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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8ea519157c5c1536e0b0cece8af322eab2e2b221
| 31
|
py
|
Python
|
models/__init__.py
|
AminJun/Effenwick
|
36633683f2812347314adf3241158c720b3cad93
|
[
"BSD-3-Clause"
] | 19
|
2018-04-26T12:43:56.000Z
|
2020-09-14T12:48:49.000Z
|
models/__init__.py
|
AminJun/Effenwick
|
36633683f2812347314adf3241158c720b3cad93
|
[
"BSD-3-Clause"
] | 3
|
2018-04-26T12:00:01.000Z
|
2018-09-18T14:36:50.000Z
|
models/__init__.py
|
AminJun/Effenwick
|
36633683f2812347314adf3241158c720b3cad93
|
[
"BSD-3-Clause"
] | 2
|
2019-03-28T00:52:47.000Z
|
2019-10-26T06:58:34.000Z
|
from .densenet import DenseNet
| 15.5
| 30
| 0.83871
| 4
| 31
| 6.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.962963
| 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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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d957c2fd9a7cf51a441d89633973a3ac806e5d9d
| 36
|
py
|
Python
|
__init__.py
|
dwind/TextToCoNLL
|
cd712cd380604ca78cc65c8653287a26eb66c711
|
[
"Apache-2.0"
] | 3
|
2020-06-09T18:16:31.000Z
|
2021-02-18T10:14:29.000Z
|
__init__.py
|
dwind/TextToCoNLL
|
cd712cd380604ca78cc65c8653287a26eb66c711
|
[
"Apache-2.0"
] | null | null | null |
__init__.py
|
dwind/TextToCoNLL
|
cd712cd380604ca78cc65c8653287a26eb66c711
|
[
"Apache-2.0"
] | 1
|
2020-10-09T12:24:31.000Z
|
2020-10-09T12:24:31.000Z
|
from .converter import text_to_conll
| 36
| 36
| 0.888889
| 6
| 36
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 1
| 36
| 36
| 0.909091
| 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
| 1
| 0
|
0
| 6
|
7943f3c6c44c7bf86810740444594d5429abe8f5
| 170
|
py
|
Python
|
app/api/index.py
|
benranderson/fmeca
|
aedbde0ec20417cace3df01e09f193dd525ba0e2
|
[
"MIT"
] | null | null | null |
app/api/index.py
|
benranderson/fmeca
|
aedbde0ec20417cace3df01e09f193dd525ba0e2
|
[
"MIT"
] | 11
|
2017-11-09T22:23:50.000Z
|
2017-11-30T16:40:22.000Z
|
app/api/index.py
|
benranderson/fmeca
|
aedbde0ec20417cace3df01e09f193dd525ba0e2
|
[
"MIT"
] | 3
|
2017-11-10T09:52:07.000Z
|
2022-01-28T11:00:17.000Z
|
from flask import jsonify
from . import api
@api.route('/', methods=['GET'])
def index():
return jsonify("Welcome to the fmeca API. Check out '/api/facilities/'.")
| 21.25
| 77
| 0.676471
| 24
| 170
| 4.791667
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.158824
| 170
| 7
| 78
| 24.285714
| 0.804196
| 0
| 0
| 0
| 0
| 0
| 0.347059
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
79715f5998b145f6e10e2255b387f8959d0ad610
| 191
|
py
|
Python
|
FrcScoutingWebsite/Forms/__init__.py
|
Fightfx1/Frc-5951-Makers-Assemble-scouting-website
|
d90eeee64f440b0d2f2ff0c7d92cfb435c6dd6aa
|
[
"MIT"
] | 1
|
2020-03-16T22:23:30.000Z
|
2020-03-16T22:23:30.000Z
|
FrcScoutingWebsite/Forms/__init__.py
|
Fightfx1/Frc-5951-Makers-Assemble-scouting-website
|
d90eeee64f440b0d2f2ff0c7d92cfb435c6dd6aa
|
[
"MIT"
] | null | null | null |
FrcScoutingWebsite/Forms/__init__.py
|
Fightfx1/Frc-5951-Makers-Assemble-scouting-website
|
d90eeee64f440b0d2f2ff0c7d92cfb435c6dd6aa
|
[
"MIT"
] | null | null | null |
from FrcScoutingWebsite.Forms.LoginForm import LoginForm
from FrcScoutingWebsite.Forms.SettingForm import SettingsForm
from FrcScoutingWebsite.Forms.AddScouterForm import AddMemberToTeam_Form
| 63.666667
| 72
| 0.910995
| 19
| 191
| 9.105263
| 0.526316
| 0.381503
| 0.468208
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057592
| 191
| 3
| 72
| 63.666667
| 0.961111
| 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
| 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
| 6
|
798bac4d0af4c33d8a7737f259f7870bccf6f972
| 38
|
py
|
Python
|
src/apps/startposes/viewsets/__init__.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 27
|
2020-05-03T11:01:27.000Z
|
2022-03-17T05:33:10.000Z
|
src/apps/startposes/viewsets/__init__.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 54
|
2020-05-09T01:18:41.000Z
|
2022-01-22T10:31:15.000Z
|
src/apps/startposes/viewsets/__init__.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 9
|
2020-09-29T11:31:32.000Z
|
2022-03-09T01:37:50.000Z
|
from .startpos import StartPosViewSet
| 19
| 37
| 0.868421
| 4
| 38
| 8.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 38
| 1
| 38
| 38
| 0.970588
| 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
| 1
| 0
|
0
| 6
|
799cc4e798f10fef14b7e1d73558239359e289df
| 113
|
py
|
Python
|
django-server/movies/views/__init__.py
|
Huangming222/angular-django-movies
|
12bb03f2235e271cbdf33025d52e6feedba4b71f
|
[
"MIT"
] | 40
|
2017-06-27T14:26:34.000Z
|
2022-01-02T20:50:03.000Z
|
django-server/movies/views/__init__.py
|
Huangming222/angular-django-movies
|
12bb03f2235e271cbdf33025d52e6feedba4b71f
|
[
"MIT"
] | 7
|
2020-06-05T20:32:54.000Z
|
2022-02-10T17:30:34.000Z
|
django-server/movies/views/__init__.py
|
phamcong/angular2-django-movies-cloned
|
c5402b9fb22e099787f7fcfc51951d98f78853c7
|
[
"MIT"
] | 24
|
2017-09-04T15:54:30.000Z
|
2021-06-08T05:34:12.000Z
|
from .comments import *
from .movies import *
from .ratings import *
from .auth import *
from .user_data import *
| 22.6
| 24
| 0.743363
| 16
| 113
| 5.1875
| 0.5
| 0.481928
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168142
| 113
| 5
| 24
| 22.6
| 0.882979
| 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
| 1
| 0
|
0
| 6
|
79cd88f87bc61a3ac2e2cdaa019563e907df8149
| 1,067
|
py
|
Python
|
env/lib/python2.7/UserDict.py
|
essien1990/Flask-Mysqldb
|
e0917b90c45a0aaf922bfa672ddb479cb450a02d
|
[
"MIT"
] | null | null | null |
env/lib/python2.7/UserDict.py
|
essien1990/Flask-Mysqldb
|
e0917b90c45a0aaf922bfa672ddb479cb450a02d
|
[
"MIT"
] | 6
|
2020-06-05T22:57:03.000Z
|
2021-06-10T18:48:39.000Z
|
env/lib/python2.7/UserDict.py
|
essien1990/Flask-Mysqldb
|
e0917b90c45a0aaf922bfa672ddb479cb450a02d
|
[
"MIT"
] | 1
|
2021-12-16T17:09:52.000Z
|
2021-12-16T17:09:52.000Z
|
XSym
0075
b535cba88ebbec6fd9c903b93b8d9b16
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/UserDict.py
| 213.4
| 948
| 0.096532
| 15
| 1,067
| 6.866667
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.208696
| 0.892221
| 1,067
| 5
| 948
| 213.4
| 0.686957
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
79d3af5cebf88609ea580dda1d0a155d48779b3e
| 110
|
py
|
Python
|
emailverifier/exceptions/invalid_argument_exception.py
|
whois-api-llc/python-email-verifier
|
159d8837a293430c0115995950c7d970a9ea282d
|
[
"MIT"
] | null | null | null |
emailverifier/exceptions/invalid_argument_exception.py
|
whois-api-llc/python-email-verifier
|
159d8837a293430c0115995950c7d970a9ea282d
|
[
"MIT"
] | null | null | null |
emailverifier/exceptions/invalid_argument_exception.py
|
whois-api-llc/python-email-verifier
|
159d8837a293430c0115995950c7d970a9ea282d
|
[
"MIT"
] | 2
|
2020-03-18T12:46:50.000Z
|
2020-12-02T11:56:31.000Z
|
from .api_base_exception import ApiBaseException
class InvalidArgumentException(ApiBaseException):
pass
| 18.333333
| 49
| 0.845455
| 10
| 110
| 9.1
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118182
| 110
| 5
| 50
| 22
| 0.938144
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
8dabf8132f277e5f2523a75ab83350e1084820eb
| 28
|
py
|
Python
|
puzzlehunt_server/settings/__init__.py
|
adenylyl/pi_day_puzzle_hunt
|
aa01cef427bc5f524e89558da72a2f79b0c78514
|
[
"MIT"
] | null | null | null |
puzzlehunt_server/settings/__init__.py
|
adenylyl/pi_day_puzzle_hunt
|
aa01cef427bc5f524e89558da72a2f79b0c78514
|
[
"MIT"
] | null | null | null |
puzzlehunt_server/settings/__init__.py
|
adenylyl/pi_day_puzzle_hunt
|
aa01cef427bc5f524e89558da72a2f79b0c78514
|
[
"MIT"
] | null | null | null |
from .base_settings import *
| 28
| 28
| 0.821429
| 4
| 28
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.88
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| 0
| 0
| 0
| 0
| 0
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| 0
| 1
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| true
| 0
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| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8de282f9d0dcab4cae7a05994cc70ba87c20abce
| 28
|
py
|
Python
|
dgp_graph/__init__.py
|
naiqili/GPGene-TCBB
|
30f889b583930ba58030e7d903dd7ef98da4824c
|
[
"MIT"
] | 1
|
2021-03-19T11:25:33.000Z
|
2021-03-19T11:25:33.000Z
|
dgp_graph/__init__.py
|
naiqili/GPGene-TCBB
|
30f889b583930ba58030e7d903dd7ef98da4824c
|
[
"MIT"
] | null | null | null |
dgp_graph/__init__.py
|
naiqili/GPGene-TCBB
|
30f889b583930ba58030e7d903dd7ef98da4824c
|
[
"MIT"
] | null | null | null |
from .impl_parallel import *
| 28
| 28
| 0.821429
| 4
| 28
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.88
| 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
| 1
| 0
| null | 0
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| 0
| 0
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| 1
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
eb975538a209cd6749d36e7396c4cea1c20634f9
| 102,833
|
py
|
Python
|
tests/test_nnn.py
|
zchvsre/TreeCorr
|
825dc0a9d4754f9d98ebcf9c26dee9597915d650
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
tests/test_nnn.py
|
zchvsre/TreeCorr
|
825dc0a9d4754f9d98ebcf9c26dee9597915d650
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
tests/test_nnn.py
|
zchvsre/TreeCorr
|
825dc0a9d4754f9d98ebcf9c26dee9597915d650
|
[
"BSD-2-Clause-FreeBSD"
] | 1
|
2020-12-14T16:23:33.000Z
|
2020-12-14T16:23:33.000Z
|
# Copyright (c) 2003-2019 by Mike Jarvis
#
# TreeCorr is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
from __future__ import print_function
import numpy as np
import treecorr
import os
import coord
from test_helper import get_script_name, do_pickle, assert_raises, CaptureLog
def test_log_binning():
import math
# Test some basic properties of the base class
def check_arrays(nnn):
np.testing.assert_almost_equal(nnn.bin_size * nnn.nbins, math.log(nnn.max_sep/nnn.min_sep))
np.testing.assert_almost_equal(nnn.ubin_size * nnn.nubins, nnn.max_u-nnn.min_u)
np.testing.assert_almost_equal(nnn.vbin_size * nnn.nvbins, nnn.max_v-nnn.min_v)
#print('logr = ',nnn.logr1d)
np.testing.assert_equal(nnn.logr1d.shape, (nnn.nbins,) )
np.testing.assert_almost_equal(nnn.logr1d[0], math.log(nnn.min_sep) + 0.5*nnn.bin_size)
np.testing.assert_almost_equal(nnn.logr1d[-1], math.log(nnn.max_sep) - 0.5*nnn.bin_size)
np.testing.assert_equal(nnn.logr.shape, (nnn.nbins, nnn.nubins, 2*nnn.nvbins) )
np.testing.assert_almost_equal(nnn.logr[:,0,0], nnn.logr1d)
np.testing.assert_almost_equal(nnn.logr[:,-1,-1], nnn.logr1d)
assert len(nnn.logr) == nnn.nbins
#print('u = ',nnn.u1d)
np.testing.assert_equal(nnn.u1d.shape, (nnn.nubins,) )
np.testing.assert_almost_equal(nnn.u1d[0], nnn.min_u + 0.5*nnn.ubin_size)
np.testing.assert_almost_equal(nnn.u1d[-1], nnn.max_u - 0.5*nnn.ubin_size)
np.testing.assert_equal(nnn.u.shape, (nnn.nbins, nnn.nubins, 2*nnn.nvbins) )
np.testing.assert_almost_equal(nnn.u[0,:,0], nnn.u1d)
np.testing.assert_almost_equal(nnn.u[-1,:,-1], nnn.u1d)
#print('v = ',nnn.v1d)
np.testing.assert_equal(nnn.v1d.shape, (2*nnn.nvbins,) )
np.testing.assert_almost_equal(nnn.v1d[0], -nnn.max_v + 0.5*nnn.vbin_size)
np.testing.assert_almost_equal(nnn.v1d[-1], nnn.max_v - 0.5*nnn.vbin_size)
np.testing.assert_almost_equal(nnn.v1d[nnn.nvbins], nnn.min_v + 0.5*nnn.vbin_size)
np.testing.assert_almost_equal(nnn.v1d[nnn.nvbins-1], -nnn.min_v - 0.5*nnn.vbin_size)
np.testing.assert_equal(nnn.v.shape, (nnn.nbins, nnn.nubins, 2*nnn.nvbins) )
np.testing.assert_almost_equal(nnn.v[0,0,:], nnn.v1d)
np.testing.assert_almost_equal(nnn.v[-1,-1,:], nnn.v1d)
def check_defaultuv(nnn):
assert nnn.min_u == 0.
assert nnn.max_u == 1.
assert nnn.nubins == np.ceil(1./nnn.ubin_size)
assert nnn.min_v == 0.
assert nnn.max_v == 1.
assert nnn.nvbins == np.ceil(1./nnn.vbin_size)
# Check the different ways to set up the binning:
# Omit bin_size
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, bin_type='LogRUV')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.nbins == 20
check_defaultuv(nnn)
check_arrays(nnn)
# Specify min, max, n for u,v too.
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20,
min_u=0.2, max_u=0.9, nubins=12,
min_v=0., max_v=0.2, nvbins=2)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.nbins == 20
assert nnn.min_u == 0.2
assert nnn.max_u == 0.9
assert nnn.nubins == 12
assert nnn.min_v == 0.
assert nnn.max_v == 0.2
assert nnn.nvbins == 2
check_arrays(nnn)
# Omit min_sep
nnn = treecorr.NNNCorrelation(max_sep=20, nbins=20, bin_size=0.1)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size == 0.1
assert nnn.max_sep == 20.
assert nnn.nbins == 20
check_defaultuv(nnn)
check_arrays(nnn)
# Specify max, n, bs for u,v too.
nnn = treecorr.NNNCorrelation(max_sep=20, nbins=20, bin_size=0.1,
max_u=0.9, nubins=3, ubin_size=0.05,
max_v=0.4, nvbins=4, vbin_size=0.05)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size == 0.1
assert nnn.max_sep == 20.
assert nnn.nbins == 20
assert np.isclose(nnn.ubin_size, 0.05)
assert np.isclose(nnn.min_u, 0.75)
assert nnn.max_u == 0.9
assert nnn.nubins == 3
assert np.isclose(nnn.vbin_size, 0.05)
assert np.isclose(nnn.min_v, 0.2)
assert nnn.max_v == 0.4
assert nnn.nvbins == 4
check_arrays(nnn)
# Omit max_sep
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=20, bin_size=0.1)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size == 0.1
assert nnn.min_sep == 5.
assert nnn.nbins == 20
check_defaultuv(nnn)
check_arrays(nnn)
# Specify min, n, bs for u,v too.
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=20, bin_size=0.1,
min_u=0.7, nubins=4, ubin_size=0.05,
min_v=0.2, nvbins=4, vbin_size=0.05)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_sep == 5.
assert nnn.bin_size == 0.1
assert nnn.nbins == 20
assert nnn.min_u == 0.7
assert np.isclose(nnn.ubin_size, 0.05)
assert nnn.nubins == 4
assert nnn.min_v == 0.2
assert nnn.max_v == 0.4
assert np.isclose(nnn.vbin_size, 0.05)
assert nnn.nvbins == 4
check_arrays(nnn)
# Omit nbins
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size <= 0.1
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
check_defaultuv(nnn)
check_arrays(nnn)
# Specify min, max, bs for u,v too.
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
min_u=0.2, max_u=0.9, ubin_size=0.03,
min_v=0.1, max_v=0.3, vbin_size=0.07)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.bin_size <= 0.1
assert nnn.min_u == 0.2
assert nnn.max_u == 0.9
assert nnn.nubins == 24
assert np.isclose(nnn.ubin_size, 0.7/24)
assert nnn.min_v == 0.1
assert nnn.max_v == 0.3
assert nnn.nvbins == 3
assert np.isclose(nnn.vbin_size, 0.2/3)
check_arrays(nnn)
# If only one of min/max v are set, respect that
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
min_u=0.2, ubin_size=0.03,
min_v=0.2, vbin_size=0.07)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_u == 0.2
assert nnn.max_u == 1.
assert nnn.nubins == 27
assert np.isclose(nnn.ubin_size, 0.8/27)
assert nnn.min_v == 0.2
assert nnn.max_v == 1.
assert nnn.nvbins == 12
assert np.isclose(nnn.vbin_size, 0.8/12)
check_arrays(nnn)
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
max_u=0.2, ubin_size=0.03,
max_v=0.2, vbin_size=0.07)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.min_u == 0.
assert nnn.max_u == 0.2
assert nnn.nubins == 7
assert np.isclose(nnn.ubin_size, 0.2/7)
assert nnn.min_v == 0.
assert nnn.max_v == 0.2
assert nnn.nvbins == 3
assert np.isclose(nnn.vbin_size, 0.2/3)
check_arrays(nnn)
# If only vbin_size is set for v, automatically figure out others.
# (And if necessary adjust the bin_size down a bit.)
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
ubin_size=0.3, vbin_size=0.3)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size <= 0.1
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.min_u == 0.
assert nnn.max_u == 1.
assert nnn.nubins == 4
assert np.isclose(nnn.ubin_size, 0.25)
assert nnn.min_v == 0.
assert nnn.max_v == 1.
assert nnn.nvbins == 4
assert np.isclose(nnn.vbin_size, 0.25)
check_arrays(nnn)
# If only nvbins is set for v, automatically figure out others.
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
nubins=5, nvbins=5)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size <= 0.1
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.min_u == 0.
assert nnn.max_u == 1.
assert nnn.nubins == 5
assert np.isclose(nnn.ubin_size,0.2)
assert nnn.min_v == 0.
assert nnn.max_v == 1.
assert nnn.nvbins == 5
assert np.isclose(nnn.vbin_size,0.2)
check_arrays(nnn)
# If both nvbins and vbin_size are set, set min/max automatically
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, bin_size=0.1,
ubin_size=0.1, nubins=5,
vbin_size=0.1, nvbins=5)
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
assert nnn.bin_size <= 0.1
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.ubin_size == 0.1
assert nnn.nubins == 5
assert nnn.max_u == 1.
assert np.isclose(nnn.min_u,0.5)
assert nnn.vbin_size == 0.1
assert nnn.nvbins == 5
assert nnn.min_v == 0.
assert np.isclose(nnn.max_v,0.5)
check_arrays(nnn)
assert_raises(TypeError, treecorr.NNNCorrelation)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5)
assert_raises(TypeError, treecorr.NNNCorrelation, max_sep=20)
assert_raises(TypeError, treecorr.NNNCorrelation, bin_size=0.1)
assert_raises(TypeError, treecorr.NNNCorrelation, nbins=20)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, max_sep=20)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, bin_size=0.1)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, nbins=20)
assert_raises(TypeError, treecorr.NNNCorrelation, max_sep=20, bin_size=0.1)
assert_raises(TypeError, treecorr.NNNCorrelation, max_sep=20, nbins=20)
assert_raises(TypeError, treecorr.NNNCorrelation, bin_size=0.1, nbins=20)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1, nbins=20)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, bin_size=0.1)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20,
bin_type='Log')
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20,
bin_type='Linear')
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20,
bin_type='TwoD')
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20,
bin_type='Invalid')
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_u=0.3, max_u = 0.9, ubin_size=0.1, nubins=6)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_u=0.9, max_u = 0.3)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_u=-0.1, max_u = 0.3)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_u=0.1, max_u = 1.3)
assert_raises(TypeError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_v=0.1, max_v = 0.9, vbin_size=0.1, nvbins=9)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_v=0.9, max_v = 0.3)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_v=-0.1, max_v = 0.3)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=5, max_sep=20, bin_size=0.1,
min_v=0.1, max_v = 1.3)
assert_raises(ValueError, treecorr.NNNCorrelation, min_sep=20, max_sep=5, nbins=20,
split_method='invalid')
# Check the use of sep_units
# radians
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, sep_units='radians')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
np.testing.assert_almost_equal(nnn.min_sep, 5.)
np.testing.assert_almost_equal(nnn.max_sep, 20.)
np.testing.assert_almost_equal(nnn._min_sep, 5.)
np.testing.assert_almost_equal(nnn._max_sep, 20.)
assert nnn.min_sep == 5.
assert nnn.max_sep == 20.
assert nnn.nbins == 20
check_defaultuv(nnn)
check_arrays(nnn)
# arcsec
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, sep_units='arcsec')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
np.testing.assert_almost_equal(nnn.min_sep, 5.)
np.testing.assert_almost_equal(nnn.max_sep, 20.)
np.testing.assert_almost_equal(nnn._min_sep, 5. * math.pi/180/3600)
np.testing.assert_almost_equal(nnn._max_sep, 20. * math.pi/180/3600)
assert nnn.nbins == 20
np.testing.assert_almost_equal(nnn.bin_size * nnn.nbins, math.log(nnn.max_sep/nnn.min_sep))
# Note that logr is in the separation units, not radians.
np.testing.assert_almost_equal(nnn.logr[0], math.log(5) + 0.5*nnn.bin_size)
np.testing.assert_almost_equal(nnn.logr[-1], math.log(20) - 0.5*nnn.bin_size)
assert len(nnn.logr) == nnn.nbins
check_defaultuv(nnn)
# arcmin
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, sep_units='arcmin')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
np.testing.assert_almost_equal(nnn.min_sep, 5.)
np.testing.assert_almost_equal(nnn.max_sep, 20.)
np.testing.assert_almost_equal(nnn._min_sep, 5. * math.pi/180/60)
np.testing.assert_almost_equal(nnn._max_sep, 20. * math.pi/180/60)
assert nnn.nbins == 20
np.testing.assert_almost_equal(nnn.bin_size * nnn.nbins, math.log(nnn.max_sep/nnn.min_sep))
np.testing.assert_almost_equal(nnn.logr[0], math.log(5) + 0.5*nnn.bin_size)
np.testing.assert_almost_equal(nnn.logr[-1], math.log(20) - 0.5*nnn.bin_size)
assert len(nnn.logr) == nnn.nbins
check_defaultuv(nnn)
# degrees
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, sep_units='degrees')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
np.testing.assert_almost_equal(nnn.min_sep, 5.)
np.testing.assert_almost_equal(nnn.max_sep, 20.)
np.testing.assert_almost_equal(nnn._min_sep, 5. * math.pi/180)
np.testing.assert_almost_equal(nnn._max_sep, 20. * math.pi/180)
assert nnn.nbins == 20
np.testing.assert_almost_equal(nnn.bin_size * nnn.nbins, math.log(nnn.max_sep/nnn.min_sep))
np.testing.assert_almost_equal(nnn.logr[0], math.log(5) + 0.5*nnn.bin_size)
np.testing.assert_almost_equal(nnn.logr[-1], math.log(20) - 0.5*nnn.bin_size)
assert len(nnn.logr) == nnn.nbins
check_defaultuv(nnn)
# hours
nnn = treecorr.NNNCorrelation(min_sep=5, max_sep=20, nbins=20, sep_units='hours')
#print(nnn.min_sep,nnn.max_sep,nnn.bin_size,nnn.nbins)
#print(nnn.min_u,nnn.max_u,nnn.ubin_size,nnn.nubins)
#print(nnn.min_v,nnn.max_v,nnn.vbin_size,nnn.nvbins)
np.testing.assert_almost_equal(nnn.min_sep, 5.)
np.testing.assert_almost_equal(nnn.max_sep, 20.)
np.testing.assert_almost_equal(nnn._min_sep, 5. * math.pi/12)
np.testing.assert_almost_equal(nnn._max_sep, 20. * math.pi/12)
assert nnn.nbins == 20
np.testing.assert_almost_equal(nnn.bin_size * nnn.nbins, math.log(nnn.max_sep/nnn.min_sep))
np.testing.assert_almost_equal(nnn.logr[0], math.log(5) + 0.5*nnn.bin_size)
np.testing.assert_almost_equal(nnn.logr[-1], math.log(20) - 0.5*nnn.bin_size)
assert len(nnn.logr) == nnn.nbins
check_defaultuv(nnn)
# Check bin_slop
# Start with default behavior
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.1,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 1.0
assert nnn.bin_size == 0.1
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.1)
np.testing.assert_almost_equal(nnn.bu, 0.03)
np.testing.assert_almost_equal(nnn.bv, 0.07)
# Explicitly set bin_slop=1.0 does the same thing.
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.1, bin_slop=1.0,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 1.0
assert nnn.bin_size == 0.1
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.1)
np.testing.assert_almost_equal(nnn.bu, 0.03)
np.testing.assert_almost_equal(nnn.bv, 0.07)
# Use a smaller bin_slop
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.1, bin_slop=0.2,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 0.2
assert nnn.bin_size == 0.1
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.02)
np.testing.assert_almost_equal(nnn.bu, 0.006)
np.testing.assert_almost_equal(nnn.bv, 0.014)
# Use bin_slop == 0
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.1, bin_slop=0.0,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 0.0
assert nnn.bin_size == 0.1
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.0)
np.testing.assert_almost_equal(nnn.bu, 0.0)
np.testing.assert_almost_equal(nnn.bv, 0.0)
# Bigger bin_slop
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.1, bin_slop=2.0,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07, verbose=0)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 2.0
assert nnn.bin_size == 0.1
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.2)
np.testing.assert_almost_equal(nnn.bu, 0.06)
np.testing.assert_almost_equal(nnn.bv, 0.14)
# With bin_size > 0.1, explicit bin_slop=1.0 is accepted.
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.4, bin_slop=1.0,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07, verbose=0)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_slop == 1.0
assert nnn.bin_size == 0.4
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.4)
np.testing.assert_almost_equal(nnn.bu, 0.03)
np.testing.assert_almost_equal(nnn.bv, 0.07)
# But implicit bin_slop is reduced so that b = 0.1
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.4,
min_u=0., max_u=0.9, ubin_size=0.03,
min_v=0., max_v=0.21, vbin_size=0.07)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_size == 0.4
assert np.isclose(nnn.ubin_size, 0.03)
assert np.isclose(nnn.vbin_size, 0.07)
np.testing.assert_almost_equal(nnn.b, 0.1)
np.testing.assert_almost_equal(nnn.bu, 0.03)
np.testing.assert_almost_equal(nnn.bv, 0.07)
np.testing.assert_almost_equal(nnn.bin_slop, 0.25)
# Separately for each of the three parameters
nnn = treecorr.NNNCorrelation(min_sep=5, nbins=14, bin_size=0.05,
min_u=0., max_u=0.9, ubin_size=0.3,
min_v=0., max_v=0.17, vbin_size=0.17)
#print(nnn.bin_size,nnn.bin_slop,nnn.b)
#print(nnn.ubin_size,nnn.bu)
#print(nnn.vbin_size,nnn.bv)
assert nnn.bin_size == 0.05
assert np.isclose(nnn.ubin_size, 0.3)
assert np.isclose(nnn.vbin_size, 0.17)
np.testing.assert_almost_equal(nnn.b, 0.05)
np.testing.assert_almost_equal(nnn.bu, 0.1)
np.testing.assert_almost_equal(nnn.bv, 0.1)
np.testing.assert_almost_equal(nnn.bin_slop, 1.0) # The stored bin_slop is just for lnr
def is_ccw(x1,y1, x2,y2, x3,y3):
# Calculate the cross product of 1->2 with 1->3
x2 -= x1
x3 -= x1
y2 -= y1
y3 -= y1
return x2*y3-x3*y2 > 0.
def test_direct_count_auto():
# If the catalogs are small enough, we can do a direct count of the number of triangles
# to see if comes out right. This should exactly match the treecorr code if bin_slop=0.
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) )
cat = treecorr.Catalog(x=x, y=y)
min_sep = 1.
max_sep = 50.
nbins = 50
min_u = 0.13
max_u = 0.89
nubins = 10
min_v = 0.13
max_v = 0.59
nvbins = 10
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True, verbose=1)
ddd.process(cat)
log_min_sep = np.log(min_sep)
log_max_sep = np.log(max_sep)
true_ntri = np.zeros( (nbins, nubins, 2*nvbins) )
bin_size = (log_max_sep - log_min_sep) / nbins
ubin_size = (max_u-min_u) / nubins
vbin_size = (max_v-min_v) / nvbins
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
dij = np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2)
dik = np.sqrt((x[i]-x[k])**2 + (y[i]-y[k])**2)
djk = np.sqrt((x[j]-x[k])**2 + (y[j]-y[k])**2)
if dij == 0.: continue
if dik == 0.: continue
if djk == 0.: continue
ccw = True
if dij < dik:
if dik < djk:
d3 = dij; d2 = dik; d1 = djk;
ccw = is_ccw(x[i],y[i],x[j],y[j],x[k],y[k])
elif dij < djk:
d3 = dij; d2 = djk; d1 = dik;
ccw = is_ccw(x[j],y[j],x[i],y[i],x[k],y[k])
else:
d3 = djk; d2 = dij; d1 = dik;
ccw = is_ccw(x[j],y[j],x[k],y[k],x[i],y[i])
else:
if dij < djk:
d3 = dik; d2 = dij; d1 = djk;
ccw = is_ccw(x[i],y[i],x[k],y[k],x[j],y[j])
elif dik < djk:
d3 = dik; d2 = djk; d1 = dij;
ccw = is_ccw(x[k],y[k],x[i],y[i],x[j],y[j])
else:
d3 = djk; d2 = dik; d1 = dij;
ccw = is_ccw(x[k],y[k],x[j],y[j],x[i],y[i])
r = d2
u = d3/d2
v = (d1-d2)/d3
if r < min_sep or r >= max_sep: continue
if u < min_u or u >= max_u: continue
if v < min_v or v >= max_v: continue
if not ccw:
v = -v
kr = int(np.floor( (np.log(r)-log_min_sep) / bin_size ))
ku = int(np.floor( (u-min_u) / ubin_size ))
if v > 0:
kv = int(np.floor( (v-min_v) / vbin_size )) + nvbins
else:
kv = int(np.floor( (v-(-max_v)) / vbin_size ))
assert 0 <= kr < nbins
assert 0 <= ku < nubins
assert 0 <= kv < 2*nvbins
true_ntri[kr,ku,kv] += 1
nz = np.where((ddd.ntri > 0) | (true_ntri > 0))
print('non-zero at:')
print(nz)
print('d1 = ',ddd.meand1[nz])
print('d2 = ',ddd.meand2[nz])
print('d3 = ',ddd.meand3[nz])
print('rnom = ',ddd.rnom[nz])
print('u = ',ddd.u[nz])
print('v = ',ddd.v[nz])
print('ddd.ntri = ',ddd.ntri[nz])
print('true_ntri = ',true_ntri[nz])
print('diff = ',ddd.ntri[nz] - true_ntri[nz])
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Check that running via the corr3 script works correctly.
file_name = os.path.join('data','nnn_direct_data.dat')
with open(file_name, 'w') as fid:
for i in range(ngal):
fid.write(('%.20f %.20f\n')%(x[i],y[i]))
L = 10*s
nrand = ngal
rx = (rng.random_sample(nrand)-0.5) * L
ry = (rng.random_sample(nrand)-0.5) * L
rcat = treecorr.Catalog(x=rx, y=ry)
rand_file_name = os.path.join('data','nnn_direct_rand.dat')
with open(rand_file_name, 'w') as fid:
for i in range(nrand):
fid.write(('%.20f %.20f\n')%(rx[i],ry[i]))
rrr = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True, verbose=0)
rrr.process(rcat)
zeta, varzeta = ddd.calculateZeta(rrr)
# First do this via the corr3 function.
config = treecorr.config.read_config('configs/nnn_direct.yaml')
logger = treecorr.config.setup_logger(0)
treecorr.corr3(config, logger)
corr3_output = np.genfromtxt(os.path.join('output','nnn_direct.out'), names=True,
skip_header=1)
print('corr3_output = ',corr3_output)
print('corr3_output.dtype = ',corr3_output.dtype)
print('rnom = ',ddd.rnom.flatten())
print(' ',corr3_output['r_nom'])
np.testing.assert_allclose(corr3_output['r_nom'], ddd.rnom.flatten(), rtol=1.e-3)
print('unom = ',ddd.u.flatten())
print(' ',corr3_output['u_nom'])
np.testing.assert_allclose(corr3_output['u_nom'], ddd.u.flatten(), rtol=1.e-3)
print('vnom = ',ddd.v.flatten())
print(' ',corr3_output['v_nom'])
np.testing.assert_allclose(corr3_output['v_nom'], ddd.v.flatten(), rtol=1.e-3)
print('DDD = ',ddd.ntri.flatten())
print(' ',corr3_output['DDD'])
np.testing.assert_allclose(corr3_output['DDD'], ddd.ntri.flatten(), rtol=1.e-3)
np.testing.assert_allclose(corr3_output['ntri'], ddd.ntri.flatten(), rtol=1.e-3)
print('RRR = ',rrr.ntri.flatten())
print(' ',corr3_output['RRR'])
np.testing.assert_allclose(corr3_output['RRR'], rrr.ntri.flatten(), rtol=1.e-3)
print('zeta = ',zeta.flatten())
print('from corr3 output = ',corr3_output['zeta'])
print('diff = ',corr3_output['zeta']-zeta.flatten())
diff_index = np.where(np.abs(corr3_output['zeta']-zeta.flatten()) > 1.e-5)[0]
print('different at ',diff_index)
print('zeta[diffs] = ',zeta.flatten()[diff_index])
print('corr3.zeta[diffs] = ',corr3_output['zeta'][diff_index])
print('diff[diffs] = ',zeta.flatten()[diff_index] - corr3_output['zeta'][diff_index])
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
# Now calling out to the external corr3 executable.
# This is the only time we test the corr3 executable. All other tests use corr3 function.
import subprocess
corr3_exe = get_script_name('corr3')
p = subprocess.Popen( [corr3_exe,"configs/nnn_direct.yaml","verbose=0"] )
p.communicate()
corr3_output = np.genfromtxt(os.path.join('output','nnn_direct.out'), names=True,
skip_header=1)
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
# Repeat with binslop = 0, since the code flow is different from bture=True
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# And again with no top-level recursion
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1, max_top=0)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# This should be equivalent to processing a cross correlation with each catalog being
# the same thing.
ddd.clear()
ddd.process(cat,cat,cat, num_threads=2)
#print('ddd.ntri = ',ddd.ntri)
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Invalid to omit file_name
config['verbose'] = 0
del config['file_name']
with assert_raises(TypeError):
treecorr.corr3(config)
config['file_name'] = 'data/nnn_direct_data.dat'
# OK to not have rand_file_name
# Also, check the automatic setting of output_dots=True when verbose=2.
# It's not too annoying if we also set max_top = 0.
del config['rand_file_name']
config['verbose'] = 2
config['max_top'] = 0
treecorr.corr3(config)
data = np.genfromtxt(config['nnn_file_name'], names=True, skip_header=1)
np.testing.assert_array_equal(data['ntri'], true_ntri.flatten())
assert 'zeta' not in data.dtype.names
# Check a few basic operations with a GGCorrelation object.
do_pickle(ddd)
ddd2 = ddd.copy()
ddd2 += ddd
np.testing.assert_allclose(ddd2.ntri, 2*ddd.ntri)
np.testing.assert_allclose(ddd2.weight, 2*ddd.weight)
np.testing.assert_allclose(ddd2.meand1, 2*ddd.meand1)
np.testing.assert_allclose(ddd2.meand2, 2*ddd.meand2)
np.testing.assert_allclose(ddd2.meand3, 2*ddd.meand3)
np.testing.assert_allclose(ddd2.meanlogd1, 2*ddd.meanlogd1)
np.testing.assert_allclose(ddd2.meanlogd2, 2*ddd.meanlogd2)
np.testing.assert_allclose(ddd2.meanlogd3, 2*ddd.meanlogd3)
np.testing.assert_allclose(ddd2.meanu, 2*ddd.meanu)
np.testing.assert_allclose(ddd2.meanv, 2*ddd.meanv)
ddd2.clear()
ddd2 += ddd
np.testing.assert_allclose(ddd2.ntri, ddd.ntri)
np.testing.assert_allclose(ddd2.weight, ddd.weight)
np.testing.assert_allclose(ddd2.meand1, ddd.meand1)
np.testing.assert_allclose(ddd2.meand2, ddd.meand2)
np.testing.assert_allclose(ddd2.meand3, ddd.meand3)
np.testing.assert_allclose(ddd2.meanlogd1, ddd.meanlogd1)
np.testing.assert_allclose(ddd2.meanlogd2, ddd.meanlogd2)
np.testing.assert_allclose(ddd2.meanlogd3, ddd.meanlogd3)
np.testing.assert_allclose(ddd2.meanu, ddd.meanu)
np.testing.assert_allclose(ddd2.meanv, ddd.meanv)
ascii_name = 'output/nnn_ascii.txt'
ddd.write(ascii_name, precision=16)
ddd3 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
ddd3.read(ascii_name)
np.testing.assert_allclose(ddd3.ntri, ddd.ntri)
np.testing.assert_allclose(ddd3.weight, ddd.weight)
np.testing.assert_allclose(ddd3.meand1, ddd.meand1)
np.testing.assert_allclose(ddd3.meand2, ddd.meand2)
np.testing.assert_allclose(ddd3.meand3, ddd.meand3)
np.testing.assert_allclose(ddd3.meanlogd1, ddd.meanlogd1)
np.testing.assert_allclose(ddd3.meanlogd2, ddd.meanlogd2)
np.testing.assert_allclose(ddd3.meanlogd3, ddd.meanlogd3)
np.testing.assert_allclose(ddd3.meanu, ddd.meanu)
np.testing.assert_allclose(ddd3.meanv, ddd.meanv)
with assert_raises(TypeError):
ddd2 += config
ddd4 = treecorr.NNNCorrelation(min_sep=min_sep/2, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd4
ddd5 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep*2, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd5
ddd6 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins*2,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd6
ddd7 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u-0.1, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd7
ddd8 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u+0.1, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd8
ddd9 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins*2,
min_v=min_v, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd9
ddd10 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v-0.1, max_v=max_v, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd10
ddd11 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v+0.1, nvbins=nvbins)
with assert_raises(ValueError):
ddd2 += ddd11
ddd12 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins*2)
with assert_raises(ValueError):
ddd2 += ddd12
# Check that adding results with different coords or metric emits a warning.
cat2 = treecorr.Catalog(x=x, y=y, z=x)
with CaptureLog() as cl:
ddd13 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
logger=cl.logger)
ddd13.process_auto(cat2)
ddd13 += ddd2
print(cl.output)
assert "Detected a change in catalog coordinate systems" in cl.output
with CaptureLog() as cl:
ddd14 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
logger=cl.logger)
ddd14.process_auto(cat2, metric='Arc')
ddd14 += ddd2
assert "Detected a change in metric" in cl.output
# Check fits I/O
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
fits_name = 'output/nnn_fits.fits'
ddd.write(fits_name)
ddd15 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins)
ddd15.read(fits_name)
np.testing.assert_allclose(ddd15.ntri, ddd.ntri)
np.testing.assert_allclose(ddd15.weight, ddd.weight)
np.testing.assert_allclose(ddd15.meand1, ddd.meand1)
np.testing.assert_allclose(ddd15.meand2, ddd.meand2)
np.testing.assert_allclose(ddd15.meand3, ddd.meand3)
np.testing.assert_allclose(ddd15.meanlogd1, ddd.meanlogd1)
np.testing.assert_allclose(ddd15.meanlogd2, ddd.meanlogd2)
np.testing.assert_allclose(ddd15.meanlogd3, ddd.meanlogd3)
np.testing.assert_allclose(ddd15.meanu, ddd.meanu)
np.testing.assert_allclose(ddd15.meanv, ddd.meanv)
def test_direct_count_cross():
# If the catalogs are small enough, we can do a direct count of the number of triangles
# to see if comes out right. This should exactly match the treecorr code if brute=True
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x1 = rng.normal(0,s, (ngal,) )
y1 = rng.normal(0,s, (ngal,) )
cat1 = treecorr.Catalog(x=x1, y=y1)
x2 = rng.normal(0,s, (ngal,) )
y2 = rng.normal(0,s, (ngal,) )
cat2 = treecorr.Catalog(x=x2, y=y2)
x3 = rng.normal(0,s, (ngal,) )
y3 = rng.normal(0,s, (ngal,) )
cat3 = treecorr.Catalog(x=x3, y=y3)
min_sep = 1.
max_sep = 50.
nbins = 50
min_u = 0.13
max_u = 0.89
nubins = 10
min_v = 0.13
max_v = 0.59
nvbins = 10
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True, verbose=1)
ddd.process(cat1, cat2, cat3)
#print('ddd.ntri = ',ddd.ntri)
log_min_sep = np.log(min_sep)
log_max_sep = np.log(max_sep)
true_ntri = np.zeros( (nbins, nubins, 2*nvbins) )
bin_size = (log_max_sep - log_min_sep) / nbins
ubin_size = (max_u-min_u) / nubins
vbin_size = (max_v-min_v) / nvbins
for i in range(ngal):
for j in range(ngal):
for k in range(ngal):
d3 = np.sqrt((x1[i]-x2[j])**2 + (y1[i]-y2[j])**2)
d2 = np.sqrt((x1[i]-x3[k])**2 + (y1[i]-y3[k])**2)
d1 = np.sqrt((x2[j]-x3[k])**2 + (y2[j]-y3[k])**2)
if d3 == 0.: continue
if d2 == 0.: continue
if d1 == 0.: continue
if d1 < d2 or d2 < d3: continue;
ccw = is_ccw(x1[i],y1[i],x2[j],y2[j],x3[k],y3[k])
r = d2
u = d3/d2
v = (d1-d2)/d3
if r < min_sep or r >= max_sep: continue
if u < min_u or u >= max_u: continue
if v < min_v or v >= max_v: continue
if not ccw:
v = -v
kr = int(np.floor( (np.log(r)-log_min_sep) / bin_size ))
ku = int(np.floor( (u-min_u) / ubin_size ))
if v > 0:
kv = int(np.floor( (v-min_v) / vbin_size )) + nvbins
else:
kv = int(np.floor( (v-(-max_v)) / vbin_size ))
assert 0 <= kr < nbins
assert 0 <= ku < nubins
assert 0 <= kv < 2*nvbins
true_ntri[kr,ku,kv] += 1
#print('true_ntri = ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Repeat with binslop = 0
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1)
ddd.process(cat1, cat2, cat3)
#print('binslop > 0: ddd.ntri = ',ddd.ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# And again with no top-level recursion
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1, max_top=0)
ddd.process(cat1, cat2, cat3)
#print('max_top = 0: ddd.ntri = ',ddd.ntri)
#print('true_ntri = ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Check that running via the corr3 script works correctly.
config = treecorr.config.read_config('configs/nnn_direct_cross.yaml')
cat1.write(config['file_name'])
cat2.write(config['file_name2'])
cat3.write(config['file_name3'])
L = 10*s
nrand = ngal
for rname in ['rand_file_name', 'rand_file_name2', 'rand_file_name3']:
rx = (rng.random_sample(nrand)-0.5) * L
ry = (rng.random_sample(nrand)-0.5) * L
rcat = treecorr.Catalog(x=rx, y=ry)
rcat.write(config[rname])
config = treecorr.config.read_config('configs/nnn_direct_cross.yaml')
config['verbose'] = 0
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_direct_cross.out'), names=True,
skip_header=1)
print('corr3_output = ',corr3_output)
print('corr3_output.dtype = ',corr3_output.dtype)
print('rnom = ',ddd.rnom.flatten())
print(' ',corr3_output['r_nom'])
np.testing.assert_allclose(corr3_output['r_nom'], ddd.rnom.flatten(), rtol=1.e-3)
print('unom = ',ddd.u.flatten())
print(' ',corr3_output['u_nom'])
np.testing.assert_allclose(corr3_output['u_nom'], ddd.u.flatten(), rtol=1.e-3)
print('vnom = ',ddd.v.flatten())
print(' ',corr3_output['v_nom'])
np.testing.assert_allclose(corr3_output['v_nom'], ddd.v.flatten(), rtol=1.e-3)
print('DDD = ',ddd.ntri.flatten())
print(' ',corr3_output['DDD'])
np.testing.assert_allclose(corr3_output['DDD'], ddd.ntri.flatten(), rtol=1.e-3)
np.testing.assert_allclose(corr3_output['ntri'], ddd.ntri.flatten(), rtol=1.e-3)
# Invalid to have rand_file_name2 but not file_name2
del config['file_name2']
with assert_raises(TypeError):
treecorr.corr3(config)
config['file_name2'] = 'data/nnn_direct_cross_data2.dat'
# Invalid to have rand_file_name3 but not file_name3
del config['file_name3']
with assert_raises(TypeError):
treecorr.corr3(config)
config['file_name3'] = 'data/nnn_direct_cross_data3.dat'
# Invalid when doing rands, to be missing one rand_file_name2
del config['rand_file_name']
with assert_raises(TypeError):
treecorr.corr3(config)
config['rand_file_name'] = 'data/nnn_direct_cross_rand1.dat'
del config['rand_file_name2']
with assert_raises(TypeError):
treecorr.corr3(config)
config['rand_file_name2'] = 'data/nnn_direct_cross_rand2.dat'
del config['rand_file_name3']
with assert_raises(TypeError):
treecorr.corr3(config)
config['rand_file_name3'] = 'data/nnn_direct_cross_rand3.dat'
# Currently not implemented to only have cat2 or cat3
with assert_raises(NotImplementedError):
ddd.process(cat1, cat2=cat2)
with assert_raises(NotImplementedError):
ddd.process(cat1, cat3=cat3)
with assert_raises(NotImplementedError):
ddd.process_cross21(cat1, cat2)
del config['rand_file_name3']
del config['file_name3']
print('config = ',config)
with assert_raises(NotImplementedError):
treecorr.corr3(config)
config['file_name3'] = 'data/nnn_direct_cross_data3.dat'
config['rand_file_name3'] = 'data/nnn_direct_cross_rand3.dat'
del config['rand_file_name2']
del config['file_name2']
print('config = ',config)
with assert_raises(NotImplementedError):
treecorr.corr3(config)
config['file_name2'] = 'data/nnn_direct_cross_data2.dat'
config['rand_file_name2'] = 'data/nnn_direct_cross_rand2.dat'
def test_direct_spherical():
# Repeat in spherical coords
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) ) + 200 # Put everything at large y, so small angle on sky
z = rng.normal(0,s, (ngal,) )
w = rng.random_sample(ngal)
ra, dec = coord.CelestialCoord.xyz_to_radec(x,y,z)
cat = treecorr.Catalog(ra=ra, dec=dec, ra_units='rad', dec_units='rad', w=w)
min_sep = 1.
bin_size = 0.2
nrbins = 10
nubins = 5
nvbins = 5
max_sep = min_sep * np.exp(nrbins * bin_size)
ddd = treecorr.NNNCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
sep_units='deg', brute=True)
ddd.process(cat, num_threads=2)
r = np.sqrt(x**2 + y**2 + z**2)
x /= r; y /= r; z /= r
north_pole = coord.CelestialCoord(0*coord.radians, 90*coord.degrees)
true_ntri = np.zeros((nrbins, nubins, 2*nvbins), dtype=int)
true_weight = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
rad_min_sep = min_sep * coord.degrees / coord.radians
rad_max_sep = max_sep * coord.degrees / coord.radians
c = [coord.CelestialCoord(r*coord.radians, d*coord.radians) for (r,d) in zip(ra, dec)]
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
d12 = np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2 + (z[i]-z[j])**2)
d23 = np.sqrt((x[j]-x[k])**2 + (y[j]-y[k])**2 + (z[j]-z[k])**2)
d31 = np.sqrt((x[k]-x[i])**2 + (y[k]-y[i])**2 + (z[k]-z[i])**2)
d3, d2, d1 = sorted([d12, d23, d31])
rindex = np.floor(np.log(d2/rad_min_sep) / bin_size).astype(int)
if rindex < 0 or rindex >= nrbins: continue
if [d1, d2, d3] == [d23, d31, d12]: ii,jj,kk = i,j,k
elif [d1, d2, d3] == [d23, d12, d31]: ii,jj,kk = i,k,j
elif [d1, d2, d3] == [d31, d12, d23]: ii,jj,kk = j,k,i
elif [d1, d2, d3] == [d31, d23, d12]: ii,jj,kk = j,i,k
elif [d1, d2, d3] == [d12, d23, d31]: ii,jj,kk = k,i,j
elif [d1, d2, d3] == [d12, d31, d23]: ii,jj,kk = k,j,i
else: assert False
# Now use ii, jj, kk rather than i,j,k, to get the indices
# that correspond to the points in the right order.
u = d3/d2
v = (d1-d2)/d3
if ( ((x[jj]-x[ii])*(y[kk]-y[ii]) - (x[kk]-x[ii])*(y[jj]-y[ii])) * z[ii] +
((y[jj]-y[ii])*(z[kk]-z[ii]) - (y[kk]-y[ii])*(z[jj]-z[ii])) * x[ii] +
((z[jj]-z[ii])*(x[kk]-x[ii]) - (z[kk]-z[ii])*(x[jj]-x[ii])) * y[ii] ) > 0:
v = -v
uindex = np.floor(u / bin_size).astype(int)
assert 0 <= uindex < nubins
vindex = np.floor((v+1) / bin_size).astype(int)
assert 0 <= vindex < 2*nvbins
www = w[i] * w[j] * w[k]
true_ntri[rindex,uindex,vindex] += 1
true_weight[rindex,uindex,vindex] += www
np.testing.assert_array_equal(ddd.ntri, true_ntri)
np.testing.assert_allclose(ddd.weight, true_weight, rtol=1.e-5, atol=1.e-8)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr3 script works correctly.
config = treecorr.config.read_config('configs/nnn_direct_spherical.yaml')
cat.write(config['file_name'])
treecorr.corr3(config)
data = fitsio.read(config['nnn_file_name'])
np.testing.assert_allclose(data['r_nom'], ddd.rnom.flatten())
np.testing.assert_allclose(data['u_nom'], ddd.u.flatten())
np.testing.assert_allclose(data['v_nom'], ddd.v.flatten())
np.testing.assert_allclose(data['ntri'], ddd.ntri.flatten())
np.testing.assert_allclose(data['DDD'], ddd.weight.flatten())
# Repeat with binslop = 0
# And don't do any top-level recursion so we actually test not going to the leaves.
ddd = treecorr.NNNCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
sep_units='deg', bin_slop=0, max_top=0)
ddd.process(cat)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
np.testing.assert_allclose(ddd.weight, true_weight, rtol=1.e-5, atol=1.e-8)
def test_direct_arc():
# Repeat the spherical test with metric='Arc'
ngal = 5
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) ) + 200 # Large angles this time.
z = rng.normal(0,s, (ngal,) )
w = rng.random_sample(ngal)
ra, dec = coord.CelestialCoord.xyz_to_radec(x,y,z)
cat = treecorr.Catalog(ra=ra, dec=dec, ra_units='rad', dec_units='rad', w=w)
min_sep = 1.
max_sep = 180.
nrbins = 50
nubins = 5
nvbins = 5
bin_size = np.log((max_sep / min_sep)) / nrbins
ubin_size = 0.2
vbin_size = 0.2
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nrbins,
nubins=nubins, ubin_size=ubin_size,
nvbins=nvbins, vbin_size=vbin_size,
sep_units='deg', brute=True)
ddd.process(cat, metric='Arc')
r = np.sqrt(x**2 + y**2 + z**2)
x /= r; y /= r; z /= r
north_pole = coord.CelestialCoord(0*coord.radians, 90*coord.degrees)
true_ntri = np.zeros((nrbins, nubins, 2*nvbins), dtype=int)
true_weight = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
c = [coord.CelestialCoord(r*coord.radians, d*coord.radians) for (r,d) in zip(ra, dec)]
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
d12 = c[i].distanceTo(c[j]) / coord.degrees
d23 = c[j].distanceTo(c[k]) / coord.degrees
d31 = c[k].distanceTo(c[i]) / coord.degrees
d3, d2, d1 = sorted([d12, d23, d31])
rindex = np.floor(np.log(d2/min_sep) / bin_size).astype(int)
if rindex < 0 or rindex >= nrbins: continue
if [d1, d2, d3] == [d23, d31, d12]: ii,jj,kk = i,j,k
elif [d1, d2, d3] == [d23, d12, d31]: ii,jj,kk = i,k,j
elif [d1, d2, d3] == [d31, d12, d23]: ii,jj,kk = j,k,i
elif [d1, d2, d3] == [d31, d23, d12]: ii,jj,kk = j,i,k
elif [d1, d2, d3] == [d12, d23, d31]: ii,jj,kk = k,i,j
elif [d1, d2, d3] == [d12, d31, d23]: ii,jj,kk = k,j,i
else: assert False
# Now use ii, jj, kk rather than i,j,k, to get the indices
# that correspond to the points in the right order.
u = d3/d2
v = (d1-d2)/d3
if ( ((x[jj]-x[ii])*(y[kk]-y[ii]) - (x[kk]-x[ii])*(y[jj]-y[ii])) * z[ii] +
((y[jj]-y[ii])*(z[kk]-z[ii]) - (y[kk]-y[ii])*(z[jj]-z[ii])) * x[ii] +
((z[jj]-z[ii])*(x[kk]-x[ii]) - (z[kk]-z[ii])*(x[jj]-x[ii])) * y[ii] ) > 0:
v = -v
uindex = np.floor(u / ubin_size).astype(int)
assert 0 <= uindex < nubins
vindex = np.floor((v+1) / vbin_size).astype(int)
assert 0 <= vindex < 2*nvbins
www = w[i] * w[j] * w[k]
true_ntri[rindex,uindex,vindex] += 1
true_weight[rindex,uindex,vindex] += www
np.testing.assert_array_equal(ddd.ntri, true_ntri)
np.testing.assert_allclose(ddd.weight, true_weight, rtol=1.e-5, atol=1.e-8)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr3 script works correctly.
config = treecorr.config.read_config('configs/nnn_direct_arc.yaml')
cat.write(config['file_name'])
treecorr.corr3(config)
data = fitsio.read(config['nnn_file_name'])
np.testing.assert_allclose(data['r_nom'], ddd.rnom.flatten())
np.testing.assert_allclose(data['u_nom'], ddd.u.flatten())
np.testing.assert_allclose(data['v_nom'], ddd.v.flatten())
np.testing.assert_allclose(data['ntri'], ddd.ntri.flatten())
np.testing.assert_allclose(data['DDD'], ddd.weight.flatten())
# Repeat with binslop = 0
# And don't do any top-level recursion so we actually test not going to the leaves.
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nrbins,
nubins=nubins, ubin_size=ubin_size,
nvbins=nvbins, vbin_size=vbin_size,
sep_units='deg', bin_slop=0, max_top=0)
ddd.process(cat)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
np.testing.assert_allclose(ddd.weight, true_weight, rtol=1.e-5, atol=1.e-8)
def test_direct_partial():
# Test the two ways to only use parts of a catalog:
ngal = 100
s = 10.
rng = np.random.RandomState(8675309)
x1 = rng.normal(0,s, (ngal,) )
y1 = rng.normal(0,s, (ngal,) )
cat1a = treecorr.Catalog(x=x1, y=y1, first_row=28, last_row=84)
x2 = rng.normal(0,s, (ngal,) )
y2 = rng.normal(0,s, (ngal,) )
cat2a = treecorr.Catalog(x=x2, y=y2, first_row=48, last_row=99)
x3 = rng.normal(0,s, (ngal,) )
y3 = rng.normal(0,s, (ngal,) )
cat3a = treecorr.Catalog(x=x3, y=y3, first_row=22, last_row=67)
min_sep = 1.
max_sep = 50.
nbins = 50
min_u = 0.13
max_u = 0.89
nubins = 10
min_v = 0.13
max_v = 0.59
nvbins = 10
ddda = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True)
ddda.process(cat1a, cat2a, cat3a)
#print('ddda.ntri = ',ddda.ntri)
log_min_sep = np.log(min_sep)
log_max_sep = np.log(max_sep)
true_ntri = np.zeros( (nbins, nubins, 2*nvbins) )
bin_size = (log_max_sep - log_min_sep) / nbins
ubin_size = (max_u-min_u) / nubins
vbin_size = (max_v-min_v) / nvbins
for i in range(27,84):
for j in range(47,99):
for k in range(21,67):
d3 = np.sqrt((x1[i]-x2[j])**2 + (y1[i]-y2[j])**2)
d2 = np.sqrt((x1[i]-x3[k])**2 + (y1[i]-y3[k])**2)
d1 = np.sqrt((x2[j]-x3[k])**2 + (y2[j]-y3[k])**2)
if d3 == 0.: continue
if d2 == 0.: continue
if d1 == 0.: continue
if d1 < d2 or d2 < d3: continue;
ccw = is_ccw(x1[i],y1[i],x2[j],y2[j],x3[k],y3[k])
r = d2
u = d3/d2
v = (d1-d2)/d3
if r < min_sep or r >= max_sep: continue
if u < min_u or u >= max_u: continue
if v < min_v or v >= max_v: continue
if not ccw:
v = -v
kr = int(np.floor( (np.log(r)-log_min_sep) / bin_size ))
ku = int(np.floor( (u-min_u) / ubin_size ))
if v > 0:
kv = int(np.floor( (v-min_v) / vbin_size )) + nvbins
else:
kv = int(np.floor( (v-(-max_v)) / vbin_size ))
assert 0 <= kr < nbins
assert 0 <= ku < nubins
assert 0 <= kv < 2*nvbins
true_ntri[kr,ku,kv] += 1
print('true_ntri = ',true_ntri)
print('diff = ',ddda.ntri - true_ntri)
np.testing.assert_array_equal(ddda.ntri, true_ntri)
# Now check that we get the same thing with all the points, but with w=0 for the ones
# we don't want.
w1 = np.zeros(ngal)
w1[27:84] = 1.
w2 = np.zeros(ngal)
w2[47:99] = 1.
w3 = np.zeros(ngal)
w3[21:67] = 1.
cat1b = treecorr.Catalog(x=x1, y=y1, w=w1)
cat2b = treecorr.Catalog(x=x2, y=y2, w=w2)
cat3b = treecorr.Catalog(x=x3, y=y3, w=w3)
dddb = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True)
dddb.process(cat1b, cat2b, cat3b)
#print('dddb.ntri = ',dddb.ntri)
#print('diff = ',dddb.ntri - true_ntri)
np.testing.assert_array_equal(dddb.ntri, true_ntri)
def is_ccw_3d(x1,y1,z1, x2,y2,z2, x3,y3,z3):
# Calculate the cross product of 1->2 with 1->3
x2 -= x1
x3 -= x1
y2 -= y1
y3 -= y1
z2 -= z1
z3 -= z1
# The cross product:
x = y2*z3-y3*z2
y = z2*x3-z3*x2
z = x2*y3-x3*y2
# ccw if the cross product is in the opposite direction of (x1,y1,z1) from (0,0,0)
return x*x1 + y*y1 + z*z1 < 0.
def test_direct_3d_auto():
# This is the same as the above test, but using the 3d correlations
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(312, s, (ngal,) )
y = rng.normal(728, s, (ngal,) )
z = rng.normal(-932, s, (ngal,) )
r = np.sqrt( x*x + y*y + z*z )
dec = np.arcsin(z/r)
ra = np.arctan2(y,x)
cat = treecorr.Catalog(ra=ra, dec=dec, r=r, ra_units='rad', dec_units='rad')
min_sep = 1.
max_sep = 50.
nbins = 50
min_u = 0.13
max_u = 0.89
nubins = 10
min_v = 0.13
max_v = 0.59
nvbins = 10
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True, verbose=1)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
log_min_sep = np.log(min_sep)
log_max_sep = np.log(max_sep)
true_ntri = np.zeros( (nbins, nubins, 2*nvbins) )
bin_size = (log_max_sep - log_min_sep) / nbins
ubin_size = (max_u-min_u) / nubins
vbin_size = (max_v-min_v) / nvbins
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
dij = np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2 + (z[i]-z[j])**2)
dik = np.sqrt((x[i]-x[k])**2 + (y[i]-y[k])**2 + (z[i]-z[k])**2)
djk = np.sqrt((x[j]-x[k])**2 + (y[j]-y[k])**2 + (z[j]-z[k])**2)
if dij == 0.: continue
if dik == 0.: continue
if djk == 0.: continue
ccw = True
if dij < dik:
if dik < djk:
d3 = dij; d2 = dik; d1 = djk;
ccw = is_ccw_3d(x[i],y[i],z[i],x[j],y[j],z[j],x[k],y[k],z[k])
elif dij < djk:
d3 = dij; d2 = djk; d1 = dik;
ccw = is_ccw_3d(x[j],y[j],z[j],x[i],y[i],z[i],x[k],y[k],z[k])
else:
d3 = djk; d2 = dij; d1 = dik;
ccw = is_ccw_3d(x[j],y[j],z[j],x[k],y[k],z[k],x[i],y[i],z[i])
else:
if dij < djk:
d3 = dik; d2 = dij; d1 = djk;
ccw = is_ccw_3d(x[i],y[i],z[i],x[k],y[k],z[k],x[j],y[j],z[j])
elif dik < djk:
d3 = dik; d2 = djk; d1 = dij;
ccw = is_ccw_3d(x[k],y[k],z[k],x[i],y[i],z[i],x[j],y[j],z[j])
else:
d3 = djk; d2 = dik; d1 = dij;
ccw = is_ccw_3d(x[k],y[k],z[k],x[j],y[j],z[j],x[i],y[i],z[i])
r = d2
u = d3/d2
v = (d1-d2)/d3
if r < min_sep or r >= max_sep: continue
if u < min_u or u >= max_u: continue
if v < min_v or v >= max_v: continue
if not ccw:
v = -v
kr = int(np.floor( (np.log(r)-log_min_sep) / bin_size ))
ku = int(np.floor( (u-min_u) / ubin_size ))
if v > 0:
kv = int(np.floor( (v-min_v) / vbin_size )) + nvbins
else:
kv = int(np.floor( (v-(-max_v)) / vbin_size ))
assert 0 <= kr < nbins
assert 0 <= ku < nubins
assert 0 <= kv < 2*nvbins
true_ntri[kr,ku,kv] += 1
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Repeat with binslop = 0
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# And again with no top-level recursion
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1, max_top=0)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# And compare to the cross correlation
ddd.clear()
ddd.process(cat,cat,cat)
#print('ddd.ntri = ',ddd.ntri)
#print('true_ntri => ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Also compare to using x,y,z rather than ra,dec,r
cat = treecorr.Catalog(x=x, y=y, z=z)
ddd.process(cat)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
def test_direct_3d_cross():
# This is the same as the above test, but using the 3d correlations
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x1 = rng.normal(312, s, (ngal,) )
y1 = rng.normal(728, s, (ngal,) )
z1 = rng.normal(-932, s, (ngal,) )
r1 = np.sqrt( x1*x1 + y1*y1 + z1*z1 )
dec1 = np.arcsin(z1/r1)
ra1 = np.arctan2(y1,x1)
cat1 = treecorr.Catalog(ra=ra1, dec=dec1, r=r1, ra_units='rad', dec_units='rad')
x2 = rng.normal(312, s, (ngal,) )
y2 = rng.normal(728, s, (ngal,) )
z2 = rng.normal(-932, s, (ngal,) )
r2 = np.sqrt( x2*x2 + y2*y2 + z2*z2 )
dec2 = np.arcsin(z2/r2)
ra2 = np.arctan2(y2,x2)
cat2 = treecorr.Catalog(ra=ra2, dec=dec2, r=r2, ra_units='rad', dec_units='rad')
x3 = rng.normal(312, s, (ngal,) )
y3 = rng.normal(728, s, (ngal,) )
z3 = rng.normal(-932, s, (ngal,) )
r3 = np.sqrt( x3*x3 + y3*y3 + z3*z3 )
dec3 = np.arcsin(z3/r3)
ra3 = np.arctan2(y3,x3)
cat3 = treecorr.Catalog(ra=ra3, dec=dec3, r=r3, ra_units='rad', dec_units='rad')
min_sep = 1.
max_sep = 50.
nbins = 50
min_u = 0.13
max_u = 0.89
nubins = 10
min_v = 0.13
max_v = 0.59
nvbins = 10
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
brute=True, verbose=1)
ddd.process(cat1, cat2, cat3)
#print('ddd.ntri = ',ddd.ntri)
log_min_sep = np.log(min_sep)
log_max_sep = np.log(max_sep)
true_ntri = np.zeros( (nbins, nubins, 2*nvbins) )
bin_size = (log_max_sep - log_min_sep) / nbins
ubin_size = (max_u-min_u) / nubins
vbin_size = (max_v-min_v) / nvbins
for i in range(ngal):
for j in range(ngal):
for k in range(ngal):
d1sq = (x2[j]-x3[k])**2 + (y2[j]-y3[k])**2 + (z2[j]-z3[k])**2
d2sq = (x1[i]-x3[k])**2 + (y1[i]-y3[k])**2 + (z1[i]-z3[k])**2
d3sq = (x1[i]-x2[j])**2 + (y1[i]-y2[j])**2 + (z1[i]-z2[j])**2
d1 = np.sqrt(d1sq)
d2 = np.sqrt(d2sq)
d3 = np.sqrt(d3sq)
if d3 == 0.: continue
if d2 == 0.: continue
if d1 == 0.: continue
if d1 < d2 or d2 < d3: continue;
ccw = is_ccw_3d(x1[i],y1[i],z1[i],x2[j],y2[j],z2[j],x3[k],y3[k],z3[k])
r = d2
u = d3/d2
v = (d1-d2)/d3
if r < min_sep or r >= max_sep: continue
if u < min_u or u >= max_u: continue
if v < min_v or v >= max_v: continue
if not ccw:
v = -v
kr = int(np.floor( (np.log(r)-log_min_sep) / bin_size ))
ku = int(np.floor( (u-min_u) / ubin_size ))
if v > 0:
kv = int(np.floor( (v-min_v) / vbin_size )) + nvbins
else:
kv = int(np.floor( (v-(-max_v)) / vbin_size ))
assert 0 <= kr < nbins
assert 0 <= ku < nubins
assert 0 <= kv < 2*nvbins
true_ntri[kr,ku,kv] += 1
#print('true_ntri = ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Repeat with binslop = 0
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1)
ddd.process(cat1, cat2, cat3)
#print('binslop > 0: ddd.ntri = ',ddd.ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# And again with no top-level recursion
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, nubins=nubins,
min_v=min_v, max_v=max_v, nvbins=nvbins,
bin_slop=0, verbose=1, max_top=0)
ddd.process(cat1, cat2, cat3)
#print('max_top = 0: ddd.ntri = ',ddd.ntri)
#print('true_ntri = ',true_ntri)
#print('diff = ',ddd.ntri - true_ntri)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
# Also compare to using x,y,z rather than ra,dec,r
cat1 = treecorr.Catalog(x=x1, y=y1, z=z1)
cat2 = treecorr.Catalog(x=x2, y=y2, z=z2)
cat3 = treecorr.Catalog(x=x3, y=y3, z=z3)
ddd.process(cat1, cat2, cat3)
np.testing.assert_array_equal(ddd.ntri, true_ntri)
def test_nnn():
# Use a simple probability distribution for the galaxies:
#
# n(r) = (2pi s^2)^-1 exp(-r^2/2s^2)
#
# The Fourier transform is: n~(k) = exp(-s^2 k^2/2)
# B(k1,k2) = <n~(k1) n~(k2) n~(-k1-k2)>
# = exp(-s^2 (|k1|^2 + |k2|^2 - k1.k2))
# = exp(-s^2 (|k1|^2 + |k2|^2 + |k3|^2)/2)
#
# zeta(r1,r2) = (1/2pi)^4 int(d^2k1 int(d^2k2 exp(ik1.x1) exp(ik2.x2) B(k1,k2) ))
# = exp(-(x1^2 + y1^2 + x2^2 + y2^2 - x1x2 - y1y2)/3s^2) / 12 pi^2 s^4
# = exp(-(d1^2 + d2^2 + d3^2)/6s^2) / 12 pi^2 s^4
#
# This is also derivable as:
# zeta(r1,r2) = int(dx int(dy n(x,y) n(x+x1,y+y1) n(x+x2,y+y2)))
# which is also analytically integrable and gives the same answer.
#
# However, we need to correct for the uniform density background, so the real result
# is this minus 1/L^4 divided by 1/L^4. So:
#
# zeta(r1,r2) = 1/(12 pi^2) (L/s)^4 exp(-(d1^2+d2^2+d3^2)/6s^2) - 1
# Doing the full correlation function takes a long time. Here, we just test a small range
# of separations and a moderate range for u, v, which gives us a variety of triangle lengths.
s = 10.
if __name__ == "__main__":
ngal = 20000
nrand = 2 * ngal
L = 50. * s # Not infinity, so this introduces some error. Our integrals were to infinity.
tol_factor = 1
else:
ngal = 2000
nrand = ngal
L = 20. * s
tol_factor = 5
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) )
min_sep = 11.
max_sep = 13.
nbins = 2
min_u = 0.6
max_u = 0.9
nubins = 3
min_v = 0.5
max_v = 0.9
nvbins = 5
cat = treecorr.Catalog(x=x, y=y, x_units='arcmin', y_units='arcmin')
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins,
sep_units='arcmin', verbose=1)
ddd.process(cat)
#print('ddd.ntri = ',ddd.ntri)
# log(<d>) != <logd>, but it should be close:
print('meanlogd1 - log(meand1) = ',ddd.meanlogd1 - np.log(ddd.meand1))
print('meanlogd2 - log(meand2) = ',ddd.meanlogd2 - np.log(ddd.meand2))
print('meanlogd3 - log(meand3) = ',ddd.meanlogd3 - np.log(ddd.meand3))
print('meand3 / meand2 = ',ddd.meand3 / ddd.meand2)
print('meanu = ',ddd.meanu)
print('max diff = ',np.max(np.abs(ddd.meand3/ddd.meand2 -ddd.meanu)))
print('max rel diff = ',np.max(np.abs((ddd.meand3/ddd.meand2 -ddd.meanu)/ddd.meanu)))
print('(meand1 - meand2)/meand3 = ',(ddd.meand1-ddd.meand2) / ddd.meand3)
print('meanv = ',ddd.meanv)
print('max diff = ',np.max(np.abs((ddd.meand1-ddd.meand2)/ddd.meand3 -np.abs(ddd.meanv))))
print('max rel diff = ',np.max(np.abs(((ddd.meand1-ddd.meand2)/ddd.meand3-np.abs(ddd.meanv))/ddd.meanv)))
np.testing.assert_allclose(ddd.meanlogd1, np.log(ddd.meand1), rtol=1.e-3)
np.testing.assert_allclose(ddd.meanlogd2, np.log(ddd.meand2), rtol=1.e-3)
np.testing.assert_allclose(ddd.meanlogd3, np.log(ddd.meand3), rtol=1.e-3)
np.testing.assert_allclose(ddd.meand3/ddd.meand2, ddd.meanu, rtol=1.e-5 * tol_factor)
np.testing.assert_allclose((ddd.meand1-ddd.meand2)/ddd.meand3, np.abs(ddd.meanv),
rtol=1.e-5 * tol_factor, atol=1.e-5 * tol_factor)
np.testing.assert_allclose(ddd.meanlogd3-ddd.meanlogd2, np.log(ddd.meanu),
atol=1.e-3 * tol_factor)
np.testing.assert_allclose(np.log(ddd.meand1-ddd.meand2)-ddd.meanlogd3,
np.log(np.abs(ddd.meanv)), atol=2.e-3 * tol_factor)
rx = (rng.random_sample(nrand)-0.5) * L
ry = (rng.random_sample(nrand)-0.5) * L
rand = treecorr.Catalog(x=rx,y=ry, x_units='arcmin', y_units='arcmin')
rrr = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins,
sep_units='arcmin', verbose=1)
rrr.process(rand)
#print('rrr.ntri = ',rrr.ntri)
d1 = ddd.meand1
d2 = ddd.meand2
d3 = ddd.meand3
#print('rnom = ',np.exp(ddd.logr))
#print('unom = ',ddd.u)
#print('vnom = ',ddd.v)
#print('d1 = ',d1)
#print('d2 = ',d2)
#print('d3 = ',d3)
true_zeta = (1./(12.*np.pi**2)) * (L/s)**4 * np.exp(-(d1**2+d2**2+d3**2)/(6.*s**2)) - 1.
zeta, varzeta = ddd.calculateZeta(rrr)
print('zeta = ',zeta)
print('true_zeta = ',true_zeta)
print('ratio = ',zeta / true_zeta)
print('diff = ',zeta - true_zeta)
print('max rel diff = ',np.max(np.abs((zeta - true_zeta)/true_zeta)))
np.testing.assert_allclose(zeta, true_zeta, rtol=0.1*tol_factor)
np.testing.assert_allclose(np.log(np.abs(zeta)), np.log(np.abs(true_zeta)),
atol=0.1*tol_factor)
# Check that we get the same result using the corr3 function
cat.write(os.path.join('data','nnn_data.dat'))
rand.write(os.path.join('data','nnn_rand.dat'))
config = treecorr.config.read_config('configs/nnn.yaml')
config['verbose'] = 0
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn.out'), names=True, skip_header=1)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check the fits write option
out_file_name1 = os.path.join('output','nnn_out1.fits')
ddd.write(out_file_name1)
data = fitsio.read(out_file_name1)
np.testing.assert_almost_equal(data['r_nom'], np.exp(ddd.logr).flatten())
np.testing.assert_almost_equal(data['u_nom'], ddd.u.flatten())
np.testing.assert_almost_equal(data['v_nom'], ddd.v.flatten())
np.testing.assert_almost_equal(data['meand1'], ddd.meand1.flatten())
np.testing.assert_almost_equal(data['meanlogd1'], ddd.meanlogd1.flatten())
np.testing.assert_almost_equal(data['meand2'], ddd.meand2.flatten())
np.testing.assert_almost_equal(data['meanlogd2'], ddd.meanlogd2.flatten())
np.testing.assert_almost_equal(data['meand3'], ddd.meand3.flatten())
np.testing.assert_almost_equal(data['meanlogd3'], ddd.meanlogd3.flatten())
np.testing.assert_almost_equal(data['meanu'], ddd.meanu.flatten())
np.testing.assert_almost_equal(data['meanv'], ddd.meanv.flatten())
np.testing.assert_almost_equal(data['ntri'], ddd.ntri.flatten())
header = fitsio.read_header(out_file_name1, 1)
np.testing.assert_almost_equal(header['tot']/ddd.tot, 1.)
out_file_name2 = os.path.join('output','nnn_out2.fits')
ddd.write(out_file_name2, rrr)
data = fitsio.read(out_file_name2)
np.testing.assert_almost_equal(data['r_nom'], np.exp(ddd.logr).flatten())
np.testing.assert_almost_equal(data['u_nom'], ddd.u.flatten())
np.testing.assert_almost_equal(data['v_nom'], ddd.v.flatten())
np.testing.assert_almost_equal(data['meand1'], ddd.meand1.flatten())
np.testing.assert_almost_equal(data['meanlogd1'], ddd.meanlogd1.flatten())
np.testing.assert_almost_equal(data['meand2'], ddd.meand2.flatten())
np.testing.assert_almost_equal(data['meanlogd2'], ddd.meanlogd2.flatten())
np.testing.assert_almost_equal(data['meand3'], ddd.meand3.flatten())
np.testing.assert_almost_equal(data['meanlogd3'], ddd.meanlogd3.flatten())
np.testing.assert_almost_equal(data['meanu'], ddd.meanu.flatten())
np.testing.assert_almost_equal(data['meanv'], ddd.meanv.flatten())
np.testing.assert_almost_equal(data['zeta'], zeta.flatten())
np.testing.assert_almost_equal(data['sigma_zeta'], np.sqrt(varzeta).flatten())
np.testing.assert_almost_equal(data['DDD'], ddd.ntri.flatten())
np.testing.assert_almost_equal(data['RRR'], rrr.ntri.flatten() * (ddd.tot / rrr.tot))
header = fitsio.read_header(out_file_name2, 1)
np.testing.assert_almost_equal(header['tot']/ddd.tot, 1.)
# Check the read function
# Note: These don't need the flatten. The read function should reshape them to the right shape.
ddd2 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins,
sep_units='arcmin', verbose=1)
ddd2.read(out_file_name1)
np.testing.assert_almost_equal(ddd2.logr, ddd.logr)
np.testing.assert_almost_equal(ddd2.u, ddd.u)
np.testing.assert_almost_equal(ddd2.v, ddd.v)
np.testing.assert_almost_equal(ddd2.meand1, ddd.meand1)
np.testing.assert_almost_equal(ddd2.meanlogd1, ddd.meanlogd1)
np.testing.assert_almost_equal(ddd2.meand2, ddd.meand2)
np.testing.assert_almost_equal(ddd2.meanlogd2, ddd.meanlogd2)
np.testing.assert_almost_equal(ddd2.meand3, ddd.meand3)
np.testing.assert_almost_equal(ddd2.meanlogd3, ddd.meanlogd3)
np.testing.assert_almost_equal(ddd2.meanu, ddd.meanu)
np.testing.assert_almost_equal(ddd2.meanv, ddd.meanv)
np.testing.assert_almost_equal(ddd2.ntri, ddd.ntri)
np.testing.assert_almost_equal(ddd2.tot/ddd.tot, 1.)
assert ddd2.coords == ddd.coords
assert ddd2.metric == ddd.metric
assert ddd2.sep_units == ddd.sep_units
assert ddd2.bin_type == ddd.bin_type
ddd2.read(out_file_name2)
np.testing.assert_almost_equal(ddd2.logr, ddd.logr)
np.testing.assert_almost_equal(ddd2.u, ddd.u)
np.testing.assert_almost_equal(ddd2.v, ddd.v)
np.testing.assert_almost_equal(ddd2.meand1, ddd.meand1)
np.testing.assert_almost_equal(ddd2.meanlogd1, ddd.meanlogd1)
np.testing.assert_almost_equal(ddd2.meand2, ddd.meand2)
np.testing.assert_almost_equal(ddd2.meanlogd2, ddd.meanlogd2)
np.testing.assert_almost_equal(ddd2.meand3, ddd.meand3)
np.testing.assert_almost_equal(ddd2.meanlogd3, ddd.meanlogd3)
np.testing.assert_almost_equal(ddd2.meanu, ddd.meanu)
np.testing.assert_almost_equal(ddd2.meanv, ddd.meanv)
np.testing.assert_almost_equal(ddd2.ntri, ddd.ntri)
np.testing.assert_almost_equal(ddd2.tot/ddd.tot, 1.)
assert ddd2.coords == ddd.coords
assert ddd2.metric == ddd.metric
assert ddd2.sep_units == ddd.sep_units
assert ddd2.bin_type == ddd.bin_type
# Test compensated zeta
# First just check the mechanics.
# If we don't actually do all the cross terms, then compensated is the same as simple.
zeta2, varzeta2 = ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
print('fake compensated zeta = ',zeta2)
np.testing.assert_allclose(zeta2, zeta)
np.testing.assert_allclose(varzeta2, varzeta)
with assert_raises(TypeError):
ddd.calculateZeta(drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,drr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr)
rrr2 = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, sep_units='arcmin')
with assert_raises(ValueError):
ddd.calculateZeta(rrr2,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr2,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr2,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr2,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr2,drd=rrr,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr2,rdd=rrr)
with assert_raises(ValueError):
ddd.calculateZeta(rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr2)
out_file_name3 = os.path.join('output','nnn_out3.fits')
with assert_raises(TypeError):
ddd.write(out_file_name3,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,drr=rrr,rrd=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,drr=rrr,rdr=rrr,ddr=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,drr=rrr,rdr=rrr,rrd=rrr,drd=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,rdd=rrr)
with assert_raises(TypeError):
ddd.write(out_file_name3,rrr=rrr,drr=rrr,rdr=rrr,rrd=rrr,ddr=rrr,drd=rrr)
# It's too slow to test the real calculation in nosetests runs, so we stop here if not main.
if __name__ != '__main__':
return
# This version computes the three-point function after subtracting off the appropriate
# two-point functions xi(d1) + xi(d2) + xi(d3), where [cf. test_nn() in test_nn.py]
# xi(r) = 1/4pi (L/s)^2 exp(-r^2/4s^2) - 1
ddr = ddd.copy()
drd = ddd.copy()
rdd = ddd.copy()
drr = ddd.copy()
rdr = ddd.copy()
rrd = ddd.copy()
ddr.process(cat,cat,rand)
drd.process(cat,rand,cat)
rdd.process(rand,cat,cat)
drr.process(cat,rand,rand)
rdr.process(rand,cat,rand)
rrd.process(rand,rand,cat)
zeta, varzeta = ddd.calculateZeta(rrr,drr,rdr,rrd,ddr,drd,rdd)
print('compensated zeta = ',zeta)
xi1 = (1./(4.*np.pi)) * (L/s)**2 * np.exp(-d1**2/(4.*s**2)) - 1.
xi2 = (1./(4.*np.pi)) * (L/s)**2 * np.exp(-d2**2/(4.*s**2)) - 1.
xi3 = (1./(4.*np.pi)) * (L/s)**2 * np.exp(-d3**2/(4.*s**2)) - 1.
print('xi1 = ',xi1)
print('xi2 = ',xi2)
print('xi3 = ',xi3)
print('true_zeta + xi1 + xi2 + xi3 = ',true_zeta)
true_zeta -= xi1 + xi2 + xi3
print('true_zeta => ',true_zeta)
print('ratio = ',zeta / true_zeta)
print('diff = ',zeta - true_zeta)
print('max rel diff = ',np.max(np.abs((zeta - true_zeta)/true_zeta)))
np.testing.assert_allclose(zeta, true_zeta, rtol=0.1*tol_factor)
np.testing.assert_allclose(np.log(np.abs(zeta)), np.log(np.abs(true_zeta)), atol=0.1*tol_factor)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
out_file_name3 = os.path.join('output','nnn_out3.fits')
ddd.write(out_file_name3, rrr,drr,rdr,rrd,ddr,drd,rdd)
data = fitsio.read(out_file_name3)
np.testing.assert_almost_equal(data['r_nom'], np.exp(ddd.logr).flatten())
np.testing.assert_almost_equal(data['u_nom'], ddd.u.flatten())
np.testing.assert_almost_equal(data['v_nom'], ddd.v.flatten())
np.testing.assert_almost_equal(data['meand1'], ddd.meand1.flatten())
np.testing.assert_almost_equal(data['meanlogd1'], ddd.meanlogd1.flatten())
np.testing.assert_almost_equal(data['meand2'], ddd.meand2.flatten())
np.testing.assert_almost_equal(data['meanlogd2'], ddd.meanlogd2.flatten())
np.testing.assert_almost_equal(data['meand3'], ddd.meand3.flatten())
np.testing.assert_almost_equal(data['meanlogd3'], ddd.meanlogd3.flatten())
np.testing.assert_almost_equal(data['meanu'], ddd.meanu.flatten())
np.testing.assert_almost_equal(data['meanv'], ddd.meanv.flatten())
np.testing.assert_almost_equal(data['zeta'], zeta.flatten())
np.testing.assert_almost_equal(data['sigma_zeta'], np.sqrt(varzeta).flatten())
np.testing.assert_almost_equal(data['DDD'], ddd.ntri.flatten())
np.testing.assert_almost_equal(data['RRR'], rrr.ntri.flatten() * (ddd.tot / rrr.tot))
np.testing.assert_almost_equal(data['DRR'], drr.ntri.flatten() * (ddd.tot / drr.tot))
np.testing.assert_almost_equal(data['RDR'], rdr.ntri.flatten() * (ddd.tot / rdr.tot))
np.testing.assert_almost_equal(data['RRD'], rrd.ntri.flatten() * (ddd.tot / rrd.tot))
np.testing.assert_almost_equal(data['DDR'], ddr.ntri.flatten() * (ddd.tot / ddr.tot))
np.testing.assert_almost_equal(data['DRD'], drd.ntri.flatten() * (ddd.tot / drd.tot))
np.testing.assert_almost_equal(data['RDD'], rdd.ntri.flatten() * (ddd.tot / rdd.tot))
header = fitsio.read_header(out_file_name3, 1)
np.testing.assert_almost_equal(header['tot']/ddd.tot, 1.)
ddd2.read(out_file_name3)
np.testing.assert_almost_equal(ddd2.logr, ddd.logr)
np.testing.assert_almost_equal(ddd2.u, ddd.u)
np.testing.assert_almost_equal(ddd2.v, ddd.v)
np.testing.assert_almost_equal(ddd2.meand1, ddd.meand1)
np.testing.assert_almost_equal(ddd2.meanlogd1, ddd.meanlogd1)
np.testing.assert_almost_equal(ddd2.meand2, ddd.meand2)
np.testing.assert_almost_equal(ddd2.meanlogd2, ddd.meanlogd2)
np.testing.assert_almost_equal(ddd2.meand3, ddd.meand3)
np.testing.assert_almost_equal(ddd2.meanlogd3, ddd.meanlogd3)
np.testing.assert_almost_equal(ddd2.meanu, ddd.meanu)
np.testing.assert_almost_equal(ddd2.meanv, ddd.meanv)
np.testing.assert_almost_equal(ddd2.ntri, ddd.ntri)
np.testing.assert_almost_equal(ddd2.tot/ddd.tot, 1.)
assert ddd2.coords == ddd.coords
assert ddd2.metric == ddd.metric
assert ddd2.sep_units == ddd.sep_units
assert ddd2.bin_type == ddd.bin_type
config = treecorr.config.read_config('configs/nnn_compensated.yaml')
config['verbose'] = 0
treecorr.corr3(config)
corr3_outfile = os.path.join('output','nnn_compensated.fits')
corr3_output = fitsio.read(corr3_outfile)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_almost_equal(corr3_output['r_nom'], np.exp(ddd.logr).flatten())
np.testing.assert_almost_equal(corr3_output['u_nom'], ddd.u.flatten())
np.testing.assert_almost_equal(corr3_output['v_nom'], ddd.v.flatten())
np.testing.assert_almost_equal(corr3_output['meand1'], ddd.meand1.flatten())
np.testing.assert_almost_equal(corr3_output['meanlogd1'], ddd.meanlogd1.flatten())
np.testing.assert_almost_equal(corr3_output['meand2'], ddd.meand2.flatten())
np.testing.assert_almost_equal(corr3_output['meanlogd2'], ddd.meanlogd2.flatten())
np.testing.assert_almost_equal(corr3_output['meand3'], ddd.meand3.flatten())
np.testing.assert_almost_equal(corr3_output['meanlogd3'], ddd.meanlogd3.flatten())
np.testing.assert_almost_equal(corr3_output['meanu'], ddd.meanu.flatten())
np.testing.assert_almost_equal(corr3_output['meanv'], ddd.meanv.flatten())
np.testing.assert_almost_equal(corr3_output['zeta'], zeta.flatten())
np.testing.assert_almost_equal(corr3_output['sigma_zeta'], np.sqrt(varzeta).flatten())
np.testing.assert_almost_equal(corr3_output['DDD'], ddd.ntri.flatten())
np.testing.assert_almost_equal(corr3_output['RRR'], rrr.ntri.flatten() * (ddd.tot / rrr.tot))
np.testing.assert_almost_equal(corr3_output['DRR'], drr.ntri.flatten() * (ddd.tot / drr.tot))
np.testing.assert_almost_equal(corr3_output['RDR'], rdr.ntri.flatten() * (ddd.tot / rdr.tot))
np.testing.assert_almost_equal(corr3_output['RRD'], rrd.ntri.flatten() * (ddd.tot / rrd.tot))
np.testing.assert_almost_equal(corr3_output['DDR'], ddr.ntri.flatten() * (ddd.tot / ddr.tot))
np.testing.assert_almost_equal(corr3_output['DRD'], drd.ntri.flatten() * (ddd.tot / drd.tot))
np.testing.assert_almost_equal(corr3_output['RDD'], rdd.ntri.flatten() * (ddd.tot / rdd.tot))
header = fitsio.read_header(corr3_outfile, 1)
np.testing.assert_almost_equal(header['tot']/ddd.tot, 1.)
def test_3d():
# For this one, build a Gaussian cloud around some random point in 3D space and do the
# correlation function in 3D.
#
# The 3D Fourier transform is: n~(k) = exp(-s^2 k^2/2)
# B(k1,k2) = <n~(k1) n~(k2) n~(-k1-k2)>
# = exp(-s^2 (|k1|^2 + |k2|^2 - k1.k2))
# = exp(-s^2 (|k1|^2 + |k2|^2 + |k3|^2)/2)
# as before, except now k1,k2 are 3d vectors, not 2d.
#
# zeta(r1,r2) = (1/2pi)^4 int(d^2k1 int(d^2k2 exp(ik1.x1) exp(ik2.x2) B(k1,k2) ))
# = exp(-(x1^2 + y1^2 + x2^2 + y2^2 - x1x2 - y1y2)/3s^2) / 12 pi^2 s^4
# = exp(-(d1^2 + d2^2 + d3^2)/6s^2) / 24 sqrt(3) pi^3 s^6
#
# And again, this is also derivable as:
# zeta(r1,r2) = int(dx int(dy int(dz n(x,y,z) n(x+x1,y+y1,z+z1) n(x+x2,y+y2,z+z2)))
# which is also analytically integrable and gives the same answer.
#
# However, we need to correct for the uniform density background, so the real result
# is this minus 1/L^6 divided by 1/L^6. So:
#
# zeta(r1,r2) = 1/(24 sqrt(3) pi^3) (L/s)^4 exp(-(d1^2+d2^2+d3^2)/6s^2) - 1
# Doing the full correlation function takes a long time. Here, we just test a small range
# of separations and a moderate range for u, v, which gives us a variety of triangle lengths.
xcen = 823 # Mpc maybe?
ycen = 342
zcen = -672
s = 10.
if __name__ == "__main__":
ngal = 5000
nrand = 20 * ngal
L = 50. * s
tol_factor = 1
else:
ngal = 1000
nrand = 5 * ngal
L = 20. * s
tol_factor = 5
rng = np.random.RandomState(8675309)
x = rng.normal(xcen, s, (ngal,) )
y = rng.normal(ycen, s, (ngal,) )
z = rng.normal(zcen, s, (ngal,) )
r = np.sqrt(x*x+y*y+z*z)
dec = np.arcsin(z/r) * (coord.radians / coord.degrees)
ra = np.arctan2(y,x) * (coord.radians / coord.degrees)
min_sep = 10.
max_sep = 20.
nbins = 8
min_u = 0.9
max_u = 1.0
nubins = 1
min_v = 0.
max_v = 0.05
nvbins = 1
cat = treecorr.Catalog(ra=ra, dec=dec, r=r, ra_units='deg', dec_units='deg')
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, verbose=1)
ddd.process(cat)
print('ddd.ntri = ',ddd.ntri.flatten())
rx = (rng.random_sample(nrand)-0.5) * L + xcen
ry = (rng.random_sample(nrand)-0.5) * L + ycen
rz = (rng.random_sample(nrand)-0.5) * L + zcen
rr = np.sqrt(rx*rx+ry*ry+rz*rz)
rdec = np.arcsin(rz/rr) * (coord.radians / coord.degrees)
rra = np.arctan2(ry,rx) * (coord.radians / coord.degrees)
rand = treecorr.Catalog(ra=rra, dec=rdec, r=rr, ra_units='deg', dec_units='deg')
rrr = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, verbose=1)
rrr.process(rand)
print('rrr.ntri = ',rrr.ntri.flatten())
d1 = ddd.meand1
d2 = ddd.meand2
d3 = ddd.meand3
print('rnom = ',np.exp(ddd.logr).flatten())
print('unom = ',ddd.u.flatten())
print('vnom = ',ddd.v.flatten())
print('d1 = ',d1.flatten())
print('d2 = ',d2.flatten())
print('d3 = ',d3.flatten())
true_zeta = ((1./(24.*np.sqrt(3)*np.pi**3)) * (L/s)**6 *
np.exp(-(d1**2+d2**2+d3**2)/(6.*s**2)) - 1.)
zeta, varzeta = ddd.calculateZeta(rrr)
print('zeta = ',zeta.flatten())
print('true_zeta = ',true_zeta.flatten())
print('ratio = ',(zeta / true_zeta).flatten())
print('diff = ',(zeta - true_zeta).flatten())
print('max rel diff = ',np.max(np.abs((zeta - true_zeta)/true_zeta)))
np.testing.assert_allclose(zeta, true_zeta, rtol=0.1*tol_factor)
np.testing.assert_allclose(np.log(np.abs(zeta)), np.log(np.abs(true_zeta)),
atol=0.1*tol_factor)
# Check that we get the same result using the corr3 functin:
cat.write(os.path.join('data','nnn_3d_data.dat'))
rand.write(os.path.join('data','nnn_3d_rand.dat'))
config = treecorr.config.read_config('configs/nnn_3d.yaml')
config['verbose'] = 0
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_3d.out'), names=True, skip_header=1)
print('zeta = ',zeta.flatten())
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
# Check that we get the same thing when using x,y,z rather than ra,dec,r
cat = treecorr.Catalog(x=x, y=y, z=z)
rand = treecorr.Catalog(x=rx, y=ry, z=rz)
ddd.process(cat)
rrr.process(rand)
zeta, varzeta = ddd.calculateZeta(rrr)
np.testing.assert_allclose(zeta, true_zeta, rtol=0.1*tol_factor)
np.testing.assert_allclose(np.log(np.abs(zeta)), np.log(np.abs(true_zeta)),
atol=0.1*tol_factor)
def test_list():
# Test that we can use a list of files for either data or rand or both.
data_cats = []
rand_cats = []
ncats = 3
ngal = 100
nrand = 2 * ngal
s = 10.
L = 50. * s
rng = np.random.RandomState(8675309)
min_sep = 30.
max_sep = 50.
nbins = 3
min_u = 0
max_u = 0.2
nubins = 2
min_v = 0.5
max_v = 0.9
nvbins = 2
x = rng.normal(0,s, (ngal,ncats) )
y = rng.normal(0,s, (ngal,ncats) )
data_cats = [ treecorr.Catalog(x=x[:,k], y=y[:,k]) for k in range(ncats) ]
rx = (rng.random_sample((nrand,ncats))-0.5) * L
ry = (rng.random_sample((nrand,ncats))-0.5) * L
rand_cats = [ treecorr.Catalog(x=rx[:,k], y=ry[:,k]) for k in range(ncats) ]
ddd = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, bin_slop=0.1, verbose=1)
ddd.process(data_cats)
print('From multiple catalogs: ddd.ntri = ',ddd.ntri)
# Now do the same thing with one big catalog
dddx = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, bin_slop=0.1, verbose=1)
data_catx = treecorr.Catalog(x=x.reshape( (ngal*ncats,) ), y=y.reshape( (ngal*ncats,) ))
dddx.process(data_catx)
print('From single catalog: dddx.ntri = ',dddx.ntri)
# Only test to rtol=0.1, since there are now differences between the auto and cross related
# to how they characterize triangles especially when d1 ~= d2 or d2 ~= d3.
np.testing.assert_allclose(ddd.ntri, dddx.ntri, rtol=0.1)
rrr = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, bin_slop=0.1, verbose=1)
rrr.process(rand_cats)
print('rrr.ntri = ',rrr.ntri)
rrrx = treecorr.NNNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins, bin_slop=0.1, verbose=1)
rand_catx = treecorr.Catalog(x=rx.reshape( (nrand*ncats,) ), y=ry.reshape( (nrand*ncats,) ))
rrrx.process(rand_catx)
print('rrrx.ntri = ',rrrx.ntri)
np.testing.assert_allclose(ddd.ntri, dddx.ntri, rtol=0.1)
zeta, varzeta = ddd.calculateZeta(rrr)
zetax, varzetax = dddx.calculateZeta(rrrx)
print('zeta = ',zeta)
print('zetax = ',zetax)
#print('ratio = ',zeta/zetax)
#print('diff = ',zeta-zetax)
np.testing.assert_allclose(zeta, zetax, rtol=0.1)
# Check that we get the same result using the corr3 function:
file_list = []
rand_file_list = []
for k in range(ncats):
file_name = os.path.join('data','nnn_list_data%d.dat'%k)
data_cats[k].write(file_name)
file_list.append(file_name)
rand_file_name = os.path.join('data','nnn_list_rand%d.dat'%k)
rand_cats[k].write(rand_file_name)
rand_file_list.append(rand_file_name)
list_name = os.path.join('data','nnn_list_data_files.txt')
with open(list_name, 'w') as fid:
for file_name in file_list:
fid.write('%s\n'%file_name)
rand_list_name = os.path.join('data','nnn_list_rand_files.txt')
with open(rand_list_name, 'w') as fid:
for file_name in rand_file_list:
fid.write('%s\n'%file_name)
file_namex = os.path.join('data','nnn_list_datax.dat')
data_catx.write(file_namex)
rand_file_namex = os.path.join('data','nnn_list_randx.dat')
rand_catx.write(rand_file_namex)
config = treecorr.config.read_config('configs/nnn_list1.yaml')
config['verbose'] = 0
config['bin_slop'] = 0.1
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_list1.out'), names=True, skip_header=1)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
config = treecorr.config.read_config('configs/nnn_list2.json')
config['verbose'] = 0
config['bin_slop'] = 0.1
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_list2.out'), names=True, skip_header=1)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=0.05)
config = treecorr.config.read_config('configs/nnn_list3.params')
config['verbose'] = 0
config['bin_slop'] = 0.1
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_list3.out'), names=True, skip_header=1)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=0.05)
config = treecorr.config.read_config('configs/nnn_list4.config', file_type='params')
config['verbose'] = 0
config['bin_slop'] = 0.1
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','nnn_list4.out'), names=True, skip_header=1)
print('zeta = ',zeta)
print('from corr3 output = ',corr3_output['zeta'])
print('ratio = ',corr3_output['zeta']/zeta.flatten())
print('diff = ',corr3_output['zeta']-zeta.flatten())
np.testing.assert_allclose(corr3_output['zeta'], zeta.flatten(), rtol=1.e-3)
if __name__ == '__main__':
test_log_binning()
test_direct_count_auto()
test_direct_count_cross()
test_direct_spherical()
test_direct_arc()
test_direct_partial()
test_direct_3d_auto()
test_direct_3d_cross()
test_nnn()
test_3d()
test_list()
| 44.924858
| 109
| 0.608453
| 16,214
| 102,833
| 3.690946
| 0.043789
| 0.04647
| 0.07745
| 0.06562
| 0.858902
| 0.824062
| 0.802506
| 0.769705
| 0.723168
| 0.696082
| 0
| 0.042185
| 0.243424
| 102,833
| 2,288
| 110
| 44.944493
| 0.727022
| 0.126798
| 0
| 0.624722
| 0
| 0
| 0.047254
| 0.007073
| 0
| 0
| 0
| 0
| 0.314588
| 1
| 0.008352
| false
| 0
| 0.010022
| 0
| 0.022829
| 0.069042
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
eba703fa0615046b1e99b1b8bcce0fe7c95f2222
| 91
|
py
|
Python
|
Language Skills/Python/Unit 09 Exam Statistics/01 Review/1-Lets look at those grades.py
|
WarHatch/Codecademy-Exercise-Answers
|
1fe3684d7edfa712747bce8e595e89409446eb94
|
[
"MIT"
] | 346
|
2016-02-22T20:21:10.000Z
|
2022-01-27T20:55:53.000Z
|
Language Skills/Python/Unit 9/Review/1-Let's look at those grades!_.py
|
vpstudios/Codecademy-Exercise-Answers
|
ebd0ee8197a8001465636f52c69592ea6745aa0c
|
[
"MIT"
] | 55
|
2016-04-07T13:58:44.000Z
|
2020-06-25T12:20:24.000Z
|
Language Skills/Python/Unit 9/Review/1-Let's look at those grades!_.py
|
vpstudios/Codecademy-Exercise-Answers
|
ebd0ee8197a8001465636f52c69592ea6745aa0c
|
[
"MIT"
] | 477
|
2016-02-21T06:17:02.000Z
|
2021-12-22T10:08:01.000Z
|
grades = [100, 100, 90, 40, 80, 100, 85, 70, 90, 65, 90, 85, 50.5]
print "Grades:", grades
| 30.333333
| 66
| 0.582418
| 18
| 91
| 2.944444
| 0.611111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.410959
| 0.197802
| 91
| 2
| 67
| 45.5
| 0.315068
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
ebdbafd1b2f41d9f712e09d7b0bde15680514e16
| 26
|
py
|
Python
|
Python/libraries/recognizers-sequence/recognizers_sequence/sequence/portuguese/__init__.py
|
XiaoxiaoMa0815/Recognizers-Text
|
d9a4bc939348bd79b5982345255961dff5f356c6
|
[
"MIT"
] | 2
|
2017-08-22T11:21:19.000Z
|
2017-09-17T20:06:00.000Z
|
Python/libraries/recognizers-sequence/recognizers_sequence/sequence/portuguese/__init__.py
|
XiaoxiaoMa0815/Recognizers-Text
|
d9a4bc939348bd79b5982345255961dff5f356c6
|
[
"MIT"
] | 76
|
2018-11-09T18:19:44.000Z
|
2019-08-20T20:29:53.000Z
|
Python/libraries/recognizers-sequence/recognizers_sequence/sequence/portuguese/__init__.py
|
XiaoxiaoMa0815/Recognizers-Text
|
d9a4bc939348bd79b5982345255961dff5f356c6
|
[
"MIT"
] | 6
|
2017-05-04T17:24:59.000Z
|
2019-07-23T15:48:44.000Z
|
from .extractors import *
| 13
| 25
| 0.769231
| 3
| 26
| 6.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 26
| 1
| 26
| 26
| 0.909091
| 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
| 1
| 0
|
0
| 6
|
ebe74047f4eedf479a37c111edc4ea7934854110
| 1,961
|
py
|
Python
|
device/modes.py
|
RegieKI/regie-core-production
|
0b57b8afe34553b02f27ef66f17a1568456a9d78
|
[
"MIT"
] | null | null | null |
device/modes.py
|
RegieKI/regie-core-production
|
0b57b8afe34553b02f27ef66f17a1568456a9d78
|
[
"MIT"
] | null | null | null |
device/modes.py
|
RegieKI/regie-core-production
|
0b57b8afe34553b02f27ef66f17a1568456a9d78
|
[
"MIT"
] | null | null | null |
PDAC_MODES = {
"debug" : {
"session-id" : "debug",
"sources" : {
"audio" : { "active" : False },
"video" : { "active" : True },
"heartrate" : { "active" : False },
},
"inference" : None,
"transform" : False,
"sinks" : {
"rstp" : { "active" : True },
"file" : { "active" : False },
"window" : { "active" : False },
}
},
"rtsp-only" : {
"session-id" : "live",
"sources" : {
"audio" : { "active" : False },
"video" : { "active" : True },
"heartrate" : { "active" : False },
},
"inference" : None,
"transform" : False,
"sinks" : {
"rstp" : { "active" : True },
"file" : { "active" : False },
"window" : { "active" : False },
}
},
"face-extraction-rtsp" : {
"session-id" : "face-extraction",
"sources" : {
"audio" : { "active" : False },
"video" : { "active" : True },
"heartrate" : { "active" : False },
},
"inference" : "face-extraction",
"transform" : True,
"sinks" : {
"rstp" : { "active" : True },
"file" : { "active" : False },
"window" : { "active" : False },
}
},
"pose-detection-rtsp" : {
"session-id" : "pose-detection",
"sources" : {
"audio" : { "active" : False },
"video" : { "active" : True },
"heartrate" : { "active" : False },
},
"inference" : "pose-detection",
"transform" : None,
"sinks" : {
"rstp" : { "active" : True },
"file" : { "active" : False },
"window" : { "active" : False },
}
}
}
| 31.126984
| 47
| 0.352371
| 123
| 1,961
| 5.609756
| 0.211382
| 0.255072
| 0.104348
| 0.133333
| 0.736232
| 0.736232
| 0.736232
| 0.736232
| 0.736232
| 0.736232
| 0
| 0
| 0.464559
| 1,961
| 62
| 48
| 31.629032
| 0.657143
| 0
| 0
| 0.580645
| 0
| 0
| 0.283529
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 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
| 6
|
ccde7cc9f15d65fd53f288c87a5c436e7ffdc640
| 8,550
|
py
|
Python
|
src/Coinpaprika/CoinpaprikaExchanges.py
|
coinpaper/coinpaprika-api-python-client
|
f819e38cf8657c9751ee55e638f3ed33d32346a4
|
[
"MIT"
] | 5
|
2021-01-30T10:54:33.000Z
|
2022-02-18T03:05:03.000Z
|
src/Coinpaprika/CoinpaprikaExchanges.py
|
coinpaper/coinpaprika-api-python-client
|
f819e38cf8657c9751ee55e638f3ed33d32346a4
|
[
"MIT"
] | 1
|
2021-09-01T14:51:49.000Z
|
2021-09-01T14:51:49.000Z
|
src/Coinpaprika/CoinpaprikaExchanges.py
|
coinpaper/coinpaprika-api-python-client
|
f819e38cf8657c9751ee55e638f3ed33d32346a4
|
[
"MIT"
] | null | null | null |
from typing import List, Dict
from .Coinpaprika import Coinpaprika
class CoinpaprikaExchanges:
def __call__(self, quotes=tuple(["USD"])) -> List[Dict]:
"""
List exchanges
:param quotes: List of quotes to return. Currently allowed values: BTC, ETH,
USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN,
BRL, THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB,
COP, PEN, ARS, ISK
:return: [
{
"id": "binance",
"name": "Binance",
"active": true,
"website_status": true,
"api_status": true,
"description": "Binance is a Malta-based cryptocurrency exchange founded in July 2017",
"message": "Currently under maintenance",
"links": {
"website": [
"https://www.binance.com/"
],
"twitter": [
"https://twitter.com/binance"
]
},
"markets_data_fetched": true,
"adjusted_rank": 1,
"reported_rank": 3,
"currencies": 150,
"markets": 385,
"fiats": [
{
"name": "US Dollars",
"symbol": "USD"
}
],
"quotes": {
"$KEY": {
"reported_volume_24h": 794020873,
"adjusted_volume_24h": 794020873,
"reported_volume_7d": 153060819,
"adjusted_volume_7d": 153060819,
"reported_volume_30d": 301246828,
"adjusted_volume_30d": 301246828
}
},
"last_updated": "2018-11-14T07:20:41Z"
}
]
"""
return self.all(quotes)
@staticmethod
def all(quotes=tuple(["USD"])) -> List[Dict]:
"""
List exchanges
:param quotes: List of quotes to return. Currently allowed values: BTC, ETH,
USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN,
BRL, THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB,
COP, PEN, ARS, ISK
:return: [
{
"id": "binance",
"name": "Binance",
"active": true,
"website_status": true,
"api_status": true,
"description": "Binance is a Malta-based cryptocurrency exchange founded in July 2017",
"message": "Currently under maintenance",
"links": {
"website": [
"https://www.binance.com/"
],
"twitter": [
"https://twitter.com/binance"
]
},
"markets_data_fetched": true,
"adjusted_rank": 1,
"reported_rank": 3,
"currencies": 150,
"markets": 385,
"fiats": [
{
"name": "US Dollars",
"symbol": "USD"
}
],
"quotes": {
"$KEY": {
"reported_volume_24h": 794020873,
"adjusted_volume_24h": 794020873,
"reported_volume_7d": 153060819,
"adjusted_volume_7d": 153060819,
"reported_volume_30d": 301246828,
"adjusted_volume_30d": 301246828
}
},
"last_updated": "2018-11-14T07:20:41Z"
}
]
"""
quotes_str = ",".join(quotes)
exchanges = Coinpaprika.get(f"/exchanges", params={"quotes": quotes_str})
for exchange in exchanges:
Coinpaprika.convert_date_in_dict(exchange, "last_updated")
return exchanges
@staticmethod
def with_id(exchange_id: str, quotes=tuple(["USD"])) -> Dict:
"""
Get exchange by ID
:param exchange_id: Example: binance
:param quotes: List of quotes to return. Currently allowed values: BTC, ETH,
USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN,
BRL, THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB,
COP, PEN, ARS, ISK
:return: {
"id": "binance",
"name": "Binance",
"active": true,
"website_status": true,
"api_status": true,
"description": "Binance is a Malta-based cryptocurrency exchange founded in July 2017",
"message": "Currently under maintenance",
"links": {
"website": [
"https://www.binance.com/"
],
"twitter": [
"https://twitter.com/binance"
]
},
"markets_data_fetched": true,
"adjusted_rank": 1,
"reported_rank": 3,
"currencies": 150,
"markets": 385,
"fiats": [
{
"name": "US Dollars",
"symbol": "USD"
}
],
"quotes": {
"$KEY": {
"reported_volume_24h": 794020873,
"adjusted_volume_24h": 794020873,
"reported_volume_7d": 153060819,
"adjusted_volume_7d": 153060819,
"reported_volume_30d": 301246828,
"adjusted_volume_30d": 301246828
}
},
"last_updated": "2018-11-14T07:20:41Z"
}
"""
quotes_str = ",".join(quotes)
exchange = Coinpaprika.get(f"/exchanges/{exchange_id}", params={"quotes": quotes_str})
Coinpaprika.convert_date_in_dict(exchange, "last_updated")
return exchange
@staticmethod
def markets(exchange_id: str, quotes=tuple(["USD"])) -> List[Dict]:
"""
List markets by exchange ID
:param exchange_id: Example: binance
:param quotes: List of quotes to return. Currently allowed values: BTC, ETH,
USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN,
BRL, THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB,
COP, PEN, ARS, ISK
:return: [
{
"pair": "BTC/USDT",
"base_currency_id": "btc-bitcoin",
"base_currency_name": "Bitcoin",
"quote_currency_id": "usdt-tether",
"quote_currency_name": "Tether",
"market_url": "https://www.binance.com/en/trade/BTC_USDT",
"category": "Spot",
"fee_type": "Percentage",
"outlier": false,
"reported_volume_24h_share": 30.29,
"quotes": {
"$KEY": {
"price": 4582.6967796728,
"volume_24h": 229658776.19514218
}
},
"last_updated": "2018-11-14T07:20:41Z"
}
]
"""
quotes_str = ",".join(quotes)
market_pairs = Coinpaprika.get(f"/exchanges/{exchange_id}/markets", params={"quotes": quotes_str})
for market_pair in market_pairs:
Coinpaprika.convert_date_in_dict(market_pair, "last_updated")
return market_pairs
| 41.707317
| 112
| 0.416023
| 711
| 8,550
| 4.852321
| 0.225035
| 0.04058
| 0.031304
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| 0.750435
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| 0.750435
| 0.71971
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| 0.071762
| 0.473567
| 8,550
| 204
| 113
| 41.911765
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| 0.694854
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| 0.16
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| 1
| 1
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| 0
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| 0
| 0
|
0
| 6
|
69373954a18aace53e483c6707982a7e1a8583a4
| 198
|
py
|
Python
|
navycut/core/__init__.py
|
navycut/navycut
|
1d49621105c7c4683d52a3d2c853ae7165b9dc0d
|
[
"MIT"
] | 13
|
2021-04-26T04:00:36.000Z
|
2021-09-18T19:57:58.000Z
|
navycut/core/__init__.py
|
FlaskAio/navycut
|
40f378f1710a26645df8d726c4d1caf33097da50
|
[
"MIT"
] | 21
|
2021-09-27T03:19:21.000Z
|
2022-03-31T03:20:59.000Z
|
navycut/core/__init__.py
|
FlaskAio/navycut
|
40f378f1710a26645df8d726c4d1caf33097da50
|
[
"MIT"
] | 7
|
2021-07-21T06:21:55.000Z
|
2021-09-02T17:58:04.000Z
|
""" import default app and default sisterapp from here """
from .app_config import Navycut, app
from .app_config import AppSister
"""Written by: Aniket Sarkar(https://github.com/marktennyson)"""
| 24.75
| 64
| 0.752525
| 27
| 198
| 5.444444
| 0.666667
| 0.095238
| 0.176871
| 0.258503
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| 198
| 8
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| null | 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
694513c964673674a180b2543f72ac155eb254f6
| 141
|
py
|
Python
|
transplant/modules/__init__.py
|
tiagocri/ecg-transfer-learning-master
|
ce27589f87c09a3df8b4abf1644eede579378725
|
[
"MIT"
] | 28
|
2020-10-19T15:41:41.000Z
|
2022-03-28T05:03:10.000Z
|
transplant/modules/__init__.py
|
tiagocri/ecg-transfer-learning-master
|
ce27589f87c09a3df8b4abf1644eede579378725
|
[
"MIT"
] | null | null | null |
transplant/modules/__init__.py
|
tiagocri/ecg-transfer-learning-master
|
ce27589f87c09a3df8b4abf1644eede579378725
|
[
"MIT"
] | 11
|
2021-04-05T17:05:39.000Z
|
2022-02-09T12:23:23.000Z
|
import transplant.modules.attn_pool
import transplant.modules.resnet1d
import transplant.modules.transformer
import transplant.modules.utils
| 28.2
| 37
| 0.886525
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| 141
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| 0
| 0
|
0
| 6
|
15da695494c2c929012bf14ce262e465b2f0a8bc
| 38,810
|
py
|
Python
|
util/data/gen/combase.dll.py
|
56kyle/bloons_auto
|
419d55b51d1cddc49099593970adf1c67985b389
|
[
"MIT"
] | null | null | null |
util/data/gen/combase.dll.py
|
56kyle/bloons_auto
|
419d55b51d1cddc49099593970adf1c67985b389
|
[
"MIT"
] | null | null | null |
util/data/gen/combase.dll.py
|
56kyle/bloons_auto
|
419d55b51d1cddc49099593970adf1c67985b389
|
[
"MIT"
] | null | null | null |
symbols = []
exports = [{'type': 'function', 'name': 'CLIPFORMAT_UserFree', 'address': '0x7ffb3c80ab60'}, {'type': 'function', 'name': 'CLIPFORMAT_UserFree64', 'address': '0x7ffb3c7176a0'}, {'type': 'function', 'name': 'CLIPFORMAT_UserMarshal', 'address': '0x7ffb3c80ae10'}, {'type': 'function', 'name': 'CLIPFORMAT_UserMarshal64', 'address': '0x7ffb3c6ecd40'}, {'type': 'function', 'name': 'CLIPFORMAT_UserSize', 'address': '0x7ffb3c80af70'}, {'type': 'function', 'name': 'CLIPFORMAT_UserSize64', 'address': '0x7ffb3c6ecc10'}, {'type': 'function', 'name': 'CLIPFORMAT_UserUnmarshal', 'address': '0x7ffb3c80b090'}, {'type': 'function', 'name': 'CLIPFORMAT_UserUnmarshal64', 'address': '0x7ffb3c6ed380'}, {'type': 'function', 'name': 'CLSIDFromOle1Class', 'address': '0x7ffb3c6e80a0'}, {'type': 'function', 'name': 'CLSIDFromProgID', 'address': '0x7ffb3c6c8380'}, {'type': 'function', 'name': 'CLSIDFromProgIDEx', 'address': '0x7ffb3c6c7ec0'}, {'type': 'function', 'name': 'CLSIDFromString', 'address': '0x7ffb3c6e84c0'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer2_Connect', 'address': '0x7ffb3c831ee0'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer2_Disconnect', 'address': '0x7ffb3c6f6690'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer2_Release', 'address': '0x7ffb3c831f30'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_AddRef', 'address': '0x7ffb3c6f3550'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_Connect', 'address': '0x7ffb3c831fb0'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_Disconnect', 'address': '0x7ffb3c6f58c0'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_Invoke', 'address': '0x7ffb3c832000'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_QueryInterface', 'address': '0x7ffb3c832110'}, {'type': 'function', 'name': 'CStdAsyncStubBuffer_Release', 'address': '0x7ffb3c832240'}, {'type': 'function', 'name': 'CStdStubBuffer2_Connect', 'address': '0x7ffb3c63d140'}, {'type': 'function', 'name': 'CStdStubBuffer2_CountRefs', 'address': '0x7ffb3c8323b0'}, {'type': 'function', 'name': 'CStdStubBuffer2_Disconnect', 'address': '0x7ffb3c6f6690'}, {'type': 'function', 'name': 'CStdStubBuffer2_QueryInterface', 'address': '0x7ffb3c6f3bf0'}, {'type': 'function', 'name': 'CStdStubBuffer_AddRef', 'address': '0x7ffb3c6f3550'}, {'type': 'function', 'name': 'CStdStubBuffer_Connect', 'address': '0x7ffb3c63d180'}, {'type': 'function', 'name': 'CStdStubBuffer_CountRefs', 'address': '0x7ffb3c832490'}, {'type': 'function', 'name': 'CStdStubBuffer_DebugServerQueryInterface', 'address': '0x7ffb3c8324b0'}, {'type': 'function', 'name': 'CStdStubBuffer_DebugServerRelease', 'address': '0x7ffb3c6437f0'}, {'type': 'function', 'name': 'CStdStubBuffer_Disconnect', 'address': '0x7ffb3c6f58c0'}, {'type': 'function', 'name': 'CStdStubBuffer_Invoke', 'address': '0x7ffb3c6e9800'}, {'type': 'function', 'name': 'CStdStubBuffer_IsIIDSupported', 'address': '0x7ffb3c8324d0'}, {'type': 'function', 'name': 'CStdStubBuffer_QueryInterface', 'address': '0x7ffb3c6f3c60'}, {'type': 'function', 'name': 'CleanupComl2StateInAllTls', 'address': '0x7ffb3c70d330'}, {'type': 'function', 'name': 'CleanupOleStateInAllTls', 'address': '0x7ffb3c703140'}, {'type': 'function', 'name': 'CleanupTlsComl2State', 'address': '0x7ffb3c70d3a0'}, {'type': 'function', 'name': 'CleanupTlsOleState', 'address': '0x7ffb3c7031b0'}, {'type': 'function', 'name': 'ClearCleanupFlag', 'address': '0x7ffb3c7c89e0'}, {'type': 'function', 'name': 'CoAddRefServerProcess', 'address': '0x7ffb3c6f2cb0'}, {'type': 'function', 'name': 'CoAllowUnmarshalerCLSID', 'address': '0x7ffb3c6be9e0'}, {'type': 'function', 'name': 'CoCancelCall', 'address': '0x7ffb3c7d4660'}, {'type': 'function', 'name': 'CoCopyProxy', 'address': '0x7ffb3c6e0280'}, {'type': 'function', 'name': 'CoCreateErrorInfo', 'address': '0x7ffb3c713750'}, {'type': 'function', 'name': 'CoCreateFreeThreadedMarshaler', 'address': '0x7ffb3c666400'}, {'type': 'function', 'name': 'CoCreateGuid', 'address': '0x7ffb3c6b16e0'}, {'type': 'function', 'name': 'CoCreateInstance', 'address': '0x7ffb3c65c030'}, {'type': 'function', 'name': 'CoCreateInstanceEx', 'address': '0x7ffb3c6f7b60'}, {'type': 'function', 'name': 'CoCreateInstanceFromApp', 'address': '0x7ffb3c713f20'}, {'type': 'function', 'name': 'CoCreateObjectInContext', 'address': '0x7ffb3c7d87e0'}, {'type': 'function', 'name': 'CoDeactivateObject', 'address': '0x7ffb3c7d8b60'}, {'type': 'function', 'name': 'CoDecodeProxy', 'address': '0x7ffb3c7d8c30'}, {'type': 'function', 'name': 'CoDecrementMTAUsage', 'address': '0x7ffb3c6c74b0'}, {'type': 'function', 'name': 'CoDisableCallCancellation', 'address': '0x7ffb3c702990'}, {'type': 'function', 'name': 'CoDisconnectContext', 'address': '0x7ffb3c6d3120'}, {'type': 'function', 'name': 'CoDisconnectObject', 'address': '0x7ffb3c6d1550'}, {'type': 'function', 'name': 'CoEnableCallCancellation', 'address': '0x7ffb3c703240'}, {'type': 'function', 'name': 'CoFileTimeNow', 'address': '0x7ffb3c642a70'}, {'type': 'function', 'name': 'CoFreeUnusedLibraries', 'address': '0x7ffb3c6c8e90'}, {'type': 'function', 'name': 'CoFreeUnusedLibrariesEx', 'address': '0x7ffb3c6c95f0'}, {'type': 'function', 'name': 'CoGetActivationState', 'address': '0x7ffb3c7d8c60'}, {'type': 'function', 'name': 'CoGetApartmentID', 'address': '0x7ffb3c7dc7a0'}, {'type': 'function', 'name': 'CoGetApartmentType', 'address': '0x7ffb3c6e5cb0'}, {'type': 'function', 'name': 'CoGetCallContext', 'address': '0x7ffb3c6b90f0'}, {'type': 'function', 'name': 'CoGetCallState', 'address': '0x7ffb3c7d8ca0'}, {'type': 'function', 'name': 'CoGetCallerTID', 'address': '0x7ffb3c712980'}, {'type': 'function', 'name': 'CoGetCancelObject', 'address': '0x7ffb3c7d46c0'}, {'type': 'function', 'name': 'CoGetClassObject', 'address': '0x7ffb3c68b340'}, {'type': 'function', 'name': 'CoGetClassVersion', 'address': '0x7ffb3c7c8330'}, {'type': 'function', 'name': 'CoGetContextToken', 'address': '0x7ffb3c6f0430'}, {'type': 'function', 'name': 'CoGetCurrentLogicalThreadId', 'address': '0x7ffb3c697b50'}, {'type': 'function', 'name': 'CoGetCurrentProcess', 'address': '0x7ffb3c7187d0'}, {'type': 'function', 'name': 'CoGetDefaultContext', 'address': '0x7ffb3c7d8ce0'}, {'type': 'function', 'name': 'CoGetErrorInfo', 'address': '0x7ffb3c701e40'}, {'type': 'function', 'name': 'CoGetInstanceFromFile', 'address': '0x7ffb3c807300'}, {'type': 'function', 'name': 'CoGetInstanceFromIStorage', 'address': '0x7ffb3c807440'}, {'type': 'function', 'name': 'CoGetInterfaceAndReleaseStream', 'address': '0x7ffb3c708320'}, {'type': 'function', 'name': 'CoGetMalloc', 'address': '0x7ffb3c6eec40'}, {'type': 'function', 'name': 'CoGetMarshalSizeMax', 'address': '0x7ffb3c6e0fc0'}, {'type': 'function', 'name': 'CoGetModuleArchitecture', 'address': '0x7ffb3c707620'}, {'type': 'function', 'name': 'CoGetModuleType', 'address': '0x7ffb3c810c10'}, {'type': 'function', 'name': 'CoGetObjectContext', 'address': '0x7ffb3c7033d0'}, {'type': 'function', 'name': 'CoGetPSClsid', 'address': '0x7ffb3c70b000'}, {'type': 'function', 'name': 'CoGetProcessIdentifier', 'address': '0x7ffb3c697fe0'}, {'type': 'function', 'name': 'CoGetStandardMarshal', 'address': '0x7ffb3c675610'}, {'type': 'function', 'name': 'CoGetStdMarshalEx', 'address': '0x7ffb3c6ce120'}, {'type': 'function', 'name': 'CoGetSystemSecurityPermissions', 'address': '0x7ffb3c7d8e20'}, {'type': 'function', 'name': 'CoGetTreatAsClass', 'address': '0x7ffb3c687f60'}, {'type': 'function', 'name': 'CoImpersonateClient', 'address': '0x7ffb3c6b8fd0'}, {'type': 'function', 'name': 'CoIncrementMTAUsage', 'address': '0x7ffb3c702710'}, {'type': 'function', 'name': 'CoInitializeEx', 'address': '0x7ffb3c68b9e0'}, {'type': 'function', 'name': 'CoInitializeSecurity', 'address': '0x7ffb3c6d8250'}, {'type': 'function', 'name': 'CoInitializeWOW', 'address': '0x7ffb3c7c8ab0'}, {'type': 'function', 'name': 'CoInvalidateRemoteMachineBindings', 'address': '0x7ffb3c7d8e50'}, {'type': 'function', 'name': 'CoIsHandlerConnected', 'address': '0x7ffb3c7d8e70'}, {'type': 'function', 'name': 'CoLockObjectExternal', 'address': '0x7ffb3c71feb0'}, {'type': 'function', 'name': 'CoMarshalHresult', 'address': '0x7ffb3c7c8430'}, {'type': 'function', 'name': 'CoMarshalInterThreadInterfaceInStream', 'address': '0x7ffb3c6f1480'}, {'type': 'function', 'name': 'CoMarshalInterface', 'address': '0x7ffb3c668d00'}, {'type': 'function', 'name': 'CoPopServiceDomain', 'address': '0x7ffb3c7e0ba0'}, {'type': 'function', 'name': 'CoPushServiceDomain', 'address': '0x7ffb3c7e0bc0'}, {'type': 'function', 'name': 'CoQueryAuthenticationServices', 'address': '0x7ffb3c7d63b0'}, {'type': 'function', 'name': 'CoQueryClientBlanket', 'address': '0x7ffb3c6b8f20'}, {'type': 'function', 'name': 'CoQueryProxyBlanket', 'address': '0x7ffb3c703940'}, {'type': 'function', 'name': 'CoReactivateObject', 'address': '0x7ffb3c7d8ef0'}, {'type': 'function', 'name': 'CoRegisterActivationFilter', 'address': '0x7ffb3c720fd0'}, {'type': 'function', 'name': 'CoRegisterClassObject', 'address': '0x7ffb3c649f00'}, {'type': 'function', 'name': 'CoRegisterConsoleHandles', 'address': '0x7ffb3c7cc7a0'}, {'type': 'function', 'name': 'CoRegisterDeviceCatalog', 'address': '0x7ffb3c813e70'}, {'type': 'function', 'name': 'CoRegisterInitializeSpy', 'address': '0x7ffb3c6f9670'}, {'type': 'function', 'name': 'CoRegisterMallocSpy', 'address': '0x7ffb3c7c9600'}, {'type': 'function', 'name': 'CoRegisterMessageFilter', 'address': '0x7ffb3c7046a0'}, {'type': 'function', 'name': 'CoRegisterPSClsid', 'address': '0x7ffb3c69def0'}, {'type': 'function', 'name': 'CoRegisterRacActivationToken', 'address': '0x7ffb3c7150e0'}, {'type': 'function', 'name': 'CoRegisterSurrogate', 'address': '0x7ffb3c642ef0'}, {'type': 'function', 'name': 'CoRegisterSurrogateEx', 'address': '0x7ffb3c6c8a40'}, {'type': 'function', 'name': 'CoReleaseMarshalData', 'address': '0x7ffb3c68b8c0'}, {'type': 'function', 'name': 'CoReleaseServerProcess', 'address': '0x7ffb3c6f2bd0'}, {'type': 'function', 'name': 'CoResumeClassObjects', 'address': '0x7ffb3c6d3d70'}, {'type': 'function', 'name': 'CoRetireServer', 'address': '0x7ffb3c7d8fa0'}, {'type': 'function', 'name': 'CoRevertToSelf', 'address': '0x7ffb3c6b9060'}, {'type': 'function', 'name': 'CoRevokeClassObject', 'address': '0x7ffb3c6d1d00'}, {'type': 'function', 'name': 'CoRevokeConsoleHandles', 'address': '0x7ffb3c7cc8a0'}, {'type': 'function', 'name': 'CoRevokeDeviceCatalog', 'address': '0x7ffb3c813f20'}, {'type': 'function', 'name': 'CoRevokeInitializeSpy', 'address': '0x7ffb3c6f9c00'}, {'type': 'function', 'name': 'CoRevokeMallocSpy', 'address': '0x7ffb3c7c9730'}, {'type': 'function', 'name': 'CoRevokeRacActivationToken', 'address': '0x7ffb3c715370'}, {'type': 'function', 'name': 'CoSetCancelObject', 'address': '0x7ffb3c7d4770'}, {'type': 'function', 'name': 'CoSetErrorInfo', 'address': '0x7ffb3c6b6b10'}, {'type': 'function', 'name': 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'WdtpInterfacePointer_UserSize', 'address': '0x7ffb3c71f400'}, {'type': 'function', 'name': 'WdtpInterfacePointer_UserSize64', 'address': '0x7ffb3c6e0e60'}, {'type': 'function', 'name': 'WdtpInterfacePointer_UserUnmarshal', 'address': '0x7ffb3c71f520'}, {'type': 'function', 'name': 'WdtpInterfacePointer_UserUnmarshal64', 'address': '0x7ffb3c6ecf00'}, {'type': 'function', 'name': 'WindowsCompareStringOrdinal', 'address': '0x7ffb3c6e99f0'}, {'type': 'function', 'name': 'WindowsConcatString', 'address': '0x7ffb3c6861f0'}, {'type': 'function', 'name': 'WindowsCreateString', 'address': '0x7ffb3c685b80'}, {'type': 'function', 'name': 'WindowsCreateStringReference', 'address': '0x7ffb3c684440'}, {'type': 'function', 'name': 'WindowsDeleteString', 'address': '0x7ffb3c685840'}, {'type': 'function', 'name': 'WindowsDeleteStringBuffer', 'address': '0x7ffb3c70b680'}, {'type': 'function', 'name': 'WindowsDuplicateString', 'address': '0x7ffb3c685590'}, {'type': 'function', 'name': 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0
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15e8ebc72a33ab4fb93e585b56a6924c564a02b0
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py
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Python
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willard/plot/__init__.py
|
cfr2ak/willard
|
c45e51f2a27f0727b5e55269b266dfbaa752a6f9
|
[
"MIT"
] | 1
|
2022-01-13T22:30:16.000Z
|
2022-01-13T22:30:16.000Z
|
willard/plot/__init__.py
|
cfr2ak/willard
|
c45e51f2a27f0727b5e55269b266dfbaa752a6f9
|
[
"MIT"
] | 7
|
2019-10-14T04:31:51.000Z
|
2019-11-19T11:27:43.000Z
|
willard/plot/__init__.py
|
cfr2ak/willard
|
c45e51f2a27f0727b5e55269b266dfbaa752a6f9
|
[
"MIT"
] | null | null | null |
from .qplot import qplot
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15ff5fd98c3847d606e1b97e4b1652969cca1e3f
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py
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Python
|
app/commands/__init__.py
|
RobertPrellwitz/RiskTool
|
6d304e75ccd8a49c01c97976673938581e1e0a5c
|
[
"MIT"
] | null | null | null |
app/commands/__init__.py
|
RobertPrellwitz/RiskTool
|
6d304e75ccd8a49c01c97976673938581e1e0a5c
|
[
"MIT"
] | null | null | null |
app/commands/__init__.py
|
RobertPrellwitz/RiskTool
|
6d304e75ccd8a49c01c97976673938581e1e0a5c
|
[
"MIT"
] | null | null | null |
from .init_db import InitDbCommand
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0
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c614f7d8dfdfc3a2545729b668aa3f29281ea6ab
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|
py
|
Python
|
tests/test_signal.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | 1
|
2021-01-09T15:21:04.000Z
|
2021-01-09T15:21:04.000Z
|
tests/test_signal.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | 10
|
2020-12-18T02:35:18.000Z
|
2021-05-11T09:21:33.000Z
|
tests/test_signal.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | null | null | null |
import speelysis
def test_fir():
assert speelysis.fir
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0
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c63308febe6ee1548bd94c999a050ded28bc879e
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py
|
Python
|
three/geometry/__init__.py
|
jpiland16/three.py-packaged
|
53026f1637eff31bbdbeb32dac6bb4ec608ff4a6
|
[
"MIT"
] | null | null | null |
three/geometry/__init__.py
|
jpiland16/three.py-packaged
|
53026f1637eff31bbdbeb32dac6bb4ec608ff4a6
|
[
"MIT"
] | null | null | null |
three/geometry/__init__.py
|
jpiland16/three.py-packaged
|
53026f1637eff31bbdbeb32dac6bb4ec608ff4a6
|
[
"MIT"
] | null | null | null |
from three.geometry.Geometry import *
from three.geometry.PointGeometry import *
from three.geometry.LineGeometry import *
from three.geometry.CurveGeometry import *
from three.geometry.SurfaceGeometry import *
from three.geometry.QuadGeometry import *
from three.geometry.BoxGeometry import *
from three.geometry.SphereGeometry import *
from three.geometry.TorusGeometry import *
from three.geometry.CylinderGeometry import *
from three.geometry.ConeGeometry import *
from three.geometry.PyramidGeometry import *
from three.geometry.PrismGeometry import *
from three.geometry.TubeGeometry import *
from three.geometry.OctahedronGeometry import *
from three.geometry.IcosahedronGeometry import *
from three.geometry.RingGeometry import *
from three.geometry.CircleGeometry import *
from three.geometry.PolygonGeometry import *
from three.geometry.OBJGeometry import *
from three.geometry.OBJExtruder import *
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0
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|
d6952c59e9b8808a6f3df56caf26408faa0d5967
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|
py
|
Python
|
gym_brt/envs/reinforcementlearning_extensions/__init__.py
|
Data-Science-in-Mechanical-Engineering/vision-based-furuta-pendulum
|
84bfc5a089a2a8ace250f030f0298d45a3f9772f
|
[
"MIT"
] | null | null | null |
gym_brt/envs/reinforcementlearning_extensions/__init__.py
|
Data-Science-in-Mechanical-Engineering/vision-based-furuta-pendulum
|
84bfc5a089a2a8ace250f030f0298d45a3f9772f
|
[
"MIT"
] | null | null | null |
gym_brt/envs/reinforcementlearning_extensions/__init__.py
|
Data-Science-in-Mechanical-Engineering/vision-based-furuta-pendulum
|
84bfc5a089a2a8ace250f030f0298d45a3f9772f
|
[
"MIT"
] | null | null | null |
from gym_brt.envs.reinforcementlearning_extensions.wrapper import TrigonometricObservationWrapper, \
convert_single_state, convert_states_array, ImageObservationWrapper, CalibrationWrapper, ExponentialRewardWrapper
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0
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d69f7a517fb262ef63a9b71ac683050ce036c85a
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|
py
|
Python
|
src/lib/commonlibs/math/__init__.py
|
airinnova/commonlibs
|
e4ed133827aaadec931e5c682806ebe68ea8b750
|
[
"Apache-2.0"
] | 1
|
2019-08-13T18:49:27.000Z
|
2019-08-13T18:49:27.000Z
|
src/lib/commonlibs/math/__init__.py
|
airinnova/commonlibs
|
e4ed133827aaadec931e5c682806ebe68ea8b750
|
[
"Apache-2.0"
] | 1
|
2020-03-27T12:53:11.000Z
|
2020-04-25T15:36:28.000Z
|
src/lib/commonlibs/math/__init__.py
|
airinnova/commonlibs
|
e4ed133827aaadec931e5c682806ebe68ea8b750
|
[
"Apache-2.0"
] | 3
|
2019-09-20T18:47:24.000Z
|
2020-06-04T12:06:49.000Z
|
from . import interpolation
from . import vectors
| 16.666667
| 27
| 0.8
| 6
| 50
| 6.666667
| 0.666667
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 50
| 2
| 28
| 25
| 0.952381
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| true
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| null | 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d6cecfbfd4fb93aa03f54f0092664203b5705700
| 638
|
py
|
Python
|
hahaha_utils/__init__.py
|
MrDannyWu/hahaha_utils
|
9d8b678062a3046a77dbcfe2ad720ef903330d36
|
[
"MIT"
] | null | null | null |
hahaha_utils/__init__.py
|
MrDannyWu/hahaha_utils
|
9d8b678062a3046a77dbcfe2ad720ef903330d36
|
[
"MIT"
] | null | null | null |
hahaha_utils/__init__.py
|
MrDannyWu/hahaha_utils
|
9d8b678062a3046a77dbcfe2ad720ef903330d36
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File: __init__.py.py
Description: Danny Python工具包
Author: Danny
Create Date: 2020/04/10
-------------------------------------------------
Modify:
2020/04/10:
-------------------------------------------------
"""
from hahaha_utils.mysql_client import MySQLClient
from hahaha_utils.log_handler import LogHandler
from hahaha_utils.web_request import *
from hahaha_utils.config_loca import *
from hahaha_utils.config_inte import *
from hahaha_utils.config_test import *
from hahaha_utils.config_prod import *
| 33.578947
| 49
| 0.543887
| 65
| 638
| 5.061538
| 0.507692
| 0.212766
| 0.319149
| 0.255319
| 0.328267
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031895
| 0.164577
| 638
| 19
| 50
| 33.578947
| 0.585366
| 0.526646
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d6d5e4f2f8e1f08426e7ed2b083c688adf2fa498
| 59
|
py
|
Python
|
app/blueprints/api/__init__.py
|
Anioko/TestApp
|
95fa8d27ca8e7a074e62f92609427a378844e621
|
[
"MIT"
] | null | null | null |
app/blueprints/api/__init__.py
|
Anioko/TestApp
|
95fa8d27ca8e7a074e62f92609427a378844e621
|
[
"MIT"
] | 1
|
2021-06-02T01:53:47.000Z
|
2021-06-02T01:53:47.000Z
|
app/blueprints/api/__init__.py
|
Anioko/TestApp
|
95fa8d27ca8e7a074e62f92609427a378844e621
|
[
"MIT"
] | null | null | null |
from app.blueprints.api.views import api, main_api # noqa
| 29.5
| 58
| 0.779661
| 10
| 59
| 4.5
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 1
| 59
| 59
| 0.882353
| 0.067797
| 0
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| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
| 1
| 1
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| 0
| null | 0
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| 0
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| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
d6e0d79d773c54786c04cca39515248f4021a808
| 3,502
|
py
|
Python
|
nesta/core/batchables/meetup/tests/_test_group_details.py
|
anniyanvr/nesta
|
4b3ae79922cebde0ad33e08ac4c40b9a10e8e7c3
|
[
"MIT"
] | 13
|
2019-06-18T16:53:53.000Z
|
2021-03-04T10:58:52.000Z
|
nesta/core/batchables/meetup/tests/_test_group_details.py
|
nestauk/old_nesta_daps
|
4b3ae79922cebde0ad33e08ac4c40b9a10e8e7c3
|
[
"MIT"
] | 208
|
2018-08-10T13:15:40.000Z
|
2021-07-21T10:16:07.000Z
|
nesta/core/batchables/meetup/tests/_test_group_details.py
|
nestauk/old_nesta_daps
|
4b3ae79922cebde0ad33e08ac4c40b9a10e8e7c3
|
[
"MIT"
] | 8
|
2018-09-20T15:19:23.000Z
|
2020-12-15T17:41:34.000Z
|
import pytest
from sqlalchemy.orm import sessionmaker
from nesta.core.orms.meetup_orm import Base
from nesta.core.orms.orm_utils import get_mysql_engine
from unittest import mock
from unittest import TestCase
import os
from nesta.core.batchables.meetup import group_details
environ = {"BATCHPAR_group_urlnames":("[b'art@34', b'french@5', b'ibd-214', b'newintown@48', b'Westchester-Singles', b'socialnetwork@113', b'broward-bellydance', b'localpolitics-76', b'bookclub@511', b'women@57', b'newyorkcultureclub', b'TheUrbanTriangle', b'phoenixsingles', b'VegasVolunteers', b'expat-171', b'newintown@822', b'newintown@839', b'newtech@76', b'sdsongwriters', b'NJPARealEstateInvestorsREIAGroup', b'NYProfessionals', b'PBSN-NJ', b'entrepreneur@1775', b'TheOrlandoGirlsNightOutClub', b'entrepreneur@1906', b'ottawa-social', b'MBC-Houston', b'ocphotoshop', b'friends-961', b'www-hiking-ilkley-org', b'_fns_NYC-Web-Devs', b'docwong', b'30s-and-40s-Generation-X-Group', b'_fns_The-Cambridge-Expats-New-in-Town-Meetup-Group', b'AloneinToronto', b'Earning-Passive-Income-Through-Real-Estate', b'Free-QuickBooks-Workshop', b'Models-Makeup-Artists-and-Photographers-Meetup', b'_fns_Closed-Group123', b'NetworkingInPerson', b'NEOWordPress', b'Project-Management-Professionals-Network-Group', b'_fns_BrusselsNightLife', b'getonbase', b'250Kfromhome', b'Duluth-Rocking-Singles', b'JiveNation', b'denver-single-social-girlfriend-connection', b'Sydney-Volleyball-in-the-Park', b'NorthCarolinaInternetMarketers', b'Oregon-Freedom-Network', b'CLOSING-DOWN-RIGHT-NOW-RIGHT-NOW', b'IamHappyProjectOaklandCA', b'London-Over-50s-Friendship-Club', b'HmmmBooks', b'AfricaDmv', b'AllThingsFUN', b'Dinner-Parties-for-Singles', b'Art-of-Living-Canton-to-Farmington-Hills', b'Red-Deer-Business-Networking-Group', b'PersonalDevelopmentLovers', b'www-spiritisminorlando-com', b'I-am-happy-Project-Port-Harcourt', b'oshonewyork', b'Rockland-Bergen-Orange-Westchester-Singles-45-to-65', b'madisonphp', b'GardenPool-org', b'CFIC-Regina', b'NYC-SINGLES-over-40-MOVIE-BRUNCH-DINNER-CLUB', b'The-Gold-Coast-Aus-Single-Parents-Meetup-Group', b'PAIR-site', b'DFWYBP', b'Austin-Social-Escapades', b'Bay-Area-Search', b'UpstateCreativeWriters', b'StartupNewark', b'Building-Trades-Network', b'Singles-Association-of-Long-Island', b'San-Francisco-Bay-Area-Highly-Sensitive-Person-HSP-Group', b'Atlanta-INTJ-adaptees', b'The-Freelance-Jungle', b'Jams-Around-Brisbane', b'SpartaBusinessNetworking', b'The-John-de-Ruiter-Freiburg-Meetup-Group-JDR', b'Grupo-HELA', b'AfricansinDFW', b'Toronto-Network-Marketers', b'Jacksonville-Miniatures-Group', b'Single-Expats-in-Denmark', b'HealthTechnologyForum-DC', b'2c15846d-07c0-407f-99c1-0abebaa8a960']"),
"BATCHPAR_outinfo":("s3://nesta-production-intermediate/DUMMY"),
"BATCHPAR_db":"production_tests",
"BATCHPAR_config": os.environ["MYSQLDBCONF"],
"MEETUP_API_KEYS": os.environ["MEETUP_API_KEYS"]}
class TestRun(TestCase):
engine = get_mysql_engine("MYSQLDBCONF", "mysqldb")
Session = sessionmaker(engine)
def setUp(self):
'''Create the temporary table'''
Base.metadata.create_all(self.engine)
def tearDown(self):
'''Drop the temporary table'''
Base.metadata.drop_all(self.engine)
@mock.patch.dict(os.environ, environ)
@mock.patch('nesta.core.batchables.meetup.group_details.run.boto3')
def test_group_details(self, boto3):
n = group_details.run.run()
self.assertGreater(n, 0)
| 97.277778
| 2,425
| 0.754997
| 489
| 3,502
| 5.343558
| 0.537832
| 0.016073
| 0.014925
| 0.013012
| 0.022197
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025521
| 0.082524
| 3,502
| 35
| 2,426
| 100.057143
| 0.787737
| 0.014563
| 0
| 0
| 0
| 0.04
| 0.760174
| 0.523547
| 0
| 0
| 0
| 0
| 0.04
| 1
| 0.12
| false
| 0.04
| 0.32
| 0
| 0.56
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ba98da081ce33c9e029495bbbf93e9c4c33f4b09
| 30
|
py
|
Python
|
db_orm_models/non_blocking/presence_db_models/__init__.py
|
bbcawodu/nav-online-backend
|
3085ad686b253ea82478eb2fc365f51dda6d9d96
|
[
"MIT"
] | null | null | null |
db_orm_models/non_blocking/presence_db_models/__init__.py
|
bbcawodu/nav-online-backend
|
3085ad686b253ea82478eb2fc365f51dda6d9d96
|
[
"MIT"
] | null | null | null |
db_orm_models/non_blocking/presence_db_models/__init__.py
|
bbcawodu/nav-online-backend
|
3085ad686b253ea82478eb2fc365f51dda6d9d96
|
[
"MIT"
] | null | null | null |
from browsing_models import *
| 15
| 29
| 0.833333
| 4
| 30
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 30
| 1
| 30
| 30
| 0.923077
| 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
| 1
| 0
|
0
| 6
|
bae4b191b6a81c9bd450224aaa8ffb1c0f5ea390
| 29
|
py
|
Python
|
StatusUpdate/__init__.py
|
superadm1n/StatusUpdate
|
8871a7f7e1cda3158084bfe60565f7a3e31c12af
|
[
"MIT"
] | null | null | null |
StatusUpdate/__init__.py
|
superadm1n/StatusUpdate
|
8871a7f7e1cda3158084bfe60565f7a3e31c12af
|
[
"MIT"
] | 2
|
2018-04-19T04:10:12.000Z
|
2018-04-20T12:51:42.000Z
|
StatusUpdate/__init__.py
|
superadm1n/StatusUpdate
|
8871a7f7e1cda3158084bfe60565f7a3e31c12af
|
[
"MIT"
] | null | null | null |
from .statusupdate import *
| 9.666667
| 27
| 0.758621
| 3
| 29
| 7.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172414
| 29
| 2
| 28
| 14.5
| 0.916667
| 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
| 1
| 0
|
0
| 6
|
2405b607784a4fb6fb9c2a8a7a4de94f29ab50cf
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/cachy/utils.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/cachy/utils.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/cachy/utils.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/c3/62/69/0b08cb0c4f86a5d532b919e4400e215f28fe72697d7ad67845eb924064
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.46875
| 0
| 96
| 1
| 96
| 96
| 0.427083
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2405d120ed471a31b4d35c27aa69af2278abd442
| 36
|
py
|
Python
|
test.py
|
DmitrTRC/rep_list
|
0313a92d82cab8aa854b01bb58ef9c3814cc7279
|
[
"MIT"
] | null | null | null |
test.py
|
DmitrTRC/rep_list
|
0313a92d82cab8aa854b01bb58ef9c3814cc7279
|
[
"MIT"
] | null | null | null |
test.py
|
DmitrTRC/rep_list
|
0313a92d82cab8aa854b01bb58ef9c3814cc7279
|
[
"MIT"
] | null | null | null |
def test_ok():
print("ok")
| 9
| 19
| 0.472222
| 5
| 36
| 3.2
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 36
| 3
| 20
| 12
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
240d14b337275a5de665ee8ec035af44e42773a5
| 9,116
|
py
|
Python
|
ENIAC/api/loop_stack/loop_indicators/percentchange_indicator.py
|
Ahrli/fast_tools
|
144d764e4f169d3ab3753dcc6a79db9f9449de59
|
[
"Apache-2.0"
] | 1
|
2021-12-11T16:33:47.000Z
|
2021-12-11T16:33:47.000Z
|
ENIAC/api/loop_stack/loop_indicators/percentchange_indicator.py
|
webclinic017/fast_tools
|
144d764e4f169d3ab3753dcc6a79db9f9449de59
|
[
"Apache-2.0"
] | null | null | null |
ENIAC/api/loop_stack/loop_indicators/percentchange_indicator.py
|
webclinic017/fast_tools
|
144d764e4f169d3ab3753dcc6a79db9f9449de59
|
[
"Apache-2.0"
] | 3
|
2021-11-22T09:46:43.000Z
|
2022-01-28T22:33:07.000Z
|
import backtrader.indicators as btind
from . import compare_price as compare
from .base_indicator import iBaseIndicator
class iPctChangeCompare(iBaseIndicator):
'''
因子：平均移动线比较数值
传入参数：
rule = {"args": ["5"], #ema周期
"logic":{"compare": "eq","byValue": 1,"byMax": 5,}, # 周期结果比较
}
'''
lines = ('pctchange',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeCompare, self).__init__()
self.pctchange = btind.PercentChange(self.data.close, period=self.args[0])
def next(self):
self.lines.pctchange[0] = compare(self.pctchange[0], self.logic)
# print(self.pctchange[0], self.data.close[0], self.data.datetime.date())
@classmethod
def judge(cls, cond):
return int(cond['args'][0])
class iPctChangeCrossGolden(iBaseIndicator):
'''
因子：金叉
传入参数：
rule = {"args": ["5","10"], #短均线周期, 长均线周期
"logic":{"compare": "eq","byValue": 1,"byMax": 5,}, # 金叉情况比较大小， 短比长高多少
}
'''
lines = ('goldencross',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeCrossGolden, self).__init__()
self.pctchange_short = btind.PercentChange(self.data.close, period=self.args[0])
self.pctchange_long = btind.PercentChange(self.data.close, period=self.args[1])
self.cross = btind.CrossOver(self.pctchange_short, self.pctchange_long)
def next(self):
if self.cross[0] == 1:
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.goldencross[0] = compare(self.pctchange_short[0] - self.pctchange_long[0], self.logic) and \
self.pctchange_short[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.goldencross[0] = compare(self.pctchange_short[0] - self.pctchange_long[0], self.logic) and \
self.pctchange_short[0] < 0
else:
self.lines.goldencross[0] = compare(self.pctchange_short[0] - self.pctchange_long[0], self.logic)
else:
self.lines.goldencross[0] = False
# print(self.cross[0],self.pctchange_short[0], self.pctchange_long[0], self.data.datetime.date())
@classmethod
def judge(cls, cond):
return int(cond['args'][0])
class iPctChangeCrossDie(iBaseIndicator):
'''
因子：死叉
传入参数：
rule = {"args": ["5","10"], #短均线周期, 长均线周期
"logic":{"compare": "eq","byValue": 1,"byMax": 5,}, # 金叉情况比较大小， 短比长高多少
}
'''
lines = ('goldencross',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeCrossDie, self).__init__()
self.pctchange_short = btind.PercentChange(self.data.close, period=self.args[0])
self.pctchange_long = btind.PercentChange(self.data.close, period=self.args[1])
self.cross = btind.CrossOver(self.pctchange_short, self.pctchange_long)
def next(self):
if self.cross[0] == -1:
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.goldencross[0] = compare(self.pctchange_long[0] - self.pctchange_short[0], self.logic) and self.pctchange_short[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.goldencross[0] = compare(self.pctchange_long[0] - self.pctchange_short[0], self.logic) and self.pctchange_short[0] < 0
else:
self.lines.goldencross[0] = compare(self.pctchange_long[0] - self.pctchange_short[0], self.logic)
else:
self.lines.goldencross[0] = False
@classmethod
def judge(cls, cond):
return int(cond['args'][0])
class iPctChangeLong(iBaseIndicator):
'''
传入参数：
rule = {"args": [5,10, 5], # 连续N日短均线, 连续N日长均线, 连续N天
"logic":{"position":0},
}
'''
lines = ('pctchangelong',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeLong, self).__init__()
self.pctchange_short = btind.PercentChange(self.data.close, period=self.args[0])
self.pctchange_long = btind.PercentChange(self.data.close, period=self.args[1])
def next(self):
pctchangelong = set(
[ self.pctchange_short[i] > self.pctchange_long[i] for i in range(1 - self.args[2], 1)])
if len(pctchangelong) == 1 and True in pctchangelong:
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.pctchangelong[0] = True and self.pctchange_short[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.pctchangelong[0] = True and self.pctchange_short[0] < 0
else:
self.lines.pctchangelong[0] = True
else:
self.lines.pctchangelong[0] = False
@classmethod
def judge(cls, cond):
return int(cond['args'][1]) + int(cond['args'][2])
class iPctChangeShort(iBaseIndicator):
'''
传入参数：
rule = {"args": [5,10, 5], # 连续N日短均线, 连续N日长均线, 连续N天
"logic":{"position":0},
}
'''
lines = ('pctchangeshort',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeShort, self).__init__()
self.pctchange_short = btind.PercentChange(self.data.close, period=self.args[0])
self.pctchange_long = btind.PercentChange(self.data.close, period=self.args[1])
def next(self):
pctchangeshort = set(
[self.data.close[i] < self.pctchange_short[i] < self.pctchange_long[i] for i in range(1 - self.args[2], 1)])
if len(pctchangeshort) == 1 and True in pctchangeshort:
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.pctchangeshort[0] = True and self.pctchange_short[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.pctchangeshort[0] = True and self.pctchange_short[0] < 0
else:
self.lines.pctchangeshort[0] = True
else:
self.lines.pctchangeshort[0] = False
@classmethod
def judge(cls, cond):
return int(cond['args'][1]) + int(cond['args'][2])
class iPctChangeTop(iBaseIndicator):
'''
传入参数：
rule = {"args": [5,3], # 连续N日短均线, 连续N日长均线, 连续N天
"logic":{"position":0},
}
'''
lines = ('pctchangetop',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeTop, self).__init__()
self.pctchange = btind.PercentChange(self.data.close, period=self.args[0])
def next(self):
_list = list(self.pctchange.get(size=self.args[1]))
if len(_list) == self.args[1] and self.pctchange[0] == max(_list):
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.pctchangetop[0] = True and self.pctchange[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.pctchangetop[0] = True and self.pctchange[0] < 0
else:
self.lines.pctchangetop[0] = True
else:
self.lines.pctchangetop[0] = False
class iPctChangeBottom(iBaseIndicator):
'''
因子：最近 n 天 最低点
rule = {"args": [5,3], # 连续N日短均线, 连续N日长均线, 连续N天
"logic":{"position":0},
}
'''
lines = ('pctchangebottom',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeBottom, self).__init__()
self.pctchange = btind.PercentChange(self.data.close, period=self.args[0])
def next(self):
_list = list(self.pctchange.get(size=self.args[1]))
if len(_list) == self.args[1] and self.pctchange[0] == min(_list):
if self.logic["position"] == 1: # 出现在上部 > 0
self.lines.pctchangebottom[0] = True and self.pctchange[0] > 0
elif self.logic["position"] == -1: # 出现在下部 <0
self.lines.pctchangebottom[0] = True and self.pctchange[0] < 0
else:
self.lines.pctchangebottom[0] = True
else:
self.lines.pctchangebottom[0] = False
# print(self.pctchange[0], self.data.close[0], self.data.datetime.date())
class iPctChangeHigh(iBaseIndicator):
'''
连续n天低于某个数值
传入参数：
rule = {"args": [6,2] # 周期 连续N天
"logic":{"compare": "eq","byValue": 1,"byMax": 5,},
'''
lines = ('ehigh',)
params = dict(rule=list())
def __init__(self):
super(iPctChangeHigh, self).__init__()
self.pctchange = btind.PercentChange(self.data.close, period=self.args[0])
def next(self):
_list = [(i) for i in list(self.pctchange.get(size=self.args[1]))]
_list.sort()
if len(_list) == self.args[1]:
_list = (list(map(lambda d: compare(d, self.logic), _list)))
_list = [ i for i in _list if i is True ]
else:
_list = []
if len(_list) == self.args[1]:
self.lines.ehigh[0] = True
else:
self.lines.ehigh[0] = False
# print(self.pctchange[0], self.data.close[0], self.data.datetime.date())
| 35.889764
| 146
| 0.573168
| 1,078
| 9,116
| 4.736549
| 0.102041
| 0.140031
| 0.081081
| 0.055817
| 0.827654
| 0.785155
| 0.7736
| 0.762632
| 0.716216
| 0.688797
| 0
| 0.025703
| 0.274462
| 9,116
| 253
| 147
| 36.031621
| 0.746296
| 0.152589
| 0
| 0.577922
| 0
| 0
| 0.028591
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.136364
| false
| 0
| 0.019481
| 0.032468
| 0.344156
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
24111e142fa803f04a9ad891ef184fcae91a5577
| 84
|
py
|
Python
|
metric/__init__.py
|
Muyun99/dysegmentation
|
3f46ee09fb509323c4573f4284e077f8ec26cd4c
|
[
"MIT"
] | 2
|
2021-04-28T14:47:30.000Z
|
2021-04-28T14:54:17.000Z
|
metric/__init__.py
|
Muyun99/dysegmentation
|
3f46ee09fb509323c4573f4284e077f8ec26cd4c
|
[
"MIT"
] | null | null | null |
metric/__init__.py
|
Muyun99/dysegmentation
|
3f46ee09fb509323c4573f4284e077f8ec26cd4c
|
[
"MIT"
] | 1
|
2021-06-16T06:12:26.000Z
|
2021-06-16T06:12:26.000Z
|
from .implement import eval_net_unet_dice, eval_net_unet_miou, eval_net_unet_bfscore
| 84
| 84
| 0.904762
| 15
| 84
| 4.466667
| 0.6
| 0.313433
| 0.492537
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.059524
| 84
| 1
| 84
| 84
| 0.848101
| 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
| 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
| 6
|
2425d37a5104fb7889dbbc9557e6206403b1913d
| 40,540
|
py
|
Python
|
pybind/slxos/v17r_2_00/mpls_state/ldp/ldp_fec_statistics/__init__.py
|
extremenetworks/pybind
|
44c467e71b2b425be63867aba6e6fa28b2cfe7fb
|
[
"Apache-2.0"
] | null | null | null |
pybind/slxos/v17r_2_00/mpls_state/ldp/ldp_fec_statistics/__init__.py
|
extremenetworks/pybind
|
44c467e71b2b425be63867aba6e6fa28b2cfe7fb
|
[
"Apache-2.0"
] | null | null | null |
pybind/slxos/v17r_2_00/mpls_state/ldp/ldp_fec_statistics/__init__.py
|
extremenetworks/pybind
|
44c467e71b2b425be63867aba6e6fa28b2cfe7fb
|
[
"Apache-2.0"
] | 1
|
2021-11-05T22:15:42.000Z
|
2021-11-05T22:15:42.000Z
|
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class ldp_fec_statistics(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-mpls-operational - based on the path /mpls-state/ldp/ldp-fec-statistics. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: LDP FEC statistics
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__number_of_prefix_fec','__number_of_prefix_fec_installed','__number_of_prefix_fec_filtered_in','__number_of_prefix_fec_filtered_out','__number_of_vc_fec_128','__number_of_vc_fec_129','__number_of_vc_fec_installed','__number_of_route_upd_proc_errors','__number_of_vc_fec_proc_errors','__number_of_prefix_fec_lwd','__number_of_vc_fec',)
_yang_name = 'ldp-fec-statistics'
_rest_name = 'ldp-fec-statistics'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__number_of_prefix_fec = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec", rest_name="number-of-prefix-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_vc_fec_installed = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-installed", rest_name="number-of-vc-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_vc_fec_proc_errors = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-proc-errors", rest_name="number-of-vc-fec-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_vc_fec = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec", rest_name="number-of-vc-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_prefix_fec_lwd = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-lwd", rest_name="number-of-prefix-fec-lwd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_prefix_fec_filtered_out = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-out", rest_name="number-of-prefix-fec-filtered-out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_vc_fec_128 = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-128", rest_name="number-of-vc-fec-128", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_vc_fec_129 = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-129", rest_name="number-of-vc-fec-129", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_route_upd_proc_errors = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-route-upd-proc-errors", rest_name="number-of-route-upd-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_prefix_fec_installed = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-installed", rest_name="number-of-prefix-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__number_of_prefix_fec_filtered_in = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-in", rest_name="number-of-prefix-fec-filtered-in", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'mpls-state', u'ldp', u'ldp-fec-statistics']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'mpls-state', u'ldp', u'ldp-fec-statistics']
def _get_number_of_prefix_fec(self):
"""
Getter method for number_of_prefix_fec, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec (uint32)
YANG Description: The total number of prefix FECs in the LDP FEC database
"""
return self.__number_of_prefix_fec
def _set_number_of_prefix_fec(self, v, load=False):
"""
Setter method for number_of_prefix_fec, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_prefix_fec is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_prefix_fec() directly.
YANG Description: The total number of prefix FECs in the LDP FEC database
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec", rest_name="number-of-prefix-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_prefix_fec must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec", rest_name="number-of-prefix-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_prefix_fec = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_prefix_fec(self):
self.__number_of_prefix_fec = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec", rest_name="number-of-prefix-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_prefix_fec_installed(self):
"""
Getter method for number_of_prefix_fec_installed, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_installed (uint32)
YANG Description: Total number of prefix FECs installed
"""
return self.__number_of_prefix_fec_installed
def _set_number_of_prefix_fec_installed(self, v, load=False):
"""
Setter method for number_of_prefix_fec_installed, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_installed (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_prefix_fec_installed is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_prefix_fec_installed() directly.
YANG Description: Total number of prefix FECs installed
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-installed", rest_name="number-of-prefix-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_prefix_fec_installed must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-installed", rest_name="number-of-prefix-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_prefix_fec_installed = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_prefix_fec_installed(self):
self.__number_of_prefix_fec_installed = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-installed", rest_name="number-of-prefix-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_prefix_fec_filtered_in(self):
"""
Getter method for number_of_prefix_fec_filtered_in, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_filtered_in (uint32)
YANG Description: Number of FECs from this peer which are filtered due to the inbound FEC filter configurations
"""
return self.__number_of_prefix_fec_filtered_in
def _set_number_of_prefix_fec_filtered_in(self, v, load=False):
"""
Setter method for number_of_prefix_fec_filtered_in, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_filtered_in (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_prefix_fec_filtered_in is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_prefix_fec_filtered_in() directly.
YANG Description: Number of FECs from this peer which are filtered due to the inbound FEC filter configurations
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-in", rest_name="number-of-prefix-fec-filtered-in", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_prefix_fec_filtered_in must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-in", rest_name="number-of-prefix-fec-filtered-in", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_prefix_fec_filtered_in = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_prefix_fec_filtered_in(self):
self.__number_of_prefix_fec_filtered_in = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-in", rest_name="number-of-prefix-fec-filtered-in", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_prefix_fec_filtered_out(self):
"""
Getter method for number_of_prefix_fec_filtered_out, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_filtered_out (uint32)
YANG Description: Number of FECs from this peer which are filtered due to the outbound FEC filter configurations
"""
return self.__number_of_prefix_fec_filtered_out
def _set_number_of_prefix_fec_filtered_out(self, v, load=False):
"""
Setter method for number_of_prefix_fec_filtered_out, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_filtered_out (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_prefix_fec_filtered_out is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_prefix_fec_filtered_out() directly.
YANG Description: Number of FECs from this peer which are filtered due to the outbound FEC filter configurations
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-out", rest_name="number-of-prefix-fec-filtered-out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_prefix_fec_filtered_out must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-out", rest_name="number-of-prefix-fec-filtered-out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_prefix_fec_filtered_out = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_prefix_fec_filtered_out(self):
self.__number_of_prefix_fec_filtered_out = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-filtered-out", rest_name="number-of-prefix-fec-filtered-out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_vc_fec_128(self):
"""
Getter method for number_of_vc_fec_128, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_128 (uint32)
YANG Description: The total number of VC FECs for type 128. The FEC type for VC FEC can be 128 or 129
"""
return self.__number_of_vc_fec_128
def _set_number_of_vc_fec_128(self, v, load=False):
"""
Setter method for number_of_vc_fec_128, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_128 (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_vc_fec_128 is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_vc_fec_128() directly.
YANG Description: The total number of VC FECs for type 128. The FEC type for VC FEC can be 128 or 129
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-128", rest_name="number-of-vc-fec-128", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_vc_fec_128 must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-128", rest_name="number-of-vc-fec-128", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_vc_fec_128 = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_vc_fec_128(self):
self.__number_of_vc_fec_128 = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-128", rest_name="number-of-vc-fec-128", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_vc_fec_129(self):
"""
Getter method for number_of_vc_fec_129, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_129 (uint32)
YANG Description: The total number of VC FECs for type 129. The FEC type for VC FEC can be 128 or 129
"""
return self.__number_of_vc_fec_129
def _set_number_of_vc_fec_129(self, v, load=False):
"""
Setter method for number_of_vc_fec_129, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_129 (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_vc_fec_129 is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_vc_fec_129() directly.
YANG Description: The total number of VC FECs for type 129. The FEC type for VC FEC can be 128 or 129
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-129", rest_name="number-of-vc-fec-129", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_vc_fec_129 must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-129", rest_name="number-of-vc-fec-129", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_vc_fec_129 = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_vc_fec_129(self):
self.__number_of_vc_fec_129 = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-129", rest_name="number-of-vc-fec-129", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_vc_fec_installed(self):
"""
Getter method for number_of_vc_fec_installed, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_installed (uint32)
YANG Description: Total number of vc FECs installed
"""
return self.__number_of_vc_fec_installed
def _set_number_of_vc_fec_installed(self, v, load=False):
"""
Setter method for number_of_vc_fec_installed, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_installed (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_vc_fec_installed is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_vc_fec_installed() directly.
YANG Description: Total number of vc FECs installed
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-installed", rest_name="number-of-vc-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_vc_fec_installed must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-installed", rest_name="number-of-vc-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_vc_fec_installed = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_vc_fec_installed(self):
self.__number_of_vc_fec_installed = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-installed", rest_name="number-of-vc-fec-installed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_route_upd_proc_errors(self):
"""
Getter method for number_of_route_upd_proc_errors, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_route_upd_proc_errors (uint32)
YANG Description: The total number of route update processing errors for L3 FEC prefix
"""
return self.__number_of_route_upd_proc_errors
def _set_number_of_route_upd_proc_errors(self, v, load=False):
"""
Setter method for number_of_route_upd_proc_errors, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_route_upd_proc_errors (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_route_upd_proc_errors is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_route_upd_proc_errors() directly.
YANG Description: The total number of route update processing errors for L3 FEC prefix
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-route-upd-proc-errors", rest_name="number-of-route-upd-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_route_upd_proc_errors must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-route-upd-proc-errors", rest_name="number-of-route-upd-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_route_upd_proc_errors = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_route_upd_proc_errors(self):
self.__number_of_route_upd_proc_errors = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-route-upd-proc-errors", rest_name="number-of-route-upd-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_vc_fec_proc_errors(self):
"""
Getter method for number_of_vc_fec_proc_errors, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_proc_errors (uint32)
YANG Description: The total number of L3 VC FEC internal processing errors
"""
return self.__number_of_vc_fec_proc_errors
def _set_number_of_vc_fec_proc_errors(self, v, load=False):
"""
Setter method for number_of_vc_fec_proc_errors, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec_proc_errors (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_vc_fec_proc_errors is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_vc_fec_proc_errors() directly.
YANG Description: The total number of L3 VC FEC internal processing errors
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-proc-errors", rest_name="number-of-vc-fec-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_vc_fec_proc_errors must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-proc-errors", rest_name="number-of-vc-fec-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_vc_fec_proc_errors = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_vc_fec_proc_errors(self):
self.__number_of_vc_fec_proc_errors = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec-proc-errors", rest_name="number-of-vc-fec-proc-errors", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_prefix_fec_lwd(self):
"""
Getter method for number_of_prefix_fec_lwd, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_lwd (uint32)
YANG Description: The total number of FECs which currently have the label withdrawal delay timer running
"""
return self.__number_of_prefix_fec_lwd
def _set_number_of_prefix_fec_lwd(self, v, load=False):
"""
Setter method for number_of_prefix_fec_lwd, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_prefix_fec_lwd (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_prefix_fec_lwd is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_prefix_fec_lwd() directly.
YANG Description: The total number of FECs which currently have the label withdrawal delay timer running
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-lwd", rest_name="number-of-prefix-fec-lwd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_prefix_fec_lwd must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-lwd", rest_name="number-of-prefix-fec-lwd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_prefix_fec_lwd = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_prefix_fec_lwd(self):
self.__number_of_prefix_fec_lwd = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-prefix-fec-lwd", rest_name="number-of-prefix-fec-lwd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_number_of_vc_fec(self):
"""
Getter method for number_of_vc_fec, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec (uint32)
YANG Description: The total number of vc FECs in the LDP FEC database
"""
return self.__number_of_vc_fec
def _set_number_of_vc_fec(self, v, load=False):
"""
Setter method for number_of_vc_fec, mapped from YANG variable /mpls_state/ldp/ldp_fec_statistics/number_of_vc_fec (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_number_of_vc_fec is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_number_of_vc_fec() directly.
YANG Description: The total number of vc FECs in the LDP FEC database
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec", rest_name="number-of-vc-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """number_of_vc_fec must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec", rest_name="number-of-vc-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__number_of_vc_fec = t
if hasattr(self, '_set'):
self._set()
def _unset_number_of_vc_fec(self):
self.__number_of_vc_fec = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="number-of-vc-fec", rest_name="number-of-vc-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
number_of_prefix_fec = __builtin__.property(_get_number_of_prefix_fec)
number_of_prefix_fec_installed = __builtin__.property(_get_number_of_prefix_fec_installed)
number_of_prefix_fec_filtered_in = __builtin__.property(_get_number_of_prefix_fec_filtered_in)
number_of_prefix_fec_filtered_out = __builtin__.property(_get_number_of_prefix_fec_filtered_out)
number_of_vc_fec_128 = __builtin__.property(_get_number_of_vc_fec_128)
number_of_vc_fec_129 = __builtin__.property(_get_number_of_vc_fec_129)
number_of_vc_fec_installed = __builtin__.property(_get_number_of_vc_fec_installed)
number_of_route_upd_proc_errors = __builtin__.property(_get_number_of_route_upd_proc_errors)
number_of_vc_fec_proc_errors = __builtin__.property(_get_number_of_vc_fec_proc_errors)
number_of_prefix_fec_lwd = __builtin__.property(_get_number_of_prefix_fec_lwd)
number_of_vc_fec = __builtin__.property(_get_number_of_vc_fec)
_pyangbind_elements = {'number_of_prefix_fec': number_of_prefix_fec, 'number_of_prefix_fec_installed': number_of_prefix_fec_installed, 'number_of_prefix_fec_filtered_in': number_of_prefix_fec_filtered_in, 'number_of_prefix_fec_filtered_out': number_of_prefix_fec_filtered_out, 'number_of_vc_fec_128': number_of_vc_fec_128, 'number_of_vc_fec_129': number_of_vc_fec_129, 'number_of_vc_fec_installed': number_of_vc_fec_installed, 'number_of_route_upd_proc_errors': number_of_route_upd_proc_errors, 'number_of_vc_fec_proc_errors': number_of_vc_fec_proc_errors, 'number_of_prefix_fec_lwd': number_of_prefix_fec_lwd, 'number_of_vc_fec': number_of_vc_fec, }
| 78.111753
| 652
| 0.766182
| 5,911
| 40,540
| 4.919472
| 0.034174
| 0.087761
| 0.049176
| 0.078923
| 0.947385
| 0.938616
| 0.920458
| 0.905396
| 0.892431
| 0.886585
| 0
| 0.026637
| 0.114702
| 40,540
| 518
| 653
| 78.262548
| 0.783589
| 0.202985
| 0
| 0.496528
| 0
| 0.038194
| 0.374109
| 0.241591
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| false
| 0
| 0.027778
| 0
| 0.267361
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
2443c72b04844429dd62b0915d6e84776a48dac7
| 35
|
py
|
Python
|
thesis_toolbox/plot/__init__.py
|
MasterOnDust/Thesis_toolbox
|
a889dfb856dd73e11d5d292be82e94d682f9dec2
|
[
"MIT"
] | null | null | null |
thesis_toolbox/plot/__init__.py
|
MasterOnDust/Thesis_toolbox
|
a889dfb856dd73e11d5d292be82e94d682f9dec2
|
[
"MIT"
] | null | null | null |
thesis_toolbox/plot/__init__.py
|
MasterOnDust/Thesis_toolbox
|
a889dfb856dd73e11d5d292be82e94d682f9dec2
|
[
"MIT"
] | null | null | null |
from .tools import map_large_scale
| 17.5
| 34
| 0.857143
| 6
| 35
| 4.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.903226
| 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
| 1
| 0
|
0
| 6
|
79f9736c87e19aab663539afe63661e7b8d3f9cc
| 375
|
py
|
Python
|
subrepos/wtsi-hgi.python-common/hgicommon/data_source/__init__.py
|
wtsi-hgi/openstack-tenant-cleanup
|
d998016f44c54666f76f90d8d3efa90e12730fff
|
[
"MIT"
] | null | null | null |
subrepos/wtsi-hgi.python-common/hgicommon/data_source/__init__.py
|
wtsi-hgi/openstack-tenant-cleanup
|
d998016f44c54666f76f90d8d3efa90e12730fff
|
[
"MIT"
] | 5
|
2017-05-10T11:27:42.000Z
|
2017-07-08T14:36:19.000Z
|
subrepos/wtsi-hgi.python-common/hgicommon/data_source/__init__.py
|
wtsi-hgi/openstack-tenant-cleanup
|
d998016f44c54666f76f90d8d3efa90e12730fff
|
[
"MIT"
] | null | null | null |
from hgicommon.data_source.common import DataSource
from hgicommon.data_source.basic import ListDataSource, MultiDataSource
from hgicommon.data_source.static_from_file import FilesDataSource, SynchronisedFilesDataSource
from hgicommon.data_source.dynamic_from_file import register, unregister, registration_event_listenable_map,\
RegisteringDataSource, RegistrationEvent
| 62.5
| 109
| 0.890667
| 41
| 375
| 7.878049
| 0.560976
| 0.160991
| 0.210526
| 0.28483
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.069333
| 375
| 5
| 110
| 75
| 0.925501
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.8
| 0
| 0.8
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cef4c8acd0a1b73d94bf9e6e123cf2b1729ba534
| 3,772
|
py
|
Python
|
tests/test_errors.py
|
sa-bpelakh/sparql-endpoint-fixture
|
b88d1fd5189da4a8652fcd0dc1bfad65c1a4a6a6
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_errors.py
|
sa-bpelakh/sparql-endpoint-fixture
|
b88d1fd5189da4a8652fcd0dc1bfad65c1a4a6a6
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_errors.py
|
sa-bpelakh/sparql-endpoint-fixture
|
b88d1fd5189da4a8652fcd0dc1bfad65c1a4a6a6
|
[
"BSD-3-Clause"
] | null | null | null |
import requests
def test_missing_query_get(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
query = "select distinct ?class where { [] a ?class } order by ?class"
response = requests.get(url=repo_uri, params={'wrong': query}, headers={'Accept': 'application/json'})
assert response.status_code == 400
def test_bad_result_format(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
query = "select distinct ?class where { [] a ?class } order by ?class"
response = requests.get(url=repo_uri, params={'query': query}, headers={'Accept': 'application/unknown'})
assert response.status_code == 415
def server_fail():
raise Exception('Fake Server Failure')
def test_query_eval_error(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
endpoint.graph.query = server_fail
query = "select distinct ?class where { [] a ?class } order by ?class"
response = requests.get(url=repo_uri, params={'query': query}, headers={'Accept': 'application/json'})
assert response.status_code == 500
def test_update_eval_error(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
endpoint.graph.update = server_fail
update = "insert { ?instance a ?super } " \
"where { ?instance a/<http://www.w3.org/2000/01/rdf-schema#subClassOf> ?super }"
response = requests.post(url=repo_uri, data=update.encode('utf-8'),
headers={'Content-Type': 'application/sparql-update'})
assert response.status_code == 500
def test_missing_query_post(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
query = "select distinct ?class where { [] a ?class } order by ?class"
response = requests.post(url=repo_uri, data={'wrong': query}, headers={'Accept': 'application/json'})
assert response.status_code == 400
def test_post_bad_content(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
update = "insert { ?instance a ?super } " \
"where { ?instance a/<http://www.w3.org/2000/01/rdf-schema#subClassOf> ?super }"
response = requests.post(url=repo_uri, data=update.encode('utf-8'),
headers={'Content-Type': 'application/bad-content-type'})
assert response.status_code == 415
def test_malformed_query(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
query = "definitely not a SPARQL query"
response = requests.get(url=repo_uri, params={'query': query}, headers={'Accept': 'application/json'})
assert response.status_code == 400
def test_malformed_update(sparql_endpoint):
repo_uri = 'https://my.rdfdb.com/repo/sparql'
rdf_files = ['tests/upper_ontology.ttl']
endpoint = sparql_endpoint(repo_uri, rdf_files) # noqa: F841
update = "definitely not a SPARQL update"
response = requests.get(url=repo_uri, params={'update': update}, headers={'Accept': 'application/json'})
assert response.status_code == 400
| 41.911111
| 109
| 0.68982
| 499
| 3,772
| 5.02004
| 0.158317
| 0.067066
| 0.11497
| 0.134132
| 0.88024
| 0.88024
| 0.856287
| 0.817565
| 0.817565
| 0.795609
| 0
| 0.020447
| 0.170201
| 3,772
| 89
| 110
| 42.382022
| 0.779872
| 0.023065
| 0
| 0.676923
| 0
| 0.030769
| 0.335963
| 0.066649
| 0
| 0
| 0
| 0
| 0.123077
| 1
| 0.138462
| false
| 0
| 0.015385
| 0
| 0.153846
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
061f44eb9b2b4f97fd2e0ae4697b95fe343c54ad
| 995
|
py
|
Python
|
apps/keepass/password_manager.py
|
timo95/knausj_talon
|
735b6fda064b12c832fce2ba4ca8e0186e7db48a
|
[
"MIT"
] | 1
|
2021-09-08T05:45:03.000Z
|
2021-09-08T05:45:03.000Z
|
apps/keepass/password_manager.py
|
timo95/knausj_talon
|
735b6fda064b12c832fce2ba4ca8e0186e7db48a
|
[
"MIT"
] | null | null | null |
apps/keepass/password_manager.py
|
timo95/knausj_talon
|
735b6fda064b12c832fce2ba4ca8e0186e7db48a
|
[
"MIT"
] | null | null | null |
from talon import Module
# --- Tag definition ---
mod = Module()
mod.tag("password_manager", desc="Common password manager actions")
# --- Define actions ---
@mod.action_class
class Actions:
def password_manager_entry_new():
"""Create new password entry"""
def password_manager_entry_edit():
"""Edit password entry"""
def password_manager_entry_clone():
"""Clone password entry"""
def password_manager_entry_copy():
"""Copy password entry"""
def password_manager_entry_paste():
"""Paste password entry"""
def password_manager_entry_delete():
"""Delete password entry"""
def password_manager_password_copy():
"""Copy password"""
def password_manager_password_fill():
"""Fill username and password"""
def password_manager_user_copy():
"""Copy username"""
def password_manager_url_copy():
"""Copy url"""
def password_manager_url_open():
"""Open url in browser"""
| 30.151515
| 67
| 0.655276
| 112
| 995
| 5.508929
| 0.285714
| 0.316045
| 0.320908
| 0.223663
| 0.341977
| 0.291734
| 0
| 0
| 0
| 0
| 0
| 0
| 0.21608
| 995
| 32
| 68
| 31.09375
| 0.791026
| 0.261307
| 0
| 0
| 0
| 0
| 0.069322
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.6875
| false
| 0.75
| 0.0625
| 0
| 0.8125
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
062b5603711ab833602c21e16071216c7ad5a3d9
| 204
|
py
|
Python
|
ckan_wit/__init__.py
|
Ethel-Ecc/wit
|
fb58d99eb1a132a25ffcd06a4bc824d8c9e861dc
|
[
"MIT"
] | 1
|
2021-03-26T10:56:57.000Z
|
2021-03-26T10:56:57.000Z
|
ckan_wit/__init__.py
|
Ethel-Ecc/wit
|
fb58d99eb1a132a25ffcd06a4bc824d8c9e861dc
|
[
"MIT"
] | null | null | null |
ckan_wit/__init__.py
|
Ethel-Ecc/wit
|
fb58d99eb1a132a25ffcd06a4bc824d8c9e861dc
|
[
"MIT"
] | null | null | null |
# def proxy_settings_1():
# ext_proxy = {'http': 'http://proxy.ciss.de:3128', 'https': 'http://proxy.ciss.de:3128'}
# local_noProxy = {'http': None, 'https': None}
# return ext_proxy['https']
| 40.8
| 93
| 0.612745
| 28
| 204
| 4.285714
| 0.5
| 0.133333
| 0.216667
| 0.25
| 0.316667
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052326
| 0.156863
| 204
| 5
| 94
| 40.8
| 0.645349
| 0.955882
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 1
| 1
| 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
| 6
|
06696076e5127c9dfa8fb192449de19f3a11fa21
| 270
|
py
|
Python
|
three.py/helpers/__init__.py
|
lukestanley/three.py
|
a3fa99cb3553aca8c74ceabb8203edeb55450803
|
[
"MIT"
] | 80
|
2019-04-04T13:41:32.000Z
|
2022-01-12T18:40:19.000Z
|
three.py/helpers/__init__.py
|
lukestanley/three.py
|
a3fa99cb3553aca8c74ceabb8203edeb55450803
|
[
"MIT"
] | 9
|
2019-04-04T14:43:50.000Z
|
2020-03-29T04:50:53.000Z
|
three.py/helpers/__init__.py
|
lukestanley/three.py
|
a3fa99cb3553aca8c74ceabb8203edeb55450803
|
[
"MIT"
] | 17
|
2019-04-04T14:20:42.000Z
|
2022-03-03T16:26:29.000Z
|
from helpers.AxesHelper import *
from helpers.GridHelper import *
from helpers.BoxHelper import *
from helpers.VertexNormalHelper import *
from helpers.DirectionalLightHelper import *
from helpers.PointLightHelper import *
from helpers.OrthographicCameraHelper import *
| 33.75
| 46
| 0.844444
| 28
| 270
| 8.142857
| 0.357143
| 0.337719
| 0.447368
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103704
| 270
| 7
| 47
| 38.571429
| 0.942149
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| 1
| 0
| 0
| 0
|
0
| 6
|
231b8619d88c10a7aac24ed51b917f23a8d3dc8f
| 76
|
py
|
Python
|
routes/__init__.py
|
qzhqzh/six-year-phoenix
|
5fb164108f86de6a1d619f936e0ae69507c6ec60
|
[
"MIT"
] | null | null | null |
routes/__init__.py
|
qzhqzh/six-year-phoenix
|
5fb164108f86de6a1d619f936e0ae69507c6ec60
|
[
"MIT"
] | null | null | null |
routes/__init__.py
|
qzhqzh/six-year-phoenix
|
5fb164108f86de6a1d619f936e0ae69507c6ec60
|
[
"MIT"
] | null | null | null |
from .test import test
def init_app(app):
app.register_blueprint(test)
| 15.2
| 32
| 0.75
| 12
| 76
| 4.583333
| 0.666667
| 0.218182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 76
| 4
| 33
| 19
| 0.859375
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
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| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0.333333
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| null | 1
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| null | 0
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| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
23228ef22343f9774a58156a5365243870b4fddf
| 43
|
py
|
Python
|
scieldas/services/dockerhub/__init__.py
|
autophagy/scieldas
|
0159f45321e4edb2211010e096dfc09f5c103df0
|
[
"MIT"
] | 6
|
2018-06-13T06:54:12.000Z
|
2021-01-15T13:59:33.000Z
|
scieldas/services/dockerhub/__init__.py
|
Autophagy/scieldas
|
0159f45321e4edb2211010e096dfc09f5c103df0
|
[
"MIT"
] | 1
|
2020-06-21T17:56:32.000Z
|
2020-06-23T07:48:57.000Z
|
scieldas/services/dockerhub/__init__.py
|
autophagy/scieldas
|
0159f45321e4edb2211010e096dfc09f5c103df0
|
[
"MIT"
] | null | null | null |
from .app import BuildStatus, Pulls, Stars
| 21.5
| 42
| 0.790698
| 6
| 43
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.139535
| 43
| 1
| 43
| 43
| 0.918919
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
88bd89a5e24f2b959aa1b80148b5d80f4ba99061
| 11,710
|
py
|
Python
|
mmseg/models/decode_heads/vit_mla_la_head_convfuse_contrast.py
|
cocolord/mmsegmentation
|
45db7269d7aa40f8aac5ddaabf7e1b4b01353ca5
|
[
"Apache-2.0"
] | null | null | null |
mmseg/models/decode_heads/vit_mla_la_head_convfuse_contrast.py
|
cocolord/mmsegmentation
|
45db7269d7aa40f8aac5ddaabf7e1b4b01353ca5
|
[
"Apache-2.0"
] | null | null | null |
mmseg/models/decode_heads/vit_mla_la_head_convfuse_contrast.py
|
cocolord/mmsegmentation
|
45db7269d7aa40f8aac5ddaabf7e1b4b01353ca5
|
[
"Apache-2.0"
] | null | null | null |
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
import math
import numpy as np
from .helpers import load_pretrained
from .layers import DropPath, to_2tuple, trunc_normal_
from ..losses import accuracy
from ..builder import HEADS
from .decode_head import BaseDecodeHead
from ..backbones.vit import Block
from mmcv.cnn import build_norm_layer
from mmcv.runner import auto_fp16, force_fp32
from mmseg.ops import resize
from .vit_mla_head import MLAHead
class Layer_Att(nn.Module):
def __init__(self):
super(Layer_Att, self).__init__()
self.gamma = nn.Parameter(torch.randn(1,requires_grad=True))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : input feature maps(B, N, C, H, W)
returns :
out : attention map + input feature (B, NC, H, W)
"""
m_batchsize, N, C, height, width = x.size()
proj_query = x.contiguous().view(m_batchsize, N, -1)
proj_key = x.contiguous().view(m_batchsize, N, -1).permute(0, 2, 1)
energy = torch.bmm(proj_query, proj_key)#b x n x n
energy_new = torch.max(
energy, -1, keepdim=True)[0].expand_as(energy)-energy
attention = self.softmax(energy_new)
proj_value = x.contiguous().view(m_batchsize, N, -1)
out = torch.bmm(attention, proj_value)
out = out.contiguous().view(m_batchsize, N, C, height, width)
out = self.gamma*out + x
out = out.contiguous().view(m_batchsize, -1, height, width)
return out
class ProjectionHead(nn.Module):
def __init__(self, dim_in, proj_dim=256, proj='convmlp'):
super(ProjectionHead, self).__init__()
if proj == 'linear':
self.proj = nn.Conv2d(dim_in, proj_dim, kernel_size=1)
elif proj == 'convmlp':
self.proj = nn.Sequential(
nn.Conv2d(dim_in, dim_in, kernel_size=1),
nn.BatchNorm2d(dim_in),
nn.ReLU(),
nn.Conv2d(dim_in, proj_dim, kernel_size=1)
)
def forward(self, x):
return F.normalize(self.proj(x), p=2, dim=1)
@HEADS.register_module()
class VIT_MLALAConvFuseContrastHead(BaseDecodeHead):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=768, mla_channels=256, mlahead_channels=128,
norm_layer=nn.BatchNorm2d, norm_cfg=None, **kwargs):
super(VIT_MLALAConvFuseContrastHead, self).__init__(**kwargs)
self.img_size = img_size
self.norm_cfg = norm_cfg
self.mla_channels = mla_channels
self.BatchNorm = norm_layer
self.mlahead_channels = mlahead_channels
self.mlahead = MLAHead(mla_channels=self.mla_channels, mlahead_channels=self.mlahead_channels, norm_cfg=self.norm_cfg)
self.fuse1 = nn.Conv2d(4 * self.mlahead_channels, self.mlahead_channels, 3, 1, 1)
self.fuse2 = nn.Conv2d(2 * self.mlahead_channels, self.mlahead_channels, 3, 1, 1)
self.cls = nn.Conv2d(self.mlahead_channels, self.num_classes, 3, padding=1)
self.la_conv = Layer_Att()
self.proj = ProjectionHead(256)
def forward(self, inputs):
x = self.mlahead(inputs[0], inputs[1], inputs[2], inputs[3])
# print('----mlahead---x.size()',x.size())
# print('---input[4] ---',inputs[4].size())
b,c,h,w = x.size()
# print('---x before la---',x.size())
# print('---x before fuse1---',x.size())
x = self.fuse1(x)
# print('---x before cat---',x.size())
x = torch.cat((x,inputs[4]), dim=1)
# print('---x after cat---',x.size())
x = self.la_conv(x.view(b,2,-1,h,w))
x_proj = self.proj(x)
# print('---x after la---',x.size())
x = self.fuse2(x)
# y = x.cpu().detach().view(-1,x.size(1)).numpy()
# np.save("/home/juan/Donglusen/Workspace/mmsegmentation/tests/tsne_embedding.npy", y)
# print('---x after fuse2---',x.size())
x = self.cls(x)
# y = x.cpu().detach().view(-1,x.size(1)).numpy()
# np.save("/home/juan/Donglusen/Workspace/mmsegmentation/tests/test_label.npy", y)
x = F.interpolate(x, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
x_proj = F.interpolate(x_proj, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
return x, x_proj
def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
gt_semantic_seg (Tensor): Semantic segmentation masks
used if the architecture supports semantic segmentation task.
train_cfg (dict): The training config.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
seg_logits, x_embed = self.forward(inputs)
losses = self.losses(seg_logits, gt_semantic_seg, x_embed)
return losses
def forward_test(self, inputs, img_metas, test_cfg):
"""Forward function for testing.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
test_cfg (dict): The testing config.
Returns:
Tensor: Output segmentation map.
"""
return self.forward(inputs)[0]
def losses(self, seg_logit, seg_label,embed=None):
"""Compute segmentation loss."""
loss = dict()
seg_logit = resize(
input=seg_logit,
size=seg_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
if self.sampler is not None:
seg_weight = self.sampler.sample(seg_logit, seg_label)
else:
seg_weight = None
seg_label = seg_label.squeeze(1)
# print("seg_logit.shape", seg_logit.shape)
# print("seg_label.shape", seg_label.shape)
loss['loss_seg'] = self.loss_decode(
seg_logit,
seg_label,
embed )
loss['acc_seg'] = accuracy(seg_logit, seg_label)
return loss
@HEADS.register_module()
class VIT_MLALAConvFuseContrastMemHead(BaseDecodeHead):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=768, mla_channels=256, mlahead_channels=128,
norm_layer=nn.BatchNorm2d, norm_cfg=None, **kwargs):
super(VIT_MLALAConvFuseContrastMemHead, self).__init__(**kwargs)
self.img_size = img_size
self.norm_cfg = norm_cfg
self.mla_channels = mla_channels
self.BatchNorm = norm_layer
self.mlahead_channels = mlahead_channels
self.mlahead = MLAHead(mla_channels=self.mla_channels, mlahead_channels=self.mlahead_channels, norm_cfg=self.norm_cfg)
self.fuse1 = nn.Conv2d(4 * self.mlahead_channels, self.mlahead_channels, 3, 1, 1)
self.fuse2 = nn.Conv2d(2 * self.mlahead_channels, self.mlahead_channels, 3, 1, 1)
self.cls = nn.Conv2d(self.mlahead_channels, self.num_classes, 3, padding=1)
self.la_conv = Layer_Att()
self.proj = ProjectionHead(256)
def forward(self, inputs):
x = self.mlahead(inputs[0], inputs[1], inputs[2], inputs[3])
# print('----mlahead---x.size()',x.size())
# print('---input[4] ---',inputs[4].size())
b,c,h,w = x.size()
# print('---x before la---',x.size())
# print('---x before fuse1---',x.size())
x = self.fuse1(x)
# print('---x before cat---',x.size())
x = torch.cat((x,inputs[4]), dim=1)
# print('---x after cat---',x.size())
x = self.la_conv(x.view(b,2,-1,h,w))
x_proj = self.proj(x)
# print('---x after la---',x.size())
x = self.fuse2(x)
# y = x.cpu().detach().view(-1,x.size(1)).numpy()
# np.save("/home/juan/Donglusen/Workspace/mmsegmentation/tests/tsne_embedding.npy", y)
# print('---x after fuse2---',x.size())
x = self.cls(x)
# y = x.cpu().detach().view(-1,x.size(1)).numpy()
# np.save("/home/juan/Donglusen/Workspace/mmsegmentation/tests/test_label.npy", y)
x = F.interpolate(x, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
x_proj = F.interpolate(x_proj, size=self.img_size, mode='bilinear', align_corners=self.align_corners)
return x, x_proj, queue
def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
gt_semantic_seg (Tensor): Semantic segmentation masks
used if the architecture supports semantic segmentation task.
train_cfg (dict): The training config.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
seg_logits, x_embed = self.forward(inputs)
losses = self.losses(seg_logits, gt_semantic_seg, x_embed)
return losses
def forward_test(self, inputs, img_metas, test_cfg):
"""Forward function for testing.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
test_cfg (dict): The testing config.
Returns:
Tensor: Output segmentation map.
"""
return self.forward(inputs)[0]
def losses(self, seg_logit, seg_label,embed=None):
"""Compute segmentation loss."""
loss = dict()
seg_logit = resize(
input=seg_logit,
size=seg_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
if self.sampler is not None:
seg_weight = self.sampler.sample(seg_logit, seg_label)
else:
seg_weight = None
seg_label = seg_label.squeeze(1)
# print("seg_logit.shape", seg_logit.shape)
# print("seg_label.shape", seg_label.shape)
loss['loss_seg'] = self.loss_decode(
seg_logit,
seg_label,
embed )
loss['acc_seg'] = accuracy(seg_logit, seg_label)
return loss
| 43.857678
| 126
| 0.608454
| 1,550
| 11,710
| 4.420645
| 0.152903
| 0.018243
| 0.038821
| 0.030356
| 0.83596
| 0.818009
| 0.803999
| 0.792177
| 0.792177
| 0.783129
| 0
| 0.014607
| 0.263365
| 11,710
| 266
| 127
| 44.022556
| 0.779736
| 0.334927
| 0
| 0.653595
| 0
| 0
| 0.013487
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.091503
| false
| 0
| 0.104575
| 0.006536
| 0.287582
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
001dd7fc0b77e12164c0515c986e844689486b87
| 30
|
py
|
Python
|
tfcaps/util/__init__.py
|
ericup/tensorflow-capsules
|
e4856a9026bccc80aca553382e6fb6f8072746cd
|
[
"Apache-2.0"
] | 1
|
2019-03-02T21:35:47.000Z
|
2019-03-02T21:35:47.000Z
|
tfcaps/util/__init__.py
|
ericup/tensorflow-capsules
|
e4856a9026bccc80aca553382e6fb6f8072746cd
|
[
"Apache-2.0"
] | null | null | null |
tfcaps/util/__init__.py
|
ericup/tensorflow-capsules
|
e4856a9026bccc80aca553382e6fb6f8072746cd
|
[
"Apache-2.0"
] | null | null | null |
from .util import force_tuple
| 15
| 29
| 0.833333
| 5
| 30
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 30
| 1
| 30
| 30
| 0.923077
| 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
| 1
| 0
|
0
| 6
|
cc35e192638828716f012e8683a4cfdc5153f56f
| 137
|
py
|
Python
|
webapp/ml/knn/clustering.py
|
Kandy16/img-search-cnn
|
afc787e9284f1c8cb28beb2224bf076c04bb931f
|
[
"Apache-2.0"
] | 3
|
2017-12-04T12:31:18.000Z
|
2018-08-12T23:45:55.000Z
|
webapp/ml/knn/clustering.py
|
Kandy16/img-search-cnn
|
afc787e9284f1c8cb28beb2224bf076c04bb931f
|
[
"Apache-2.0"
] | 1
|
2018-02-03T13:59:55.000Z
|
2018-02-03T13:59:55.000Z
|
webapp/ml/knn/clustering.py
|
Kandy16/img-search-cnn
|
afc787e9284f1c8cb28beb2224bf076c04bb931f
|
[
"Apache-2.0"
] | 1
|
2019-03-12T10:43:06.000Z
|
2019-03-12T10:43:06.000Z
|
class Clustering:
def __init__(self):
self._cluster = {}
def _query(self, jpt):
return self._cluster[jpt]
| 19.571429
| 33
| 0.576642
| 15
| 137
| 4.8
| 0.6
| 0.305556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.313869
| 137
| 7
| 33
| 19.571429
| 0.765957
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
cc5b5c49ce956b04472b5aa40f49094669d70893
| 79
|
py
|
Python
|
test.py
|
shakirshakeelzargar/flask-heroku-sample
|
81edeaf51ea3d66806681e52a6469a46d4ced107
|
[
"MIT"
] | null | null | null |
test.py
|
shakirshakeelzargar/flask-heroku-sample
|
81edeaf51ea3d66806681e52a6469a46d4ced107
|
[
"MIT"
] | null | null | null |
test.py
|
shakirshakeelzargar/flask-heroku-sample
|
81edeaf51ea3d66806681e52a6469a46d4ced107
|
[
"MIT"
] | null | null | null |
import os
print(os.path.join(os.getcwd(),"Resume_Shakir_July2020_public.docx"))
| 39.5
| 69
| 0.810127
| 13
| 79
| 4.692308
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051948
| 0.025316
| 79
| 2
| 69
| 39.5
| 0.74026
| 0
| 0
| 0
| 0
| 0
| 0.425
| 0.425
| 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
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
cc758c3fcd6ab8fde3b1a1df43568406739aed0b
| 118
|
py
|
Python
|
app/translation/__init__.py
|
TartuNLP/translation-api
|
b3eefb2daa7ce734078f48e5c4483e8d71adce12
|
[
"MIT"
] | 2
|
2022-01-04T22:45:09.000Z
|
2022-01-28T22:33:17.000Z
|
app/translation/__init__.py
|
TartuNLP/translation-api
|
b3eefb2daa7ce734078f48e5c4483e8d71adce12
|
[
"MIT"
] | null | null | null |
app/translation/__init__.py
|
TartuNLP/translation-api
|
b3eefb2daa7ce734078f48e5c4483e8d71adce12
|
[
"MIT"
] | null | null | null |
from .v1_schemas import *
from .v2_schemas import *
from .v1_router import v1_router
from .v2_router import v2_router
| 23.6
| 32
| 0.813559
| 20
| 118
| 4.5
| 0.3
| 0.133333
| 0.377778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.135593
| 118
| 4
| 33
| 29.5
| 0.823529
| 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
| 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
| 6
|
ccb6da3095d17ea11205a8f587af7ca39065f1ce
| 36
|
py
|
Python
|
src/carrier/templates/__init__.py
|
Project-0/carrier
|
651df143b42a8cdbda23e7fde78e3bf187e6fb56
|
[
"MIT"
] | null | null | null |
src/carrier/templates/__init__.py
|
Project-0/carrier
|
651df143b42a8cdbda23e7fde78e3bf187e6fb56
|
[
"MIT"
] | null | null | null |
src/carrier/templates/__init__.py
|
Project-0/carrier
|
651df143b42a8cdbda23e7fde78e3bf187e6fb56
|
[
"MIT"
] | null | null | null |
from .atlanta_carrier import DOMAINS
| 36
| 36
| 0.888889
| 5
| 36
| 6.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 1
| 36
| 36
| 0.939394
| 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
| 1
| 0
|
0
| 6
|
ccc28e6ef850e1969cab038f02f5127737b38e6e
| 142
|
py
|
Python
|
tournamentcontrol/competition/signals/custom.py
|
goodtune/vitriolic
|
d135eecf7acbc229a872585ebafb8bbefca52df4
|
[
"BSD-3-Clause"
] | null | null | null |
tournamentcontrol/competition/signals/custom.py
|
goodtune/vitriolic
|
d135eecf7acbc229a872585ebafb8bbefca52df4
|
[
"BSD-3-Clause"
] | 28
|
2016-12-09T21:14:19.000Z
|
2022-01-11T07:17:16.000Z
|
tournamentcontrol/competition/signals/custom.py
|
goodtune/vitriolic
|
d135eecf7acbc229a872585ebafb8bbefca52df4
|
[
"BSD-3-Clause"
] | null | null | null |
from django.dispatch import Signal
match_forfeit = Signal(providing_args=["match", "team"])
score_updated = Signal(providing_args=["match"])
| 28.4
| 56
| 0.774648
| 18
| 142
| 5.888889
| 0.666667
| 0.283019
| 0.358491
| 0.45283
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084507
| 142
| 4
| 57
| 35.5
| 0.815385
| 0
| 0
| 0
| 0
| 0
| 0.098592
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
aed602876f2447b6ff9572f790d870bf1ac61c0f
| 3,980
|
py
|
Python
|
tests/functional/test_links.py
|
jounile/nollanet
|
7bea20934d3f5e09658a9d31c3b05c15416398a0
|
[
"MIT"
] | 3
|
2019-10-13T08:37:13.000Z
|
2020-02-16T12:24:11.000Z
|
tests/functional/test_links.py
|
jounile/nollanet
|
7bea20934d3f5e09658a9d31c3b05c15416398a0
|
[
"MIT"
] | 5
|
2019-11-13T15:56:52.000Z
|
2021-04-30T20:58:19.000Z
|
tests/functional/test_links.py
|
jounile/nollanet
|
7bea20934d3f5e09658a9d31c3b05c15416398a0
|
[
"MIT"
] | 1
|
2020-04-08T21:09:52.000Z
|
2020-04-08T21:09:52.000Z
|
import pytest
from requests import get
from urllib.parse import urljoin
def test_valid_new_link_page(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/links/new' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/new'))
assert response.status_code == 200
assert '<h1>New link</h1>' in response.text
def test_invalid_new_link_page(wait_for_api):
"""
GIVEN a user has not logged in
WHEN the '/links/new' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/new'))
assert response.status_code == 200
assert '<div class="flash">Please login first</div>' in response.text
def test_valid_link_categories_page(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/links/categories' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/categories'))
assert response.status_code == 200
assert '<h1>Link categories</h1>' in response.text
def test_invalid_link_categories_page(wait_for_api):
"""
GIVEN a user has not logged in
WHEN the '/links/categories' page is navigated to (GET)
THEN check user is redirected to homepage because not authorized and flash message is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/categories'))
assert response.status_code == 200
assert '<div class="flash">Please login first</div>' in response.text
def test_valid_new_link_category_page(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/links/category/new' page is navigated to (GET)
THEN check check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/category/new'))
assert response.status_code == 200
assert '<h1>New link category</h1>' in response.text
def test_invalid_new_link_category_page(wait_for_api):
"""
GIVEN a user has not logged
WHEN the '/links/category/new' page is navigated to (GET)
THEN check check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/links/category/new'))
assert response.status_code == 200
assert '<div class="flash">Please login first</div>' in response.text
def test_valid_new_link(wait_for_api, login_user):
"""
GIVEN a user has logged in
WHEN the '/links/new' page is navigated to (POST)
THEN check check the response is valid and flash message is correct
"""
new_link = dict(category=1, name='Test link', url='http://www.example.com')
request_session, api_url = wait_for_api
response = request_session.post(urljoin(api_url, '/links/new'), data=new_link, allow_redirects=True)
assert response.status_code == 200
assert '<div class="flash">New link created</div>' in response.text
def test_valid_new_link_category(wait_for_api, login_user):
"""
GIVEN a user has logged in
WHEN the '/links/category/new' page is navigated to (POST)
THEN check check the response is valid and flash message is correct
"""
new_category = dict(category_name='Test category')
request_session, api_url = wait_for_api
response = request_session.post(urljoin(api_url, '/links/category/new'), data=new_category, allow_redirects=True)
assert response.status_code == 200
assert '<div class="flash">New link category created</div>' in response.text
| 42.340426
| 117
| 0.723116
| 605
| 3,980
| 4.558678
| 0.119008
| 0.040609
| 0.058013
| 0.037708
| 0.91008
| 0.887962
| 0.871284
| 0.85678
| 0.85678
| 0.827049
| 0
| 0.009527
| 0.182412
| 3,980
| 93
| 118
| 42.795699
| 0.838045
| 0.310302
| 0
| 0.555556
| 0
| 0
| 0.176563
| 0
| 0
| 0
| 0
| 0
| 0.355556
| 1
| 0.177778
| false
| 0
| 0.066667
| 0
| 0.244444
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
aef181cea8346b1aaaf58513b0696a4f07e4b604
| 36
|
py
|
Python
|
flask/Runnerly/monolith/model/__init__.py
|
mir-dhaka/coding_playground
|
f20138e404c27e008b2902d4be2e9f9d4c25b11f
|
[
"Apache-2.0"
] | 2
|
2019-05-23T06:05:20.000Z
|
2019-11-17T01:35:45.000Z
|
flask/Runnerly/monolith/model/__init__.py
|
mir-dhaka/coding_playground
|
f20138e404c27e008b2902d4be2e9f9d4c25b11f
|
[
"Apache-2.0"
] | 2
|
2020-08-29T12:29:11.000Z
|
2020-08-29T12:30:14.000Z
|
flask/Runnerly/monolith/model/__init__.py
|
mir-dhaka/coding_playground
|
f20138e404c27e008b2902d4be2e9f9d4c25b11f
|
[
"Apache-2.0"
] | 2
|
2020-07-18T17:07:36.000Z
|
2021-12-06T02:21:15.000Z
|
from .database import User, db, Run
| 18
| 35
| 0.75
| 6
| 36
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 36
| 1
| 36
| 36
| 0.9
| 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
| 1
| 0
|
0
| 6
|
aef619ff83447998c678ab67a6743cb54b6d9f3d
| 117
|
py
|
Python
|
pytorch_toolkit/nncf/examples/common/models/classification/__init__.py
|
shixinlishixinli/openvino_training_extensions
|
cd34e5ceb8ae016e14b8b43b033f82bd5e11949e
|
[
"Apache-2.0"
] | null | null | null |
pytorch_toolkit/nncf/examples/common/models/classification/__init__.py
|
shixinlishixinli/openvino_training_extensions
|
cd34e5ceb8ae016e14b8b43b033f82bd5e11949e
|
[
"Apache-2.0"
] | null | null | null |
pytorch_toolkit/nncf/examples/common/models/classification/__init__.py
|
shixinlishixinli/openvino_training_extensions
|
cd34e5ceb8ae016e14b8b43b033f82bd5e11949e
|
[
"Apache-2.0"
] | null | null | null |
from .inceptionv3_cifar100 import *
from .mobilenetV2 import *
from .resnet_cifar import *
from .squeezenet import *
| 23.4
| 35
| 0.794872
| 14
| 117
| 6.5
| 0.571429
| 0.32967
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.049505
| 0.136752
| 117
| 4
| 36
| 29.25
| 0.851485
| 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
| 1
| 0
|
0
| 6
|
4e02345a8aaf82f889408ec74acd8e674cf82c50
| 477
|
py
|
Python
|
roboticstoolbox/models/ETS/__init__.py
|
tassos/robotics-toolbox-python
|
51aa8bbb3663a7c815f9880d538d61e7c85bc470
|
[
"MIT"
] | 749
|
2015-04-28T03:02:30.000Z
|
2022-03-31T06:55:12.000Z
|
roboticstoolbox/models/ETS/__init__.py
|
tassos/robotics-toolbox-python
|
51aa8bbb3663a7c815f9880d538d61e7c85bc470
|
[
"MIT"
] | 226
|
2015-04-16T22:22:55.000Z
|
2022-03-24T16:42:28.000Z
|
roboticstoolbox/models/ETS/__init__.py
|
tassos/robotics-toolbox-python
|
51aa8bbb3663a7c815f9880d538d61e7c85bc470
|
[
"MIT"
] | 251
|
2015-04-30T23:52:35.000Z
|
2022-03-27T13:32:16.000Z
|
from roboticstoolbox.models.ETS.Panda import Panda
from roboticstoolbox.models.ETS.Frankie import Frankie
from roboticstoolbox.models.ETS.Puma560 import Puma560
from roboticstoolbox.models.ETS.Planar_Y import Planar_Y
from roboticstoolbox.models.ETS.Planar2 import Planar2
from roboticstoolbox.models.ETS.GenericSeven import GenericSeven
from roboticstoolbox.models.ETS.Omni import Omni
__all__ = ["Panda", "Frankie", "Puma560", "Planar_Y", "Planar2", "GenericSeven", "Omni"]
| 47.7
| 88
| 0.828092
| 60
| 477
| 6.466667
| 0.233333
| 0.342784
| 0.451031
| 0.505155
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027335
| 0.079665
| 477
| 9
| 89
| 53
| 0.856492
| 0
| 0
| 0
| 0
| 0
| 0.104822
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.875
| 0
| 0.875
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9dc3c3689242b366e0c9e2575faafbc3482fe7df
| 157
|
py
|
Python
|
digistore/digistore/doctype/plan/test_plan.py
|
yrestom/digistore
|
374e7fcd1751590de476bbc09d4637a3f2dc0c70
|
[
"MIT"
] | 19
|
2021-11-14T11:47:46.000Z
|
2022-02-09T02:51:44.000Z
|
digistore/digistore/doctype/plan/test_plan.py
|
yrestom/digistore
|
374e7fcd1751590de476bbc09d4637a3f2dc0c70
|
[
"MIT"
] | 1
|
2021-12-10T17:38:49.000Z
|
2021-12-19T18:23:36.000Z
|
digistore/digistore/doctype/plan/test_plan.py
|
yrestom/digistore
|
374e7fcd1751590de476bbc09d4637a3f2dc0c70
|
[
"MIT"
] | 6
|
2021-11-15T04:25:25.000Z
|
2022-02-16T03:30:26.000Z
|
# Copyright (c) 2021, Mohammad Hussain Nagaria and contributors
# See license.txt
# import frappe
import unittest
class TestPlan(unittest.TestCase):
pass
| 17.444444
| 63
| 0.783439
| 20
| 157
| 6.15
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029851
| 0.146497
| 157
| 8
| 64
| 19.625
| 0.88806
| 0.579618
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
d1b20fdbdfeb932d279ee052138ecd5fec57e2c3
| 47
|
py
|
Python
|
flask_sk_testing/__init__.py
|
saktika/flask_sk_testing
|
35ad97ffee4b6d732d10bd4cddadd49b8d05f53c
|
[
"MIT"
] | null | null | null |
flask_sk_testing/__init__.py
|
saktika/flask_sk_testing
|
35ad97ffee4b6d732d10bd4cddadd49b8d05f53c
|
[
"MIT"
] | null | null | null |
flask_sk_testing/__init__.py
|
saktika/flask_sk_testing
|
35ad97ffee4b6d732d10bd4cddadd49b8d05f53c
|
[
"MIT"
] | null | null | null |
from flask_sk_testing.sk_string import SkString
| 47
| 47
| 0.914894
| 8
| 47
| 5
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06383
| 47
| 1
| 47
| 47
| 0.909091
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 1
| 0
| true
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| 1
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d1e14082e91c3b06e3d75bcdf76fc6d00c20041b
| 40
|
py
|
Python
|
geo_loc/__init__.py
|
sha31dev/geo_loc
|
2a7b86c47aeb03f4059c793b693ec1bd49502276
|
[
"MIT"
] | null | null | null |
geo_loc/__init__.py
|
sha31dev/geo_loc
|
2a7b86c47aeb03f4059c793b693ec1bd49502276
|
[
"MIT"
] | null | null | null |
geo_loc/__init__.py
|
sha31dev/geo_loc
|
2a7b86c47aeb03f4059c793b693ec1bd49502276
|
[
"MIT"
] | null | null | null |
from geo_loc.src.maxmind import Maxmind
| 20
| 39
| 0.85
| 7
| 40
| 4.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 40
| 1
| 40
| 40
| 0.916667
| 0
| 0
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| 0
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| 0
| 0
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| 0
| 0
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| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d1e732c2fc1fe5c8d5defe883b9a3fc79fc040a1
| 47
|
py
|
Python
|
garnet_redis/__init__.py
|
ukinti/garnet-redis-storage
|
eb412cc91edfba3f468dc340cc6b4b0ea0b60960
|
[
"MIT"
] | null | null | null |
garnet_redis/__init__.py
|
ukinti/garnet-redis-storage
|
eb412cc91edfba3f468dc340cc6b4b0ea0b60960
|
[
"MIT"
] | null | null | null |
garnet_redis/__init__.py
|
ukinti/garnet-redis-storage
|
eb412cc91edfba3f468dc340cc6b4b0ea0b60960
|
[
"MIT"
] | null | null | null |
from garnet_redis._storage import RedisStorage
| 23.5
| 46
| 0.893617
| 6
| 47
| 6.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 47
| 1
| 47
| 47
| 0.930233
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
| 1
| 0
| null | 0
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| 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
| 6
|
060153ef84c8610221f2a7f942344021755bfe92
| 365
|
py
|
Python
|
Numbers genenne.py
|
SixLeopard/Python-Random-NUmber-Generator
|
7c66d97f9a4332f26228685f575c170f9bd6e191
|
[
"MIT"
] | 2
|
2019-03-04T02:55:02.000Z
|
2019-05-03T01:47:03.000Z
|
Numbers genenne.py
|
SixLeopard/Python-Random-NUmber-Generator
|
7c66d97f9a4332f26228685f575c170f9bd6e191
|
[
"MIT"
] | null | null | null |
Numbers genenne.py
|
SixLeopard/Python-Random-NUmber-Generator
|
7c66d97f9a4332f26228685f575c170f9bd6e191
|
[
"MIT"
] | 2
|
2021-02-08T16:34:48.000Z
|
2021-02-22T21:38:07.000Z
|
import random
yes = "yes"
f = open('Numbes.txt','w')
while yes == "yes":
lol = str(random.randint(100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000,999999999999999999999999999999999999999999999999999999999990000000000000000000000000000000000000000000000000000000000))
f.write(lol)
| 45.625
| 267
| 0.838356
| 21
| 365
| 14.571429
| 0.714286
| 0.039216
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.702703
| 0.087671
| 365
| 7
| 268
| 52.142857
| 0.216216
| 0
| 0
| 0
| 0
| 0
| 0.047619
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 0.166667
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ae47c873fc9fd66e9657f0582b454df9ab66438b
| 48
|
py
|
Python
|
core/gateways/__init__.py
|
jklemm/py-dnd
|
9f20a0e4f484297e80170cb529c0af8da0cf1032
|
[
"MIT"
] | 9
|
2015-04-16T20:13:20.000Z
|
2021-11-29T18:56:16.000Z
|
core/gateways/__init__.py
|
jklemm/py-dnd
|
9f20a0e4f484297e80170cb529c0af8da0cf1032
|
[
"MIT"
] | 1
|
2015-04-16T20:12:43.000Z
|
2015-04-18T12:25:48.000Z
|
core/gateways/__init__.py
|
jklemm/py-dnd
|
9f20a0e4f484297e80170cb529c0af8da0cf1032
|
[
"MIT"
] | 2
|
2021-11-27T23:49:52.000Z
|
2021-11-29T18:56:19.000Z
|
from .character_gateway import CharacterGateway
| 24
| 47
| 0.895833
| 5
| 48
| 8.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 48
| 1
| 48
| 48
| 0.954545
| 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
| 1
| 0
|
0
| 6
|
ae57ff5f2d17ae3cd31713d678581149a7e5fd65
| 48
|
py
|
Python
|
file_validator/exception/__init__.py
|
sujavarghese/data-validator
|
e0c5d94da797cb43b17d6ee193d337cbcb602f49
|
[
"MIT"
] | null | null | null |
file_validator/exception/__init__.py
|
sujavarghese/data-validator
|
e0c5d94da797cb43b17d6ee193d337cbcb602f49
|
[
"MIT"
] | null | null | null |
file_validator/exception/__init__.py
|
sujavarghese/data-validator
|
e0c5d94da797cb43b17d6ee193d337cbcb602f49
|
[
"MIT"
] | null | null | null |
from file_validator.exception.exception import *
| 48
| 48
| 0.875
| 6
| 48
| 6.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 48
| 1
| 48
| 48
| 0.911111
| 0
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 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
| 1
| 0
|
0
| 6
|
883b6f2cfca48e98c869df3f379015de7da28c25
| 854
|
py
|
Python
|
ws/build/gscam/catkin_generated/pkg.develspace.context.pc.py
|
Prasanna-icefire/redtail
|
780886023831a5bb4108b6a2885385c3df314e18
|
[
"BSD-3-Clause"
] | null | null | null |
ws/build/gscam/catkin_generated/pkg.develspace.context.pc.py
|
Prasanna-icefire/redtail
|
780886023831a5bb4108b6a2885385c3df314e18
|
[
"BSD-3-Clause"
] | null | null | null |
ws/build/gscam/catkin_generated/pkg.develspace.context.pc.py
|
Prasanna-icefire/redtail
|
780886023831a5bb4108b6a2885385c3df314e18
|
[
"BSD-3-Clause"
] | null | null | null |
# generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/home/icefire/ws/src/gscam/include;/usr/include/gstreamer-1.0;/usr/include/glib-2.0;/usr/lib/aarch64-linux-gnu/glib-2.0/include".split(';') if "/home/icefire/ws/src/gscam/include;/usr/include/gstreamer-1.0;/usr/include/glib-2.0;/usr/lib/aarch64-linux-gnu/glib-2.0/include" != "" else []
PROJECT_CATKIN_DEPENDS = "roscpp;nodelet;image_transport;sensor_msgs;camera_calibration_parsers;camera_info_manager".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lgscam;-lgstapp-1.0;-lgstbase-1.0;-lgstreamer-1.0;-lgobject-2.0;-lglib-2.0".split(';') if "-lgscam;-lgstapp-1.0;-lgstbase-1.0;-lgstreamer-1.0;-lgobject-2.0;-lglib-2.0" != "" else []
PROJECT_NAME = "gscam"
PROJECT_SPACE_DIR = "/home/icefire/ws/devel"
PROJECT_VERSION = "1.0.1"
| 94.888889
| 321
| 0.750585
| 141
| 854
| 4.390071
| 0.41844
| 0.029079
| 0.038772
| 0.051696
| 0.494346
| 0.494346
| 0.494346
| 0.494346
| 0.494346
| 0.494346
| 0
| 0.047853
| 0.045667
| 854
| 8
| 322
| 106.75
| 0.711656
| 0.063232
| 0
| 0
| 1
| 0.571429
| 0.662907
| 0.645363
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
88a7022476899e22d04e2d1ebf446f2827af8d2e
| 978
|
py
|
Python
|
tests/test_sekolah.py
|
hexatester/dapodik
|
d89c0fb899c89e866527f6b7b57f741abd6444ea
|
[
"MIT"
] | 4
|
2021-02-01T15:19:35.000Z
|
2022-01-26T02:47:21.000Z
|
tests/test_sekolah.py
|
hexatester/dapodik
|
d89c0fb899c89e866527f6b7b57f741abd6444ea
|
[
"MIT"
] | 3
|
2020-01-08T17:07:15.000Z
|
2020-01-08T18:05:12.000Z
|
tests/test_sekolah.py
|
hexatester/dapodik
|
d89c0fb899c89e866527f6b7b57f741abd6444ea
|
[
"MIT"
] | 2
|
2021-08-04T13:48:08.000Z
|
2021-12-25T02:36:49.000Z
|
import attr
from dapodik.base import BaseDapodik
from dapodik.sekolah import AkreditasiSp
from dapodik.sekolah import BlockGrant
from dapodik.sekolah import JurusanSp
from dapodik.sekolah import Kepanitiaan
from dapodik.sekolah import ProgramInklusi
from dapodik.sekolah import Sanitasi
from dapodik.sekolah import SekolahLongitudinal
from dapodik.sekolah import SekolahPaud
from dapodik.sekolah import Sekolah
from dapodik.sekolah import Semester
from dapodik.sekolah import Yayasan
from dapodik.sekolah import BaseSekolah
def test_base_sekolah():
assert issubclass(BaseSekolah, BaseDapodik)
def test_member():
assert attr.has(AkreditasiSp)
assert attr.has(BlockGrant)
assert attr.has(JurusanSp)
assert attr.has(Kepanitiaan)
assert attr.has(ProgramInklusi)
assert attr.has(Sanitasi)
assert attr.has(SekolahLongitudinal)
assert attr.has(SekolahPaud)
assert attr.has(Sekolah)
assert attr.has(Semester)
assert attr.has(Yayasan)
| 29.636364
| 47
| 0.805726
| 122
| 978
| 6.434426
| 0.196721
| 0.182166
| 0.275159
| 0.366879
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.138037
| 978
| 32
| 48
| 30.5625
| 0.931198
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| 0
| 0
| 0.428571
| 1
| 0.071429
| true
| 0
| 0.5
| 0
| 0.571429
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ee05b3e5a06558416f745723b8b6fef22767769f
| 3,233
|
py
|
Python
|
filter/xarray/butter.py
|
wy2136/wython
|
0eaa9db335d57052806ae956afe6a34705407628
|
[
"MIT"
] | 1
|
2022-03-21T21:24:40.000Z
|
2022-03-21T21:24:40.000Z
|
filter/xarray/butter.py
|
wy2136/wython
|
0eaa9db335d57052806ae956afe6a34705407628
|
[
"MIT"
] | null | null | null |
filter/xarray/butter.py
|
wy2136/wython
|
0eaa9db335d57052806ae956afe6a34705407628
|
[
"MIT"
] | null | null | null |
'''
Butterworth filter: lowpass, highpass and bandpass.
Author: Wenchang Yang (wenchang@princeton.edu)
'''
import numpy as np
# import matplotlib.pyplot as plt
import xarray as xr
from ..butter import lowpass as lp
from ..butter import highpass as hp
from ..butter import bandpass as bp
def lowpass(da, cutoff=0.25, order=2, dim=None, fs=1.0):
'''Butterworth lowpass filter for xarray.DataArray input data.
*Parameters*:
da: xarray.DataArray.
cutoff: float, low-frequency cutoff, default=0.25 (unit is sample freqency).
order: int, default=2.
dim: str, default=None (the first dimension).
fs: number, sample freqency, default=1.0
*Return*:
DataArray, lowpassed da.'''
if dim is None:
dim = da.dims[0]
axis = 0
else:
axis = da.dims.index(dim)
# numpy version lowpass
Y = lp(da.data, cutoff=cutoff, order=order, axis=axis, fs=fs)
# wrap the result into a DataArray
dims = da.dims
coords = da.coords
attrs = da.attrs.copy()
s = 'cutoff={}, order={}, dim={}, fs={}'.format(cutoff, order, dim, fs)
attrs['Forward-backward Butterworth lowpass'] = s
return xr.DataArray(Y, dims=dims, coords=coords, attrs=attrs)
def highpass(da, cutoff=0.25, order=2, dim=None, fs=1.0):
'''Butterworth highpass filter for xarray.DataArray input data.
*Parameters*:
da: xarray.DataArray.
cutoff: float, high-frequency cutoff, default=0.25 (unit is sample freqency).
order: int, default=2.
dim: str, default=None (the first dimension).
fs: number, sample freqency, default=1.0
*Return*:
DataArray, highpassed da.'''
if dim is None:
dim = da.dims[0]
axis = 0
else:
axis = da.dims.index(dim)
# numpy version lowpass
Y = hp(da.data, cutoff=cutoff, order=order, axis=axis, fs=fs)
# wrap the result into a DataArray
dims = da.dims
coords = da.coords
attrs = da.attrs.copy()
s = 'cutoff={}, order={}, dim="{}", fs={}'.format(cutoff, order, dim, fs)
attrs['Forward-backward Butterworth highpass'] = s
return xr.DataArray(Y, dims=dims, coords=coords, attrs=attrs)
def bandpass(da, cutoff=(0.125, 0.375), order=2, dim=None, fs=1.0):
'''Butterworth bandpass filter for xarray.DataArray input data.
*Parameters*:
da: xarray.DataArray.
cutoff: (float, float), low/high-frequency cut, default=(0.125, 0.375) (unit is sample freqency).
order: int, default=2.
dim: str, default=None (the first dimension).
fs: number, sample freqency, default=1.0
*Return*:
DataArray, bandpassed da.'''
if dim is None:
dim = da.dims[0]
axis = 0
else:
axis = da.dims.index(dim)
# numpy version lowpass
Y = bp(da.data, cutoff=cutoff, order=order,
axis=axis, fs=fs)
# wrap the result into a DataArray
dims = da.dims
coords = da.coords
attrs = da.attrs.copy()
s = 'lowcut={}, highcut={}, order={}, dim="{}", fs={}'.format(
cutoff[0], cutoff[1], order, dim, fs)
attrs['Forward-backward Butterworth bandpass'] = s
return xr.DataArray(Y, dims=dims, coords=coords, attrs=attrs)
| 30.5
| 105
| 0.623879
| 449
| 3,233
| 4.492205
| 0.184855
| 0.026772
| 0.029747
| 0.03173
| 0.795736
| 0.784829
| 0.784829
| 0.764502
| 0.75062
| 0.75062
| 0
| 0.022005
| 0.240953
| 3,233
| 105
| 106
| 30.790476
| 0.799919
| 0.419115
| 0
| 0.586957
| 0
| 0
| 0.130063
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.065217
| false
| 0.195652
| 0.108696
| 0
| 0.23913
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
ee2781767f8255ebdc34aa2953a81a6fefbee2db
| 110
|
py
|
Python
|
gsheets2pandas/__init__.py
|
lucashadfield/gsheets2pandas
|
b2a653bf87a4c0a4851014b8e1cf951ff015b2f5
|
[
"MIT"
] | 2
|
2019-07-06T05:03:02.000Z
|
2019-12-11T19:18:38.000Z
|
gsheets2pandas/__init__.py
|
lucashadfield/gsheets2pandas
|
b2a653bf87a4c0a4851014b8e1cf951ff015b2f5
|
[
"MIT"
] | null | null | null |
gsheets2pandas/__init__.py
|
lucashadfield/gsheets2pandas
|
b2a653bf87a4c0a4851014b8e1cf951ff015b2f5
|
[
"MIT"
] | null | null | null |
from .reader import GSheetReader, read_gsheet
from .reader import CLIENT_SECRET_PATH, CLIENT_CREDENTIALS_PATH
| 36.666667
| 63
| 0.872727
| 15
| 110
| 6.066667
| 0.666667
| 0.21978
| 0.351648
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 110
| 2
| 64
| 55
| 0.91
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
ee59e1078cc5da33a3001bcfecc73fc210506e28
| 3,007
|
py
|
Python
|
tests/test_miniparser.py
|
wbwqq/miniparser
|
807c2b8b6f208d65af9b0b858f82e690e8e0990a
|
[
"MIT"
] | 3
|
2022-01-22T13:26:23.000Z
|
2022-02-15T10:40:13.000Z
|
tests/test_miniparser.py
|
wbwqq/miniparser
|
807c2b8b6f208d65af9b0b858f82e690e8e0990a
|
[
"MIT"
] | null | null | null |
tests/test_miniparser.py
|
wbwqq/miniparser
|
807c2b8b6f208d65af9b0b858f82e690e8e0990a
|
[
"MIT"
] | null | null | null |
import unittest
import miniparser
class TestMiniParser(unittest.TestCase):
def test_return_all_args(self):
miniparser.add_command('v', view_args, nargs=-1, help='return all args')
sys_argv_simulation = ['dummy_filename.py', 'v', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg', 'second_arg')
self.assertEqual(actual, expected)
def test_return_first_arg(self):
miniparser.add_command('v', view_args, nargs=1, help='return first arg')
sys_argv_simulation = ['dummy_filename.py', 'v', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg',)
self.assertEqual(actual, expected)
def test_return_two_args(self):
miniparser.add_command('v', view_args, nargs=2, help='return two args')
sys_argv_simulation = ['dummy_filename.py', 'v', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg', 'second_arg')
self.assertEqual(actual, expected)
def test_no_arg(self):
miniparser.add_command('v', view_args, nargs=0, help='return first arg')
sys_argv_simulation = ['dummy_filename.py', 'v', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ()
self.assertEqual(actual, expected)
def test_return_all_args_empty_cmd(self):
miniparser.add_command('', view_args, nargs=-1, help='return all args')
sys_argv_simulation = ['dummy_filename.py', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg', 'second_arg')
self.assertEqual(actual, expected)
def test_return_first_arg_empty_cmd(self):
miniparser.add_command('', view_args, nargs=1, help='return first arg')
sys_argv_simulation = ['dummy_filename.py', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg',)
self.assertEqual(actual, expected)
def test_return_two_args_empty_cmd(self):
miniparser.add_command('', view_args, nargs=2, help='return two args')
sys_argv_simulation = ['dummy_filename.py', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ('first_arg', 'second_arg')
self.assertEqual(actual, expected)
def test_no_arg_empty_cmd(self):
miniparser.add_command('', view_args, nargs=0, help='return first arg')
sys_argv_simulation = ['dummy_filename.py', 'first_arg', 'second_arg']
actual = miniparser.parser(sys_argv_simulation)
expected = ()
self.assertEqual(actual, expected)
# Dummy test functions
def view_args(*something):
return something
def no_arg():
return 'no arg'
if __name__ == '__main__':
unittest.main()
| 42.352113
| 84
| 0.663452
| 366
| 3,007
| 5.117486
| 0.117486
| 0.085424
| 0.145222
| 0.108916
| 0.915109
| 0.904965
| 0.904965
| 0.898025
| 0.898025
| 0.869194
| 0
| 0.003388
| 0.214832
| 3,007
| 71
| 85
| 42.352113
| 0.78992
| 0.006651
| 0
| 0.561404
| 0
| 0
| 0.18107
| 0
| 0
| 0
| 0
| 0
| 0.140351
| 1
| 0.175439
| false
| 0
| 0.035088
| 0.035088
| 0.263158
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
ee5e3d81fc0cf35c9e7765ea1097c3a5145e8779
| 162
|
py
|
Python
|
msgclient/views.py
|
InstanteSports/ies-msgclient
|
cdc7abdf0a34f41c0ac6b9af4642fb403ff29e40
|
[
"MIT"
] | null | null | null |
msgclient/views.py
|
InstanteSports/ies-msgclient
|
cdc7abdf0a34f41c0ac6b9af4642fb403ff29e40
|
[
"MIT"
] | null | null | null |
msgclient/views.py
|
InstanteSports/ies-msgclient
|
cdc7abdf0a34f41c0ac6b9af4642fb403ff29e40
|
[
"MIT"
] | null | null | null |
from rest_framework.views import APIView
from rest_framework.generics import ListAPIView
from rest_framework.response import Response
# Create your views here.
| 23.142857
| 47
| 0.851852
| 22
| 162
| 6.136364
| 0.545455
| 0.177778
| 0.377778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117284
| 162
| 6
| 48
| 27
| 0.944056
| 0.141975
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c9e74c57acb5c344b725e3967df9644a397ed7a8
| 595
|
py
|
Python
|
Isogrid Builder.py
|
DDecoene/Isogrid-Builder
|
5b86955b7a671f3cb302481e3de1d36af151b0f9
|
[
"MIT"
] | null | null | null |
Isogrid Builder.py
|
DDecoene/Isogrid-Builder
|
5b86955b7a671f3cb302481e3de1d36af151b0f9
|
[
"MIT"
] | null | null | null |
Isogrid Builder.py
|
DDecoene/Isogrid-Builder
|
5b86955b7a671f3cb302481e3de1d36af151b0f9
|
[
"MIT"
] | null | null | null |
#Author-Dennis Decoene
#Description-
import adsk.core, adsk.fusion, adsk.cam, traceback
def run(context):
ui = None
try:
app = adsk.core.Application.get()
ui = app.userInterface
ui.messageBox('Hello addin')
except:
if ui:
ui.messageBox('Failed:\n{}'.format(traceback.format_exc()))
def stop(context):
ui = None
try:
app = adsk.core.Application.get()
ui = app.userInterface
ui.messageBox('Stop addin')
except:
if ui:
ui.messageBox('Failed:\n{}'.format(traceback.format_exc()))
| 22.037037
| 71
| 0.591597
| 70
| 595
| 5
| 0.428571
| 0.137143
| 0.074286
| 0.091429
| 0.737143
| 0.737143
| 0.737143
| 0.737143
| 0.737143
| 0.737143
| 0
| 0
| 0.27395
| 595
| 26
| 72
| 22.884615
| 0.810185
| 0.055462
| 0
| 0.736842
| 0
| 0
| 0.076786
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105263
| false
| 0
| 0.052632
| 0
| 0.157895
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
4e4926d345bcd69c7137621e72e2ba0c8cf20679
| 195
|
py
|
Python
|
fault/tester/__init__.py
|
makaimann/fault
|
8c805415f398e64971d18fbd3014bc0b59fb38b8
|
[
"BSD-3-Clause"
] | 31
|
2018-07-16T15:03:14.000Z
|
2022-03-10T08:36:09.000Z
|
fault/tester/__init__.py
|
makaimann/fault
|
8c805415f398e64971d18fbd3014bc0b59fb38b8
|
[
"BSD-3-Clause"
] | 216
|
2018-07-18T20:00:34.000Z
|
2021-10-05T17:40:47.000Z
|
fault/tester/__init__.py
|
makaimann/fault
|
8c805415f398e64971d18fbd3014bc0b59fb38b8
|
[
"BSD-3-Clause"
] | 10
|
2019-02-17T00:56:58.000Z
|
2021-11-05T13:31:37.000Z
|
from .base import TesterBase
from .staged_tester import Tester
from .symbolic_tester import SymbolicTester
from .interactive_tester import PythonTester
from .synchronous import SynchronousTester
| 32.5
| 44
| 0.871795
| 23
| 195
| 7.26087
| 0.521739
| 0.215569
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 195
| 5
| 45
| 39
| 0.954286
| 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
| 1
| 0
|
0
| 6
|
4e82bf43541b2ca9bd1abde450130ee02f3d6112
| 2,835
|
py
|
Python
|
app/tests/test_utils.py
|
mimipeshy/Politicov2
|
c2bcdcc9bfbec75e7701aa1bfdb51055287e59e8
|
[
"MIT"
] | 1
|
2020-05-29T23:04:07.000Z
|
2020-05-29T23:04:07.000Z
|
app/tests/test_utils.py
|
mimipeshy/Politicov2
|
c2bcdcc9bfbec75e7701aa1bfdb51055287e59e8
|
[
"MIT"
] | null | null | null |
app/tests/test_utils.py
|
mimipeshy/Politicov2
|
c2bcdcc9bfbec75e7701aa1bfdb51055287e59e8
|
[
"MIT"
] | 1
|
2019-02-21T13:35:49.000Z
|
2019-02-21T13:35:49.000Z
|
import json
import unittest
from app.tests.base_test import BaseTests
class ValidationTests(BaseTests):
def test_empty_strings(self):
"""Tests API can get all offices"""
token = self.get_token()
response = self.client.post('/api/v2/party', data=self.empty_party_name,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
def test_empty_logo_string(self):
token = self.get_token()
response = self.client.post('/api/v2/party', data=self.empty_logoUrl,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
def test_empty_hqaddress_string(self):
token = self.get_token()
response = self.client.post('/api/v2/party', data=self.empty_hqAddress,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
# def test_short_name_length(self):
# token = self.get_token()
# response = self.client.post('/api/v2/party', data=self.length_name,
# headers=dict(Authorization="Bearer " + token),
# content_type='application/json')
# self.assertEqual(response.status_code, 400)
# response = self.client.post('/api/v2/party', data=self.length_hqAddress,
# headers=dict(Authorization="Bearer " + token),
# content_type='application/json')
# self.assertEqual(response.status_code, 400)
def test_validate_logo(self):
token = self.get_token()
response = self.client.post('/api/v2/party', data=self.missing_http,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
response = self.client.post('/api/v2/party', data=self.missing_body,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
response = self.client.post('/api/v2/party', data=self.missing_path,
headers=dict(Authorization="Bearer " + token),
content_type='application/json')
self.assertEqual(response.status_code, 400)
if __name__ == '__main__':
unittest.main()
| 48.050847
| 84
| 0.562257
| 282
| 2,835
| 5.468085
| 0.184397
| 0.062257
| 0.093385
| 0.114137
| 0.85668
| 0.85668
| 0.85668
| 0.85668
| 0.85668
| 0.85668
| 0
| 0.016789
| 0.32769
| 2,835
| 58
| 85
| 48.87931
| 0.792235
| 0.220811
| 0
| 0.578947
| 0
| 0
| 0.102097
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 1
| 0.105263
| false
| 0
| 0.078947
| 0
| 0.210526
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
4e88c0638f2480af2c4e1162f3804b724247e58f
| 35
|
py
|
Python
|
elliot/recommender/content_based/VSM/__init__.py
|
gategill/elliot
|
113763ba6d595976e14ead2e3d460d9705cd882e
|
[
"Apache-2.0"
] | 175
|
2021-03-04T15:46:25.000Z
|
2022-03-31T05:56:58.000Z
|
elliot/recommender/content_based/VSM/__init__.py
|
gategill/elliot
|
113763ba6d595976e14ead2e3d460d9705cd882e
|
[
"Apache-2.0"
] | 15
|
2021-03-06T17:53:56.000Z
|
2022-03-24T17:02:07.000Z
|
elliot/recommender/content_based/VSM/__init__.py
|
gategill/elliot
|
113763ba6d595976e14ead2e3d460d9705cd882e
|
[
"Apache-2.0"
] | 39
|
2021-03-04T15:46:26.000Z
|
2022-03-09T15:37:12.000Z
|
from .vector_space_model import VSM
| 35
| 35
| 0.885714
| 6
| 35
| 4.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 35
| 1
| 35
| 35
| 0.90625
| 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
| 1
| 0
|
0
| 6
|
4e8aff20b91a9eec381eda4a2525647cf72938d2
| 43
|
py
|
Python
|
app/train/__init__.py
|
toshiks/number_recognizer
|
5dee9da830d1a790578eceb923ebf7ffc776339b
|
[
"MIT"
] | null | null | null |
app/train/__init__.py
|
toshiks/number_recognizer
|
5dee9da830d1a790578eceb923ebf7ffc776339b
|
[
"MIT"
] | null | null | null |
app/train/__init__.py
|
toshiks/number_recognizer
|
5dee9da830d1a790578eceb923ebf7ffc776339b
|
[
"MIT"
] | null | null | null |
from .train_recognition import train_model
| 21.5
| 42
| 0.883721
| 6
| 43
| 6
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 43
| 1
| 43
| 43
| 0.923077
| 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
| 1
| 0
|
0
| 6
|
4ec1f876acefbc39d3ca1821c2aeb36f3575293d
| 31
|
py
|
Python
|
plotcollector/__init__.py
|
dacts23/plotcollector
|
984273e8220b5df829dfb2578e0001be11b4adf9
|
[
"MIT"
] | null | null | null |
plotcollector/__init__.py
|
dacts23/plotcollector
|
984273e8220b5df829dfb2578e0001be11b4adf9
|
[
"MIT"
] | null | null | null |
plotcollector/__init__.py
|
dacts23/plotcollector
|
984273e8220b5df829dfb2578e0001be11b4adf9
|
[
"MIT"
] | null | null | null |
from .plotcollector import view
| 31
| 31
| 0.870968
| 4
| 31
| 6.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096774
| 31
| 1
| 31
| 31
| 0.964286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
094b163883863336ea5164328de59ee01c3b730f
| 22,977
|
py
|
Python
|
main.py
|
Samurai6/oracleManager
|
d601f7df0b1636bb69532af498aadf5cfab2adda
|
[
"MIT"
] | null | null | null |
main.py
|
Samurai6/oracleManager
|
d601f7df0b1636bb69532af498aadf5cfab2adda
|
[
"MIT"
] | null | null | null |
main.py
|
Samurai6/oracleManager
|
d601f7df0b1636bb69532af498aadf5cfab2adda
|
[
"MIT"
] | null | null | null |
import pywaves as py
import pandas as pd
py.setNode('https://privatenode.blackturtle.eu', 'TN', 'L')
py.setMatcher('https://privatematcher.blackturtle.eu')
py.DEFAULT_BASE_FEE = 2000000
address = py.Address(
seed="inputyourseedhere")
oracle = py.Oracle(
seed="inputyourseedhere")
##Black liating scams
SCAM_URL = "https://raw.githubusercontent.com/BlackTurtle123/TN-community/master/scam-v2.csv"
wine_csv_url = SCAM_URL
wine_data = pd.read_csv(wine_csv_url, header=None, nrows=None)
for value in wine_data.values:
print(value[0])
oracle.storeData('scam_<'+value[0]+'>','boolean',True,minimalFee=2000000)
##Add basic data provider info
oracle.storeData('data_provider_version', 'integer', 0, minimalFee=2000000)
oracle.storeData('data_provider_name', 'string', 'Turtle Network', minimalFee=2000000)
oracle.storeData('data_provider_email', 'string', 'support@turtlenetwork.eu', minimalFee=2000000)
oracle.storeData('data_provider_lang_list', 'string', 'en', minimalFee=2000000)
oracle.storeData('data_provider_link', 'string', 'https://turtlenetwork.eu', minimalFee=2000000)
oracle.storeData('data_provider_description_<en>', 'string', 'Description https://turtlenetwork.eu', minimalFee=2000000)
oracle.storeData('data_provider_logo_meta', 'string', 'data:image/svg+xml;base64', minimalFee=2000000)
oracle.storeData('data_provider_logo', 'string',
"PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHhtbG5zOnhsaW5rPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5L3hsaW5rIiB3aWR0aD0iMzAiIGhlaWdodD0iMzAiIHZpZXdCb3g9IjAgMCAzMCAzMCI+CiAgPGRlZnM+CiAgICA8Y2xpcFBhdGggaWQ9ImNsaXAtY3VzdG9tXzEiPgogICAgICA8cmVjdCB3aWR0aD0iMzAiIGhlaWdodD0iMzAiLz4KICAgIDwvY2xpcFBhdGg+CiAgPC9kZWZzPgogIDxnIGlkPSJjdXN0b21fMSIgZGF0YS1uYW1lPSJjdXN0b20g4oCTIDEiIGNsaXAtcGF0aD0idXJsKCNjbGlwLWN1c3RvbV8xKSI+CiAgICA8cmVjdCB3aWR0aD0iMzAiIGhlaWdodD0iMzAiIGZpbGw9IiNmZmYiLz4KICAgIDxnIGlkPSJHcm91cF8xIiBkYXRhLW5hbWU9Ikdyb3VwIDEiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDMuMzU5IDAuNjU3KSI+CiAgICAgIDxwYXRoIGlkPSJQYXRoXzgiIGRhdGEtbmFtZT0iUGF0aCA4IiBkPSJNNDAyLjU2LTM2OC4zbC0uMTY0LjYxNiwxLjE2NiwxLjY2OS4zMzcuMTc1aDQuMzc2bC4zMjctLjE3NSwxLjE2OC0xLjY2OS0uMTU5LS42MTZBOC4wNzMsOC4wNzMsMCwwLDAsNDAyLjU2LTM2OC4zWiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTM5NC42NyAzNzAuMDc5KSIgZmlsbD0iIzE2NTMxNiIvPgogICAgICA8cGF0aCBpZD0iUGF0aF85IiBkYXRhLW5hbWU9IlBhdGggOSIgZD0iTTY1NC4yMTUtMzExLjAzOGwtMS4xMDYsMS41Njl2LjUyOWwxLjgxNCwyLjU2LjM0NC4yMTFoM2wuMjI2LS41NzRhMTEuNDc3LDExLjQ3NywwLDAsMC0zLjY4LTQuMzkxWiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTYzOC41MTggMzEzLjY4NykiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfMTAiIGRhdGEtbmFtZT0iUGF0aCAxMCIgZD0iTTY1OS42NzMtMTExLjk3bC0uNzUxLS4yNjlINjU1LjgzbC0uMjguMTUyLTEuODYxLDIuNjI2di41NDhsMS44NjEsMi42MS4yOC4xNjFoNC40NDJsLjUtLjQ4MkExNS41MywxNS41MywwLDAsMCw2NTkuNjczLTExMS45N1oiIHRyYW5zZm9ybT0idHJhbnNsYXRlKC02MzkuMDgxIDEyMC4yMzcpIiBmaWxsPSIjMTY1MzE2Ii8+CiAgICAgIDxwYXRoIGlkPSJQYXRoXzExIiBkYXRhLW5hbWU9IlBhdGggMTEiIGQ9Ik02NTkuODYxLDEyNy45MTRoLTQuNDE4bC0uMzQ3LjE4Mi0xLjg0MiwyLjYzdi41MWwxLjg0MiwyLjYxLjM0Ny4xNzNoMy4wODhsLjcyNi0uMjQ4YTE2LjI0NCwxNi4yNDQsMCwwLDAsMS4xMDctNS4zMTlaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgtNjM4LjY1OCAtMTEzLjMzOSkiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfMTIiIGRhdGEtbmFtZT0iUGF0aCAxMiIgZD0iTTY1OC43NzEsMzY5LjIyaC0zLjAxNGwtLjI3NC4xNTYtMS44NTksMi42MDl2LjU0bDEuMSwxLjU2Ni42MTEuMDgxQTEyLjQ0NCwxMi40NDQsMCwwLDAsNjU5LDM2OS44MTVaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgtNjM5LjAxOSAtMzQ4LjAzOCkiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfMTMiIGRhdGEtbmFtZT0iUGF0aCAxMyIgZD0iTTQwMy43OTMsNDg5LjQzNWwtLjI3OS4xMzUtMS4yMSwxLjY4LjE1NC42MzZhOC4zNDcsOC4zNDcsMCwwLDAsNy4wNywwbC4xNDctLjYzNi0xLjE4MS0xLjY4LS4yODEtLjEzNVoiIHRyYW5zZm9ybT0idHJhbnNsYXRlKC0zOTQuNTggLTQ2NC45NikiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfMTQiIGRhdGEtbmFtZT0iUGF0aCAxNCIgZD0iTTIyNy40NjUsMzc0LjUzOGwuNTc4LS4xLDEuMTM1LTEuNTU5di0uNWwtMS44NjUtMi42NC0uMy0uMTVoLTMuMDIybC0uMjExLjU2QTExLjY2MywxMS42NjMsMCwwLDAsMjI3LjQ2NSwzNzQuNTM4WiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTIyMC45NDYgLTM0OC4zOTEpIiBmaWxsPSIjMTY1MzE2Ii8+CiAgICAgIDxwYXRoIGlkPSJQYXRoXzE1IiBkYXRhLW5hbWU9IlBhdGggMTUiIGQ9Ik0xNjEuNDc5LDEyOC44ODFsLjQ2Mi0uNTA3aDQuNDM1bC4zNTIuMTY0LDEuODYsMi42MzN2LjUwNmwtMS44NiwyLjYzMi0uMy4xNjFoLTMuMTEybC0uNzQ5LS4yNTJBMTQuODcyLDE0Ljg3MiwwLDAsMSwxNjEuNDc5LDEyOC44ODFaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgtMTYwLjM1IC0xMTMuNzg2KSIgZmlsbD0iIzE2NTMxNiIvPgogICAgICA8cGF0aCBpZD0iUGF0aF8xNiIgZGF0YS1uYW1lPSJQYXRoIDE2IiBkPSJNMTYxLjc3Mi0xMTEuNDZsLjc0My0uMjI4aDMuMTA4bC4zMTQuMTc2LDEuODMyLDIuNjJ2LjUxMmwtMS44MzIsMi42LS4zMTQuMTc2LTQuNDU5LjAzNS0uNTEtLjUxM0ExNy43NjIsMTcuNzYyLDAsMCwxLDE2MS43NzItMTExLjQ2WiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTE1OS41NDggMTE5LjcwMSkiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfMTciIGRhdGEtbmFtZT0iUGF0aCAxNyIgZD0iTTIyNy4zMS0zMTEuMTczbC41NjguMUwyMjktMzA5LjUxMVYtMzA5bC0xLjgzNiwyLjYtLjMyMi4xN2gtM2wtLjI0NC0uNTU2QTExLjgwNSwxMS44MDUsMCwwLDEsMjI3LjMxLTMxMS4xNzNaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgtMjIwLjc2OCAzMTMuNzI0KSIgZmlsbD0iIzE2NTMxNiIvPgogICAgICA8cGF0aCBpZD0iUGF0aF8xOCIgZGF0YS1uYW1lPSJQYXRoIDE4IiBkPSJNMzc5LjU4Ni0yMzJoNC40NjRsLjMxNi4yLDEuODI0LDIuNjA3di41MTdsLTEuNzgxLDIuNTA1LS4zNTkuMjMzaC00LjQ2NGwtLjMtLjEyNi0xLjgzMy0yLjYxMnYtLjUxN2wxLjgzMy0yLjYwN1oiIHRyYW5zZm9ybT0idHJhbnNsYXRlKC0zNzAuNDA2IDIzNi43MTYpIiBmaWxsPSIjMTY1MzE2Ii8+CiAgICAgIDxwYXRoIGlkPSJQYXRoXzE5IiBkYXRhLW5hbWU9IlBhdGggMTkiIGQ9Ik0zNzcsMTEuMTU5bDEuODUtMi42MDkuMzIzLS4xODZoNC40NGwuMzE4LjE4NiwxLjgzNCwyLjYwOXYuNDg5bC0xLjgzNCwyLjYwOC0uMzE4LjIwOWgtNC40NGwtLjMyMy0uMTc0TDM3NywxMS42NzdaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgtMzY5Ljk3IDIuOTM2KSIgZmlsbD0iIzE2NTMxNiIvPgogICAgICA8cGF0aCBpZD0iUGF0aF8yMCIgZGF0YS1uYW1lPSJQYXRoIDIwIiBkPSJNMzc3LDI1Mi4ybDEuODc4LTIuNjMzLjI3NS0uMTQ0aDQuNDcxbC4yNjMuMTQ0LDEuODY0LDIuNjMzdi41bC0xLjgsMi41NTEtLjMzMS4yMzJoLTQuNDcxbC0uMjc1LS4xNjVMMzc3LDI1Mi43WiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTM2OS45NyAtMjMxLjUyMikiIGZpbGw9IiMxNjUzMTYiLz4KICAgICAgPHBhdGggaWQ9IlBhdGhfNiIgZGF0YS1uYW1lPSJQYXRoIDYiIGQ9Ik05Ny40MzMtNDI2LjgyOWwuNzE1LS4yMjgtLjI5My0uNzI2YTEyLjc0OCwxMi43NDgsMCwwLDEsNC4zMDgtNS4wOWwuOTE3LjE0Mi4yMTktLjg0NWE5LjI4NCw5LjI4NCwwLDAsMSw4LjUzOCwwbC4xODMuODQ1Ljk5Mi0uMTM4YTEyLjg3NSwxMi44NzUsMCwwLDEsNC4yNjEsNS4wNjNsLS4yOS43NjEuNzUzLjIzMmExNy4zNjIsMTcuMzYyLDAsMCwxLDEuMjM5LDYuMDEzbC0uNTQ3LjUzNi41NDcuNTc0YTE2Ljc4MywxNi43ODMsMCwwLDEtMS4yNDEsNi4wMTJsLS43NTguMjMzLjMuNzU2YTEzLjI4NCwxMy4yODQsMCwwLDEtNC4zLDUuMDVsLS45MS0uMTQxLS4yNDguODUzYTkuNjI4LDkuNjI4LDAsMCwxLTguNTE3LDBsLS4yNDEtLjg1OC0uOTE1LjE4OWExMy41MTQsMTMuNTE0LDAsMCwxLTQuMjgzLTUuMWwuMzEzLS43NjEtLjc5My0uMjEzYTIxLjExMywyMS4xMTMsMCwwLDEtMS4yMzEtNi4wMThsLjYxMS0uNTU5LS42MTEtLjU5MUExNy44NTIsMTcuODUyLDAsMCwxLDk3LjQzMy00MjYuODI5WiIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTk2LjE1OSA0MzQuNjA0KSIgZmlsbD0ibm9uZSIgc3Ryb2tlPSIjNzA3MDcwIiBzdHJva2Utd2lkdGg9IjAuNSIvPgogICAgPC9nPgogIDwvZz4KPC9zdmc+Cg==",
minimalFee=2000000)
def add_token(ticker, status, email, asset_id,description='Description',link='example.com',logo='blabla',version=0):
data = [{
'type': 'string',
'key': 'ticker_<' + asset_id + '>',
'value': ticker
},
{
'type': 'integer',
'key': 'status_<' + asset_id + '>',
'value': status
},
{
'type': 'integer',
'key': 'version_<' + asset_id + '>',
'value': version
},
{
'type': 'string',
'key': 'email_<' + asset_id + '>',
'value': email
},
{
'type': 'string',
'key': 'logo_<' + asset_id + '>',
'value': logo
},
{
'type': 'string',
'key': 'description_<en>_<' + asset_id + '>',
'value': description
},
{
'type': 'string',
'key': 'link_<' + asset_id + '>',
'value': link
}
]
ticker_tx = address.dataTransaction(data, baseFee=2000000, minimalFee=2500000)
print(ticker_tx)
###
#SCAM = -2,
#SUSPICIOUS = -1,
#NOT_VERIFY = 0,
#DETAILED = 1,
#VERIFIED = 2
#SVG_LIST_BASE64
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sent=''
etho=''
tusd='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'
# TODO: Status 2 seems to be when an asset is active and verified
add_token('ARRR', 2, 'info@rcanelabs.com','9gTWnHstaAkhD7hEGBqNdgcAru5872d8Xf1QmoPrz5iz','CryptoBrokers Officially Backed Gateway Token for ARRR. 1:1 Gateway Peg','https://rcanelabs.com','',0)
add_token('ACL', 2, 'info@rcanelabs.com', '3HFgaMssup9ssSL139sptsxc2EXLY3Qx5ykY7hf5LD2B','ArcaneLabs Platform System Token is utilized in CryptoBrokers technology solutions ','https://rcanelabs.com',acl,0)
add_token('BCH', 2, 'support@turtlenetwork.eu', 'Fr2kNhe7XR3E16W7Mfh7NhNcsQWLXx3hSLjoFgpbFsNj','Bitcoin Cash proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu','',0)
add_token('BTC', 2, 'support@turtlenetwork.eu', '5Asy9P3xjcvBAgbeyiitZhBRJZJ2TPGSZJz9ihDTnB3d','Bitcoin proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu','')
add_token('DASH', 2, 'support@turtlenetwork.eu', 'A62sRG58HFbWUNvFoEEjX4U3txXKcLm11MXWWS429qpN','Dash proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu','')
add_token('DOGE', 2, 'support@turtlenetwork.eu', 'HDeemVktm2Z68RMkyA7AexhpaCqot1By7adBzaN9j5Xg','Doge proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu','')
add_token('ETH', 2, 'support@turtlenetwork.eu', '6Mh41byVWPg8JVCfuwG5CAPCh9Q7gnuaAVxjDfVNDmcD','ETH proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu','')
add_token('ETHO', 2, 'support@kgbconcepts.network','GzHRyYtdwvaGSkUC4i8d3Xzmsz9aXBWdrpMNszi6bcvR','proxy','https://ether1.org',etho,0)
add_token('HN', 2, '------', '3GvqjyJFBe1fpiYnGsmiZ1YJTkYiRktQ86M2KMzcTb2s','Hellenic Node project','https://www.hellenicnode.eu','',0)
add_token('LTC', 2, 'support@turtlenetwork.eu', '3vB9hXHTCYbPiQNuyxCQgXF6AvFg51ozGKL9QkwoCwaS','LTC proxy token for the gateway operated by Black Turtle BVBA.','https://turtlenetwork.eu')
add_token('MLT', 2, '------', '7jcY6DDYsSo7NuZEAruWhrB5apebA2cERhrBx6RFk5tL')
add_token('MN', 2, '------', 'DfutD8DdUhDphHoaMds2RQhw7oCmsf7W41s2zR5ZDq9F')
add_token('SCOM', 2, '------', '7tC2ZukogadhvHUdKQyWJ2cbk6T1viTCFJMgevVeTY1Y')
add_token('SSYS', 2, '------', 'HBxBjymrCC8TuL8rwCLr2vakDEq4obqkMwYYPEZtTauA')
add_token('TN', 2, 'support@turtlenetwork.eu', 'TN','Native currency','https://turtlenetwork.eu',tn,0)
add_token('SENT', 2, 'info@rcanelabs.com','AVJc3uYu8HdjQEx4rroaq6v4Xv4L1etG1tQPeuqs38Sf','CryptoBrokers Officially Backed Gateway Token for SENTinel 0xa44E5137293E855B1b7bC7E2C6f8cD796fFCB037. 1:1 Gateway Peg','https://rcanelabs.com',sent,0)
add_token('TUSD', 2, 'info@rcanelabs.com','2R7raH74LuuiCbJbcv3Aa7g14WY1vYPUGushCUJFwW1f','CryptoBrokers Officially Backed Gateway Token for TrueUSD 0x0000000000085d4780B73119b644AE5ecd22b376 1:1 Gateway Peg','https://rcanelabs.com',tusd,0)
add_token('WAVES', 2, 'support@turtlenetwork.eu', 'EzwaF58ssALcUCZ9FbyeD1GTSteoZAQZEDTqBAXHfq8y','WAVES proxy token for the gateway operated by Black Turtle BVBA.')
add_token('WGR', 2, 'support@turtlenetwork.eu', '91NnG9iyUs3ZT3tqK1oQ3ddpgAkE7v5Kbcgp2hhnDhqd','WGR proxy token for the gateway operated by Black Turtle BVBA.')
#Not verified
add_token('BTN', 0, '------', 'ExbYSuz4DZwf9grp3K8s3CSbNtE9fob2DtTKgbLGFXsJ')
add_token('FR', 0, '------', '4xUv25qFsjQ1Gd6oCmzU14FoPMSDXrwub5PbKRgETf97')
add_token('KA', 0, '------', '5Dy1qVUzEwq6WEUGMy7CkkkbmFuxb2RTBRfs4JKc5b88')
| 194.720339
| 5,292
| 0.919528
| 674
| 22,977
| 31.235905
| 0.296736
| 0.00874
| 0.01045
| 0.009832
| 0.55365
| 0.55365
| 0.541111
| 0.534508
| 0.531706
| 0.525626
| 0
| 0.117612
| 0.037516
| 22,977
| 117
| 5,293
| 196.384615
| 0.834366
| 0.008878
| 0
| 0.098901
| 0
| 0.010989
| 0.891203
| 0.804552
| 0
| 1
| 0.003691
| 0.008547
| 0
| 1
| 0.010989
| false
| 0
| 0.021978
| 0
| 0.032967
| 0.021978
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
11b4c38697c921ac668b1301b68f2f923ce10366
| 35
|
py
|
Python
|
src/rhml_server/rhml_server/Deploy/__init__.py
|
PycT/RhythmicML
|
abf3eea273dcaa97b9308772c8054cfc60b77a4f
|
[
"Apache-2.0"
] | null | null | null |
src/rhml_server/rhml_server/Deploy/__init__.py
|
PycT/RhythmicML
|
abf3eea273dcaa97b9308772c8054cfc60b77a4f
|
[
"Apache-2.0"
] | null | null | null |
src/rhml_server/rhml_server/Deploy/__init__.py
|
PycT/RhythmicML
|
abf3eea273dcaa97b9308772c8054cfc60b77a4f
|
[
"Apache-2.0"
] | null | null | null |
from .helpers import configuration;
| 35
| 35
| 0.857143
| 4
| 35
| 7.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 35
| 1
| 35
| 35
| 0.9375
| 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
| 1
| 0
|
0
| 6
|
11cc42482e4ed2cfac461454f31197005315e26b
| 31
|
py
|
Python
|
coramin/domain_reduction/__init__.py
|
jsiirola/Coramin
|
e5dd359ca885794c05447ff28f0f8aaf9c86da6e
|
[
"BSD-3-Clause"
] | null | null | null |
coramin/domain_reduction/__init__.py
|
jsiirola/Coramin
|
e5dd359ca885794c05447ff28f0f8aaf9c86da6e
|
[
"BSD-3-Clause"
] | null | null | null |
coramin/domain_reduction/__init__.py
|
jsiirola/Coramin
|
e5dd359ca885794c05447ff28f0f8aaf9c86da6e
|
[
"BSD-3-Clause"
] | null | null | null |
from .obbt import perform_obbt
| 15.5
| 30
| 0.83871
| 5
| 31
| 5
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.925926
| 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
| 1
| 0
|
0
| 6
|
11fa45a5f15066e1c05320704be156c8acadb848
| 518,419
|
py
|
Python
|
python/datadict/core/os/base/ngrams.py
|
jiportilla/ontology
|
8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40
|
[
"MIT"
] | null | null | null |
python/datadict/core/os/base/ngrams.py
|
jiportilla/ontology
|
8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40
|
[
"MIT"
] | null | null | null |
python/datadict/core/os/base/ngrams.py
|
jiportilla/ontology
|
8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# pylint:disable=bad-whitespace
# pylint:disable=line-too-long
# pylint:disable=too-many-lines
# pylint:disable=invalid-name
# #########################################################
#
# ************** !! WARNING !! ***************
# ******* THIS FILE WAS AUTO-GENERATED *******
# ********* DO NOT MODIFY THIS FILE **********
#
# #########################################################
the_entity_ngrams = {
'gram-1': [ '1337',
'1337X',
'1337x',
'1610',
'1708',
'1709',
'1711',
'1802',
'4Chan',
'4chan',
'ADO.NET',
'AIX',
'ANTLR',
'API',
'AS/400',
'ASIC',
'ASP',
'AWS',
'Abbvie',
'Access',
'Accessibility',
'Account',
'Accountability',
'Accountant',
'Accounting',
'Accumulo',
'Accurately',
'ActiveMQ',
'Activerecord',
'Activity',
'Adabas',
'Adaboost',
'Adaptability',
'Adobe',
'Aecom',
'Aesthetics',
'Age',
'Agent',
'Agile',
'Agricultural',
'Ai',
'AiCure',
'Aimbot',
'Aimbotting',
'Airavata',
'Ajax',
'Algebra',
'Algo',
'Algorithm',
'Algorithmic',
'Alibaba',
'Alignment',
'AllegroGraph',
'Alliance',
'Allianz',
'Alloy',
'Allura',
'Almirall',
'Altcoin',
'Amazon',
'Ambari',
'Ameriprise',
'Amex',
'Amgen',
'Amro',
'Analyst',
'Analytics',
'Android',
'AngularJS',
'Annotation',
'Ansible',
'Ant',
'Anthropology',
'Apache',
'Apmm',
'Apple',
'Appraisal',
'Appreciation',
'Approval',
'Archaeology',
'Architect',
'Architecture',
'Archive',
'Argument',
'Arla',
'ArrangoDB',
'Artifact',
'Artist',
'Assembly',
'Assertiveness',
'Assessment',
'Asset',
'Assistant',
'Assurance',
'Astellas',
'Astrazeneca',
'Astrobiology',
'Astronomy',
'Astrophysics',
'Atlassian',
'Attentiveness',
'Audit',
'Aurora',
'Authentication',
'Authorization',
'Autocad',
'Autoencoder',
'Automation',
'Automotive',
'Availability',
'Avis',
'Award',
'Awareness',
'Awk',
'Axelos',
'BES',
'BPEL',
'BPM',
'Backend',
'Backup',
'Badge',
'Bandwidth',
'Bank',
'Barclays',
'Basf',
'Bash',
'Battery',
'Bayer',
'Beam',
'Behavior',
'Benchmark',
'Benefit',
'Bes',
'Bezel',
'Bharti',
'Bias',
'Biochemistry',
'Bioinformatics',
'Biology',
'Biometrics',
'Biopharmaceutical',
'Biophysics',
'Biostatistics',
'Biotech',
'Biotechnology',
'Bitcoin',
'Bitmain',
'BizTalk',
'Blackberry',
'Blade',
'Blockchain',
'Bluemix',
'Bombardier',
'Bonus',
'Bootcamp',
'Booting',
'Botnet',
'Bourne',
'Bp2i',
'Brand',
'Brava!',
'Budget',
'Bullying',
'BusyBox',
'C#',
'C4.5',
'CAMS',
'CC-CEM',
'CC-SDWAN',
'CC-SHAREFILE',
'CCA-N',
'CCA-V',
'CCDA',
'CCDE',
'CCDP',
'CCE-V',
'CCENT',
'CCP-M',
'CCP-N',
'CCP-V',
'CICS',
'CLI',
'COBOL',
'CPLEX',
'CPU',
'CRM',
'CSA',
'CSS',
'Cafeteria',
'Calculus',
'Camel',
'Cameyo',
'Campus',
'Capability',
'Capacity',
'Carlsberg',
'Carrefour',
'Cart',
'Casandra',
'Caterpillar',
'Ceedo',
'CentOS',
'Centurylink',
'Certification',
'Chanel',
'Change',
'Channel',
'Checkpoint',
'Chef',
'Chemistry',
'Chitchat',
'Chrysler',
'Churn',
'Cibc',
'Cifs',
'Circuit',
'Cisco',
'Citi',
'Citibank',
'Citigroup',
'Citrix',
'Clarity',
'Classics',
'Cleco',
'Clique',
'Clojure',
'Cloud',
'CloudBurst',
'Cloudera',
'Cluster',
'Coach',
'Coachable',
'Coffeescript',
'Cognizant',
'Cognos',
'Collaborate',
'Collaboration',
'Collection',
'Command',
'Commerce',
'CommonStore',
'Communication',
'Company',
'Competitiveness',
'Compiler',
'Compliance',
'Computerworld',
'Concurrency',
'Conda',
'Confidence',
'Configurability',
'Configuration',
'Connectivity',
'Construction',
'Consultant',
'Consulting',
'Container',
'Containerization',
'Continuity',
'Contract',
'Contractor',
'Conversation',
'Conversion',
'Cooperation',
'Coordinator',
'Corporation',
'Correlation',
'Cosmology',
'Cost',
'CouchDB',
'Couchbase',
'Counterfeit',
'Coursera',
'Courtesy',
'Creative',
'Creativity',
'Cryptocurrency',
'Cryptography',
'Cryptomining',
'Csa',
'Culture',
'Customer',
'Cyberdefense',
'Cybersecurity',
'Cython',
'D3.js',
'DB2',
'DCFM',
'DHCP',
'DNS',
'DNSSec',
'Dashboard',
'Data',
'Database',
'Datacap',
'Dbscan',
'Ddos',
'Ddosed',
'Ddoser',
'Ddosing',
'Deadline',
'Debugging',
'Decommission',
'Dedication',
'Defect',
'Defra',
'Degree',
'Delegation',
'Deliverable',
'Dell',
'Delphi',
'Demisto',
'Demotivating',
'Dependability',
'Deployment',
'Dermatology',
'Designer',
'Deskside',
'Developer',
'Device',
'Devop',
'Diageo',
'Dimension',
'Disagree',
'Discoverability',
'DisplayWrite/370',
'Disruption',
'Diversity',
'Django',
'Docker',
'Dockercon',
'Document',
'Documentation',
'Downtime',
'Drupal',
'Dynamo',
'EBS',
'EC2',
'EJB',
'ESSL',
'ETL',
'Earning',
'Eclipse',
'Ecology',
'Econometrics',
'Economic',
'Economics',
'Education',
'Elasticsearch',
'EmberJS',
'Emotion',
'Empathy',
'Employee',
'Encourage',
'Encryption',
'Endeca',
'Energy',
'Engineer',
'Engineering',
'English',
'Enterprise',
'Enthusiasm',
'Entity',
'Entomology',
'Entrepreneur',
'Environment',
'Enzyme',
'Equality',
'Equations',
'Ergonomics',
'Erlang',
'Estimation',
'Ethereum',
'Ethernet',
'Etihad',
'Evangelize',
'Exchange',
'Executive',
'Expectation',
'Experience',
'Extensibility',
'FTP',
'Facebook',
'Facilitation',
'Failover',
'Fairfight',
'Feasibility',
'Fiber',
'FileNet',
'Finance',
'Financial',
'Fintech',
'Firefox',
'Firewall',
'Firmware',
'Flask',
'Flexibility',
'Flume',
'Focus',
'Forecast',
'Forms',
'Forsensics',
'FortiGate',
'Fortinet',
'Fpga',
'Fpgas',
'Framework',
'Freebsd',
'Freepbx',
'French',
'Friendliness',
'GDDM',
'GDPR',
'GIAC',
'GPFS',
'Gateway',
'Geochemistry',
'Geography',
'Geology',
'Geometry',
'Geomorphology',
'Geophysics',
'Geosciences',
'German',
'Gis',
'Git',
'GitHub',
'Glad',
'GoLang',
'Google',
'Governance',
'Government',
'Gpgpu',
'Grafana',
'GraphQL',
'Graphic',
'Greeting',
'Groupware',
'Growth',
'Guideline',
'H1B',
'HALDB',
'HAProxy',
'HBase',
'HDFS',
'HIPPA',
'HP',
'HP-UX',
'HTML',
'HTTP',
'HTTPS',
'Hackability',
'Hacker',
'Hadoop',
'Harassment',
'Hardware',
'Hartford',
'HashiCorp',
'Health',
'Hello',
'Heuristic',
'Heuristics',
'Hibernate',
'History',
'Hive',
'Hololens',
'Honda',
'Honesty',
'Honeypot',
'HornetQ',
'Hortonworks',
'Hosting',
'Hudson',
'Humanities',
'Humorous',
'Hydrogeology',
'Hydrology',
'Hyperledger',
'Hyperparameter',
'Hypervisor',
'Hypothesis',
'IBM',
'IDMS',
'IEEE',
'IHS',
'ILOG',
'IMS',
'IPC',
'IPSec',
'IPv4',
'IPv6',
'IPython',
'ISACA',
'ISAM',
'ISC',
'ISMS',
'ISPF',
'ITIL',
'ITaaS',
'Ica',
'Id3',
'Identification',
'Identity',
'Image',
'Impala',
'Implement',
'Improve',
'Inadequate',
'Incident',
'Inclusive',
'Independence',
'Independent',
'Index',
'Industry',
'Influence',
'InfluxDB',
'InfoSphere',
'Informatics',
'Informix',
'Infosec',
'Infrastructure',
'Innovation',
'Insight',
'Inspire',
'Install',
'Insurance',
'Integration',
'Interface',
'Internet',
'Interoperability',
'Interview',
'Interviewing',
'Intuition',
'Intuitiveness',
'Inventor',
'Inventory',
'Investment',
'Invoice',
'Iscsi',
'J2Ee',
'JACL',
'JCL',
'JIRA',
'JSON',
'JSP',
'Java',
'Javascript',
'Jboss',
'Jdbc',
'Jenkins',
'Jit',
'Jython',
'KDB',
'KPI',
'Kafka',
'Kaiser',
'Kbank',
'Keepalived',
'Keylogged',
'Keylogger',
'Keynesian',
'Kibana',
'Kubernetes',
'L33T',
'Language',
'Lasso',
'Layoff',
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'Leadership',
'Learning',
'Leet',
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'Libquantum',
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'LinkedIn',
'Linux',
'Lisp',
'Listen',
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'LoadLeveler',
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'LogStash',
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'Loss',
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'MQSeries',
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'Microsoft',
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'Modeler',
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'Monitor',
'Monitoring',
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'MuleSoft',
'Multilevel',
'Multitasking',
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'NAT',
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'NetNOVO',
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'Netcat',
'Netcool',
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'Networking',
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'Numpy',
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'Office',
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'PCI',
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'SQL',
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'trust',
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'xhtml',
'xmind',
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'xpath',
'xslt',
'yaml',
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'yarn',
'yml',
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'z/OS',
'z/VM',
'z/VSE',
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'zvm',
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'Cognos Now!',
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'Cognos Query',
'Cognos Statistics',
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'Collaborative Filtering',
'Collaborative Software',
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'Communication Device',
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'Communication Protocol',
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'Communications Server',
'Community Cloud',
'Company Asset',
'Comparative Literature',
'Comparative Religion',
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'Competitive Workplace',
'Complex Circuit',
'Complexity Theory',
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'Compliance Policy',
'Compliance Test',
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'Computational Linguistics',
'Computational Neuroscience',
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'Computer Model',
'Computer Operator',
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'Computer Security',
'Computer Vision',
'Computing Platform',
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'Consumer Good',
'Consumer Research',
'Container Software',
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'Content Classification',
'Content Collector',
'Content Designer',
'Content Integrator',
'Content Management',
'Content Manager',
'Content Navigator',
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'Continental Philosophy',
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'Continuous Integration',
'Contract Renewal',
'Contract Template',
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'Control Protocol',
'Control Theory',
'Corporate Audit',
'Corporate Customer',
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'Corrective Action',
'Correlation Explanation',
'Correspondence Analysis',
'Cosmos DB',
'Cost Cutting',
'Cost Reduction',
'Couch DB',
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'Creative Director',
'Credit Card',
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'Critical Thinking',
'Cross Platform',
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'Cubist Model',
'Cultural Anthropology',
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'Customer Behavior',
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'DB2 Archive',
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'DB2 Merge',
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'DB2 Path',
'DB2 Performance',
'DB2 Query',
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'DB2 SQL',
'DB2 Server',
'DB2 Storage',
'DB2 Test',
'DB2 Tools',
'DB2 Universal',
'DB2 Utilities',
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'Data Audit',
'Data Center',
'Data Dictionary',
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'Data Error',
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'Data Lake',
'Data Management',
'Data Mart',
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'Data Migration',
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'Data Model',
'Data Privacy',
'Data Processing',
'Data Quality',
'Data Science',
'Data Scientist',
'Data Security',
'Data Server',
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'Data Structure',
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'Data Warehouse',
'Data Wrangling',
'Database Administrator',
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'Database Function',
'Database Index',
'Database Query',
'Database Schema',
'Database Skill',
'Database Table',
'Databases Modernization',
'Ddos Attack',
'Deal Making',
'Debian Linux',
'Debug Tool',
'Decentralized Application',
'Decision Center',
'Decision Document',
'Decision Making',
'Decision Stump',
'Decision Tree',
'Declarative Language',
'Deep Learning',
'Defense Industry',
'Delivery Manager',
'Delivery Model',
'Delivery Role',
'Delivery Skill',
'Delivery Specialist',
'Delivery Time',
'Dell Certification',
'Denial-Of-Service Attack',
'Density Estimation',
'Deployment Environment',
'Deployment Skill',
'Design Aesthetics',
'Design Aptitude',
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'Design Document',
'Design Pattern',
'Design Skill',
'Design Thinking',
'Desktop Hardware',
'Desktop Support',
'Deterministic Optimization',
'Deutsche Bank',
'Development Methodology',
'Development Team',
'Device Driver',
'Device Programming',
'Device Support',
'DiVincenzo Criteria',
'Differential Equations',
'Digital Artifact',
'Digital Asset',
'Digital Image',
'Digital Marketing',
'Digital Network',
'Digital Pen',
'Digital Structure',
'Digital Transformation',
'Dimensionality Reduction',
'Disability Awareness',
'Disaster Recovery',
'Discriminant Function',
'Disk Drive',
'Disk Encryption',
'Display Device',
'Display Resolution',
'Dispute Resolution',
'Distance Education',
'Distribute System',
'Distributed Cloud',
'Distributed Computing',
'Distributed Environment',
'Distributed Ledger',
'Distributed Memory',
'Distributed Skill',
'Distributed Software',
'Diversity Awareness',
'Divisive Clustering',
'Doctoral Degree',
'Document Classification',
'Document Clustering',
'Document Database',
'Document Manager',
'Domain Language',
'Domain Security',
'Domain Skill',
'Domain Training',
'Drug Entrepreneur',
'Dummy Account',
'Dummy Coding',
'Dynamics 365',
'E Commerce',
'ELK Stack',
'Earth Science',
'Economic History',
'Economic Theory',
'Edge Cloud',
'Edition 2016',
'Edition 2018',
'Education Policy',
'Educational Field',
'Effective Communicator',
'Elastic Net',
'Elections Canada',
'Electric Circuit',
'Electrical Device',
'Electrical Engineering',
'Electrical Regulator',
'Electrical State',
'Elliptic Envelope',
'Embedded Linux',
'Embedded System',
'Emerging Technologies',
'Emerging Technology',
'Emotion Management',
'Emotional Intelligence',
'Emotional Skill',
'Employee Appreciation',
'Employee Benefit',
'Employee Pay',
'Employee Recognition',
'Employee Training',
'Employee Treatment',
'Encryption Software',
'End User',
'Energy Industry',
'Engagement Plan',
'Engagement Process',
'Engagement Training',
'English Literature',
'Enterprise Architect',
'Enterprise Cloud',
'Enterprise Infrastructure',
'Enterprise Java',
'Enterprise Network',
'Enterprise Plan',
'Enterprise Records',
'Entity Provenance',
'Environmental Science',
'Equal Opportunity',
'Equipment Failure',
'Ergonomic Sensitivity',
'Ethical Guideline',
'Event Log',
'Event Processing',
'Exchange Server',
'Execution Model',
'Executive Role',
'Experimental Physics',
'Expert Role',
'Expert System',
'External Device',
'External Transfer',
'Extreme Programming',
'F1 Score',
'Facility Management',
'Fact Table',
'Factor Analysis',
'Fair Profit',
'False Negative',
'False Positive',
'Fannie Mae',
'Fault Tolerant',
'Feasibility Study',
'Feature Creep',
'Feature Engineering',
'Feature Extraction',
'Feature Selection',
'Federated Identity',
'Fedora Linux',
'Field Inventory',
'File System',
'FileNet Application',
'FileNet Business',
'FileNet Capture',
'FileNet Connectors',
'FileNet Content',
'FileNet Fax',
'FileNet IDM',
'FileNet Image',
'FileNet Team',
'FileNet eForms',
'FileNet eProcess',
'Financial Benefit',
'Financial Company',
'Financial Instrument',
'Financial Measurement',
'Financial Plan',
'Financial Service',
'Financial Skill',
'Financial Technology',
'Find Similar',
'Fine Art',
'Firewall Configuration',
'First Responsibility',
'Fixed Asset',
'Flexible Environment',
'Flexible Work',
'Fluid Dynamics',
'Fluid Mechanics',
'Follow Instructions',
'Follow Regulations',
'Follow Rules',
'Following Direction',
'Formal Language',
'Forms Designer',
'Forms Server',
'Forms Turbo',
'Forms Viewer',
'FortiGate Carrier',
'FortiGate Enterprise',
'FortiGate UTM',
'Forward Proxy',
'Forward Selection',
'Frame Relay',
'Free Software',
'Front End',
'Full Stack',
'Functional Programming',
'Functional Requirement',
'Fundamental Physics',
'Funding Round',
'GASF Certification',
'GAWN Certification',
'GCCC Certification',
'GCDA Certification',
'GCED Certification',
'GCFA Certification',
'GCFE Certification',
'GCIA Certification',
'GCIH Certification',
'GCIP Certification',
'GCPM Certification',
'GCTI Certification',
'GCUX Certification',
'GCWN Certification',
'GDAT Certification',
'GDSA Certification',
'GEVA Certification',
'GIAC Certification',
'GISCP Certification',
'GISF Certification',
'GISP Certification',
'GLEG Certification',
'GMOB Certification',
'GMON Certification',
'GNFA Certification',
'GPEN Certification',
'GPL License',
'GPL3 License',
'GPPA Certification',
'GPYC Certification',
'GREM Certification',
'GRID Certification',
'GSE Certification',
'GSEC Certification',
'GSLC Certification',
'GSNA Certification',
'GSSP Certification',
'GSTRT Certification',
'GWAPT Certification',
'GWEB Certification',
'GXPN Certification',
'Game Designer',
'Game Engine',
'Game Programming',
'Genetic Algorithms',
'Georgia Tech',
'Ginni Rometty',
'Global Data',
'Global Retention',
'Global Training',
'Go Live',
'Good Attitude',
'Good Quality',
'Google Cardboard',
'Google Certification',
'Google Cloud',
'Gradient Descent',
'Graduate Degree',
'Graph Algorithm',
'Graph Database',
'Graph Theory',
'Graphic Artist',
'Graphic Designer',
'Graphical Model',
'Green Hat',
'Ground Truth',
'HCISPP Certification',
'HP Cloud',
'HTTP Server',
'Handheld Computer',
'Hard Disk',
'Hardware Deployment',
'Hardware Design',
'Hardware Platform',
'Hardware Skill',
'Hardware Test',
'Hardware Testing',
'Hardware Troubleshooting',
'Hardware Virtualization',
'Hash Tree',
'Health Care',
'Health Informatics',
'Health Insurance',
'Health Net',
'Healthcare Provider',
'Help Desk',
'Hierarchical Clustering',
'Hierarchical Database',
'High Availability',
'High Quality',
'High Scalability',
'High Skill',
'High Workload',
'Highly Recommended',
'Hikma Pharmaceuticals',
'Hiring Manager',
'Historical Linguistics',
'Home Network',
'Home Office',
'Hortonworks Certification',
'Host Access',
'Human Resources',
'Hybrid cloud',
'Hyperledger Fabric',
'I O',
'IBM AIX',
'IBM Agile',
'IBM Badge',
'IBM Certification',
'IBM Cloud',
'IBM DB2',
'IBM Employee',
'IBM GBS',
'IBM GTS',
'IBM Internal',
'IBM Mainframe',
'IBM Notes',
'IBM Offering',
'IBM Power',
'IBM Product',
'IBM Q',
'IBM SameTime',
'IBM Software',
'IBM Storage',
'IBM Team',
'IEEE 802.11',
'IEEE 802.3',
'IEEE Standard',
'ILOG AMPL',
'ILOG CPLEX',
'ILOG Inventory',
'ILOG JViews',
'ILOG LogicNet',
'ILOG OPL-CPLEX',
'ILOG Server',
'ILOG Telecom',
'ILOG Views',
'IMAC Coordinator',
'IMS Audit',
'IMS Batch',
'IMS Buffer',
'IMS Cloning',
'IMS Command',
'IMS Configuration',
'IMS Connect',
'IMS DEDB',
'IMS Database',
'IMS Extended',
'IMS Fast',
'IMS High',
'IMS Index',
'IMS Library',
'IMS Network',
'IMS Online',
'IMS Parallel',
'IMS Parameter',
'IMS Performance',
'IMS Problem',
'IMS Program',
'IMS Queue',
'IMS Recovery',
'IMS Sequential',
'IMS Sysplex',
'IMS Tools',
'ISA Certification',
'ISACA Certification',
'ISC Certification',
'ISMS Certification',
'ISO 27001',
'ISO Certification',
'ISO Standard',
'ISPF Productivity',
'IT Architect',
'IT Manager',
'IT Service',
'IT Specialist',
'ITIL Certification',
'Ibm Aix',
'Ibm Cloud',
'Ibm Db2',
'Ibm Internal',
'Ibm Notes',
'Ibm Offering',
'Ibm Power',
'Ibm Sametime',
'Ibm Storage',
'Ibm Team',
'Identity Governance',
'Identity Management',
'Ieee 802.11',
'Ieee 802.3',
'Image Recognition',
'Implementation Role',
'Incident Manager',
'Incident Reduction',
'Incident Ticket',
'Incidental Benefit',
'Individual Role',
'Industry Context',
'Industry Insight',
'Industry Standard',
'Industry Trend',
'InfoSphere BigInsights',
'InfoSphere Change',
'InfoSphere Classic',
'InfoSphere Content',
'InfoSphere Data',
'InfoSphere DataStage',
'InfoSphere Discovery',
'InfoSphere FastTrack',
'InfoSphere Global',
'InfoSphere Guardium',
'InfoSphere Information',
'InfoSphere MashupHub',
'InfoSphere Master',
'InfoSphere Optim',
'InfoSphere QualityStage',
'InfoSphere Replication',
'InfoSphere Streams',
'Information Architect',
'Information Architecture',
'Information Developer',
'Information Governance',
'Information Management',
'Information Retrieval',
'Information Security',
'Information Server',
'Information System',
'Information Technology',
'Informix 4GL',
'Informix Data',
'Informix ESQL/COBOL',
'Informix Enterprise',
'Informix Extended',
'Informix Growth',
'Informix OnLine',
'Informix Servers',
'Informix Tools',
'Infosec Certification',
'Infrastructure Architect',
'Infrastructure Manager',
'Infrastructure Platform',
'Infrastructure Security',
'Infrastructure Service',
'Infrastructure Specialist',
'Initiate Address',
'Initiate Master',
'Innovative Programs',
'Innovative Role',
'Inorganic Chemistry',
'Inspiring People',
'Inspur K-UX',
'Insurance Company',
'Insurance Industry',
'Insurance Process',
'Integrated Circuit',
'Integration Architect',
'Integration Framework',
'Integration Platform',
'Integration Skill',
'Interaction Designer',
'Intercultural Competence',
'Internal Device',
'Internal Process',
'Internal Transfer',
'International Economics',
'Internet Explorer',
'Internet Protocol',
'Interpersonal Skills',
'Interview Skill',
'Inventory Management',
'Inventory Process',
'Inventory Tracking',
'Ip Ban',
'Ireland Bank',
'Isolation Forest',
'It Service',
'JMP Certification',
'Janus Graph',
'Java Certification',
'Java Foundations',
'Java Servlet',
'Job Posting',
'Job Security',
'Joint Venture',
'Juniper Certification',
'Juniper Networks',
'Jupyter Notebook',
'Just-In-Time Manufacturing',
'Kafka Messaging',
'Kernel Pca',
'Kernel Virtualization',
'Keystroke Logging',
'Killer App',
'Killer Application',
'Killer Feature',
'Knowledge Engineer',
'Knowledge Graph',
'Knowledge Management',
'Knowledge Share',
'Kohonen Map',
'Korn Shell',
'Kshape Clustering',
'Labor Claim',
'Lambda Calculus',
'Lead Developer',
'Leadership Role',
'Leadership Situation',
'Lean Manufacturing',
'Learning Motivation',
'Learning Presentation',
'Learning Provider',
'Legacy Modernization',
'Legacy System',
'Legal Document',
'Level 1',
'Level 2',
'Level 3',
'License Renewal',
'Life Insurance',
'Lifecycle Management',
'Line Management',
'Line Manager',
'Linear Algebra',
'Linear Model',
'Linear Programming',
'Linear Regression',
'Linus Torvalds',
'Linux Certification',
'Listening Skill',
'Literary Theory',
'Live Training',
'Living Organism',
'Lloyds Bank',
'Load Balancer',
'Load Test',
'Load Testing',
'Log File',
'Logical Partition',
'Logical Thinking',
'Logistic Regression',
'Logo Design',
'Lotus ActiveInsight',
'Lotus Connector',
'Lotus Domino',
'Lotus End',
'Lotus Enterprise',
'Lotus Expeditor',
'Lotus Foundations',
'Lotus Notes',
'Lotus Protector',
'Lotus Quickr',
'Lotus SmartSuite',
'Lotus Symphony',
'Lotus Workflow',
'Lotus iNotes',
'Low Skill',
'Loyalty Marketing',
'MCDBA Certification',
'MCSA Certification',
'MCSD Certification',
'MCSE Certification',
'MCTS Certification',
'MIT License',
'MOS Certification',
'MQSeries Integrator',
'MTA Certification',
'Machine Learning',
'Macro Language',
'Mailbox Client',
'Mainframe Computer',
'Majorana Fermion',
'Managed Cloud',
'Management Conversation',
'Management Methodology',
'Management Skill',
'Managment Enablement',
'Managment Training',
'Mandrake Linux',
'Manufacturing Company',
'Manufacturing Method',
'Marine Biology',
'Market Standard',
'Marketing Research',
'Marketing Role',
'Markov Model',
'Markup Language',
'Master Data',
'Master Inventor',
'Masters Degree',
'Materials Engineering',
'Materials Science',
'Math Skill',
'Math Software',
'Mathematical Finance',
'Mathematical Logic',
'Maximo Adapter',
'Maximo Archiving',
'Maximo Asset',
'Maximo Calibration',
'Maximo Change',
'Maximo Compliance',
'Maximo Contract',
'Maximo Data',
'Maximo Discovery',
'Maximo Everyplace',
'Maximo MainControl',
'Maximo Mobile',
'Maximo Online',
'Maximo Space',
'Maximo Spatial',
'Mechanical Engineering',
'Media Configuration',
'Media Content',
'Media Service',
'Medical Benefit',
'Medical Pay',
'Medical Skill',
'Meeting Deadlines',
'Meeting Minutes',
'Meets Deadlines',
'Memory-Mapped I/O',
'Mental Health',
'Merge Tool',
'Merkle Tree',
'Message Queue',
'Messaging Extension',
'Metro Life',
'Micro Service',
'Microsoft Azure',
'Microsoft Basic',
'Microsoft Certification',
'Microsoft Excel',
'Microsoft Hololens',
'Microsoft IIS',
'Microsoft Office',
'Microsoft Powerpoint',
'Microsoft Silverlight',
'Microsoft Visio',
'Microsoft Word',
'Middleware Modernization',
'Migration Skill',
'Migration Support',
'Miller Coors',
'Mind Map',
'Mining Protocol',
'Minority Employee',
'Mistakes Paid',
'Mixture Model',
'Mobile Computing',
'Mobile Device',
'Mobile Network',
'Mobile Office',
'Mobile Skill',
'Mobile Team',
'Mobile Virtualization',
'Model Explainability',
'Model Test',
'Model Testing',
'Modeling Language',
'Modern Economics',
'Modern Physics',
'Modular Design',
'Molecular Biology',
'Moller Maersk',
'Monitoring Software',
'Monte Carlo',
'Montreal Bank',
'Motion Designer',
'Multi Dimension',
'Multi Threaded',
'Multiclass Classifier',
'Multicloud Enabler',
'Multidimensional Scaling',
'Multilevel Grid',
'Multiprotocol Network',
'NS Lookup',
'NY State',
'Name Server',
'National Railroad',
'Natural Language',
'Natural Resources',
'Natural Science',
'Navigational Database',
'Negative Emotion',
'Negative Leadership',
'Negative Morale',
'Negative Situation',
'Negative Workplace',
'Negotiation Skill',
'Net Framework',
'NetNOVO Application',
'Netcool GUI',
'Netcool Omnibus',
'Netcool Realtime',
'Netcool Service',
'Netcool System',
'Netezza High',
'Network Administrator',
'Network Architect',
'Network Configuration',
'Network Database',
'Network Design',
'Network Device',
'Network Downtime',
'Network Drive',
'Network Gateway',
'Network Management',
'Network Methodology',
'Network Model',
'Network Outage',
'Network Packet',
'Network Product',
'Network Protocol',
'Network Provisioning',
'Network Security',
'Network Service',
'Network Skill',
'Network Specialist',
'Network Standard',
'Network Support',
'Network Tool',
'Neural Nets',
'Neural Network',
'New Cheat',
'New Employee',
'New Hire',
'New Manager',
'News Media',
'Newtonian Physics',
'No Change',
'No Conversation',
'No Problem',
'No Promotion',
'Node Red',
'Norwegian Wood',
'Notebook Interface',
'Nuage Networks',
'Nuclear Physics',
'Nueral Networks',
'Number Theory',
'Numerical Analysis',
'Numerical Method',
'Numerical Methods',
'OMEGAMON z/OS',
'Object Oriented',
'Object Pascal',
'Oculus Vr',
'Offering Manager',
'Offshore Team',
'Ohio State',
'Omni Channel',
'Online Training',
'Onsite Training',
'Open Source',
'Open Stack',
'OpenShift Dedicated',
'OpenShift Online',
'OpenShift Origin',
'OpenShift Platform',
'OpenShift Virtualization',
'Operating Model',
'Operating System',
'Operation Analyst',
'Operation Research',
'Operational Analytics',
'Operational Model',
'Operational Role',
'Operational Skill',
'Operations Analyst',
'Operations Architect',
'Operations Management',
'Operations Manager',
'Operations Research',
'Operations Role',
'Opportunity Owner',
'Optical Network',
'Optim Database',
'Optim High',
'Optim Performance',
'Optim pureQuery',
'Optim z/OS',
'Optimization Algorithm',
'Optimization Method',
'Optimization Skill',
'Oracle CCB',
'Oracle CRM',
'Oracle Ccb',
'Oracle Certification',
'Oracle Clinical',
'Oracle Cloud',
'Oracle Clusterware',
'Oracle Coherence',
'Oracle Commerce',
'Oracle Designer',
'Oracle Discoverer',
'Oracle EBS',
'Oracle Exadata',
'Oracle Exalogic',
'Oracle Financials',
'Oracle Flashback',
'Oracle Forms',
'Oracle Help',
'Oracle Hyperion',
'Oracle Integration',
'Oracle JDeveloper',
'Oracle Java',
'Oracle Linux',
'Oracle Multimedia',
'Oracle OLAP',
'Oracle Office',
'Oracle Payables',
'Oracle Portal',
'Oracle PowerBrowser',
'Oracle RAC',
'Oracle Racing',
'Oracle Receivables',
'Oracle Reports',
'Oracle Retail',
'Oracle SCM',
'Oracle Solaris',
'Oracle Spatial',
'Oracle Text',
'Oracle Tutor',
'Oracle VDI',
'Oracle VM',
'Oracle WebCenter',
'Oracle Weblogic',
'Oracle database',
'Oracle interMedia',
'Oracle metadata',
'Oracle script',
'Orchestration Framework',
'Order Management',
'Ordinal Regression',
'Organic Chemistry',
'Organizational Benefit',
'Organizational Focus',
'Organizational Issue',
'Organizational Pay',
'Organizational Role',
'Organizational Structure',
'Outlier Detection',
'Outsourcing Contract',
'Over-Specified Query',
'P2P Encryption',
'PAQSA Certification',
'PC Support',
'PCI Certification',
'PRINCE2 Certification',
'Package Manager',
'Packet Switching',
'Packet Trace',
'Paid Account',
'Parallel Computing',
'Parallel Engineering',
'Parallel Environment',
'Parse Tree',
'Particle Physics',
'Parzen Windows',
'Password Compliance',
'Pay Gap',
'Pay Level',
'Pay Penalty',
'Pay Process',
'Pay Raise',
'Payroll Software',
'Pc Power',
'Pc Support',
'Penetration Test',
'People Manager',
'Peoplesoft Certification',
'Performance Appraisal',
'Performance Expectation',
'Performance Indicator',
'Performance Management',
'Performance Metric',
'Performance Optimization',
'Performance Professional',
'Performance Report',
'Personal Cloud',
'Personal Computer',
'Personal Life',
'Personal Pc',
'Petroleum Industry',
'Pharmaceutical Company',
'Philips Medical',
'Philosophy Student',
'Phishing Link',
'Physical Anthropology',
'Physical Artifact',
'Physical Chemistry',
'Physical Health',
'Physical Location',
'Physical Property',
'Physical Science',
'Physical Structure',
'Plan Development',
'Plant Biology',
'Platform Analytics',
'Platform HDFS',
'Platform HPC',
'Platform Hdfs',
'Platform ISF',
'Platform LSF',
'Platform MPI',
'Platform Manager',
'Platform RTM',
'Platform Specific',
'Platform Symphony',
'Point-To-Point Encryption',
'Point-to-Point Encryption',
'PointBase Embedded',
'Poisson Regression',
'Poli Science',
'Political Economy',
'Political Philosophy',
'Political Science',
'Political Theory',
'Polynomial Regression',
'Poor Pay',
'Portfolio Alignment',
'Positive Attitude',
'Positive Emotion',
'Positive Leadership',
'Positive Morale',
'Positive Situation',
'Positive Workplace',
'Post Graduate',
'Potential Employee',
'Power Management',
'Power Server',
'PowerHA SystemMirror',
'PowerVM Lx86',
'PowerVM VIOS',
'PowerVM Virtual',
'Presales Role',
'Presales Skill',
'Presales Workshop',
'Presentation Layer',
'Presentation Skill',
'Price Optimization',
'Pricing Info',
'Pricing Process',
'Printing Failure',
'Privacy Regulation',
'Private Blockchain',
'Private cloud',
'Problem Solving',
'Procedural Language',
'Process Automation',
'Process Change',
'Process Consultant',
'Process Continuity',
'Process Improvement',
'Process Mining',
'Process Role',
'Procurement Manager',
'Product Designer',
'Product Management',
'Product Manager',
'Product Offering',
'Production Environment',
'Professional Growth',
'Professional Hire',
'Professional Pay',
'Professional Role',
'Professional Services',
'Professions Certification',
'Program Manager',
'Programming Language',
'Programming Skill',
'Project Administrator',
'Project Assistant',
'Project Estimation',
'Project Executive',
'Project Manager',
'Project Plan',
'Project Scope',
'Project Status',
'Project Team',
'Project Transfer',
'Property Graph',
'Proventia Desktop',
'Proventia Endpoint',
'Proventia Management',
'Proventia Network',
'Proventia Virtualized',
'Provide Value',
'Provisioning Skill',
'Proxy Server',
'Public Administration',
'Public Blockchain',
'Public cloud',
'PureApplication System',
'PureData System',
'Python Library',
'QIR Certification',
'QSA Certification',
'Quadratic Programming',
'Quality Assurance',
'Quality Control',
'Quality Improvement',
'Quality Management',
'Quality Planning',
'Quantitative Finance',
'Quantum Advantage',
'Quantum Algorithms',
'Quantum Bit',
'Quantum Cloud',
'Quantum Computing',
'Quantum Entanglement',
'Quantum Interference',
'Quantum Mechanics',
'Quantum Operation',
'Quantum Parallelism',
'Quantum Physics',
'Quantum Programming',
'Quantum Research',
'Quantum Vertex',
'Query Disambigutation',
'Query Language',
'R Language',
'R-Series Platform',
'RACF Database',
'Rackspace Cloud',
'Random Forest',
'Random Tree',
'Rational Ada',
'Rational Apex',
'Rational Application',
'Rational Asset',
'Rational Automation',
'Rational Build',
'Rational COBOL',
'Rational Change',
'Rational ClearCase',
'Rational ClearDDTS',
'Rational ClearQuest',
'Rational Computer',
'Rational DOORS',
'Rational Dashboard',
'Rational Data',
'Rational Developer',
'Rational Development',
'Rational Elite',
'Rational Engineering',
'Rational Functional',
'Rational Host',
'Rational Insight',
'Rational Lifecycle',
'Rational Logiscope',
'Rational Modeling',
'Rational Open',
'Rational Performance',
'Rational Policy',
'Rational ProjectConsole',
'Rational Purify',
'Rational PurifyPlus',
'Rational Quality',
'Rational RequisitePro',
'Rational Robot',
'Rational Rose',
'Rational Service',
'Rational SoDA',
'Rational Software',
'Rational Statemate',
'Rational Suite',
'Rational Synergy',
'Rational System',
'Rational Systems',
'Rational Tau',
'Rational Team',
'Rational Test',
'Rational TestManager',
'Rational Web',
'Readable Medium',
'Reasonable Prices',
'Reciprocal Averaging',
'Recommendation Systems',
'Recruiting Document',
'Recruiting Role',
'Recruitment Strategy',
'Recruitment System',
'Red Hat',
'RedHat CloudForms',
'RedHat Linux',
'Redhat 3scale',
'Redhat Certification',
'Redhat Fuse',
'Redhat Portfolio',
'Redhat Virtualization',
'Reduce Costs',
'Regression Model',
'Regression Test',
'Regression Testing',
'Regression Tree',
'Regularized Regression',
'Reinforcement Learning',
'Relational Database',
'Release Process',
'Release Schedule',
'Release Strategy',
'Reliability Engineer',
'Reliability-centered maintenance',
'Rembo Auto-Backup',
'Remote Team',
'Reporting Analyst',
'Reporting Software',
'Request Find',
'Requirements Document',
'Requirements Gathering',
'Research Skill',
'Resistive Circuit',
'Resolving Issues',
'Resource Management',
'Resource Staffing',
'Rest API',
'Result Artifact',
'Results Oriented',
'Retail Security',
'Retirement Plan',
'Revenue Forecast',
'Revenue Growth',
'Revenue Loss',
'Reverse Proxy',
'Ridge Regression',
'Risk Management',
'Risk Manager',
'Risk Mitigation',
'Robotic Device',
'Robust Optimization',
'Routing Protocol',
'Rules Engine',
'Runtime Environment',
'Runtime Process',
'Russian Linux',
'SAN Data',
'SAP ADS',
'SAP AG',
'SAP APO',
'SAP ASE',
'SAP Afaria',
'SAP Analytics',
'SAP Anywhere',
'SAP Architect',
'SAP Arena',
'SAP Ariba',
'SAP Banking',
'SAP Business',
'SAP BusinessObjects',
'SAP Certification',
'SAP Cloud',
'SAP Concur',
'SAP DB',
'SAP EHSM',
'SAP ERP',
'SAP ESOA',
'SAP EWM',
'SAP Enterprise',
'SAP Fiori',
'SAP GUI',
'SAP HANA',
'SAP Hosting',
'SAP IQ',
'SAP Implementation',
'SAP Jam',
'SAP MM',
'SAP Migration',
'SAP Mobile',
'SAP NetWeaver',
'SAP Open',
'SAP PI',
'SAP PLM',
'SAP PP',
'SAP PRD2',
'SAP Project',
'SAP R3',
'SAP SCM',
'SAP SD',
'SAP SE',
'SAP SRM',
'SAP Sybase',
'SAP TECHED',
'SAP XI',
'SAS Certification',
'SCLM Advanced',
'SCLM Suite',
'SMP 3.0',
'SNIA Certification',
'SOA Architect',
'SOA Policy',
'SPSS Amos',
'SPSS AnswerTree',
'SPSS Collaboration',
'SPSS Data',
'SPSS Modeler',
'SPSS Risk',
'SPSS SamplePower',
'SPSS Statistics',
'SPSS Text',
'SQL Server',
'SSCP Certification',
'Safety Conscious',
'Sales Conversion',
'Sales Manager',
'Sales Role',
'Sales Skill',
'Sales Workshop',
'Salesforce Cloud',
"Sammon'S Mapping",
'Sap Implementation',
'Satoshi Nakamoto',
'Scientific Computing',
'Scientific Programming',
'Scientific Skill',
'Scope Description',
'Screen Definition',
'Screen Quality',
'Script Kiddie',
'Script Kiddy',
'Scripting Language',
'Scrum Master',
'Search Engine',
'Securities Offering',
'Security AppScan',
'Security Architect',
'Security Breach',
'Security Flaw',
'Security Key',
'Security Model',
'Security Network',
'Security Policy',
'Security Privileged',
'Security Product',
'Security Skill',
'Security Software',
'Security Specialist',
'Security Team',
'Security Virtual',
'Security zSecure',
'Self Directed',
'Self Monitoring',
'Self Supervising',
'Selling Skills',
'Semantic Web',
'Semiconductor Device',
'Senior Management',
'Sequence Diagram',
'Sequential Model',
'Serial Port',
'Server Configuration',
'Server Consolidation',
'Server Operation',
'Server Security',
'Server Skill',
'Server Support',
'Service Coordinator',
'Service Designer',
'Service Desk',
'Service Level',
'Service Management',
'Service Manager',
'Service Monitor',
'Service Offering',
'Service Provider',
'Service Provisioning',
'Serviced Promptly',
'Set Theory',
'Severance Pay',
'Sexist Workplace',
'Sexual Harassment',
'Shell Script',
'ShowCase Essbase',
'ShowCase Reporting',
'Signal Process',
'Signal Processing',
'Simplex Methods',
'Six Sigma',
'Sizing Skill',
'Skill Assessment',
'Skill Gap',
'Skill Level',
'Skills Growth',
'Skills Transfer',
'Smart Contract',
'Smart Phone',
'Smart Watch',
'SmartCloud Application',
'SmartCloud Connections',
'SmartCloud Engage',
'SmartCloud Meetings',
'SmartCloud Monitoring',
'SmartCloud Provisioning',
'SmartCloud iNotes',
'Smarter Commerce',
'Social Assistance',
'Social Media',
'Social Psychology',
'Social Science',
'Social Sciences',
'Social Scientist',
'Social Skills',
'Social Software',
'Social Studies',
'Social Study',
'Social Theory',
'Soft Clustering',
'Soft Skill',
'Software AG',
'Software Compliance',
'Software Component',
'Software Defect',
'Software Deployment',
'Software Design',
'Software Engineer',
'Software Environment',
'Software Installion',
'Software Library',
'Software License',
'Software Methodology',
'Software Metric',
'Software Migration',
'Software Model',
'Software Platform',
'Software Portfolio',
'Software Project',
'Software Quality',
'Software Service',
'Software Skill',
'Software Test',
'Software Testing',
'Software Troubleshooting',
'Solution Architect',
'Solution Design',
'Solution Manager',
'Solution Representative',
'Solution Skill',
'Solution Strategy',
'Solution Value',
'Source Control',
'Source Safe',
'Space Physics',
'Spectral Clustering',
'Speech Recognition',
'Spend Approval',
'Squid Cache',
'Staffing Plan',
'Stakeholder Role',
'Star Schema',
'State Diagram',
'State Street',
'Static Analysis',
'Static Test',
'Static Testing',
'Statistical Algorithm',
'Statistical Application',
'Statistical Model',
'Statistical Outcome',
'Statistical Software',
'Status Document',
'Sterling Configure',
'Sterling Connect:Direct',
'Sterling Connect:Enterprise',
'Sterling Connect:Express',
'Sterling Gentran:Director',
'Sterling Gentran:Server',
'Sterling Selling',
'Sterling Warehouse',
'Stochastic Calculus',
'Stochastic Optimization',
'Storage Administration',
'Storage Architect',
'Storage Device',
'Storage Enterprise',
'Storage Manager',
'Storage Platform',
'Storage Product',
'Storage Skill',
'Storage Software',
'Storage Specialist',
'Stored Procedure',
'Strategic Planning',
'Strategic Skill',
'Strategy Document',
'Streaming Service',
'Stress Management',
'Structured Data',
'Structured Document',
'Structured Language',
'Structured Prediction',
'Successful Coaching',
'Sun Pharmaceutical',
'Supervised Learning',
'Supplier Management',
'Supplier Network',
'Supply Chain',
'Support Services',
'Support Specialist',
'SurePOS ACE/EPS',
'Suse Linux',
'Swimlane Diagram',
'Switch Failover',
'Switch Failure',
'Sybase ASA',
'Sybase ASE',
'Sybase IQ',
'Sybase MobiLink',
'Sybase iAnywhere',
'Symantec NetBackup',
'Symbolic Logic',
'Symmetric Cryptography',
'System Administrator',
'System Artifact',
'System Clock',
'System Configuration',
'System Design',
'System Engineer',
'System Language',
'System Manager',
'System Operator',
'System Programmer',
'System Programming',
'System R',
'System Reliability',
'System Role',
'System Transformation',
'Systems Design',
'Systems Expert',
'Systems Management',
'TPF Toolkit',
'TRIRIGA Portfolio',
'Tabular Database',
'Talent Acquisition',
'Tape Library',
'Td Bank',
'Teaching Skill',
'Team Building',
'Team Culture',
'Team Formation',
'Team Lead',
'Team Meeting',
'Team Member',
'Team Player',
'Team Skill',
'Team Work',
'Technical Architect',
'Technical Communication',
'Technical Consultant',
'Technical Enablement',
'Technical Framework',
'Technical Lead',
'Technical Role',
'Technical Services',
'Technical Skill',
'Technical Specialist',
'Technical Team',
'Technical Training',
'Technical Workshop',
'Technical Writer',
'Technical Writing',
'Technology Savvy',
'Teradata Aster',
'Test Architect',
'Test Estimation',
'Test Lead',
'Test Manager',
'Test Plan',
'Test Script',
'Test Specialist',
'Test Strategy',
'Test User',
'Testing Outcome',
'Testing Skill',
'Text Editor',
'Text Mining',
'Theoretical Physics',
'Tibco ESB',
'Ticketing Tool',
'Time Management',
'Time Series',
'Tivoli AF/Integrated',
'Tivoli AF/OPERATOR',
'Tivoli Access',
'Tivoli Advanced',
'Tivoli Alert',
'Tivoli Allocation',
'Tivoli Analyzer',
'Tivoli Application',
'Tivoli Asset',
'Tivoli Automated',
'Tivoli Availability',
'Tivoli Business',
'Tivoli Capacity',
'Tivoli Central',
'Tivoli Change',
'Tivoli Command',
'Tivoli Compliance',
'Tivoli Composite',
'Tivoli Comprehensive',
'Tivoli Configuration',
'Tivoli Continuous',
'Tivoli Contract',
'Tivoli Decision',
'Tivoli Directory',
'Tivoli Dynamic',
'Tivoli ETEWatch',
'Tivoli Endpoint',
'Tivoli Event',
'Tivoli Federated',
'Tivoli Foundations',
'Tivoli Identity',
'Tivoli Information',
'Tivoli IntelliWatch',
'Tivoli Key',
'Tivoli License',
'Tivoli Management',
'Tivoli Monitoring',
'Tivoli NetView',
'Tivoli Netcool',
'Tivoli Netcool/Impact',
'Tivoli Netcool/Reporter',
'Tivoli Netcool/Webtop',
'Tivoli Network',
'Tivoli OMEGACENTER',
'Tivoli OMEGAMON',
'Tivoli OMNIbus',
'Tivoli Output',
'Tivoli Performance',
'Tivoli Policy',
'Tivoli Privacy',
'Tivoli Provisioning',
'Tivoli Release',
'Tivoli SANergy',
'Tivoli Security',
'Tivoli Service',
'Tivoli Storage',
'Tivoli System',
'Tivoli Tape',
'Tivoli Unified',
'Tivoli Usage',
'Tivoli Web',
'Tivoli Workload',
'Tivoli zSecure',
'Tool Training',
'Topic Model',
'TotalStorage Productivity',
'Trade Off',
'Trade Offs',
'Trained Classifier',
'Transaction Data',
'Transaction Processing',
'Transformation Model',
'Transformation Role',
'Transformation Skill',
'Transition Manager',
'Translation Cloud',
'Travel Visa',
'Tree Ensemble',
'Triple Store',
'True Negative',
'True Positive',
'Twitter Bot',
'Type System',
'Type Theory',
'Typing Skill',
'UX Designer',
'Ubuntu Linux',
'Ui Design',
'Under-Specified Query',
'Undergraduate Degree',
'Unica Campaign',
'Unica CustomerInsight',
'Unica Detect',
'Unica Interact',
'Unica Interactive',
'Unica Leads',
'Unica Marketing',
'Unica Optimize',
'Unica PredictiveInsight',
'Unica eMessage',
'Unified Messaging',
'Unit Testing',
'Unix Administrator',
'Unix Filesystem',
'Unstructured Data',
'Unsupervised Learning',
'Usability Engineer',
'Usability Skill',
'Use Case',
'User Authentication',
'User Guide',
'User Interface',
'User Management',
'User Research',
'User Support',
'VERSA Analytics',
'VERSA CSG',
'VERSA Cloud',
'VERSA Director',
'VERSA FlexVNF',
'VERSA Titan',
'VMWare Certification',
'VMWare Company',
'VMWare ESXi',
'VMWare Fusion',
'VMWare Infrastructure',
'VMWare NSX',
'VMWare Software',
'VMWare Workstation',
'VMware SDWAN',
'VOIP Protocol',
'VS FORTRAN',
'Value Chain',
'Value Education',
'Value Proposition',
'Vector Autoregression',
'Vector Quantization',
'Vendor Management',
'Venture Round',
'Veritas Software',
'Versa Networks',
'Versa Product',
'Version Control',
'Vice President',
'Video Conference',
'Video Configuration',
'Vif Regression',
'Virtual Appliance',
'Virtual Cloud',
'Virtual Desktop',
'Virtual Machine',
'Virtual Meeting',
'Virtual Network',
'Virtual Server',
'Virtual Storage',
'Virtual Team',
'Virtual Training',
'Virtualization Infrastructure',
'Virtualization Platform',
'Virtualization Software',
'Virtualization Tool',
'Virtuo Mediation',
'Visual Basic',
'Visual Dashboard',
'Visual Designer',
'VisualAge Generator',
'VisualAge Pacbase',
'VisualAge Smalltalk',
'Visualization Skill',
'Visualization Software',
'Vmware Vsphere',
'Voltage Regulator',
'Vr App',
'Vr Technology',
'Vulnerability Management',
'W3C Standard',
'Wall Hacks',
'Watson Annotator',
'Watson Discovery',
'Watson Explorer',
'Watson Group',
'Watson Studio',
'Wearable Device',
'Web App',
'Web Application',
'Web Applications',
'Web Browser',
'Web Designer',
'Web Developer',
'Web Developers',
'Web Language',
'Web Mail',
'Web Server',
'Web Service',
'WebSphere Adapter',
'WebSphere Appliance',
'WebSphere Application',
'WebSphere Business',
'WebSphere Cast',
'WebSphere Commerce',
'WebSphere Data',
'WebSphere DataPower',
'WebSphere Decision',
'WebSphere Developer',
'WebSphere Development',
'WebSphere Digital',
'WebSphere Dynamic',
'WebSphere End',
'WebSphere Enterprise',
'WebSphere Event',
'WebSphere Everyplace',
'WebSphere Front',
'WebSphere Host',
'WebSphere ILOG',
'WebSphere IP',
'WebSphere Industry',
'WebSphere MQ',
'WebSphere Message',
'WebSphere Multichannel',
'WebSphere Operational',
'WebSphere Partner',
'WebSphere Portal',
'WebSphere Process',
'WebSphere Service',
'WebSphere Studio',
'WebSphere Telecom',
'WebSphere Transaction',
'WebSphere Translation',
'WebSphere Voice',
'WebSphere XML',
'WebSphere eXtended',
'WebSphere sMash',
'Webcenter Certification',
'Weblogic Certification',
'Westpac Bank',
'Williams Glyn',
'Windows Desktop',
'Windows NT',
'Windows Nt',
'Windows Powershell',
'Windows Server',
'Word Embeddings',
'Work Culture',
'Work Environment',
'Work Ethic',
'Work Experience',
'Work Optimization',
'Work Product',
'Work-Life Balance',
'Workflow Language',
'Workflow Software',
'Workforce Management',
'Workload Deployer',
'Workload Distribution',
'Workload Simulator',
'Workplace Harassment',
'Workplace Situation',
'Workplace Training',
'Writing Skill',
'XML Standard',
'Xcel Energy',
'Zero Defects',
'abbvie inc.',
'abstract algebra',
'acceptance test',
'acceptance testing',
'accepting feedback',
'access attempt',
'access control',
'access failure',
'access manager',
'account architect',
'account team',
'accounts payable',
'ace insurance',
'activation training',
'active directory',
'activity diagram',
'activity provenance',
'activity report',
'administrative role',
'administrative services',
'adobe acrobat',
'adobe air',
'adobe analytics',
'adobe animate',
'adobe apollo',
'adobe atmosphere',
'adobe audition',
'adobe authorware',
'adobe blazeds',
'adobe brackets',
'adobe bridge',
'adobe browserlab',
'adobe buzzword',
'adobe campaign',
'adobe captivate',
'adobe coldfusion',
'adobe color',
'adobe connect',
'adobe contribute',
'adobe creativesync',
'adobe dimension',
'adobe director',
'adobe displaytalk',
'adobe distiller',
'adobe dreamweaver',
'adobe edge',
'adobe encore',
'adobe fdk',
'adobe fireworks',
'adobe flash',
'adobe flex',
'adobe fonts',
'adobe framemaker',
'adobe freehand',
'adobe fresco',
'adobe golive',
'adobe homesite',
'adobe illustrator',
'adobe imageready',
'adobe imagestyler',
'adobe incopy',
'adobe indesign',
'adobe jenson',
'adobe jrun',
'adobe lasertalk',
'adobe leanprint',
'adobe lightroom',
'adobe livecycle',
'adobe livemotion',
'adobe max',
'adobe media',
'adobe muse',
'adobe onlocation',
'adobe originals',
'adobe ovation',
'adobe pagemaker',
'adobe pagemill',
'adobe pdf',
'adobe persuasion',
'adobe photodeluxe',
'adobe photoshop',
'adobe portfolio',
'adobe postscript',
'adobe prelude',
'adobe premiere',
'adobe presenter',
'adobe pressready',
'adobe presswise',
'adobe primetime',
'adobe ps',
'adobe reader',
'adobe rgb',
'adobe robohelp',
'adobe scout',
'adobe shockwave',
'adobe sign',
'adobe social',
'adobe soundbooth',
'adobe spark',
'adobe speedgrade',
'adobe stock',
'adobe story',
'adobe streamline',
'adobe target',
'adobe type',
'adobe ultra',
'adobe voco',
'adobe xd',
'advanced analytics',
'agglomerative clustering',
'agile artifact',
'agricultural industry',
'aim bot',
'air canada',
'airtel africa',
'aix 5.2',
'aix 5.3',
'aix certification',
'algo opvar',
'algo strategic',
'algorithm analysis',
'algorithm design',
'aliababa certification',
'alpine linux',
'amazon neptune',
'american express',
'analysis document',
'analysis skill',
'analytical activity',
'analytical chemistry',
'analytical skill',
'analytics asset',
'and Leadership',
'and leadership',
'antergos linux',
'anthropology course',
'antivirus software',
'anzo graph',
'apache cassandra',
'apache license',
'apache software',
'apache spark',
'api management',
'application administrator',
'application architect',
'application architecture',
'application developer',
'application framework',
'application manager',
'application performance',
'application recovery',
'application server',
'application time',
'application virtualization',
'apps modernization',
'arch linux',
'architectural pattern',
'ariba integration',
'arizona state',
'art director',
'art history',
'artificial intelligence',
'artistic aptitude',
'asp.net mvc',
'assessment workshop',
'asset management',
'asset optimization',
'associates degree',
'astellas pharma',
'astra linux',
'asv certification',
'atlas ediscovery',
'audio configuration',
'audit readiness',
'audit skill',
'australia bank',
'austrian school',
'authorization code',
'authorization security',
'automated provisioning',
'automated transformation',
'automotive industry',
'awareness training',
'aws certification',
'aws cli',
'aws cloudtrail',
'aws cognito',
'aws dynamodb',
'aws ec2',
'aws elemental',
'aws govcloud',
'aws lambda',
'aws s3',
'aws stats',
'axa tech',
'azure certification',
'azure developer',
'bachelors degree',
'back end',
'backward elimination',
'bad process',
'bad quality',
'batch job',
'batch optimization',
'bayesian network',
'behavioral economics',
'bell canada',
'benefit pay',
'best practice',
'bi reporting',
'bid strategy',
'big data',
'binary classifier',
'biological anthropology',
'biological science',
'bitcoin mining',
'blackboard inc',
'blade server',
'blame culture',
'blockchain block',
'blockchain company',
'blockchain framework',
'blockchain infrastructure',
'blockchain project',
'bluemix nlc',
'boehringer ingelheim',
'bonus pay',
'bot account',
'bot net',
'branch transformation',
'brand management',
'brand role',
'brand specialist',
'brava! enterprise',
'bristol-myers squibb',
'broadband network',
'bsd license',
'build automation',
'business advisor',
'business analyst',
'business case',
'business continuity',
'business dashboard',
'business development',
'business entity',
'business ethics',
'business framework',
'business intelligence',
'business lead',
'business leader',
'business logic',
'business methodology',
'business model',
'business network',
'business opportunity',
'business pitch',
'business process',
'business reporting',
'business requirement',
'business role',
'business skill',
'business software',
'business specialist',
'business stakeholder',
'business storytelling',
'business terminology',
'c language',
'c sharp',
'ca state',
'caching proxy',
'call monitoring',
'canada bank',
'candidate pool',
'cap certification',
'capacity plan',
'capacity skill',
'capital asset',
'career conversation',
'career growth',
'carphone warehouse',
'case manager',
'case study',
'category theory',
'ccie security',
'ccie wireless',
'ccna industrial',
'ccna security',
'ccna wireless',
'ccnp security',
'ccnp wireless',
'ccsk certification',
'ccsp certification',
'ceph storage',
'cgeit certification',
'chair role',
'change management',
'change request',
'chat bot',
'cheat engine',
'checkpoint certification',
'chemistry major',
'cics batch',
'cics business',
'cics configuration',
'cics deployment',
'cics interdependency',
'cics online',
'cics performance',
'cics transaction',
'cics vsam',
'circuit board',
'circuit design',
'cisa certification',
'cisco aci',
'cisco certification',
'cism certification',
'cissp certification',
'citizens financial',
'citrix certification',
'citrix cloud',
'citrix online',
'citrix receiver',
'citrix software',
'citrix winframe',
'citrix workspace',
'citrix xenapp',
'citrix xendesktop',
'civil engineering',
'cj healthcare',
'claim of',
'claim training',
'clarity 7',
'class training',
'classical mechanics',
'classical study',
'classification model',
'classification tree',
'client account',
'client environment',
'client focus',
'client growth',
'client industry',
'client interview',
'client meeting',
'client mission',
'client relationship',
'client request',
'client requirement',
'client role',
'client satisfaction',
'client server',
'client skill',
'client success',
'client training',
'client transformation',
'client travel',
'cloud 9',
'cloud analytics',
'cloud application',
'cloud architect',
'cloud backup',
'cloud cms',
'cloud collaboration',
'cloud communications',
'cloud computing',
'cloud container',
'cloud database',
'cloud deployment',
'cloud desktop',
'cloud developer',
'cloud elements',
'cloud engineering',
'cloud feedback',
'cloud files',
'cloud front',
'cloud gate',
'cloud gateway',
'cloud hosting',
'cloud ide',
'cloud infrastructure',
'cloud management',
'cloud manufacturing',
'cloud microphysics',
'cloud migration',
'cloud printing',
'cloud provider',
'cloud research',
'cloud robotics',
'cloud security',
'cloud server',
'cloud service',
'cloud skill',
'cloud software',
'cloud storage',
'cloud tool',
'cloud trail',
'cloud watch',
'cloudera certification',
'cluster analysis',
'cluster computing',
'cluster systems',
'clustering model',
'cmos device',
'coaching skill',
'coca cola',
'code quality',
'cognitive framework',
'cognitive neuroscience',
'cognitive psychology',
'cognitive science',
'cognitive skill',
'cognitive test',
'cognitive testing',
'cognitive training',
'cognos 8',
'cognos analysis',
'cognos application',
'cognos business',
'cognos consolidator',
'cognos controller',
'cognos customer',
'cognos decisionstream',
'cognos express',
'cognos finance',
'cognos financial',
'cognos impromptu',
'cognos insight',
'cognos mobile',
'cognos noticecast',
'cognos now!',
'cognos planning',
'cognos powerplay',
'cognos query',
'cognos statistics',
'cognos supply',
'cognos tm1',
'cognos visualizer',
'collaborative culture',
'collaborative filtering',
'collaborative software',
'combat logger',
'commerical license',
'commerical software',
'common lisp',
'communication bus',
'communication controller',
'communication designer',
'communication device',
'communication player',
'communication protocol',
'communication security',
'communication skill',
'communications server',
'community cloud',
'company asset',
'comparative literature',
'comparative religion',
'comparative studies',
'competitive analysis',
'competitive research',
'competitive workplace',
'complex circuit',
'complexity theory',
'compliance engineer',
'compliance lead',
'compliance policy',
'compliance test',
'compliance testing',
'computational biology',
'computational chemistry',
'computational complexity',
'computational linguistics',
'computational neuroscience',
'computational physics',
'computational science',
'computer architecture',
'computer configuration',
'computer hardware',
'computer model',
'computer operator',
'computer science',
'computer security',
'computer vision',
'computing platform',
'concept artist',
'configuration management',
'conflict resolution',
'connections enterprise',
'constantly strive',
'consulting skill',
'consumer good',
'consumer research',
'container software',
'content analytics',
'content analyzer',
'content classification',
'content collector',
'content designer',
'content integrator',
'content management',
'content manager',
'content navigator',
'continental casualty',
'continental philosophy',
'continuous deployment',
'continuous improvement',
'continuous integration',
'contract renewal',
'contract template',
'contrail cloud',
'contrail product',
'contrail sdwan',
'control framework',
'control protocol',
'control theory',
'corporate audit',
'corporate customer',
'corporate division',
'corporate finance',
'corrective action',
'correlation explanation',
'correspondence analysis',
'cosmos db',
'cost cutting',
'cost reduction',
'couch db',
'creative cloud',
'creative director',
'credit card',
'crisc certification',
'crisp dm',
'critical observation',
'critical theory',
'critical thinking',
'cross platform',
'cryptographic protocol',
'crystal report',
'csa certification',
'cssa certification',
'csslp certification',
'cubist model',
'cultural anthropology',
'cultural studies',
'cultural study',
'customer behavior',
'customer benefit',
'customer engagement',
'customer experience',
'customer management',
'customer oriented',
'customer service',
'customer support',
'customer team',
'cybersecurity company',
'danske bank',
'data analysis',
'data analyst',
'data architect',
'data architecture',
'data artifact',
'data audit',
'data center',
'data dictionary',
'data dimension',
'data encryption',
'data error',
'data governance',
'data lake',
'data management',
'data mart',
'data methodology',
'data migration',
'data mining',
'data model',
'data privacy',
'data processing',
'data quality',
'data science',
'data scientist',
'data security',
'data server',
'data skill',
'data storage',
'data structure',
'data studio',
'data warehouse',
'data wrangling',
'database administrator',
'database certification',
'database design',
'database dimension',
'database function',
'database index',
'database query',
'database schema',
'database skill',
'database table',
'databases modernization',
'db2 administration',
'db2 alphablox',
'db2 analytics',
'db2 archive',
'db2 audit',
'db2 automation',
'db2 bind',
'db2 buffer',
'db2 change',
'db2 cloning',
'db2 data',
'db2 everyplace',
'db2 high',
'db2 log',
'db2 merge',
'db2 object',
'db2 optimization',
'db2 path',
'db2 performance',
'db2 query',
'db2 recovery',
'db2 server',
'db2 sql',
'db2 storage',
'db2 test',
'db2 tools',
'db2 universal',
'db2 utilities',
'ddos attack',
'deal making',
'debian linux',
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'decentralized application',
'decision center',
'decision document',
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'decision stump',
'decision tree',
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'deutsche bank',
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'differential equations',
'digital artifact',
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'digital network',
'digital pen',
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'digital transformation',
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'discriminant function',
'disk drive',
'disk encryption',
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'display resolution',
'dispute resolution',
'distance education',
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'distributed cloud',
'distributed computing',
'distributed environment',
'distributed ledger',
'distributed memory',
'distributed skill',
'distributed software',
'diversity awareness',
'divincenzo criteria',
'divisive clustering',
'doctoral degree',
'document classification',
'document clustering',
'document database',
'document manager',
'domain language',
'domain security',
'domain skill',
'domain training',
'drug entrepreneur',
'dummy account',
'dummy coding',
'dynamics 365',
'e Commerce',
'e commerce',
'eDiscovery Analyzer',
'eDiscovery Manager',
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'economic history',
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'edition 2018',
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'i o',
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'i2 COPLINK',
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'i2 coplink',
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'lloyds bank',
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'sybase ianywhere',
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'technical workshop',
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'text mining',
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'ticketing tool',
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'tivoli access',
'tivoli advanced',
'tivoli afintegrated',
'tivoli afoperator',
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'tivoli business',
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'tivoli change',
'tivoli command',
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'tivoli composite',
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'tivoli continuous',
'tivoli contract',
'tivoli decision',
'tivoli directory',
'tivoli dynamic',
'tivoli endpoint',
'tivoli etewatch',
'tivoli event',
'tivoli federated',
'tivoli foundations',
'tivoli identity',
'tivoli information',
'tivoli intelliwatch',
'tivoli key',
'tivoli license',
'tivoli management',
'tivoli monitoring',
'tivoli netcool',
'tivoli netcoolimpact',
'tivoli netcoolreporter',
'tivoli netcoolwebtop',
'tivoli netview',
'tivoli network',
'tivoli omegacenter',
'tivoli omegamon',
'tivoli omnibus',
'tivoli output',
'tivoli performance',
'tivoli policy',
'tivoli privacy',
'tivoli provisioning',
'tivoli release',
'tivoli sanergy',
'tivoli security',
'tivoli service',
'tivoli storage',
'tivoli system',
'tivoli tape',
'tivoli unified',
'tivoli usage',
'tivoli web',
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'tivoli zsecure',
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'trade offs',
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'true positive',
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'type theory',
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'vector quantization',
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'gram-3': [ 'AIX 5.2 Workload',
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'Adobe Audience Manager',
'Adobe CS Live',
'Adobe Camera Raw',
'Adobe Capture CC',
'Adobe Character Animator',
'Adobe ColdFusion Builder',
'Adobe Comp CC',
'Adobe Content Server',
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'Adobe Creative Suite',
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'Adobe Digital Editions',
'Adobe Document Cloud',
'Adobe Dynamic Link',
'Adobe Edge Animate',
'Adobe Edge Code',
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'Adobe Flex Builder',
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'Adobe PDF JobReady',
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'Azure Developer Associate',
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'Blackberry Mobile Device',
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'Certified Microsoft PowerPoint',
'Certified Microsoft Word',
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'Certified Teamwork Administrator',
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'Change Management Software',
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'Cisco Routing Certification',
'Cisco Secure ACS',
'Cisco Security Certification',
'Cisco Wireless Certification',
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'Citrix Cloud Certification',
'Citrix Netscaler Certification',
'Citrix Networking Certification',
'Citrix Presentation Server',
'Citrix Sharefile Certified',
'Citrix Virtualization Certification',
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'Cloud Computing Issues',
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'CommonStore for Lotus',
'CommonStore for SAP',
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'Contrail Service Orchestration',
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'Crossing the Line',
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'DB2 Archive Log',
'DB2 Audit Management',
'DB2 Automation Tool',
'DB2 Bind Manager',
'DB2 Buffer Pool',
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'DB2 Change Management',
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'DB2 Merge Backup',
'DB2 Object Comparison',
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'DB2 Query Monitor',
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'DB2 SQL Performance',
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'DB2 Universal Database',
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'DB2 Utilities Solution',
'DB2 Utilities Suite',
'DB2 Warehouse Manager',
'DB2 XML Extender',
'Data Access Layer',
'Data Analysis Skill',
'Data Center Specialist',
'Data Communication Hardware',
'Data Integration Skill',
'Data Modeling Skill',
'Data Privacy Regulation',
'Data Scientist Workbench',
'Data Server Client',
'Data Storage Library',
'Data Studio pureQuery',
'Data at Rest',
'Datacap FastDoc Capture',
'Datacap Taskmaster Capture',
'Deep Belief Networks',
'Deep Learning Design',
'Delivery Project Executive',
'Delivery Project Manager',
'Dell Cloud Certification',
'Dell Networking Certification',
'Dell Security Certification',
'Dell Server Certification',
'Dell Storage Certification',
'Dense Wavelength Network',
'Density Based Clustering',
'Desire To Learn',
'Development And Advancement',
'Device Programming Skill',
'Dimension Reduction Model',
'DisplayWrite/370 for MVS/CICS',
'Disposal and Governance',
'Distributed File System',
'Document Management System',
'Domain Name Server',
'Dow Chemical Company',
'Dynamics 365 Fundamentals',
'Elastic Compute Cloud',
'End User License',
'Engineering and Scientific',
'Enhanced Access Control',
'Enterprise Content Management',
'Enterprise Content Manager',
'Enterprise Resource Planning',
'Essential Team Member',
'Establishing Interpersonal Relationships',
'Extract Transform Load',
'Fabasoft Folio Cloud',
'Fiber over Ethernet',
'Field Effect Transistor',
'Field-Programmable Gate Array',
'FileNet Application Connector',
'FileNet Business Activity',
'FileNet Business Process',
'FileNet Content Manager',
'FileNet Content Services',
'FileNet Email Manager',
'FileNet Forms Manager',
'FileNet IDM Desktop/WEB',
'FileNet Image Manager',
'FileNet Image Services',
'FileNet Records Crawler',
'FileNet Rendition Engine',
'FileNet Report Manager',
'FileNet System Monitor',
'FileNet Team Collaboration',
'Finite Element Analysis',
'Finite Element Method',
'Firebase Cloud Messaging',
'First Line Manager',
'Flat File Database',
'Forward Stagewise Selection',
'Forward Stepwise Selection',
'Fractional Processor Unit',
'Free Market Economics',
'Full Stack Developer',
'Fuzzy C Means',
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'GIAC Python Coder',
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'GIAC Security Expert',
'GIAC Security Leadership',
'GIAC Strategic Planning',
'GTS Claim Training',
'Gaussian Mixture Model',
'Gaussian Naïve Bayes',
'General Ledger Accounting',
'Generalized Linear Model',
'Geographic Information System',
'German Chemical Company',
'German Multinational Pharmaceutical',
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'Giac Python Coder',
'Giac Security Essentials',
'Giac Security Expert',
'Giac Security Leadership',
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'Giving Clear Feedback',
'Global Data Synchronization',
'Global Retention Policy',
'Google Ads Certification',
'Google Cloud Certification',
'Google Cloud Connect',
'Google Cloud Dataflow',
'Google Cloud Dataproc',
'Google Cloud Datastore',
'Google Cloud Messaging',
'Google Cloud Platform',
'Google Cloud Print',
'Google Cloud Storage',
'Google Cloud functions',
'Google Developers Certification',
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'Graphical User Interface',
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'Green Hat Tester',
'Green Hat Virtual',
'Grid Based Clustering',
'HP Cloud Services',
'HP Converged Cloud',
'Hardware Inventory Process',
'Health And Safety',
'Health Care Company',
'Health Care Security',
'Hidden Markov Model',
'Hierarchical Storage Management',
'High Avaiability Cluster',
'High Level Language',
'High Performance Computing',
'High Performance Microprocessor',
'Highly Capable Leaders',
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'Hortonworks Certified Developer',
'Hortonworks Certified Professional',
'Hortonworks Spark Certification',
'Host Access Client',
'Human Computer Interaction',
'Human Readable Medium',
'Human Resources Policy',
'Hybrid Cloud Gateway',
'IBM Business Process',
'IBM Cloud Video',
'IBM Corporate Division',
'IBM Master Inventor',
'IBM Platform Software',
'IBM Private Cloud',
'IBM Q Certification',
'IBM Quantum Computing',
'IBM System I',
'IBM System P',
'IBM System Z',
'IBM WebSphere MQ',
'IBM cloud computing',
'IEEE Cloud Computing',
'ILOG CP Optimizer',
'ILOG CPLEX Optimization',
'ILOG DB Link',
'ILOG Elixir Enterprise',
'ILOG Fab PowerOps',
'ILOG JViews Charts',
'ILOG JViews Diagrammer',
'ILOG JViews Enterprise',
'ILOG JViews Gantt',
'ILOG JViews Graph',
'ILOG JViews Maps',
'ILOG ODM Enterprise',
'ILOG OPL-CPLEX -ODM',
'ILOG OPL-CPLEX Development',
'ILOG Plant PowerOps',
'ILOG Transport PowerOps',
'ILOG Transportation Analyst',
'IMS Audit Management',
'IMS Batch Backout',
'IMS Batch Terminal',
'IMS Buffer Pool',
'IMS Cloning Tool',
'IMS Command Control',
'IMS Configuration Manager',
'IMS Connect Extensions',
'IMS DEDB Fast',
'IMS Database Control',
'IMS Database Recovery',
'IMS Database Repair',
'IMS Database Solution',
'IMS Enterprise Suite',
'IMS Extended Terminal',
'IMS Fast Path',
'IMS High Availability',
'IMS High Performance',
'IMS Index Builder',
'IMS Library Integrity',
'IMS Online Reorganization',
'IMS Parallel Reorganization',
'IMS Parameter Manager',
'IMS Performance Analyzer',
'IMS Performance Monitor',
'IMS Performance Solution',
'IMS Problem Investigator',
'IMS Program Restart',
'IMS Queue Control',
'IMS Recovery Expert',
'IMS Recovery Solution',
'IMS Sequential Randomizer',
'IMS Sysplex Manager',
'IMS Tools Knowledge',
'IOS XR Specialist',
'ISO 9001 Certification',
'ISPF Productivity Tool',
'ISPF for z/OS',
'IT Management Consultant',
'IT Operations Analytics',
'IT Service Management',
'ITIL Foundation Certification',
'Ibm Master Inventor',
'Ibm System I',
'Ibm System P',
'Inclusive Work Environment',
'Incremental Selection Model',
'InfoSphere Balanced Warehouse',
'InfoSphere Blueprint Director',
'InfoSphere Business Glossary',
'InfoSphere Change Data',
'InfoSphere Classic Connector',
'InfoSphere Classic Data',
'InfoSphere Classic Federation',
'InfoSphere Classic Replication',
'InfoSphere Classification Module',
'InfoSphere Content Collector',
'InfoSphere Data Architect',
'InfoSphere Data Event',
'InfoSphere Data Replication',
'InfoSphere Enterprise Records',
'InfoSphere Federation Server',
'InfoSphere Global Name',
'InfoSphere Guardium Data',
'InfoSphere Guardium Vulnerability',
'InfoSphere Identity Insight',
'InfoSphere Information Analyzer',
'InfoSphere Information Server',
'InfoSphere Information Services',
'InfoSphere Master Data',
'InfoSphere Metadata Workbench',
'InfoSphere Optim Configuration',
'InfoSphere Optim High',
'InfoSphere Optim Performance',
'InfoSphere Optim Query',
'InfoSphere Optim pureQuery',
'InfoSphere Replication Server',
'InfoSphere Traceability Server',
'InfoSphere Warehouse Packs',
'Information Lifecycle Management',
'Information Management Software',
'Information Server Data',
'Informix C-ISAM DataBlade',
'Informix Data Director',
'Informix DataBlade Modules',
'Informix Enterprise Gateway',
'Informix Extended Parallel',
'Informix Growth Warehouse',
'Informix Standard Engine',
'Infrastructure as Code',
'Initial Coin Offering',
'Initial Public Offering',
'Initiate Address Verification',
'Initiate Master Data',
'Integrated Data Store',
'Integrated Development Environment',
'Interactive Voice Response',
'Internal Security Assessor',
'International Standards Organization',
'Internet of Things',
'Ip Address Blocking',
'It Operations Analytics',
'It Service Management',
'JD Edwards Certification',
'Japanese Pharmaceutical Company',
'Java Database Connectivity',
'Journey to Cloud',
'K Means Clustering',
'K Medians Clustering',
'K Medoids Clustering',
'K Modes Clustering',
'K Nearest Neighbors',
'K Prototypes Clustering',
'Kernel Density Estimation',
'Key Value Database',
'Lack of Time',
'Lack of Training',
'Language Integrated Query',
'Latent Dirichlet Allocation',
'Latent Semantic Analysis',
'Level 1 Certification',
'Level 2 Certification',
'Level 3 Certification',
'License Metric Tool',
'License Use Management',
'Linear Discriminant Analysis',
'Liquid Crystal Display',
'Local Area Network',
'Local Linear Embedding',
'Local Outlier Factor',
'Long Term Focus',
'Lotus Domino Access',
'Lotus Domino Designer',
'Lotus Domino Document',
'Lotus EasySync Pro',
'Lotus Enterprise Integrator',
'Lotus Foundations Branch',
'Lotus Foundations Reach',
'Lotus Foundations Start',
'Lotus Mobile Connect',
'Lotus Notes Traveler',
'Lotus Quickr Connectors',
'Lotus Quickr Content',
'Lotus Workforce Management',
'Low Level Language',
'Low Pay Level',
'M5 Model Tree',
'MQSeries Integrator Agent',
'MTA: Cloud Fundamentals',
'MTA: Database Fundamentals',
'MTA: Networking Fundamentals',
'MTA: Security Fundamentals',
'Mainframe Operating System',
'Managed Private Cloud',
'Managing Difficult Conversations',
'Managing Remote Teams',
'Managing Virtual Teams',
'Markov Decision Process',
'Markov Random Fields',
'Master Data Management',
'Master of Arts',
'Master of Business',
'Master of Engineering',
'Master of Science',
'Master of Technology',
'Maximo Asset Configuration',
'Maximo Asset Management',
'Maximo Asset Navigator',
'Maximo Business Object',
'Maximo Change Manager',
'Maximo Data Center',
'Maximo Enterprise Adapter',
'Maximo Field Control',
'Maximo Migration Manager',
'Maximo Mobile Audit',
'Maximo Mobile Inventory',
'Maximo Mobile Suite',
'Maximo Mobile Work',
'Maximo Online Commerce',
'Maximo SLA Manager',
'Maximo Space Management',
'Maximo Spatial Asset',
'Maximo Usage Monitor',
'Maximo for Government',
'Maximo for Life',
'Maximo for Nuclear',
'Maximo for Service',
'Maximo for Transportation',
'Maximo for Utilities',
'Mean Shift Clustering',
'Media and Entertainment',
'Merger and Acquisition',
'Message Broker Software',
'Message Oriented Middleware',
'Metrica Performance Manager',
'Metrica Service Manager',
'Microsoft 365 Certification',
'Microsoft App Builder',
'Microsoft Application Virtualization',
'Microsoft Business Applications',
'Microsoft Certified HTML5',
'Microsoft Certified Productivity',
'Microsoft Certified Professional',
'Microsoft Core Infrastructure',
'Microsoft Exchange Server',
'Microsoft Foundation Class',
'Microsoft Visual Studio',
'Mixed Integer Programming',
'Mobile App Development',
'Mobile Cloud Computing',
'Mobile Cloud Storage',
'Mobile Operating System',
'Mobility Computing Platform',
'Mode Seeking Algorithm',
'Moving Average Model',
'Mta: Cloud Fundamentals',
'Mta: Database Fundamentals',
'Mta: Networking Fundamentals',
'Mta: Security Fundamentals',
'Multi Modal Database',
'Multi Tenant Cloud',
'Multinomial Logistic Regression',
'Multinomial Naïve Bayes',
'My New Certification',
'Naive Bayes Classifier',
'Natural Language Classification',
'Natural Language Generation',
'Natural Language Processing',
'Natural Language Toolkit',
'Negative Pay Situation',
'Negative Team Culture',
'Netcool Network Management',
'Netcool Service Monitor',
'Netcool/Service Monitor Reporter',
'Netezza High Capacity',
'Network Equipment Provider',
'Network Performance Reporting',
'Network Security Policy',
'Network Services Product',
'Network Support Specialist',
'Neural Language Model',
'No Career Growth',
'No Skills Growth',
'Nokia Nuage SDN',
'Non Functional Requirement',
'Non Linear Programming',
'Non Profit Company',
'Novia Scotia Bank',
'Nubifer Cloud Portal',
'OMEGAMON z/OS Management',
'OmniFind Discovery Edition',
'OmniFind Enterprise Edition',
'OmniFind Yahoo! Edition',
'Open Source Framework',
'Open Source License',
'Open Source Software',
'Open Telekom Cloud',
'OpenPages GRC Platform',
'OpenShift Site Reliablity',
'Operating System Environment',
'Operating System Virtualization',
'Operational Decision Management',
'Operations Production Analyst',
'Optim Database Administrator',
'Optim Development Studio',
'Optim High Performance',
'Optim Performance Manager',
'Optim pureQuery Runtime',
'Oracle Application Certification',
'Oracle Architect Certification',
'Oracle Business Intelligence',
'Oracle Business Rules',
'Oracle Call Interface',
'Oracle Cash Management',
'Oracle Cloud Certification',
'Oracle Cloud Fn',
'Oracle Cloud Platform',
'Oracle Cluster Registry',
'Oracle Commerce Certification',
'Oracle Content Management',
'Oracle Data Guard',
'Oracle Data Integrator',
'Oracle Data Mining',
'Oracle Database Appliance',
'Oracle Developer Studio',
'Oracle Developer Suite',
'Oracle EBS Certification',
'Oracle Ebusiness Certification',
'Oracle Enterprise Linux',
'Oracle Enterprise Manager',
'Oracle Entitlements Server',
'Oracle Express Edition',
'Oracle Fusion Applications',
'Oracle Fusion Architecture',
'Oracle Fusion Middleware',
'Oracle Grid Engine',
'Oracle HTTP Server',
'Oracle Health Sciences',
'Oracle Hyperion Certification',
'Oracle Identity Management',
'Oracle Identity Manager',
'Oracle Internet Directory',
'Oracle Java VisualVM',
'Oracle Management Server',
'Oracle Mobile Certification',
'Oracle Net Services',
'Oracle NoSQL Database',
'Oracle Policy Automation',
'Oracle R Enterprise',
'Oracle SOA Suite',
'Oracle SQL Developer',
'Oracle Service Bus',
'Oracle Service Registry',
'Oracle Siebel CRM',
'Oracle Solaris Studio',
'Oracle Technology Network',
'Oracle Ultra Search',
'Oracle Unified Directory',
'Oracle Unified Method',
'Oracle User Group',
'Oracle VM VirtualBox',
'Oracle Virtual Directory',
'Oracle Warehouse Builder',
'Oracle Web Cache',
'Oracle WebLogic Platform',
'Oracle Weblogic Server',
'Oracle bone script',
'Oracle data cartridge',
'P2PE Assessor Certification',
'PMI Scheduling Professional',
'PRINCE2 Agile Certification',
'PRINCE2 Foundation Certification',
'PRINCE2 Practitioner Certification',
'PaizaCloud Cloud IDE',
'Palo Alto Networks',
'Parallel Environment Developer',
'Parallel Environment Runtime',
'Partition Load Manager',
'Pay Level Decrease',
'Pay Level Increase',
'Platform Application Center',
'Platform Cluster Manager',
'Platform HDFS Support',
'Platform Process Manager',
'Platform RTM Data',
'Point of Sale',
'Portfolio Management Professional',
'Positive Team Culture',
'Positive Work Ethic',
'PowerHA SystemMirror Enterprise',
'PowerHA SystemMirror Standard',
'PowerSC Express Edition',
'PowerSC Standard Edition',
'PowerVM VIOS Enterprise',
'PowerVM VIOS Standard',
'PowerVM Virtual I/O',
'Principal Component Regression',
'Principal Components Analysis',
'Process Orchestration 7.50',
'Program Management Professional',
'Project Management Professional',
'Project Management Software',
'Project Manager Certification',
'Project Procurement Manager',
'Proof of Concept',
'Proper Business Etiquette',
'Protecting The Environment',
'Proventia Desktop Endpoint',
'Proventia Endpoint Secure',
'Proventia Management SiteProtector',
'Proventia Network Multi-Function',
'Proventia Web Filter',
'Proxmox Virtual Environment',
'Public Key Cryptography',
'Python Visualization Library',
'Qualified Security Assessor',
'Quality of Service',
'Quantum Assembly Language',
'Quantum Complexity Theory',
'Quantum Computation Language',
'Quantum Computing Platform',
'Quantum Fault Tolerance',
'Quantum Ready Workshop',
'Quantum Tape Library',
'Quick Response Manufacturing',
'REXX for CICS',
'Rational Ada Developer',
'Rational Ada Embedded',
'Rational Application Developer',
'Rational Asset Analyzer',
'Rational Asset Manager',
'Rational Automation Framework',
'Rational Build Forge',
'Rational Business Developer',
'Rational COBOL Generation',
'Rational COBOL Runtime',
'Rational ClearCase Change',
'Rational ClearCase LT',
'Rational ClearCase MultiSite',
'Rational ClearQuest MultiSite',
'Rational Computer Based',
'Rational DOORS Analyst',
'Rational DOORS Web',
'Rational Development Studio',
'Rational Elite Support',
'Rational Engineering Lifecycle',
'Rational Focal Point',
'Rational Functional Tester',
'Rational Host Access',
'Rational Host Integration',
'Rational Host On-Demand',
'Rational Lifecycle Integration',
'Rational Lifecycle Package',
'Rational Manual Tester',
'Rational Method Composer',
'Rational Modeling Extension',
'Rational Open Access',
'Rational Performance Test',
'Rational Performance Tester',
'Rational Policy Tester',
'Rational Portfolio Manager',
'Rational Professional Bundle',
'Rational Programming Patterns',
'Rational Project Conductor',
'Rational Publishing Engine',
'Rational Purify family',
'Rational PurifyPlus Enterprise',
'Rational PurifyPlus family',
'Rational Quality Manager',
'Rational Requirements Composer',
'Rational Rhapsody family',
'Rational Rose Data',
'Rational Rose Developer',
'Rational Rose Enterprise',
'Rational Rose Modeler',
'Rational Rose Technical',
'Rational Rose family',
'Rational SDL Suite',
'Rational Service Tester',
'Rational Software Analyzer',
'Rational Software Architect',
'Rational Software Modeler',
'Rational Suite DevelopmentStudio',
'Rational System Architect',
'Rational Systems Developer',
'Rational Systems Tester',
'Rational TTCN Suite',
'Rational Team Concert',
'Rational Team Unifying',
'Rational Team Webtop',
'Rational Test RealTime',
'Rational Test Virtualization',
'Rational Test Workbench',
'Rational Transformation Workbench',
'Rational Visual Test',
'Rational Web Developer',
'Recurrent Neural Networks',
'Red Hat Architect',
'Red Hat DevOps',
'Red Hat Developer',
'Red Hat JBoss',
'Red Hat OpenShift',
'Red Hat Platform',
'Redhat Certified Engineer',
'Redhat Certified Specialist',
'Redhat OpenShift Certification',
'Redhat OpenStack Certification',
'Redhat Security Certification',
'Redhat Virtualization Certification',
'Regularized Discriminant Analysis',
'Rembo Toolkit Professional',
'Remote Procedure Call',
'Request For Proposal',
'Request For Service',
'Request for Proposal',
'Request for Service',
'Responsibility To Stockholders',
'Retail Data Warehouse',
'Risk Management Skill',
'Robotic Process Automation',
'SAN Data Center',
'SAP Ariba Catalogs',
'SAP Ariba Certification',
'SAP Ariba Contracts',
'SAP Ariba Procurement',
'SAP Ariba Sourcing',
'SAP BI Accelerator',
'SAP Business ByDesign',
'SAP Business Connector',
'SAP Business Intelligence',
'SAP Business Objects',
'SAP Business One',
'SAP Business Suite',
'SAP Certified Application',
'SAP Certified Associate',
'SAP Certified Development',
'SAP Certified Professional',
'SAP Certified Technology',
'SAP Cloud Platform',
'SAP Community Network',
'SAP Converged Cloud',
'SAP Convergent Charging',
'SAP Enable Now',
'SAP Enterprise Learning',
'SAP Enterprise Portal',
'SAP Exchange Infrastructure',
'SAP Global Certification',
'SAP HANA 2.0',
'SAP HANA Certification',
'SAP Hybris Billing',
'SAP Knowledge Warehouse',
'SAP Logon Ticket',
'SAP NetWeaver Mobile',
'SAP NetWeaver Portal',
'SAP Predictive Analytics',
'SAP Process Integration',
'SAP S/4HANA 1610',
'SAP S/4HANA 1709',
'SAP S/4HANA 1809',
'SAP S/4HANA Cloud',
'SAP Sales Cloud',
'SAP Solution Composer',
'SAP Solution Manager',
'SAP for Insurance',
'SAP for Retail',
'SAS Administration Certification',
'SAS Analytics Certification',
'SAS Enterprise Guide',
'SAS Enterprise Miner',
'SAS Foundation Certification',
'SAS Other Certification',
'SCLM Administrator Toolkit',
'SCLM Advanced Edition',
'SCLM Developer Toolkit',
'SCLM Suite Administrator',
'SOA Policy Gateway',
'SOA Policy Pattern',
'SPSS Data Collection',
'SPSS Decision Management',
'SPSS Event Builder',
'SPSS Interaction Builder',
'SPSS Model Builder',
'SPSS Risk Control',
'SPSS Text Analytics',
'SPSS Visualization Designer',
'SQL Server 2012/2014',
'SUSE OpenStack Cloud',
'Safe Swiss Cloud',
'Sage Business Cloud',
'Sales Delivery Executive',
'Salesforce Commerce Cloud',
'Salesforce Marketing Cloud',
'Sametime Unified Telephony',
'Scientific Programming Language',
'Scotia SmartModel Device',
'Screen Definition Facility',
'Second Line Manager',
'Security Account Manager',
'Security AppScan Enterprise',
'Security AppScan Source',
'Security AppScan family',
'Security Delivery Specialist',
'Security Identity Manager',
'Security Key Lifecycle',
'Security Network Active',
'Security Network Controller',
'Security Network Protection',
'Security Privileged Identity',
'Security Server Protection',
'Security SiteProtector System',
'Security zSecure Admin',
'Security zSecure Administration',
'Security zSecure Alert',
'Security zSecure Audit',
'Security zSecure CICS',
'Security zSecure Command',
'Security zSecure Manager',
'Security zSecure Visual',
'Self Organizing Map',
'Sense Of Security',
'Serial Peripheral Interface',
'Server Message Block',
'Server Resource Management',
'Server Support Specialist',
'Service Availability Manager',
'Service Integration Leader',
'Service Level Agreement',
'Service Management Skill',
'Service Oriented Architecture',
'Shopping Ads Certification',
'Short Term Focus',
'ShowCase Web Analysis',
'Simple Cloud API',
'Smart Analytic Optimizer',
'Smart Analytics System',
'SmartCloud Application Performance',
'SmartCloud Control Desk',
'SmartCloud Cost Management',
'SmartCloud Engage Advanced',
'SmartCloud Engage Standard',
'SmartCloud Patch Management',
'So Many Hackers',
'Social Media Company',
'Social Media Skill',
'Soft K Means',
'Software Defined Network',
'Software Design Pattern',
'Software Development Lifecycle',
'Software Development Process',
'Software Portfolio Alignment',
'Software Service Planner',
'Solution Sales Manager',
'Spanish Pharmaceutical Company',
'Speech Recognition System',
'Sql Server 2012/2014',
'Static Program Analysis',
'Statistical Model Testing',
'Staying On Task',
'Steady State Support',
'Sterling B2B Integrator',
'Sterling Connect:Enterprise Gateway',
'Sterling Control Center',
'Sterling File Gateway',
'Sterling Order Management',
'Sterling Secure Proxy',
'Sterling Warehouse Management',
'Stochastic Gradient Descent',
'Storage Access Method',
'Storage Administration Workbench',
'Storage Area Network',
'Storage Enterprise Resource',
'Subject Matter Expert',
'Suggestions And Complaints',
'Support Vector Classification',
'Support Vector Machine',
'Suse Linux Distributions',
'Sybase SQL Server',
'Symantec Workspace Virtualization',
'System Programming Skill',
'System Security Architect',
'Systems Directory Management',
'Systems Management Specialist',
'Systems Network Architecture',
'TRIRIGA Application Platform',
'TRIRIGA CAD Integrator/Publisher',
'TRIRIGA Energy Optimization',
'TRIRIGA Portfolio Data',
'TXSeries for Multiplatforms',
'Takeda Pharmaceutical Company',
'Technical Solution Architect',
'Technical Solution Manager',
'Technical Team Lead',
'Technology Trend Awareness',
'Telecommunications Data Warehouse',
'Teleprocessing Network Simulator',
'Theory of Constraints',
'Time Series Clustering',
'Time Series Database',
'Tivoli AF/Integrated Resource',
'Tivoli Access Manager',
'Tivoli Advanced Audit',
'Tivoli Advanced Backup',
'Tivoli Advanced Catalog',
'Tivoli Advanced Reporting',
'Tivoli Alert Adapter',
'Tivoli Allocation Optimizer',
'Tivoli Application Dependency',
'Tivoli Asset Discovery',
'Tivoli Asset Management',
'Tivoli Automated Tape',
'Tivoli Availability Process',
'Tivoli Business Continuity',
'Tivoli Business Service',
'Tivoli Business Systems',
'Tivoli Capacity Process',
'Tivoli Central Control',
'Tivoli Command Center',
'Tivoli Common Reporting',
'Tivoli Composite Application',
'Tivoli Configuration Manager',
'Tivoli Continuous Data',
'Tivoli Data Warehouse',
'Tivoli Decision Support',
'Tivoli Directory Integrator',
'Tivoli Directory Server',
'Tivoli Dynamic Workload',
'Tivoli ETEWatch Customizer',
'Tivoli ETEWatch Enterprise',
'Tivoli ETEWatch Starter',
'Tivoli Endpoint Manager',
'Tivoli Enterprise Console',
'Tivoli Event Pump',
'Tivoli Federated Identity',
'Tivoli Foundations Application',
'Tivoli Foundations Service',
'Tivoli Identity Manager',
'Tivoli Information Management',
'Tivoli Integration Composer',
'Tivoli IntelliWatch Pinnacle',
'Tivoli Intelligent Orchestrator',
'Tivoli Intrusion Manager',
'Tivoli Kernel Services',
'Tivoli Key Lifecycle',
'Tivoli Management Framework',
'Tivoli Management Solution',
'Tivoli Monitoring Active',
'Tivoli Monitoring Express',
'Tivoli Monitoring V6',
'Tivoli NetView Access',
'Tivoli NetView Distribution',
'Tivoli NetView File',
'Tivoli NetView Performance',
'Tivoli Netcool Carrier',
'Tivoli Netcool Configuration',
'Tivoli Netcool Performance',
'Tivoli Netcool Service',
'Tivoli Netcool/OMNIbus Gateways',
'Tivoli OMEGACENTER Gateway',
'Tivoli OMEGAMON Alert',
'Tivoli OMEGAMON DE',
'Tivoli OMEGAMON II',
'Tivoli OMEGAMON Monitoring',
'Tivoli OMEGAMON XE',
'Tivoli Output Manager',
'Tivoli Performance Analyzer',
'Tivoli Performance Modeler',
'Tivoli Policy Driven',
'Tivoli Privacy Manager',
'Tivoli Provisioning Manager',
'Tivoli Release Process',
'Tivoli Remote Control',
'Tivoli Risk Manager',
'Tivoli Security Compliance',
'Tivoli Security Information',
'Tivoli Security Management',
'Tivoli Security Operations',
'Tivoli Security Policy',
'Tivoli Service Automation',
'Tivoli Service Level',
'Tivoli Service Manager',
'Tivoli Service Request',
'Tivoli Storage FlashCopy',
'Tivoli Storage Manager',
'Tivoli Storage Optimizer',
'Tivoli Storage Process',
'Tivoli Storage Productivity',
'Tivoli Storage Resource',
'Tivoli Switch Analyzer',
'Tivoli System Automation',
'Tivoli Tape Optimizer',
'Tivoli Unified Process',
'Tivoli Unified Single',
'Tivoli Universal Agent',
'Tivoli User Administration',
'Tivoli Web Access',
'Tivoli Web Availability',
'Tivoli Web Response',
'Tivoli Web Segment',
'Tivoli Web Site',
'Tivoli Workload Scheduler',
'Tivoli zSecure Admin',
'Tivoli zSecure Alert',
'Tivoli zSecure Audit',
'Tivoli zSecure CICS',
'Tivoli zSecure Command',
'Tivoli zSecure Manager',
'Tivoli zSecure Visual',
'Total Productive Maintenance',
'Total Quality Management',
'TotalStorage Productivity Center',
'Train the Trainers',
'Transaction Processing Facility',
'Transmission Control Protocol',
'Tree Based Model',
'Unica Distributed Marketing',
'Unica Interactive Marketing',
'Unica Marketing Operations',
'Unica Marketing Platform',
'Unica NetInsight OnDemand',
'Unified Method Framework',
'Universal Windows Platform',
'User Acceptance Test',
'User Centered Design',
'User Interface Design',
'VMWare Horizon View',
'VMWare Thin App',
'VMware Certified Associate',
'VMware Certified Professional',
'VMware Cloud Foundation',
'VTAM for VSE',
'VTAM for VSE/ESA',
'Value Driven Maintenance',
'Video Game Exploit',
'Virtual Computing Platform',
'Virtual Private Cloud',
'Virtual Runtime Environment',
'VisualAge Generator EGL',
'Watson Conversation Service',
'Watson Data Platform',
'Watson Support Agent',
'Watson Wealth Advisor',
'Web Application Framework',
'WebSphere Adapters Family',
'WebSphere Appliance Management',
'WebSphere Application Accelerator',
'WebSphere Application Server',
'WebSphere Business Compass',
'WebSphere Business Events',
'WebSphere Business Integration',
'WebSphere Business Modeler',
'WebSphere Business Monitor',
'WebSphere Business Services',
'WebSphere Cast Iron',
'WebSphere CloudBurst Appliance',
'WebSphere Commerce Enterprise',
'WebSphere Commerce Professional',
'WebSphere Customer Center',
'WebSphere Dashboard Framework',
'WebSphere Data Interchange',
'WebSphere DataPower B2B',
'WebSphere DataPower Edge',
'WebSphere DataPower Integration',
'WebSphere DataPower Low',
'WebSphere DataPower SOA',
'WebSphere DataPower Service',
'WebSphere DataPower XC10',
'WebSphere DataPower XML',
'WebSphere Decision Center',
'WebSphere Decision Server',
'WebSphere Developer Debugger',
'WebSphere Development Studio',
'WebSphere Digital Media',
'WebSphere Dynamic Process',
'WebSphere Enterprise Service',
'WebSphere Event Broker',
'WebSphere Everyplace Client',
'WebSphere Everyplace Custom',
'WebSphere Everyplace Micro',
'WebSphere Everyplace Server',
'WebSphere Extended Deployment',
'WebSphere Front Office',
'WebSphere Host Access',
'WebSphere Host Publisher',
'WebSphere ILOG Decision',
'WebSphere ILOG JRules',
'WebSphere ILOG Rule',
'WebSphere ILOG Rules',
'WebSphere IP Multimedia',
'WebSphere Industry Content',
'WebSphere Integration Developer',
'WebSphere InterChange Server',
'WebSphere Lombardi Edition',
'WebSphere MQ Everyplace',
'WebSphere MQ Hypervisor',
'WebSphere MQ Integrator',
'WebSphere MQ Low',
'WebSphere MQ Workflow',
'WebSphere Message Broker',
'WebSphere Operational Decision',
'WebSphere Partner Gateway',
'WebSphere Portal End',
'WebSphere Portlet Factory',
'WebSphere Premises Server',
'WebSphere Presence Server',
'WebSphere Process Server',
'WebSphere Product Center',
'WebSphere Real Time',
'WebSphere Remote Server',
'WebSphere Sensor Events',
'WebSphere Service Registry',
'WebSphere Studio Application',
'WebSphere Studio Asset',
'WebSphere Studio Enterprise',
'WebSphere Studio Site',
'WebSphere Studio Workload',
'WebSphere Studio family',
'WebSphere Transaction Cluster',
'WebSphere Transcoding Publisher',
'WebSphere Transformation Extender',
'WebSphere Translation Server',
'WebSphere Virtual Enterprise',
'WebSphere Voice Response',
'WebSphere Voice Server',
'WebSphere Voice Toolkit',
'WebSphere XML Document',
'WebSphere eXtended Transaction',
'WebSphere eXtreme Scale',
'Wide Area Network',
'Willingness To Learn',
'Windows Management Instrumentation',
'Windows Server 2012',
'Windows Server 2016',
'Work From Home',
'Work Life Balance',
'Work from Home',
'Workload Deployer Pattern',
'World Wide Web',
'active record pattern',
'adobe 3d reviewer',
'adobe acrobat 3d',
'adobe acrobat connect',
'adobe acrobat inproduction',
'adobe acrobat x',
'adobe after effects',
'adobe audience manager',
'adobe camera raw',
'adobe capture cc',
'adobe character animator',
'adobe coldfusion builder',
'adobe comp cc',
'adobe content server',
'adobe content viewer',
'adobe creative cloud',
'adobe creative suite',
'adobe cs live',
'adobe device central',
'adobe digital editions',
'adobe dng converter',
'adobe document cloud',
'adobe dynamic link',
'adobe edge animate',
'adobe edge code',
'adobe edge reflow',
'adobe elearning suite',
'adobe experience design',
'adobe experience manager',
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'dow chemical company',
'dynamics 365 fundamentals',
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'master data management',
'master of arts',
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'maximo asset configuration',
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'mean shift clustering',
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'microsoft 365 certification',
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'Red Hat Databases Modernization',
'Red Hat Microsoft Apps',
'Red Hat Middleware Architect',
'Red Hat Middleware Modernization',
'Red Hat Open Source',
'Red Hat OpenShift Virtualization',
'Red Hat Site Reliability',
'Redhat Ansible Automation Certification',
'Redhat Business Rules Certification',
'Redhat Camel Development Certification',
'Redhat Certified System Administrator',
'Redhat Configuration Management Certification',
'Redhat Data Virtualization Certification',
'Redhat Identity Management Certification',
'Redhat Messaging Administration Certification',
'Redhat OpenShift Administration Certification',
'Redhat Security Linux Certification',
'Responsibility to the Nature',
'Responsible To The Communities',
'Risk Aware Recommender Systems',
'SAN Data Center Fabric',
'SAP Activate Project Manager',
'SAP Ariba SNAP Deployment',
'SAP Ariba Spend Analysis',
'SAP Ariba Supplier Management',
'SAP Business Information Warehouse',
'SAP Catalog Content Management',
'SAP Central Process Scheduling',
'SAP Certified Application Associate',
'SAP Certified Application Professional',
'SAP Certified Application Specialist',
'SAP Certified Development Associate',
'SAP Certified Development Professional',
'SAP Certified Development Specialist',
'SAP Certified Integration Associate',
'SAP Certified Technology Associate',
'SAP Certified Technology Professional',
'SAP Certified Technology Specialist',
'SAP Cloud Platform Integration',
'SAP Cloud for Customer',
'SAP Composite Application Framework',
'SAP Customer Data Cloud',
'SAP Enterprise Buyer Professional',
'SAP Financial Services Network',
'SAP Fiori Application Developer',
'SAP Fiori System Administration',
'SAP HANA 2.0 SPS02',
'SAP HANA 2.0 SPS03',
'SAP HANA Cloud Platform',
'SAP Hybris Commerce 6.0',
'SAP Internet Transaction Server',
'SAP Lumira Designer 2.1',
'SAP Master Data Governance',
'SAP Master Data Management',
'SAP NetWeaver Application Server',
'SAP NetWeaver Business Intelligence',
'SAP NetWeaver Business Warehouse',
'SAP NetWeaver Composition Environment',
'SAP NetWeaver Developer Studio',
'SAP NetWeaver Development Infrastructure',
'SAP NetWeaver Identity Management',
'SAP NetWeaver Process Integration',
'SAP NetWeaver Single Sign-On',
'SAP Rapid Deployment Solutions',
'SAP S/4HANA Sales Upskilling',
'SAP Sales Cloud 1811',
'SAP Service Cloud 1811',
'SAP Shipping Services Network',
'SAP Strategic Enterprise Management',
'SAP Success Factors Certification',
'SAP SuccessFactors Compensation Q1/2019',
'SAP SuccessFactors Reporting Q1/2019',
'SAP Supply Chain Management',
'SAP Transportation Management 9.5',
'SAP Web Application Server',
'SAS Advanced Analytics Certification',
'SAS Data Management Certification',
'SCADA Security Architect Certification',
'SCLM Suite Administrator Workbench',
'SNIA Certified Information Architect',
'SNIA Certified Storage Architect',
'SNIA Certified Storage Engineer',
'SNIA Certified Storage Professional',
'SOA Policy Gateway Pattern',
'SPSS Collaboration and Deployment',
'SPSS Data Collection Heritage',
'SPSS Risk Control Builder',
'SQL 2016 BI Development',
'SQL 2016 Database Administration',
'SQL 2016 Database Development',
'SUSE Cloud Application Platform',
'Scada Security Architect Certification',
'Screen Definition Facility II',
'Security Key Lifecycle Manager',
'Security Network Active Bypass',
'Security Privileged Identity Manager',
'Security zSecure CICS Toolkit',
'Security zSecure Command Verifier',
'Seeded Latent Dirchlet Allocation',
'Series C Financing Round',
'Session Manager for z/OS',
'Show What You Know',
'Siebel 8 Consultant Exam',
'SmartCloud Application Performance Management',
'SmartCloud Entry for Power',
'Software as a Service',
'Sql 2016 Bi Development',
'Sql 2016 Database Administration',
'Sql 2016 Database Development',
'Sql Server Integration Services',
'Sql Server Management Studio',
'Sterling Configure and Price',
'Sterling Connect:Direct for HP',
'Sterling Connect:Direct for Microsoft',
'Sterling Connect:Direct for OpenVMS',
'Sterling Connect:Direct for UNIX',
'Sterling Connect:Direct for VM/VSE',
'Sterling Connect:Direct for i5/OS',
'Sterling Connect:Direct for z/OS',
'Sterling Connect:Enterprise for UNIX',
'Sterling Connect:Enterprise for z/OS',
'Sterling Connect:Express for Microsoft',
'Sterling Connect:Express for UNIX',
'Sterling Connect:Express for z/OS',
'Sterling Gentran for z/OS',
'Sterling Gentran:Basic for zSeries',
'Sterling Gentran:Server for Microsoft',
'Sterling Gentran:Server for UNIX',
'Sterling Gentran:Server for iSeries',
'Sterling Selling and Fulfillment',
'Sterling Warehouse Management System',
'Storage Enterprise Resource Planner',
'Storage Networking Certification Program',
'Storage Networking Industry Association',
'Supplier Relationship Management 7.2',
'Systems Director Management Console',
'TRIRIGA Portfolio Data Manager',
'Tape Manager for z/VM',
'Telco Cloud Computing Platform',
'Thinking Outside The Box',
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'Tivoli AF/OPERATOR on z/OS',
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'Tivoli Business Service Manager',
'Tivoli Capacity Process Manager',
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'Tivoli Continuous Data Protection',
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'Tivoli Dynamic Workload Broker',
'Tivoli ETEWatch Enterprise Edition',
'Tivoli ETEWatch Starter Kit',
'Tivoli ETEWatch for Citrix',
'Tivoli ETEWatch for Custom',
'Tivoli ETEWatch for TN3270',
'Tivoli Federated Identity Manager',
'Tivoli Foundations Application Manager',
'Tivoli Foundations Service Manager',
'Tivoli Identity Manager Express',
'Tivoli Identity and Access',
'Tivoli Key Lifecycle Manager',
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'Tivoli Monitoring for Applications',
'Tivoli Monitoring for Business',
'Tivoli Monitoring for Cluster',
'Tivoli Monitoring for Energy',
'Tivoli Monitoring for Messaging',
'Tivoli Monitoring for Microsoft',
'Tivoli Monitoring for Virtual',
'Tivoli NetView Access Services',
'Tivoli NetView Distribution Manager',
'Tivoli NetView File Transfer',
'Tivoli NetView Performance Monitor',
'Tivoli NetView for z/OS',
'Tivoli Netcool Carrier VoIP',
'Tivoli Netcool Configuration Manager',
'Tivoli Netcool Network Mediation',
'Tivoli Netcool Performance Flow',
'Tivoli Netcool Performance Manager',
'Tivoli Netcool Service Quality',
'Tivoli OMEGAMON Alert Manager',
'Tivoli OMEGAMON Monitoring Agent',
'Tivoli OMEGAMON XE Management',
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'Tivoli Provisioning Manager Express',
'Tivoli Release Process Manager',
'Tivoli Security Compliance Manager',
'Tivoli Security Operations Manager',
'Tivoli Security Policy Manager',
'Tivoli Service Automation Manager',
'Tivoli Service Level Advisor',
'Tivoli Service Manager Quick',
'Tivoli Service Request Manager',
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'Tivoli Storage Manager Express',
'Tivoli Storage Manager Extended',
'Tivoli Storage Manager FastBack',
'Tivoli Storage Manager HSM',
'Tivoli Storage Manager Suite',
'Tivoli Storage Process Manager',
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'Tivoli Storage Resource Manager',
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'Tivoli Unified Process Composer',
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'Truncated Singular Value Decomposition',
'Unica Interactive Marketing OnDemand',
'Unified Messaging for WebSphere',
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'VMware Certified Design Expert',
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'Virtual Local Area Network',
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'azure ai engineer associate',
'azure data engineer associate',
'azure data scientist associate',
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'azure security engineer associate',
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'cisco video network specialist',
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'citrix microsoft azure certified',
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'cloud container platform architecture',
'cloud container re platform',
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'cloud foundry certified developer',
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'cloudburst for power systems',
'cloudera certified data engineer',
'cloudera data analyst certification',
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'commonstore for exchange server',
'commonstore for lotus domino',
'communication controller for linux',
'communications server for aix',
'communications server for linux',
'communications server for windows',
'connections enterprise content edition',
'content analytics with enterprise',
'content collector for sap',
'content manager enterprise edition',
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'core configuration & ats',
'cplex optimizer for zos',
'data at rest encryption',
'data encryption for ims',
'data engineering with azure',
'data server client packages',
'data studio purequery runtime',
'db2 archive log accelerator',
'db2 audit management expert',
'db2 buffer pool analyzer',
'db2 certified database administrator',
'db2 certified database associate',
'db2 change accumulation tool',
'db2 change management expert',
'db2 data archive expert',
'db2 data links manager',
'db2 datapropagator for iseries',
'db2 high performance unload',
'db2 imageplus for zos',
'db2 log analysis tool',
'db2 object comparison tool',
'db2 query management facility',
'db2 server for vse',
'db2 test database generator',
'db2 toolkit for multiplatforms',
'db2 tools for linux',
'db2 tools for zos',
'db2 universal database enterprise',
'db2 universal database personal',
'db2 utilities enhancement tool',
'db2 utilities solution pack',
'dealing with difficult personalities',
'dealing with difficult situations',
'dealing with office politics',
'debug tool for vseesa',
'debug tool for zos',
'decision center for zos',
'decision server for zos',
'dell converged infrastructure certification',
'dell data protection certification',
'dell data science certifictation',
'dell enterprise architect certification',
'disposal and governance management',
'doctorate of computer science',
'domain driven data mining',
'dynamics 365 for marketing',
'dynamics 365 for operations',
'dynamics 365 for sales',
'emc elastic cloud storage',
'enterprise cobol for zos',
'enterprise pli for zos',
'experiment with new ideas',
'exponential family linear model',
'fair share of taxes',
'fault analyzer for zos',
'file manager for zos',
'filenet business activity monitor',
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'filenet business process manager',
'filenet connectors for microsoft',
'filenet idm desktopweb servicesopen',
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'filenet team collaboration manager',
'financials functional consultant associate',
'fujitsu global cloud platform',
'functions well under pressure',
'general data protection regulation',
'general parallel file system',
'giac advanced smartphone forensics',
'giac certified detection analyst',
'giac certified enterprise defender',
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'giac certified intrusion analyst',
'giac certified project manager',
'giac continuous monitoring certification',
'giac critical controls certification',
'giac critical infrastructure protection',
'giac cyber threat intelligence',
'giac defending advanced threats',
'giac defensible security architecture',
'giac enterprise vulnerability assessor',
'giac information security professional',
'giac network forensic analyst',
'giac reverse engineering malware',
'giac secure software programmer',
'google ads display certification',
'google ads search certification',
'google ads video certification',
'google certified android developer',
'graphical data display manager',
'green hat virtual integration',
'guided latent dirchlet allocation',
'healthcare provider data warehouse',
'high availability cluster multi-processing',
'host access client package',
'human factors and ergonomics',
'i2 Analysts Notebook Connector',
'i2 Analysts Notebook Premium',
'i2 Fraud Intelligence Analysis',
'i2 Intelligence Analysis Platform',
'i2 analysts notebook connector',
'i2 analysts notebook premium',
'i2 fraud intelligence analysis',
'i2 intelligence analysis platform',
'ibm q associate ambassador',
'ibm q intermediate ambassador',
'ibm q senior ambassador',
'ilog cplex optimization studio',
'ilog diagram for net',
'ilog gantt for net',
'ilog inventory and product',
'ilog jviews graph layout',
'ilog opl-cplex development bundles',
'ilog telecom graphic objects',
'ims audit management expert',
'ims batch backout manager',
'ims batch terminal simulator',
'ims buffer pool analyzer',
'ims command control facility',
'ims database control suite',
'ims database recovery facility',
'ims database repair facility',
'ims database solution pack',
'ims datapropagator for zos',
'ims dedb fast recovery',
'ims extended terminal option',
'ims fast path solution',
'ims high availability large',
'ims high performance change',
'ims high performance fast',
'ims high performance image',
'ims high performance load',
'ims high performance pointer',
'ims high performance prefix',
'ims high performance unload',
'ims library integrity utilities',
'ims online reorganization facility',
'ims performance solution pack',
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'ims queue control facility',
'ims recovery solution pack',
'ims sequential randomizer generator',
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'indian multinational pharmaceutical company',
'information server data quality',
'informix enterprise gateway manager',
'informix extended parallel server',
'informix growth warehouse edition',
'infosphere change data capture',
'infosphere classic data event',
'infosphere data event publisher',
'infosphere global name recognition',
'infosphere guardium data encryption',
'infosphere guardium vulnerability assessment',
'infosphere information services director',
'infosphere master data management',
'infosphere optim configuration manager',
'infosphere optim high performance',
'infosphere optim performance manager',
'infosphere optim purequery runtime',
'infosphere optim query capture',
'infosphere optim query tuner',
'infosphere optim query workload',
'infrastructure as a service',
'initiate address verification interface',
'initiate master data service',
'insurance process and service',
'integrated services digital network',
'isms internal auditor certification',
'isms lead auditor certification',
'isms lead implementer certification',
'itil service design certification',
'itil service management certification',
'java platform, enterprise edition',
'job specific soft skills',
'juniper networks certified professional',
'juniper networks technical certification',
'kernel k means clustering',
'level 1 customer service',
'level 2 customer service',
'level 3 customer service',
'lightweight directory access protocol',
'linear dimension reduction model',
'linear support vector classification',
'linear support vector machine',
'linear support vector regression',
'linux foundation certified engineer',
'linux foundation certified sysadmin',
'load leveler for linux',
'lotus connector for sap',
'lotus domino document manager',
'lotus end of support',
'lotus foundations branch office',
'lotus protector for mail',
'lotus quickr content integrator',
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'markov chain monte carlo',
'master of computer science',
'maximo adapter for microsoft',
'maximo adapter for primavera',
'maximo archiving with optim',
'maximo asset configuration manager',
'maximo asset management essentials',
'maximo asset management scheduler',
'maximo change and corrective',
'maximo compliance assistance documentation',
'maximo contract and procurement',
'maximo data center infrastructure',
'maximo for life sciences',
'maximo for nuclear power',
'maximo for service providers',
'maximo mobile audit manager',
'maximo mobile inventory manager',
'maximo mobile work manager',
'maximo online commerce system',
'maximo spatial asset management',
'merge tool for zos',
'messaging extension for web',
'microsoft certified bi development',
'microsoft certified bi reporting',
'microsoft certified blockbased languages',
'microsoft certified cloud fundamentals',
'microsoft certified database administration',
'microsoft certified database administrator',
'microsoft certified database development',
'microsoft certified database fundamentals',
'microsoft certified development fundamentals',
'microsoft certified java programmer',
'microsoft certified javascript programmer',
'microsoft certified mobility fundamentals',
'microsoft certified networking fundamentals',
'microsoft certified python programmer',
'microsoft certified security fundamentals',
'microsoft certified sql server',
'microsoft certified web applications',
'microsoft certified windows fundamentals',
'migration utility for zos',
'mixed effects logistic regression',
'mixed integer linear programming',
'mobile web specialist certification',
'mos: microsoft access 2016',
'mos: microsoft excel 2016',
'mos: microsoft outlook 2016',
'mos: microsoft powerpoint 2016',
'mta: software development fundamentals',
'multi criteria recommender systems',
'multinational pharmaceutical company with',
'multivariate gaussian mixture model',
'my new cloud certification',
'netcool for security management',
'netcool omnibus virtual operator',
'netcool realtime active dashboards',
'netcool system service monitor',
'netezza high capacity appliance',
'nfx series network services',
'noisy intermediate scale quantum',
'omegamon zos management console',
'open cloud computing interface',
'open source nlp software',
'openjs nodejs application developer',
'openjs nodejs services developer',
'operations manager for zvm',
'optim high performance unload',
'optim move for db2',
'oracle big data appliance',
'oracle business activity monitoring',
'oracle business intelligence beans',
'oracle cluster file system',
'oracle communications calendar server',
'oracle communications messaging server',
'oracle enterprise messaging service',
'oracle enterprise service bus',
'oracle essbase 11 essentials',
'oracle help for java',
'oracle iplanet web server',
'oracle real application testing',
'oracle secure global desktop',
'oracle software configuration manager',
'oracle spatial and graph',
'oracle web services manager',
'ordinary least squares regression',
'orion molecular cloud complex',
'palo alto network certification',
'parallel engineering and scientific',
'parallel environment developer edition',
'parallel environment for aix',
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'parallel environment runtime edition',
'partial least squares regression',
'pci security standards council',
'platform hdfs support server',
'platform hpc for system',
'platform rtm data collectors',
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'pmi risk management professional',
'powerha systemmirror enterprise edition',
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'pqedit for distributed systems',
'prince2 agile foundation certification',
'prince2 agile practitioner certification',
'probabilistic latent semantic analysis',
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'proventia network mail filter',
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'puredata system for operational',
'puredata system for transactions',
'qualified integrators and resellers',
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'red hat commercial middleware',
'red hat databases modernization',
'red hat microsoft apps',
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'red hat middleware modernization',
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'red hat site reliability',
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'redhat business rules certification',
'redhat camel development certification',
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'redhat data virtualization certification',
'redhat identity management certification',
'redhat messaging administration certification',
'redhat openshift administration certification',
'redhat security linux certification',
'responsibility to the nature',
'responsible to the communities',
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'san data center fabric',
'sap activate project manager',
'sap ariba snap deployment',
'sap ariba spend analysis',
'sap ariba supplier management',
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'sap catalog content management',
'sap central process scheduling',
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py
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sample-viewer-api/src/static/data/compile_cvisb_data/clean_hla/__init__.py
|
cvisb/cvisb_data
|
81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed
|
[
"MIT"
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|
2020-02-18T08:16:45.000Z
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2021-04-11T18:58:02.000Z
|
sample-viewer-api/src/static/data/compile_cvisb_data/clean_hla/__init__.py
|
cvisb/cvisb_data
|
81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed
|
[
"MIT"
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2019-09-30T22:26:36.000Z
|
2021-11-17T00:34:38.000Z
|
sample-viewer-api/src/static/data/compile_cvisb_data/clean_hla/__init__.py
|
cvisb/cvisb_data
|
81ebf22782f2c44f8aa8ab9437cc4fb54248c3ed
|
[
"MIT"
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2020-07-01T21:15:18.000Z
|
2020-07-01T21:15:18.000Z
|
from .clean_hla import clean_hla
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py
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Python
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fastsom/som/__init__.py
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kireygroup/fastsom
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18388c666635350d960c732974f3460687562651
|
[
"MIT"
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2020-04-21T12:36:47.000Z
|
2022-02-22T11:45:22.000Z
|
fastsom/som/__init__.py
|
kireygroup/fastsom
|
18388c666635350d960c732974f3460687562651
|
[
"MIT"
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|
2020-04-21T11:20:28.000Z
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2021-09-08T01:55:33.000Z
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fastsom/som/__init__.py
|
kireygroup/fastsom
|
18388c666635350d960c732974f3460687562651
|
[
"MIT"
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2021-02-25T13:21:54.000Z
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2021-09-29T00:25:16.000Z
|
"""
"""
from .distance import *
from .neighborhood import *
from .som import *
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py
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python3/lib/python3.6/site-packages/tensorflow/python/keras/api/_v1/keras/__init__.py
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TruongThuyLiem/keras2tensorflow
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726f2370160701081cb43fbd8b56154c10d7ad63
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2022-01-14T19:51:26.000Z
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python3/lib/python3.6/site-packages/tensorflow/python/keras/api/_v1/keras/__init__.py
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TruongThuyLiem/keras2tensorflow
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726f2370160701081cb43fbd8b56154c10d7ad63
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[
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python3/lib/python3.6/site-packages/tensorflow/python/keras/api/_v1/keras/__init__.py
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TruongThuyLiem/keras2tensorflow
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726f2370160701081cb43fbd8b56154c10d7ad63
|
[
"MIT"
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2020-08-03T13:02:06.000Z
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2020-11-04T03:15:44.000Z
|
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Implementation of the Keras API meant to be a high-level API for TensorFlow.
Detailed documentation and user guides are available at
[keras.io](https://keras.io).
"""
from __future__ import print_function as _print_function
from tensorflow.python.keras import Input
from tensorflow.python.keras import Model
from tensorflow.python.keras import Sequential
from tensorflow.python.keras import __version__
from tensorflow.python.keras.api._v1.keras import activations
from tensorflow.python.keras.api._v1.keras import applications
from tensorflow.python.keras.api._v1.keras import backend
from tensorflow.python.keras.api._v1.keras import callbacks
from tensorflow.python.keras.api._v1.keras import constraints
from tensorflow.python.keras.api._v1.keras import datasets
from tensorflow.python.keras.api._v1.keras import estimator
from tensorflow.python.keras.api._v1.keras import experimental
from tensorflow.python.keras.api._v1.keras import initializers
from tensorflow.python.keras.api._v1.keras import layers
from tensorflow.python.keras.api._v1.keras import losses
from tensorflow.python.keras.api._v1.keras import metrics
from tensorflow.python.keras.api._v1.keras import mixed_precision
from tensorflow.python.keras.api._v1.keras import models
from tensorflow.python.keras.api._v1.keras import optimizers
from tensorflow.python.keras.api._v1.keras import preprocessing
from tensorflow.python.keras.api._v1.keras import regularizers
from tensorflow.python.keras.api._v1.keras import utils
from tensorflow.python.keras.api._v1.keras import wrappers
del _print_function
import sys as _sys
from tensorflow.python.util import deprecation_wrapper as _deprecation_wrapper
if not isinstance(_sys.modules[__name__], _deprecation_wrapper.DeprecationWrapper):
_sys.modules[__name__] = _deprecation_wrapper.DeprecationWrapper(
_sys.modules[__name__], "keras")
| 45.454545
| 83
| 0.8365
| 287
| 2,000
| 5.627178
| 0.275261
| 0.247678
| 0.297214
| 0.356037
| 0.629721
| 0.552941
| 0.552941
| 0.552941
| 0.070588
| 0
| 0
| 0.010388
| 0.0855
| 2,000
| 43
| 84
| 46.511628
| 0.872608
| 0.145
| 0
| 0
| 1
| 0
| 0.002939
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.866667
| 0
| 0.866667
| 0.066667
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
eed753e88fff6da7819eb8b85bbe84a5247d96aa
| 25
|
py
|
Python
|
models/__init__.py
|
danieldemedziuk/odoo12-lists
|
7d61646b4e5dfa2f84d7b1789660105e9fa8fc38
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
danieldemedziuk/odoo12-lists
|
7d61646b4e5dfa2f84d7b1789660105e9fa8fc38
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
danieldemedziuk/odoo12-lists
|
7d61646b4e5dfa2f84d7b1789660105e9fa8fc38
|
[
"MIT"
] | 1
|
2020-11-05T14:07:57.000Z
|
2020-11-05T14:07:57.000Z
|
from . import lists_line
| 12.5
| 24
| 0.8
| 4
| 25
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.904762
| 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
| 1
| 0
|
0
| 6
|
e10fb359e72b7870251586c33760ab31ce053e4f
| 44
|
py
|
Python
|
continual_learning/methods/task_incremental/multi_task/gg/ewc/__init__.py
|
jaryP/ContinualAI
|
7d9b7614066d219ebd72049692da23ad6ec132b0
|
[
"MIT"
] | null | null | null |
continual_learning/methods/task_incremental/multi_task/gg/ewc/__init__.py
|
jaryP/ContinualAI
|
7d9b7614066d219ebd72049692da23ad6ec132b0
|
[
"MIT"
] | null | null | null |
continual_learning/methods/task_incremental/multi_task/gg/ewc/__init__.py
|
jaryP/ContinualAI
|
7d9b7614066d219ebd72049692da23ad6ec132b0
|
[
"MIT"
] | null | null | null |
from .EWC import ElasticWeightConsolidation
| 22
| 43
| 0.886364
| 4
| 44
| 9.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.975
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6d7ef0ebfab502d68a878bcda578f0edc7798bdd
| 19
|
py
|
Python
|
UR_Control/ur_online_control/ur/__init__.py
|
ytakzk/Gradual-Assemblies
|
48579e7a2d73d51b95a685fc1b757024c96011d4
|
[
"MIT"
] | 5
|
2021-08-12T07:20:02.000Z
|
2022-02-26T02:48:32.000Z
|
UR_Control/ur_online_control/ur/__init__.py
|
ytakzk/Gradual-Assemblies
|
48579e7a2d73d51b95a685fc1b757024c96011d4
|
[
"MIT"
] | 7
|
2021-01-20T16:31:21.000Z
|
2021-01-21T15:36:45.000Z
|
UR_Control/ur_online_control/ur/__init__.py
|
ytakzk/Gradual-Assemblies
|
48579e7a2d73d51b95a685fc1b757024c96011d4
|
[
"MIT"
] | 2
|
2020-11-19T14:26:41.000Z
|
2020-12-11T13:32:55.000Z
|
from .ur import UR
| 9.5
| 18
| 0.736842
| 4
| 19
| 3.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 19
| 1
| 19
| 19
| 0.933333
| 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
| 1
| 0
|
0
| 6
|
096ba48fccb08af660c4146594ec58d590fcfb43
| 8,569
|
py
|
Python
|
test.py
|
raj-patra/cryptogram
|
923c923b324d3b4829265baa472af687faf74f09
|
[
"MIT"
] | 1
|
2021-11-21T17:21:22.000Z
|
2021-11-21T17:21:22.000Z
|
test.py
|
raj-patra/cryptogram
|
923c923b324d3b4829265baa472af687faf74f09
|
[
"MIT"
] | 1
|
2021-07-25T02:02:15.000Z
|
2021-07-25T02:02:15.000Z
|
test.py
|
raj-patra/cryptogram
|
923c923b324d3b4829265baa472af687faf74f09
|
[
"MIT"
] | null | null | null |
import unittest
from cryptogram.cypher import Cypher
from cryptogram.encode import Encode
from cryptogram.transform import Transform
class TestCryptogram(unittest.TestCase):
def setUp(self) -> None:
self.message = "Hello World"
self.enc_obj = Encode()
self.trf_obj = Transform()
self.cyp_obj = Cypher()
return super().setUp()
def test_encode_init(self):
self.assertEqual(str, type(self.enc_obj.__str__()))
self.assertEqual(list, type(self.enc_obj.__engines__()))
def test_encode_base64(self):
encoded = self.enc_obj.encode(message=self.message, engine="base64")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="base64")
self.assertEqual(decoded["decoded_message"], self.message)
def test_encode_base32(self):
encoded = self.enc_obj.encode(message=self.message, engine="base32")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="base32")
self.assertEqual(decoded["decoded_message"], self.message)
def test_encode_base16(self):
encoded = self.enc_obj.encode(message=self.message, engine="base16")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="base16")
self.assertEqual(decoded["decoded_message"], self.message)
def test_encode_ascii85(self):
encoded = self.enc_obj.encode(message=self.message, engine="ascii85")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="ascii85")
self.assertEqual(decoded["decoded_message"], self.message)
def test_encode_url(self):
encoded = self.enc_obj.encode(message=self.message, engine="url")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="url")
self.assertEqual(decoded["decoded_message"], self.message)
def test_encode_base85(self):
encoded = self.enc_obj.encode(message=self.message, engine="base85")
decoded = self.enc_obj.decode(message=encoded["encoded_message"], engine="base85")
self.assertEqual(decoded["decoded_message"], self.message)
def test_transform_init(self):
self.assertEqual(str, type(self.trf_obj.__str__()))
self.assertEqual(list, type(self.trf_obj.__engines__()))
def test_transform_reverse(self):
transformed = self.trf_obj.transform(message=self.message, engine="reverse")
self.assertEqual(transformed["transformed_message"], self.message[::-1])
transformed = self.trf_obj.transform(message=404, engine="reverse")
self.assertEqual("", transformed["transformed_message"])
def test_transform_numeric(self):
transformed = self.trf_obj.transform(message=self.message, engine="numeric", key="binary")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="numeric", key="octal")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="numeric", key="decimal")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="numeric", key="hexadecimal")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="numeric", key="test")
self.assertEqual("", transformed["transformed_message"])
def test_transform_case(self):
transformed = self.trf_obj.transform(message=self.message, engine="case", key="upper")
self.assertEqual(self.message.upper(), transformed["transformed_message"])
transformed = self.trf_obj.transform(message=self.message, engine="case", key="lower")
self.assertEqual(self.message.lower(), transformed["transformed_message"])
transformed = self.trf_obj.transform(message=self.message, engine="case", key="capitalize")
self.assertEqual(self.message.capitalize(), transformed["transformed_message"])
transformed = self.trf_obj.transform(message=self.message, engine="case", key="alternating")
self.assertEqual(len(self.message), len(transformed["transformed_message"]))
transformed = self.trf_obj.transform(message=self.message, engine="case", key="inverse")
self.assertEqual(len(self.message), len(transformed["transformed_message"]))
transformed = self.trf_obj.transform(message=self.message, engine="case", key="test")
self.assertEqual("", transformed["transformed_message"])
transformed = self.trf_obj.transform(message=123, engine="case", key="test")
self.assertEqual("", transformed["transformed_message"])
def test_transform_morse(self):
encrypted = self.trf_obj.transform(message=self.message, engine="morse", key="encrypt")
decrypted = self.trf_obj.transform(message=encrypted["transformed_message"], engine=encrypted["engine"], key="decrypt")
self.assertEqual(len(decrypted["transformed_message"].strip()), len(self.message))
encrypted = self.trf_obj.transform(message=self.message+'!', engine="morse", key="encrypt")
self.assertEqual(len(encrypted["transformed_message"]), 0)
encrypted = self.trf_obj.transform(message=self.message, engine="morse", key="test")
self.assertEqual(len(encrypted["transformed_message"]), 0)
def test_transform_alphabetic(self):
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key="nato")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key="dutch")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key="german")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key="swedish")
self.assertEqual(3, len(transformed.keys()))
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key="test")
self.assertEqual(0, len(transformed['transformed_message']))
transformed = self.trf_obj.transform(message=self.message, engine="alphabetic", key=123)
self.assertEqual(0, len(transformed['transformed_message']))
transformed = self.trf_obj.transform(message=123, engine="alphabetic", key="nato")
self.assertEqual(0, len(transformed['transformed_message']))
transformed = self.trf_obj.transform(message="👀", engine="alphabetic", key="nato")
self.assertEqual(1, len(transformed['transformed_message']))
def test_cypher_init(self):
self.assertEqual(str, type(self.cyp_obj.__str__()))
self.assertEqual(list, type(self.cyp_obj.__engines__()))
def test_cypher_caesar(self):
encrypted = self.cyp_obj.encrypt(message=self.message, engine="caesar", key=1)
decrypted = self.cyp_obj.decrypt(message=encrypted["encrypted_message"], engine=encrypted["engine"], key=encrypted["key"])
self.assertEqual(decrypted["decrypted_message"], self.message)
encrypted = self.cyp_obj.encrypt(message=self.message, engine="caesar", key="hello")
decrypted = self.cyp_obj.decrypt(message=encrypted["encrypted_message"], engine=encrypted["engine"], key=encrypted["key"])
self.assertEqual(decrypted["decrypted_message"], self.message)
def test_cypher_shifting_caesar(self):
encrypted = self.cyp_obj.encrypt(message=self.message, engine="shifting_caesar", key=1)
decrypted = self.cyp_obj.decrypt(message=encrypted["encrypted_message"], engine=encrypted["engine"], key=encrypted["key"])
self.assertEqual(decrypted["decrypted_message"], self.message)
encrypted = self.cyp_obj.encrypt(message=self.message, engine="shifting_caesar", key="hello")
decrypted = self.cyp_obj.decrypt(message=encrypted["encrypted_message"], engine=encrypted["engine"], key=encrypted["key"])
self.assertEqual(decrypted["decrypted_message"], self.message)
| 50.111111
| 130
| 0.673941
| 955
| 8,569
| 5.889005
| 0.078534
| 0.095839
| 0.134424
| 0.13229
| 0.847795
| 0.830014
| 0.812945
| 0.749467
| 0.738798
| 0.725107
| 0
| 0.008487
| 0.188703
| 8,569
| 171
| 131
| 50.111111
| 0.800345
| 0
| 0
| 0.289474
| 0
| 0
| 0.133372
| 0
| 0
| 0
| 0
| 0
| 0.359649
| 1
| 0.149123
| false
| 0
| 0.035088
| 0
| 0.201754
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
09bae9ae003ea34e5534550474fecc8c2f65bb8e
| 40
|
py
|
Python
|
steam_reviews/__init__.py
|
tommyxu97/SteamReviews
|
da025ca7623908dcebddad8fe39fcd3881411ab2
|
[
"MIT"
] | 2
|
2021-01-16T09:34:55.000Z
|
2021-01-16T14:28:17.000Z
|
steam_reviews/__init__.py
|
tommyxu97/SteamReviews
|
da025ca7623908dcebddad8fe39fcd3881411ab2
|
[
"MIT"
] | null | null | null |
steam_reviews/__init__.py
|
tommyxu97/SteamReviews
|
da025ca7623908dcebddad8fe39fcd3881411ab2
|
[
"MIT"
] | null | null | null |
from .review_loader import ReviewLoader
| 20
| 39
| 0.875
| 5
| 40
| 6.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 40
| 1
| 40
| 40
| 0.944444
| 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
| 1
| 0
|
0
| 6
|
09c0ed37d78164ee40f8275a34f0a9a897f4d733
| 111
|
py
|
Python
|
geo-mapping/jupyter_notebook/config.py
|
m-duval/geo_mapping
|
501540793732d0fb0aac26a45b475701f763590e
|
[
"MIT"
] | null | null | null |
geo-mapping/jupyter_notebook/config.py
|
m-duval/geo_mapping
|
501540793732d0fb0aac26a45b475701f763590e
|
[
"MIT"
] | null | null | null |
geo-mapping/jupyter_notebook/config.py
|
m-duval/geo_mapping
|
501540793732d0fb0aac26a45b475701f763590e
|
[
"MIT"
] | null | null | null |
census_key = "6b9ffb3dcc6d7b2a4786668d349a8bcc4855e720"
google_key = "AIzaSyCUGxOSyCpiom_dAlxYwQHVUUBeykyNePw"
| 37
| 55
| 0.891892
| 7
| 111
| 13.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.228571
| 0.054054
| 111
| 3
| 56
| 37
| 0.685714
| 0
| 0
| 0
| 0
| 0
| 0.705357
| 0.705357
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
61fc087fbef38612ce2c48239489cc0200d7d165
| 170
|
py
|
Python
|
morfist/__init__.py
|
systemallica/morfist
|
6a4fd33971558829abdc10d47ec4cea08657002e
|
[
"MIT"
] | 3
|
2020-08-10T22:18:45.000Z
|
2021-03-20T20:32:26.000Z
|
morfist/__init__.py
|
systemallica/morfist
|
6a4fd33971558829abdc10d47ec4cea08657002e
|
[
"MIT"
] | null | null | null |
morfist/__init__.py
|
systemallica/morfist
|
6a4fd33971558829abdc10d47ec4cea08657002e
|
[
"MIT"
] | 1
|
2020-08-11T02:08:13.000Z
|
2020-08-11T02:08:13.000Z
|
from morfist.algo.evaluation import cross_validation
from morfist.core.MixedRandomForest import MixedRandomForest
from morfist.legacy.core import MixedRandomForestLegacy
| 42.5
| 60
| 0.894118
| 19
| 170
| 7.947368
| 0.578947
| 0.218543
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070588
| 170
| 3
| 61
| 56.666667
| 0.955696
| 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
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
fec5a92b0e210209fc7efee94350b2430f360834
| 39
|
py
|
Python
|
homework/76_homework.py
|
kaixiang1992/python-learning
|
c74aa4bec0c72e26cf18f138e6faf110ed64d8c0
|
[
"MIT"
] | null | null | null |
homework/76_homework.py
|
kaixiang1992/python-learning
|
c74aa4bec0c72e26cf18f138e6faf110ed64d8c0
|
[
"MIT"
] | 7
|
2020-06-05T23:24:41.000Z
|
2021-06-10T19:02:09.000Z
|
homework/76_homework.py
|
kaixiang1992/python-learning
|
c74aa4bec0c72e26cf18f138e6faf110ed64d8c0
|
[
"MIT"
] | null | null | null |
'''
@description 2019/09/22 20:53
'''
| 7.8
| 29
| 0.589744
| 6
| 39
| 3.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.363636
| 0.153846
| 39
| 4
| 30
| 9.75
| 0.333333
| 0.74359
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
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| null | 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3a4310d426472aa644e4b247a3f9ef7938a14b80
| 125
|
py
|
Python
|
chatbot/agents.py
|
s0hv/chatbot
|
9e00aaabb6c430169900d540c15a36f4ce9974cf
|
[
"MIT"
] | 1
|
2022-01-21T14:41:46.000Z
|
2022-01-21T14:41:46.000Z
|
chatbot/agents.py
|
s0hv/chatbot
|
9e00aaabb6c430169900d540c15a36f4ce9974cf
|
[
"MIT"
] | null | null | null |
chatbot/agents.py
|
s0hv/chatbot
|
9e00aaabb6c430169900d540c15a36f4ce9974cf
|
[
"MIT"
] | null | null | null |
from parlai.chat_service.services.websocket.agents import WebsocketAgent
class ConversationAgent(WebsocketAgent):
pass
| 20.833333
| 72
| 0.84
| 13
| 125
| 8
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104
| 125
| 5
| 73
| 25
| 0.928571
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
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| 0
| null | 0
| 0
| 0
| 0
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| 0
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| 0
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| 1
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| null | 0
| 0
| 0
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| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
3a4d2bc660d7dc2c5766e1832a5e52f519e8c6fd
| 660
|
py
|
Python
|
philter_lite/filters/filter_db.py
|
sourcery-ai-bot/philter-lite
|
b1e6d58c04c166c04dcd7391615cfc5f3cbcd5b1
|
[
"BSD-3-Clause"
] | null | null | null |
philter_lite/filters/filter_db.py
|
sourcery-ai-bot/philter-lite
|
b1e6d58c04c166c04dcd7391615cfc5f3cbcd5b1
|
[
"BSD-3-Clause"
] | 1
|
2021-11-16T14:18:09.000Z
|
2021-11-16T14:18:09.000Z
|
philter_lite/filters/filter_db.py
|
sourcery-ai-bot/philter-lite
|
b1e6d58c04c166c04dcd7391615cfc5f3cbcd5b1
|
[
"BSD-3-Clause"
] | 3
|
2021-11-16T13:52:53.000Z
|
2022-03-29T19:56:15.000Z
|
from importlib import resources
from typing import Any, Dict, MutableMapping
import toml
from philter_lite import filters
def load_regex_db() -> MutableMapping[str, Any]:
return toml.loads(resources.read_text(filters, "regex.toml"))
def load_regex_context_db() -> MutableMapping[str, Any]:
return toml.loads(resources.read_text(filters, "regex_context.toml"))
def load_set_db() -> MutableMapping[str, Any]:
return toml.loads(resources.read_text(filters, "set.toml"))
regex_db: MutableMapping[str, Any] = load_regex_db()
regex_context_db: MutableMapping[str, Any] = load_regex_context_db()
set_db: MutableMapping[str, Any] = load_set_db()
| 27.5
| 73
| 0.766667
| 93
| 660
| 5.193548
| 0.247312
| 0.198758
| 0.236025
| 0.273292
| 0.664596
| 0.57764
| 0.399586
| 0.399586
| 0.399586
| 0.399586
| 0
| 0
| 0.116667
| 660
| 23
| 74
| 28.695652
| 0.828473
| 0
| 0
| 0
| 0
| 0
| 0.054545
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.230769
| true
| 0
| 0.307692
| 0.230769
| 0.769231
| 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
| 0
| 1
| 1
| 0
| 0
|
0
| 6
|
3a57ffbe250b4a8385a7e0a19e672dac690bf34b
| 131
|
py
|
Python
|
pdetools/__init__.py
|
jinanloubani/aTEAM
|
0999799fafbdc36ae09cdd91d99a5a7316803143
|
[
"MIT"
] | 23
|
2018-05-25T02:16:59.000Z
|
2022-03-24T06:56:34.000Z
|
pdetools/__init__.py
|
jinanloubani/aTEAM
|
0999799fafbdc36ae09cdd91d99a5a7316803143
|
[
"MIT"
] | 1
|
2019-06-11T06:59:21.000Z
|
2019-06-11T06:59:40.000Z
|
pdetools/__init__.py
|
jinanloubani/aTEAM
|
0999799fafbdc36ae09cdd91d99a5a7316803143
|
[
"MIT"
] | 8
|
2018-08-29T16:43:12.000Z
|
2022-01-17T11:54:40.000Z
|
"""pde tool box"""
from .stepper import *
from .upwind import *
from .spectral import *
from .init import *
from .example import *
| 18.714286
| 23
| 0.70229
| 18
| 131
| 5.111111
| 0.555556
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175573
| 131
| 6
| 24
| 21.833333
| 0.851852
| 0.091603
| 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
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
28ae38772f9a9fa23cd9eb3897e7a8b124fcb791
| 49
|
py
|
Python
|
amulet/__init__.py
|
Podshot/Amulet-Core
|
678a722daa5e4487d193a7e947ccceacac325fd2
|
[
"MIT"
] | null | null | null |
amulet/__init__.py
|
Podshot/Amulet-Core
|
678a722daa5e4487d193a7e947ccceacac325fd2
|
[
"MIT"
] | null | null | null |
amulet/__init__.py
|
Podshot/Amulet-Core
|
678a722daa5e4487d193a7e947ccceacac325fd2
|
[
"MIT"
] | null | null | null |
from .api import *
from .world_interface import *
| 24.5
| 30
| 0.77551
| 7
| 49
| 5.285714
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 49
| 2
| 30
| 24.5
| 0.880952
| 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
| 1
| 0
|
0
| 6
|
3a8ea5402279bc3b9fbc7df1e6df1c07c353f88b
| 99
|
py
|
Python
|
kpnet/__init__.py
|
K-ran/kpnet
|
adec263139f79c5a723ee2fa1831c23f34f4896c
|
[
"MIT"
] | 1
|
2017-11-21T20:39:24.000Z
|
2017-11-21T20:39:24.000Z
|
kpnet/__init__.py
|
K-ran/kpnet
|
adec263139f79c5a723ee2fa1831c23f34f4896c
|
[
"MIT"
] | null | null | null |
kpnet/__init__.py
|
K-ran/kpnet
|
adec263139f79c5a723ee2fa1831c23f34f4896c
|
[
"MIT"
] | null | null | null |
# import sys; print(sys.path)
from kpnet.NeuralNet import NeuralNet
from kpnet.Layer import Layer
| 24.75
| 37
| 0.79798
| 15
| 99
| 5.266667
| 0.533333
| 0.227848
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131313
| 99
| 3
| 38
| 33
| 0.918605
| 0.272727
| 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
| 1
| 0
|
0
| 6
|
c91abbbc2e5069655861cd3c1c037da50a5469d0
| 79
|
py
|
Python
|
tests/test_image.py
|
Nasko29/progimage-python
|
4ae6de092e1f60b5612b43eb8c36f82e0d466c67
|
[
"MIT"
] | null | null | null |
tests/test_image.py
|
Nasko29/progimage-python
|
4ae6de092e1f60b5612b43eb8c36f82e0d466c67
|
[
"MIT"
] | 5
|
2021-03-18T23:34:32.000Z
|
2022-03-11T23:44:11.000Z
|
tests/test_image.py
|
Nasko29/progimage-python
|
4ae6de092e1f60b5612b43eb8c36f82e0d466c67
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
import pytest
def test_something():
assert(3) == 3
| 13.166667
| 22
| 0.670886
| 12
| 79
| 4.333333
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046154
| 0.177215
| 79
| 6
| 23
| 13.166667
| 0.753846
| 0.265823
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c94614850c903ab2614c7a128b31d8d24e54a988
| 8,017
|
py
|
Python
|
tests/doodledashboard/it/features/test_secrets.py
|
FlyingTopHat/Slack-Dashboard
|
28ed478d8927b8e71db48a521493919a9c0b5797
|
[
"MIT"
] | 1
|
2019-09-21T09:03:54.000Z
|
2019-09-21T09:03:54.000Z
|
tests/doodledashboard/it/features/test_secrets.py
|
FlyingTopHat/Slack-Dashboard
|
28ed478d8927b8e71db48a521493919a9c0b5797
|
[
"MIT"
] | 46
|
2018-01-01T12:56:30.000Z
|
2020-02-11T08:30:42.000Z
|
tests/doodledashboard/it/features/test_secrets.py
|
FlyingTopHat/Slack-Dashboard
|
28ed478d8927b8e71db48a521493919a9c0b5797
|
[
"MIT"
] | 2
|
2018-08-26T08:25:49.000Z
|
2020-11-04T03:47:08.000Z
|
import json
import os
import os.path
import unittest
from click.testing import CliRunner
from doodledashboard.cli import start, view
from doodledashboard.component import StaticComponentSource, DataFeedCreator
from doodledashboard.datafeeds.text import TextFeed
from doodledashboard.secrets_store import SecretNotFound
from tests.doodledashboard.it.support import CliTestCase
class SecretLeakerCreator(DataFeedCreator):
@staticmethod
def get_id():
return "leak-secrets"
def create(self, options, secret_store):
id_of_secret = options["secret-id"]
secret = secret_store.get(id_of_secret)
if secret:
return TextFeed(secret)
else:
raise SecretNotFound(self, id_of_secret)
class StartCommand(CliTestCase):
dashboard_that_outputs_messages_containing_test = """
dashboard:
display:
type: console
options:
seconds-per-notifications: 0
data-feeds:
- type: leak-secrets
options:
secret-id: twitter-api
notifications:
- title: Leaked secret
type: text-from-message
"""
def test_secrets_available_to_data_feed_config(self):
secrets = "twitter-api: This secret has been printed to the console"
StaticComponentSource.add(SecretLeakerCreator)
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("config.yml", self.dashboard_that_outputs_messages_containing_test)
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, start, "--once config.yml --secrets secrets.yml")
self.assertIn("This secret has been printed to the console", result.output)
self.assertEqual(0, result.exit_code)
def test_friendly_error_message_output_if_data_feed_throws_secret_not_found(self):
secrets = ""
config_for_notification_that_prints_password = """
dashboard:
data-feeds:
- type: leak-secrets
options:
secret-id: twitter-api
"""
StaticComponentSource.add(SecretLeakerCreator)
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("config.yml", config_for_notification_that_prints_password)
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, start, "--once config.yml --secrets secrets.yml")
err_msg = "The secret 'twitter-api' is missing from your secrets file according to the data feed config " \
"'leak-secrets'"
self.assertIn(err_msg, result.output)
self.assertEqual(1, result.exit_code)
def test_secrets_not_found_info_shown_for_default_secrets_not_existing_when_verbose(self):
setattr(os.path, 'expanduser', lambda path: '/dummy-user-directory')
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, start, "--once --verbose")
self.assertIn("Secrets file not found:", result.output)
self.assertIn("/dummy-user-directory/.doodledashboard/secrets.yml", result.output)
self.assertEqual(0, result.exit_code)
def test_secrets_not_found_info_not_shown_for_default_secrets_not_existing_when_not_verbose(self):
setattr(os.path, 'expanduser', lambda path: '/dummy-user-directory')
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, start, "--once")
self.assertNotIn("Secrets file not found:", result.output)
self.assertEqual(0, result.exit_code)
def test_useful_error_if_secret_file_provided_does_not_exist(self):
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, start, "--once --secrets i-dont-exist.yml")
self.assertIn("Path \"i-dont-exist.yml\" does not exist.", result.output)
self.assertEqual(2, result.exit_code)
def test_useful_error_if_secret_file_contains_invalid_yaml(self):
secrets = ":"
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, start, "--once --secrets secrets.yml")
self.assertIn("Error while parsing", result.output)
self.assertEqual(1, result.exit_code)
class ViewCommand(CliTestCase):
def test_secrets_available_to_data_feeds(self):
secrets = "twitter-api: This is a secret"
config_for_notification_that_prints_password = """
dashboard:
data-feeds:
- type: leak-secrets
options:
secret-id: twitter-api
"""
StaticComponentSource.add(SecretLeakerCreator)
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("config.yml", config_for_notification_that_prints_password)
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, view, "datafeeds config.yml --secrets secrets.yml")
data = json.loads(result.output)
self.assertEqual(1, len(data["source-data"]), "Source data should contain 1 message")
self.assertEqual("This is a secret", data["source-data"][0]["text"], "Secret is in message")
self.assertEqual(0, result.exit_code)
def test_friendly_error_message_output_if_data_feed_throws_secret_not_found(self):
secrets = ""
config_for_notification_that_prints_password = """
dashboard:
data-feeds:
- type: leak-secrets
options:
secret-id: twitter-api
"""
StaticComponentSource.add(SecretLeakerCreator)
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("config.yml", config_for_notification_that_prints_password)
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, view, "datafeeds config.yml --secrets secrets.yml")
err_msg = "The secret 'twitter-api' is missing from your secrets file according to the data feed config " \
"'leak-secrets'"
self.assertIn(err_msg, result.output)
self.assertEqual(1, result.exit_code)
def test_secrets_not_found_info_shown_for_default_secrets_not_existing_when_verbose(self):
setattr(os.path, 'expanduser', lambda path: '/dummy-user-directory')
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, view, "datafeeds --verbose")
self.assertIn("Secrets file not found: /dummy-user-directory/.doodledashboard/secrets", result.output)
self.assertEqual(0, result.exit_code)
def test_secrets_not_found_info_not_shown_for_default_secrets_not_existing_when_not_verbose(self):
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, view)
self.assertNotIn("Secrets file not found:", result.output)
self.assertEqual(2, result.exit_code)
def test_useful_error_if_secret_file_provided_does_not_exist(self):
runner = CliRunner()
with runner.isolated_filesystem():
result = self.call_cli(runner, view, "datafeeds --secrets i-dont-exist.yml")
self.assertIn("Path \"i-dont-exist.yml\" does not exist.", result.output)
self.assertEqual(2, result.exit_code)
def test_useful_error_if_secret_file_contains_invalid_yaml(self):
secrets = ":"
runner = CliRunner()
with runner.isolated_filesystem():
self.save_file("secrets.yml", secrets)
result = self.call_cli(runner, view, "datafeeds --secrets secrets.yml")
self.assertIn("Error while parsing", result.output)
self.assertEqual(1, result.exit_code)
if __name__ == '__main__':
unittest.main()
| 37.816038
| 115
| 0.666085
| 925
| 8,017
| 5.527568
| 0.15027
| 0.041072
| 0.040681
| 0.058674
| 0.823196
| 0.79073
| 0.762957
| 0.744964
| 0.730882
| 0.730882
| 0
| 0.002614
| 0.236497
| 8,017
| 211
| 116
| 37.995261
| 0.832707
| 0
| 0
| 0.679012
| 0
| 0
| 0.257328
| 0.023076
| 0
| 0
| 0
| 0
| 0.160494
| 1
| 0.08642
| false
| 0.037037
| 0.061728
| 0.006173
| 0.185185
| 0.049383
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
a30d52db082dde48fbe054da12c8fb1933ecaaaa
| 44
|
py
|
Python
|
tools/Polygraphy/polygraphy/tools/base/__init__.py
|
martellz/TensorRT
|
f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4
|
[
"Apache-2.0"
] | 4
|
2021-04-16T13:49:38.000Z
|
2022-01-16T08:58:07.000Z
|
tools/Polygraphy/polygraphy/tools/base/__init__.py
|
martellz/TensorRT
|
f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4
|
[
"Apache-2.0"
] | null | null | null |
tools/Polygraphy/polygraphy/tools/base/__init__.py
|
martellz/TensorRT
|
f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4
|
[
"Apache-2.0"
] | 2
|
2021-02-04T14:46:10.000Z
|
2021-02-04T14:56:08.000Z
|
from polygraphy.tools.base.tool import Tool
| 22
| 43
| 0.840909
| 7
| 44
| 5.285714
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.925
| 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
| 1
| 0
|
0
| 6
|
a31fde165d07ffc6f37e7e84b472e897e34c11b1
| 38
|
wsgi
|
Python
|
url_shortener.wsgi
|
zstone12/tinyURL
|
76dd96e5b283896b85687dc91246947bd8542122
|
[
"MIT"
] | null | null | null |
url_shortener.wsgi
|
zstone12/tinyURL
|
76dd96e5b283896b85687dc91246947bd8542122
|
[
"MIT"
] | 16
|
2019-02-25T10:52:28.000Z
|
2021-04-30T20:43:16.000Z
|
url_shortener.wsgi
|
zstone12/tinyURL
|
76dd96e5b283896b85687dc91246947bd8542122
|
[
"MIT"
] | 1
|
2019-02-25T10:58:15.000Z
|
2019-02-25T10:58:15.000Z
|
from manage import app as application
| 19
| 37
| 0.842105
| 6
| 38
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 38
| 1
| 38
| 38
| 1
| 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
| 1
| 0
|
0
| 6
|
a3271f01dc65e72bc3ff653a33f91eca01dba2e5
| 91
|
py
|
Python
|
authApp/serializers/__init__.py
|
xlausae/4a-docs
|
e2d1038153f170d32d8edbe5ac3fe616ef554206
|
[
"MIT"
] | 1
|
2021-11-29T14:17:07.000Z
|
2021-11-29T14:17:07.000Z
|
authApp/serializers/__init__.py
|
xlausae/4a-docs
|
e2d1038153f170d32d8edbe5ac3fe616ef554206
|
[
"MIT"
] | 2
|
2021-11-18T17:09:21.000Z
|
2021-11-19T21:59:11.000Z
|
authApp/serializers/__init__.py
|
xlausae/4a-docs
|
e2d1038153f170d32d8edbe5ac3fe616ef554206
|
[
"MIT"
] | 1
|
2021-11-18T03:19:28.000Z
|
2021-11-18T03:19:28.000Z
|
from .userSerializer import UserSerializer
from .productSerializer import ProductSerializer
| 45.5
| 48
| 0.901099
| 8
| 91
| 10.25
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 91
| 2
| 48
| 45.5
| 0.97619
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a329819c9845cd96ae1949b2e56170f55d5228ca
| 73
|
py
|
Python
|
snekcord/http/__init__.py
|
asleep-cult/snakecord
|
9b46b62674da9ceeeda688f3e3040616da518277
|
[
"MIT"
] | 6
|
2020-12-15T04:40:18.000Z
|
2021-01-24T06:54:58.000Z
|
snekcord/http/__init__.py
|
blanketsucks/snakecord
|
9b46b62674da9ceeeda688f3e3040616da518277
|
[
"MIT"
] | 13
|
2021-02-07T23:22:44.000Z
|
2021-04-19T13:00:50.000Z
|
snekcord/http/__init__.py
|
blanketsucks/snakecord
|
9b46b62674da9ceeeda688f3e3040616da518277
|
[
"MIT"
] | 3
|
2021-03-03T17:50:48.000Z
|
2021-04-02T00:17:55.000Z
|
from .endpoints import *
from .ratelimit import *
from .session import *
| 18.25
| 24
| 0.753425
| 9
| 73
| 6.111111
| 0.555556
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164384
| 73
| 3
| 25
| 24.333333
| 0.901639
| 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
| 1
| 0
|
0
| 6
|
a3883f2866c6ee38d1d184a607b008368e035c7b
| 7,574
|
py
|
Python
|
Trab002/trab.py
|
gustavodiel/PIM
|
9e001887bc67943f7f7d210d87dda49768bc00e7
|
[
"MIT"
] | null | null | null |
Trab002/trab.py
|
gustavodiel/PIM
|
9e001887bc67943f7f7d210d87dda49768bc00e7
|
[
"MIT"
] | null | null | null |
Trab002/trab.py
|
gustavodiel/PIM
|
9e001887bc67943f7f7d210d87dda49768bc00e7
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from scipy import ndimage
from PIL import Image
import scipy
import numpy as np
import cv2
import os
nomeImagem="muitas_crateras.jpg"
def medianBlur(img):
img_blur=cv2.medianBlur(img,7);
return img_blur
def averageBlur(img):
kernel = np.ones((5,5),np.float32)/25
img_blur=cv2.filter2D(img,-1,kernel);
return img_blur
def highBoostBlur(img, img_blur, c):
print(type(img))
mag = cv2.Laplacian(img, cv2.CV_16S, ksize=int(c*2))
mag = cv2.convertScaleAbs(mag)
print(type(mag))
return mag#np.subtract(img, img_blur)
def noFiltro(img):
pixels=np.array(img.getdata())
ii,jj=img.size
pixels=pixels.reshape(jj,ii)
dx=ndimage.sobel(img,0) #Ox
dy=ndimage.sobel(img,1) #Oy
mag=np.hypot(dx,dy)#magnetude
#mag*=255.0/np.max(mag) #normalização
np.place(dx,dx==0,1)
divided=np.divide(dy,dx)
direc=np.arctan(divided)*180/np.pi
for i in range(len(direc)):
for j in range(len(direc[i])):
if(direc[i][j]<=30):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i][j-1]+mag[i][j+1]):
pixels[i][j]=mag[i][j]
else:
pixels[i][j]=0
elif(direc[i][j]>30 and direc[i][j]<=60):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j+1]+mag[i-1][j-1]):
pixels[i][j]=mag[i][j]
else:
pixels[i][j]=0
elif(direc[i][j]>60 and direc[i][j]<=90):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j]+mag[i-1][j]):
pixels[i][j]=mag[i][j]
else:
pixels[i][j]=0
elif(direc[i][j]>90 and direc[i][j]<=120):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j-1]+mag[i-1][j+1]):
pixels[i][j]=mag[i][j]
else:
pixels[i][j]=0
pixels=pixels.astype(np.uint8)
result=Image.fromarray(pixels)
result.save('results/semFiltro.png')
def media(img):
pixels=np.array(img.getdata())
ii,jj=img.size
pixels=pixels.astype(np.uint8)
pixels=averageBlur(pixels)
pixels=pixels.reshape(jj,ii)
result=Image.fromarray(pixels)
dx=ndimage.sobel(img,0) #Ox
dy=ndimage.sobel(img,1) #Oy
mag=np.hypot(dx,dy)#magnetude
#mag*=255.0/np.max(mag) #normalização
np.place(dx,dx==0,1)
divided=np.divide(dy,dx)
direc=np.arctan(divided)*180/np.pi
pixels2=np.zeros((jj,ii))
for i in range(len(direc)):
for j in range(len(direc[i])):
if(direc[i][j]<=30):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i][j-1]+mag[i][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>30 and direc[i][j]<=60):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j+1]+mag[i-1][j-1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>60 and direc[i][j]<=90):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j]+mag[i-1][j]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>90 and direc[i][j]<=120):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j-1]+mag[i-1][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
pixels2=pixels2.astype(np.uint8)
result=Image.fromarray(pixels2)
result.save('results/media.png')
def mediana(img):
pixels=np.array(img.getdata())
ii,jj=img.size
pixels=pixels.astype(np.uint8)
pixels=medianBlur(pixels)
pixels=pixels.reshape(jj,ii)
result=Image.fromarray(pixels)
dx=ndimage.sobel(img,0) #Ox
dy=ndimage.sobel(img,1) #Oy
mag=np.hypot(dx,dy)#magnetude
#mag*=255.0/np.max(mag) #normalização
np.place(dx,dx==0,1)
divided=np.divide(dy,dx)
direc=np.arctan(divided)*180/np.pi
pixels2=np.zeros((jj,ii))
for i in range(len(direc)):
for j in range(len(direc[i])):
if(direc[i][j]<=30):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i][j-1]+mag[i][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>30 and direc[i][j]<=60):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j+1]+mag[i-1][j-1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>60 and direc[i][j]<=90):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j]+mag[i-1][j]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>90 and direc[i][j]<=120):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j-1]+mag[i-1][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
pixels2=pixels2.astype(np.uint8)
result=Image.fromarray(pixels2)
result.save('results/mediana.png')
def passaAlta(img):
pixels=np.array(img.getdata())
ii,jj=img.size
pixels=pixels.astype(np.uint8)
mpixels=medianBlur(pixels)
mpixels=mpixels.astype(np.uint8)
pixels=highBoostBlur(pixels,mpixels,1.5)
pixels=pixels.reshape(jj,ii)
result=Image.fromarray(pixels)
result.save('results/passaaltaBlur1.5.png')
dx=ndimage.sobel(img,0) #Ox
dy=ndimage.sobel(img,1) #Oy
mag=np.hypot(dx,dy)#magnetude
mag*=255.0/np.max(mag) #normalização
np.place(dx,dx==0,1)
divided=np.divide(dy,dx)
direc=np.arctan(divided)*180/np.pi
pixels2=np.zeros((jj,ii))
for i in range(len(direc)):
for j in range(len(direc[i])):
if(direc[i][j]<=30):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i][j-1]+mag[i][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>30 and direc[i][j]<=60):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j+1]+mag[i-1][j-1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>60 and direc[i][j]<=90):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j]+mag[i-1][j]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
elif(direc[i][j]>90 and direc[i][j]<=120):
if(j-1>=0 and i-1>=0 and i+1<jj and j+1 <ii and mag[i][j]>mag[i+1][j-1]+mag[i-1][j+1]):
pixels2[i][j]=pixels[i][j]
else:
pixels2[i][j]=0
pixels2=pixels2.astype(np.uint8)
result=Image.fromarray(pixels2)
result.save('results/passaAlta1.5.png')
if __name__=="__main__":
img=Image.open(nomeImagem).convert(mode="L")
'''print "Imagem sem filtro"
noFiltro(img)
print "Media"
media(img)
print "Mediana"
mediana(img)
print "High pass"'''
passaAlta(img)
| 37.127451
| 103
| 0.512675
| 1,327
| 7,574
| 2.914092
| 0.089676
| 0.05172
| 0.041376
| 0.049651
| 0.796742
| 0.790794
| 0.787432
| 0.771916
| 0.771916
| 0.759245
| 0
| 0.062011
| 0.290996
| 7,574
| 203
| 104
| 37.310345
| 0.658101
| 0.031291
| 0
| 0.794444
| 0
| 0
| 0.019097
| 0.010176
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038889
| false
| 0.022222
| 0.038889
| 0
| 0.094444
| 0.011111
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6e760ac76503b482d1ea760f8d7fbcf71336654e
| 144
|
py
|
Python
|
chapter2/2.2/print_test.py
|
yifengyou/crazy-python
|
28099bd5011de6981a7c5412783952cc7601ae0c
|
[
"Unlicense"
] | null | null | null |
chapter2/2.2/print_test.py
|
yifengyou/crazy-python
|
28099bd5011de6981a7c5412783952cc7601ae0c
|
[
"Unlicense"
] | null | null | null |
chapter2/2.2/print_test.py
|
yifengyou/crazy-python
|
28099bd5011de6981a7c5412783952cc7601ae0c
|
[
"Unlicense"
] | null | null | null |
user_name = "huyankai"
user_age = 24
print(user_name, "今年", str(user_age), sep=">>>>",end="")
print(user_name, "今年", str(user_age), sep=">>>>")
| 28.8
| 56
| 0.625
| 23
| 144
| 3.652174
| 0.434783
| 0.285714
| 0.309524
| 0.357143
| 0.666667
| 0.666667
| 0.666667
| 0.666667
| 0
| 0
| 0
| 0.015385
| 0.097222
| 144
| 4
| 57
| 36
| 0.630769
| 0
| 0
| 0
| 0
| 0
| 0.138889
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
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