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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d1f311807379af0e44d3a92b78c48e68bd3314bb
| 98
|
py
|
Python
|
mysite/storehouse/apis/views/__init__.py
|
othmankurdi/storehouse
|
c702abac6ad7bceef59913485ae7ead4f0f884d7
|
[
"MIT"
] | null | null | null |
mysite/storehouse/apis/views/__init__.py
|
othmankurdi/storehouse
|
c702abac6ad7bceef59913485ae7ead4f0f884d7
|
[
"MIT"
] | 3
|
2021-11-28T10:18:00.000Z
|
2021-11-28T10:39:55.000Z
|
mysite/storehouse/apis/views/__init__.py
|
othmankurdi/storehouse
|
c702abac6ad7bceef59913485ae7ead4f0f884d7
|
[
"MIT"
] | null | null | null |
from ._user import UserView
from ._category import CategoryView
from ._product import ProductView
| 24.5
| 35
| 0.846939
| 12
| 98
| 6.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122449
| 98
| 3
| 36
| 32.666667
| 0.930233
| 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
|
ae2f269d8e3b2b34bfbc2f98c4d7af13e2e35ea5
| 101
|
py
|
Python
|
server/backend/app/db/utils/__init__.py
|
chemetc/maskcam
|
4841c2c49235844765e8c2164f5dd03a7d28bdad
|
[
"MIT"
] | 179
|
2021-03-16T15:15:49.000Z
|
2022-03-30T14:13:14.000Z
|
server/backend/app/db/utils/__init__.py
|
chemetc/maskcam
|
4841c2c49235844765e8c2164f5dd03a7d28bdad
|
[
"MIT"
] | 22
|
2021-04-10T17:04:47.000Z
|
2022-03-15T22:48:16.000Z
|
server/backend/app/db/utils/__init__.py
|
chemetc/maskcam
|
4841c2c49235844765e8c2164f5dd03a7d28bdad
|
[
"MIT"
] | 63
|
2021-03-24T13:35:32.000Z
|
2022-02-23T10:10:42.000Z
|
from .enums import StatisticTypeEnum
from .utils import convert_timestamp_to_datetime, get_enum_type
| 33.666667
| 63
| 0.881188
| 14
| 101
| 6
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089109
| 101
| 2
| 64
| 50.5
| 0.913043
| 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
|
ae7072011b1b0725667362b68186c16e02907b59
| 103
|
py
|
Python
|
pythonx/lints/sh/__init__.py
|
maralla/validator.vim
|
fd5ec0891cbd035bd572e74d684b8afd852b87bf
|
[
"MIT"
] | 255
|
2016-09-08T12:12:26.000Z
|
2022-03-10T01:50:06.000Z
|
pythonx/lints/sh/__init__.py
|
maralla/vim-fixup
|
fd5ec0891cbd035bd572e74d684b8afd852b87bf
|
[
"MIT"
] | 56
|
2016-09-09T05:53:24.000Z
|
2020-11-11T16:02:05.000Z
|
pythonx/lints/sh/__init__.py
|
maralla/vim-linter
|
fd5ec0891cbd035bd572e74d684b8afd852b87bf
|
[
"MIT"
] | 23
|
2016-09-09T13:37:51.000Z
|
2019-04-08T22:31:24.000Z
|
# -*- coding: utf-8 -*-
from .sh import ShLint # noqa
from .shellcheck import ShellcheckLint # noqa
| 20.6
| 46
| 0.679612
| 13
| 103
| 5.384615
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012048
| 0.194175
| 103
| 4
| 47
| 25.75
| 0.831325
| 0.300971
| 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
|
881e1f558a2d7a8c337dcb8a6d540f63717c008a
| 68
|
py
|
Python
|
graystillplays.py
|
KaynWest/graystillplays-python-module
|
513e2f67c840ea6a9d06737f8ef5e14a7b31a499
|
[
"Apache-2.0"
] | null | null | null |
graystillplays.py
|
KaynWest/graystillplays-python-module
|
513e2f67c840ea6a9d06737f8ef5e14a7b31a499
|
[
"Apache-2.0"
] | null | null | null |
graystillplays.py
|
KaynWest/graystillplays-python-module
|
513e2f67c840ea6a9d06737f8ef5e14a7b31a499
|
[
"Apache-2.0"
] | null | null | null |
def nophysics():
print("We don't need physics where we're going")
| 17
| 49
| 0.705882
| 12
| 68
| 4
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.161765
| 68
| 3
| 50
| 22.666667
| 0.842105
| 0
| 0
| 0
| 0
| 0
| 0.573529
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
885072872ef109d95c17482f73f985b37b9810c3
| 118
|
py
|
Python
|
shared/tasks.py
|
ByK95/discount_code
|
76b7babfbccaa211d842f7b0f5c55e88e7c355cb
|
[
"MIT"
] | 1
|
2022-01-20T10:30:05.000Z
|
2022-01-20T10:30:05.000Z
|
shared/tasks.py
|
ByK95/discount_code
|
76b7babfbccaa211d842f7b0f5c55e88e7c355cb
|
[
"MIT"
] | null | null | null |
shared/tasks.py
|
ByK95/discount_code
|
76b7babfbccaa211d842f7b0f5c55e88e7c355cb
|
[
"MIT"
] | null | null | null |
import os
from celery import Celery
celery = Celery("web", broker=os.environ["CELERY_BROKER_URL"], backend="rpc://")
| 23.6
| 80
| 0.737288
| 17
| 118
| 5
| 0.588235
| 0.282353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101695
| 118
| 4
| 81
| 29.5
| 0.801887
| 0
| 0
| 0
| 0
| 0
| 0.220339
| 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
|
8858863c8ab74a30a2943b67ffa2e55b853ccf36
| 52
|
py
|
Python
|
experiment_tracker/__init__.py
|
MarkusZopf/Experiment-Tracker
|
02744a32bef4f0019af8cf3658de9628513661b5
|
[
"MIT"
] | 3
|
2020-07-23T11:18:57.000Z
|
2021-03-27T22:33:28.000Z
|
experiment_tracker/__init__.py
|
MarkusZopf/Experiment-Tracker
|
02744a32bef4f0019af8cf3658de9628513661b5
|
[
"MIT"
] | null | null | null |
experiment_tracker/__init__.py
|
MarkusZopf/Experiment-Tracker
|
02744a32bef4f0019af8cf3658de9628513661b5
|
[
"MIT"
] | null | null | null |
from experiment_tracker.Experiment import Experiment
| 52
| 52
| 0.923077
| 6
| 52
| 7.833333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057692
| 52
| 1
| 52
| 52
| 0.959184
| 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
|
88606ca6764ffb135cf685129a85888ac95e94ce
| 402
|
gyp
|
Python
|
binding.gyp
|
tauu/win-audio
|
5ccb6a4c1e8b6eb336ba11d1568edd4a5ecd0de1
|
[
"MIT"
] | null | null | null |
binding.gyp
|
tauu/win-audio
|
5ccb6a4c1e8b6eb336ba11d1568edd4a5ecd0de1
|
[
"MIT"
] | null | null | null |
binding.gyp
|
tauu/win-audio
|
5ccb6a4c1e8b6eb336ba11d1568edd4a5ecd0de1
|
[
"MIT"
] | null | null | null |
{
"targets": [
{
"target_name": "audio",
'conditions': [
['OS=="win"', {
"sources": ["audio-napi.cc"],
"cflags" : [ "-lole32", "-loleaut32"]
}],
['OS=="linux"', {
"sources": ["audio-napi_dummy.cc"]
}],
['OS=="mac"', {
"sources": ["audio-napi_dummy.cc"]
}],
]
}
]
}
| 20.1
| 48
| 0.340796
| 28
| 402
| 4.785714
| 0.571429
| 0.268657
| 0.358209
| 0.313433
| 0.343284
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017167
| 0.420398
| 402
| 19
| 49
| 21.157895
| 0.55794
| 0
| 0
| 0.263158
| 0
| 0
| 0.409922
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
886f0bc4004d64a5332c6afb24dfcc22367b8725
| 174
|
py
|
Python
|
uf/third.py
|
dumpmemory/unif
|
a301d7207791664fb107edda607c55f2d50dd17d
|
[
"Apache-2.0"
] | null | null | null |
uf/third.py
|
dumpmemory/unif
|
a301d7207791664fb107edda607c55f2d50dd17d
|
[
"Apache-2.0"
] | null | null | null |
uf/third.py
|
dumpmemory/unif
|
a301d7207791664fb107edda607c55f2d50dd17d
|
[
"Apache-2.0"
] | null | null | null |
""" Version control of dependencies. """
import tensorflow as tf
if tf.__version__.startswith("2"):
import tensorflow.compat.v1 as tf
tf.disable_eager_execution()
| 19.333333
| 40
| 0.729885
| 23
| 174
| 5.26087
| 0.695652
| 0.264463
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013699
| 0.16092
| 174
| 8
| 41
| 21.75
| 0.815068
| 0.183908
| 0
| 0
| 0
| 0
| 0.007463
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
888d80ebf6ecf7607f89ac742b73ffbe57d097b6
| 1,440
|
py
|
Python
|
python/anyascii/_data/_0d1.py
|
casept/anyascii
|
d4f426b91751254b68eaa84c6cd23099edd668e6
|
[
"ISC"
] | null | null | null |
python/anyascii/_data/_0d1.py
|
casept/anyascii
|
d4f426b91751254b68eaa84c6cd23099edd668e6
|
[
"ISC"
] | null | null | null |
python/anyascii/_data/_0d1.py
|
casept/anyascii
|
d4f426b91751254b68eaa84c6cd23099edd668e6
|
[
"ISC"
] | null | null | null |
b='Tyal Tyalg Tyalm Tyalb Tyals Tyalt Tyalp Tyalh Tyam Tyab Tyabs Tyas Tyass Tyang Tyaj Tyach Tyak Tyat Tyap Tyah Tyae Tyaeg Tyaekk Tyaegs Tyaen Tyaenj Tyaenh Tyaed Tyael Tyaelg Tyaelm Tyaelb Tyaels Tyaelt Tyaelp Tyaelh Tyaem Tyaeb Tyaebs Tyaes Tyaess Tyaeng Tyaej Tyaech Tyaek Tyaet Tyaep Tyaeh Teo Teog Teokk Teogs Teon Teonj Teonh Teod Teol Teolg Teolm Teolb Teols Teolt Teolp Teolh Teom Teob Teobs Teos Teoss Teong Teoj Teoch Teok Teot Teop Teoh Te Teg Tekk Tegs Ten Tenj Tenh Ted Tel Telg Telm Telb Tels Telt Telp Telh Tem Teb Tebs Tes Tess Teng Tej Tech Tek Tet Tep Teh Tyeo Tyeog Tyeokk Tyeogs Tyeon Tyeonj Tyeonh Tyeod Tyeol Tyeolg Tyeolm Tyeolb Tyeols Tyeolt Tyeolp Tyeolh Tyeom Tyeob Tyeobs Tyeos Tyeoss Tyeong Tyeoj Tyeoch Tyeok Tyeot Tyeop Tyeoh Tye Tyeg Tyekk Tyegs Tyen Tyenj Tyenh Tyed Tyel Tyelg Tyelm Tyelb Tyels Tyelt Tyelp Tyelh Tyem Tyeb Tyebs Tyes Tyess Tyeng Tyej Tyech Tyek Tyet Tyep Tyeh To Tog Tokk Togs Ton Tonj Tonh Tod Tol Tolg Tolm Tolb Tols Tolt Tolp Tolh Tom Tob Tobs Tos Toss Tong Toj Toch Tok Tot Top Toh Twa Twag Twakk Twags Twan Twanj Twanh Twad Twal Twalg Twalm Twalb Twals Twalt Twalp Twalh Twam Twab Twabs Twas Twass Twang Twaj Twach Twak Twat Twap Twah Twae Twaeg Twaekk Twaegs Twaen Twaenj Twaenh Twaed Twael Twaelg Twaelm Twaelb Twaels Twaelt Twaelp Twaelh Twaem Twaeb Twaebs Twaes Twaess Twaeng Twaej Twaech Twaek Twaet Twaep Twaeh Toe Toeg Toekk Toegs Toen Toenj Toenh Toed Toel Toelg Toelm Toelb'
| 1,440
| 1,440
| 0.820833
| 257
| 1,440
| 4.599222
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177083
| 1,440
| 1
| 1,440
| 1,440
| 0.997468
| 0
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| 0
| 1
| 0.99653
| 0
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| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
88aaac76c87445923cb8b5add3c69ab4982e771e
| 211
|
py
|
Python
|
tools/short_all.py
|
jcrocholl/nxdom
|
16e93561a0cc5b5aa5be88d60c2d19d018d92dfa
|
[
"MIT"
] | 14
|
2015-02-25T18:03:32.000Z
|
2021-11-16T11:10:44.000Z
|
tools/short_all.py
|
jcrocholl/nxdom
|
16e93561a0cc5b5aa5be88d60c2d19d018d92dfa
|
[
"MIT"
] | null | null | null |
tools/short_all.py
|
jcrocholl/nxdom
|
16e93561a0cc5b5aa5be88d60c2d19d018d92dfa
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
LETTERS = 'abcdefghijklmnopqrstuvwxyz'
for c1 in LETTERS:
for c2 in LETTERS:
for c3 in LETTERS:
for c4 in LETTERS:
print ''.join((c1, c2, c3, c4))
| 21.1
| 47
| 0.57346
| 28
| 211
| 4.321429
| 0.5
| 0.297521
| 0.297521
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055944
| 0.322275
| 211
| 9
| 48
| 23.444444
| 0.79021
| 0.094787
| 0
| 0
| 0
| 0
| 0.136842
| 0.136842
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.166667
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
88b13c4085766f836676ece937081cd869a1beb7
| 304
|
py
|
Python
|
test/integration/version-5/testinfra/test_kibana.py
|
sensidev/kibana-formula
|
73e6f085d9d45a08d59669bffc983cc48d30cb98
|
[
"Apache-2.0"
] | 10
|
2016-11-01T14:57:39.000Z
|
2021-11-28T21:00:03.000Z
|
test/integration/version-5/testinfra/test_kibana.py
|
sensidev/kibana-formula
|
73e6f085d9d45a08d59669bffc983cc48d30cb98
|
[
"Apache-2.0"
] | 22
|
2016-09-05T13:46:40.000Z
|
2022-01-13T16:47:24.000Z
|
test/integration/version-5/testinfra/test_kibana.py
|
sensidev/kibana-formula
|
73e6f085d9d45a08d59669bffc983cc48d30cb98
|
[
"Apache-2.0"
] | 50
|
2016-08-02T05:51:56.000Z
|
2021-11-28T21:00:04.000Z
|
import testinfra
def test_package_in_installed(Package):
kibana = Package('kibana')
assert kibana.is_installed
assert kibana.version.startswith('5.')
def test_service_is_running_and_enabled(Service):
kibana = Service('kibana')
assert kibana.is_running
assert kibana.is_enabled
| 23.384615
| 49
| 0.759868
| 39
| 304
| 5.641026
| 0.435897
| 0.218182
| 0.190909
| 0.181818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003891
| 0.154605
| 304
| 12
| 50
| 25.333333
| 0.85214
| 0
| 0
| 0
| 0
| 0
| 0.046053
| 0
| 0
| 0
| 0
| 0
| 0.444444
| 1
| 0.222222
| false
| 0
| 0.111111
| 0
| 0.333333
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
88b2181f7da7d917a5b19f0c1a49e19f7f3716b5
| 33
|
py
|
Python
|
tkcomponents/__init__.py
|
immijimmi/tkcomponents
|
c9f5d08ddf6d78a80927fa89727e71eb3e09715f
|
[
"MIT"
] | null | null | null |
tkcomponents/__init__.py
|
immijimmi/tkcomponents
|
c9f5d08ddf6d78a80927fa89727e71eb3e09715f
|
[
"MIT"
] | null | null | null |
tkcomponents/__init__.py
|
immijimmi/tkcomponents
|
c9f5d08ddf6d78a80927fa89727e71eb3e09715f
|
[
"MIT"
] | null | null | null |
from .component import Component
| 16.5
| 32
| 0.848485
| 4
| 33
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.965517
| 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
|
ee0323c6c88f356ac323383e4ac98f810a717ae7
| 143
|
py
|
Python
|
edit/core/hook/evaluation/__init__.py
|
tpoisonooo/basicVSR_mge
|
53df836a7dcc075083ef7c9ff7cabea69fec3192
|
[
"Apache-2.0"
] | 28
|
2021-03-23T09:00:33.000Z
|
2022-03-10T03:55:00.000Z
|
edit/core/hook/evaluation/__init__.py
|
tpoisonooo/basicVSR_mge
|
53df836a7dcc075083ef7c9ff7cabea69fec3192
|
[
"Apache-2.0"
] | 2
|
2021-04-17T20:08:55.000Z
|
2022-02-01T17:48:55.000Z
|
edit/core/hook/evaluation/__init__.py
|
tpoisonooo/basicVSR_mge
|
53df836a7dcc075083ef7c9ff7cabea69fec3192
|
[
"Apache-2.0"
] | 5
|
2021-05-19T07:35:56.000Z
|
2022-01-13T02:11:50.000Z
|
from .metrics import (connectivity, gradient_error, mse, niqe, psnr, ssim, sad, reorder_image, fid, lpips)
from .eval_hooks import EvalIterHook
| 71.5
| 106
| 0.797203
| 20
| 143
| 5.55
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111888
| 143
| 2
| 107
| 71.5
| 0.874016
| 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
|
ee17930aa680a1631657e231ed3ccb4570e33c64
| 28
|
py
|
Python
|
pyiArduinoI2Crelay/__init__.py
|
tremaru/Py_iarduino_I2C_Relay
|
a2caefb84dc52f97c72b7c71dd7abf8e2f63c800
|
[
"MIT"
] | null | null | null |
pyiArduinoI2Crelay/__init__.py
|
tremaru/Py_iarduino_I2C_Relay
|
a2caefb84dc52f97c72b7c71dd7abf8e2f63c800
|
[
"MIT"
] | null | null | null |
pyiArduinoI2Crelay/__init__.py
|
tremaru/Py_iarduino_I2C_Relay
|
a2caefb84dc52f97c72b7c71dd7abf8e2f63c800
|
[
"MIT"
] | 1
|
2021-11-09T13:16:14.000Z
|
2021-11-09T13:16:14.000Z
|
name = "pyiArduinoI2Crelay"
| 14
| 27
| 0.785714
| 2
| 28
| 11
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04
| 0.107143
| 28
| 1
| 28
| 28
| 0.84
| 0
| 0
| 0
| 0
| 0
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ee1df833d45c4dca461db279278e1910ab6110fb
| 144
|
py
|
Python
|
task_error.py
|
paritajohari/TaskPlanner
|
fb931e53293cf0d1ae3145b050559a57dc93c427
|
[
"MIT"
] | null | null | null |
task_error.py
|
paritajohari/TaskPlanner
|
fb931e53293cf0d1ae3145b050559a57dc93c427
|
[
"MIT"
] | null | null | null |
task_error.py
|
paritajohari/TaskPlanner
|
fb931e53293cf0d1ae3145b050559a57dc93c427
|
[
"MIT"
] | null | null | null |
class TaskError(Exception):
def __init__(self, msg):
self.msg = msg
def print_msg(self):
return ("Exception raised: ", self.msg)
| 14.4
| 41
| 0.659722
| 19
| 144
| 4.736842
| 0.526316
| 0.233333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.208333
| 144
| 10
| 42
| 14.4
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0.124138
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.2
| 0.8
| 0.2
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
ee34edae869222fc5c2aeba65da6caca09ef010c
| 36
|
py
|
Python
|
common/__init__.py
|
delpapa/CritSORN
|
cdad55d55f39e04f568ca1bc0c6036bec8db08fb
|
[
"MIT"
] | null | null | null |
common/__init__.py
|
delpapa/CritSORN
|
cdad55d55f39e04f568ca1bc0c6036bec8db08fb
|
[
"MIT"
] | null | null | null |
common/__init__.py
|
delpapa/CritSORN
|
cdad55d55f39e04f568ca1bc0c6036bec8db08fb
|
[
"MIT"
] | null | null | null |
import utils
utils.backup(__file__)
| 12
| 22
| 0.833333
| 5
| 36
| 5.2
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 2
| 23
| 18
| 0.787879
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
ee3785eaedffed4ab3623bb25b5e5a939db130dc
| 296
|
py
|
Python
|
muscle_manager_protocol/__init__.py
|
DongweiYe/muscle3
|
0c2fcf5f62995b8639fc84ce1b983c8a8e6248d0
|
[
"Apache-2.0"
] | 11
|
2018-03-12T10:43:46.000Z
|
2020-06-01T10:58:56.000Z
|
muscle_manager_protocol/__init__.py
|
DongweiYe/muscle3
|
0c2fcf5f62995b8639fc84ce1b983c8a8e6248d0
|
[
"Apache-2.0"
] | 85
|
2018-03-03T15:10:56.000Z
|
2022-03-18T14:05:14.000Z
|
muscle_manager_protocol/__init__.py
|
DongweiYe/muscle3
|
0c2fcf5f62995b8639fc84ce1b983c8a8e6248d0
|
[
"Apache-2.0"
] | 6
|
2018-03-12T10:47:11.000Z
|
2022-02-03T13:44:07.000Z
|
import os
import sys
# The gRPC generated code contains an absolute import. So either it
# needs to be in the top-level directory, or the user needs to modify
# their PYTHONPATH environment variable, or we add it to the path here.
sys.path.append(os.path.dirname(os.path.expanduser(__file__)))
| 32.888889
| 71
| 0.777027
| 51
| 296
| 4.431373
| 0.686275
| 0.061947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155405
| 296
| 8
| 72
| 37
| 0.904
| 0.685811
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
ee3b6c302c663efbea4d850eed169962f6559d39
| 401
|
py
|
Python
|
backend/userapp/models.py
|
Lenend-KPU/LBS-Platform
|
75ba24db8969248e74e9d974638977de1c0bc36a
|
[
"MIT"
] | 15
|
2020-12-23T13:56:49.000Z
|
2021-12-10T11:04:23.000Z
|
backend/userapp/models.py
|
Lenend-KPU/LBS-Platform
|
75ba24db8969248e74e9d974638977de1c0bc36a
|
[
"MIT"
] | 41
|
2021-03-19T07:51:48.000Z
|
2021-11-22T09:45:46.000Z
|
backend/userapp/models.py
|
Lenend-KPU/LBS-Platform
|
75ba24db8969248e74e9d974638977de1c0bc36a
|
[
"MIT"
] | 3
|
2021-03-24T15:18:24.000Z
|
2021-09-11T14:51:35.000Z
|
from django.db import models
max_length = 100
class User(models.Model):
user_name = models.CharField(max_length=max_length)
# 헬퍼 함수를 통해 해시로 변환한 값
user_password = models.CharField(max_length=max_length)
user_email = models.CharField(max_length=max_length, unique=True)
user_address = models.CharField(max_length=max_length)
def __str__(self):
return str(super().pk)
| 26.733333
| 69
| 0.735661
| 59
| 401
| 4.711864
| 0.508475
| 0.291367
| 0.258993
| 0.345324
| 0.47482
| 0.47482
| 0
| 0
| 0
| 0
| 0
| 0.009036
| 0.17207
| 401
| 14
| 70
| 28.642857
| 0.828313
| 0.047382
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0.111111
| 0.111111
| 0.111111
| 0.888889
| 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
| 1
| 0
| 1
| 0
| 0
|
0
| 5
|
ee54821dca0e380b159e533f4578aa42f3b1b2ad
| 75
|
py
|
Python
|
tcli.py
|
farooq-teqniqly/typer-cli
|
2c24c62e9e31cfc84d11a8019eae04be0ef5b013
|
[
"MIT"
] | null | null | null |
tcli.py
|
farooq-teqniqly/typer-cli
|
2c24c62e9e31cfc84d11a8019eae04be0ef5b013
|
[
"MIT"
] | null | null | null |
tcli.py
|
farooq-teqniqly/typer-cli
|
2c24c62e9e31cfc84d11a8019eae04be0ef5b013
|
[
"MIT"
] | null | null | null |
from tcli import create_cli
if __name__ == "__main__":
create_cli()()
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| 27
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0
| 5
|
ee64536e1ba8d43629ec9cd3fd49a1fcb76e2d4d
| 201,094
|
py
|
Python
|
countyFDARestaurants.py
|
tgadf/census
|
d9ebaba6823cb537735d77bcaea5e411b2725d9b
|
[
"MIT"
] | null | null | null |
countyFDARestaurants.py
|
tgadf/census
|
d9ebaba6823cb537735d77bcaea5e411b2725d9b
|
[
"MIT"
] | null | null | null |
countyFDARestaurants.py
|
tgadf/census
|
d9ebaba6823cb537735d77bcaea5e411b2725d9b
|
[
"MIT"
] | null | null | null |
def getCountyFDARestaurantsData(county):
data = {"01001": {"FFRPTH14": 0.649878148, "FSRPTH14": 0.523512952}, "01003": {"FFRPTH14": 0.659633903, "FSRPTH14": 1.104387065}, "01005": {"FFRPTH14": 0.818239298, "FSRPTH14": 0.55789043}, "01007": {"FFRPTH14": 0.22216297899999998, "FSRPTH14": 0.22216297899999998}, "01009": {"FFRPTH14": 0.363831667, "FSRPTH14": 0.259879762}, "01011": {"FFRPTH14": 0.2787068, "FSRPTH14": 0.092902267}, "01013": {"FFRPTH14": 0.837603469, "FSRPTH14": 0.492707923}, "01015": {"FFRPTH14": 0.888574485, "FSRPTH14": 0.66427413}, "01017": {"FFRPTH14": 0.763000352, "FSRPTH14": 0.469538678}, "01019": {"FFRPTH14": 0.576103238, "FSRPTH14": 0.42247570799999995}, "01021": {"FFRPTH14": 0.455259384, "FSRPTH14": 0.409733446}, "01023": {"FFRPTH14": 0.45034902, "FSRPTH14": 0.37529085}, "01025": {"FFRPTH14": 1.042293045, "FSRPTH14": 0.60132291}, "01027": {"FFRPTH14": 0.295159386, "FSRPTH14": 0.442739079}, "01029": {"FFRPTH14": 0.530503979, "FSRPTH14": 0.132625995}, "01031": {"FFRPTH14": 0.667858335, "FSRPTH14": 0.550000982}, "01033": {"FFRPTH14": 0.953376235, "FSRPTH14": 0.696698018}, "01035": {"FFRPTH14": 0.5524861879999999, "FSRPTH14": 0.31570639300000003}, "01037": {"FFRPTH14": 0.091861106, "FSRPTH14": 0.0}, "01039": {"FFRPTH14": 0.633011552, "FSRPTH14": 0.474758664}, "01041": {"FFRPTH14": 0.357730557, "FSRPTH14": 0.429276669}, "01043": {"FFRPTH14": 0.651994735, "FSRPTH14": 0.590485798}, "01045": {"FFRPTH14": 0.687090777, "FSRPTH14": 0.6062565679999999}, "01047": {"FFRPTH14": 0.45551533200000005, "FSRPTH14": 0.359617367}, "01049": {"FFRPTH14": 0.605079856, "FSRPTH14": 0.43622036200000003}, "01051": {"FFRPTH14": 0.555713351, "FSRPTH14": 0.469269052}, "01053": {"FFRPTH14": 0.715554024, "FSRPTH14": 0.530040018}, "01055": {"FFRPTH14": 0.7630564759999999, "FSRPTH14": 0.550559736}, "01057": {"FFRPTH14": 0.53336494, "FSRPTH14": 0.53336494}, "01059": {"FFRPTH14": 0.5696022279999999, "FSRPTH14": 0.506313091}, "01061": {"FFRPTH14": 0.37436358200000003, "FSRPTH14": 0.41179994}, "01063": {"FFRPTH14": 0.11691804, "FSRPTH14": 0.233836081}, "01065": {"FFRPTH14": 0.461011591, "FSRPTH14": 0.263435195}, "01067": {"FFRPTH14": 0.581733566, "FSRPTH14": 0.639906923}, "01069": {"FFRPTH14": 0.930964652, "FSRPTH14": 0.739013177}, "01071": {"FFRPTH14": 0.550650337, "FSRPTH14": 0.474698566}, "01073": {"FFRPTH14": 0.9715599290000001, "FSRPTH14": 0.5690132920000001}, "01075": {"FFRPTH14": 0.42595484899999997, "FSRPTH14": 0.7099247479999999}, "01077": {"FFRPTH14": 0.773395205, "FSRPTH14": 0.751912005}, "01079": {"FFRPTH14": 0.477940078, "FSRPTH14": 0.5078113329999999}, "01081": {"FFRPTH14": 0.82979482, "FSRPTH14": 0.706622152}, "01083": {"FFRPTH14": 0.561754436, "FSRPTH14": 0.429576922}, "01085": {"FFRPTH14": 0.0, "FSRPTH14": 0.189035917}, "01087": {"FFRPTH14": 0.46332046299999996, "FSRPTH14": 0.10296010300000001}, "01089": {"FFRPTH14": 0.9420523609999999, "FSRPTH14": 0.6451631320000001}, "01091": {"FFRPTH14": 0.94480358, "FSRPTH14": 0.447538538}, "01093": {"FFRPTH14": 0.561593604, "FSRPTH14": 0.660698358}, "01095": {"FFRPTH14": 0.750243036, "FSRPTH14": 0.6340081999999999}, "01097": {"FFRPTH14": 0.60945792, "FSRPTH14": 0.522736635}, "01099": {"FFRPTH14": 0.410078826, "FSRPTH14": 0.45564314}, "01101": {"FFRPTH14": 0.928427112, "FSRPTH14": 0.623372489}, "01103": {"FFRPTH14": 0.827710753, "FSRPTH14": 0.560167883}, "01105": {"FFRPTH14": 0.10177081199999999, "FSRPTH14": 0.610624873}, "01107": {"FFRPTH14": 0.39283083700000004, "FSRPTH14": 0.294623128}, "01109": {"FFRPTH14": 0.9883494559999999, "FSRPTH14": 0.658899638}, "01111": {"FFRPTH14": 0.399307866, "FSRPTH14": 0.310572785}, "01113": {"FFRPTH14": 0.7381559520000001, "FSRPTH14": 0.352301704}, "01115": {"FFRPTH14": 0.5651868, "FSRPTH14": 0.5075146779999999}, "01117": {"FFRPTH14": 0.6822965809999999, "FSRPTH14": 0.6387457360000001}, "01119": {"FFRPTH14": 0.53167249, "FSRPTH14": 0.45571927700000003}, "01121": {"FFRPTH14": 0.602542977, "FSRPTH14": 0.454981432}, "01123": {"FFRPTH14": 0.48584963, "FSRPTH14": 0.412972185}, "01125": {"FFRPTH14": 0.796194093, "FSRPTH14": 0.543983542}, "01127": {"FFRPTH14": 0.626231461, "FSRPTH14": 0.519313895}, "01129": {"FFRPTH14": 0.178210764, "FSRPTH14": 0.356421528}, "01131": {"FFRPTH14": 0.45053162700000005, "FSRPTH14": 0.45053162700000005}, "01133": {"FFRPTH14": 0.414078675, "FSRPTH14": 0.621118012}, "02013": {"FFRPTH14": 0.297619048, "FSRPTH14": 0.297619048}, "02016": {"FFRPTH14": 0.0, "FSRPTH14": 0.347826087}, "02020": {"FFRPTH14": 0.677718348, "FSRPTH14": 0.767416365}, "02050": {"FFRPTH14": 0.39176180899999996, "FSRPTH14": 0.39176180899999996}, "02060": {"FFRPTH14": 0.0, "FSRPTH14": 2.089864159}, "02068": {"FFRPTH14": 1.041124414, "FSRPTH14": 3.64393545}, "02070": {"FFRPTH14": 0.200481155, "FSRPTH14": 0.40096231}, "02090": {"FFRPTH14": 0.49317109, "FSRPTH14": 0.654206548}, "02100": {"FFRPTH14": 0.38971161299999996, "FSRPTH14": 2.33826968}, "02105": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02110": {"FFRPTH14": 0.9566129729999999, "FSRPTH14": 0.894896007}, "02122": {"FFRPTH14": 0.678532282, "FSRPTH14": 1.3396663009999998}, "02130": {"FFRPTH14": 0.870385145, "FSRPTH14": 0.870385145}, "02150": {"FFRPTH14": 0.500500501, "FSRPTH14": 0.786500787}, "02164": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02170": {"FFRPTH14": 0.439304469, "FSRPTH14": 0.51081915}, "02180": {"FFRPTH14": 0.101864113, "FSRPTH14": 1.018641133}, "02185": {"FFRPTH14": 0.206121818, "FSRPTH14": 0.515304545}, "02188": {"FFRPTH14": 0.25916807, "FSRPTH14": 0.129584035}, "02195": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02198": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02220": {"FFRPTH14": 0.449438202, "FSRPTH14": 0.898876404}, "02230": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02240": {"FFRPTH14": 0.28855865, "FSRPTH14": 1.0099552729999999}, "02261": {"FFRPTH14": 0.94856661, "FSRPTH14": 1.5809443509999999}, "02270": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02275": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02282": {"FFRPTH14": 0.0, "FSRPTH14": 0.0}, "02290": {"FFRPTH14": 0.0, "FSRPTH14": 0.180277628}, "04001": {"FFRPTH14": 0.222754358, "FSRPTH14": 0.250598652}, "04003": {"FFRPTH14": 0.47862657700000005, "FSRPTH14": 0.753248384}, "04005": {"FFRPTH14": 0.871573626, "FSRPTH14": 1.176624395}, "04007": {"FFRPTH14": 0.640072291, "FSRPTH14": 1.035411058}, "04009": {"FFRPTH14": 0.421529626, "FSRPTH14": 0.316147219}, "04011": {"FFRPTH14": 0.213995292, "FSRPTH14": 0.7489835220000001}, "04012": {"FFRPTH14": 0.7414364090000001, "FSRPTH14": 1.482872819}, "04013": {"FFRPTH14": 0.68041841, "FSRPTH14": 0.583285684}, "04015": {"FFRPTH14": 0.5605794620000001, "FSRPTH14": 0.634339918}, "04017": {"FFRPTH14": 0.499532844, "FSRPTH14": 0.721547442}, "04019": {"FFRPTH14": 0.608253129, "FSRPTH14": 0.648073301}, "04021": {"FFRPTH14": 0.28612801600000004, "FSRPTH14": 0.271199598}, "04023": {"FFRPTH14": 0.578220366, "FSRPTH14": 0.856622765}, "04025": {"FFRPTH14": 0.584891521, "FSRPTH14": 0.9367403259999999}, "04027": {"FFRPTH14": 0.575654253, "FSRPTH14": 0.37884938}, "05001": {"FFRPTH14": 0.591588685, "FSRPTH14": 0.645369474}, "05003": {"FFRPTH14": 0.716058812, "FSRPTH14": 0.286423525}, "05005": {"FFRPTH14": 0.783219522, "FSRPTH14": 0.758743912}, "05007": {"FFRPTH14": 0.660281197, "FSRPTH14": 0.631393895}, "05009": {"FFRPTH14": 0.618346059, "FSRPTH14": 0.672115281}, "05011": {"FFRPTH14": 0.538213132, "FSRPTH14": 0.538213132}, "05013": {"FFRPTH14": 0.384467512, "FSRPTH14": 0.576701269}, "05015": {"FFRPTH14": 0.540657439, "FSRPTH14": 1.838235294}, "05017": {"FFRPTH14": 0.447227191, "FSRPTH14": 0.178890877}, "05019": {"FFRPTH14": 0.841601701, "FSRPTH14": 0.48724309}, "05021": {"FFRPTH14": 0.595316841, "FSRPTH14": 0.529170525}, "05023": {"FFRPTH14": 0.6241710229999999, "FSRPTH14": 0.9362565340000001}, "05025": {"FFRPTH14": 0.118357202, "FSRPTH14": 0.118357202}, "05027": {"FFRPTH14": 0.919232858, "FSRPTH14": 0.33426649399999997}, "05029": {"FFRPTH14": 0.33202106, "FSRPTH14": 0.569178959}, "05031": {"FFRPTH14": 0.7413332290000001, "FSRPTH14": 0.682806922}, "05033": {"FFRPTH14": 0.45383081799999997, "FSRPTH14": 0.486247305}, "05035": {"FFRPTH14": 0.6862032779999999, "FSRPTH14": 0.403648987}, "05037": {"FFRPTH14": 0.696580949, "FSRPTH14": 0.40633888700000004}, "05039": {"FFRPTH14": 0.386847195, "FSRPTH14": 0.7736943909999999}, "05041": {"FFRPTH14": 0.733855186, "FSRPTH14": 0.896934116}, "05043": {"FFRPTH14": 0.805498872, "FSRPTH14": 0.6980990229999999}, "05045": {"FFRPTH14": 0.654146794, "FSRPTH14": 0.579623741}, "05047": {"FFRPTH14": 0.336983993, "FSRPTH14": 0.730131985}, "05049": {"FFRPTH14": 0.329896907, "FSRPTH14": 0.329896907}, "05051": {"FFRPTH14": 0.873389367, "FSRPTH14": 1.027516903}, "05053": {"FFRPTH14": 0.330687831, "FSRPTH14": 0.496031746}, "05055": {"FFRPTH14": 0.64082025, "FSRPTH14": 0.503501625}, "05057": {"FFRPTH14": 0.49267702799999996, "FSRPTH14": 0.582254669}, "05059": {"FFRPTH14": 0.419563654, "FSRPTH14": 0.269719492}, "05061": {"FFRPTH14": 0.666666667, "FSRPTH14": 0.592592593}, "05063": {"FFRPTH14": 0.568197191, "FSRPTH14": 0.730539246}, "05065": {"FFRPTH14": 0.22245291399999997, "FSRPTH14": 0.5190568}, "05067": {"FFRPTH14": 0.5132884679999999, "FSRPTH14": 0.456256416}, "05069": {"FFRPTH14": 0.7745826240000001, "FSRPTH14": 0.442618643}, "05071": {"FFRPTH14": 0.422995578, "FSRPTH14": 0.422995578}, "05073": {"FFRPTH14": 0.421881592, "FSRPTH14": 0.14062719699999998}, "05075": {"FFRPTH14": 0.35437954, "FSRPTH14": 0.649695824}, "05077": {"FFRPTH14": 0.304259635, "FSRPTH14": 0.101419878}, "05079": {"FFRPTH14": 0.357909807, "FSRPTH14": 0.429491768}, "05081": {"FFRPTH14": 0.47877433799999997, "FSRPTH14": 0.23938716899999998}, "05083": {"FFRPTH14": 0.592039348, "FSRPTH14": 0.409873395}, "05085": {"FFRPTH14": 0.531045181, "FSRPTH14": 0.43322106899999996}, "05087": {"FFRPTH14": 0.190597205, "FSRPTH14": 0.698856417}, "05089": {"FFRPTH14": 0.30549276, "FSRPTH14": 0.672084072}, "05091": {"FFRPTH14": 0.552638851, "FSRPTH14": 0.5065856129999999}, "05093": {"FFRPTH14": 0.723409065, "FSRPTH14": 0.542556799}, "05095": {"FFRPTH14": 0.6594566079999999, "FSRPTH14": 0.6594566079999999}, "05097": {"FFRPTH14": 0.440431623, "FSRPTH14": 0.660647434}, "05099": {"FFRPTH14": 0.34391837700000005, "FSRPTH14": 0.229278918}, "05101": {"FFRPTH14": 0.12651821900000002, "FSRPTH14": 0.6325910929999999}, "05103": {"FFRPTH14": 0.644433704, "FSRPTH14": 0.36249395799999995}, "05105": {"FFRPTH14": 0.09760859, "FSRPTH14": 0.390434358}, "05107": {"FFRPTH14": 0.351229303, "FSRPTH14": 0.45158053200000003}, "05109": {"FFRPTH14": 0.45355587799999997, "FSRPTH14": 0.725689405}, "05111": {"FFRPTH14": 0.7011465809999999, "FSRPTH14": 0.536170915}, "05113": {"FFRPTH14": 0.39555006200000004, "FSRPTH14": 0.939431397}, "05115": {"FFRPTH14": 0.8227717920000001, "FSRPTH14": 0.537966171}, "05117": {"FFRPTH14": 0.12042389199999999, "FSRPTH14": 0.48169556799999996}, "05119": {"FFRPTH14": 0.8759822970000001, "FSRPTH14": 0.883621678}, "05121": {"FFRPTH14": 0.455295658, "FSRPTH14": 0.5691195720000001}, "05123": {"FFRPTH14": 0.37176103200000005, "FSRPTH14": 0.44611323799999997}, "05125": {"FFRPTH14": 0.553063888, "FSRPTH14": 0.337023306}, "05127": {"FFRPTH14": 0.374076499, "FSRPTH14": 0.46759562299999996}, "05129": {"FFRPTH14": 0.504477235, "FSRPTH14": 0.37835792700000004}, "05131": {"FFRPTH14": 0.7572411179999999, "FSRPTH14": 0.788792831}, "05133": {"FFRPTH14": 0.40169861100000004, "FSRPTH14": 0.631240675}, "05135": {"FFRPTH14": 0.532355377, "FSRPTH14": 1.005560156}, "05137": {"FFRPTH14": 0.8003841840000001, "FSRPTH14": 0.640307348}, "05139": {"FFRPTH14": 0.6711909909999999, "FSRPTH14": 0.6960499170000001}, "05141": {"FFRPTH14": 0.356061955, "FSRPTH14": 0.71212391}, "05143": {"FFRPTH14": 0.711076488, "FSRPTH14": 0.8741258740000001}, "05145": {"FFRPTH14": 0.763436482, "FSRPTH14": 0.5853013029999999}, "05147": {"FFRPTH14": 0.578871201, "FSRPTH14": 0.28943560100000004}, "05149": {"FFRPTH14": 0.41000410000000004, "FSRPTH14": 0.227780056}, "06001": {"FFRPTH14": 0.767262951, "FSRPTH14": 0.96590708}, "06003": {"FFRPTH14": 1.792114695, "FSRPTH14": 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{"FFRPTH14": 0.4671338, "FSRPTH14": 2.268935602}, "55009": {"FFRPTH14": 0.670121167, "FSRPTH14": 0.8454435659999999}, "55011": {"FFRPTH14": 0.227479527, "FSRPTH14": 1.5923566880000002}, "55013": {"FFRPTH14": 0.26096033399999996, "FSRPTH14": 1.891962422}, "55015": {"FFRPTH14": 0.42431957299999995, "FSRPTH14": 0.686993595}, "55017": {"FFRPTH14": 0.551528522, "FSRPTH14": 0.8194138040000001}, "55019": {"FFRPTH14": 0.319553787, "FSRPTH14": 0.8134096390000001}, "55021": {"FFRPTH14": 0.582884395, "FSRPTH14": 1.2010951159999999}, "55023": {"FFRPTH14": 0.488042948, "FSRPTH14": 1.037091264}, "55025": {"FFRPTH14": 0.8115688259999999, "FSRPTH14": 0.8541810320000001}, "55027": {"FFRPTH14": 0.429019803, "FSRPTH14": 0.598369725}, "55029": {"FFRPTH14": 0.8283512209999999, "FSRPTH14": 3.3134048839999997}, "55031": {"FFRPTH14": 0.503455536, "FSRPTH14": 1.1671014690000001}, "55033": {"FFRPTH14": 0.473987135, "FSRPTH14": 0.7674077420000001}, "55035": {"FFRPTH14": 0.87629475, "FSRPTH14": 0.777834666}, "55037": {"FFRPTH14": 0.446328944, "FSRPTH14": 1.7853157780000002}, "55039": {"FFRPTH14": 0.579801295, "FSRPTH14": 0.766516967}, "55041": {"FFRPTH14": 0.328695081, "FSRPTH14": 1.533910376}, "55043": {"FFRPTH14": 0.424472786, "FSRPTH14": 0.8103571359999999}, "55045": {"FFRPTH14": 0.43169738, "FSRPTH14": 0.998300192}, "55047": {"FFRPTH14": 0.26544914, "FSRPTH14": 1.2741558720000001}, "55049": {"FFRPTH14": 0.419727177, "FSRPTH14": 1.049317943}, "55051": {"FFRPTH14": 0.676018252, "FSRPTH14": 3.380091262}, "55053": {"FFRPTH14": 0.629478985, "FSRPTH14": 1.1136935890000002}, "55055": {"FFRPTH14": 0.497659814, "FSRPTH14": 0.805734937}, "55057": {"FFRPTH14": 0.303087706, "FSRPTH14": 0.795605228}, "55059": {"FFRPTH14": 0.654496989, "FSRPTH14": 0.737796606}, "55061": {"FFRPTH14": 0.391312855, "FSRPTH14": 1.173938564}, "55063": {"FFRPTH14": 0.677902907, "FSRPTH14": 0.88127378}, "55065": {"FFRPTH14": 0.296683083, "FSRPTH14": 0.949385866}, "55067": {"FFRPTH14": 0.566718187, "FSRPTH14": 1.3910355490000001}, "55069": {"FFRPTH14": 0.526445092, "FSRPTH14": 1.052890184}, "55071": {"FFRPTH14": 0.42415169700000005, "FSRPTH14": 0.873253493}, "55073": {"FFRPTH14": 0.574458683, "FSRPTH14": 0.7880394759999999}, "55075": {"FFRPTH14": 0.629570439, "FSRPTH14": 1.477069107}, "55077": {"FFRPTH14": 0.398671096, "FSRPTH14": 0.9302325579999999}, "55078": {"FFRPTH14": 0.221141088, "FSRPTH14": 0.0}, "55079": {"FFRPTH14": 0.6430323520000001, "FSRPTH14": 0.6587160679999999}, "55081": {"FFRPTH14": 0.484805747, "FSRPTH14": 0.7051719959999999}, "55083": {"FFRPTH14": 0.42761311700000004, "FSRPTH14": 1.256113531}, "55085": {"FFRPTH14": 0.759216039, "FSRPTH14": 1.827742316}, "55087": {"FFRPTH14": 0.714262167, "FSRPTH14": 0.939529466}, "55089": {"FFRPTH14": 0.60592203, "FSRPTH14": 0.8345718529999999}, "55091": {"FFRPTH14": 0.6816632579999999, "FSRPTH14": 1.4996591680000002}, "55093": {"FFRPTH14": 0.537135602, "FSRPTH14": 0.805703403}, "55095": {"FFRPTH14": 0.575546193, "FSRPTH14": 0.897852062}, "55097": {"FFRPTH14": 0.638460884, "FSRPTH14": 0.950597316}, "55099": {"FFRPTH14": 0.365630713, "FSRPTH14": 1.535648995}, "55101": {"FFRPTH14": 0.538011816, "FSRPTH14": 0.660985945}, "55103": {"FFRPTH14": 0.452949836, "FSRPTH14": 0.736043483}, "55105": {"FFRPTH14": 0.6452093210000001, "FSRPTH14": 0.7630841009999999}, "55107": {"FFRPTH14": 0.418614386, "FSRPTH14": 0.837228773}, "55109": {"FFRPTH14": 0.564782904, "FSRPTH14": 0.8068327209999999}, "55111": {"FFRPTH14": 0.85201723, "FSRPTH14": 1.309582038}, "55113": {"FFRPTH14": 0.5475451720000001, "FSRPTH14": 3.1027559769999997}, "55115": {"FFRPTH14": 0.384809639, "FSRPTH14": 1.106327714}, "55117": {"FFRPTH14": 0.589816983, "FSRPTH14": 0.867377917}, "55119": {"FFRPTH14": 0.29211295, "FSRPTH14": 0.827653359}, "55121": {"FFRPTH14": 0.47443152899999996, "FSRPTH14": 0.84719916}, "55123": {"FFRPTH14": 0.362294974, "FSRPTH14": 0.889269482}, "55125": {"FFRPTH14": 0.747733433, "FSRPTH14": 4.065800542}, "55127": {"FFRPTH14": 0.676152115, "FSRPTH14": 1.0818433840000001}, "55129": {"FFRPTH14": 0.318593093, "FSRPTH14": 2.166433032}, "55131": {"FFRPTH14": 0.562847558, "FSRPTH14": 0.5778568270000001}, "55133": {"FFRPTH14": 0.652969493, "FSRPTH14": 0.756735962}, "55135": {"FFRPTH14": 0.7106365, "FSRPTH14": 0.941113202}, "55137": {"FFRPTH14": 0.330879312, "FSRPTH14": 1.323517247}, "55139": {"FFRPTH14": 0.5427376389999999, "FSRPTH14": 0.7256166270000001}, "55141": {"FFRPTH14": 0.557004673, "FSRPTH14": 0.8966416690000001}, "56001": {"FFRPTH14": 0.7140779140000001, "FSRPTH14": 0.925656555}, "56003": {"FFRPTH14": 0.419111484, "FSRPTH14": 0.586756077}, "56005": {"FFRPTH14": 0.68294702, "FSRPTH14": 0.641556291}, "56007": {"FFRPTH14": 0.44152895200000003, "FSRPTH14": 1.6399646780000001}, "56009": {"FFRPTH14": 0.56749663, "FSRPTH14": 1.20593034}, "56011": {"FFRPTH14": 0.82781457, "FSRPTH14": 1.793598234}, "56013": {"FFRPTH14": 0.49136427299999996, "FSRPTH14": 0.9581603320000001}, "56015": {"FFRPTH14": 0.813970697, "FSRPTH14": 0.8879680329999999}, "56017": {"FFRPTH14": 1.03820598, "FSRPTH14": 1.6611295680000002}, "56019": {"FFRPTH14": 0.8165169720000001, "FSRPTH14": 0.9331622540000001}, "56021": {"FFRPTH14": 0.674350808, "FSRPTH14": 0.549855274}, "56023": {"FFRPTH14": 0.6463079660000001, "FSRPTH14": 1.1848979370000001}, "56025": {"FFRPTH14": 0.649318828, "FSRPTH14": 0.784083113}, "56027": {"FFRPTH14": 1.218026797, "FSRPTH14": 1.218026797}, "56029": {"FFRPTH14": 0.620925179, "FSRPTH14": 1.37983373}, "56031": {"FFRPTH14": 0.568246392, "FSRPTH14": 1.4774406180000001}, "56033": {"FFRPTH14": 0.732551945, "FSRPTH14": 0.99893447}, "56035": {"FFRPTH14": 0.298299692, "FSRPTH14": 1.193198767}, "56037": {"FFRPTH14": 0.733170407, "FSRPTH14": 0.710953122}, "56039": {"FFRPTH14": 1.003052769, "FSRPTH14": 2.398604448}, "56041": {"FFRPTH14": 0.76540375, "FSRPTH14": 0.76540375}, "56043": {"FFRPTH14": 0.7209805340000001, "FSRPTH14": 1.321797645}, "56045": {"FFRPTH14": 0.416608804, "FSRPTH14": 1.527565616}}
try:
retval = data[county]
except:
retval = None
return retval
| 25,136.75
| 200,982
| 0.687246
| 22,014
| 201,094
| 6.277869
| 0.408104
| 0.196084
| 0.011577
| 0.020839
| 0.02971
| 0.00492
| 0
| 0
| 0
| 0
| 0
| 0.541647
| 0.078257
| 201,094
| 7
| 200,983
| 28,727.714286
| 0.203947
| 0
| 0
| 0
| 0
| 0
| 0.32822
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0
| 0
| 0.285714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ee735b67bc86acbfb2a7fb6d5c4dc1ea7b4a9dd6
| 140
|
py
|
Python
|
classify.py
|
Social-Developers-Club/cfc-model-server
|
52e5681f725cd22ce5133d5709356b21560ab0f5
|
[
"Apache-2.0"
] | null | null | null |
classify.py
|
Social-Developers-Club/cfc-model-server
|
52e5681f725cd22ce5133d5709356b21560ab0f5
|
[
"Apache-2.0"
] | null | null | null |
classify.py
|
Social-Developers-Club/cfc-model-server
|
52e5681f725cd22ce5133d5709356b21560ab0f5
|
[
"Apache-2.0"
] | 1
|
2020-03-23T00:12:29.000Z
|
2020-03-23T00:12:29.000Z
|
"""
IDE: PyCharm
Project: semantic-match-classifier
Author: Robin
Filename: classify.py
Date: 21.03.2020
TODO: load model and classify
"""
| 14
| 34
| 0.75
| 20
| 140
| 5.25
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.065574
| 0.128571
| 140
| 9
| 35
| 15.555556
| 0.795082
| 0.928571
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.111111
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ee7e6405ccb264561cc573695c0780404d9fa04a
| 144
|
py
|
Python
|
votingbooth/votingbooth/views.py
|
msexauer/ask-meanything
|
7b5279246df03c37beab09de193705c3c4d9a8ee
|
[
"MIT"
] | null | null | null |
votingbooth/votingbooth/views.py
|
msexauer/ask-meanything
|
7b5279246df03c37beab09de193705c3c4d9a8ee
|
[
"MIT"
] | null | null | null |
votingbooth/votingbooth/views.py
|
msexauer/ask-meanything
|
7b5279246df03c37beab09de193705c3c4d9a8ee
|
[
"MIT"
] | null | null | null |
from django.http import HttpResponse, HttpResponseRedirect, HttpRequest
def redirect_to_home(request):
return HttpResponseRedirect('admin')
| 36
| 71
| 0.833333
| 15
| 144
| 7.866667
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097222
| 144
| 4
| 72
| 36
| 0.907692
| 0
| 0
| 0
| 0
| 0
| 0.034483
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 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
| 1
| 0
| 0
|
0
| 5
|
c9a6bb95a55c66fa1ea19fa5d277f0d820ec844b
| 19
|
py
|
Python
|
Submissions/German Bodenbender/test.py
|
germanbodenbender/Programming_with_python_2021
|
e94beb538aff7822e0cffe256e6409def5534a4e
|
[
"Apache-2.0"
] | null | null | null |
Submissions/German Bodenbender/test.py
|
germanbodenbender/Programming_with_python_2021
|
e94beb538aff7822e0cffe256e6409def5534a4e
|
[
"Apache-2.0"
] | null | null | null |
Submissions/German Bodenbender/test.py
|
germanbodenbender/Programming_with_python_2021
|
e94beb538aff7822e0cffe256e6409def5534a4e
|
[
"Apache-2.0"
] | null | null | null |
#test pepe
#caramba
| 9.5
| 10
| 0.789474
| 3
| 19
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 19
| 2
| 11
| 9.5
| 0.882353
| 0.842105
| 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
|
c9be428fcde987cfcbac63eaedcfe3b9361c192d
| 42,000
|
py
|
Python
|
trainShape.py
|
bsrvasulu/patternRecognitionCNNModel
|
03e81e633f7edee306bf8305f72325db4359991d
|
[
"MIT"
] | 1
|
2020-03-18T16:02:06.000Z
|
2020-03-18T16:02:06.000Z
|
trainShape.py
|
bsrvasulu/patternRecognitionCNNModel
|
03e81e633f7edee306bf8305f72325db4359991d
|
[
"MIT"
] | null | null | null |
trainShape.py
|
bsrvasulu/patternRecognitionCNNModel
|
03e81e633f7edee306bf8305f72325db4359991d
|
[
"MIT"
] | 1
|
2020-03-18T16:02:07.000Z
|
2020-03-18T16:02:07.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 4 12:53:03 2019
@author: Sreenivasulu Bachu
"""
import numpy as np
import tensorflow as tf
from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D, core, ZeroPadding2D, BatchNormalization, Activation, Flatten, Dense
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
import keras.layers
from keras.layers.merge import concatenate
from keras.utils import *
from keras.initializers import glorot_uniform
from scipy.ndimage.filters import gaussian_filter, gaussian_laplace
import matplotlib.pyplot as plt
from skimage.filters import threshold_otsu, threshold_local
from scipy.ndimage.morphology import binary_erosion
from sklearn.metrics import confusion_matrix, classification_report
import sys, os, fnmatch
import pandas as pd
import math
import statistics
import pickle
#from imageprocess import *
class LossObj(object):
def __init__(self):
self.losses = []
self.dice_coef = []
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.lossObj = LossObj()
self.lossObj.losses = []
self.lossObj.dice_coef = []
self.lossObj.accuracy = []
def on_epoch_end(self, batch, logs={}):
self.lossObj.losses.append(logs.get('loss'))
self.lossObj.dice_coef.append(logs.get('dice_coef_mod'))
self.lossObj.accuracy.append(logs.get('acc'))
def on_batch_end(self, batch, logs={}):
self.lossObj.losses.append(logs.get('loss'))
self.lossObj.dice_coef.append(logs.get('dice_coef_mod'))
self.lossObj.accuracy.append(logs.get('acc'))
def get_LossObj(self):
return self.lossObj
#def get_dice_coeff(self):
# return self.dice_coef
def dice_coef_mod(y_true, y_pred, smooth=1):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
y_pred_f = K.clip(y_pred_f, 0., 1.)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss_mod(y_true, y_pred):
return 1.0 -dice_coef_mod(y_true, y_pred)
class trainShapes(object):
def __init__(self, shape, img_rows = 256, img_cols = 256, dataDir = './images'):
self.dataDir = dataDir
self.img_rows = img_rows
self.img_cols = img_cols
self.shape = shape
self.num_channels = 1
def prepare_network_chanal_last(self):
inputs = Input((self.img_rows, self.img_cols, self.num_channels))
# zero padding
#zeroPadX = ZeroPadding2D(padding = (4, 4))(inputs)
conv0 = Conv2D(8, (2, 2), strides=(1, 1), padding='same', data_format='channels_last')(inputs)
conv0 = BatchNormalization(axis = 3)(conv0)
conv0 = Activation('relu')(conv0)
pool0 = MaxPooling2D((2, 2), strides=(1, 1), padding='same', data_format='channels_last')(conv0)
conv01 = Conv2D(8, (2, 2), strides=(1, 1), padding='same', data_format='channels_last')(pool0)
conv01 = BatchNormalization(axis = 3)(conv01)
conv01 = Activation('relu')(conv01)
pool01 = MaxPooling2D((2, 2), strides=(1, 1), padding='same', data_format='channels_last')(conv01)
conv1 = Conv2D(16, (4, 4), padding='same', data_format='channels_last')(pool01)
conv1 = BatchNormalization(axis = 3)(conv1)
conv1 = Activation('relu')(conv1)
pool1 = MaxPooling2D((4, 4), strides=(1, 1), padding='same', data_format='channels_last')(conv1)
conv11 = Conv2D(16, (4, 4), padding='same', data_format='channels_last')(pool1)
conv11 = BatchNormalization(axis = 3)(conv11)
conv11 = Activation('relu')(conv11)
pool11 = MaxPooling2D((4, 4), strides=(1, 1), padding='same', data_format='channels_last')(conv11)
conv2 = Conv2D(32, (8, 8), padding='same', data_format='channels_last')(pool11)
conv2 = BatchNormalization(axis = 3)(conv2)
conv2 = Activation('relu')(conv2)
pool2 = MaxPooling2D((8, 8), strides=(1, 1), padding='same', data_format='channels_last')(conv2)
conv3 = Conv2D(1, (1, 1), padding='same',data_format='channels_last')(pool2)
conv3 = Activation('relu')(conv3)
model = Model(inputs = inputs, outputs = conv3)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss_mod,metrics=[dice_coef_mod])
#model.compile(optimizer='adam', loss='mse',metrics=['accuracy'])
return model
def prepare_network_chanal_last_reduced_size(self):
inputs = Input((self.img_rows, self.img_cols, self.num_channels))
# zero padding
#zeroPadX = ZeroPadding2D(padding = (4, 4))(inputs)
conv0 = Conv2D(8, (2, 2), strides=(1, 1), padding='same', data_format='channels_last')(inputs)
conv0 = BatchNormalization(axis = 3)(conv0)
conv0 = Activation('relu')(conv0)
pool0 = MaxPooling2D((2, 2), strides=(1, 1), padding='same', data_format='channels_last')(conv0)
conv01 = Conv2D(8, (2, 2), strides=(1, 1), padding='same', data_format='channels_last')(pool0)
conv01 = BatchNormalization(axis = 3)(conv01)
conv01 = Activation('relu')(conv01)
pool01 = MaxPooling2D((2, 2), strides=(2, 2), padding='same', data_format='channels_last')(conv01)
conv1 = Conv2D(16, (4, 4), padding='same', data_format='channels_last')(pool01)
conv1 = BatchNormalization(axis = 3)(conv1)
conv1 = Activation('relu')(conv1)
pool1 = MaxPooling2D((4, 4), strides=(1, 1), padding='same', data_format='channels_last')(conv1)
conv11 = Conv2D(16, (4, 4), padding='same', data_format='channels_last')(pool1)
conv11 = BatchNormalization(axis = 3)(conv11)
conv11 = Activation('relu')(conv11)
pool11 = MaxPooling2D((4, 4), strides=(2, 2), padding='same', data_format='channels_last')(conv11)
conv2 = Conv2D(32, (8, 8), padding='same', data_format='channels_last')(pool11)
conv2 = BatchNormalization(axis = 3)(conv2)
conv2 = Activation('relu')(conv2)
pool2 = MaxPooling2D((8, 8), strides=(1, 1), padding='same', data_format='channels_last')(conv2)
conv3 = Conv2D(1, (1, 1), padding='same',data_format='channels_last')(pool2)
conv3 = Activation('relu')(conv3)
model = Model(inputs = inputs, outputs = conv3)
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss_mod,metrics=[dice_coef_mod])
#model.compile(optimizer='adam', loss='mse',metrics=['accuracy'])
return model
def prepare_network_chanal_last_classification(self, classes = 4):
inputs = Input((self.img_rows, self.img_cols, self.num_channels))
# zero padding
zeroPadX = ZeroPadding2D(padding = (4, 4))(inputs)
conv0 = Conv2D(8, (8, 8), strides=(1, 1), padding='same', data_format='channels_last')(zeroPadX)
conv0 = BatchNormalization(axis = 3)(conv0)
conv0 = Activation('relu')(conv0)
pool0 = MaxPooling2D(pool_size=(8, 8), strides=(8, 8), padding='same', data_format='channels_last')(conv0)
conv1 = Conv2D(16, (4, 4),strides=(1, 1), padding='same', data_format='channels_last')(pool0)
conv1 = BatchNormalization(axis = 3)(conv1)
conv1 = Activation('relu')(conv1)
pool1 = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), padding='same', data_format='channels_last')(conv1)
conv2 = Conv2D(32, (2, 2), strides=(1, 1), padding='same', data_format='channels_last')(pool1)
conv2 = BatchNormalization(axis = 3)(conv2)
conv2 = Activation('relu')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', data_format='channels_last')(conv2)
# output layer
X = Flatten()(pool2)
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
#X = Dense(classes, activation=keras.activations.softmax(X, dim=axis), name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs = inputs, outputs = X, name='Shapes4')
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
def one_hot_encodeing(self, y):
# convert integers to dummy variables (i.e. one hot encoded)
#dummy_y = np_utils.to_categorical(y)
encoded_y = to_categorical(y, num_classes=4, dtype='float32')
#nb_classes = 6
#targets = np.array([[2, 3, 4, 0]]).reshape(-1)
#one_hot_targets = np.eye(nb_classes)[targets]
#return one_hot_targets
return encoded_y
def randomData(self, X, Y):
m = X.shape[0]
permutation = list(np.random.permutation(m))
shuffled_X = X[permutation, :]
shuffled_Y = Y[permutation, :]#.reshape(Y.shape[0], m)
return shuffled_X, shuffled_Y
def save_object(self, obj, filename):
with open(filename, 'wb') as output: # Overwrites any existing file.
pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)
def noiseImagesXY(self, count=20, shape=(256, 256)):
X_data = []
Y_data = []
for entry in range(count):
X = np.zeros([256, 256, 1], dtype="float_")
Y_data.append(np.zeros([shape[0], shape[1], 1], dtype="float_"))
num_noise = np.random.randint(20,256*5)
pt_random = np.random.randint(0, 255, (num_noise, 2))
for (i, j) in pt_random:
X[i, j, 0] = 1
X_data.append(X.copy())
X_data = np.asarray(X_data)
Y_data = np.asarray(Y_data)
return X_data, Y_data
def noiseImagesXY_clasification(self, count=20):
X_data = []
y_data = []
for entry in range(count):
X = np.zeros([256, 256, 1], dtype="float_")
num_noise = np.random.randint(20,256*5)
pt_random = np.random.randint(0, 255, (num_noise, 2))
for (i, j) in pt_random:
X[i, j, 0] = 1
X_data.append(X.copy())
X_data = np.asarray(X_data)
y_data = np.zeros(shape=(X_data.shape[0], 1))
return X_data, y_data
def readImagesXY_classification(self):
X_r = np.load('X_data_train_rectangle.npy')
y_r = np.ones(shape=(X_r.shape[0], 1)) * 2#np.load('y_data_train_rectangle.npy')
X_c = np.load('X_data_train_circle.npy')
y_c = np.ones(shape=(X_c.shape[0], 1))
X_l = np.load('X_data_train_line.npy')
y_l = np.ones(shape=(X_l.shape[0], 1)) * 3
X = np.concatenate((X_r, X_c, X_l), axis = 0)
y = np.concatenate((y_r, y_c, y_l), axis = 0)
return X, y
def readImagesXY_test_classification(self):
X_r = np.load('X_data_test_rectangle.npy')
y_r = np.ones(shape=(X_r.shape[0], 1)) * 2#np.load('y_data_train_rectangle.npy')
X_c = np.load('X_data_test_circle.npy')
y_c = np.ones(shape=(X_c.shape[0], 1))
X_l = np.load('X_data_test_line.npy')
y_l = np.ones(shape=(X_l.shape[0], 1)) * 3
X = np.concatenate((X_r, X_c, X_l), axis = 0)
y = np.concatenate((y_r, y_c, y_l), axis = 0)
return X, y
def readImagesXY(self):
X = np.load('X_data_train_rectangle.npy')
y = np.load('y_data_train_rectangle.npy')
return X, y
def readImagesXY_test(self):
X = np.load('X_data_test_rectangle.npy')
y = np.load('y_data_test_rectangle.npy')
return X, y
def readNonClassImagesXY(self):
X = np.load('X_data_train_circle.npy')
y = np.zeros(X.shape)
return X, y
def readNonClassImagesXY_test(self):
X = np.load('X_data_test_circle.npy')
y = np.zeros(X.shape)
return X, y
def get_mody(self, y, y_class):
y_c = y_class
if(self.shape == 'CIRCLE'):
y_c = y_class == 1
elif(self.shape == 'RECTANGLE'):
y_c = y_class == 2
elif(self.shape == 'LINE'):
y_c = y_class == 3
ym = y.reshape((y.shape[0], y.shape[1]*y.shape[2]*y.shape[3]))
ym = ym * y_c
return ym.reshape(y.shape);
def train_channel_last_classification(self):
model = self.prepare_network_chanal_last_classification()
print(model.summary())
history = LossHistory()
for iter in range(2):
for fileCount in range(51):
print('File: ' + ".\\npyXYFiles\\X_data_" + str(fileCount)+ '.npy')
X = np.load(".\\npyXYFiles\\X_data_" + str(fileCount)+ '.npy')
y = np.load(".\\npyXYFiles\\y_data_class_" + str(fileCount)+ '.npy')
#y_class = np.load(".\\npyXYFiles\\y_data_class_" + str(fileCount)+ '.npy')
#X, y = train_shapes.readImagesXY_classification()
X_n, y_n = train_shapes.noiseImagesXY_clasification(int(len(X)/4))
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
y = train_shapes.one_hot_encodeing(y)
X, y = train_shapes.randomData(X, y)
model.fit(X, y, batch_size=8, nb_epoch=20, verbose=1, callbacks=[history])
if fileCount % 10 == 0 :
model.save(self.shape + '_' + str(iter) + '_' + str(fileCount) + '.h5')
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
self.save_object(history.get_LossObj(), self.shape + '.pkl')
# save model
# serialize model to JSON
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save(self.shape + '.h5')
self.save_object(history.get_LossObj(), self.shape + '.pkl')
print("Saved model to disk")
def train_shape_channel_last(self):
model = self.prepare_network_chanal_last_reduced_size()
print(model.summary())
history = LossHistory()
for iter in range(2):
for fileCount in range(51):
'''
print('File: ' + ".\\npyXYFiles_size64\\X_data_" + str(fileCount)+ '.npy')
X = np.load(".\\npyXYFiles\\X_data_" + str(fileCount)+ '.npy')
y = np.load(".\\npyXYFiles\\y_data_" + str(fileCount)+ '.npy')
y_c = np.load(".\\npyXYFiles\\y_data_class_" + str(fileCount)+ '.npy')
'''
print('File: ' + "./npyXYFiles_size64/X_data_" + str(fileCount)+ '.npy')
X = np.load("./npyXYFiles_size64/X_data_" + str(fileCount)+ '.npy')
y = np.load("./npyXYFiles_size64/y_data_" + str(fileCount)+ '.npy')
y_c = np.load("./npyXYFiles_size64/y_data_class_" + str(fileCount)+ '.npy')
y = self.get_mody(y, y_c)
X_n, y_n = train_shapes.noiseImagesXY(int(len(X)/4), shape=(y.shape[1], y.shape[2]))
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
X, y = train_shapes.randomData(X, y)
model.fit(X, y, batch_size=8, nb_epoch=20, verbose=1, callbacks=[history])
if fileCount % 10 == 0 :
model.save(self.shape + '_' + str(iter) + '_' + str(fileCount) + '.h5')
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
self.save_object(history.get_LossObj(), self.shape + '.pkl')
# save model
# serialize model to JSON
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save(self.shape + '.h5')
self.save_object(history.get_LossObj(), self.shape + '.pkl')
print("Saved model to disk")
'''
model = self.prepare_network_chanal_last()
print(model.summary())
history = LossHistory()
#imageProc = imageprocess(dataDir = self.dataDir)
X, y = self.readImagesXY()
X_n, y_n = self.noiseImagesXY(len(X))
#X_n2, y_n2 = self.readNonClassImagesXY()
#X_n3, y_n3 = self.noiseImagesXY(len(X_n2))
#append noise
X = np.concatenate((X, X_n, X_n2, X_n3), axis = 0)
y = np.concatenate((y, y_n, y_n2, y_n3), axis = 0)
X, y = self.randomData(X, y)
print('X.shape', X.shape)
print('Y.shape', y.shape)
model.fit(X, y, batch_size=8, nb_epoch=11, verbose=1, callbacks=[history])
# save model
# serialize model to JSON
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save(self.shape + '.h5')
self.save_object(history.get_LossObj(), self.shape + '.pkl')
print("Saved model to disk")
'''
def retrieve_fitmodel_channel_last(self):
# load json and create model
json_file = open(self.shape + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights(self.shape + '.h5')
print("Loaded model from disk")
# compile
model.compile(optimizer = Adam(lr=2e-5), loss = dice_coef_loss_mod, metrics = [dice_coef_mod])
print(model.summary())
imageProc = imageprocess(dataDir = self.dataDir)
X, Y = imageProc.convertImagesXY()
print('X.shape', X.shape)
print('Y.shape', Y.shape)
model.fit(X, Y, batch_size=8, nb_epoch=20, verbose=1, callbacks=[history])
# save model
# serialize model to JSON
model_json = model.to_json()
with open(self.shape + ".json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save(self.shape + '.h5')
print("Saved model to disk")
def calculate_stats_classification(self):
# load json and create model
json_file = open(self.shape + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights(self.shape + '.h5')
print("Loaded model from disk")
# compile
model.compile(optimizer = 'adam', loss = dice_coef_loss_mod, metrics = [dice_coef_mod])
print(model.summary())
#X, y = train_shapes.readImagesXY_classification()
#X, y = train_shapes.readImagesXY_test_classification()
#X_n, y_n = train_shapes.noiseImagesXY_clasification(len(X))
#X = np.concatenate((X, X_n), axis = 0)
#y = np.concatenate((y, y_n), axis = 0)
#y = train_shapes.one_hot_encodeing(y)
y_t = []
y_pt = []
for fileCount in range(20):
print('File: ' + ".\\npyXYFiles-test\\X_data_" + str(fileCount)+ '.npy')
X = np.load(".\\npyXYFiles-test\\X_data_" + str(fileCount)+ '.npy')
y = np.load(".\\npyXYFiles-test\\y_data_class_" + str(fileCount)+ '.npy')
X_n, y_n = train_shapes.noiseImagesXY_clasification(int(len(X)/4))
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
#y = train_shapes.one_hot_encodeing(y)
#X, y = train_shapes.randomData(X, y)
#all shapes
#print('--------------------------------')
#print('X.shape: ', X.shape)
#print('y.shape: ', y.shape)
#print('--------------------------------')
y_predict = model.predict(X)
y_predict_labels = np.argmax(y_predict, axis=1)
#print('y_predict_labels.shape: ', y_predict_labels.shape)
y = y.reshape(y_predict_labels.shape)
y_t = np.append(y_t, y)
y_pt = np.append(y_pt, y_predict_labels)
#print('y_predict:', y_predict_labels)
#print('y_predict:', y)
confusion = confusion_matrix(y_t, y_pt)
print('confusion matrix:\n', confusion)
print('classification_report:\n', classification_report(y_t, y_pt))
def calculate_stats_2(self):
# load json and create model
json_file = open(self.shape + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights(self.shape + '.h5')
print("Loaded model from disk")
# compile
model.compile(optimizer = 'adam', loss = dice_coef_loss_mod, metrics = [dice_coef_mod])
print(model.summary())
m_pts = 0
e_points = 0
detected_pts = 0
missing_shapes = 0
noPattern_shapes = 0;
pattern_shapes = 0
extra_detected_shapes = 0
y_t = []
y_pt = []
for fileCount in range(51):
'''
#test
print('File: ' + "./npyXYFiles-test-size64/X_data_" + str(fileCount)+ '.npy')
X = np.load("./npyXYFiles-test-size64/X_data_" + str(fileCount)+ '.npy')
y = np.load("./npyXYFiles-test-size64/y_data_" + str(fileCount)+ '.npy')
y_c = np.load("./npyXYFiles-test-size64/y_data_class_" + str(fileCount)+ '.npy')
'''
#training
print('File: ' + "./npyXYFiles_size64/X_data_" + str(fileCount)+ '.npy')
X = np.load("./npyXYFiles_size64/X_data_" + str(fileCount)+ '.npy')
y = np.load("./npyXYFiles_size64/y_data_" + str(fileCount)+ '.npy')
y_c = np.load("./npyXYFiles_size64/y_data_class_" + str(fileCount)+ '.npy')
y = self.get_mody(y, y_c)
X_n, y_n = train_shapes.noiseImagesXY(int(len(X)/4), shape=(y.shape[1], y.shape[2]))
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
X, y = train_shapes.randomData(X, y)
Yp = model.predict(X)
tempYp = Yp[:,:,:,0]*y[:,:,:,0]
idx = tempYp > 0.5
tempYp[idx] = 1.0
idx = tempYp <= 0.5
tempYp[idx] = 0.0
tempActYp = Yp[:,:,:,0]
idx = tempActYp > 0.5
tempActYp[idx] = 1.0
idx = tempActYp <= 0.5
tempActYp[idx] = 0.0
tempY = y[:,:,:,0]#*X[:,:,:,0]
yyp_result = tempYp*tempY
for i in range(0,len(X)):
tempYp_sum = np.sum(tempYp[i])
tempY_sum = np.sum(tempY[i])
yp_sum = np.sum(tempActYp[i])
if (tempY_sum == 0 and tempYp_sum > 7):
extra_detected_shapes += 1
'''
print('Extra points detected: ', tempYp_sum)
plt.figure(1)
plt.subplot('121')
plt.title('X')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.title('y')
plt.imshow(y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
plt.title('Yp')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
plt.title('Yp * y')
temp = tempActYp[i,:,:]*y[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
'''
elif tempY_sum != 0 and tempYp_sum == 0 :
missing_shapes += 1
'''
print('Missing points count: ', tempY_sum)
plt.figure(1)
plt.subplot('121')
plt.title('X')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.title('y')
plt.imshow(y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
plt.title('Yp')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
plt.title('Yp * y')
temp = tempActYp[i,:,:]*y[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
'''
elif tempY_sum != 0 :
pattern_shapes += 1
'''
print('Actual points count: ', tempY_sum)
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.title('X')
plt.subplot('122')
plt.imshow(y[i,:,:,0])
plt.title('y')
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.title('Yp')
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*y[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.title('Yp * y')
plt.imshow(temp)
plt.show()
'''
else :
noPattern_shapes += 1
'''
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
'''
yyp_result_sum = np.sum(yyp_result[i])
detected_pts += yyp_result_sum
m_pts += tempY_sum - yyp_result_sum
e_points += tempYp_sum - yyp_result_sum
y_t = np.append(y_t, (np.sum(tempY.reshape(tempY.shape[0], tempY.shape[1]*tempY.shape[2]), axis=1) > 0))
y_pt = np.append(y_pt, (np.sum(tempYp.reshape(tempYp.shape[0], tempYp.shape[1]*tempYp.shape[2]), axis=1) > 7))
print('-------------------------------------------------')
print('Stats - - -')
print('detected points: ', detected_pts)
print('Missing points: ', m_pts)
print('Extra Detected points: ', e_points)
print('Shape images: ', pattern_shapes)
print('No shape image: ', noPattern_shapes)
print('Missing images: ', missing_shapes)
print('Extra Detected images: ', extra_detected_shapes)
print('-------------------------------------------------')
confusion = confusion_matrix(y_t, y_pt)
print('confusion matrix:\n', confusion)
print('classification_report:\n', classification_report(y_t, y_pt))
def calculate_stats(self):
# load json and create model
json_file = open(self.shape + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights(self.shape + '.h5')
print("Loaded model from disk")
# compile
model.compile(optimizer = 'adam', loss = dice_coef_loss_mod, metrics = [dice_coef_mod])
print(model.summary())
#imageProc = imageprocess(dataDir = self.dataDir)
#X, Y = imageProc.convertImagesXY()
#X, y = self.readImagesXY()
#X_n, y_n = self.noiseImagesXY(len(X))
X_n2, y_n2 = self.readImagesXY_test()
X_n3, y_n3 = self.noiseImagesXY(len(X_n2))
#append noise
X = np.concatenate((X_n2, X_n3), axis = 0)
y = np.concatenate((y_n2, y_n3), axis = 0)
X, Y = self.randomData(X, y)
print('X.shape', X.shape)
print('Y.shape', y.shape)
'''
X, y = self.readImagesXY()
X_n, y_n = self.noiseImagesXY(len(X))
#append noise
X = np.concatenate((X, X_n, X), axis = 0)
y = np.concatenate((y, y_n, y), axis = 0)
X, Y = self.randomData(X, y)
'''
'''
X_n, y_n = imageProc.noiseImagesXY(len(X))
#append noise
X = np.concatenate((X, X_n, X), axis = 0)
y = np.concatenate((y, y_n, y), axis = 0)
X, Y = self.randomData(X, y)
'''
#all shapes
print('--------------------------------')
print('X.shape: ', X.shape)
print('Y.shape: ', Y.shape)
print('--------------------------------')
Yp = model.predict(X)
tempYp = Yp[:,:,:,0]*X[:,:,:,0]
idx = tempYp > 0.5
tempYp[idx] = 1.0
idx = tempYp <= 0.5
tempYp[idx] = 0.0
tempActYp = Yp[:,:,:,0]
idx = tempActYp > 0.5
tempActYp[idx] = 1.0
idx = tempActYp <= 0.5
tempActYp[idx] = 0.0
tempY = Y[:,:,:,0]*X[:,:,:,0]
yyp_result = tempYp*tempY
m_pts = 0
e_points = 0
detected_pts = 0
missing_shapes = 0
noPattern_shapes = 0;
pattern_shapes = 0
extra_detected_shapes = 0
for i in range(0,len(X)):
tempYp_sum = np.sum(tempYp[i])
tempY_sum = np.sum(tempY[i])
yp_sum = np.sum(tempActYp[i])
if (tempY_sum == 0 and tempYp_sum > 5):
extra_detected_shapes += 1
print('Extra points detected: ', tempYp_sum)
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(Y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
elif tempY_sum != 0 and tempYp_sum == 0 :
missing_shapes += 1
'''
print('Missing points count: ', tempY_sum)
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(Y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
'''
elif tempY_sum != 0 :
pattern_shapes += 1
print('Actual points count: ', tempY_sum)
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(Y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
else :
noPattern_shapes += 1
'''
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(Y[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
temp = tempActYp[i,:,:]#*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.subplot('122')
temp = tempActYp[i,:,:]*X[i,:,:,0]
idx = temp > 0.5
temp[idx] = 1.0
idx = temp <= 0.5
temp[idx] = 0.0
plt.imshow(temp)
plt.show()
'''
yyp_result_sum = np.sum(yyp_result[i])
detected_pts += yyp_result_sum
m_pts += tempY_sum - yyp_result_sum
e_points += tempYp_sum - yyp_result_sum
print('-------------------------------------------------')
print('Stats - - -')
print('detected points: ', detected_pts)
print('Missing points: ', m_pts)
print('Extra Detected points: ', e_points)
print('Shape images: ', pattern_shapes)
print('No shape image: ', noPattern_shapes)
print('Missing images: ', missing_shapes)
print('Extra Detected images: ', extra_detected_shapes)
print('-------------------------------------------------')
def showDIceCoeffientTrend(self):
# retrive and show
with open(self.shape + '.pkl', 'rb') as input:
lossObj = pickle.load(input)
#print('lossObj.losses = ', lossObj.losses)
#print('lossObj.dice_coef = ', lossObj.dice_coef)
plt.title('Loss trend')
plt.xlabel('epoch count')
plt.ylabel('Loss')
#plt.plot(lossObj.dice_coef)
plt.plot(lossObj.losses)
plt.show()
plt.title('Accuracy trend')
plt.xlabel('epoch count')
plt.ylabel('Accuracy')
#plt.plot(lossObj.dice_coef)
plt.plot(lossObj.accuracy)
plt.show()
def saveDataXY(self, listDir=[]):
X_data = np.zeros([1, 256, 256, 1], dtype="float_")
y_data = np.zeros([1, 256, 256, 1], dtype="float_")
imageProc = imageprocess(dataDir = '')
for dirName in listDir:
imageProc.dataDir = dirName
X, y = imageProc.convertImagesXY()
X_data = np.concatenate((X_data, X.copy()), axis = 0)
y_data = np.concatenate((y_data, y.copy()), axis = 0)
np.save('X_data', X_data)
np.save('y_data', y_data)
def showDataXY(self, X_data, y_data):
print(X_data.shape)
print(y_data.shape)
for i in range(0,len(X_data)):
plt.figure(1)
plt.subplot('121')
plt.imshow(X_data[i, :, :, 0])
plt.subplot('122')
plt.imshow(y_data[i, :, :, 0])
plt.show()
def getShape(self, shape):
switcher = {
0: 'No Shape',
1: 'Circle',
2: 'Rectangle/Square',
3: 'Line'
}
return switcher.get(shape, 'No Shape')
def showData10RandomShapes(self):
fileCount = np.random.randint(50)
X = np.load(".\\npyXYFiles\\X_data_" + str(fileCount)+ '.npy')
y = np.load(".\\npyXYFiles\\y_data_class_" + str(fileCount)+ '.npy')
X_n, y_n = train_shapes.noiseImagesXY_clasification(int(len(X)/4))
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
X, y = train_shapes.randomData(X, y)
random10 = np.random.randint(0, X.shape[0], 10)
for i in range(0,len(random10)):
print('Shape: ', self.getShape(y[random10[i]][0]))
plt.figure(1)
plt.subplot('111')
plt.imshow(X[random10[i], :, :, 0])
plt.show()
if __name__ == '__main__':
gpu_id = 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
train_shapes = trainShapes(shape='RECTANGLE', dataDir = './train_images')
#train_shapes.saveDataXY(listDir=['train_line_images_data', 'train_line_images _data_rotate_left', 'train_line_images_data_rotate_right', 'train_line_images_data_rotate_right_right'])
#train_shapes.saveDataXY(listDir=['train_circles_images_data'])
#train_shapes.train_channel_last_classification()
#train_shapes.calculate_stats_classification()
#train_shapes.train_shape_channel_last()
train_shapes.calculate_stats_2()
#train_shapes.showDIceCoeffientTrend()
#train_shapes = trainShapes(shape='CIRCLE', dataDir = './test_images')
#train_shapes.calculate_stats()
#print(train_shapes.onehot_encodeing())
#train_shapes.showData10RandomShapes()
#X, y = train_shapes.readNonClassImagesXY()
#train_shapes.showDataXY(X, y)
'''
X, y = train_shapes.readImagesXY_classification()
X_n, y_n = train_shapes.noiseImagesXY_clasification(len(X))
#append noise
X = np.concatenate((X, X_n), axis = 0)
y = np.concatenate((y, y_n), axis = 0)
print(X.shape)
print(y.shape)
df = pd.DataFrame(y)
print(df[0].unique())
encode_y = train_shapes.one_hot_encodeing(y)
X, y = train_shapes.randomData(X, encode_y)
print(y[0:20])
'''
'''
fileCount = 0;
X = np.load(".\\npyXYFiles\\X_data_" + str(fileCount)+ '.npy')
y = np.load(".\\npyXYFiles\\y_data_" + str(fileCount)+ '.npy')
y_class = np.load(".\\npyXYFiles\\y_data_class_" + str(fileCount)+ '.npy')
y_m = train_shapes.get_mody(y, y_class)
print(y_m.shape)
for i in range(len(X)):
plt.figure(1)
plt.subplot('121')
plt.imshow(X[i,:,:,0])
plt.subplot('122')
plt.imshow(y_m[i,:,:,0])
plt.show()
plt.figure(2)
plt.subplot('121')
plt.imshow(y[i,:,:,0])
plt.show()
'''
| 41.832669
| 187
| 0.500619
| 4,964
| 42,000
| 4.062853
| 0.073127
| 0.010908
| 0.012693
| 0.01428
| 0.793187
| 0.765768
| 0.750496
| 0.724712
| 0.704135
| 0.695409
| 0
| 0.037053
| 0.354214
| 42,000
| 1,004
| 188
| 41.832669
| 0.706522
| 0.076619
| 0
| 0.633452
| 0
| 0
| 0.091775
| 0.035401
| 0
| 0
| 0
| 0
| 0
| 1
| 0.060498
| false
| 0
| 0.037367
| 0.003559
| 0.135231
| 0.096085
| 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
|
c9d23ca0d87aee2941eb4ca969bbf28e98802bd9
| 955
|
py
|
Python
|
benches/test_ops_gf.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | 1
|
2022-01-01T07:48:29.000Z
|
2022-01-01T07:48:29.000Z
|
benches/test_ops_gf.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | null | null | null |
benches/test_ops_gf.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | null | null | null |
import crypto_math as gf
import pytest
ITERATION = 10**4
'''
def test_add(benchmark):
F7 = gf.GF(7)
F7_4 = gf.field_extension(F7,4)
x = F7_4([1,2])
y = F7_4([2,4])
def f():
_ = x + y
benchmark(f)
def test_sub(benchmark):
F7 = gf.GF(7)
F7_4 = gf.field_extension(F7,4)
x = F7_4([1,2])
y = F7_4([2,4])
def f():
_ = x - y
benchmark(f)
'''
def test_mul_copy(benchmark):
import galois_fields_copy as gf
F7 = gf.GF(7)
F7_4 = gf.field_extension(F7,4)
x = F7_4([1,2])
y = F7_4([2,4])
def f():
_ = x * y
benchmark(f)
def test_mul(benchmark):
F7 = gf.GF(7)
F7_4 = gf.field_extension(F7,4)
x = F7_4([1,2])
y = F7_4([2,4])
def f():
_ = x * y
benchmark(f)
'''
def test_div(benchmark):
F7 = gf.GF(7)
F7_4 = gf.field_extension(F7,4)
x = F7_4([1,2])
y = F7_4([2,4])
def f():
_ = x / y
benchmark(f)
'''
| 17.363636
| 35
| 0.510995
| 172
| 955
| 2.639535
| 0.162791
| 0.132159
| 0.066079
| 0.077093
| 0.781938
| 0.781938
| 0.781938
| 0.781938
| 0.781938
| 0.781938
| 0
| 0.110942
| 0.310995
| 955
| 55
| 36
| 17.363636
| 0.579027
| 0
| 0
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.15
| 0
| 0.35
| 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
|
a000454b5e18e27637b12b1a0f9b89cc478a5f3d
| 250
|
py
|
Python
|
Curso-em-video/Aula_107/teste.py
|
JhonAI13/Curso_python
|
27dedb0effa2c26140f46392e993b8e7a27d6eb3
|
[
"MIT"
] | null | null | null |
Curso-em-video/Aula_107/teste.py
|
JhonAI13/Curso_python
|
27dedb0effa2c26140f46392e993b8e7a27d6eb3
|
[
"MIT"
] | null | null | null |
Curso-em-video/Aula_107/teste.py
|
JhonAI13/Curso_python
|
27dedb0effa2c26140f46392e993b8e7a27d6eb3
|
[
"MIT"
] | null | null | null |
import moeda
p = float(input('Digite o preço: R$'))
print(f"""A metade de {moeda.moeda(p)} é {moeda.moeda(moeda.dobro(p))}.
O dobro de {moeda.moeda(p)} é {moeda.moeda(moeda.metade(p))}.
Aumentando 10%, temos {moeda.moeda(moeda.aumentar(p, 10))}""")
| 35.714286
| 71
| 0.672
| 44
| 250
| 3.818182
| 0.454545
| 0.47619
| 0.267857
| 0.154762
| 0.345238
| 0.345238
| 0.345238
| 0.345238
| 0
| 0
| 0
| 0.017857
| 0.104
| 250
| 6
| 72
| 41.666667
| 0.732143
| 0
| 0
| 0
| 0
| 0.4
| 0.8
| 0.364
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0.2
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a0094293746599eccfebc640bc28cab5d5880add
| 25
|
py
|
Python
|
dymos/phase/__init__.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 104
|
2018-09-08T16:52:27.000Z
|
2022-03-10T23:35:30.000Z
|
dymos/phase/__init__.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 628
|
2018-06-27T20:32:59.000Z
|
2022-03-31T19:24:32.000Z
|
dymos/phase/__init__.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 46
|
2018-06-27T20:54:07.000Z
|
2021-12-19T07:23:32.000Z
|
from .phase import Phase
| 12.5
| 24
| 0.8
| 4
| 25
| 5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.952381
| 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
|
4e5b77ad5f1dc65b2fdbea632c71361385bfab2b
| 171
|
py
|
Python
|
indexer/src/annotators/id_annotator.py
|
alliance-genome/agr_archive_initial_prototype
|
8559303de20e55886cc5bc7c2153f9357fc0ca2f
|
[
"MIT"
] | 9
|
2016-10-03T16:10:39.000Z
|
2016-10-10T16:22:52.000Z
|
indexer/src/annotators/id_annotator.py
|
alliance-genome/agr
|
8559303de20e55886cc5bc7c2153f9357fc0ca2f
|
[
"MIT"
] | 168
|
2017-02-06T17:07:20.000Z
|
2017-08-23T21:23:55.000Z
|
indexer/src/annotators/id_annotator.py
|
alliance-genome/agr_prototype
|
8559303de20e55886cc5bc7c2153f9357fc0ca2f
|
[
"MIT"
] | 12
|
2016-10-04T22:01:48.000Z
|
2017-02-01T21:17:33.000Z
|
class DoAnnotator:
# get the gene, disease_dataset in bulk, do_dataset
@staticmethod
def attach_annotations(id, dataset):
return dataset[id]
| 21.375
| 55
| 0.666667
| 20
| 171
| 5.55
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.269006
| 171
| 7
| 56
| 24.428571
| 0.888
| 0.28655
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
4e707d0fdd09752450c1f3d990948788b8cfcd28
| 336
|
py
|
Python
|
pages/widgets.py
|
isergart/gradient
|
837d882f5ab07f2a9847d0212698cdc2d9312125
|
[
"MIT"
] | null | null | null |
pages/widgets.py
|
isergart/gradient
|
837d882f5ab07f2a9847d0212698cdc2d9312125
|
[
"MIT"
] | null | null | null |
pages/widgets.py
|
isergart/gradient
|
837d882f5ab07f2a9847d0212698cdc2d9312125
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from django import forms
class Editor(forms.Textarea):
"""CKEditor widget"""
class Media:
css = {'all': ('pages/ckeditor/init/styles.css',)}
js = ('admin/js/vendor/jquery/jquery.min.js', 'pages/ckeditor/ckeditor.js', 'pages/ckeditor/adapters/jquery.js', 'pages/ckeditor/init/init.js',)
| 33.6
| 152
| 0.64881
| 44
| 336
| 4.954545
| 0.545455
| 0.238532
| 0.206422
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003509
| 0.151786
| 336
| 9
| 153
| 37.333333
| 0.761404
| 0.113095
| 0
| 0
| 0
| 0
| 0.530822
| 0.520548
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.6
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
4e9a62ee17a233ca162c9263e3f86f3276434f80
| 190
|
py
|
Python
|
0x04-python-more_data_structures/6-print_sorted_dictionary.py
|
omarcherni007/holbertonschool-higher_level_programming
|
65f3430ab0310f85368d73cb72e139631e8c6f1e
|
[
"MIT"
] | 1
|
2022-01-04T11:07:56.000Z
|
2022-01-04T11:07:56.000Z
|
0x04-python-more_data_structures/6-print_sorted_dictionary.py
|
omarcherni007/holbertonschool-higher_level_programming
|
65f3430ab0310f85368d73cb72e139631e8c6f1e
|
[
"MIT"
] | null | null | null |
0x04-python-more_data_structures/6-print_sorted_dictionary.py
|
omarcherni007/holbertonschool-higher_level_programming
|
65f3430ab0310f85368d73cb72e139631e8c6f1e
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
def print_sorted_dictionary(a_dictionary):
sorted_dictionary = sorted(a_dictionary.items())
for k, v in sorted_dictionary:
print('{0}: {1}'.format(k, v))
| 27.142857
| 52
| 0.684211
| 27
| 190
| 4.592593
| 0.592593
| 0.387097
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018868
| 0.163158
| 190
| 6
| 53
| 31.666667
| 0.761006
| 0.089474
| 0
| 0
| 0
| 0
| 0.046512
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.25
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
4ebdc90109d2acf88d7c53aac6becc87d89bbb77
| 101
|
py
|
Python
|
yaost/__init__.py
|
ariloulaleelay/yaost
|
3dd692c830dc3077b0f6b89de57e9e1433570f2b
|
[
"MIT"
] | 2
|
2020-04-05T11:18:24.000Z
|
2020-08-03T12:08:13.000Z
|
yaost/__init__.py
|
ariloulaleelay/yaost
|
3dd692c830dc3077b0f6b89de57e9e1433570f2b
|
[
"MIT"
] | null | null | null |
yaost/__init__.py
|
ariloulaleelay/yaost
|
3dd692c830dc3077b0f6b89de57e9e1433570f2b
|
[
"MIT"
] | 1
|
2020-04-05T11:18:00.000Z
|
2020-04-05T11:18:00.000Z
|
from .project import Project # noqa
from .base import Vector # noqa
from .path import Path # noqa
| 25.25
| 36
| 0.732673
| 15
| 101
| 4.933333
| 0.466667
| 0.216216
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.207921
| 101
| 3
| 37
| 33.666667
| 0.925
| 0.138614
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
4ec4de4cfb3cf182f11e7e7c9a917533d588a80a
| 1,026
|
py
|
Python
|
FizzBuzz/test_fizzbuzz.py
|
asfelix/wtdd
|
38ffc47ae230727dcc64f29eda44df173579c30d
|
[
"Apache-2.0"
] | null | null | null |
FizzBuzz/test_fizzbuzz.py
|
asfelix/wtdd
|
38ffc47ae230727dcc64f29eda44df173579c30d
|
[
"Apache-2.0"
] | null | null | null |
FizzBuzz/test_fizzbuzz.py
|
asfelix/wtdd
|
38ffc47ae230727dcc64f29eda44df173579c30d
|
[
"Apache-2.0"
] | 1
|
2019-11-12T02:59:59.000Z
|
2019-11-12T02:59:59.000Z
|
import unittest
from fizzbuzz import robot
class FizzBuzzTest(unittest.TestCase):
def test_say_1_when_1(self):
self.assertEqual(robot(1), '1')
def test_say_2_when_2(self):
self.assertEqual(robot(2), '2')
def test_say_4_when_4(self):
self.assertEqual(robot(4), '4')
def test_say_3_when_3(self):
self.assertEqual(robot(3), 'Fizz')
def test_say_6_when_6(self):
self.assertEqual(robot(6), 'Fizz')
def test_say_9_when_9(self):
self.assertEqual(robot(9), 'Fizz')
def test_say_5_when_5(self):
self.assertEqual(robot(5), 'Buzz')
def test_say_10_when_10(self):
self.assertEqual(robot(10), 'Buzz')
def test_say_20_when_20(self):
self.assertEqual(robot(20), 'Buzz')
def test_say_15_when_15(self):
self.assertEqual(robot(15), 'FizzBuzz')
def test_say_30_when_30(self):
self.assertEqual(robot(30), 'FizzBuzz')
def test_say_45_when_45(self):
self.assertEqual(robot(45), 'FizzBuzz')
| 24.428571
| 47
| 0.658869
| 154
| 1,026
| 4.077922
| 0.181818
| 0.133758
| 0.191083
| 0.458599
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066502
| 0.208577
| 1,026
| 41
| 48
| 25.02439
| 0.706897
| 0
| 0
| 0
| 0
| 0
| 0.049708
| 0
| 0
| 0
| 0
| 0
| 0.444444
| 1
| 0.444444
| false
| 0
| 0.074074
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
14db0560125a2f126db95baf43e186914ffab209
| 110
|
py
|
Python
|
study/w3resource/exercises/python-basic/031 - 060/python-basic - 053.py
|
gustavomarquezinho/python
|
e36779aa5c4bfaf88c587f05db5bd447fd41e4a2
|
[
"MIT"
] | null | null | null |
study/w3resource/exercises/python-basic/031 - 060/python-basic - 053.py
|
gustavomarquezinho/python
|
e36779aa5c4bfaf88c587f05db5bd447fd41e4a2
|
[
"MIT"
] | null | null | null |
study/w3resource/exercises/python-basic/031 - 060/python-basic - 053.py
|
gustavomarquezinho/python
|
e36779aa5c4bfaf88c587f05db5bd447fd41e4a2
|
[
"MIT"
] | null | null | null |
# 053 - Write a python program to access environment variables.
from os import environ
print(environ['PATH'])
| 27.5
| 63
| 0.772727
| 16
| 110
| 5.3125
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031915
| 0.145455
| 110
| 4
| 64
| 27.5
| 0.87234
| 0.554545
| 0
| 0
| 0
| 0
| 0.083333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
090c1f68c5035d91d9285cf7d463c94a3c7b2ec2
| 987
|
py
|
Python
|
ros/src/map_collector/src/common.py
|
jkulhanek/robot-visual-navigation
|
ddc63df38d326e9225981bf89608043c77d950e8
|
[
"MIT"
] | 13
|
2020-11-01T05:04:58.000Z
|
2022-03-23T00:15:54.000Z
|
ros/src/map_collector/src/common.py
|
leefree-GIT/robot-visual-navigation
|
ddc63df38d326e9225981bf89608043c77d950e8
|
[
"MIT"
] | 5
|
2021-03-31T13:12:11.000Z
|
2022-03-29T09:25:55.000Z
|
ros/src/map_collector/src/common.py
|
leefree-GIT/robot-visual-navigation
|
ddc63df38d326e9225981bf89608043c77d950e8
|
[
"MIT"
] | 5
|
2021-02-25T03:19:03.000Z
|
2022-03-23T00:16:03.000Z
|
class Proxy(object):
def __init__(self, inner):
object.__setattr__(
self,
"_obj",
inner
)
#
# proxying (special cases)
#
def __getattribute__(self, name):
value = getattr(object.__getattribute__(self, "_obj"), name)
if callable(value):
fn = value.__func__
value = lambda *args,**kwargs: fn(self, *args, **kwargs)
return value
def __delattr__(self, name):
delattr(object.__getattribute__(self, "_obj"), name)
def __setattr__(self, name, value):
setattr(object.__getattribute__(self, "_obj"), name, value)
def __nonzero__(self):
return bool(object.__getattribute__(self, "_obj"))
def __str__(self):
return str(object.__getattribute__(self, "_obj"))
def __repr__(self):
return repr(object.__getattribute__(self, "_obj"))
def __hash__(self):
return hash(object.__getattribute__(self, "_obj"))
| 28.2
| 68
| 0.601824
| 100
| 987
| 5.18
| 0.3
| 0.108108
| 0.297297
| 0.337838
| 0.330116
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.273556
| 987
| 35
| 69
| 28.2
| 0.722455
| 0.024316
| 0
| 0
| 0
| 0
| 0.033333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.32
| false
| 0
| 0
| 0.16
| 0.56
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
092d9fb5ab6f2cc369f7239812eacff14b572ad0
| 508
|
py
|
Python
|
tests/test_vexmpp.py
|
nicfit/vexmpp
|
e67070d2822da8356345976fb15d365935b550a6
|
[
"MIT"
] | null | null | null |
tests/test_vexmpp.py
|
nicfit/vexmpp
|
e67070d2822da8356345976fb15d365935b550a6
|
[
"MIT"
] | 349
|
2017-02-18T22:48:17.000Z
|
2021-12-13T19:50:23.000Z
|
tests/test_vexmpp.py
|
nicfit/vexmpp
|
e67070d2822da8356345976fb15d365935b550a6
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import vexmpp
"""
test_vexmpp
----------------------------------
Tests for `vexmpp` module.
"""
def test_metadata():
assert vexmpp.version
assert vexmpp.__about__.__license__
assert vexmpp.__about__.__project_name__
assert vexmpp.__about__.__author__
assert vexmpp.__about__.__author_email__
assert vexmpp.__about__.__version__
assert vexmpp.__about__.__version_info__
assert vexmpp.__about__.__release__
assert vexmpp.__about__.__version_txt__
| 24.190476
| 44
| 0.712598
| 53
| 508
| 5.509434
| 0.415094
| 0.369863
| 0.465753
| 0.246575
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002304
| 0.145669
| 508
| 20
| 45
| 25.4
| 0.670507
| 0.041339
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.818182
| 1
| 0.090909
| true
| 0
| 0.090909
| 0
| 0.181818
| 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
|
0960831046536c7369256d0c80d07eb1d8ba8d90
| 69
|
py
|
Python
|
task01.py
|
gadamslr/PythonTest
|
e9bb057818fc4e024aab603fc240e52bc8292291
|
[
"CC0-1.0"
] | null | null | null |
task01.py
|
gadamslr/PythonTest
|
e9bb057818fc4e024aab603fc240e52bc8292291
|
[
"CC0-1.0"
] | null | null | null |
task01.py
|
gadamslr/PythonTest
|
e9bb057818fc4e024aab603fc240e52bc8292291
|
[
"CC0-1.0"
] | null | null | null |
def hello():
print("Hello world ")
return "Hello world"
hello()
| 11.5
| 23
| 0.637681
| 9
| 69
| 4.888889
| 0.555556
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.202899
| 69
| 5
| 24
| 13.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0
| 0
| 0.5
| 0.25
| 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
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
116e75d9ed0c49434106a0b6bfe0b052b52592df
| 4,281
|
py
|
Python
|
testcases/indicator_tests/savitzkygolaytests.py
|
quantwizard-com/pythonbacktest
|
7056c2804c30ca571eb43dc1ae4cc3d537f6613e
|
[
"Apache-2.0"
] | null | null | null |
testcases/indicator_tests/savitzkygolaytests.py
|
quantwizard-com/pythonbacktest
|
7056c2804c30ca571eb43dc1ae4cc3d537f6613e
|
[
"Apache-2.0"
] | null | null | null |
testcases/indicator_tests/savitzkygolaytests.py
|
quantwizard-com/pythonbacktest
|
7056c2804c30ca571eb43dc1ae4cc3d537f6613e
|
[
"Apache-2.0"
] | null | null | null |
import unittest
import random
from pythonbacktest.indicator import SavitzkyGolay
from scipy.signal import savgol_filter
class SavitzkyGolayTests(unittest.TestCase):
def test_10singlevalues(self):
sg_indicator = SavitzkyGolay(window_size=3, polyorder=1, level=1)
test_data = [t for t in range(0, 9)]
for record in test_data:
sg_indicator.on_new_upstream_value(record)
self.assertEqual(test_data, sg_indicator._SavitzkyGolay__data_storage)
self.assertEqual(len(test_data), len(sg_indicator.all_result))
def test_10growing_list(self):
sg_indicator = SavitzkyGolay(window_size=3, polyorder=1, level=1)
test_data = []
for i in range(1, 11):
test_data.append([s for s in range(0, i)])
for test_record in test_data:
sg_indicator.on_new_upstream_value(test_record)
self.assertEqual(test_record, sg_indicator._SavitzkyGolay__data_storage)
def test_10elements_list_1none(self):
sg_indicator = SavitzkyGolay(window_size=3, polyorder=1, level=1)
test_data = [None] + [t for t in range(1, 9)]
sg_indicator.on_new_upstream_value(test_data)
self.assertEqual(test_data, sg_indicator._SavitzkyGolay__data_storage)
self.assertEqual(len(test_data), len(sg_indicator.all_result))
self.assertIsNone(sg_indicator.all_result[0])
def test_10elements_list_5nones(self):
sg_indicator = SavitzkyGolay(window_size=3, polyorder=1, level=1)
test_data = [None] * 5 + [t for t in range(1, 9)]
sg_indicator.on_new_upstream_value(test_data)
self.assertEqual(test_data, sg_indicator._SavitzkyGolay__data_storage)
self.assertEqual(len(test_data), len(sg_indicator.all_result))
self.assertEqual([None] * 5, sg_indicator.all_result[0:5])
def test_100elements_list_with_real_data(self):
WINDOW_SIZE = 21
POLYORDER = 1
sg_indicator = SavitzkyGolay(window_size=WINDOW_SIZE, polyorder=POLYORDER, level=1)
input_data = [random.randint(0, 20) for x in range(100)]
expected_result = savgol_filter(input_data, window_length=WINDOW_SIZE, polyorder=POLYORDER)
expected_result = list(expected_result)
# execute
sg_indicator.on_new_upstream_value(input_data)
actual_result = sg_indicator.all_result
# we're interested only in single thing: do the results match?
self.assertEqual(expected_result, actual_result)
def test_100elements_list_with_growing_data_passed_collection(self):
WINDOW_SIZE = 21
POLYORDER = 1
EXPERIMENTS = 100
input_data = [random.randint(0, 20) for x in range(100)]
sg_indicator = SavitzkyGolay(window_size=WINDOW_SIZE, polyorder=POLYORDER, level=1)
for i in range(0, 100):
input_data.append(random.randint(0, 20))
expected_result = savgol_filter(input_data, window_length=WINDOW_SIZE, polyorder=POLYORDER)
expected_result = list(expected_result)
# execute
sg_indicator.on_new_upstream_value(input_data)
actual_result = sg_indicator.all_result
# we're interested only in single thing: do the results match?
self.assertEqual(expected_result, actual_result)
def test_100elements_list_with_growing_data_passed_single_int(self):
WINDOW_SIZE = 21
POLYORDER = 1
EXPERIMENTS = 4600
input_data = []
sg_indicator = SavitzkyGolay(window_size=WINDOW_SIZE, polyorder=POLYORDER, level=1)
for i in range(0, EXPERIMENTS):
random_int = random.randint(0, 20)
input_data.append(random_int)
expected_result = [None] * len(input_data) if len(input_data) < WINDOW_SIZE \
else savgol_filter(input_data, window_length=WINDOW_SIZE, polyorder=POLYORDER)
expected_result = list(expected_result)
# execute
sg_indicator.on_new_upstream_value(random_int)
actual_result = sg_indicator.all_result
# we're interested only in single thing: do the results match?
self.assertEqual(expected_result, actual_result)
if __name__ == "__main__":
unittest.main()
| 35.97479
| 103
| 0.690493
| 551
| 4,281
| 5.032668
| 0.157895
| 0.103137
| 0.095204
| 0.057699
| 0.789037
| 0.747566
| 0.73819
| 0.710061
| 0.710061
| 0.710061
| 0
| 0.026683
| 0.229619
| 4,281
| 118
| 104
| 36.279661
| 0.81413
| 0.04812
| 0
| 0.486486
| 0
| 0
| 0.001967
| 0
| 0
| 0
| 0
| 0
| 0.162162
| 1
| 0.094595
| false
| 0.027027
| 0.054054
| 0
| 0.162162
| 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
|
119477ec95f872983b7c60921ee299022b4f88c9
| 154
|
py
|
Python
|
tests/installation_test.py
|
Fladdimir/casymda
|
6cf599bed2229c4aff9bca31350604b38ef76138
|
[
"MIT"
] | 19
|
2020-04-18T14:47:37.000Z
|
2022-03-26T14:18:21.000Z
|
tests/installation_test.py
|
Fladdimir/casymda
|
6cf599bed2229c4aff9bca31350604b38ef76138
|
[
"MIT"
] | 4
|
2020-03-17T21:01:58.000Z
|
2021-09-24T21:07:25.000Z
|
tests/installation_test.py
|
Fladdimir/casymda
|
6cf599bed2229c4aff9bca31350604b38ef76138
|
[
"MIT"
] | 4
|
2020-05-09T16:31:57.000Z
|
2022-01-23T09:11:19.000Z
|
"""test the installation"""
from casymda import __version__
def test_version():
"""version should not be none"""
assert __version__ is not None
| 19.25
| 36
| 0.714286
| 20
| 154
| 5.05
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188312
| 154
| 7
| 37
| 22
| 0.808
| 0.311688
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
11a0a1f2ebe3c2a46c23f28631fa879d3e57d676
| 7,082
|
py
|
Python
|
cohesity_management_sdk/cohesity_client.py
|
sachinthakare-cohesity/management-sdk-python
|
c95f67b7d387d5bab8392be43190e598280ae7b5
|
[
"MIT"
] | null | null | null |
cohesity_management_sdk/cohesity_client.py
|
sachinthakare-cohesity/management-sdk-python
|
c95f67b7d387d5bab8392be43190e598280ae7b5
|
[
"MIT"
] | null | null | null |
cohesity_management_sdk/cohesity_client.py
|
sachinthakare-cohesity/management-sdk-python
|
c95f67b7d387d5bab8392be43190e598280ae7b5
|
[
"MIT"
] | null | null | null |
# Copyright 2019 Cohesity Inc.
# -*- coding: utf-8 -*-
from cohesity_management_sdk.decorators import lazy_property
from cohesity_management_sdk.configuration import Configuration
from cohesity_management_sdk.http.auth.auth_manager import AuthManager
from cohesity_management_sdk.controllers.alerts import Alerts
from cohesity_management_sdk.controllers.active_directory import ActiveDirectory
from cohesity_management_sdk.controllers.tenant import Tenant
from cohesity_management_sdk.controllers.static_route import StaticRoute
from cohesity_management_sdk.controllers.preferences import Preferences
from cohesity_management_sdk.controllers.notifications import Notifications
from cohesity_management_sdk.controllers.principals import Principals
from cohesity_management_sdk.controllers.routes import Routes
from cohesity_management_sdk.controllers.remote_cluster import RemoteCluster
from cohesity_management_sdk.controllers.nodes import Nodes
from cohesity_management_sdk.controllers.interface_group import InterfaceGroup
from cohesity_management_sdk.controllers.clusters import Clusters
from cohesity_management_sdk.controllers.certificates import Certificates
from cohesity_management_sdk.controllers.app import App
from cohesity_management_sdk.controllers.app_instance import AppInstance
from cohesity_management_sdk.controllers.vlan import Vlan
from cohesity_management_sdk.controllers.views import Views
from cohesity_management_sdk.controllers.view_boxes import ViewBoxes
from cohesity_management_sdk.controllers.restore_tasks import RestoreTasks
from cohesity_management_sdk.controllers.vaults import Vaults
from cohesity_management_sdk.controllers.tenants import Tenants
from cohesity_management_sdk.controllers.statistics import Statistics
from cohesity_management_sdk.controllers.smb_file_opens import SMBFileOpens
from cohesity_management_sdk.controllers.search import Search
from cohesity_management_sdk.controllers.roles import Roles
from cohesity_management_sdk.controllers.remote_restore import RemoteRestore
from cohesity_management_sdk.controllers.protection_sources import ProtectionSources
from cohesity_management_sdk.controllers.protection_runs import ProtectionRuns
from cohesity_management_sdk.controllers.protection_policies import ProtectionPolicies
from cohesity_management_sdk.controllers.protection_jobs import ProtectionJobs
from cohesity_management_sdk.controllers.audit import Audit
from cohesity_management_sdk.controllers.kms_configuration import KmsConfiguration
from cohesity_management_sdk.controllers.privileges import Privileges
from cohesity_management_sdk.controllers.ldap_provider import LdapProvider
from cohesity_management_sdk.controllers.mimport import Import
from cohesity_management_sdk.controllers.idps import Idps
from cohesity_management_sdk.controllers.groups import Groups
from cohesity_management_sdk.controllers.dashboard import Dashboard
from cohesity_management_sdk.controllers.cluster_partitions import ClusterPartitions
from cohesity_management_sdk.controllers.export import Export
from cohesity_management_sdk.controllers.cluster import Cluster
from cohesity_management_sdk.controllers.access_tokens import AccessTokens
class CohesityClient(object):
auth = AuthManager
config = Configuration
@lazy_property
def alerts(self):
return Alerts()
@lazy_property
def active_directory(self):
return ActiveDirectory()
@lazy_property
def tenant(self):
return Tenant()
@lazy_property
def static_route(self):
return StaticRoute()
@lazy_property
def preferences(self):
return Preferences()
@lazy_property
def notifications(self):
return Notifications()
@lazy_property
def principals(self):
return Principals()
@lazy_property
def routes(self):
return Routes()
@lazy_property
def remote_cluster(self):
return RemoteCluster()
@lazy_property
def nodes(self):
return Nodes()
@lazy_property
def interface_group(self):
return InterfaceGroup()
@lazy_property
def clusters(self):
return Clusters()
@lazy_property
def certificates(self):
return Certificates()
@lazy_property
def app(self):
return App()
@lazy_property
def app_instance(self):
return AppInstance()
@lazy_property
def vlan(self):
return Vlan()
@lazy_property
def views(self):
return Views()
@lazy_property
def view_boxes(self):
return ViewBoxes()
@lazy_property
def restore_tasks(self):
return RestoreTasks()
@lazy_property
def vaults(self):
return Vaults()
@lazy_property
def tenants(self):
return Tenants()
@lazy_property
def statistics(self):
return Statistics()
@lazy_property
def smb_file_opens(self):
return SMBFileOpens()
@lazy_property
def search(self):
return Search()
@lazy_property
def roles(self):
return Roles()
@lazy_property
def remote_restore(self):
return RemoteRestore()
@lazy_property
def protection_sources(self):
return ProtectionSources()
@lazy_property
def protection_runs(self):
return ProtectionRuns()
@lazy_property
def protection_policies(self):
return ProtectionPolicies()
@lazy_property
def protection_jobs(self):
return ProtectionJobs()
@lazy_property
def audit(self):
return Audit()
@lazy_property
def kms_configuration(self):
return KmsConfiguration()
@lazy_property
def privileges(self):
return Privileges()
@lazy_property
def ldap_provider(self):
return LdapProvider()
@lazy_property
def mimport(self):
return Import()
@lazy_property
def idps(self):
return Idps()
@lazy_property
def groups(self):
return Groups()
@lazy_property
def dashboard(self):
return Dashboard()
@lazy_property
def cluster_partitions(self):
return ClusterPartitions()
@lazy_property
def export(self):
return Export()
@lazy_property
def cluster(self):
return Cluster()
@lazy_property
def access_tokens(self):
return AccessTokens()
def __init__(self,
cluster_vip=None,
username=None,
password=None,
domain=None,
auth_token=None):
#CohesityPatch
if cluster_vip == None:
raise Exception("Specify cluster VIP")
if auth_token != None:
Configuration.auth_token = auth_token
if username != None:
Configuration.username = username
if password != None:
Configuration.password = password
Configuration.auth_token = None # Flushing existing token.
if domain != None:
Configuration.domain = domain
Configuration.cluster_vip = cluster_vip
| 28.788618
| 86
| 0.743858
| 769
| 7,082
| 6.612484
| 0.141743
| 0.106195
| 0.19469
| 0.221239
| 0.311504
| 0.084956
| 0
| 0
| 0
| 0
| 0
| 0.000882
| 0.199096
| 7,082
| 245
| 87
| 28.906122
| 0.895628
| 0.012426
| 0
| 0.21875
| 0
| 0
| 0.002719
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.223958
| false
| 0.015625
| 0.244792
| 0.21875
| 0.703125
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
11a9573fe6fb2059fe2ca583ba745fe3732f0a95
| 185
|
py
|
Python
|
jarvis_discord/__init__.py
|
upsilon-group/jarvis-discord
|
9113703fd3811541eb17cae461f9b52bc10417fa
|
[
"MIT"
] | 1
|
2018-08-30T23:34:09.000Z
|
2018-08-30T23:34:09.000Z
|
jarvis_discord/__init__.py
|
upsilon-group/jarvis-discord
|
9113703fd3811541eb17cae461f9b52bc10417fa
|
[
"MIT"
] | 10
|
2020-07-09T06:27:53.000Z
|
2021-06-25T15:26:07.000Z
|
jarvis_discord/__init__.py
|
Luoskate/jarvis-discord
|
9113703fd3811541eb17cae461f9b52bc10417fa
|
[
"MIT"
] | 1
|
2020-07-31T15:22:19.000Z
|
2020-07-31T15:22:19.000Z
|
"""Jarvis Discord BOT.
AUTHOR : Luoskate
VERSION : 1.1
"""
from .config import Config
from .utils import JarvisHelpCommand
from .converters import GuildConverter
from .embeds import *
| 18.5
| 38
| 0.772973
| 23
| 185
| 6.217391
| 0.652174
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012658
| 0.145946
| 185
| 9
| 39
| 20.555556
| 0.892405
| 0.281081
| 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
|
11c25b6c9ae3a39fb64a4fbc08133c7cb763228e
| 268
|
py
|
Python
|
AutoMark/admin.py
|
tyanakiev/AutoMark
|
4e44a9f7c448f02bc4abc05c7a45a67fc71aa3f9
|
[
"MIT"
] | 1
|
2018-02-25T06:43:13.000Z
|
2018-02-25T06:43:13.000Z
|
AutoMark/admin.py
|
tyanakiev/AutoMark
|
4e44a9f7c448f02bc4abc05c7a45a67fc71aa3f9
|
[
"MIT"
] | 4
|
2021-04-17T03:55:49.000Z
|
2022-02-10T10:29:08.000Z
|
AutoMark/admin.py
|
tyanakiev/AutoMark
|
4e44a9f7c448f02bc4abc05c7a45a67fc71aa3f9
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from AutoMark.models import InstagramAccount, InstagramSettings, InstagramCeleryTask
# Register your models here.
admin.site.register(InstagramAccount)
admin.site.register(InstagramSettings)
admin.site.register(InstagramCeleryTask)
| 26.8
| 84
| 0.854478
| 28
| 268
| 8.178571
| 0.5
| 0.117904
| 0.222707
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078358
| 268
| 9
| 85
| 29.777778
| 0.927126
| 0.097015
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 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
|
11d53a6a65b5fb8a9ecff1613387bc3bfbe0b1e4
| 351
|
py
|
Python
|
heliosburn/django/hbproject/webui/exceptions.py
|
thecodeteam/heliosburn
|
513f6335c9788948d82e5c9285d7869f3ff4cc10
|
[
"MIT"
] | null | null | null |
heliosburn/django/hbproject/webui/exceptions.py
|
thecodeteam/heliosburn
|
513f6335c9788948d82e5c9285d7869f3ff4cc10
|
[
"MIT"
] | null | null | null |
heliosburn/django/hbproject/webui/exceptions.py
|
thecodeteam/heliosburn
|
513f6335c9788948d82e5c9285d7869f3ff4cc10
|
[
"MIT"
] | 1
|
2020-09-17T18:19:05.000Z
|
2020-09-17T18:19:05.000Z
|
class UnauthorizedException(Exception):
pass
class BadRequestException(Exception):
pass
class NotFoundException(Exception):
pass
class RedirectException(Exception):
pass
class ServerErrorException(Exception):
pass
class UnexpectedException(Exception):
pass
class LocationHeaderNotFoundException(Exception):
pass
| 13.5
| 49
| 0.769231
| 28
| 351
| 9.642857
| 0.357143
| 0.337037
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.17094
| 351
| 26
| 50
| 13.5
| 0.927835
| 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
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
eec391a83b8061e2bdf8066313189671d0bfb64b
| 57
|
py
|
Python
|
datadog/threadstats/__init__.py
|
Censio/datadogpy
|
0a52e54cd104caa99b822f0cc14237ab7f01539f
|
[
"BSD-3-Clause"
] | 2
|
2017-02-17T19:58:58.000Z
|
2018-02-13T17:55:49.000Z
|
datadog/threadstats/__init__.py
|
Censio/datadogpy
|
0a52e54cd104caa99b822f0cc14237ab7f01539f
|
[
"BSD-3-Clause"
] | null | null | null |
datadog/threadstats/__init__.py
|
Censio/datadogpy
|
0a52e54cd104caa99b822f0cc14237ab7f01539f
|
[
"BSD-3-Clause"
] | null | null | null |
from datadog.threadstats.base import ThreadStats # noqa
| 28.5
| 56
| 0.824561
| 7
| 57
| 6.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122807
| 57
| 1
| 57
| 57
| 0.94
| 0.070175
| 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
|
eede27936282c2fd08fce082f85254841f4f8175
| 149
|
py
|
Python
|
w2vembeddings/__init__.py
|
LG-1/w2vembeddings
|
9daccecbc1ee788a97da6e7d4efe5bebd7bdb045
|
[
"MIT"
] | 1
|
2019-01-07T03:56:30.000Z
|
2019-01-07T03:56:30.000Z
|
w2vembeddings/__init__.py
|
LG-1/w2vembeddings
|
9daccecbc1ee788a97da6e7d4efe5bebd7bdb045
|
[
"MIT"
] | null | null | null |
w2vembeddings/__init__.py
|
LG-1/w2vembeddings
|
9daccecbc1ee788a97da6e7d4efe5bebd7bdb045
|
[
"MIT"
] | null | null | null |
__version__ = '0.1.2'
from w2vembeddings.embedding import Embedding
from w2vembeddings.w2vemb import EMB
from w2vembeddings.managedb import ManageDB
| 29.8
| 45
| 0.845638
| 19
| 149
| 6.421053
| 0.578947
| 0.418033
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052239
| 0.100671
| 149
| 4
| 46
| 37.25
| 0.858209
| 0
| 0
| 0
| 0
| 0
| 0.033557
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e122dac2cd41e3f94b7fb47033366e052b400d3d
| 249
|
py
|
Python
|
src/pandas_profiling_study/report/presentation/flavours/html/warnings.py
|
lucasiscoviciMoon/pandas-profiling-study
|
142d3b0f5e3139cdb531819f637a407682fa5684
|
[
"MIT"
] | null | null | null |
src/pandas_profiling_study/report/presentation/flavours/html/warnings.py
|
lucasiscoviciMoon/pandas-profiling-study
|
142d3b0f5e3139cdb531819f637a407682fa5684
|
[
"MIT"
] | null | null | null |
src/pandas_profiling_study/report/presentation/flavours/html/warnings.py
|
lucasiscoviciMoon/pandas-profiling-study
|
142d3b0f5e3139cdb531819f637a407682fa5684
|
[
"MIT"
] | 1
|
2020-04-25T15:20:39.000Z
|
2020-04-25T15:20:39.000Z
|
from .....report.presentation.core.warnings import Warnings
from .....report.presentation.flavours.html import templates
class HTMLWarnings(Warnings):
def render(self):
return templates.template("warnings.html").render(**self.content)
| 31.125
| 73
| 0.751004
| 28
| 249
| 6.678571
| 0.607143
| 0.106952
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11245
| 249
| 7
| 74
| 35.571429
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0.052209
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0.2
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
|
0
| 5
|
014db594ef6a914035d1d74272ee7c621b846fc9
| 131
|
py
|
Python
|
qcdb/intf_dftd3/__init__.py
|
nuwandesilva/qcdb
|
b47fb2ed550fc4176198ddb1dbea3724d6704d23
|
[
"BSD-3-Clause"
] | 1
|
2019-02-20T20:18:02.000Z
|
2019-02-20T20:18:02.000Z
|
qcdb/iface_dftd3/__init__.py
|
vivacebelles/qcdb
|
5bbdcb5c833277647a36bb0a5982abb56bf29b20
|
[
"BSD-3-Clause"
] | null | null | null |
qcdb/iface_dftd3/__init__.py
|
vivacebelles/qcdb
|
5bbdcb5c833277647a36bb0a5982abb56bf29b20
|
[
"BSD-3-Clause"
] | null | null | null |
from .dashparam import dashcoeff, full_dash_keys, dash_alias, dash_server, dftd3_list
from .runner import run_dftd3, alt_run_dftd3
| 43.666667
| 85
| 0.847328
| 21
| 131
| 4.904762
| 0.666667
| 0.15534
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025424
| 0.099237
| 131
| 2
| 86
| 65.5
| 0.847458
| 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
|
0160dd81ab2cde2489cd2dc785b027c1e64a7553
| 73
|
py
|
Python
|
input/helpers/__init__.py
|
ml-boringtao/rnn
|
d7c7fd3ced77d7db061e4077a3532f74d2788886
|
[
"MIT"
] | 3
|
2018-03-24T15:28:18.000Z
|
2021-07-26T11:42:28.000Z
|
input/helpers/__init__.py
|
ml-boringtao/rnn
|
d7c7fd3ced77d7db061e4077a3532f74d2788886
|
[
"MIT"
] | null | null | null |
input/helpers/__init__.py
|
ml-boringtao/rnn
|
d7c7fd3ced77d7db061e4077a3532f74d2788886
|
[
"MIT"
] | 2
|
2020-10-20T14:48:01.000Z
|
2021-02-09T14:51:31.000Z
|
from .hdf5datasetwriter import HDF5DatasetWriter
from .utils import Utils
| 36.5
| 48
| 0.876712
| 8
| 73
| 8
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030303
| 0.09589
| 73
| 2
| 49
| 36.5
| 0.939394
| 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
|
0166c34232d06377747bd515afc5dfe5ec7bd48a
| 24,716
|
py
|
Python
|
pair-classifier/tests/tests.py
|
kingdido999/bugswarm
|
8ff2b3e71ca2598c354e8481c6b887cd5988816a
|
[
"BSD-3-Clause"
] | 18
|
2019-12-27T06:53:39.000Z
|
2022-03-03T03:05:35.000Z
|
pair-classifier/tests/tests.py
|
kingdido999/bugswarm
|
8ff2b3e71ca2598c354e8481c6b887cd5988816a
|
[
"BSD-3-Clause"
] | 13
|
2020-01-10T17:11:38.000Z
|
2021-12-13T20:34:38.000Z
|
pair-classifier/tests/tests.py
|
kingdido999/bugswarm
|
8ff2b3e71ca2598c354e8481c6b887cd5988816a
|
[
"BSD-3-Clause"
] | 10
|
2020-01-10T17:36:57.000Z
|
2021-09-13T19:51:43.000Z
|
import unittest
import sys
sys.path.append("../")
from pair_classifier.classify_bugs import process_error # noqa: E402
class Test(unittest.TestCase):
@staticmethod
def read_file_to_list(log_path):
lines = []
with open(log_path) as f:
for line in f:
lines.append(line.rstrip())
return lines
def compare_error_dict(self, result: dict, should_be: dict):
self.assertDictEqual(should_be, result)
def compare_user_def_error(self, result: list, should_be: list):
result, should_be = set(result), set(should_be)
self.assertSetEqual(should_be, result)
def compare_confidence(self, result: float, should_be: float):
# print(result, should_be)
self.assertEqual(should_be, result)
def test_java_mvn_process_error_1(self):
log = "334968079.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {"NullPointerException": 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_2(self):
log = "71816517.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 2})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_3(self):
log = "90868641.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 4})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_4(self):
log = "255051211.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 146})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_5(self):
log = "93618854.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 38,
'IllegalStateException': 24, 'UnsupportedOperationException': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_6(self):
log = "123642638.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'InvocationTargetException': 1, 'NullPointerException': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_process_error_7(self):
log = "201546728.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'IllegalStateException': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_process_error_1(self):
log = "84151798.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'UnboundLocalError': 2, 'URLError': 3})
self.compare_user_def_error(user_def_errors, ['URLError'])
def test_python_process_error_2(self):
log = "387279901.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_process_error_3(self):
log = "163925598.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
def test_python_process_error_4(self):
log = "79576031.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'TypeError': 39})
self.compare_user_def_error(user_def_errors, [])
def test_python_unittest_process_error_5(self):
log = "109227526.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'UnicodeDecodeError': 1, 'AssertionError': 2, 'BadRequestKeyError': 2})
self.compare_user_def_error(user_def_errors, ['BadRequestKeyError'])
def test_python_unittest_process_error_6(self):
log = "71127915.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'PermissionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_unittest_process_error_7(self):
log = "83097609.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_unittest_process_error_8(self):
log = "107475404.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_unittest_process_error_9(self):
log = "356963348.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 2})
self.compare_user_def_error(user_def_errors, [])
# def test_python_unittest_process_error_10(self):
# log = "367963035.log"
# file_path = "logs/" + log
# lines = self.read_file_to_list(file_path)
# lang = "python"
# error_dict, user_def_errors, confidence = process_error(lang, lines)
# self.compare_error_dict(error_dict, {'ImportError': 1, 'ValueError': 1})
# self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_1(self):
log = "360721043.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 10})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_2(self):
log = "214979627.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_3(self):
log = "331910347.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AttributeError': 4})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_4(self):
log = "316134246.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'TypeError': 10, 'SystemExit': 10, 'RuntimeError': 4})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_5(self):
log = "405742384_modified.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 8, 'FooBar': 36})
self.compare_user_def_error(user_def_errors, ['FooBar'])
def test_python_pytest_process_error_6(self):
log = "83739366.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'TemplateSyntaxError': 5})
self.compare_user_def_error(user_def_errors, ['TemplateSyntaxError'])
def test_python_pytest_process_error_7(self):
log = "107125259.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1, 'AttributeError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_8(self):
log = "389597748.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 6, 'SystemExit': 2})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_9(self):
log = "403765814.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'DocTestFailure': 1})
self.compare_user_def_error(user_def_errors, ['DocTestFailure'])
def test_python_pytest_process_error_10(self):
log = "330142563.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_11(self):
log = "46673191.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'SyntaxError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_12(self):
log = "375673938.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'ModuleNotFoundError': 94, 'ImportError': 28})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_13(self):
log = "287718761.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'PicklingError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_process_error_14(self):
log = "344823668.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'SyntaxError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_python_pytest_successful_build(self):
log = "405750843.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "python"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Test whether "Exception1 at [...] Caused By: Exception2 at [...]" is counted correctly in Maven logs.
def test_java_mvn_causedby_process_error_1(self):
log = "110208140.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'ComponentLookupException': 2, 'ProvisionException': 2})
self.compare_user_def_error(user_def_errors, ['ProvisionException', 'ComponentLookupException'])
def test_java_mvn_causedby_process_error_2(self):
log = "166980116.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {
'NullPointerException': 2, 'ExecutionException': 1, 'YamcsApiException': 1})
self.compare_user_def_error(user_def_errors, ['YamcsApiException'])
# Test whether the classifier catches exception names that don't end in 'Exception' or 'Error'.
def test_java_mvn_nonstandard_name_process_error(self):
log = "108400121.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'ArgumentsAreDifferent': 1})
self.compare_user_def_error(user_def_errors, ['ArgumentsAreDifferent'])
# Test whether the classifier identifies traces of the form "[INFO] java.lang.NullPointerException" (or similar)
def test_java_mvn_text_before_exception_process_error(self):
log = "75144750.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'ExceptionInInitializerError': 1,
'RuntimeException': 2, 'InternalCompilerException': 2,
'HostedModeException': 2})
self.compare_user_def_error(user_def_errors, ['InternalCompilerException', 'HostedModeException'])
# Test whether the classifier counts exceptions that are subclasses of other classes.
# These come in the format "ParentClass$SubClass".
def test_java_mvn_exception_is_subclass_process_error(self):
log = "136259688.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertFailedException': 1})
self.compare_user_def_error(user_def_errors, ['AssertFailedException'])
def test_java_mvn_semicolon_after_exception_process_error(self):
log = "102665470.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'SAXParseException': 1})
self.compare_user_def_error(user_def_errors, [])
# Edge case: in 408889048.log, there is no exception at the start of the stack trace after the "<<< FAILURE!".
# Instead, there is an explanation of
# what went wrong. The rest of the stack trace proceeds normally, with a "Caused by: java.lang.AssertionError"
# some way down the trace. This test makes sure that AssertionError is counted.
def test_java_mvn_no_initial_exception(self):
log = "408889048.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_process_error_1(self):
log = "114302339.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 11, 'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_process_error_2(self):
log = "67967396.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'TimeoutException': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_process_error_3(self):
log = "64373562.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 2})
self.compare_user_def_error(user_def_errors, [])
# Test whether "Exception1 at [...] Caused By: Exception2 at [...]" is counted correctly in Gradle logs.
def test_java_gradle_causedby_process_error_1(self):
log = "64037267.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'RuntimeException': 1, 'TimeoutException': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_causedby_process_error_2(self):
log = "144826559.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'RuntimeException': 70, 'NoSuchMethodException': 71, 'AssertionError': 3})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_causedby_process_error_3(self):
log = "63073864.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 46})
self.compare_user_def_error(user_def_errors, [])
def test_java_gradle_sameline_process_error(self):
log = "358767427.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'AssertionError': 1})
self.compare_user_def_error(user_def_errors, [])
def test_java_ant_process_error(self):
log = "233645906.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'RuntimeException': 52})
self.compare_user_def_error(user_def_errors, [])
# Test to make sure that no errors are counted on successful builds
# (with no stack traces in their logs).
def test_java_mvn_successful_build(self):
log = "95797603.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Some logs of successful builds have stack traces from exceptions that don't make the build fail.
# Since the classifier is only supposed to find exceptions that make the build fail,
# it shouldn't count exceptions from those stack traces.
def test_java_mvn_successful_build_with_stacktraces(self):
log = "232256103.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Same as test_java_mvn_successful_build, but with a log from a Gradle build.
def test_java_gradle_successful_build(self):
log = "64374491.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Same as test_java_mvn_successful_build_with_stacktraces, but with a log from a Gradle build.
def test_java_gradle_successful_build_with_stacktraces(self):
log = "67980613.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Same as test_java_mvn_successful_build, but with a log from an Ant build.
def test_java_ant_successful_build(self):
log = "233655405.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {})
self.compare_user_def_error(user_def_errors, [])
# Make sure the classifier doesn't count function names ending with 'exception' or 'error' as exceptions.
# (The log this tests references a function called 'sqlException'.)
def test_java_mvn_funcname_process_error(self):
log = "290369132.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {'NullPointerException': 31})
self.compare_user_def_error(user_def_errors, [])
def test_java_mvn_bare_exception_no_causedby(self):
log = "117115625.log"
file_path = "logs/" + log
lines = self.read_file_to_list(file_path)
lang = "java"
error_dict, user_def_errors, confidence = process_error(lang, lines)
self.compare_error_dict(error_dict, {
'NullPointerException': 53, 'PersistenceException': 46, 'BuilderException': 46,
'Exception': 1})
self.compare_user_def_error(user_def_errors, ['PersistenceException', 'BuilderException'])
if __name__ == '__main__':
unittest.main()
| 45.517495
| 119
| 0.676404
| 3,185
| 24,716
| 4.859655
| 0.086342
| 0.09478
| 0.090709
| 0.049748
| 0.787763
| 0.76276
| 0.746931
| 0.736529
| 0.731878
| 0.705324
| 0
| 0.032453
| 0.223297
| 24,716
| 542
| 120
| 45.601476
| 0.773819
| 0.082376
| 0
| 0.607539
| 0
| 0
| 0.116385
| 0.015365
| 0
| 0
| 0
| 0
| 0.044346
| 1
| 0.126386
| false
| 0
| 0.008869
| 0
| 0.13969
| 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
|
017a4ec011344f593f110821e2a3e44ea03f74bd
| 116
|
py
|
Python
|
bot/plugins/__init__.py
|
invincibleJai/osio-chatbot
|
312bb38d027ba52757c33cf1c5c9c4514e20aa75
|
[
"Apache-2.0"
] | 2
|
2018-08-10T16:45:54.000Z
|
2020-08-12T04:56:32.000Z
|
bot/plugins/__init__.py
|
invincibleJai/osio-chatbot
|
312bb38d027ba52757c33cf1c5c9c4514e20aa75
|
[
"Apache-2.0"
] | 9
|
2020-01-28T22:23:26.000Z
|
2022-02-09T23:51:52.000Z
|
plugins/mattermost/__init__.py
|
ravsa/osio-chatbot-backend
|
31574898ed51ea553ab5102fc5093d20ee11c049
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Initialize package."""
from .mattermost import mattermost_runner
| 19.333333
| 41
| 0.689655
| 14
| 116
| 5.642857
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019608
| 0.12069
| 116
| 5
| 42
| 23.2
| 0.754902
| 0.543103
| 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
|
6d6be976f40c681e448365f94bed9ca4198f23b9
| 117
|
py
|
Python
|
RT_Group/main/admin.py
|
DiForzza/site_one
|
440efe197e1ecaab3416460f1827738d160e48b6
|
[
"Apache-2.0"
] | null | null | null |
RT_Group/main/admin.py
|
DiForzza/site_one
|
440efe197e1ecaab3416460f1827738d160e48b6
|
[
"Apache-2.0"
] | null | null | null |
RT_Group/main/admin.py
|
DiForzza/site_one
|
440efe197e1ecaab3416460f1827738d160e48b6
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
from .models import Task, Test
admin.site.register(Task)
admin.site.register(Test)
| 19.5
| 32
| 0.803419
| 18
| 117
| 5.222222
| 0.555556
| 0.191489
| 0.361702
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 117
| 5
| 33
| 23.4
| 0.895238
| 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
|
6d740f70557c3248f205ce078ff07e81db75828f
| 105
|
py
|
Python
|
atcoder/pbc2019a.py
|
tomato-300yen/coding
|
db6f440a96d8c83f486005c650461a69f27e3926
|
[
"MIT"
] | null | null | null |
atcoder/pbc2019a.py
|
tomato-300yen/coding
|
db6f440a96d8c83f486005c650461a69f27e3926
|
[
"MIT"
] | null | null | null |
atcoder/pbc2019a.py
|
tomato-300yen/coding
|
db6f440a96d8c83f486005c650461a69f27e3926
|
[
"MIT"
] | null | null | null |
A, B, C = map(int, input().split())
print("Yes" if (A <= C and C <= B) or (B <= C and C <= A) else "No")
| 35
| 68
| 0.485714
| 23
| 105
| 2.217391
| 0.608696
| 0.078431
| 0.196078
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.238095
| 105
| 2
| 69
| 52.5
| 0.6375
| 0
| 0
| 0
| 0
| 0
| 0.047619
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 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
| 0
| 0
| 0
| 1
|
0
| 5
|
6d8b4a619d1414b62c1b73a0213acb786caa0581
| 1,081
|
py
|
Python
|
test/test_nmf.py
|
mariolpantunes/nmf
|
5f481bacc5c54beba31fc041aeacce63f63791db
|
[
"MIT"
] | null | null | null |
test/test_nmf.py
|
mariolpantunes/nmf
|
5f481bacc5c54beba31fc041aeacce63f63791db
|
[
"MIT"
] | null | null | null |
test/test_nmf.py
|
mariolpantunes/nmf
|
5f481bacc5c54beba31fc041aeacce63f63791db
|
[
"MIT"
] | null | null | null |
import unittest
import numpy as np
import nmf.nmf as nmf
class TestSum(unittest.TestCase):
def test_nmf_mu_00(self):
X = np.random.rand(5,5)
Xr, W, H, cost = nmf.nmf_mu(X, k=2)
np.testing.assert_almost_equal(X, Xr, decimal=0)
def test_nmf_mu_kl_00(self):
X = np.random.rand(5,5)
Xr, W, H, cost = nmf.nmf_mu_kl(X, k=5)
self.assertAlmostEqual(0.0, nmf.cost_kl(X, Xr), delta=0.01)
def test_nmf_mu_kl_01(self):
X = np.array([[1,0,3], [0,2,0], [4,5,6]])
Xr, W, H, cost = nmf.nmf_mu_kl(X, k=3)
self.assertAlmostEqual(0.0, nmf.cost_kl(X, Xr), delta=0.01)
def test_nmf_mu_is_00(self):
X = np.random.rand(5,5)
Xr, W, H, cost = nmf.nmf_mu_is(X, k=5)
self.assertAlmostEqual(0.0, nmf.cost_is(X, Xr), delta=0.01)
def test_nmf_mu_is_01(self):
X = np.array([[1,0,3], [0,2,0], [4,5,6]])
Xr, W, H, cost = nmf.nmf_mu_is(X, k=3)
self.assertAlmostEqual(0.0, nmf.cost_is(X, Xr), delta=0.01)
if __name__ == '__main__':
unittest.main()
| 30.885714
| 67
| 0.580019
| 207
| 1,081
| 2.826087
| 0.207729
| 0.08547
| 0.08547
| 0.102564
| 0.747009
| 0.719658
| 0.719658
| 0.719658
| 0.719658
| 0.712821
| 0
| 0.073801
| 0.247919
| 1,081
| 34
| 68
| 31.794118
| 0.645756
| 0
| 0
| 0.346154
| 0
| 0
| 0.007401
| 0
| 0
| 0
| 0
| 0
| 0.192308
| 1
| 0.192308
| false
| 0
| 0.115385
| 0
| 0.346154
| 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
|
098ac9ae3edc273135ad871861c7b93b32dbf246
| 8,848
|
py
|
Python
|
src/players/migrations/0024_rename_tmp_to_player.py
|
reddcoin-project/ReddConnect
|
5c212683de6b80b81fd15ed05239c3a1b46c3afd
|
[
"BSD-3-Clause"
] | 2
|
2019-02-24T00:20:47.000Z
|
2020-04-24T15:50:31.000Z
|
src/players/migrations/0024_rename_tmp_to_player.py
|
reddcoin-project/ReddConnect
|
5c212683de6b80b81fd15ed05239c3a1b46c3afd
|
[
"BSD-3-Clause"
] | null | null | null |
src/players/migrations/0024_rename_tmp_to_player.py
|
reddcoin-project/ReddConnect
|
5c212683de6b80b81fd15ed05239c3a1b46c3afd
|
[
"BSD-3-Clause"
] | 1
|
2019-01-05T15:51:37.000Z
|
2019-01-05T15:51:37.000Z
|
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
db.rename_table('players_playerdbtmp', 'players_playerdb')
db.rename_table('players_playerdbtmp_groups', 'players_playerdb_groups')
db.rename_column('players_playerdb_groups', 'playerdbtmp_id', 'playerdb_id')
db.rename_table('players_playerdbtmp_user_permissions', 'players_playerdb_user_permissions')
db.rename_column('players_playerdb_user_permissions', 'playerdbtmp_id', 'playerdb_id')
def backwards(self, orm):
db.rename_table('players_playerdb_groups', 'players_playerdbtmp_groups')
db.rename_column('players_playerdbtmp_groups', 'playerdb_id', 'playerdbtmp_id')
db.rename_table('players_playerdb_user_permissions', 'players_playerdbtmp_user_permissions')
db.rename_column('players_playerdbtmp_user_permissions', 'playerdb_id', 'playerdbtmp_id')
models = {
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'players.playerdb': {
'Meta': {'object_name': 'PlayerDB'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'db_attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['typeclasses.Attribute']", 'null': 'True', 'symmetrical': 'False'}),
'db_cmdset_storage': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}),
'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'db_is_connected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'db_permissions': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'db_typeclass_path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'players.playerdbtmp': {
'Meta': {'object_name': 'PlayerDBtmp'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'db_attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['typeclasses.Attribute']", 'null': 'True', 'symmetrical': 'False'}),
'db_cmdset_storage': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}),
'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'db_is_connected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'db_permissions': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'db_typeclass_path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'players.playernick': {
'Meta': {'unique_together': "(('db_nick', 'db_type', 'db_obj'),)", 'object_name': 'PlayerNick'},
'db_nick': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'db_obj': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['players.PlayerDB']"}),
'db_real': ('django.db.models.fields.TextField', [], {}),
'db_type': ('django.db.models.fields.CharField', [], {'default': "'inputline'", 'max_length': '16', 'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
},
u'typeclasses.attribute': {
'Meta': {'object_name': 'Attribute'},
'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'db_value': ('src.utils.picklefield.PickledObjectField', [], {'null': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
}
}
complete_apps = ['players']
| 79.711712
| 187
| 0.590981
| 940
| 8,848
| 5.378723
| 0.121277
| 0.099684
| 0.171677
| 0.245253
| 0.792919
| 0.739122
| 0.714201
| 0.714201
| 0.695411
| 0.658228
| 0
| 0.009878
| 0.176198
| 8,848
| 110
| 188
| 80.436364
| 0.68377
| 0.002373
| 0
| 0.485437
| 0
| 0
| 0.597167
| 0.33847
| 0
| 0
| 0
| 0
| 0
| 1
| 0.019417
| false
| 0.019417
| 0.038835
| 0
| 0.087379
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
09b85e2525b9b4dc25e388085ed4a6db6a1370f5
| 178
|
py
|
Python
|
yb_backend/introspection.py
|
yugabyte/yb-django
|
5d05896b4f6062102fe534922c9154a54a3c01d8
|
[
"Apache-2.0"
] | 5
|
2021-10-30T19:00:12.000Z
|
2022-02-26T04:54:03.000Z
|
yb_backend/introspection.py
|
yugabyte/yb-django
|
5d05896b4f6062102fe534922c9154a54a3c01d8
|
[
"Apache-2.0"
] | null | null | null |
yb_backend/introspection.py
|
yugabyte/yb-django
|
5d05896b4f6062102fe534922c9154a54a3c01d8
|
[
"Apache-2.0"
] | null | null | null |
from django.db.backends.postgresql.introspection import (
DatabaseIntrospection as PGDatabaseIntrospection,
)
class DatabaseIntrospection(PGDatabaseIntrospection):
pass
| 25.428571
| 57
| 0.831461
| 14
| 178
| 10.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11236
| 178
| 7
| 58
| 25.428571
| 0.936709
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.2
| 0.2
| 0
| 0.4
| 0
| 1
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| 1
| null | 0
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| 0
| 0
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| 0
| 0
| 0
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
61d365cad64a1a3785ca78e5c451f67949a9cda4
| 73
|
py
|
Python
|
2483.py
|
heltonricardo/URI
|
160cca22d94aa667177c9ebf2a1c9864c5e55b41
|
[
"MIT"
] | 6
|
2021-04-13T00:33:43.000Z
|
2022-02-10T10:23:59.000Z
|
2483.py
|
heltonricardo/URI
|
160cca22d94aa667177c9ebf2a1c9864c5e55b41
|
[
"MIT"
] | null | null | null |
2483.py
|
heltonricardo/URI
|
160cca22d94aa667177c9ebf2a1c9864c5e55b41
|
[
"MIT"
] | 3
|
2021-03-23T18:42:24.000Z
|
2022-02-10T10:24:07.000Z
|
print('Feliz nat', end='')
print('a' * int(input()), end='')
print('l!')
| 18.25
| 33
| 0.534247
| 11
| 73
| 3.545455
| 0.727273
| 0.410256
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109589
| 73
| 3
| 34
| 24.333333
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0.164384
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
|
61d969a36b8a4befeaf9c49d05885f4b782de22e
| 83
|
py
|
Python
|
testing/example_scripts/marks/marks_considered_keywords/test_marks_as_keywords.py
|
tinkerlin/pytest
|
bed3918cbc800682681a26c163f4cb0868b3a612
|
[
"MIT"
] | 5,079
|
2015-01-01T03:39:46.000Z
|
2022-03-31T07:38:22.000Z
|
testing/example_scripts/marks/marks_considered_keywords/test_marks_as_keywords.py
|
tinkerlin/pytest
|
bed3918cbc800682681a26c163f4cb0868b3a612
|
[
"MIT"
] | 1,623
|
2015-01-01T08:06:24.000Z
|
2022-03-30T19:48:52.000Z
|
testing/example_scripts/marks/marks_considered_keywords/test_marks_as_keywords.py
|
tinkerlin/pytest
|
bed3918cbc800682681a26c163f4cb0868b3a612
|
[
"MIT"
] | 2,033
|
2015-01-04T07:18:02.000Z
|
2022-03-28T19:55:47.000Z
|
# -*- coding: utf-8 -*-
import pytest
@pytest.mark.foo
def test_mark():
pass
| 10.375
| 23
| 0.614458
| 12
| 83
| 4.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015152
| 0.204819
| 83
| 7
| 24
| 11.857143
| 0.742424
| 0.253012
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0.25
| 0.25
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
61ef4122e83df463d5cfbbe365944fa6d26ac63c
| 181
|
py
|
Python
|
igit/models/__init__.py
|
jmosbacher/igit
|
62b613fd5bed28b603160dc998c02106ee4fdef0
|
[
"Apache-2.0"
] | null | null | null |
igit/models/__init__.py
|
jmosbacher/igit
|
62b613fd5bed28b603160dc998c02106ee4fdef0
|
[
"Apache-2.0"
] | 107
|
2021-06-28T02:10:11.000Z
|
2022-03-30T02:38:03.000Z
|
igit/models/__init__.py
|
jmosbacher/igit
|
62b613fd5bed28b603160dc998c02106ee4fdef0
|
[
"Apache-2.0"
] | null | null | null |
from intervaltree import Interval, IntervalTree
from .blob import Blob
from .packet import ObjectPacket
from .reference import *
from .repo import RepoIndex
from .user import User
| 22.625
| 47
| 0.81768
| 24
| 181
| 6.166667
| 0.458333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.143646
| 181
| 7
| 48
| 25.857143
| 0.954839
| 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
|
111d3a2309885f7003f6ce937ad9313d5cd29785
| 174
|
py
|
Python
|
torchio/data/__init__.py
|
nwschurink/torchio
|
9cb4319200ca328102a370d58b39be1c3b0b4cdc
|
[
"MIT"
] | 1
|
2020-03-19T08:30:18.000Z
|
2020-03-19T08:30:18.000Z
|
torchio/data/__init__.py
|
nwschurink/torchio
|
9cb4319200ca328102a370d58b39be1c3b0b4cdc
|
[
"MIT"
] | null | null | null |
torchio/data/__init__.py
|
nwschurink/torchio
|
9cb4319200ca328102a370d58b39be1c3b0b4cdc
|
[
"MIT"
] | null | null | null |
from .queue import Queue
from .sampler import ImageSampler, LabelSampler
from .images import ImagesDataset, Image, Subject
from .inference import GridSampler, GridAggregator
| 34.8
| 50
| 0.83908
| 20
| 174
| 7.3
| 0.65
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114943
| 174
| 4
| 51
| 43.5
| 0.948052
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
113585a8cad3c9d4e0ae67e6620cef7a46fbb8c7
| 206
|
py
|
Python
|
Programiz/Swap Two Variables/Using a temporary variable/Sol.py
|
Pandz18/C-Programs
|
9d9b47516d3f65d348f9f72b9c0edda8246e9fab
|
[
"MIT"
] | null | null | null |
Programiz/Swap Two Variables/Using a temporary variable/Sol.py
|
Pandz18/C-Programs
|
9d9b47516d3f65d348f9f72b9c0edda8246e9fab
|
[
"MIT"
] | null | null | null |
Programiz/Swap Two Variables/Using a temporary variable/Sol.py
|
Pandz18/C-Programs
|
9d9b47516d3f65d348f9f72b9c0edda8246e9fab
|
[
"MIT"
] | null | null | null |
a=int(input("Enter value of a"))
b=int(input("Enter value of b"))
print("Value of a " + str(a))
print("Value of b " + str(b))
temp=a
a=b
b=temp
print("Value of a " + str(a))
print("Value of b " + str(b))
| 17.166667
| 32
| 0.601942
| 44
| 206
| 2.818182
| 0.227273
| 0.33871
| 0.387097
| 0.290323
| 0.870968
| 0.548387
| 0.548387
| 0.548387
| 0.548387
| 0.548387
| 0
| 0
| 0.179612
| 206
| 11
| 33
| 18.727273
| 0.733728
| 0
| 0
| 0.444444
| 0
| 0
| 0.368932
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.444444
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
11369634159761d8771829f1d4a7b0dc4f1a31e1
| 111
|
py
|
Python
|
pysharpen/methods/sharpening/__init__.py
|
Vladimir-Kozub/pansharpen
|
2d6210e09fd1630e66980e6cd539879be17068bf
|
[
"MIT"
] | 1
|
2021-06-01T21:07:31.000Z
|
2021-06-01T21:07:31.000Z
|
pysharpen/methods/sharpening/__init__.py
|
Vladimir-Kozub/pansharpen
|
2d6210e09fd1630e66980e6cd539879be17068bf
|
[
"MIT"
] | 6
|
2019-12-30T10:40:44.000Z
|
2021-07-30T19:53:58.000Z
|
pysharpen/methods/sharpening/__init__.py
|
Vladimir-Kozub/pansharpen
|
2d6210e09fd1630e66980e6cd539879be17068bf
|
[
"MIT"
] | 3
|
2019-10-28T14:54:11.000Z
|
2021-03-02T13:33:22.000Z
|
from .brovey import BroveyPansharpening
from .gihs import GIHSPansharpening
from .ihs import IHSPansharpening
| 37
| 40
| 0.855856
| 12
| 111
| 7.916667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117117
| 111
| 3
| 41
| 37
| 0.969388
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 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
|
113b254e5f08aa02f89286dc490c91acd7aaa166
| 278
|
py
|
Python
|
datas/models.py
|
sear-azazel/monitoring
|
88e7d31ddf3258da0b45f735ced147a331af720c
|
[
"MIT"
] | null | null | null |
datas/models.py
|
sear-azazel/monitoring
|
88e7d31ddf3258da0b45f735ced147a331af720c
|
[
"MIT"
] | null | null | null |
datas/models.py
|
sear-azazel/monitoring
|
88e7d31ddf3258da0b45f735ced147a331af720c
|
[
"MIT"
] | null | null | null |
from django.db import models
from django.utils import timezone
class Recognition(models.Model):
recognition_text = models.CharField(max_length=200)
recognition_date = models.DateTimeField(default=timezone.now)
pub_date = models.DateTimeField(default=timezone.now)
| 30.888889
| 65
| 0.798561
| 35
| 278
| 6.228571
| 0.571429
| 0.091743
| 0.211009
| 0.275229
| 0.376147
| 0.376147
| 0
| 0
| 0
| 0
| 0
| 0.012245
| 0.118705
| 278
| 8
| 66
| 34.75
| 0.877551
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 0
| 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
| 1
| 0
|
0
| 5
|
114794698d39a92cd98dd2345707b44af967f977
| 168
|
py
|
Python
|
kafka-consumer/consumer/utils/__init__.py
|
shiv12095/realtimeviz
|
ee2bf10b5f9467212f9a9ce8957d80456ebd0259
|
[
"MIT"
] | 1
|
2021-03-03T13:54:15.000Z
|
2021-03-03T13:54:15.000Z
|
backend/server/utils/__init__.py
|
shiv12095/realtimeviz
|
ee2bf10b5f9467212f9a9ce8957d80456ebd0259
|
[
"MIT"
] | null | null | null |
backend/server/utils/__init__.py
|
shiv12095/realtimeviz
|
ee2bf10b5f9467212f9a9ce8957d80456ebd0259
|
[
"MIT"
] | 1
|
2021-03-03T13:59:48.000Z
|
2021-03-03T13:59:48.000Z
|
from .file_utils import FileUtils
from .time_utils import TimeUtils
from .logger import Logger
from .constants import Constants
from .server_config import ServerConfig
| 28
| 39
| 0.85119
| 23
| 168
| 6.086957
| 0.521739
| 0.157143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 168
| 5
| 40
| 33.6
| 0.945946
| 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
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1158d87622ef2fbcb541262d539f76e2716bba86
| 120
|
py
|
Python
|
enthought/developer/features/dock_control_feature.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 3
|
2016-12-09T06:05:18.000Z
|
2018-03-01T13:00:29.000Z
|
enthought/developer/features/dock_control_feature.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 1
|
2020-12-02T00:51:32.000Z
|
2020-12-02T08:48:55.000Z
|
enthought/developer/features/dock_control_feature.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | null | null | null |
# proxy module
from __future__ import absolute_import
from etsdevtools.developer.features.dock_control_feature import *
| 30
| 65
| 0.866667
| 15
| 120
| 6.466667
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091667
| 120
| 3
| 66
| 40
| 0.889908
| 0.1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1161087c38bade0b901629b0f87206fbc4f24c3d
| 632
|
py
|
Python
|
bigo_test/complexity.py
|
nvn-nil/bigo_test
|
e2bd45f84315d27368a6ef19ae720ee7a4fb2f93
|
[
"MIT"
] | null | null | null |
bigo_test/complexity.py
|
nvn-nil/bigo_test
|
e2bd45f84315d27368a6ef19ae720ee7a4fb2f93
|
[
"MIT"
] | null | null | null |
bigo_test/complexity.py
|
nvn-nil/bigo_test
|
e2bd45f84315d27368a6ef19ae720ee7a4fb2f93
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from big_o import complexities
class BaseComplexity:
pass
class Constant(complexities.Constant, BaseComplexity):
pass
class Linear(complexities.Linear, BaseComplexity):
pass
class Quadratic(complexities.Quadratic, BaseComplexity):
pass
class Cubic(complexities.Cubic, BaseComplexity):
pass
class Logarithmic(complexities.Logarithmic, BaseComplexity):
pass
class Linearithmic(complexities.Linearithmic, BaseComplexity):
pass
class Polynomial(complexities.Polynomial, BaseComplexity):
pass
class Exponential(complexities.Exponential, BaseComplexity):
pass
| 16.205128
| 62
| 0.765823
| 59
| 632
| 8.186441
| 0.322034
| 0.335404
| 0.380952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.001873
| 0.155063
| 632
| 38
| 63
| 16.631579
| 0.902622
| 0.033228
| 0
| 0.473684
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.473684
| 0.052632
| 0
| 0.526316
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
fefc22d24a61890feac7b1479718ddb745ef172c
| 121
|
py
|
Python
|
lcs/strategies/action_selection/__init__.py
|
Gab0/pyalcs
|
da68f8ef454939d45b53f5ac53c1c5fd40e65ffc
|
[
"MIT"
] | null | null | null |
lcs/strategies/action_selection/__init__.py
|
Gab0/pyalcs
|
da68f8ef454939d45b53f5ac53c1c5fd40e65ffc
|
[
"MIT"
] | null | null | null |
lcs/strategies/action_selection/__init__.py
|
Gab0/pyalcs
|
da68f8ef454939d45b53f5ac53c1c5fd40e65ffc
|
[
"MIT"
] | null | null | null |
from .EpsilonGreedy import EpsilonGreedy
from .ActionDelay import ActionDelay
from .KnowledgeArray import KnowledgeArray
| 30.25
| 42
| 0.876033
| 12
| 121
| 8.833333
| 0.416667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.099174
| 121
| 3
| 43
| 40.333333
| 0.972477
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| null | 0
| 0
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| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3a0b75850859dd62b7e114acaae6336d0c75b24f
| 159
|
py
|
Python
|
h5Nastran/h5Nastran/__init__.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 293
|
2015-03-22T20:22:01.000Z
|
2022-03-14T20:28:24.000Z
|
h5Nastran/h5Nastran/__init__.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 512
|
2015-03-14T18:39:27.000Z
|
2022-03-31T16:15:43.000Z
|
h5Nastran/h5Nastran/__init__.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 136
|
2015-03-19T03:26:06.000Z
|
2022-03-25T22:14:54.000Z
|
from __future__ import print_function, absolute_import
from .exceptions import pyNastranReadBdfError, pyNastranWriteBdfError
from .h5nastran import H5Nastran
| 31.8
| 69
| 0.880503
| 16
| 159
| 8.375
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013889
| 0.09434
| 159
| 4
| 70
| 39.75
| 0.916667
| 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
|
3a4bdf012eecdc16c5bd589165a8f2fedad6878a
| 6,042
|
py
|
Python
|
tests/utils/test_procrustes.py
|
greenfieldvision/psiz
|
37068530a78e08792e827ee55cf55e627add115e
|
[
"Apache-2.0"
] | 21
|
2020-04-03T21:10:05.000Z
|
2021-12-02T01:31:11.000Z
|
tests/utils/test_procrustes.py
|
greenfieldvision/psiz
|
37068530a78e08792e827ee55cf55e627add115e
|
[
"Apache-2.0"
] | 14
|
2020-04-10T00:48:02.000Z
|
2021-05-25T18:06:55.000Z
|
tests/utils/test_procrustes.py
|
rgerkin/psiz
|
d540738462b6436a08a472d5e349ca2b813e6d47
|
[
"Apache-2.0"
] | 4
|
2020-10-13T16:46:14.000Z
|
2021-11-10T00:08:47.000Z
|
# -*- coding: utf-8 -*-
# Copyright 2020 The PsiZ Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Module for testing utils.py."""
import pytest
import numpy as np
import psiz.utils
@pytest.fixture(scope="module")
def z0():
"""Create random set of points."""
z0 = np.array([
[0.46472851, 0.09534286],
[0.90612827, 0.21031482],
[0.46595517, 0.92022067],
[0.51457351, 0.88226988],
[0.24506303, 0.75287697],
[0.69773745, 0.25095083],
[0.71550351, 0.14846334],
[0.24825323, 0.96021703],
[0.85497989, 0.9114596],
[0.35982138, 0.85040905]
])
return z0
def test_simple_rotation_0(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (without scaling or reflection).
s = np.array([[1, 0], [0, 1]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
def test_simple_rotation_1(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (without scaling or reflection).
s = np.array([[1, 0], [0, 1]])
r = psiz.utils.rotation_matrix(-np.pi/2.1)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
def test_scaled_rotation(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (with scaling).
s = np.array([[2, 0], [0, 2]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
def test_scaled_rotation_no_scale(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (with scaling).
s = np.array([[2, 0], [0, 2]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=False
)
z0_rot = np.matmul(z0_centered, r_recov)
z0_rot_desired = .5 * z1_centered
np.testing.assert_almost_equal(z0_rot_desired, z0_rot, decimal=2)
def test_x_reflection_rotation(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (with scaling and reflection).
s = np.array([[-1, 0], [0, 1]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
def test_y_reflection_rotation(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (with scaling and reflection).
s = np.array([[1, 0], [0, -1]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
def test_xy_reflection_rotation(z0):
"""Test procrustean solution for simple problem."""
# Assemble rotation matrix (with scaling and reflection).
s = np.array([[-1, 0], [0, -1]])
r = psiz.utils.rotation_matrix(np.pi/4)
rs = np.matmul(s, r)
# Center `z0`.
z0_centered = z0 - np.mean(z0, axis=0, keepdims=True)
# Apply rotation to centered `z0` data.
z1 = np.matmul(z0_centered, rs)
z1_centered = z1
# Attempt to recover original set of points.
r_recov = psiz.utils.procrustes_rotation(
z0, z1, scale=True
)
z0_rot = np.matmul(z0_centered, r_recov)
np.testing.assert_almost_equal(z1_centered, z0_rot, decimal=2)
| 29.910891
| 78
| 0.649619
| 885
| 6,042
| 4.303955
| 0.171751
| 0.044106
| 0.036755
| 0.066159
| 0.770806
| 0.76398
| 0.758729
| 0.758729
| 0.751116
| 0.751116
| 0
| 0.075559
| 0.215823
| 6,042
| 201
| 79
| 30.059701
| 0.728366
| 0.344091
| 0
| 0.621359
| 0
| 0
| 0.001548
| 0
| 0
| 0
| 0
| 0
| 0.067961
| 1
| 0.07767
| false
| 0
| 0.029126
| 0
| 0.116505
| 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
|
3a4d22904a112f6cc1a23268abe7747d535296b8
| 90,572
|
py
|
Python
|
venv/lib/python3.8/site-packages/mogp_emulator/MeanFunction.py
|
AndrewKirby2/data_synthesis
|
656858137a348fd5dcb57bcd04bdfece2b9eac1b
|
[
"MIT"
] | 21
|
2021-01-20T07:02:12.000Z
|
2022-03-30T21:09:04.000Z
|
venv/lib/python3.8/site-packages/mogp_emulator/MeanFunction.py
|
AndrewKirby2/data_synthesis
|
656858137a348fd5dcb57bcd04bdfece2b9eac1b
|
[
"MIT"
] | 114
|
2019-04-25T14:53:11.000Z
|
2021-01-06T17:07:41.000Z
|
venv/lib/python3.8/site-packages/mogp_emulator/MeanFunction.py
|
AndrewKirby2/data_synthesis
|
656858137a348fd5dcb57bcd04bdfece2b9eac1b
|
[
"MIT"
] | 8
|
2021-02-02T08:56:12.000Z
|
2022-02-15T10:03:15.000Z
|
"""
**MeanFunction Module**
The MeanFunction module contains classes used for constructing mean functions for GP emulators.
A base ``MeanBase`` class is provided, which implements basic operations to combine fixed
functions and fitting parameters. The basic operations ``f1 + f2``, ``f1*f2``, ``f1**f2``
and ``f1(f2)`` are available, though not all possible combinations will make sense.
Particular cases where combinations do not make sense often use classes that represent free
fitting parameters, or if you attempt to raise a mean function to a power that is not
independent of the inputs. These operations will create new derived classes ``MeanSum``,
``MeanProduct``, ``MeanPower``, and ``MeanComposite``, from which more complex
regression functions can be formed. The derived sum, product, power, and composite mean classes
call the necessary methods to compute the function and derivatives from the more basic
classes and then combine them using sum, product, power, and chain rules for function evaluation
and derivatives.
The basic building blocks are fixed mean functions, derived from ``FixedMean``, and free
parameters, represented by the ``Coefficient`` class. Incuded fixed functions include
``ConstantMean`` and ``LinearMean``. Additional derived ``FixedMean`` functions can be
created by initializing a new ``FixedMean`` instance where the user provides a fixed
function and its derivative, and these can be combined to form arbitrarily complex mean functions.
Future improvements will extend the number of pre-defined function options.
One implementation note: ``CompositeMean`` does not implement the Hessian, as computing this
requires mixed partials involving inputs and parameters that are not normally implemented.
If a composite mean is required with a Hessian Computation, the user must implement this.
Additionally, note that given mean function may have a number of parameters that depends on
the shape of the input. Since the mean function does not store input, but rather provides
a way to collate functions and derivatives together in a single object, the number of parameters
can vary based on the inputs. This is particularly true for the provided ``PolynomialMean``
class, which fits a polynomial function of a fixed degree to each input parameter. Thus,
the number of parameters depends on the input shape.
In addition to manually creating a mean function by composing fixed functions and fitting parameters,
a ``MeanBase`` subclass can be created by using the ``MeanFunction`` function. ``MeanFunction``
is a functional interface for creating ``MeanBase`` subclasses from a string formula.
The formula langauge supports the operations described above as expected (see below for some
examples), with the option to first parse the formula using the Python library ``patsy``
before converting the terms to the respective subclasses of ``MeanBase``. Formulas specify
input variables using either ``x[i]`` or ``inputs[i]`` to represent the dependent variables,
and can explicitly include a leading ``"y ="`` or ``"y ~"`` (which will be ignored). Optionally,
named variables can be mapped to input dimensions by providing a dictionary mapping strings
to integer indices. Any other variables in the formula will be assumed to be fitting
coefficients. Note that the formula parser does not make any effort to simplify expressions
(such as having identical terms or a term with redundant fitting parameters), so it is up
to the user to get things correct. Converting a mean function instance to a string can
be very helpful in determining if the parsing led to any problems, see below.
Example: ::
>>> from mogp_emulator.MeanFunction import Coefficient, LinearMean, MeanFunction
>>> mf1 = Coefficient() + Coefficient()*LinearMean()
>>> print(mf1)
c + c*x[0]
>>> mf2 = LinearMean(1)*LinearMean(2)
>>> print(mf2)
x[1]*x[2]
>>> mf3 = mf1(mf2)
>>> print(mf3)
c + c*x[1]*x[2]
>>> mf4 = Coefficient()*LinearMean()**2
>>> print(mf4)
c*x[0]^2
>>> mf5 = MeanFunction("x[0]")
>>> print(mf5)
c + c*x[0]
>>> mf6 = MeanFunction("y = a + b*x[0]", use_patsy=False)
>>> print(mf6)
c + c*x[0]
>>> mf7 = MeanFunction("a*b", {"a": 0, "b": 1})
>>> print(mf7)
c + c*x[0] + c*x[1] + c*x[0]*x[1]
"""
import numpy as np
from functools import partial
from inspect import signature
from mogp_emulator.formula import mean_from_patsy_formula, mean_from_string
def MeanFunction(formula, inputdict={}, use_patsy=True):
"""
Create a mean function from a formula
This is the functional interface to creating a mean function from a string formula.
This method takes a string as an input, an optional dictionary that map strings to
integer indices in the input data, and an optional boolean flag that indicates if
the user would like to have the formula parsed with patsy before being converted
to a mean function.
The string formulas can be specified in several ways. The formula LHS is implicitly
always ``"y = "`` or ``"y ~ "``, though these can be explicitly provided as well.
The RHS may contain a set of terms containing the add, multiply, power, and
call operations much in the same way that the operations would be entered as
regular python code. Parentheses are used to indicated prececence as well as
the call operation, and square brackets indicate an indexing operation on the
inputs. Inputs may be specified as either a string such as ``"x[0]"``,
``"inputs[0]"``, or a string that can be mapped to an integer index with the
optional dictionary passed to the function. Any strings not representing operations
or inputs as described above are interpreted as follows: if the string can
be converted into a number, then it is interpreted as a ``ConstantMean`` fixed
mean function object; otherwise it is assumed to represent a fitting coefficient.
Note that this means many characters that do not represent operations within this
mean function language but would not normally be considered as python variables
will nonetheless be converted into fitting coefficients -- it is up to the user
to get this right.
Expressions that are repeated or redundant will not be simplified, so the user should
take care that the provided expression is sensible as a mean function and will not
cause problems when fitting.
Additional special cases to be aware of:
* ``call`` cannot be used as a variable name, if this is parsed as a token an exception
will be raised.
* ``I`` is the identity operator, it simply returns the given value. It is useful
if you wish to use patsy to evaluate a formula but protect a part of the string
formula from being expanded based on the rules in patsy. If ``I`` is encountered
in any other context, an exception will be raised.
Examples: ::
>>> from mogp_emulator.MeanFunction import MeanFunction
>>> mf1 = MeanFunction("x[0]")
>>> print(mf1)
c + c*x[0]
>>> mf2 = MeanFunction("y = a + b*x[0]", use_patsy=False)
>>> print(mf2)
c + c*x[0]
>>> mf3 = MeanFunction("a*b", {"a": 0, "b": 1})
>>> print(mf3)
c + c*x[0] + c*x[1] + c*x[0]*x[1]
:param formula: string representing the desired mean function formula
:type formula: str
:param inputdict: dictionary used to map variables to input indices. Maps
strings to integer indices (must be non-negative). Optional,
default is ``{}``.
:type inputdict: dict
:param use_patsy: Boolean flag indicating if the string is to be parsed using
patsy library. Optional, default is ``True``. If patsy is not
installed, the basic string parser will be used.
:type use_patsy: bool
:returns: New subclass of ``MeanBase`` implementing the given formula
:rtype: subclass of MeanBase (exact type will depend on the formula that is provided)
"""
if formula is None or (isinstance(formula, str) and formula.strip() == ""):
return ConstantMean(0.)
if not isinstance(formula, str):
raise ValueError("input formula must be a string")
if use_patsy:
mf = mean_from_patsy_formula(formula, inputdict)
else:
mf = mean_from_string(formula, inputdict)
return mf
class MeanBase(object):
"""
Base mean function class
The base class for the mean function implementation includes code for checking inputs and
implements sum, product, power, and composition methods to allow more complicated functions
to be built up from fixed functions and fitting coefficients. Subclasses need to implement
the following methods:
* ``get_n_params`` which returns the number of parameters for a given input size. This is
usually a constant, but can be more complicated (such as the provided ``PolynomialMean``
class)
* ``mean_f`` computes the mean function for the provided inputs and parameters
* ``mean_deriv`` computes the derivative with respect to the parameters
* ``mean_hessian`` computes the hessian with respect to the parameters
* ``mean_inputderiv`` computes the derivate with respect to the inputs
The base class does not have any attributes, but subclasses will usually have some
attributes that must be set and so are likely to need a ``__init__`` method.
"""
def _check_inputs(self, x, params):
"""
Check the shape of the inputs and reshape if needed
This method checks that the inputs and parameters are consistent for the provided
mean function. In particular, the following must be met:
* The inputs ``x`` must be a 2D numpy array, though if it is 1D it is reshaped to add
a second dimenion of length 1.
* ``params`` must be a 1D numpy array. If a multi-dimensional array is provided, it
will be flattened.
* ``params`` must have a length that is the same as the return value of ``get_n_params``
when called with the inputs. Note that some mean functions may have different
numbers of parameters depending on the inputs, so this may not be known in advance.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: tuple containing the reshaped ``x`` and ``params`` arrays
:rtype: tuple containing two ndarrays
"""
x = np.array(x)
params = np.array(params).flatten()
if len(x.shape) == 1:
x = np.reshape(x, (-1, 1))
assert len(x.shape) == 2, "inputs must be a 1D or 2D array"
assert len(params.shape) == 1, "params must be a 1D array"
assert len(params) == self.get_n_params(x), "bad length for params"
return x, params
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
raise NotImplementedError("base mean function does not implement a particular function")
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
raise NotImplementedError("base mean function does not implement a particular function")
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
raise NotImplementedError("base mean function does not implement a particular function")
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, n_params, x.shape[0])`` holding the value
of the mean function second derivaties with respect to each parameter pair (first twp axes)
for each input point (last axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
raise NotImplementedError("base mean function does not implement a particular function")
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
raise NotImplementedError("base mean function does not implement a particular function")
def __add__(self, other):
"""
Adds two mean functions
This method adds two mean functions, returning a ``MeanSum`` object. If the second
argument is a float or integer, it is converted to a ``ConstantMean`` object. If
the second argument is neither a subclass of ``MeanBase`` nor a float/int,
an exception is raised.
:param other: Second ``MeanBase`` (or float/integer) to be added
:type other: subclass of MeanBase or float or int
:returns: ``MeanSum`` instance
:rtype: MeanSum
"""
if issubclass(type(other), MeanBase):
return MeanSum(self, other)
elif isinstance(other, (float, int)):
return MeanSum(self, ConstantMean(other))
else:
raise TypeError("other function cannot be added with a MeanBase")
def __radd__(self, other):
"""
Right adds two mean functions
This method adds two mean functions, returning a ``MeanSum`` object. If the second
argument is a float or integer, it is converted to a ``ConstantMean`` object. If
the second argument is neither a subclass of ``MeanBase`` nor a float/int,
an exception is raised.
:param other: Second ``MeanBase`` (or float/integer) to be added
:type other: subclass of MeanBase or float or int
:returns: ``MeanSum`` instance
:rtype: MeanSum
"""
if issubclass(type(other), MeanBase):
return MeanSum(other, self)
elif isinstance(other, (float, int)):
return MeanSum(ConstantMean(other), self)
else:
raise TypeError("other function cannot be added with a MeanBase")
def __mul__(self, other):
"""
Multiplies two mean functions
This method multiples two mean functions, returning a ``MeanProduct`` object. If
the second argument is a float or integer, it is converted to a ``ConstantMean``
object. If the second argument is neither a subclass of ``MeanBase`` nor a
float/int, an exception is raised.
:param other: Second ``MeanBase`` (or float/integer) to be multiplied
:type other: subclass of MeanBase or float or int
:returns: ``MeanProduct`` instance
:rtype: MeanProduct
"""
if issubclass(type(other), MeanBase):
return MeanProduct(self, other)
elif isinstance(other, (float, int)):
return MeanProduct(self, ConstantMean(other))
else:
raise TypeError("other function cannot be multiplied with a MeanBase")
def __rmul__(self, other):
"""
Right multiplies two mean functions
This method multiples two mean functions, returning a ``MeanProduct`` object. If
the second argument is a float or integer, it is converted to a ``ConstantMean``
object. If the second argument is neither a subclass of ``MeanBase`` nor a
float/int, an exception is raised.
:param other: Second ``MeanBase`` (or float/integer) to be multiplied
:type other: subclass of MeanBase or float or int
:returns: ``MeanProduct`` instance
:rtype: MeanProduct
"""
if issubclass(type(other), MeanBase):
return MeanProduct(other, self)
elif isinstance(other, (float, int)):
return MeanProduct(ConstantMean(other), self)
else:
raise TypeError("other function cannot be multipled with a MeanBase")
def __pow__(self, exp):
"""
Raises a mean function to a power
This method raises a mean function to a power, returning a ``MeanPower`` object.
The second argument can only be a mean function that returns a value that is
independent of its input, in particular a ``Coefficient`` or a ``ConstantMean``
(or a float or integer, from which a new ``ConstantMean`` will be created)
are the only acceptable types for the ``exp`` argument.
:param exp: Mean function exponent, must be a ``Coefficient`` or a ``ConstantMean``
object, or a float/int from which a new ``ConstantMean`` will be
created.
:type exp: Coefficient, ConstantMean, float, or int
:returns: ``MeanPower``instance
:rtype: MeanPower
"""
if isinstance(exp, (float, int)):
return MeanPower(self, ConstantMean(exp))
elif isinstance(exp, (Coefficient, ConstantMean)):
return MeanPower(self, exp)
else:
raise TypeError("MeanBase can only be raised to a power that is a ConstantMean, " +
"Coefficient, or float/int")
def __rpow__(self, base):
"""
Right raises a mean function to a power
This method right raises a mean function to a power, meaning that the base
is potentially not a mean function. Returns a ``MeanPower`` object. The ``self``
argument can only be a mean function that returns a value that is
independent of its input, in particular a ``Coefficient`` or a ``ConstantMean``.
The base can be any ``MeanBase`` instance, or a float or integer, from which a new
``ConstantMean`` will be created.
:param base: Mean function base, must be a ``MeanBase`` subclass
object, or a float/int from which a new ``ConstantMean`` will be
created.
:type base: MeanBase subclass, float, or int
:returns: ``MeanPower``instance
:rtype: MeanPower
"""
if not isinstance(self, (Coefficient, ConstantMean)):
raise TypeError("arbitrary mean functions cannot serve as the exponent when " +
"raising a mean function to a power")
if isinstance(base, (float, int)):
return MeanPower(ConstantMean(base), self)
elif issubclass(type(base), MeanBase):
return MeanPower(base, self)
else:
raise TypeError("base in a MeanPower must be a MeanBase or a float/int")
def __call__(self, other):
"""
Composes two mean functions
This method multiples two mean functions, returning a ``MeanComposite`` object.
If the second argument is not a subclass of ``MeanBase``, an exception is
raised.
:param other: Second ``MeanBase`` to be composed
:type other: subclass of MeanBase
:returns: ``MeanComposite`` instance
:rtype: MeanComposite
"""
if issubclass(type(other), MeanBase):
return MeanComposite(self, other)
else:
raise TypeError("other function cannot be composed with a MeanBase")
class MeanSum(MeanBase):
"""
Class representing the sum of two mean functions
This derived class represents the sum of two mean functions, and does the necessary
bookkeeping needed to compute the required function and derivatives. The code does
not do any checks to confirm that it makes sense to add these particular mean functions --
in particular, adding two ``Coefficient`` classes is the same as having a single
one, but the code will not attempt to simplify this so it is up to the user to get it
right.
:ivar f1: first ``MeanBase`` to be added
:type f1: subclass of MeanBase
:ivar f2: second ``MeanBase`` to be added
:type f2: subclass of MeanBase
"""
def __init__(self, f1, f2):
"""
Create a new instance of two added mean functions
Creates an instance of to added mean functions. Inputs are the two functions
to be added, which must be subclasses of the base ``MeanBase`` class.
:param f1: first ``MeanBase`` to be added
:type f1: subclass of MeanBase
:param f2: second ``MeanBase`` to be added
:type f2: subclass of MeanBase
:returns: new ``MeanSum`` instance
:rtype: MeanSum
"""
if not issubclass(type(f1), MeanBase):
raise TypeError("inputs to MeanSum must be subclasses of MeanBase")
if not issubclass(type(f2), MeanBase):
raise TypeError("inputs to MeanSum must be subclasses of MeanBase")
self.f1 = f1
self.f2 = f2
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return self.f1.get_n_params(x) + self.f2.get_n_params(x)
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
For ``MeanSum``, this method applies the sum rule to the results of computing
the mean for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_f(x, params[:switch]) +
self.f2.mean_f(x, params[switch:]))
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
For ``MeanSum``, this method applies the sum rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
deriv = np.zeros((self.get_n_params(x), x.shape[0]))
deriv[:switch] = self.f1.mean_deriv(x, params[:switch])
deriv[switch:] = self.f2.mean_deriv(x, params[switch:])
return deriv
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, n_params, x.shape[0])`` holding the value
of the mean function second derivaties with respect to each parameter pair (first twp axes)
for each input point (last axis).
For ``MeanSum``, this method applies the sum rule to the results of computing
the Hessian for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
hess = np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))
hess[:switch, :switch] = self.f1.mean_hessian(x, params[:switch])
hess[switch:, switch:] = self.f2.mean_hessian(x, params[switch:])
return hess
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
For ``MeanSum``, this method applies the sum rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_inputderiv(x, params[:switch]) +
self.f2.mean_inputderiv(x, params[switch:]))
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "{} + {}".format(self.f1, self.f2)
class MeanProduct(MeanBase):
"""
Class representing the product of two mean functions
This derived class represents the product of two mean functions, and does the necessary
bookkeeping needed to compute the required function and derivatives. The code does
not do any checks to confirm that it makes sense to multiply these particular mean functions --
in particular, multiplying two ``Coefficient`` classes is the same as having a single
one, but the code will not attempt to simplify this so it is up to the user to get it
right.
:ivar f1: first ``MeanBase`` to be multiplied
:type f1: subclass of MeanBase
:ivar f2: second ``MeanBase`` to be multiplied
:type f2: subclass of MeanBase
"""
def __init__(self, f1, f2):
"""
Create a new instance of two mulitplied mean functions
Creates an instance of to multiplied mean functions. Inputs are the two functions
to be multiplied, which must be subclasses of the base ``MeanBase`` class.
:param f1: first ``MeanBase`` to be multiplied
:type f1: subclass of MeanBase
:param f2: second ``MeanBase`` to be multiplied
:type f2: subclass of MeanBase
:returns: new ``MeanProduct`` instance
:rtype: MeanProduct
"""
if not issubclass(type(f1), MeanBase):
raise TypeError("inputs to MeanProduct must be subclasses of MeanBase")
if not issubclass(type(f2), MeanBase):
raise TypeError("inputs to MeanProduct must be subclasses of MeanBase")
self.f1 = f1
self.f2 = f2
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return self.f1.get_n_params(x) + self.f2.get_n_params(x)
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
For ``MeanProduct``, this method applies the product rule to the results of computing
the mean for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_f(x, params[:switch])*
self.f2.mean_f(x, params[switch:]))
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
For ``MeanProduct``, this method applies the product rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
deriv = np.zeros((self.get_n_params(x), x.shape[0]))
deriv[:switch] = (self.f1.mean_deriv(x, params[:switch])*
self.f2.mean_f(x, params[switch:]))
deriv[switch:] = (self.f1.mean_f(x, params[:switch])*
self.f2.mean_deriv(x, params[switch:]))
return deriv
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, n_params, x.shape[0])`` holding the value
of the mean function second derivaties with respect to each parameter pair (first twp axes)
for each input point (last axis).
For ``MeanProduct``, this method applies the product rule to the results of computing
the Hessian for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
hess = np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))
hess[:switch, :switch] = (self.f1.mean_hessian(x, params[:switch])*
self.f2.mean_f(x, params[switch:]))
hess[:switch, switch:, :] = (self.f1.mean_deriv(x, params[:switch])[:,np.newaxis,:]*
self.f2.mean_deriv(x, params[switch:])[np.newaxis,:,:])
hess[switch:, :switch, :] = np.transpose(hess[:switch, switch:, :], (1, 0, 2))
hess[switch:, switch:] = (self.f1.mean_f(x, params[:switch])*
self.f2.mean_hessian(x, params[switch:]))
return hess
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
For ``MeanProduct``, this method applies the product rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_inputderiv(x, params[:switch])*
self.f2.mean_f(x, params[switch:]) +
self.f1.mean_f(x, params[:switch])*
self.f2.mean_inputderiv(x, params[switch:]))
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "{}*{}".format(self.f1, self.f2)
class MeanPower(MeanBase):
"""
Class representing a mean function raised to a power
This derived class represents a mean function raised to a power, and does the necessary
bookkeeping needed to compute the required function and derivatives. The code requires
that the exponent be either a ``Coefficient``, ``ConstantMean``, ``float``, or ``int``
as the output of the exponent mean function must be independent of the inputs to make
sense. If input is a float or int, a ``ConstantMean`` instance will be created.
:ivar f1: first ``MeanBase`` to be raised to the given exponent
:type f1: subclass of MeanBase
:ivar f2: second ``MeanBase`` indicating the exponent. Must be a ``Coefficient``,
``ConstantMean``, or float/int (from which a ``ConstantMean`` object will
be created)
:type f2: Coefficient, ConstantMean, float, or int
"""
def __init__(self, f1, f2):
"""
Create a new instance of a mean function raised to a power
Creates an instance of a mean function raised to a power. Inputs are the two
functions (base, exponent), the first of which must be subclass of the base
``MeanBase`` class, and the second must be a ``Coefficient`` or a
``ConstantMean`` (or a float or int, from which a ``ConstantMean`` will
be created).
:param f1: first ``MeanBase`` serving as the base
:type f1: subclass of MeanBase
:param f2: second ``MeanBase`` serving as the exponent, must be a ``Coefficient``,
``ConstantMean``, ``float``, or ``int``
:type f2: Coefficient, ConstantMean, float, or int
:returns: new ``MeanPower`` instance
:rtype: MeanPower
"""
if not issubclass(type(f1), MeanBase):
raise TypeError("first input to MeanPower must be a subclass of MeanBase")
if isinstance(f2, (float, int)):
f2 = ConstantMean(f2)
if not isinstance(f2, (ConstantMean, Coefficient)):
raise TypeError("second input to MeanPower must be a Coefficient, ConstantMean, "
"float, or int")
self.f1 = f1
self.f2 = f2
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return self.f1.get_n_params(x) + self.f2.get_n_params(x)
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
For ``MeanProduct``, this method applies the product rule to the results of computing
the mean for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_f(x, params[:switch])**
self.f2.mean_f(x, params[switch:]))
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
For ``MeanPpwer``, this method applies the power rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
exp = self.f2.mean_f(x, params[switch:])
nonzeroexp = True
if np.allclose(exp, 0.):
nonzeroexp = False
deriv = np.zeros((self.get_n_params(x), x.shape[0]))
if nonzeroexp:
deriv[:switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*
self.f1.mean_deriv(x, params[:switch]))
# only evaluate if f2 has parameters, as f1 could be negative and taking the log will
# raise an error even though this calculation is ultimately ignored in this case
if not self.f2.get_n_params(x) == 0:
deriv[switch:] = (np.log(self.f1.mean_f(x, params[:switch]))*
self.f1.mean_f(x, params[:switch])**exp*
self.f2.mean_deriv(x, params[switch:]))
return deriv
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, n_params, x.shape[0])`` holding the value
of the mean function second derivaties with respect to each parameter pair (first twp axes)
for each input point (last axis).
For ``MeanPower``, this method applies the power rule to the results of computing
the Hessian for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
exp = self.f2.mean_f(x, params[switch:])
nonzeroexp = True
if np.allclose(exp, 0.):
nonzeroexp = False
nononeexp = True
if np.allclose(exp, 1.):
nononeexp = False
hess = np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))
if nonzeroexp and nononeexp:
hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*
self.f1.mean_hessian(x, params[:switch]) +
exp*(exp - 1.)*self.f1.mean_f(x, params[:switch])**(exp - 2.)*
self.f1.mean_deriv(x, params[:switch]))
elif nonzeroexp:
hess[:switch, :switch] = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*
self.f1.mean_hessian(x, params[:switch]))
if not self.f2.get_n_params(x) == 0:
if nonzeroexp:
hess[:switch, switch:, :] = (self.f1.mean_f(x, params[:switch])**(exp - 1.)*
(exp*np.log(self.f1.mean_f(x, params[:switch])) + 1.)*
self.f1.mean_deriv(x, params[:switch])[:,np.newaxis,:]*
self.f2.mean_deriv(x, params[switch:])[np.newaxis,:,:])
hess[switch:, :switch, :] = np.transpose(hess[:switch, switch:, :], (1, 0, 2))
hess[switch:, switch:] = (self.f1.mean_f(x, params[:switch])**exp*
(np.log(self.f1.mean_f(x, params[:switch]))**2*
self.f2.mean_deriv(x, params[switch:])**2 +
np.log(self.f1.mean_f(x, params[:switch]))*
self.f2.mean_hessian(x, params[switch:])))
return hess
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
For ``MeanPower``, this method applies the power rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
exp = self.f2.mean_f(x, params[switch:])
nonzeroexp = True
if np.allclose(exp, 0.):
nonzeroexp = False
inputderiv = np.zeros((x.shape[1], x.shape[0]))
if nonzeroexp:
inputderiv = (exp*self.f1.mean_f(x, params[:switch])**(exp - 1.)*
self.f1.mean_inputderiv(x, params[:switch]))
return inputderiv
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "{}^{}".format(self.f1, self.f2)
class MeanComposite(MeanBase):
"""
Class representing the composition of two mean functions
This derived class represents the composition of two mean functions, and does the necessary
bookkeeping needed to compute the required function and derivatives. The code does
not do any checks to confirm that it makes sense to compose these particular mean
functions -- in particular, applying a ``Coefficient`` class to another function will
simply wipe out the second function. This will not raise an error, but the code will not
attempt to alert the user to this so it is up to the user to get it right.
Because the Hessian computation requires mixed partials that are not normally implemented
in the ``MeanBase`` class, the Hessian computation is not currently implemented.
If you require Hessian computation for a composite mean function, you must implement
it yourself.
Note that since the outer function takes as its input the output of the second function,
the outer function can only ever have an index of 0 due to the fixed output shape of
a mean function. This will produce an error when attempting to evaluate the function
or its derivatives, but will not cause an error when initializing a ``MeanComposite``
object.
:ivar f1: first ``MeanBase`` to be applied to the second
:type f1: subclass of MeanBase
:ivar f2: second ``MeanBase`` to be composed as the input to the first
:type f2: subclass of MeanBase
"""
def __init__(self, f1, f2):
"""
Create a new instance of two composed mean functions
Creates an instance of to composed mean functions. Inputs are the two functions
to be composed (``f1(f2)``), which must be subclasses of the base ``MeanBase``
class.
:param f1: first ``MeanBase`` to be applied to the second
:type f1: subclass of MeanBase
:param f2: second ``MeanBase`` to be composed as the input to the first
:type f2: subclass of MeanBase
:returns: new ``MeanComposite`` instance
:rtype: MeanComposite
"""
if not issubclass(type(f1), MeanBase):
raise TypeError("inputs to MeanComposite must be subclasses of MeanBase")
if not issubclass(type(f2), MeanBase):
raise TypeError("inputs to MeanComposite must be subclasses of MeanBase")
self.f1 = f1
self.f2 = f2
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return self.f1.get_n_params(np.zeros((x.shape[0], 1))) + self.f2.get_n_params(x)
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
For ``MeanComposite``, this method applies the output of the second function as
input to the first function.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return self.f1.mean_f(np.reshape(self.f2.mean_f(x, params[switch:]), (-1, 1)),
params[:switch])
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
For ``MeanComposite``, this method applies the chain rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
deriv = np.zeros((self.get_n_params(x), x.shape[0]))
f2 = np.reshape(self.f2.mean_f(x, params[switch:]), (-1, 1))
deriv[:switch] = self.f1.mean_deriv(f2, params[:switch])
deriv[switch:] = (self.f1.mean_inputderiv(f2, params[:switch])*
self.f2.mean_deriv(x, params[switch:]))
return deriv
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
For ``MeanComposite``, this method applies the chain rule to the results of computing
the derivative for the individual functions.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
switch = self.f1.get_n_params(x)
return (self.f1.mean_inputderiv(np.reshape(self.f2.mean_f(x, params[switch:]), (-1, 1)),
params[:switch])*
self.f2.mean_inputderiv(x, params[switch:]))
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "{}({})".format(self.f1, self.f2)
class FixedMean(MeanBase):
"""
Class representing a fixed mean function with no parameters
Class representing a mean function with a fixed function (and optional derivative)
and no fitting parameters. The user must provide these functions when initializing
the instance.
:ivar f: fixed mean function, must be callable and take a single argument (the inputs)
:type f: function
:ivar deriv: fixed derivative function (optional if no derivatives are needed), must
be callable and take a single argument (the inputs)
:type deriv: function or None
"""
def __init__(self, f, deriv=None):
"""
Initialize a class instance representing a fixed mean function with no parameters
Create a class instance representing a mean function with a fixed function
(and optional derivative) and no fitting parameters. The user must provide these
functions, though the derivative is optional. The code will check that the provided
arguments are callable, but will not confirm that the inputs and outputs are the
correct type/shape.
:param f: fixed mean function, must be callable and take a single argument (the inputs)
:type f: function
:param deriv: fixed derivative function (optional if no derivatives are needed), must
be callable and take a single argument (the inputs)
:type deriv: function or None
:returns: new ``FixedMean`` instance
:rtype: FixedMean
"""
assert callable(f), "fixed mean function must be a callable function"
if not deriv is None:
assert callable(deriv), "mean function derivative must be a callable function"
self.f = f
self.deriv = deriv
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
For a ``FixedMean`` class, this is zero.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return 0
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. For ``FixedMean`` classes, there are no parameters so the
``params`` argument should be an array of length zero. Returns a numpy array of shape
``(x.shape[0],)`` holding the value of the mean function for each input point.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input (zero in this case)
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return self.f(x)
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``FixedMean`` classes, there are no parameters so the ``params`` argument
should be an array of length zero. Returns a numpy array of shape
``(n_params, x.shape[0])`` holding the value of the mean function derivative with
respect to each parameter (first axis) for each input point (second axis). Since
fixed means have no parameters, this will just be an array of zeros.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.zeros((self.get_n_params(x), x.shape[0]))
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``FixedMean`` classes, there are no parameters so the ``params`` argument
should be an array of length zero. Returns a numpy array of shape
``(n_params, n_params, x.shape[0])`` holding the value of the mean function
second derivaties with respect to each parameter pair (first twp axes) for each
input point (last axis). Since fixed means have no parameters, this will just
be an array of zeros.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``FixedMean`` classes, there are no parameters so the ``params`` argument
should be an array of length zero. Returns a numpy array of shape
``(x.shape[1], x.shape[0])`` holding the value of the mean function derivative
with respect to each input (first axis) for each input point (second axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
if self.deriv is None:
raise RuntimeError("Derivative function was not provided with this FixedMean")
else:
return self.deriv(x)
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "f"
def fixed_f(x, index, f):
"""
Dummy function to index into x and apply a function
Usage is intended to be with a fixed mean function, where an index and specific mean
function are meant to be bound using partial before setting it as the ``f`` attribute of
``FixedMean``
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param index: integer index to be applied to the second axis of ``x``, used to select
a particular input variable. Must be non-negative and less than the
length of the second axis of the inputs.
:type index: int
:param f: fixed mean function, must be callable and take a single argument (the inputs)
:type f: function
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
assert callable(f), "fixed mean function must be callable"
assert index >= 0, "provided index cannot be negative"
assert x.ndim == 2, "x must have 2 dimensions"
try:
val = f(x[:,index])
except IndexError:
raise IndexError("provided mean function index is out of range")
return val
def fixed_inputderiv(x, index, deriv):
"""
Dummy function to index into x and apply a derivative function
Usage is intended to be with a fixed mean function, where an index and specific derivative
function are meant to be bound using partial before setting it as the ``deriv`` attribute of
``FixedMean``
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param index: integer index to be applied to the second axis of ``x``, used to select
a particular input variable. Must be non-negative and less than the
length of the second axis of the inputs.
:type index: int
:param deriv: fixed derivative function, must be callable and take a single argument (the inputs)
:type deriv: function
:returns: Value of mean derivative evaluated at all input points, numpy array of shape
``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
assert callable(deriv), "fixed mean function derivative must be callable"
assert index >= 0, "provided index cannot be negative"
assert x.ndim == 2, "x must have 2 dimensions"
try:
out = np.zeros((x.shape[1], x.shape[0]))
out[index, :] = deriv(np.transpose(x[:, index]))
except IndexError:
raise IndexError("provided mean function index is out of range")
return out
def one(x):
"""
Function to return an array of ones with the same shape as the input
Function to return a numpy array of ones with the same shape as the input. Used in
linear mean functions to evaluate derivatives.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:returns: Numpy array of ones with the same shape as x
:rtype: ndarray
"""
return np.ones(x.shape)
def const_f(x, val):
"""
Function to return an array of a constant value
Function to return a numpy array of a constant value with the correct shape for a given
input. Used in constant mean functions to evaluate the function.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param val: value of output, must be a float
:type val: float
:returns: Numpy array of ``val`` with shape ``(x.shape[0],)``
:rtype: ndarray
"""
assert x.ndim == 2, "x must have 2 dimensions"
return np.broadcast_to(val, x.shape[0])
def const_deriv(x):
"""
Function to return an array of zeros with the transposed shape of the inputs
Function to return a numpy array of zeros with the shape that is transpose of the
shape of the input. Used in constant mean functions to evaluate the derivative.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:returns: Numpy array of zeros with shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
assert x.ndim == 2, "x must have 2 dimensions"
return np.zeros((x.shape[1], x.shape[0]))
class ConstantMean(FixedMean):
"""
Class representing a constant fixed mean function
Subclass of ``FixedMean`` where the function is a constant, with the value
provided when ``ConstantMean`` is initialized. Uses utility functions to bind the
value to the ``fixed_f`` function and sets that as the ``f`` attribute.
:ivar f: fixed mean function, must be callable and take a single argument (the inputs)
:type f: function
:ivar deriv: fixed derivative function (optional if no derivatives are needed), must
be callable and take a single argument (the inputs)
:type deriv: function
"""
def __init__(self, val):
"""
Initialize a new ConstantMean
Create a new ``ConstantMean`` instance with the given constant value.
:param val: Constant mean function value, must be a float or an integer
:type val: float or int
:returns: new ``ConstantMean`` instance
:rtype: ConstantMean
"""
if not isinstance(val, (float, int)):
raise TypeError("val must be a float or an integer")
self.f = partial(const_f, val=val)
self.deriv = const_deriv
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
val = signature(self.f).parameters['val'].default
return "{}".format(val)
class LinearMean(FixedMean):
"""
Class representing a linear fixed mean function
Subclass of ``FixedMean`` where the function is a linear function. By default the
function is linear in the first input dimension, though any non-negative integer index
can be provided to control which input is used in the linear function. Uses utility
functions to bind the correct function to the ``fixed_f`` function and sets that as
the ``f`` attribute and similar with the ``fixed_deriv`` utility function and the
``deriv`` attribute.
:ivar f: fixed mean function, must be callable and take a single argument (the inputs)
:type f: function
:ivar deriv: fixed derivative function, must be callable and take a single argument
(the inputs)
:type deriv: function
"""
def __init__(self, index=0):
"""
Initialize a new LinearMean
Create a new ``LinearMean`` instance with the given index value. This index is used
to select the dimension of the input for evaluating the function.
:param index: integer index to be applied to the second axis of ``x``, used to select
a particular input variable. Must be non-negative and less than the
length of the second axis of the inputs.
:type index: int
:returns: new ``LinearMean`` instance
:rtype: LinearMean
"""
self.f = partial(fixed_f, index=index, f=np.array)
self.deriv = partial(fixed_inputderiv, index=index, deriv=one)
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
index = signature(self.f).parameters["index"].default
return "x[{}]".format(index)
class Coefficient(MeanBase):
"""
Class representing a single fitting parameter in a mean function
Class representing a mean function with single free fitting parameter. Does not require any
internal state as the parameter value is stored/set externally through fitting routines.
"""
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which possibly depends on x.
For a ``Coefficient`` class, this is always 1.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return 1
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. For ``Coefficient`` classes, the inputs are ignored and the
function returns the value of the parameter broadcasting it appropriately given the
shape of the inputs. Thus, the ``params`` argument should always be an array of length
one. Returns a numpy array of shape ``(x.shape[0],)`` holding the value of the
parameter for each input point.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input (one in this case)
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.broadcast_to(params, x.shape[0])
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``Coefficient`` classes, the inputs are ignored and the derivative function
returns one, broadcasting it appropriately given the shape of the inputs.
Returns a numpy array of ones with shape ``(1, x.shape[0])`` holding the value
of the mean function derivative with respect to each parameter (first axis) for
each input point (second axis). Since coefficients are single parameters, this
will just be an array of ones.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.ones((self.get_n_params(x), x.shape[0]))
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``Coefficient`` classes, there is only a single parameter so the ``params``
argument should be an array of length one. Returns a numpy array of shape
``(n_params, n_params, x.shape[0])`` holding the value of the mean function
second derivaties with respect to each parameter pair (first twp axes) for each
input point (last axis). Since coefficients depend linearly on a single parameter,
this will always be an array of zeros.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.zeros((self.get_n_params(x), self.get_n_params(x), x.shape[0]))
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
For ``Coefficient`` classes, there is a single parameters so the ``params`` argument
should be an array of length one. Returns a numpy array of shape
``(x.shape[1], x.shape[0])`` holding the value of the mean function derivative
with respect to each input (first axis) for each input point (second axis).
Since coefficients do not depend on the inputs, this is just an array of zeros.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
return np.zeros((x.shape[1], x.shape[0]))
def __str__(self):
"""
Returns a string representation
Return a formula-like representation of the Mean Function. Useful for confirming
that a formula was correctly parsed.
"""
return "c"
class PolynomialMean(MeanBase):
"""
Polynomial mean function class
A ``PolynomialMean`` is a mean function where every input dimension is fit to a fixed
degree polynomial. The degree must be provided when creating the class instance. The
number of parameters depends on the degree and the shape of the inputs, since a separate
set of parameters are used for each input dimension.
:ivar degree: Polynomial degree, must be a positive integer
:type degree: int
"""
def __init__(self, degree):
"""
Create a new polynomial mean function instance
A ``PolynomialMean`` is a mean function where every input dimension is fit to a fixed
degree polynomial. The degree must be provided when creating the class instance. The
number of parameters depends on the degree and the shape of the inputs, since a separate
set of parameters are used for each input dimension. Must provide the degree when
initializing.
:param degree: Polynomial degree, must be a positive integer
:type degree: int
:returns: new ``PolynomialMean`` instance
:rtype: PolynomialMean
"""
assert int(degree) > 0, "degree must be a positive integer"
self.degree = int(degree)
def get_n_params(self, x):
"""
Determine the number of parameters
Returns the number of parameters for the mean function, which depends on x.
:param x: Input array
:type x: ndarray
:returns: number of parameters
:rtype: int
"""
return x.shape[1]*self.degree + 1
def mean_f(self, x, params):
"""
Returns value of mean function
Method to compute the value of the mean function for the inputs and parameters provided.
Shapes of ``x`` and ``params`` must be consistent based on the return value of the
``get_n_params`` method. Returns a numpy array of shape ``(x.shape[0],)`` holding
the value of the mean function for each input point.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function evaluated at all input points, numpy array of shape
``(x.shape[0],)``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
n_params = self.get_n_params(x)
indices = np.arange(0, n_params - 1) % x.shape[1]
expon = np.arange(0, n_params - 1) // x.shape[1] + 1
output = params[0] + np.sum(params[1:]*x[:, indices]**expon, axis = 1)
return output
def mean_deriv(self, x, params):
"""
Returns value of mean function derivative wrt the parameters
Method to compute the value of the mean function derivative with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, x.shape[0])`` holding the value of the mean
function derivative with respect to each parameter (first axis) for each input point
(second axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
n_params = self.get_n_params(x)
deriv = np.zeros((n_params, x.shape[0]))
deriv[0] = np.ones(x.shape[0])
indices = np.arange(0, n_params - 1) % x.shape[1]
expon = np.arange(0, n_params - 1) // x.shape[1] + 1
deriv[1:,:] = np.transpose(x[:, indices]**expon)
return deriv
def mean_hessian(self, x, params):
"""
Returns value of mean function Hessian wrt the parameters
Method to compute the value of the mean function Hessian with respect to the
parameters for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(n_params, n_params, x.shape[0])`` holding the
value of the mean function second derivaties with respect to each parameter pair
(first twp axes) for each input point (last axis). Since polynomial means depend
linearly on all input parameters, this will always be an array of zeros.
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function Hessian with respect to the parameters evaluated
at all input points, numpy array of shape ``(n_parmas, n_params, x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
n_params = self.get_n_params(x)
hess = np.zeros((n_params, n_params, x.shape[0]))
return hess
def mean_inputderiv(self, x, params):
"""
Returns value of mean function derivative wrt the inputs
Method to compute the value of the mean function derivative with respect to the
inputs for the inputs and parameters provided. Shapes of ``x`` and ``params``
must be consistent based on the return value of the ``get_n_params`` method.
Returns a numpy array of shape ``(x.shape[1], x.shape[0])`` holding the value of the mean
function derivative with respect to each input (first axis) for each input point
(second axis).
:param x: Inputs, must be a 1D or 2D numpy array (if 1D a second dimension will be added)
:type x: ndarray
:param params: Parameters, must be a 1D numpy array (of more than 1D will be flattened)
and have the same length as the number of parameters required for the
provided input
:type params: ndarray
:returns: Value of mean function derivative with respect to the inputs evaluated
at all input points, numpy array of shape ``(x.shape[1], x.shape[0])``
:rtype: ndarray
"""
x, params = self._check_inputs(x, params)
expon = np.reshape(np.arange(0, x.shape[0]*x.shape[1]*self.degree)//x.shape[1]//self.degree,
(self.degree, x.shape[0]*x.shape[1]))
x_indices = np.reshape(np.arange(0, x.shape[0]*x.shape[1]*self.degree) % (x.shape[0]*x.shape[1]),
(self.degree, x.shape[0]*x.shape[1]))
param_indices = np.reshape(np.arange(0, x.shape[0]*x.shape[1]*self.degree) % x.shape[1],
(self.degree, x.shape[0]*x.shape[1])) + expon*x.shape[1]
param_indices = np.reshape(param_indices, (self.degree, x.shape[0]*x.shape[1]))
output = np.sum((expon + 1.)*params[1:][param_indices]*x.flatten()[x_indices]**expon, axis=0)
return np.transpose(np.reshape(output, (x.shape[0], x.shape[1])))
def __str__(self):
"""
Returns a string representation
Return a string representation of the polynomial mean
"""
return "Polynomial mean of degree {}".format(self.degree)
| 45.354031
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| 0.009526
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0
| 5
|
3a52e34d437749ccd259783e2bfa67cbe68fa911
| 6,552
|
py
|
Python
|
aqsa_apps/import_from_file/views_check_csv_backup.py
|
yulaymusin/aqsa
|
e691c5827f9f341c73cb318cfde7920a1afb1c88
|
[
"MIT"
] | 13
|
2018-10-11T19:31:15.000Z
|
2022-02-13T10:42:43.000Z
|
aqsa_apps/import_from_file/views_check_csv_backup.py
|
yulaymusin/aqsa
|
e691c5827f9f341c73cb318cfde7920a1afb1c88
|
[
"MIT"
] | 7
|
2020-06-05T19:15:29.000Z
|
2022-02-10T06:53:59.000Z
|
aqsa_apps/import_from_file/views_check_csv_backup.py
|
yulaymusin/aqsa
|
e691c5827f9f341c73cb318cfde7920a1afb1c88
|
[
"MIT"
] | 4
|
2019-01-23T06:16:32.000Z
|
2022-03-04T20:31:13.000Z
|
# Author of Aqsa: Yulay Musin
from django.contrib.auth.decorators import login_required
from django.shortcuts import get_object_or_404
from . import models as m
from . import viewxins_check_csv_backup as vxccb
from . import forms as f
from django.shortcuts import render
from django.utils.translation import ugettext as _
import os
from django.conf import settings
from aqsa_apps.wallet_tag_etc import models as wte_m
@login_required
def check_csv_wallets(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=2)
if not import_from_file.checked or import_from_file.no_error is None:
no_error, ___, ___ = vxccb.csv_checker_of_wallet_tag_etc(import_from_file.file.path, f.Wallet)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('CSV with wallets have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import wallets'),
'links': (m.ImportFromFile.links['list'],),
})
@login_required
def check_csv_categories(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=3)
if not import_from_file.checked or import_from_file.no_error is None:
no_error, ___, ___ = vxccb.csv_checker_of_wallet_tag_etc(import_from_file.file.path, f.Category)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('CSV with categories have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import categories'),
'links': (m.ImportFromFile.links['list'],),
})
@login_required
def check_csv_tags(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=4)
if not import_from_file.checked or import_from_file.no_error is None:
no_error, ___, ___ = vxccb.csv_checker_of_wallet_tag_etc(import_from_file.file.path, f.Tag)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('CSV with tags have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import tags'),
'links': (m.ImportFromFile.links['list'],),
})
@login_required
def check_csv_contacts(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=5)
if not import_from_file.checked or import_from_file.no_error is None:
no_error, ___, ___ = vxccb.csv_checker_of_wallet_tag_etc(import_from_file.file.path, f.Contact)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('CSV with contacts have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import contacts'),
'links': (m.ImportFromFile.links['list'],),
})
@login_required
def check_csv_transactions(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=6)
if not import_from_file.checked or import_from_file.no_error is None:
wallets = wte_m.Wallet.objects.filter(owner=request.user).values_list('name', 'currency')
names_and_currencies_of_wallets = dict((x, y) for x, y in wallets)
names_of_categories = wte_m.Category.objects.filter(owner=request.user).values_list('name', flat=True)
names_of_tags = wte_m.Tag.objects.filter(owner=request.user).values_list('name', flat=True)
names_of_contacts = wte_m.Contact.objects.filter(owner=request.user).values_list('name', flat=True)
no_error = vxccb.csv_checker_of_transaction(
import_from_file.file.path,
names_and_currencies_of_wallets, names_of_categories, names_of_tags, names_of_contacts
)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('CSV with transactions have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import transactions'),
'links': (m.ImportFromFile.links['list'],),
})
@login_required
def check_aqsa_backup(request, pk):
import_from_file = get_object_or_404(m.ImportFromFile, owner=request.user, pk=pk, variety=7)
if not import_from_file.checked or import_from_file.no_error is None:
unzipped_csv_path = os.path.join(settings.MEDIA_ROOT, os.path.join('import_from_file', pk))
no_error, ___, names_and_currencies_of_wallets = vxccb.csv_checker_of_wallet_tag_etc(
os.path.join(unzipped_csv_path, 'wallets.csv'), f.Wallet
)
if no_error:
no_error, names_of_categories, ___ = vxccb.csv_checker_of_wallet_tag_etc(
os.path.join(unzipped_csv_path, 'categories.csv'), f.Category
)
if no_error:
no_error, names_of_tags, ___ = vxccb.csv_checker_of_wallet_tag_etc(
os.path.join(unzipped_csv_path, 'tags.csv'), f.Tag
)
if no_error:
no_error, names_of_contacts, ___ = vxccb.csv_checker_of_wallet_tag_etc(
os.path.join(unzipped_csv_path, 'contacts.csv'), f.Contact
)
if no_error:
# Here we will not do DB queries and check what kind of "wallet_tag_etc" user have. We will think uploaded
# ZIP was made by "export_to_file" app and CSV-files of "wallet_tag_etc" of ZIP have everything what needs.
no_error = vxccb.csv_checker_of_transaction(
os.path.join(unzipped_csv_path, 'transactions.csv'),
names_and_currencies_of_wallets, names_of_categories, names_of_tags, names_of_contacts
)
import_from_file.mark_as_checked(no_error)
return render(request=request, template_name='import_from_file/check_file_show_error_or_ok.html', context={
'title': _('Aqsa-Backup have been checked'),
'import_from_file': import_from_file,
'submit_btn': _('Confirm to import data from Aqsa-Backup'),
'links': (m.ImportFromFile.links['list'],),
})
| 44.876712
| 119
| 0.719017
| 946
| 6,552
| 4.572939
| 0.134249
| 0.110957
| 0.15534
| 0.039297
| 0.772538
| 0.742718
| 0.736939
| 0.704808
| 0.694868
| 0.68331
| 0
| 0.005035
| 0.181624
| 6,552
| 145
| 120
| 45.186207
| 0.801753
| 0.036325
| 0
| 0.454545
| 0
| 0
| 0.160063
| 0.046593
| 0
| 0
| 0
| 0
| 0
| 1
| 0.054545
| false
| 0
| 0.527273
| 0
| 0.636364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3a5871eb1c1dd62974c0d991fca245412e2d17c2
| 120
|
py
|
Python
|
graph/model/__init__.py
|
KMU-AELAB/LayoutNet_pytorch
|
a3a325c41ee10b556c8d258b2d3a1909d913a507
|
[
"MIT"
] | null | null | null |
graph/model/__init__.py
|
KMU-AELAB/LayoutNet_pytorch
|
a3a325c41ee10b556c8d258b2d3a1909d913a507
|
[
"MIT"
] | null | null | null |
graph/model/__init__.py
|
KMU-AELAB/LayoutNet_pytorch
|
a3a325c41ee10b556c8d258b2d3a1909d913a507
|
[
"MIT"
] | null | null | null |
from .encoder import Encoder
from .decoder import Edge, Corner
from .regressor import Regressor
from .model import Model
| 30
| 33
| 0.825
| 17
| 120
| 5.823529
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 120
| 4
| 34
| 30
| 0.951923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
|
28aaf024670c00df056dab6a7883f6ca311141ff
| 190
|
py
|
Python
|
molotov/__init__.py
|
tarekziade/molotov
|
e7e6f030892cc1244a886be96da7479eab689d52
|
[
"Apache-2.0"
] | 10
|
2017-08-28T09:49:13.000Z
|
2021-11-09T11:59:18.000Z
|
molotov/__init__.py
|
tarekziade/molotov
|
e7e6f030892cc1244a886be96da7479eab689d52
|
[
"Apache-2.0"
] | null | null | null |
molotov/__init__.py
|
tarekziade/molotov
|
e7e6f030892cc1244a886be96da7479eab689d52
|
[
"Apache-2.0"
] | null | null | null |
try:
from molotov import patch # NOQA
from molotov.fmwk import scenario # NOQA
except ImportError:
pass # first import
__version__ = '0.1'
| 23.75
| 58
| 0.563158
| 20
| 190
| 5.15
| 0.75
| 0.213592
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017094
| 0.384211
| 190
| 7
| 59
| 27.142857
| 0.863248
| 0.115789
| 0
| 0
| 0
| 0
| 0.018293
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.166667
| 0.5
| 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
| 0
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
28ca3a74e35d63029544a42b5e41674186417713
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/setuptools/_vendor/packaging/version.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/setuptools/_vendor/packaging/version.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/setuptools/_vendor/packaging/version.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/fd/f2/d1/36b16bc5870755fca8f2f93d8fcb3a24cf0dff1b12c5516be91272728f
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.385417
| 0
| 96
| 1
| 96
| 96
| 0.510417
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
28d1fc4c3b112d327ee7bbccaf7f0c0e45aca3e5
| 113
|
py
|
Python
|
src/evolvepy/__init__.py
|
EltonCN/evolvepy
|
4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc
|
[
"MIT"
] | 1
|
2022-01-13T21:11:53.000Z
|
2022-01-13T21:11:53.000Z
|
src/evolvepy/__init__.py
|
EltonCN/evolvepy
|
4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc
|
[
"MIT"
] | null | null | null |
src/evolvepy/__init__.py
|
EltonCN/evolvepy
|
4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc
|
[
"MIT"
] | null | null | null |
from .evolver import Evolver
from .configurable import Configurable
from . import generator, evaluator, callbacks
| 37.666667
| 45
| 0.840708
| 13
| 113
| 7.307692
| 0.538462
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115044
| 113
| 3
| 45
| 37.666667
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e915ceae2b275ad3703c60d4db614efff8adb558
| 196
|
py
|
Python
|
temperature_web_control/plugin/__init__.py
|
JQIamo/temperature-control-app
|
7ba91e8a7caa0506b0daf1d6394eb07d400182b9
|
[
"MIT"
] | null | null | null |
temperature_web_control/plugin/__init__.py
|
JQIamo/temperature-control-app
|
7ba91e8a7caa0506b0daf1d6394eb07d400182b9
|
[
"MIT"
] | null | null | null |
temperature_web_control/plugin/__init__.py
|
JQIamo/temperature-control-app
|
7ba91e8a7caa0506b0daf1d6394eb07d400182b9
|
[
"MIT"
] | null | null | null |
import os
from temperature_web_control.utils import list_all_modules
# Automatically load available drivers
plugins = list_all_modules("(.*)_plugin", os.path.dirname(os.path.abspath(__file__)))
| 28
| 85
| 0.811224
| 27
| 196
| 5.481481
| 0.740741
| 0.094595
| 0.189189
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086735
| 196
| 7
| 85
| 28
| 0.826816
| 0.183673
| 0
| 0
| 0
| 0
| 0.069182
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e9211ece6b65016817755e46b413560a11b9429c
| 156
|
py
|
Python
|
src/numberGenerator/rng.py
|
pooyadav/cpso-for-nn-training
|
f643d00e37a6500126561c429263c94f09d870e9
|
[
"MIT"
] | 1
|
2020-01-19T00:27:42.000Z
|
2020-01-19T00:27:42.000Z
|
src/numberGenerator/rng.py
|
Sharzy92/cpso-for-nn-training
|
f643d00e37a6500126561c429263c94f09d870e9
|
[
"MIT"
] | null | null | null |
src/numberGenerator/rng.py
|
Sharzy92/cpso-for-nn-training
|
f643d00e37a6500126561c429263c94f09d870e9
|
[
"MIT"
] | 2
|
2019-11-18T14:52:51.000Z
|
2020-01-19T00:27:26.000Z
|
import random
from numberGenerator.ng import NG
class RNG(NG):
def __init__(self):
pass
def random(self):
return random.random()
| 14.181818
| 33
| 0.647436
| 20
| 156
| 4.85
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.269231
| 156
| 11
| 34
| 14.181818
| 0.850877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0.142857
| 0.285714
| 0.142857
| 0.857143
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 5
|
3a620f4b2bd9fc9e84bac171539e6b0aab7d31ed
| 357
|
py
|
Python
|
p2p/exchange/__init__.py
|
wschwab/trinity
|
f94c1aa1642dd5d83eb6a89e48205abda234de79
|
[
"MIT"
] | null | null | null |
p2p/exchange/__init__.py
|
wschwab/trinity
|
f94c1aa1642dd5d83eb6a89e48205abda234de79
|
[
"MIT"
] | 2
|
2019-04-30T06:22:12.000Z
|
2019-06-14T04:27:18.000Z
|
p2p/exchange/__init__.py
|
wschwab/trinity
|
f94c1aa1642dd5d83eb6a89e48205abda234de79
|
[
"MIT"
] | null | null | null |
from .abc import ExchangeAPI, PerformanceAPI, ValidatorAPI # noqa: F401
from .exchange import BaseExchange # noqa: F401
from .logic import ExchangeLogic # noqa: F401
from .normalizers import BaseNormalizer, NoopNormalizer # noqa: F401
from .tracker import BasePerformanceTracker # noqa: F401
from .validator import noop_payload_validator # noqa: F401
| 51
| 72
| 0.798319
| 41
| 357
| 6.902439
| 0.487805
| 0.169611
| 0.212014
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.142857
| 357
| 6
| 73
| 59.5
| 0.866013
| 0.182073
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 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
| 1
| 0
|
0
| 5
|
3a9eb863c19594799283402a2121dbf1dbbdff58
| 92
|
py
|
Python
|
sqaodpy/sqaod/common/__init__.py
|
rickyHong/Qubo-GPU-repl
|
a2bea6857885d318cd3aa6b6ed37dc6e7f011433
|
[
"Apache-2.0"
] | 51
|
2018-01-04T06:26:07.000Z
|
2022-03-31T12:05:16.000Z
|
sqaodpy/sqaod/common/__init__.py
|
rickyHong/Qubo-GPU-repl
|
a2bea6857885d318cd3aa6b6ed37dc6e7f011433
|
[
"Apache-2.0"
] | 63
|
2018-02-21T10:57:26.000Z
|
2020-10-20T18:25:25.000Z
|
sqaodpy/sqaod/common/__init__.py
|
rickyHong/Qubo-GPU-repl
|
a2bea6857885d318cd3aa6b6ed37dc6e7f011433
|
[
"Apache-2.0"
] | 15
|
2018-01-18T16:56:15.000Z
|
2021-09-16T12:19:43.000Z
|
from .common import *
from .summary import *
from . import checkers
from . import docstring
| 18.4
| 23
| 0.76087
| 12
| 92
| 5.833333
| 0.5
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 92
| 4
| 24
| 23
| 0.921053
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3acbf3b5a2c313d13873fcbb521b75e5e21fdd6b
| 447
|
py
|
Python
|
python/src/constants/main.py
|
wagmiog/scripts
|
80aab6f4de064a83db120daaf3959166385195c1
|
[
"MIT"
] | 2
|
2021-12-31T18:40:42.000Z
|
2021-12-31T18:43:23.000Z
|
python/src/constants/main.py
|
anongothdev/scripts
|
6406bc23f1482312a8ad006218de4c6372d6e725
|
[
"MIT"
] | null | null | null |
python/src/constants/main.py
|
anongothdev/scripts
|
6406bc23f1482312a8ad006218de4c6372d6e725
|
[
"MIT"
] | null | null | null |
# PNG ADDRESS
PNG = "0x60781C2586D68229fde47564546784ab3fACA982"
# PGL PNG/AVAX
LP_PNG_AVAX = "0xd7538cABBf8605BdE1f4901B47B8D42c61DE0367"
# FACTORY ADDRESS
FACTORY = '0xefa94DE7a4656D787667C749f7E1223D71E9FD88'
# STAKING CONTRACTS
# EARN AVAX
STAKING_AVAX = "0xD49B406A7A29D64e081164F6C3353C599A2EeAE9"
# EARN OOE
STAKING_OOE = "0xf0eFf017644680B9878429137ccb2c041b4Fb701"
# EARN APEIN
STAKING_APEIN = "0xfe1d712363f2B1971818DBA935eEC13Ddea474cc"
| 34.384615
| 60
| 0.856823
| 32
| 447
| 11.8125
| 0.53125
| 0.037037
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.396088
| 0.085011
| 447
| 13
| 60
| 34.384615
| 0.528117
| 0.196868
| 0
| 0
| 0
| 0
| 0.715909
| 0.715909
| 0
| 0
| 0.715909
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
aaf1ca13b30e68af546ae723a0359703d1476571
| 116
|
py
|
Python
|
math-and-algorithm/059.py
|
silphire/training-with-books
|
bd07f7376996828b6cb4000d654cdc5f53d1c589
|
[
"MIT"
] | null | null | null |
math-and-algorithm/059.py
|
silphire/training-with-books
|
bd07f7376996828b6cb4000d654cdc5f53d1c589
|
[
"MIT"
] | 4
|
2020-01-04T14:05:45.000Z
|
2020-01-19T14:53:03.000Z
|
math-and-algorithm/059.py
|
silphire/training-with-books
|
bd07f7376996828b6cb4000d654cdc5f53d1c589
|
[
"MIT"
] | null | null | null |
# https://atcoder.jp/contests/math-and-algorithm/tasks/math_and_algorithm_ay
print([6, 2, 4, 8][int(input()) % 4])
| 29
| 76
| 0.706897
| 20
| 116
| 3.95
| 0.8
| 0.177215
| 0.405063
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046729
| 0.077586
| 116
| 3
| 77
| 38.666667
| 0.691589
| 0.637931
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 0
| 0
| 0
| 1
|
0
| 5
|
c908689edf617dc2b6de048930b09b793d2062e6
| 360
|
py
|
Python
|
src/top_sites_check/interface.py
|
deeso/top-sites-check
|
f128c68476a6366f4155a05140fda04ec60e0204
|
[
"Apache-2.0"
] | null | null | null |
src/top_sites_check/interface.py
|
deeso/top-sites-check
|
f128c68476a6366f4155a05140fda04ec60e0204
|
[
"Apache-2.0"
] | null | null | null |
src/top_sites_check/interface.py
|
deeso/top-sites-check
|
f128c68476a6366f4155a05140fda04ec60e0204
|
[
"Apache-2.0"
] | null | null | null |
class ServiceInterface(object):
def __init__(self, **kargs):
pass
def update(self, **kargs):
raise Exception("Not implemented for this class")
def load(self, **kargs):
raise Exception("Not implemented for this class")
def check(self, domain=None, **kargs):
raise Exception("Not implemented for this class")
| 24
| 57
| 0.644444
| 43
| 360
| 5.302326
| 0.44186
| 0.118421
| 0.25
| 0.289474
| 0.653509
| 0.653509
| 0.653509
| 0.653509
| 0.45614
| 0.45614
| 0
| 0
| 0.241667
| 360
| 14
| 58
| 25.714286
| 0.835165
| 0
| 0
| 0.333333
| 0
| 0
| 0.250696
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.444444
| false
| 0.111111
| 0
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
c9109658e516b1a084b257c6bcefb8c3f97fa7b6
| 41
|
py
|
Python
|
e2j2/exceptions.py
|
TheLastProject/e2j2
|
4d2071f6d974e383fe54400a7d4082d968449011
|
[
"MIT"
] | 9
|
2019-05-23T09:46:01.000Z
|
2020-09-01T06:50:23.000Z
|
e2j2/exceptions.py
|
TheLastProject/e2j2
|
4d2071f6d974e383fe54400a7d4082d968449011
|
[
"MIT"
] | 5
|
2017-08-21T12:14:02.000Z
|
2019-10-23T11:43:53.000Z
|
e2j2/exceptions.py
|
TheLastProject/e2j2
|
4d2071f6d974e383fe54400a7d4082d968449011
|
[
"MIT"
] | 2
|
2017-08-07T07:05:45.000Z
|
2019-10-18T13:06:11.000Z
|
class E2j2Exception(Exception):
pass
| 13.666667
| 31
| 0.756098
| 4
| 41
| 7.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.170732
| 41
| 2
| 32
| 20.5
| 0.852941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
c912e9404e24aa48ed3fd2dbd984fb56ce7ec5db
| 66
|
py
|
Python
|
pandas_genomics/io/plink/__init__.py
|
tomszar/pandas-genomics
|
13cd67c9e3a43e29420fc153ccf1cc60a6c7c009
|
[
"BSD-3-Clause"
] | 38
|
2020-09-22T18:41:18.000Z
|
2022-02-14T19:39:54.000Z
|
pandas_genomics/io/plink/__init__.py
|
tomszar/pandas-genomics
|
13cd67c9e3a43e29420fc153ccf1cc60a6c7c009
|
[
"BSD-3-Clause"
] | 24
|
2020-10-23T14:15:25.000Z
|
2022-02-14T19:42:05.000Z
|
pandas_genomics/io/plink/__init__.py
|
tomszar/pandas-genomics
|
13cd67c9e3a43e29420fc153ccf1cc60a6c7c009
|
[
"BSD-3-Clause"
] | 8
|
2020-10-22T21:12:03.000Z
|
2021-11-02T14:26:14.000Z
|
from .from_plink import from_plink
from .to_plink import to_plink
| 22
| 34
| 0.848485
| 12
| 66
| 4.333333
| 0.333333
| 0.346154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 66
| 2
| 35
| 33
| 0.896552
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
c91c969a159f16a09cf5fd8cf5943cc5de03d806
| 602
|
py
|
Python
|
allure-pytest-bdd/test/links_tests/links_test.py
|
Duisus/allure-python
|
09402db43da00bb3edb59767d5cc3826457c3f1a
|
[
"Apache-2.0"
] | 1
|
2021-01-08T12:52:32.000Z
|
2021-01-08T12:52:32.000Z
|
allure-pytest-bdd/test/links_tests/links_test.py
|
Duisus/allure-python
|
09402db43da00bb3edb59767d5cc3826457c3f1a
|
[
"Apache-2.0"
] | null | null | null |
allure-pytest-bdd/test/links_tests/links_test.py
|
Duisus/allure-python
|
09402db43da00bb3edb59767d5cc3826457c3f1a
|
[
"Apache-2.0"
] | null | null | null |
from pytest_bdd import scenario
@scenario("links_features\\link_issue_test_case_link.feature", "Default link")
def test_default_link():
pass
@scenario("links_features\\link_issue_test_case_link.feature", "Issue link")
def test_issue_link():
pass
@scenario("links_features\\link_issue_test_case_link.feature", "Test case link")
def test_test_case_link():
pass
@scenario("links_features\\link_without_name.feature", "Link without name")
def test_link_without_name():
pass
@scenario("links_features\\all_links_type.feature", "All links type")
def test_all_links_type():
pass
| 22.296296
| 80
| 0.772425
| 87
| 602
| 4.942529
| 0.218391
| 0.151163
| 0.244186
| 0.232558
| 0.455814
| 0.455814
| 0.37907
| 0.37907
| 0.37907
| 0.265116
| 0
| 0
| 0.111296
| 602
| 26
| 81
| 23.153846
| 0.803738
| 0
| 0
| 0.3125
| 0
| 0
| 0.486711
| 0.375415
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3125
| true
| 0.3125
| 0.0625
| 0
| 0.375
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
c928a3e3455dfa05750ddf525003a68980ae0788
| 157
|
py
|
Python
|
ifm3d_ros_msgs/utils/ifm3d_ros_utils/__init__.py
|
lovepark/ifm3d-ros
|
335b0a75cc3e289723fa41a5dddbb32ae7adacf8
|
[
"Apache-2.0"
] | 9
|
2017-10-17T13:34:01.000Z
|
2018-10-14T16:17:00.000Z
|
ifm3d_ros_msgs/utils/ifm3d_ros_utils/__init__.py
|
lovepark/ifm3d-ros
|
335b0a75cc3e289723fa41a5dddbb32ae7adacf8
|
[
"Apache-2.0"
] | 10
|
2017-04-03T14:10:55.000Z
|
2018-10-30T08:59:31.000Z
|
ifm3d_ros_msgs/utils/ifm3d_ros_utils/__init__.py
|
lovepark/ifm3d-ros
|
335b0a75cc3e289723fa41a5dddbb32ae7adacf8
|
[
"Apache-2.0"
] | 14
|
2017-05-01T18:45:12.000Z
|
2018-10-18T12:09:00.000Z
|
# SPDX-License-Identifier: Apache-2.0
# Copyright (C) 2021 ifm electronic, gmbh
from ._DumpClient import DumpClient
from ._ConfigClient import ConfigClient
| 26.166667
| 41
| 0.802548
| 20
| 157
| 6.2
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 0.121019
| 157
| 5
| 42
| 31.4
| 0.855072
| 0.477707
| 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
|
a344b3d03d0de9d4a69945176f852c4a940915ec
| 27
|
py
|
Python
|
mk.py
|
mk-knight23/Text-to-Speech
|
a9a3e5f98a6c56408112a6277ab171574667088c
|
[
"MIT"
] | null | null | null |
mk.py
|
mk-knight23/Text-to-Speech
|
a9a3e5f98a6c56408112a6277ab171574667088c
|
[
"MIT"
] | null | null | null |
mk.py
|
mk-knight23/Text-to-Speech
|
a9a3e5f98a6c56408112a6277ab171574667088c
|
[
"MIT"
] | null | null | null |
print("kazi")
print(98*96)
| 13.5
| 14
| 0.666667
| 5
| 27
| 3.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 0.074074
| 27
| 2
| 15
| 13.5
| 0.56
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 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
|
a38191a9b0d8ea3824d1db0498233b4f975cdf19
| 14,345
|
py
|
Python
|
backend/api/views/RolProyectoViewSet.py
|
kukiamarilla/polijira
|
510dbc1473db973ac71fc68fa5a9b758b90a780b
|
[
"MIT"
] | 1
|
2022-03-02T02:28:49.000Z
|
2022-03-02T02:28:49.000Z
|
backend/api/views/RolProyectoViewSet.py
|
kukiamarilla/polijira
|
510dbc1473db973ac71fc68fa5a9b758b90a780b
|
[
"MIT"
] | 22
|
2021-09-01T17:44:25.000Z
|
2021-10-07T19:39:09.000Z
|
backend/api/views/RolProyectoViewSet.py
|
kukiamarilla/polijira
|
510dbc1473db973ac71fc68fa5a9b758b90a780b
|
[
"MIT"
] | null | null | null |
from django.db import transaction
from backend.api.decorators import FormValidator
from rest_framework.decorators import action
from backend.api.serializers import RolProyectoSerializer, PermisoProyectoSerializer
from backend.api.models import Usuario, Proyecto, PermisoProyecto, RolProyecto, Miembro
from rest_framework import viewsets, status
from rest_framework.response import Response
from backend.api.forms import \
CreateRolProyectoForm, \
UpdateRolProyectoForm, \
AgregarPermisoRolProyectoForm
class RolProyectoViewSet(viewsets.ViewSet):
"""
RolProyectoViewSet View para RolProyecto
Args:
views (ViewSet): Tipo de clase basado en View
"""
def retrieve(self, request, pk=None):
"""
retrieve Obtiene un rol de proyecto
Args:
request (Any): request
pk (integer, opcional): Primary Key
Returns:
JSON: un rol de proyecto
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not (miembro.tiene_permiso("ver_roles_proyecto") and
miembro.tiene_permiso("ver_permisos_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": ["ver_roles_proyecto", "ver_permisos_proyecto"]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
serializer = RolProyectoSerializer(rol, many=False)
return Response(serializer.data)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
@transaction.atomic
@FormValidator(form=CreateRolProyectoForm)
def create(self, request):
"""
create Crea un rol de proyecto
Args:
request (Any): request
Returns:
JSON: Rol de proyecto
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
proyecto = Proyecto.objects.get(pk=request.data["proyecto"])
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=proyecto)
if not (miembro.tiene_permiso("crear_roles_proyecto") and
miembro.tiene_permiso("ver_permisos_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": ["crear_roles_proyecto", "ver_permisos_proyecto"]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
permisos = request.data["permisos"]
rol = RolProyecto.objects.create(nombre=request.data["nombre"], proyecto=proyecto)
for p in permisos:
perm = PermisoProyecto.objects.get(pk=p["id"])
rol.agregar_permiso(perm)
serializer = RolProyectoSerializer(rol, many=False)
return Response(serializer.data)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
def destroy(self, request, pk=None):
"""
destroy Elimina un rol de proyecto
Args:
request (Any): request
pk (integer, opcional): Primary Key
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not (miembro.tiene_permiso("ver_roles_proyecto")
and miembro.tiene_permiso("eliminar_roles_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": [
"ver_roles_proyecto",
"eliminar_roles_proyecto"
]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
if Miembro.objects.filter(rol__pk=pk).count():
response = {"message": "Rol asignado a un miembro de proyecto, no se puede eliminar"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol.delete()
response = {"message": "Rol de Proyecto Eliminado."}
return Response(response)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
@FormValidator(UpdateRolProyectoForm)
def update(self, request, pk=None):
"""
update Obtiene un rol de proyecto mediante su pk
Args:
request (Any): request
pk (integer, opcional): Primary Key
Returns:
JSON: rol de proyecto modificado en formato json
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not (miembro.tiene_permiso("ver_permisos_proyecto") and
miembro.tiene_permiso("ver_roles_proyecto") and
miembro.tiene_permiso("modificar_roles_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": [
"ver_permisos_proyecto",
"ver_roles_proyecto",
"modificar_roles_proyecto"
]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
if (miembro.rol.pk == int(pk)):
response = {"message": "No puedes modificar tu propio rol"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol = RolProyecto.objects.get(pk=pk)
if rol.nombre == "Scrum Master":
response = {
"message": "No se puede modificar el rol Scrum Master",
"error": "forbidden"
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol_db = RolProyecto.objects.filter(nombre=request.data["nombre"], proyecto=rol.proyecto)
if len(rol_db) > 0:
response = {
"message": "Ya existe un rol con ese nombre",
"error": "forbidden"
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol.nombre = request.data["nombre"]
rol.save()
serializer = RolProyectoSerializer(rol, many=False)
return Response(serializer.data)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
@action(detail=True, methods=["GET"])
def permisos(self, request, pk=None):
"""
permisos Lista los permisos de un rol de proyecto
request (Any): request
pk (integer, opcional): Primary Key
Returns:
JSON: lista de permisos del rol de proyecto con la pk especificada
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not miembro.tiene_permiso("ver_roles_proyecto"):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": ["ver_roles_proyecto"]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
permisos = rol.permisos.all()
serializer = PermisoProyectoSerializer(permisos, many=True)
return Response(serializer.data)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
@permisos.mapping.post
def agregar_permiso(self, request, pk=None):
"""
agregar_permiso Agrega un permiso a un rol de proyecto
Args:
request (Any): request
pk (integer, opcional): Primary Key
Returns:
JSON: Rol de proyecto con nuevo permiso agregado en formato json
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not (miembro.tiene_permiso("ver_permisos_proyecto")
and miembro.tiene_permiso("modificar_roles_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": [
"ver_permisos_proyecto",
"modificar_roles_proyecto"
]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
form = AgregarPermisoRolProyectoForm(request.data)
if not form.is_valid():
response = {
"message": "Error de validacion",
"errors": form.errors
}
return Response(response, status=status.HTTP_422_UNPROCESSABLE_ENTITY)
if (miembro.rol.pk == int(pk)):
response = {"message": "No puedes modificar tu propio rol"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol = RolProyecto.objects.get(pk=pk)
if rol.nombre == "Scrum Master":
response = {
"message": "No se puede modificar el rol Scrum Master",
"error": "forbidden"
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
permiso = PermisoProyecto.objects.get(pk=request.data["id"])
rol.agregar_permiso(permiso)
serializer = RolProyectoSerializer(rol, many=False)
return Response(serializer.data)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
@permisos.mapping.delete
def eliminar_permiso(self, request, pk=None):
"""
eliminar_permiso Elimina un permiso de un rol de proyecto
Args:
request (Any): request
pk (integer, opcional): Primary Key
"""
try:
usuario_request = Usuario.objects.get(user=request.user)
rol = RolProyecto.objects.get(pk=pk)
miembro = Miembro.objects.get(usuario=usuario_request, proyecto=rol.proyecto)
if not (miembro.tiene_permiso("ver_permisos_proyecto")
and miembro.tiene_permiso("modificar_roles_proyecto")):
response = {
"message": "No tiene permiso para realizar esta acción",
"permission_required": [
"ver_permisos_proyecto",
"modificar_roles_proyecto"
]
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
if (miembro.rol.pk == int(pk)):
response = {"message": "No puedes modificar tu propio rol"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol = RolProyecto.objects.get(pk=pk)
if rol.nombre == "Scrum Master":
response = {
"message": "No se puede modificar el rol Scrum Master",
"error": "forbidden"
}
return Response(response, status=status.HTTP_403_FORBIDDEN)
permiso = PermisoProyecto.objects.get(pk=request.data["id"])
if rol.permisos.all().count() < 2:
response = {"message": "El rol de proyecto no se puede quedar sin permisos"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
rol.eliminar_permiso(permiso)
serializer = RolProyectoSerializer(rol, many=False)
return Response(serializer.data)
except PermisoProyecto.DoesNotExist:
response = {"message": "No existe el permiso de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except RolProyecto.DoesNotExist:
response = {"message": "No existe el rol de proyecto"}
return Response(response, status=status.HTTP_404_NOT_FOUND)
except Miembro.DoesNotExist:
response = {"message": "Usted no es miembro de este proyecto"}
return Response(response, status=status.HTTP_403_FORBIDDEN)
| 45.977564
| 101
| 0.593935
| 1,430
| 14,345
| 5.834965
| 0.11049
| 0.063758
| 0.084372
| 0.104027
| 0.787872
| 0.760307
| 0.747483
| 0.747483
| 0.736817
| 0.724113
| 0
| 0.009733
| 0.319554
| 14,345
| 311
| 102
| 46.125402
| 0.845098
| 0.082328
| 0
| 0.645299
| 0
| 0
| 0.175335
| 0.03138
| 0
| 0
| 0
| 0
| 0
| 1
| 0.029915
| false
| 0
| 0.034188
| 0
| 0.230769
| 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
|
6e793cbdbdcad886ae6fbef5aeee1e88788ce9dd
| 136
|
py
|
Python
|
server/api/models/__init__.py
|
koiic/favorite-things
|
f34944dfbc78e454c6245b76f036f6dd24d018eb
|
[
"MIT"
] | null | null | null |
server/api/models/__init__.py
|
koiic/favorite-things
|
f34944dfbc78e454c6245b76f036f6dd24d018eb
|
[
"MIT"
] | 5
|
2020-07-17T10:43:13.000Z
|
2022-02-26T12:16:12.000Z
|
server/api/models/__init__.py
|
koiic/favorite-things
|
f34944dfbc78e454c6245b76f036f6dd24d018eb
|
[
"MIT"
] | null | null | null |
from .audit import Audit
from .category import Category
from .favorite import Favorite
from .user import User
from .database import db
| 19.428571
| 30
| 0.808824
| 20
| 136
| 5.5
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.154412
| 136
| 6
| 31
| 22.666667
| 0.956522
| 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
|
6e990cd39c3f94ba36b0a62c92cb3bd6181d8a96
| 1,625
|
py
|
Python
|
tests/utils/test_ZeropadLayer.py
|
jamesjiang52/Reshade
|
ddc87424c50030b4606c4eb5ec61b4be1d4cad98
|
[
"MIT"
] | null | null | null |
tests/utils/test_ZeropadLayer.py
|
jamesjiang52/Reshade
|
ddc87424c50030b4606c4eb5ec61b4be1d4cad98
|
[
"MIT"
] | null | null | null |
tests/utils/test_ZeropadLayer.py
|
jamesjiang52/Reshade
|
ddc87424c50030b4606c4eb5ec61b4be1d4cad98
|
[
"MIT"
] | null | null | null |
import reshade as rs
class TestZeropadLayer:
def test_ZeropadLayer(self):
inputs = rs.ConnectionLayer(depth=2, height=3, width=3)
inputs.values = [
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
[[10, 11, 12],
[13, 14, 15],
[16, 17, 18]]
]
outputs = rs.ConnectionLayer(depth=2, height=8, width=8)
rs.utils.ZeropadLayer(inputs, outputs, 3, 2, 4, 1)
assert outputs.values == [
[[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, 2, 3, 0, 0],
[0, 0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 7, 8, 9, 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, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 10, 11, 12, 0, 0],
[0, 0, 0, 13, 14, 15, 0, 0],
[0, 0, 0, 16, 17, 18, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]]
]
outputs = rs.ConnectionLayer(depth=2, height=5, width=5)
rs.utils.ZeropadLayer(inputs, outputs, 1, 1, 1, 1)
assert outputs.values == [
[[0, 0, 0, 0, 0],
[0, 1, 2, 3, 0],
[0, 4, 5, 6, 0],
[0, 7, 8, 9, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 10, 11, 12, 0],
[0, 13, 14, 15, 0],
[0, 16, 17, 18, 0],
[0, 0, 0, 0, 0]],
]
| 29.017857
| 64
| 0.329231
| 259
| 1,625
| 2.061776
| 0.146718
| 0.479401
| 0.640449
| 0.779026
| 0.750936
| 0.569288
| 0.434457
| 0.385768
| 0.385768
| 0.385768
| 0
| 0.276498
| 0.465846
| 1,625
| 55
| 65
| 29.545455
| 0.33871
| 0
| 0
| 0.304348
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 1
| 0.021739
| false
| 0
| 0.021739
| 0
| 0.065217
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6e9b79f9a74fd53e00de482157a0696bd4ebd2e6
| 137
|
py
|
Python
|
Draw/random_shapes.py
|
Joevaen/Scikit-image_On_CT
|
e3bf0eeadc50691041b4b7c44a19d07546a85001
|
[
"Apache-2.0"
] | null | null | null |
Draw/random_shapes.py
|
Joevaen/Scikit-image_On_CT
|
e3bf0eeadc50691041b4b7c44a19d07546a85001
|
[
"Apache-2.0"
] | null | null | null |
Draw/random_shapes.py
|
Joevaen/Scikit-image_On_CT
|
e3bf0eeadc50691041b4b7c44a19d07546a85001
|
[
"Apache-2.0"
] | null | null | null |
# 生成具有随机形状的图像，并用边框标记。
import skimage.draw
image, labels = skimage.draw.random_shapes((32, 32), max_shapes=3)
print(image)
print(labels)
| 19.571429
| 66
| 0.766423
| 20
| 137
| 5.15
| 0.65
| 0.213592
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.040323
| 0.094891
| 137
| 6
| 67
| 22.833333
| 0.790323
| 0.138686
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 0.5
| 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
|
6eaed71022cce7d261feb988ee4cff688b390a70
| 12
|
py
|
Python
|
Lib/test/test_compiler/testcorpus/95_annotation_module.py
|
diogommartins/cinder
|
79103e9119cbecef3b085ccf2878f00c26e1d175
|
[
"CNRI-Python-GPL-Compatible"
] | 1,886
|
2021-05-03T23:58:43.000Z
|
2022-03-31T19:15:58.000Z
|
Lib/test/test_compiler/testcorpus/95_annotation_module.py
|
diogommartins/cinder
|
79103e9119cbecef3b085ccf2878f00c26e1d175
|
[
"CNRI-Python-GPL-Compatible"
] | 70
|
2021-05-04T23:25:35.000Z
|
2022-03-31T18:42:08.000Z
|
Lib/test/test_compiler/testcorpus/95_annotation_module.py
|
diogommartins/cinder
|
79103e9119cbecef3b085ccf2878f00c26e1d175
|
[
"CNRI-Python-GPL-Compatible"
] | 52
|
2021-05-04T21:26:03.000Z
|
2022-03-08T18:02:56.000Z
|
z: int = 5
| 4
| 10
| 0.416667
| 3
| 12
| 1.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 0.416667
| 12
| 2
| 11
| 6
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 5
|
6e20bb4d243ca51a54bf6d82a760d4031fb587a3
| 75
|
py
|
Python
|
app/backend/callbacks/__init__.py
|
Arthurdb1999/dash-plotly-example
|
a54dce5e233aab75746a433665a4a958a77e4b58
|
[
"MIT"
] | null | null | null |
app/backend/callbacks/__init__.py
|
Arthurdb1999/dash-plotly-example
|
a54dce5e233aab75746a433665a4a958a77e4b58
|
[
"MIT"
] | null | null | null |
app/backend/callbacks/__init__.py
|
Arthurdb1999/dash-plotly-example
|
a54dce5e233aab75746a433665a4a958a77e4b58
|
[
"MIT"
] | null | null | null |
# Lista de callbacks
from app.backend.callbacks.callbacks_main import *
| 12.5
| 50
| 0.786667
| 10
| 75
| 5.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146667
| 75
| 5
| 51
| 15
| 0.90625
| 0.24
| 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
|
6e27ca4ff4e87d0ff72c7412eb2284448a407d8a
| 17,075
|
py
|
Python
|
DonaldTrump.py
|
lanceculnane/DonaldTrumpVa
|
7151087894b654e1f96e4875575f2093bdd58258
|
[
"CC0-1.0"
] | null | null | null |
DonaldTrump.py
|
lanceculnane/DonaldTrumpVa
|
7151087894b654e1f96e4875575f2093bdd58258
|
[
"CC0-1.0"
] | null | null | null |
DonaldTrump.py
|
lanceculnane/DonaldTrumpVa
|
7151087894b654e1f96e4875575f2093bdd58258
|
[
"CC0-1.0"
] | null | null | null |
####Donald Trump estimation game haahhaaha
# this is based on his assertion that he will
# personally answer calls of any veterans who are
# having trouble navigating the VA system
# it estimates how many days he will spend in his
# presidency (if elected) answering VA calls
print """
WWMxxxxxMWWWMMMMMWWWMMxxxMMMMMMxxxxMMWMMMxMWWWMMxxxxxxMMMMxxxxxxxMMMMMMMMMxxMMWWMWWMxxxMWWWMMMWWMMMMMMMxxMMMMxxxxxMMMWWWMMMMMMWW@@@WWWMMMMMxxxMMWW@@WW
WWMxxxxxMWWWMMMMMWWWMMxxxMMMMMMxxxxMMWMMMxMWWWWMxxxxxxMMMMxxxxxxxMMMMMMMMMxxMMWWMWWMxxxMMWMMMMWWMMMxMxnnnnnnnxxxxxxxMMWWMMMMMMWW@@WWWWMMMxxxxxMMMWW@WW
WMMxnnxxMWWWMMMMMWWWMMxxxMMMMMMxxxxMMWMMMxMWWWWMMxxxxxMMMMxxxnnxxMMMMMMMMMxxMMWWMWWMxxxxMWMMMMWWMxxnnz##+++**i*+#nnxMMWWMMxxMWWW@WWWWWMMxxxxxxxMMWW@WW
WMxxnnxxMWWWMMMMMWWWMMxxxMMMMMMMxxxMMWMMMxMWWWWMMxxxxxMMMMxxxnnxxxMMMMMMMMxxMMWWMWWMxxxxMWMMxMMMxxnnz+******ii;:;;*+nxMWMxxxMMWWWWWWWMMxxxxxxxxxMMWWWW
WMMxnnxxMWWWMMMMMWWWMMxxxMMMMMMMxxxMMWMMxxMWWWWMMxxxxxMMMMxxnnxxxxxMMMMMMxxxMMWWWWWMxxnxMWMxMxMxnnzzz++*******ii:;;:;i*#nxxxxMWWWWWWWMxxxxxxxxxxxMWWWW
WMMxnnxxMWWWMxxMMWWWMMxxxMMMMMMMxxxMMWMMxxMMWWWMMxxxxxMMMMxxnnxxxxxMMMMMMxxxMMMWWWWMxxnxMWMMMxMnnnzz#+*********ii;;:::::;+nxxMMWWWWWWMxxxxxxxxxxxMWWWW
WWMxnnxxMMWWMMMMMWWWMMxxxMMMMMMMxxxMMWMMxxMMWWWMMxxnxxMMMMxxnnnxxxxMMMMMMMxxMMMWWWWMxxnxMWWMxxnnnnnnz#+++*+++**iii;;::,:ii;*zxMWWWWWMMxxxxxxxxxxxMWWWW
WWMxnnxxMMWWMMMMMWWWMMxxxMMMMMMxxxxMMWMMxxMMWWWMxxnnxxMMMxxxnnnxxxxMMMMMMMxxMMMWWWWMxxnxWWWxxxnnnnnnxz##+++#++++***ii;::::;i;i+MWWWWMMxxxxxxxxxxxxMWWW
WWMxnnxxMMWWMMMMWWWWMMxxxMMMMMMxxxxMMWMMxxMMWWMMxxnnnxxMMMxxnnnxxxxMMMMMMMxxMMMMWWWMxxxxMWWxxxMMMWWMxnzz#+++*++++**ii;;;;;;i;i;inWMWMMxxxxxxxxxxxxMWWW
WMxxxxxxMMWMMxMMWWWWMMxxxMMMMMMxxxxMWWWMxxMMWxnxMxnnnxMMMMxxxnnxxxxMMMMMMMxxxMMMMMWMMxxxMWMxMWWWxxnnnnnz###+++++***i;ii;;;iiiii;;#WMMMxnnxxxxnnnxxMWWW
WMxxnnxxMMWMMxMMWWWWMMxxxMMMMMMxnxxMMWWMxxMMni;inxnnxxMMMMxxxnnxxxxMMMMMMMxxxMMMMWWMxxxxMMMWWMxnzz######z#+#++++****ii;;iiiiiiiii;+MxxnnnnnnnnnnxxMWWW
WMxxnnxxMMWMMxMMWWWWMMxxMMMMMMMxnnxMMWWMxxxM+;;:*nnnxxMMMMxxxnnxxxxMMMMMMMxxxMMMMWWMxxxxMWWMxnz#+#++++++##+++++++*****i;i***ii****i#xxxnnnznnnnnnxMWWW
WMxxnnxxMMWMMxMMWWWWMMMMMMMMMMMxnnxMMWMMxxxxi;;:;nnnnxMMMMxxxnnxxxxMMMMMMMxxxMMMWWWMxxxMWMxxxnz##+*++++++#++*+++++***i*i;i********+i*nxnnnznnnnnnxMWWW
WMxxnnnxxMMMMxxMWWWWMMMMMMMMMMMxnnxMMWMMxxxz*;;;;zxnnxMMMMxxnnnnxxxMMMMMMMxxxMMMMWWMMxxxxxMMxz##++++++++*+##+++*++**ii**iiii*****+++**nnnnnnnnnnnxMMMW
WMxxnnnxxMMMMxxMWWWWMMMMMMMMMMMxnnxMMWMMxxx+i;;iiznnnxxMMMxxnnnnxxxMMMMMMMxxxMMMMWWMMxxxxMxnzzz##**************++++**iii*iii******+++**znznnnnnnnnxMMM
WWMxnnnxxMMMMxxMMWWWMMMMMMMMMMMxnnxMMWMMMxx*iiii*znnnxxMMMxxnnnnxxxMMMMMMMxxxMMWMMMMMMnxMMxxnz#+**********iiii****++*iiiiiii***++**+++*#nnznzzznnnxxMM
WWMxnnnxxMWMMxxMMWWWMMMMMMWMMMMxnnxMMWMMMMn*ii;i*nxnnxxxMxxxnnnnxxxMMMMMMMxxxMMWMMMMMxxxxxnz#++**i****iii;;;;iiii**++**i;;iii***+**+*+++zzzzzzznnnxxxM
WWMxnnnxxMMMMxxMMWWWMMMMMWWWMMMxxxxxMWMMMxz*i;;;*xnnnnxxxxxnnnnnxxxxMMMMMMxxxMWWMMMWMMMMxnnz+++i;iiiiii;;;;;;;;;ii*****iii;ii*i*****+**++zzzzzzznnnxxx
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"""
# image from http://www.asciify.net/ascii/ascii/4454
print "\tThe following calculation is based on claims by presidential hopeful Donald Trump."
print "\t-"*100
print '\tSpeaking about VA concerns, he claimed that " if the complaint was valid and not addressed, it would \n\t “be brought directly to me '
print '\tand I will pick up the phone and fix it myself if I have to.”-Trump'
print "\t-"*100
print "\thttp://www.huffingtonpost.com/entry/donald-trump-veterans-hotline_us_5783f248e4b01edea78f08c9"
print "\t-"*100
print "\tSo if elected, lets see how many hours or days he will spend on the phone helping people with"
print "\tVA hospital concerns in his first 4-year term!"
print "\t*"*100
vets = int(raw_input("\tThere are 22 million vets in the US. On a monthly basis, what percentage of them do you \n\tthink are having veteran paperwork issues and may need to talk to President Trump? (enter a number w/o any symbols): "))
v_time = int(raw_input("\tHow long do you think the average person is going to need to talk to President Trump \n\tin order to fully explain their situation so that he may personally and effectively help them? (answer in minutes): "))
answer = 22000000*vets*12*v_time
print "\t*"*100
print "\tAccording to your estimates, in his first 4-year term, President Trump will spend ", answer, " minutes"
print "\ton the phone helping veterans. What a guy!"
print "\t-"*100
print "\tThat's ", answer/60, " hours"
print "\t...which is ", answer/1440, " days"
print "\t...which is ", answer/525600, " YEARS"
print "\t...which is ", answer/126144000, " times the age of the United States"
print "\t...so... out of the 1460 days in his 4-yr term, he will spend ", ((answer/1440)/1460)*100, " percentage of his time on the phone."
print "\t...making America Great Again one phone call at a time!"
print "\t-"*100
| 126.481481
| 236
| 0.63672
| 1,327
| 17,075
| 8.188395
| 0.415222
| 0.010123
| 0.004417
| 0.006442
| 0.019418
| 0.008283
| 0
| 0
| 0
| 0
| 0
| 0.005524
| 0.035139
| 17,075
| 134
| 237
| 127.425373
| 0.654021
| 0.018272
| 0
| 0.056
| 0
| 0.408
| 0.973736
| 0.868979
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.176
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6e432dce570efc715e8c14b346f01ec1acd4dce9
| 106
|
py
|
Python
|
jhu_primitives/nearest_neighbor_nomination/__init__.py
|
remram44/primitives-interfaces
|
f6d305d6f65fc8c89c14bef6f2b8b4d86d44005b
|
[
"Apache-2.0"
] | null | null | null |
jhu_primitives/nearest_neighbor_nomination/__init__.py
|
remram44/primitives-interfaces
|
f6d305d6f65fc8c89c14bef6f2b8b4d86d44005b
|
[
"Apache-2.0"
] | 23
|
2017-09-20T08:12:13.000Z
|
2022-03-01T01:49:11.000Z
|
jhu_primitives/nearest_neighbor_nomination/__init__.py
|
remram44/primitives-interfaces
|
f6d305d6f65fc8c89c14bef6f2b8b4d86d44005b
|
[
"Apache-2.0"
] | 8
|
2018-05-14T18:44:38.000Z
|
2021-03-18T19:53:23.000Z
|
from __future__ import absolute_import
from .nearest_neighbor_nomination import NearestNeighborNomination
| 35.333333
| 66
| 0.915094
| 11
| 106
| 8.181818
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075472
| 106
| 2
| 67
| 53
| 0.918367
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| true
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| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
282c52b3a4f06321ff046f748599c948542d4b70
| 403
|
py
|
Python
|
ismo/samples/sample_generator_factory.py
|
kjetil-lye/iterative_surrogate_optimization
|
f5de412daab1180612837f4c950203ad87d62f7e
|
[
"MIT"
] | 6
|
2020-10-20T14:03:50.000Z
|
2021-11-02T15:08:55.000Z
|
ismo/samples/sample_generator_factory.py
|
kjetil-lye/iterative_surrogate_optimization
|
f5de412daab1180612837f4c950203ad87d62f7e
|
[
"MIT"
] | 3
|
2020-11-13T19:04:10.000Z
|
2022-02-10T02:12:18.000Z
|
ismo/samples/sample_generator_factory.py
|
kjetil-lye/iterative_surrogate_optimization
|
f5de412daab1180612837f4c950203ad87d62f7e
|
[
"MIT"
] | 3
|
2020-10-20T14:03:53.000Z
|
2021-03-19T23:11:34.000Z
|
from ismo.samples import MonteCarlo, Sobol
class SampleGeneratorFactory(object):
def __init__(self):
self.known_names = {
'monte-carlo' : MonteCarlo,
'sobol' : Sobol
}
def create_sample_generator(self, name):
return self.known_names[name]()
def create_sample_generator(name):
return SampleGeneratorFactory().create_sample_generator(name)
| 23.705882
| 65
| 0.677419
| 42
| 403
| 6.214286
| 0.5
| 0.137931
| 0.241379
| 0.183908
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 403
| 16
| 66
| 25.1875
| 0.841935
| 0
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| 0.039702
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| 1
| 0.272727
| false
| 0
| 0.090909
| 0.181818
| 0.636364
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
284f6f4b7078afb237f57370ce346f793b581a77
| 2,786
|
py
|
Python
|
tests/test_roi.py
|
betatim/cornerstone_widget
|
c22fafd4d8fe148f6b2349518188eb0bee5f18f1
|
[
"Apache-2.0"
] | 24
|
2018-09-07T10:40:07.000Z
|
2022-02-01T21:18:00.000Z
|
tests/test_roi.py
|
betatim/cornerstone_widget
|
c22fafd4d8fe148f6b2349518188eb0bee5f18f1
|
[
"Apache-2.0"
] | 26
|
2018-09-04T16:32:46.000Z
|
2018-10-08T09:11:50.000Z
|
tests/test_roi.py
|
betatim/cornerstone_widget
|
c22fafd4d8fe148f6b2349518188eb0bee5f18f1
|
[
"Apache-2.0"
] | 3
|
2018-09-17T12:56:16.000Z
|
2019-12-03T06:30:34.000Z
|
import json
from cornerstone_widget import get_bbox_handles
_test_bbox_json = """
{"imageIdToolState": {"": {"rectangleRoi": {"data": [{"visible": true, "active": false, "invalidated": false, "handles": {"start": {"x": 553.3138489596392, "y": 449.722433543228, "highlight": true, "active": false}, "end": {"x": 835.5569648554714, "y": 705.8887398182495, "highlight": true, "active": false}, "textBox": {"active": false, "hasMoved": false, "movesIndependently": false, "drawnIndependently": true, "allowedOutsideImage": true, "hasBoundingBox": true, "x": 835.5569648554714, "y": 577.8055866807388, "boundingBox": {"width": 150.8333282470703, "height": 65, "left": 312.93333435058605, "top": 195.39999389648438}}}, "meanStdDev": {"count": 72731, "mean": 137.81189589033562, "variance": 484.0080783665253, "stdDev": 22.00018359847311}, "area": 72301.17647058812}]}}}, "elementToolState": {}, "elementViewport": {}, "viewing_time": 77.17544794082642}
"""
_test_bbox_json_2 = """
{"imageIdToolState": {"": {"rectangleRoi": {"data": [{"visible": true, "active": false, "invalidated": false, "handles": {"start": {"x": 196.03125, "y": 417.8125, "highlight": true, "active": false}, "end": {"x": 478.03125, "y": 625.8125, "highlight": true, "active": false}, "textBox": {"active": false, "hasMoved": false, "movesIndependently": false, "drawnIndependently": true, "allowedOutsideImage": true, "hasBoundingBox": true, "x": 478.03125, "y": 521.8125, "boundingBox": {"width": 150.9033203125, "height": 65, "left": 239.015625, "top": 228.40625}}}, "meanStdDev": {"count": 58656, "mean": 145.6067352181388, "variance": 1398.8774714024185, "stdDev": 37.40157044032267}, "area": 58656}, {"visible": true, "active": true, "invalidated": false, "handles": {"start": {"x": 658.03125, "y": 497.8125, "highlight": true, "active": false}, "end": {"x": 912.03125, "y": 577.8125, "highlight": true, "active": false}, "textBox": {"active": false, "hasMoved": false, "movesIndependently": false, "drawnIndependently": true, "allowedOutsideImage": true, "hasBoundingBox": true, "x": 912.03125, "y": 537.8125, "boundingBox": {"width": 150.9033203125, "height": 65, "left": 456.015625, "top": 236.40625}}}, "meanStdDev": {"count": 20320, "mean": 136.35415690597338, "variance": 813.4574721617173, "stdDev": 28.521175855173244}, "area": 20320}]}}}, "elementToolState": {}, "elementViewport": {}, "viewing_time": 63.09548878669739}"""
def test_bbox_parser():
a_bbox = get_bbox_handles(json.loads(_test_bbox_json))
assert len(a_bbox) == 1
assert len(a_bbox[0]['x']) == 2
assert a_bbox[0]['x'][0] > 500
assert a_bbox[0]['x'][1] < 900
b_bbox = get_bbox_handles(json.loads(_test_bbox_json_2))
assert len(b_bbox) == 2
assert b_bbox[0]['x'][0] < 200
assert b_bbox[0]['x'][1] > 450
| 121.130435
| 1,429
| 0.668342
| 327
| 2,786
| 5.590214
| 0.363914
| 0.066193
| 0.065646
| 0.078775
| 0.51477
| 0.47046
| 0.455142
| 0.420131
| 0.370897
| 0.328228
| 0
| 0.22008
| 0.106246
| 2,786
| 22
| 1,430
| 126.636364
| 0.514056
| 0
| 0
| 0
| 0
| 0.117647
| 0.824838
| 0.085068
| 0
| 0
| 0
| 0
| 0.411765
| 1
| 0.058824
| false
| 0
| 0.117647
| 0
| 0.176471
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 1
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| 1
| 1
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
2856ba22f26fa86ccc6cbca6d7fa1f6e37c3c73e
| 1,867
|
py
|
Python
|
correlations/tests/corr3d_test.py
|
FilippoAleotti/Dwarf-Tensorflow
|
3b44d3a791b75e93f8d1cabded736440ce84bca0
|
[
"Apache-2.0"
] | 16
|
2019-12-04T14:42:07.000Z
|
2022-03-22T05:06:01.000Z
|
correlations/tests/corr3d_test.py
|
FilippoAleotti/Dwarf-Tensorflow
|
3b44d3a791b75e93f8d1cabded736440ce84bca0
|
[
"Apache-2.0"
] | 10
|
2020-04-12T03:26:25.000Z
|
2022-03-12T00:14:16.000Z
|
correlations/tests/corr3d_test.py
|
FilippoAleotti/Dwarf-Tensorflow
|
3b44d3a791b75e93f8d1cabded736440ce84bca0
|
[
"Apache-2.0"
] | 3
|
2020-03-08T01:50:44.000Z
|
2020-07-10T07:51:47.000Z
|
import tensorflow as tf
import numpy as np
from external_packages.correlation3D.ops import correlation3D as cuda_corr
from correlations.correlation3D import correlation3D as native_corr
import os
class Corr3DTest(tf.test.TestCase):
def test_equals_mdd0(self):
x = np.random.rand(2,480,640,128)
y = np.random.rand(2,480,640,128)
with self.test_session():
x = tf.convert_to_tensor(x, dtype=tf.float32)
y = tf.convert_to_tensor(y, dtype=tf.float32)
md= 3
mdd=0
native_corr_res = native_corr(x,y,pad=md, kernel_size=1, max_displacement=md, stride_1=1, stride_2=1, max_depth_displacement=mdd).eval()
cuda_corr_res = cuda_corr(x,y,pad=md, kernel_size=1, max_displacement=md, stride_1=1, stride_2=1, max_depth_displacement=mdd).eval()
assert cuda_corr_res.shape == native_corr_res.shape
print(np.max(cuda_corr_res - native_corr_res))
number_errors = np.sum(np.abs(cuda_corr_res - native_corr_res) > 0.01)
self.assertAllEqual(number_errors, 0)
def test_equals_mdd1(self):
x = np.random.rand(2,480,640,128)
y = np.random.rand(2,480,640,128)
with self.test_session():
x = tf.convert_to_tensor(x, dtype=tf.float32)
y = tf.convert_to_tensor(y, dtype=tf.float32)
md= 4
mdd=1
native_corr_res = native_corr(x,y,pad=md, kernel_size=1, max_displacement=md, stride_1=1, stride_2=1, max_depth_displacement=mdd).eval()
cuda_corr_res = cuda_corr(x,y,pad=md, kernel_size=1, max_displacement=md, stride_1=1, stride_2=1, max_depth_displacement=mdd).eval()
number_errors = np.sum(np.abs(cuda_corr_res - native_corr_res) > 0.01)
self.assertAllEqual(number_errors, 0)
if __name__ == '__main__':
tf.test.main()
| 49.131579
| 148
| 0.667381
| 293
| 1,867
| 3.976109
| 0.228669
| 0.072103
| 0.066953
| 0.072961
| 0.733047
| 0.733047
| 0.712446
| 0.712446
| 0.712446
| 0.712446
| 0
| 0.059794
| 0.220675
| 1,867
| 38
| 149
| 49.131579
| 0.740893
| 0
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| 0.529412
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| 0.004283
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| 0.088235
| 1
| 0.058824
| false
| 0
| 0.147059
| 0
| 0.235294
| 0.029412
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 5
|
28a394aad03f9fc8db0394d5e951a8cc4603fc0e
| 183
|
py
|
Python
|
autofaiss/__init__.py
|
rom1504/autofaiss
|
d61c15bb1ad49e94ab29bd05b280d49387431ece
|
[
"Apache-2.0"
] | 195
|
2021-05-04T17:33:36.000Z
|
2022-03-31T20:35:13.000Z
|
autofaiss/__init__.py
|
rom1504/autofaiss
|
d61c15bb1ad49e94ab29bd05b280d49387431ece
|
[
"Apache-2.0"
] | 69
|
2021-06-11T20:31:40.000Z
|
2022-03-31T21:48:27.000Z
|
autofaiss/__init__.py
|
rom1504/autofaiss
|
d61c15bb1ad49e94ab29bd05b280d49387431ece
|
[
"Apache-2.0"
] | 20
|
2021-07-31T12:09:03.000Z
|
2022-03-10T10:29:54.000Z
|
# pylint: disable=unused-import,missing-docstring
from autofaiss.external.quantize import build_index, score_index, tune_index
from autofaiss.version import __author__, __version__
| 30.5
| 76
| 0.846995
| 23
| 183
| 6.26087
| 0.695652
| 0.180556
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| 0
| 0.087432
| 183
| 5
| 77
| 36.6
| 0.862275
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| 1
| 0
| 1
| 0
|
0
| 5
|
9542f0a667b0870153b36e4402dd5a711fa0476e
| 3,225
|
py
|
Python
|
ingenico/direct/sdk/merchant/i_merchant_client.py
|
Ingenico/direct-sdk-python3
|
d2b30b8e8afb307153a1f19ac4c054d5344449ce
|
[
"Apache-2.0"
] | null | null | null |
ingenico/direct/sdk/merchant/i_merchant_client.py
|
Ingenico/direct-sdk-python3
|
d2b30b8e8afb307153a1f19ac4c054d5344449ce
|
[
"Apache-2.0"
] | 1
|
2021-03-30T12:55:39.000Z
|
2021-04-08T08:23:27.000Z
|
ingenico/direct/sdk/merchant/i_merchant_client.py
|
Ingenico/direct-sdk-python3
|
d2b30b8e8afb307153a1f19ac4c054d5344449ce
|
[
"Apache-2.0"
] | null | null | null |
#
# This class was auto-generated from the API references found at
# https://support.direct.ingenico.com/documentation/api/reference/
#
from abc import ABC, abstractmethod
from ingenico.direct.sdk.merchant.hostedcheckout.hosted_checkout_client import HostedCheckoutClient
from ingenico.direct.sdk.merchant.hostedtokenization.hosted_tokenization_client import HostedTokenizationClient
from ingenico.direct.sdk.merchant.payments.payments_client import PaymentsClient
from ingenico.direct.sdk.merchant.payouts.payouts_client import PayoutsClient
from ingenico.direct.sdk.merchant.productgroups.product_groups_client import ProductGroupsClient
from ingenico.direct.sdk.merchant.products.products_client import ProductsClient
from ingenico.direct.sdk.merchant.services.services_client import ServicesClient
from ingenico.direct.sdk.merchant.sessions.sessions_client import SessionsClient
from ingenico.direct.sdk.merchant.tokens.tokens_client import TokensClient
class IMerchantClient(ABC):
"""
Merchant client interface. Thread-safe.
"""
@abstractmethod
def hosted_checkout(self) -> HostedCheckoutClient:
"""
Resource /v2/{merchantId}/hostedcheckouts
:return: :class:`ingenico.direct.sdk.merchant.hostedcheckout.i_hosted_checkout_client.IHostedCheckoutClient`
"""
@abstractmethod
def hosted_tokenization(self) -> HostedTokenizationClient:
"""
Resource /v2/{merchantId}/hostedtokenizations
:return: :class:`ingenico.direct.sdk.merchant.hostedtokenization.i_hosted_tokenization_client.IHostedTokenizationClient`
"""
@abstractmethod
def payments(self) -> PaymentsClient:
"""
Resource /v2/{merchantId}/payments
:return: :class:`ingenico.direct.sdk.merchant.payments.i_payments_client.IPaymentsClient`
"""
@abstractmethod
def payouts(self) -> PayoutsClient:
"""
Resource /v2/{merchantId}/payouts
:return: :class:`ingenico.direct.sdk.merchant.payouts.i_payouts_client.IPayoutsClient`
"""
@abstractmethod
def product_groups(self) -> ProductGroupsClient:
"""
Resource /v2/{merchantId}/productgroups
:return: :class:`ingenico.direct.sdk.merchant.productgroups.i_product_groups_client.IProductGroupsClient`
"""
@abstractmethod
def products(self) -> ProductsClient:
"""
Resource /v2/{merchantId}/products
:return: :class:`ingenico.direct.sdk.merchant.products.i_products_client.IProductsClient`
"""
@abstractmethod
def services(self) -> ServicesClient:
"""
Resource /v2/{merchantId}/services
:return: :class:`ingenico.direct.sdk.merchant.services.i_services_client.IServicesClient`
"""
@abstractmethod
def sessions(self) -> SessionsClient:
"""
Resource /v2/{merchantId}/sessions
:return: :class:`ingenico.direct.sdk.merchant.sessions.i_sessions_client.ISessionsClient`
"""
@abstractmethod
def tokens(self) -> TokensClient:
"""
Resource /v2/{merchantId}/tokens
:return: :class:`ingenico.direct.sdk.merchant.tokens.i_tokens_client.ITokensClient`
"""
| 34.677419
| 128
| 0.724341
| 311
| 3,225
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| 0
| 0.003371
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| 92
| 129
| 35.054348
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| 0.310345
| false
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| 0.344828
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| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
957e6f56b79c08e6ede44a3ddbbbefc525a1a548
| 299
|
py
|
Python
|
tests/__init__.py
|
actively-ai/fastcluster
|
569c3e38e749ba2f3c3a665a04f5f8e0a71d6d37
|
[
"BSD-2-Clause"
] | 92
|
2016-03-12T09:27:31.000Z
|
2022-03-20T23:48:45.000Z
|
tests/__init__.py
|
actively-ai/fastcluster
|
569c3e38e749ba2f3c3a665a04f5f8e0a71d6d37
|
[
"BSD-2-Clause"
] | 27
|
2016-04-28T04:51:51.000Z
|
2022-02-27T13:50:29.000Z
|
tests/__init__.py
|
actively-ai/fastcluster
|
569c3e38e749ba2f3c3a665a04f5f8e0a71d6d37
|
[
"BSD-2-Clause"
] | 29
|
2016-03-16T14:18:26.000Z
|
2022-03-30T23:26:34.000Z
|
import unittest
class fastcluster_test(unittest.TestCase):
def test(self):
from tests.test import test
test(10)
def test_nan(self):
from tests.nantest import test
test()
def test_vector(self):
from tests.vectortest import test
test(10)
| 19.933333
| 42
| 0.632107
| 38
| 299
| 4.894737
| 0.394737
| 0.112903
| 0.209677
| 0.172043
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.018957
| 0.294314
| 299
| 14
| 43
| 21.357143
| 0.862559
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| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0.363636
| 0
| 0.727273
| 0
| 0
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| 0
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| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 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
|
958d1996892cade235bfddc20013a54273b03436
| 2,615
|
py
|
Python
|
tests/test_alpha_geodesic.py
|
ISMHinoLab/geodesical_skew_divergence
|
293648a30e86bdd14749af5b107f1d3687d67700
|
[
"MIT"
] | 7
|
2021-04-01T09:21:49.000Z
|
2022-03-24T05:28:22.000Z
|
tests/test_alpha_geodesic.py
|
ISMHinoLab/geodesical_skew_divergence
|
293648a30e86bdd14749af5b107f1d3687d67700
|
[
"MIT"
] | 21
|
2021-04-01T02:56:54.000Z
|
2021-05-07T01:02:09.000Z
|
tests/test_alpha_geodesic.py
|
ISMHinoLab/geodesical_skew_divergence
|
293648a30e86bdd14749af5b107f1d3687d67700
|
[
"MIT"
] | 2
|
2021-04-12T15:00:17.000Z
|
2021-04-26T03:10:26.000Z
|
import unittest
import torch
from gs_divergence.alpha_geodesic import alpha_geodesic
class TestAlphaGeodesic(unittest.TestCase):
def test_alpha_minus_1(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=-1, lmd=0.5)
self.assertTrue(torch.equal(g, ((a+b) / 2)))
def test_alpha_1(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=1, lmd=0.5)
res = torch.exp(0.5 * torch.log(a) + 0.5 * torch.log(b))
self.assertTrue(torch.equal(g, res))
def test_alpha_0(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=0, lmd=0.5)
res = (0.5 * torch.sqrt(a) + 0.5 * torch.sqrt(b))**2
self.assertTrue(torch.equal(g, res))
def test_alpha_3(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=3, lmd=0.5)
res = 1 / (0.5 * 1/a + 0.5 * 1/b)
self.assertTrue(torch.equal(g, res))
def test_alpha_inf(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=float('inf'), lmd=0.5)
res = torch.min(a, b)
self.assertTrue(torch.equal(g, res))
def test_alpha_minus_inf(self):
a = torch.Tensor([1, 2, 3])
b = torch.Tensor([4, 5, 6])
g = alpha_geodesic(a, b, alpha=-float('inf'), lmd=0.5)
res = torch.max(a, b)
self.assertTrue(torch.equal(g, res))
def test_value_0(self):
a = torch.Tensor([0, 1, 2])
b = torch.Tensor([1, 2, 3])
g = alpha_geodesic(a, b, alpha=-1, lmd=0.5)
self.assertTrue(torch.isinf(g).sum() == 0)
def test_value_0_2d(self):
a = torch.Tensor([[0.1, 0.2, 0.7], [0.5, 0.5, 0.0]])
b = torch.Tensor([[0.4, 0.4, 0.2], [0.2, 0.1, 0.7]])
g = alpha_geodesic(a, b, alpha=1, lmd=0.5)
self.assertTrue(torch.isinf(g).sum() == 0)
def test_value_inf(self):
a = torch.Tensor([[0.1, 0.2, 0.7], [0.5, 0.5, 0.0]])
b = torch.Tensor([[0.4, 0.4, 0.2], [0.2, 0.1, 0.7]])
g = alpha_geodesic(a, b, alpha=100, lmd=0.5)
res = torch.min(a, b)
self.assertTrue(torch.all(torch.isclose(g, res)))
def test_grad(self):
a = torch.tensor([[0.1, 0.2, 0.7], [0.5, 0.5, 0.0]], requires_grad=True)
b = torch.tensor([[0.4, 0.4, 0.2], [0.2, 0.1, 0.7]])
g = alpha_geodesic(a, b, alpha=1, lmd=0.5)
self.assertIsNotNone(g.grad_fn)
| 30.406977
| 80
| 0.532314
| 462
| 2,615
| 2.9329
| 0.108225
| 0.032472
| 0.073801
| 0.118081
| 0.772694
| 0.748339
| 0.735055
| 0.735055
| 0.735055
| 0.705535
| 0
| 0.089052
| 0.269981
| 2,615
| 85
| 81
| 30.764706
| 0.620744
| 0
| 0
| 0.5
| 0
| 0
| 0.002294
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 1
| 0.166667
| false
| 0
| 0.05
| 0
| 0.233333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
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| null | 0
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| 0
| 0
| 0
|
0
| 5
|
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