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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
78cd572e0697f113d97c683234ec1a8991c4609e
| 41
|
py
|
Python
|
tests/helpers/__init__.py
|
denpamusic/PyPlumIO
|
16e8049977cbfbb1bd6c830beb751e65ee270295
|
[
"MIT"
] | 1
|
2022-03-24T21:44:59.000Z
|
2022-03-24T21:44:59.000Z
|
tests/helpers/__init__.py
|
denpamusic/PyPlumIO
|
16e8049977cbfbb1bd6c830beb751e65ee270295
|
[
"MIT"
] | null | null | null |
tests/helpers/__init__.py
|
denpamusic/PyPlumIO
|
16e8049977cbfbb1bd6c830beb751e65ee270295
|
[
"MIT"
] | null | null | null |
"""Contains tests for helper classes."""
| 20.5
| 40
| 0.707317
| 5
| 41
| 5.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 41
| 1
| 41
| 41
| 0.805556
| 0.829268
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1530bd915e3aea53bd5beba64113bec051918529
| 253
|
py
|
Python
|
estorage/__init__.py
|
EnergyModels/estorage
|
0f84c87632dba1ff0564ffb68f59ece314f67022
|
[
"MIT"
] | null | null | null |
estorage/__init__.py
|
EnergyModels/estorage
|
0f84c87632dba1ff0564ffb68f59ece314f67022
|
[
"MIT"
] | null | null | null |
estorage/__init__.py
|
EnergyModels/estorage
|
0f84c87632dba1ff0564ffb68f59ece314f67022
|
[
"MIT"
] | null | null | null |
from .acaes_0D import ACAES_0D
from .acaes_idealgas_0D import ACAES_IDEALGAS_0D
from .compressor_sizing import SIZE_AIR_CMP
from .turbine_sizing import SIZE_AIR_TRB
from .compressor_design import DESIGN_AIR_CMP
from .turbine_design import DESIGN_AIR_TRB
| 42.166667
| 48
| 0.885375
| 42
| 253
| 4.904762
| 0.309524
| 0.087379
| 0.126214
| 0.184466
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017391
| 0.090909
| 253
| 6
| 49
| 42.166667
| 0.878261
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 5
|
1554b530da5606043811a93945881eea653cc8db
| 3,976
|
py
|
Python
|
server/swagger_server/models/__init__.py
|
fabric-testbed/fabric-core-api
|
8ce79fd16e1020271487967743a89b7a2346bf45
|
[
"MIT"
] | null | null | null |
server/swagger_server/models/__init__.py
|
fabric-testbed/fabric-core-api
|
8ce79fd16e1020271487967743a89b7a2346bf45
|
[
"MIT"
] | null | null | null |
server/swagger_server/models/__init__.py
|
fabric-testbed/fabric-core-api
|
8ce79fd16e1020271487967743a89b7a2346bf45
|
[
"MIT"
] | null | null | null |
# coding: utf-8
# flake8: noqa
from __future__ import absolute_import
# import models into model package
from swagger_server.models.api_options import ApiOptions
from swagger_server.models.api_options_details import ApiOptionsDetails
from swagger_server.models.api_options_one import ApiOptionsOne
from swagger_server.models.api_options_one_api_endpoints import ApiOptionsOneApiEndpoints
from swagger_server.models.bastionkeys import Bastionkeys
from swagger_server.models.bastionkeys_one import BastionkeysOne
from swagger_server.models.facility_update import FacilityUpdate
from swagger_server.models.facility_update_patch import FacilityUpdatePatch
from swagger_server.models.facility_update_post import FacilityUpdatePost
from swagger_server.models.people import People
from swagger_server.models.people_details import PeopleDetails
from swagger_server.models.people_one import PeopleOne
from swagger_server.models.people_one_roles import PeopleOneRoles
from swagger_server.models.people_patch import PeoplePatch
from swagger_server.models.person import Person
from swagger_server.models.preferences import Preferences
from swagger_server.models.profile_people import ProfilePeople
from swagger_server.models.profile_people_other_identities import ProfilePeopleOtherIdentities
from swagger_server.models.profile_people_personal_pages import ProfilePeoplePersonalPages
from swagger_server.models.profile_projects import ProfileProjects
from swagger_server.models.profile_projects_references import ProfileProjectsReferences
from swagger_server.models.project import Project
from swagger_server.models.project_membership import ProjectMembership
from swagger_server.models.projects import Projects
from swagger_server.models.projects_details import ProjectsDetails
from swagger_server.models.projects_one import ProjectsOne
from swagger_server.models.projects_patch import ProjectsPatch
from swagger_server.models.projects_personnel_patch import ProjectsPersonnelPatch
from swagger_server.models.projects_post import ProjectsPost
from swagger_server.models.projects_tags_patch import ProjectsTagsPatch
from swagger_server.models.sshkey_pair import SshkeyPair
from swagger_server.models.sshkey_pair_data import SshkeyPairData
from swagger_server.models.sshkeys import Sshkeys
from swagger_server.models.sshkeys_one import SshkeysOne
from swagger_server.models.sshkeys_post import SshkeysPost
from swagger_server.models.sshkeys_put import SshkeysPut
from swagger_server.models.status200_ok_no_content import Status200OkNoContent
from swagger_server.models.status200_ok_no_content_data import Status200OkNoContentData
from swagger_server.models.status200_ok_paginated import Status200OkPaginated
from swagger_server.models.status200_ok_paginated_links import Status200OkPaginatedLinks
from swagger_server.models.status200_ok_single import Status200OkSingle
from swagger_server.models.status400_bad_request import Status400BadRequest
from swagger_server.models.status400_bad_request_errors import Status400BadRequestErrors
from swagger_server.models.status401_unauthorized import Status401Unauthorized
from swagger_server.models.status401_unauthorized_errors import Status401UnauthorizedErrors
from swagger_server.models.status403_forbidden import Status403Forbidden
from swagger_server.models.status403_forbidden_errors import Status403ForbiddenErrors
from swagger_server.models.status404_not_found import Status404NotFound
from swagger_server.models.status404_not_found_errors import Status404NotFoundErrors
from swagger_server.models.status500_internal_server_error import Status500InternalServerError
from swagger_server.models.status500_internal_server_error_errors import Status500InternalServerErrorErrors
from swagger_server.models.updates import Updates
from swagger_server.models.version import Version
from swagger_server.models.version_data import VersionData
from swagger_server.models.whoami import Whoami
from swagger_server.models.whoami_data import WhoamiData
| 64.129032
| 107
| 0.909708
| 491
| 3,976
| 7.071283
| 0.234216
| 0.177419
| 0.274194
| 0.370968
| 0.540035
| 0.347062
| 0.146889
| 0.054147
| 0
| 0
| 0
| 0.024632
| 0.060614
| 3,976
| 61
| 108
| 65.180328
| 0.904953
| 0.014839
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 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
| 5
|
ecb9918134a7442c7cdb6357865b8d1394dc71d6
| 92
|
py
|
Python
|
urls/project/dev.py
|
cuzen1/teracy-tutorial
|
00326d086512805190c91708e1a275889d76e37c
|
[
"BSD-3-Clause"
] | null | null | null |
urls/project/dev.py
|
cuzen1/teracy-tutorial
|
00326d086512805190c91708e1a275889d76e37c
|
[
"BSD-3-Clause"
] | null | null | null |
urls/project/dev.py
|
cuzen1/teracy-tutorial
|
00326d086512805190c91708e1a275889d76e37c
|
[
"BSD-3-Clause"
] | null | null | null |
"""
project specic settings for urls in developing mode
"""
from urls.dev import * # noqa
| 15.333333
| 51
| 0.706522
| 13
| 92
| 5
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195652
| 92
| 5
| 52
| 18.4
| 0.878378
| 0.619565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
01e525212b0eb9095ce724cb8916c2667d1d5034
| 73
|
py
|
Python
|
bl21.py
|
rinapyktina/bl
|
38546465c8802be184fbd44ae521af54a5ec504f
|
[
"MIT"
] | null | null | null |
bl21.py
|
rinapyktina/bl
|
38546465c8802be184fbd44ae521af54a5ec504f
|
[
"MIT"
] | null | null | null |
bl21.py
|
rinapyktina/bl
|
38546465c8802be184fbd44ae521af54a5ec504f
|
[
"MIT"
] | null | null | null |
d = 3255
f = d // 15-17
z = d // (f*5)
d = d % (z+1)
print("d = ", d)
| 14.6
| 16
| 0.356164
| 17
| 73
| 1.529412
| 0.529412
| 0.153846
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.208333
| 0.342466
| 73
| 5
| 16
| 14.6
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.2
| 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 | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
17236650b128de54ac3e707372fbb29bc0f9f4e4
| 1,104
|
py
|
Python
|
sdk/python/pulumi_azure_native/advisor/__init__.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/advisor/__init__.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/advisor/__init__.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
from .. import _utilities
import typing
# Export this package's modules as members:
from .get_suppression import *
from .suppression import *
# Make subpackages available:
if typing.TYPE_CHECKING:
import pulumi_azure_native.advisor.v20160712preview as __v20160712preview
v20160712preview = __v20160712preview
import pulumi_azure_native.advisor.v20170331 as __v20170331
v20170331 = __v20170331
import pulumi_azure_native.advisor.v20170419 as __v20170419
v20170419 = __v20170419
import pulumi_azure_native.advisor.v20200101 as __v20200101
v20200101 = __v20200101
else:
v20160712preview = _utilities.lazy_import('pulumi_azure_native.advisor.v20160712preview')
v20170331 = _utilities.lazy_import('pulumi_azure_native.advisor.v20170331')
v20170419 = _utilities.lazy_import('pulumi_azure_native.advisor.v20170419')
v20200101 = _utilities.lazy_import('pulumi_azure_native.advisor.v20200101')
| 40.888889
| 93
| 0.791667
| 131
| 1,104
| 6.343511
| 0.396947
| 0.115523
| 0.163658
| 0.22142
| 0.454874
| 0.454874
| 0.20698
| 0
| 0
| 0
| 0
| 0.202518
| 0.136775
| 1,104
| 26
| 94
| 42.461538
| 0.669465
| 0.209239
| 0
| 0
| 1
| 0
| 0.178984
| 0.178984
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1755f13217e501089c0142ea16ea694eb326ca8c
| 158
|
py
|
Python
|
pygitops/_constants.py
|
jashparekh/pygitops
|
7c612169be63774dab97f6a5a921132c619c15ca
|
[
"MIT"
] | 10
|
2021-05-07T17:53:05.000Z
|
2022-03-25T02:36:48.000Z
|
pygitops/_constants.py
|
jashparekh/pygitops
|
7c612169be63774dab97f6a5a921132c619c15ca
|
[
"MIT"
] | 110
|
2021-05-07T16:37:43.000Z
|
2022-03-28T20:09:09.000Z
|
pygitops/_constants.py
|
jashparekh/pygitops
|
7c612169be63774dab97f6a5a921132c619c15ca
|
[
"MIT"
] | 1
|
2021-10-20T01:22:17.000Z
|
2021-10-20T01:22:17.000Z
|
GIT_BRANCH_MASTER = "master"
GIT_BRANCH_MAIN = "main"
GITHUB_PUBLIC_DOMAIN_NAME = "github.com"
KNOWN_DEFAULT_BRANCHES = (GIT_BRANCH_MASTER, GIT_BRANCH_MAIN)
| 26.333333
| 61
| 0.822785
| 23
| 158
| 5.086957
| 0.521739
| 0.307692
| 0.25641
| 0.324786
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088608
| 158
| 5
| 62
| 31.6
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0.126582
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
176a9dc53fdca90cee708990536a54912cab7861
| 166
|
py
|
Python
|
mentoria_credentials.py
|
paulohenriqueviegasmartins/mentoria_yhub_26_01_2022
|
35b1dafc7859bb0cd66e7a30defe0667f4c7d704
|
[
"Apache-2.0"
] | 1
|
2022-02-24T01:08:26.000Z
|
2022-02-24T01:08:26.000Z
|
mentoria_credentials.py
|
paulohenriqueviegasmartins/mentoria_yhub_26_01_2022
|
35b1dafc7859bb0cd66e7a30defe0667f4c7d704
|
[
"Apache-2.0"
] | null | null | null |
mentoria_credentials.py
|
paulohenriqueviegasmartins/mentoria_yhub_26_01_2022
|
35b1dafc7859bb0cd66e7a30defe0667f4c7d704
|
[
"Apache-2.0"
] | null | null | null |
# Nunca faça isso:
Access Key ID: "ALLDAJDUABDSAFHABFHDABFHDABHDAB"
Secret Access key: "zJIDAFBDSAUQEHQB9Q41daufdshunqeqOOOAdbbas413541N541B54B967"
Zone: us-east-1
| 23.714286
| 79
| 0.837349
| 15
| 166
| 9.266667
| 0.866667
| 0.129496
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 0.096386
| 166
| 6
| 80
| 27.666667
| 0.806667
| 0.096386
| 0
| 0
| 0
| 0
| 0.605442
| 0.605442
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bd59b3990a4e53ec29a5c3d836ccc693e2bc52c3
| 50
|
py
|
Python
|
HelloWorld.py
|
romy421kumari/Hello-World
|
fd5e1cf9d0792a4ab10d7e36b2062db7ec71a638
|
[
"MIT"
] | null | null | null |
HelloWorld.py
|
romy421kumari/Hello-World
|
fd5e1cf9d0792a4ab10d7e36b2062db7ec71a638
|
[
"MIT"
] | null | null | null |
HelloWorld.py
|
romy421kumari/Hello-World
|
fd5e1cf9d0792a4ab10d7e36b2062db7ec71a638
|
[
"MIT"
] | null | null | null |
print('Hello World')
print('hello hacktoberfest')
| 16.666667
| 28
| 0.76
| 6
| 50
| 6.333333
| 0.666667
| 0.526316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 50
| 2
| 29
| 25
| 0.826087
| 0
| 0
| 0
| 0
| 0
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
bd7ba11fef910ad6a35ca1bea5913650f8213af5
| 121
|
py
|
Python
|
svgbatch/tests/__init__.py
|
tartley/svgload
|
19662a51dbd53a2839625914f037eba4039fc9f5
|
[
"BSD-3-Clause"
] | 8
|
2016-07-07T07:34:52.000Z
|
2022-03-04T00:02:18.000Z
|
svgbatch/tests/__init__.py
|
tartley/svgload
|
19662a51dbd53a2839625914f037eba4039fc9f5
|
[
"BSD-3-Clause"
] | null | null | null |
svgbatch/tests/__init__.py
|
tartley/svgload
|
19662a51dbd53a2839625914f037eba4039fc9f5
|
[
"BSD-3-Clause"
] | null | null | null |
from sys import path
from os.path import abspath, dirname, join
path.append(abspath(join(dirname(__file__), '..')))
| 24.2
| 52
| 0.719008
| 17
| 121
| 4.882353
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140496
| 121
| 4
| 53
| 30.25
| 0.798077
| 0
| 0
| 0
| 0
| 0
| 0.017241
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bd87f3669e47dab77b2165c5028f3dab665959a9
| 24,282
|
py
|
Python
|
sdk/python/pulumi_aws_native/cur/report_definition.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 29
|
2021-09-30T19:32:07.000Z
|
2022-03-22T21:06:08.000Z
|
sdk/python/pulumi_aws_native/cur/report_definition.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 232
|
2021-09-30T19:26:26.000Z
|
2022-03-31T23:22:06.000Z
|
sdk/python/pulumi_aws_native/cur/report_definition.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 4
|
2021-11-10T19:42:01.000Z
|
2022-02-05T10:15:49.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from ._enums import *
__all__ = ['ReportDefinitionArgs', 'ReportDefinition']
@pulumi.input_type
class ReportDefinitionArgs:
def __init__(__self__, *,
compression: pulumi.Input['ReportDefinitionCompression'],
format: pulumi.Input['ReportDefinitionFormat'],
refresh_closed_reports: pulumi.Input[bool],
report_name: pulumi.Input[str],
report_versioning: pulumi.Input['ReportDefinitionReportVersioning'],
s3_bucket: pulumi.Input[str],
s3_prefix: pulumi.Input[str],
s3_region: pulumi.Input[str],
time_unit: pulumi.Input['ReportDefinitionTimeUnit'],
additional_artifacts: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]]] = None,
additional_schema_elements: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]]] = None,
billing_view_arn: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a ReportDefinition resource.
:param pulumi.Input['ReportDefinitionCompression'] compression: The compression format that AWS uses for the report.
:param pulumi.Input['ReportDefinitionFormat'] format: The format that AWS saves the report in.
:param pulumi.Input[bool] refresh_closed_reports: Whether you want Amazon Web Services to update your reports after they have been finalized if Amazon Web Services detects charges related to previous months. These charges can include refunds, credits, or support fees.
:param pulumi.Input[str] report_name: The name of the report that you want to create. The name must be unique, is case sensitive, and can't include spaces.
:param pulumi.Input['ReportDefinitionReportVersioning'] report_versioning: Whether you want Amazon Web Services to overwrite the previous version of each report or to deliver the report in addition to the previous versions.
:param pulumi.Input[str] s3_bucket: The S3 bucket where AWS delivers the report.
:param pulumi.Input[str] s3_prefix: The prefix that AWS adds to the report name when AWS delivers the report. Your prefix can't include spaces.
:param pulumi.Input[str] s3_region: The region of the S3 bucket that AWS delivers the report into.
:param pulumi.Input['ReportDefinitionTimeUnit'] time_unit: The granularity of the line items in the report.
:param pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]] additional_artifacts: A list of manifests that you want Amazon Web Services to create for this report.
:param pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]] additional_schema_elements: A list of strings that indicate additional content that Amazon Web Services includes in the report, such as individual resource IDs.
:param pulumi.Input[str] billing_view_arn: The Amazon resource name of the billing view. You can get this value by using the billing view service public APIs.
"""
pulumi.set(__self__, "compression", compression)
pulumi.set(__self__, "format", format)
pulumi.set(__self__, "refresh_closed_reports", refresh_closed_reports)
pulumi.set(__self__, "report_name", report_name)
pulumi.set(__self__, "report_versioning", report_versioning)
pulumi.set(__self__, "s3_bucket", s3_bucket)
pulumi.set(__self__, "s3_prefix", s3_prefix)
pulumi.set(__self__, "s3_region", s3_region)
pulumi.set(__self__, "time_unit", time_unit)
if additional_artifacts is not None:
pulumi.set(__self__, "additional_artifacts", additional_artifacts)
if additional_schema_elements is not None:
pulumi.set(__self__, "additional_schema_elements", additional_schema_elements)
if billing_view_arn is not None:
pulumi.set(__self__, "billing_view_arn", billing_view_arn)
@property
@pulumi.getter
def compression(self) -> pulumi.Input['ReportDefinitionCompression']:
"""
The compression format that AWS uses for the report.
"""
return pulumi.get(self, "compression")
@compression.setter
def compression(self, value: pulumi.Input['ReportDefinitionCompression']):
pulumi.set(self, "compression", value)
@property
@pulumi.getter
def format(self) -> pulumi.Input['ReportDefinitionFormat']:
"""
The format that AWS saves the report in.
"""
return pulumi.get(self, "format")
@format.setter
def format(self, value: pulumi.Input['ReportDefinitionFormat']):
pulumi.set(self, "format", value)
@property
@pulumi.getter(name="refreshClosedReports")
def refresh_closed_reports(self) -> pulumi.Input[bool]:
"""
Whether you want Amazon Web Services to update your reports after they have been finalized if Amazon Web Services detects charges related to previous months. These charges can include refunds, credits, or support fees.
"""
return pulumi.get(self, "refresh_closed_reports")
@refresh_closed_reports.setter
def refresh_closed_reports(self, value: pulumi.Input[bool]):
pulumi.set(self, "refresh_closed_reports", value)
@property
@pulumi.getter(name="reportName")
def report_name(self) -> pulumi.Input[str]:
"""
The name of the report that you want to create. The name must be unique, is case sensitive, and can't include spaces.
"""
return pulumi.get(self, "report_name")
@report_name.setter
def report_name(self, value: pulumi.Input[str]):
pulumi.set(self, "report_name", value)
@property
@pulumi.getter(name="reportVersioning")
def report_versioning(self) -> pulumi.Input['ReportDefinitionReportVersioning']:
"""
Whether you want Amazon Web Services to overwrite the previous version of each report or to deliver the report in addition to the previous versions.
"""
return pulumi.get(self, "report_versioning")
@report_versioning.setter
def report_versioning(self, value: pulumi.Input['ReportDefinitionReportVersioning']):
pulumi.set(self, "report_versioning", value)
@property
@pulumi.getter(name="s3Bucket")
def s3_bucket(self) -> pulumi.Input[str]:
"""
The S3 bucket where AWS delivers the report.
"""
return pulumi.get(self, "s3_bucket")
@s3_bucket.setter
def s3_bucket(self, value: pulumi.Input[str]):
pulumi.set(self, "s3_bucket", value)
@property
@pulumi.getter(name="s3Prefix")
def s3_prefix(self) -> pulumi.Input[str]:
"""
The prefix that AWS adds to the report name when AWS delivers the report. Your prefix can't include spaces.
"""
return pulumi.get(self, "s3_prefix")
@s3_prefix.setter
def s3_prefix(self, value: pulumi.Input[str]):
pulumi.set(self, "s3_prefix", value)
@property
@pulumi.getter(name="s3Region")
def s3_region(self) -> pulumi.Input[str]:
"""
The region of the S3 bucket that AWS delivers the report into.
"""
return pulumi.get(self, "s3_region")
@s3_region.setter
def s3_region(self, value: pulumi.Input[str]):
pulumi.set(self, "s3_region", value)
@property
@pulumi.getter(name="timeUnit")
def time_unit(self) -> pulumi.Input['ReportDefinitionTimeUnit']:
"""
The granularity of the line items in the report.
"""
return pulumi.get(self, "time_unit")
@time_unit.setter
def time_unit(self, value: pulumi.Input['ReportDefinitionTimeUnit']):
pulumi.set(self, "time_unit", value)
@property
@pulumi.getter(name="additionalArtifacts")
def additional_artifacts(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]]]:
"""
A list of manifests that you want Amazon Web Services to create for this report.
"""
return pulumi.get(self, "additional_artifacts")
@additional_artifacts.setter
def additional_artifacts(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]]]):
pulumi.set(self, "additional_artifacts", value)
@property
@pulumi.getter(name="additionalSchemaElements")
def additional_schema_elements(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]]]:
"""
A list of strings that indicate additional content that Amazon Web Services includes in the report, such as individual resource IDs.
"""
return pulumi.get(self, "additional_schema_elements")
@additional_schema_elements.setter
def additional_schema_elements(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]]]):
pulumi.set(self, "additional_schema_elements", value)
@property
@pulumi.getter(name="billingViewArn")
def billing_view_arn(self) -> Optional[pulumi.Input[str]]:
"""
The Amazon resource name of the billing view. You can get this value by using the billing view service public APIs.
"""
return pulumi.get(self, "billing_view_arn")
@billing_view_arn.setter
def billing_view_arn(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "billing_view_arn", value)
warnings.warn("""ReportDefinition is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""", DeprecationWarning)
class ReportDefinition(pulumi.CustomResource):
warnings.warn("""ReportDefinition is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""", DeprecationWarning)
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
additional_artifacts: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]]] = None,
additional_schema_elements: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]]] = None,
billing_view_arn: Optional[pulumi.Input[str]] = None,
compression: Optional[pulumi.Input['ReportDefinitionCompression']] = None,
format: Optional[pulumi.Input['ReportDefinitionFormat']] = None,
refresh_closed_reports: Optional[pulumi.Input[bool]] = None,
report_name: Optional[pulumi.Input[str]] = None,
report_versioning: Optional[pulumi.Input['ReportDefinitionReportVersioning']] = None,
s3_bucket: Optional[pulumi.Input[str]] = None,
s3_prefix: Optional[pulumi.Input[str]] = None,
s3_region: Optional[pulumi.Input[str]] = None,
time_unit: Optional[pulumi.Input['ReportDefinitionTimeUnit']] = None,
__props__=None):
"""
The AWS::CUR::ReportDefinition resource creates a Cost & Usage Report with user-defined settings. You can use this resource to define settings like time granularity (hourly, daily, monthly), file format (Parquet, CSV), and S3 bucket for delivery of these reports.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]] additional_artifacts: A list of manifests that you want Amazon Web Services to create for this report.
:param pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]] additional_schema_elements: A list of strings that indicate additional content that Amazon Web Services includes in the report, such as individual resource IDs.
:param pulumi.Input[str] billing_view_arn: The Amazon resource name of the billing view. You can get this value by using the billing view service public APIs.
:param pulumi.Input['ReportDefinitionCompression'] compression: The compression format that AWS uses for the report.
:param pulumi.Input['ReportDefinitionFormat'] format: The format that AWS saves the report in.
:param pulumi.Input[bool] refresh_closed_reports: Whether you want Amazon Web Services to update your reports after they have been finalized if Amazon Web Services detects charges related to previous months. These charges can include refunds, credits, or support fees.
:param pulumi.Input[str] report_name: The name of the report that you want to create. The name must be unique, is case sensitive, and can't include spaces.
:param pulumi.Input['ReportDefinitionReportVersioning'] report_versioning: Whether you want Amazon Web Services to overwrite the previous version of each report or to deliver the report in addition to the previous versions.
:param pulumi.Input[str] s3_bucket: The S3 bucket where AWS delivers the report.
:param pulumi.Input[str] s3_prefix: The prefix that AWS adds to the report name when AWS delivers the report. Your prefix can't include spaces.
:param pulumi.Input[str] s3_region: The region of the S3 bucket that AWS delivers the report into.
:param pulumi.Input['ReportDefinitionTimeUnit'] time_unit: The granularity of the line items in the report.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ReportDefinitionArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
The AWS::CUR::ReportDefinition resource creates a Cost & Usage Report with user-defined settings. You can use this resource to define settings like time granularity (hourly, daily, monthly), file format (Parquet, CSV), and S3 bucket for delivery of these reports.
:param str resource_name: The name of the resource.
:param ReportDefinitionArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ReportDefinitionArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
additional_artifacts: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalArtifactsItem']]]] = None,
additional_schema_elements: Optional[pulumi.Input[Sequence[pulumi.Input['ReportDefinitionAdditionalSchemaElementsItem']]]] = None,
billing_view_arn: Optional[pulumi.Input[str]] = None,
compression: Optional[pulumi.Input['ReportDefinitionCompression']] = None,
format: Optional[pulumi.Input['ReportDefinitionFormat']] = None,
refresh_closed_reports: Optional[pulumi.Input[bool]] = None,
report_name: Optional[pulumi.Input[str]] = None,
report_versioning: Optional[pulumi.Input['ReportDefinitionReportVersioning']] = None,
s3_bucket: Optional[pulumi.Input[str]] = None,
s3_prefix: Optional[pulumi.Input[str]] = None,
s3_region: Optional[pulumi.Input[str]] = None,
time_unit: Optional[pulumi.Input['ReportDefinitionTimeUnit']] = None,
__props__=None):
pulumi.log.warn("""ReportDefinition is deprecated: ReportDefinition is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""")
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ReportDefinitionArgs.__new__(ReportDefinitionArgs)
__props__.__dict__["additional_artifacts"] = additional_artifacts
__props__.__dict__["additional_schema_elements"] = additional_schema_elements
__props__.__dict__["billing_view_arn"] = billing_view_arn
if compression is None and not opts.urn:
raise TypeError("Missing required property 'compression'")
__props__.__dict__["compression"] = compression
if format is None and not opts.urn:
raise TypeError("Missing required property 'format'")
__props__.__dict__["format"] = format
if refresh_closed_reports is None and not opts.urn:
raise TypeError("Missing required property 'refresh_closed_reports'")
__props__.__dict__["refresh_closed_reports"] = refresh_closed_reports
if report_name is None and not opts.urn:
raise TypeError("Missing required property 'report_name'")
__props__.__dict__["report_name"] = report_name
if report_versioning is None and not opts.urn:
raise TypeError("Missing required property 'report_versioning'")
__props__.__dict__["report_versioning"] = report_versioning
if s3_bucket is None and not opts.urn:
raise TypeError("Missing required property 's3_bucket'")
__props__.__dict__["s3_bucket"] = s3_bucket
if s3_prefix is None and not opts.urn:
raise TypeError("Missing required property 's3_prefix'")
__props__.__dict__["s3_prefix"] = s3_prefix
if s3_region is None and not opts.urn:
raise TypeError("Missing required property 's3_region'")
__props__.__dict__["s3_region"] = s3_region
if time_unit is None and not opts.urn:
raise TypeError("Missing required property 'time_unit'")
__props__.__dict__["time_unit"] = time_unit
super(ReportDefinition, __self__).__init__(
'aws-native:cur:ReportDefinition',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ReportDefinition':
"""
Get an existing ReportDefinition resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = ReportDefinitionArgs.__new__(ReportDefinitionArgs)
__props__.__dict__["additional_artifacts"] = None
__props__.__dict__["additional_schema_elements"] = None
__props__.__dict__["billing_view_arn"] = None
__props__.__dict__["compression"] = None
__props__.__dict__["format"] = None
__props__.__dict__["refresh_closed_reports"] = None
__props__.__dict__["report_name"] = None
__props__.__dict__["report_versioning"] = None
__props__.__dict__["s3_bucket"] = None
__props__.__dict__["s3_prefix"] = None
__props__.__dict__["s3_region"] = None
__props__.__dict__["time_unit"] = None
return ReportDefinition(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="additionalArtifacts")
def additional_artifacts(self) -> pulumi.Output[Optional[Sequence['ReportDefinitionAdditionalArtifactsItem']]]:
"""
A list of manifests that you want Amazon Web Services to create for this report.
"""
return pulumi.get(self, "additional_artifacts")
@property
@pulumi.getter(name="additionalSchemaElements")
def additional_schema_elements(self) -> pulumi.Output[Optional[Sequence['ReportDefinitionAdditionalSchemaElementsItem']]]:
"""
A list of strings that indicate additional content that Amazon Web Services includes in the report, such as individual resource IDs.
"""
return pulumi.get(self, "additional_schema_elements")
@property
@pulumi.getter(name="billingViewArn")
def billing_view_arn(self) -> pulumi.Output[Optional[str]]:
"""
The Amazon resource name of the billing view. You can get this value by using the billing view service public APIs.
"""
return pulumi.get(self, "billing_view_arn")
@property
@pulumi.getter
def compression(self) -> pulumi.Output['ReportDefinitionCompression']:
"""
The compression format that AWS uses for the report.
"""
return pulumi.get(self, "compression")
@property
@pulumi.getter
def format(self) -> pulumi.Output['ReportDefinitionFormat']:
"""
The format that AWS saves the report in.
"""
return pulumi.get(self, "format")
@property
@pulumi.getter(name="refreshClosedReports")
def refresh_closed_reports(self) -> pulumi.Output[bool]:
"""
Whether you want Amazon Web Services to update your reports after they have been finalized if Amazon Web Services detects charges related to previous months. These charges can include refunds, credits, or support fees.
"""
return pulumi.get(self, "refresh_closed_reports")
@property
@pulumi.getter(name="reportName")
def report_name(self) -> pulumi.Output[str]:
"""
The name of the report that you want to create. The name must be unique, is case sensitive, and can't include spaces.
"""
return pulumi.get(self, "report_name")
@property
@pulumi.getter(name="reportVersioning")
def report_versioning(self) -> pulumi.Output['ReportDefinitionReportVersioning']:
"""
Whether you want Amazon Web Services to overwrite the previous version of each report or to deliver the report in addition to the previous versions.
"""
return pulumi.get(self, "report_versioning")
@property
@pulumi.getter(name="s3Bucket")
def s3_bucket(self) -> pulumi.Output[str]:
"""
The S3 bucket where AWS delivers the report.
"""
return pulumi.get(self, "s3_bucket")
@property
@pulumi.getter(name="s3Prefix")
def s3_prefix(self) -> pulumi.Output[str]:
"""
The prefix that AWS adds to the report name when AWS delivers the report. Your prefix can't include spaces.
"""
return pulumi.get(self, "s3_prefix")
@property
@pulumi.getter(name="s3Region")
def s3_region(self) -> pulumi.Output[str]:
"""
The region of the S3 bucket that AWS delivers the report into.
"""
return pulumi.get(self, "s3_region")
@property
@pulumi.getter(name="timeUnit")
def time_unit(self) -> pulumi.Output['ReportDefinitionTimeUnit']:
"""
The granularity of the line items in the report.
"""
return pulumi.get(self, "time_unit")
| 53.133479
| 276
| 0.686105
| 2,809
| 24,282
| 5.711641
| 0.08722
| 0.069247
| 0.032286
| 0.028422
| 0.807467
| 0.750436
| 0.732112
| 0.698579
| 0.671778
| 0.656071
| 0
| 0.003927
| 0.224034
| 24,282
| 456
| 277
| 53.25
| 0.847575
| 0.311589
| 0
| 0.40636
| 1
| 0.010601
| 0.21716
| 0.096115
| 0
| 0
| 0
| 0
| 0
| 1
| 0.14841
| false
| 0.003534
| 0.021201
| 0
| 0.265018
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bd9632aa03b7bc3745f47daf6e5746b44e58f668
| 460
|
py
|
Python
|
tests/settings/test_settings.py
|
ooi-data/ooi_harvester
|
7ddcad7b05b7ca5d2012ffd2517e1489a4ce489c
|
[
"MIT"
] | 4
|
2021-01-08T20:01:38.000Z
|
2022-03-11T19:03:58.000Z
|
tests/settings/test_settings.py
|
ooi-data/ooi_harvester
|
7ddcad7b05b7ca5d2012ffd2517e1489a4ce489c
|
[
"MIT"
] | 7
|
2021-01-08T16:51:50.000Z
|
2021-11-02T21:54:22.000Z
|
tests/settings/test_settings.py
|
ooi-data/ooi_harvester
|
7ddcad7b05b7ca5d2012ffd2517e1489a4ce489c
|
[
"MIT"
] | null | null | null |
def test_harvest_settings():
from ooi_harvester.settings import harvest_settings
from ooi_harvester.settings.main import HarvestSettings
assert isinstance(harvest_settings, HarvestSettings)
assert harvest_settings.storage_options.aws.key == 'minioadmin'
assert harvest_settings.storage_options.aws.secret == 'minioadmin'
assert harvest_settings.ooi_config.username == 'username'
assert harvest_settings.ooi_config.token == 'token'
| 46
| 70
| 0.797826
| 53
| 460
| 6.660377
| 0.396226
| 0.29745
| 0.23796
| 0.124646
| 0.606232
| 0.436261
| 0
| 0
| 0
| 0
| 0
| 0
| 0.126087
| 460
| 9
| 71
| 51.111111
| 0.878109
| 0
| 0
| 0
| 0
| 0
| 0.071739
| 0
| 0
| 0
| 0
| 0
| 0.625
| 1
| 0.125
| true
| 0
| 0.25
| 0
| 0.375
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bdba57ca739cd396a8dd77c5e2aab2023f5955de
| 2,711
|
py
|
Python
|
transformer/test/testYangUTCisco.py
|
Verizon/YANG-validator
|
82c976e886ac4437b36e2a7d3ad90ce3cea80b95
|
[
"Apache-2.0"
] | 2
|
2021-08-15T03:12:07.000Z
|
2021-11-18T05:27:11.000Z
|
transformer/test/testYangUTCisco.py
|
Verizon/YANG-validator
|
82c976e886ac4437b36e2a7d3ad90ce3cea80b95
|
[
"Apache-2.0"
] | 2
|
2021-08-15T03:12:52.000Z
|
2021-08-15T15:02:46.000Z
|
transformer/test/testYangUTCisco.py
|
Verizon/YANG-validator
|
82c976e886ac4437b36e2a7d3ad90ce3cea80b95
|
[
"Apache-2.0"
] | 1
|
2020-09-18T19:36:01.000Z
|
2020-09-18T19:36:01.000Z
|
# Copyright Verizon Inc.
# Licensed under the terms of the Apache License 2.0 license.
# See LICENSE file in project root for terms.
import json
import jsonToYang
#jsonStr = '{"push.push-change-update":{"push.update-data":{"alm.alarm":{"alm.owner":"","alm.source-object-id":0,"alm.perceived-severity":"critical","alm.system-received-time":"2019-04-0204: 07: 16.363","alm.probable-cause":"mplsTunnelDown","alm.root-cause-alarm-identifier":{"alm.event-identifier":0},"alm.cause-type":"cause-unknown","alm.system-update-time":"2019-04-0306: 56: 19.232","alm.description":"Device: NYCMNYWS-CSCX68Y25BMPLSTunnelwithid: nullisOperationallyDOWN","alm.node-ref":"NYCMNYWS-CSCX68Y25B","alm.category":"MPLS","alm.source-object-ref":"MD=CISCO_EPNM!ND=NYCMNYWS-CSCX68Y25B","alm.business-key":"MPLS_TUNNEL: 166.34.111.78: : : ##SubAlarm@@_421","alm.ack-state":"unacknowledged","alm.remote-interface-ip-address":"166.34.111.78","alm.alarm-identifier":{"alm.resource-object-ref":"MD=CISCO_EPNM!ND=NYCMNYWS-CSCX68Y25B","alm.probable-cause":"mplsTunnelDown","alm.event-identifier":892322899}}},"push.topic":"alarm","push.time-of-update":"2019-04-0306: 56: 19.232","push.notification-id":-9058108182000441521,"push.operation":"push: modify"}}'
jsonStr = '{"push.push-change-update": {"push.update-data": {"alm.alarm": {"alm.owner": "", "alm.source-object-id": 0, "alm.perceived-severity": "critical", "alm.system-received-time": "2019-05-08 11:00:01.366", "alm.probable-cause": "entSensorThresholdNotification", "alm.root-cause-alarm-identifier": {"alm.event-identifier": 0}, "alm.cause-type": "cause-unknown", "alm.system-update-time": "2019-06-04 05:18:54.785", "alm.description": "Alarm Duplicating: Device:166.34.111.22, Sensor 0/6-DENALI ADC DACREF1 VTUNE with current value 198 milli voltsDC has violated the threshold value 500 milli voltsDC.", "alm.node-ref": "NYCMNYWS-CSCX68Y08A", "alm.category": "Optical Networking", "alm.source-object-ref": "MD=CISCO_EPNM!ND=NYCMNYWS-CSCX68Y08A", "alm.business-key": "166.34.111.22:entSensorThresholdNotification:39098##SubAlarm@@_2", "alm.ack-state": "unacknowledged", "alm.remote-interface-ip-address": "166.34.111.22", "alm.alarm-identifier": {"alm.resource-object-ref": "MD=CISCO_EPNM!ND=NYCMNYWS-CSCX68Y08A", "alm.probable-cause": "entSensorThresholdNotification", "alm.event-identifier": 1610487892}}}, "push.topic": "alarm", "push.time-of-update": "2019-06-04 05:18:54.785", "push.notification-id": -5995635441674197083, "push.operation": "push:modify"}}'
jsonObj = json.loads(jsonStr)
#print(json.dumps(jsonObj, indent=4))
mappingDetails = jsonToYang.getMappingDetails("UT_ALARMS_CISCO")
yangJsonStr = jsonToYang.convertToYangJsonStr(mappingDetails, jsonObj)
| 123.227273
| 1,266
| 0.744006
| 373
| 2,711
| 5.383378
| 0.372654
| 0.01245
| 0.01992
| 0.031873
| 0.538845
| 0.463147
| 0.450199
| 0.437251
| 0.403386
| 0.338645
| 0
| 0.102184
| 0.054224
| 2,711
| 21
| 1,267
| 129.095238
| 0.680967
| 0.451494
| 0
| 0
| 0
| 0.166667
| 0.859174
| 0.459039
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 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
| 0
| 0
|
0
| 5
|
bdd5844982980c687894d0b3bd598994ae7f1bcb
| 60
|
py
|
Python
|
schleifen.py
|
itmm/computer-poesie
|
54884ce42a472fa7912d0b30d007354145086a46
|
[
"MIT"
] | null | null | null |
schleifen.py
|
itmm/computer-poesie
|
54884ce42a472fa7912d0b30d007354145086a46
|
[
"MIT"
] | null | null | null |
schleifen.py
|
itmm/computer-poesie
|
54884ce42a472fa7912d0b30d007354145086a46
|
[
"MIT"
] | null | null | null |
def sum(n):
return n * (n + 1) // 2
print(sum(46341))
| 10
| 27
| 0.5
| 11
| 60
| 2.727273
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162791
| 0.283333
| 60
| 5
| 28
| 12
| 0.534884
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
bdf8470bdd92d3e46706668c58c2010929e67d76
| 92
|
py
|
Python
|
{{cookiecutter.project_slug}}/{{cookiecutter.app_name}}/io/__init__.py
|
mhavel/cookiecutter-python3-quickstart
|
c1d29b4b8dbdc2a53d67699c3e8c0bde786697cf
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_slug}}/{{cookiecutter.app_name}}/io/__init__.py
|
mhavel/cookiecutter-python3-quickstart
|
c1d29b4b8dbdc2a53d67699c3e8c0bde786697cf
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_slug}}/{{cookiecutter.app_name}}/io/__init__.py
|
mhavel/cookiecutter-python3-quickstart
|
c1d29b4b8dbdc2a53d67699c3e8c0bde786697cf
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
I/O
"""
from .helpers import get_output, read, write, update_defaults
| 11.5
| 61
| 0.684783
| 14
| 92
| 4.357143
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012987
| 0.163043
| 92
| 7
| 62
| 13.142857
| 0.779221
| 0.195652
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
da008204c56605880dca66da89d0cbe802a3d5a0
| 77
|
py
|
Python
|
flask_template/api/v1_api/models/__init__.py
|
neumantm/flask-template
|
13fb6e299fd01140648c2871d0932f0f00fbd063
|
[
"MIT"
] | null | null | null |
flask_template/api/v1_api/models/__init__.py
|
neumantm/flask-template
|
13fb6e299fd01140648c2871d0932f0f00fbd063
|
[
"MIT"
] | null | null | null |
flask_template/api/v1_api/models/__init__.py
|
neumantm/flask-template
|
13fb6e299fd01140648c2871d0932f0f00fbd063
|
[
"MIT"
] | null | null | null |
"""Module containing all schemas of the API."""
from .auth import * # noqa
| 19.25
| 47
| 0.675325
| 11
| 77
| 4.727273
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.194805
| 77
| 3
| 48
| 25.666667
| 0.83871
| 0.61039
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
da300b3af43237eb285d9d0548fecd970a03e83f
| 308
|
py
|
Python
|
app/app/errors/errors.py
|
MartinHeinz/IoT-Cloud
|
2e6fddcfe2624862c9351759334a6655a896e8c7
|
[
"MIT"
] | 14
|
2019-11-17T23:49:20.000Z
|
2022-02-04T23:28:45.000Z
|
app/app/errors/errors.py
|
MartinHeinz/IoT-Cloud
|
2e6fddcfe2624862c9351759334a6655a896e8c7
|
[
"MIT"
] | 3
|
2019-12-02T18:26:11.000Z
|
2021-04-30T20:46:06.000Z
|
app/app/errors/errors.py
|
MartinHeinz/IoT-Cloud
|
2e6fddcfe2624862c9351759334a6655a896e8c7
|
[
"MIT"
] | 4
|
2018-12-28T13:41:44.000Z
|
2020-09-13T14:14:06.000Z
|
from app.consts import SOMETHING_WENT_WRONG_MSG
from app.utils import http_json_response
def handle_error(e):
if hasattr(e, "code"):
return http_json_response(False, e.code, **{"error": SOMETHING_WENT_WRONG_MSG})
return http_json_response(False, 500, **{"error": SOMETHING_WENT_WRONG_MSG})
| 34.222222
| 87
| 0.756494
| 46
| 308
| 4.717391
| 0.478261
| 0.179724
| 0.248848
| 0.290323
| 0.488479
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011236
| 0.133117
| 308
| 8
| 88
| 38.5
| 0.801498
| 0
| 0
| 0
| 0
| 0
| 0.045455
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.833333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e5d802d5fbfaf63c2c69764f6c01e07ab7cc8036
| 5,911
|
py
|
Python
|
test/unit/test_trafficsystem.py
|
ArsamAryandoust/BEVPO
|
7763e28c1fa895c6689f394b7d99f9fd878cd5b2
|
[
"MIT"
] | 1
|
2022-02-15T14:51:41.000Z
|
2022-02-15T14:51:41.000Z
|
test/unit/test_trafficsystem.py
|
ArsamAryandoust/bevpo
|
7763e28c1fa895c6689f394b7d99f9fd878cd5b2
|
[
"MIT"
] | 2
|
2021-07-13T06:16:21.000Z
|
2021-07-14T23:51:02.000Z
|
test/unit/test_trafficsystem.py
|
ArsamAryandoust/bevpo
|
7763e28c1fa895c6689f394b7d99f9fd878cd5b2
|
[
"MIT"
] | null | null | null |
import unittest
import sys
sys.path.append('/bevpo/src')
import os
import random
import bevpo.datasets.prep_ubermovement as prep_data
import bevpo.trafficsystem as trafficsystem
class TestTrafficSystem(unittest.TestCase):
""" Tests class methods defined in trafficsystem.py """
@classmethod
def setUpClass(cls):
""" Runs once before the first test. """
# set path to data Uber Movement data
path_to_data = 'data/public/Uber Movement/'
# get list of cities
city_list = os.listdir(path_to_data)
# choose particular cities or comment out for testing all cities
city_list = ['Amsterdam']
# set the ciy_list as attribute of unittest.TestCase
cls.path_to_data = path_to_data
cls.city_list = city_list
@classmethod
def tearDownClass(cls):
""" Runs once after the last test. """
print('Executed test_trafficsystem.py')
def setUp(self):
""" Runs before every test. """
pass
def tearDown(self):
""" Runs after every test. """
pass
def test_calc_od_distances(self):
""" tests if calc_od_distances method result in symmetric and possitive
distance matrix.
"""
# iterate over all cities
for city in self.city_list:
# create the base path to data
base_path = self.path_to_data + city + '/'
file_list = os.listdir(base_path)
# search directory for .json files
json_file_name = [
file for file in file_list if file.endswith('.json')
][0]
# create the full paths to json and csv data
path_to_json_data = base_path + json_file_name
# merge into city_zone coordinates
city_zone_coordinates = (
prep_data.create_city_zone_coordinates(path_to_json_data)
)
# create class instance wihtout od_distances to call
# method calc_od_distance
tfs = trafficsystem.TrafficSystem(
city_zone_coordinates,
[]
)
# check if all distances positive and greater zero
for min_val in tfs.od_distances.min():
self.assertGreater(
min_val,
0
)
# check if distance matrix is quadratic
self.assertEqual(
len(tfs.od_distances.index),
len(tfs.od_distances.columns)
)
# sample 100 random datapoints from matrix
dist_sample_1 = random.sample(
list(tfs.od_distances.index),
100
)
dist_sample_2 = random.sample(
list(tfs.od_distances.index),
100
)
for source_id, dest_id in zip(dist_sample_1, dist_sample_2):
# check if matrix entries symmetric for randomly sampled points
self.assertEqual(
tfs.od_distances.loc[source_id, dest_id],
tfs.od_distances.loc[dest_id, source_id]
)
# check if diagonal entries are set to 1 km.
self.assertEqual(
tfs.od_distances.loc[source_id, source_id],
1
)
def test_create_datatensor(self):
""" """
# iterate over all cities
for city in self.city_list:
# create the base path to data
base_path = self.path_to_data + city + '/'
file_list = os.listdir(base_path)
# search directory for .json files
json_file_name = [
file for file in file_list if file.endswith('.json')
][0]
# create the full paths to json and csv data
path_to_json_data = base_path + json_file_name
# merge into city_zone coordinates
city_zone_coordinates = (
prep_data.create_city_zone_coordinates(path_to_json_data)
)
# create class instance wihtout od_distances to call
# method calc_od_distance
tfs = trafficsystem.TrafficSystem(
city_zone_coordinates,
[]
)
# check if all distances positive and greater zero
for min_val in tfs.od_distances.min():
self.assertGreater(
min_val,
0
)
# check if distance matrix is quadratic
self.assertEqual(
len(tfs.od_distances.index),
len(tfs.od_distances.columns)
)
# sample 100 random datapoints from matrix
dist_sample_1 = random.sample(
list(tfs.od_distances.index),
100
)
dist_sample_2 = random.sample(
list(tfs.od_distances.index),
100
)
for source_id, dest_id in zip(dist_sample_1, dist_sample_2):
# check if matrix entries symmetric for randomly sampled points
self.assertEqual(
tfs.od_distances.loc[source_id, dest_id],
tfs.od_distances.loc[dest_id, source_id]
)
# check if diagonal entries are set to 1 km.
self.assertEqual(
tfs.od_distances.loc[source_id, source_id],
1
)
if __name__ == '__main__':
unittest.main()
| 29.853535
| 79
| 0.519032
| 624
| 5,911
| 4.6875
| 0.213141
| 0.075214
| 0.076581
| 0.038974
| 0.712479
| 0.712479
| 0.712479
| 0.712479
| 0.712479
| 0.712479
| 0
| 0.009918
| 0.420064
| 5,911
| 197
| 80
| 30.005076
| 0.843349
| 0.23008
| 0
| 0.603774
| 0
| 0
| 0.021286
| 0.004705
| 0
| 0
| 0
| 0
| 0.075472
| 1
| 0.056604
| false
| 0.018868
| 0.056604
| 0
| 0.122642
| 0.009434
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f9078b45641d9092a33a389535681300c7bbb737
| 3,621
|
py
|
Python
|
polls/forms.py
|
simranlotey/Blood-Bank-Management-System
|
4c51d5cfcd855fcd39bc32cb145d65bb872c2e2e
|
[
"MIT"
] | 9
|
2021-09-11T19:11:04.000Z
|
2022-02-17T14:01:12.000Z
|
polls/forms.py
|
simranlotey/Blood-Bank-Management-System
|
4c51d5cfcd855fcd39bc32cb145d65bb872c2e2e
|
[
"MIT"
] | null | null | null |
polls/forms.py
|
simranlotey/Blood-Bank-Management-System
|
4c51d5cfcd855fcd39bc32cb145d65bb872c2e2e
|
[
"MIT"
] | 8
|
2021-09-11T19:16:28.000Z
|
2022-01-16T06:45:57.000Z
|
from django import forms
from django.forms import ModelForm
from . import models
class DonorRegistration(ModelForm):
class Meta:
model = models.donor_Registration
fields = '__all__'
widgets = {'name': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Name'}),
'father_name': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Father Name'}),
'gender': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'email': forms.EmailInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your E-mail'}),
'phone_number': forms.NumberInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Phone Number'}),
'state': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your State'}),
'city': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your City'}),
'date_of_birth': forms.DateInput(
attrs={'class': 'form-control', 'required': 'True', 'type': 'date', 'placeholder': 'Enter Your D.O.B.'}),
'occupation': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Occupation'}),
'blood_group': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'home_address': forms.Textarea(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Home Address'}),
'last_donate_date': forms.DateInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your Last Donate Date'}),
'any_disease': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'allergies': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'cardiac': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'bleeding_disorder': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'hbsAg_hcv_hIV': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
}
class Search(forms.ModelForm):
class Meta:
model = models.sea_rch
fields = '__all__'
widgets = {'blood_group': forms.Select(
attrs={'class': 'form-control', 'required': 'True'}),
'state': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your State'}),
'city': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Enter Your City'}),
}
class Contact(forms.ModelForm):
class Meta:
model = models.con_tact
fields = '__all__'
widgets = {'name': forms.TextInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Name'}),
'phone_number': forms.NumberInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Phone Number'}),
'email': forms.EmailInput(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'E-mail'}),
'subject': forms.Textarea(
attrs={'class': 'form-control', 'required': 'True', 'placeholder': 'Subject...'}),
}
| 48.932432
| 117
| 0.560342
| 348
| 3,621
| 5.747126
| 0.198276
| 0.12
| 0.168
| 0.252
| 0.8005
| 0.786
| 0.752
| 0.752
| 0.5895
| 0.4925
| 0
| 0
| 0.252416
| 3,621
| 73
| 118
| 49.60274
| 0.738825
| 0
| 0
| 0.424242
| 0
| 0
| 0.379453
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.045455
| 0
| 0.136364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f90a457236a273a6cc3d459d09d35e0888b48d62
| 2,301
|
py
|
Python
|
Rosalind_HAMM.py
|
David-boo/Rosalind
|
0e43538ea635f8f00a6aa3a3a64d7ad486a80b3c
|
[
"MIT"
] | null | null | null |
Rosalind_HAMM.py
|
David-boo/Rosalind
|
0e43538ea635f8f00a6aa3a3a64d7ad486a80b3c
|
[
"MIT"
] | null | null | null |
Rosalind_HAMM.py
|
David-boo/Rosalind
|
0e43538ea635f8f00a6aa3a3a64d7ad486a80b3c
|
[
"MIT"
] | null | null | null |
# Code on Python 3.7.4
# Working @ Dec, 2020
# david-boo.github.io
# Define sequences. Then put two strings together to produce the list of pairs that we wish to compare, and a filter returns just those pairs for which seq1 != seq2
# Taking the length of the filtered list gives us the Hamming distance
seq1='AAAGAAAACGCCAACCCCCCCCCGTGCTGCAGTCTTGATTGCTGTATGAGAGATCCGGCCCTGTACGCGGTCCCCGTAGGACACTCACAGACGTCCACAGTTCTAGAAGAAGCGCTTATTCGTATTCGTCGACCTGTCCCCTGCTCCGTCCAGCGGTTAGAGTCCTACATGTACGTTGGAAGAGCACTCCCGACCGGTGCGGTTAGTACATCTTTTGTGATTTCCGAGTTCCGTGAAGGGGAGACCGATATGTACGCTCAGCATATGTCGATGCTGCAACGTGCATAAGGACGTAGACGGATACGCACAGTGATGTAAGCTTTGGACCTTGGGCCTCTACGACCTACACACGAACAGTAGTTCACACCCCTGCGCTACCGAGCATGCACTAGCGGCAGTCTTTCTCCTCCGAACGCGCCAAAGAAAATTGAATAGCTATCACCGTGCATGGGCGTGATGTGGAGTCAACATTCAGTTTGGAAAGTTCTTGCATATGTGGTCATGTACTGAGTTGTCTTCCAAGACTGCGAAATTTTCGATGCAGCGGCAACAACCGTACGTTACGCACAACTAGTAGTGGAGTTCTTGACCTTTTCGGGCAGAGTCATGCCCACCTAGCATCGCTTAAAGTCCGTAAGAAGCTATCATGGACCCACGCACGCGGGGCTTGTGAGCTAATATATCCCTGCGGGAGAAACCGAAACGGGGTAGATGGAGCGGAGCCATAGGGACACGCGGCGGCTGTTAGACACTGATGGAGCAGTTGACGGCATTTCCTACGCTAACTATCAGGGAGGGGCCCAGTACAAATCACTTCTTTAGCTATAGTTACGTGCGGACATTAGCTTTTAGTAGCTCATGGCGAATGATTTAACTAAATACCGCCACTATTGCGGGGGTACTGTTTCCCCGTTTCACAAATTTACGCGTTACCTCCCCCTTCTTAGTAGCG'
seq2='CAGTATAATAACAACCCCCTACCTAGCGGCATTCAGGGACTCGTCACAACTGATACGCCACTGATCGCGAAAGAACTGGCCAACTAAGCTAGGTGCATTCTATTATCAGAATTCAATTTTCGTATTCGAGGCACGTTCGACAGAACTCGACACTGGTCCGAGTTCCGCTTATAGATTTAACATCAGGCTCTCAAGAACGCGGCTAGAATCTCCTGCGGTGACTCCAAGGTCCATCAAGGTGGGAGTAGCGTCACCGCTCCTGATCCATCAATGACATAACAGGCCGTAGAGCGGTATCAGATGTGCTGAGTACCGTAATCATTGAGGGTTTGGGATGAAGAACGAAAGCAGGGCCTCTGGCTCACATACACTCCCTACCCAGTATGCGCTGACTGGCGTCGTTATGGTAGTAACCCGCAGAGTTCTAGTGAAAGGGGTCGTTCAGGCAAATACCGCGAGTGGGATAAATTGTCGCGCTGAGAAGGAATCGAATATGTCCTAACACACGCTGTCTTTCGATATGACGACGCCTTTCTAGATTCCTAGTCAACAGATGCACCCTAAGTACGACGAGAATAGTACAGCTCGACTTCCTCCGGGTGAGTCTTGCCCGAGAAGGCGAGCGCACAACAGATGGGTTGGTATATGCTGGGCCGACGCTGGGGACGCTGGCGGAGAGATTCGTGACTAGGAGGAGGCCAGACAAGGTTCGGGTAGGGGAGGATTAGATATCCGTGGGTATCGGTAAACCTGTGTAGGGGTGTCAATTGCGGTTCTGAGGTATACACTGAGACAAGGGCCAAACACTGATGACGACTGTAGCTGGACATACATAGCGGCACTAGTTAGAAGCAGCTGCCTTTCCACGACCAAAGTAGCCTATGTTCTCACTGGGGGGCTCCCGACTTAGGCCATCCCAACCGTGCGCCACTCGTTATATTCATAAGAGGGC'
print(sum(map(lambda x, y: 0 if x == y else 1, seq1, seq2)))
| 177
| 960
| 0.947414
| 72
| 2,301
| 30.277778
| 0.819444
| 0.007339
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006775
| 0.03781
| 2,301
| 12
| 961
| 191.75
| 0.977868
| 0.127336
| 0
| 0
| 0
| 0
| 0.956784
| 0.956784
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
008467fbeba521ddcd97afa3700bf334ffc4f60d
| 56
|
py
|
Python
|
01. Different Modes in PYTHON/subpackages/demo/math/display.py
|
penanrajput/PythonCourseContent
|
074a4af9c83a8a6b9b4608ce341ed96d1bd2e999
|
[
"MIT"
] | null | null | null |
01. Different Modes in PYTHON/subpackages/demo/math/display.py
|
penanrajput/PythonCourseContent
|
074a4af9c83a8a6b9b4608ce341ed96d1bd2e999
|
[
"MIT"
] | null | null | null |
01. Different Modes in PYTHON/subpackages/demo/math/display.py
|
penanrajput/PythonCourseContent
|
074a4af9c83a8a6b9b4608ce341ed96d1bd2e999
|
[
"MIT"
] | 1
|
2020-12-19T19:29:17.000Z
|
2020-12-19T19:29:17.000Z
|
def display():
print("inside math display function")
| 28
| 41
| 0.714286
| 7
| 56
| 5.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160714
| 56
| 2
| 41
| 28
| 0.851064
| 0
| 0
| 0
| 0
| 0
| 0.491228
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
00a85eafbf6a1ac5ead6c1b6b1b0bdfabb387f38
| 55
|
py
|
Python
|
tcn/__init__.py
|
junwang23/TemporalConvNet
|
65eabbaa136f2ce97d5cfe4c974157a61b231fdd
|
[
"MIT"
] | null | null | null |
tcn/__init__.py
|
junwang23/TemporalConvNet
|
65eabbaa136f2ce97d5cfe4c974157a61b231fdd
|
[
"MIT"
] | null | null | null |
tcn/__init__.py
|
junwang23/TemporalConvNet
|
65eabbaa136f2ce97d5cfe4c974157a61b231fdd
|
[
"MIT"
] | null | null | null |
from tcn.conv import Conv1D, DCNBlock, TemporalConvNet
| 27.5
| 54
| 0.836364
| 7
| 55
| 6.571429
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.020408
| 0.109091
| 55
| 1
| 55
| 55
| 0.918367
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
00ac5aa65814391fa6f748808b6c7d4901b4eb34
| 185
|
py
|
Python
|
dregistration/admin.py
|
netzary/quinnan
|
d6d5abd12dcdefe6ee61fe323cd82b5627d62aa3
|
[
"MIT"
] | null | null | null |
dregistration/admin.py
|
netzary/quinnan
|
d6d5abd12dcdefe6ee61fe323cd82b5627d62aa3
|
[
"MIT"
] | null | null | null |
dregistration/admin.py
|
netzary/quinnan
|
d6d5abd12dcdefe6ee61fe323cd82b5627d62aa3
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from models import *
class RegistrationProfileAdmin(admin.ModelAdmin):
pass
admin.site.register( RegistrationProfile, RegistrationProfileAdmin)
| 15.416667
| 67
| 0.821622
| 18
| 185
| 8.444444
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118919
| 185
| 12
| 67
| 15.416667
| 0.932515
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.2
| 0.4
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
00d4ef236fd2f98da365ceea162b9aca342bd4b9
| 59
|
py
|
Python
|
es_example/es_ui_hooks/__init__.py
|
force-h2020/force-bdss-plugin-enginsoft-toy-model
|
f22c0ad3cc45c3b5a7f9c4fd0b20549d7dfc9aeb
|
[
"MIT"
] | null | null | null |
es_example/es_ui_hooks/__init__.py
|
force-h2020/force-bdss-plugin-enginsoft-toy-model
|
f22c0ad3cc45c3b5a7f9c4fd0b20549d7dfc9aeb
|
[
"MIT"
] | null | null | null |
es_example/es_ui_hooks/__init__.py
|
force-h2020/force-bdss-plugin-enginsoft-toy-model
|
f22c0ad3cc45c3b5a7f9c4fd0b20549d7dfc9aeb
|
[
"MIT"
] | null | null | null |
from .es_ui_hooks_factory import ESUIHooksFactory # noqa
| 29.5
| 58
| 0.830508
| 8
| 59
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 1
| 59
| 59
| 0.901961
| 0.067797
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
00d5bf67636c24a132187663582ac0d7afa5699e
| 49
|
py
|
Python
|
resume_json/__main__.py
|
moshe742/resume-json-python
|
9413e062e1889f58d970795976a8d6b88637465c
|
[
"MIT"
] | 3
|
2021-02-14T20:27:02.000Z
|
2022-02-03T16:54:11.000Z
|
resume_json/__main__.py
|
moshe742/resume-json-python
|
9413e062e1889f58d970795976a8d6b88637465c
|
[
"MIT"
] | 10
|
2020-12-10T13:39:54.000Z
|
2020-12-29T14:18:11.000Z
|
resume_json/__main__.py
|
moshe742/resume-json-python
|
9413e062e1889f58d970795976a8d6b88637465c
|
[
"MIT"
] | 1
|
2020-12-10T12:23:58.000Z
|
2020-12-10T12:23:58.000Z
|
from resume_json.resume_cli import main
main()
| 9.8
| 39
| 0.795918
| 8
| 49
| 4.625
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 49
| 4
| 40
| 12.25
| 0.880952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
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| 0.5
| 0
| 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
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
00eb6b8db3b17f5451508e2f0e56f530b4ed9889
| 138
|
py
|
Python
|
genial/exceptions.py
|
varnion/genial
|
c0146900e4560693377b2c083e021c8f47df078d
|
[
"BSD-3-Clause"
] | 4
|
2016-12-11T14:26:47.000Z
|
2017-10-04T11:42:33.000Z
|
genial/exceptions.py
|
varnion/genial
|
c0146900e4560693377b2c083e021c8f47df078d
|
[
"BSD-3-Clause"
] | null | null | null |
genial/exceptions.py
|
varnion/genial
|
c0146900e4560693377b2c083e021c8f47df078d
|
[
"BSD-3-Clause"
] | null | null | null |
class ParseError(Exception):
pass
class UnsupportedFile(Exception):
pass
class MultipleParentsGFF(UnsupportedFile):
pass
| 11.5
| 42
| 0.746377
| 12
| 138
| 8.583333
| 0.5
| 0.252427
| 0.349515
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188406
| 138
| 11
| 43
| 12.545455
| 0.919643
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
| 0
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| 0
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| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
dab567bdf024e59ba81d82f173996735d29a7f19
| 81,034
|
py
|
Python
|
tests/test_preprocessing/test_datahandler.py
|
csiro-hydroinformatics/AI4Water
|
cdb18bd4bf298f77b381f1829045a1e790146985
|
[
"MIT"
] | 12
|
2020-10-13T08:23:17.000Z
|
2021-01-22T04:36:21.000Z
|
tests/test_preprocessing/test_datahandler.py
|
csiro-hydroinformatics/AI4Water
|
cdb18bd4bf298f77b381f1829045a1e790146985
|
[
"MIT"
] | 1
|
2020-10-15T02:42:52.000Z
|
2020-10-15T02:51:07.000Z
|
tests/test_preprocessing/test_datahandler.py
|
csiro-hydroinformatics/AI4Water
|
cdb18bd4bf298f77b381f1829045a1e790146985
|
[
"MIT"
] | 2
|
2020-11-23T04:45:38.000Z
|
2020-11-26T10:12:34.000Z
|
import os
import unittest
import random
import sys
import site # so that ai4water directory is in path
ai4_dir = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))
site.addsitedir(ai4_dir)
import scipy
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from ai4water import Model
from ai4water.preprocessing import DataHandler, SiteDistributedDataHandler
from ai4water.preprocessing.datahandler import MultiLocDataHandler
from ai4water.datasets import load_u1, arg_beach
os.environ['PYTHONHASHSEED'] = '313'
random.seed(313)
np.random.seed(313)
# todo, check last dimension of x,y
# todo test with 3d y
def _check_xy_equal_len(x, prev_y, y, lookback, num_ins, num_outs, num_examples, data_type='training'):
feat_dim = 1
if lookback > 1:
assert x.shape[1] == lookback
feat_dim = 2
assert x.shape[
feat_dim] == num_ins, f"for {data_type} x's shape is {x.shape} while num_ins of dataloader are {num_ins}"
if y is not None:
assert y.shape[1] == num_outs, f"for {data_type} y's shape is {y.shape} while num_outs of dataloader are {num_outs}"
else:
assert num_outs == 0
y = x # just for next statement to run
if prev_y is None:
prev_y = x # just for next statement to run
assert x.shape[0] == y.shape[0] == prev_y.shape[
0], f"for {data_type} xshape: {x.shape}, yshape: {y.shape}, prevyshape: {prev_y.shape}"
if num_examples:
assert x.shape[
0] == num_examples, f'for {data_type} x contains {x.shape[0]} samples while expected samples are {num_examples}'
return
def assert_xy_equal_len(x, prev_y, y, data_loader, num_examples=None, data_type='training'):
if isinstance(x, np.ndarray):
_check_xy_equal_len(x, prev_y, y, data_loader.lookback, data_loader.num_ins, data_loader.num_outs, num_examples,
data_type=data_type)
elif isinstance(x, list):
while len(y)<len(x):
y.append(None)
for idx, i in enumerate(x):
_check_xy_equal_len(i, prev_y[idx], y[idx], data_loader.lookback[idx], data_loader.num_ins[idx],
data_loader.num_outs[idx], num_examples, data_type=data_type
)
elif isinstance(x, dict):
for key, i in x.items():
_check_xy_equal_len(i, prev_y.get(key, None), y.get(key, None), data_loader.lookback[key], data_loader.num_ins[key],
data_loader.num_outs[key], num_examples, data_type=data_type
)
elif x is None: # all should be None
assert all(v is None for v in [x, prev_y, y])
else:
raise ValueError
def _check_num_examples(train_x, val_x, test_x, val_ex, test_ex, tot_obs):
val_examples = 0
if val_ex:
val_examples = val_x.shape[0]
test_examples = 0
if test_ex:
test_examples = test_x.shape[0]
xyz_samples = train_x.shape[0] + val_examples + test_examples
# todo, whould be equal
assert xyz_samples == tot_obs, f"""
data_loader has {tot_obs} examples while sum of train/val/test examples are {xyz_samples}."""
def check_num_examples(train_x, val_x, test_x, val_ex, test_ex, data_loader):
if isinstance(train_x, np.ndarray):
_check_num_examples(train_x, val_x, test_x, val_ex, test_ex, data_loader.tot_obs_for_one_df())
elif isinstance(train_x, list):
for idx in range(len(train_x)):
_check_num_examples(train_x[idx], val_x[idx], test_x[idx], val_ex, test_ex,
data_loader.tot_obs_for_one_df()[idx])
return
def check_inverse_transformation(data, data_loader, y, cols, key):
if cols is None:
# not output columns, so not checking
return
# check that after inverse transformation, we get correct y.
if data_loader.source_is_df:
train_y_ = data_loader.inverse_transform(data=pd.DataFrame(y.reshape(-1, len(cols)), columns=cols), key=key)
train_y_, index = data_loader.deindexify(train_y_, key=key)
compare_individual_item(data, key, cols, train_y_, data_loader)
elif data_loader.source_is_list:
#for idx in range(data_loader.num_sources):
# y_ = y[idx].reshape(-1, len(cols[idx]))
train_y_ = data_loader.inverse_transform(data=y, key=key)
train_y_, _ = data_loader.deindexify(train_y_, key=key)
for idx, y in enumerate(train_y_):
compare_individual_item(data[idx], f'{key}_{idx}', cols[idx], y, data_loader)
elif data_loader.source_is_dict:
train_y_ = data_loader.inverse_transform(data=y, key=key)
train_y_, _ = data_loader.deindexify(train_y_, key=key)
for src_name, val in train_y_.items():
compare_individual_item(data[src_name], f'{key}_{src_name}', cols[src_name], val, data_loader)
def compare_individual_item(data, key, cols, y, data_loader):
if y is None:
return
train_index = data_loader.indexes[key]
if y.__class__.__name__ in ['DataFrame']:
y = y.values
for i, v in zip(train_index, y):
if len(cols) == 1:
if isinstance(train_index, pd.DatetimeIndex):
# if true value in data is None, y's value should also be None
if np.isnan(data[cols].loc[i]).item():
assert np.isnan(v).item()
else:
_t = round(data[cols].loc[i].item(), 0)
_p = round(v.item(), 0)
if not np.allclose(data[cols].loc[i].item(), v.item()):
print(f'true: {_t}, : pred: {_p}, index: {i}, col: {cols}')
else:
if isinstance(v, np.ndarray):
v = round(v.item(), 3)
_true = round(data[cols].loc[i], 3).item()
_p = round(v, 3)
if _true != _p:
print(f'true: {_true}, : pred: {_p}, index: {i}, col: {cols}')
else:
if isinstance(train_index, pd.DatetimeIndex):
assert abs(data[cols].loc[i].sum() - np.nansum(v)) <= 0.00001, f'{data[cols].loc[i].sum()},: {v}'
else:
assert abs(data[cols].iloc[i].sum() - v.sum()) <= 0.00001
def check_kfold_splits(data_handler):
if data_handler.source_is_df:
splits = data_handler.KFold_splits()
for (train_x, train_y), (test_x, test_y) in splits:
... # print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
return
def assert_uniquenes(train_y, val_y, test_y, out_cols, data_loader):
if isinstance(train_y, list):
assert isinstance(val_y, list)
assert isinstance(test_y, list)
train_y = train_y[0]
val_y = val_y[0]
test_y = test_y[0]
if isinstance(train_y, dict):
train_y = list(train_y.values())[0]
assert isinstance(val_y, dict)
isinstance(test_y, dict)
val_y = list(val_y.values())[0]
test_y = list(test_y.values())[0]
if out_cols is not None:
b = train_y.reshape(-1, )
if val_y is None:
a = test_y.reshape(-1, )
else:
a = val_y.reshape(-1, )
if not len(np.intersect1d(a, b)) == 0:
raise ValueError(f'train and val have overlapping values')
if data_loader.val_data != 'same' and out_cols is not None and val_y is not None and test_y is not None:
a = test_y.reshape(-1,)
b = val_y.reshape(-1,)
assert len(np.intersect1d(a, b)) == 0, 'test and val have overlapping values'
return
def build_and_test_loader(data, config, out_cols, train_ex=None, val_ex=None, test_ex=None, save=True,
assert_uniqueness=True, check_examples=True,
true_train_y=None, true_val_y=None, true_test_y=None):
config['teacher_forcing'] = True # todo
if 'val_fraction' not in config:
config['val_fraction'] = 0.3
if 'test_fraction' not in config:
config['test_fraction'] = 0.3
data_loader = DataHandler(data=data, save=save, verbosity=0, **config)
#dl = DataLoader.from_h5('data.h5')
train_x, prev_y, train_y = data_loader.training_data(key='train')
assert_xy_equal_len(train_x, prev_y, train_y, data_loader, train_ex)
val_x, prev_y, val_y = data_loader.validation_data(key='val')
assert_xy_equal_len(val_x, prev_y, val_y, data_loader, val_ex, data_type='validation')
test_x, prev_y, test_y = data_loader.test_data(key='test')
assert_xy_equal_len(test_x, prev_y, test_y, data_loader, test_ex, data_type='test')
if check_examples:
check_num_examples(train_x, val_x, test_x, val_ex, test_ex, data_loader)
if isinstance(data, str):
data = data_loader.data
check_inverse_transformation(data, data_loader, train_y, out_cols, 'train')
if val_ex:
check_inverse_transformation(data, data_loader, val_y, out_cols, 'val')
if test_ex:
check_inverse_transformation(data, data_loader, test_y, out_cols, 'test')
check_kfold_splits(data_loader)
if assert_uniqueness:
assert_uniquenes(train_y, val_y, test_y, out_cols, data_loader)
if true_train_y is not None:
assert np.allclose(train_y, true_train_y)
if true_val_y is not None:
assert np.allclose(val_y, true_val_y)
if true_test_y is not None:
assert np.allclose(test_y, true_test_y)
return data_loader
class TestAllCases(object):
def __init__(self, input_features, output_features, lookback=3, allow_nan_labels=0, save=True):
self.input_features = input_features
self.output_features = output_features
self.lookback = lookback
self.allow_nan_labels=allow_nan_labels
self.save=save
self.run_all()
def run_all(self):
all_methods = [m for m in dir(self) if callable(getattr(self, m)) and not m.startswith('_') and m not in ['run_all']]
for m in all_methods:
getattr(self, m)()
return
def test_basic(self):
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback}
tr_examples = 49 - (self.lookback - 2) if self.lookback>1 else 49
val_examples = 22 - (self.lookback - 2) if self.lookback>1 else 22
test_examples = 30 - (self.lookback - 2) if self.lookback>1 else 30
if self.output_features == ['c']:
tty = np.arange(202, 250).reshape(-1, 1, 1)
tvy = np.arange(250, 271).reshape(-1, 1, 1)
ttesty = np.arange(271, 300).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, val_examples, test_examples,
save=self.save,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
check_examples=True,
)
assert loader.source_is_df
return
def test_with_random(self):
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random'}
tr_examples = 49 - (self.lookback - 2) if self.lookback>1 else 49
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, 20, 30,
save=self.save,
)
assert loader.source_is_df
return
def test_drop_remainder(self):
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'batch_size': 8,
'drop_remainder': True,
'train_data': 'random'}
loader = build_and_test_loader(data, config, self.output_features,
48, 16, 24,
check_examples=False,
save=self.save,
)
assert loader.source_is_df
return
def test_with_same_val_data(self):
# val_data is "same" as and train_data is make based upon fractions.
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_data': 'same'}
if self.output_features == ['c']:
tty = np.arange(202, 271).reshape(-1, 1, 1)
tvy = np.arange(271, 300).reshape(-1, 1, 1)
ttesty = np.arange(271, 300).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 71 - (self.lookback - 1) if self.lookback > 1 else 71
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, 29, 29,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save,
check_examples=False
)
assert loader.source_is_df
return
def test_with_same_val_data_and_random(self):
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'val_data': 'same'}
tr_examples = 70 - (self.lookback - 1) if self.lookback > 1 else 70
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, 30, 30,
check_examples=False,
save=self.save
)
assert loader.source_is_df
return
def test_with_no_val_data(self):
# we dont' want to have any validation_data
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_fraction': 0.0}
if self.output_features == ['c']:
tty = np.arange(202, 271).reshape(-1, 1, 1)
ttesty = np.arange(271, 300).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 71 - (self.lookback - 1) if self.lookback > 1 else 71
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, 0, 29,
true_train_y=tty,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_no_val_data_with_random(self):
# we dont' want to have any validation_data
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'val_fraction': 0.0}
tr_examples = 70 - (self.lookback - 1) if self.lookback > 1 else 70
loader = build_and_test_loader(data, config, self.output_features,
tr_examples, 0, 30,
save=self.save
)
assert loader.source_is_df
return
def test_with_no_test_data(self):
# we don't want any test_data
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'test_fraction': 0.0}
if self.output_features == ['c']:
tty = np.arange(202, 271).reshape(-1, 1, 1)
tvy = np.arange(271, 300).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 71 - (self.lookback - 1) if self.lookback > 1 else 71
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 29, 0,
true_train_y=tty,
true_val_y=tvy,
save=self.save
)
assert loader.source_is_df
return
def test_with_no_test_data_with_random(self):
# we don't want any test_data
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'test_fraction': 0.0,
'transformation': 'minmax'}
tr_examples = 15- (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 0,
save=self.save)
assert loader.source_is_df
return
def test_with_dt_index(self):
# we don't want any test_data
#print('testing test_with_dt_index', self.lookback)
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=20, freq='D'))
config = {'input_features': self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'test_fraction': 0.0,
'transformation': 'minmax'}
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 0,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals(self):
#print('testing test_with_intervals', self.lookback)
examples = 35
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=35, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'transformation': 'minmax',
'intervals': [(0, 10), (20, 35)]
}
tr_examples = 12 - (self.lookback - 1) if self.lookback > 1 else 12
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 4, 7,
save=self.save
)
assert loader.source_is_df
return
def test_with_dt_intervals(self):
# check whether indices of intervals can be datetime?
examples = 35
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=35, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'transformation': 'minmax',
'intervals': [('20110101', '20110110'), ('20110121', '20110204')]
}
tr_examples = 12 - (self.lookback - 1) if self.lookback > 1 else 12
val_examples = 7 - (self.lookback - 2) if self.lookback > 1 else 7
test_examples = 7 - (self.lookback - 2) if self.lookback > 1 else 7
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 4, 7,
save=self.save)
assert loader.source_is_df
return
def test_with_custom_train_indices(self):
#print('testing test_with_custom_train_indices')
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=20, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
'transformation': 'minmax',
}
tr_examples = 9 - (self.lookback - 2) if self.lookback > 1 else 9
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
test_examples = 8 - (self.lookback - 1) if self.lookback > 1 else 8
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, test_examples,
save=self.save)
assert loader.source_is_df
return
def test_with_custom_train_indices_no_val_data(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=20, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
'transformation': 'minmax',
'val_fraction': 0.0,
}
test_examples = 8 - (self.lookback - 1) if self.lookback > 1 else 8
loader = build_and_test_loader(data, config, self.output_features, 12, 0, test_examples,
save=self.save)
assert loader.source_is_df
return
def test_with_custom_train_indices_same_val_data(self):
#print('testing test_with_custom_train_indices_same_val_data')
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=20, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
'transformation': 'minmax',
'val_data': 'same',
}
test_examples = 8 - (self.lookback - 1) if self.lookback > 1 else 8
loader = build_and_test_loader(data, config, self.output_features, 12, 0, test_examples,
save=self.save)
assert loader.source_is_df
return
def test_with_custom_train_and_val_indices(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=20, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
'transformation': 'minmax',
'val_data': [0, 12, 14, 16, 5],
'val_fraction': 0.0,
}
test_examples = 8 - (self.lookback - 1) if self.lookback > 1 else 8
loader = build_and_test_loader(data, config, self.output_features, 12, 5, test_examples,
assert_uniqueness=False,
save=self.save,
check_examples=False
)
assert loader.source_is_df
return
# def test_with_train_and_val_and_test_indices(self):
# # todo, does it make sense to define test_data by indices
# return
def test_with_custom_train_indices_and_intervals(self):
#print('testing test_with_custom_train_indices_and_intervals', self.lookback)
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
#'transformation': 'minmax',
'intervals': [(0, 10), (20, 30)]
}
if self.output_features == ['c']:
tty = np.array([63., 64., 65., 66., 67., 68., 69., 82.]).reshape(-1, 1, 1)
tvy = np.arange(83, 87).reshape(-1, 1, 1)
ttesty = np.array([62., 87., 88., 89.]).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
test_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, test_examples,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_one_feature_transformation(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': [{'method': 'minmax', 'features': ['a']}],
}
if self.output_features == ['c']:
tty = np.arange(42, 51).reshape(-1, 1, 1)
tvy = np.arange(51, 55).reshape(-1, 1, 1)
ttesty = np.arange(55, 60).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_one_feature_multi_transformation(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': [{'method': 'minmax', 'features': ['a']}, {'method': 'zscore', 'features': ['a']}],
}
if self.output_features == ['c']:
tty = np.arange(42, 51).reshape(-1, 1, 1)
tvy = np.arange(51, 55).reshape(-1, 1, 1)
ttesty = np.arange(55, 60).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_one_feature_multi_transformation_on_diff_features(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': [{'method': 'minmax', 'features': ['a', 'b', 'c']}, {'method': 'zscore', 'features': ['c']}],
}
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
save=self.save)
assert loader.source_is_df
return
def test_with_input_transformation(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': [{'method': 'minmax', 'features': ['a', 'b']}],
}
if self.output_features == ['c']:
tty = np.arange(42, 51).reshape(-1, 1, 1)
tvy = np.arange(51, 55).reshape(-1, 1, 1)
ttesty = np.arange(55, 60).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_input_transformation_as_dict(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': {'method': 'minmax', 'features': ['a', 'b']},
}
if self.output_features == ['c']:
tty = np.arange(42, 51).reshape(-1, 1, 1)
tvy = np.arange(51, 55).reshape(-1, 1, 1)
ttesty = np.arange(55, 60).reshape(-1, 1, 1)
else:
tty, tvy, ttesty = None, None, None
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
true_train_y=tty,
true_val_y=tvy,
true_test_y=ttesty,
save=self.save)
assert loader.source_is_df
return
def test_with_output_transformation(self):
examples = 20
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'])
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'transformation': {'method': 'minmax', 'features': ['c']},
}
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_and_intervals(self):
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback, 'train_data': 'random',
'transformation': 'minmax',
'intervals': [(0, 10), (20, 30)]
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
val_examples = 5 - (self.lookback - 1) if self.lookback > 1 else 5
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_and_intervals_same_val_data(self):
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback, 'train_data': 'random', 'val_data': 'same',
'transformation': 'minmax',
'intervals': [(0, 10), (20, 30)]
}
tr_examples = 13 - (self.lookback - 1) if self.lookback > 1 else 13
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_and_intervals_no_val_data(self):
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random', 'val_fraction': 0.0,
'transformation': 'minmax',
'intervals': [(0, 10), (20, 30)]
}
tr_examples = 13 - (self.lookback - 1) if self.lookback > 1 else 13
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 0, 5,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_and_nans(self):
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
if self.output_features is not None:
data['c'].iloc[10:20] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 4, 6,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 6, 9,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_and_nans_interpolate(self):
examples = 30
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=30, freq='D'))
if self.output_features is not None:
data['b'].iloc[10:20] = np.nan
config = {'input_features': self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'nan_filler': {'method': 'KNNImputer', 'features': self.input_features},
'train_data': 'random',
}
if self.input_features == ['a']:
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
test_examples = 6
else:
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
val_examples = 6
test_examples = 9
build_and_test_loader(data, config, self.output_features,
tr_examples, val_examples, test_examples,
save=self.save)
data['c'].iloc[10:20] = np.nan
if 'b' not in self.output_features:
config = {'input_features': self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'nan_filler': {'method': 'KNNImputer', 'features': ['b']},
'train_data': 'random',
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
build_and_test_loader(data, config, self.output_features, tr_examples, 4, 6,
save=self.save)
config = {'input_features': self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'nan_filler': {'method': 'KNNImputer', 'features': ['b'], 'imputer_args': {'n_neighbors': 4}},
'train_data': 'random',
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
build_and_test_loader(data, config, self.output_features, tr_examples, 4, 6,
save=self.save)
return
def test_with_indices_and_nans_at_irregular_intervals(self):
if self.output_features is not None and len(self.output_features)>1:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['b'].iloc[20:30] = np.nan
data['c'].iloc[10:20] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 4, 6,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
loader = build_and_test_loader(data, config, self.output_features, 18, 8, 12,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 11 - (self.lookback - 1) if self.lookback > 1 else 11
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 5,
check_examples=False, # todo
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
val_examples = 7 - (self.lookback - 1) if self.lookback > 1 else 7
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, 8,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans_at_irregular_intervals(self):
# if data contains nans and we also have intervals
if self.output_features is not None and len(self.output_features) > 1:
examples = 50
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=50, freq='D'))
data['b'].iloc[20:30] = np.nan
data['c'].iloc[40:50] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'intervals': [(0, 10), (20, 50)]
}
loader = build_and_test_loader(data, config, self.output_features, 9, 4, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
loader = build_and_test_loader(data, config, self.output_features, 18, 7, 11,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans_same_val_data(self):
# if data contains nans and we also have intervals and val_data is same
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_data': 'same',
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
tr_examples = 20 - (self.lookback - 1) if self.lookback > 1 else 20
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 8, 8,
check_examples=False,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans_at_irregular_intervals_and_same_val_data(self):
# if data contains nans and we also have intervals and val_data is same
if self.output_features is not None and len(self.output_features) > 1:
examples = 50
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=50, freq='D'))
data['b'].iloc[20:30] = np.nan
data['c'].iloc[40:50] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_data': 'same',
'intervals': [(0, 10), (20, 50)]
}
loader = build_and_test_loader(data, config, self.output_features, 13, 5, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
loader = build_and_test_loader(data, config, self.output_features, 25, 11, 11,
check_examples=False,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans_no_val_data(self):
# if data contains nans and we also have intervals and val_data is same
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_fraction': 0.0,
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 0, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
tr_examples = 20 - (self.lookback - 1) if self.lookback > 1 else 20
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 0, 8,
save=self.save)
assert loader.source_is_df
return
def test_with_intervals_and_nans_at_irreg_intervals_and_no_val_data(self):
# if data contains nans and we also have intervals and val_data is same
if self.output_features is not None and len(self.output_features) > 1:
examples = 50
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=50, freq='D'))
data['b'].iloc[20:30] = np.nan
data['c'].iloc[40:50] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'val_fraction': 0.0,
'intervals': [(0, 10), (20, 50)]
}
loader = build_and_test_loader(data, config, self.output_features, 13, 0, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = self.allow_nan_labels
loader = build_and_test_loader(data, config, self.output_features, 25, 0, 11,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_intervals_and_nans(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 3, 5,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 15 - (self.lookback - 1) if self.lookback > 1 else 15
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 8, save=self.save)
assert loader.source_is_df
return
def test_with_indices_intervals_and_nans_with_same_val_data(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'val_data': 'same',
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 13 - (self.lookback - 1) if self.lookback > 1 else 13
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 5,
check_examples=False,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 20 - (self.lookback - 1) if self.lookback > 1 else 20
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 8, 8,
check_examples=False,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_intervals_and_nans_with_no_val_data(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': 'random',
'val_fraction': 0.0,
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 13 - (self.lookback - 1) if self.lookback > 1 else 13
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 0, 5,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 20 - (self.lookback - 1) if self.lookback > 1 else 20
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 0, 8,
save=self.save)
assert loader.source_is_df
return
def test_with_indices_intervals_and_nans_with_no_test_data(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback, 'train_data': 'random', 'test_fraction': 0.0,
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 13 - (self.lookback - 1) if self.lookback > 1 else 13
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 5, 0,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 20 - (self.lookback - 1) if self.lookback > 1 else 20
loader = build_and_test_loader(data, config, self.output_features, tr_examples, 8, 0,
save=self.save)
assert loader.source_is_df
return
def test_with_custom_indices_intervals_and_nans(self):
# if data contains nans and we also have intervals
if self.output_features is not None:
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
data['c'].iloc[20:30] = np.nan
config = {'input_features':self.input_features,
'output_features': self.output_features,
'lookback': self.lookback,
'train_data': [1,2,3,4,5,6,7,8,9,10,11,12],
'intervals': [(0, 10), (20, 40)]
}
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
test_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples, test_examples,
save=self.save)
assert loader.source_is_df
config['allow_nan_labels'] = 2 if len(self.output_features) == 1 else 1
tr_examples = 10 - (self.lookback - 1) if self.lookback > 1 else 10
val_examples = 6 - (self.lookback - 1) if self.lookback > 1 else 6
test_examples = 16 - (self.lookback - 1) if self.lookback > 1 else 16
loader = build_and_test_loader(data, config, self.output_features, tr_examples, val_examples,
test_examples, save=self.save)
assert loader.source_is_df
return
def test_with_random_with_transformation_of_features():
examples = 100
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
data = pd.DataFrame(data, columns=['a', 'b', 'c'], index=pd.date_range('20110101', periods=len(data), freq='D'))
data['date'] = data.index
config = {'input_features':['b'],
'output_features': ['c'],
'lookback': 5,
'train_data': 'random'}
dh = DataHandler(data, verbosity=0, **config)
x,y = dh.training_data()
return
def test_random_with_intervals():
data = np.random.randint(0, 1000, (40560, 14))
input_features = [f'input_{i}' for i in range(13)]
output_features = ['NDX']
data = pd.DataFrame(data, columns=input_features+output_features)
out = data["NDX"]
# put four chunks of missing intervals
intervals = [(100, 200), (1000, 8000), (10000, 31000)]
for interval in intervals:
st, en = interval[0], interval[1]
out[st:en] = np.nan
data["NDX"] = out
config = {
'input_features': input_features,
'output_features': output_features,
'lookback': 5,
'train_data': 'random',
'intervals': [(0, 99), (200, 999), (8000, 9999), (31000, 40560)],
}
build_and_test_loader(data, config, out_cols=output_features,
train_ex=6096, val_ex=2612, test_ex=3733,
assert_uniqueness=False,
save=False)
return
def make_cross_validator(cv, **kwargs):
model = Model(
model={'randomforestregressor': {}},
data=arg_beach(),
cross_validator=cv,
val_metric="mse",
verbosity=0,
**kwargs
)
return model
class TestCVs(object):
def test_kfold(self):
model = make_cross_validator(cv={'TimeSeriesSplit': {'n_splits': 5}})
model.cross_val_score()
model.dh.plot_TimeSeriesSplit_splits(show=False)
return
def test_loocv(self):
model = make_cross_validator(cv={'KFold': {'n_splits': 5}})
model.cross_val_score()
model.dh.plot_KFold_splits(show=False)
return
def test_tscv(self):
model = make_cross_validator(cv={'LeaveOneOut': {}}, test_fraction=0.6)
model.cross_val_score()
model.dh.plot_LeaveOneOut_splits(show=False)
return
#
# class TestDataLoader(unittest.TestCase):
#
# def test_OndDF(self):
# TestAllCases(
# input_features = ['a', 'b'],
# output_features=['c'], allow_nan_labels=2)
# return
#
# def test_OneDFTwoOut(self):
# TestAllCases(input_features = ['a'],
# output_features=['b', 'c'])
# return
#
# def test_MultiSources(self):
# test_multisource_basic()
# return
#
# def test_MultiUnequalSources(self):
# return
def test_AI4WaterDataSets():
config = {'intervals': [("20000101", "20011231")],
'input_features': ['precipitation_AWAP',
'evap_pan_SILO'],
'output_features': ['streamflow_MLd_inclInfilled'],
'dataset_args': {'stations': 1}
}
build_and_test_loader('CAMELS_AUS', config=config,
out_cols=['streamflow_MLd_inclInfilled'],
train_ex=358, val_ex=154, test_ex=219,
assert_uniqueness=False,
save=False)
return
def test_multisource_basic():
examples = 40
data = np.arange(int(examples * 4), dtype=np.int32).reshape(-1, examples).transpose()
df1 = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'],
index=pd.date_range('20110101', periods=40, freq='D'))
df2 = pd.DataFrame(np.array([5,6]).repeat(40, axis=0).reshape(40, -1), columns=['len', 'dep'],
index=pd.date_range('20110101', periods=40, freq='D'))
input_features = [['a', 'b'], ['len', 'dep']]
output_features = [['c', 'd'], []]
lookback = 4
config = {'input_features': input_features,
'output_features': output_features,
'lookback': lookback}
build_and_test_loader(data=[df1, df2], config=config, out_cols=output_features,
train_ex=18, val_ex=8, test_ex=11,
save=True)
# #testing data as a dictionary
config['input_features'] = {'cont_data': ['a', 'b'], 'static_data': ['len', 'dep']}
config['output_features'] = {'cont_data': ['c', 'd'], 'static_data': []}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=config['output_features'],
train_ex=18, val_ex=8, test_ex=11,
save=True)
# #test when output_features for one data is not provided?
config['input_features'] = {'cont_data': ['a', 'b'], 'static_data': ['len', 'dep']}
config['output_features'] = {'cont_data': ['c', 'd']}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=config['output_features'],
train_ex=18, val_ex=8, test_ex=11,
save=False)
# # #testing with transformation
config['input_features'] = {'cont_data': ['a', 'b'], 'static_data': ['len', 'dep']}
config['transformation'] = {'cont_data': 'minmax', 'static_data': 'zscore'}
config['output_features'] = {'cont_data': ['c', 'd'], 'static_data': []}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=config['output_features'],
train_ex=18, val_ex=8, test_ex=11,
save=True)
# # testing with `same` `val_data`
config['val_data'] = 'same'
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=config['output_features'],
train_ex=26, val_ex=11, test_ex=11,
save=True)
# # testing with random train indices
config['val_data'] = 'same'
config['train_data'] = random.sample(list(np.arange(37)), 25)
config['input_features'] = {'cont_data': ['a', 'b'], 'static_data': ['len', 'dep']}
config['output_features'] = {'cont_data': ['c', 'd'], 'static_data': []}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=config['output_features'],
train_ex=25, val_ex=12, test_ex=12,
save=True)
return
def test_multisource_basic2():
examples = 40
data = np.arange(int(examples * 4), dtype=np.int32).reshape(-1, examples).transpose()
df1 = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'],
index=pd.date_range('20110101', periods=40, freq='D'))
df2 = pd.DataFrame(np.array([[5],[6], [7]]).repeat(40, axis=1).transpose(), columns=['len', 'dep', 'y'],
index=pd.date_range('20110101', periods=40, freq='D'))
input_features = [['a', 'b'], ['len', 'dep']]
output_features = [['c', 'd'], ['y']]
lookback = 4
config = {'input_features': input_features,
'output_features': output_features,
'lookback': lookback}
build_and_test_loader(data=[df1, df2], config=config, out_cols=output_features,
train_ex=18, val_ex=8, test_ex=11,
save=True)
config['input_features'] = {'cont_data': ['a', 'b'], 'static_data': ['len', 'dep']}
config['output_features'] = {'cont_data': ['c', 'd'], 'static_data': ['y']}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config,
out_cols=config['output_features'],
train_ex=18, val_ex=8, test_ex=11,
save=True)
return
def test_multisource_basic3():
examples = 40
data = np.arange(int(examples * 5), dtype=np.int32).reshape(-1, examples).transpose()
y_df = pd.DataFrame(data[:, -1], columns=['y'])
y_df.loc[y_df.sample(frac=0.5).index] = np.nan
cont_df = pd.DataFrame(data[:, 0:4], columns=['a', 'b', 'c', 'd'],
index=pd.date_range('20110101', periods=40, freq='D'))
static_df = pd.DataFrame(np.array([[5],[6], [7]]).repeat(40, axis=1).transpose(), columns=['len', 'dep', 'y'],
index=pd.date_range('20110101', periods=40, freq='D'))
disc_df = pd.DataFrame(np.random.randint(0, 10, (40, 4)), columns=['cl', 'o', 'do', 'bod'],
index=pd.date_range('20110101', periods=40, freq='D'))
cont_df['y'] = y_df.values
static_df['y'] = y_df.values
disc_df['y'] = y_df.values
input_features = [['len', 'dep'], ['a', 'b'], ['cl', 'o', 'do', 'bod']]
output_features = [['y'], ['c', 'y'], ['y']]
lookback = [1, 4, 1]
config = {'input_features': input_features,
'output_features': output_features,
'test_fraction': 0.3,
'val_fraction': 0.3,
'lookback': lookback}
# build_and_test_loader(data=[static_df, cont_df, disc_df], config=config, out_cols=output_features, train_ex=6,
# val_ex=4,
# test_ex=6, save=True)
data_handler = DataHandler(data=[static_df, cont_df, disc_df], verbosity=0, **config)
data_handler.training_data()
data_handler.validation_data()
data_handler.test_data()
return
def test_multisource_multi_loc():
examples = 40
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
training_data = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
val_data = pd.DataFrame(data+1000.0, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
test_data = pd.DataFrame(data+2000, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=40, freq='D'))
dh = MultiLocDataHandler()
train_x, train_y = dh.training_data(data=training_data, input_features=['a', 'b'], output_features=['c'])
valx, val_y = dh.validation_data(data=val_data, input_features=['a', 'b'], output_features=['c'])
test_x, test_y = dh.test_data(data=test_data, input_features=['a', 'b'], output_features=['c'])
assert np.allclose(train_y.reshape(-1,), training_data['c'].values.reshape(-1, ))
assert np.allclose(val_y.reshape(-1, ), val_data['c'].values.reshape(-1, ))
assert np.allclose(test_y.reshape(-1, ), test_data['c'].values.reshape(-1, ))
return
def test_multisource_basic4():
examples = 40
data = np.arange(int(examples * 4), dtype=np.int32).reshape(-1, examples).transpose()
df1 = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'],
index=pd.date_range('20110101', periods=40, freq='D'))
df2 = pd.DataFrame(np.array([5,6]).repeat(40, axis=0).reshape(40, -1), columns=['len', 'dep'],
index=pd.date_range('20110101', periods=40, freq='D'))
input_features = {'cont_data':['a', 'b'], 'static_data':['len', 'dep']}
output_features = {'cont_data': ['c', 'd']}
lookback = {'cont_data': 4, 'static_data': 1}
config = {'input_features': input_features,
'output_features': output_features,
'lookback': lookback}
build_and_test_loader(data={'cont_data': df1, 'static_data': df2},
config=config, out_cols=output_features,
train_ex=18, val_ex=8, test_ex=11,
save=False)
return
def site_distributed_basic():
examples = 50
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
df = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=examples, freq='D'))
config = {'input_features': ['a', 'b'],
'output_features': ['c'],
'lookback': 4,
'val_fraction': 0.3,
'test_fraction': 0.3,
'verbosity': 0}
data = {'0': df, '1': df+1000, '2': df+2000, '3': df+3000}
configs = {'0': config, '1': config, '2': config, '3': config}
dh = SiteDistributedDataHandler(data, configs, verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
assert train_x.shape == (23, len(data), config['lookback'], len(config['input_features']))
assert val_x.shape == (10, len(data), config['lookback'], len(config['input_features']))
assert test_x.shape == (14, len(data), config['lookback'], len(config['input_features']))
dh = SiteDistributedDataHandler(data, configs, training_sites=['0', '1'], validation_sites=['2'],
test_sites=['3'], verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
assert train_x.shape == (len(df)-config['lookback']+1, 2, config['lookback'], len(config['input_features']))
assert val_x.shape == (len(df)-config['lookback']+1, 1, config['lookback'], len(config['input_features']))
assert test_x.shape == (len(df)-config['lookback']+1, 1, config['lookback'], len(config['input_features']))
def site_distributed_diff_lens():
examples = 50
data = np.arange(int(examples * 3), dtype=np.int32).reshape(-1, examples).transpose()
df = pd.DataFrame(data, columns=['a', 'b', 'c'],
index=pd.date_range('20110101', periods=examples, freq='D'))
config = {'input_features': ['a', 'b'],
'output_features': ['c'],
'lookback': 4,
'verbosity': 0}
data = {'0': df,
'1': pd.concat([df, df], axis=0)+1000,
'2': pd.concat([df, df, df], axis=0)+2000,
'3': df +3000
}
configs = {'0': config, '1': config, '2': config, '3': config}
#dh = SiteDistributedDataHandler(data, configs) # This should raise NotImplementedError
dh = SiteDistributedDataHandler(data, configs, allow_variable_len=True, verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
assert isinstance(train_x, dict)
dh = SiteDistributedDataHandler(data, configs, training_sites=['0', '1'], validation_sites=['2'],
test_sites=['3'], allow_variable_len=True, verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
assert isinstance(train_x, dict)
def site_distributed_multiple_srcs():
examples = 40
data = np.arange(int(examples * 4), dtype=np.int32).reshape(-1, examples).transpose()
cont_df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'],
index=pd.date_range('20110101', periods=examples, freq='D'))
static_df = pd.DataFrame(np.array([[5],[6], [7]]).repeat(examples, axis=1).transpose(), columns=['len', 'dep', 'width'],
index=pd.date_range('20110101', periods=examples, freq='D'))
config = {'input_features': {'cont_data': ['a', 'b', 'c'], 'static_data': ['len', 'dep', 'width']},
'output_features': {'cont_data': ['d']},
'lookback': {'cont_data': 4, 'static_data':1},
'verbosity': 0
}
data = {'cont_data': cont_df, 'static_data': static_df}
datas = {'0': data, '1': data, '2': data, '3': data, '4': data, '5': data, '6': data}
configs = {'0': config, '1': config, '2': config, '3': config, '4': config, '5': config, '6': config}
dh = SiteDistributedDataHandler(datas, configs, verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
dh = SiteDistributedDataHandler(datas, configs, training_sites=['0', '1', '2'],
validation_sites=['3', '4'], test_sites=['5', '6'], verbosity=0)
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
def test_with_string_index():
data = load_u1()
config = {
'input_features': ['x1', 'x2', 'x3', 'x4'],
'output_features': ['target'],
'lookback': 3,
'train_data': 'random',
'transformation': 'minmax'
}
build_and_test_loader(data, config, out_cols=['target'], train_ex=136, val_ex=58, test_ex=84)
return
def test_with_indices_and_nans():
# todo, check with two output columns
data = arg_beach()
train_idx, test_idx = train_test_split(np.arange(len(data.dropna())),
test_size=0.25, random_state=332898)
out_cols = [list(data.columns)[-1]]
config = {
'train_data': train_idx,
'input_features': list(data.columns)[0:-1],
'output_features': out_cols,
'lookback': 14,
'val_data': 'same'
}
build_and_test_loader(data,
config,
out_cols=out_cols, train_ex=163, val_ex=55, test_ex=55,
check_examples=False, save=False)
def test_file_formats(data):
csv_fname = os.path.join(os.getcwd(), "results", "data.csv")
data.to_csv(csv_fname)
xlsx_fname = os.path.join(os.getcwd(), "results", "data.xlsx")
data.to_excel(xlsx_fname, engine="xlsxwriter")
parq_fname = os.path.join(os.getcwd(), "results", "data.parquet")
data.to_parquet(parq_fname)
feather_fname = os.path.join(os.getcwd(), "results", "data.feather")
data.reset_index().to_feather(feather_fname)
nc_fname = os.path.join(os.getcwd(), "results", "data.nc")
xds = data.to_xarray()
xds.to_netcdf(nc_fname)
npz_fname = os.path.join(os.getcwd(), "results", "data.npz")
np.savez(npz_fname, data.values)
mat_fname = os.path.join(os.getcwd(), "results", "data.mat")
scipy.io.savemat(mat_fname, {'data': data.values})
dh = DataHandler(data, verbosity=0)
input_features = dh.input_features
output_features = dh.output_features
train_x, train_y = dh.training_data()
val_x, val_y = dh.validation_data()
test_x, test_y = dh.test_data()
train_x_shape, train_y_shape = train_x.shape, train_y.shape
val_x_shape, val_y_shape = val_x.shape, val_y.shape
test_x_shape, test_y_shape = test_x.shape, test_y.shape
for fname in [csv_fname,
xlsx_fname, parq_fname,
feather_fname,
nc_fname,
npz_fname,
mat_fname
]:
#print(f'readeing {fname}')
dh = DataHandler(fname,
input_features=input_features,
output_features=output_features,
verbosity=0)
train_x, train_y = dh.training_data()
assert train_x.shape == train_x_shape
assert train_y.shape == train_y_shape
val_x, val_y = dh.validation_data()
assert val_x.shape == val_x_shape
assert val_y.shape == val_y_shape
test_x, test_y = dh.test_data()
assert test_x.shape == test_x_shape
assert test_y.shape == test_y_shape
return
test_with_indices_and_nans()
test_with_string_index()
site_distributed_basic()
site_distributed_diff_lens()
site_distributed_multiple_srcs()
test_multisource_multi_loc()
test_with_random_with_transformation_of_features()
test_random_with_intervals()
test_AI4WaterDataSets()
test_multisource_basic()
test_multisource_basic2()
test_multisource_basic3()
# #test_multisource_basic4() todo
test_file_formats(arg_beach())
cv_tester = TestCVs()
cv_tester.test_loocv()
cv_tester.test_tscv()
cv_tester.test_kfold()
# TestAllCases(input_features = ['a', 'b'],
# output_features=['c'], lookback=1, save=False, allow_nan_labels=2)
# # ##testing single dataframe with single output and multiple inputs
TestAllCases(input_features = ['a', 'b'],
output_features=['c'], allow_nan_labels=2, save=False)
#
# # ## ##testing single dataframe with multiple output and sing inputs
TestAllCases(input_features = ['a'],
output_features=['b', 'c'], allow_nan_labels=1, save=False)
# #
# # ##testing single dataframe with all inputs and not output
TestAllCases(input_features = ['a', 'b', 'c'],
output_features=None)
| 44.064165
| 131
| 0.552719
| 9,875
| 81,034
| 4.315241
| 0.046987
| 0.075564
| 0.05618
| 0.029568
| 0.803182
| 0.780278
| 0.761247
| 0.738836
| 0.698167
| 0.687044
| 0
| 0.041512
| 0.321618
| 81,034
| 1,838
| 132
| 44.088139
| 0.733664
| 0.040008
| 0
| 0.630994
| 0
| 0.00417
| 0.086592
| 0.001313
| 0
| 0
| 0
| 0.000544
| 0.068103
| 1
| 0.04795
| false
| 0
| 0.009034
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| 0.101459
| 0.00139
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| null | 0
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| 1
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| 0
| 0
| 0
|
0
| 5
|
dab7308e7481dbbb0e8887ec1303e14d69550058
| 3,454
|
py
|
Python
|
src/ml_skeleton/tests/unittests/LoggerTests.py
|
girlrilaz/my_project_builder
|
8542d8860f7d010e5d61ccc96fc461bf52fa74d7
|
[
"MIT"
] | null | null | null |
src/ml_skeleton/tests/unittests/LoggerTests.py
|
girlrilaz/my_project_builder
|
8542d8860f7d010e5d61ccc96fc461bf52fa74d7
|
[
"MIT"
] | null | null | null |
src/ml_skeleton/tests/unittests/LoggerTests.py
|
girlrilaz/my_project_builder
|
8542d8860f7d010e5d61ccc96fc461bf52fa74d7
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
"""
model tests
"""
import os, sys
import csv
import unittest
from ast import literal_eval
import pandas as pd
from datetime import date
sys.path.insert(1, os.path.join('..', os.getcwd()))
## import model specific functions and variables
from utils.logger import update_train_log, update_evaluation_log #, update_processing_log
# TODO : Set the test parameters in all the unittests below
# class LoggerTest(unittest.TestCase):
# """
# test the essential functionality
# """
# def test_01_train(self):
# """
# ensure log file is created
# """
# log_test_date = date.today() #"2022-1-15" #date.today()
# log_file = os.path.join("logs", f"{log_test_date}", "model-train-subset.log")
# if os.path.exists(log_file):
# os.remove(log_file)
# ## update the log
# data_shape = (100,10)
# best_params = {'learning_rate':0.05}
# runtime = "00:00:01"
# model_version = "1.0.0"
# model_version_note = "test model"
# update_train_log(data_shape, runtime, model_version, model_version_note, best_params, subset=True)
# self.assertTrue(os.path.exists(log_file))
# def test_02_train(self):
# """
# ensure that content can be retrieved from log file
# """
# log_test_date = date.today() #"2022-1-15" #date.today()
# log_file = os.path.join("logs", f"{log_test_date}", "model-train-subset.log")
# ## update the log
# data_shape = (100,10)
# best_params = {'learning_rate':0.05}
# runtime = "00:00:01"
# model_version = 0.1
# model_version_note = "test model"
# update_train_log(data_shape, runtime, model_version, model_version_note, best_params, subset=True)
# df = pd.read_csv(log_file)
# logged_best_params = [literal_eval(i) for i in df['model_params'].copy()][-1]
# self.assertEqual(best_params, logged_best_params)
# def test_03_evaluation(self):
# """
# ensure log file is created
# """
# log_test_date = date.today() #"2022-1-15" #date.today()
# log_file = os.path.join("logs" , f"{log_test_date}" , f"model-eval-{log_test_date}.log")
# if os.path.exists(log_file):
# os.remove(log_file)
# ## update the log
# runtime = "00:00:02"
# model_version = 0.1
# eval_metrics = {"accuracy": 0.91, "roc_auc": 0.88}
# update_evaluation_log(eval_metrics, runtime, model_version)
# self.assertTrue(os.path.exists(log_file))
# def test_04_evaluation(self):
# """
# ensure that content can be retrieved from log file
# """
# log_test_date = date.today() #"2022-1-15" #date.today()
# log_file = os.path.join("logs" , f"{log_test_date}" ,f"model-eval-{log_test_date}.log")
# ## update the log
# runtime = "00:00:02"
# model_version = 0.1
# eval_metrics = {"accuracy": 0.91, "roc_auc": 0.88}
# update_evaluation_log(eval_metrics, runtime, model_version)
# df = pd.read_csv(log_file)
# logged_y_pred = [literal_eval(i) for i in df['eval_metrics'].copy()][-1]
# self.assertEqual(eval_metrics,logged_y_pred)
# ### Run the tests
# if __name__ == '__main__':
# unittest.main()
| 32.280374
| 108
| 0.59062
| 457
| 3,454
| 4.221007
| 0.236324
| 0.058061
| 0.057024
| 0.031104
| 0.695697
| 0.695697
| 0.695697
| 0.650078
| 0.650078
| 0.608606
| 0
| 0.039683
| 0.270411
| 3,454
| 106
| 109
| 32.584906
| 0.725794
| 0.854372
| 0
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| 0
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| 0.004938
| 0
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| 0
| 0
| 0.009434
| 0
| 1
| 0
| true
| 0
| 0.875
| 0
| 0.875
| 0
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| null | 0
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| null | 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
9700199429d01d4274b41bf2468d6e1c3445ef89
| 160
|
py
|
Python
|
orangeinp/universe/__init__.py
|
celeritas-project/orange-port
|
9aa2d36984a24a02ed6d14688a889d4266f7b1af
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
orangeinp/universe/__init__.py
|
celeritas-project/orange-port
|
9aa2d36984a24a02ed6d14688a889d4266f7b1af
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
orangeinp/universe/__init__.py
|
celeritas-project/orange-port
|
9aa2d36984a24a02ed6d14688a889d4266f7b1af
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Copyright 2021 UT-Battelle, LLC and SCALE Developers.
# See the top-level COPYRIGHT file for details.
from .universe import universes
| 32
| 55
| 0.7375
| 23
| 160
| 5.130435
| 0.956522
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0.037037
| 0.15625
| 160
| 4
| 56
| 40
| 0.837037
| 0.75625
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| true
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
971970ff5d1aa9c7e3bdc362c3334e9875b5ff8d
| 64
|
py
|
Python
|
lib/anod/__init__.py
|
reznikmm/GNAT-FSF-builds
|
c764199d8752e0a99be4ae103db6af78a4ff59ce
|
[
"MIT"
] | 5
|
2021-06-04T05:24:49.000Z
|
2022-01-23T11:17:29.000Z
|
lib/anod/__init__.py
|
reznikmm/GNAT-FSF-builds
|
c764199d8752e0a99be4ae103db6af78a4ff59ce
|
[
"MIT"
] | 7
|
2021-05-30T14:29:26.000Z
|
2022-01-20T09:02:46.000Z
|
lib/anod/__init__.py
|
reznikmm/GNAT-FSF-builds
|
c764199d8752e0a99be4ae103db6af78a4ff59ce
|
[
"MIT"
] | 4
|
2021-05-29T19:07:47.000Z
|
2021-11-11T05:53:14.000Z
|
"""FSFBuild anod module."""
from __future__ import annotations
| 16
| 34
| 0.765625
| 7
| 64
| 6.428571
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0.125
| 64
| 3
| 35
| 21.333333
| 0.803571
| 0.328125
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| 0
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| 0
| 1
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| true
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
972d8a5252d5398b581973d64cd374d79ded0c27
| 181
|
py
|
Python
|
noge/envs/__init__.py
|
johny-c/noge
|
88e68ba8c51ff0d63577991e233e9110cb76e228
|
[
"MIT"
] | null | null | null |
noge/envs/__init__.py
|
johny-c/noge
|
88e68ba8c51ff0d63577991e233e9110cb76e228
|
[
"MIT"
] | null | null | null |
noge/envs/__init__.py
|
johny-c/noge
|
88e68ba8c51ff0d63577991e233e9110cb76e228
|
[
"MIT"
] | null | null | null |
from .base_graph_env import BaseGraphEnv
from .online_graph_env import OnlineGraphEnv
from .dfp_wrapper import TargetMeasEnvWrapper
from .maze_graph import make_maze, maze_to_graph
| 36.2
| 48
| 0.878453
| 26
| 181
| 5.769231
| 0.538462
| 0.106667
| 0.186667
| 0
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| 0
| 0.093923
| 181
| 4
| 49
| 45.25
| 0.914634
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
97440a3ea96867f36ba7d4ac69244d2e34b45a15
| 95
|
py
|
Python
|
conformer_tf/__init__.py
|
Rishit-dagli/Conformer
|
9a89d28d2071d68dc6d7745747ff40c89bf85393
|
[
"Apache-2.0"
] | 13
|
2022-01-03T14:52:04.000Z
|
2022-03-11T10:32:26.000Z
|
conformer_tf/__init__.py
|
Rishit-dagli/Conformer
|
9a89d28d2071d68dc6d7745747ff40c89bf85393
|
[
"Apache-2.0"
] | 7
|
2022-01-20T09:35:30.000Z
|
2022-01-20T11:11:59.000Z
|
conformer_tf/__init__.py
|
Rishit-dagli/Conformer
|
9a89d28d2071d68dc6d7745747ff40c89bf85393
|
[
"Apache-2.0"
] | 2
|
2022-01-05T23:10:08.000Z
|
2022-01-21T06:29:38.000Z
|
from .conformer_tf import ConformerBlock, ConformerConvModule
from .version import __version__
| 31.666667
| 61
| 0.873684
| 10
| 95
| 7.8
| 0.7
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0.094737
| 95
| 2
| 62
| 47.5
| 0.906977
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| 1
| 0
| 1
| 0
|
0
| 5
|
975c473496bb1b7a7a5a67781ae2633c008a309c
| 1,565
|
py
|
Python
|
python/euler8.py
|
feliposz/project-euler-solutions
|
1aec50c4d2d86b515dc8607559575da9e4aecb3a
|
[
"MIT"
] | null | null | null |
python/euler8.py
|
feliposz/project-euler-solutions
|
1aec50c4d2d86b515dc8607559575da9e4aecb3a
|
[
"MIT"
] | null | null | null |
python/euler8.py
|
feliposz/project-euler-solutions
|
1aec50c4d2d86b515dc8607559575da9e4aecb3a
|
[
"MIT"
] | null | null | null |
def euler8():
"""Solves problem 7 of Project Euler."""
big = "73167176531330624919225119674426574742355349194934"
big += "96983520312774506326239578318016984801869478851843"
big += "85861560789112949495459501737958331952853208805511"
big += "12540698747158523863050715693290963295227443043557"
big += "66896648950445244523161731856403098711121722383113"
big += "62229893423380308135336276614282806444486645238749"
big += "30358907296290491560440772390713810515859307960866"
big += "70172427121883998797908792274921901699720888093776"
big += "65727333001053367881220235421809751254540594752243"
big += "52584907711670556013604839586446706324415722155397"
big += "53697817977846174064955149290862569321978468622482"
big += "83972241375657056057490261407972968652414535100474"
big += "82166370484403199890008895243450658541227588666881"
big += "16427171479924442928230863465674813919123162824586"
big += "17866458359124566529476545682848912883142607690042"
big += "24219022671055626321111109370544217506941658960408"
big += "07198403850962455444362981230987879927244284909188"
big += "84580156166097919133875499200524063689912560717606"
big += "05886116467109405077541002256983155200055935729725"
big += "71636269561882670428252483600823257530420752963450"
maxprod = 1
for i in range(len(big) - 5):
prod = 1
for j in map(int, big[i:i+5]):
prod *= j
maxprod = max(prod, maxprod)
print(maxprod)
if __name__ == "__main__":
euler8()
| 46.029412
| 63
| 0.771885
| 80
| 1,565
| 15
| 0.575
| 0.006667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.758283
| 0.151438
| 1,565
| 33
| 64
| 47.424242
| 0.145331
| 0.021725
| 0
| 0
| 0
| 0
| 0.660984
| 0.655738
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033333
| false
| 0
| 0
| 0
| 0.033333
| 0.033333
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
975db3e1568f5425ef0b6840b6f0c32bc4f97472
| 184
|
py
|
Python
|
functions/hof/practice3.py
|
Rorima/exercicios-python
|
ca78e2d2402c2aa90efd95ccaa620c0a8b42444f
|
[
"MIT"
] | null | null | null |
functions/hof/practice3.py
|
Rorima/exercicios-python
|
ca78e2d2402c2aa90efd95ccaa620c0a8b42444f
|
[
"MIT"
] | null | null | null |
functions/hof/practice3.py
|
Rorima/exercicios-python
|
ca78e2d2402c2aa90efd95ccaa620c0a8b42444f
|
[
"MIT"
] | null | null | null |
def philosophy(statement):
def thought():
return statement
return thought
question = philosophy('To B, or not to B. It depends where the bomb is.')
print(question())
| 20.444444
| 73
| 0.679348
| 25
| 184
| 5
| 0.68
| 0.048
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222826
| 184
| 8
| 74
| 23
| 0.874126
| 0
| 0
| 0
| 0
| 0
| 0.26087
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.166667
| 0.666667
| 0.166667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
c1038c4b73f9427629d940a9537e1ce5f2d350a6
| 50
|
py
|
Python
|
Topsis_101917193_Vidushi/__init__.py
|
vidushi2001/Topsis
|
0bb2d98ccd91a0c1335c5d8a843c3dd701181b5c
|
[
"MIT"
] | null | null | null |
Topsis_101917193_Vidushi/__init__.py
|
vidushi2001/Topsis
|
0bb2d98ccd91a0c1335c5d8a843c3dd701181b5c
|
[
"MIT"
] | null | null | null |
Topsis_101917193_Vidushi/__init__.py
|
vidushi2001/Topsis
|
0bb2d98ccd91a0c1335c5d8a843c3dd701181b5c
|
[
"MIT"
] | null | null | null |
from Topsis_101917193_Vidushi.topsis import topsis
| 50
| 50
| 0.92
| 7
| 50
| 6.285714
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.191489
| 0.06
| 50
| 1
| 50
| 50
| 0.744681
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c1494446edd4f5a830e875aab16dc77b76d19f7d
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/lockfile/linklockfile.py
|
GiulianaPola/select_repeats
|
17a0d053d4f874e42cf654dd142168c2ec8fbd11
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/lockfile/linklockfile.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/lockfile/linklockfile.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/0b/b3/87/dc7e0674aebcbb8150829f1f90fda43b87f25134b28f75fa6e581fa1b7
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.395833
| 0
| 96
| 1
| 96
| 96
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c15bc4805de4969be012520407355d619e16f85e
| 3,676
|
py
|
Python
|
configs/_base_/datasets/phone_detection_coco.py
|
vietnamican/mmdetection
|
458f593608ec0a416c38f18c743004992c27096d
|
[
"Apache-2.0"
] | null | null | null |
configs/_base_/datasets/phone_detection_coco.py
|
vietnamican/mmdetection
|
458f593608ec0a416c38f18c743004992c27096d
|
[
"Apache-2.0"
] | null | null | null |
configs/_base_/datasets/phone_detection_coco.py
|
vietnamican/mmdetection
|
458f593608ec0a416c38f18c743004992c27096d
|
[
"Apache-2.0"
] | null | null | null |
# dataset settings
dataset_type = 'PhoneDataset'
data_root = '/home/ubuntu/tienpv/datasets/PhoneDatasets/COCO2017/'
ann_files = '/home/ubuntu/tienpv/datasets/PhoneDatasets/COCO2017/annotations/instances_train2017_cell_phone_format_widerface.txt'
val_data_root = '/home/ubuntu/tienpv/datasets/PhoneDatasets/COCO2017/'
val_ann_files = '/home/ubuntu/tienpv/datasets/PhoneDatasets/COCO2017/annotations/instances_val2017_cell_phone_format_widerface.txt'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
gray_train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, color_type='grayscale'),
dict(type='Stack'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(320, 320),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# rgb_dataset_train = dict(
# type='RepeatDataset',
# times=2,
# dataset=dict(
# type=dataset_type,
# ann_file=ann_files,
# img_prefix=data_root,
# pipeline=train_pipeline
# )
# )
# gray_dataset_train = dict(
# type='RepeatDataset',
# times=2,
# dataset=dict(
# type=dataset_type,
# ann_file=ann_files,
# img_prefix=data_root,
# pipeline=gray_train_pipeline
# )
# )
data = dict(
samples_per_gpu=60,
workers_per_gpu=4,
# train=[rgb_dataset_train, gray_dataset_train],
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
ann_file=ann_files,
img_prefix=data_root,
pipeline=train_pipeline
)
),
val=dict(
type=dataset_type,
ann_file=val_ann_files,
img_prefix=val_data_root,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=val_ann_files,
img_prefix=val_data_root,
pipeline=test_pipeline))
| 30.633333
| 131
| 0.620783
| 459
| 3,676
| 4.705882
| 0.220044
| 0.12963
| 0.037037
| 0.043981
| 0.825463
| 0.790278
| 0.790278
| 0.790278
| 0.693981
| 0.693981
| 0
| 0.044988
| 0.232046
| 3,676
| 119
| 132
| 30.890756
| 0.720156
| 0.131937
| 0
| 0.56044
| 0
| 0
| 0.239899
| 0.118056
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c165b046bec8f4525de7546ba524408e73a2876d
| 36
|
py
|
Python
|
bin/tesliper_gui.py
|
mishioo/tesliper
|
e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327
|
[
"BSD-2-Clause"
] | null | null | null |
bin/tesliper_gui.py
|
mishioo/tesliper
|
e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327
|
[
"BSD-2-Clause"
] | 4
|
2022-02-24T18:28:39.000Z
|
2022-03-23T16:27:59.000Z
|
bin/tesliper_gui.py
|
mishioo/tesliper
|
e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327
|
[
"BSD-2-Clause"
] | null | null | null |
from tesliper import gui
gui.run()
| 9
| 24
| 0.75
| 6
| 36
| 4.5
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 36
| 3
| 25
| 12
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
c177ac0a7183145f9d72ee7385e465e4cbb4622b
| 60
|
py
|
Python
|
serverless/provider.py
|
captain-fox/serverless-builder
|
d79d120578d692dd34dd2f0a3bb75cc8ec719c81
|
[
"MIT"
] | 3
|
2022-03-16T14:25:03.000Z
|
2022-03-24T15:04:55.000Z
|
serverless/provider.py
|
captain-fox/serverless-builder
|
d79d120578d692dd34dd2f0a3bb75cc8ec719c81
|
[
"MIT"
] | 3
|
2022-01-24T20:11:15.000Z
|
2022-01-26T19:33:20.000Z
|
serverless/provider.py
|
epsyhealth/serverless-builder
|
6a1f943b5cabc4c4748234b1623a9ced6464043a
|
[
"MIT"
] | 1
|
2022-02-15T13:54:29.000Z
|
2022-02-15T13:54:29.000Z
|
from serverless.aws.provider import Provider as AWSProvider
| 30
| 59
| 0.866667
| 8
| 60
| 6.5
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 60
| 1
| 60
| 60
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c1831981e23f1236beac365b6467be8fc73c5953
| 3,280
|
py
|
Python
|
tests/test_qtool.py
|
milancio42/qtool_c
|
f84445a22cb36891cae40da230e149ec66c4cfa6
|
[
"Unlicense"
] | null | null | null |
tests/test_qtool.py
|
milancio42/qtool_c
|
f84445a22cb36891cae40da230e149ec66c4cfa6
|
[
"Unlicense"
] | null | null | null |
tests/test_qtool.py
|
milancio42/qtool_c
|
f84445a22cb36891cae40da230e149ec66c4cfa6
|
[
"Unlicense"
] | null | null | null |
import unittest
import subprocess
QTOOL = 'build/release/qtool'
TESTDB = 'tests/testdb'
HEADER = 'hostname,start_time,end_time'
class QtoolTest(unittest.TestCase):
def cmd(self, cmd, data=''):
proc = subprocess.Popen(
cmd,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
bufsize=1024*1024
)
stdout, stderr = proc.communicate(data)
return (stdout, proc.returncode)
def test_workers_not_set(self):
cmd = [QTOOL, TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22'])
out, _ = self.cmd(cmd, data.encode('utf-8'))
# extract the number of queries which returned some data (3rd line), the number after ':'
num_queries_ok = out.split(b'\n')[2].split(b':')[1].strip()
self.assertTrue(int(num_queries_ok), 1)
def test_workers_set(self):
cmd = [QTOOL, '-w', '2', TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22'])
out, _ = self.cmd(cmd, data.encode('utf-8'))
# extract the number of queries which returned some data (3rd line), the number after ':'
num_queries_ok = out.split(b'\n')[2].split(b':')[1].strip()
self.assertTrue(int(num_queries_ok) == 1)
def test_workers_set_wrong(self):
cmd = [QTOOL, '-w', '0', TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22'])
_, rc = self.cmd(cmd, data.encode('utf-8'))
self.assertTrue(rc != 0)
cmd = [QTOOL, '-w', '-1', TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22'])
_, rc = self.cmd(cmd, data.encode('utf-8'))
self.assertTrue(rc != 0)
cmd = [QTOOL, '-w', '17', TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22'])
_, rc = self.cmd(cmd, data.encode('utf-8'))
self.assertTrue(rc == 0)
def test_empty_query_params(self):
cmd = [QTOOL, TESTDB]
data = '\n'.join([HEADER])
out, _ = self.cmd(cmd, data.encode('utf-8'))
# extract the number of queries which returned some data (3rd line), the number after ':'
num_queries_ok = out.split(b'\n')[2].split(b':')[1].strip()
self.assertTrue(int(num_queries_ok) == 0)
def test_sql_inject(self):
cmd = [QTOOL, TESTDB]
data = '\n'.join([HEADER, 'host_000008;SELECT * FROM CPU_USAGE;,2017-01-01 08:59:22,2017-01-01 09:59:22'])
out, _ = self.cmd(cmd, data.encode('utf-8'))
num_queries_ok = out.split(b'\n')[2].split(b':')[1].strip()
self.assertTrue(int(num_queries_ok) == 0)
def test_input_incomplete(self):
cmd = [QTOOL, TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22'])
_, rc = self.cmd(cmd, data.encode('utf-8'))
self.assertTrue(rc != 0)
def test_input_too_long(self):
cmd = [QTOOL, TESTDB]
data = '\n'.join([HEADER, 'host_000008,2017-01-01 08:59:22,2017-01-01 09:59:22,Upssss'])
_, rc = self.cmd(cmd, data.encode('utf-8'))
self.assertTrue(rc != 0)
if __name__ == '__main__':
unittest.main()
| 39.518072
| 114
| 0.584756
| 487
| 3,280
| 3.811088
| 0.186858
| 0.064116
| 0.064655
| 0.072737
| 0.755927
| 0.755927
| 0.755927
| 0.755927
| 0.738147
| 0.738147
| 0
| 0.119617
| 0.235366
| 3,280
| 82
| 115
| 40
| 0.620415
| 0.080183
| 0
| 0.446154
| 0
| 0.107692
| 0.191235
| 0.067397
| 0
| 0
| 0
| 0
| 0.138462
| 1
| 0.123077
| false
| 0
| 0.030769
| 0
| 0.184615
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c18836f0356e8d999663e8fbc8ec3a5cf3e5d9be
| 31,082
|
py
|
Python
|
post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_16.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_16.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
post_optimization_studies/mad_analyses/vbf_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_16.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
def selection_16():
# Library import
import numpy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# Library version
matplotlib_version = matplotlib.__version__
numpy_version = numpy.__version__
# Histo binning
xBinning = numpy.linspace(-8.0,8.0,101,endpoint=True)
# Creating data sequence: middle of each bin
xData = numpy.array([-7.92,-7.76,-7.6,-7.44,-7.28,-7.12,-6.96,-6.8,-6.64,-6.48,-6.32,-6.16,-6.0,-5.84,-5.68,-5.52,-5.36,-5.2,-5.04,-4.88,-4.72,-4.56,-4.4,-4.24,-4.08,-3.92,-3.76,-3.6,-3.44,-3.28,-3.12,-2.96,-2.8,-2.64,-2.48,-2.32,-2.16,-2.0,-1.84,-1.68,-1.52,-1.36,-1.2,-1.04,-0.88,-0.72,-0.56,-0.4,-0.24,-0.08,0.08,0.24,0.4,0.56,0.72,0.88,1.04,1.2,1.36,1.52,1.68,1.84,2.0,2.16,2.32,2.48,2.64,2.8,2.96,3.12,3.28,3.44,3.6,3.76,3.92,4.08,4.24,4.4,4.56,4.72,4.88,5.04,5.2,5.36,5.52,5.68,5.84,6.0,6.16,6.32,6.48,6.64,6.8,6.96,7.12,7.28,7.44,7.6,7.76,7.92])
# Creating weights for histo: y17_sdETA_0
y17_sdETA_0_weights = numpy.array([0.0040940847512,0.0,0.0122822542536,0.00818816950241,0.0040940847512,0.0368467587608,0.0163763350048,0.0491290010145,0.0368467587608,0.0450349242632,0.0614112712681,0.0614112712681,0.143292966292,0.163763350048,0.192421967307,0.262021392077,0.286585892584,0.319338586594,0.479008019891,0.6796178607,0.732841018466,0.937545256026,1.05627356181,1.44930564993,1.67038627449,2.12482991387,2.46463884422,3.09103354716,3.87709772339,4.56899637434,5.53929560438,6.57919477918,7.74600585328,9.53921643031,11.3324270073,12.9127417533,14.9229361581,17.1583063843,19.8276482661,22.5624980959,26.0956912922,29.2890767581,31.9666106334,34.6523285022,38.5171454353,40.5437278272,42.7258460956,44.8506844094,46.9427627493,45.9806435128,46.6480029832,46.2672432854,45.3051240488,43.3972855628,40.4536478986,37.9521618837,35.3851719207,32.6339461038,29.1867248393,25.8827994611,22.6320980407,20.2165879574,17.6455019977,15.385567791,13.0642216331,11.1481911535,9.33041659599,7.84016977856,6.70201468172,5.66211950691,4.5812803646,3.79521618837,3.11559792767,2.550614376,1.85871412505,1.71951543551,1.26097779937,1.07674434557,0.876134104758,0.773781785978,0.614112712681,0.511760393901,0.364373510857,0.30705631634,0.262021392077,0.176045620302,0.171951543551,0.110540272283,0.10235211878,0.0450349242632,0.0695994247705,0.0614112712681,0.0286585892584,0.040940847512,0.040940847512,0.020470419756,0.020470419756,0.00818816950241,0.0122822542536,0.0])
# Creating weights for histo: y17_sdETA_1
y17_sdETA_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.097211543571,0.315906604611,0.80158256384,0.898949638703,0.947414679303,1.34850196335,1.59203761076,2.28423076719,2.2472769648,2.96440319324,3.00134137441,4.02183973916,4.11886755314,4.90815581495,4.89716488358,5.73316460373,5.91767927011,6.76795838906,6.82806404364,8.01837710462,7.66672337692,9.05172092014,9.31829106031,9.91353971595,10.3758677967,10.4630021702,11.2764913094,11.9438939875,10.8753183089,10.8748536777,11.3000473106,11.6029748429,11.5801678592,11.2155124686,11.7746640857,11.0924773232,10.8398300975,10.5215977734,10.0372678027,9.35620656919,8.6377425233,8.07941202161,7.91009400014,6.93748469349,6.50013054028,5.90464155815,6.02761662191,5.43125445354,4.25348242978,4.56878677622,3.68179057647,3.58375459288,2.64883408713,2.81857227941,2.06506863162,1.84681252691,1.38518139299,1.2395287213,0.898794227575,0.449483030507,0.376629137227,0.121724885372,0.0121144577013,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_2
y17_sdETA_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.592065320139,1.70689961469,2.66087422507,3.45360569358,5.4617353079,7.77144408324,9.8188148922,12.0081063758,14.2969894305,16.5957192496,19.9993031324,22.4589240395,25.8232282993,29.2568235331,32.5800465798,35.1198671717,38.9551178314,41.4663650388,44.9590945757,47.9911376858,52.1774380968,55.3518729395,59.1666986911,61.5757469802,64.9000939546,67.006425519,71.668535797,71.1946266903,71.2748718281,68.412079019,69.015322462,68.6332696588,71.3159447771,74.4259603768,72.6412291766,69.0653620407,66.6268106474,63.9078144806,61.4937663657,59.2066972974,54.8081315553,51.5256013057,47.5382195832,46.0342637235,42.3901758242,38.83670047,36.4959183973,31.5860151007,29.2367994375,25.5413992766,23.3131504727,20.5219791349,16.6562419341,13.4637829261,12.0375268383,9.62763147126,7.5002551856,5.52212163351,4.24658756658,2.61032598641,1.57593186416,0.602320747917,0.0301005048792,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_3
y17_sdETA_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00550799275372,0.401474182898,1.08926994923,1.81509539073,2.80539771166,4.06995382981,5.7756453236,7.1008227196,8.9438525674,10.808405772,13.4983094274,15.8959821232,18.5799097767,21.1212218319,24.7190796402,27.4731552366,31.2255115837,34.6206890843,37.4803745681,41.1493025167,44.097206138,48.4472967346,51.4443571353,53.8093669794,57.5785950707,58.7222011363,63.1782428739,65.9667322785,68.5176034991,68.12768055,65.6561508088,64.1732004638,64.0286145317,63.8970693651,68.1876030703,67.446188836,66.724965444,62.2137543622,60.3271090447,57.4947441677,54.9537043029,50.0632547621,47.1532952994,45.8180184196,41.2080468993,37.5301732294,34.5813230353,31.6278374789,28.2876708833,25.4718689971,21.825419103,19.2294252089,15.5934444901,13.3936217375,11.0056057082,9.14239720555,7.26581074637,5.62686278385,4.09817632124,3.00382453604,1.95780727136,1.08906072822,0.384984399067,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_4
y17_sdETA_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0029579919338,0.0424400237989,0.114463241719,0.240816029632,0.321695350734,0.499310595891,0.671038687294,0.893043313851,1.19412773424,1.43788541677,1.69842826553,2.1543682301,2.48487799767,2.89252152837,3.37495356459,3.72824331515,4.28190087706,4.66583209369,5.0369086852,5.65458404382,6.11257665995,6.56834466836,7.19525916352,7.4535301066,7.90646023706,8.33192147264,8.79164968361,9.07628323017,9.38682644206,9.51409805309,8.91324954704,8.63192685809,8.69235602185,8.86399953808,9.58530153313,9.43525875738,9.09370331147,8.55291921369,8.3272638255,8.04397706848,7.60169298862,7.11799112227,6.59821453625,6.20719664136,5.6281452826,5.21973937283,4.73567675929,4.35287588388,3.83093882298,3.41641824822,3.0118611102,2.49382573065,2.12070169838,1.78227553398,1.45166756301,1.16154103913,0.918831928458,0.674032087367,0.499374327895,0.342408813445,0.20431149971,0.11938154875,0.0404527877409,0.00197474538671,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_5
y17_sdETA_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000503397328248,0.00856985954584,0.0221796978454,0.0289871136069,0.050667773192,0.0857040367915,0.105377736688,0.145936793327,0.191322512458,0.240224813124,0.298702059573,0.359701563956,0.426243226121,0.503906653029,0.589389995752,0.647322988864,0.716416716264,0.804088595161,0.897906379755,1.00659740817,1.0730776953,1.18680035643,1.20238737512,1.31382027527,1.37204253925,1.44339401979,1.49456655664,1.55305968698,1.58656109455,1.57851672375,1.495829266,1.51694483914,1.52179722793,1.51070971158,1.61104989411,1.60400816895,1.54146364605,1.50540721251,1.43353440426,1.3914596963,1.3314818022,1.2203225691,1.15351380149,1.06271235588,0.98261313222,0.896659674322,0.837637614398,0.705832923321,0.646077083627,0.563900551045,0.489769989661,0.423713406336,0.354923113274,0.297951635726,0.239718849167,0.199156831803,0.152261862917,0.105869857253,0.0741051151501,0.0496542448859,0.0327699283657,0.0176419181264,0.00504169517428,0.000252556474281,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_6
y17_sdETA_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00172195124314,0.00458019449121,0.00658048512273,0.0120254219957,0.0200567186036,0.0223294905516,0.0400464792455,0.0429638590296,0.0546218319662,0.0658597731505,0.0872853612093,0.105925536049,0.117677268126,0.161740365356,0.171764366093,0.1898079273,0.219333209454,0.239925431804,0.25399969945,0.268568304392,0.303182111727,0.322088988786,0.336421571411,0.362746906784,0.368200611771,0.409680159342,0.383390512222,0.419471536643,0.409989357574,0.379122217073,0.357327390544,0.344372554453,0.36249049118,0.393421710589,0.411921821529,0.387617321025,0.379649143643,0.377379890537,0.326705769235,0.320666157091,0.324390031429,0.285811329084,0.278294603113,0.261106360389,0.224697543856,0.210131737994,0.182682772448,0.154305612404,0.135996738521,0.119093102124,0.0987650228347,0.0792938015226,0.0644055118009,0.0518407073355,0.0432293016627,0.0292119052983,0.0291893727146,0.0194643535725,0.0140320015573,0.00944682370633,0.00400915544096,0.000859269683059,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_7
y17_sdETA_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.63116632733e-05,8.63928393057e-05,0.000388664892436,0.000539894793195,0.000863869302605,0.00125249798642,0.00190007779469,0.00235242735904,0.0030873415983,0.00380083450096,0.00479252902394,0.00589435222459,0.00738810018286,0.00760022652435,0.00958881720643,0.0101523473423,0.01198410945,0.013688370287,0.0147880268378,0.0159617769483,0.0166941171907,0.0166942471059,0.0191548615393,0.0191112477567,0.019759457444,0.020880017766,0.0214661028532,0.0223920167115,0.0220248470816,0.022009081246,0.0207017322331,0.0196924463568,0.0202061393539,0.0223720223464,0.0218115682667,0.0220308064165,0.0212672697681,0.0201259858797,0.0207278493748,0.0202043498771,0.018659918236,0.0176894937613,0.0151811040687,0.0142060277925,0.0144200651443,0.0122818834205,0.0100418649601,0.00902870258249,0.0080102891172,0.00746964500679,0.00572340480384,0.00412268259225,0.0043198515059,0.00313015200224,0.00200875393685,0.00170296461885,0.00144348754747,0.000993501601205,0.000539767392504,0.000302448486324,0.000151213975672,8.64054536505e-05,2.1607882221e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_8
y17_sdETA_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.79832066157e-05,8.51991440142e-05,0.000198630523088,0.000198863434868,0.000141661891061,0.000113665434617,0.000284037104156,0.00019816306558,0.000397472865179,0.000482800422608,0.000595218531459,0.000738128921756,0.000933468526165,0.000737956466031,0.000851582656201,0.00116178193255,0.0015329650291,0.000651669781457,0.00144507261981,0.00147717953841,0.00130567685045,0.00164221372554,0.00184195934348,0.00158786714838,0.00139108818482,0.00156107041042,0.00130128613661,0.00201379268298,0.00178366306975,0.00167241204499,0.00164323568539,0.00153197426279,0.00141499328145,0.00184196528511,0.00167462678647,0.00118489931797,0.00127905048864,0.00127298037391,0.00124959977442,0.000823574569022,0.00124926570644,0.000937379453626,0.000963396353042,0.000737960922252,0.000679341424005,0.000369114964708,0.0004259383091,0.000196195495784,0.000225552629384,0.000170078628493,0.000139602879625,0.000167856162898,0.000142147203157,0.000141949035042,0.0,2.84595617098e-05,0.0,2.83791120796e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_9
y17_sdETA_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.81862417516,2.60355382494,5.20799132619,13.0377210592,2.60958035894,0.0,2.60878781896,10.4206471405,5.2164127845,7.80677260689,10.4233735395,7.8420158331,0.0,13.0441582835,13.0350408051,13.017767202,18.2354912729,18.2599481108,31.291393453,39.0939860941,33.8618263362,57.3091774226,44.3114448314,67.7490939364,88.6223128506,67.7748582152,132.935757298,117.305338404,91.2123535036,127.737564088,112.084687972,106.833351131,99.0380417056,54.7297885685,49.5239045296,54.77189586,33.9279328612,23.4640478772,26.0800258527,23.4492084217,23.4589142485,13.0334603396,13.0367481692,13.0337333641,7.83240229622,5.21361332256,10.4247809613,7.81067185745,5.2157859819,5.22476887076,0.0,2.60958035894,2.61222062071,7.82819156706,2.60304276932,5.21373253042,5.21053314535,0.0,2.60578993362,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_10
y17_sdETA_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.4244817581,11.5814750548,24.2345692448,30.5509913712,42.1293344768,33.7067726728,34.7575070163,28.4470717609,33.7006203549,41.0819013772,25.2740531957,43.1727275928,32.6570425539,35.8134741005,47.391701778,34.7539595259,51.6168706048,48.4433672415,78.9995336394,97.9529438876,111.666379887,123.229813529,181.137361945,203.291208781,242.288978773,293.890843724,323.320393342,381.268803308,427.677288488,466.557706573,470.788531377,472.877395315,432.860010397,471.801605378,367.636744122,366.485887441,252.740339576,245.390993663,200.125554529,169.577698955,148.470246365,98.9749828042,84.2580918131,64.2457826068,63.1832284254,56.8758520148,52.6705368221,51.6200641157,32.6515443285,38.9744150384,47.3979733717,35.8199919408,28.4402576551,26.3393545659,27.3789193164,24.2178744933,35.8105691599,35.8163174795,36.854300865,32.6451842399,15.7932426786,9.48052579438,1.05458425551,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_11
y17_sdETA_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.13977867641,15.4339516364,22.8089218469,29.7149152971,32.0213600179,35.9377505381,38.2357919934,42.3770880202,47.4503242567,40.0744318772,41.2394772135,41.0011349351,38.0099182092,38.9320565303,42.1481979747,39.6220311058,46.9888939133,54.5934073722,62.4207015066,76.4738279315,94.2017212133,114.246295293,138.890917609,164.458631155,202.932907595,235.621918012,285.60578649,330.796450736,341.569114425,371.773532429,386.29008723,383.728147886,365.287187379,356.564390239,333.976819706,271.800569848,239.528187727,199.925368476,170.661985575,132.914455202,120.931021962,87.5221817453,82.4633544003,61.4963422951,57.5863070567,47.674180796,42.1589950375,45.3801315643,40.9979457671,42.8448966996,40.0766604524,42.3818141366,45.6027201212,42.1509260582,46.5274635698,40.3005554155,37.7772203741,38.9283294304,30.63930909,24.8762711847,15.2017187281,8.9826605379,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_12
y17_sdETA_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0276982578978,0.830468650589,1.96609641265,2.71367770127,4.0431648112,5.2880006381,5.3996528012,5.56504588725,5.26125136204,5.39975667441,5.73182779665,5.09549279616,4.347047821,4.3480288458,4.56895178238,4.40294699851,3.76636654947,4.92938029192,5.17892606957,5.26044730642,6.70157272968,7.53198905895,9.41621837541,10.5222064931,14.8430397927,17.4997549625,19.8269573689,23.6489491783,28.4402627337,29.962101491,32.4829235674,33.229629628,34.1978395458,32.038703999,32.0656879515,28.6050287333,24.8940312232,19.7156899214,17.1116538595,14.2326038468,11.4356367557,9.05482422971,8.22420015402,5.75894255254,5.12229593241,5.23345565952,4.12538623045,3.79362211127,4.01553453639,4.73503735653,4.07047577205,4.18168551219,4.87399663332,5.26076277322,5.6768596309,6.06410666531,6.17476241498,5.1778988789,4.29222200003,3.57336819512,3.32340615518,1.85586692786,0.996972795315,0.110660674034,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_13
y17_sdETA_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100580399082,0.191541137076,0.35288757745,0.615093246784,0.574752783437,0.746091103064,0.766298532281,0.846927394548,1.00814688996,1.02833793527,1.09855633972,0.584423356009,0.887387763844,0.68540267296,0.635226645,0.594879506727,0.615103562579,0.664998030011,0.554717506638,0.655299968879,0.64542714604,0.846767803128,1.12892482723,1.40167688015,1.95626738115,2.30865433912,2.67172239283,3.44795139504,3.7804634565,4.38578946564,4.29534178676,4.53722898037,5.04065617112,4.40651753911,4.11260900381,3.80035473693,3.27668613552,2.5910583355,2.29871598064,1.78453791084,1.46186408397,1.34107643771,1.05892548172,0.655505677972,0.645183814635,0.604941108619,0.564532318299,0.726019599619,0.655453492184,0.584838050977,0.675423658112,0.705973582256,0.897346753914,0.997951850223,1.08870839615,1.0283609941,1.09904846383,0.897363744635,0.81619603377,0.625120806551,0.433442226674,0.332785733431,0.191615471483,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_14
y17_sdETA_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0452767212082,0.104673735684,0.127287896359,0.200887306883,0.18389780228,0.195238489355,0.223416402305,0.240523559347,0.215042303387,0.268811430004,0.226337168339,0.25178445206,0.181166480525,0.181066679863,0.1838850675,0.135836357883,0.135792305548,0.183900110699,0.186707148607,0.215041764756,0.23199591207,0.302743270508,0.319663368637,0.455441131143,0.526202647886,0.633767678784,0.659216078242,0.888329390144,1.08933808141,1.05249340044,1.00733148462,1.04396879259,1.04687970936,1.00434669843,0.865616467595,0.794978451285,0.613890649386,0.480953781605,0.469628291623,0.359281115272,0.268739253426,0.220641528373,0.181041671987,0.152776231471,0.192343385249,0.152793621563,0.11597190937,0.158395347336,0.186723307542,0.22070612564,0.209359513622,0.254566020409,0.257407453765,0.268737983796,0.254661319654,0.21498370801,0.294242939527,0.220641913109,0.198040294846,0.158444478194,0.087715048479,0.0481090747819,0.00566544968877,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_15
y17_sdETA_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00152147860754,0.00152079908585,0.0136743990382,0.0121667826732,0.0152287074067,0.0106794385082,0.0152232121443,0.01826124135,0.0259278059546,0.0182792989002,0.0137252863495,0.0152019283433,0.00915519572461,0.0183267117875,0.00912164744334,0.0167567803291,0.0107129714264,0.00914102503838,0.00913588076374,0.010678131463,0.0167616019786,0.0152535719917,0.0182751035924,0.0289936660051,0.0243303509133,0.0335008329215,0.0486869018194,0.0593489398452,0.0502764153203,0.044122808712,0.0639879694227,0.045708540527,0.0487400108711,0.0380855953059,0.0654502764633,0.0411493872469,0.0426226257234,0.024358749011,0.0365066114368,0.031950046248,0.0122072112593,0.00760202068013,0.0137693075372,0.0167082683893,0.0137357332568,0.012204469537,0.00915507991048,0.00457095923084,0.00762667136348,0.0122179536108,0.0137047588855,0.0213175880968,0.0152473085744,0.0198194259465,0.00914612558727,0.0259248633303,0.0167617083386,0.0198248384845,0.0137223791784,0.00607898803603,0.0076133456477,0.001522455937,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y17_sdETA_16
y17_sdETA_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000541027778366,0.000360854804187,0.000721259714044,0.000541558085242,0.000180370308416,0.0012646255411,0.00162525616835,0.00180536409618,0.00270915566795,0.00126453003194,0.00108241564165,0.000542329860826,0.00162398412506,0.000722757051105,0.0016253624608,0.000902001545366,0.000541534207954,0.00126372089996,0.00072231955756,0.000722409289951,0.000722296835624,0.00216618535663,0.000722911098128,0.00162629097923,0.00162361980386,0.00162672577695,0.00216723634244,0.00216767999786,0.00433348909464,0.00378943621786,0.00234629752074,0.00342954809726,0.00306879153656,0.00288817140238,0.00306864211095,0.00343114017324,0.00270876978016,0.00162501046335,0.000904444731147,0.000902406689036,0.000361518169179,0.000901370337691,0.00126489281268,0.000901400761978,0.000361015706302,0.000360657277392,0.00072223598705,0.00108402042651,0.00162568865537,0.00126416108932,0.00144397209151,0.00108302528274,0.00108284697331,0.00126508498634,0.00162593166455,0.00090354740724,0.00162565900132,0.00180653677914,0.000722266411337,0.000722824831795,0.0,0.000542114580112,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating a new Canvas
fig = plt.figure(figsize=(12,6),dpi=80)
frame = gridspec.GridSpec(1,1,right=0.7)
pad = fig.add_subplot(frame[0])
# Creating a new Stack
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights+y17_sdETA_14_weights+y17_sdETA_15_weights+y17_sdETA_16_weights,\
label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights+y17_sdETA_14_weights+y17_sdETA_15_weights,\
label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights+y17_sdETA_14_weights,\
label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights,\
label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights,\
label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights,\
label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights,\
label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights,\
label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights,\
label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights,\
label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights,\
label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights,\
label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights,\
label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights,\
label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights,\
label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights+y17_sdETA_1_weights,\
label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y17_sdETA_0_weights,\
label="$signal$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
# Axis
plt.rc('text',usetex=False)
plt.xlabel(r"$sd\eta$ $[ a_{1} a_{2} ]$ ",\
fontsize=16,color="black")
plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\
fontsize=16,color="black")
# Boundary of y-axis
ymax=(y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights+y17_sdETA_14_weights+y17_sdETA_15_weights+y17_sdETA_16_weights).max()*1.1
ymin=0 # linear scale
#ymin=min([x for x in (y17_sdETA_0_weights+y17_sdETA_1_weights+y17_sdETA_2_weights+y17_sdETA_3_weights+y17_sdETA_4_weights+y17_sdETA_5_weights+y17_sdETA_6_weights+y17_sdETA_7_weights+y17_sdETA_8_weights+y17_sdETA_9_weights+y17_sdETA_10_weights+y17_sdETA_11_weights+y17_sdETA_12_weights+y17_sdETA_13_weights+y17_sdETA_14_weights+y17_sdETA_15_weights+y17_sdETA_16_weights) if x])/100. # log scale
plt.gca().set_ylim(ymin,ymax)
# Log/Linear scale for X-axis
plt.gca().set_xscale("linear")
#plt.gca().set_xscale("log",nonposx="clip")
# Log/Linear scale for Y-axis
plt.gca().set_yscale("linear")
#plt.gca().set_yscale("log",nonposy="clip")
# Legend
plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)
# Saving the image
plt.savefig('../../HTML/MadAnalysis5job_0/selection_16.png')
plt.savefig('../../PDF/MadAnalysis5job_0/selection_16.png')
plt.savefig('../../DVI/MadAnalysis5job_0/selection_16.eps')
# Running!
if __name__ == '__main__':
selection_16()
| 160.216495
| 1,473
| 0.781835
| 5,398
| 31,082
| 4.366432
| 0.251945
| 0.101315
| 0.146754
| 0.189902
| 0.336317
| 0.336317
| 0.335002
| 0.331608
| 0.328596
| 0.328596
| 0
| 0.55941
| 0.053118
| 31,082
| 193
| 1,474
| 161.046632
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| 0
| 0.00885
| 0.035204
| 0.006852
| 0
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| 0.00885
| false
| 0
| 0.035398
| 0
| 0.044248
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| null | 0
| 0
| 1
| 0
| 0
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0
| 5
|
c19c9a913fd8a8a252c8d7c62ef2760373195fef
| 57,308
|
py
|
Python
|
kitconc/kit_tools.py
|
ilexistools/kitconc
|
695c01cef606d7b03e725b91d101ae2152f60f43
|
[
"MIT"
] | 3
|
2018-07-07T00:36:10.000Z
|
2021-11-21T13:49:42.000Z
|
build/lib.win-amd64-3.8/kitconc/kit_tools.py
|
ilexistools/kitconc
|
695c01cef606d7b03e725b91d101ae2152f60f43
|
[
"MIT"
] | null | null | null |
build/lib.win-amd64-3.8/kitconc/kit_tools.py
|
ilexistools/kitconc
|
695c01cef606d7b03e725b91d101ae2152f60f43
|
[
"MIT"
] | 1
|
2022-02-21T10:04:48.000Z
|
2022-02-21T10:04:48.000Z
|
# -*- coding: utf-8 -*-
# Author: jlopes@usp.br
import pandas as pd
from io import StringIO
import xlsxwriter
from kitconc.kit_plots import CollDist
from kitconc.kit_plots import CollGraph
class xStyles():
def xls_columns_resize(self,worksheet,sizes):
for k in sizes:
worksheet.set_column(k+':'+k,sizes[k])
def xls_header(self,workbook,font_name='Calibri',font_color='#003366',font_size=12,bold=True,align='center'):
header_style = workbook.add_format({'bold': bold, 'font_color': font_color})
header_style.set_font(font_name)
header_style.set_font_size(font_size)
header_style.set_align(align)
return header_style
def xls_column_cells(self,workbook,font_name,font_color,font_size,bold,align):
cell_style = workbook.add_format({'bold': bold, 'font_color': font_color})
cell_style.set_font(font_name)
cell_style.set_font_size(font_size)
cell_style.set_align(align)
return cell_style
class Wordlist(object):
def __init__(self,**kwargs):
self.df = None
self.tokens = kwargs.get('tokens',0)
self.types = kwargs.get('types',0)
self.typetoken=kwargs.get('typetoken',0)
self.hapax=kwargs.get('hapax',0)
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
return True
def save_excel(self,filename):
# create Excel
style = xStyles() # style object
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Wordlist'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':10,'E':3,'F':3,'G':3,'H':3,'I':3,'J':20,'K':20,'L':25,'M':20}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
info_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', '%',header_style)
worksheet.write('J1', 'TOKENS',header_style)
worksheet.write('K1', 'TYPES',header_style)
worksheet.write('L1', 'TYPE-TOKEN RATIO',header_style)
worksheet.write('M1', 'HAPAX',header_style)
# set contents
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, int(row[2]),freq_style) # FREQ
worksheet.write(i,3, float(row[3]),p_style) # %
if i == 1:
# corpus info
worksheet.write('J2', self.tokens ,info_style)
worksheet.write('K2', self.types,info_style)
worksheet.write('L2', self.typetoken,info_style)
worksheet.write('M2', self.hapax,info_style)
# close
workbook.close()
return True
class Keywords(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles() # style object
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Keywords'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':15}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
keyness_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', 'KEYNESS',header_style)
# set contents
i = 0
for kv in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(i),n_style)
worksheet.write(i,1, str(kv[1]),word_style)
worksheet.write(i,2, int(kv[2]),freq_style)
worksheet.write(i,3, float(kv[3]),keyness_style)
# close
workbook.close()
return True
class WTfreq(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles() # style object
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'WTfreq'
# resize columns
column_sizes = {'A':10, 'B':20,'C':20,'D':20,'E':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
tag_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'TAG',header_style)
worksheet.write('D1', 'FREQUENCY',header_style)
worksheet.write('E1', '%',header_style)
# set contents
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, str(row[2]),tag_style) # TAG
worksheet.write(i,3, int(row[3]),freq_style) # FREQ
worksheet.write(i,4, float(row[4]),p_style) # %
# close
workbook.close()
return True
class Wfreqinfiles(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles() # style object
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name='Range'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
range_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'RANGE',header_style)
worksheet.write('D1', '%',header_style)
# set contents
i = 0
types = 0
tokens=0
for row in self.df.itertuples(index=False):
i+=1
types+=1
tokens += int(row[2])
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, int(row[2]),range_style) # RANGE
worksheet.write(i,3, float(row[3]),p_style) # %
# close
workbook.close()
return True
class Kwic(object):
class __KwicColors(object):
def __init__(self):
self.black = '#000000'
self.blue = '#000099'
self.brown = '#604020'
self.green = '#008000'
self.orange = '#cc5200'
self.pink = '#ff0080'
self.purple = '#990099'
self.red = '#cc2900'
self.white = '#ffffff'
self.yellow='#e6e600'
def __init__(self,**kwargs):
self.colors = self.__KwicColors()
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
self.node_length = kwargs.get('node_length',1)
self.HIGHLIGHT_L1 = ['L1']
self.HIGHLIGHT_L2 = ['L2']
self.HIGHLIGHT_L3 = ['L3']
self.HIGHLIGHT_R1 = ['R1']
self.HIGHLIGHT_R2 = ['R2']
self.HIGHLIGHT_R3 = ['R3']
self.HIGHLIGHT_LEFT = 'L1 L2 L3'
self.HIGHLIGHT_RIGHT = 'R1 R2 R3'
"""
self.color_blue = '#000099'
self.color_orange = '#cc5200'
self.color_green = '#008000'
self.color_brown = '#604020'
self.color_red = '#cc2900'
self.color_yellow='#e6e600'
self.color_purple = '#990099'
self.color_pink = '#ff0080'
self.color_black = '#000000'
self.color_white = '#ffffff'
"""
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def sort(self,first='R1',second='R2',third='R3'):
horizon = ['L1','L2','L3','L4','L5','R1','R2','R3','R4','R5']
# first
if first == 'N':
df1 = self.__get_node_column('Col1')
elif first in horizon:
df1= self.__get_column(first,'Col1')
else:
df1 = self.__get_node_column('Col1')
# second
if second != None:
if second == 'N':
df2 = self.__get_node_column('Col2')
elif second in horizon:
df2= self.__get_column(second,'Col2')
else:
df2= None
else:
df2 = None
# third
if third != None:
if third == 'N':
df3 = self.__get_node_column('Col3')
elif third in horizon:
df3= self.__get_column(third,'Col3')
else:
df3= None
else:
df3 = None
# add columns
if df2 is not None and df3 is not None:
self.df['Sort'] = df1['Col1'] + df2['Col2'] + df3['Col3']
elif df2 is None and df3 is not None:
self.df['Sort'] = df1['Col1'] + df3['Col3']
elif df2 is not None and df3 is None:
self.df['Sort'] = df1['Col1'] + df2['Col2']
elif df2 is None and df3 is None:
self.df['Sort'] = df1['Col1']
# sort
self.df.sort_values(['Sort'], ascending=[True],inplace=True)
self.df = self.df.drop('Sort',1)
def __get_node_column(self,column_name):
data = []
for row in self.df.itertuples():
data.append((int(row[0]), str(row[3])))
# create DataFrame
df = pd.DataFrame(data, columns=['Idx',column_name])
data = None
return df
def __get_column(self,col,column_name):
# parse col
h = str(col[0:1]).upper()
p = int(col[-1:])
# avoid p out of range
if p > 5:
p = 5
elif p < 1:
p = 1
# adjust index
if h == 'L':
idx = 5 - p
elif h == 'R':
idx = p-1
# get values
data =[]
i = 0
for row in self.df.itertuples():
i+=1
# get horizon
if h == 'L':
hs = str(row[2]).split(' ')[-5:]
elif h == 'R':
hs = str(row[4]).split(' ')[0:5]
else:
hs = str(row[4]).split(' ')[0:5]
if len(hs) < 5:
for i in range(5-len(hs)):
hs.append(' ')
# get column
data.append((int(row[0]), hs[idx]))
# create DataFrame
df = pd.DataFrame(data, columns=['Idx',column_name])
data = None
return df
def __cleft(self,left,p1,p2,p3,h1,h2,h3,n_format,c):
left = str(left)
t = len(left)
if t < c:
left = ' ' * (c-t) + left
if t > c:
left = left[(t-c):]
arr = str(left).split(' ')
new_arr = []
for item in arr:
new_arr.append([' ' + item,n_format])
arr = None
total = len(new_arr)
if p1 is not None:
new_arr[total - p1][1] = h1
if p2 is not None:
new_arr[total - p2][1] = h2
if p3 is not None:
new_arr[total - p3][1] = h3
arr = []
for item in new_arr:
arr.append(item[1])
arr.append(item[0])
return arr
def __cright(self,right,p1,p2,p3,h1,h2,h3,n_format,c):
right = str(right)
t = len(right)
if t > c:
right = right[:c]
arr = str(right).split(' ')
new_arr = []
for item in arr:
new_arr.append([item + ' ',n_format])
arr = None
total = len(new_arr)
if p1 is not None:
if total > p1:
new_arr[p1-1][1] = h1
if p2 is not None:
if total > p2:
new_arr[p2-1][1] = h2
if p3 is not None:
if total > p3:
new_arr[p3-1][1] = h3
arr = []
for item in new_arr:
arr.append(item[1])
arr.append(item[0])
return arr
def __norm(self,s):
for r in [(' ,', ','), (' .', '.'),(' ;', ';'), (' ?', '?'),(' !', '!'),(' :', ':'), (' %', '%'),
(' )', ')'), ('( ', '('), (' ]', ']'), ('[ ', '['),(' }', '}'), ('{ ', '{')]:
s = str(s).replace(r[0],r[1])
return s
def __nleft(self,s,c):
s = self.__norm(s)
t = len(s)
if t < c:
s = ' ' * (c-t) + s
if t > c:
s = s[(t-c):]
return s
def __nright(self,s,c):
s = self.__norm(s)
t = len(s)
if t > c:
s = s[:c]
return s
def save_excel(self,filename,**kwargs):
# args
width = kwargs.get('width',50)
cols = kwargs.get('highlight',None)
left_colors = kwargs.get('left_colors',[self.colors.blue,self.colors.green, self.colors.orange])
right_colors = kwargs.get('right_colors',[self.colors.blue,self.colors.green, self.colors.orange])
if len (left_colors) < 3:
while len(left_colors) < 3:
left_colors.append(self.colors.blue)
if len (right_colors) < 3:
while len(right_colors) < 3:
right_colors.append(self.colors.blue)
if cols == None:
lc = [None,None,None]
rc = [None,None,None]
else:
lc = []
rc = []
if type(cols) != list:
cols = cols.strip().split(' ')
for item in cols:
if item.startswith('L'):
lc.append(int(item[-1:]))
if item.startswith('R'):
rc.append(int(item[-1:]))
while len(lc) < 3:
lc.append(None)
while len(rc) < 3:
rc.append(None)
if lc == [None,None,None] and rc == [None,None,None]:
kcolor = False
else:
kcolor = True
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = "KWIC"
# resize columns
column_sizes = {'A':10, 'B':150,'C':15,'D':10,'E':10,'F':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
node_style = style.xls_column_cells(workbook,'Courier New','#cc0066',10,True,'center')
node_style.set_border(0)
node_style.set_bottom_color('white')
node_style.set_bg_color("white")
left_hf1_style = style.xls_column_cells(workbook,'Courier New',left_colors[0],10,True,'center')
left_hf2_style = style.xls_column_cells(workbook,'Courier New',left_colors[1],10,True,'center')
left_hf3_style = style.xls_column_cells(workbook,'Courier New',left_colors[2],10,True,'center')
right_hf1_style = style.xls_column_cells(workbook,'Courier New',right_colors[0],10,True,'center')
right_hf2_style = style.xls_column_cells(workbook,'Courier New',right_colors[1],10,True,'center')
right_hf3_style = style.xls_column_cells(workbook,'Courier New',right_colors[2],10,True,'center')
hor_style = style.xls_column_cells(workbook,'Courier New','#000000',10,False,'center')
hor_style.set_border(0)
hor_style.set_bottom_color('white')
hor_style.set_bg_color("white")
filename_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
token_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
sent_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
file_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'CONCORDANCE',header_style)
worksheet.write('C1', 'FILENAME',header_style)
worksheet.write('D1', 'TOKEN_ID',header_style)
worksheet.write('E1', 'SENT_ID',header_style)
worksheet.write('F1', 'FILE_ID',header_style)
# set contents
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
if kcolor == True:
left = self.__cleft(row[1], lc[0], lc[1], lc[2], left_hf1_style,left_hf2_style,left_hf3_style, hor_style,width)
right = self.__cright(row[3], rc[0], rc[1], rc[2], right_hf1_style,right_hf2_style,right_hf3_style, hor_style,width)
whole = left + [node_style, ' ' + row[2] + ' '] + right
else:
whole = [hor_style, self.__nleft(row[1],width), node_style, ' ' + row[2] + ' ', hor_style, self.__nright(row[3],width)]
worksheet.write_rich_string(i,1, *whole) # CONCORDANCE
worksheet.write_rich_string(i,1, *whole) # CONCORDANCE
worksheet.write(i,2, str(row[4]),filename_style) # FILENAME
worksheet.write(i,3, int(row[5]),token_id_style) # TOKEN_ID
worksheet.write(i,4, int(row[6]),sent_id_style) # SENT_ID
worksheet.write(i,5, int(row[7]),file_id_style) # FILE_ID
# close
workbook.close()
return True
class Concordance(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
self.node_length = kwargs.get('node_length',1)
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def __norm(self,s):
for r in [(' ,', ','), (' .', '.'),(' ;', ';'), (' ?', '?'),(' !', '!'),(' :', ':'), (' %', '%'),
(' )', ')'), ('( ', '('), (' ]', ']'), ('[ ', '['),(' }', '}'), ('{ ', '{')]:
s = str(s).replace(r[0],r[1])
return s
def save_excel(self,filename,**kwargs):
node_color = kwargs.get('node_color','#cc0066')
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = "CONCORDANCE"
# resize columns
column_sizes = {'A':10, 'B':150,'C':15,'D':10,'E':10,'F':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
node_style = style.xls_column_cells(workbook,'Courier New',node_color,10,True,'left')
node_style.set_border(0)
node_style.set_bottom_color('white')
node_style.set_bg_color("white")
hor_style = style.xls_column_cells(workbook,'Courier New','#000000',10,False,'left')
hor_style.set_border(0)
hor_style.set_bottom_color('white')
hor_style.set_bg_color("white")
filename_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
token_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
sent_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
file_id_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'CONCORDANCE',header_style)
worksheet.write('C1', 'FILENAME',header_style)
worksheet.write('D1', 'TOKEN_ID',header_style)
worksheet.write('E1', 'SENT_ID',header_style)
worksheet.write('F1', 'FILE_ID',header_style)
# set contents
def highlight(sentence,position,ns,hs):
tokens = sentence.strip().split(' ')
tokens_length = len(tokens)
position = position - 1
s = []
# in the beginning
if position == 0 :
s = [ns,tokens[0] + ' '] + [hs,self.__norm(' '.join(tokens[1:]))]
# in the end
elif (position+1) == tokens_length:
s = [hs,self.__norm(' '.join(tokens[:-1])) + ' '] + [ns,tokens[position]]
# in the middle
else:
s = [hs,self.__norm(' '.join(tokens[:position])) + ' '] + [ns,tokens[position]] + [hs,' ' + self.__norm(' '.join(tokens[position+1:]))]
return s
def highlight_multi(sentence,position,ns,hs):
tokens = sentence.strip().split(' ')
tokens_length = len(tokens)
position = position - 1
s = []
# in the beginning
if position == 0:
s=[ns,self.__norm(' '.join(tokens[0:self.node_length]))] + [hs, ' ' + self.__norm(' '.join(tokens[self.node_length:]))]
# in the end
elif (position+self.node_length) == tokens_length:
s = [hs,self.__norm(' '.join(tokens[:position])) + ' '] + [ns,self.__norm(' '.join(tokens[position:]))]
# in the middle
else:
s = [hs,self.__norm(' '.join(tokens[:position])) + ' '] + [ns,self.__norm(' '.join(tokens[position:position + self.node_length]))] + [hs,' ' + self.__norm(' '.join(tokens[position+self.node_length:]))]
return s
i = 0
for row in self.df.itertuples(index=False):
i+=1
if self.node_length == 1:
whole = highlight(row[1], row[3], node_style,hor_style)
else:
whole = highlight_multi(row[1], row[3], node_style,hor_style)
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write_rich_string(i,1, *whole) # CONCORDANCE
worksheet.write(i,2, str(row[2]),filename_style) # FILENAME
worksheet.write(i,3, int(row[3]),token_id_style) # TOKEN_ID
worksheet.write(i,4, int(row[4]),sent_id_style) # SENT_ID
worksheet.write(i,5, int(row[5]),file_id_style) # FILE_ID
# close
workbook.close()
return True
class Collocates(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Collocates'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':15,'E':15,'F':15}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
left_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
right_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
association_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', 'LEFT',header_style)
worksheet.write('E1', 'RIGHT',header_style)
worksheet.write('F1', 'ASSOCIATION',header_style)
# set contents
# load wordlist
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, int(row[2]),freq_style) # FREQ
worksheet.write(i,3, float(row[3]),left_style) # LEFT
worksheet.write(i,4, float(row[4]),right_style) # RIGHT
worksheet.write(i,5, float(row[5]),association_style) # FORCE
# close
workbook.close()
return True
def plot_collgraph(self,**kwargs):
args_title = kwargs.get('title','Collocations')
args_xlabel = kwargs.get('xlabel','position of the collocate')
args_ylabel = kwargs.get('ylabel','strength of association')
args_node = kwargs.get('node','node')
args_cutoff = kwargs.get('cutoff',0.5)
args_limit = kwargs.get('limit',20)
args_stoplist = kwargs.get('stoplist',[])
collgraph = CollGraph(title=args_title,xlabel=args_xlabel,ylabel=args_ylabel,node=args_node,
cutoff=args_cutoff,limit=args_limit,stoplist=args_stoplist)
collgraph.plot_graphcoll(self)
class Ngrams(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'N-grams'
# resize columns
column_sizes = {'A':10, 'B':35,'C':15,'D':15,'E':15}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
ngram_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
range_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'N-GRAM',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', 'RANGE',header_style)
worksheet.write('E1', '%',header_style)
# set contents
# load wordlist
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),ngram_style) # N-GRAM
worksheet.write(i,2, int(row[2]),freq_style) # FREQ
worksheet.write(i,3, float(row[3]),range_style) # RANGE
worksheet.write(i,4, float(row[4]),p_style) # %
# close
workbook.close()
return True
class Clusters(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Clusters'
# resize columns
column_sizes = {'A':10, 'B':35,'C':15,'D':15,'E':15}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
cluster_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
range_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'CLUSTER',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', 'RANGE',header_style)
worksheet.write('E1', '%',header_style)
# set contents
# load wordlist
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),cluster_style) # CLUSTER
worksheet.write(i,2, int(row[2]),freq_style) # FREQ
worksheet.write(i,3, float(row[3]),range_style) # RANGE
worksheet.write(i,4, float(row[4]),p_style) # %
# close
workbook.close()
return True
class Dispersion(object):
def __init__(self,**kwargs):
self.df = None
self.dpts = {}
self.total_s1 = 0
self.total_s2 = 0
self.total_s3 = 0
self.total_s4 = 0
self.total_s5 = 0
self.encoding = kwargs.get('encoding','utf-8')
self.output_path = kwargs.get('output_path',None)
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
import os
import shutil
import PIL
from PIL import ImageDraw
# function
def create_temp_folder(path):
flag = True
try:
if not os.path.exists(path):
if not os.path.isfile(path):
os.mkdir(path)
flag = True
except:
flag = False
return flag
# function
def remove_temp_folder(path):
flag = True
try:
if os.path.exists(path):
if not os.path.isfile(path):
shutil.rmtree(path)
flag = True
except:
flag = False
return flag
# function
def draw_barcode(points):
# create rectangle
im = PIL.Image.new('RGB', (201,19), (255,255,255))
dr = PIL.ImageDraw.Draw(im)
dr.rectangle(((0,0),(200,18)), fill="white", outline="blue")
# draw lines
for point in points:
p = (point * 200)/100
dr.line(((p,0),(p,18)), fill="black", width=1)
# save file
return im
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Dispersion'
# resize columns
column_sizes = {'A':10, 'B':25,'C':10,'D':10,'E':8,'F':8,'G':8,'H':8,'I':8,'J':28,'K':1,'L':10,'M':10,'N':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
filename_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'center')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
zero_style = style.xls_column_cells(workbook,'Tahoma','#cccccc',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'FILENAME',header_style)
worksheet.write('C1', 'TOTAL',header_style)
worksheet.write('D1', 'HITS',header_style)
worksheet.write('E1', 'S1',header_style)
worksheet.write('F1', 'S2',header_style)
worksheet.write('G1', 'S3',header_style)
worksheet.write('H1', 'S4',header_style)
worksheet.write('I1', 'S5',header_style)
worksheet.write('J1', 'PLOT',header_style)
#worksheet.write('L1', 'SECTION',header_style)
#worksheet.write('M1', 'HITS',header_style)
#worksheet.write('N1', '%',header_style)
total = float(self.total_s1 + self.total_s2 + self.total_s3 + self.total_s4 + self.total_s5)
create_temp_folder(self.output_path + 'temp')
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),filename_style) # FILENAME
worksheet.write(i,2, int(row[2]),freq_style) # TOTAL
if row[3] == 0:
worksheet.write(i,3, float(row[3]),zero_style) # HITS
else:
worksheet.write(i,3, float(row[3]),freq_style) # HITS
if row[4] == 0:
worksheet.write(i,4, float(row[4]),zero_style) # S1
else:
worksheet.write(i,4, float(row[4]),freq_style) # S1
if row[5] == 0:
worksheet.write(i,5, float(row[5]),zero_style) # S2
else:
worksheet.write(i,5, float(row[5]),freq_style) # S2
if row[6] == 0:
worksheet.write(i,6, float(row[6]),zero_style) # S3
else:
worksheet.write(i,6, float(row[6]),freq_style) # S3
if row[7] == 0:
worksheet.write(i,7, float(row[7]),zero_style) # S4
else:
worksheet.write(i,7, float(row[7]),freq_style) # S4
if row[8] == 0:
worksheet.write(i,8, float(row[8]),zero_style) # S5
else:
worksheet.write(i,8, float(row[8]),freq_style) # S5
"""
# set section total contents
if i == 2:
#worksheet.write('L2', 'S1',header_style)
worksheet.write('M2', int(self.total_s1),freq_style)
worksheet.write('N2', round((self.total_s1 / total) * 100,2),freq_style)
elif i == 3:
#worksheet.write('L3', 'S2',header_style)
worksheet.write('M3', int(self.total_s2),freq_style)
worksheet.write('N3', round((self.total_s2 / total) * 100,2),freq_style)
elif i == 4:
#worksheet.write('L4', 'S3',header_style)
worksheet.write('M4', int(self.total_s3),freq_style)
worksheet.write('N4', round((self.total_s3 / total) * 100,2),freq_style)
elif i == 5:
#worksheet.write('L5', 'S4',header_style)
worksheet.write('M5', int(self.total_s4),freq_style)
worksheet.write('N5', round((self.total_s4 / total) * 100,2),freq_style)
elif i == 6:
#worksheet.write('L6', 'S5',header_style)
worksheet.write('M6', int(self.total_s5),freq_style)
worksheet.write('N6', round((self.total_s5 / total) * 100,2),freq_style)
"""
img = draw_barcode(self.dpts[row[1]])
img.save(self.output_path + 'temp/' + str(i) + '.jpg')
worksheet.insert_image('J' + str(i+1), self.output_path + 'temp/' + str(i) + '.jpg')
# close
workbook.close()
remove_temp_folder(self.output_path + 'temp')
return True
class KeywordsDispersion(object):
def __init__(self,**kwargs):
self.df = None
self.dpts = {}
self.total_s1 = 0
self.total_s2 = 0
self.total_s3 = 0
self.total_s4 = 0
self.total_s5 = 0
self.encoding = kwargs.get('encoding','utf-8')
self.output_path = kwargs.get('output_path',None)
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
import os
import shutil
import PIL
from PIL import ImageDraw
# function
def create_temp_folder(path):
flag = True
try:
if not os.path.exists(path):
if not os.path.isfile(path):
os.mkdir(path)
flag = True
except:
flag = False
return flag
# function
def remove_temp_folder(path):
flag = True
try:
if os.path.exists(path):
if not os.path.isfile(path):
shutil.rmtree(path)
flag = True
except:
flag = False
return flag
# function
def draw_barcode(points):
# create rectangle
im = PIL.Image.new('RGB', (201,19), (255,255,255))
dr = PIL.ImageDraw.Draw(im)
dr.rectangle(((0,0),(200,18)), fill="white", outline="blue")
# draw lines
for point in points:
p = (point * 200)/100
dr.line(((p,0),(p,18)), fill="black", width=1)
# save file
return im
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Keywords Dispersion'
# resize columns
column_sizes = {'A':10, 'B':25,'C':10,'D':10,'E':8,'F':8,'G':8,'H':8,'I':8,'J':28,'K':7,'L':7,'M':7,'N':7,'O':7}
style.xls_columns_resize(worksheet, column_sizes)
# formats
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'center')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
zero_style = style.xls_column_cells(workbook,'Tahoma','#cccccc',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'KEYNESS',header_style)
worksheet.write('D1', 'HITS',header_style)
worksheet.write('E1', 'S1',header_style)
worksheet.write('F1', 'S2',header_style)
worksheet.write('G1', 'S3',header_style)
worksheet.write('H1', 'S4',header_style)
worksheet.write('I1', 'S5',header_style)
worksheet.write('J1', 'PLOT',header_style)
worksheet.write('K1', 'S1%',header_style)
worksheet.write('L1', 'S2%',header_style)
worksheet.write('M1', 'S3%',header_style)
worksheet.write('N1', 'S4%',header_style)
worksheet.write('O1', 'S5%',header_style)
# set contents
create_temp_folder(self.output_path + 'temp')
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, float(row[2]),freq_style) # KEYNESS
if row[3] == 0:
worksheet.write(i,3, float(row[3]),zero_style) # HITS
else:
worksheet.write(i,3, float(row[3]),freq_style) # HITS
if row[4] == 0:
worksheet.write(i,4, float(row[4]),zero_style) # S1
else:
worksheet.write(i,4, float(row[4]),freq_style) # S1
if row[5] == 0:
worksheet.write(i,5, float(row[5]),zero_style) # S2
else:
worksheet.write(i,5, float(row[5]),freq_style) # S2
if row[6] == 0:
worksheet.write(i,6, float(row[6]),zero_style) # S3
else:
worksheet.write(i,6, float(row[6]),freq_style) # S3
if row[7] == 0:
worksheet.write(i,7, float(row[7]),zero_style) # S4
else:
worksheet.write(i,7, float(row[7]),freq_style) # S4
if row[8] == 0:
worksheet.write(i,8, float(row[8]),zero_style) # S5
else:
worksheet.write(i,8, float(row[8]),freq_style) # S5
img = draw_barcode(self.dpts[row[1]])
img.save(self.output_path + 'temp/' + str(i) + '.jpg')
worksheet.insert_image('J' + str(i+1), self.output_path + 'temp/' + str(i) + '.jpg')
s1 = round((row[4]/row[3])*100,2)
s2 = round((row[5]/row[3])*100,2)
s3 = round((row[6]/row[3])*100,2)
s4 = round((row[7]/row[3])*100,2)
s5 = round((row[8]/row[3])*100,2)
if s1 <=0:
worksheet.write(i,10, s1,zero_style) # S1%
else:
worksheet.write(i,10, s1,freq_style) # S1%
if s2 <=0:
worksheet.write(i,11, s2,zero_style) # S2%
else:
worksheet.write(i,11, s2,freq_style) # S2%
if s3 <=0:
worksheet.write(i,12, s3,zero_style) # S3%
else:
worksheet.write(i,12, s3,freq_style) # S3%
if s4 <=0:
worksheet.write(i,13, s4,zero_style) # S4%
else:
worksheet.write(i,13, s4,freq_style) # S4%
if s5 <=0:
worksheet.write(i,14, s5,zero_style) # S5%
else:
worksheet.write(i,14, s5,freq_style) # S5%
# close
workbook.close()
remove_temp_folder(self.output_path + 'temp')
return True
class Keynessxrange(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'KeywordsxRange'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':15}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
key_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'FREQUENCY',header_style)
worksheet.write('D1', 'KEYNESS',header_style)
# set contents
i = 0
for kv in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(i),n_style)
worksheet.write(i,1, kv[1],word_style)
worksheet.write(i,2, kv[2],freq_style)
worksheet.write(i,3, kv[3],key_style)
# close
workbook.close()
return True
class ComparedCollocates(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Comparison'
# resize columns
column_sizes = {'A':10, 'B':20,'C':10,'D':10,'E':15,'F':15,'G':12}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'FREQ1',header_style)
worksheet.write('D1', 'FREQ2',header_style)
worksheet.write('E1', 'ASSOCIATION1',header_style)
worksheet.write('F1', 'ASSOCIATION2',header_style)
worksheet.write('G1', 'DIFFERENCE',header_style)
# set contents
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, int(row[2]),freq_style) # FREQ1
worksheet.write(i,3, int(row[3]),freq_style) # FREQ2
worksheet.write(i,4, float(row[4]),freq_style) # AM1
worksheet.write(i,5, float(row[5]),freq_style) # AM2
worksheet.write(i,6, float(row[6]),freq_style) # PD
# close
workbook.close()
return True
class Collocations(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
def save_excel(self,filename):
# create Excel
style = xStyles()
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Collocations'
# resize columns
column_sizes = {'A':10, 'B':20,'C':7,'D':7,'E':7,'F':7,'G':7,'H':4,'I':7,'J':7,
'K':7,'L':7,'M':7,'N':8,'O':8,'P':8,'Q':15}
style.xls_columns_resize(worksheet, column_sizes)
# formats
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'L5',header_style)
worksheet.write('D1', 'L4',header_style)
worksheet.write('E1', 'L3',header_style)
worksheet.write('F1', 'L2',header_style)
worksheet.write('G1', 'L1',header_style)
worksheet.write('H1', 'N',header_style)
worksheet.write('I1', 'R1',header_style)
worksheet.write('J1', 'R2',header_style)
worksheet.write('K1', 'R3',header_style)
worksheet.write('L1', 'R4',header_style)
worksheet.write('M1', 'R5',header_style)
worksheet.write('N1', 'LEFT',header_style)
worksheet.write('O1', 'RIGHT',header_style)
worksheet.write('P1', 'TOTAL',header_style)
worksheet.write('Q1', 'ASSOCIATION',header_style)
# set contents
# add data
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, int(row[2]),freq_style) # L5
worksheet.write(i,3, int(row[3]),freq_style) # L4
worksheet.write(i,4, int(row[4]),freq_style) # L3
worksheet.write(i,5, int(row[5]),freq_style) # L2
worksheet.write(i,6, int(row[6]),freq_style) # L1
worksheet.write(i,7, '*' ,freq_style) # N
worksheet.write(i,8, int(row[7]),freq_style) # R1
worksheet.write(i,9, int(row[8]),freq_style) # R2
worksheet.write(i,10, int(row[9]),freq_style) # R3
worksheet.write(i,11, int(row[10]),freq_style) # R4
worksheet.write(i,12, int(row[11]),freq_style) # R5
worksheet.write(i,13, int(row[12]),freq_style) # LEFT
worksheet.write(i,14, int(row[13]),freq_style) # RIGHT
worksheet.write(i,15, int(row[14]),freq_style) # TOTAL
worksheet.write(i,16, float(row[15]),freq_style) # ASSOCIATION
# close
workbook.close()
return True
def plot_colldist(self,word,**kwargs):
results = self.df.loc[self.df['WORD'] == word].values.tolist()[0]
if len(results) != 0:
title = kwargs.get('title','Distribution of "' + word + '"')
show_values = kwargs.get('show_values',False)
xlabel = kwargs.get('xlabel','Horizon')
ylabel = kwargs.get('ylabel','Frequency')
colldist = CollDist()
left = list(reversed(results[2:7]))
right = results[7:12]
colldist.plot_colldist(left,right,title=title,show_values=show_values,xlabel=xlabel,ylabel=ylabel)
class Combolist(object):
def __init__(self,**kwargs):
self.df = None
self.encoding = kwargs.get('encoding','utf-8')
def read_str(self,str_table):
"""Reads data table from string"""
self.df = pd.read_csv(StringIO(str_table),sep='\t')
def save_tab(self,filename):
self.df.to_csv(filename,sep='\t',index=False)
return True
def save_excel(self,filename):
# create Excel
style = xStyles() # style object
workbook = xlsxwriter.Workbook(filename,{'constant_memory': True})
worksheet = workbook.add_worksheet()
worksheet.name = 'Wordlist'
# resize columns
column_sizes = {'A':10, 'B':20,'C':15,'D':12,'E':8,'F':15,'G':12,'H':8,'I':15,'J':15,'K':10}
style.xls_columns_resize(worksheet, column_sizes)
# styles
header_style = style.xls_header(workbook)
n_style = style.xls_column_cells(workbook,'Tahoma','#404040',11,False,'center')
word_style = style.xls_column_cells(workbook,'Tahoma','#b30000',11,False,'right')
freq_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
p_style = style.xls_column_cells(workbook,'Tahoma','#000000',11,False,'center')
# set headers
worksheet.write('A1', 'N',header_style)
worksheet.write('B1', 'WORD',header_style)
worksheet.write('C1', 'POS',header_style)
worksheet.write('D1', 'FREQUENCY',header_style)
worksheet.write('E1', 'F%',header_style)
worksheet.write('F1', 'KEYNESS',header_style)
worksheet.write('G1', 'RANGE',header_style)
worksheet.write('H1', 'R%',header_style)
worksheet.write('I1', 'STOPWORD',header_style)
worksheet.write('J1', 'LEMMA',header_style)
worksheet.write('K1', 'CAT',header_style)
# set contents
i = 0
for row in self.df.itertuples(index=False):
i+=1
worksheet.write(i,0, int(row[0]),n_style) # N
worksheet.write(i,1, str(row[1]),word_style) # WORD
worksheet.write(i,2, str(row[2]),freq_style) # POS
worksheet.write(i,3, int(row[3]),p_style) # FREQUENCY
worksheet.write(i,4, float(row[4]),p_style) # F%
worksheet.write(i,5, float(row[5]),p_style) # KEYNESS
worksheet.write(i,6, int(row[6]),p_style) # RANGE
worksheet.write(i,7, float(row[7]),p_style) # R%
worksheet.write(i,8, int(row[8]),p_style) # STOPWORD
worksheet.write(i,9, str(row[9]),p_style) # LEMMA
worksheet.write(i,10, str(row[10]),p_style) # CAT
# close
workbook.close()
return True
def cat(self,custom_function):
results = []
for row in self.df.itertuples(index=False):
r = custom_function(row)
results.append(r)
self.df['CAT'] = results
| 40.500353
| 218
| 0.553902
| 7,191
| 57,308
| 4.254763
| 0.055208
| 0.11897
| 0.059812
| 0.086613
| 0.815074
| 0.75268
| 0.738332
| 0.710975
| 0.696888
| 0.677082
| 0
| 0.044648
| 0.29495
| 57,308
| 1,415
| 219
| 40.500353
| 0.712585
| 0.046032
| 0
| 0.654171
| 0
| 0
| 0.070667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.078725
| false
| 0
| 0.012184
| 0
| 0.139644
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c1ab4bc702bace34dac853d0745686e86e8bec34
| 223
|
py
|
Python
|
backend/django_app/login_app/admin.py
|
AnujKumar1995/DjangoProject
|
c89d2e852c2ea10aa270bc2a09dd3aa5a07ce402
|
[
"MIT"
] | null | null | null |
backend/django_app/login_app/admin.py
|
AnujKumar1995/DjangoProject
|
c89d2e852c2ea10aa270bc2a09dd3aa5a07ce402
|
[
"MIT"
] | null | null | null |
backend/django_app/login_app/admin.py
|
AnujKumar1995/DjangoProject
|
c89d2e852c2ea10aa270bc2a09dd3aa5a07ce402
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from user_app import models
from . import models
# Register your models here.
admin.site.register(models.User)
admin.site.register(models.LoginUser)
admin.site.register(models.UserLoginMap)
| 27.875
| 40
| 0.825112
| 32
| 223
| 5.71875
| 0.4375
| 0.147541
| 0.278689
| 0.377049
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089686
| 223
| 8
| 40
| 27.875
| 0.901478
| 0.116592
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
c1ff0e38baada2a903557801ec5e2aa5d77d3586
| 2,830
|
py
|
Python
|
llvm-2.9/tools/clang/bindings/python/tests/cindex/test_diagnostics.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | 1
|
2016-04-09T02:58:13.000Z
|
2016-04-09T02:58:13.000Z
|
llvm-2.9/tools/clang/bindings/python/tests/cindex/test_diagnostics.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | null | null | null |
llvm-2.9/tools/clang/bindings/python/tests/cindex/test_diagnostics.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | null | null | null |
from clang.cindex import *
def tu_from_source(source):
index = Index.create()
tu = index.parse('INPUT.c', unsaved_files = [('INPUT.c', source)])
return tu
# FIXME: We need support for invalid translation units to test better.
def test_diagnostic_warning():
tu = tu_from_source("""int f0() {}\n""")
assert len(tu.diagnostics) == 1
assert tu.diagnostics[0].severity == Diagnostic.Warning
assert tu.diagnostics[0].location.line == 1
assert tu.diagnostics[0].location.column == 11
assert (tu.diagnostics[0].spelling ==
'control reaches end of non-void function')
def test_diagnostic_note():
# FIXME: We aren't getting notes here for some reason.
index = Index.create()
tu = tu_from_source("""#define A x\nvoid *A = 1;\n""")
assert len(tu.diagnostics) == 1
assert tu.diagnostics[0].severity == Diagnostic.Warning
assert tu.diagnostics[0].location.line == 2
assert tu.diagnostics[0].location.column == 7
assert 'incompatible' in tu.diagnostics[0].spelling
# assert tu.diagnostics[1].severity == Diagnostic.Note
# assert tu.diagnostics[1].location.line == 1
# assert tu.diagnostics[1].location.column == 11
# assert tu.diagnostics[1].spelling == 'instantiated from'
def test_diagnostic_fixit():
index = Index.create()
tu = tu_from_source("""struct { int f0; } x = { f0 : 1 };""")
assert len(tu.diagnostics) == 1
assert tu.diagnostics[0].severity == Diagnostic.Warning
assert tu.diagnostics[0].location.line == 1
assert tu.diagnostics[0].location.column == 31
assert tu.diagnostics[0].spelling.startswith('use of GNU old-style')
assert len(tu.diagnostics[0].fixits) == 1
assert tu.diagnostics[0].fixits[0].range.start.line == 1
assert tu.diagnostics[0].fixits[0].range.start.column == 26
assert tu.diagnostics[0].fixits[0].range.end.line == 1
assert tu.diagnostics[0].fixits[0].range.end.column == 30
assert tu.diagnostics[0].fixits[0].value == '.f0 = '
def test_diagnostic_range():
index = Index.create()
tu = tu_from_source("""void f() { int i = "a" + 1; }""")
assert len(tu.diagnostics) == 1
assert tu.diagnostics[0].severity == Diagnostic.Warning
assert tu.diagnostics[0].location.line == 1
assert tu.diagnostics[0].location.column == 16
assert tu.diagnostics[0].spelling.startswith('incompatible pointer to')
assert len(tu.diagnostics[0].fixits) == 0
assert len(tu.diagnostics[0].ranges) == 1
assert tu.diagnostics[0].ranges[0].start.line == 1
assert tu.diagnostics[0].ranges[0].start.column == 20
assert tu.diagnostics[0].ranges[0].end.line == 1
assert tu.diagnostics[0].ranges[0].end.column == 27
try:
tu.diagnostics[0].ranges[1].start.line
except IndexError:
assert True
else:
assert False
| 40.428571
| 75
| 0.672085
| 399
| 2,830
| 4.719298
| 0.233083
| 0.255443
| 0.215613
| 0.254912
| 0.665428
| 0.622411
| 0.472119
| 0.406798
| 0.332448
| 0.270313
| 0
| 0.035653
| 0.177385
| 2,830
| 69
| 76
| 41.014493
| 0.773196
| 0.118021
| 0
| 0.272727
| 0
| 0
| 0.087621
| 0
| 0
| 0
| 0
| 0.014493
| 0.618182
| 1
| 0.090909
| false
| 0
| 0.018182
| 0
| 0.127273
| 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
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a9aa95cb22c1c01fe60fac788aaa05e29f55ad73
| 3,233
|
py
|
Python
|
apps/amcm/migrations/0063_auto_20220531_1254.py
|
agsneutron/asociacion_mexicana_cuarto_milla
|
4657e1f494eb572e9b40b2804e012cdfd6193c51
|
[
"MIT"
] | null | null | null |
apps/amcm/migrations/0063_auto_20220531_1254.py
|
agsneutron/asociacion_mexicana_cuarto_milla
|
4657e1f494eb572e9b40b2804e012cdfd6193c51
|
[
"MIT"
] | null | null | null |
apps/amcm/migrations/0063_auto_20220531_1254.py
|
agsneutron/asociacion_mexicana_cuarto_milla
|
4657e1f494eb572e9b40b2804e012cdfd6193c51
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2.6 on 2022-05-31 17:54
import datetime
from django.db import migrations, models
from django.utils.timezone import utc
class Migration(migrations.Migration):
dependencies = [
('amcm', '0062_auto_20220506_1027'),
]
operations = [
migrations.AlterField(
model_name='credito',
name='fecha_pago',
field=models.DateField(blank=True, default=datetime.datetime(2022, 5, 31, 17, 54, 33, 359754, tzinfo=utc), null=True, verbose_name='Fecha de pago'),
),
migrations.AlterField(
model_name='credito',
name='fecha_registro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 359741, tzinfo=utc), verbose_name='Fecha de registro'),
),
migrations.AlterField(
model_name='cuentaspago',
name='fecha_registro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 349913, tzinfo=utc), verbose_name='Fecha de Registro'),
),
migrations.AlterField(
model_name='elegible',
name='fecha_registro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 350178, tzinfo=utc), verbose_name='Fecha de registro'),
),
migrations.AlterField(
model_name='estadocuenta',
name='fecha_registro',
field=models.DateTimeField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 360038, tzinfo=utc), editable=False, verbose_name='Fecha de registro'),
),
migrations.AlterField(
model_name='estadocuentadetalle',
name='fecha_registro',
field=models.DateTimeField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 360390, tzinfo=utc), editable=False, verbose_name='Fecha de registro'),
),
migrations.AlterField(
model_name='pago',
name='estatus_credito',
field=models.CharField(choices=[('PAGADO', 'PAGADO'), ('CREDITO', 'CREDITO'), ('ANTICIPO', 'ANTICIPO'), ('PAGO TOTAL', 'PAGO TOTAL')], default='PAGADO', max_length=15, verbose_name='Estatus del Pago'),
),
migrations.AlterField(
model_name='pago',
name='fechaPago',
field=models.DateField(blank=True, default=datetime.datetime(2022, 5, 31, 17, 54, 33, 348256, tzinfo=utc), null=True, verbose_name='Fecha del Pago'),
),
migrations.AlterField(
model_name='pago',
name='fechaRegistro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 348269, tzinfo=utc), verbose_name='Fecha de Registro'),
),
migrations.AlterField(
model_name='recibo',
name='fecha_registro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 359236, tzinfo=utc), verbose_name='Fecha de registro'),
),
migrations.AlterField(
model_name='referenciaformapago',
name='fecha_registro',
field=models.DateField(default=datetime.datetime(2022, 5, 31, 17, 54, 33, 358878, tzinfo=utc), verbose_name='Fecha de registro'),
),
]
| 45.535211
| 213
| 0.617074
| 359
| 3,233
| 5.45961
| 0.220056
| 0.082653
| 0.033673
| 0.162755
| 0.745408
| 0.743367
| 0.739286
| 0.646429
| 0.601531
| 0.601531
| 0
| 0.091921
| 0.249613
| 3,233
| 70
| 214
| 46.185714
| 0.715993
| 0.013919
| 0
| 0.53125
| 1
| 0
| 0.163214
| 0.007219
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.046875
| 0
| 0.09375
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a9aabfe5d88cbd375b61ef3806ea20e91bd8b0f6
| 166
|
py
|
Python
|
toy_package/math.py
|
toy-repo-rj/toy-python-package
|
d304454dfa2d83f313e1193e4661d9045577a0f5
|
[
"MIT"
] | null | null | null |
toy_package/math.py
|
toy-repo-rj/toy-python-package
|
d304454dfa2d83f313e1193e4661d9045577a0f5
|
[
"MIT"
] | null | null | null |
toy_package/math.py
|
toy-repo-rj/toy-python-package
|
d304454dfa2d83f313e1193e4661d9045577a0f5
|
[
"MIT"
] | null | null | null |
'''
This is a math Module
Do Some thing
'''
def add(a=0, b=0):
return a + b;
def minus(a=0, b=0):
return a - b;
def multy(a=1, b=1):
return a * b;
| 9.222222
| 21
| 0.518072
| 35
| 166
| 2.457143
| 0.457143
| 0.244186
| 0.27907
| 0.093023
| 0.348837
| 0.348837
| 0.348837
| 0.348837
| 0
| 0
| 0
| 0.051724
| 0.301205
| 166
| 17
| 22
| 9.764706
| 0.689655
| 0.210843
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
a9c0ab82f327c8e616bdd7cc4081808a46566a2e
| 135
|
py
|
Python
|
Curso_em_Video_Exercicios/ex012.py
|
Cohuzer/Exercicios-do-Curso-em-Video
|
879cbb53c54ba226e12d9972bc28eadcd521fc10
|
[
"MIT"
] | null | null | null |
Curso_em_Video_Exercicios/ex012.py
|
Cohuzer/Exercicios-do-Curso-em-Video
|
879cbb53c54ba226e12d9972bc28eadcd521fc10
|
[
"MIT"
] | null | null | null |
Curso_em_Video_Exercicios/ex012.py
|
Cohuzer/Exercicios-do-Curso-em-Video
|
879cbb53c54ba226e12d9972bc28eadcd521fc10
|
[
"MIT"
] | null | null | null |
p = float(input('Qual o preço do produto? R$'))
print('O preço do produto na promoção com desconto de 5% é {:.2f}'.format(p-(p*0.05)))
| 45
| 86
| 0.659259
| 27
| 135
| 3.296296
| 0.777778
| 0.134831
| 0.179775
| 0.337079
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 0.148148
| 135
| 2
| 87
| 67.5
| 0.730435
| 0
| 0
| 0
| 0
| 0
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
a9d11c9b0323cc7ab01bbc6518cef14dbcede31c
| 81
|
py
|
Python
|
roadworkapi/models/__init__.py
|
attiladonath/roadwork-api
|
aeab86a1461d188d553fb31c5a40fb48939fc92d
|
[
"MIT"
] | null | null | null |
roadworkapi/models/__init__.py
|
attiladonath/roadwork-api
|
aeab86a1461d188d553fb31c5a40fb48939fc92d
|
[
"MIT"
] | null | null | null |
roadworkapi/models/__init__.py
|
attiladonath/roadwork-api
|
aeab86a1461d188d553fb31c5a40fb48939fc92d
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from roadworkapi.models import db_models
| 20.25
| 40
| 0.876543
| 11
| 81
| 5.909091
| 0.636364
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 81
| 3
| 41
| 27
| 0.902778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a9de53d585f6b2393608eedd530755a9d4659c7b
| 109
|
py
|
Python
|
pyrobolearn/tools/interfaces/mouse_keyboard/__init__.py
|
Pandinosaurus/pyrobolearn
|
9cd7c060723fda7d2779fa255ac998c2c82b8436
|
[
"Apache-2.0"
] | 2
|
2021-01-21T21:08:30.000Z
|
2022-03-29T16:45:49.000Z
|
pyrobolearn/tools/interfaces/mouse_keyboard/__init__.py
|
Pandinosaurus/pyrobolearn
|
9cd7c060723fda7d2779fa255ac998c2c82b8436
|
[
"Apache-2.0"
] | null | null | null |
pyrobolearn/tools/interfaces/mouse_keyboard/__init__.py
|
Pandinosaurus/pyrobolearn
|
9cd7c060723fda7d2779fa255ac998c2c82b8436
|
[
"Apache-2.0"
] | 1
|
2020-09-29T21:25:39.000Z
|
2020-09-29T21:25:39.000Z
|
# -*- coding: utf-8 -*-
# import mouse keyboard interface
from .mousekeyboard import MouseKeyboardInterface
| 21.8
| 49
| 0.761468
| 11
| 109
| 7.545455
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010638
| 0.137615
| 109
| 4
| 50
| 27.25
| 0.87234
| 0.486239
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a9fb05d34cab5a1ae74e0add52a6905d953e51a0
| 97
|
py
|
Python
|
pytorchgp/utils/__init__.py
|
kunalghosh/gaussianprocess-in-pytorch
|
78131c869a4f3d3001029af829b76a51d996d4f4
|
[
"BSD-3-Clause"
] | null | null | null |
pytorchgp/utils/__init__.py
|
kunalghosh/gaussianprocess-in-pytorch
|
78131c869a4f3d3001029af829b76a51d996d4f4
|
[
"BSD-3-Clause"
] | null | null | null |
pytorchgp/utils/__init__.py
|
kunalghosh/gaussianprocess-in-pytorch
|
78131c869a4f3d3001029af829b76a51d996d4f4
|
[
"BSD-3-Clause"
] | null | null | null |
from .decorators import castargs_pytorch_to_numpy
from .eye import eye
from .params import Param
| 24.25
| 49
| 0.845361
| 15
| 97
| 5.266667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123711
| 97
| 3
| 50
| 32.333333
| 0.929412
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e7221865bc80cef6e9e2555783db34e1417b7be1
| 84
|
py
|
Python
|
tplink/devices/__init__.py
|
Aprelius/tplink-smart-devices
|
85caf8d2a7ca04a7ea9c609a3706e069450fbf6c
|
[
"MIT"
] | null | null | null |
tplink/devices/__init__.py
|
Aprelius/tplink-smart-devices
|
85caf8d2a7ca04a7ea9c609a3706e069450fbf6c
|
[
"MIT"
] | null | null | null |
tplink/devices/__init__.py
|
Aprelius/tplink-smart-devices
|
85caf8d2a7ca04a7ea9c609a3706e069450fbf6c
|
[
"MIT"
] | null | null | null |
from .bulb import Bulb
from .device import Device, DeviceType
from .plug import Plug
| 28
| 38
| 0.809524
| 13
| 84
| 5.230769
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 84
| 3
| 39
| 28
| 0.944444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e7d3ab0cad01a9dbb6fea0e9a1c2e1b66d287b4b
| 41
|
py
|
Python
|
tests/__init__.py
|
pymandes/django-msal
|
59bfc7e6993a7d60efe663afdd2ba55580bec7be
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
pymandes/django-msal
|
59bfc7e6993a7d60efe663afdd2ba55580bec7be
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
pymandes/django-msal
|
59bfc7e6993a7d60efe663afdd2ba55580bec7be
|
[
"MIT"
] | null | null | null |
"""Unit test package for django_msal."""
| 20.5
| 40
| 0.707317
| 6
| 41
| 4.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 41
| 1
| 41
| 41
| 0.777778
| 0.829268
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
99c10f8f11747189d2d00a9f0d93c50230acc720
| 150
|
py
|
Python
|
tests/dependencies_tests.py
|
SergeyKonnov/walbot
|
28923523299bd18b47074915c8209833683d0b8c
|
[
"MIT"
] | 2
|
2021-01-14T22:17:59.000Z
|
2021-12-31T11:18:21.000Z
|
tests/dependencies_tests.py
|
SergeyKonnov/walbot
|
28923523299bd18b47074915c8209833683d0b8c
|
[
"MIT"
] | 221
|
2020-01-31T15:04:48.000Z
|
2022-01-15T12:03:13.000Z
|
tests/dependencies_tests.py
|
aobolensk/walbot
|
f11ee6971b232cdb177284933528730b70ec67ca
|
[
"MIT"
] | 1
|
2019-11-26T18:18:46.000Z
|
2019-11-26T18:18:46.000Z
|
from src.info import BotInfo
def test_query_dependencies_info():
bot_info = BotInfo()
assert bot_info.query_dependencies_info() is not None
| 21.428571
| 57
| 0.773333
| 22
| 150
| 4.954545
| 0.636364
| 0.311927
| 0.385321
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 150
| 6
| 58
| 25
| 0.865079
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
99c531294e892d07aed1e6d374f20059f3768498
| 3,315
|
py
|
Python
|
content/grammars/antlr/PropFormulaParserLexer.py
|
marcofavorito/tl-grammars
|
7d9a17267fbf525d9a6a1beb92a46f05cf652db6
|
[
"CC-BY-4.0",
"CC0-1.0"
] | 2
|
2020-12-29T10:47:43.000Z
|
2021-07-07T13:36:47.000Z
|
content/grammars/antlr/PropFormulaParserLexer.py
|
marcofavorito/tl-grammars
|
7d9a17267fbf525d9a6a1beb92a46f05cf652db6
|
[
"CC-BY-4.0",
"CC0-1.0"
] | 6
|
2020-12-29T17:58:42.000Z
|
2021-08-06T07:42:00.000Z
|
content/grammars/antlr/PropFormulaParserLexer.py
|
marcofavorito/tl-grammars
|
7d9a17267fbf525d9a6a1beb92a46f05cf652db6
|
[
"CC-BY-4.0",
"CC0-1.0"
] | null | null | null |
# Generated from PropFormulaParser.g4 by ANTLR 4.9
from antlr4 import *
from io import StringIO
from typing.io import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\16")
buf.write("M\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7\t\7")
buf.write("\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r\3\2")
buf.write("\3\2\7\2\36\n\2\f\2\16\2!\13\2\3\3\3\3\3\3\3\3\3\3\3\4")
buf.write("\3\4\3\4\3\4\3\4\3\4\3\5\3\5\3\5\3\5\3\6\3\6\3\6\3\7\3")
buf.write("\7\3\b\3\b\3\b\5\b:\n\b\3\t\3\t\3\t\5\t?\n\t\3\n\3\n\3")
buf.write("\13\3\13\3\f\3\f\3\r\6\rH\n\r\r\r\16\rI\3\r\3\r\2\2\16")
buf.write("\3\3\5\4\7\5\t\6\13\7\r\b\17\t\21\n\23\13\25\f\27\r\31")
buf.write("\16\3\2\5\5\2C\\aac|\6\2\62;C\\aac|\5\2\13\f\17\17\"\"")
buf.write("\2P\2\3\3\2\2\2\2\5\3\2\2\2\2\7\3\2\2\2\2\t\3\2\2\2\2")
buf.write("\13\3\2\2\2\2\r\3\2\2\2\2\17\3\2\2\2\2\21\3\2\2\2\2\23")
buf.write("\3\2\2\2\2\25\3\2\2\2\2\27\3\2\2\2\2\31\3\2\2\2\3\33\3")
buf.write("\2\2\2\5\"\3\2\2\2\7\'\3\2\2\2\t-\3\2\2\2\13\61\3\2\2")
buf.write("\2\r\64\3\2\2\2\179\3\2\2\2\21>\3\2\2\2\23@\3\2\2\2\25")
buf.write("B\3\2\2\2\27D\3\2\2\2\31G\3\2\2\2\33\37\t\2\2\2\34\36")
buf.write("\t\3\2\2\35\34\3\2\2\2\36!\3\2\2\2\37\35\3\2\2\2\37 \3")
buf.write("\2\2\2 \4\3\2\2\2!\37\3\2\2\2\"#\7v\2\2#$\7t\2\2$%\7w")
buf.write("\2\2%&\7g\2\2&\6\3\2\2\2\'(\7h\2\2()\7c\2\2)*\7n\2\2*")
buf.write("+\7u\2\2+,\7g\2\2,\b\3\2\2\2-.\7>\2\2./\7/\2\2/\60\7@")
buf.write("\2\2\60\n\3\2\2\2\61\62\7/\2\2\62\63\7@\2\2\63\f\3\2\2")
buf.write("\2\64\65\7`\2\2\65\16\3\2\2\2\66\67\7~\2\2\67:\7~\2\2")
buf.write("8:\7~\2\29\66\3\2\2\298\3\2\2\2:\20\3\2\2\2;<\7(\2\2<")
buf.write("?\7(\2\2=?\7(\2\2>;\3\2\2\2>=\3\2\2\2?\22\3\2\2\2@A\7")
buf.write("#\2\2A\24\3\2\2\2BC\7*\2\2C\26\3\2\2\2DE\7+\2\2E\30\3")
buf.write("\2\2\2FH\t\4\2\2GF\3\2\2\2HI\3\2\2\2IG\3\2\2\2IJ\3\2\2")
buf.write("\2JK\3\2\2\2KL\b\r\2\2L\32\3\2\2\2\7\2\379>I\3\b\2\2")
return buf.getvalue()
class PropFormulaParserLexer(Lexer):
atn = ATNDeserializer().deserialize(serializedATN())
decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ]
NAME = 1
TRUE = 2
FALSE = 3
DOUBLEIMPLY = 4
IMPLY = 5
XOR = 6
OR = 7
AND = 8
NOT = 9
LPAREN = 10
RPAREN = 11
WS = 12
channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ]
modeNames = [ "DEFAULT_MODE" ]
literalNames = [ "<INVALID>",
]
symbolicNames = [ "<INVALID>",
"NAME", "TRUE", "FALSE", "DOUBLEIMPLY", "IMPLY", "XOR", "OR",
"AND", "NOT", "LPAREN", "RPAREN", "WS" ]
ruleNames = [ "NAME", "TRUE", "FALSE", "DOUBLEIMPLY", "IMPLY", "XOR",
"OR", "AND", "NOT", "LPAREN", "RPAREN", "WS" ]
grammarFileName = "PropFormulaParser.g4"
def __init__(self, input=None, output:TextIO = sys.stdout):
super().__init__(input, output)
self.checkVersion("4.9")
self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache())
self._actions = None
self._predicates = None
| 40.426829
| 103
| 0.528808
| 782
| 3,315
| 2.223785
| 0.203325
| 0.143761
| 0.087982
| 0.085106
| 0.227717
| 0.166187
| 0.119609
| 0.106958
| 0.074756
| 0.06843
| 0
| 0.227273
| 0.177074
| 3,315
| 81
| 104
| 40.925926
| 0.410191
| 0.01448
| 0
| 0
| 1
| 0.296875
| 0.462764
| 0.405762
| 0
| 0
| 0
| 0
| 0
| 1
| 0.03125
| false
| 0
| 0.0625
| 0
| 0.4375
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
8204f8d94c5625381d0a0d9fde3fb83e34bcde66
| 233
|
py
|
Python
|
casepro/orgs_ext/urls.py
|
rapidpro/ureport-partners
|
16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd
|
[
"BSD-3-Clause"
] | 21
|
2015-07-21T15:57:49.000Z
|
2021-11-04T18:26:35.000Z
|
casepro/orgs_ext/urls.py
|
rapidpro/ureport-partners
|
16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd
|
[
"BSD-3-Clause"
] | 357
|
2015-05-22T07:26:45.000Z
|
2022-03-12T01:08:28.000Z
|
casepro/orgs_ext/urls.py
|
rapidpro/ureport-partners
|
16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd
|
[
"BSD-3-Clause"
] | 24
|
2015-05-28T12:30:25.000Z
|
2021-11-19T01:57:38.000Z
|
from dash.orgs.views import OrgBackendCRUDL
from .views import OrgExtCRUDL, TaskExtCRUDL
urlpatterns = OrgExtCRUDL().as_urlpatterns()
urlpatterns += TaskExtCRUDL().as_urlpatterns()
urlpatterns += OrgBackendCRUDL().as_urlpatterns()
| 29.125
| 49
| 0.811159
| 23
| 233
| 8.086957
| 0.434783
| 0.209677
| 0.258065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085837
| 233
| 7
| 50
| 33.285714
| 0.873239
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 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
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
820c3788b8fcea76129f4fda5d1e2aff430ebdc4
| 68
|
py
|
Python
|
pytorch/demo.py
|
FengJunJian/tutorials_for_demo
|
36ab749241193aeb17dec14d0c7503d858c84f8e
|
[
"Apache-2.0"
] | null | null | null |
pytorch/demo.py
|
FengJunJian/tutorials_for_demo
|
36ab749241193aeb17dec14d0c7503d858c84f8e
|
[
"Apache-2.0"
] | null | null | null |
pytorch/demo.py
|
FengJunJian/tutorials_for_demo
|
36ab749241193aeb17dec14d0c7503d858c84f8e
|
[
"Apache-2.0"
] | null | null | null |
import torch
import torchvision
print(torch.cuda.is_available())
| 17
| 32
| 0.794118
| 9
| 68
| 5.888889
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 68
| 4
| 32
| 17
| 0.883333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
8218e4b80d3c760a8ec81ad3947603b274e46e72
| 577
|
py
|
Python
|
src/tensor_network/__init__.py
|
Shoooooon/TensorOrder
|
6a390c34f5f05a4c28bcdf5429da0582f34d749a
|
[
"MIT"
] | 14
|
2020-01-31T23:02:39.000Z
|
2021-12-26T06:00:13.000Z
|
src/tensor_network/__init__.py
|
Shoooooon/TensorOrder
|
6a390c34f5f05a4c28bcdf5429da0582f34d749a
|
[
"MIT"
] | 3
|
2020-06-27T21:11:46.000Z
|
2020-06-27T21:11:47.000Z
|
src/tensor_network/__init__.py
|
Shoooooon/TensorOrder
|
6a390c34f5f05a4c28bcdf5429da0582f34d749a
|
[
"MIT"
] | 1
|
2021-05-28T05:12:43.000Z
|
2021-05-28T05:12:43.000Z
|
from tensor_network.tensor_network import TensorNetwork
from tensor_network.tensor import Tensor
from tensor_network.tensor_network_constructions import ALL_CONSTRUCTIONS
from tensor_network.slicers import ALL_SLICERS
import tensor_network.tensor_apis.numpy_apis as numpy_apis
import tensor_network.tensor_apis.tensorflow_apis as tensorflow_apis
import tensor_network.tensor_apis.jax_apis as jax_apis
from tensor_network.tensor_apis.base_api import OutOfMemoryError
ALL_APIS = {
**numpy_apis.NUMPY_APIS,
**jax_apis.JAX_APIS,
**tensorflow_apis.TENSORFLOW_APIS,
}
| 33.941176
| 73
| 0.859619
| 82
| 577
| 5.670732
| 0.195122
| 0.27957
| 0.286022
| 0.197849
| 0.333333
| 0.141935
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093588
| 577
| 16
| 74
| 36.0625
| 0.889101
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.615385
| 0
| 0.615385
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
4130b4331bfe1acbb982c758781a65435b84ab3a
| 921
|
py
|
Python
|
tests/conftest.py
|
hmiladhia/nbmanips
|
967dd680d6c5ea743d5b775da6d99b4e5706ae68
|
[
"MIT"
] | 1
|
2021-05-26T09:49:05.000Z
|
2021-05-26T09:49:05.000Z
|
tests/conftest.py
|
hmiladhia/nbmanips
|
8a075ca0b3da20cf9aadefe2221443b26dfaee57
|
[
"MIT"
] | 3
|
2022-02-27T16:32:40.000Z
|
2022-02-28T18:10:13.000Z
|
tests/conftest.py
|
hmiladhia/nbmanips
|
967dd680d6c5ea743d5b775da6d99b4e5706ae68
|
[
"MIT"
] | null | null | null |
from pathlib import Path
import pytest
from nbmanips import Notebook
@pytest.fixture(scope='session')
def test_files():
return Path(__file__).parent / 'test_files'
@pytest.fixture(scope='function')
def nb1_0(test_files):
return Notebook.read_ipynb(test_files / 'nb1.ipynb')
@pytest.fixture(scope='function')
def nb3_0(test_files):
return Notebook.read_ipynb(test_files / 'nb3.ipynb')
@pytest.fixture(scope='session')
def nb1(test_files):
return Notebook.read_ipynb(test_files / 'nb1.ipynb')
@pytest.fixture(scope='session')
def nb2(test_files):
return Notebook.read_ipynb(test_files / 'nb2.ipynb')
@pytest.fixture(scope='session')
def nb3(test_files):
"""Notebook with images"""
return Notebook.read_ipynb(test_files / 'nb3.ipynb')
@pytest.fixture(scope='session')
def nb5(test_files):
"""Notebook in version 4.5"""
return Notebook.read_ipynb(test_files / 'nb5.ipynb')
| 21.418605
| 56
| 0.729642
| 129
| 921
| 5.007752
| 0.232558
| 0.195046
| 0.195046
| 0.213622
| 0.719814
| 0.614551
| 0.498452
| 0.498452
| 0.434985
| 0.417957
| 0
| 0.019925
| 0.128122
| 921
| 42
| 57
| 21.928571
| 0.784558
| 0.047774
| 0
| 0.458333
| 0
| 0
| 0.132794
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.291667
| false
| 0
| 0.125
| 0.208333
| 0.708333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
414e4ccedf600db1aa61e7572c23de5f87fa041b
| 144
|
py
|
Python
|
pykinect_azure/k4arecord/__init__.py
|
necoxt/pyKinectAzure
|
1e1fa845bd8299b7534a647f12ca0b49c5bc57d4
|
[
"MIT"
] | 170
|
2020-06-29T05:37:49.000Z
|
2022-03-30T01:09:16.000Z
|
pykinect_azure/k4arecord/__init__.py
|
necoxt/pyKinectAzure
|
1e1fa845bd8299b7534a647f12ca0b49c5bc57d4
|
[
"MIT"
] | 49
|
2020-06-29T06:30:58.000Z
|
2022-03-31T04:04:35.000Z
|
pykinect_azure/k4arecord/__init__.py
|
necoxt/pyKinectAzure
|
1e1fa845bd8299b7534a647f12ca0b49c5bc57d4
|
[
"MIT"
] | 52
|
2020-06-29T11:15:03.000Z
|
2022-03-24T08:53:24.000Z
|
from .datablock import Datablock
from .record import Record
from .record_configuration import RecordConfiguration
from .playback import Playback
| 36
| 53
| 0.868056
| 17
| 144
| 7.294118
| 0.411765
| 0.16129
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104167
| 144
| 4
| 54
| 36
| 0.96124
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
415bab8a0f77c38ac3ad84f6a9d7680f22afdab1
| 117
|
py
|
Python
|
image_manipulations/__init__.py
|
idkidkaaa/streamlit-image-editor
|
4584c3ebbe272673a3086409ef093e9d3df9e485
|
[
"MIT"
] | null | null | null |
image_manipulations/__init__.py
|
idkidkaaa/streamlit-image-editor
|
4584c3ebbe272673a3086409ef093e9d3df9e485
|
[
"MIT"
] | null | null | null |
image_manipulations/__init__.py
|
idkidkaaa/streamlit-image-editor
|
4584c3ebbe272673a3086409ef093e9d3df9e485
|
[
"MIT"
] | null | null | null |
from .adjust_hsv import adjust_hsv
from .modify_rgb import modify_rgb
from .crop import crop
from .noise import noise
| 29.25
| 34
| 0.837607
| 20
| 117
| 4.7
| 0.4
| 0.191489
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.128205
| 117
| 4
| 35
| 29.25
| 0.921569
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
4174bdd97e081b228f44cd2ab273fca986c7f3b5
| 61
|
py
|
Python
|
pyllusion/RodFrame/__init__.py
|
RealityBending/Pyllusion
|
daca536b439fa39348ed9822e5db3b24ec0bbd23
|
[
"MIT"
] | 17
|
2020-09-30T07:00:57.000Z
|
2022-03-01T19:01:27.000Z
|
pyllusion/RodFrame/__init__.py
|
RealityBending/Pyllusion
|
daca536b439fa39348ed9822e5db3b24ec0bbd23
|
[
"MIT"
] | 11
|
2020-10-05T09:43:26.000Z
|
2022-03-16T07:08:04.000Z
|
pyllusion/RodFrame/__init__.py
|
RealityBending/Pyllusion
|
daca536b439fa39348ed9822e5db3b24ec0bbd23
|
[
"MIT"
] | 3
|
2021-01-15T07:31:09.000Z
|
2022-02-11T06:46:23.000Z
|
"""
Pyllusion submodule.
"""
from .RodFrame import RodFrame
| 10.166667
| 30
| 0.721311
| 6
| 61
| 7.333333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147541
| 61
| 5
| 31
| 12.2
| 0.846154
| 0.327869
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
68d687f17b734b93404c9e587d5caf6d01c35b01
| 24
|
py
|
Python
|
pytoan/pyplot/__init__.py
|
toandaominh1997/pytoan
|
e0abf302960d44647ae8a4a5b9cc9c12e682e208
|
[
"MIT"
] | 6
|
2019-06-10T16:04:23.000Z
|
2020-11-28T11:26:19.000Z
|
pytoan/pyplot/__init__.py
|
toandaominh1997/pytoan
|
e0abf302960d44647ae8a4a5b9cc9c12e682e208
|
[
"MIT"
] | null | null | null |
pytoan/pyplot/__init__.py
|
toandaominh1997/pytoan
|
e0abf302960d44647ae8a4a5b9cc9c12e682e208
|
[
"MIT"
] | 1
|
2020-01-04T11:36:56.000Z
|
2020-01-04T11:36:56.000Z
|
from .show import imshow
| 24
| 24
| 0.833333
| 4
| 24
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 24
| 1
| 24
| 24
| 0.952381
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
68f87d8c803f6680a5e50678f658cb7f88f7eba9
| 134
|
py
|
Python
|
tests/test_math.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | 1
|
2021-01-09T15:21:04.000Z
|
2021-01-09T15:21:04.000Z
|
tests/test_math.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | 10
|
2020-12-18T02:35:18.000Z
|
2021-05-11T09:21:33.000Z
|
tests/test_math.py
|
conf8o/speelysis
|
0a584fb1b5386f7b913cefd1bc4dcf0e776cae42
|
[
"MIT"
] | null | null | null |
import speelysis
def test_time_axis():
assert speelysis.time_axis
def test_sin_wave():
assert speelysis.sin_wave
| 13.4
| 31
| 0.708955
| 18
| 134
| 4.944444
| 0.5
| 0.157303
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.231343
| 134
| 9
| 32
| 14.888889
| 0.864078
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.4
| true
| 0
| 0.2
| 0
| 0.6
| 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
| 0
| 0
| 1
| 0
|
0
| 5
|
ec3432db2d0f9493f782929f0a8f764fabc21880
| 79
|
py
|
Python
|
bf_cython.py
|
physicodes/password-cracking
|
29c14ed393ab21be9aaea185d72abbfa71312b8f
|
[
"MIT"
] | null | null | null |
bf_cython.py
|
physicodes/password-cracking
|
29c14ed393ab21be9aaea185d72abbfa71312b8f
|
[
"MIT"
] | null | null | null |
bf_cython.py
|
physicodes/password-cracking
|
29c14ed393ab21be9aaea185d72abbfa71312b8f
|
[
"MIT"
] | null | null | null |
import pyximport; pyximport.install()
import bf_ctools
print("Hello, world!")
| 15.8
| 37
| 0.772152
| 10
| 79
| 6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101266
| 79
| 4
| 38
| 19.75
| 0.84507
| 0
| 0
| 0
| 0
| 0
| 0.164557
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6b5954657df60624d64e66fd75919d68112011af
| 42
|
py
|
Python
|
mysite/plans/temp.py
|
tenderghost/FlyPersonalAssistant
|
f9b379a42c32ff1ea73803d25cce7be04f8ec497
|
[
"MIT"
] | 1
|
2018-01-07T16:45:31.000Z
|
2018-01-07T16:45:31.000Z
|
mysite/plans/temp.py
|
tenderghost/FlyPersonalAssistant
|
f9b379a42c32ff1ea73803d25cce7be04f8ec497
|
[
"MIT"
] | null | null | null |
mysite/plans/temp.py
|
tenderghost/FlyPersonalAssistant
|
f9b379a42c32ff1ea73803d25cce7be04f8ec497
|
[
"MIT"
] | null | null | null |
pi = 3.14915926
n = round(pi, 2)
print(n)
| 10.5
| 16
| 0.619048
| 9
| 42
| 2.888889
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.294118
| 0.190476
| 42
| 4
| 17
| 10.5
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6b5b56f03fe4b1193915cd7350f7dc9b32611ea4
| 171
|
py
|
Python
|
y/datatypes.py
|
cartercarlson/ypricemagic
|
f17fec155db7fb44ee624cd6e75193f17c6238cf
|
[
"MIT"
] | 1
|
2022-03-28T16:07:07.000Z
|
2022-03-28T16:07:07.000Z
|
y/datatypes.py
|
cartercarlson/ypricemagic
|
f17fec155db7fb44ee624cd6e75193f17c6238cf
|
[
"MIT"
] | null | null | null |
y/datatypes.py
|
cartercarlson/ypricemagic
|
f17fec155db7fb44ee624cd6e75193f17c6238cf
|
[
"MIT"
] | null | null | null |
class UsdValue(float):
def __init__(self, v) -> None:
super().__init__()
class UsdPrice(float):
def __init__(self, v) -> None:
super().__init__()
| 21.375
| 34
| 0.596491
| 20
| 171
| 4.3
| 0.5
| 0.186047
| 0.27907
| 0.372093
| 0.697674
| 0.697674
| 0.697674
| 0.697674
| 0
| 0
| 0
| 0
| 0.239766
| 171
| 8
| 35
| 21.375
| 0.661538
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6bb44538f3097e128912cc310e346615c2278082
| 32
|
py
|
Python
|
main.py
|
YCBDM/ApiTestEngine
|
cebc5f6596ddc56d34809602453fb14237cea2c3
|
[
"MIT"
] | 6
|
2017-08-11T08:23:50.000Z
|
2021-06-23T12:24:50.000Z
|
main.py
|
zengjiaqi/ApiTestEngine
|
cebc5f6596ddc56d34809602453fb14237cea2c3
|
[
"MIT"
] | null | null | null |
main.py
|
zengjiaqi/ApiTestEngine
|
cebc5f6596ddc56d34809602453fb14237cea2c3
|
[
"MIT"
] | 3
|
2020-09-17T08:58:07.000Z
|
2020-10-21T08:52:10.000Z
|
from ate.main import main
main()
| 16
| 25
| 0.78125
| 6
| 32
| 4.166667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 2
| 26
| 16
| 0.892857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
6bbeb697758b0b48f2fd9d7957f0e874f4051f43
| 1,256
|
py
|
Python
|
app/models/acl.py
|
witlov/excel-to-database
|
6a0b7ef820bd8c34dafc69c1e47cb1cfe9c52365
|
[
"MIT"
] | 2
|
2019-03-15T16:22:01.000Z
|
2020-01-17T16:21:29.000Z
|
app/models/acl.py
|
witlov/excel-to-database
|
6a0b7ef820bd8c34dafc69c1e47cb1cfe9c52365
|
[
"MIT"
] | 3
|
2020-06-10T09:40:57.000Z
|
2021-06-22T14:17:54.000Z
|
app/models/acl.py
|
witlov/excel-to-database
|
6a0b7ef820bd8c34dafc69c1e47cb1cfe9c52365
|
[
"MIT"
] | 4
|
2019-03-15T16:21:11.000Z
|
2020-07-31T08:21:30.000Z
|
from datetime import datetime
from app import login
from hashlib import md5
from random import random
from flask_login import UserMixin
from werkzeug.security import generate_password_hash, check_password_hash
import json
@login.user_loader
def load_user(id):
return User.get(id)
class User(UserMixin):
@staticmethod
def get(username):
with open('auth/auth.json', 'r') as fp:
retrieved = json.load(fp).get(username, None)
if retrieved:
return User(username=username, **retrieved)
return None
def __init__(self, username, password_hash, password_salt, path=''):
self.id = username
self.username = username
self.password_salt = password_salt
self.password_hash = password_hash
self.path = path
def set_password(self, password):
self.password_hash = generate_password_hash(self.password_salt + password)
def check_password(self, password):
return check_password_hash(self.password_hash, self.password_salt + password)
@staticmethod
def get_user_password_hash(salt, password):
return generate_password_hash(salt + password)
def __repr__(self):
return '<User {}>'.format(self.username)
| 30.634146
| 85
| 0.696656
| 156
| 1,256
| 5.378205
| 0.275641
| 0.15733
| 0.076281
| 0.085816
| 0.085816
| 0.085816
| 0
| 0
| 0
| 0
| 0
| 0.001022
| 0.221338
| 1,256
| 40
| 86
| 31.4
| 0.856851
| 0
| 0
| 0.060606
| 1
| 0
| 0.019108
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.212121
| false
| 0.30303
| 0.212121
| 0.121212
| 0.636364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 5
|
6bc68c1190f80be61335690c72f73e3205234bc9
| 94
|
py
|
Python
|
elastic_app/admin.py
|
vrcgal/endnote_enter
|
97f301cc555813b190a67260fee2aa21113d3280
|
[
"MIT"
] | null | null | null |
elastic_app/admin.py
|
vrcgal/endnote_enter
|
97f301cc555813b190a67260fee2aa21113d3280
|
[
"MIT"
] | 3
|
2020-06-05T17:38:00.000Z
|
2021-06-01T21:59:32.000Z
|
elastic_app/admin.py
|
vrcgal/endnote_enter
|
97f301cc555813b190a67260fee2aa21113d3280
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Citation
admin.site.register(Citation)
| 15.666667
| 32
| 0.819149
| 13
| 94
| 5.923077
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117021
| 94
| 5
| 33
| 18.8
| 0.927711
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6be0a958c1abc5b1989604ead48997c3a637ec71
| 79
|
py
|
Python
|
keras2onnx/ktf2onnx/tf2onnx/version.py
|
ashaazami/keras-onnx
|
0e66937886c8256bdde366b9ac5dc67b68c9f56e
|
[
"MIT"
] | 1
|
2020-03-02T10:35:45.000Z
|
2020-03-02T10:35:45.000Z
|
keras2onnx/ktf2onnx/tf2onnx/version.py
|
souptc/keras-onnx
|
c08d52bf4d4ec2bba69ec4ffd2ea14f47fecb1f5
|
[
"MIT"
] | null | null | null |
keras2onnx/ktf2onnx/tf2onnx/version.py
|
souptc/keras-onnx
|
c08d52bf4d4ec2bba69ec4ffd2ea14f47fecb1f5
|
[
"MIT"
] | null | null | null |
version = '1.5.3'
git_version = '7b598d55547c33d114db49f2a50920da7a672935'
| 19.75
| 57
| 0.772152
| 7
| 79
| 8.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.463768
| 0.126582
| 79
| 3
| 58
| 26.333333
| 0.405797
| 0
| 0
| 0
| 0
| 0
| 0.6
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6be2b1a262bc1e9f9e403c8292ec3427532262e2
| 2,365
|
py
|
Python
|
python/fbs/LqrGainMatrix.py
|
oliverlee/biketest
|
074b0b03455021c52a13efe583b1816bc5daad4e
|
[
"BSD-2-Clause"
] | 3
|
2016-12-14T01:22:27.000Z
|
2020-04-07T05:15:04.000Z
|
python/fbs/LqrGainMatrix.py
|
oliverlee/biketest
|
074b0b03455021c52a13efe583b1816bc5daad4e
|
[
"BSD-2-Clause"
] | 7
|
2017-01-12T15:20:57.000Z
|
2017-07-02T16:09:37.000Z
|
python/fbs/LqrGainMatrix.py
|
oliverlee/biketest
|
074b0b03455021c52a13efe583b1816bc5daad4e
|
[
"BSD-2-Clause"
] | 1
|
2020-04-07T05:15:05.000Z
|
2020-04-07T05:15:05.000Z
|
# automatically generated, do not modify
# namespace: fbs
import flatbuffers
class LqrGainMatrix(object):
__slots__ = ['_tab']
# LqrGainMatrix
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
# LqrGainMatrix
def K00(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(0))
# LqrGainMatrix
def K01(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(8))
# LqrGainMatrix
def K02(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(16))
# LqrGainMatrix
def K03(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(24))
# LqrGainMatrix
def K04(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(32))
# LqrGainMatrix
def K10(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(40))
# LqrGainMatrix
def K11(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(48))
# LqrGainMatrix
def K12(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(56))
# LqrGainMatrix
def K13(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(64))
# LqrGainMatrix
def K14(self): return self._tab.Get(flatbuffers.number_types.Float64Flags, self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(72))
def CreateLqrGainMatrix(builder, k00, k01, k02, k03, k04, k10, k11, k12, k13, k14):
builder.Prep(8, 80)
builder.PrependFloat64(k14)
builder.PrependFloat64(k13)
builder.PrependFloat64(k12)
builder.PrependFloat64(k11)
builder.PrependFloat64(k10)
builder.PrependFloat64(k04)
builder.PrependFloat64(k03)
builder.PrependFloat64(k02)
builder.PrependFloat64(k01)
builder.PrependFloat64(k00)
return builder.Offset()
| 49.270833
| 146
| 0.762791
| 297
| 2,365
| 5.885522
| 0.181818
| 0.084096
| 0.251716
| 0.097254
| 0.600687
| 0.600687
| 0.600687
| 0.600687
| 0.600687
| 0.600687
| 0
| 0.058482
| 0.125159
| 2,365
| 47
| 147
| 50.319149
| 0.78637
| 0.087526
| 0
| 0
| 1
| 0
| 0.001866
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0
| 0.035714
| 0.357143
| 0.571429
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
6bfebdcae153a13ce5cbf76da06ddcf0e415d3c0
| 6,273
|
py
|
Python
|
ozone-framework-python-server/appconf/handlers.py
|
aamduka/ozone
|
3fdbf232f5ea70661204a632e45310ca9d374973
|
[
"Apache-2.0"
] | 6
|
2020-02-21T22:06:31.000Z
|
2020-12-08T10:48:07.000Z
|
ozone-framework-python-server/appconf/handlers.py
|
aamduka/ozone
|
3fdbf232f5ea70661204a632e45310ca9d374973
|
[
"Apache-2.0"
] | 12
|
2019-12-26T17:38:40.000Z
|
2022-02-10T14:15:55.000Z
|
ozone-framework-python-server/appconf/handlers.py
|
aamduka/ozone
|
3fdbf232f5ea70661204a632e45310ca9d374973
|
[
"Apache-2.0"
] | 4
|
2019-09-20T01:20:33.000Z
|
2020-09-05T01:15:51.000Z
|
from appconf.models import ApplicationConfiguration
from django.contrib.auth.signals import user_logged_in, user_logged_out, user_login_failed
from django.dispatch import receiver
import logging
from django.conf import settings
from config.owf_utils.log_middleware import session_expired
logger = logging.getLogger('owf.enable.cef.object.access.logging')
def get_client_ip(request):
x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')
if x_forwarded_for:
ip = x_forwarded_for.split(',')[0]
else:
ip = request.META.get('REMOTE_ADDR')
return str(ip)
@receiver(user_logged_in)
def on_login(sender, user, request, **kwargs):
obj_access_control = ApplicationConfiguration.objects.get(title='Enable CEF Object Access Logging',
group_name='AUDITING',
code='owf.enable.cef.object.access.logging').value
if (obj_access_control.startswith('t')) or (obj_access_control.startswith('T')):
sec_level = ApplicationConfiguration.objects.get(title='Security Level',
code='owf.security.level').value
if sec_level.startswith('D') or sec_level.startswith('d'):
logger.debug(f'IP: {get_client_ip(request)} User: {request.user.username} [USER LOGIN]: LOGIN SUCCESS - '
f'ACCESS GRANTED USER [{request.user.username}] with EMAIL [{request.user.email}]')
elif sec_level.startswith('I') or sec_level.startswith('i'):
logger.info(f'IP: {get_client_ip(request)} User: {request.user.username} [USER LOGIN]: LOGIN SUCCESS - '
f'ACCESS GRANTED USER [{request.user.username}] with EMAIL [{request.user.email}] ')
else:
pass
# TODO: SESSION EXPIRE TAG
@receiver(user_logged_out)
def on_logout(sender, user, request, **kwargs):
obj_access_control = ApplicationConfiguration.objects.get(title='Enable CEF Object Access Logging',
group_name='AUDITING',
code='owf.enable.cef.object.access.logging').value
if (obj_access_control.startswith('t')) or (obj_access_control.startswith('T')):
sec_level = ApplicationConfiguration.objects.get(title='Security Level',
code='owf.security.level').value
if sec_level.startswith('D') or sec_level.startswith('d'):
try:
return logger.debug(
f'IP: {get_client_ip(request)} '
f'SessionID: {request.session.session_key} '
f'USER: {request.user.username} '
f'[USER LOGOUT] with EMAIL {user.email} with LAST LOGIN DATE [ {user.last_login} ]'
)
except SystemError:
return logger.debug(
f'[USER LOGOUT]'
)
elif sec_level.startswith('I') or sec_level.startswith('i'):
return logger.info(
f'IP: {get_client_ip(request)} SessionID: {request.session.session_key} '
f'USER: {request.user.username} '
f'[USER LOGOUT]'
)
else:
pass
@receiver(user_login_failed)
def on_login_failed(sender, credentials, request, **kwargs):
obj_access_control = ApplicationConfiguration.objects.get(title='Enable CEF Object Access Logging',
group_name='AUDITING',
code='owf.enable.cef.object.access.logging').value
if (obj_access_control.startswith('t')) or (obj_access_control.startswith('T')):
sec_level = ApplicationConfiguration.objects.get(title='Security Level',
code='owf.security.level').value
if sec_level.startswith('D') or sec_level.startswith('d'):
return logger.debug(f'IP: {get_client_ip(request)} '
f'USER: {credentials["username"]}'
f' [USER LOGIN]: ACCESS DENIED with FAILURE MSG: [Login for {credentials["username"]} '
f'attempted with authenticated credentials')
elif sec_level.startswith('I') or sec_level.startswith('i'):
return logger.info(f'IP: {get_client_ip(request)} '
f'USER: {credentials["username"]}'
f'[USER LOGIN]: ACCESS DENIED with FAILURE MSG: [Login for {credentials["username"]}] '
f'attempted with authenticated credentials')
else:
pass
@receiver(session_expired)
def on_done(sender, user, request, **kwargs):
obj_access_control = ApplicationConfiguration.objects.get(title='Enable CEF Object Access Logging',
group_name='AUDITING',
code='owf.enable.cef.object.access.logging').value
if (obj_access_control.startswith('t')) or (obj_access_control.startswith('T')):
sec_level = ApplicationConfiguration.objects.get(title='Security Level',
code='owf.security.level').value
if sec_level.startswith('D') or sec_level.startswith('d'):
return logger.debug(f'IP: {get_client_ip(request)} '
f'SessionID: {request.session.session_key} '
f'USER: {user.username} [USER SESSION TIMEOUT], '
f'with ID [{user.id}], with EMAIL [{user.email}], '
f'with LAST LOGIN DATE [{user.last_login}] '
)
elif sec_level.startswith('I') or sec_level.startswith('i'):
return logger.info(f'IP: {get_client_ip(request)} '
f'SessionID: {request.session.session_key} '
f'USER: {user.username} [USER SESSION TIMEOUT]'
)
else:
pass
| 55.513274
| 119
| 0.555556
| 655
| 6,273
| 5.163359
| 0.154198
| 0.047309
| 0.085157
| 0.055884
| 0.790656
| 0.790656
| 0.78149
| 0.763749
| 0.75754
| 0.75754
| 0
| 0.00024
| 0.33684
| 6,273
| 112
| 120
| 56.008929
| 0.81274
| 0.003826
| 0
| 0.6
| 0
| 0.03
| 0.296782
| 0.12406
| 0
| 0
| 0
| 0.008929
| 0
| 1
| 0.05
| false
| 0.04
| 0.06
| 0
| 0.19
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
d4084bc360141e6a7ed8a89dcba398eac2afe219
| 888
|
py
|
Python
|
getClassDescription.py
|
pavanKumar6720/Open-Images-class-hierarchy
|
f08905f9291316148450e0f6f6b61bde77bc0080
|
[
"Apache-2.0"
] | null | null | null |
getClassDescription.py
|
pavanKumar6720/Open-Images-class-hierarchy
|
f08905f9291316148450e0f6f6b61bde77bc0080
|
[
"Apache-2.0"
] | null | null | null |
getClassDescription.py
|
pavanKumar6720/Open-Images-class-hierarchy
|
f08905f9291316148450e0f6f6b61bde77bc0080
|
[
"Apache-2.0"
] | null | null | null |
import csv
def get_code(class_name,file_loc):
with open(file_loc,encoding='utf-8') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
rows = []
#print (csv_reader)
for row in csv_reader:
#print (row)
rows.append(row)
codes = [item[0] for item in rows]
names = [item[1] for item in rows]
#print (names[5])
try:
ind = names.index(class_name)
return codes[ind]
except:
return class_name
def get_name(class_code,file_loc):
with open(file_loc,encoding='utf-8') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
rows = []
#print (csv_reader)
for row in csv_reader:
#print (row)
rows.append(row)
codes = [item[0] for item in rows]
names = [item[1] for item in rows]
#print (names[5])
try:
ind = codes.index(class_code)
return names[ind]
except:
return class_code
| 24
| 52
| 0.640766
| 138
| 888
| 3.963768
| 0.246377
| 0.131627
| 0.087751
| 0.095064
| 0.716636
| 0.716636
| 0.716636
| 0.716636
| 0.716636
| 0.716636
| 0
| 0.011713
| 0.230856
| 888
| 37
| 53
| 24
| 0.789165
| 0.101351
| 0
| 0.666667
| 0
| 0
| 0.015132
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.074074
| false
| 0
| 0.037037
| 0
| 0.259259
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
d415121c8565132e06cbb054b64484b9f0fe2466
| 76
|
py
|
Python
|
filecleaver/__init__.py
|
fprimex/filecleaver
|
d2930d42836582dfe689117e901bac66c1a3d6f2
|
[
"Apache-2.0"
] | 2
|
2017-07-22T08:22:05.000Z
|
2019-02-13T21:48:51.000Z
|
filecleaver/__init__.py
|
fprimex/filecleaver
|
d2930d42836582dfe689117e901bac66c1a3d6f2
|
[
"Apache-2.0"
] | 2
|
2017-12-15T01:18:37.000Z
|
2021-08-08T18:56:51.000Z
|
filecleaver/__init__.py
|
fprimex/filecleaver
|
d2930d42836582dfe689117e901bac66c1a3d6f2
|
[
"Apache-2.0"
] | 2
|
2015-03-12T21:07:48.000Z
|
2018-02-27T09:43:31.000Z
|
from .filecleaver import FileChunk, FileChunkReadError, cleave, cleavebytes
| 38
| 75
| 0.855263
| 7
| 76
| 9.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092105
| 76
| 1
| 76
| 76
| 0.942029
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
d4343404ce7c0609941046d205341725a1d49b37
| 87
|
py
|
Python
|
tests/feedback/constants.py
|
felix781/market-access-public-frontend
|
26e7594a86976df941ba97b7d0084364837405db
|
[
"MIT"
] | null | null | null |
tests/feedback/constants.py
|
felix781/market-access-public-frontend
|
26e7594a86976df941ba97b7d0084364837405db
|
[
"MIT"
] | 6
|
2020-12-01T17:46:21.000Z
|
2021-06-07T09:43:10.000Z
|
tests/feedback/constants.py
|
felix781/market-access-public-frontend
|
26e7594a86976df941ba97b7d0084364837405db
|
[
"MIT"
] | 2
|
2021-02-09T09:37:42.000Z
|
2021-03-10T17:37:06.000Z
|
class ErrorHTML:
FIELD_ERROR = '<span class="govuk-visually-hidden">Error:</span>'
| 29
| 69
| 0.712644
| 11
| 87
| 5.545455
| 0.727273
| 0.295082
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114943
| 87
| 2
| 70
| 43.5
| 0.792208
| 0
| 0
| 0
| 0
| 0
| 0.563218
| 0.494253
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 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
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
2e164fdeeb52e22ed976f9759eedd732ced19ed2
| 692
|
py
|
Python
|
pytorchvideo_trainer/pytorchvideo_trainer/__init__.py
|
ricklentz/pytorchvideo
|
874d27cb55b9d7e9df6cd0881e2d7fe9f262532b
|
[
"Apache-2.0"
] | null | null | null |
pytorchvideo_trainer/pytorchvideo_trainer/__init__.py
|
ricklentz/pytorchvideo
|
874d27cb55b9d7e9df6cd0881e2d7fe9f262532b
|
[
"Apache-2.0"
] | null | null | null |
pytorchvideo_trainer/pytorchvideo_trainer/__init__.py
|
ricklentz/pytorchvideo
|
874d27cb55b9d7e9df6cd0881e2d7fe9f262532b
|
[
"Apache-2.0"
] | 1
|
2022-03-11T04:59:19.000Z
|
2022-03-11T04:59:19.000Z
|
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
def register_components() -> None:
"""
Calls register_components() for all subfolders so we can register
subcomponents to Hydra's ConfigStore.
"""
import pytorchvideo_trainer.datamodule.datamodule # noqa
import pytorchvideo_trainer.module.byol # noqa
import pytorchvideo_trainer.module.lr_policy # noqa
import pytorchvideo_trainer.module.moco_v2 # noqa
import pytorchvideo_trainer.module.optimizer # noqa
import pytorchvideo_trainer.module.simclr # noqa
import pytorchvideo_trainer.module.video_classification # noqa
import pytorchvideo_trainer.train_app # noqa
| 40.705882
| 71
| 0.761561
| 80
| 692
| 6.4125
| 0.525
| 0.280702
| 0.389864
| 0.395712
| 0.409357
| 0
| 0
| 0
| 0
| 0
| 0
| 0.001748
| 0.17341
| 692
| 16
| 72
| 43.25
| 0.895105
| 0.309249
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| true
| 0
| 0.888889
| 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
| 0
| 0
|
0
| 5
|
2e2fd17ddb0dc349f5d4d0c4af86865934ceb503
| 58
|
py
|
Python
|
source/Calamari.Tests/Fixtures/Python/Scripts/parameters.py
|
tfly0072/Calamari
|
9f6085c2912a06760515e58b743a9697ff183284
|
[
"Apache-2.0"
] | 198
|
2015-04-13T02:15:30.000Z
|
2022-03-06T02:03:37.000Z
|
source/Calamari.Tests/Fixtures/Python/Scripts/parameters.py
|
tfly0072/Calamari
|
9f6085c2912a06760515e58b743a9697ff183284
|
[
"Apache-2.0"
] | 320
|
2015-04-13T03:51:17.000Z
|
2022-03-10T06:58:48.000Z
|
source/Calamari.Tests/Fixtures/Python/Scripts/parameters.py
|
tfly0072/Calamari
|
9f6085c2912a06760515e58b743a9697ff183284
|
[
"Apache-2.0"
] | 134
|
2015-04-13T22:30:35.000Z
|
2022-01-31T16:14:44.000Z
|
print("Parameters {} {}".format(sys.argv[1], sys.argv[2]))
| 58
| 58
| 0.637931
| 9
| 58
| 4.111111
| 0.777778
| 0.378378
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.036364
| 0.051724
| 58
| 1
| 58
| 58
| 0.636364
| 0
| 0
| 0
| 0
| 0
| 0.271186
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
2e3025b26d1c367df8b8a250650a555d56ec0cd7
| 29,553
|
py
|
Python
|
main/test/test_views.py
|
NikOneZ1/createfolio
|
822869f6fba5d5792c932a0fe7c2bd0c0c348e56
|
[
"MIT"
] | 1
|
2021-12-02T12:30:18.000Z
|
2021-12-02T12:30:18.000Z
|
main/test/test_views.py
|
NikOneZ1/createfolio
|
822869f6fba5d5792c932a0fe7c2bd0c0c348e56
|
[
"MIT"
] | 14
|
2021-12-03T12:03:25.000Z
|
2021-12-20T03:29:02.000Z
|
main/test/test_views.py
|
NikOneZ1/createfolio
|
822869f6fba5d5792c932a0fe7c2bd0c0c348e56
|
[
"MIT"
] | null | null | null |
from django.test import TestCase, Client
from django.urls import reverse
from django.contrib.auth.models import User
from rest_framework import status
from ..models import Portfolio, Project, Contact
from ..serializers import PortfolioSerializer, ProjectSerializer, ContactSerializer
import json
class ViewTestClass(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
username_1 = 'test1'
username_2 = 'test2'
password = 'testtesttest'
cls.user_1 = User.objects.create_user(username=username_1, password=password)
data = {'username': username_1, 'password': password}
cls.user1_token = cls.client.post('/auth/jwt/create/', data=data).data['access']
cls.user_2 = User.objects.create_user(username=username_2, password=password)
data = {'username': username_2, 'password': password}
cls.user2_token = cls.client.post('/auth/jwt/create/', data=data).data['access']
def setUp(self):
"""Creating first portfolio"""
self.portfolio_1 = Portfolio.objects.create(
image=None,
header='Portfolio 1 test header',
about_me='Some text in portfolio 1 about me part.',
link='portfolio1',
user=self.user_1
)
Project.objects.create(
name='Google',
description='Some information about google.',
image=None,
project_link='https://www.google.com',
portfolio=self.portfolio_1
)
Project.objects.create(
name='Bing',
description='Some information about bing.',
image=None,
project_link='https://www.bing.com',
portfolio=self.portfolio_1
)
Contact.objects.create(
social_network='GitHub',
link='https://www.github.com',
logo=None,
portfolio=self.portfolio_1
)
Contact.objects.create(
social_network='Linkedin',
link='https://www.linkedin.com',
logo=None,
portfolio=self.portfolio_1
)
"""Creating second portfolio"""
self.portfolio_2 = Portfolio.objects.create(
image=None,
header='Portfolio 2 test header',
about_me='Some text in portfolio 2 about me part.',
link='portfolio2',
user=self.user_1
)
Project.objects.create(
name='YouTube',
description='Some information about youtube.',
image=None,
project_link='https://www.youtube.com',
portfolio=self.portfolio_2
)
Project.objects.create(
name='RealPython',
description='Some information about realpython.',
image=None,
project_link='https://www.realpython.com',
portfolio=self.portfolio_2
)
Contact.objects.create(
social_network='Facebook',
link='https://www.facebook.com',
logo=None,
portfolio=self.portfolio_2
)
Contact.objects.create(
social_network='Instagram',
link='https://www.instagram.com',
logo=None,
portfolio=self.portfolio_2
)
"""Creating portfolio for second user"""
self.portfolio_3 = Portfolio.objects.create(
image=None,
header='Portfolio 3 test header',
about_me='Some text in portfolio 3 about me part.',
link='portfolio3',
user=self.user_2
)
Project.objects.create(
name='Wikipedia',
description='Some information about wikipedia.',
image=None,
project_link='https://www.wikipedia.org',
portfolio=self.portfolio_3
)
Project.objects.create(
name='Ebay',
description='Some information about ebay.',
image=None,
project_link='https://www.ebay.com',
portfolio=self.portfolio_3
)
Contact.objects.create(
social_network='Twitter',
link='https://www.twitter.com',
logo=None,
portfolio=self.portfolio_3
)
Contact.objects.create(
social_network='Reddit',
link='https://www.reddit.com',
logo=None,
portfolio=self.portfolio_3
)
def test_get_portfolio_without_auth(self):
"""
Try to get portfolio without authorization
:return: Portfolio with the corresponding link
"""
resp = self.client.get(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}))
portfolio = Portfolio.objects.get(pk=self.portfolio_1.pk)
serializer = PortfolioSerializer(portfolio)
self.assertEqual(resp.data, serializer.data)
self.assertEqual(resp.status_code, status.HTTP_200_OK)
def test_get_portfolio_with_auth(self):
"""
Try to get portfolio without authorization
:return: Portfolio with the corresponding link
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.get(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}), **headers)
portfolio = Portfolio.objects.get(pk=self.portfolio_1.pk)
serializer = PortfolioSerializer(portfolio)
self.assertEqual(resp.data, serializer.data)
self.assertEqual(resp.status_code, status.HTTP_200_OK)
def test_portfolio_create_without_auth(self):
"""
Try to create portfolio without authorization
:return: 401 ERROR
"""
data = {
"header": "Created portfolio",
"about_me": "Created portfolio about me.",
"link": "created_portfolio2",
"projects": [
{
"name": "Created project 1",
"description": "Description of created project 1",
"project_link": "https://www.google.com"
},
{
"name": "Created project 2",
"description": "Description of created project 2",
"project_link": "https://www.google.com"
}
],
"contacts": [
{
"social_network": "Created contact 1",
"link": "https://www.google.com"
},
{
"social_network": "Created contact 2",
"link": "https://www.github.com"
}
]
}
resp = self.client.post(
reverse('api_create_portfolio'),
content_type='application/json',
data=json.dumps(data)
)
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_portfolio_create_with_auth(self):
"""
Try to create portfolio with authorized user
:return: Status 201 CREATED
"""
data = {
"header": "Created portfolio",
"about_me": "Created portfolio about me.",
"link": "created_portfolio2",
"projects": [
{
"name": "Created project 1",
"description": "Description of created project 1",
"project_link": "https://www.google.com"
},
{
"name": "Created project 2",
"description": "Description of created project 2",
"project_link": "https://www.google.com"
}
],
"contacts": [
{
"social_network": "Created contact 1",
"link": "https://www.google.com"
},
{
"social_network": "Created contact 2",
"link": "https://www.github.com"
}
],
"user": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.post(
reverse('api_create_portfolio'),
content_type='application/json',
data=json.dumps(data),
**headers
)
self.assertEqual(resp.status_code, status.HTTP_201_CREATED)
def test_get_user_portfolio_with_auth(self):
"""
Try to GET portfolio with authorized user
:return: All portfolios of authorized user
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.get(reverse('api_user_portfolio'), **headers)
portfolios = Portfolio.objects.filter(user=self.user_1)
serializer = PortfolioSerializer(portfolios, many=True)
self.assertEqual(serializer.data, resp.data)
def test_get_user_portfolio_without_auth(self):
"""
Try to get user portfolio without authorization
:return: 401 ERROR
"""
resp = self.client.get(reverse('api_user_portfolio'))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_patch_portfolio_without_auth(self):
"""
Try to PATCH portfolio without authorization
:return: 401 ERROR
"""
data = {
"id": 1,
"header": "Updated portfolio",
"about_me": "Updated portfolio about me.",
"link": "created_portfolio2_upd",
"projects": [
{
"id": 1,
"name": "Updated project 1",
"description": "Description of updated project 1",
"project_link": "https://www.google.com"
},
{
"id": 2,
"name": "Updated project 2_upd",
"description": "Description of updated project 2",
"project_link": "https://www.google.com"
}
],
"contacts": [
{
"id": 1,
"social_network": "Updated contact 1",
"link": "https://www.google.com"
},
{
"id": 2,
"social_network": "Updated contact 2",
"link": "https://www.github.com"
}
]
}
resp = self.client.patch(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}),
content_type='application/json',
data=json.dumps(data))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_patch_portfolio_with_other_user(self):
"""
Change user in updated portfolio (user should not be changed)
:return: Updated portfolio with not changed user
"""
data = {
"id": 1,
"header": "Updated portfolio",
"about_me": "Updated portfolio about me.",
"link": "created_portfolio2_upd",
"projects": [
{
"id": 1,
"name": "Updated project 1",
"description": "Description of updated project 1",
"project_link": "https://www.google.com"
},
{
"id": 2,
"name": "Updated project 2",
"description": "Description of updated project 2",
"project_link": "https://www.google.com"
}
],
"contacts": [
{
"id": 1,
"social_network": "Updated contact 1",
"link": "https://www.google.com"
},
{
"id": 2,
"social_network": "Updated contact 2",
"link": "https://www.github.com"
}
],
"user": 2
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}),
content_type='application/json',
data=json.dumps(data),
**headers
)
self.assertEqual(resp.status_code, status.HTTP_200_OK)
self.assertNotEqual(resp.data["user"], data["user"])
def test_patch_portfolio_with_auth(self):
"""
PATCH portfolio of authorized user
:return: Status 200 OK and Updated portfolio
"""
data = {
"id": 1,
"image": None,
"header": "Updated portfolio",
"about_me": "Updated portfolio about me.",
"link": "created_portfolio2_upd",
"projects": [
{
"id": 1,
"name": "Updated project 1",
"description": "Description of updated project 1",
"image": None,
"project_link": "https://www.google.com",
"portfolio": 1
},
{
"id": 2,
"name": "Updated project 2",
"description": "Description of updated project 2",
"image": None,
"project_link": "https://www.google.com",
"portfolio": 1
}
],
"contacts": [
{
"id": 1,
"social_network": "Updated contact 1",
"link": "https://www.google.com",
"logo": None,
"portfolio": 1,
},
{
"id": 2,
"social_network": "Updated contact 2",
"link": "https://www.github.com",
"logo": None,
"portfolio": 1
}
],
"user": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}),
content_type='application/json',
data=json.dumps(data),
**headers
)
self.assertEqual(resp.status_code, status.HTTP_200_OK)
self.assertEqual(json.dumps(resp.data), json.dumps(data))
def test_patch_portfolio_other_user(self):
"""
PATCH portfolio of other user
:return: Status 403
"""
data = {
"id": 3,
"header": "Updated portfolio",
"about_me": "Updated portfolio about me.",
"link": "created_portfolio2_upd",
"projects": [
{
"id": 5,
"name": "Updated project 1",
"description": "Description of updated project 1",
"project_link": "https://www.google.com"
},
{
"id": 6,
"name": "Updated project 2",
"description": "Description of updated project 2",
"project_link": "https://www.google.com"
}
],
"contacts": [
{
"id": 5,
"social_network": "Updated contact 1",
"link": "https://www.google.com",
},
{
"id": 6,
"social_network": "Updated contact 2",
"link": "https://www.github.com",
}
]
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_portfolio', kwargs={'link': self.portfolio_3.link}),
content_type='application/json',
data=json.dumps(data),
**headers
)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
def test_delete_portfolio_without_auth(self):
"""
Try to delete portfolio without authorization
:return: Status 401
"""
resp = self.client.delete(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_delete_portfolio_with_auth(self):
"""
Try to delete portfolio of authorized user
:return: Status 204
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_portfolio', kwargs={'link': self.portfolio_1.link}),
**headers)
self.assertEqual(resp.status_code, status.HTTP_204_NO_CONTENT)
def test_delete_portfolio_other_user(self):
"""
Try to delete portfolio of other user
:return: Status 403
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_portfolio', kwargs={'link': self.portfolio_3.link}),
**headers)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
def test_create_project_without_auth(self):
"""
Try to create project without authorization
:return: Status 401
"""
data = {
"name": "Created project",
"description": "Description of created project",
"portfolio": 1
}
resp = self.client.post(reverse('api_create_project'),
content_type='application/json',
data=json.dumps(data))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_create_project_other_user(self):
"""
Try to create project to portfolio of other user
:return: Status 400
"""
data = {
"name": "Created project",
"description": "Description of created project",
"project_link": "https://www.google.com",
"portfolio": 3
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.post(reverse('api_create_project'),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)
def test_create_project_with_auth(self):
"""
Try to create project with auth
:return: Status 201
"""
data = {
"name": "Created project",
"description": "Description of created project",
"project_link": "https://www.google.com",
"portfolio": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.post(reverse('api_create_project'),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_201_CREATED)
def test_update_project_without_auth(self):
"""
Try to update project without authentication
:return: Status 401
"""
data = {
"id": 1,
"name": "Updated project",
"description": "Description of updated project",
"project_link": "https://www.google.com",
}
resp = self.client.patch(reverse('api_update_project', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_update_project_other_user(self):
"""
Try to update project of other user
:return: Status 403
"""
data = {
"name": "Updated project",
"description": "Description of updated project"
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_project', kwargs={'pk': 5}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
def test_update_portfolio_in_project(self):
"""
Try to change portfolio in project
:return: Status 400
"""
data = {
"name": "Updated project",
"description": "Description of updated project",
"portfolio": 3
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_project', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)
def test_update_project_with_auth(self):
"""
Try to update project with authorization
:return: Status 200
"""
data = {
"name": "Updated project",
"description": "Description of updated project"
}
returned_data = {
"id": 1,
"name": "Updated project",
"description": "Description of updated project",
"image": None,
"project_link": "https://www.google.com",
"portfolio": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_project', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(json.dumps(resp.data), json.dumps(returned_data))
self.assertEqual(resp.status_code, status.HTTP_200_OK)
def test_delete_project_without_auth(self):
"""
Try to delete project without authorization
:return: Status 401
"""
resp = self.client.delete(reverse('api_update_project', kwargs={'pk': 1}))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_delete_project_with_auth(self):
"""
Try to delete project with authorization
:return: Status 204
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_update_project', kwargs={'pk': 1}), **headers)
self.assertEqual(resp.status_code, status.HTTP_204_NO_CONTENT)
def test_delete_project_other_user(self):
"""
Try to delete project of other user
:return: Status 403
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_update_project', kwargs={'pk': 5}), **headers)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
def test_create_contact_without_auth(self):
"""
Try to create contact without authorization
:return: Status 401
"""
data = {
"social_network": "Created network",
"link": "https://www.facebook.com",
"logo": None,
"portfolio": 1
}
resp = self.client.post(reverse('api_create_contact'),
content_type='application/json',
data=json.dumps(data))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_create_contact_other_user(self):
"""
Try to create contact to portfolio of other user
:return: Status 400
"""
data = {
"social_network": "Created network",
"link": "https://www.facebook.com",
"logo": None,
"portfolio": 3
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.post(reverse('api_create_contact'),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)
def test_create_contact_with_auth(self):
"""
Try to create contact with authorization
:return: Status 201
"""
data = {
"social_network": "Created network",
"link": "https://www.facebook.com",
"logo": None,
"portfolio": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.post(reverse('api_create_contact'),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_201_CREATED)
def test_update_contact_without_auth(self):
"""
Try to update contact without authentication
:return: Status 401
"""
data = {
"social_network": "Updated network",
"link": "https://www.facebook.com",
}
resp = self.client.patch(reverse('api_update_contact', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_update_contact_other_user(self):
"""
Try to update contact of other user
:return: Status 403
"""
data = {
"social_network": "Updated network",
"link": "https://www.facebook.com",
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_contact', kwargs={'pk': 5}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
def test_update_portfolio_in_contact(self):
"""
Try to change portfolio in contact
:return: Status 400
"""
data = {
"social_network": "Updated network",
"link": "https://www.facebook.com",
"portfolio": 3
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_contact', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)
def test_update_contact_with_auth(self):
"""
Try to update contact with authentication
:return: Status 200
"""
data = {
"social_network": "Updated network",
"link": "https://www.facebook.com",
}
returned_data = {
"id": 1,
"social_network": "Updated network",
"link": "https://www.facebook.com",
"logo": None,
"portfolio": 1
}
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.patch(reverse('api_update_contact', kwargs={'pk': 1}),
content_type='application/json',
data=json.dumps(data),
**headers)
self.assertEqual(json.dumps(resp.data), json.dumps(returned_data))
self.assertEqual(resp.status_code, status.HTTP_200_OK)
def test_delete_contact_without_auth(self):
"""
Try to delete contact without authorization
:return: Status 401
"""
resp = self.client.delete(reverse('api_update_contact', kwargs={'pk': 1}))
self.assertEqual(resp.status_code, status.HTTP_401_UNAUTHORIZED)
def test_delete_contact_with_auth(self):
"""
Try to delete contact with authorization
:return: Status 204
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_update_contact', kwargs={'pk': 1}), **headers)
self.assertEqual(resp.status_code, status.HTTP_204_NO_CONTENT)
def test_delete_contact_other_user(self):
"""
Try to delete contact of other user
:return: Status 403
"""
headers = {"HTTP_AUTHORIZATION": "JWT " + self.user1_token}
resp = self.client.delete(reverse('api_update_contact', kwargs={'pk': 5}), **headers)
self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)
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| 107
| 0.516022
| 2,776
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| 5.322767
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| 37.551461
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0
| 5
|
5cf3709f8d9654c6d51e78d6fb00391a52ccbbb4
| 99
|
py
|
Python
|
src/page_dewarp/debug_utils/__init__.py
|
vuonglv1612/page-dewarp
|
68063db040ba97964a22f68a6056467dacd2952f
|
[
"MIT"
] | null | null | null |
src/page_dewarp/debug_utils/__init__.py
|
vuonglv1612/page-dewarp
|
68063db040ba97964a22f68a6056467dacd2952f
|
[
"MIT"
] | null | null | null |
src/page_dewarp/debug_utils/__init__.py
|
vuonglv1612/page-dewarp
|
68063db040ba97964a22f68a6056467dacd2952f
|
[
"MIT"
] | null | null | null |
from .colours import cCOLOURS
from .viewer import debug_show
__all__ = ["cCOLOURS", "debug_show"]
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| 36
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0
| 5
|
5cf9d1aa36b1da1fa687e1429e518c6838239ea7
| 227
|
py
|
Python
|
backend/tests/test_currency.py
|
deti/boss
|
bc0cfe3067bf1cbf26789f7443a36e7cdd2ac869
|
[
"Apache-2.0"
] | 7
|
2018-05-20T08:56:08.000Z
|
2022-03-11T15:50:54.000Z
|
backend/tests/test_currency.py
|
deti/boss
|
bc0cfe3067bf1cbf26789f7443a36e7cdd2ac869
|
[
"Apache-2.0"
] | 2
|
2021-06-08T21:12:51.000Z
|
2022-01-13T01:25:27.000Z
|
backend/tests/test_currency.py
|
deti/boss
|
bc0cfe3067bf1cbf26789f7443a36e7cdd2ac869
|
[
"Apache-2.0"
] | 5
|
2016-10-09T14:52:09.000Z
|
2020-12-25T01:04:35.000Z
|
from tests.base import TestCaseApi
class TestCurrencyApi(TestCaseApi):
def test_currency(self):
self.assertTrue(self.admin_client.currency.list())
self.assertTrue(self.admin_client.currency.list_active())
| 28.375
| 65
| 0.757709
| 27
| 227
| 6.222222
| 0.592593
| 0.166667
| 0.214286
| 0.27381
| 0.488095
| 0.488095
| 0.488095
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| 227
| 7
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| 32.428571
| 0.861538
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| 0
|
0
| 5
|
cf03baad2f51c9df90fa6fdc9e89b7e97e38f634
| 119
|
py
|
Python
|
espaloma/nn/__init__.py
|
cschlick/espaloma
|
cae5664446d0c89025de5eb827f507d8af64e2d4
|
[
"MIT"
] | 60
|
2020-05-15T13:21:55.000Z
|
2022-03-29T17:53:17.000Z
|
espaloma/nn/__init__.py
|
cschlick/espaloma
|
cae5664446d0c89025de5eb827f507d8af64e2d4
|
[
"MIT"
] | 72
|
2020-04-16T18:49:51.000Z
|
2022-03-25T14:24:52.000Z
|
espaloma/nn/__init__.py
|
cschlick/espaloma
|
cae5664446d0c89025de5eb827f507d8af64e2d4
|
[
"MIT"
] | 5
|
2020-11-13T19:24:09.000Z
|
2022-01-19T20:49:08.000Z
|
from . import baselines, layers, readout, sequential
from .layers import dgl_legacy
from .sequential import Sequential
| 29.75
| 52
| 0.823529
| 15
| 119
| 6.466667
| 0.533333
| 0
| 0
| 0
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| 3
| 53
| 39.666667
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0
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|
cf2a383b4bb1e3acd1e97226d2c75e0e629dc8d4
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|
py
|
Python
|
vnpy/api/okex/__init__.py
|
aiqtt/vnpy
|
0efcc3efbe920a55afdadb9993ad540e3886819c
|
[
"MIT"
] | 4
|
2018-08-23T03:13:34.000Z
|
2021-07-03T06:22:33.000Z
|
vnpy/api/okex/__init__.py
|
uniwin/vnpydjv
|
7f76e50501fe0244b3ae7f71002539cd2bd44b79
|
[
"MIT"
] | null | null | null |
vnpy/api/okex/__init__.py
|
uniwin/vnpydjv
|
7f76e50501fe0244b3ae7f71002539cd2bd44b79
|
[
"MIT"
] | null | null | null |
# encoding: UTF-8
from vnpy.api.okex.vnokex import WsSpotApi, WsFuturesApi,SPOT_SYMBOL, CONTRACT_SYMBOL, SPOT_CURRENCY,CONTRACT_TYPE
| 44.333333
| 114
| 0.842105
| 19
| 133
| 5.684211
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0
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|
cf4fcccbc52d7dcaac81318da7fb14c77a3fef17
| 161
|
py
|
Python
|
buildserver/sil_snomed_server/data_types/custom_types.py
|
poppingtonic/terminology-server
|
788375e4f666b9344dc4f5faebee63fab58f1f57
|
[
"MIT"
] | null | null | null |
buildserver/sil_snomed_server/data_types/custom_types.py
|
poppingtonic/terminology-server
|
788375e4f666b9344dc4f5faebee63fab58f1f57
|
[
"MIT"
] | null | null | null |
buildserver/sil_snomed_server/data_types/custom_types.py
|
poppingtonic/terminology-server
|
788375e4f666b9344dc4f5faebee63fab58f1f57
|
[
"MIT"
] | 1
|
2021-08-20T00:01:48.000Z
|
2021-08-20T00:01:48.000Z
|
import sqlalchemy as sa
from sqlalchemy_utils.types.uuid import UUIDType
class UUID(UUIDType):
def __repr__(self):
return "sa.dialects.postgresql.UUID()"
| 23
| 48
| 0.776398
| 22
| 161
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| 6
| 49
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| 1
| 1
| 0
|
0
| 5
|
cf61a0321c22ae3efcbb5b63454069163d3c1688
| 128
|
py
|
Python
|
Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/PRSCondition/LADcoronaryRpController.py
|
carthurs/CRIMSONGUI
|
1464df9c4d04cf3ba131ca90b91988a06845c68e
|
[
"BSD-3-Clause"
] | 10
|
2020-09-17T18:55:31.000Z
|
2022-02-23T02:52:38.000Z
|
Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/PRSCondition/LADcoronaryRpController.py
|
carthurs/CRIMSONGUI
|
1464df9c4d04cf3ba131ca90b91988a06845c68e
|
[
"BSD-3-Clause"
] | null | null | null |
Documentation/GuidesFromPlosCompBioPaper/ExampleCaseC/AdditionalInputFiles/PRSCondition/LADcoronaryRpController.py
|
carthurs/CRIMSONGUI
|
1464df9c4d04cf3ba131ca90b91988a06845c68e
|
[
"BSD-3-Clause"
] | 3
|
2021-05-19T09:02:21.000Z
|
2021-07-26T17:39:57.000Z
|
version https://git-lfs.github.com/spec/v1
oid sha256:83d46ffaadc46c43ea37cc9e316cf18250ce5ad73accf7b3abd96b5ae8ac5761
size 712
| 32
| 75
| 0.882813
| 13
| 128
| 8.692308
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| 128
| 3
| 76
| 42.666667
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0
| 5
|
cf66be295360bececa07da0a9b3d8495f37ab008
| 113
|
py
|
Python
|
module/preprocess/__init__.py
|
anirudhagar13/engine_nwc_pattern_detection
|
afb6e39c531d8a3071341c39c64b09cd2bab6713
|
[
"MIT"
] | null | null | null |
module/preprocess/__init__.py
|
anirudhagar13/engine_nwc_pattern_detection
|
afb6e39c531d8a3071341c39c64b09cd2bab6713
|
[
"MIT"
] | null | null | null |
module/preprocess/__init__.py
|
anirudhagar13/engine_nwc_pattern_detection
|
afb6e39c531d8a3071341c39c64b09cd2bab6713
|
[
"MIT"
] | 1
|
2021-02-23T01:37:40.000Z
|
2021-02-23T01:37:40.000Z
|
from .invalid_idx_preprocess import InvalidIndexPreprocessor
from .preprocess_strategy import PreprocessStrategy
| 37.666667
| 60
| 0.911504
| 11
| 113
| 9.090909
| 0.727273
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| 2
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| 1
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|
0
| 5
|
d87eab6e1bc19d71a9c1bfea6cc640f9cfba8b5f
| 91
|
py
|
Python
|
_mod_Community/ParticleRenderer/init.py
|
tianlunjiang/_NukeStudio_v2
|
5ed9b9217aff16d903bdcda5c2f1e1cd3bebe367
|
[
"CNRI-Python"
] | 6
|
2019-08-27T01:30:15.000Z
|
2020-11-17T00:40:01.000Z
|
_mod_Community/ParticleRenderer/init.py
|
tianlunjiang/_NukeMods
|
47861bfc273262abba55b9f9a61782a5d89479b1
|
[
"CNRI-Python"
] | 2
|
2019-01-22T04:09:28.000Z
|
2019-01-23T15:11:39.000Z
|
_mod_Community/ParticleRenderer/init.py
|
tianlunjiang/_NukeMods
|
47861bfc273262abba55b9f9a61782a5d89479b1
|
[
"CNRI-Python"
] | 1
|
2020-08-03T22:43:23.000Z
|
2020-08-03T22:43:23.000Z
|
import ParticleRenderer
nuke.pluginAddPath( './ToolSets' )
nuke.pluginAddPath( './icons' )
| 22.75
| 34
| 0.758242
| 8
| 91
| 8.625
| 0.75
| 0.492754
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| 0
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| 91
| 4
| 35
| 22.75
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| 1
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| 0
| 0
|
0
| 5
|
d8b27f1d571fa9526f3e344ee0ef42aad129969b
| 119
|
py
|
Python
|
enthought/block_canvas/function_tools/extension_function_info.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 3
|
2016-12-09T06:05:18.000Z
|
2018-03-01T13:00:29.000Z
|
enthought/block_canvas/function_tools/extension_function_info.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | 1
|
2020-12-02T00:51:32.000Z
|
2020-12-02T08:48:55.000Z
|
enthought/block_canvas/function_tools/extension_function_info.py
|
enthought/etsproxy
|
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
|
[
"BSD-3-Clause"
] | null | null | null |
# proxy module
from __future__ import absolute_import
from blockcanvas.function_tools.extension_function_info import *
| 29.75
| 64
| 0.87395
| 15
| 119
| 6.4
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0.092437
| 119
| 3
| 65
| 39.666667
| 0.888889
| 0.10084
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| 1
| 0
|
0
| 5
|
d8e84299ce3acb903138d2fe0465c05d683012e5
| 86
|
py
|
Python
|
module00/ex01/exec.py
|
kotabrog/bootcamp_python
|
41251363d8f62d39451650dcd55e0c1522b1ddcb
|
[
"MIT"
] | null | null | null |
module00/ex01/exec.py
|
kotabrog/bootcamp_python
|
41251363d8f62d39451650dcd55e0c1522b1ddcb
|
[
"MIT"
] | null | null | null |
module00/ex01/exec.py
|
kotabrog/bootcamp_python
|
41251363d8f62d39451650dcd55e0c1522b1ddcb
|
[
"MIT"
] | null | null | null |
from sys import argv
print(*list(map(lambda x: x[::-1].swapcase(), argv[1:]))[::-1])
| 21.5
| 63
| 0.604651
| 15
| 86
| 3.466667
| 0.733333
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0.038961
| 0.104651
| 86
| 3
| 64
| 28.666667
| 0.636364
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
2b20f87da8cf8cc34d672d6e1d0277eadc1159ed
| 104
|
py
|
Python
|
Mathematics/harmonic mean/python/harmonic_mean.py
|
iabhimanyu/Algorithms
|
adefcd165e591d2338be8fc8a62629ff072620dd
|
[
"MIT"
] | 715
|
2018-10-01T21:30:10.000Z
|
2022-03-23T09:14:10.000Z
|
Mathematics/harmonic mean/python/harmonic_mean.py
|
iabhimanyu/Algorithms
|
adefcd165e591d2338be8fc8a62629ff072620dd
|
[
"MIT"
] | 157
|
2018-10-01T20:53:11.000Z
|
2021-08-03T07:00:58.000Z
|
Mathematics/harmonic mean/python/harmonic_mean.py
|
iabhimanyu/Algorithms
|
adefcd165e591d2338be8fc8a62629ff072620dd
|
[
"MIT"
] | 1,225
|
2018-10-01T20:56:22.000Z
|
2022-02-22T04:00:27.000Z
|
def harmonic(a, b):
return (2*a*b)/(a + b);
a, b = map(int, input().split())
print(harmonic(a, b))
| 17.333333
| 32
| 0.548077
| 20
| 104
| 2.85
| 0.55
| 0.175439
| 0.350877
| 0.140351
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011765
| 0.182692
| 104
| 5
| 33
| 20.8
| 0.658824
| 0
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| false
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| null | 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
2b33a4033349a7ca072e384b02fb2176b4374a30
| 2,326
|
py
|
Python
|
muitiprocess_or_threads.py
|
masa1203/python_asyncio_practice
|
e06e7d67c29ffbb8adb385a754471c38b4604c67
|
[
"MIT"
] | null | null | null |
muitiprocess_or_threads.py
|
masa1203/python_asyncio_practice
|
e06e7d67c29ffbb8adb385a754471c38b4604c67
|
[
"MIT"
] | null | null | null |
muitiprocess_or_threads.py
|
masa1203/python_asyncio_practice
|
e06e7d67c29ffbb8adb385a754471c38b4604c67
|
[
"MIT"
] | null | null | null |
import concurrent.futures
import os
import time
LEARGE_TEXT = "some string" * 10000000
def io_bound(file_name):
with open(file_name, "w+") as f:
f.write(LEARGE_TEXT)
f.seek(0)
f.read()
os.remove(file_name)
return "Future is done!"
def cpu_bound():
i = 0
while i < 10000000:
i = i + 1 - 2 + 3 - 4 + 5
return "Future is done!"
if __name__ == "__main__":
# start = time.time()
# print(io_bound("1.txt"))
# print(io_bound("2.txt"))
# end = time.time()
# print("I/O bound: Sync {:.4f}\n".format(end - start))
# with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executer:
# start = time.time()
# future1 = executer.submit(io_bound, "1.txt")
# future2 = executer.submit(io_bound, "2.txt")
# print(future1.result())
# print(future2.result())
# end = time.time()
# print("I/O bound: Thread {:.4f}\n".format(end - start))
# with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executer:
# start = time.time()
# future1 = executer.submit(io_bound, "1.txt")
# future2 = executer.submit(io_bound, "2.txt")
# print(future1.result())
# print(future2.result())
# end = time.time()
# print("The number of cpu: {}".format(os.cpu_count()))
# print("I/O bound: Process {:.4f}\n".format(end - start))
start = time.time()
print(cpu_bound())
print(cpu_bound())
end = time.time()
print("I/O bound: Sync {:.4f}\n".format(end - start))
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executer:
start = time.time()
future1 = executer.submit(cpu_bound)
future2 = executer.submit(cpu_bound)
print(future1.result())
print(future2.result())
end = time.time()
print("I/O bound: Thread {:.4f}\n".format(end - start))
with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executer:
start = time.time()
future1 = executer.submit(cpu_bound)
future2 = executer.submit(cpu_bound)
print(future1.result())
print(future2.result())
end = time.time()
print("The number of cpu: {}".format(os.cpu_count()))
print("I/O bound: Process {:.4f}\n".format(end - start))
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| 77
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| 0.071642
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| 0
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| 0.250645
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| 75
| 78
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| 0.051282
| false
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| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
2b3431109cf34c79a91fff15a524a5e5203f24f1
| 101,396
|
py
|
Python
|
fn_proofpoint_trap/fn_proofpoint_trap/util/customize.py
|
rudimeyer/resilient-community-apps
|
7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00
|
[
"MIT"
] | 1
|
2020-08-25T03:43:07.000Z
|
2020-08-25T03:43:07.000Z
|
fn_proofpoint_trap/fn_proofpoint_trap/util/customize.py
|
rudimeyer/resilient-community-apps
|
7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00
|
[
"MIT"
] | 1
|
2019-07-08T16:57:48.000Z
|
2019-07-08T16:57:48.000Z
|
fn_proofpoint_trap/fn_proofpoint_trap/util/customize.py
|
rudimeyer/resilient-community-apps
|
7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Generate the Resilient customizations required for fn_proofpoint_trap"""
from __future__ import print_function
from resilient_circuits.util import *
def codegen_reload_data():
"""Parameters to codegen used to generate the fn_proofpoint_trap package"""
reload_params = {"package": u"fn_proofpoint_trap",
"incident_fields": [u"proofpoint_trap_incident_id"],
"action_fields": [u"trap_description", u"trap_duration", u"trap_expiration", u"trap_list_id"],
"function_params": [u"trap_description", u"trap_duration", u"trap_expiration", u"trap_incident_id", u"trap_list_id", u"trap_member", u"trap_member_id", u"trap_members_type"],
"datatables": [u"proofpoint_trap_events", u"trap_list_members"],
"message_destinations": [u"fn_proofpoint_trap"],
"functions": [u"fn_proofpoint_trap_add_members_to_list", u"fn_proofpoint_trap_delete_list_member", u"fn_proofpoint_trap_get_incident_details", u"fn_proofpoint_trap_get_list_members", u"fn_proofpoint_trap_update_list_member"],
"phases": [],
"automatic_tasks": [],
"scripts": [],
"workflows": [u"wf_proofpoint_trap_add_member_to_list", u"wf_proofpoint_trap_delete_list_member", u"wf_proofpoint_trap_get_incident_details", u"wf_proofpoint_trap_get_list_members", u"wf_proofpoint_trap_update_list_member"],
"actions": [u"Example: Proofpoint TRAP: Add Member to List", u"Example: Proofpoint TRAP: Delete List Member", u"Example: Proofpoint TRAP: Get Incident Details", u"Example: Proofpoint TRAP: Get List Members", u"Example: Proofpoint TRAP: Update List Member"],
"incident_artifact_types": []
}
return reload_params
def customization_data(client=None):
"""Produce any customization definitions (types, fields, message destinations, etc)
that should be installed by `resilient-circuits customize`
"""
# This import data contains:
# Incident fields:
# proofpoint_trap_incident_id
# Action fields:
# trap_description
# trap_duration
# trap_expiration
# trap_list_id
# Function inputs:
# trap_description
# trap_duration
# trap_expiration
# trap_incident_id
# trap_list_id
# trap_member
# trap_member_id
# trap_members_type
# DataTables:
# proofpoint_trap_events
# trap_list_members
# Message Destinations:
# fn_proofpoint_trap
# Functions:
# fn_proofpoint_trap_add_members_to_list
# fn_proofpoint_trap_delete_list_member
# fn_proofpoint_trap_get_incident_details
# fn_proofpoint_trap_get_list_members
# fn_proofpoint_trap_update_list_member
# Workflows:
# wf_proofpoint_trap_add_member_to_list
# wf_proofpoint_trap_delete_list_member
# wf_proofpoint_trap_get_incident_details
# wf_proofpoint_trap_get_list_members
# wf_proofpoint_trap_update_list_member
# Rules:
# Example: Proofpoint TRAP: Add Member to List
# Example: Proofpoint TRAP: Delete List Member
# Example: Proofpoint TRAP: Get Incident Details
# Example: Proofpoint TRAP: Get List Members
# Example: Proofpoint TRAP: Update List Member
yield ImportDefinition(u"""
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dGlvbnMiOiBbXX1dfV19
"""
)
| 75.219585
| 278
| 0.974979
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| 101,396
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| 0.789532
| 0.005533
| 0.002432
| 0.001206
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| 0.003932
| 0.000993
| 0.000993
| 0.000993
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| 0.11538
| 0.021036
| 101,396
| 1,348
| 279
| 75.219585
| 0.878736
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0
| 5
|
99134854fd8a9a503c20ec37de53d0a62104b82f
| 54
|
py
|
Python
|
entrypoint.py
|
HakierGrzonzo/pyPub
|
cffe03599cf53306d1539d647d97e49d90f0b14c
|
[
"MIT"
] | 1
|
2021-02-17T18:39:54.000Z
|
2021-02-17T18:39:54.000Z
|
entrypoint.py
|
HakierGrzonzo/pyPub
|
cffe03599cf53306d1539d647d97e49d90f0b14c
|
[
"MIT"
] | null | null | null |
entrypoint.py
|
HakierGrzonzo/pyPub
|
cffe03599cf53306d1539d647d97e49d90f0b14c
|
[
"MIT"
] | null | null | null |
import json
from app import ui, args, config
ui.run()
| 13.5
| 32
| 0.740741
| 10
| 54
| 4
| 0.8
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| 0
| 0
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| 0
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| 0.166667
| 54
| 4
| 33
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0
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|
9935098af2f8a1176fc4540bd57de9a5ac9d7eb7
| 115
|
py
|
Python
|
{{cookiecutter.project_name}}/service/tests/crud/conftest.py
|
ProjectTemplates/python-backend-service
|
5266916e54faaf236bc972a2cd7bb1217e8a8625
|
[
"MIT"
] | 7
|
2020-07-28T18:45:20.000Z
|
2021-12-11T23:33:49.000Z
|
{{cookiecutter.project_name}}/tests/crud/conftest.py
|
KovalevVasiliy/python-fastapi-backend
|
e9ed466c00bae2eeb0b4271b013cc8dacd98acf0
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_name}}/tests/crud/conftest.py
|
KovalevVasiliy/python-fastapi-backend
|
e9ed466c00bae2eeb0b4271b013cc8dacd98acf0
|
[
"MIT"
] | 1
|
2020-05-10T20:26:02.000Z
|
2020-05-10T20:26:02.000Z
|
import pytest
from services.dependencies import get_db
@pytest.fixture()
def session():
yield from get_db()
| 12.777778
| 40
| 0.747826
| 16
| 115
| 5.25
| 0.6875
| 0.119048
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| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0.165217
| 115
| 8
| 41
| 14.375
| 0.875
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
993b95faf3bc85398847fb6c2af5b9cf4a09508d
| 52
|
py
|
Python
|
diptrace/__init__.py
|
snhobbs/DiptraceSchematicApi
|
de2a8b7492844c3506f245bc250e755df57caadd
|
[
"BSD-2-Clause"
] | null | null | null |
diptrace/__init__.py
|
snhobbs/DiptraceSchematicApi
|
de2a8b7492844c3506f245bc250e755df57caadd
|
[
"BSD-2-Clause"
] | 2
|
2017-07-12T16:57:05.000Z
|
2017-07-20T15:09:03.000Z
|
diptrace/__init__.py
|
snhobbs/DiptraceSchematicApi
|
de2a8b7492844c3506f245bc250e755df57caadd
|
[
"BSD-2-Clause"
] | null | null | null |
from . schematicWriter import Part, SchematicWriter
| 26
| 51
| 0.846154
| 5
| 52
| 8.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 52
| 1
| 52
| 52
| 0.956522
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| 1
| 0
| 1
| 0
|
0
| 5
|
997653b9ef081fd96ff5a3763abf541a623750eb
| 213
|
py
|
Python
|
extra_script.py
|
atoomnetmarc/Reset-timeout
|
196cab0ac00c1bb036de085bff453be03626a1d9
|
[
"Apache-2.0"
] | 1
|
2021-11-11T23:48:05.000Z
|
2021-11-11T23:48:05.000Z
|
extra_script.py
|
atoomnetmarc/Reset-timeout
|
196cab0ac00c1bb036de085bff453be03626a1d9
|
[
"Apache-2.0"
] | null | null | null |
extra_script.py
|
atoomnetmarc/Reset-timeout
|
196cab0ac00c1bb036de085bff453be03626a1d9
|
[
"Apache-2.0"
] | 1
|
2021-09-17T16:21:44.000Z
|
2021-09-17T16:21:44.000Z
|
Import("env", "projenv")
from shutil import copyfile
def copyhex(*args, **kwargs):
copyfile(str(kwargs['target'][0]), 'hex/'+env['BOARD_MCU']+'.hex')
env.AddPostAction("$BUILD_DIR/${PROGNAME}.hex", copyhex)
| 26.625
| 70
| 0.680751
| 28
| 213
| 5.107143
| 0.714286
| 0.083916
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005155
| 0.089202
| 213
| 7
| 71
| 30.428571
| 0.731959
| 0
| 0
| 0
| 0
| 0
| 0.276995
| 0.122066
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0
| 0.6
| 0
| 1
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| 0
| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
513d57e7e24109d44d56d09e631b7b2368470ebe
| 284
|
py
|
Python
|
jinahub/indexers/searcher/compound/FaissPostgresIndexer/tests/integration/test_dummy.py
|
albertocarpentieri/executors
|
3b025b6106fca9dba3c2569b0e60da050273fa6e
|
[
"Apache-2.0"
] | 29
|
2021-07-26T07:16:38.000Z
|
2022-03-27T15:10:34.000Z
|
jinahub/indexers/searcher/compound/FaissPostgresIndexer/tests/integration/test_dummy.py
|
albertocarpentieri/executors
|
3b025b6106fca9dba3c2569b0e60da050273fa6e
|
[
"Apache-2.0"
] | 176
|
2021-07-23T08:30:21.000Z
|
2022-03-14T12:29:06.000Z
|
jinahub/indexers/searcher/compound/FaissPostgresIndexer/tests/integration/test_dummy.py
|
albertocarpentieri/executors
|
3b025b6106fca9dba3c2569b0e60da050273fa6e
|
[
"Apache-2.0"
] | 16
|
2021-07-26T20:55:40.000Z
|
2022-03-18T15:32:17.000Z
|
# tests require to import from Faiss module
# so thus require PYTHONPATH
# the other option would be installing via requirements
# but that would always be a different version
# only required for CI. DO NOT add real tests here, but in top-level integration
def test_dummy():
pass
| 35.5
| 80
| 0.774648
| 46
| 284
| 4.76087
| 0.891304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18662
| 284
| 7
| 81
| 40.571429
| 0.948052
| 0.866197
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
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| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
5ac88e6c27fd12ec91876d1b4ae28023ba326d96
| 213
|
py
|
Python
|
ark/utils/metacluster_remap_gui/__init__.py
|
ngreenwald/segmentation
|
8bc87c2db96434a24194040f7ea754af2caf5e5f
|
[
"Apache-2.0"
] | 1
|
2020-01-15T22:23:41.000Z
|
2020-01-15T22:23:41.000Z
|
ark/utils/metacluster_remap_gui/__init__.py
|
ngreenwald/segmentation
|
8bc87c2db96434a24194040f7ea754af2caf5e5f
|
[
"Apache-2.0"
] | 103
|
2020-01-06T23:32:43.000Z
|
2020-08-14T04:42:00.000Z
|
ark/utils/metacluster_remap_gui/__init__.py
|
ngreenwald/segmentation
|
8bc87c2db96434a24194040f7ea754af2caf5e5f
|
[
"Apache-2.0"
] | 5
|
2020-02-21T14:00:20.000Z
|
2020-07-02T07:41:33.000Z
|
from .file_reader import metaclusterdata_from_files
from .metaclusterdata import MetaClusterData
from .metaclustergui import MetaClusterGui
__all__ = [MetaClusterGui, MetaClusterData, metaclusterdata_from_files]
| 35.5
| 71
| 0.877934
| 21
| 213
| 8.47619
| 0.380952
| 0.320225
| 0.280899
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084507
| 213
| 5
| 72
| 42.6
| 0.912821
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
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| 0
| null | 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
5affcda4a8c2e1e8aba84617b88fe9878aa3194e
| 82
|
py
|
Python
|
NLP/Text2SQL-BASELINE/text2sql/models/__init__.py
|
zhangyimi/Research
|
866f91d9774a38d205d6e9a3b1ee6293748261b3
|
[
"Apache-2.0"
] | 1,319
|
2020-02-14T10:42:07.000Z
|
2022-03-31T15:42:18.000Z
|
NLP/Text2SQL-BASELINE/text2sql/models/__init__.py
|
green9989/Research
|
94519a72e7936c77f62a31709634b72c09aabf74
|
[
"Apache-2.0"
] | 192
|
2020-02-14T02:53:34.000Z
|
2022-03-31T02:25:48.000Z
|
NLP/Text2SQL-BASELINE/text2sql/models/__init__.py
|
green9989/Research
|
94519a72e7936c77f62a31709634b72c09aabf74
|
[
"Apache-2.0"
] | 720
|
2020-02-14T02:12:38.000Z
|
2022-03-31T12:21:15.000Z
|
# -*- coding: utf-8 -*-
"""text2sql models"""
from .enc_dec import EncDecModel
| 11.714286
| 32
| 0.634146
| 10
| 82
| 5.1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029412
| 0.170732
| 82
| 6
| 33
| 13.666667
| 0.720588
| 0.463415
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
8514b05423de218f2f90ab1a2f160cf5760c8c21
| 425
|
py
|
Python
|
kinopoisk_unofficial/client/persons_api_client.py
|
masterWeber/kinopoisk-api-unofficial-client
|
5c95e1ec6e43bd302399b63a1525ee7e61724155
|
[
"MIT"
] | 2
|
2021-11-13T12:23:41.000Z
|
2021-12-24T14:09:49.000Z
|
kinopoisk_unofficial/client/persons_api_client.py
|
masterWeber/kinopoisk-api-unofficial-client
|
5c95e1ec6e43bd302399b63a1525ee7e61724155
|
[
"MIT"
] | 1
|
2022-03-29T19:13:24.000Z
|
2022-03-30T18:57:23.000Z
|
kinopoisk_unofficial/client/persons_api_client.py
|
masterWeber/kinopoisk-api-unofficial-client
|
5c95e1ec6e43bd302399b63a1525ee7e61724155
|
[
"MIT"
] | 1
|
2021-11-13T12:30:01.000Z
|
2021-11-13T12:30:01.000Z
|
from kinopoisk_unofficial.client.api_client import ApiClient
from kinopoisk_unofficial.request.persons.person_by_name_request import PersonByNameRequest
from kinopoisk_unofficial.response.persons.person_by_name_response import PersonByNameResponse
class PersonsApiClient(ApiClient):
def send_person_by_name_request(self, request: PersonByNameRequest) -> PersonByNameResponse:
return self._send_request(request)
| 47.222222
| 96
| 0.865882
| 48
| 425
| 7.333333
| 0.4375
| 0.110795
| 0.196023
| 0.107955
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084706
| 425
| 8
| 97
| 53.125
| 0.904884
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.5
| 0.166667
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
851f2e31e3a6a9164238cae5d8441f99c7368fa1
| 22
|
py
|
Python
|
Python/hello_earth.py
|
kennethsequeira/Hello-world
|
464227bc7d9778a4a2a4044fe415a629003ea77f
|
[
"MIT"
] | 1,428
|
2018-10-03T15:15:17.000Z
|
2019-03-31T18:38:36.000Z
|
Python/hello_earth.py
|
kennethsequeira/Hello-world
|
464227bc7d9778a4a2a4044fe415a629003ea77f
|
[
"MIT"
] | 1,162
|
2018-10-03T15:05:49.000Z
|
2018-10-18T14:17:52.000Z
|
Python/hello_earth.py
|
kennethsequeira/Hello-world
|
464227bc7d9778a4a2a4044fe415a629003ea77f
|
[
"MIT"
] | 3,909
|
2018-10-03T15:07:19.000Z
|
2019-03-31T18:39:08.000Z
|
print('hello earth!')
| 11
| 21
| 0.681818
| 3
| 22
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 22
| 1
| 22
| 22
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0.545455
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
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