278008
| 0.206662
| 0.317941
| 0.272521
| 0.618471
| 0.618471
| 0.618471
| 0.589705
| 0
| 0
| 0
| 0.00445
| 0.252732
| 2,105
| 78
| 78
| 26.987179
| 0.835346
| 0.342043
| 0
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| 1
| 0.466667
| false
| 0.466667
| 0.022222
| 0
| 0.511111
| 0
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| 1
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| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
b80e105a06cf1ea9e09c4ffa7f6f93fb80c05b9a
| 3,344
|
py
|
Python
|
17tensorflow/01.seq2seq_demo.py
|
KEVINYZY/python-tutorial
|
ae43536908eb8af56c34865f52a6e8644edc4fa3
|
[
"Apache-2.0"
] | 2
|
2021-01-04T10:44:44.000Z
|
2022-02-13T07:53:41.000Z
|
17tensorflow/01.seq2seq_demo.py
|
zm79287/python-tutorial
|
d0f7348e1da4ff954e3add66e1aae55d599283ee
|
[
"Apache-2.0"
] | null | null | null |
17tensorflow/01.seq2seq_demo.py
|
zm79287/python-tutorial
|
d0f7348e1da4ff954e3add66e1aae55d599283ee
|
[
"Apache-2.0"
] | 2
|
2020-11-23T08:58:51.000Z
|
2022-02-13T07:53:42.000Z
|
# -*- coding: utf-8 -*-
"""
@author:XuMing（xuming624@qq.com)
@description:
"""
from seq2seq import SimpleSeq2Seq, Seq2Seq, AttentionSeq2Seq
import numpy as np
from keras.utils.test_utils import keras_test
input_length = 5
input_dim = 3
output_length = 3
output_dim = 4
samples = 100
hidden_dim = 24
@keras_test
def test_SimpleSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
print(x)
print(y)
models += [SimpleSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [SimpleSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, nb_epoch=1)
@keras_test
def test_Seq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True)]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True, depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1)
model = Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True, depth=2, teacher_force=True)
model.compile(loss='mse', optimizer='sgd')
model.fit([x, y], y, epochs=1)
@keras_test
def test_AttentionSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=3)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1)
# test_SimpleSeq2Seq()
# test_Seq2Seq()
# test_AttentionSeq2Seq()
from seq2seq.models import AttentionSeq2Seq
model = AttentionSeq2Seq(input_dim=5, input_length=7, hidden_dim=10, output_length=8, output_dim=20, depth=4)
model.compile(loss='mse', optimizer='rmsprop')
| 38.436782
| 109
| 0.684809
| 439
| 3,344
| 4.931663
| 0.138952
| 0.138568
| 0.110855
| 0.114088
| 0.771363
| 0.742725
| 0.742725
| 0.742725
| 0.742725
| 0.742725
| 0
| 0.020127
| 0.197667
| 3,344
| 87
| 110
| 38.436782
| 0.786806
| 0.038876
| 0
| 0.6
| 0
| 0
| 0.010612
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.05
| false
| 0
| 0.066667
| 0
| 0.116667
| 0.033333
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
629e2faddf7f32cc91c61e978dde208e127c8427
| 112
|
py
|
Python
|
dashboard/source/data/__init__.py
|
mueller-stephan/car-classification
|
6b620091e071535ea112a6e19c1d15de21dadbd1
|
[
"MIT"
] | 2
|
2020-03-21T19:26:39.000Z
|
2021-06-14T18:45:10.000Z
|
dashboard/source/data/__init__.py
|
fabianmax/car-classification
|
2c32a57b0404c2da6c341a6f496c9912fbd19b50
|
[
"MIT"
] | null | null | null |
dashboard/source/data/__init__.py
|
fabianmax/car-classification
|
2c32a57b0404c2da6c341a6f496c9912fbd19b50
|
[
"MIT"
] | 5
|
2020-02-26T13:35:23.000Z
|
2022-03-11T20:05:34.000Z
|
from .data import GameData, ItemLabel
from .labels import LABELS
__all__ = ['GameData', 'ItemLabel', 'LABELS']
| 22.4
| 45
| 0.741071
| 13
| 112
| 6.076923
| 0.538462
| 0.43038
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133929
| 112
| 4
| 46
| 28
| 0.814433
| 0
| 0
| 0
| 0
| 0
| 0.205357
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 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
| 1
| 0
|
0
| 5
|
62ce60b2eeda9ac7bf96320971a8b4a925d3febf
| 247
|
py
|
Python
|
postcodeinfo/apps/postcode_api/importers/postcode_gss_code_importer.py
|
UKHomeOffice/postcodeinfo
|
f268a6b6d6d7ac55cb2fb307e98275348e30a287
|
[
"MIT"
] | null | null | null |
postcodeinfo/apps/postcode_api/importers/postcode_gss_code_importer.py
|
UKHomeOffice/postcodeinfo
|
f268a6b6d6d7ac55cb2fb307e98275348e30a287
|
[
"MIT"
] | null | null | null |
postcodeinfo/apps/postcode_api/importers/postcode_gss_code_importer.py
|
UKHomeOffice/postcodeinfo
|
f268a6b6d6d7ac55cb2fb307e98275348e30a287
|
[
"MIT"
] | 1
|
2021-04-11T09:12:19.000Z
|
2021-04-11T09:12:19.000Z
|
from postcode_api.importers.psql_import_adapter import PSQLImportAdapter
class PostcodeGssCodeImporter(object):
def import_postcode_gss_codes(self, filepaths):
PSQLImportAdapter().import_csv('postcode_gss_code_import.sh', filepaths)
| 35.285714
| 80
| 0.825911
| 28
| 247
| 6.928571
| 0.678571
| 0.113402
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101215
| 247
| 6
| 81
| 41.166667
| 0.873874
| 0
| 0
| 0
| 0
| 0
| 0.109312
| 0.109312
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 1
| 0
| 1.5
| 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
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
62feae5cebd3a443a308ad20f4b109cf3c510a1b
| 81
|
py
|
Python
|
machine_learning/supervised/perceptron.py
|
saforem2/machine_learning
|
239661d0c532cae51b18411abd1ff435477ff4ba
|
[
"MIT"
] | null | null | null |
machine_learning/supervised/perceptron.py
|
saforem2/machine_learning
|
239661d0c532cae51b18411abd1ff435477ff4ba
|
[
"MIT"
] | null | null | null |
machine_learning/supervised/perceptron.py
|
saforem2/machine_learning
|
239661d0c532cae51b18411abd1ff435477ff4ba
|
[
"MIT"
] | null | null | null |
from __future__ import print_function, division
import math
import numpy as np
| 13.5
| 47
| 0.82716
| 12
| 81
| 5.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160494
| 81
| 5
| 48
| 16.2
| 0.911765
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 5
|
1a026c7c729746c92336cc1c7bb7b18d2bab4d57
| 122
|
py
|
Python
|
nl/stores/__init__.py
|
philomelus/nl3
|
0b70b018a94d964bd224015ceb23dcc370462de4
|
[
"MIT"
] | null | null | null |
nl/stores/__init__.py
|
philomelus/nl3
|
0b70b018a94d964bd224015ceb23dcc370462de4
|
[
"MIT"
] | null | null | null |
nl/stores/__init__.py
|
philomelus/nl3
|
0b70b018a94d964bd224015ceb23dcc370462de4
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
bp = Blueprint('stores', __name__, url_prefix='/stores')
from nl.stores import handlers
| 12.2
| 56
| 0.745902
| 16
| 122
| 5.375
| 0.6875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155738
| 122
| 9
| 57
| 13.555556
| 0.834951
| 0
| 0
| 0
| 0
| 0
| 0.108333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 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
| 1
|
0
| 5
|
1a1c98401ce3887482134626b4cf871fb2132c81
| 123
|
py
|
Python
|
ecomsite/shop/admin.py
|
Lebana-source/ProyectoTienda
|
a7a6aa726e0f4fca7bdfa9803113de354791d4fa
|
[
"MIT"
] | null | null | null |
ecomsite/shop/admin.py
|
Lebana-source/ProyectoTienda
|
a7a6aa726e0f4fca7bdfa9803113de354791d4fa
|
[
"MIT"
] | null | null | null |
ecomsite/shop/admin.py
|
Lebana-source/ProyectoTienda
|
a7a6aa726e0f4fca7bdfa9803113de354791d4fa
|
[
"MIT"
] | 1
|
2021-07-06T20:39:45.000Z
|
2021-07-06T20:39:45.000Z
|
from django.contrib import admin
from .models import Products
# Register your models here.
admin.site.register(Products)
| 17.571429
| 32
| 0.804878
| 17
| 123
| 5.823529
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130081
| 123
| 6
| 33
| 20.5
| 0.925234
| 0.211382
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a7ece191fd109845d8259adbb41b4715510de3de
| 160
|
py
|
Python
|
src/cgr_gwas_qc/__init__.py
|
Monia234/NCI-GwasQc
|
9e3ca52085c891e1d4d7972e5337c4a1888f992c
|
[
"MIT"
] | null | null | null |
src/cgr_gwas_qc/__init__.py
|
Monia234/NCI-GwasQc
|
9e3ca52085c891e1d4d7972e5337c4a1888f992c
|
[
"MIT"
] | 43
|
2021-03-02T04:10:01.000Z
|
2022-03-16T20:26:55.000Z
|
src/cgr_gwas_qc/__init__.py
|
Monia234/NCI-GwasQc
|
9e3ca52085c891e1d4d7972e5337c4a1888f992c
|
[
"MIT"
] | 2
|
2021-03-02T12:27:00.000Z
|
2021-12-16T03:22:20.000Z
|
from cgr_gwas_qc.config import ConfigMgr
def load_config(pytest=False) -> ConfigMgr:
"""Load the config manager."""
return ConfigMgr.instance(pytest)
| 22.857143
| 43
| 0.74375
| 21
| 160
| 5.52381
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 160
| 6
| 44
| 26.666667
| 0.852941
| 0.15
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| 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
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c50776cfd6078933ed9076d44883b2eb95d41631
| 151
|
py
|
Python
|
Ejercicio7.py
|
Edroll/PC-3
|
55d9dbd12c8093d626184884aaf5ce12ee7dcc4c
|
[
"Apache-2.0"
] | null | null | null |
Ejercicio7.py
|
Edroll/PC-3
|
55d9dbd12c8093d626184884aaf5ce12ee7dcc4c
|
[
"Apache-2.0"
] | null | null | null |
Ejercicio7.py
|
Edroll/PC-3
|
55d9dbd12c8093d626184884aaf5ce12ee7dcc4c
|
[
"Apache-2.0"
] | null | null | null |
# 1
#1 1
#1 1 1
p = int(input("Ingrese un numero: "))
for n in range(1,p):
print(" "*(p-1) + "1")
if(p != 1):
print(" 1 1 ")
| 16.777778
| 38
| 0.410596
| 28
| 151
| 2.214286
| 0.464286
| 0.225806
| 0.193548
| 0.193548
| 0.096774
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123711
| 0.357616
| 151
| 9
| 39
| 16.777778
| 0.515464
| 0.059603
| 0
| 0
| 0
| 0
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.4
| 1
| 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
| 5
|
c51181ee53360e67c5f95bd1cbf5dacb39e8860c
| 58
|
py
|
Python
|
sales_register/domain/entities/fields/__init__.py
|
tamercuba/purchase-system
|
cfd3e4fecbd96c130f620d11491fa14979c0d996
|
[
"MIT"
] | null | null | null |
sales_register/domain/entities/fields/__init__.py
|
tamercuba/purchase-system
|
cfd3e4fecbd96c130f620d11491fa14979c0d996
|
[
"MIT"
] | 6
|
2021-05-15T21:44:19.000Z
|
2021-05-23T22:20:13.000Z
|
sales_register/domain/entities/fields/__init__.py
|
tamercuba/sales-register
|
cfd3e4fecbd96c130f620d11491fa14979c0d996
|
[
"MIT"
] | null | null | null |
from domain.entities.fields.sale_status import SaleStatus
| 29
| 57
| 0.87931
| 8
| 58
| 6.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068966
| 58
| 1
| 58
| 58
| 0.925926
| 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
| 1
| 0
|
0
| 5
|
c54c1483f4716b59f33bad7f7907682044cfcf44
| 32
|
py
|
Python
|
problem/01000~09999/09655/9655.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 1
|
2019-04-19T16:37:44.000Z
|
2019-04-19T16:37:44.000Z
|
problem/01000~09999/09659/9659.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 1
|
2019-04-20T11:42:44.000Z
|
2019-04-20T11:42:44.000Z
|
problem/01000~09999/09659/9659.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 3
|
2019-04-19T16:37:47.000Z
|
2021-10-25T00:45:00.000Z
|
print('CSYK'[int(input())%2::2])
| 32
| 32
| 0.59375
| 6
| 32
| 3.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 0
| 32
| 1
| 32
| 32
| 0.53125
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 1
|
0
| 5
|
c55ffdc81b6548c97d372a776e036e5e1e11e2bf
| 120
|
py
|
Python
|
books/admin.py
|
ajmaln/StackLibrary
|
988c94b7a66b3f9fd24026b289bb3cdb8420dbb4
|
[
"MIT"
] | 1
|
2018-04-01T15:36:28.000Z
|
2018-04-01T15:36:28.000Z
|
books/admin.py
|
ajmaln/StackLibrary
|
988c94b7a66b3f9fd24026b289bb3cdb8420dbb4
|
[
"MIT"
] | null | null | null |
books/admin.py
|
ajmaln/StackLibrary
|
988c94b7a66b3f9fd24026b289bb3cdb8420dbb4
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Book, Issue
admin.site.register(Book)
admin.site.register(Issue)
| 17.142857
| 32
| 0.8
| 18
| 120
| 5.333333
| 0.555556
| 0.1875
| 0.354167
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108333
| 120
| 6
| 33
| 20
| 0.897196
| 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
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
c56f8989db98a243cdc7a022d1e9155a02f8bf0d
| 39
|
py
|
Python
|
recipes-samples/demo-application/demo-application-bluetooth/__init__.py
|
gkiagia/meta-st-openstlinux
|
2c1ae43b5d06b14e1506a3d678e4e82ab8dec9e8
|
[
"MIT"
] | 46
|
2019-02-28T09:58:10.000Z
|
2022-03-15T07:37:54.000Z
|
recipes-samples/demo-application/demo-application-bluetooth/__init__.py
|
gkiagia/meta-st-openstlinux
|
2c1ae43b5d06b14e1506a3d678e4e82ab8dec9e8
|
[
"MIT"
] | 3
|
2019-11-06T15:01:08.000Z
|
2021-08-30T14:32:09.000Z
|
recipes-samples/demo-application/demo-application-bluetooth/__init__.py
|
gkiagia/meta-st-openstlinux
|
2c1ae43b5d06b14e1506a3d678e4e82ab8dec9e8
|
[
"MIT"
] | 22
|
2019-03-27T10:09:47.000Z
|
2022-03-21T11:04:19.000Z
|
#print('Importing bluetooth __init__')
| 19.5
| 38
| 0.794872
| 4
| 39
| 6.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 39
| 1
| 39
| 39
| 0.75
| 0.948718
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c5754e6900db1d29299b53069ec7bb4d5d50e24a
| 6,508
|
py
|
Python
|
conduit/hydra/conduit/fair/data/datamodules/conf.py
|
wearepal/conduit
|
a16e575181c0464aa63ee4c029eac19021ce79ba
|
[
"MIT"
] | null | null | null |
conduit/hydra/conduit/fair/data/datamodules/conf.py
|
wearepal/conduit
|
a16e575181c0464aa63ee4c029eac19021ce79ba
|
[
"MIT"
] | 5
|
2022-03-18T12:54:45.000Z
|
2022-03-30T19:14:16.000Z
|
conduit/hydra/conduit/fair/data/datamodules/conf.py
|
wearepal/conduit
|
a16e575181c0464aa63ee4c029eac19021ce79ba
|
[
"MIT"
] | 1
|
2022-03-24T03:52:44.000Z
|
2022-03-24T03:52:44.000Z
|
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/master/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from conduit.fair.data.datamodules.tabular.admissions import AdmissionsSens
from conduit.fair.data.datamodules.tabular.adult import AdultSens
from conduit.fair.data.datamodules.tabular.compas import CompasSens
from conduit.fair.data.datamodules.tabular.credit import CreditSens
from conduit.fair.data.datamodules.tabular.crime import CrimeSens
from conduit.fair.data.datamodules.tabular.german import GermanSens
from conduit.fair.data.datamodules.tabular.health import HealthSens
from conduit.fair.data.datamodules.tabular.law import LawSens
from conduit.fair.data.datamodules.tabular.sqf import SqfSens
from omegaconf import MISSING
from ranzen.torch.data import TrainingMode
from typing import Any
from typing import Optional
@dataclass
class AdmissionsDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.AdmissionsDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: AdmissionsSens = AdmissionsSens.gender
disc_feats_only: bool = False
@dataclass
class AdultDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.AdultDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
bin_nationality: bool = False
sens_feat: AdultSens = AdultSens.sex
bin_race: bool = False
disc_feats_only: bool = False
@dataclass
class CompasDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.CompasDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: CompasSens = CompasSens.sex
disc_feats_only: bool = False
@dataclass
class CreditDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.CreditDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: CreditSens = CreditSens.sex
disc_feats_only: bool = False
@dataclass
class CrimeDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.CrimeDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: CrimeSens = CrimeSens.raceBinary
disc_feats_only: bool = False
@dataclass
class GermanDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.GermanDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: GermanSens = GermanSens.sex
disc_feats_only: bool = False
@dataclass
class HealthDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.HealthDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: HealthSens = HealthSens.sex
disc_feats_only: bool = False
@dataclass
class LawDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.LawDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: LawSens = LawSens.sex
disc_feats_only: bool = False
@dataclass
class SqfDataModuleConf:
_target_: str = "conduit.fair.data.datamodules.SqfDataModule"
train_batch_size: int = 64
eval_batch_size: Optional[int] = None
val_prop: float = 0.2
test_prop: float = 0.2
num_workers: int = 0
seed: int = 47
persist_workers: bool = False
pin_memory: bool = True
stratified_sampling: bool = False
instance_weighting: bool = False
training_mode: TrainingMode = TrainingMode.epoch
scaler: Any = MISSING # ScalerType
invert_s: bool = False
sens_feat: SqfSens = SqfSens.sex
disc_feats_only: bool = False
| 32.059113
| 75
| 0.721727
| 828
| 6,508
| 5.47343
| 0.149758
| 0.093336
| 0.059576
| 0.103266
| 0.775596
| 0.775596
| 0.627096
| 0.603266
| 0.560238
| 0.560238
| 0
| 0.015794
| 0.202213
| 6,508
| 202
| 76
| 32.217822
| 0.857088
| 0.037646
| 0
| 0.758427
| 1
| 0
| 0.065621
| 0.065621
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.011236
| 0.078652
| 0
| 0.949438
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
3db2a075e027ca4fb48cea7e3b132ea959076ea6
| 173
|
py
|
Python
|
geekshop/mainapp/admin.py
|
A1eksAwP/GB-Internet-Store
|
a7cffcf3e7a73efc0cad3cd67ccdbaf245889ce5
|
[
"MIT"
] | null | null | null |
geekshop/mainapp/admin.py
|
A1eksAwP/GB-Internet-Store
|
a7cffcf3e7a73efc0cad3cd67ccdbaf245889ce5
|
[
"MIT"
] | null | null | null |
geekshop/mainapp/admin.py
|
A1eksAwP/GB-Internet-Store
|
a7cffcf3e7a73efc0cad3cd67ccdbaf245889ce5
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import ProductCategory, Product
# Register your models here.
admin.site.register(ProductCategory)
admin.site.register(Product)
| 28.833333
| 44
| 0.83237
| 22
| 173
| 6.545455
| 0.545455
| 0.125
| 0.236111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092486
| 173
| 6
| 45
| 28.833333
| 0.917197
| 0.150289
| 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
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3dc9170c9b4b644a7fcfa6389f705247a830adc9
| 61
|
py
|
Python
|
enthought/pyface/ui/null/resource_manager.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 3
|
2016-12-09T06:05:18.000Z
|
2018-03-01T13:00:29.000Z
|
enthought/pyface/ui/null/resource_manager.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 1
|
2020-12-02T00:51:32.000Z
|
2020-12-02T08:48:55.000Z
|
enthought/pyface/ui/null/resource_manager.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | null | null | null |
# proxy module
from pyface.ui.null.resource_manager import *
| 20.333333
| 45
| 0.803279
| 9
| 61
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114754
| 61
| 2
| 46
| 30.5
| 0.888889
| 0.196721
| 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
| 1
| 0
|
0
| 5
|
ad00601a36931f95fd3671418966719240da2ef2
| 174
|
py
|
Python
|
ini/__init__.py
|
ClementJ18/ini_parser
|
68bee79af6319cc967c3fa13e4ba7b5ef0485fa0
|
[
"MIT"
] | null | null | null |
ini/__init__.py
|
ClementJ18/ini_parser
|
68bee79af6319cc967c3fa13e4ba7b5ef0485fa0
|
[
"MIT"
] | null | null | null |
ini/__init__.py
|
ClementJ18/ini_parser
|
68bee79af6319cc967c3fa13e4ba7b5ef0485fa0
|
[
"MIT"
] | null | null | null |
from .parser import GameParser
from .ini_objects import *
from .enums import *
from .types import *
from .behaviors import *
from . import interactions
__version__ = "0.1a"
| 19.333333
| 30
| 0.758621
| 23
| 174
| 5.521739
| 0.565217
| 0.314961
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013699
| 0.16092
| 174
| 8
| 31
| 21.75
| 0.856164
| 0
| 0
| 0
| 0
| 0
| 0.022989
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.857143
| 0
| 0.857143
| 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
| 1
| 0
|
0
| 5
|
b18e2762f676c1ff93b8e7b9267ad281eb3ab209
| 144
|
py
|
Python
|
tests/test_transcription.py
|
ABignaud/bacchus
|
f3f4698652cdac6305d6eeb0197de01c04b8f2a3
|
[
"BSD-3-Clause"
] | 1
|
2022-02-02T11:45:21.000Z
|
2022-02-02T11:45:21.000Z
|
tests/test_transcription.py
|
ABignaud/bacchus
|
f3f4698652cdac6305d6eeb0197de01c04b8f2a3
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_transcription.py
|
ABignaud/bacchus
|
f3f4698652cdac6305d6eeb0197de01c04b8f2a3
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Module to test functions from transcription module."""
import bacchus.transcription as bct
| 18
| 57
| 0.701389
| 19
| 144
| 5.315789
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01626
| 0.145833
| 144
| 8
| 58
| 18
| 0.804878
| 0.659722
| 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
| 1
| 0
|
0
| 5
|
b1a5d01d6ac1c695643025155f4d460b6f0e9e95
| 244
|
py
|
Python
|
mmocr/models/textrecog/recognizer/satrn.py
|
hongxuenong/mmocr
|
e8e3a059f8f2e4fca96af37751c33563fc48e2ba
|
[
"Apache-2.0"
] | 2,261
|
2021-04-08T03:45:41.000Z
|
2022-03-31T23:37:46.000Z
|
mmocr/models/textrecog/recognizer/satrn.py
|
hongxuenong/mmocr
|
e8e3a059f8f2e4fca96af37751c33563fc48e2ba
|
[
"Apache-2.0"
] | 789
|
2021-04-08T05:40:13.000Z
|
2022-03-31T09:42:39.000Z
|
mmocr/models/textrecog/recognizer/satrn.py
|
hongxuenong/mmocr
|
e8e3a059f8f2e4fca96af37751c33563fc48e2ba
|
[
"Apache-2.0"
] | 432
|
2021-04-08T03:56:16.000Z
|
2022-03-30T18:44:43.000Z
|
from mmocr.models.builder import DETECTORS
from .encode_decode_recognizer import EncodeDecodeRecognizer
@DETECTORS.register_module()
class SATRN(EncodeDecodeRecognizer):
"""Implementation of `SATRN <https://arxiv.org/abs/1910.04396>`_"""
| 30.5
| 71
| 0.803279
| 27
| 244
| 7.111111
| 0.814815
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.040359
| 0.086066
| 244
| 7
| 72
| 34.857143
| 0.820628
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.75
| 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
| 1
| 0
|
0
| 5
|
490d8055f3f52afe8e1db6afc1ca3cd8050eaab9
| 176
|
py
|
Python
|
tests/conftest.py
|
cevanwells/oliver
|
7b7cf6a7110825bfe477572fba0ea4800e78f8ad
|
[
"Apache-2.0"
] | null | null | null |
tests/conftest.py
|
cevanwells/oliver
|
7b7cf6a7110825bfe477572fba0ea4800e78f8ad
|
[
"Apache-2.0"
] | null | null | null |
tests/conftest.py
|
cevanwells/oliver
|
7b7cf6a7110825bfe477572fba0ea4800e78f8ad
|
[
"Apache-2.0"
] | null | null | null |
import pytest
import oliver
@pytest.fixture
def app():
app = oliver.create_app('testing')
yield app
@pytest.fixture
def client(app):
return app.test_client()
| 11
| 38
| 0.693182
| 24
| 176
| 5
| 0.5
| 0.216667
| 0.266667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.198864
| 176
| 15
| 39
| 11.733333
| 0.851064
| 0
| 0
| 0.222222
| 0
| 0
| 0.039773
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.222222
| false
| 0
| 0.222222
| 0.111111
| 0.555556
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
493fd5f028a7782e214c2f092c6457c32490cd74
| 1,699
|
py
|
Python
|
utils_test.py
|
sendoswww/matlab_utils_for_python
|
8cc029350785ce593c6b83f4995d26527ec1d912
|
[
"MIT"
] | null | null | null |
utils_test.py
|
sendoswww/matlab_utils_for_python
|
8cc029350785ce593c6b83f4995d26527ec1d912
|
[
"MIT"
] | 1
|
2017-04-18T15:25:34.000Z
|
2017-04-18T15:44:07.000Z
|
utils_test.py
|
sendoswww/matlab_utils_for_python
|
8cc029350785ce593c6b83f4995d26527ec1d912
|
[
"MIT"
] | null | null | null |
"""
Script with sample usage of the functions in matlab_utils.py
Copyright (c) 2017 Andrew Sendonaris.
"""
# In this script, we convert the following Matlab script for use in Python
"""
function D = mydist(X)
if isempty(X)
error('Input matrix is empty\n');
end
% Get the number of points
num_points = size(X, 1);
if num_points < 2
error('Number of points should be more than one\n');
end
% Initialize the result
D = zeros(num_points, num_points);
for i = 1:num_points-1
for j = 1:num_points
if(i < j)
% Ensure the matrix is symmetric
D(i,j) = sqrt(sum((X(i,:)-X(j,:)).^2));
D(j,i) = D(i,j);
end
end
end
end
X = [[1, 2, 3]; [4, 5, 6]; [7, 8, 9]];
D = mydist(X);
fprintf('D = [\n')
for I = [1:size(D,1)]
fprintf(' %5.2f %5.2f %5.2f\n', D(I,:))
end
fprintf(']\n')
"""
from matlab_utils import *
def mydist(X):
if isempty(X):
error('Input matrix is empty\n');
end
# Get the number of points
num_points = size(X, 1);
if num_points < 2:
error('Number of points should be more than one\n');
end
# Initialize the result
D = zeros(num_points, num_points);
for i in mrange[1:num_points-1]:
for j in mrange[1:num_points]:
if(i < j):
# Ensure the matrix is symmetric
D[i,j] = sqrt(sum((X[i,:]-X[j,:])**2));
D[j,i] = D[i,j];
end
end
end
return D
end
X = marray([[1, 2, 3], [4, 5, 6], [7, 8, 9]]);
D = mydist(X);
fprintf('D = [\n')
for I in mrange[1:size(D,1)]:
fprintf(' %5.2f %5.2f %5.2f\n', *D[I,:])
end
fprintf(']\n')
| 19.988235
| 75
| 0.52678
| 285
| 1,699
| 3.091228
| 0.252632
| 0.122588
| 0.063564
| 0.027242
| 0.780931
| 0.753689
| 0.719637
| 0.719637
| 0.719637
| 0.719637
| 0
| 0.042445
| 0.306651
| 1,699
| 84
| 76
| 20.22619
| 0.705433
| 0.147734
| 0
| 0.259259
| 0
| 0
| 0.143498
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.037037
| false
| 0
| 0.037037
| 0
| 0.111111
| 0.111111
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
497213027ab30ba1143b135a37f0056e25e3fee5
| 58
|
py
|
Python
|
tests/web/fronts.py
|
brumar/eel-for-transcrypt
|
28cf5e0aa55a3c885b63d79d1ffae1370be644d2
|
[
"MIT"
] | 1
|
2019-12-31T13:53:05.000Z
|
2019-12-31T13:53:05.000Z
|
tests/web/fronts.py
|
brumar/eel-for-transcrypt
|
28cf5e0aa55a3c885b63d79d1ffae1370be644d2
|
[
"MIT"
] | 1
|
2021-11-15T17:48:03.000Z
|
2021-11-15T17:48:03.000Z
|
tests/web/fronts.py
|
brumar/eel-for-transcrypt
|
28cf5e0aa55a3c885b63d79d1ffae1370be644d2
|
[
"MIT"
] | null | null | null |
from frontendscrypt import *
from anotherfrontend import *
| 29
| 29
| 0.844828
| 6
| 58
| 8.166667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12069
| 58
| 2
| 29
| 29
| 0.960784
| 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
| 1
| 0
|
0
| 5
|
499f2bb38f16f4328fec7555c38ac669ab60a16c
| 5,788
|
py
|
Python
|
src/flat_estimators/classifiers/elr.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 11
|
2021-11-01T21:38:17.000Z
|
2022-03-30T11:46:32.000Z
|
src/flat_estimators/classifiers/elr.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 7
|
2021-10-30T16:55:47.000Z
|
2021-12-04T18:51:50.000Z
|
src/flat_estimators/classifiers/elr.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 1
|
2021-11-18T10:35:29.000Z
|
2021-11-18T10:35:29.000Z
|
import copy
import numpy as np
from sklearn.decomposition import PCA
import statsmodels.api as sm
class ELRClassifier:
def __init__(self, pca=-999, preprocessing='none', verbose=False, **kwargs):
self.kwargs = kwargs
self.an_thresh = 0.67
self.bn_thresh = 0.33
self.pca = pca
self.preprocessing = preprocessing
self.verbose=verbose
def fit(self, x, y):
self.models = [MultivariateELRClassifier(pca=self.pca, preprocessing=self.preprocessing, verbose=self.verbose, **self.kwargs) for i in range(x.shape[1])]
for i in range(x.shape[1]):
self.models[i].bn_thresh = self.bn_thresh
self.models[i].an_thresh = self.an_thresh
self.models[i].fit(x[:,i].reshape(-1,1), y)
def predict_proba(self, x):
res = []
for i in range(x.shape[1]):
res.append(self.models[i].predict_proba(x[:,i].reshape(-1,1)))
res = np.stack(res, axis=0)
return np.nanmean(res, axis=0)
def predict(self, x):
res = []
for i in range(x.shape[1]):
res.append(self.models[i].predict_proba(x[:,i].reshape(-1,1)))
res = np.stack(res, axis=0)
return np.argmax(np.nanmean(res, axis=0), axis=-1)
class MultivariateELRClassifier:
def __init__(self, pca=-999, preprocessing='none', verbose=False, **kwargs):
self.kwargs = kwargs
self.pca_retained = pca if pca != -1 else None
self.an_thresh = 0.67
self.bn_thresh = 0.33
self.preprocessing = preprocessing
self.verbose=verbose
def fit(self, x, y):
# first, take care of preprocessing
x2 = copy.deepcopy(x)
y2 = copy.deepcopy(y)
if self.preprocessing == 'std':
self.mean, self.std = x.mean(axis=0), x.std(axis=0)
x2 = (copy.deepcopy(x) - self.mean) / self.std # scales to std normal dist
if self.verbose:
print('{} Applied Standard Normal Scaling to ELR '.format(dt.datetime.now()))
if self.preprocessing == 'minmax':
self.min, self.max = x.min(axis=0), x.max(axis=0)
x2 = ((copy.deepcopy(x) - self.min) / (self.max - self.min) ) * 2 - 1 #scales to [-1, 1]
if self.verbose:
print('{} Applied MinMax Scaling to ELR '.format(dt.datetime.now()))
# now, if anything needs to train a PCA on the input data, do it (PCA initialization, or PCA transformation)
if self.pca_retained != -999:
self.pca = PCA(n_components=None if self.pca_retained == -1 else self.pca_retained )
self.pca.fit(x2)
if self.verbose:
print('{} Fit PCA on X with n_compnents={} for ELR'.format(dt.datetime.now(), None if self.pca_retained==-999 or self.pca_retained == -1 else self.pca_retained))
x2 = self.pca.transform(x2)
if self.verbose:
print('{} Applied PCA Transformation to X '.format(dt.datetime.now()))
try:
y2 = np.vstack([np.sum(y2[:,1:], axis=-1).reshape(-1,1), y2[:,2].reshape(-1,1)]) # exceeded 0.33, exceeded 0.66
x_bn = np.hstack([x2, np.ones((x2.shape[0], 1)) * self.bn_thresh])
x_an = np.hstack([x2, np.ones((x2.shape[0], 1)) * self.an_thresh])
x2 = np.vstack([x_bn, x_an])
#x = sm.add_constant(x)
model = sm.GLM(y2, sm.add_constant(x2, has_constant='add'), family=sm.families.Binomial())
self.model = model.fit()
if self.verbose:
print('{} Fit ELR '.format(dt.datetime.now()))
except:
pass
def predict_proba(self, x):
x2 = copy.deepcopy(x)
if self.preprocessing == 'std':
x2 = (copy.deepcopy(x) - self.mean) / self.std # scales to std normal dist
if self.verbose:
print('{} Applied Standard Normal Scaling to ELR '.format(dt.datetime.now()))
if self.preprocessing == 'minmax':
x2 = ((copy.deepcopy(x) - self.min) / (self.max - self.min) ) * 2 - 1 #scales to [-1, 1]
if self.verbose:
print('{} Applied MinMax Scaling to ELR '.format(dt.datetime.now()))
# now, if anything needs to train a PCA on the input data, do it (PCA initialization, or PCA transformation)
if self.pca_retained != -999:
x2 = self.pca.transform(x2)
if self.verbose:
print('{} Applied PCA Transformation to X '.format(dt.datetime.now()))
try:
x_bn = np.hstack([x2, np.ones((x.shape[0], 1)) * self.bn_thresh])
x_bn = sm.add_constant(x_bn, has_constant='add')
bn = 1 - self.model.predict(x_bn).reshape(-1, 1)
x_an = np.hstack([x2, np.ones((x.shape[0], 1)) * self.an_thresh])
x_an = sm.add_constant(x_an, has_constant='add')
an = self.model.predict(x_an).reshape(-1, 1)
nn = 1 - (an + bn)
return np.hstack([bn, nn, an])
except:
return np.hstack([np.ones((x2.shape[0], 1)) * 0.33, np.ones((x2.shape[0], 1)) * 0.34, np.ones((x.shape[0], 1)) * 0.33])
def predict(self, x):
x2 = copy.deepcopy(x)
if self.preprocessing == 'std':
x2 = (copy.deepcopy(x) - self.mean) / self.std # scales to std normal dist
if self.verbose:
print('{} Applied Standard Normal Scaling to ELR '.format(dt.datetime.now()))
if self.preprocessing == 'minmax':
x2 = ((copy.deepcopy(x) - self.min) / (self.max - self.min) ) * 2 - 1 #scales to [-1, 1]
if self.verbose:
print('{} Applied MinMax Scaling to ELR '.format(dt.datetime.now()))
# now, if anything needs to train a PCA on the input data, do it (PCA initialization, or PCA transformation)
if self.pca_retained != -999:
x2 = self.pca.transform(x2)
if self.verbose:
print('{} Applied PCA Transformation to X '.format(dt.datetime.now()))
try:
x_bn = np.hstack([x2, np.ones((x.shape[0], 1)) * self.bn_thresh])
x_bn = sm.add_constant(x_bn, has_constant='add')
bn = 1 - self.model.predict(x_bn).reshape(-1, 1)
x_an = np.hstack([x2, np.ones((x.shape[0], 1)) * self.an_thresh])
x_an = sm.add_constant(x_an, has_constant='add')
an = self.model.predict(x_an).reshape(-1, 1)
nn = 1 - (an + bn)
return np.argmax(np.hstack([bn, nn, an]), axis=-1)
except:
return np.argmax(np.hstack([np.ones((x2.shape[0], 1)) * 0.33, np.ones((x2.shape[0], 1)) * 0.34, np.ones((x.shape[0], 1)) * 0.33]), axis=-1)
| 39.917241
| 165
| 0.653594
| 969
| 5,788
| 3.833849
| 0.119711
| 0.035532
| 0.022611
| 0.053297
| 0.788964
| 0.725168
| 0.723015
| 0.706864
| 0.688022
| 0.688022
| 0
| 0.034927
| 0.16897
| 5,788
| 144
| 166
| 40.194444
| 0.737422
| 0.09226
| 0
| 0.733333
| 0
| 0
| 0.082809
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.066667
| false
| 0.008333
| 0.033333
| 0
| 0.166667
| 0.091667
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 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
| 5
|
b8f25076995bfacdadf36cb95a58aac493ce8b9c
| 765
|
py
|
Python
|
src/syft/lib/tensorflow/tensorflow.py
|
chinmayshah99/PySyft
|
c26c7c9478df37da7d0327a67a5987c2dfd91cbe
|
[
"MIT"
] | 2
|
2020-03-06T15:51:52.000Z
|
2020-03-08T13:14:24.000Z
|
src/syft/lib/tensorflow/tensorflow.py
|
chinmayshah99/PySyft
|
c26c7c9478df37da7d0327a67a5987c2dfd91cbe
|
[
"MIT"
] | 5
|
2020-12-03T21:06:20.000Z
|
2020-12-31T03:46:57.000Z
|
src/syft/lib/tensorflow/tensorflow.py
|
chinmayshah99/PySyft
|
c26c7c9478df37da7d0327a67a5987c2dfd91cbe
|
[
"MIT"
] | 1
|
2020-09-29T06:21:17.000Z
|
2020-09-29T06:21:17.000Z
|
# from ..ast.globals import Globals
#
# import tensorflow
#
# allowlist = {} # (path: str, return_type:type)
# allowlist["torch.tensor"] = "torch.Tensor"
# allowlist["torch.Tensor"] = "torch.Tensor"
# allowlist["torch.Tensor.__add__"] = "torch.Tensor"
# allowlist["torch.Tensor.__sub__"] = "torch.Tensor"
# allowlist["torch.zeros"] = "torch.Tensor"
# allowlist["torch.ones"] = "torch.Tensor"
# allowlist["torch.nn.Linear"] = "torch.nn.Linear"
# # allowlist.add("torch.nn.Linear.parameters")
#
# ast = Globals()
#
# for method, return_type_name in allowlist.items():
# ast.add_path(path=method, framework_reference=tensorflow, return_type_name=return_type_name)
#
# for klass in ast.classes:
# klass.create_pointer_class()
# klass.create_send_method()
#
| 31.875
| 98
| 0.708497
| 95
| 765
| 5.484211
| 0.336842
| 0.211132
| 0.230326
| 0.287908
| 0.216891
| 0.15739
| 0.15739
| 0.15739
| 0
| 0
| 0
| 0
| 0.115033
| 765
| 23
| 99
| 33.26087
| 0.769572
| 0.935948
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 1
| 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
| 5
|
770483f94b543fd02f4fc324aa59640d3b221727
| 6,206
|
py
|
Python
|
src/backend/tasks_io/handlers/tests/frc_api_event_matches_test.py
|
bovlb/the-blue-alliance
|
29389649d96fe060688f218d463e642dcebfd6cc
|
[
"MIT"
] | null | null | null |
src/backend/tasks_io/handlers/tests/frc_api_event_matches_test.py
|
bovlb/the-blue-alliance
|
29389649d96fe060688f218d463e642dcebfd6cc
|
[
"MIT"
] | null | null | null |
src/backend/tasks_io/handlers/tests/frc_api_event_matches_test.py
|
bovlb/the-blue-alliance
|
29389649d96fe060688f218d463e642dcebfd6cc
|
[
"MIT"
] | null | null | null |
import datetime
import json
from typing import Dict, Optional
from unittest import mock
from freezegun import freeze_time
from google.appengine.ext import ndb, testbed
from werkzeug.test import Client
from backend.common.consts.comp_level import CompLevel
from backend.common.consts.event_type import EventType
from backend.common.models.event import Event
from backend.common.models.match import Match
from backend.tasks_io.datafeeds.datafeed_fms_api import DatafeedFMSAPI
def create_event(official: bool, remap_teams: Optional[Dict[str, str]] = None) -> None:
Event(
id="2020nyny",
year=2020,
event_short="nyny",
event_type_enum=EventType.REGIONAL,
start_date=datetime.datetime(2020, 4, 1),
end_date=datetime.datetime(2020, 4, 2),
official=official,
remap_teams=remap_teams,
).put()
def test_enqueue_bad_when(tasks_client: Client) -> None:
resp = tasks_client.get("/tasks/enqueue/fmsapi_matches/asdf")
assert resp.status_code == 404
@freeze_time("2020-4-1")
def test_enqueue_current(
tasks_client: Client, taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub
) -> None:
create_event(official=True)
resp = tasks_client.get("/tasks/enqueue/fmsapi_matches/now")
assert resp.status_code == 200
assert len(resp.data) > 0
tasks = taskqueue_stub.get_filtered_tasks(queue_names="datafeed")
assert len(tasks) == 1
expected_keys = ["2020nyny"]
assert [f"/tasks/get/fmsapi_matches/{k}" for k in expected_keys] == [
t.url for t in tasks
]
@freeze_time("2020-4-1")
def test_enqueue_current_skips_unofficial(
tasks_client: Client, taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub
) -> None:
create_event(official=False)
resp = tasks_client.get("/tasks/enqueue/fmsapi_matches/now")
assert resp.status_code == 200
assert len(resp.data) > 0
tasks = taskqueue_stub.get_filtered_tasks(queue_names="datafeed")
assert len(tasks) == 0
@freeze_time("2020-4-1")
def test_enqueue_current_no_output_in_taskqueue(
tasks_client: Client, taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub
) -> None:
resp = tasks_client.get(
"/tasks/enqueue/fmsapi_matches/now",
headers={"X-Appengine-Taskname": "test"},
)
assert resp.status_code == 200
assert len(resp.data) == 0
tasks = taskqueue_stub.get_filtered_tasks(queue_names="datafeed")
assert len(tasks) == 0
def test_enqueue_explicit_year(
tasks_client: Client, taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub
) -> None:
create_event(official=True)
resp = tasks_client.get("/tasks/enqueue/fmsapi_matches/2020")
assert resp.status_code == 200
tasks = taskqueue_stub.get_filtered_tasks(queue_names="datafeed")
assert len(tasks) == 1
assert tasks[0].url == "/tasks/get/fmsapi_matches/2020nyny"
def test_enqueue_explicit_year_skips_unofficial(
tasks_client: Client, taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub
) -> None:
create_event(official=False)
resp = tasks_client.get("/tasks/enqueue/fmsapi_matches/2020")
assert resp.status_code == 200
tasks = taskqueue_stub.get_filtered_tasks(queue_names="datafeed")
assert len(tasks) == 0
def test_get_bad_key(tasks_client: Client) -> None:
resp = tasks_client.get("/tasks/get/fmsapi_matches/asdf")
assert resp.status_code == 404
def test_get_no_event(tasks_client: Client) -> None:
resp = tasks_client.get("/tasks/get/fmsapi_matches/2020nyny")
assert resp.status_code == 404
@mock.patch.object(DatafeedFMSAPI, "get_event_matches")
def test_get_no_matches(fmsapi_matches_mock, tasks_client: Client) -> None:
create_event(official=True)
fmsapi_matches_mock.return_value = []
resp = tasks_client.get("/tasks/get/fmsapi_matches/2020nyny")
assert resp.status_code == 200
assert len(resp.data) > 0
@mock.patch.object(DatafeedFMSAPI, "get_event_matches")
def test_get_no_matches_no_output_in_taskqueue(
fmsapi_matches_mock, tasks_client: Client
) -> None:
create_event(official=True)
fmsapi_matches_mock.return_value = []
resp = tasks_client.get(
"/tasks/get/fmsapi_matches/2020nyny",
headers={"X-Appengine-Taskname": "test"},
)
assert resp.status_code == 200
assert len(resp.data) == 0
@mock.patch.object(DatafeedFMSAPI, "get_event_matches")
def test_get(
fmsapi_matches_mock,
tasks_client: Client,
) -> None:
create_event(official=True)
matches = [
Match(
id="2020nyny_qm1",
event=ndb.Key(Event, "2020nyny"),
comp_level=CompLevel.QM,
set_number=1,
match_number=1,
year=2020,
alliances_json=json.dumps({"red": {"score": "-1"}, "blue": {"score": -1}}),
)
]
fmsapi_matches_mock.return_value = matches
resp = tasks_client.get("/tasks/get/fmsapi_matches/2020nyny")
assert resp.status_code == 200
assert len(resp.data) > 0
# Make sure we write objects
assert Match.get_by_id("2020nyny_qm1") is not None
@mock.patch.object(DatafeedFMSAPI, "get_event_matches")
def test_get_remap_teams(
fmsapi_matches_mock,
tasks_client: Client,
taskqueue_stub: testbed.taskqueue_stub.TaskQueueServiceStub,
) -> None:
create_event(official=True, remap_teams={"frc254": "frc9000"})
matches = [
Match(
id="2020nyny_qm1",
event=ndb.Key(Event, "2020nyny"),
comp_level=CompLevel.QM,
set_number=1,
match_number=1,
year=2020,
alliances_json=json.dumps(
{
"red": {"score": "-1", "teams": ["frc254"]},
"blue": {"score": -1, "teams": []},
}
),
team_key_names=["frc254"],
)
]
fmsapi_matches_mock.return_value = matches
resp = tasks_client.get("/tasks/get/fmsapi_matches/2020nyny")
assert resp.status_code == 200
assert len(resp.data) > 0
# Make sure we write objects
m = Match.get_by_id("2020nyny_qm1")
assert m is not None
assert m.team_key_names == ["frc9000"]
| 30.722772
| 87
| 0.688688
| 798
| 6,206
| 5.102757
| 0.161654
| 0.064833
| 0.050098
| 0.053045
| 0.777259
| 0.746071
| 0.730599
| 0.730599
| 0.717338
| 0.663065
| 0
| 0.036881
| 0.196101
| 6,206
| 201
| 88
| 30.875622
| 0.779314
| 0.00854
| 0
| 0.592357
| 0
| 0
| 0.131382
| 0.075447
| 0
| 0
| 0
| 0
| 0.184713
| 1
| 0.082803
| false
| 0
| 0.076433
| 0
| 0.159236
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
771de7b3a7f3cf79ea114375dd7f127e9f152561
| 18,110
|
py
|
Python
|
tests/test_keyterms.py
|
zf109/textacy
|
103dd7e248ace0f3db1ef130e7f490f81a6c89df
|
[
"Apache-2.0"
] | null | null | null |
tests/test_keyterms.py
|
zf109/textacy
|
103dd7e248ace0f3db1ef130e7f490f81a6c89df
|
[
"Apache-2.0"
] | null | null | null |
tests/test_keyterms.py
|
zf109/textacy
|
103dd7e248ace0f3db1ef130e7f490f81a6c89df
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Note: Results of weighting nodes in a network by pagerank are random bc the
algorithm relies on a random walk. Consequently, keyterm rankings aren't
necessarily the same across runs.
"""
from __future__ import absolute_import, unicode_literals
import pytest
from textacy import cache, keyterms, preprocess_text
from textacy.spacier import utils as spacy_utils
@pytest.fixture(scope="module")
def spacy_doc():
spacy_lang = cache.load_spacy_lang("en")
text = """
Friedman joined the London bureau of United Press International after completing his master's degree. He was dispatched a year later to Beirut, where he lived from June 1979 to May 1981 while covering the Lebanon Civil War. He was hired by The New York Times as a reporter in 1981 and re-dispatched to Beirut at the start of the 1982 Israeli invasion of Lebanon. His coverage of the war, particularly the Sabra and Shatila massacre, won him the Pulitzer Prize for International Reporting (shared with Loren Jenkins of The Washington Post). Alongside David K. Shipler he also won the George Polk Award for foreign reporting.
In June 1984, Friedman was transferred to Jerusalem, where he served as the New York Times Jerusalem Bureau Chief until February 1988. That year he received a second Pulitzer Prize for International Reporting, which cited his coverage of the First Palestinian Intifada. He wrote a book, From Beirut to Jerusalem, describing his experiences in the Middle East, which won the 1989 U.S. National Book Award for Nonfiction.
Friedman covered Secretary of State James Baker during the administration of President George H. W. Bush. Following the election of Bill Clinton in 1992, Friedman became the White House correspondent for the New York Times. In 1994, he began to write more about foreign policy and economics, and moved to the op-ed page of The New York Times the following year as a foreign affairs columnist. In 2002, Friedman won the Pulitzer Prize for Commentary for his "clarity of vision, based on extensive reporting, in commenting on the worldwide impact of the terrorist threat."
In February 2002, Friedman met Saudi Crown Prince Abdullah and encouraged him to make a comprehensive attempt to end the Arab-Israeli conflict by normalizing Arab relations with Israel in exchange for the return of refugees alongside an end to the Israel territorial occupations. Abdullah proposed the Arab Peace Initiative at the Beirut Summit that March, which Friedman has since strongly supported.
Friedman received the 2004 Overseas Press Club Award for lifetime achievement and was named to the Order of the British Empire by Queen Elizabeth II.
In May 2011, The New York Times reported that President Barack Obama "has sounded out" Friedman concerning Middle East issues.
"""
spacy_doc = spacy_lang(preprocess_text(text), disable=["parser"])
return spacy_doc
@pytest.fixture(scope="module")
def empty_spacy_doc():
spacy_lang = cache.load_spacy_lang("en")
return spacy_lang("")
class TestSGRank(object):
def test_base(self, spacy_doc):
expected = [
"new york times",
"york times jerusalem bureau chief",
"friedman",
"president george h. w.",
"george polk award",
"pulitzer prize",
"u.s. national book award",
"international reporting",
"beirut",
"washington post",
]
observed = [term for term, _ in keyterms.sgrank(spacy_doc)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# assert e == o
def test_ngrams_1(self, spacy_doc):
expected = ["friedman", "international", "beirut", "bureau", "york"]
observed = [term for term, _ in keyterms.sgrank(spacy_doc, ngrams=1, n_keyterms=5)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# assert e == o
def test_ngrams_1_2_3(self, spacy_doc):
expected = [
"new york times",
"friedman",
"pulitzer prize",
"beirut",
"international reporting",
]
observed = [
term for term, _ in keyterms.sgrank(spacy_doc, ngrams=(1, 2, 3), n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_n_keyterms(self, spacy_doc):
expected = [
"new york times",
"new york times jerusalem bureau chief",
"friedman",
"president george h. w. bush",
"david k. shipler",
]
observed = [term for term, _ in keyterms.sgrank(spacy_doc, n_keyterms=5)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
observed = [term for term, _ in keyterms.sgrank(spacy_doc, n_keyterms=0.1)]
assert len(observed) > 0
def test_norm_lower(self, spacy_doc):
expected = [
"new york times",
"president george h. w. bush",
"friedman",
"new york times jerusalem bureau",
"george polk award",
]
observed = [
term for term, _ in keyterms.sgrank(spacy_doc, normalize="lower", n_keyterms=5)
]
assert len(expected) == len(observed)
for term in observed:
assert term == term.lower()
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_none(self, spacy_doc):
expected = [
"New York Times",
"New York Times Jerusalem Bureau Chief",
"Friedman",
"President George H. W. Bush",
"George Polk Award",
]
observed = [
term for term, _ in keyterms.sgrank(spacy_doc, normalize=None, n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_normalized_str(self, spacy_doc):
expected = [
"New York Times",
"New York Times Jerusalem Bureau Chief",
"Friedman",
"President George H. W. Bush",
"George Polk Award",
]
observed = [
term
for term, _ in keyterms.sgrank(
spacy_doc, normalize=spacy_utils.get_normalized_text, n_keyterms=5
)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_window_width(self, spacy_doc):
expected = [
"new york times",
"friedman",
"new york times jerusalem",
"times jerusalem bureau",
"second pulitzer prize",
]
observed = [
term for term, _ in keyterms.sgrank(spacy_doc, window_width=50, n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
class TestTextRank(object):
def test_base(self, spacy_doc):
expected = [
"friedman",
"beirut",
"reporting",
"arab",
"new",
"award",
"foreign",
"year",
"times",
"jerusalem",
]
observed = [term for term, _ in keyterms.textrank(spacy_doc)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_textrank_n_keyterms(self, spacy_doc):
expected = ["friedman", "beirut", "reporting", "arab", "new"]
observed = [term for term, _ in keyterms.textrank(spacy_doc, n_keyterms=5)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_lower(self, spacy_doc):
expected = ["friedman", "beirut", "reporting", "arab", "new"]
observed = [
term
for term, _ in keyterms.textrank(spacy_doc, normalize="lower", n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
for term in observed:
assert term == term.lower()
def test_norm_none(self, spacy_doc):
expected = ["Friedman", "Beirut", "New", "Arab", "Award"]
observed = [
term for term, _ in keyterms.textrank(spacy_doc, normalize=None, n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_normalized_str(self, spacy_doc):
expected = ["Friedman", "Beirut", "New", "Award", "foreign"]
observed = [
term
for term, _ in keyterms.textrank(
spacy_doc, normalize=spacy_utils.get_normalized_text, n_keyterms=5
)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
class TestSingleRank(object):
def test_base(self, spacy_doc):
expected = [
"new york times jerusalem bureau",
"new york times",
"friedman",
"foreign reporting",
"international reporting",
"pulitzer prize",
"book award",
"press international",
"president george",
"beirut",
]
observed = [term for term, _ in keyterms.singlerank(spacy_doc)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_singlegrank_n_keyterms(self, spacy_doc):
expected = [
"new york times jerusalem bureau",
"new york times",
"friedman",
"foreign reporting",
"international reporting",
]
observed = [term for term, _ in keyterms.singlerank(spacy_doc, n_keyterms=5)]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_lower(self, spacy_doc):
expected = [
"new york times jerusalem bureau",
"new york times",
"friedman",
"foreign reporting",
"international reporting",
]
observed = [
term
for term, _ in keyterms.singlerank(spacy_doc, normalize="lower", n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
for term in observed:
assert term == term.lower()
def test_norm_none(self, spacy_doc):
expected = [
"New York Times Jerusalem",
"New York Times",
"Friedman",
"Pulitzer Prize",
"foreign reporting",
]
observed = [
term for term, _ in keyterms.singlerank(spacy_doc, normalize=None, n_keyterms=5)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_norm_normalized_str(self, spacy_doc):
expected = [
"New York Times Jerusalem",
"New York Times",
"Friedman",
"Pulitzer Prize",
"foreign reporting",
]
observed = [
term
for term, _ in keyterms.singlerank(
spacy_doc, normalize=spacy_utils.get_normalized_text, n_keyterms=5
)
]
assert len(expected) == len(observed)
# can't do this owing to randomness of results
# for e, o in zip(expected, observed):
# asert e == o
def test_key_terms_from_semantic_network(spacy_doc):
# let's just make sure that these run without exception
_ = keyterms.key_terms_from_semantic_network(spacy_doc, ranking_algo="divrank")
_ = keyterms.key_terms_from_semantic_network(spacy_doc, ranking_algo="bestcoverage")
def test_key_terms_from_semantic_network_empty(empty_spacy_doc):
key_terms = keyterms.key_terms_from_semantic_network(empty_spacy_doc)
assert isinstance(key_terms, list) is True
assert len(key_terms) == 0
def test_most_discriminating_terms(spacy_doc):
text1 = """Friedman joined the London bureau of United Press International after completing his master's degree. He was dispatched a year later to Beirut, where he lived from June 1979 to May 1981 while covering the Lebanon Civil War. He was hired by The New York Times as a reporter in 1981 and re-dispatched to Beirut at the start of the 1982 Israeli invasion of Lebanon. His coverage of the war, particularly the Sabra and Shatila massacre, won him the Pulitzer Prize for International Reporting (shared with Loren Jenkins of The Washington Post). Alongside David K. Shipler he also won the George Polk Award for foreign reporting.
In June 1984, Friedman was transferred to Jerusalem, where he served as the New York Times Jerusalem Bureau Chief until February 1988. That year he received a second Pulitzer Prize for International Reporting, which cited his coverage of the First Palestinian Intifada. He wrote a book, From Beirut to Jerusalem, describing his experiences in the Middle East, which won the 1989 U.S. National Book Award for Nonfiction.
Friedman covered Secretary of State James Baker during the administration of President George H. W. Bush. Following the election of Bill Clinton in 1992, Friedman became the White House correspondent for the New York Times. In 1994, he began to write more about foreign policy and economics, and moved to the op-ed page of The New York Times the following year as a foreign affairs columnist. In 2002, Friedman won the Pulitzer Prize for Commentary for his "clarity of vision, based on extensive reporting, in commenting on the worldwide impact of the terrorist threat."
In February 2002, Friedman met Saudi Crown Prince Abdullah and encouraged him to make a comprehensive attempt to end the Arab-Israeli conflict by normalizing Arab relations with Israel in exchange for the return of refugees alongside an end to the Israel territorial occupations. Abdullah proposed the Arab Peace Initiative at the Beirut Summit that March, which Friedman has since strongly supported.
Friedman received the 2004 Overseas Press Club Award for lifetime achievement and was named to the Order of the British Empire by Queen Elizabeth II.
In May 2011, The New York Times reported that President Barack Obama "has sounded out" Friedman concerning Middle East issues."""
text2 = """In 1954, Bucksbaum and his brother Martin borrowed $1.2 million and built the first shopping center in Cedar Rapids, Iowa, anchored by a fourth family grocery store.
They expanded into enclosed malls which mirrored the continued movement to the suburbs seen in the 1960s.
By 1964, their company - then named General Management - owned five malls anchored by the Younkers department store.
In 1972, the company became publicly traded on the New York Stock Exchange under the name General Growth Properties and became the second-largest owner, developer, and manager of regional shopping malls in the country.
Bucksbaum served as its Chairman and Chief Executive Officer and under his tenure, he formed two Real estate investment trusts and expanded the company's portfolio of malls and shopping centers via more than $36 billion in acquisitions.
In 1984, General Growth sold 19 malls for $800 million to Equitable Real Estate, which was deemed the “nation’s largest single asset real estate transaction” to date.
In 1995, his brother Martin died and he re-located the company to Chicago.
In 2004, General Growth purchased The Rouse Company for $14.2 billion.
By 2007, General Growth was the second-largest REIT owning 194 malls with over 200 million square feet in 44 states.
In 2008, General Growth filed for Chapter 11 bankruptcy protection after the collapse of the stock market."""
doc1 = [line.split() for line in text1.split("\n")]
doc2 = [line.split() for line in text2.split("\n")]
expected = (["Friedman", "Times"], ["General", "malls"])
observed = keyterms.most_discriminating_terms(
doc1 + doc2, [True] * len(doc1) + [False] * len(doc2), top_n_terms=2
)
print(observed)
assert expected == observed
def test_aggregate_term_variants():
# TODO: the actual results are NOT what i'd expect; figure out why
terms = set([
"vice versa",
"vice-versa",
"vice/versa",
"BJD",
"Burton Jacob DeWilde",
"the big black cat named Rico",
"the black cat named Rico",
])
result1 = keyterms.aggregate_term_variants(terms)
result2 = keyterms.aggregate_term_variants(
terms, acro_defs={"BJD": "Burton Jacob DeWilde"})
assert len(result2) <= len(result1) <= len(terms)
| 46.917098
| 638
| 0.645334
| 2,383
| 18,110
| 4.821653
| 0.188418
| 0.032724
| 0.03342
| 0.031419
| 0.782071
| 0.757528
| 0.751697
| 0.742298
| 0.728808
| 0.703046
| 0
| 0.015839
| 0.278355
| 18,110
| 385
| 639
| 47.038961
| 0.863341
| 0.1164
| 0
| 0.514493
| 0
| 0.065217
| 0.484126
| 0
| 0
| 0
| 0
| 0.002597
| 0.094203
| 1
| 0.086957
| false
| 0
| 0.014493
| 0
| 0.119565
| 0.003623
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
772f072094280aaafb301b5e093ffe7cc7806389
| 108
|
py
|
Python
|
scaling_fl/sacred/__init__.py
|
Peltarion/scaling_fl
|
011f845a6472e9a3df338351b8970b0fc70cf242
|
[
"Apache-2.0"
] | 2
|
2021-04-10T09:51:32.000Z
|
2021-09-09T15:29:31.000Z
|
scaling_fl/sacred/__init__.py
|
Peltarion/scaling_fl
|
011f845a6472e9a3df338351b8970b0fc70cf242
|
[
"Apache-2.0"
] | null | null | null |
scaling_fl/sacred/__init__.py
|
Peltarion/scaling_fl
|
011f845a6472e9a3df338351b8970b0fc70cf242
|
[
"Apache-2.0"
] | 1
|
2021-09-13T09:32:10.000Z
|
2021-09-13T09:32:10.000Z
|
from sacred import SETTINGS
from .mongoobserver import observer_from_env
SETTINGS['CAPTURE_MODE'] = 'sys'
| 18
| 44
| 0.805556
| 14
| 108
| 6
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12037
| 108
| 5
| 45
| 21.6
| 0.884211
| 0
| 0
| 0
| 0
| 0
| 0.138889
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
623fad3932fdae3b4cc05b052d24851f20dc14b2
| 157
|
py
|
Python
|
server/app/services/device_data/views/_reports_type/__init__.py
|
goodfree/ActorCloud
|
e8db470830ea6f6f208ad43c2e56a2e8976bc468
|
[
"Apache-2.0"
] | 173
|
2019-06-10T07:14:49.000Z
|
2022-03-31T08:42:36.000Z
|
server/app/services/device_data/views/_reports_type/__init__.py
|
zlyz12345/ActorCloud
|
9c34b371c23464981323ef9865d9913bde1fe09c
|
[
"Apache-2.0"
] | 27
|
2019-06-12T08:25:29.000Z
|
2022-02-26T11:37:15.000Z
|
server/app/services/device_data/views/_reports_type/__init__.py
|
zlyz12345/ActorCloud
|
9c34b371c23464981323ef9865d9913bde1fe09c
|
[
"Apache-2.0"
] | 67
|
2019-06-10T08:40:05.000Z
|
2022-03-09T03:43:56.000Z
|
from .aggr_events import devices_event_aggr_data
__all__ = ['REPORTS_TYPE_FUNC']
REPORTS_TYPE_FUNC = {
'devicesEventAggr': devices_event_aggr_data
}
| 15.7
| 48
| 0.789809
| 20
| 157
| 5.45
| 0.6
| 0.220183
| 0.293578
| 0.366972
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133758
| 157
| 9
| 49
| 17.444444
| 0.801471
| 0
| 0
| 0
| 0
| 0
| 0.210191
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0
| 1
| 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
| 5
|
6258610a4ba9fb73d7d3af867c25d9aa32c8f562
| 447
|
py
|
Python
|
src/metrics/__init__.py
|
Guillin/honeycomb
|
4741ecc4ec6949073bdae738db3bb48bbf85e50f
|
[
"RSA-MD"
] | null | null | null |
src/metrics/__init__.py
|
Guillin/honeycomb
|
4741ecc4ec6949073bdae738db3bb48bbf85e50f
|
[
"RSA-MD"
] | null | null | null |
src/metrics/__init__.py
|
Guillin/honeycomb
|
4741ecc4ec6949073bdae738db3bb48bbf85e50f
|
[
"RSA-MD"
] | null | null | null |
from .classification import auc
from .classification import logloss
from .classification import plot_roc_curve
from .classification import plot_pr_curve
from .regression import mae
from .regression import r2
from .regression import mape
from .regression import gini
from .regression import rmse
from .regression import kappa
__all__ = ['auc', 'logloss', 'plot_roc_curve', 'plot_pr_curve',
'mae', 'r2', 'mape', 'gini', 'rmse', 'kappa']
| 31.928571
| 63
| 0.762864
| 59
| 447
| 5.576271
| 0.288136
| 0.255319
| 0.364742
| 0.170213
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005222
| 0.143177
| 447
| 14
| 64
| 31.928571
| 0.853786
| 0
| 0
| 0
| 0
| 0
| 0.131696
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.833333
| 0
| 0.833333
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
627201e22f8a993ecbcd1fca02813ecf40503511
| 580
|
py
|
Python
|
packages/watchmen-model/src/watchmen_model/console/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
packages/watchmen-model/src/watchmen_model/console/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
packages/watchmen-model/src/watchmen_model/console/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
from .connected_space import ConnectedSpace
from .connected_space_graphic import ConnectedSpaceGraphic, SubjectGraphic, TopicGraphic
from .dashboard import Dashboard, DashboardParagraph, DashboardReport
from .data_result_set import SubjectDataResultSet
from .report import Report, ReportDimension, ReportFunnel, ReportFunnelType, ReportIndicator, ReportIndicatorArithmetic
from .subject import Subject, SubjectDataset, SubjectDatasetColumn, SubjectDatasetCriteria, \
SubjectDatasetCriteriaIndicator, SubjectDatasetCriteriaIndicatorArithmetic, SubjectDatasetJoin, SubjectJoinType
| 72.5
| 119
| 0.887931
| 45
| 580
| 11.333333
| 0.666667
| 0.05098
| 0.070588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072414
| 580
| 7
| 120
| 82.857143
| 0.947955
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.857143
| 0
| 0.857143
| 0
| 0
| 0
| 1
| 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
| 5
|
627dd31f84a9f48c4659192e75f118d2d9c8a537
| 7,607
|
py
|
Python
|
src/test_addLatLong.py
|
rleir/latlong-col
|
e24d27e070ba274185ccff0c4f3b7d688cebc278
|
[
"Apache-2.0"
] | null | null | null |
src/test_addLatLong.py
|
rleir/latlong-col
|
e24d27e070ba274185ccff0c4f3b7d688cebc278
|
[
"Apache-2.0"
] | 1
|
2019-09-09T20:33:28.000Z
|
2019-09-09T20:33:28.000Z
|
src/test_addLatLong.py
|
rleir/latlong-col
|
e24d27e070ba274185ccff0c4f3b7d688cebc278
|
[
"Apache-2.0"
] | null | null | null |
import pytest
import addLatLong
import filecmp
from shutil import copyfile
# init contents
test_initlocFileName = "testData/testInitLoc.json"
# the test loc DB
test_locFileName = "testData/testLoc.json"
# output file names for several tests
test_locCountsFilename = "testData/testLocCounts.json"
test_locCountsGeoJSON = "testData/testLocCounts.geojson"
test_locInstFilename = "testData/testLocInst.json"
test_locInstGeoJSON = "testData/testLocInst.geojson"
def init_test_loc_file():
'''the loc file should not change in many tests below'''
copyfile(test_initlocFileName, test_locFileName)
def test_open_json():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
return True
# each unique addr with one or two rows
# a Country
# b city, Country
# c city, prov, Country
#
# one unique addr with inst permutations: one two three or four rows
# one with no inst
# one with instname only
# one with instdept only
# one with instname and instdept
#
# d instname, city, prov, Country
# e instdpt, instname, city, prov, Country
# f instdpt, city, prov, Country
# g each permutation?
def test_b_one_row():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# test with one row in the input, having InstName, Prov, Country
a1.scan_spreadsheet("testData/test_B_one.xlsx")
assert a1.all_data["Gloucester Ontario Canada"]["magnitude"] == 0
assert a1.all_data["Ontario Canada"]["magnitude"] == 1
a1.write_location_DB()
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
# output locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsFilename,
"testData/test_B_oneLocCountsRef.json", shallow=False)
assert filecmp.cmp(test_locInstFilename,
"testData/test_B_oneLocInstRef.json", shallow=False)
return True
def test_b_two_rows_same_addr():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# test with two rows in the input, both having InstName, Prov, Country
a1.scan_spreadsheet("testData/test_B_two.xlsx")
assert a1.all_data["Ontario Canada"]["magnitude"] == 2
a1.write_location_DB()
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
# output locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsFilename,
"testData/test_B_twoLocCountsRef.json", shallow=False)
# output loc Inst files should equal the test check files
assert filecmp.cmp(test_locInstFilename,
"testData/test_B_twoLocInstRef.json", shallow=False)
return True
def test_c_one_row():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# test with one row in the input, having InstName, City, Prov, Country
test_xlsxFileName = "testData/test_C_one.xlsx"
a1.scan_spreadsheet(test_xlsxFileName)
assert a1.all_data["Cornwall Ontario Canada"]["magnitude"] == 1
a1.write_location_DB()
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
# output locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsFilename,
"testData/test_C_oneLocCountsRef.json", shallow=False)
assert filecmp.cmp(test_locInstFilename,
"testData/test_C_oneLocInstRef.json", shallow=False)
return True
def test_c_two_rows_same_addr():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# test with two rows in the input,
# both having InstName, City, Prov, Country
test_xlsxFileName = "testData/test_C_two.xlsx"
a1.scan_spreadsheet(test_xlsxFileName)
assert a1.all_data["Cornwall Ontario Canada"]["magnitude"] == 2
a1.write_location_DB()
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
# output locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsFilename,
"testData/test_C_twoLocCountsRef.json", shallow=False)
# output loc Inst files should equal the test check files
assert filecmp.cmp(test_locInstFilename,
"testData/test_C_twoLocInstRef.json", shallow=False)
return True
def test_a_one_row():
init_test_loc_file()
a1 = addLatLong.AcqInfo(test_locFileName,
test_locCountsFilename,
test_locCountsGeoJSON,
test_locInstFilename,
test_locInstGeoJSON)
# test with one row in the input, having InstName, Prov, Country
a1.scan_spreadsheet("testData/test_A_one.xlsx")
assert a1.all_data["Russia"]["magnitude"] == 1
a1.write_location_DB()
# output locations DB should be unchanged,
# and should equal the test check file
assert filecmp.cmp(test_locFileName,
test_initlocFileName, shallow=False)
# debug locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsFilename,
"testData/test_A_oneLocCountsRef.json", shallow=False)
assert filecmp.cmp(test_locInstFilename,
"testData/test_A_oneLocInstRef.json", shallow=False)
# output locations counts and inst files
# should equal the test reference files
assert filecmp.cmp(test_locCountsGeoJSON,
"testData/test_A_oneLocCountsRef.geojson", shallow=False)
assert filecmp.cmp(test_locInstGeoJSON,
"testData/test_A_oneLocInstRef.geojson", shallow=False)
return True
| 37.658416
| 80
| 0.642566
| 829
| 7,607
| 5.699638
| 0.135103
| 0.049524
| 0.060952
| 0.07619
| 0.789418
| 0.788571
| 0.772487
| 0.766138
| 0.725926
| 0.725926
| 0
| 0.005214
| 0.294071
| 7,607
| 201
| 81
| 37.845771
| 0.874674
| 0.267517
| 0
| 0.622807
| 0
| 0
| 0.156091
| 0.127266
| 0
| 0
| 0
| 0
| 0.210526
| 1
| 0.061404
| false
| 0
| 0.035088
| 0
| 0.149123
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
6289e18a67270d46e83b443f234d0397024d211d
| 1,473
|
py
|
Python
|
vivialconnect/resources/user.py
|
Localvox/vivialconnect-python
|
7063ae9f42246d20928d7a370ff90e2c7d93ccf2
|
[
"MIT"
] | null | null | null |
vivialconnect/resources/user.py
|
Localvox/vivialconnect-python
|
7063ae9f42246d20928d7a370ff90e2c7d93ccf2
|
[
"MIT"
] | 1
|
2018-05-23T18:00:26.000Z
|
2018-05-23T18:30:50.000Z
|
vivialconnect/resources/user.py
|
Localvox/vivialconnect-python
|
7063ae9f42246d20928d7a370ff90e2c7d93ccf2
|
[
"MIT"
] | null | null | null |
"""
.. module:: user
:synopsis: User module.
"""
from vivialconnect.resources.resource import Resource
from vivialconnect.resources.countable import Countable
class User(Resource, Countable):
"""Use the User resource to manage users and user passwords in the API.
User properties
============= ======================
Field Description
============= ======================
id Unique identifier of the user object.
date_created Creation date (UTC) of the user in ISO 8601 format.
date_modified Last modification date (UTC) of the user in ISO 8601 format.
account_id Unique identifier of the account that this user is part of.
username User's username for logging in to the account. *Max. length:* 128 characters.
first_name User's first name. *Max. length:* 128 characters.
last_name User's last name. *Max. length:* 128 characters.
email User's email address. *Max. length:* 128 characters.
============= ======================
Example request to retrieve a list of users accociated with the account id 12345::
from vivialconnect import Resource, User
Resource.api_key = ""
Resource.api_secret = ""
Resource.api_account_id = "12345"
def list_users():
users = User.find()
for user in users:
print(user.id, user.first_name, user.last_name)
list_users()
"""
pass
| 32.021739
| 95
| 0.606925
| 178
| 1,473
| 4.949438
| 0.382022
| 0.031782
| 0.054484
| 0.099886
| 0.181612
| 0.070375
| 0.070375
| 0.070375
| 0.070375
| 0
| 0
| 0.027397
| 0.256619
| 1,473
| 45
| 96
| 32.733333
| 0.777169
| 0.814664
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
659f11d45577b5aa900e36f584de86db4cd46278
| 471
|
py
|
Python
|
lisapi/routes.py
|
bergpb/LisaPi
|
0e9c18c38d3d9485b438cf8d880a06f044562cc4
|
[
"MIT"
] | 7
|
2018-11-14T00:52:06.000Z
|
2021-04-10T12:26:47.000Z
|
lisapi/routes.py
|
bergpb/LisaPi
|
0e9c18c38d3d9485b438cf8d880a06f044562cc4
|
[
"MIT"
] | 8
|
2019-01-14T23:31:02.000Z
|
2020-04-24T02:25:59.000Z
|
lisapi/routes.py
|
bergpb/LisaPi
|
0e9c18c38d3d9485b438cf8d880a06f044562cc4
|
[
"MIT"
] | 4
|
2019-10-01T16:31:59.000Z
|
2022-02-21T03:33:04.000Z
|
from lisapi.errors import errors as errors_blueprint
from lisapi.auth import auth as auth_blueprint
from lisapi.main import main as main_blueprint
from lisapi.pin import pin as pin_blueprint
from lisapi.pwa import pwa as pwa_blueprint
def init_app(app):
app.register_blueprint(auth_blueprint)
app.register_blueprint(main_blueprint)
app.register_blueprint(pwa_blueprint)
app.register_blueprint(pin_blueprint)
app.register_blueprint(errors_blueprint)
| 33.642857
| 52
| 0.825902
| 69
| 471
| 5.405797
| 0.202899
| 0.134048
| 0.268097
| 0.310992
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125265
| 471
| 13
| 53
| 36.230769
| 0.90534
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0
| 0.454545
| 0
| 0.545455
| 0.909091
| 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
| 1
|
0
| 5
|
65a9a98ee0d2b05ef3c5f8c4d8c5462e27385911
| 159
|
py
|
Python
|
tests/test_bytes.py
|
Dogeek/mys
|
193259a634c3ab1d9058b9ff79a0462ae86274b7
|
[
"MIT"
] | 83
|
2020-08-18T18:48:46.000Z
|
2021-01-01T17:00:45.000Z
|
tests/test_bytes.py
|
Dogeek/mys
|
193259a634c3ab1d9058b9ff79a0462ae86274b7
|
[
"MIT"
] | 31
|
2021-01-05T00:32:36.000Z
|
2022-02-23T13:34:33.000Z
|
tests/test_bytes.py
|
Dogeek/mys
|
193259a634c3ab1d9058b9ff79a0462ae86274b7
|
[
"MIT"
] | 7
|
2021-01-03T11:53:03.000Z
|
2022-02-22T17:49:42.000Z
|
from .utils import TestCase
from .utils import build_and_test_module
class Test(TestCase):
def test_bytes(self):
build_and_test_module('bytes')
| 17.666667
| 40
| 0.748428
| 23
| 159
| 4.869565
| 0.521739
| 0.160714
| 0.267857
| 0.321429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176101
| 159
| 8
| 41
| 19.875
| 0.854962
| 0
| 0
| 0
| 0
| 0
| 0.031447
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 0
|
0
| 5
|
028eff74a8e1337d081196014f5d1ee84718677c
| 183
|
py
|
Python
|
packs/jenkins/actions/build_job.py
|
jonico/st2contrib
|
149c9c553f24360d91a14fef7ea6146707de75fd
|
[
"Apache-2.0"
] | 164
|
2015-01-17T16:08:33.000Z
|
2021-08-03T02:34:07.000Z
|
packs/jenkins/actions/build_job.py
|
jonico/st2contrib
|
149c9c553f24360d91a14fef7ea6146707de75fd
|
[
"Apache-2.0"
] | 442
|
2015-01-01T11:19:01.000Z
|
2017-09-06T23:26:17.000Z
|
packs/jenkins/actions/build_job.py
|
jonico/st2contrib
|
149c9c553f24360d91a14fef7ea6146707de75fd
|
[
"Apache-2.0"
] | 202
|
2015-01-13T00:37:40.000Z
|
2020-11-07T11:30:10.000Z
|
from lib import action
class BuildProject(action.JenkinsBaseAction):
def run(self, project, branch="master"):
return self.jenkins.build_job(project, {'branch': branch})
| 26.142857
| 66
| 0.726776
| 22
| 183
| 6
| 0.772727
| 0.19697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153005
| 183
| 6
| 67
| 30.5
| 0.851613
| 0
| 0
| 0
| 0
| 0
| 0.065574
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 1
| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
02afc851a641b8451509955a016e874ad9d8b73e
| 3,731
|
py
|
Python
|
benchmark/benchmark.py
|
megagonlabs/ginza
|
bbda75c87f35e40bbcfef9ff3dfd6c4fae018e98
|
[
"MIT"
] | 581
|
2019-04-02T03:12:03.000Z
|
2022-03-26T13:25:27.000Z
|
benchmark/benchmark.py
|
megagonlabs/ginza
|
bbda75c87f35e40bbcfef9ff3dfd6c4fae018e98
|
[
"MIT"
] | 88
|
2019-04-03T03:45:48.000Z
|
2022-03-28T12:03:06.000Z
|
benchmark/benchmark.py
|
megagonlabs/ginza
|
bbda75c87f35e40bbcfef9ff3dfd6c4fae018e98
|
[
"MIT"
] | 56
|
2019-04-03T01:41:38.000Z
|
2022-03-20T02:45:43.000Z
|
from datetime import datetime
import json
import sys
REPEAT = 5
BATCH_SIZE = 128
assert len(sys.argv) == 1 or len(sys.argv) == 2 and sys.argv[1] == "-g", "Usage: python {sys.argv[0]} {sys.argv[1]} [-g]"
require_gpu = len(sys.argv) == 2 and sys.argv[1] == "-g"
if require_gpu:
device = "GPU"
else:
device = "CPU"
sents = [_.rstrip("\n") for _ in sys.stdin]
results = {}
print("timestamp ", "[msec]", "device", 'procedure description', sep="\t", file=sys.stderr)
start = datetime.now()
prev = start
print(start, 0, f'benchmark started with {len(sents)} sentences', sep="\t", file=sys.stderr)
import spacy
if require_gpu:
spacy.require_gpu()
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"import spacy"] = [dur]
print(lap, dur, device, 'import spacy', sep="\t", file=sys.stderr)
prev = lap
nlp = spacy.load("ja_ginza")
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"spacy.load('ja_ginza')"] = [dur]
print(lap, dur, device, f'spacy.load("ja_ginza")', sep="\t", file=sys.stderr)
prev = lap
results[f"ja_ginza->nlp(batch={BATCH_SIZE})"] = []
for repeat in range(1, REPEAT + 1):
for _ in range((len(sents) - 1) // BATCH_SIZE + 1):
doc = nlp("\n".join(sents[_ * BATCH_SIZE:(_ + 1) * BATCH_SIZE]))
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"ja_ginza->nlp(batch={BATCH_SIZE})"].append(dur / len(sents))
print(
lap,
dur,
device,
f'#{repeat} ja_ginza->nlp(batch={BATCH_SIZE}): {dur / len(sents):.03f}[msec/sent]',
sep="\t", file=sys.stderr,
)
prev = lap
results[f"ja_ginza->nlp(batch=1)"] = []
for repeat in range(1, REPEAT + 1):
for sent in sents:
doc = nlp(sent)
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"ja_ginza->nlp(batch=1)"].append(dur / len(sents))
print(
lap,
dur,
device,
f'#{repeat} ja_ginza->nlp(batch=1): {dur / len(sents):.03f}[msec/sent]',
sep="\t", file=sys.stderr,
)
prev = lap
nlp = spacy.load("ja_ginza_electra")
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"spacy.load('ja_ginza_electra')"] = [dur]
print(lap, dur, device, f'spacy.load("ja_ginza_electra")', sep="\t", file=sys.stderr)
prev = lap
results[f"ja_ginza_electra->nlp(batch={BATCH_SIZE})"] = []
for repeat in range(1, REPEAT + 1):
for _ in range((len(sents) - 1) // BATCH_SIZE + 1):
doc = nlp("\n".join(sents[_ * BATCH_SIZE:(_ + 1) * BATCH_SIZE]))
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"ja_ginza_electra->nlp(batch={BATCH_SIZE})"].append(dur / len(sents))
print(
lap,
dur,
device,
f'#{repeat} ja_ginza_electra->nlp(batch={BATCH_SIZE}): {dur / len(sents):.03f}[msec/sent]',
sep="\t", file=sys.stderr,
)
prev = lap
results[f"ja_ginza_electra->nlp(batch=1)"] = []
for repeat in range(1, REPEAT + 1):
for sent in sents:
doc = nlp(sent)
lap = datetime.now()
dur = int((lap - prev).total_seconds() * 1000)
results[f"ja_ginza_electra->nlp(batch=1)"].append(dur / len(sents))
print(
lap,
dur,
device,
f'#{repeat} ja_ginza_electra->nlp(batch=1): {dur / len(sents):.03f}[msec/sent]',
sep="\t", file=sys.stderr,
)
prev = lap
dur = int((lap - start).total_seconds() * 1000)
print(lap, dur, device, 'total', sep="\t", file=sys.stderr)
for k, v in results.items():
l = sorted(v)
results[k] = l[len(l) // 2]
json.dump(
{"device": device, "results": results},
sys.stdout,
ensure_ascii=False,
)
print()
| 29.611111
| 121
| 0.597159
| 554
| 3,731
| 3.911552
| 0.142599
| 0.058145
| 0.036917
| 0.050761
| 0.759114
| 0.72312
| 0.716659
| 0.716659
| 0.716659
| 0.695431
| 0
| 0.024679
| 0.207183
| 3,731
| 125
| 122
| 29.848
| 0.707911
| 0
| 0
| 0.509259
| 0
| 0.037037
| 0.247119
| 0.159207
| 0
| 0
| 0
| 0
| 0.009259
| 1
| 0
| false
| 0
| 0.055556
| 0
| 0.055556
| 0.101852
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
02c493a8505fd5dcaefe94dff4c6a5b0eae1540d
| 61
|
py
|
Python
|
updateapi.py
|
BioKIC/NEON-Biorepo-Collections
|
ee32bbb46269ec829aca2281058fc2077f164701
|
[
"CC0-1.0"
] | null | null | null |
updateapi.py
|
BioKIC/NEON-Biorepo-Collections
|
ee32bbb46269ec829aca2281058fc2077f164701
|
[
"CC0-1.0"
] | 2
|
2021-01-29T16:05:29.000Z
|
2021-04-08T19:25:43.000Z
|
updateapi.py
|
BioKIC/NEON-Biorepo-Collections
|
ee32bbb46269ec829aca2281058fc2077f164701
|
[
"CC0-1.0"
] | 1
|
2021-01-29T15:59:00.000Z
|
2021-01-29T15:59:00.000Z
|
# Import files as changes are pushed
import colls, external
| 20.333333
| 37
| 0.786885
| 9
| 61
| 5.333333
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180328
| 61
| 2
| 38
| 30.5
| 0.96
| 0.557377
| 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
| 1
| 0
|
0
| 5
|
f31bebcc1ebe1bb267743d117b697bb3e0323d36
| 274
|
py
|
Python
|
metrics/losses.py
|
sajith-rahim/papyrus
|
1f027274670b6492caaeb09e6ad6f80d2ebff390
|
[
"Apache-2.0"
] | null | null | null |
metrics/losses.py
|
sajith-rahim/papyrus
|
1f027274670b6492caaeb09e6ad6f80d2ebff390
|
[
"Apache-2.0"
] | null | null | null |
metrics/losses.py
|
sajith-rahim/papyrus
|
1f027274670b6492caaeb09e6ad6f80d2ebff390
|
[
"Apache-2.0"
] | null | null | null |
import torch.nn.functional as F
def nll_loss(output, target):
""" negative log likelihhod loss """
return F.nll_loss(output, target)
def binary_cross_entropy(output, target):
""" binary cross entropy loss """
return F.binary_cross_entropy(output, target)
| 24.909091
| 49
| 0.718978
| 38
| 274
| 5.026316
| 0.473684
| 0.251309
| 0.282723
| 0.198953
| 0.314136
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.171533
| 274
| 11
| 49
| 24.909091
| 0.84141
| 0.20073
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0
| 1
| 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
| 1
| 0
|
0
| 5
|
f324504230679ab319edfc950bae3fd8da37d7f9
| 4,225
|
py
|
Python
|
dataloader.py
|
AI-Huang/WGAN_PyTorch
|
a15365a3c0d4decec15da4bd74e39e65299e88aa
|
[
"MIT"
] | null | null | null |
dataloader.py
|
AI-Huang/WGAN_PyTorch
|
a15365a3c0d4decec15da4bd74e39e65299e88aa
|
[
"MIT"
] | null | null | null |
dataloader.py
|
AI-Huang/WGAN_PyTorch
|
a15365a3c0d4decec15da4bd74e39e65299e88aa
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : Dec-01-21 14:09
# @Author : Kan HUANG (kan.huang@connect.ust.hk)
import torch
from torchvision import datasets, transforms
def get_dataloader(args):
if args.dataset.lower() == 'mnist':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_root, train=False, download=True,
transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
elif args.dataset.lower() == 'svhn':
train_loader = torch.utils.data.DataLoader(
datasets.SVHN(args.data_root, split='train', download=True,
transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(
(0.43768206, 0.44376972, 0.47280434), (0.19803014, 0.20101564, 0.19703615)),
# transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.SVHN(args.data_root, split='test', download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.43768206, 0.44376972, 0.47280434), (0.19803014, 0.20101564, 0.19703615)),
# transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
elif args.dataset.lower() == 'cifar10':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.data_root, train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])),
batch_size=args.batch_size, shuffle=True, num_workers=2)
else:
raise ValueError(f"Unknown dataset name: {args.dataset}.")
return train_loader, test_loader
class Namespace:
def __init__(self, dataset="svhn", batch_size=256):
dataset = dataset.lower()
self.dataset = dataset
self.data_root = None
if dataset == "svhn":
self.data_root = "~/.datasets/SVHN"
elif dataset == "cifar10":
self.data_root = "~/.datasets"
self.batch_size = batch_size
def main():
args = Namespace()
train_loader, test_loader = get_dataloader(args)
if __name__ == "__main__":
main()
| 41.831683
| 110
| 0.501538
| 411
| 4,225
| 5.024331
| 0.223844
| 0.065375
| 0.014528
| 0.01937
| 0.717191
| 0.717191
| 0.717191
| 0.709927
| 0.709927
| 0.709927
| 0
| 0.096984
| 0.380118
| 4,225
| 100
| 111
| 42.25
| 0.691485
| 0.052071
| 0
| 0.573333
| 0
| 0
| 0.028007
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04
| false
| 0
| 0.026667
| 0
| 0.093333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
b840334f2f5b8f66a6a43fbc94e06499e189104c
| 178
|
py
|
Python
|
molsysmt/tools/biopython_Seq/is_biopython_Seq.py
|
dprada/molsysmt
|
83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d
|
[
"MIT"
] | null | null | null |
molsysmt/tools/biopython_Seq/is_biopython_Seq.py
|
dprada/molsysmt
|
83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d
|
[
"MIT"
] | null | null | null |
molsysmt/tools/biopython_Seq/is_biopython_Seq.py
|
dprada/molsysmt
|
83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d
|
[
"MIT"
] | null | null | null |
_item_fullname_='Bio.Seq.Seq'
def is_biopython_Seq(item):
item_fullname = item.__class__.__module__+'.'+item.__class__.__name__
return _item_fullname_==item_fullname
| 19.777778
| 73
| 0.775281
| 23
| 178
| 4.869565
| 0.478261
| 0.428571
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11236
| 178
| 8
| 74
| 22.25
| 0.708861
| 0
| 0
| 0
| 0
| 0
| 0.067797
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.5
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b8582cb9dd9ff9160365ce9d51b1c5907e4a9c57
| 25,147
|
py
|
Python
|
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/lan_4d193b8edf8a184f36859f87c27564fa.py
|
Vibaswan/ixnetwork_restpy
|
239fedc7050890746cbabd71ea1e91c68d9e5cad
|
[
"MIT"
] | null | null | null |
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/lan_4d193b8edf8a184f36859f87c27564fa.py
|
Vibaswan/ixnetwork_restpy
|
239fedc7050890746cbabd71ea1e91c68d9e5cad
|
[
"MIT"
] | null | null | null |
ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/lan_4d193b8edf8a184f36859f87c27564fa.py
|
Vibaswan/ixnetwork_restpy
|
239fedc7050890746cbabd71ea1e91c68d9e5cad
|
[
"MIT"
] | null | null | null |
# MIT LICENSE
#
# Copyright 1997 - 2020 by IXIA Keysight
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from ixnetwork_restpy.base import Base
from ixnetwork_restpy.files import Files
class Lan(Base):
"""This object holds the list of statically-configured LANs for the port.
The Lan class encapsulates a list of lan resources that are managed by the user.
A list of resources can be retrieved from the server using the Lan.find() method.
The list can be managed by using the Lan.add() and Lan.remove() methods.
"""
__slots__ = ()
_SDM_NAME = 'lan'
_SDM_ATT_MAP = {
'AtmEncapsulation': 'atmEncapsulation',
'Bmac': 'bmac',
'Count': 'count',
'CountPerVc': 'countPerVc',
'EnableBmac': 'enableBmac',
'EnableIncrementMac': 'enableIncrementMac',
'EnableIncrementVlan': 'enableIncrementVlan',
'EnableSiteId': 'enableSiteId',
'EnableVlan': 'enableVlan',
'Enabled': 'enabled',
'FrEncapsulation': 'frEncapsulation',
'IncrementPerVcVlanMode': 'incrementPerVcVlanMode',
'IncrementVlanMode': 'incrementVlanMode',
'IncremetVlanMode': 'incremetVlanMode',
'Mac': 'mac',
'MacRangeMode': 'macRangeMode',
'NumberOfVcs': 'numberOfVcs',
'SiteId': 'siteId',
'SkipVlanIdZero': 'skipVlanIdZero',
'Tpid': 'tpid',
'TrafficGroupId': 'trafficGroupId',
'VlanCount': 'vlanCount',
'VlanId': 'vlanId',
'VlanPriority': 'vlanPriority',
}
def __init__(self, parent):
super(Lan, self).__init__(parent)
@property
def AtmEncapsulation(self):
"""
Returns
-------
- str(None | /api/v1/sessions/1/ixnetwork/vport/.../atm): Select the ATM VPI/VCI Name from the list configured in the atm object.
"""
return self._get_attribute(self._SDM_ATT_MAP['AtmEncapsulation'])
@AtmEncapsulation.setter
def AtmEncapsulation(self, value):
self._set_attribute(self._SDM_ATT_MAP['AtmEncapsulation'], value)
@property
def Bmac(self):
"""
Returns
-------
- str:
"""
return self._get_attribute(self._SDM_ATT_MAP['Bmac'])
@Bmac.setter
def Bmac(self, value):
self._set_attribute(self._SDM_ATT_MAP['Bmac'], value)
@property
def Count(self):
"""
Returns
-------
- number: If the VLAN is enabled, then this is the number of MAC address/VLAN combinations that will be created.
"""
return self._get_attribute(self._SDM_ATT_MAP['Count'])
@Count.setter
def Count(self, value):
self._set_attribute(self._SDM_ATT_MAP['Count'], value)
@property
def CountPerVc(self):
"""
Returns
-------
- number: The total count per VC in this bundled mode.
"""
return self._get_attribute(self._SDM_ATT_MAP['CountPerVc'])
@CountPerVc.setter
def CountPerVc(self, value):
self._set_attribute(self._SDM_ATT_MAP['CountPerVc'], value)
@property
def EnableBmac(self):
"""
Returns
-------
- bool:
"""
return self._get_attribute(self._SDM_ATT_MAP['EnableBmac'])
@EnableBmac.setter
def EnableBmac(self, value):
self._set_attribute(self._SDM_ATT_MAP['EnableBmac'], value)
@property
def EnableIncrementMac(self):
"""
Returns
-------
- bool: Enables the use of multiple MAC addresses, which are incremented for each additional address. The default increment is 00 00 00 00 00 01.
"""
return self._get_attribute(self._SDM_ATT_MAP['EnableIncrementMac'])
@EnableIncrementMac.setter
def EnableIncrementMac(self, value):
self._set_attribute(self._SDM_ATT_MAP['EnableIncrementMac'], value)
@property
def EnableIncrementVlan(self):
"""
Returns
-------
- bool: Enables the use of multiple VLANs, which are incremented for each additional VLAN. The default increment is 1.
"""
return self._get_attribute(self._SDM_ATT_MAP['EnableIncrementVlan'])
@EnableIncrementVlan.setter
def EnableIncrementVlan(self, value):
self._set_attribute(self._SDM_ATT_MAP['EnableIncrementVlan'], value)
@property
def EnableSiteId(self):
"""
Returns
-------
- bool: Enables this site identifier (ID).
"""
return self._get_attribute(self._SDM_ATT_MAP['EnableSiteId'])
@EnableSiteId.setter
def EnableSiteId(self, value):
self._set_attribute(self._SDM_ATT_MAP['EnableSiteId'], value)
@property
def EnableVlan(self):
"""
Returns
-------
- bool: Enables the use of VLANs.
"""
return self._get_attribute(self._SDM_ATT_MAP['EnableVlan'])
@EnableVlan.setter
def EnableVlan(self, value):
self._set_attribute(self._SDM_ATT_MAP['EnableVlan'], value)
@property
def Enabled(self):
"""
Returns
-------
- bool: Enables this LAN entry.
"""
return self._get_attribute(self._SDM_ATT_MAP['Enabled'])
@Enabled.setter
def Enabled(self, value):
self._set_attribute(self._SDM_ATT_MAP['Enabled'], value)
@property
def FrEncapsulation(self):
"""
Returns
-------
- str(None | /api/v1/sessions/1/ixnetwork/vport/.../fr): Selects the Frame Relay encapsulation for the LAN based on the configuration of the fr object.
"""
return self._get_attribute(self._SDM_ATT_MAP['FrEncapsulation'])
@FrEncapsulation.setter
def FrEncapsulation(self, value):
self._set_attribute(self._SDM_ATT_MAP['FrEncapsulation'], value)
@property
def IncrementPerVcVlanMode(self):
"""
Returns
-------
- str(noIncrement | parallelIncrement | innerFirst | outerFirst): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
"""
return self._get_attribute(self._SDM_ATT_MAP['IncrementPerVcVlanMode'])
@IncrementPerVcVlanMode.setter
def IncrementPerVcVlanMode(self, value):
self._set_attribute(self._SDM_ATT_MAP['IncrementPerVcVlanMode'], value)
@property
def IncrementVlanMode(self):
"""
Returns
-------
- str(noIncrement | parallelIncrement | innerFirst | outerFirst): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
"""
return self._get_attribute(self._SDM_ATT_MAP['IncrementVlanMode'])
@IncrementVlanMode.setter
def IncrementVlanMode(self, value):
self._set_attribute(self._SDM_ATT_MAP['IncrementVlanMode'], value)
@property
def IncremetVlanMode(self):
"""DEPRECATED
Returns
-------
- str(noIncrement | parallelIncrement | innerFirst | outerFirst): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
"""
return self._get_attribute(self._SDM_ATT_MAP['IncremetVlanMode'])
@IncremetVlanMode.setter
def IncremetVlanMode(self, value):
self._set_attribute(self._SDM_ATT_MAP['IncremetVlanMode'], value)
@property
def Mac(self):
"""
Returns
-------
- str: The first MAC address in the range.
"""
return self._get_attribute(self._SDM_ATT_MAP['Mac'])
@Mac.setter
def Mac(self, value):
self._set_attribute(self._SDM_ATT_MAP['Mac'], value)
@property
def MacRangeMode(self):
"""
Returns
-------
- str(normal | bundled): Indicates the available MAC range mode.
"""
return self._get_attribute(self._SDM_ATT_MAP['MacRangeMode'])
@MacRangeMode.setter
def MacRangeMode(self, value):
self._set_attribute(self._SDM_ATT_MAP['MacRangeMode'], value)
@property
def NumberOfVcs(self):
"""
Returns
-------
- number: The total number of VCs in this mode.
"""
return self._get_attribute(self._SDM_ATT_MAP['NumberOfVcs'])
@NumberOfVcs.setter
def NumberOfVcs(self, value):
self._set_attribute(self._SDM_ATT_MAP['NumberOfVcs'], value)
@property
def SiteId(self):
"""
Returns
-------
- number: The value of the site identifier (ID). The valid range is 0 to 4,294,967,295. The default is 0.
"""
return self._get_attribute(self._SDM_ATT_MAP['SiteId'])
@SiteId.setter
def SiteId(self, value):
self._set_attribute(self._SDM_ATT_MAP['SiteId'], value)
@property
def SkipVlanIdZero(self):
"""
Returns
-------
- bool: Skip the value of vlad id, if the vlan id value is equal to zero.
"""
return self._get_attribute(self._SDM_ATT_MAP['SkipVlanIdZero'])
@SkipVlanIdZero.setter
def SkipVlanIdZero(self, value):
self._set_attribute(self._SDM_ATT_MAP['SkipVlanIdZero'], value)
@property
def Tpid(self):
"""
Returns
-------
- str: Tag Protocol Identifier / TPID (hex). The EtherType that identifies the protocol header that follows the VLAN header (tag).The dropdown list contains the available TPIDs. Choose one of: 0x8100 (the default), 0x88a8, 0x9100, 0x9200.
"""
return self._get_attribute(self._SDM_ATT_MAP['Tpid'])
@Tpid.setter
def Tpid(self, value):
self._set_attribute(self._SDM_ATT_MAP['Tpid'], value)
@property
def TrafficGroupId(self):
"""
Returns
-------
- str(None | /api/v1/sessions/1/ixnetwork/traffic/.../trafficGroup): The name of the group to which this port is assigned, for the purpose of creating traffic streams among source/destination members of the group.
"""
return self._get_attribute(self._SDM_ATT_MAP['TrafficGroupId'])
@TrafficGroupId.setter
def TrafficGroupId(self, value):
self._set_attribute(self._SDM_ATT_MAP['TrafficGroupId'], value)
@property
def VlanCount(self):
"""
Returns
-------
- number: The number of VLANs created.
"""
return self._get_attribute(self._SDM_ATT_MAP['VlanCount'])
@VlanCount.setter
def VlanCount(self, value):
self._set_attribute(self._SDM_ATT_MAP['VlanCount'], value)
@property
def VlanId(self):
"""
Returns
-------
- str: The identifier for the first VLAN in the range.
"""
return self._get_attribute(self._SDM_ATT_MAP['VlanId'])
@VlanId.setter
def VlanId(self, value):
self._set_attribute(self._SDM_ATT_MAP['VlanId'], value)
@property
def VlanPriority(self):
"""
Returns
-------
- str: The User Priority for this VLAN. A value from 0 through 7. The use and interpretation of this field is defined in ISO/IEC 15802-3.
"""
return self._get_attribute(self._SDM_ATT_MAP['VlanPriority'])
@VlanPriority.setter
def VlanPriority(self, value):
self._set_attribute(self._SDM_ATT_MAP['VlanPriority'], value)
def update(self, AtmEncapsulation=None, Bmac=None, Count=None, CountPerVc=None, EnableBmac=None, EnableIncrementMac=None, EnableIncrementVlan=None, EnableSiteId=None, EnableVlan=None, Enabled=None, FrEncapsulation=None, IncrementPerVcVlanMode=None, IncrementVlanMode=None, IncremetVlanMode=None, Mac=None, MacRangeMode=None, NumberOfVcs=None, SiteId=None, SkipVlanIdZero=None, Tpid=None, TrafficGroupId=None, VlanCount=None, VlanId=None, VlanPriority=None):
"""Updates lan resource on the server.
Args
----
- AtmEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../atm)): Select the ATM VPI/VCI Name from the list configured in the atm object.
- Bmac (str):
- Count (number): If the VLAN is enabled, then this is the number of MAC address/VLAN combinations that will be created.
- CountPerVc (number): The total count per VC in this bundled mode.
- EnableBmac (bool):
- EnableIncrementMac (bool): Enables the use of multiple MAC addresses, which are incremented for each additional address. The default increment is 00 00 00 00 00 01.
- EnableIncrementVlan (bool): Enables the use of multiple VLANs, which are incremented for each additional VLAN. The default increment is 1.
- EnableSiteId (bool): Enables this site identifier (ID).
- EnableVlan (bool): Enables the use of VLANs.
- Enabled (bool): Enables this LAN entry.
- FrEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../fr)): Selects the Frame Relay encapsulation for the LAN based on the configuration of the fr object.
- IncrementPerVcVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncrementVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncremetVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- Mac (str): The first MAC address in the range.
- MacRangeMode (str(normal | bundled)): Indicates the available MAC range mode.
- NumberOfVcs (number): The total number of VCs in this mode.
- SiteId (number): The value of the site identifier (ID). The valid range is 0 to 4,294,967,295. The default is 0.
- SkipVlanIdZero (bool): Skip the value of vlad id, if the vlan id value is equal to zero.
- Tpid (str): Tag Protocol Identifier / TPID (hex). The EtherType that identifies the protocol header that follows the VLAN header (tag).The dropdown list contains the available TPIDs. Choose one of: 0x8100 (the default), 0x88a8, 0x9100, 0x9200.
- TrafficGroupId (str(None | /api/v1/sessions/1/ixnetwork/traffic/.../trafficGroup)): The name of the group to which this port is assigned, for the purpose of creating traffic streams among source/destination members of the group.
- VlanCount (number): The number of VLANs created.
- VlanId (str): The identifier for the first VLAN in the range.
- VlanPriority (str): The User Priority for this VLAN. A value from 0 through 7. The use and interpretation of this field is defined in ISO/IEC 15802-3.
Raises
------
- ServerError: The server has encountered an uncategorized error condition
"""
return self._update(self._map_locals(self._SDM_ATT_MAP, locals()))
def add(self, AtmEncapsulation=None, Bmac=None, Count=None, CountPerVc=None, EnableBmac=None, EnableIncrementMac=None, EnableIncrementVlan=None, EnableSiteId=None, EnableVlan=None, Enabled=None, FrEncapsulation=None, IncrementPerVcVlanMode=None, IncrementVlanMode=None, IncremetVlanMode=None, Mac=None, MacRangeMode=None, NumberOfVcs=None, SiteId=None, SkipVlanIdZero=None, Tpid=None, TrafficGroupId=None, VlanCount=None, VlanId=None, VlanPriority=None):
"""Adds a new lan resource on the server and adds it to the container.
Args
----
- AtmEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../atm)): Select the ATM VPI/VCI Name from the list configured in the atm object.
- Bmac (str):
- Count (number): If the VLAN is enabled, then this is the number of MAC address/VLAN combinations that will be created.
- CountPerVc (number): The total count per VC in this bundled mode.
- EnableBmac (bool):
- EnableIncrementMac (bool): Enables the use of multiple MAC addresses, which are incremented for each additional address. The default increment is 00 00 00 00 00 01.
- EnableIncrementVlan (bool): Enables the use of multiple VLANs, which are incremented for each additional VLAN. The default increment is 1.
- EnableSiteId (bool): Enables this site identifier (ID).
- EnableVlan (bool): Enables the use of VLANs.
- Enabled (bool): Enables this LAN entry.
- FrEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../fr)): Selects the Frame Relay encapsulation for the LAN based on the configuration of the fr object.
- IncrementPerVcVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncrementVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncremetVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- Mac (str): The first MAC address in the range.
- MacRangeMode (str(normal | bundled)): Indicates the available MAC range mode.
- NumberOfVcs (number): The total number of VCs in this mode.
- SiteId (number): The value of the site identifier (ID). The valid range is 0 to 4,294,967,295. The default is 0.
- SkipVlanIdZero (bool): Skip the value of vlad id, if the vlan id value is equal to zero.
- Tpid (str): Tag Protocol Identifier / TPID (hex). The EtherType that identifies the protocol header that follows the VLAN header (tag).The dropdown list contains the available TPIDs. Choose one of: 0x8100 (the default), 0x88a8, 0x9100, 0x9200.
- TrafficGroupId (str(None | /api/v1/sessions/1/ixnetwork/traffic/.../trafficGroup)): The name of the group to which this port is assigned, for the purpose of creating traffic streams among source/destination members of the group.
- VlanCount (number): The number of VLANs created.
- VlanId (str): The identifier for the first VLAN in the range.
- VlanPriority (str): The User Priority for this VLAN. A value from 0 through 7. The use and interpretation of this field is defined in ISO/IEC 15802-3.
Returns
-------
- self: This instance with all currently retrieved lan resources using find and the newly added lan resources available through an iterator or index
Raises
------
- ServerError: The server has encountered an uncategorized error condition
"""
return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))
def remove(self):
"""Deletes all the contained lan resources in this instance from the server.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
self._delete()
def find(self, AtmEncapsulation=None, Bmac=None, Count=None, CountPerVc=None, EnableBmac=None, EnableIncrementMac=None, EnableIncrementVlan=None, EnableSiteId=None, EnableVlan=None, Enabled=None, FrEncapsulation=None, IncrementPerVcVlanMode=None, IncrementVlanMode=None, IncremetVlanMode=None, Mac=None, MacRangeMode=None, NumberOfVcs=None, SiteId=None, SkipVlanIdZero=None, Tpid=None, TrafficGroupId=None, VlanCount=None, VlanId=None, VlanPriority=None):
"""Finds and retrieves lan resources from the server.
All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve lan resources from the server.
To retrieve an exact match ensure the parameter value starts with ^ and ends with $
By default the find method takes no parameters and will retrieve all lan resources from the server.
Args
----
- AtmEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../atm)): Select the ATM VPI/VCI Name from the list configured in the atm object.
- Bmac (str):
- Count (number): If the VLAN is enabled, then this is the number of MAC address/VLAN combinations that will be created.
- CountPerVc (number): The total count per VC in this bundled mode.
- EnableBmac (bool):
- EnableIncrementMac (bool): Enables the use of multiple MAC addresses, which are incremented for each additional address. The default increment is 00 00 00 00 00 01.
- EnableIncrementVlan (bool): Enables the use of multiple VLANs, which are incremented for each additional VLAN. The default increment is 1.
- EnableSiteId (bool): Enables this site identifier (ID).
- EnableVlan (bool): Enables the use of VLANs.
- Enabled (bool): Enables this LAN entry.
- FrEncapsulation (str(None | /api/v1/sessions/1/ixnetwork/vport/.../fr)): Selects the Frame Relay encapsulation for the LAN based on the configuration of the fr object.
- IncrementPerVcVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncrementVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- IncremetVlanMode (str(noIncrement | parallelIncrement | innerFirst | outerFirst)): If true, enables the use of multiple VLANs, which are incremented for each additional VLAN per VC. The default increment is 1.
- Mac (str): The first MAC address in the range.
- MacRangeMode (str(normal | bundled)): Indicates the available MAC range mode.
- NumberOfVcs (number): The total number of VCs in this mode.
- SiteId (number): The value of the site identifier (ID). The valid range is 0 to 4,294,967,295. The default is 0.
- SkipVlanIdZero (bool): Skip the value of vlad id, if the vlan id value is equal to zero.
- Tpid (str): Tag Protocol Identifier / TPID (hex). The EtherType that identifies the protocol header that follows the VLAN header (tag).The dropdown list contains the available TPIDs. Choose one of: 0x8100 (the default), 0x88a8, 0x9100, 0x9200.
- TrafficGroupId (str(None | /api/v1/sessions/1/ixnetwork/traffic/.../trafficGroup)): The name of the group to which this port is assigned, for the purpose of creating traffic streams among source/destination members of the group.
- VlanCount (number): The number of VLANs created.
- VlanId (str): The identifier for the first VLAN in the range.
- VlanPriority (str): The User Priority for this VLAN. A value from 0 through 7. The use and interpretation of this field is defined in ISO/IEC 15802-3.
Returns
-------
- self: This instance with matching lan resources retrieved from the server available through an iterator or index
Raises
------
- ServerError: The server has encountered an uncategorized error condition
"""
return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))
def read(self, href):
"""Retrieves a single instance of lan data from the server.
Args
----
- href (str): An href to the instance to be retrieved
Returns
-------
- self: This instance with the lan resources from the server available through an iterator or index
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
return self._read(href)
| 50.294
| 461
| 0.675747
| 3,114
| 25,147
| 5.367373
| 0.104689
| 0.018667
| 0.028
| 0.039667
| 0.766543
| 0.750748
| 0.71473
| 0.712935
| 0.687328
| 0.60572
| 0
| 0.013009
| 0.229689
| 25,147
| 499
| 462
| 50.39479
| 0.849827
| 0.568418
| 0
| 0.128342
| 0
| 0
| 0.122255
| 0.009861
| 0
| 0
| 0
| 0
| 0
| 1
| 0.28877
| false
| 0
| 0.010695
| 0
| 0.470588
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
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| 1
| 0
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| null | 0
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|
0
| 5
|
b86202d8a7d216ce3d835a5d9ac20b588ff476c5
| 9,366
|
py
|
Python
|
app/src/routers/tensor.py
|
PSauerborn/tensor-trigger
|
a87bde55c4024b4bb0eae16b7ad15a1fe8f3ce64
|
[
"MIT"
] | 1
|
2021-11-14T08:51:39.000Z
|
2021-11-14T08:51:39.000Z
|
app/src/routers/tensor.py
|
PSauerborn/tensor-trigger
|
a87bde55c4024b4bb0eae16b7ad15a1fe8f3ce64
|
[
"MIT"
] | null | null | null |
app/src/routers/tensor.py
|
PSauerborn/tensor-trigger
|
a87bde55c4024b4bb0eae16b7ad15a1fe8f3ce64
|
[
"MIT"
] | null | null | null |
"""Module containing API router for tensor
trigger functionality"""
import logging
import json
from fastapi import APIRouter, Depends, status
from fastapi.responses import JSONResponse
from fastapi.encoders import jsonable_encoder as je
from src.utils import get_user,json_response_with_message, parse_base64_file
from src.persistence.postgres import get_user_model, insert_async_job
from src.persistence.s3 import retrieve_s3_file, upload_s3_file
from src.config import PG_CREDENTIALS, MESSAGE_BROKER_URL, JOB_EXCHANGE_NAME, \
JOB_EXCHANGE_TYPE, JOB_ROUTING_KEY
from src.logic.tensor import validate_data_point, run_model, \
run_model_batched, validate_csv_file
from src.models.tensor import ProcessRequest, BatchProcessRequest, \
AsyncBatchProcessRequest, TrainModelRequest
from src.services.rabbitmq import write_to_exchange
LOGGER = logging.getLogger(__name__)
ROUTER = APIRouter()
@ROUTER.post('/run')
async def run_model_handler(r: ProcessRequest, uid: str = Depends(get_user())) -> JSONResponse:
"""API handler used to run model
for a given datapoint
Args:
model_id (UUID): UUID of model to run
uid (str, optional): [description]. Defaults to Depends(get_user()).
Returns:
JSONResponse: [description]
"""
LOGGER.debug('received request to run model for user %s', uid)
# get model metadata from postgres server. return
# 404 error code if model cannot be found
model_meta = get_user_model(PG_CREDENTIALS, uid, r.model_id)
if model_meta is None:
LOGGER.error('unable to retrieve model %s for user %s', r.model_id, uid)
return json_response_with_message(status.HTTP_404_NOT_FOUND, 'Cannot find specified model')
schema = model_meta.model_schema
# validate provided input vector against
# the schema registered against the model
if not validate_data_point(r.input_vector, schema.get('input_schema')):
LOGGER.error('unable to validate data point %s against schema %s', r.input_vector, model_meta.model_schema)
return json_response_with_message(status.HTTP_400_BAD_REQUEST, 'Invalid input vector')
# retrieve hd5 file from s3 storage and run tensorflow model
s3_data = retrieve_s3_file('/tensor-trigger/' + str(r.model_id))
results = run_model(s3_data, r.input_vector, schema.get('input_schema'), schema.get('output_schema'))
if results is None:
LOGGER.error('unable to run model %s', r.model_id)
return json_response_with_message(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal server error')
content = {'http_code': status.HTTP_200_OK, 'output': results}
return JSONResponse(status_code=status.HTTP_200_OK, content=je(content))
@ROUTER.post('/run/batch')
async def batch_model_handler(r: BatchProcessRequest, uid: str = Depends(get_user())) -> JSONResponse:
"""API handler used to handle batch processing
of model data
Args:
r (BatchProcessRequest): [description]
uid (str, optional): [description]. Defaults to Depends(get_user()).
Returns:
JSONResponse: [description]
"""
LOGGER.debug('received request to batch process data for user %s', uid)
# get model metadata from postgres server. return
# 404 error code if model cannot be found
model_meta = get_user_model(PG_CREDENTIALS, uid, r.model_id)
if model_meta is None:
LOGGER.error('unable to retrieve model %s for user %s', r.model_id, uid)
return json_response_with_message(status.HTTP_404_NOT_FOUND, 'Cannot find specified model')
schema = model_meta.model_schema
# validate provided input vectors against
# the schema registered against the model
invalid_points = any(not validate_data_point(x, schema.get('input_schema')) for x in r.input_vectors)
if invalid_points:
LOGGER.error('unable to validate data point(s) against schema %s', schema)
return json_response_with_message(status.HTTP_400_BAD_REQUEST, 'Invalid input vector(s)')
# retrieve hd5 file from s3 storage and run tensorflow model
s3_data = retrieve_s3_file('/tensor-trigger/' + str(r.model_id))
results = run_model_batched(s3_data, r.input_vectors, schema.get('input_schema'), schema.get('output_schema'))
if results is None:
LOGGER.error('unable to run model %s', r.model_id)
return json_response_with_message(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal server error')
content = {'http_code': status.HTTP_200_OK, 'output': results}
return JSONResponse(status_code=status.HTTP_200_OK, content=je(content))
@ROUTER.post('/run/async')
async def async_batch_model_handler(r: AsyncBatchProcessRequest, uid: str = Depends(get_user())) -> JSONResponse:
"""API handler used to handle batch processing
of model data
Args:
r (BatchProcessRequest): [description]
uid (str, optional): [description]. Defaults to Depends(get_user()).
Returns:
JSONResponse: [description]
"""
LOGGER.debug('received request to batch process data async for user %s', uid)
# get model metadata from postgres server. return
# 404 error code if model cannot be found
model_meta = get_user_model(PG_CREDENTIALS, uid, r.model_id)
if model_meta is None:
LOGGER.error('unable to retrieve model %s for user %s', r.model_id, uid)
return json_response_with_message(status.HTTP_404_NOT_FOUND, 'Cannot find specified model')
schema = model_meta.model_schema
try:
meta, bytes_data = parse_base64_file(r.input_data)
if not validate_csv_file(bytes_data, schema.get('input_schema')):
LOGGER.error('CSV validation failed')
raise ValueError
bytes_data.seek(0)
except Exception:
LOGGER.exception('unable to validate file data')
return json_response_with_message(status.HTTP_400_BAD_REQUEST, 'Invalid input data')
# insert job into database and upload input data to s3
job_id = insert_async_job(PG_CREDENTIALS, r.model_id, meta.file_size)
upload_s3_file(bytes_data, '/tensor-trigger/input-data' + str(job_id))
content = {'http_code': status.HTTP_201_CREATED,
'message': 'Successfully queued job',
'job_id': job_id}
# send event to RabbitMQ broker to trigger worker
event = {'job_id': str(job_id),
'event_type': 'model_run',
'event': {'model_id': str(r.model_id), 'user': uid}}
write_to_exchange(MESSAGE_BROKER_URL,
JOB_EXCHANGE_NAME,
json.dumps(event),
JOB_EXCHANGE_TYPE,
JOB_ROUTING_KEY)
return JSONResponse(status_code=status.HTTP_201_CREATED, content=je(content))
@ROUTER.patch('/train')
async def train_model_handler(r: TrainModelRequest, uid: str = Depends(get_user())) -> JSONResponse:
"""API handler used to handle batch processing
of model data
Args:
r (BatchProcessRequest): [description]
uid (str, optional): [description]. Defaults to Depends(get_user()).
Returns:
JSONResponse: [description]
"""
LOGGER.debug('received request to batch process data async for user %s', uid)
# get model metadata from postgres server. return
# 404 error code if model cannot be found
meta = get_user_model(PG_CREDENTIALS, uid, r.model_id)
if meta is None:
LOGGER.error('unable to retrieve model %s for user %s', r.model_id, uid)
return json_response_with_message(status.HTTP_404_NOT_FOUND, 'Cannot find specified model')
schema = meta.model_schema
# validate provided input vectors against
# the schema registered against the model
invalid_input_points = any(not validate_data_point(x, schema.get('input_schema')) for x in r.input_vectors)
if invalid_input_points:
LOGGER.error('unable to validate data point(s) against schema %s', meta.model_schema)
return json_response_with_message(status.HTTP_400_BAD_REQUEST, 'Invalid input vector(s)')
# validate provided output vectors against
# the schema registered against the model
invalid_output_points = any(not validate_data_point(x, schema.get('output_schema')) for x in r.output_vectors)
if invalid_output_points:
LOGGER.error('unable to validate data point(s) against schema %s', meta.model_schema)
return json_response_with_message(status.HTTP_400_BAD_REQUEST, 'Invalid output vector(s)')
# insert job into database and generate new event
job_id = insert_async_job(PG_CREDENTIALS, r.model_id, 0)
event = {'job_id': str(job_id),
'event_type': 'model_train',
'event': {
'model_id': str(r.model_id),
'user': uid,
'epochs': r.epochs,
'input_vectors': r.input_vectors,
'output_vectors': r.output_vectors}}
# send event to RabbitMQ broker to trigger worker
write_to_exchange(MESSAGE_BROKER_URL,
JOB_EXCHANGE_NAME,
json.dumps(event),
JOB_EXCHANGE_TYPE,
JOB_ROUTING_KEY)
content = {'http_code': status.HTTP_201_CREATED,
'message': 'Successfully queued job',
'job_id': job_id}
return JSONResponse(status_code=status.HTTP_201_CREATED, content=je(content))
| 43.562791
| 115
| 0.704676
| 1,271
| 9,366
| 4.959874
| 0.130606
| 0.021098
| 0.020305
| 0.043782
| 0.768242
| 0.758566
| 0.744289
| 0.730806
| 0.718115
| 0.683534
| 0
| 0.012097
| 0.205637
| 9,366
| 214
| 116
| 43.766355
| 0.835215
| 0.112001
| 0
| 0.462185
| 0
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| 0.185471
| 0.003577
| 0
| 0
| 0
| 0
| 0
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| 0
| false
| 0
| 0.10084
| 0
| 0.226891
| 0
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| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
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| null | 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b862af1d5936704c2909e5b0c9ae838270c47ea6
| 154
|
py
|
Python
|
todos_app/todos_app/forms_workshop/urls.py
|
BoyanPeychinov/python_web_basics
|
2f892ac119f7fe3a5c03fc5e7b35670dc609a70f
|
[
"MIT"
] | 1
|
2021-07-20T12:16:34.000Z
|
2021-07-20T12:16:34.000Z
|
todos_app/todos_app/forms_workshop/urls.py
|
BoyanPeychinov/python_web_basics
|
2f892ac119f7fe3a5c03fc5e7b35670dc609a70f
|
[
"MIT"
] | null | null | null |
todos_app/todos_app/forms_workshop/urls.py
|
BoyanPeychinov/python_web_basics
|
2f892ac119f7fe3a5c03fc5e7b35670dc609a70f
|
[
"MIT"
] | null | null | null |
from django.urls import path
from todos_app.forms_workshop.views import show_form_data
urlpatterns = [
path('', show_form_data, name='show form'),
]
| 22
| 57
| 0.75974
| 23
| 154
| 4.826087
| 0.652174
| 0.216216
| 0.216216
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 154
| 7
| 58
| 22
| 0.834586
| 0
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| 0
| 0.058065
| 0
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| false
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| null | 1
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b87e59d8c7046aefb109449a96633ef2acbbaf10
| 60
|
py
|
Python
|
KD_Lib/Pruning/weight_threshold/__init__.py
|
NeelayS/KD_Lib
|
c3f8c7cef76772d14862260e61c1d1c52c58f58e
|
[
"MIT"
] | null | null | null |
KD_Lib/Pruning/weight_threshold/__init__.py
|
NeelayS/KD_Lib
|
c3f8c7cef76772d14862260e61c1d1c52c58f58e
|
[
"MIT"
] | null | null | null |
KD_Lib/Pruning/weight_threshold/__init__.py
|
NeelayS/KD_Lib
|
c3f8c7cef76772d14862260e61c1d1c52c58f58e
|
[
"MIT"
] | null | null | null |
from .weight_threshold_pruning import WeightThresholdPruner
| 30
| 59
| 0.916667
| 6
| 60
| 8.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066667
| 60
| 1
| 60
| 60
| 0.946429
| 0
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| 0
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| 0
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| true
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| 1
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| null | 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b887554eb3423c6c58c0c654d8e5044b635c4810
| 417
|
py
|
Python
|
python/graphscope/experimental/nx/tests/algorithms/forward/isomorphism/test_match_helpers.py
|
wenyuanyu/GraphScope
|
a40ccaf70557e608d8b091eb25ab04477f99ce21
|
[
"Apache-2.0"
] | 2
|
2020-12-15T08:42:10.000Z
|
2022-01-14T09:13:16.000Z
|
python/graphscope/experimental/nx/tests/algorithms/forward/isomorphism/test_match_helpers.py
|
wenyuanyu/GraphScope
|
a40ccaf70557e608d8b091eb25ab04477f99ce21
|
[
"Apache-2.0"
] | 1
|
2020-12-22T13:15:40.000Z
|
2020-12-22T13:15:40.000Z
|
python/graphscope/experimental/nx/tests/algorithms/forward/isomorphism/test_match_helpers.py
|
wenyuanyu/GraphScope
|
a40ccaf70557e608d8b091eb25ab04477f99ce21
|
[
"Apache-2.0"
] | 1
|
2021-11-23T03:40:43.000Z
|
2021-11-23T03:40:43.000Z
|
import networkx.algorithms.isomorphism.tests.test_match_helpers
import pytest
from graphscope.experimental.nx.utils.compat import import_as_graphscope_nx
import_as_graphscope_nx(networkx.algorithms.isomorphism.tests.test_match_helpers,
decorators=pytest.mark.usefixtures("graphscope_session"))
@pytest.mark.skip(reason="not support multigraph")
class TestGenericMultiEdgeMatch():
pass
| 32.076923
| 81
| 0.805755
| 48
| 417
| 6.770833
| 0.583333
| 0.110769
| 0.178462
| 0.209231
| 0.307692
| 0.307692
| 0.307692
| 0
| 0
| 0
| 0
| 0
| 0.115108
| 417
| 12
| 82
| 34.75
| 0.880759
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| 0.095923
| 0
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| true
| 0.125
| 0.5
| 0
| 0.625
| 0
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| null | 0
| 0
| 1
| 0
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| 0
| 0
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| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
b8879c266a78858010ea839c2a7387e1ba517b6f
| 9,841
|
py
|
Python
|
Python Simulator/Frontier Exploration/backup/1 (initial backup)/GridUI.py
|
yiorgosk/Path-Planning-Simulator
|
84847d0068a3fd6fa30098b99a75dff237768a73
|
[
"MIT"
] | 50
|
2018-11-15T08:42:49.000Z
|
2022-03-20T10:51:58.000Z
|
Python Simulator/Frontier Exploration/backup/1 (initial backup)/GridUI.py
|
yiorgosk/Path-Planning-Simulator
|
84847d0068a3fd6fa30098b99a75dff237768a73
|
[
"MIT"
] | null | null | null |
Python Simulator/Frontier Exploration/backup/1 (initial backup)/GridUI.py
|
yiorgosk/Path-Planning-Simulator
|
84847d0068a3fd6fa30098b99a75dff237768a73
|
[
"MIT"
] | 24
|
2019-02-03T06:11:58.000Z
|
2022-03-15T06:18:39.000Z
|
# The MIT License (MIT)
# Copyright (c) 2015 INSPIRE Lab, BITS Pilani
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Defines a Tkinter based GUI class for visualizing the simulation
"""
from math import floor
from time import sleep
from Tkinter import Tk, Canvas, Frame, BOTH
# The GridUI class
class GridUI(Frame):
def __init__(self, parent, height, width, cellSize, grid, robots, frontier):
Frame.__init__(self, parent)
self.parent = parent
self.initialize(height, width, cellSize, grid, robots, frontier)
# Method to draw a grid of specified height and width
def initialize(self, height, width, cellSize, grid, robots, frontier):
self.parent.title('Grid')
self.pack(fill = BOTH, expand = 1)
self.canvas = Canvas(self)
startX = cellSize
startY = cellSize
endX = startX + (cellSize * width)
endY = startY + (cellSize * height)
curX = startX
curY = startY
rectIdx = 0
xIdx = 0
yIdx = 0
while curX != endX and curY != endY:
# print 'x, y:', xIdx, yIdx
# First, check if the current location corresponds to that of any robot
robotFlag = False
for robot in robots:
if robot.curX == xIdx and robot.curY == yIdx:
robotFlag = True
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#00FF00', width = 2)
# Then check if it corresponds to an obstacle
if grid.cells[xIdx][yIdx].obstacle == True:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#000000', width = 2)
elif robotFlag == False:
# Then check if it corresponds to a frontier cell
frontierFlag = False
for pt in frontier:
if pt[0] == xIdx and pt[1] == yIdx:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#00FFFF', width = 2)
frontierFlag = True
if frontierFlag == False:
if grid.cells[xIdx][yIdx].visited == True:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#FFFFFF', width = 2)
else:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#777777', width = 2)
curX = curX + cellSize
if curX == endX and curY != endY:
curX = startX
xIdx += 1
curY = curY + cellSize
yIdx = 0
# Move to the next iteration of the loop
continue
elif curX == endX and curY == endY:
break
rectIdx += 1
yIdx += 1
self.canvas.pack(fill = BOTH, expand = 1)
# Method to redraw the positions of the robots and the frontier
def redraw(self, height, width, cellSize, grid, robots, frontier):
self.parent.title('Grid2')
self.pack(fill = BOTH, expand = 1)
# canvas = Canvas(self.parent)
self.canvas.delete('all')
startX = cellSize
startY = cellSize
endX = startX + (cellSize * width)
endY = startY + (cellSize * height)
curX = startX
curY = startY
rectIdx = 0
xIdx = 0
yIdx = 0
while curX != endX and curY != endY:
# print 'x, y:', xIdx, yIdx
# First, check if the current location corresponds to that of any robot
robotFlag = False
if grid.cells[xIdx][yIdx].centroid == True:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'indian red', width = 2)
else:
for robot in robots:
if robot.curX == xIdx and robot.curY == yIdx:
robotFlag = True
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#00FF00', width = 2)
# Then check if it corresponds to an obstacle
if grid.cells[xIdx][yIdx].obstacle == True:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#000000', width = 2)
elif robotFlag == False:
# Then check if it corresponds to a frontier cell
frontierFlag = False
for pt in frontier:
if pt[0] == xIdx and pt[1] == yIdx:
if grid.cells[xIdx][yIdx].cluster==-1:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#00FFFF', width = 2)
if grid.cells[xIdx][yIdx].cluster == 0:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'midnight blue', width = 2)
if grid.cells[xIdx][yIdx].cluster == 1:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'peach puff', width = 2)
if grid.cells[xIdx][yIdx].cluster == 2:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'gold', width = 2)
if grid.cells[xIdx][yIdx].cluster == 3:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'slate blue', width = 2)
if grid.cells[xIdx][yIdx].cluster == 4:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'royal blue', width = 2)
if grid.cells[xIdx][yIdx].cluster == 5:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'red', width = 2)
if grid.cells[xIdx][yIdx].cluster == 6:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'dark green', width = 2)
if grid.cells[xIdx][yIdx].cluster == 7:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'yellow', width = 2)
if grid.cells[xIdx][yIdx].cluster == 8:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'orange', width = 2)
if grid.cells[xIdx][yIdx].cluster == 9:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'hot pink', width = 2)
frontierFlag = True
if frontierFlag == False:
if grid.cells[xIdx][yIdx].visited == True:
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#FFFFFF', width = 2)
#else:
#if grid.cells[xIdx][yIdx].cluster != -1:
# if grid.cells[xIdx][yIdx].cluster == 0:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'midnight blue', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 1:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'peach puff', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 2:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'dim gray', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 3:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'slate blue', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 4:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'royal blue', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 5:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'red', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 6:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'dark green', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 7:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'gold', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 8:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'orange', width = 2)
# if grid.cells[xIdx][yIdx].cluster == 9:
# self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = 'hot pink', width = 2)
else :
self.canvas.create_rectangle(curX, curY, curX + cellSize, curY + cellSize, outline = '#0000FF', fill = '#777777', width = 2)
curX = curX + cellSize
if curX == endX and curY != endY:
curX = startX
xIdx += 1
curY = curY + cellSize
yIdx = 0
# Move to the next iteration of the loop
continue
elif curX == endX and curY == endY:
break
rectIdx += 1
yIdx += 1
self.canvas.pack(fill = BOTH, expand = 1)
| 45.985981
| 141
| 0.640585
| 1,294
| 9,841
| 4.841577
| 0.166924
| 0.055866
| 0.07917
| 0.123703
| 0.788667
| 0.788667
| 0.76648
| 0.762171
| 0.762171
| 0.762171
| 0
| 0.031839
| 0.234021
| 9,841
| 213
| 142
| 46.201878
| 0.799284
| 0.352911
| 0
| 0.691667
| 0
| 0
| 0.052424
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.025
| false
| 0
| 0.025
| 0
| 0.058333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 5
|
b89e9dd003bf4048d5763e783565f19266efbada
| 6,741
|
py
|
Python
|
connect4/test_connect4.py
|
ThomasKidder/alpha-zero-general
|
e894d91a5d6afceb0e04a1a5ff2234fac8cc1fb2
|
[
"MIT"
] | null | null | null |
connect4/test_connect4.py
|
ThomasKidder/alpha-zero-general
|
e894d91a5d6afceb0e04a1a5ff2234fac8cc1fb2
|
[
"MIT"
] | null | null | null |
connect4/test_connect4.py
|
ThomasKidder/alpha-zero-general
|
e894d91a5d6afceb0e04a1a5ff2234fac8cc1fb2
|
[
"MIT"
] | null | null | null |
# """
# To run tests:
# pytest-3 connect4
# """
# from collections import namedtuple
# import textwrap
# import numpy as np
# from .Connect4Game import Connect4Game
# # Tuple of (Board, Player, Game) to simplify testing.
# BPGTuple = namedtuple('BPGTuple', 'board player game')
# def init_board_from_moves(moves, height=None, width=None):
# """Returns a BPGTuple based on series of specified moved."""
# game = Connect4Game(height=height, width=width)
# board, player = game.getInitBoard(), 1
# for move in moves:
# board, player = game.getNextState(board, player, move)
# return BPGTuple(board, player, game)
# def init_board_from_array(board, player):
# """Returns a BPGTuple based on series of specified moved."""
# game = Connect4Game(height=len(board), width=len(board[0]))
# return BPGTuple(board, player, game)
# def test_simple_moves():
# board, player, game = init_board_from_moves([4, 5, 4, 3, 0, 6])
# expected = textwrap.dedent("""\
# [[ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 1. 0. 0.]
# [ 1. 0. 0. -1. 1. -1. -1.]]""")
# assert expected == game.stringRepresentation(board)
# def test_overfull_column():
# for height in range(1, 10):
# # Fill to max height is ok
# init_board_from_moves([4] * height, height=height)
# # Check overfilling causes an error.
# try:
# init_board_from_moves([4] * (height + 1), height=height)
# assert False, "Expected error when overfilling column"
# except ValueError:
# pass # Expected.
# def test_get_valid_moves():
# """Tests vector of valid moved is correct."""
# move_valid_pairs = [
# ([], [True] * 7),
# ([0, 1, 2, 3, 4, 5, 6], [True] * 7),
# ([0, 1, 2, 3, 4, 5, 6] * 5, [True] * 7),
# ([0, 1, 2, 3, 4, 5, 6] * 6, [False] * 7),
# ([0, 1, 2] * 3 + [3, 4, 5, 6] * 6, [True] * 3 + [False] * 4),
# ]
# for moves, expected_valid in move_valid_pairs:
# board, player, game = init_board_from_moves(moves)
# assert (np.array(expected_valid) == game.getValidMoves(board, player)).all()
# def test_symmetries():
# """Tests symetric board are produced."""
# board, player, game = init_board_from_moves([0, 0, 1, 0, 6])
# pi = [0.1, 0.2, 0.3]
# (board1, pi1), (board2, pi2) = game.getSymmetries(board, pi)
# assert [0.1, 0.2, 0.3] == pi1 and [0.3, 0.2, 0.1] == pi2
# expected_board1 = textwrap.dedent("""\
# [[ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [-1. 0. 0. 0. 0. 0. 0.]
# [-1. 0. 0. 0. 0. 0. 0.]
# [ 1. 1. 0. 0. 0. 0. 1.]]""")
# assert expected_board1 == game.stringRepresentation(board1)
# expected_board2 = textwrap.dedent("""\
# [[ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 0. 0. -1.]
# [ 0. 0. 0. 0. 0. 0. -1.]
# [ 1. 0. 0. 0. 0. 1. 1.]]""")
# assert expected_board2 == game.stringRepresentation(board2)
# def test_game_ended():
# """Tests game end detection logic based on fixed boards."""
# array_end_state_pairs = [
# (np.array([[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]), 1, 0),
# (np.array([[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 1, 0, 0, 0],
# [0, 0, 1, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]), 1, 1),
# (np.array([[0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 1, 0, 0, 0],
# [0, 0, 1, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]), -1, -1),
# (np.array([[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 1, 0, 0, 0, 0],
# [0, 0, 0, 1, 0, 0, 0],
# [0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0, 1, 0]]), -1, -1),
# (np.array([[0, 0, 0, -1],
# [0, 0, -1, 0],
# [0, -1, 0, 0],
# [-1, 0, 0, 0]]), 1, -1),
# (np.array([[0, 0, 0, 0, 1],
# [0, 0, 0, 1, 0],
# [0, 0, 1, 0, 0],
# [0, 1, 0, 0, 0]]), -1, -1),
# (np.array([[1, 0, 0, 0, 0],
# [0, 1, 0, 0, 0],
# [0, 0, 1, 0, 0],
# [0, 0, 0, 1, 0]]), -1, -1),
# (np.array([[ 0, 0, 0, 0, 0, 0, 0],
# [ 0, 0, 0, -1, 0, 0, 0],
# [ 0, 0, 0, -1, 0, 0, 1],
# [ 0, 0, 0, 1, 1, -1, -1],
# [ 0, 0, 0, -1, 1, 1, 1],
# [ 0, -1, 0, -1, 1, -1, 1]]), -1, 0),
# (np.array([[ 0., 0., 0., 0., 0., 0., 0.],
# [ 0., 0., 0., -1., 0., 0., 0.],
# [ 1., 0., 1., -1., 0., 0., 0.],
# [-1., -1., 1., 1., 0., 0., 0.],
# [ 1., 1., 1., -1., 0., 0., 0.],
# [ 1., -1., 1., -1., 0., -1., 0.]]), -1, -1),
# (np.array([[ 0., 0., 0., 1., 0., 0., 0.,],
# [ 0., 0., 0., 1., 0., 0., 0.,],
# [ 0., 0., 0., -1., 0., 0., 0.,],
# [ 0., 0., 1., 1., -1., 0., -1.,],
# [ 0., 0., -1., 1., 1., 1., 1.,],
# [-1., 0., -1., 1., -1., -1., -1.,],]), 1, 1),
# ]
# for np_pieces, player, expected_end_state in array_end_state_pairs:
# board, player, game = init_board_from_array(np_pieces, player)
# end_state = game.getGameEnded(board, player)
# assert expected_end_state == end_state, ("expected=%s, actual=%s, board=\n%s" % (expected_end_state, end_state, board))
# def test_immutable_move():
# """Test original board is not mutated whtn getNextState() called."""
# board, player, game = init_board_from_moves([1, 2, 3, 3, 4])
# original_board_string = game.stringRepresentation(board)
# new_np_pieces, new_player = game.getNextState(board, 3, -1)
# assert original_board_string == game.stringRepresentation(board)
# assert original_board_string != game.stringRepresentation(new_np_pieces)
| 40.125
| 129
| 0.409583
| 957
| 6,741
| 2.804598
| 0.120167
| 0.225782
| 0.278316
| 0.302534
| 0.497765
| 0.467958
| 0.373323
| 0.305514
| 0.272355
| 0.258942
| 0
| 0.13756
| 0.379914
| 6,741
| 167
| 130
| 40.365269
| 0.504545
| 0.953568
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 1
| 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
| 5
|
b8b181ea7a51c91780a25b8fb1238857dd12be5c
| 1,526
|
py
|
Python
|
tests/deferred_function/test_set_central_widget.py
|
KD-Group/quite
|
49f62a182d5c49bc812290e189a8e05d630bd6ab
|
[
"MIT"
] | 4
|
2019-04-08T04:13:33.000Z
|
2020-12-25T13:15:10.000Z
|
tests/deferred_function/test_set_central_widget.py
|
KD-Group/quite
|
49f62a182d5c49bc812290e189a8e05d630bd6ab
|
[
"MIT"
] | 2
|
2018-05-28T02:59:14.000Z
|
2018-06-27T13:44:01.000Z
|
tests/deferred_function/test_set_central_widget.py
|
SF-Zhou/quite
|
49f62a182d5c49bc812290e189a8e05d630bd6ab
|
[
"MIT"
] | 3
|
2018-01-03T11:29:57.000Z
|
2018-03-12T00:34:21.000Z
|
import unittest
import quite
class MyTestCase(unittest.TestCase):
def test_set_main_window_central_widget(self):
before_central_widget = quite.Widget()
after_central_widget = quite.Widget()
main_window = quite.MainWindow()
main_window.set_central_widget(before_central_widget)
self.assertEqual(id(before_central_widget), id(main_window.center_widget))
main_window.set_central_widget(after_central_widget)
self.assertEqual(id(after_central_widget), id(main_window.center_widget))
def test_set_dock_window_central_widget(self):
before_central_widget = quite.Widget()
after_central_widget = quite.Widget()
dock_widget = quite.DockWidget()
dock_widget.set_central_widget(before_central_widget)
self.assertEqual(id(before_central_widget), id(dock_widget.center_widget))
dock_widget.set_central_widget(after_central_widget)
self.assertEqual(id(after_central_widget), id(dock_widget.center_widget))
def test_set_widget_central_widget(self):
before_central_widget = quite.Widget()
after_central_widget = quite.Widget()
contain_widget = quite.Widget()
contain_widget.set_central_widget(before_central_widget)
self.assertEqual(id(before_central_widget), id(contain_widget.center_widget))
contain_widget.set_central_widget(after_central_widget)
self.assertEqual(id(after_central_widget), id(contain_widget.center_widget))
| 47.6875
| 86
| 0.742464
| 187
| 1,526
| 5.614973
| 0.122995
| 0.334286
| 0.145714
| 0.137143
| 0.87619
| 0.813333
| 0.794286
| 0.662857
| 0.662857
| 0.662857
| 0
| 0
| 0.178244
| 1,526
| 31
| 87
| 49.225806
| 0.837321
| 0
| 0
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 1
| 0.111111
| false
| 0
| 0.074074
| 0
| 0.222222
| 0
| 0
| 0
| 0
| null | 1
| 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
| 5
|
b29f3b81ca2a64241ad7fd43246b48d7f0c39223
| 18
|
py
|
Python
|
fastai_extensions/__init__.py
|
leoitcode/bugslife
|
bc2af0816bc37320a5125f9d901e98503d24b6fe
|
[
"MIT"
] | 1
|
2020-01-25T19:03:17.000Z
|
2020-01-25T19:03:17.000Z
|
fastai_extensions/__init__.py
|
leoitcode/bugslife
|
bc2af0816bc37320a5125f9d901e98503d24b6fe
|
[
"MIT"
] | 8
|
2020-03-07T02:35:28.000Z
|
2022-03-12T00:13:16.000Z
|
fastai_extensions/__init__.py
|
leoitcode/bugslife
|
bc2af0816bc37320a5125f9d901e98503d24b6fe
|
[
"MIT"
] | null | null | null |
from .exp import *
| 18
| 18
| 0.722222
| 3
| 18
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 18
| 1
| 18
| 18
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
a22cd866f84f2796ae85fa720a5067fd3b860a6d
| 88
|
py
|
Python
|
mvc/mvc_exceptions.py
|
jackdbd/design-patterns
|
7675aa8cec3ce1e4bec18bbb4f9f47a71c16d3eb
|
[
"MIT"
] | 55
|
2017-09-18T18:54:18.000Z
|
2022-03-05T09:30:51.000Z
|
mvc/mvc_exceptions.py
|
jackaljack/design-patterns
|
7675aa8cec3ce1e4bec18bbb4f9f47a71c16d3eb
|
[
"MIT"
] | 62
|
2017-09-04T10:31:11.000Z
|
2020-01-07T23:28:01.000Z
|
mvc/mvc_exceptions.py
|
alankuras/design-patterns
|
7675aa8cec3ce1e4bec18bbb4f9f47a71c16d3eb
|
[
"MIT"
] | 27
|
2017-10-01T05:31:34.000Z
|
2022-01-15T17:24:01.000Z
|
class ItemAlreadyStored(Exception):
pass
class ItemNotStored(Exception):
pass
| 12.571429
| 35
| 0.75
| 8
| 88
| 8.25
| 0.625
| 0.393939
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 88
| 6
| 36
| 14.666667
| 0.916667
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
a26f022cde71910969b4135792482d78dbfa7751
| 86
|
py
|
Python
|
python/ParentChildTree/ParentChildTree/setup.py
|
sourabharvikar3/samplePrograms
|
d78278a45fcd20d966d2e7da6db2888f3681e93b
|
[
"MIT"
] | null | null | null |
python/ParentChildTree/ParentChildTree/setup.py
|
sourabharvikar3/samplePrograms
|
d78278a45fcd20d966d2e7da6db2888f3681e93b
|
[
"MIT"
] | null | null | null |
python/ParentChildTree/ParentChildTree/setup.py
|
sourabharvikar3/samplePrograms
|
d78278a45fcd20d966d2e7da6db2888f3681e93b
|
[
"MIT"
] | null | null | null |
from distutils.core import setup
import py2exe
setup(console=['ParentChildTree.py'])
| 17.2
| 37
| 0.802326
| 11
| 86
| 6.272727
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012821
| 0.093023
| 86
| 4
| 38
| 21.5
| 0.871795
| 0
| 0
| 0
| 0
| 0
| 0.209302
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a275798967634f41ddc9ed37a60ac0e5de9251de
| 225
|
py
|
Python
|
src/pyfonycore/bootstrap/config/raw/kernel_class_resolver.py
|
pyfony/core
|
32cb2e959590307fb845ccafec90b8264fdad4ab
|
[
"MIT"
] | null | null | null |
src/pyfonycore/bootstrap/config/raw/kernel_class_resolver.py
|
pyfony/core
|
32cb2e959590307fb845ccafec90b8264fdad4ab
|
[
"MIT"
] | null | null | null |
src/pyfonycore/bootstrap/config/raw/kernel_class_resolver.py
|
pyfony/core
|
32cb2e959590307fb845ccafec90b8264fdad4ab
|
[
"MIT"
] | null | null | null |
from injecta.module import attribute_loader
from pyfonycore.Kernel import Kernel
def resolve(raw_config):
return attribute_loader.load(*raw_config["kernel_class"].split(":")) if "kernel_class" in raw_config else Kernel
| 32.142857
| 116
| 0.8
| 32
| 225
| 5.40625
| 0.59375
| 0.156069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106667
| 225
| 6
| 117
| 37.5
| 0.860697
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0.25
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
a284abfef4373df7a23c1b6ed71bbdd89c0bda1a
| 73
|
py
|
Python
|
src/inception/image/shadow/__init__.py
|
jercytryn/inception
|
f7489cccdd308b5bb2d6ea04588dc67113e995b5
|
[
"MIT"
] | 6
|
2015-06-02T02:41:12.000Z
|
2021-08-16T11:15:58.000Z
|
src/inception/image/shadow/__init__.py
|
jercytryn/inception
|
f7489cccdd308b5bb2d6ea04588dc67113e995b5
|
[
"MIT"
] | 2
|
2015-05-20T08:13:07.000Z
|
2015-05-20T08:13:07.000Z
|
src/inception/image/shadow/__init__.py
|
jercytryn/inception
|
f7489cccdd308b5bb2d6ea04588dc67113e995b5
|
[
"MIT"
] | 2
|
2015-09-11T02:03:11.000Z
|
2016-07-05T21:13:56.000Z
|
"""
Shadow-based image manipulations
"""
from shadow import create_shadow
| 18.25
| 32
| 0.794521
| 9
| 73
| 6.333333
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109589
| 73
| 4
| 33
| 18.25
| 0.876923
| 0.438356
| 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
| 1
| 0
|
0
| 5
|
a2a66c00465d0063eb22a61d3adaa0d78885d914
| 558
|
py
|
Python
|
src/30-binary-search/py/__main__.py
|
MichaelZalla/data-structures-review
|
4ef1a70cf28031160eafc81fe3557791c58aed32
|
[
"MIT"
] | null | null | null |
src/30-binary-search/py/__main__.py
|
MichaelZalla/data-structures-review
|
4ef1a70cf28031160eafc81fe3557791c58aed32
|
[
"MIT"
] | null | null | null |
src/30-binary-search/py/__main__.py
|
MichaelZalla/data-structures-review
|
4ef1a70cf28031160eafc81fe3557791c58aed32
|
[
"MIT"
] | null | null | null |
from BinarySearch import binarySearch
# Binary search, base case: Empty sequence
assert(binarySearch([], 4) == -1)
# Binary search, base case: 1-element sequence
assert(binarySearch([2], 1) == -1)
assert(binarySearch([2], 2) == 0)
# Binary search, recursive case: 3-element sequence
assert(binarySearch([5,7,8], 4) == -1)
assert(binarySearch([5,7,8], 5) == 0)
assert(binarySearch([5,7,8], 7) == 1)
assert(binarySearch([5,7,8], 8) == 2)
assert(binarySearch([5,7,8], 9) == -1)
# Binary search, recursive case:
assert(binarySearch([0,0,0,0,0], 0) == 2)
| 24.26087
| 51
| 0.664875
| 86
| 558
| 4.313953
| 0.244186
| 0.436658
| 0.256065
| 0.269542
| 0.304582
| 0.118598
| 0
| 0
| 0
| 0
| 0
| 0.086777
| 0.132616
| 558
| 22
| 52
| 25.363636
| 0.679752
| 0.297491
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.9
| 1
| 0
| true
| 0
| 0.1
| 0
| 0.1
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a2c9ad6d0cb5bf34aca6ee335b827cee5f11c455
| 33
|
py
|
Python
|
nes/bus/devices/cartridge/cartridges/__init__.py
|
Hexadorsimal/pynes
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | 1
|
2017-05-13T18:57:09.000Z
|
2017-05-13T18:57:09.000Z
|
nes/bus/devices/cartridge/cartridges/__init__.py
|
Hexadorsimal/py6502
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | 7
|
2020-10-24T17:16:56.000Z
|
2020-11-01T14:10:23.000Z
|
nes/bus/devices/cartridge/cartridges/__init__.py
|
Hexadorsimal/pynes
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | null | null | null |
from .nrom import NromCartridge
| 11
| 31
| 0.818182
| 4
| 33
| 6.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151515
| 33
| 2
| 32
| 16.5
| 0.964286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
a2f54ff328634138d979b4a2a80787cae12e1010
| 1,085
|
py
|
Python
|
tools/leetcode.054.Spiral Matrix/leetcode.054.Spiral Matrix.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | 4
|
2015-10-10T00:30:55.000Z
|
2020-07-27T19:45:54.000Z
|
tools/leetcode.054.Spiral Matrix/leetcode.054.Spiral Matrix.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | null | null | null |
tools/leetcode.054.Spiral Matrix/leetcode.054.Spiral Matrix.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | null | null | null |
class Solution:
# @param {integer[][]} matrix
# @return {integer[]}
def spiralOrder(self, matrix):
if not matrix: return []
u = 0
o = len(matrix)
l = 0
r = len(matrix[0])
res = []
while u < o and l < r:
i = u
j = l
if j == r: break
while j < r:
res.append(matrix[i][j])
j += 1
j -= 1
i += 1
if i == o: break
while i < o:
res.append(matrix[i][j])
i += 1
i -= 1
j -= 1
if j == l-1:
break
while j >= l:
print(matrix[i][j])
res.append(matrix[i][j])
j -= 1
j += 1
i -= 1
if i == u: break
while i > u:
res.append(matrix[i][j])
i -= 1
u += 1
o -= 1
l += 1
r -= 1
return res
| 1,085
| 1,085
| 0.287558
| 120
| 1,085
| 2.6
| 0.216667
| 0.112179
| 0.128205
| 0.205128
| 0.288462
| 0.288462
| 0.288462
| 0.166667
| 0.166667
| 0.166667
| 0
| 0.041284
| 0.598157
| 1,085
| 1
| 1,085
| 1,085
| 0.674312
| 0.043318
| 0
| 0.35
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.025
| false
| 0
| 0
| 0
| 0.075
| 0.025
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
0c1d2e0da713730edeb96ef85beea64c6583090d
| 564
|
py
|
Python
|
doc/src/modules/galgebra/GA/BasicGAtest.py
|
sn6uv/sympy
|
5b149c2f72847e4785c65358b09d99b29f101dd5
|
[
"BSD-3-Clause"
] | 2
|
2015-05-11T12:26:38.000Z
|
2016-08-19T00:11:03.000Z
|
doc/src/modules/galgebra/GA/BasicGAtest.py
|
goodok/sympy
|
de84ed2139125a755ea7b6ba91d945d9fbbe5ed9
|
[
"BSD-3-Clause"
] | null | null | null |
doc/src/modules/galgebra/GA/BasicGAtest.py
|
goodok/sympy
|
de84ed2139125a755ea7b6ba91d945d9fbbe5ed9
|
[
"BSD-3-Clause"
] | null | null | null |
a,b,c,d,e = MV.setup('a b c d e')
MV.set_str_format(1)
print 'e|(a^b) =',e|(a^b)
print 'e|(a^b^c) =',e|(a^b^c)
print 'a*(b^c)-b*(a^c)+c*(a^b) =',a*(b^c)-b*(a^c)+c*(a^b)
print 'e|(a^b^c^d) =',e|(a^b^c^d)
print -d*(a^b^c)+c*(a^b^d)-b*(a^c^d)+a*(b^c^d)
print (a^b)|(c^d)
e|(a^b) = {-(b.e)}a
+{(a.e)}b
e|(a^b^c) = {(c.e)}a^b
+{-(b.e)}a^c
+{(a.e)}b^c
a*(b^c)-b*(a^c)+c*(a^b) = {3}a^b^c
e|(a^b^c^d) = {-(d.e)}a^b^c
+{(c.e)}a^b^d
+{-(b.e)}a^c^d
+{(a.e)}b^c^d
{4}a^b^c^d
{(a.d)*(b.c) - (a.c)*(b.d)}1
| 19.448276
| 65
| 0.359929
| 162
| 564
| 1.240741
| 0.08642
| 0.258706
| 0.253731
| 0.159204
| 0.61194
| 0.522388
| 0.422886
| 0.21393
| 0.134328
| 0
| 0
| 0.008969
| 0.20922
| 564
| 28
| 66
| 20.142857
| 0.441704
| 0
| 0
| 0
| 0
| 0
| 0.118794
| 0.04078
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.3
| 0
| 0
| 1
| null | 1
| 1
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
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