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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
418f61799280733e6ccd844f14ac7a7c940d4228 | 74 | py | Python | python/function/volume.py | kobdani/tutorials | aea4528d587a6d9547ab78273646556fd7ffb105 | [
"MIT"
] | null | null | null | python/function/volume.py | kobdani/tutorials | aea4528d587a6d9547ab78273646556fd7ffb105 | [
"MIT"
] | null | null | null | python/function/volume.py | kobdani/tutorials | aea4528d587a6d9547ab78273646556fd7ffb105 | [
"MIT"
] | null | null | null | def sphere_volume(r):
return (4/3)*3.14*pow(r,3)
print(sphere_volume(4)) | 18.5 | 27 | 0.702703 | 16 | 74 | 3.125 | 0.625 | 0.48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102941 | 0.081081 | 74 | 4 | 28 | 18.5 | 0.632353 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
419b29d02a25d18b1cd5fe3d015a6520b3c477f2 | 136 | py | Python | pyplanning/solvers/__init__.py | kbvatral/pyplanning | f85f2976d5a167b61f782a74e1b72d83654e07ff | [
"MIT"
] | null | null | null | pyplanning/solvers/__init__.py | kbvatral/pyplanning | f85f2976d5a167b61f782a74e1b72d83654e07ff | [
"MIT"
] | null | null | null | pyplanning/solvers/__init__.py | kbvatral/pyplanning | f85f2976d5a167b61f782a74e1b72d83654e07ff | [
"MIT"
] | null | null | null | from .search import search_plan
from .graphplan import graph_plan
from . import search
from . import heuristics
from . import graphplan | 22.666667 | 33 | 0.816176 | 19 | 136 | 5.736842 | 0.368421 | 0.275229 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.147059 | 136 | 6 | 34 | 22.666667 | 0.939655 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
41be89a25ad729698199a6ddb0587fbe038bce43 | 292 | py | Python | portfolio_backtester/__init__.py | zhudechuan/portfolio_backtester | 01a04008788afe598bb6333127447f6f1f7b0e35 | [
"MIT"
] | null | null | null | portfolio_backtester/__init__.py | zhudechuan/portfolio_backtester | 01a04008788afe598bb6333127447f6f1f7b0e35 | [
"MIT"
] | null | null | null | portfolio_backtester/__init__.py | zhudechuan/portfolio_backtester | 01a04008788afe598bb6333127447f6f1f7b0e35 | [
"MIT"
] | null | null | null | from portfolio_backtester.backtest_model import backtest_model
from portfolio_backtester.backtest_model import naive_alloc, iv_alloc, min_var, basic_mean_variance, FF_3_factor_model, \
hrp_alloc, Bayes_Stein_shrink, no_short_sell
from portfolio_backtester.backtest_model import fetch_data | 73 | 121 | 0.883562 | 43 | 292 | 5.511628 | 0.604651 | 0.219409 | 0.291139 | 0.392405 | 0.531646 | 0.531646 | 0 | 0 | 0 | 0 | 0 | 0.003717 | 0.078767 | 292 | 4 | 122 | 73 | 0.877323 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
ec22f61a3ad82ecbf3480d456056f463af3a28ca | 237 | py | Python | python/inherit.py | robotlightsyou/test | 015f13943fc402d8ce86c5f6d2f5a7d032b3340a | [
"MIT"
] | 2 | 2019-05-26T15:09:34.000Z | 2021-09-12T08:01:23.000Z | python/inherit.py | robotlightsyou/test | 015f13943fc402d8ce86c5f6d2f5a7d032b3340a | [
"MIT"
] | null | null | null | python/inherit.py | robotlightsyou/test | 015f13943fc402d8ce86c5f6d2f5a7d032b3340a | [
"MIT"
] | 1 | 2021-04-11T20:28:21.000Z | 2021-04-11T20:28:21.000Z | # class Foo:
# class Bar(Foo):
# pass
class Parent1:
def foo(self):
return 'parent1'
class Parent2:
def foo(self):
return 'parent2'
class Child(Parent1, Parent2):
pass
print(Child().foo())
| 11.285714 | 30 | 0.565401 | 28 | 237 | 4.785714 | 0.392857 | 0.089552 | 0.149254 | 0.238806 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036585 | 0.308017 | 237 | 20 | 31 | 11.85 | 0.780488 | 0.181435 | 0 | 0.222222 | 0 | 0 | 0.073684 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0.111111 | 0 | 0.222222 | 0.777778 | 0.111111 | 1 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
ec4329f5827802583fb00ed87fa89ef2f060b6b9 | 17,338 | py | Python | likes/api.py | CMPUT404-stev-sand-pant-ashw-mehr/CMPUT404-stev-sand-pant-ashw-mehr-repo | 0f96d938e9e3ec51103f2b20cb9673bd0b145343 | [
"MIT"
] | null | null | null | likes/api.py | CMPUT404-stev-sand-pant-ashw-mehr/CMPUT404-stev-sand-pant-ashw-mehr-repo | 0f96d938e9e3ec51103f2b20cb9673bd0b145343 | [
"MIT"
] | 50 | 2021-10-08T00:01:43.000Z | 2021-12-06T06:34:29.000Z | likes/api.py | CMPUT404-stev-sand-pant-ashw-mehr/CMPUT404-stev-sand-pant-ashw-mehr-repo | 0f96d938e9e3ec51103f2b20cb9673bd0b145343 | [
"MIT"
] | null | null | null | from author.models import Author
from comment.models import Comment
from likes.models import Like
from post.models import Post
from rest_framework import viewsets, status
from rest_framework.response import Response
from .serializers import LikeSerializer
from author.serializer import AuthorSerializer
from accounts.permissions import CustomAuthentication, AccessPermission
from accounts.helper import is_valid_node
from drf_yasg.utils import swagger_auto_schema
from drf_yasg import openapi
import json
from urllib.parse import urlparse
class PostLikeViewSet(viewsets.ModelViewSet):
authentication_classes = (CustomAuthentication,)
permission_classes = (AccessPermission,)
serializer_class = LikeSerializer
@swagger_auto_schema(
operation_description="GET /service/author/< AUTHOR_ID >/post/< POST_ID >/likes",
responses={
"200": openapi.Response(
description="OK",
examples={
"application/json": {
"@context": "https://www.w3.org/ns/activitystreams",
"summary": "Lara Croft Likes your post",
"type": "Like",
"author":{
"type":"author",
"id":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"host":"http://127.0.0.1:5454/",
"displayName":"Lara Croft",
"url":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"github":"http://github.com/laracroft",
"profileImage": "https://i.imgur.com/k7XVwpB.jpeg"
},
"object":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e/posts/764efa883dda1e11db47671c4a3bbd9e"
}
}
),
"403": openapi.Response(
description="Forbidden",
examples={
"application/json":{"message":"Node not allowed"}
}
),
},
tags=['Get Post Likes'],
)
def get_post_likes(self, request, author_id, post_id):
# node check
valid = is_valid_node(request)
if not valid:
return Response({"message":"Node not allowed"}, status=status.HTTP_403_FORBIDDEN)
try:
Author.objects.exclude(is_active=False).get(id=author_id)
except:
return Response({"detail": "author not found"}, status=status.HTTP_404_NOT_FOUND)
query_set = Like.objects.filter(post=Post.objects.get(id=post_id))
response = LikeSerializer(query_set, many=True).data
for likeObj in response:
likeObj['@context'] = "https://www.w3.org/ns/activitystreams"
likeObj['author'] = AuthorSerializer(Author.objects.get(id=likeObj["author"])).data
name = likeObj['author']["displayName"]
return Response(response, status=status.HTTP_200_OK)
@swagger_auto_schema(
operation_description="POST /service/author/< AUTHOR_ID >/post/< POST_ID >/likes",
responses={
"200": openapi.Response(
description="OK",
examples={
"application/json": {
"@context": "https://www.w3.org/ns/activitystreams",
"summary": "Lara Croft Likes your post",
"type": "Like",
"author":{
"type":"author",
"id":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"host":"http://127.0.0.1:5454/",
"displayName":"Lara Croft",
"url":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"github":"http://github.com/laracroft",
"profileImage": "https://i.imgur.com/k7XVwpB.jpeg"
},
"object":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e/posts/764efa883dda1e11db47671c4a3bbd9e"
}
}
),
"403": openapi.Response(
description="Forbidden",
examples={
"application/json":{"message":"Node not allowed"}
}
),
"409": openapi.Response(
description="Conflict",
examples={
"application/json":{"message": "error"}
}
),
},
tags=['Like Post'],
)
def add_post_like(self, request, author_id, post_id):
# node check
valid = is_valid_node(request)
if not valid:
return Response({"message":"Node not allowed"}, status=status.HTTP_403_FORBIDDEN)
try:
Author.objects.exclude(is_active=False).get(id=author_id)
except:
return Response({"detail": "author not found"}, status=status.HTTP_404_NOT_FOUND)
result, obj = add_author_to_database(request=request)
if not result:
return Response(obj, status=status.HTTP_400_BAD_REQUEST)
else:
author_inst = obj
try:
query_set = Post.objects.get(id=post_id).like_set.create(author=author_inst, object=request.build_absolute_uri().strip("/likes"))
except Exception as e:
return Response({"message": e.args}, status=status.HTTP_409_CONFLICT)
response = LikeSerializer(query_set).data
response['@context'] = "https://www.w3.org/ns/activitystreams"
response['author'] = AuthorSerializer(Author.objects.get(id=response["author"])).data
name = response['author']["displayName"]
response["summary"] = f"{name} Likes your post"
return Response(response, status=status.HTTP_200_OK)
class CommentLikeViewSet(viewsets.ModelViewSet):
authentication_classes = (CustomAuthentication,)
permission_classes = (AccessPermission,)
serializer_class = LikeSerializer
@swagger_auto_schema(
operation_description="GET /service/author/< AUTHOR_ID >/post/< POST_ID >/comments/{ COMMENT_ID }/likes",
responses={
"200": openapi.Response(
description="OK",
examples={
"application/json": {
"@context": "https://www.w3.org/ns/activitystreams",
"summary": "Lara Croft Likes your comment",
"type": "Like",
"author":{
"type":"author",
"id":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"host":"http://127.0.0.1:5454/",
"displayName":"Lara Croft",
"url":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"github":"http://github.com/laracroft",
"profileImage": "https://i.imgur.com/k7XVwpB.jpeg"
},
"object":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e/posts/764efa883dda1e11db47671c4a3bbd9e/comments/764efa883dda1e11db47671c4a3bbd9f"
}
}
),
"403": openapi.Response(
description="Forbidden",
examples={
"application/json":{"message":"Node not allowed"}
}
),
},
tags=['Get Comment Likes'],
)
def get_comment_likes(self, request, author_id, post_id, comment_id):
# node check
valid = is_valid_node(request)
if not valid:
return Response({"message":"Node not allowed"}, status=status.HTTP_403_FORBIDDEN)
try:
Author.objects.exclude(is_active=False).get(id=author_id)
except:
return Response({"detail": "author not found"}, status=status.HTTP_404_NOT_FOUND)
query_set = Like.objects.filter(comment=Comment.objects.get(id=comment_id))
response = LikeSerializer(query_set, many=True).data
for likeObj in response:
likeObj['@context'] = "https://www.w3.org/ns/activitystreams"
likeObj['author'] = AuthorSerializer(Author.objects.get(id=likeObj["author"])).data
name = likeObj['author']["displayName"]
likeObj["summary"] = f"{name} Likes your comment"
return Response(response, status=status.HTTP_200_OK)
@swagger_auto_schema(
operation_description="POST /service/author/< AUTHOR_ID >/post/< POST_ID >/comments/{ COMMENT_ID }/likes",
responses={
"200": openapi.Response(
description="OK",
examples={
"application/json": {
"@context": "https://www.w3.org/ns/activitystreams",
"summary": "Lara Croft Likes your comment",
"type": "Like",
"author":{
"type":"author",
"id":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"host":"http://127.0.0.1:5454/",
"displayName":"Lara Croft",
"url":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"github":"http://github.com/laracroft",
"profileImage": "https://i.imgur.com/k7XVwpB.jpeg"
},
"object":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e/posts/764efa883dda1e11db47671c4a3bbd9e/comments/764efa883dda1e11db47671c4a3bbd9f"
}
}
),
"403": openapi.Response(
description="Forbidden",
examples={
"application/json":{"message":"Node not allowed"}
}
),
"409": openapi.Response(
description="Conflict",
examples={
"application/json":{"message": "error"}
}
),
},
tags=['Like Comment'],
)
def add_comment_like(self, request, author_id, post_id, comment_id):
# node check
valid = is_valid_node(request)
if not valid:
return Response({"message":"Node not allowed"}, status=status.HTTP_403_FORBIDDEN)
try:
Author.objects.exclude(is_active=False).get(id=author_id)
except:
return Response({"detail": "author not found"}, status=status.HTTP_404_NOT_FOUND)
try:
Post.objects.get(id=post_id)
except:
return Response({"detail": "post not found"}, status=status.HTTP_404_NOT_FOUND)
result, obj = add_author_to_database(request=request)
if not result:
return Response(obj, status=status.HTTP_400_BAD_REQUEST)
else:
author_inst = obj
try:
query_set = Comment.objects.get(id=comment_id).like_set.create(author=author_inst, object=request.build_absolute_uri().strip("/likes"))
except Exception as e:
return Response({"message": e.args}, status=status.HTTP_409_CONFLICT)
response = LikeSerializer(query_set).data
response['@context'] = "https://www.w3.org/ns/activitystreams"
response['author'] = AuthorSerializer(Author.objects.get(id=response["author"])).data
name = response['author']["displayName"]
response["summary"] = f"{name} Likes your comment"
response["object"] = request.build_absolute_uri().strip("/likes")
return Response(response, status=status.HTTP_200_OK)
class AuthorLikeViewSet(viewsets.ModelViewSet):
authentication_classes = (CustomAuthentication,)
permission_classes = (AccessPermission,)
serializer_class = LikeSerializer
@swagger_auto_schema(
operation_description="GET /service/author/< AUTHOR_ID >/liked",
responses={
"200": openapi.Response(
description="OK",
examples={
"type":"liked",
"items":[
{
"@context": "https://www.w3.org/ns/activitystreams",
"summary": "Lara Croft Likes your post",
"type": "Like",
"author":{
"type":"author",
"id":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"host":"http://127.0.0.1:5454/",
"displayName":"Lara Croft",
"url":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e",
"github":"http://github.com/laracroft",
"profileImage": "https://i.imgur.com/k7XVwpB.jpeg"
},
"object":"http://127.0.0.1:5454/author/9de17f29c12e8f97bcbbd34cc908f1baba40658e/posts/764efa883dda1e11db47671c4a3bbd9e"
}
]
}
),
"403": openapi.Response(
description="Forbidden",
examples={
"application/json":{"message":"Node not allowed"}
}
),
"404": openapi.Response(
description="Not Found",
examples={
"application/json":{"detail": "author not found"}
}
),
},
tags=['Get Comment Likes'],
)
def get_likes(self, request, author_id):
# node check
valid = is_valid_node(request)
if not valid:
return Response({"message":"Node not allowed"}, status=status.HTTP_403_FORBIDDEN)
if not Author.objects.exclude(is_active=False).filter(id=author_id).exists():
return Response({"detail": "author not found"}, status=status.HTTP_404_NOT_FOUND)
query_set = Like.objects.filter(author=author_id).all()
data = LikeSerializer(query_set, many=True).data
for likeObj in data:
likeObj['@context'] = "https://www.w3.org/ns/activitystreams"
likeObj['author'] = AuthorSerializer(Author.objects.get(id=likeObj["author"])).data
name = likeObj['author']["displayName"]
objUrlsplitted = likeObj['object'].split('/')
if {"post", "posts"}.intersection(set(objUrlsplitted)):
ptype = "post"
else:
ptype = "comment"
likeObj["summary"] = f"{name} Likes your {ptype}"
response = {
'type': 'liked',
"items": data
}
return Response(response, status=status.HTTP_200_OK)
def add_author_to_database(request):
try:
if hasattr(request, "data") and "author" in request.data:
author_json = request.data["author"]
else:
request_data = json.loads(request.body.decode('utf-8'))
author_json = request_data["author"]
if type(author_json) == dict:
author_dict = author_json
else:
author_dict = json.loads(author_json)
except json.JSONDecodeError as e:
return False, {"detail": f"Invalid author JSON: {e.msg}"}
except:
try:
author = Author.objects.get(user = request.user)
return True, author
except:
return False, {"detail": "JSON author missing"}
author_validation = AuthorSerializer(data=author_dict)
if not author_validation.is_valid():
return False, author_validation.error_messages
else:
if urlparse(author_dict["id"]).hostname in ("social-dis.herokuapp.com", "127.0.0.1"):
author_path = urlparse(author_dict["id"]).path
if author_path[-1] == '/':
author_path = author_path[:-1]
author_id = author_path.split("/")[-1]
else:
author_id = author_dict["id"]
author, created = Author.objects.get_or_create(id = author_id)
author_dict.pop("id")
if not created:
author_dict.pop("url")
author_dict.pop("host")
for key, value in author_dict.items():
try:
setattr(author, key, value)
except:
pass
author.is_active = True
author.save()
return True, author
| 41.778313 | 185 | 0.533337 | 1,586 | 17,338 | 5.703657 | 0.111602 | 0.021225 | 0.011607 | 0.013929 | 0.809087 | 0.794937 | 0.766195 | 0.754588 | 0.749834 | 0.734137 | 0 | 0.067038 | 0.347849 | 17,338 | 415 | 186 | 41.778313 | 0.732997 | 0.003115 | 0 | 0.650704 | 0 | 0.005634 | 0.250477 | 0.001389 | 0 | 0 | 0 | 0 | 0 | 1 | 0.016901 | false | 0.002817 | 0.039437 | 0 | 0.160563 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ec4efef29eba506f0ad7d12897a03be963c19958 | 483 | py | Python | samples/server/petstore/flaskConnexion-python2/controllers/user_controller.py | victoracn/swagger-codegen | b51285c810ee6024d937a8f234a77910af6fdb33 | [
"Apache-2.0"
] | 1 | 2016-11-04T01:12:31.000Z | 2016-11-04T01:12:31.000Z | samples/server/petstore/flaskConnexion-python2/controllers/user_controller.py | victoracn/swagger-codegen | b51285c810ee6024d937a8f234a77910af6fdb33 | [
"Apache-2.0"
] | null | null | null | samples/server/petstore/flaskConnexion-python2/controllers/user_controller.py | victoracn/swagger-codegen | b51285c810ee6024d937a8f234a77910af6fdb33 | [
"Apache-2.0"
] | 3 | 2018-09-03T12:58:01.000Z | 2021-02-19T06:00:30.000Z |
def create_user(body):
return 'do some magic!'
def create_users_with_array_input(body):
return 'do some magic!'
def create_users_with_list_input(body):
return 'do some magic!'
def delete_user(username):
return 'do some magic!'
def get_user_by_name(username):
return 'do some magic!'
def login_user(username, password):
return 'do some magic!'
def logout_user():
return 'do some magic!'
def update_user(username, body):
return 'do some magic!'
| 19.32 | 40 | 0.710145 | 73 | 483 | 4.479452 | 0.30137 | 0.195719 | 0.293578 | 0.415902 | 0.700306 | 0.513761 | 0.342508 | 0.238532 | 0.238532 | 0 | 0 | 0 | 0.186335 | 483 | 24 | 41 | 20.125 | 0.832061 | 0 | 0 | 0.5 | 0 | 0 | 0.232365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0.0625 | 0 | 0.5 | 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 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
6b7d8ce573cade9789103ec126aaa3618ac0fe3b | 68 | py | Python | src/lk_db/ents/both/EntRegionSnap.py | nuuuwan/lk_db | ac0abfa47ba31b0d4c2c8566b3101b83749bd45d | [
"MIT"
] | null | null | null | src/lk_db/ents/both/EntRegionSnap.py | nuuuwan/lk_db | ac0abfa47ba31b0d4c2c8566b3101b83749bd45d | [
"MIT"
] | null | null | null | src/lk_db/ents/both/EntRegionSnap.py | nuuuwan/lk_db | ac0abfa47ba31b0d4c2c8566b3101b83749bd45d | [
"MIT"
] | null | null | null | from lk_db.ents.Ent import Ent
class EntRegionSnap(Ent):
pass
| 11.333333 | 30 | 0.735294 | 11 | 68 | 4.454545 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.191176 | 68 | 5 | 31 | 13.6 | 0.890909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
6bc5568c31df6b8a0bad1a0410f012df32c61dc1 | 47 | py | Python | examples/math.isfinite/ex2.py | mcorne/python-by-example | 15339c0909c84b51075587a6a66391100971c033 | [
"MIT"
] | null | null | null | examples/math.isfinite/ex2.py | mcorne/python-by-example | 15339c0909c84b51075587a6a66391100971c033 | [
"MIT"
] | null | null | null | examples/math.isfinite/ex2.py | mcorne/python-by-example | 15339c0909c84b51075587a6a66391100971c033 | [
"MIT"
] | null | null | null | import math
print(math.isfinite(float('inf')))
| 15.666667 | 34 | 0.744681 | 7 | 47 | 5 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06383 | 47 | 2 | 35 | 23.5 | 0.795455 | 0 | 0 | 0 | 0 | 0 | 0.06383 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 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 | 1 | 0 | 6 |
d45038924ef8b818769e934ce879e881a8e76ffa | 131 | py | Python | ROS_ws/src/lab2/traj_planning_ros/src/iLQR/ellipsoid/__init__.py | kenarvyas/ECE346 | f0229a9f3e03ca06e2d8fa74f9208fea5b2c29c7 | [
"MIT"
] | 4 | 2022-02-04T03:08:53.000Z | 2022-03-24T13:17:46.000Z | ROS_ws/src/lab2/traj_planning_ros/src/iLQR/ellipsoid/__init__.py | kenarvyas/ECE346 | f0229a9f3e03ca06e2d8fa74f9208fea5b2c29c7 | [
"MIT"
] | null | null | null | ROS_ws/src/lab2/traj_planning_ros/src/iLQR/ellipsoid/__init__.py | kenarvyas/ECE346 | f0229a9f3e03ca06e2d8fa74f9208fea5b2c29c7 | [
"MIT"
] | 12 | 2022-01-28T05:07:56.000Z | 2022-03-30T02:43:05.000Z | from .ellipsoid import Ellipsoid
from .dyn_sys import DynSys
from .reach import Reach
from .plot_ellipsoids import plot_ellipsoids
| 26.2 | 44 | 0.847328 | 19 | 131 | 5.684211 | 0.473684 | 0.259259 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122137 | 131 | 4 | 45 | 32.75 | 0.93913 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d46ddee3d84f0d1a49dcfc159a722eef872963ce | 40 | py | Python | ferizefirst/__init__.py | spetcha/ferizefirst | 87615ec4fd6cf4b33659fe9f91707d889035cee1 | [
"MIT"
] | null | null | null | ferizefirst/__init__.py | spetcha/ferizefirst | 87615ec4fd6cf4b33659fe9f91707d889035cee1 | [
"MIT"
] | null | null | null | ferizefirst/__init__.py | spetcha/ferizefirst | 87615ec4fd6cf4b33659fe9f91707d889035cee1 | [
"MIT"
] | null | null | null | from ferizefirst.myname import fullname
| 20 | 39 | 0.875 | 5 | 40 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.972222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d46f47e48bf2e023fa5faa461a2b76cd65fb5318 | 167 | py | Python | boa3_test/test_sc/interop_test/stdlib/Base58CheckDecode.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 25 | 2020-07-22T19:37:43.000Z | 2022-03-08T03:23:55.000Z | boa3_test/test_sc/interop_test/stdlib/Base58CheckDecode.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 419 | 2020-04-23T17:48:14.000Z | 2022-03-31T13:17:45.000Z | boa3_test/test_sc/interop_test/stdlib/Base58CheckDecode.py | hal0x2328/neo3-boa | 6825a3533384cb01660773050719402a9703065b | [
"Apache-2.0"
] | 15 | 2020-05-21T21:54:24.000Z | 2021-11-18T06:17:24.000Z | from boa3.builtin import public
from boa3.builtin.interop.stdlib import base58_check_decode
@public
def main(key: str) -> bytes:
return base58_check_decode(key)
| 20.875 | 59 | 0.790419 | 25 | 167 | 5.12 | 0.64 | 0.125 | 0.234375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.041379 | 0.131737 | 167 | 7 | 60 | 23.857143 | 0.841379 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0.2 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
d48a9bb059e7c001a6cabe244f7f00c0783cb046 | 19 | py | Python | malaya_speech/train/model/stft/__init__.py | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 111 | 2020-08-31T04:58:54.000Z | 2022-03-29T15:44:18.000Z | malaya_speech/train/model/stft/__init__.py | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 14 | 2020-12-16T07:27:22.000Z | 2022-03-15T17:39:01.000Z | malaya_speech/train/model/stft/__init__.py | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 29 | 2021-02-09T08:57:15.000Z | 2022-03-12T14:09:19.000Z | from . import loss
| 9.5 | 18 | 0.736842 | 3 | 19 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.210526 | 19 | 1 | 19 | 19 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2e52565f653537d26f22fd293cd1868cbe2bb16b | 48 | py | Python | CodeWars/Python/6 kyu/Bit Counting/main.py | opastushkov/codewars-solutions | 0132a24259a4e87f926048318332dcb4d94858ca | [
"MIT"
] | null | null | null | CodeWars/Python/6 kyu/Bit Counting/main.py | opastushkov/codewars-solutions | 0132a24259a4e87f926048318332dcb4d94858ca | [
"MIT"
] | null | null | null | CodeWars/Python/6 kyu/Bit Counting/main.py | opastushkov/codewars-solutions | 0132a24259a4e87f926048318332dcb4d94858ca | [
"MIT"
] | null | null | null | def countBits(n):
return (bin(n)).count('1') | 24 | 30 | 0.604167 | 8 | 48 | 3.625 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02439 | 0.145833 | 48 | 2 | 30 | 24 | 0.682927 | 0 | 0 | 0 | 0 | 0 | 0.020408 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
2e52dd3f03a01ad5c32e5f98c476139fa5853a95 | 119 | py | Python | receitas/views.py | G-ilian/Pyhton-Web | 5ac8a6ef955accba863100c05419105bb7fcb715 | [
"MIT"
] | null | null | null | receitas/views.py | G-ilian/Pyhton-Web | 5ac8a6ef955accba863100c05419105bb7fcb715 | [
"MIT"
] | null | null | null | receitas/views.py | G-ilian/Pyhton-Web | 5ac8a6ef955accba863100c05419105bb7fcb715 | [
"MIT"
] | null | null | null | from django.shortcuts import render
# Create your views here.
def index(request):
return render(request,'index.html') | 23.8 | 36 | 0.781513 | 17 | 119 | 5.470588 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 119 | 5 | 36 | 23.8 | 0.885714 | 0.193277 | 0 | 0 | 0 | 0 | 0.105263 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
2e8d405c85727cc284cfa63775be3aebaac741a3 | 3,271 | py | Python | stac_overflow/utils/datasets_test.py | stefanistrate/drivendata-stac-overflow | ff9ef1900ce02e5cb1e18d111ab571cf48b3a644 | [
"MIT"
] | null | null | null | stac_overflow/utils/datasets_test.py | stefanistrate/drivendata-stac-overflow | ff9ef1900ce02e5cb1e18d111ab571cf48b3a644 | [
"MIT"
] | 2 | 2022-02-01T17:09:38.000Z | 2022-02-01T17:09:51.000Z | stac_overflow/utils/datasets_test.py | stefanistrate/drivendata-stac-overflow | ff9ef1900ce02e5cb1e18d111ab571cf48b3a644 | [
"MIT"
] | null | null | null | """Tests for datasets utilities."""
import albumentations
import numpy as np
import tensorflow as tf
from stac_overflow.utils.datasets import augment_image_dataset
class TestAugmentImageDataset:
"""Test `augment_image_dataset()`."""
def _build_dataset(self):
return tf.data.Dataset.from_tensor_slices((
# images
[
[
[[-1], [-2], [-3], [-4]],
[[-5], [-6], [-7], [-8]],
[[-9], [10], [11], [12]],
],
[
[[101], [102], [103], [104]],
[[105], [106], [107], [108]],
[[109], [110], [111], [112]],
],
],
# labels
[
[
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
],
[
[0, 0, 1, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
],
],
))
def test_without_augment_labels(self):
ds = self._build_dataset()
transforms = albumentations.Compose([albumentations.Transpose(p=1.0)])
new_ds = augment_image_dataset(ds, transforms)
new_ds = list(new_ds.as_numpy_iterator())
assert len(new_ds) == 2 # list
assert len(new_ds[0]) == 2 # tuple
np.testing.assert_array_equal(new_ds[0][0], [
[[-1], [-5], [-9]],
[[-2], [-6], [10]],
[[-3], [-7], [11]],
[[-4], [-8], [12]],
])
np.testing.assert_array_equal(new_ds[0][1], [
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
])
assert len(new_ds[1]) == 2 # tuple
np.testing.assert_array_equal(new_ds[1][0], [
[[101], [105], [109]],
[[102], [106], [110]],
[[103], [107], [111]],
[[104], [108], [112]],
])
np.testing.assert_array_equal(new_ds[1][1], [
[0, 0, 1, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
])
def test_with_augment_labels(self):
ds = self._build_dataset()
transforms = albumentations.Compose([albumentations.Transpose(p=1.0)])
new_ds = augment_image_dataset(ds, transforms, augment_labels=True)
new_ds = list(new_ds.as_numpy_iterator())
assert len(new_ds) == 2 # list
assert len(new_ds[0]) == 2 # tuple
np.testing.assert_array_equal(new_ds[0][0], [
[[-1], [-5], [-9]],
[[-2], [-6], [10]],
[[-3], [-7], [11]],
[[-4], [-8], [12]],
])
np.testing.assert_array_equal(new_ds[0][1], [
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0],
])
assert len(new_ds[1]) == 2 # tuple
np.testing.assert_array_equal(new_ds[1][0], [
[[101], [105], [109]],
[[102], [106], [110]],
[[103], [107], [111]],
[[104], [108], [112]],
])
np.testing.assert_array_equal(new_ds[1][1], [
[0, 0, 1],
[0, 1, 0],
[1, 0, 0],
[0, 0, 0],
])
| 30.858491 | 78 | 0.389483 | 367 | 3,271 | 3.297003 | 0.196185 | 0.059504 | 0.042149 | 0.042975 | 0.726446 | 0.72562 | 0.720661 | 0.720661 | 0.720661 | 0.720661 | 0 | 0.133648 | 0.416692 | 3,271 | 105 | 79 | 31.152381 | 0.500524 | 0.033629 | 0 | 0.744681 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148936 | 1 | 0.031915 | false | 0 | 0.042553 | 0.010638 | 0.095745 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5cea3f263101de9ea2182db79dc68cf4887c1d54 | 147 | py | Python | teste.py | guihlr/teste_travis | 32c3cbd4e45c14b3afafaca71ddf1bc51067953d | [
"Apache-2.0"
] | null | null | null | teste.py | guihlr/teste_travis | 32c3cbd4e45c14b3afafaca71ddf1bc51067953d | [
"Apache-2.0"
] | null | null | null | teste.py | guihlr/teste_travis | 32c3cbd4e45c14b3afafaca71ddf1bc51067953d | [
"Apache-2.0"
] | null | null | null | import pytest
from main import soma
from main import multi
def test_soma():
assert soma(6, 4) == 10
def test_multi():
assert multi(2, 4) == 8 | 16.333333 | 25 | 0.693878 | 26 | 147 | 3.846154 | 0.538462 | 0.16 | 0.28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.059322 | 0.197279 | 147 | 9 | 26 | 16.333333 | 0.788136 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.285714 | 1 | 0.285714 | true | 0 | 0.428571 | 0 | 0.714286 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
cf0ad8ecf683fb87d12c1d50b9afc8f71ae46e80 | 24,736 | py | Python | src/script_map.py | summerlight/anlp | d7c25d2da4f118febaa5fe410a1f1f584d7ad1ed | [
"Apache-2.0"
] | 1 | 2019-09-28T22:25:22.000Z | 2019-09-28T22:25:22.000Z | src/script_map.py | summerlight/anlp | d7c25d2da4f118febaa5fe410a1f1f584d7ad1ed | [
"Apache-2.0"
] | 8 | 2016-03-12T23:09:13.000Z | 2016-04-30T06:38:38.000Z | src/script_map.py | summerlight/anlp | d7c25d2da4f118febaa5fe410a1f1f584d7ad1ed | [
"Apache-2.0"
] | 1 | 2022-03-01T23:45:51.000Z | 2022-03-01T23:45:51.000Z | import numpy
# generated by unicode_script.py
CODEPOINT = numpy.array([0x40, 0x5a, 0x60, 0x7a, 0xa9, 0xaa, 0xb9, 0xba, 0xbf, 0xd6, 0xd7, 0xf6, 0xf7, 0x2b8, 0x2df, 0x2e4, 0x2e9, 0x2eb, 0x2ff, 0x36f, 0x373, 0x374, 0x377, 0x379, 0x37d, 0x37e, 0x37f, 0x383, 0x384, 0x385, 0x386, 0x387, 0x38a, 0x38b, 0x38c, 0x38d, 0x3a1, 0x3a2, 0x3e1, 0x3ef, 0x3ff, 0x484, 0x486, 0x52f, 0x530, 0x556, 0x558, 0x55f, 0x560, 0x587, 0x588, 0x589, 0x58a, 0x58c, 0x58f, 0x590, 0x5c7, 0x5cf, 0x5ea, 0x5ef, 0x5f4, 0x5ff, 0x604, 0x605, 0x60b, 0x60c, 0x61a, 0x61c, 0x61d, 0x61e, 0x61f, 0x63f, 0x640, 0x64a, 0x655, 0x66f, 0x670, 0x6dc, 0x6dd, 0x6ff, 0x70d, 0x70e, 0x74a, 0x74c, 0x74f, 0x77f, 0x7b1, 0x7bf, 0x7fa, 0x7ff, 0x82d, 0x82f, 0x83e, 0x83f, 0x85b, 0x85d, 0x85e, 0x89f, 0x8b4, 0x8e2, 0x8ff, 0x950, 0x952, 0x963, 0x965, 0x97f, 0x983, 0x984, 0x98c, 0x98e, 0x990, 0x992, 0x9a8, 0x9a9, 0x9b0, 0x9b1, 0x9b2, 0x9b5, 0x9b9, 0x9bb, 0x9c4, 0x9c6, 0x9c8, 0x9ca, 0x9ce, 0x9d6, 0x9d7, 0x9db, 0x9dd, 0x9de, 0x9e3, 0x9e5, 0x9fb, 0xa00, 0xa03, 0xa04, 0xa0a, 0xa0e, 0xa10, 0xa12, 0xa28, 0xa29, 0xa30, 0xa31, 0xa33, 0xa34, 0xa36, 0xa37, 0xa39, 0xa3b, 0xa3c, 0xa3d, 0xa42, 0xa46, 0xa48, 0xa4a, 0xa4d, 0xa50, 0xa51, 0xa58, 0xa5c, 0xa5d, 0xa5e, 0xa65, 0xa75, 0xa80, 0xa83, 0xa84, 0xa8d, 0xa8e, 0xa91, 0xa92, 0xaa8, 0xaa9, 0xab0, 0xab1, 0xab3, 0xab4, 0xab9, 0xabb, 0xac5, 0xac6, 0xac9, 0xaca, 0xacd, 0xacf, 0xad0, 0xadf, 0xae3, 0xae5, 0xaf1, 0xaf8, 0xaf9, 0xb00, 0xb03, 0xb04, 0xb0c, 0xb0e, 0xb10, 0xb12, 0xb28, 0xb29, 0xb30, 0xb31, 0xb33, 0xb34, 0xb39, 0xb3b, 0xb44, 0xb46, 0xb48, 0xb4a, 0xb4d, 0xb55, 0xb57, 0xb5b, 0xb5d, 0xb5e, 0xb63, 0xb65, 0xb77, 0xb81, 0xb83, 0xb84, 0xb8a, 0xb8d, 0xb90, 0xb91, 0xb95, 0xb98, 0xb9a, 0xb9b, 0xb9c, 0xb9d, 0xb9f, 0xba2, 0xba4, 0xba7, 0xbaa, 0xbad, 0xbb9, 0xbbd, 0xbc2, 0xbc5, 0xbc8, 0xbc9, 0xbcd, 0xbcf, 0xbd0, 0xbd6, 0xbd7, 0xbe5, 0xbfa, 0xbff, 0xc03, 0xc04, 0xc0c, 0xc0d, 0xc10, 0xc11, 0xc28, 0xc29, 0xc39, 0xc3c, 0xc44, 0xc45, 0xc48, 0xc49, 0xc4d, 0xc54, 0xc56, 0xc57, 0xc5a, 0xc5f, 0xc63, 0xc65, 0xc6f, 0xc77, 0xc7f, 0xc80, 0xc83, 0xc84, 0xc8c, 0xc8d, 0xc90, 0xc91, 0xca8, 0xca9, 0xcb3, 0xcb4, 0xcb9, 0xcbb, 0xcc4, 0xcc5, 0xcc8, 0xcc9, 0xccd, 0xcd4, 0xcd6, 0xcdd, 0xcde, 0xcdf, 0xce3, 0xce5, 0xcef, 0xcf0, 0xcf2, 0xd00, 0xd03, 0xd04, 0xd0c, 0xd0d, 0xd10, 0xd11, 0xd3a, 0xd3c, 0xd44, 0xd45, 0xd48, 0xd49, 0xd4e, 0xd56, 0xd57, 0xd5e, 0xd63, 0xd65, 0xd75, 0xd78, 0xd7f, 0xd81, 0xd83, 0xd84, 0xd96, 0xd99, 0xdb1, 0xdb2, 0xdbb, 0xdbc, 0xdbd, 0xdbf, 0xdc6, 0xdc9, 0xdca, 0xdce, 0xdd4, 0xdd5, 0xdd6, 0xdd7, 0xddf, 0xde5, 0xdef, 0xdf1, 0xdf4, 0xe00, 0xe3a, 0xe3e, 0xe3f, 0xe5b, 0xe80, 0xe82, 0xe83, 0xe84, 0xe86, 0xe88, 0xe89, 0xe8a, 0xe8c, 0xe8d, 0xe93, 0xe97, 0xe98, 0xe9f, 0xea0, 0xea3, 0xea4, 0xea5, 0xea6, 0xea7, 0xea9, 0xeab, 0xeac, 0xeb9, 0xeba, 0xebd, 0xebf, 0xec4, 0xec5, 0xec6, 0xec7, 0xecd, 0xecf, 0xed9, 0xedb, 0xedf, 0xeff, 0xf47, 0xf48, 0xf6c, 0xf70, 0xf97, 0xf98, 0xfbc, 0xfbd, 0xfcc, 0xfcd, 0xfd4, 0xfd8, 0xfda, 0xfff, 0x109f, 0x10c5, 0x10c6, 0x10c7, 0x10cc, 0x10cd, 0x10cf, 0x10fa, 0x10fb, 0x10ff, 0x11ff, 0x1248, 0x1249, 0x124d, 0x124f, 0x1256, 0x1257, 0x1258, 0x1259, 0x125d, 0x125f, 0x1288, 0x1289, 0x128d, 0x128f, 0x12b0, 0x12b1, 0x12b5, 0x12b7, 0x12be, 0x12bf, 0x12c0, 0x12c1, 0x12c5, 0x12c7, 0x12d6, 0x12d7, 0x1310, 0x1311, 0x1315, 0x1317, 0x135a, 0x135c, 0x137c, 0x137f, 0x1399, 0x139f, 0x13f5, 0x13f7, 0x13fd, 0x167f, 0x169c, 0x169f, 0x16ea, 0x16ed, 0x16f8, 0x16ff, 0x170c, 0x170d, 0x1714, 0x171f, 0x1734, 0x1736, 0x173f, 0x1753, 0x175f, 0x176c, 0x176d, 0x1770, 0x1771, 0x1773, 0x177f, 0x17dd, 0x17df, 0x17e9, 0x17ef, 0x17f9, 0x17ff, 0x1801, 0x1803, 0x1804, 0x1805, 0x180e, 0x180f, 0x1819, 0x181f, 0x1877, 0x187f, 0x18aa, 0x18ff, 0x191e, 0x191f, 0x192b, 0x192f, 0x193b, 0x193f, 0x1940, 0x1943, 0x194f, 0x19df, 0x19ff, 0x1a1b, 0x1a1d, 0x1a1f, 0x1aaf, 0x1abe, 0x1aff, 0x1b4b, 0x1b4f, 0x1b7c, 0x1b7f, 0x1bbf, 0x1bf3, 0x1bfb, 0x1bff, 0x1c37, 0x1c3a, 0x1c49, 0x1c4c, 0x1c4f, 0x1cbf, 0x1cc7, 0x1ccf, 0x1cd2, 0x1cd3, 0x1ce0, 0x1ce1, 0x1ce8, 0x1cec, 0x1ced, 0x1cf3, 0x1cf4, 0x1cf6, 0x1cf7, 0x1cf9, 0x1cff, 0x1d25, 0x1d2a, 0x1d2b, 0x1d5c, 0x1d61, 0x1d65, 0x1d6a, 0x1d77, 0x1d78, 0x1dbe, 0x1dbf, 0x1df5, 0x1dfb, 0x1dff, 0x1eff, 0x1f15, 0x1f17, 0x1f1d, 0x1f1f, 0x1f45, 0x1f47, 0x1f4d, 0x1f4f, 0x1f57, 0x1f58, 0x1f59, 0x1f5a, 0x1f5b, 0x1f5c, 0x1f5d, 0x1f5e, 0x1f7d, 0x1f7f, 0x1fb4, 0x1fb5, 0x1fc4, 0x1fc5, 0x1fd3, 0x1fd5, 0x1fdb, 0x1fdc, 0x1fef, 0x1ff1, 0x1ff4, 0x1ff5, 0x1ffe, 0x1fff, 0x200b, 0x200d, 0x2064, 0x2065, 0x2070, 0x2071, 0x2073, 0x207e, 0x207f, 0x208e, 0x208f, 0x209c, 0x209f, 0x20be, 0x20cf, 0x20f0, 0x20ff, 0x2125, 0x2126, 0x2129, 0x212b, 0x2131, 0x2132, 0x214d, 0x214e, 0x215f, 0x2188, 0x218b, 0x218f, 0x23fa, 0x23ff, 0x2426, 0x243f, 0x244a, 0x245f, 0x27ff, 0x28ff, 0x2b73, 0x2b75, 0x2b95, 0x2b97, 0x2bb9, 0x2bbc, 0x2bc8, 0x2bc9, 0x2bd1, 0x2beb, 0x2bef, 0x2bff, 0x2c2e, 0x2c2f, 0x2c5e, 0x2c5f, 0x2c7f, 0x2cf3, 0x2cf8, 0x2cff, 0x2d25, 0x2d26, 0x2d27, 0x2d2c, 0x2d2d, 0x2d2f, 0x2d67, 0x2d6e, 0x2d70, 0x2d7e, 0x2d7f, 0x2d96, 0x2d9f, 0x2da6, 0x2da7, 0x2dae, 0x2daf, 0x2db6, 0x2db7, 0x2dbe, 0x2dbf, 0x2dc6, 0x2dc7, 0x2dce, 0x2dcf, 0x2dd6, 0x2dd7, 0x2dde, 0x2ddf, 0x2dff, 0x2e42, 0x2e7f, 0x2e99, 0x2e9a, 0x2ef3, 0x2eff, 0x2fd5, 0x2fef, 0x2ffb, 0x2fff, 0x3004, 0x3005, 0x3006, 0x3007, 0x3020, 0x3029, 0x302d, 0x302f, 0x3037, 0x303b, 0x303f, 0x3040, 0x3096, 0x3098, 0x309a, 0x309c, 0x309f, 0x30a0, 0x30fa, 0x30fc, 0x30ff, 0x3104, 0x312d, 0x3130, 0x318e, 0x318f, 0x319f, 0x31ba, 0x31bf, 0x31e3, 0x31ef, 0x31ff, 0x321e, 0x321f, 0x325f, 0x327e, 0x32cf, 0x32fe, 0x32ff, 0x3357, 0x33ff, 0x4db5, 0x4dbf, 0x4dff, 0x9fd5, 0x9fff, 0xa48c, 0xa48f, 0xa4c6, 0xa4cf, 0xa4ff, 0xa62b, 0xa63f, 0xa69f, 0xa6f7, 0xa6ff, 0xa721, 0xa787, 0xa78a, 0xa7ad, 0xa7af, 0xa7b7, 0xa7f6, 0xa7ff, 0xa82f, 0xa839, 0xa87f, 0xa8c4, 0xa8cd, 0xa8d9, 0xa8df, 0xa8fd, 0xa92d, 0xa92e, 0xa92f, 0xa953, 0xa95e, 0xa95f, 0xa97c, 0xa97f, 0xa9cd, 0xa9ce, 0xa9cf, 0xa9d9, 0xa9dd, 0xa9df, 0xa9fe, 0xa9ff, 0xaa36, 0xaa3f, 0xaa4d, 0xaa4f, 0xaa59, 0xaa5b, 0xaa5f, 0xaa7f, 0xab00, 0xab06, 0xab08, 0xab0e, 0xab10, 0xab16, 0xab1f, 0xab26, 0xab27, 0xab2e, 0xab2f, 0xab5a, 0xab5b, 0xab64, 0xab65, 0xab6f, 0xabbf, 0xabff, 0xd7a3, 0xd7af, 0xd7c6, 0xd7ca, 0xd7fb, 0xf8ff, 0xfa6d, 0xfa6f, 0xfad9, 0xfaff, 0xfb06, 0xfb12, 0xfb17, 0xfb1c, 0xfb36, 0xfb37, 0xfb3c, 0xfb3d, 0xfb3e, 0xfb3f, 0xfb41, 0xfb42, 0xfb44, 0xfb45, 0xfb4f, 0xfbc1, 0xfbd2, 0xfd3d, 0xfd3f, 0xfd4f, 0xfd8f, 0xfd91, 0xfdc7, 0xfdef, 0xfdfd, 0xfdff, 0xfe0f, 0xfe19, 0xfe1f, 0xfe2d, 0xfe2f, 0xfe52, 0xfe53, 0xfe66, 0xfe67, 0xfe6b, 0xfe6f, 0xfe74, 0xfe75, 0xfefc, 0xfefe, 0xfeff, 0xff00, 0xff20, 0xff3a, 0xff40, 0xff5a, 0xff65, 0xff6f, 0xff70, 0xff9d, 0xff9f, 0xffbe, 0xffc1, 0xffc7, 0xffc9, 0xffcf, 0xffd1, 0xffd7, 0xffd9, 0xffdc, 0xffdf, 0xffe6, 0xffe7, 0xffee, 0xfff8, 0xfffd, 0x100ff, 0x10102, 0x10106, 0x10133, 0x10136, 0x1013f, 0x1018c, 0x1018f, 0x1019b, 0x1019f, 0x101a0, 0x101cf, 0x101fc, 0x101fd, 0x1027f, 0x1029c, 0x1029f, 0x102d0, 0x102df, 0x102e0, 0x102fb, 0x1032f, 0x1034a, 0x1037f, 0x1039d, 0x1039e, 0x1039f, 0x103ff, 0x1044f, 0x1047f, 0x1049d, 0x1049f, 0x104a9, 0x104ff, 0x10527, 0x107ff, 0x10805, 0x10807, 0x10808, 0x10809, 0x10835, 0x10836, 0x10838, 0x1083b, 0x1083c, 0x1083e, 0x1083f, 0x1085f, 0x1087f, 0x1089e, 0x108a6, 0x108af, 0x108df, 0x108f2, 0x108f3, 0x108f5, 0x108fa, 0x108ff, 0x1091b, 0x1091e, 0x1091f, 0x10939, 0x1093e, 0x1093f, 0x109ff, 0x10a03, 0x10a04, 0x10a06, 0x10a0b, 0x10a13, 0x10a14, 0x10a17, 0x10a18, 0x10a33, 0x10a37, 0x10a3a, 0x10a3e, 0x10a47, 0x10a4f, 0x10a58, 0x10abf, 0x10ae6, 0x10aea, 0x10af6, 0x10aff, 0x10b35, 0x10b38, 0x10b3f, 0x10e5f, 0x10e7e, 0x10fff, 0x1104d, 0x11051, 0x1106f, 0x1107e, 0x1107f, 0x110c1, 0x110ff, 0x11134, 0x11135, 0x11143, 0x1114f, 0x11176, 0x1117f, 0x111cd, 0x111cf, 0x111df, 0x111e0, 0x111f4, 0x111ff, 0x11211, 0x11212, 0x1123d, 0x1127f, 0x11286, 0x11287, 0x11288, 0x11289, 0x1128d, 0x1128e, 0x1129d, 0x1129e, 0x112a9, 0x112af, 0x112ea, 0x112ef, 0x112f9, 0x112ff, 0x11303, 0x11304, 0x1130c, 0x1130e, 0x11310, 0x11312, 0x11328, 0x11329, 0x11330, 0x11331, 0x11333, 0x11334, 0x11339, 0x1133b, 0x11344, 0x11346, 0x11348, 0x1134a, 0x1134d, 0x1134f, 0x11350, 0x11356, 0x11357, 0x1135c, 0x11363, 0x11365, 0x1136c, 0x1136f, 0x11374, 0x1147f, 0x114c7, 0x114cf, 0x114d9, 0x1157f, 0x115b5, 0x115b7, 0x115dd, 0x115ff, 0x11644, 0x1164f, 0x11659, 0x1167f, 0x116b7, 0x116bf, 0x116c9, 0x116ff, 0x11719, 0x1171c, 0x1172b, 0x1172f, 0x1173f, 0x11fff, 0x12399, 0x123ff, 0x1246e, 0x1246f, 0x12474, 0x1247f, 0x12543, 0x167ff, 0x16a38, 0x16a3f, 0x16a5e, 0x16a5f, 0x16a69, 0x16a6d, 0x16a6f, 0x16eff, 0x16f44, 0x16f4f, 0x16f7e, 0x16f8e, 0x16f9f, 0x1afff, 0x1b000, 0x1b001, 0x1bbff, 0x1bc6a, 0x1bc6f, 0x1bc7c, 0x1bc7f, 0x1bc88, 0x1bc8f, 0x1bc99, 0x1bc9b, 0x1bc9f, 0x1bca3, 0x1cfff, 0x1d0f5, 0x1d0ff, 0x1d126, 0x1d128, 0x1d166, 0x1d169, 0x1d17a, 0x1d182, 0x1d184, 0x1d18b, 0x1d1a9, 0x1d1ad, 0x1d1e8, 0x1d1ff, 0x1d245, 0x1d2ff, 0x1d356, 0x1d35f, 0x1d371, 0x1d3ff, 0x1d454, 0x1d455, 0x1d49c, 0x1d49d, 0x1d49f, 0x1d4a1, 0x1d4a2, 0x1d4a4, 0x1d4a6, 0x1d4a8, 0x1d4ac, 0x1d4ad, 0x1d4b9, 0x1d4ba, 0x1d4bb, 0x1d4bc, 0x1d4c3, 0x1d4c4, 0x1d505, 0x1d506, 0x1d50a, 0x1d50c, 0x1d514, 0x1d515, 0x1d51c, 0x1d51d, 0x1d539, 0x1d53a, 0x1d53e, 0x1d53f, 0x1d544, 0x1d545, 0x1d546, 0x1d549, 0x1d550, 0x1d551, 0x1d6a5, 0x1d6a7, 0x1d7cb, 0x1d7cd, 0x1d7ff, 0x1da8b, 0x1da9a, 0x1da9f, 0x1daa0, 0x1daaf, 0x1edff, 0x1ee03, 0x1ee04, 0x1ee1f, 0x1ee20, 0x1ee22, 0x1ee23, 0x1ee24, 0x1ee26, 0x1ee27, 0x1ee28, 0x1ee32, 0x1ee33, 0x1ee37, 0x1ee38, 0x1ee39, 0x1ee3a, 0x1ee3b, 0x1ee41, 0x1ee42, 0x1ee46, 0x1ee47, 0x1ee48, 0x1ee49, 0x1ee4a, 0x1ee4b, 0x1ee4c, 0x1ee4f, 0x1ee50, 0x1ee52, 0x1ee53, 0x1ee54, 0x1ee56, 0x1ee57, 0x1ee58, 0x1ee59, 0x1ee5a, 0x1ee5b, 0x1ee5c, 0x1ee5d, 0x1ee5e, 0x1ee5f, 0x1ee60, 0x1ee62, 0x1ee63, 0x1ee64, 0x1ee66, 0x1ee6a, 0x1ee6b, 0x1ee72, 0x1ee73, 0x1ee77, 0x1ee78, 0x1ee7c, 0x1ee7d, 0x1ee7e, 0x1ee7f, 0x1ee89, 0x1ee8a, 0x1ee9b, 0x1eea0, 0x1eea3, 0x1eea4, 0x1eea9, 0x1eeaa, 0x1eebb, 0x1eeef, 0x1eef1, 0x1efff, 0x1f02b, 0x1f02f, 0x1f093, 0x1f09f, 0x1f0ae, 0x1f0b0, 0x1f0bf, 0x1f0c0, 0x1f0cf, 0x1f0d0, 0x1f0f5, 0x1f0ff, 0x1f10c, 0x1f10f, 0x1f12e, 0x1f12f, 0x1f16b, 0x1f16f, 0x1f19a, 0x1f1e5, 0x1f1ff, 0x1f200, 0x1f202, 0x1f20f, 0x1f23a, 0x1f23f, 0x1f248, 0x1f24f, 0x1f251, 0x1f2ff, 0x1f579, 0x1f57a, 0x1f5a3, 0x1f5a4, 0x1f6d0, 0x1f6df, 0x1f6ec, 0x1f6ef, 0x1f6f3, 0x1f6ff, 0x1f773, 0x1f77f, 0x1f7d4, 0x1f7ff, 0x1f80b, 0x1f80f, 0x1f847, 0x1f84f, 0x1f859, 0x1f85f, 0x1f887, 0x1f88f, 0x1f8ad, 0x1f90f, 0x1f918, 0x1f97f, 0x1f984, 0x1f9bf, 0x1f9c0, 0x1ffff, 0x2a6d6, 0x2a6ff, 0x2b734, 0x2b73f, 0x2b81d, 0x2b81f, 0x2cea1, 0x2f7ff, 0x2fa1d, 0xe0000, 0xe0001, 0xe001f, 0xe007f, 0xe00ff, 0xe01ef]) # noqa
SCRIPT = ['Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Bopomofo', 'Common', 'Inherited', 'Greek', 'Common', 'Greek', 'Unknown', 'Greek', 'Common', 'Greek', 'Unknown', 'Greek', 'Common', 'Greek', 'Common', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Coptic', 'Greek', 'Cyrillic', 'Inherited', 'Cyrillic', 'Unknown', 'Armenian', 'Unknown', 'Armenian', 'Unknown', 'Armenian', 'Unknown', 'Common', 'Armenian', 'Unknown', 'Armenian', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Arabic', 'Common', 'Arabic', 'Common', 'Arabic', 'Common', 'Unknown', 'Arabic', 'Common', 'Arabic', 'Common', 'Arabic', 'Inherited', 'Arabic', 'Inherited', 'Arabic', 'Common', 'Arabic', 'Syriac', 'Unknown', 'Syriac', 'Unknown', 'Syriac', 'Arabic', 'Thaana', 'Unknown', 'Nko', 'Unknown', 'Samaritan', 'Unknown', 'Samaritan', 'Unknown', 'Mandaic', 'Unknown', 'Mandaic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Devanagari', 'Inherited', 'Devanagari', 'Common', 'Devanagari', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Bengali', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gurmukhi', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Gujarati', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Oriya', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Tamil', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Telugu', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Kannada', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Malayalam', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Sinhala', 'Unknown', 'Thai', 'Unknown', 'Common', 'Thai', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Lao', 'Unknown', 'Tibetan', 'Unknown', 'Tibetan', 'Unknown', 'Tibetan', 'Unknown', 'Tibetan', 'Unknown', 'Tibetan', 'Unknown', 'Tibetan', 'Common', 'Tibetan', 'Unknown', 'Myanmar', 'Georgian', 'Unknown', 'Georgian', 'Unknown', 'Georgian', 'Unknown', 'Georgian', 'Common', 'Georgian', 'Hangul', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Cherokee', 'Unknown', 'Cherokee', 'Unknown', 'Ogham', 'Unknown', 'Runic', 'Common', 'Runic', 'Unknown', 'Tagalog', 'Unknown', 'Tagalog', 'Unknown', 'Hanunoo', 'Common', 'Unknown', 'Buhid', 'Unknown', 'Tagbanwa', 'Unknown', 'Tagbanwa', 'Unknown', 'Tagbanwa', 'Unknown', 'Khmer', 'Unknown', 'Khmer', 'Unknown', 'Khmer', 'Unknown', 'Mongolian', 'Common', 'Mongolian', 'Common', 'Mongolian', 'Unknown', 'Mongolian', 'Unknown', 'Mongolian', 'Unknown', 'Mongolian', 'Unknown', 'Limbu', 'Unknown', 'Limbu', 'Unknown', 'Limbu', 'Unknown', 'Limbu', 'Unknown', 'Limbu', 'Unknown', 'Khmer', 'Buginese', 'Unknown', 'Buginese', 'Unknown', 'Inherited', 'Unknown', 'Balinese', 'Unknown', 'Balinese', 'Unknown', 'Sundanese', 'Batak', 'Unknown', 'Batak', 'Lepcha', 'Unknown', 'Lepcha', 'Unknown', 'Lepcha', 'Unknown', 'Sundanese', 'Unknown', 'Inherited', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Unknown', 'Inherited', 'Unknown', 'Latin', 'Greek', 'Cyrillic', 'Latin', 'Greek', 'Latin', 'Greek', 'Latin', 'Cyrillic', 'Latin', 'Greek', 'Inherited', 'Unknown', 'Inherited', 'Latin', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Greek', 'Unknown', 'Common', 'Inherited', 'Common', 'Unknown', 'Common', 'Latin', 'Unknown', 'Common', 'Latin', 'Common', 'Unknown', 'Latin', 'Unknown', 'Common', 'Unknown', 'Inherited', 'Unknown', 'Common', 'Greek', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Braille', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Glagolitic', 'Unknown', 'Glagolitic', 'Unknown', 'Latin', 'Coptic', 'Unknown', 'Coptic', 'Georgian', 'Unknown', 'Georgian', 'Unknown', 'Georgian', 'Unknown', 'Tifinagh', 'Unknown', 'Tifinagh', 'Unknown', 'Tifinagh', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Cyrillic', 'Common', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Common', 'Unknown', 'Common', 'Han', 'Common', 'Han', 'Common', 'Han', 'Inherited', 'Hangul', 'Common', 'Han', 'Common', 'Unknown', 'Hiragana', 'Unknown', 'Inherited', 'Common', 'Hiragana', 'Common', 'Katakana', 'Common', 'Katakana', 'Unknown', 'Bopomofo', 'Unknown', 'Hangul', 'Unknown', 'Common', 'Bopomofo', 'Unknown', 'Common', 'Unknown', 'Katakana', 'Hangul', 'Unknown', 'Common', 'Hangul', 'Common', 'Katakana', 'Unknown', 'Katakana', 'Common', 'Han', 'Unknown', 'Common', 'Han', 'Unknown', 'Yi', 'Unknown', 'Yi', 'Unknown', 'Lisu', 'Vai', 'Unknown', 'Cyrillic', 'Bamum', 'Unknown', 'Common', 'Latin', 'Common', 'Latin', 'Unknown', 'Latin', 'Unknown', 'Latin', 'Unknown', 'Common', 'Unknown', 'Saurashtra', 'Unknown', 'Saurashtra', 'Unknown', 'Devanagari', 'Unknown', 'Common', 'Unknown', 'Rejang', 'Unknown', 'Rejang', 'Hangul', 'Unknown', 'Javanese', 'Unknown', 'Common', 'Javanese', 'Unknown', 'Javanese', 'Myanmar', 'Unknown', 'Cham', 'Unknown', 'Cham', 'Unknown', 'Cham', 'Unknown', 'Cham', 'Myanmar', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Ethiopic', 'Unknown', 'Latin', 'Common', 'Latin', 'Greek', 'Unknown', 'Cherokee', 'Unknown', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Latin', 'Unknown', 'Armenian', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Unknown', 'Hebrew', 'Arabic', 'Unknown', 'Arabic', 'Common', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Inherited', 'Common', 'Unknown', 'Inherited', 'Cyrillic', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Common', 'Unknown', 'Common', 'Latin', 'Common', 'Latin', 'Common', 'Katakana', 'Common', 'Katakana', 'Common', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Hangul', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Greek', 'Unknown', 'Common', 'Unknown', 'Greek', 'Unknown', 'Common', 'Inherited', 'Unknown', 'Lycian', 'Unknown', 'Carian', 'Unknown', 'Inherited', 'Common', 'Unknown', 'Gothic', 'Unknown', 'Ugaritic', 'Unknown', 'Ugaritic', 'Unknown', 'Deseret', 'Shavian', 'Osmanya', 'Unknown', 'Osmanya', 'Unknown', 'Elbasan', 'Unknown', 'Cypriot', 'Unknown', 'Cypriot', 'Unknown', 'Cypriot', 'Unknown', 'Cypriot', 'Unknown', 'Cypriot', 'Unknown', 'Cypriot', 'Unknown', 'Palmyrene', 'Nabataean', 'Unknown', 'Nabataean', 'Unknown', 'Hatran', 'Unknown', 'Hatran', 'Unknown', 'Hatran', 'Phoenician', 'Unknown', 'Phoenician', 'Lydian', 'Unknown', 'Lydian', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Kharoshthi', 'Unknown', 'Manichaean', 'Unknown', 'Manichaean', 'Unknown', 'Avestan', 'Unknown', 'Avestan', 'Unknown', 'Arabic', 'Unknown', 'Brahmi', 'Unknown', 'Brahmi', 'Unknown', 'Brahmi', 'Kaithi', 'Unknown', 'Chakma', 'Unknown', 'Chakma', 'Unknown', 'Mahajani', 'Unknown', 'Sharada', 'Unknown', 'Sharada', 'Unknown', 'Sinhala', 'Unknown', 'Khojki', 'Unknown', 'Khojki', 'Unknown', 'Multani', 'Unknown', 'Multani', 'Unknown', 'Multani', 'Unknown', 'Multani', 'Unknown', 'Multani', 'Unknown', 'Khudawadi', 'Unknown', 'Khudawadi', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Grantha', 'Unknown', 'Tirhuta', 'Unknown', 'Tirhuta', 'Unknown', 'Siddham', 'Unknown', 'Siddham', 'Unknown', 'Modi', 'Unknown', 'Modi', 'Unknown', 'Takri', 'Unknown', 'Takri', 'Unknown', 'Ahom', 'Unknown', 'Ahom', 'Unknown', 'Ahom', 'Unknown', 'Cuneiform', 'Unknown', 'Cuneiform', 'Unknown', 'Cuneiform', 'Unknown', 'Cuneiform', 'Unknown', 'Bamum', 'Unknown', 'Mro', 'Unknown', 'Mro', 'Unknown', 'Mro', 'Unknown', 'Miao', 'Unknown', 'Miao', 'Unknown', 'Miao', 'Unknown', 'Katakana', 'Hiragana', 'Unknown', 'Duployan', 'Unknown', 'Duployan', 'Unknown', 'Duployan', 'Unknown', 'Duployan', 'Unknown', 'Duployan', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Inherited', 'Common', 'Unknown', 'Greek', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'SignWriting', 'Unknown', 'SignWriting', 'Unknown', 'SignWriting', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Arabic', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Hiragana', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Han', 'Unknown', 'Common', 'Unknown', 'Common', 'Unknown', 'Inherited', 'Unknown'] # noqa
def script(chr):
return SCRIPT[numpy.searchsorted(CODEPOINT, ord(chr))]
def script_str(seq):
conv = [ord(c) for c in seq]
result = numpy.searchsorted(CODEPOINT, conv)
return (SCRIPT[r] for r in result)
| 1,546 | 13,980 | 0.67901 | 2,659 | 24,736 | 6.315908 | 0.536668 | 0.073538 | 0.090509 | 0.103728 | 0.384244 | 0.355008 | 0.337382 | 0.319221 | 0.307312 | 0.287365 | 0 | 0.197398 | 0.108102 | 24,736 | 15 | 13,981 | 1,649.066667 | 0.56382 | 0.001617 | 0 | 0 | 1 | 0 | 0.353758 | 0 | 0 | 0 | 0.317512 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.111111 | 0.111111 | 0.555556 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
cf2d3e189ef2bdd60e7dada3e2f334e928ea947b | 483 | py | Python | 05.04.2022/saidaEx1.py | N0N4T0/python-codes | ac2b884f86749a8b179ff972cdb316ec4e005b32 | [
"MIT"
] | null | null | null | 05.04.2022/saidaEx1.py | N0N4T0/python-codes | ac2b884f86749a8b179ff972cdb316ec4e005b32 | [
"MIT"
] | null | null | null | 05.04.2022/saidaEx1.py | N0N4T0/python-codes | ac2b884f86749a8b179ff972cdb316ec4e005b32 | [
"MIT"
] | null | null | null | print("Mostra sempre os sinais")
print('{:+f}; {:+f}'.format(3.14, -3.14)) # mostra sempre os sinais
print("\n") # quebra a linha
print("Mostra um epaço para números positivos")
print('{: f}; {: f}'.format(3.14, -3.14)) # Mostra um epaço para números positivos
print("\n") # quebra a linha
print("Exibe somente o sinal de menos")
print('{:f}; {:f}'.format(3.14, -3.14)) # exibe somente o sinal de menos
print('{:-f}; {:-f}'.format(3.14, -3.14)) # exibe somente o sinal de menos | 43.909091 | 83 | 0.637681 | 83 | 483 | 3.710843 | 0.289157 | 0.077922 | 0.090909 | 0.168831 | 0.983766 | 0.873377 | 0.74026 | 0.529221 | 0.529221 | 0.366883 | 0 | 0.058252 | 0.146998 | 483 | 11 | 84 | 43.909091 | 0.68932 | 0.318841 | 0 | 0.444444 | 0 | 0 | 0.436533 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
cf54e691a676b2f09078558b2cc478413e830731 | 21,240 | py | Python | ngnrouterconfigPython/ngnrouterlib/__init__.py | kamiyn/ngnrouterconfig | 2d9edd17546759904a818a96c41d73636b4f6339 | [
"MIT"
] | null | null | null | ngnrouterconfigPython/ngnrouterlib/__init__.py | kamiyn/ngnrouterconfig | 2d9edd17546759904a818a96c41d73636b4f6339 | [
"MIT"
] | null | null | null | ngnrouterconfigPython/ngnrouterlib/__init__.py | kamiyn/ngnrouterconfig | 2d9edd17546759904a818a96c41d73636b4f6339 | [
"MIT"
] | null | null | null | import json
import os
import sys
import re
from time import sleep
import time
import pexpect # 追加ライブラリ
import jinja2 # 追加ライブラリ
from termcolor import colored, cprint # 追加ライブラリ
def doNgconf(filename):
# config の中身を解析する
routerconfig, sendlines = readrouterconfig(filename)
# ログファイル名の作成
nowstr = time.strftime('%Y%m%d%H%M%S')
filewithoutext, fileext = os.path.splitext(filename)
logfilename = filewithoutext + "_" + nowstr + ".log"
if routerconfig["routertype"] == "rtx-ssh":
rtx_ssh_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "rtx-telnet":
rtx_telnet_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "ix-telnet":
ix_telnet_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "ix-direct":
ix_direct_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "century-direct":
century_direct_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "edgecore-telnet":
edgecore_telnet_login(routerconfig, sendlines, logfilename)
elif routerconfig["routertype"] == "edgecore-direct":
edgecore_direct_login(routerconfig, sendlines, logfilename)
else:
print("unknown routertype: " + routerconfig["routertype"])
return logfilename
class ConfigHolder(object):
def __init__(self):
self.configFile = ""
self.outputFile = ""
self.regexFile = []
self.renotFile = []
def __str__(self):
return "configFile: " + self.configFile + "\tregexFile: " + ",".join(self.regexFile) + "\trenotFile: " + ",".join(self.renotFile)
def confirmToRun(self):
with open(self.configFile) as f1:
print(colored("========投入 " + self.configFile, 'cyan'))
print(f1.read())
for r in self.regexFile:
with open(r) as f2:
print(colored("========検証 " + r, 'cyan'))
print(f2.read())
for r in self.renotFile:
with open(r) as f2:
print(colored("\n========存在しない検証 " + r, 'cyan'))
print(f2.read())
input(colored("実行するには Enter を押して下さい", 'cyan'))
return True
def Run(self):
logfilename = doNgconf(self.configFile)
for r in self.regexFile:
with open(r) as f2:
print(colored("\n========検証 " + r, 'cyan'))
print(f2.read())
if not checkre(r, logfilename):
print(colored("XXXXXXXX検証に失敗しました " + r + "\n", 'red'))
raise Exception()
else:
print(colored("========検証に成功しました " + r + "\n", 'green'))
for r in self.renotFile:
with open(r) as f2:
print(colored("\n========存在しない検証 " + r, 'cyan'))
print(f2.read())
if checkre(r, logfilename):
print(colored("XXXXXXXX検証に失敗しました " + r + "\n", 'red'))
raise Exception()
else:
print(colored("========検証に成功しました " + r + "\n", 'green'))
@staticmethod
def appendFile(filename, result):
resultlen = len(result)
if filename.endswith(".re"):
result[resultlen-1].regexFile.append(filename)
elif filename.endswith(".nre"):
result[resultlen-1].renotFile.append(filename)
else:
holder = ConfigHolder()
holder.configFile = filename
result.append(holder)
return result
def expandTemplate(dict, templatedir, outputdir):
'''
expandTemplate
templatedir にあるテンプレートファイルについて、
dict をパラメータとして展開したファイルを
outputdir に生成する
'''
templateLoader = jinja2.FileSystemLoader(searchpath=templatedir)
templateEnv = jinja2.Environment(loader=templateLoader)
result = []
for templateFilename in [x for x in templateEnv.list_templates() if x.find("/") == -1]:
outputpath = os.path.join(
outputdir, dict["id"] + "_" + templateFilename)
print(templateFilename)
template = templateEnv.get_template(templateFilename)
outputText = template.render(dict)
with open(outputpath, "w") as fp:
fp.write(outputText)
# result.append(outputpath)
result = ConfigHolder.appendFile(outputpath, result)
return result
def readrouterconfig(filename):
'''
readrouterconfig
1行目を JSON として解釈し、辞書にして タプルの第一要素として返す
2行目以降を タプルの第二要素として返す
1行目のJSONの 値 について、 $ で始まる値については環境変数を参照して展開する
'''
#
with open(filename, "r") as fp:
jsonstr = fp.readline()
sendlines = fp.readlines()
jsonobj = json.loads(jsonstr)
for k, v in jsonobj.items():
if (v.startswith("$") and os.getenv(v[1::])):
jsonobj[k] = os.getenv(v[1::])
return (jsonobj, sendlines)
def checkre(pfile, tfile):
with open(pfile) as f:
patternStr = f.readline().strip()
with open(tfile) as f2:
targetStr = f2.read()
return re.search(patternStr, targetStr, re.DOTALL)
class multifile(object):
def __init__(self, files):
self._files = files
def __getattr__(self, attr, *args):
return self._wrap(attr, *args)
def _wrap(self, attr, *args):
def g(*a, **kw):
for f in self._files:
res = getattr(f, attr, *args)(*a, **kw)
return res
return g
def ix_telnet_login(routerconfig, sendlines, logfilename):
'''
ix_telnet_login
IX にコンフィグ送信し、送信状態をログファイルに記録する関数
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "ix_telnet", "username": "ログインユーザー名", "password": "ログインパスワード", "centerrouter":"", "centeruser":"", "centerpassword":"" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["centerrouter"])
child.logfile_read = logfp
child.ignore_sighup = True
try:
# center router login
child.expect('login: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["centeruser"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["centerpassword"] + "\r")
# ユーザーログイン
child.expect('# ', timeout=timeout)
sleep(sleepspan)
child.send("telnet " + routerconfig["router"] + "\r")
child.expect('login: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["username"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\r")
child.expect('# ', timeout=timeout)
# プロンプト文字列の取得
sleep(sleepspan)
# ルータのヘッダー文字列に含まれない文字列をコメント送信することで、ヘッダーをスキップする
child.send("! non-existent-lines\r")
child.expect('non-existent-lines', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != "#"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
# 管理者モードに変更
sleep(sleepspan)
child.send('configure' + "\r")
child.expect(prom, timeout=timeout)
sleep(sleepspan)
child.send('svintr-config' + "\r")
child.expect(prom, timeout=timeout)
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\r")
if (line.startswith("reload y")): # リロードはプロンプトを出力せず、先に進めない
print(colored("========reload しています。ルータの起動を待って下さい ", 'yellow'))
sleep(sleepspan * 10)
return
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(sleepspan)
child.send('exit' + "\r")
child.expect(prom, timeout=timeout)
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
finally:
logfp.flush()
logfp1.close() # stdout は close したくない
child.close()
def ix_direct_login(routerconfig, sendlines, logfilename):
'''
ix_direct_login
IX にコンフィグ送信し、送信状態をログファイルに記録する関数 (ルーターを中継しない)
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "ix_direct", "username": "ログインユーザー名", "password": "ログインパスワード" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["router"])
child.logfile_read = logfp
try:
# ユーザーログイン
child.expect('login: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["username"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\r")
child.expect('# ', timeout=timeout)
# プロンプト文字列の取得
sleep(sleepspan)
# ルータのヘッダー文字列に含まれない文字列をコメント送信することで、ヘッダーをスキップする
child.send("! non-existent-lines\r")
child.expect('non-existent-lines', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != "#"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
# 管理者モードに変更
sleep(sleepspan)
child.send('configure' + "\r")
child.expect(prom, timeout=timeout)
sleep(sleepspan)
child.send('svintr-config' + "\r")
child.expect(prom, timeout=timeout)
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\r")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(sleepspan)
child.send('exit' + "\r")
child.expect(prom, timeout=timeout)
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
finally:
logfp.flush()
logfp1.close() # stdout は close したくない
child.close()
def century_direct_login(routerconfig, sendlines, logfilename):
'''
century_direct_login
Century にコンフィグ送信し、送信状態をログファイルに記録する関数 (ルーターを中継しない)
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "century_direct", "username": "ログインユーザー名", "password": "ログインパスワード" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["router"])
child.logfile_read = logfp
try:
# ユーザーログイン
child.expect('login: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["username"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\r")
child.expect('#', timeout=timeout)
# プロンプト文字列の取得
sleep(sleepspan)
# ルータのヘッダー文字列に含まれない文字列をコメント送信することで、ヘッダーをスキップする
child.send("! non-existent-lines\r")
child.expect('non-existent-lines', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != "#"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
prom = "\n"+ prom.strip() # Centuryはエコーバックの改行コードが \r
#
sleep(sleepspan)
child.send('terminal length 0' + "\r")
child.expect(prom, timeout=timeout)
# configure モード
sleep(sleepspan)
child.send('configure terminal' + "\r")
child.expect(prom, timeout=timeout)
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\r")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(sleepspan)
child.send('exit' + "\r")
child.expect(prom, timeout=timeout)
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
child.send('exit' + "\r")
sleep(sleepspan)
finally:
logfp.flush()
logfp1.close() # stdout は close したくない
child.close()
def rtx_telnet_login(routerconfig, sendlines, logfilename):
'''
rtx_telnet_login
RTX810, RTX1200 にコンフィグ送信し、送信状態をログファイルに記録する関数
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "rtx-telnet", "password": "ログインパスワード", "adminpassword": "管理者パスワード" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["router"])
child.logfile_read = logfp
try:
# ユーザーログイン
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\n")
child.expect('> ', timeout=timeout)
# プロンプト文字列の取得
sleep(sleepspan)
# ルータのヘッダー文字列に含まれない文字列をコメント送信することで、ヘッダーをスキップする
child.send("# non-existent-lines\n")
child.expect('non-existent-lines', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != ">"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
# 管理者モードに変更
sleep(sleepspan)
child.send('administrator' + "\n")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["adminpassword"] + "\n")
child.expect(prom, timeout=timeout)
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\n")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(sleepspan)
child.send('exit' + "\n")
child.expect(prom + '> ', timeout=timeout)
sleep(sleepspan)
child.send('exit' + "\n")
sleep(sleepspan)
finally:
logfp.flush()
logfp1.close() # stdout は close したくない
child.close()
def rtx_ssh_login(routerconfig, sendlines, logfilename):
'''
rtx_ssh_login
RTX810, RTX1200 にコンフィグ送信し、送信状態をログファイルに記録する関数
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "rtx-ssh", "username": "ログインユーザー名", "password": "ログインパスワード", "adminpassword": "管理者パスワード" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('ssh -l ' + routerconfig["username"] + " " + routerconfig["router"])
child.logfile_read = logfp
try:
# ユーザーログイン
child.expect('password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\n")
child.expect('> ', timeout=timeout)
# プロンプト文字列の取得
sleep(sleepspan)
# ルータのヘッダー文字列に含まれない文字列をコメント送信することで、ヘッダーをスキップする
child.send("# non-existent-lines\n")
child.expect('non-existent-lines', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != ">"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
# 管理者モードに変更
sleep(sleepspan)
child.send('administrator' + "\n")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["adminpassword"] + "\n")
child.expect(prom, timeout=timeout)
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\n")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(sleepspan)
child.send('exit' + "\n")
child.expect(prom + '> ', timeout=timeout)
sleep(sleepspan)
child.send('exit' + "\n")
sleep(sleepspan)
finally:
logfp.flush()
logfp1.close() # stdout は close したくない
child.close()
def edgecore_direct_login(routerconfig, sendlines, logfilename):
'''
edgecore_direct_login
Edge-Core にコンフィグ送信し、送信状態をログファイルに記録する関数
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "edgecore_telnet", "username": "ログインユーザー名", "password": "ログインパスワード" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["router"])
child.logfile_read = logfp
try:
# ユーザーログイン
child.expect('Username: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["username"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\r")
child.expect('#', timeout=timeout)
# プロンプト文字列の取得 と terminal length 0 を同時に実施する
sleep(sleepspan)
child.send("terminal length 0\r")
child.expect('terminal length 0', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != "#"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
prom = "\n"+ prom.strip()
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\r")
if (re.match(r"^copy running-config startup-config", line)):
child.expect("]:")
child.send("\r")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(timeout)
child.send('exit' + "\r")
try:
child.expect(prom, timeout=timeout)
except pexpect.EOF as ex:
print("この物件への処理が完了しました")
finally:
logfp.flush()
sys.stdout.buffer.flush()
logfp1.close() # stdout は close したくない
child.close()
def edgecore_telnet_login(routerconfig, sendlines, logfilename):
'''
edgecore_telnet_login
Edge-Core にコンフィグ送信し、送信状態をログファイルに記録する関数
ファイルの中身は以下のような内容であるこのを期待している。1行目はJSON、2行目以降は ルータに送信したい内容
"router": "10.0.0.1", "routertype": "edgecore_telnet", "username": "ログインユーザー名", "password": "ログインパスワード", "centerrouter":"", "centeruser":"", "centerpassword":"" }
show config
'''
sleepspan = 0.5
timeout = 5
logfp1 = open(logfilename, 'wb')
logfp = multifile([sys.stdout.buffer, logfp1])
child = pexpect.spawn('telnet ' + routerconfig["centerrouter"])
child.logfile_read = logfp
child.ignore_sighup = True
try:
# center router login
child.expect('login: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["centeruser"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["centerpassword"] + "\r")
# ユーザーログイン
child.expect('# ', timeout=timeout)
sleep(sleepspan)
child.send("telnet " + routerconfig["router"] + "\r")
child.expect('Username: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["username"] + "\r")
child.expect('Password: ', timeout=timeout)
sleep(sleepspan)
child.send(routerconfig["password"] + "\r")
child.expect('#', timeout=timeout)
# プロンプト文字列の取得 と terminal length 0 を同時に実施する
sleep(sleepspan)
child.send("terminal length 0\r")
child.expect('terminal length 0', timeout=timeout) # この時点では改行コードを含まない
prom = ""
prom1 = ""
while (prom1 != "#"):
prom = prom + prom1
c = child.read(1)
prom1 = chr(c[0])
prom = "\n"+ prom.strip()
# config を送信する
for line in sendlines:
if (re.match(r"^\s*$", line)):
continue
sleep(sleepspan)
child.send(line + "\r")
if (re.match(r"^copy running-config startup-config", line)):
child.expect("]:")
child.send("\r")
child.expect(prom) # 標準のタイムアウト 30秒を利用する
sleep(timeout)
child.send('exit' + "\r")
try:
child.expect(prom, timeout=timeout)
except pexpect.EOF as ex:
print("この物件への処理が完了しました")
finally:
logfp.flush()
sys.stdout.buffer.flush()
logfp1.close() # stdout は close したくない
child.close()
| 33.135725 | 174 | 0.570951 | 2,070 | 21,240 | 5.819807 | 0.125121 | 0.070889 | 0.078858 | 0.095459 | 0.790985 | 0.785756 | 0.748734 | 0.743505 | 0.724911 | 0.713124 | 0 | 0.012873 | 0.294115 | 21,240 | 640 | 175 | 33.1875 | 0.790636 | 0.157345 | 0 | 0.757447 | 0 | 0 | 0.097307 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042553 | false | 0.046809 | 0.019149 | 0.004255 | 0.089362 | 0.042553 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d856a4c7f36c1584eb876c64217a2d7fa7188a3d | 2,999 | py | Python | tests/objects/test_boolobject.py | mswart/topaz | 4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae | [
"BSD-3-Clause"
] | 241 | 2015-01-02T18:49:09.000Z | 2022-03-15T15:08:45.000Z | tests/objects/test_boolobject.py | mswart/topaz | 4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae | [
"BSD-3-Clause"
] | 16 | 2015-05-04T21:31:08.000Z | 2020-06-04T22:49:36.000Z | tests/objects/test_boolobject.py | mswart/topaz | 4bc02d6f4bf29c20f045223ecb6ae8a5cc9df2ae | [
"BSD-3-Clause"
] | 24 | 2015-02-15T05:35:11.000Z | 2022-03-22T13:29:04.000Z | from ..base import BaseTopazTest
class TestTrueObject(BaseTopazTest):
def test_name(self, space):
space.execute("TrueClass")
def test_to_s(self, space):
w_res = space.execute("return true.to_s")
assert space.str_w(w_res) == "true"
def test_inspect(self, space):
w_res = space.execute("return true.inspect")
assert space.str_w(w_res) == "true"
def test_eql(self, space):
w_res = space.execute("return true == false")
assert self.unwrap(space, w_res) is False
w_res = space.execute("return true == true")
assert self.unwrap(space, w_res) is True
def test_and(self, space):
w_res = space.execute("return true & 3")
assert w_res is space.w_true
w_res = space.execute("return true & false")
assert w_res is space.w_false
def test_or(self, space):
w_res = space.execute("return true | 3")
assert w_res is space.w_true
w_res = space.execute("return true | nil")
assert w_res is space.w_true
def test_xor(self, space):
assert space.execute("return true ^ nil") is space.w_true
assert space.execute("return true ^ false") is space.w_true
assert space.execute("return true ^ true") is space.w_false
assert space.execute("return true ^ 1") is space.w_false
def test_singleton_class(self, space):
w_res = space.execute("return true.singleton_class == TrueClass")
assert w_res is space.w_true
class TestFalseObject(BaseTopazTest):
def test_name(self, space):
space.execute("FalseClass")
def test_to_s(self, space):
w_res = space.execute("return false.to_s")
assert space.str_w(w_res) == "false"
def test_inspect(self, space):
w_res = space.execute("return false.inspect")
assert space.str_w(w_res) == "false"
def test_eql(self, space):
w_res = space.execute("return false == false")
assert self.unwrap(space, w_res) is True
w_res = space.execute("return false == true")
assert self.unwrap(space, w_res) is False
def test_and(self, space):
w_res = space.execute("return false & 3")
assert w_res is space.w_false
w_res = space.execute("return false & false")
assert w_res is space.w_false
def test_or(self, space):
w_res = space.execute("return false | 3")
assert w_res is space.w_true
w_res = space.execute("return false | nil")
assert w_res is space.w_false
def test_xor(self, space):
assert space.execute("return false ^ nil") is space.w_false
assert space.execute("return false ^ false") is space.w_false
assert space.execute("return false ^ true") is space.w_true
assert space.execute("return false ^ 1") is space.w_true
def test_singleton_class(self, space):
w_res = space.execute("return false.singleton_class == FalseClass")
assert w_res is space.w_true
| 34.872093 | 75 | 0.643881 | 442 | 2,999 | 4.183258 | 0.079186 | 0.07788 | 0.25311 | 0.15576 | 0.927528 | 0.904813 | 0.877231 | 0.810708 | 0.607355 | 0.40887 | 0 | 0.002658 | 0.247416 | 2,999 | 85 | 76 | 35.282353 | 0.816571 | 0 | 0 | 0.523077 | 0 | 0 | 0.183061 | 0.007002 | 0 | 0 | 0 | 0 | 0.4 | 1 | 0.246154 | false | 0 | 0.015385 | 0 | 0.292308 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d86918a29016474297b3ba84ff44902b9dff2987 | 136 | py | Python | scripts/npc/autogen_npc9400024.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | scripts/npc/autogen_npc9400024.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | scripts/npc/autogen_npc9400024.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | # ParentID: 9400024
# Character field ID when accessed: 180000000
# ObjectID: 1000019
# Object Position Y: -85
# Object Position X: 112
| 22.666667 | 45 | 0.75 | 18 | 136 | 5.666667 | 0.888889 | 0.27451 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.247788 | 0.169118 | 136 | 5 | 46 | 27.2 | 0.654867 | 0.919118 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d873e3ff8440b6e30f25837bd8ab7b48e7d00b9c | 192 | py | Python | form/views.py | lroyland/PropertyLeadForm | b97156d71091981a5c345bf94d7399f2ba29df08 | [
"BSD-3-Clause"
] | null | null | null | form/views.py | lroyland/PropertyLeadForm | b97156d71091981a5c345bf94d7399f2ba29df08 | [
"BSD-3-Clause"
] | null | null | null | form/views.py | lroyland/PropertyLeadForm | b97156d71091981a5c345bf94d7399f2ba29df08 | [
"BSD-3-Clause"
] | null | null | null | from django.shortcuts import render
from django.http import HttpResponse
def index(request):
return HttpResponse("Testing basic Http Response in form views.py")
# Create your views here.
| 27.428571 | 71 | 0.791667 | 27 | 192 | 5.62963 | 0.777778 | 0.131579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145833 | 192 | 6 | 72 | 32 | 0.926829 | 0.119792 | 0 | 0 | 0 | 0 | 0.263473 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.5 | 0.25 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
d88c2cfc0be43df74bdfc5f48d5404d8a99bcdb5 | 29 | py | Python | gfapy/line/unknown/__init__.py | ujjwalsh/gfapy | 891ef3df695f20c67809e5a54549c876d90690b4 | [
"ISC"
] | 44 | 2017-03-18T08:08:04.000Z | 2021-11-10T16:11:15.000Z | gfapy/line/unknown/__init__.py | ujjwalsh/gfapy | 891ef3df695f20c67809e5a54549c876d90690b4 | [
"ISC"
] | 22 | 2017-04-04T21:20:31.000Z | 2022-03-09T19:05:30.000Z | gfapy/line/unknown/__init__.py | ujjwalsh/gfapy | 891ef3df695f20c67809e5a54549c876d90690b4 | [
"ISC"
] | 5 | 2017-07-07T02:56:56.000Z | 2020-09-30T20:10:49.000Z | from .unknown import Unknown
| 14.5 | 28 | 0.827586 | 4 | 29 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d89ecaa6a7270c3e7b5b9e59135c08ff98a5beb0 | 26 | py | Python | kalliope/stt/google/__init__.py | G10DRAS/kalliope | 4c6586bd4c5ff0ca2b51cbf02f042d9ed0c9742d | [
"MIT"
] | null | null | null | kalliope/stt/google/__init__.py | G10DRAS/kalliope | 4c6586bd4c5ff0ca2b51cbf02f042d9ed0c9742d | [
"MIT"
] | null | null | null | kalliope/stt/google/__init__.py | G10DRAS/kalliope | 4c6586bd4c5ff0ca2b51cbf02f042d9ed0c9742d | [
"MIT"
] | null | null | null | from google import Google
| 13 | 25 | 0.846154 | 4 | 26 | 5.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 26 | 1 | 26 | 26 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d8a8ef19d79b811de69f72de6220f603172249bf | 19,461 | py | Python | qcodes/tests/dataset/test_dataset_in_memory.py | edumur/Qcodes | ac262035c299a872995cecd210f0e84b0b85d751 | [
"MIT"
] | null | null | null | qcodes/tests/dataset/test_dataset_in_memory.py | edumur/Qcodes | ac262035c299a872995cecd210f0e84b0b85d751 | [
"MIT"
] | 62 | 2021-12-23T04:32:54.000Z | 2022-03-31T04:28:12.000Z | qcodes/tests/dataset/test_dataset_in_memory.py | edumur/Qcodes | ac262035c299a872995cecd210f0e84b0b85d751 | [
"MIT"
] | null | null | null | import contextlib
import os
import shutil
import sqlite3
from pathlib import Path
import hypothesis.strategies as hst
import numpy as np
import pytest
import xarray
from hypothesis import HealthCheck, given, settings
from numpy.testing import assert_almost_equal
from qcodes import load_by_id
from qcodes.dataset import load_by_run_spec
from qcodes.dataset.data_set_in_memory import DataSetInMem
from qcodes.dataset.data_set_protocol import DataSetType
from qcodes.dataset.sqlite.connection import ConnectionPlus, atomic_transaction
from qcodes.station import Station
def test_dataset_in_memory_reload_from_db(
meas_with_registered_param, DMM, DAC, tmp_path
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
paramspecs = ds.get_parameters()
assert len(paramspecs) == 2
assert paramspecs[0].name == "dummy_dac_ch1"
assert paramspecs[1].name == "dummy_dmm_v1"
ds.export(export_type="netcdf", path=str(tmp_path))
assert isinstance(ds, DataSetInMem)
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
@settings(
deadline=None,
suppress_health_check=(HealthCheck.function_scoped_fixture,),
max_examples=10,
)
@given(
shape1=hst.integers(min_value=1, max_value=100),
shape2=hst.integers(min_value=1, max_value=100),
)
def test_dataset_in_memory_reload_from_db_2d(
meas_with_registered_param_2d, DMM, DAC, tmp_path, shape1, shape2
):
meas_with_registered_param_2d.set_shapes(
{
DMM.v1.full_name: (shape1, shape2),
}
)
i = 0
with meas_with_registered_param_2d.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, shape1):
for set_v2 in np.linspace(0, 100, shape2):
DAC.ch1.set(set_v)
DAC.ch2.set(set_v2)
datasaver.add_result(
(DAC.ch1, set_v), (DAC.ch2, set_v2), (DMM.v1, float(i))
)
i = i + 1
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
paramspecs = ds.get_parameters()
assert len(paramspecs) == 3
assert paramspecs[0].name == "dummy_dac_ch1"
assert paramspecs[1].name == "dummy_dac_ch2"
assert paramspecs[2].name == "dummy_dmm_v1"
# if the indexes (their order) are not correct here, the exported xarray, and thus
# the exported netcdf will have a wrong order of axes in the data, so that
# the loaded data will have the coordinates inverted. Hence we assert that
# the order is exactly the same as declared via Measurement.register_parameter
# calls above
assert tuple(ds.cache.to_pandas_dataframe().index.names) == (
"dummy_dac_ch1",
"dummy_dac_ch2",
)
ds.export(export_type="netcdf", path=str(tmp_path))
assert isinstance(ds, DataSetInMem)
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
@settings(
deadline=None,
suppress_health_check=(HealthCheck.function_scoped_fixture,),
max_examples=10,
)
@given(
shape1=hst.integers(min_value=1, max_value=10),
shape2=hst.integers(min_value=1, max_value=10),
shape3=hst.integers(min_value=1, max_value=10),
)
def test_dataset_in_memory_reload_from_db_3d(
meas_with_registered_param_3d, DMM, DAC3D, tmp_path, shape1, shape2, shape3
):
meas_with_registered_param_3d.set_shapes(
{
DMM.v1.full_name: (shape1, shape2, shape3),
}
)
i = 0
with meas_with_registered_param_3d.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, shape1):
for set_v2 in np.linspace(0, 100, shape2):
for set_v3 in np.linspace(0, 400, shape3):
DAC3D.ch1.set(set_v)
DAC3D.ch2.set(set_v2)
DAC3D.ch3.set(set_v3)
datasaver.add_result(
(DAC3D.ch1, set_v),
(DAC3D.ch2, set_v2),
(DAC3D.ch3, set_v3),
(DMM.v1, float(i)),
)
i = i + 1
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
paramspecs = ds.get_parameters()
assert len(paramspecs) == 4
assert paramspecs[0].name == "dummy_dac_ch1"
assert paramspecs[1].name == "dummy_dac_ch2"
assert paramspecs[2].name == "dummy_dac_ch3"
assert paramspecs[3].name == "dummy_dmm_v1"
# if the indexes (their order) are not correct here, the exported xarray, and thus
# the exported netcdf will have a wrong order of axes in the data, so that
# the loaded data will have the coordinates inverted. Hence we assert that
# the order is exactly the same as declared via Measurement.register_parameter
# calls above
assert tuple(ds.cache.to_pandas_dataframe().index.names) == (
"dummy_dac_ch1",
"dummy_dac_ch2",
"dummy_dac_ch3",
)
ds.export(export_type="netcdf", path=str(tmp_path))
assert isinstance(ds, DataSetInMem)
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def test_dataset_in_memory_without_cache_raises(
meas_with_registered_param, DMM, DAC, tmp_path
):
with pytest.raises(
RuntimeError,
match="Cannot disable the in memory cache for a dataset that is only in memory.",
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem, in_memory_cache=False
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
def test_dataset_in_memory_reload_from_db_complex(
meas_with_registered_param_complex, DAC, complex_num_instrument, tmp_path
):
with meas_with_registered_param_complex.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = complex_num_instrument.complex_num()
datasaver.add_result(
(DAC.ch1, set_v), (complex_num_instrument.complex_num, get_v)
)
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
ds.export(export_type="netcdf", path=str(tmp_path))
assert isinstance(ds, DataSetInMem)
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def test_dataset_in_memory_reload_from_netcdf_complex(
meas_with_registered_param_complex, DAC, complex_num_instrument, tmp_path
):
with meas_with_registered_param_complex.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = complex_num_instrument.complex_num()
datasaver.add_result(
(DAC.ch1, set_v), (complex_num_instrument.complex_num, get_v)
)
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
ds.add_metadata("someothermetadatatag", 42)
ds.export(export_type="netcdf", path=str(tmp_path))
assert isinstance(ds, DataSetInMem)
loaded_ds = DataSetInMem._load_from_netcdf(
tmp_path / f"qcodes_{ds.captured_run_id}_{ds.guid}.nc"
)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def test_dataset_in_memory_no_export_warns(
meas_with_registered_param, DMM, DAC, tmp_path
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
assert isinstance(ds, DataSetInMem)
ds.export(export_type="netcdf", path=str(tmp_path))
os.remove(ds.export_info.export_paths["nc"])
with pytest.warns(
UserWarning, match="Could not load raw data for dataset with guid"
):
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.cache.data() == {}
def test_dataset_in_memory_missing_file_warns(
meas_with_registered_param, DMM, DAC, tmp_path
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
assert isinstance(ds, DataSetInMem)
with pytest.warns(UserWarning, match="No raw data stored for dataset with guid"):
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.cache.data() == {}
def test_dataset_in_reload_from_netcdf(meas_with_registered_param, DMM, DAC, tmp_path):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
assert isinstance(ds, DataSetInMem)
ds.export(export_type="netcdf", path=str(tmp_path))
ds.add_metadata("metadata_added_after_export", 69)
loaded_ds = DataSetInMem._load_from_netcdf(
tmp_path / f"qcodes_{ds.captured_run_id}_{ds.guid}.nc"
)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def test_dataset_load_from_netcdf_and_db(
meas_with_registered_param, DMM, DAC, tmp_path
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
path_to_db = datasaver.dataset._path_to_db
ds = datasaver.dataset
ds.add_metadata("mymetadatatag", 42)
assert ds.run_id == 2
assert isinstance(ds, DataSetInMem)
ds.export(export_type="netcdf", path=str(tmp_path))
ds.add_metadata("metadata_added_after_export", 69)
loaded_ds = DataSetInMem._load_from_netcdf(
tmp_path / f"qcodes_{ds.captured_run_id}_{ds.guid}.nc", path_to_db=path_to_db
)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.run_id == ds.run_id
compare_datasets(ds, loaded_ds)
def test_dataset_in_memory_does_not_create_runs_table(
meas_with_registered_param, DMM, DAC, tmp_path
):
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
dbfile = datasaver.dataset._path_to_db
conn = ConnectionPlus(sqlite3.connect(dbfile))
tables_query = 'SELECT * FROM sqlite_master WHERE TYPE = "table"'
tables = list(atomic_transaction(conn, tables_query).fetchall())
assert len(tables) == 4
tablenames = tuple(table[1] for table in tables)
assert all(ds.name not in table_name for table_name in tablenames)
def test_load_from_netcdf_and_write_metadata_to_db(empty_temp_db):
netcdf_file_path = (
Path(__file__).parent / "fixtures" / "db_files" / "netcdf" / "qcodes_2.nc"
)
if not os.path.exists(str(netcdf_file_path)):
pytest.skip("No netcdf fixtures found.")
ds = DataSetInMem._load_from_netcdf(netcdf_file_path)
ds.write_metadata_to_db()
loaded_ds = load_by_run_spec(captured_run_id=ds.captured_run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.captured_run_id == ds.captured_run_id
assert loaded_ds.captured_counter == ds.captured_counter
assert loaded_ds.run_timestamp_raw == ds.run_timestamp_raw
assert loaded_ds.completed_timestamp_raw == ds.completed_timestamp_raw
compare_datasets(ds, loaded_ds)
# now we attempt to write again. This should be a noop so everything should
# stay the same
ds.write_metadata_to_db()
loaded_ds = load_by_run_spec(captured_run_id=ds.captured_run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.captured_run_id == ds.captured_run_id
assert loaded_ds.captured_counter == ds.captured_counter
assert loaded_ds.run_timestamp_raw == ds.run_timestamp_raw
assert loaded_ds.completed_timestamp_raw == ds.completed_timestamp_raw
compare_datasets(ds, loaded_ds)
def test_load_from_netcdf_no_db_file(non_created_db):
netcdf_file_path = (
Path(__file__).parent / "fixtures" / "db_files" / "netcdf" / "qcodes_2.nc"
)
if not os.path.exists(str(netcdf_file_path)):
pytest.skip("No netcdf fixtures found.")
ds = DataSetInMem._load_from_netcdf(netcdf_file_path)
ds.write_metadata_to_db()
loaded_ds = load_by_run_spec(captured_run_id=ds.captured_run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def test_load_from_db(meas_with_registered_param, DMM, DAC, tmp_path):
Station(DAC, DMM)
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("foo", "bar")
ds.export(export_type="netcdf", path=tmp_path)
ds.add_metadata("metadata_added_after_export", 69)
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.snapshot == ds.snapshot
assert loaded_ds.export_info == ds.export_info
assert loaded_ds.metadata == ds.metadata
assert "foo" in loaded_ds.metadata.keys()
assert "export_info" in loaded_ds.metadata.keys()
assert "metadata_added_after_export" in loaded_ds.metadata.keys()
assert loaded_ds.metadata["metadata_added_after_export"] == 69
compare_datasets(ds, loaded_ds)
def test_load_from_netcdf_legacy_version(non_created_db):
# Qcodes 0.26 exported netcdf files did not contain
# the parent dataset links and used a different engine to write data
# check that it still loads correctly
netcdf_file_path = (
Path(__file__).parent / "fixtures" / "db_files" / "netcdf" / "qcodes_v26.nc"
)
if not os.path.exists(str(netcdf_file_path)):
pytest.skip("No netcdf fixtures found.")
ds = DataSetInMem._load_from_netcdf(netcdf_file_path)
ds.write_metadata_to_db()
loaded_ds = load_by_run_spec(captured_run_id=ds.captured_run_id)
assert isinstance(loaded_ds, DataSetInMem)
compare_datasets(ds, loaded_ds)
def compare_datasets(ds, loaded_ds):
assert ds.the_same_dataset_as(loaded_ds)
assert len(ds) == len(loaded_ds)
assert len(ds) != 0
for outer_var, inner_dict in ds.cache.data().items():
for inner_var, expected_data in inner_dict.items():
assert (
expected_data.shape
== loaded_ds.cache.data()[outer_var][inner_var].shape
)
assert_almost_equal(
expected_data,
loaded_ds.cache.data()[outer_var][inner_var],
)
xds = ds.cache.to_xarray_dataset()
loaded_xds = loaded_ds.cache.to_xarray_dataset()
assert xds.sizes == loaded_xds.sizes
assert all(xds == loaded_xds)
def test_load_from_db_dataset_moved(meas_with_registered_param, DMM, DAC, tmp_path):
Station(DAC, DMM)
with meas_with_registered_param.run(
dataset_class=DataSetType.DataSetInMem
) as datasaver:
for set_v in np.linspace(0, 25, 10):
DAC.ch1.set(set_v)
get_v = DMM.v1()
datasaver.add_result((DAC.ch1, set_v), (DMM.v1, get_v))
ds = datasaver.dataset
ds.add_metadata("foo", "bar")
ds.export(export_type="netcdf", path=tmp_path)
ds.add_metadata("metadata_added_after_export", 69)
export_path = ds.export_info.export_paths["nc"]
with contextlib.closing(xarray.open_dataset(export_path)) as xr_ds:
assert xr_ds.attrs["metadata_added_after_export"] == 69
new_path = str(Path(export_path).parent / "someotherfilename.nc")
shutil.move(export_path, new_path)
with pytest.warns(
UserWarning, match="Could not load raw data for dataset with guid"
):
loaded_ds = load_by_id(ds.run_id)
assert isinstance(loaded_ds, DataSetInMem)
assert loaded_ds.snapshot == ds.snapshot
assert loaded_ds.export_info == ds.export_info
assert loaded_ds.metadata == ds.metadata
assert "foo" in loaded_ds.metadata.keys()
assert "export_info" in loaded_ds.metadata.keys()
assert "metadata_added_after_export" in loaded_ds.metadata.keys()
assert loaded_ds.cache.data() == {}
with pytest.warns(
UserWarning, match="Could not add metadata to the exported NetCDF file"
):
ds.add_metadata("metadata_added_after_move", 696)
with contextlib.closing(xarray.open_dataset(new_path)) as new_xr_ds:
assert new_xr_ds.attrs["metadata_added_after_export"] == 69
assert "metadata_added_after_move" not in new_xr_ds.attrs
loaded_ds.set_netcdf_location(new_path)
assert loaded_ds.cache.data().keys() == ds.cache.data().keys()
with contextlib.closing(xarray.open_dataset(new_path)) as new_xr_ds:
assert new_xr_ds.attrs["metadata_added_after_export"] == 69
assert "metadata_added_after_move" not in new_xr_ds.attrs
# This should have effect neither on the loaded_ds nor on the netcdf file
with pytest.warns(
UserWarning, match="Could not add metadata to the exported NetCDF file"
):
ds.add_metadata(
"metadata_added_to_old_dataset_after_set_new_netcdf_location", 696977
)
loaded_ds.add_metadata("metadata_added_after_set_new_netcdf_location", 6969)
with contextlib.closing(xarray.open_dataset(new_path)) as new_xr_ds:
assert new_xr_ds.attrs["metadata_added_after_export"] == 69
assert "metadata_added_after_move" not in new_xr_ds.attrs
assert (
"metadata_added_to_old_dataset_after_set_new_netcdf_location"
not in new_xr_ds.attrs
)
assert new_xr_ds.attrs["metadata_added_after_set_new_netcdf_location"] == 6969
| 34.383392 | 89 | 0.68943 | 2,748 | 19,461 | 4.566594 | 0.098617 | 0.049088 | 0.041597 | 0.053152 | 0.846442 | 0.806279 | 0.799426 | 0.78564 | 0.741095 | 0.733445 | 0 | 0.019587 | 0.215611 | 19,461 | 565 | 90 | 34.444248 | 0.802489 | 0.048507 | 0 | 0.682648 | 0 | 0 | 0.091297 | 0.039081 | 0 | 0 | 0 | 0 | 0.191781 | 1 | 0.038813 | false | 0 | 0.038813 | 0 | 0.077626 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d8e463af9711e1daab390c78f54ec56f5c494c2e | 155 | py | Python | jupiter/domain/smart_lists/smart_list_tag_name.py | horia141/jupiter | 2c721d1d44e1cd2607ad9936e54a20ea254741dc | [
"MIT"
] | 15 | 2019-05-05T14:34:58.000Z | 2022-02-25T09:57:28.000Z | jupiter/domain/smart_lists/smart_list_tag_name.py | horia141/jupiter | 2c721d1d44e1cd2607ad9936e54a20ea254741dc | [
"MIT"
] | 3 | 2020-02-22T16:09:39.000Z | 2021-12-18T21:33:06.000Z | jupiter/domain/smart_lists/smart_list_tag_name.py | horia141/jupiter | 2c721d1d44e1cd2607ad9936e54a20ea254741dc | [
"MIT"
] | null | null | null | """The name of a smart list tag."""
from jupiter.domain.tag_name import TagName
class SmartListTagName(TagName):
"""The name of a smart list tag."""
| 22.142857 | 43 | 0.709677 | 24 | 155 | 4.541667 | 0.583333 | 0.12844 | 0.165138 | 0.183486 | 0.40367 | 0.40367 | 0.40367 | 0 | 0 | 0 | 0 | 0 | 0.167742 | 155 | 6 | 44 | 25.833333 | 0.844961 | 0.380645 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 1 | 0 | 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 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2b0a25b8d64e3274b22eae0414c0862ac46e3afa | 208 | py | Python | weather/urls.py | debugleader/Django-WeatherX | 0f7544165078d485e3b277fc05b066ad9026f32a | [
"MIT"
] | 7 | 2020-09-16T07:46:33.000Z | 2021-10-19T16:45:30.000Z | weather/urls.py | debugleader/Django-WeatherX | 0f7544165078d485e3b277fc05b066ad9026f32a | [
"MIT"
] | null | null | null | weather/urls.py | debugleader/Django-WeatherX | 0f7544165078d485e3b277fc05b066ad9026f32a | [
"MIT"
] | null | null | null | from django.urls import path
from .views import index_name, delete_city
urlpatterns = [
path('', index_name, name = 'indexo'),
path('/delete/<city_name>/', delete_city, name = 'delete_city')
] | 29.714286 | 68 | 0.673077 | 27 | 208 | 4.962963 | 0.444444 | 0.298507 | 0.313433 | 0.298507 | 0.283582 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.182692 | 208 | 7 | 69 | 29.714286 | 0.788235 | 0 | 0 | 0 | 0 | 0 | 0.182266 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
2b34abfc137fda65d2c44eda58684ba43ab6809d | 73 | py | Python | proxyUtil/__pyinstaller/__init__.py | actlaboratory/pypac | 32956b3b3f7ca2cd7f00b674bfc6e9457dc37602 | [
"Apache-2.0"
] | null | null | null | proxyUtil/__pyinstaller/__init__.py | actlaboratory/pypac | 32956b3b3f7ca2cd7f00b674bfc6e9457dc37602 | [
"Apache-2.0"
] | null | null | null | proxyUtil/__pyinstaller/__init__.py | actlaboratory/pypac | 32956b3b3f7ca2cd7f00b674bfc6e9457dc37602 | [
"Apache-2.0"
] | null | null | null | import os
def get_module_path():
return [os.path.dirname(__file__)]
| 14.6 | 38 | 0.726027 | 11 | 73 | 4.272727 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150685 | 73 | 4 | 39 | 18.25 | 0.758065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 6 |
2b4441e87c645b85d036159624fc60a64daafc2e | 37 | py | Python | Backend/__init__.py | spd1-3/pesto-dolphins | 0848baee637f2c475e71eeee80794316a54d390d | [
"MIT"
] | 1 | 2021-02-03T18:02:39.000Z | 2021-02-03T18:02:39.000Z | Backend/__init__.py | spd1-3/pesto-dolphins | 0848baee637f2c475e71eeee80794316a54d390d | [
"MIT"
] | 1 | 2021-03-23T17:11:05.000Z | 2021-03-23T17:11:05.000Z | Backend/__init__.py | spd1-3/pesto-dolphins | 0848baee637f2c475e71eeee80794316a54d390d | [
"MIT"
] | null | null | null | from Server.models import make_user
| 12.333333 | 35 | 0.837838 | 6 | 37 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135135 | 37 | 2 | 36 | 18.5 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2b468ffbd5ed0c8bb1e3642a3acfa6082450388e | 27 | py | Python | __init__.py | ziplantil/wikitextlib | cbe649d049b20729e50548c5585db74c5e9243e6 | [
"MIT"
] | null | null | null | __init__.py | ziplantil/wikitextlib | cbe649d049b20729e50548c5585db74c5e9243e6 | [
"MIT"
] | null | null | null | __init__.py | ziplantil/wikitextlib | cbe649d049b20729e50548c5585db74c5e9243e6 | [
"MIT"
] | null | null | null | from wikitextlib import *
| 9 | 25 | 0.777778 | 3 | 27 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.185185 | 27 | 2 | 26 | 13.5 | 0.954545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
511f0c1f8fffe7f59dc4ba34811807edee951f9d | 41 | py | Python | lambdasoc/periph/__init__.py | supersat/lambdasoc | c2662ef4cf485330dd137e1993394416c5497de5 | [
"BSD-2-Clause"
] | 29 | 2020-04-05T08:46:57.000Z | 2021-11-28T21:28:20.000Z | lambdasoc/periph/__init__.py | supersat/lambdasoc | c2662ef4cf485330dd137e1993394416c5497de5 | [
"BSD-2-Clause"
] | 13 | 2020-04-24T04:47:18.000Z | 2021-08-21T23:58:39.000Z | lambdasoc/periph/__init__.py | supersat/lambdasoc | c2662ef4cf485330dd137e1993394416c5497de5 | [
"BSD-2-Clause"
] | 16 | 2020-06-08T11:22:14.000Z | 2022-01-29T04:36:03.000Z | from .base import *
from .event import *
| 13.666667 | 20 | 0.707317 | 6 | 41 | 4.833333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195122 | 41 | 2 | 21 | 20.5 | 0.878788 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5150b1149add8fa93580d427a82a7354bc7110d1 | 94 | py | Python | SDED-Ragic/.ipynb_checkpoints/config-checkpoint.py | lanesharman/OpenDoors | c601fc067a4def1aea2d67995ce9f3baa8386501 | [
"Apache-2.0"
] | null | null | null | SDED-Ragic/.ipynb_checkpoints/config-checkpoint.py | lanesharman/OpenDoors | c601fc067a4def1aea2d67995ce9f3baa8386501 | [
"Apache-2.0"
] | null | null | null | SDED-Ragic/.ipynb_checkpoints/config-checkpoint.py | lanesharman/OpenDoors | c601fc067a4def1aea2d67995ce9f3baa8386501 | [
"Apache-2.0"
] | null | null | null | api = 'ODlWbjdDd3hEYlRFODJXaG0vY2lsamNOTmVJR05hOHFGb3RraHZDeTRDNFgvK05ucmtUdVl0VXM5MUo0bFF0Vw' | 94 | 94 | 0.946809 | 2 | 94 | 44.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 0.021277 | 94 | 1 | 94 | 94 | 0.836957 | 0 | 0 | 0 | 0 | 0 | 0.905263 | 0.905263 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
515c9a4037a7ce3097f854fc4b6088125e5927e2 | 10,290 | py | Python | tests/ignite/metrics/test_accuracy.py | Acidburn0zzz/ignite | 0ea52729740ddd5e2da543527232ad23b0c9c97f | [
"BSD-3-Clause"
] | 1 | 2018-12-30T04:11:33.000Z | 2018-12-30T04:11:33.000Z | tests/ignite/metrics/test_accuracy.py | Acidburn0zzz/ignite | 0ea52729740ddd5e2da543527232ad23b0c9c97f | [
"BSD-3-Clause"
] | null | null | null | tests/ignite/metrics/test_accuracy.py | Acidburn0zzz/ignite | 0ea52729740ddd5e2da543527232ad23b0c9c97f | [
"BSD-3-Clause"
] | null | null | null | from ignite.exceptions import NotComputableError
from ignite.metrics import Accuracy
import pytest
import torch
from sklearn.metrics import accuracy_score
torch.manual_seed(12)
def test_no_update():
acc = Accuracy()
with pytest.raises(NotComputableError):
acc.compute()
def test__check_shape():
acc = Accuracy()
# Check squeezed dimensions
y_pred, y = acc._check_shape((torch.randint(0, 2, size=(10, 1, 5, 6)).type(torch.LongTensor),
torch.randint(0, 2, size=(10, 5, 6)).type(torch.LongTensor)))
assert y_pred.shape == (10, 5, 6)
assert y.shape == (10, 5, 6)
y_pred, y = acc._check_shape((torch.randint(0, 2, size=(10, 5, 6)).type(torch.LongTensor),
torch.randint(0, 2, size=(10, 1, 5, 6)).type(torch.LongTensor)))
assert y_pred.shape == (10, 5, 6)
assert y.shape == (10, 5, 6)
def test_binary_wrong_inputs():
acc = Accuracy()
with pytest.raises(ValueError):
# y has not only 0 or 1 values
acc.update((torch.randint(0, 2, size=(10,)).type(torch.LongTensor),
torch.arange(0, 10).type(torch.LongTensor)))
# TODO: Uncomment the following after 0.1.2 release
# with pytest.raises(ValueError):
# # y_pred values are not thresholded to 0, 1 values
# acc.update((torch.rand(10, 1),
# torch.randint(0, 2, size=(10,)).type(torch.LongTensor)))
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.randint(0, 2, size=(10,)).type(torch.LongTensor),
torch.randint(0, 2, size=(10, 5)).type(torch.LongTensor)))
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.randint(0, 2, size=(10, 5, 6)).type(torch.LongTensor),
torch.randint(0, 2, size=(10,)).type(torch.LongTensor)))
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.randint(0, 2, size=(10,)).type(torch.LongTensor),
torch.randint(0, 2, size=(10, 5, 6)).type(torch.LongTensor)))
def test_binary_input_N():
# Binary accuracy on input of shape (N, 1) or (N, )
acc = Accuracy()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(10, 1)).type(torch.LongTensor)
y_pred = torch.rand(10, 1)
y = torch.randint(0, 2, size=(10,)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(10, )).type(torch.LongTensor)
y_pred = torch.rand(10)
y = torch.randint(0, 2, size=(10,)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_binary_input_NL():
# Binary accuracy on input of shape (N, L)
acc = Accuracy()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(10, 5)).type(torch.LongTensor)
y_pred = torch.rand(10, 5)
y = torch.randint(0, 2, size=(10, 5)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(10, 1, 5)).type(torch.LongTensor)
y_pred = torch.rand(10, 1, 5)
y = torch.randint(0, 2, size=(10, 1, 5)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_binary_input_NHW():
# Binary accuracy on input of shape (N, H, W, ...)
acc = Accuracy()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(4, 12, 10)).type(torch.LongTensor)
y_pred = torch.rand(4, 12, 10)
y = torch.randint(0, 2, size=(4, 12, 10)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(4, 1, 12, 10)).type(torch.LongTensor)
y_pred = torch.rand(4, 1, 12, 10)
y = torch.randint(0, 2, size=(4, 1, 12, 10)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
# TODO: y_pred should be binary after 0.1.2 release
# y_pred = torch.randint(0, 2, size=(4, 12, 10, 8)).type(torch.LongTensor)
y_pred = torch.rand(4, 12, 10, 8)
y = torch.randint(0, 2, size=(4, 12, 10, 8)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y = y.numpy().ravel()
# np_y_pred = y_pred.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')
assert acc._type == 'binary'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_multiclass_wrong_inputs():
acc = Accuracy()
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.rand(10, 5, 4),
torch.randint(0, 2, size=(10,)).type(torch.LongTensor)))
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.rand(10, 5, 6),
torch.randint(0, 5, size=(10, 5)).type(torch.LongTensor)))
with pytest.raises(ValueError):
# incompatible shapes
acc.update((torch.rand(10),
torch.randint(0, 5, size=(10, 5, 6)).type(torch.LongTensor)))
def test_multiclass_input_N():
# Multiclass input data of shape (N, ) and (N, C)
acc = Accuracy()
y_pred = torch.rand(10, 4)
y = torch.randint(0, 4, size=(10,)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
y_pred = torch.rand(4, 10)
y = torch.randint(0, 10, size=(4, 1)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
# 2-classes
acc.reset()
y_pred = torch.rand(4, 2)
y = torch.randint(0, 2, size=(4, 1)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_multiclass_input_NL():
# Multiclass input data of shape (N, L) and (N, C, L)
acc = Accuracy()
y_pred = torch.rand(10, 4, 5)
y = torch.randint(0, 4, size=(10, 5)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
y_pred = torch.rand(4, 10, 5)
y = torch.randint(0, 10, size=(4, 5)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_multiclass_input_NHW():
# Multiclass input data of shape (N, H, W, ...) and (N, C, H, W, ...)
acc = Accuracy()
y_pred = torch.rand(4, 5, 12, 10)
y = torch.randint(0, 5, size=(4, 12, 10)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
acc.reset()
y_pred = torch.rand(4, 5, 10, 12, 8)
y = torch.randint(0, 5, size=(4, 10, 12, 8)).type(torch.LongTensor)
acc.update((y_pred, y))
np_y_pred = y_pred.numpy().argmax(axis=1).ravel()
np_y = y.numpy().ravel()
assert acc._type == 'multiclass'
assert isinstance(acc.compute(), float)
assert accuracy_score(np_y, np_y_pred) == pytest.approx(acc.compute())
def test_incorrect_type():
acc = Accuracy()
# Start as binary data
y_pred = torch.rand(4,)
y = torch.ones(4).type(torch.LongTensor)
acc.update((y_pred, y))
# And add a multiclass data
y_pred = torch.rand(4, 4)
y = torch.ones(4).type(torch.LongTensor)
with pytest.raises(RuntimeError):
acc.update((y_pred, y))
| 36.489362 | 98 | 0.625753 | 1,587 | 10,290 | 3.909263 | 0.061122 | 0.086235 | 0.037718 | 0.063185 | 0.918762 | 0.898453 | 0.871534 | 0.83285 | 0.794487 | 0.772566 | 0 | 0.042702 | 0.205734 | 10,290 | 281 | 99 | 36.619217 | 0.716383 | 0.180758 | 0 | 0.682796 | 0 | 0 | 0.015862 | 0 | 0 | 0 | 0 | 0.003559 | 0.247312 | 1 | 0.05914 | false | 0 | 0.026882 | 0 | 0.086022 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
51640087989fb93312714f07de7eb58362e16b47 | 194 | py | Python | tensor_fcn/networks/__init__.py | dubvulture/tensor_fcn | 90bc2023ef8d0b237044e806267ac033af1b02b4 | [
"MIT"
] | 3 | 2018-07-16T14:12:28.000Z | 2021-09-07T01:16:24.000Z | tensor_fcn/networks/__init__.py | dubvulture/tensor_fcn | 90bc2023ef8d0b237044e806267ac033af1b02b4 | [
"MIT"
] | 1 | 2017-10-11T14:01:08.000Z | 2017-10-23T08:59:00.000Z | tensor_fcn/networks/__init__.py | dubvulture/tensor_fcn | 90bc2023ef8d0b237044e806267ac033af1b02b4 | [
"MIT"
] | 2 | 2017-11-14T02:30:22.000Z | 2018-11-19T09:36:13.000Z | from __future__ import absolute_import
from tensor_fcn.networks.vgg_net import create_vgg16
from tensor_fcn.networks.vgg_net import create_vgg19
from tensor_fcn.networks.fcn import create_fcn
| 27.714286 | 52 | 0.876289 | 31 | 194 | 5.064516 | 0.387097 | 0.191083 | 0.248408 | 0.401274 | 0.496815 | 0.496815 | 0.496815 | 0.496815 | 0 | 0 | 0 | 0.022727 | 0.092784 | 194 | 6 | 53 | 32.333333 | 0.869318 | 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 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5a924a068a63812ff015eaa0e4e3a784e5c8f94b | 45 | py | Python | finnhub_api/__init__.py | humdings/finnhub-api | 9edd735af03035cdb3740667aa97273d2b9e716f | [
"MIT"
] | null | null | null | finnhub_api/__init__.py | humdings/finnhub-api | 9edd735af03035cdb3740667aa97273d2b9e716f | [
"MIT"
] | null | null | null | finnhub_api/__init__.py | humdings/finnhub-api | 9edd735af03035cdb3740667aa97273d2b9e716f | [
"MIT"
] | null | null | null | from finnhub_api.client import FinnHubClient
| 22.5 | 44 | 0.888889 | 6 | 45 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 45 | 1 | 45 | 45 | 0.95122 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5abe2a3cab5fdb4dccb7d8d781d21d6bcaece5c6 | 49 | py | Python | drivers/lockInAmp/__init__.py | mv20100/phd_code | 2262c71c7c35aed5759c4b0e058fe74c44e5266b | [
"MIT"
] | null | null | null | drivers/lockInAmp/__init__.py | mv20100/phd_code | 2262c71c7c35aed5759c4b0e058fe74c44e5266b | [
"MIT"
] | null | null | null | drivers/lockInAmp/__init__.py | mv20100/phd_code | 2262c71c7c35aed5759c4b0e058fe74c44e5266b | [
"MIT"
] | null | null | null | from .sr830 import SR830
__all__ = ['SR830', ] | 16.333333 | 25 | 0.653061 | 6 | 49 | 4.666667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.230769 | 0.204082 | 49 | 3 | 26 | 16.333333 | 0.487179 | 0 | 0 | 0 | 0 | 0 | 0.104167 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 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 | 1 | 0 | 0 | 0 | 0 | 6 |
5aeef70e18fc7ad3200d0cd255f53f80dd513384 | 74 | py | Python | model/__init__.py | ishine/FastVocoder | ac716e6df8cd03dbfc4a969d8a5ed42c055c38aa | [
"MIT"
] | 116 | 2021-05-30T13:27:19.000Z | 2022-03-28T12:52:41.000Z | model/__init__.py | ishine/FastVocoder | ac716e6df8cd03dbfc4a969d8a5ed42c055c38aa | [
"MIT"
] | 9 | 2021-06-23T05:33:41.000Z | 2022-02-22T09:27:53.000Z | model/__init__.py | ishine/FastVocoder | ac716e6df8cd03dbfc4a969d8a5ed42c055c38aa | [
"MIT"
] | 17 | 2021-05-30T14:18:31.000Z | 2022-03-25T04:58:22.000Z | from .discriminator import *
from .loss import *
from .generator import *
| 18.5 | 28 | 0.756757 | 9 | 74 | 6.222222 | 0.555556 | 0.357143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.162162 | 74 | 3 | 29 | 24.666667 | 0.903226 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
518129639498ea71336fbb785485470ac20067f0 | 22,224 | py | Python | run_dengueinfections.py | hnguyenbhsai/immune-response-to-dengue | c3311b4a06d33b36c44d54cc8712072f8f9491e4 | [
"MIT"
] | null | null | null | run_dengueinfections.py | hnguyenbhsai/immune-response-to-dengue | c3311b4a06d33b36c44d54cc8712072f8f9491e4 | [
"MIT"
] | null | null | null | run_dengueinfections.py | hnguyenbhsai/immune-response-to-dengue | c3311b4a06d33b36c44d54cc8712072f8f9491e4 | [
"MIT"
] | null | null | null | import math
import random
import func
import sys
#get command-line optionsccc
data_file = str(sys.argv[1])+ ".txt"
infection = str(sys.argv[2])
print( "Writing to data file " + data_file)
#immunization/infection parameters
equil_interval = 100.0
finfection_interval = 0.0 #days
sinfection_interval = 365.0 #days
#antigen paramaters
ag_decay = 8.0 #12 for malaria
stimulation = 1.0
ag_replication_v = 6.25
ag_replication0 = 6.2
ag_replication1 = 6.2
ag_replication2 = 6.2
ag_replication3 = 6.2
#immune system parameters
Tcd4_initial = 1150
Tcd8_initial = 1050
Bcell_initial = 50000000 #see Smith & Perelson PNAS 1999
Bcell_carry = 5000 #see Kuppers et al. EMBO J 1993
Bcell_aff = 10.0
Tcell_carry = 5000
AB_aff = 2.5
tau = 8.0 # time interval for constants (hrs) see Zhang et al Immune Lett. 1988; Liu et al Eur J. Immun. 1991
knB = round(random.triangular(0.0848, 0.2488, 0.1479), 4)
kT4 = round(random.triangular(0.0496, 0.1223, 0.0735), 4)
kT8 = round(random.triangular(0.0949, 0.2225, 0.1306), 4)
print('knB, kT4, kT8 ', knB, kT4, kT8)
#define antigens
antigen_list = func.ReadAgFile( "./ep_file.txt" )
#set up antigen population
V0 = func.Antigen("V0", 0, antigen_list[0])
V1 = func.Antigen("V1", 0, antigen_list[1])
V2 = func.Antigen("V2", 0, antigen_list[2])
V3 = func.Antigen("V3", 0, antigen_list[3])
#set up populations
nB_gc = func.BCell("GC_B", 0, antigen_list )
nB_stim = func.BCell("Stimulated_B", 0, antigen_list )
nB_me = func.BCell("Memory_B", 0, antigen_list )
nB_pls = func.BCell("SL_Plasma_B", 0, antigen_list )
nB_pll = func.BCell("LL_Plasma_B", 0, antigen_list )
nAB = func.BCell("Antibody", 0, antigen_list )
print('set up population for nB')
nB = func.BCell("Naive_B", Bcell_initial, antigen_list )
print('set up population for Tcd4')
Tcd4 = func.Population("T_cd4", Tcd4_initial)
print('set up population for Tcd4_stim')
Tcd4_stim = func.Population("T_cd4_stim", 0)
print('set up population for Tcd4_memory')
Tcd4_me = func.Population("T_cd4_me", 0)
print('set up population for Tcd8')
Tcd8 = func.Population("T_cd8", Tcd8_initial)
nB_Tstim = func.BCell("Tstimulated_B", 0, antigen_list )
Tcd8_stim = func.Population("T_cd8_stim", 0)
AbClear = func.Population("AbClearance", 0)
T8Clear = func.Population("T8Clearance", 0)
GC_population = func.GroupPopulation("GC Bcell Population")
GC_population.add_population(nB_gc)
GC_population.add_population(nB_stim)
GC_population.add_population(nB_Tstim)
T4_population = func.GroupPopulation("CD4 T-cell Population")
T4_population.add_population(Tcd4)
T4_population.add_population(Tcd4_stim)
T8_population = func.GroupPopulation("CD8 T-cell Population")
T8_population.add_population(Tcd8)
T8_population.add_population(Tcd8_stim)
#set up population list
PopulationList = []
PopulationList.append(nB_gc)
PopulationList.append(nB_stim)
PopulationList.append(nB_me)
PopulationList.append(nB_pls)
PopulationList.append(nAB)
PopulationList.append(nB)
PopulationList.append(V0)
PopulationList.append(V1)
PopulationList.append(V2)
PopulationList.append(V3)
PopulationList.append(Tcd4)
PopulationList.append(Tcd4_stim)
PopulationList.append(Tcd4_me)
PopulationList.append(Tcd8)
PopulationList.append(nB_Tstim)
PopulationList.append(nB_pll)
PopulationList.append(Tcd8_stim)
PopulationList.append(AbClear)
PopulationList.append(T8Clear)
#### EQUILIBRATION
#define a system of reactions
A0 = func.TotalReaction()
##Description: Naive B cell formation in bone marrow
eq3 = func.Formation("Naive B cell formation", float(tau/knB), nB) #produces 250 cells every 108hrs, formerly 0.432
A0.add_reaction(eq3)
##Description: Basal B cell decay rate
eq4b = func.Decay("Naive B cell decay", float(tau/108.0), nB)
A0.add_reaction(eq4b)
##Description: T CD4 cell formation
eq12a = func.Formation("T CD4 cell formation", float(tau/kT4), Tcd4)
A0.add_reaction(eq12a)
##Description: T CD4 cell decay rate
eq12b = func.Decay("T CD4 cell decay", float(tau/108.0), Tcd4)
A0.add_reaction(eq12b)
##Description: T CD8 cell formation
eq13a = func.Formation("T CD8 cell formation", float(tau/kT8), Tcd8)
A0.add_reaction(eq13a)
##Description: T CD8 cell decay rate
eq13b = func.Decay("T CD8 cell decay", float(tau/108.0), Tcd8)
A0.add_reaction(eq13b)
#carry out simulation
output = func.FileOutput( data_file, 0.1, PopulationList, antigen_list )
if (equil_interval > 0.0):
output.start()
###Equilibrium (just naive B cell decay and formation) starting at day 0
total_time = float(equil_interval * 3.0 )
t = 0.0
while( t < total_time ):
dt = A0.MC_TimeStep()
t += dt
A0.MC_React()
output.write( t )
#### FIRST INFECTION
ag_initial = 100
Tstimulation1 = 1200.0
Tstimulation2 = 2400.0
Tstimulation3 = 8.0
Abclearance = 0.00025
T8clearance = 0.000025
#define a system of reactions
A0 = func.TotalReaction()
print('Setting up first reaction', len(A0))
##Description: Naive B cell formation in bone marrow
eq3 = func.Formation("Naive B cell formation", float(tau/knB), nB) #produces 250 cells every 108hrs, formerly 0.432
A0.add_reaction(eq3)
##Description: Basal B cell decay rate
eq4b = func.Decay("Naive B cell decay", float(tau/108.0), nB)
A0.add_reaction(eq4b)
##Description: T CD4 cell formation
eq12a = func.Formation("T CD4 cell formation", float(tau/kT4), Tcd4)
A0.add_reaction(eq12a)
##Description: T CD4 cell decay rate
eq12b = func.Decay("T CD4 cell decay", float(tau/108.0), Tcd4)
A0.add_reaction(eq12b)
##Description: T CD8 cell formation
eq13a = func.Formation("T CD8 cell formation", float(tau/kT8), Tcd8)
A0.add_reaction(eq13a)
##Description: T CD8 cell decay rate
eq13b = func.Decay("T CD8 cell decay", float(tau/108.0), Tcd8)
A0.add_reaction(eq13b)
##Description: Circulating B cell antigen stimulation
##Naive B cell stimulation is external to the GC
eq1a = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V0, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1b = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V1, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1c = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V2, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1d = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V3, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
A0.add_reaction(eq1a)
A0.add_reaction(eq1b)
A0.add_reaction(eq1c)
A0.add_reaction(eq1d)
##Description: Germinal Center B cell antigen stimulation
## binding affinity with antigen scales exponentially with Hamming Distance
eq2a = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V0, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity") #max stimulate rate half life 15min (0.25hr)
eq2b = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V1, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
eq2c = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V2, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
eq2d = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V3, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
A0.add_reaction(eq2a)
A0.add_reaction(eq2b)
A0.add_reaction(eq2c)
A0.add_reaction(eq2d)
##Description: Additional stimulation of antigen-stimulated GC B cell by T_cd4 cell
eq14 = func.TStimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 cell", float(tau/Tstimulation1), nB_stim, Tcd4, nB_Tstim, Tcd4_stim) #cell cycle is tau
A0.add_reaction(eq14)
##Description: Additional stimulation of antigen-stimulated GC B cell by T_cd4 memory cell
eq15 = func.TStimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 memory cell", float(tau/Tstimulation2), nB_stim, Tcd4_me, nB_Tstim, Tcd4_stim) #cell cycle is tau
A0.add_reaction(eq15)
##Description: Stimulation of T_cd8 cell
eq20 = func.T8Stimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 memory cell", float(tau/Tstimulation3), Tcd4_stim, Tcd8, Tcd8_stim) #cell cycle is tau
A0.add_reaction(eq20)
##Description: Germinal Center B cell decay, governed by apoptosis. Modeled as a logistic function
## with a pre-defined GC population carrying capacity
eq4a = func.PopulationDecay("B Cell decay", float(tau/(tau+1.0)), float(tau/108.0), Bcell_carry, GC_population, nB_gc, nB_stim, nB_Tstim) #base half life of 4.5 days (108hrs)
A0.add_reaction(eq4a)
eq19a = func.TPopulationDecay("Stimulated T Cell decay", float(tau/(tau+200.0)), float(tau/21600.0), Tcell_carry, T4_population, Tcd4_stim) #base half life of ?
#eq19a = func.Decay("T CD4 cell decay", float(tau/108.0), Tcd4_stim)
A0.add_reaction(eq19a)
eq19b = func.TPopulationDecay("Stimulated T Cell decay", float(tau/(tau+200.0)), float(tau/21600.0), Tcell_carry, T8_population, Tcd8_stim) #base half life of ?
#eq19b = func.Decay("T CD8 cell decay", float(tau/108.0), Tcd8_stim)
A0.add_reaction(eq19b)
##Description: Germinal Center B cell differentiation rate, modeling affinity-dependent T help
eq5a = func.BDifferentiation("B cell differentiation", float(tau/60.0), nB_stim, nB_gc, 1.0) #cell cycle is tau
A0.add_reaction(eq5a)
eq5b = func.Differentiation("B cell differentiation", float(tau/8.0), nB_Tstim, nB_gc, nB_stim, nB_me, nB_pls, nB_pll, 1.0) #cell cycle is tau
A0.add_reaction(eq5b)
##Description: TCD4 cell differentiation by mutation and into memory cell
eq16 = func.TDifferentiation("TCD4 cell differentiation", float(tau/15.0), Tcd4_stim, Tcd4, Tcd4_me) #cell cycle is tau
A0.add_reaction(eq16)
##Description: TCD8 cell differentiation
eq21 = func.T8Differentiation("TCD8 cell differentiation", float(tau/180.0), Tcd8_stim, Tcd8) #cell cycle is tau
A0.add_reaction(eq21)
##Description: Antibody production by Plasma cells
eq6a = func.Production("Antibody Production", 1.0, nB_pls, nAB)
A0.add_reaction(eq6a)
eq6b = func.Production("Antibody Production", 0.1, nB_pll, nAB)
A0.add_reaction(eq6b)
##Description: Antibody decay rate
eq7 = func.Decay("Antibody Decay", float(tau/360.0), nAB) #half life of 10 days (360hrs)
A0.add_reaction(eq7)
##Description: Intrinsic antigen decay
eq8a = func.Decay("Antigen Decay", float(tau/ag_decay), V0)
eq8b = func.Decay("Antigen Decay", float(tau/ag_decay), V1)
eq8c = func.Decay("Antigen Decay", float(tau/ag_decay), V2)
eq8d = func.Decay("Antigen Decay", float(tau/ag_decay), V3)
A0.add_reaction(eq8a)
A0.add_reaction(eq8b)
A0.add_reaction(eq8c)
A0.add_reaction(eq8d)
##Description: Antibody-dependent antigen clearance
eq8e = func.AbClearance("Antigen Clearance", Abclearance, V0, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8f = func.AbClearance("Antigen Clearance", Abclearance, V1, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8g = func.AbClearance("Antigen Clearance", Abclearance, V2, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8h = func.AbClearance("Antigen Clearance", Abclearance, V3, nAB, 10000.0, AB_aff, "clearance", AbClear)
A0.add_reaction(eq8e)
A0.add_reaction(eq8f)
A0.add_reaction(eq8g)
A0.add_reaction(eq8h)
##Description: CD8-dependent antigen clearance
eq17a = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V0, Tcd8_stim, T8Clear)
eq17b = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V1, Tcd8_stim, T8Clear)
eq17c = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V2, Tcd8_stim, T8Clear)
eq17d = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V3, Tcd8_stim, T8Clear)
A0.add_reaction(eq17a)
A0.add_reaction(eq17b)
A0.add_reaction(eq17c)
A0.add_reaction(eq17d)
##Description: Plasma cell decay rate
eq9 = func.Decay("Plasma B cell decay", float(tau/72.0), nB_pls) #half life of 3 days (72hrs)
A0.add_reaction(eq9)
##Description: Circulating memory cell stimulation rate
eq10a = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V0, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10b = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V1, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10c = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V2, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10d = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V3, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
A0.add_reaction(eq10a)
A0.add_reaction(eq10b)
A0.add_reaction(eq10c)
A0.add_reaction(eq10d)
##Description: viral replication
eq11a = func.Replication("Viral replication", float(tau/ag_replication0), V0)
eq11b = func.Replication("Viral replication", float(tau/ag_replication1), V1)
eq11c = func.Replication("Viral replication", float(tau/ag_replication2), V2)
eq11d = func.Replication("Viral replication", float(tau/ag_replication3), V3)
A0.add_reaction(eq11a)
A0.add_reaction(eq11b)
A0.add_reaction(eq11c)
A0.add_reaction(eq11d)
###first infection
if (finfection_interval > 0):
if (infection == "monovalent"):
V0.increase( ag_initial)
if (infection == "polyvalent"):
V0.increase( ag_initial/4)
V1.increase( ag_initial/4)
V2.increase( ag_initial/4)
V3.increase( ag_initial/4)
total_time = total_time + float(finfection_interval * 3.0)
while( t <= total_time ):
dt = A0.MC_TimeStep()
t += dt
A0.MC_React()
output.write( t )
#### SECOND INFECTION
ag_initial = 100
Tstimulation1 = 1200.0
Tstimulation2 = 2400.0
Tstimulation3 = 8.0
Abclearance = 0.00025
T8clearance = 0.000025
#define a system of reactions
A0 = func.TotalReaction()
print('Setting up second reaction', len(A0))
##Description: Naive B cell formation in bone marrow
eq3 = func.Formation("Naive B cell formation", float(tau/knB), nB) #produces 250 cells every 108hrs, formerly 0.432
A0.add_reaction(eq3)
##Description: Basal B cell decay rate
eq4b = func.Decay("Naive B cell decay", float(tau/108.0), nB)
A0.add_reaction(eq4b)
##Description: T CD4 cell formation
eq12a = func.Formation("T CD4 cell formation", float(tau/kT4), Tcd4)
A0.add_reaction(eq12a)
##Description: T CD4 cell decay rate
eq12b = func.Decay("T CD4 cell decay", float(tau/108.0), Tcd4)
A0.add_reaction(eq12b)
##Description: T CD8 cell formation
eq13a = func.Formation("T CD8 cell formation", float(tau/kT8), Tcd8)
A0.add_reaction(eq13a)
##Description: T CD8 cell decay rate
eq13b = func.Decay("T CD8 cell decay", float(tau/108.0), Tcd8)
A0.add_reaction(eq13b)
##Description: Circulating B cell antigen stimulation
##Naive B cell stimulation is external to the GC
eq1a = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V0, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1b = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V1, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1c = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V2, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
eq1d = func.Stimulation("Free Naive B Cell Stimulation", float(tau/360.0), V3, nB, nB_gc, float(stimulation/6.0), Bcell_aff, "immunogenicity")
A0.add_reaction(eq1a)
A0.add_reaction(eq1b)
A0.add_reaction(eq1c)
A0.add_reaction(eq1d)
##Description: Germinal Center B cell antigen stimulation
## binding affinity with antigen scales exponentially with Hamming Distance
eq2a = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V0, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity") #max stimulate rate half life 15min (0.25hr)
eq2b = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V1, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
eq2c = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V2, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
eq2d = func.Stimulation("GC B Cell Stimulation", float(tau/20.0), V3, nB_gc, nB_stim, float(stimulation/0.45), Bcell_aff, "immunogenicity")
A0.add_reaction(eq2a)
A0.add_reaction(eq2b)
A0.add_reaction(eq2c)
A0.add_reaction(eq2d)
##Description: Additional stimulation of antigen-stimulated GC B cell by T_cd4 cell
eq14 = func.TStimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 cell", float(tau/Tstimulation1), nB_stim, Tcd4, nB_Tstim, Tcd4_stim) #cell cycle is tau
A0.add_reaction(eq14)
##Description: Additional stimulation of antigen-stimulated GC B cell by T_cd4 memory cell
eq15 = func.TStimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 memory cell", float(tau/Tstimulation2), nB_stim, Tcd4_me, nB_Tstim, Tcd4_stim) #cell cycle is tau
A0.add_reaction(eq15)
##Description: Stimulation of T_cd8 cell
eq20 = func.T8Stimulation("Stimulation of antigen-stimulated GC B cell by T_cd4 memory cell", float(tau/Tstimulation3), Tcd4_stim, Tcd8, Tcd8_stim) #cell cycle is tau
A0.add_reaction(eq20)
##Description: Germinal Center B cell decay, governed by apoptosis. Modeled as a logistic function
## with a pre-defined GC population carrying capacity
eq4a = func.PopulationDecay("B Cell decay", float(tau/(tau+1.0)), float(tau/108.0), Bcell_carry, GC_population, nB_gc, nB_stim, nB_Tstim) #base half life of 4.5 days (108hrs)
A0.add_reaction(eq4a)
eq19a = func.TPopulationDecay("Stimulated T Cell decay", float(tau/(tau+200.0)), float(tau/21600.0), Tcell_carry, T4_population, Tcd4_stim) #base half life of ?
#eq19a = func.Decay("T CD4 cell decay", float(tau/108.0), Tcd4_stim)
A0.add_reaction(eq19a)
eq19b = func.TPopulationDecay("Stimulated T Cell decay", float(tau/(tau+200.0)), float(tau/21600.0), Tcell_carry, T8_population, Tcd8_stim) #base half life of ?
#eq19b = func.Decay("T CD8 cell decay", float(tau/108.0), Tcd8_stim)
A0.add_reaction(eq19b)
##Description: Germinal Center B cell differentiation rate, modeling affinity-dependent T help
eq5a = func.BDifferentiation("B cell differentiation", float(tau/60.0), nB_stim, nB_gc, 1.0) #cell cycle is tau
A0.add_reaction(eq5a)
eq5b = func.Differentiation("B cell differentiation", float(tau/8.0), nB_Tstim, nB_gc, nB_stim, nB_me, nB_pls, nB_pll, 1.0) #cell cycle is tau
A0.add_reaction(eq5b)
##Description: TCD4 cell differentiation by mutation and into memory cell
eq16 = func.TDifferentiation("TCD4 cell differentiation", float(tau/15.0), Tcd4_stim, Tcd4, Tcd4_me) #cell cycle is tau
A0.add_reaction(eq16)
##Description: TCD8 cell differentiation
eq21 = func.T8Differentiation("TCD8 cell differentiation", float(tau/180.0), Tcd8_stim, Tcd8) #cell cycle is tau
A0.add_reaction(eq21)
##Description: Antibody production by Plasma cells
eq6a = func.Production("Antibody Production", 1.0, nB_pls, nAB)
A0.add_reaction(eq6a)
eq6b = func.Production("Antibody Production", 0.1, nB_pll, nAB)
A0.add_reaction(eq6b)
##Description: Antibody decay rate
eq7 = func.Decay("Antibody Decay", float(tau/360.0), nAB) #half life of 10 days (360hrs)
A0.add_reaction(eq7)
##Description: Intrinsic antigen decay
eq8a = func.Decay("Antigen Decay", float(tau/ag_decay), V0)
eq8b = func.Decay("Antigen Decay", float(tau/ag_decay), V1)
eq8c = func.Decay("Antigen Decay", float(tau/ag_decay), V2)
eq8d = func.Decay("Antigen Decay", float(tau/ag_decay), V3)
A0.add_reaction(eq8a)
A0.add_reaction(eq8b)
A0.add_reaction(eq8c)
A0.add_reaction(eq8d)
##Description: Antibody-dependent antigen clearance
eq8e = func.AbClearance("Antigen Clearance", Abclearance, V0, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8f = func.AbClearance("Antigen Clearance", Abclearance, V1, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8g = func.AbClearance("Antigen Clearance", Abclearance, V2, nAB, 10000.0, AB_aff, "clearance", AbClear)
eq8h = func.AbClearance("Antigen Clearance", Abclearance, V3, nAB, 10000.0, AB_aff, "clearance", AbClear)
A0.add_reaction(eq8e)
A0.add_reaction(eq8f)
A0.add_reaction(eq8g)
A0.add_reaction(eq8h)
##Description: CD8-dependent antigen clearance
eq17a = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V0, Tcd8_stim, T8Clear)
eq17b = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V1, Tcd8_stim, T8Clear)
eq17c = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V2, Tcd8_stim, T8Clear)
eq17d = func.TClearance("Antigen Clearance by CD8 T cell", T8clearance, V3, Tcd8_stim, T8Clear)
A0.add_reaction(eq17a)
A0.add_reaction(eq17b)
A0.add_reaction(eq17c)
A0.add_reaction(eq17d)
##Description: Plasma cell decay rate
eq9 = func.Decay("Plasma B cell decay", float(tau/72.0), nB_pls) #half life of 3 days (72hrs)
A0.add_reaction(eq9)
##Description: Circulating memory cell stimulation rate
eq10a = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V0, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10b = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V1, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10c = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V2, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
eq10d = func.MStimulation("Memory B cell Simulation", float(stimulation/80.0), V3, nB_me, nB_pls, nB_pll, float(tau/24.0), Bcell_aff, "immunogenicity")
A0.add_reaction(eq10a)
A0.add_reaction(eq10b)
A0.add_reaction(eq10c)
A0.add_reaction(eq10d)
##Description: viral replication
eq11a = func.Replication("Viral replication", float(tau/ag_replication0), V0)
eq11b = func.Replication("Viral replication", float(tau/ag_replication1), V1)
eq11c = func.Replication("Viral replication", float(tau/ag_replication2), V2)
eq11d = func.Replication("Viral replication", float(tau/ag_replication3), V3)
A0.add_reaction(eq11a)
A0.add_reaction(eq11b)
A0.add_reaction(eq11c)
A0.add_reaction(eq11d)
###second infection
if (sinfection_interval > 0):
if (infection == "monovalent"):
PopulationList[6].increase( ag_initial)
if (infection == "polyvalent"):
PopulationList[6].increase( ag_initial/4 )
PopulationList[7].increase( ag_initial/4 )
PopulationList[8].increase( ag_initial/4 )
PopulationList[9].increase( ag_initial/4 )
total_time = total_time + float(sinfection_interval * 3.0)
while( t <= total_time ):
dt = A0.MC_TimeStep()
t += dt
A0.MC_React()
output.write( t )
output.finish()
| 43.069767 | 185 | 0.755805 | 3,466 | 22,224 | 4.725332 | 0.092325 | 0.031139 | 0.080962 | 0.021798 | 0.853462 | 0.824887 | 0.815179 | 0.811027 | 0.808402 | 0.803395 | 0 | 0.068853 | 0.116451 | 22,224 | 515 | 186 | 43.153398 | 0.765227 | 0.204464 | 0 | 0.688953 | 0 | 0 | 0.186496 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.011628 | 0 | 0.011628 | 0.026163 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
519c3e3c47ee472e1a3beb07b41907e90a9abffb | 20 | py | Python | __init__.py | secretsauceai/natex-py | 7c5657f4a63296da87d6d64762ed0120d4dc119f | [
"MIT"
] | 1 | 2021-06-07T11:57:59.000Z | 2021-06-07T11:57:59.000Z | __init__.py | secretsauceai/natex-py | 7c5657f4a63296da87d6d64762ed0120d4dc119f | [
"MIT"
] | null | null | null | __init__.py | secretsauceai/natex-py | 7c5657f4a63296da87d6d64762ed0120d4dc119f | [
"MIT"
] | 1 | 2021-02-04T14:11:34.000Z | 2021-02-04T14:11:34.000Z | from .natex import * | 20 | 20 | 0.75 | 3 | 20 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 20 | 1 | 20 | 20 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a403d655c784ac9b2e857f7076cfba81abe52e51 | 13,848 | py | Python | brainscore/benchmarks/_neural_common_extra.py | arjunsinghrathore/brain-score | 4cacdc38d30fc41580d364059e6ae26fb21020e4 | [
"MIT"
] | null | null | null | brainscore/benchmarks/_neural_common_extra.py | arjunsinghrathore/brain-score | 4cacdc38d30fc41580d364059e6ae26fb21020e4 | [
"MIT"
] | null | null | null | brainscore/benchmarks/_neural_common_extra.py | arjunsinghrathore/brain-score | 4cacdc38d30fc41580d364059e6ae26fb21020e4 | [
"MIT"
] | null | null | null | import numpy as np
import os
import pandas as pd
import xarray as xr
from brainio_base.assemblies import array_is_element, walk_coords
from brainscore.benchmarks import BenchmarkBase, ceil_score
from brainscore.benchmarks.screen import place_on_screen
from brainscore.model_interface import BrainModel
from brainscore.benchmarks._neural_common import explained_variance, timebins_from_assembly
from brainio_base.stimuli import StimulusSet
from brainscore.metrics.regression_extra import take_gram, unflatten
class NeuralBenchmarkCovariate(BenchmarkBase):
def __init__(self, identifier, assembly, covariate_image_dir, similarity_metric, visual_degrees, number_of_trials, **kwargs):
super(NeuralBenchmarkCovariate, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
self._similarity_metric = similarity_metric
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._visual_degrees = visual_degrees
self._number_of_trials = number_of_trials
self.covariate_image_dir = covariate_image_dir
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Find 'twin' image set whose model activations will serve as covariate
brainio_dir = os.getenv('BRAINIO_HOME', os.path.join(os.path.expanduser('~'), '.brainio'))
covariate_stimulus_set = pd.DataFrame(stimulus_set)
covariate_stimulus_set = StimulusSet(covariate_stimulus_set)
#covariate_stimulus_set = stimulus_set.copy(deep=True)
covariate_stimulus_set.identifier = stimulus_set.identifier + '_' + self.covariate_image_dir
covariate_stimulus_set.image_paths = {
k: os.path.join(brainio_dir,
self.covariate_image_dir,
os.path.basename(v)) for k, v in stimulus_set.image_paths.items()}
source_assembly = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials)
source_assembly.attrs['stimulus_set_identifier'] = stimulus_set.identifier
covariate_assembly = candidate.look_at(covariate_stimulus_set, number_of_trials=self._number_of_trials)
covariate_assembly.attrs['stimulus_set_identifier'] = covariate_stimulus_set.identifier
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks
covariate_assembly = covariate_assembly.squeeze('time_bin') # static case for these benchmarks
raw_score = self._similarity_metric(source_assembly, covariate_assembly, self._assembly)
return explained_variance(raw_score, self.ceiling)
class CacheFeaturesCovariate(BenchmarkBase):
def __init__(self, identifier, assembly, covariate_image_dir, similarity_metric, visual_degrees, number_of_trials, **kwargs):
super(CacheFeaturesCovariate, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
self._similarity_metric = similarity_metric
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._visual_degrees = visual_degrees
self._number_of_trials = number_of_trials
self.covariate_image_dir = covariate_image_dir
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Find 'twin' image set whose model activations will serve as covariate
brainio_dir = os.getenv('BRAINIO_HOME', os.path.join(os.path.expanduser('~'), '.brainio'))
covariate_stimulus_set = pd.DataFrame(stimulus_set)
covariate_stimulus_set = StimulusSet(covariate_stimulus_set)
#covariate_stimulus_set = stimulus_set.copy(deep=True)
covariate_stimulus_set.identifier = stimulus_set.identifier + '_' + self.covariate_image_dir
covariate_stimulus_set.image_paths = {
k: os.path.join(brainio_dir,
self.covariate_image_dir,
os.path.basename(v)) for k, v in stimulus_set.image_paths.items()}
source_assembly = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials)
source_assembly.attrs['stimulus_set_identifier'] = stimulus_set.identifier
covariate_assembly = candidate.look_at(covariate_stimulus_set, number_of_trials=self._number_of_trials)
covariate_assembly.attrs['stimulus_set_identifier'] = covariate_stimulus_set.identifier
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks
covariate_assembly = covariate_assembly.squeeze('time_bin') # static case for these benchmarks
#raw_score = self._similarity_metric(source_assembly, covariate_assembly, self._assembly)
return 0
class NeuralBenchmarkCovariateGram(BenchmarkBase):
def __init__(self, identifier, assembly, covariate_image_dir, similarity_metric, visual_degrees, number_of_trials, gram, **kwargs):
super(NeuralBenchmarkCovariateGram, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
self._similarity_metric = similarity_metric
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._visual_degrees = visual_degrees
self._number_of_trials = number_of_trials
self.covariate_image_dir = covariate_image_dir
self.gram = gram
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Find 'twin' image set whose model activations will serve as covariate
brainio_dir = os.getenv('BRAINIO_HOME', os.path.join(os.path.expanduser('~'), '.brainio'))
covariate_stimulus_set = pd.DataFrame(stimulus_set)
covariate_stimulus_set = StimulusSet(covariate_stimulus_set)
#covariate_stimulus_set = stimulus_set.copy(deep=True)
covariate_stimulus_set.identifier = stimulus_set.identifier + '_' + self.covariate_image_dir
covariate_stimulus_set.image_paths = {
k: os.path.join(brainio_dir,
self.covariate_image_dir,
os.path.basename(v)) for k, v in stimulus_set.image_paths.items()}
source_assembly = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials)
source_assembly.attrs['stimulus_set_identifier'] = stimulus_set.identifier
covariate_assembly = candidate.look_at(covariate_stimulus_set, number_of_trials=self._number_of_trials)
covariate_assembly.attrs['stimulus_set_identifier'] = covariate_stimulus_set.identifier
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks
covariate_assembly = covariate_assembly.squeeze('time_bin') # static case for these benchmarks
if self.gram:
model = covariate_assembly.model.values[0]
layer = covariate_assembly.layer.values[0]
fname = os.path.join(brainio_dir, self.covariate_image_dir, '_'.join([model, layer.replace('/', '_'), covariate_assembly.stimulus_set_identifier, 'gram.nc']))
covariate_assembly = gram_on_all(covariate_assembly, fname = fname)
source_assembly, covariate_assembly = source_assembly.sortby('image_id'), covariate_assembly.sortby('image_id')
covariate_assembly = covariate_assembly.rename({'image_id': 'presentation'})
covariate_assembly = covariate_assembly.assign_coords({'presentation': source_assembly.presentation.coords.to_index()})
covariate_assembly = covariate_assembly.assign_coords({'neuroid': pd.MultiIndex.from_tuples(list(zip([model]*covariate_assembly.shape[0],
[layer]*covariate_assembly.shape[0])), names=['model', 'layer'])})
covariate_assembly.attrs['stimulus_set_identifier'] = covariate_stimulus_set.identifier
raw_score = self._similarity_metric(source_assembly, covariate_assembly, self._assembly)
return explained_variance(raw_score, self.ceiling)
class NeuralBenchmarkImageDir(BenchmarkBase):
def __init__(self, identifier, assembly, image_dir, similarity_metric, visual_degrees, number_of_trials, **kwargs):
super(NeuralBenchmarkImageDir, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
self._similarity_metric = similarity_metric
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._visual_degrees = visual_degrees
self._number_of_trials = number_of_trials
self.image_dir = image_dir
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Find 'twin' image set whose model activations we need
brainio_dir = os.getenv('BRAINIO_HOME', os.path.join(os.path.expanduser('~'), '.brainio'))
stimulus_set_from_dir = pd.DataFrame(stimulus_set)
stimulus_set_from_dir = StimulusSet(stimulus_set_from_dir)
# stimulus_set_from_dir = stimulus_set.copy(deep=True)
stimulus_set_from_dir.identifier = stimulus_set.identifier + '_' + self.image_dir
stimulus_set_from_dir.image_paths = {
k: os.path.join(brainio_dir,
self.image_dir,
os.path.basename(v)) for k, v in stimulus_set.image_paths.items()}
source_assembly = candidate.look_at(stimulus_set_from_dir, number_of_trials=self._number_of_trials)
source_assembly.attrs['stimulus_set_identifier'] = stimulus_set_from_dir.identifier
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks
raw_score = self._similarity_metric(source_assembly, self._assembly)
return explained_variance(raw_score, self.ceiling)
class ToleranceCeiling(BenchmarkBase):
def __init__(self, identifier, assembly, similarity_metric, visual_degrees, number_of_trials, **kwargs):
super(ToleranceCeiling, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
self._similarity_metric = similarity_metric
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._visual_degrees = visual_degrees
self._number_of_trials = number_of_trials
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
raw_score = self._similarity_metric(self._assembly)
return explained_variance(raw_score)
class NeuralBenchmarkCeiling(BenchmarkBase):
def __init__(self, identifier, assembly, number_of_trials, **kwargs):
super(NeuralBenchmarkCeiling, self).__init__(identifier=identifier, **kwargs)
self._assembly = assembly
region = np.unique(self._assembly['region'])
assert len(region) == 1
self.region = region[0]
timebins = timebins_from_assembly(self._assembly)
self.timebins = timebins
self._number_of_trials = number_of_trials
def __call__(self):
return self.ceiling
def gram_on_all(assembly, fname):
if os.path.isfile(fname):
assembly = xr.open_dataarray(fname)
else:
assembly = assembly.T
image_ids = assembly.image_id.values
assembly = unflatten(assembly, channel_coord=None)
assembly = assembly.reshape(list(assembly.shape[0:2]) + [-1])
assembly = take_gram(assembly)
assembly = assembly.T
assembly = xr.DataArray(assembly, dims=['neuroid','image_id'], coords={'image_id':image_ids})
assembly.to_netcdf(fname)
return assembly
| 56.522449 | 171 | 0.698296 | 1,560 | 13,848 | 5.809615 | 0.096795 | 0.089816 | 0.049432 | 0.025819 | 0.818713 | 0.802604 | 0.765861 | 0.761117 | 0.755158 | 0.740814 | 0 | 0.001847 | 0.218082 | 13,848 | 244 | 172 | 56.754098 | 0.83515 | 0.057337 | 0 | 0.673367 | 0 | 0 | 0.038621 | 0.014385 | 0 | 0 | 0 | 0 | 0.030151 | 1 | 0.065327 | false | 0 | 0.055276 | 0.005025 | 0.18593 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cffd5f646cdea0652fedaba6d29bdd71abd5394f | 29,827 | py | Python | imagedataextractor/grid_splitting.py | ktm2/ImageDataExtractor | fb0cd8c0baddc1d9583e2c73b4905380fe08c26b | [
"MIT"
] | 6 | 2019-11-28T16:40:01.000Z | 2021-06-16T08:48:44.000Z | imagedataextractor/grid_splitting.py | ktm2/ImageDataExtractor | fb0cd8c0baddc1d9583e2c73b4905380fe08c26b | [
"MIT"
] | 1 | 2020-08-28T12:32:54.000Z | 2020-08-28T12:38:43.000Z | imagedataextractor/grid_splitting.py | ktm2/ImageDataExtractor | fb0cd8c0baddc1d9583e2c73b4905380fe08c26b | [
"MIT"
] | 2 | 2020-03-26T00:20:21.000Z | 2020-05-11T06:53:05.000Z | #Author: Karim Mukaddem
from .img_utils import *
def line_detection_and_split(gimg, show = False, eval_img = True):
'''Detects vertical and horizontal lines in a figure along a (2,3,4)x(2,3,4) grid and splits
into constituent images.
:param numpy.ndarray gimg: grayscale input figure.
:param bool show: display steps for debugging.
:param bool eval_img: output an additional image for debugging.
:return list img_split_vertically: list of images in figure
:return numpy.ndarray evaluation_img: evaluation image, none if eval_img = False
'''
#Measure input figure.
rows = len(gimg)
cols = len(gimg[0])
#Create copies of figure to draw on (for debugging/evaluation)
drawingimg = cv2.cvtColor(gimg,cv2.COLOR_GRAY2RGB)
evaluation_img = drawingimg.copy()
#Apply visual filtereing and edge detection.
low_threshold = 200
high_threshold = 255
edges = cv2.Canny(gimg, low_threshold, high_threshold)
if show == True:
show_image(edges)
print(cols,rows)
min_line_length = int(0.5*min(rows,cols))
max_line_gap = int(0.5*min(rows,cols))
theta = np.pi/180
# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments.
lines = cv2.HoughLinesP(edges, 1, theta, 50, np.array([]),min_line_length, max_line_gap)
if lines is None:
print("no straight lines found")
return None, evaluation_img
horizontal_lines = []
vertical_lines = []
# X describes column number, Y describes row number.
# take only horizontal/vertical lines.
for line in lines:
for x1,y1,x2,y2 in line:
if x1 == x2:
vertical_lines.append(line[0])
cv2.line(drawingimg,(x1,y1),(x2,y2),(255,0,0),1)
if y1 == y2:
horizontal_lines.append(line[0])
cv2.line(drawingimg,(x1,y1),(x2,y2),(255,0,0),1)
x1s=[line[0] for line in vertical_lines]
y1s=[line[1] for line in horizontal_lines]
#Checking along which fractions of image dimensions straight lines exist.
horizontally_in_half = 0
vertically_in_half = 0
horizontally_in_thirds = 0
vertically_in_thirds = 0
horizontally_in_four = 0
vertically_in_four = 0
#Fractional tolerance along splitting points.
tolerance = 0.015
for line in horizontal_lines:
if (rows/2) - int(rows*tolerance) < line[1] == line[3] < (rows/2) + int(rows*tolerance):
horizontally_in_half += 1
horizontally_in_four += 1
if (rows/3) - int(rows*tolerance) < line[1] == line[3] < (rows/3) + int(rows*tolerance):
horizontally_in_thirds += 1
if (2*rows/3) - int(rows*tolerance) < line[1] == line[3] < (2*rows/3) + int(rows*tolerance):
horizontally_in_thirds += 1
if (rows/4) - int(rows*tolerance) < line[1] == line[3] < (rows/4) + int(rows*tolerance):
horizontally_in_four += 1
if (3*rows/4) - int(rows*tolerance) < line[1] == line[3] < (3*rows/4) + int(rows*tolerance):
horizontally_in_four += 1
for line in vertical_lines:
if (cols/2) - int(cols*tolerance) < line[0] == line[2] < (cols/2) + int(cols*tolerance):
vertically_in_half += 1
vertically_in_four += 1
if (cols/3) - int(cols*tolerance) < line[0] == line[2] < (cols/3) + int(cols*tolerance):
vertically_in_thirds += 1
if (2*cols/3) - int(cols*tolerance) < line[0] == line[2] < (2*cols/3) + int(cols*tolerance):
vertically_in_thirds += 1
if (cols/4) - int(cols*tolerance) < line[0] == line[2] < (cols/4) + int(cols*tolerance):
vertically_in_four += 1
if (3*cols/4) - int(cols*tolerance) < line[0] == line[2] < (3*cols/4) + int(cols*tolerance):
vertically_in_four += 1
if show == True:
print("horizontally_in_half", horizontally_in_half)
print("horizontally_in_thirds", horizontally_in_thirds)
print("horizontally_in_four", horizontally_in_four)
print("vertically_in_half", vertically_in_half)
print("vertically_in_thirds", vertically_in_thirds)
print("vertically_in_four", vertically_in_four)
show_image(drawingimg)
img_split_horizontally = []
#Split horizontally in four, using the previously detected straight lines closest to the "grid" lines.
if horizontally_in_four > horizontally_in_half > 0:
#Finding where to split.
distance_to_1st_splitline=[line[1] - rows/4 for line in horizontal_lines]
from_bottom_1st = [a for a in distance_to_1st_splitline if rows * tolerance >= a >= 0]
from_top_1st = [a for a in distance_to_1st_splitline if 0 >= a >= -rows * tolerance]
if len(from_bottom_1st) == 0:
closest_from_bottom_1st = rows/4
else:
closest_from_bottom_1st=y1s[distance_to_1st_splitline.index(min(from_bottom_1st))]
if len(from_top_1st) == 0:
closest_from_top_1st = rows/4
else:
closest_from_top_1st=y1s[distance_to_1st_splitline.index(max(from_top_1st))]
distance_to_2nd_splitline=[line[1] - 2*rows/4 for line in horizontal_lines]
from_bottom_2nd = [a for a in distance_to_2nd_splitline if rows * tolerance >= a >= 0]
from_top_2nd = [a for a in distance_to_2nd_splitline if 0 >= a >= -rows * tolerance]
if len(from_bottom_2nd) == 0:
closest_from_bottom_2nd = 2*rows/4
else:
closest_from_bottom_2nd=y1s[distance_to_2nd_splitline.index(min(from_bottom_2nd))]
if len(from_top_2nd) == 0:
closest_from_top_2nd = 2*rows/4
else:
closest_from_top_2nd=y1s[distance_to_2nd_splitline.index(max(from_top_2nd))]
distance_to_3rd_splitline=[line[1] - 3*rows/4 for line in horizontal_lines]
from_bottom_3rd = [a for a in distance_to_3rd_splitline if rows * tolerance >= a >= 0]
from_top_3rd = [a for a in distance_to_3rd_splitline if 0 >= a >= -rows * tolerance]
if len(from_bottom_3rd) == 0:
closest_from_bottom_3rd = 3*rows/4
else:
closest_from_bottom_3rd=y1s[distance_to_3rd_splitline.index(min(from_bottom_3rd))]
if len(from_top_3rd) == 0:
closest_from_top_3rd = 3*rows/4
else:
closest_from_top_3rd=y1s[distance_to_3rd_splitline.index(max(from_top_3rd))]
#Splitting the image and appending to output.
img_0 = gimg[0:closest_from_top_1st,0:cols]
img_1 = gimg[closest_from_bottom_1st:closest_from_top_2nd,0:cols]
img_2 = gimg[closest_from_bottom_2nd:closest_from_top_3rd,0:cols]
img_3 = gimg[closest_from_bottom_3rd:rows,0:cols]
img_split_horizontally.append(img_0)
img_split_horizontally.append(img_1)
img_split_horizontally.append(img_2)
img_split_horizontally.append(img_3)
if eval_img == True:
cv2.line(evaluation_img,(0,closest_from_top_1st),(cols,closest_from_top_1st),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom_1st),(cols,closest_from_bottom_1st),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_top_2nd),(cols,closest_from_top_2nd),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom_2nd),(cols,closest_from_bottom_2nd),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_top_3rd),(cols,closest_from_top_3rd),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom_3rd),(cols,closest_from_bottom_3rd),(0,0,255),1)
elif horizontally_in_half > 0:
distance_to_midline=[line[1] - rows/2 for line in horizontal_lines]
from_bottom = [a for a in distance_to_midline if rows * tolerance >= a >= 0]
from_top = [a for a in distance_to_midline if 0 >= a >= -rows * tolerance]
if len(from_bottom) == 0:
closest_from_bottom = rows/2
else:
closest_from_bottom=y1s[distance_to_midline.index(min(from_bottom))]
if len(from_top) == 0:
closest_from_top = rows/2
else:
closest_from_top=y1s[distance_to_midline.index(max(from_top))]
img_top_half = gimg[0:closest_from_top,0:cols]
img_bottom_half = gimg[closest_from_bottom:rows,0:cols]
img_split_horizontally.append(img_top_half)
img_split_horizontally.append(img_bottom_half)
if eval_img == True:
cv2.line(evaluation_img,(0,closest_from_top),(cols,closest_from_top),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom),(cols,closest_from_bottom),(0,0,255),1)
elif horizontally_in_thirds > 1:
distance_to_1st_splitline=[line[1] - rows/3 for line in horizontal_lines]
from_bottom_1st = [a for a in distance_to_1st_splitline if rows * tolerance >= a >= 0]
from_top_1st = [a for a in distance_to_1st_splitline if 0 >= a >= -rows * tolerance]
if len(from_bottom_1st) == 0:
closest_from_bottom_1st = rows/3
else:
closest_from_bottom_1st=y1s[distance_to_1st_splitline.index(min(from_bottom_1st))]
if len(from_top_1st) == 0:
closest_from_top_1st = rows/3
else:
closest_from_top_1st=y1s[distance_to_1st_splitline.index(max(from_top_1st))]
distance_to_2nd_splitline=[line[1] - 2*rows/3 for line in horizontal_lines]
from_bottom_2nd = [a for a in distance_to_2nd_splitline if rows * tolerance >= a >= 0]
from_top_2nd = [a for a in distance_to_2nd_splitline if 0 >= a >= -rows * tolerance]
if len(from_bottom_2nd) == 0:
closest_from_bottom_2nd = 2*rows/3
else:
closest_from_bottom_2nd=y1s[distance_to_2nd_splitline.index(min(from_bottom_2nd))]
if len(from_top_2nd) == 0:
closest_from_top_2nd = 2*rows/3
else:
closest_from_top_2nd=y1s[distance_to_2nd_splitline.index(max(from_top_2nd))]
img_0 = gimg[0:closest_from_top_1st,0:cols]
img_1 = gimg[closest_from_bottom_1st:closest_from_top_2nd,0:cols]
img_2 = gimg[closest_from_bottom_2nd:rows,0:cols]
img_split_horizontally.append(img_0)
img_split_horizontally.append(img_1)
img_split_horizontally.append(img_2)
if eval_img == True:
cv2.line(evaluation_img,(0,closest_from_top_1st),(cols,closest_from_top_1st),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom_1st),(cols,closest_from_bottom_1st),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_top_2nd),(cols,closest_from_top_2nd),(0,0,255),1)
cv2.line(evaluation_img,(0,closest_from_bottom_2nd),(cols,closest_from_bottom_2nd),(0,0,255),1)
else:
img_split_horizontally.append(gimg)
if show == True:
for i in img_split_horizontally:
show_image(i)
#Same process as above but vertical splits.
img_split_vertically = []
if vertically_in_four > vertically_in_half > 0:
distance_to_1st_splitline=[line[0] - cols/4 for line in vertical_lines]
from_right_1st = [a for a in distance_to_1st_splitline if cols * tolerance >= a >= 0]
from_left_1st = [a for a in distance_to_1st_splitline if 0 >= a >= -cols * tolerance]
if len(from_right_1st) == 0:
closest_from_right_1st = cols/4
else:
closest_from_right_1st=x1s[distance_to_1st_splitline.index(min(from_right_1st))]
if len(from_left_1st) == 0:
closest_from_left_1st = cols/4
else:
closest_from_left_1st=x1s[distance_to_1st_splitline.index(max(from_left_1st))]
distance_to_2nd_splitline=[line[0] - 2*cols/4 for line in vertical_lines]
from_right_2nd = [a for a in distance_to_2nd_splitline if cols * tolerance >= a >= 0]
from_left_2nd = [a for a in distance_to_2nd_splitline if 0 >= a >= -cols * tolerance]
if len(from_right_2nd) == 0:
closest_from_right_2nd = 2*cols/4
else:
closest_from_right_2nd=x1s[distance_to_2nd_splitline.index(min(from_right_2nd))]
if len(from_left_2nd) == 0:
closest_from_left_2nd = 2*cols/4
else:
closest_from_left_2nd=x1s[distance_to_2nd_splitline.index(max(from_left_2nd))]
distance_to_3rd_splitline=[line[0] - 3*cols/4 for line in vertical_lines]
from_right_3rd = [a for a in distance_to_3rd_splitline if cols * tolerance >= a >= 0]
from_left_3rd = [a for a in distance_to_3rd_splitline if 0 >= a >= -cols * tolerance]
if len(from_right_3rd) == 0:
closest_from_right_3rd = 3*cols/4
else:
closest_from_right_3rd=x1s[distance_to_3rd_splitline.index(min(from_right_3rd))]
if len(from_left_3rd) == 0:
closest_from_left_3rd = 3*cols/4
else:
closest_from_left_3rd=x1s[distance_to_3rd_splitline.index(max(from_left_3rd))]
if len(img_split_horizontally) == 1:
img_0 = img_split_horizontally[0]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:closest_from_left_3rd]
img_0_3 = img_0[0:len(img_0),closest_from_right_3rd:len(img_0[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_0_3)
if len(img_split_horizontally) == 2:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:closest_from_left_3rd]
img_0_3 = img_0[0:len(img_0),closest_from_right_3rd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:closest_from_left_3rd]
img_1_3 = img_1[0:len(img_1),closest_from_right_3rd:len(img_1[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_0_3)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
img_split_vertically.append(img_1_3)
if len(img_split_horizontally) == 3:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:closest_from_left_3rd]
img_0_3 = img_0[0:len(img_0),closest_from_right_3rd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:closest_from_left_3rd]
img_1_3 = img_1[0:len(img_1),closest_from_right_3rd:len(img_1[0])]
img_2_0 = img_2[0:len(img_2),0:closest_from_left_1st]
img_2_1 = img_2[0:len(img_2),closest_from_right_1st:closest_from_left_2nd]
img_2_2 = img_2[0:len(img_2),closest_from_right_2nd:closest_from_left_3rd]
img_2_3 = img_2[0:len(img_2),closest_from_right_3rd:len(img_2[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_0_3)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
img_split_vertically.append(img_1_3)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
img_split_vertically.append(img_2_2)
img_split_vertically.append(img_2_3)
if len(img_split_horizontally) == 4:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_3 = img_split_horizontally[3]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:closest_from_left_3rd]
img_0_3 = img_0[0:len(img_0),closest_from_right_3rd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:closest_from_left_3rd]
img_1_3 = img_1[0:len(img_1),closest_from_right_3rd:len(img_1[0])]
img_2_0 = img_2[0:len(img_2),0:closest_from_left_1st]
img_2_1 = img_2[0:len(img_2),closest_from_right_1st:closest_from_left_2nd]
img_2_2 = img_2[0:len(img_2),closest_from_right_2nd:closest_from_left_3rd]
img_2_3 = img_2[0:len(img_2),closest_from_right_3rd:len(img_2[0])]
img_3_0 = img_3[0:len(img_3),0:closest_from_left_1st]
img_3_1 = img_3[0:len(img_3),closest_from_right_1st:closest_from_left_2nd]
img_3_2 = img_3[0:len(img_3),closest_from_right_2nd:closest_from_left_3rd]
img_3_3 = img_3[0:len(img_3),closest_from_right_3rd:len(img_3[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_0_3)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
img_split_vertically.append(img_1_3)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
img_split_vertically.append(img_2_2)
img_split_vertically.append(img_2_3)
img_split_vertically.append(img_3_0)
img_split_vertically.append(img_3_1)
img_split_vertically.append(img_3_2)
img_split_vertically.append(img_3_3)
if eval_img == True:
cv2.line(evaluation_img,(closest_from_left_1st,0),(closest_from_left_1st,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right_1st,0),(closest_from_right_1st,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_left_2nd,0),(closest_from_left_2nd,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right_2nd,0),(closest_from_right_2nd,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_left_3rd,0),(closest_from_left_3rd,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right_3rd,0),(closest_from_right_3rd,rows),(0,0,255),1)
elif vertically_in_half > 0:
distance_to_midline=[line[0] - cols/2 for line in vertical_lines]
from_left = [a for a in distance_to_midline if cols * tolerance >= a >= 0]
from_right = [a for a in distance_to_midline if 0 >= a >= -cols * tolerance]
if len(from_left) == 0:
closest_from_left = cols/2
else:
closest_from_left=x1s[distance_to_midline.index(min(from_left))]
if len(from_right) == 0:
closest_from_right = cols/2
else:
closest_from_right=x1s[distance_to_midline.index(max(from_right))]
if len(img_split_horizontally) == 1:
img_0 = img_split_horizontally[0]
img_0_0 = img_0[0:len(img_0),0:closest_from_left]
img_0_1 = img_0[0:len(img_0),closest_from_right:cols]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
if len(img_split_horizontally) == 2:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_0_0 = img_0[0:len(img_0),0:closest_from_left]
img_0_1 = img_0[0:len(img_0),closest_from_right:cols]
img_1_0 = img_1[0:len(img_1),0:closest_from_left]
img_1_1 = img_1[0:len(img_1),closest_from_right:cols]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
if len(img_split_horizontally) == 3:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_0_0 = img_0[0:len(img_0),0:closest_from_left]
img_0_1 = img_0[0:len(img_0),closest_from_right:cols]
img_1_0 = img_1[0:len(img_1),0:closest_from_left]
img_1_1 = img_1[0:len(img_1),closest_from_right:cols]
img_2_0 = img_2[0:len(img_2),0:closest_from_left]
img_2_1 = img_2[0:len(img_2),closest_from_right:cols]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
if len(img_split_horizontally) == 4:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_3 = img_split_horizontally[3]
img_0_0 = img_0[0:len(img_0),0:closest_from_left]
img_0_1 = img_0[0:len(img_0),closest_from_right:cols]
img_1_0 = img_1[0:len(img_1),0:closest_from_left]
img_1_1 = img_1[0:len(img_1),closest_from_right:cols]
img_2_0 = img_2[0:len(img_2),0:closest_from_left]
img_2_1 = img_2[0:len(img_2),closest_from_right:cols]
img_3_0 = img_2[0:len(img_3),0:closest_from_left]
img_3_1 = img_2[0:len(img_3),closest_from_right:cols]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
img_split_vertically.append(img_3_0)
img_split_vertically.append(img_3_1)
if eval_img == True:
cv2.line(evaluation_img,(closest_from_left,0),(closest_from_left,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right,0),(closest_from_right,rows),(0,0,255),1)
elif vertically_in_thirds > 1:
distance_to_1st_splitline=[line[0] - cols/3 for line in vertical_lines]
from_right_1st = [a for a in distance_to_1st_splitline if cols * tolerance >= a >= 0]
from_left_1st = [a for a in distance_to_1st_splitline if 0 >= a >= -cols * tolerance]
if len(from_right_1st) == 0:
closest_from_right_1st = cols/3
else:
closest_from_right_1st=x1s[distance_to_1st_splitline.index(min(from_right_1st))]
if len(from_left_1st) == 0:
closest_from_left_1st = cols/3
else:
closest_from_left_1st=x1s[distance_to_1st_splitline.index(max(from_left_1st))]
distance_to_2nd_splitline=[line[0] - 2*cols/3 for line in vertical_lines]
from_right_2nd = [a for a in distance_to_2nd_splitline if cols * tolerance >= a >= 0]
from_left_2nd = [a for a in distance_to_2nd_splitline if 0 >= a >= -cols * tolerance]
if len(from_right_2nd) == 0:
closest_from_right_2nd = 2*cols/3
else:
closest_from_right_2nd=x1s[distance_to_2nd_splitline.index(min(from_right_2nd))]
if len(from_left_2nd) == 0:
closest_from_left_2nd = 2*cols/3
else:
closest_from_left_2nd=x1s[distance_to_2nd_splitline.index(max(from_left_2nd))]
if len(img_split_horizontally) == 1:
img_0 = img_split_horizontally[0]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:len(img_0[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
if len(img_split_horizontally) == 2:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:len(img_1[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
if len(img_split_horizontally) == 3:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:len(img_1[0])]
img_2_0 = img_1[0:len(img_2),0:closest_from_left_1st]
img_2_1 = img_1[0:len(img_2),closest_from_right_1st:closest_from_left_2nd]
img_2_2 = img_1[0:len(img_2),closest_from_right_2nd:len(img_2[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
img_split_vertically.append(img_2_2)
if len(img_split_horizontally) == 4:
img_0 = img_split_horizontally[0]
img_1 = img_split_horizontally[1]
img_2 = img_split_horizontally[2]
img_3 = img_split_horizontally[3]
img_0_0 = img_0[0:len(img_0),0:closest_from_left_1st]
img_0_1 = img_0[0:len(img_0),closest_from_right_1st:closest_from_left_2nd]
img_0_2 = img_0[0:len(img_0),closest_from_right_2nd:len(img_0[0])]
img_1_0 = img_1[0:len(img_1),0:closest_from_left_1st]
img_1_1 = img_1[0:len(img_1),closest_from_right_1st:closest_from_left_2nd]
img_1_2 = img_1[0:len(img_1),closest_from_right_2nd:len(img_1[0])]
img_2_0 = img_1[0:len(img_2),0:closest_from_left_1st]
img_2_1 = img_1[0:len(img_2),closest_from_right_1st:closest_from_left_2nd]
img_2_2 = img_1[0:len(img_2),closest_from_right_2nd:len(img_2[0])]
img_3_0 = img_1[0:len(img_3),0:closest_from_left_1st]
img_3_1 = img_1[0:len(img_3),closest_from_right_1st:closest_from_left_2nd]
img_3_2 = img_1[0:len(img_3),closest_from_right_2nd:len(img_2[0])]
img_split_vertically.append(img_0_0)
img_split_vertically.append(img_0_1)
img_split_vertically.append(img_0_2)
img_split_vertically.append(img_1_0)
img_split_vertically.append(img_1_1)
img_split_vertically.append(img_1_2)
img_split_vertically.append(img_2_0)
img_split_vertically.append(img_2_1)
img_split_vertically.append(img_2_2)
img_split_vertically.append(img_3_0)
img_split_vertically.append(img_3_1)
img_split_vertically.append(img_3_2)
if eval_img == True:
cv2.line(evaluation_img,(closest_from_left_1st,0),(closest_from_left_1st,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right_1st,0),(closest_from_right_1st,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_left_2nd,0),(closest_from_left_2nd,rows),(0,0,255),1)
cv2.line(evaluation_img,(closest_from_right_2nd,0),(closest_from_right_2nd,rows),(0,0,255),1)
else:
img_split_vertically = img_split_horizontally
if show == True:
for i in img_split_vertically:
show_image(i)
show_image(evaluation_img)
return img_split_vertically, evaluation_img
| 38.141944 | 107 | 0.661012 | 4,775 | 29,827 | 3.706806 | 0.033717 | 0.141695 | 0.071186 | 0.122034 | 0.871243 | 0.838531 | 0.816441 | 0.78661 | 0.757175 | 0.731017 | 0 | 0.069066 | 0.238844 | 29,827 | 782 | 108 | 38.141944 | 0.710567 | 0.036511 | 0 | 0.682927 | 0 | 0 | 0.004915 | 0.000767 | 0 | 0 | 0 | 0 | 0 | 1 | 0.002033 | false | 0 | 0.002033 | 0 | 0.00813 | 0.01626 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
320e9d6e782dae574ff35d12dea94c409bb4b3f9 | 219 | py | Python | demography/serializers/__init__.py | The-Politico/politico-civic-demography | 080bb964b64b06db7fd04386530e893ceed1cf98 | [
"MIT"
] | null | null | null | demography/serializers/__init__.py | The-Politico/politico-civic-demography | 080bb964b64b06db7fd04386530e893ceed1cf98 | [
"MIT"
] | null | null | null | demography/serializers/__init__.py | The-Politico/politico-civic-demography | 080bb964b64b06db7fd04386530e893ceed1cf98 | [
"MIT"
] | null | null | null | # flake8: noqa
from .census_estimate import CensusEstimateSerializer
from .census_label import CensusLabelSerializer
from .census_table import CensusTableSerializer
from .census_variable import CensusVariableSerializer
| 36.5 | 53 | 0.885845 | 22 | 219 | 8.636364 | 0.590909 | 0.210526 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005 | 0.086758 | 219 | 5 | 54 | 43.8 | 0.945 | 0.054795 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5c7277869ef46020073ca70e368733a07f30f2c1 | 50 | py | Python | fedrec/multiprocessing/__init__.py | ayush268/RecoEdge | b6554c646bae602704f44ae80571a4c319f90fd2 | [
"Apache-2.0"
] | 68 | 2021-06-20T07:54:48.000Z | 2022-02-19T16:11:01.000Z | fedrec/multiprocessing/__init__.py | ayush268/RecoEdge | b6554c646bae602704f44ae80571a4c319f90fd2 | [
"Apache-2.0"
] | 100 | 2021-06-24T13:33:24.000Z | 2022-02-23T10:30:27.000Z | fedrec/multiprocessing/__init__.py | ayush268/RecoEdge | b6554c646bae602704f44ae80571a4c319f90fd2 | [
"Apache-2.0"
] | 38 | 2021-07-13T12:16:24.000Z | 2022-02-26T05:08:28.000Z | from . import jobber
from . import process_manager | 25 | 29 | 0.82 | 7 | 50 | 5.714286 | 0.714286 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.14 | 50 | 2 | 29 | 25 | 0.930233 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5cc0639f6fe4dd4ef0e2b735f0ca0b3a703ae0e6 | 32 | py | Python | theseus/base/optimizers/scalers/__init__.py | kaylode/Custom-Template | b2f11bfacf2b03b793476a19781f9046fab6fd82 | [
"MIT"
] | 2 | 2022-02-18T04:41:29.000Z | 2022-03-12T09:04:14.000Z | theseus/base/optimizers/scalers/__init__.py | kaylode/mediaeval21-vsa | 8c5e7d612393d511331124931843c2ed07192c1b | [
"MIT"
] | 8 | 2022-02-16T17:01:28.000Z | 2022-03-28T02:53:45.000Z | theseus/base/optimizers/scalers/__init__.py | lannguyen0910/theseus | 5c08fb2f4a9c7ffa395788e6a0ade43780e8bd7d | [
"MIT"
] | 3 | 2022-02-13T05:00:13.000Z | 2022-03-02T00:11:27.000Z | from .native import NativeScaler | 32 | 32 | 0.875 | 4 | 32 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 32 | 1 | 32 | 32 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5ccaa4a3bfba79d4a0894e7e9c6707223a2dd9a3 | 46 | py | Python | performance/__main__.py | skeptycal/python_performance | d8c2108c614cf13295aba1b42ce0425750743359 | [
"MIT"
] | null | null | null | performance/__main__.py | skeptycal/python_performance | d8c2108c614cf13295aba1b42ce0425750743359 | [
"MIT"
] | null | null | null | performance/__main__.py | skeptycal/python_performance | d8c2108c614cf13295aba1b42ce0425750743359 | [
"MIT"
] | null | null | null | import performance.cli
performance.cli.main()
| 15.333333 | 22 | 0.826087 | 6 | 46 | 6.333333 | 0.666667 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.065217 | 46 | 2 | 23 | 23 | 0.883721 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
7a5ba4ac4ebe8b96e0545bbebf2a4ee2bb5f8fef | 5,092 | py | Python | pyHalo/Cosmology/lookup_tables/lookup_sheth99.py | ryu57/pyHalo | 61b9ab49d76f3552f5680b2e457fbd3e49b9cc89 | [
"MIT"
] | 7 | 2020-12-09T23:58:34.000Z | 2022-03-13T12:18:32.000Z | pyHalo/Cosmology/lookup_tables/lookup_sheth99.py | ryu57/pyHalo | 61b9ab49d76f3552f5680b2e457fbd3e49b9cc89 | [
"MIT"
] | 8 | 2020-10-12T21:30:22.000Z | 2022-01-25T16:04:54.000Z | pyHalo/Cosmology/lookup_tables/lookup_sheth99.py | ryu57/pyHalo | 61b9ab49d76f3552f5680b2e457fbd3e49b9cc89 | [
"MIT"
] | 6 | 2021-06-07T16:30:41.000Z | 2022-01-12T16:58:04.000Z | import numpy as np
norm_z_dV = [6.1335e-08, 6.165e-08, 6.1969e-08, 6.2289e-08, 6.2612e-08, 6.2936e-08, 6.3262e-08, 6.3591e-08, 6.392e-08, 6.4252e-08, 6.4584e-08, 6.4918e-08, 6.5252e-08, 6.5588e-08, 6.5924e-08, 6.6261e-08, 6.6598e-08, 6.6936e-08, 6.7274e-08, 6.7612e-08, 6.795e-08, 6.8287e-08, 6.8625e-08, 6.8962e-08, 6.9299e-08, 6.9636e-08, 6.9972e-08, 7.0307e-08, 7.0641e-08, 7.0975e-08, 7.1308e-08, 7.164e-08, 7.1971e-08, 7.23e-08, 7.2629e-08, 7.2956e-08, 7.3282e-08, 7.3606e-08, 7.3929e-08, 7.4251e-08, 7.4571e-08, 7.4889e-08, 7.5205e-08, 7.552e-08, 7.5833e-08, 7.6144e-08, 7.6453e-08, 7.676e-08, 7.7065e-08, 7.7367e-08, 7.7668e-08, 7.7967e-08, 7.8263e-08, 7.8558e-08, 7.885e-08, 7.914e-08, 7.9428e-08, 7.9713e-08, 7.9996e-08, 8.0277e-08, 8.0555e-08, 8.0831e-08, 8.1105e-08, 8.1376e-08, 8.1644e-08, 8.191e-08, 8.2174e-08, 8.2435e-08, 8.2693e-08, 8.2949e-08, 8.3202e-08, 8.3452e-08, 8.37e-08, 8.3945e-08, 8.4188e-08, 8.4427e-08, 8.4664e-08, 8.4898e-08, 8.513e-08, 8.5358e-08, 8.5584e-08, 8.5808e-08, 8.6028e-08, 8.6246e-08, 8.646e-08, 8.6672e-08, 8.6882e-08, 8.7088e-08, 8.7292e-08, 8.7492e-08, 8.769e-08, 8.7885e-08, 8.8078e-08, 8.8267e-08, 8.8454e-08, 8.8637e-08, 8.8818e-08, 8.8996e-08, 8.9171e-08, 8.9343e-08, 8.9512e-08, 8.9679e-08, 8.9842e-08, 9.0003e-08, 9.0161e-08, 9.0316e-08, 9.0468e-08, 9.0617e-08, 9.0763e-08, 9.0906e-08, 9.1046e-08, 9.1184e-08, 9.1318e-08, 9.145e-08, 9.1579e-08, 9.1704e-08, 9.1827e-08, 9.1947e-08, 9.2065e-08, 9.2179e-08, 9.229e-08, 9.2399e-08, 9.2505e-08, 9.2607e-08, 9.2707e-08, 9.2804e-08, 9.2899e-08, 9.299e-08, 9.3079e-08, 9.3164e-08, 9.3247e-08, 9.3327e-08, 9.3404e-08, 9.3479e-08, 9.355e-08, 9.3619e-08, 9.3685e-08, 9.3748e-08, 9.3808e-08, 9.3866e-08, 9.392e-08, 9.3972e-08, 9.4022e-08, 9.4068e-08, 9.4112e-08, 9.4153e-08, 9.4191e-08, 9.4226e-08, 9.4259e-08, 9.4289e-08, 9.4316e-08, 9.4341e-08, 9.4362e-08, 9.4382e-08, 9.4398e-08, 9.4412e-08, 9.4423e-08, 9.4431e-08, 9.4437e-08, 9.444e-08, 9.4441e-08, 9.4439e-08, 9.4434e-08, 9.4427e-08, 9.4417e-08, 9.4404e-08, 9.4389e-08, 9.4372e-08, 9.4352e-08, 9.4329e-08, 9.4304e-08, 9.4276e-08, 9.4245e-08, 9.4213e-08, 9.4177e-08, 9.414e-08, 9.4099e-08, 9.4057e-08, 9.4012e-08, 9.3964e-08, 9.3914e-08, 9.3862e-08, 9.3807e-08, 9.375e-08, 9.369e-08, 9.3628e-08, 9.3564e-08, 9.3497e-08, 9.3428e-08, 9.3357e-08, 9.3283e-08, 9.3207e-08, 9.3129e-08, 9.3049e-08, 9.2966e-08, 9.2881e-08, 9.2794e-08, 9.2704e-08, 9.2613e-08, 9.2519e-08, 9.2423e-08, ]
plaw_index_z = [-1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.91, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.92, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.93, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.94, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.95, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.96, -1.97, -1.97, -1.97, ]
z_range = np.array([0.01, 0.03, 0.05, 0.07, 0.09, 0.11, 0.13, 0.15, 0.17, 0.19, 0.21, 0.23, 0.25, 0.27, 0.29, 0.31, 0.33, 0.35, 0.37, 0.39, 0.41, 0.43, 0.45, 0.47, 0.49, 0.51, 0.53, 0.55, 0.57, 0.59, 0.61, 0.63, 0.65, 0.67, 0.69, 0.71, 0.73, 0.75, 0.77, 0.79, 0.81, 0.83, 0.85, 0.87, 0.89, 0.91, 0.93, 0.95, 0.97, 0.99, 1.01, 1.03, 1.05, 1.07, 1.09, 1.11, 1.13, 1.15, 1.17, 1.19, 1.21, 1.23, 1.25, 1.27, 1.29, 1.31, 1.33, 1.35, 1.37, 1.39, 1.41, 1.43, 1.45, 1.47, 1.49, 1.51, 1.53, 1.55, 1.57, 1.59, 1.61, 1.63, 1.65, 1.67, 1.69, 1.71, 1.73, 1.75, 1.77, 1.79, 1.81, 1.83, 1.85, 1.87, 1.89, 1.91, 1.93, 1.95, 1.97, 1.99, 2.01, 2.03, 2.05, 2.07, 2.09, 2.11, 2.13, 2.15, 2.17, 2.19, 2.21, 2.23, 2.25, 2.27, 2.29, 2.31, 2.33, 2.35, 2.37, 2.39, 2.41, 2.43, 2.45, 2.47, 2.49, 2.51, 2.53, 2.55, 2.57, 2.59, 2.61, 2.63, 2.65, 2.67, 2.69, 2.71, 2.73, 2.75, 2.77, 2.79, 2.81, 2.83, 2.85, 2.87, 2.89, 2.91, 2.93, 2.95, 2.97, 2.99, 3.01, 3.03, 3.05, 3.07, 3.09, 3.11, 3.13, 3.15, 3.17, 3.19, 3.21, 3.23, 3.25, 3.27, 3.29, 3.31, 3.33, 3.35, 3.37, 3.39, 3.41, 3.43, 3.45, 3.47, 3.49, 3.51, 3.53, 3.55, 3.57, 3.59, 3.61, 3.63, 3.65, 3.67, 3.69, 3.71, 3.73, 3.75, 3.77, 3.79, 3.81, 3.83, 3.85, 3.87, 3.89, 3.91, 3.93, 3.95, 3.97, 3.99, 4.01, ])
delta_z = 0.02
| 636.5 | 2,401 | 0.559505 | 1,425 | 5,092 | 1.995088 | 0.192982 | 0.103412 | 0.102708 | 0.149842 | 0.209286 | 0.209286 | 0.209286 | 0.209286 | 0.209286 | 0.209286 | 0 | 0.579512 | 0.121956 | 5,092 | 7 | 2,402 | 727.428571 | 0.056363 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 0.2 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7a752246e71d26d0f7729e203d3ab0aed89e6a61 | 33,192 | py | Python | koku/api/provider/test/test_serializers.py | rubik-ai/koku | 3255d1c217b7b6685cb2e130bf4e025946e76fac | [
"Apache-2.0"
] | 157 | 2018-04-30T16:27:53.000Z | 2022-03-31T08:17:21.000Z | koku/api/provider/test/test_serializers.py | rubik-ai/koku | 3255d1c217b7b6685cb2e130bf4e025946e76fac | [
"Apache-2.0"
] | 3,250 | 2018-04-26T14:14:25.000Z | 2022-03-31T23:49:15.000Z | koku/api/provider/test/test_serializers.py | rubik-ai/koku | 3255d1c217b7b6685cb2e130bf4e025946e76fac | [
"Apache-2.0"
] | 65 | 2018-05-10T14:11:50.000Z | 2022-03-18T19:22:58.000Z | #
# Copyright 2021 Red Hat Inc.
# SPDX-License-Identifier: Apache-2.0
#
"""Test the Provider serializers."""
import copy
import random
import uuid
from itertools import permutations
from unittest.mock import patch
from faker import Faker
from rest_framework import serializers
from rest_framework.exceptions import ValidationError
from api.iam.serializers import create_schema_name
from api.iam.test.iam_test_case import IamTestCase
from api.provider.models import Provider
from api.provider.models import Sources
from api.provider.serializers import AdminProviderSerializer
from api.provider.serializers import ProviderSerializer
from api.provider.serializers import REPORT_PREFIX_MAX_LENGTH
from providers.provider_access import ProviderAccessor
from providers.provider_errors import ProviderErrors
FAKE = Faker()
class ProviderSerializerTest(IamTestCase):
"""Tests for the customer serializer."""
def setUp(self):
"""Create test case objects."""
super().setUp()
self.generic_providers = {
Provider.PROVIDER_OCP: {
"name": "test_provider",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": "my-ocp-cluster-1"}},
"billing_source": {},
},
Provider.PROVIDER_AWS: {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "arn:aws:s3:::my_s3_bucket"}},
"billing_source": {"data_source": {"bucket": "my_s3_bucket"}},
},
Provider.PROVIDER_AZURE: {
"name": "test_provider",
"type": Provider.PROVIDER_AZURE.lower(),
"authentication": {
"credentials": {
"subscription_id": "12345678-1234-5678-1234-567812345678",
"tenant_id": "12345678-1234-5678-1234-567812345678",
"client_id": "12345678-1234-5678-1234-567812345678",
"client_secret": "12345",
}
},
"billing_source": {"data_source": {"resource_group": {}, "storage_account": {}}},
},
}
def test_create_all_providers(self):
"""Tests that adding all unique providers together is successful."""
list_of_uuids = []
initial_date_updated = self.customer.date_updated
self.assertIsNotNone(initial_date_updated)
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(
data=self.generic_providers[Provider.PROVIDER_AZURE], context=self.request_context
)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
list_of_uuids.append(instance.uuid)
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(
data=self.generic_providers[Provider.PROVIDER_AWS], context=self.request_context
)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
list_of_uuids.append(instance.uuid)
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(
data=self.generic_providers[Provider.PROVIDER_OCP], context=self.request_context
)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
list_of_uuids.append(instance.uuid)
for a, b in permutations(list_of_uuids, 2):
self.assertNotEqual(a, b)
self.assertGreater(self.customer.date_updated, initial_date_updated)
def test_create_provider_fails_user(self):
"""Test creating a provider fails with no user."""
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "arn:aws:s3:::my_s3_bucket"}},
"billing_source": {"data_source": {"bucket": "my_s3_bucket"}},
}
serializer = ProviderSerializer(data=provider)
if serializer.is_valid(raise_exception=True):
with self.assertRaises(serializers.ValidationError):
serializer.save()
def test_create_provider_fails_customer(self):
"""Test creating a provider where customer is not found for user."""
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "arn:aws:s3:::my_s3_bucket"}},
"billing_source": {"data_source": {"bucket": "my_s3_bucket"}},
}
user_data = self._create_user_data()
alt_request_context = self._create_request_context(
self.create_mock_customer_data(), user_data, create_tenant=True
)
request = alt_request_context["request"]
request.user.customer = None
serializer = ProviderSerializer(data=provider, context=alt_request_context)
if serializer.is_valid(raise_exception=True):
with self.assertRaises(serializers.ValidationError):
serializer.save()
def test_create_aws_provider(self):
"""Test creating a provider."""
iam_arn = "arn:aws:s3:::my_s3_bucket"
bucket_name = "my_s3_bucket"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": iam_arn}},
"billing_source": {"data_source": {"bucket": bucket_name}},
}
instance = None
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_create_ocp_provider(self):
"""Test creating an OCP provider."""
cluster_id = "my-ocp-cluster-1"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": cluster_id}},
"billing_source": {},
}
instance = None
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_create_ocp_source_with_existing_provider(self):
"""Test creating an OCP Source when the provider already exists."""
cluster_id = "my-ocp-cluster-1"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": cluster_id}},
"billing_source": {},
}
instance = None
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
# Add Source without provider uuid
sources = Sources.objects.create(
source_id=1, auth_header="testheader", offset=1, authentication={"cluster_id": cluster_id}
)
sources.save()
# Verify ValidationError is raised when another source is added with an existing
# provider.
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
with self.assertRaises(serializers.ValidationError):
serializer.save()
def test_create_provider_with_credentials_and_data_source(self):
"""Test creating a provider with data_source field instead of bucket."""
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "four"}},
"billing_source": {"data_source": {"bucket": "bar"}},
}
instance = None
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_create_provider_with_credentials_and_role_arn(self):
"""Test creating a provider with credentials and role_arn fields should fail."""
iam_arn = "arn:aws:s3:::my_s3_bucket"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "four"}, "role_arn": iam_arn},
"billing_source": {"data_source": {"bucket": "bar"}},
}
user_data = self._create_user_data()
alt_request_context = self._create_request_context(
self.create_mock_customer_data(), user_data, create_tenant=True
)
request = alt_request_context["request"]
request.user.customer = None
serializer = ProviderSerializer(data=provider, context=alt_request_context)
if serializer.is_valid(raise_exception=True):
with self.assertRaises(serializers.ValidationError):
serializer.save()
def test_create_provider_with_bucket_and_data_source(self):
"""Test creating a provider with data_source and bucket fields should fail."""
bucket_name = "my_s3_bucket"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "four"}},
"billing_source": {"data_source": {"bucket": "bar"}, "bucket": bucket_name},
}
user_data = self._create_user_data()
alt_request_context = self._create_request_context(
self.create_mock_customer_data(), user_data, create_tenant=True
)
request = alt_request_context["request"]
request.user.customer = None
serializer = ProviderSerializer(data=provider, context=alt_request_context)
if serializer.is_valid(raise_exception=True):
with self.assertRaises(serializers.ValidationError):
serializer.save()
def test_create_provider_two_providers_shared_billing_record(self):
"""Test that the same blank billing entry is used for all OCP providers."""
cluster_id = "my-ocp-cluster-1"
provider = {
"name": "test_provider_one",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": cluster_id}},
"billing_source": {},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
provider_one = serializer.save()
cluster_id = "my-ocp-cluster-2"
provider["name"] = "test_provider_two"
provider["authentication"] = {"credentials": {"cluster_id": cluster_id}}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
provider_two = serializer.save()
self.assertEqual(provider_one.billing_source_id, provider_two.billing_source_id)
def test_missing_creds_parameters_exception(self):
"""Test that ValidationError is raised when there are missing parameters."""
fields = ["subscription_id", "tenant_id", "client_id", "client_secret"]
credentials = {
"subscription_id": FAKE.uuid4(),
"tenant_id": FAKE.uuid4(),
"client_id": FAKE.uuid4(),
"client_secret": FAKE.word(),
}
source_name = {"resource_group": FAKE.word(), "storage_account": FAKE.word()}
del credentials[random.choice(fields)]
provider = {
"name": FAKE.word(),
"type": Provider.PROVIDER_AZURE.lower(),
"authentication": {"credentials": credentials},
"billing_source": {"data_source": source_name},
}
with self.assertRaises(ValidationError):
serializer = ProviderSerializer(data=provider, context=self.request_context)
serializer.is_valid(raise_exception=True)
def test_missing_source_parameters_exception(self):
"""Test that ValidationError is raised when there are missing parameters."""
fields = ["resource_group", "storage_account"]
credentials = {
"subscription_id": FAKE.uuid4(),
"tenant_id": FAKE.uuid4(),
"client_id": FAKE.uuid4(),
"client_secret": FAKE.word(),
}
source_name = {"resource_group": FAKE.word(), "storage_account": FAKE.word()}
del source_name[random.choice(fields)]
provider = {
"name": FAKE.word(),
"type": Provider.PROVIDER_AZURE.lower(),
"authentication": credentials,
"billing_source": {"data_source": source_name},
}
with self.assertRaises(ValidationError):
serializer = ProviderSerializer(data=provider, context=self.request_context)
serializer.is_valid(raise_exception=True)
def test_create_gcp_provider(self):
"""Test that the same blank billing entry is used for all OCP providers."""
provider = {
"name": "test_provider_one",
"type": Provider.PROVIDER_GCP.lower(),
"authentication": {"credentials": {"project_id": "gcp_project"}},
"billing_source": {
"data_source": {"dataset": "test_dataset", "table_id": "test_table_id", "report_prefix": "precious"}
},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_update_with_duplication_error(self):
provider = self.generic_providers[Provider.PROVIDER_AWS]
provider2 = copy.deepcopy(provider)
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save() # add first provider
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
provider2["billing_source"] = {"data_source": {"bucket": "my_new_bucket"}}
serializer = ProviderSerializer(data=provider2, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save() # add second provider
serializer.validated_data["billing_source"] = provider.get("billing_source")
if serializer.is_valid(raise_exception=True):
with self.assertRaises(ValidationError) as excCtx:
serializer.update(serializer.instance, serializer.validated_data) # try to make second match first
validationErr = excCtx.exception.detail[ProviderErrors.DUPLICATE_AUTH][0]
self.assertTrue("Cost management does not allow duplicate accounts" in str(validationErr))
def test_error_providers_with_same_auth_or_billing_source(self):
"""Test that the errors are wrapped correctly."""
provider = self.generic_providers[Provider.PROVIDER_OCP]
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
with self.assertRaises(ValidationError) as excCtx:
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
validationErr = excCtx.exception.detail[ProviderErrors.DUPLICATE_AUTH][0]
self.assertTrue("Cost management does not allow duplicate accounts" in str(validationErr))
def test_error_update_provider_with_used_auth_or_billing_source(self):
p1 = {
"name": "test_provider_1",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": "my-ocp-cluster-1"}},
"billing_source": {},
}
p2 = {
"name": "test_provider_2",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": "my-ocp-cluster-2"}},
"billing_source": {},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=p1, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
serializer = ProviderSerializer(data=p2, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
d = {
"name": "test_provider_2",
"type": Provider.PROVIDER_OCP.lower(),
"authentication": {"credentials": {"cluster_id": "my-ocp-cluster-1"}},
"billing_source": {},
}
with self.assertRaises(ValidationError) as excCtx:
serializer = ProviderSerializer(instance, data=d, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
validationErr = excCtx.exception.detail[ProviderErrors.DUPLICATE_AUTH][0]
self.assertTrue("Cost management does not allow duplicate accounts" in str(validationErr))
def test_create_gcp_provider_validate_no_data_source_bucket(self):
"""Test the data_source.bucket validation for GCP provider."""
provider = {
"name": "test_provider_val_data_source",
"type": Provider.PROVIDER_GCP.lower(),
"authentication": {"credentials": {"project_id": "gcp_project"}},
"billing_source": {"data_source": {"potato": ""}},
}
with self.assertRaises(ValidationError) as e:
serializer = ProviderSerializer(data=provider, context=self.request_context)
serializer.is_valid(raise_exception=True)
self.assertEqual(e.exception.status_code, 400)
self.assertEqual(
str(e.exception.detail["billing_source"]["data_source"]["provider.data_source"][0]),
"One or more required fields is invalid/missing. Required fields are ['dataset']",
)
def test_create_gcp_provider_validate_report_prefix_too_long(self):
"""Test the data_source.report_prefix validation for GCP provider."""
provider = {
"name": "test_provider_val_data_source",
"type": Provider.PROVIDER_GCP.lower(),
"authentication": {"credentials": {"project_id": "gcp_project"}},
"billing_source": {
"data_source": {
"dataset": "test_dataset",
"table_id": "test_table_id",
"report_prefix": "an-unnecessarily-long-prefix-that-is-here-simply-for-the-purpose-of"
"testing-the-custom-validator-the-checks-for-too-long-of-a-report_prefix",
}
},
}
with self.assertRaises(ValidationError) as e:
serializer = ProviderSerializer(data=provider, context=self.request_context)
serializer.is_valid(raise_exception=True)
self.assertEqual(e.exception.status_code, 400)
self.assertEqual(
str(e.exception.detail["billing_source"]["data_source"]["data_source.report_prefix"][0]),
f"Ensure this field has no more than {REPORT_PREFIX_MAX_LENGTH} characters.",
)
def test_create_ibm_provider(self):
"""Test that the same blank billing entry is used for all OCP providers."""
provider = {
"name": "test_provider_ibm",
"type": Provider.PROVIDER_IBM.lower(),
"authentication": {"credentials": {"iam_token": "1111-1111-1111-1111"}},
"billing_source": {"data_source": {"enterprise_id": "2222-2222-2222-2222"}},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_create_provider_invalid_type(self):
"""Test that an invalid provider type is not validated."""
iam_arn = "arn:aws:s3:::my_s3_bucket"
bucket_name = "my_s3_bucket"
provider = {
"name": "test_provider",
"type": "Bad",
"authentication": {"credentials": {"role_arn": iam_arn}},
"billing_source": {"data_source": {"bucket": bucket_name}},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
with self.assertRaises(ValidationError):
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
def test_create_same_provider_different_customers(self):
"""Test that the same provider can be created for 2 different customers."""
user_data = self._create_user_data()
alt_request_context = self._create_request_context(
self.create_mock_customer_data(), user_data, create_tenant=True
)
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(
data=self.generic_providers[Provider.PROVIDER_AZURE], context=self.request_context
)
if serializer.is_valid(raise_exception=True):
instance1 = serializer.save()
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = ProviderSerializer(
data=self.generic_providers[Provider.PROVIDER_AZURE], context=alt_request_context
)
if serializer.is_valid(raise_exception=True):
instance2 = serializer.save()
self.assertNotEqual(instance1.uuid, instance2.uuid)
self.assertEqual(instance1.billing_source_id, instance2.billing_source_id)
self.assertEqual(instance1.authentication_id, instance2.authentication_id)
def test_create_provider_for_demo_account(self):
"""Test creating a provider for a demo account."""
provider = {
"name": "test_provider_one",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "three"}},
"billing_source": {"data_source": {"bucket": "foo"}},
}
instance = None
account_id = self.customer_data.get("account_id")
with self.settings(DEMO_ACCOUNTS={account_id: {}}):
with patch.object(ProviderAccessor, "cost_usage_source_ready") as mock_method:
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
mock_method.assert_called()
aws_cred = "four"
provider = {
"name": "test_provider_two",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": aws_cred}},
"billing_source": {"data_source": {"bucket": "bar"}},
}
demo_accounts = {aws_cred: {"report_prefix": "cur", "report_name": "awscost", "source_type": "AWS"}}
with self.settings(DEMO_ACCOUNTS={account_id: demo_accounts}):
with patch.object(ProviderAccessor, "cost_usage_source_ready") as mock_method:
serializer = ProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
instance = serializer.save()
mock_method.assert_not_called()
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsInstance(instance.uuid, uuid.UUID)
self.assertTrue(instance.active)
self.assertIsNone(schema_name)
self.assertFalse("schema_name" in serializer.data["customer"])
def test_demo_credentials(self):
"""Test the demo credentials property is created as expected."""
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "four"}},
"billing_source": {"data_source": {"bucket": "bar"}},
}
aws_cred = "arn:aws:iam::999:role/DEMO"
azure_cred = "123"
gcp_cred = "my-gcp-project"
demo_accounts = {
self.schema_name: {
aws_cred: {"report_prefix": "cur", "report_name": "awscost", "source_type": "AWS"},
azure_cred: {
"report_name": "report",
"report_prefix": "prefix",
"container_name": "container",
"source_type": "Azure",
},
gcp_cred: {"dataset": "dataset", "table_id": "table_id", "source_type": "GCP"},
}
}
with self.settings(DEMO_ACCOUNTS=demo_accounts):
with patch.object(ProviderAccessor, "cost_usage_source_ready"):
serializer = ProviderSerializer(data=provider, context=self.request_context)
demo_credentials = serializer.demo_credentials
self.assertIn(Provider.PROVIDER_AWS, demo_credentials)
self.assertIn(Provider.PROVIDER_AZURE, demo_credentials)
self.assertIn(Provider.PROVIDER_GCP, demo_credentials)
self.assertEqual(demo_credentials.get(Provider.PROVIDER_AWS), [{"role_arn": aws_cred}])
self.assertEqual(demo_credentials.get(Provider.PROVIDER_AZURE), [{"client_id": azure_cred}])
self.assertEqual(demo_credentials.get(Provider.PROVIDER_GCP), [{"project_id": gcp_cred}])
def test_is_demo_account(self):
"""Test that we correctly identify demo accounts."""
aws_cred = "arn:aws:iam::999:role/DEMO"
azure_cred = "123"
gcp_cred = "my-gcp-project"
provider = {
"name": "test_provider_one",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": "four"}},
"billing_source": {"data_source": {"bucket": "bar"}},
}
demo_accounts = {
self.schema_name: {
aws_cred: {"report_prefix": "cur", "report_name": "awscost", "source_type": "AWS"},
azure_cred: {
"report_name": "report",
"report_prefix": "prefix",
"container_name": "container",
"source_type": "Azure",
},
gcp_cred: {"dataset": "dataset", "table_id": "table_id", "source_type": "GCP"},
}
}
with self.settings(DEMO_ACCOUNTS=demo_accounts):
with patch.object(ProviderAccessor, "cost_usage_source_ready"):
serializer = ProviderSerializer(data=provider, context=self.request_context)
self.assertFalse(
serializer._is_demo_account(
Provider.PROVIDER_AWS, provider.get("authentication", {}).get("credentials")
)
)
provider = {
"name": "test_provider_two",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": aws_cred}},
"billing_source": {"data_source": {"bucket": "bar"}},
}
with self.settings(DEMO_ACCOUNTS=demo_accounts):
with patch.object(ProviderAccessor, "cost_usage_source_ready"):
serializer = ProviderSerializer(data=provider, context=self.request_context)
self.assertTrue(
serializer._is_demo_account(
Provider.PROVIDER_AWS, provider.get("authentication", {}).get("credentials")
)
)
class AdminProviderSerializerTest(IamTestCase):
"""Tests for the admin customer serializer."""
def setUp(self):
"""Create test case objects."""
super().setUp()
def test_schema_name_present_on_customer(self):
"""Test that schema_name is returned on customer."""
iam_arn = "arn:aws:s3:::my_s3_bucket"
bucket_name = "my_s3_bucket"
provider = {
"name": "test_provider",
"type": Provider.PROVIDER_AWS.lower(),
"authentication": {"credentials": {"role_arn": iam_arn}},
"billing_source": {"data_source": {"bucket": bucket_name}},
}
with patch.object(ProviderAccessor, "cost_usage_source_ready", returns=True):
serializer = AdminProviderSerializer(data=provider, context=self.request_context)
if serializer.is_valid(raise_exception=True):
serializer.save()
account = self.customer.account_id
expected_schema_name = create_schema_name(account)
schema_name = serializer.data["customer"].get("schema_name")
self.assertIsNotNone(schema_name)
self.assertEqual(schema_name, expected_schema_name)
| 47.417143 | 119 | 0.627651 | 3,372 | 33,192 | 5.925563 | 0.083926 | 0.026025 | 0.054452 | 0.037436 | 0.818878 | 0.795556 | 0.766128 | 0.750213 | 0.737601 | 0.732846 | 0 | 0.008894 | 0.258165 | 33,192 | 699 | 120 | 47.484979 | 0.802583 | 0.052965 | 0 | 0.660441 | 0 | 0.003396 | 0.177375 | 0.036984 | 0 | 0 | 0 | 0 | 0.129032 | 1 | 0.04584 | false | 0 | 0.028862 | 0 | 0.078098 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7ab7667e6d417cc6b70a6d9ca587be93eef8f55d | 28 | py | Python | argmin/__init__.py | argmin-rs/pyargmin | 9c58ef8c7ee0997d72471d8150227e4aeafe8914 | [
"Apache-2.0",
"MIT"
] | null | null | null | argmin/__init__.py | argmin-rs/pyargmin | 9c58ef8c7ee0997d72471d8150227e4aeafe8914 | [
"Apache-2.0",
"MIT"
] | 56 | 2019-07-16T20:32:32.000Z | 2022-03-21T07:10:56.000Z | argmin/__init__.py | argmin-rs/pyargmin | 9c58ef8c7ee0997d72471d8150227e4aeafe8914 | [
"Apache-2.0",
"MIT"
] | 1 | 2019-07-16T20:33:26.000Z | 2019-07-16T20:33:26.000Z | from ._lib import * # noqa
| 14 | 27 | 0.642857 | 4 | 28 | 4.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 28 | 1 | 28 | 28 | 0.809524 | 0.142857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8fa301c7234a34eaae5579f539a20a65f24c2e37 | 197 | py | Python | TranscriptomicPipelines/t_utilities/t_data_exceptions.py | g-simmons/OCB | 217d9b8eaaefad97c52741b3eac9c18ae1def51a | [
"Apache-2.0"
] | null | null | null | TranscriptomicPipelines/t_utilities/t_data_exceptions.py | g-simmons/OCB | 217d9b8eaaefad97c52741b3eac9c18ae1def51a | [
"Apache-2.0"
] | null | null | null | TranscriptomicPipelines/t_utilities/t_data_exceptions.py | g-simmons/OCB | 217d9b8eaaefad97c52741b3eac9c18ae1def51a | [
"Apache-2.0"
] | null | null | null | #Definition of data exceptions
class OriDataMatrixIsNotReady(Exception):
pass
class InvalidOriDataMatrixPath(Exception):
pass
class FailedToWriteOriDataMatrix(Exception):
pass | 21.888889 | 44 | 0.77665 | 16 | 197 | 9.5625 | 0.625 | 0.254902 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.172589 | 197 | 9 | 45 | 21.888889 | 0.93865 | 0.147208 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
8fab1fec50b9c5207d13012edc5f885ec1940dee | 248 | py | Python | tests/test_experiment.py | dsjardim/fraud-detection-mlops | 6c907f8c6e3fb452e4ebe1bc8fc2c42e9191f896 | [
"MIT"
] | null | null | null | tests/test_experiment.py | dsjardim/fraud-detection-mlops | 6c907f8c6e3fb452e4ebe1bc8fc2c42e9191f896 | [
"MIT"
] | null | null | null | tests/test_experiment.py | dsjardim/fraud-detection-mlops | 6c907f8c6e3fb452e4ebe1bc8fc2c42e9191f896 | [
"MIT"
] | 1 | 2022-02-14T13:47:22.000Z | 2022-02-14T13:47:22.000Z | import os
def test_model_creation():
assert os.path.exists("data/model.pickle")
def test_metrics_creation():
assert os.path.exists("data/metrics.json")
def test_dataset_availability():
assert os.path.exists("data/creditcard.csv")
| 17.714286 | 48 | 0.737903 | 35 | 248 | 5.057143 | 0.485714 | 0.118644 | 0.20339 | 0.305085 | 0.463277 | 0.338983 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 248 | 13 | 49 | 19.076923 | 0.819444 | 0 | 0 | 0 | 0 | 0 | 0.21371 | 0 | 0 | 0 | 0 | 0 | 0.428571 | 1 | 0.428571 | true | 0 | 0.142857 | 0 | 0.571429 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
891a8c1ef033229f3032d2abd1d000d13f7ed7e1 | 46 | py | Python | src/max.py | toicca/planetary-simulator-sc2 | f673eb0fbb49b72fdaf861782fa476b13d798aa0 | [
"MIT"
] | null | null | null | src/max.py | toicca/planetary-simulator-sc2 | f673eb0fbb49b72fdaf861782fa476b13d798aa0 | [
"MIT"
] | null | null | null | src/max.py | toicca/planetary-simulator-sc2 | f673eb0fbb49b72fdaf861782fa476b13d798aa0 | [
"MIT"
] | null | null | null | lista=[[1,2,3],[4,5,6][7,8,9]]
print(lista)
| 15.333333 | 31 | 0.521739 | 12 | 46 | 2 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 0.086957 | 46 | 2 | 32 | 23 | 0.357143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 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 | 1 | 0 | 6 |
8f05742eef5746f4b1bf266f6a1f1f5643ebe54e | 94 | py | Python | policosm/extractors/__init__.py | ComplexCity/policosm | 548d4d694df49603f91cd45af7fe50ced79aea68 | [
"MIT"
] | 6 | 2017-06-05T07:30:46.000Z | 2022-03-07T00:47:22.000Z | policosm/extractors/__init__.py | ComplexCity/policosm | 548d4d694df49603f91cd45af7fe50ced79aea68 | [
"MIT"
] | 1 | 2017-12-14T05:40:42.000Z | 2017-12-14T05:40:42.000Z | policosm/extractors/__init__.py | ComplexCity/policosm | 548d4d694df49603f91cd45af7fe50ced79aea68 | [
"MIT"
] | 1 | 2020-10-22T19:18:30.000Z | 2020-10-22T19:18:30.000Z | from .buildingsPolygons import *
from .roadsGraph import *
from .transportationGraph import *
| 23.5 | 34 | 0.808511 | 9 | 94 | 8.444444 | 0.555556 | 0.263158 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12766 | 94 | 3 | 35 | 31.333333 | 0.926829 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8f133d8cd3679d0085527bccc6be4c7d181b99ac | 242 | py | Python | tests/test_utils.py | Wen777/python-ddp-server | 61221a6fc7da8707a887f81a279b9fbfa2930ff8 | [
"Apache-2.0"
] | 1 | 2015-11-06T06:39:22.000Z | 2015-11-06T06:39:22.000Z | tests/test_utils.py | Wen777/python-ddp-server | 61221a6fc7da8707a887f81a279b9fbfa2930ff8 | [
"Apache-2.0"
] | null | null | null | tests/test_utils.py | Wen777/python-ddp-server | 61221a6fc7da8707a887f81a279b9fbfa2930ff8 | [
"Apache-2.0"
] | 1 | 2018-10-12T08:01:46.000Z | 2018-10-12T08:01:46.000Z | from ddpserver import utils
from nose.tools import assert_equal, assert_not_equal
def test_gen_id():
assert_not_equal(utils.gen_id(), utils.gen_id())
assert_equal(len(utils.gen_id()), 17)
assert_equal(len(utils.gen_id(10)), 10)
| 26.888889 | 53 | 0.747934 | 41 | 242 | 4.097561 | 0.390244 | 0.14881 | 0.238095 | 0.22619 | 0.285714 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0.028436 | 0.128099 | 242 | 8 | 54 | 30.25 | 0.767773 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.666667 | 1 | 0.166667 | true | 0 | 0.333333 | 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 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
8f29974423cb277d1c38d37919b96da139140ca5 | 67 | py | Python | lib/__init__.py | n2westman/CS410_Project | f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83 | [
"MIT"
] | null | null | null | lib/__init__.py | n2westman/CS410_Project | f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83 | [
"MIT"
] | null | null | null | lib/__init__.py | n2westman/CS410_Project | f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83 | [
"MIT"
] | null | null | null | import lib.data
import lib.train
import lib.utils
import lib.model
| 13.4 | 16 | 0.820896 | 12 | 67 | 4.583333 | 0.5 | 0.654545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119403 | 67 | 4 | 17 | 16.75 | 0.932203 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8f610d613615cf48b8316dd5014672dde96753cc | 177 | py | Python | app/database/__init__.py | LuxQuad/ozet-core-api | bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b | [
"MIT"
] | null | null | null | app/database/__init__.py | LuxQuad/ozet-core-api | bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b | [
"MIT"
] | 5 | 2021-08-10T03:38:31.000Z | 2021-08-11T12:39:34.000Z | app/database/__init__.py | LuxQuad/ozet-core-api | bf0cd9e4b58bf9b7e805843df4dfe7320afa7e4b | [
"MIT"
] | null | null | null | """
@Author:
Bart Kim
@Note:
"""
from .esume import base as esume_base
from .esume import session_local as esume_session_local
from .esume import engine as esume_engine
| 14.75 | 55 | 0.745763 | 27 | 177 | 4.703704 | 0.444444 | 0.212598 | 0.354331 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.180791 | 177 | 11 | 56 | 16.090909 | 0.875862 | 0.169492 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
56c6778f65df72113b6a3de1370c39822f42e6b9 | 2,576 | py | Python | tests/cli/test_customization.py | selectel/python-selvpcclient | 99955064215c2be18b568e5e9b34f17087ec304f | [
"Apache-2.0"
] | 7 | 2017-07-15T12:44:23.000Z | 2020-03-24T09:45:11.000Z | tests/cli/test_customization.py | selectel/python-selvpcclient | 99955064215c2be18b568e5e9b34f17087ec304f | [
"Apache-2.0"
] | 13 | 2017-07-05T09:34:09.000Z | 2021-04-20T08:18:46.000Z | tests/cli/test_customization.py | selectel/python-selvpcclient | 99955064215c2be18b568e5e9b34f17087ec304f | [
"Apache-2.0"
] | 9 | 2017-06-29T13:51:35.000Z | 2021-06-26T21:00:49.000Z | import pytest
from tests.cli import make_client, run_cmd
from tests.util import answers
from tests.util import params
def test_show_theme_b64():
client = make_client(return_value=answers.CUSTOMIZATION_SHOW)
args = ['customization show', '--show-base64']
output = run_cmd(args, client, json_output=True)
assert output["color"] == "00ffee"
assert output["logo"] == params.LOGO_BASE64
assert output["brand_color"] == "00ffee"
def test_show_no_theme_b64():
client = make_client(return_value=answers.CUSTOMIZATION_NO_THEME)
args = ['customization show', '--show-base64']
output = run_cmd(args, client, json_output=True)
assert output["color"] == ""
assert output["logo"] == ""
assert output["brand_color"] == ""
def test_show_theme_b64_short():
client = make_client(return_value=answers.CUSTOMIZATION_SHOW)
args = ['customization show', '--show-short-base64']
output = run_cmd(args, client, json_output=True)
assert output["color"] == "00ffee"
assert output["logo"] == params.LOGO_BASE64_SHORTEN
assert output["brand_color"] == "00ffee"
def test_show_no_theme_b64_short():
client = make_client(return_value=answers.CUSTOMIZATION_NO_THEME)
args = ['customization show', '--show-short-base64']
output = run_cmd(args, client, json_output=True)
assert output["color"] == ""
assert output["logo"] == ""
assert output["brand_color"] == ""
def test_show_theme():
client = make_client(return_value=answers.CUSTOMIZATION_SHOW)
args = ['customization show']
output = run_cmd(args, client, json_output=True)
assert output["color"] == "00ffee"
assert output["logo"] is True
assert output["brand_color"] == "00ffee"
def test_show_no_theme():
client = make_client(return_value=answers.CUSTOMIZATION_NO_THEME)
args = ['customization show']
output = run_cmd(args, client, json_output=True)
assert output["color"] == ""
assert output["logo"] is False
assert output["brand_color"] == ""
def test_update_theme():
client = make_client(return_value=answers.CUSTOMIZATION_UPDATE)
args = ['customization update',
'--color', '00eeff',
'--brand-color', '00ffee']
output = run_cmd(args, client, json_output=True)
assert output["color"] == "00eeff"
assert output["brand_color"] == "00ffee"
def test_customization_delete_without_confirm_flag():
client = make_client(return_value={})
args = ['customization delete']
with pytest.raises(SystemExit):
run_cmd(args, client)
| 28 | 69 | 0.6875 | 317 | 2,576 | 5.33123 | 0.138801 | 0.142012 | 0.07574 | 0.104142 | 0.830178 | 0.807692 | 0.790533 | 0.769822 | 0.739053 | 0.73432 | 0 | 0.01885 | 0.176242 | 2,576 | 91 | 70 | 28.307692 | 0.777568 | 0 | 0 | 0.576271 | 0 | 0 | 0.166149 | 0 | 0 | 0 | 0 | 0 | 0.338983 | 1 | 0.135593 | false | 0 | 0.067797 | 0 | 0.20339 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
56d38268a683dd59dde946751d9f1e95248f6638 | 106 | py | Python | mothulity/messages/__init__.py | dizak/mothulity | 9eb7e290649a3226891d8210897084d2bfacdad6 | [
"BSD-3-Clause"
] | 4 | 2017-04-21T09:33:03.000Z | 2021-06-15T18:02:00.000Z | mothulity/messages/__init__.py | dariusz-izak-doktorat/mothulity | 9eb7e290649a3226891d8210897084d2bfacdad6 | [
"BSD-3-Clause"
] | 104 | 2017-06-28T14:20:15.000Z | 2020-07-20T10:08:45.000Z | mothulity/messages/__init__.py | dizak/mothur_script_creator | 9eb7e290649a3226891d8210897084d2bfacdad6 | [
"BSD-3-Clause"
] | 5 | 2017-09-07T12:39:25.000Z | 2020-11-09T12:52:20.000Z | #pylint: disable=missing-module-docstring
from . import errors
from . import info
from . import warnings
| 17.666667 | 41 | 0.783019 | 14 | 106 | 5.928571 | 0.714286 | 0.361446 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.141509 | 106 | 5 | 42 | 21.2 | 0.912088 | 0.377358 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
712f01b25d8792a727603d4397bb7e3395dace3f | 174 | py | Python | OpenCV/opencv-4.5.3/build_gcc_win86/python_loader/cv2/config.py | lithiumice/DE1_libonnx | 6ba59c90aa0c4c6bbb6106c77e9ea97a224041b0 | [
"MIT"
] | null | null | null | OpenCV/opencv-4.5.3/build_gcc_win86/python_loader/cv2/config.py | lithiumice/DE1_libonnx | 6ba59c90aa0c4c6bbb6106c77e9ea97a224041b0 | [
"MIT"
] | null | null | null | OpenCV/opencv-4.5.3/build_gcc_win86/python_loader/cv2/config.py | lithiumice/DE1_libonnx | 6ba59c90aa0c4c6bbb6106c77e9ea97a224041b0 | [
"MIT"
] | null | null | null | import os
BINARIES_PATHS = [
'/mnt/d/ProgramFiles/intelFPGA/18.1/hld/board/terasic/de1_soc/tests/DE1_libonnx/OpenCV/opencv-4.5.3/build_gcc_win86/lib'
] + BINARIES_PATHS
| 29 | 124 | 0.775862 | 29 | 174 | 4.448276 | 0.862069 | 0.20155 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0625 | 0.08046 | 174 | 5 | 125 | 34.8 | 0.74375 | 0 | 0 | 0 | 0 | 0.25 | 0.678161 | 0.678161 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 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 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7144aef3176a1ade0c02e66dbb2ce3c3ebace5b2 | 124 | py | Python | devapp/admin.py | bashmak/djing | 8cc0c670600254d288178acd47965f7b3db6856e | [
"Unlicense"
] | 23 | 2017-04-27T20:13:22.000Z | 2022-03-16T12:47:29.000Z | devapp/admin.py | bashmak/djing | 8cc0c670600254d288178acd47965f7b3db6856e | [
"Unlicense"
] | 2 | 2017-04-04T15:03:12.000Z | 2021-01-26T15:30:57.000Z | devapp/admin.py | bashmak/djing | 8cc0c670600254d288178acd47965f7b3db6856e | [
"Unlicense"
] | 13 | 2017-08-22T16:00:03.000Z | 2022-03-20T03:12:15.000Z | from django.contrib import admin
from . import models
admin.site.register(models.Device)
admin.site.register(models.Port)
| 17.714286 | 34 | 0.806452 | 18 | 124 | 5.555556 | 0.555556 | 0.18 | 0.34 | 0.46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096774 | 124 | 6 | 35 | 20.666667 | 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 | 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 | 6 |
8555df432d65144afdbbc46c8a6a14b9b951f5df | 1,709 | py | Python | tests/test_motutils.py | agat59/sahil6 | 1f413d3cba7fe058f663f8e0ff8c72981817c325 | [
"MIT"
] | 1 | 2022-02-24T06:23:47.000Z | 2022-02-24T06:23:47.000Z | tests/test_motutils.py | agat59/sahil6 | 1f413d3cba7fe058f663f8e0ff8c72981817c325 | [
"MIT"
] | 1 | 2022-03-11T13:48:28.000Z | 2022-03-11T13:48:28.000Z | tests/test_motutils.py | agat59/sahil6 | 1f413d3cba7fe058f663f8e0ff8c72981817c325 | [
"MIT"
] | 1 | 2021-12-30T11:05:29.000Z | 2021-12-30T11:05:29.000Z | # OBSS SAHI Tool
# Code written by Fatih C Akyon, 2020.
import os
import shutil
import unittest
class TestMotUtils(unittest.TestCase):
def test_mot_vid(self):
from sahi.utils.mot import MotAnnotation, MotFrame, MotVideo
export_dir = "tests/data/mot/"
if os.path.isdir(export_dir):
shutil.rmtree(export_dir, ignore_errors=True)
mot_video = MotVideo(name="video.mp4")
# frame 0
mot_frame = MotFrame()
mot_detection = MotAnnotation(bbox=[10, 10, 100, 100])
mot_frame.add_annotation(mot_detection)
mot_video.add_frame(mot_frame)
# frame 1
mot_frame = MotFrame()
mot_detection = MotAnnotation(bbox=[12, 12, 98, 98])
mot_frame.add_annotation(mot_detection)
mot_detection = MotAnnotation(bbox=[95, 95, 98, 98])
mot_frame.add_annotation(mot_detection)
mot_video.add_frame(mot_frame)
# export
mot_video.export(export_dir=export_dir, type="gt", exist_ok=True)
mot_video = MotVideo(name="video.mp4")
# frame 0
mot_frame = MotFrame()
mot_detection = MotAnnotation(bbox=[10, 10, 100, 100])
mot_frame.add_annotation(mot_detection)
mot_video.add_frame(mot_frame)
# frame 1
mot_frame = MotFrame()
mot_detection = MotAnnotation(bbox=[12, 12, 98, 98])
mot_frame.add_annotation(mot_detection)
mot_detection = MotAnnotation(bbox=[95, 95, 98, 98])
mot_frame.add_annotation(mot_detection)
mot_video.add_frame(mot_frame)
# export
mot_video.export(export_dir=export_dir, type="det", exist_ok=True)
if __name__ == "__main__":
unittest.main()
| 32.865385 | 74 | 0.653013 | 222 | 1,709 | 4.738739 | 0.265766 | 0.106464 | 0.142586 | 0.165399 | 0.718631 | 0.718631 | 0.718631 | 0.718631 | 0.718631 | 0.718631 | 0 | 0.048025 | 0.244587 | 1,709 | 51 | 75 | 33.509804 | 0.766847 | 0.056758 | 0 | 0.628571 | 0 | 0 | 0.028696 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.028571 | false | 0 | 0.114286 | 0 | 0.171429 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
85632f9dbcc9b6710b370bfe53a15fc266c5f9c0 | 1,580 | py | Python | tests/test__mixin.py | Infinidat/infi.pyutils | a53dd8971558d87cafcf9c17a95959e74bf6c5c8 | [
"BSD-3-Clause"
] | 1 | 2022-02-12T20:30:55.000Z | 2022-02-12T20:30:55.000Z | tests/test__mixin.py | Infinidat/infi.pyutils | a53dd8971558d87cafcf9c17a95959e74bf6c5c8 | [
"BSD-3-Clause"
] | 5 | 2015-11-08T14:50:42.000Z | 2020-06-23T14:42:33.000Z | tests/test__mixin.py | Infinidat/infi.pyutils | a53dd8971558d87cafcf9c17a95959e74bf6c5c8 | [
"BSD-3-Clause"
] | 4 | 2015-02-22T09:06:59.000Z | 2022-02-12T20:30:55.000Z | from infi.pyutils.mixin import *
def test_install_mixin__simple():
class A(object):
pass
class B(object):
def foo(self):
self.a = 1
a = A()
install_mixin(a, B)
a.foo()
assert a.a == 1
def test_install_mixin__multiple_inheritence1():
class A(object):
pass
class B(object):
pass
class C(object):
pass
class D(A, B, C):
pass
class E(object):
def foo(self):
self.a = 1
d = D()
install_mixin(d, E)
d.foo()
assert d.a == 1
def test_install_mixin__multiple_inheritence2():
class A(object):
pass
class B(object):
pass
class C(object):
pass
class D(A, B, C):
pass
class E(object):
def foo(self):
self.a = 1
d = D()
install_mixin(d, E)
assert issubclass(d.__class__, E)
d.foo()
assert d.a == 1
def test_install_mixin__multiple_inheritence3():
class A(object):
pass
class B(object):
pass
class C(object):
pass
class E(object):
def foo(self):
self.a = 2
class D(A, B, C):
class D_E(E):
def foo(self):
self.a = 1
d = D()
install_mixin(d, E)
assert issubclass(d.__class__, E)
assert isinstance(d, E)
assert isinstance(d, D)
install_mixin(d, D.D_E)
assert issubclass(d.__class__, D.D_E)
assert isinstance(d, E)
assert isinstance(d, D)
assert isinstance(d, D.D_E)
d.foo()
assert d.a == 1
| 19.036145 | 48 | 0.527848 | 224 | 1,580 | 3.544643 | 0.125 | 0.13602 | 0.188917 | 0.088161 | 0.819899 | 0.790932 | 0.760705 | 0.668766 | 0.649874 | 0.530227 | 0 | 0.011823 | 0.357595 | 1,580 | 82 | 49 | 19.268293 | 0.770443 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171429 | 1 | 0.128571 | false | 0.171429 | 0.014286 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
858671964d8f6b6ce020619ab1756e74c13ec9fa | 98 | py | Python | app/charges/__init__.py | JONGWE1/BankManagement | 363ecdc950ee9c38538b83cddaf1c1d8bd6322d0 | [
"MIT"
] | 1 | 2019-09-10T15:01:28.000Z | 2019-09-10T15:01:28.000Z | app/charges/__init__.py | JONGWE1/BankManagement | 363ecdc950ee9c38538b83cddaf1c1d8bd6322d0 | [
"MIT"
] | null | null | null | app/charges/__init__.py | JONGWE1/BankManagement | 363ecdc950ee9c38538b83cddaf1c1d8bd6322d0 | [
"MIT"
] | null | null | null | from flask import Blueprint
charges = Blueprint('charges', __name__)#设定蓝本的名称
from . import views | 19.6 | 48 | 0.785714 | 12 | 98 | 6.083333 | 0.666667 | 0.438356 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.132653 | 98 | 5 | 49 | 19.6 | 0.858824 | 0.071429 | 0 | 0 | 0 | 0 | 0.076923 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
859dee84cedc826aaa7df5007cf24cff131bd381 | 9,416 | py | Python | app/test/unittest/test_log.py | michalkoziara/IoT-RESTful-Webservice | ecb0f3e09cded3190f3646e5cd6c913056d94981 | [
"bzip2-1.0.6"
] | 2 | 2021-09-24T02:45:32.000Z | 2021-11-15T09:44:44.000Z | app/test/unittest/test_log.py | PKramek/IoT-RESTful-Webservice-1 | ecb0f3e09cded3190f3646e5cd6c913056d94981 | [
"bzip2-1.0.6"
] | null | null | null | app/test/unittest/test_log.py | PKramek/IoT-RESTful-Webservice-1 | ecb0f3e09cded3190f3646e5cd6c913056d94981 | [
"bzip2-1.0.6"
] | 1 | 2021-09-11T11:47:32.000Z | 2021-09-11T11:47:32.000Z | from unittest.mock import patch
import pytest
from app.main.repository.device_group_repository import DeviceGroupRepository
from app.main.repository.log_repository import LogRepository
from app.main.service.log_service import LogService
from app.main.util.constants import Constants
def test_log_exception_should_log_data_when_valid_product_key_and_data(
get_log_default_values,
get_device_group_default_values,
create_device_group):
log_service_instance = LogService.get_instance()
test_product_key = 'test product key'
device_group_values = get_device_group_default_values()
device_group_values['product_key'] = test_product_key
user_device_group = create_device_group(device_group_values)
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate=log_default_values['creation_date'].strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
with patch.object(
DeviceGroupRepository,
'get_device_group_by_product_key'
) as get_device_group_by_product_key_mock:
get_device_group_by_product_key_mock.return_value = user_device_group
with patch.object(LogRepository, 'save') as save_mock:
save_mock.return_value = True
result = log_service_instance.log_exception(log_values, test_product_key)
args = save_mock.call_args_list[0][0]
created_log = args[0]
assert result == Constants.RESPONSE_MESSAGE_CREATED
assert log_default_values['type'] == created_log.type
assert log_default_values['creation_date'] == created_log.creation_date
assert log_default_values['error_message'] == created_log.error_message
assert log_default_values['stack_trace'] == created_log.stack_trace
assert log_default_values['payload'] == created_log.payload
assert log_default_values['time'] == created_log.time
def test_log_exception_should_not_log_data_when_product_key_is_none(
get_log_default_values):
log_service_instance = LogService.get_instance()
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate=log_default_values['creation_date'].strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
result = log_service_instance.log_exception(log_values, None)
assert result == Constants.RESPONSE_MESSAGE_BAD_REQUEST
def test_log_exception_should_not_log_data_when_type_is_invalid(
get_log_default_values):
log_service_instance = LogService.get_instance()
log_default_values = get_log_default_values()
log_values = dict(
type='for sure not type',
creationDate=log_default_values['creation_date'].strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
result = log_service_instance.log_exception(log_values, 'product_key')
assert result == Constants.RESPONSE_MESSAGE_BAD_REQUEST
def test_log_exception_should_not_log_data_when_creation_date_is_none(
get_log_default_values):
log_service_instance = LogService.get_instance()
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate=None,
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
result = log_service_instance.log_exception(log_values, 'product_key')
assert result == Constants.RESPONSE_MESSAGE_BAD_REQUEST
def test_log_exception_should_not_log_data_when_creation_date_is_invalid(
get_log_default_values):
log_service_instance = LogService.get_instance()
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate='badly formatted date',
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
result = log_service_instance.log_exception(log_values, 'product_key')
assert result == Constants.RESPONSE_MESSAGE_ERROR
def test_log_exception_should_log_data_when_invalid_product_key(
get_log_default_values):
log_service_instance = LogService.get_instance()
test_product_key = 'test product key'
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate=log_default_values['creation_date'].strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
with patch.object(
DeviceGroupRepository,
'get_device_group_by_product_key'
) as get_device_group_by_product_key_mock:
get_device_group_by_product_key_mock.return_value = None
result = log_service_instance.log_exception(log_values, test_product_key)
assert result == Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND
def test_log_exception_should_log_data_when_logger_set_off(
get_log_default_values):
log_service_instance = LogService.get_instance()
test_product_key = 'test product key'
log_default_values = get_log_default_values()
log_values = dict(
type=log_default_values['type'],
creationDate=log_default_values['creation_date'].strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
errorMessage=log_default_values['error_message'],
stackTrace=log_default_values['stack_trace'],
payload=log_default_values['payload'],
time=log_default_values['time']
)
with patch.object(Constants, 'LOGGER_LEVEL_OFF', 'ALL'):
result = log_service_instance.log_exception(log_values, test_product_key)
assert result == Constants.RESPONSE_MESSAGE_LOGGER_LEVEL_OFF
def test_get_log_values_for_device_group_should_return_log_values_when_valid_product_key(
get_device_group_default_values,
create_device_group,
create_log):
log_service_instance = LogService.get_instance()
test_product_key = 'test product key'
device_group_values = get_device_group_default_values()
device_group_values['product_key'] = test_product_key
user_device_group = create_device_group(device_group_values)
log = create_log()
with patch.object(
DeviceGroupRepository,
'get_device_group_by_admin_id_and_product_key'
) as get_device_group_by_admin_id_and_product_key_mock:
get_device_group_by_admin_id_and_product_key_mock.return_value = user_device_group
with patch.object(LogRepository, 'get_logs_by_device_group_id') as get_logs_by_device_group_id_mock:
get_logs_by_device_group_id_mock.return_value = [log]
result, result_values = log_service_instance.get_log_values_for_device_group(
test_product_key,
'admin_id'
)
assert result == Constants.RESPONSE_MESSAGE_OK
assert result_values
assert result_values[0]['type'] == log.type
assert result_values[0]['errorMessage'] == log.error_message
assert result_values[0]['stackTrace'] == log.stack_trace
assert result_values[0]['payload'] == log.payload
assert result_values[0]['time'] == log.time
assert result_values[0]['creationDate'] == log.creation_date.strftime('%Y-%m-%dT%H:%M:%S.%fZ')
@pytest.mark.parametrize("product_key, admin_id", [
("test product key", None),
(None, "admin id")])
def test_get_log_values_for_device_group_should_return_error_message_when_no_parameter(product_key, admin_id):
log_service_instance = LogService.get_instance()
result, result_values = log_service_instance.get_log_values_for_device_group(product_key, admin_id)
assert result == Constants.RESPONSE_MESSAGE_BAD_REQUEST
assert result_values is None
def test_get_log_values_for_device_group_should_not_return_values_when_no_device_group():
log_service_instance = LogService.get_instance()
test_product_key = 'test product key'
with patch.object(
DeviceGroupRepository,
'get_device_group_by_admin_id_and_product_key'
) as get_device_group_by_admin_id_and_product_key_mock:
get_device_group_by_admin_id_and_product_key_mock.return_value = None
result, result_values = log_service_instance.get_log_values_for_device_group(
test_product_key,
'admin_id'
)
assert result == Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND
assert result_values is None
if __name__ == '__main__':
pytest.main(['app/unittest/{}.py'.format(__file__)])
| 37.217391 | 110 | 0.742778 | 1,233 | 9,416 | 5.154096 | 0.080292 | 0.143194 | 0.166168 | 0.041857 | 0.815736 | 0.789614 | 0.779072 | 0.768057 | 0.738946 | 0.712667 | 0 | 0.001153 | 0.170667 | 9,416 | 252 | 111 | 37.365079 | 0.812652 | 0 | 0 | 0.634409 | 0 | 0 | 0.108326 | 0.032179 | 0 | 0 | 0 | 0 | 0.134409 | 1 | 0.053763 | false | 0 | 0.032258 | 0 | 0.086022 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
85a43ee28fee3793eb9def8112f172e95cee4118 | 144 | py | Python | packages/conan/recipes/pyside/test_package/test.py | boberfly/aswf-docker | 96fa383baa4584b0d389df4759b7cdb832a57873 | [
"Apache-2.0"
] | 3 | 2019-07-02T20:05:35.000Z | 2021-09-14T17:35:25.000Z | packages/conan/recipes/pyside/test_package/test.py | boberfly/aswf-docker | 96fa383baa4584b0d389df4759b7cdb832a57873 | [
"Apache-2.0"
] | null | null | null | packages/conan/recipes/pyside/test_package/test.py | boberfly/aswf-docker | 96fa383baa4584b0d389df4759b7cdb832a57873 | [
"Apache-2.0"
] | null | null | null | try:
from PySide2.QtWidgets import QApplication
except ImportError:
import sys
import pprint
pprint.pprint(sys.path)
raise
| 16 | 46 | 0.715278 | 17 | 144 | 6.058824 | 0.705882 | 0.23301 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009091 | 0.236111 | 144 | 8 | 47 | 18 | 0.927273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.571429 | 0 | 0.571429 | 0.285714 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
85abfdff9fc1c0af1a8e9ea538cf7b689260f044 | 142 | py | Python | backend/apps/project_manager/admin.py | HerlanAssis/simple-project-manager | 800c833ec0cbeba848264753d79c5ecedc54cc39 | [
"MIT"
] | 1 | 2019-06-14T20:34:19.000Z | 2019-06-14T20:34:19.000Z | backend/apps/project_manager/admin.py | HerlanAssis/simple-project-manager | 800c833ec0cbeba848264753d79c5ecedc54cc39 | [
"MIT"
] | 3 | 2020-02-11T23:42:20.000Z | 2020-06-25T17:35:48.000Z | backend/apps/project_manager/admin.py | HerlanAssis/simple-project-manager | 800c833ec0cbeba848264753d79c5ecedc54cc39 | [
"MIT"
] | null | null | null | from django.contrib import admin
# from .models import Category, Ingredient
# admin.site.register(Category)
# admin.site.register(Ingredient) | 28.4 | 42 | 0.802817 | 18 | 142 | 6.333333 | 0.555556 | 0.157895 | 0.298246 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098592 | 142 | 5 | 43 | 28.4 | 0.890625 | 0.71831 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a42888fc98a09b596899bf5cc82ca084cf94eada | 30 | py | Python | skyrouter/__init__.py | tombulled/sky-hub | 49b3357ccf43b11b015958cf33bae6b40f06f9d5 | [
"MIT"
] | null | null | null | skyrouter/__init__.py | tombulled/sky-hub | 49b3357ccf43b11b015958cf33bae6b40f06f9d5 | [
"MIT"
] | null | null | null | skyrouter/__init__.py | tombulled/sky-hub | 49b3357ccf43b11b015958cf33bae6b40f06f9d5 | [
"MIT"
] | null | null | null | from .client import SkyRouter
| 15 | 29 | 0.833333 | 4 | 30 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 30 | 1 | 30 | 30 | 0.961538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a46b2fe765af3c8ab5ff66c441d4b19ddd58388a | 26 | py | Python | pytope/zonotope/__init__.py | gleasonj/pytope | 32604a532b8e9277d68a65b0433083f2131853e6 | [
"MIT"
] | null | null | null | pytope/zonotope/__init__.py | gleasonj/pytope | 32604a532b8e9277d68a65b0433083f2131853e6 | [
"MIT"
] | null | null | null | pytope/zonotope/__init__.py | gleasonj/pytope | 32604a532b8e9277d68a65b0433083f2131853e6 | [
"MIT"
] | null | null | null | from .base import Zonotope | 26 | 26 | 0.846154 | 4 | 26 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 26 | 1 | 26 | 26 | 0.956522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a48577775ee4ab1f6816bc3f9ee9273e1b2bc478 | 194 | py | Python | bitmovin_api_sdk/encoding/encodings/keyframes/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 11 | 2019-07-03T10:41:16.000Z | 2022-02-25T21:48:06.000Z | bitmovin_api_sdk/encoding/encodings/keyframes/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 8 | 2019-11-23T00:01:25.000Z | 2021-04-29T12:30:31.000Z | bitmovin_api_sdk/encoding/encodings/keyframes/__init__.py | jaythecaesarean/bitmovin-api-sdk-python | 48166511fcb9082041c552ace55a9b66cc59b794 | [
"MIT"
] | 13 | 2020-01-02T14:58:18.000Z | 2022-03-26T12:10:30.000Z | from bitmovin_api_sdk.encoding.encodings.keyframes.keyframes_api import KeyframesApi
from bitmovin_api_sdk.encoding.encodings.keyframes.keyframe_list_query_params import KeyframeListQueryParams
| 64.666667 | 108 | 0.917526 | 24 | 194 | 7.083333 | 0.583333 | 0.141176 | 0.176471 | 0.211765 | 0.517647 | 0.517647 | 0.517647 | 0 | 0 | 0 | 0 | 0 | 0.041237 | 194 | 2 | 109 | 97 | 0.913978 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f11fdd24d8cb8acbc659b55bcbec3c59406f8b75 | 11,749 | py | Python | src/DataJoin/common/data_join_service_pb2_grpc.py | huangwei19/9nfl | 4d7cf8a00eaddac45cedfc3df7a6a65ba70d8e55 | [
"Apache-2.0"
] | 103 | 2020-09-07T09:05:30.000Z | 2022-03-10T11:13:43.000Z | src/DataJoin/common/data_join_service_pb2_grpc.py | dubaokun/9nfl | cc82255a1c25155c825bf8eded9074dd118dbaf5 | [
"Apache-2.0"
] | 12 | 2020-09-07T07:52:52.000Z | 2020-11-27T07:43:51.000Z | src/DataJoin/common/data_join_service_pb2_grpc.py | dubaokun/9nfl | cc82255a1c25155c825bf8eded9074dd118dbaf5 | [
"Apache-2.0"
] | 29 | 2020-09-07T09:15:23.000Z | 2022-02-22T04:00:16.000Z | # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
import grpc
from DataJoin.common import common_pb2 as DataJoin_dot_common_dot_common__pb2
from DataJoin.common import data_join_service_pb2 as DataJoin_dot_common_dot_data__join__service__pb2
from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
class DataJoinMasterServiceStub(object):
# missing associated documentation comment in .proto file
pass
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.GetDataSource = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/GetDataSource',
request_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.DataSource.FromString,
)
self.GetDataSourceStatus = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/GetDataSourceStatus',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceStatus.FromString,
)
self.AbortDataSource = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/AbortDataSource',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.Status.FromString,
)
self.RequestJoinPartition = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/RequestJoinPartition',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataResponse.FromString,
)
self.FinishJoinPartition = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/FinishJoinPartition',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.Status.FromString,
)
self.QueryRawDataManifest = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/QueryRawDataManifest',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataManifest.FromString,
)
self.FinishRawData = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/FinishRawData',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.Status.FromString,
)
self.AddRawData = channel.unary_unary(
'/DataJoin.common.DataJoinMasterService/AddRawData',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.Status.FromString,
)
class DataJoinMasterServiceServicer(object):
# missing associated documentation comment in .proto file
pass
def GetDataSource(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetDataSourceStatus(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AbortDataSource(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def RequestJoinPartition(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def FinishJoinPartition(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def QueryRawDataManifest(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def FinishRawData(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AddRawData(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_DataJoinMasterServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'GetDataSource': grpc.unary_unary_rpc_method_handler(
servicer.GetDataSource,
request_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.DataSource.SerializeToString,
),
'GetDataSourceStatus': grpc.unary_unary_rpc_method_handler(
servicer.GetDataSourceStatus,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceRequest.FromString,
response_serializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceStatus.SerializeToString,
),
'AbortDataSource': grpc.unary_unary_rpc_method_handler(
servicer.AbortDataSource,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.DataSourceRequest.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.Status.SerializeToString,
),
'RequestJoinPartition': grpc.unary_unary_rpc_method_handler(
servicer.RequestJoinPartition,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.FromString,
response_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataResponse.SerializeToString,
),
'FinishJoinPartition': grpc.unary_unary_rpc_method_handler(
servicer.FinishJoinPartition,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.Status.SerializeToString,
),
'QueryRawDataManifest': grpc.unary_unary_rpc_method_handler(
servicer.QueryRawDataManifest,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.FromString,
response_serializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataManifest.SerializeToString,
),
'FinishRawData': grpc.unary_unary_rpc_method_handler(
servicer.FinishRawData,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.Status.SerializeToString,
),
'AddRawData': grpc.unary_unary_rpc_method_handler(
servicer.AddRawData,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.RawDataRequest.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.Status.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'DataJoin.common.DataJoinMasterService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
class DataJoinServiceStub(object):
# missing associated documentation comment in .proto file
pass
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.StartPartition = channel.unary_unary(
'/DataJoin.common.DataJoinService/StartPartition',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.StartPartitionRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.StartPartitionResponse.FromString,
)
self.SyncPartition = channel.unary_unary(
'/DataJoin.common.DataJoinService/SyncPartition',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.SyncPartitionRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_common__pb2.Status.FromString,
)
self.FinishPartition = channel.unary_unary(
'/DataJoin.common.DataJoinService/FinishPartition',
request_serializer=DataJoin_dot_common_dot_data__join__service__pb2.FinishPartitionRequest.SerializeToString,
response_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.FinishPartitionResponse.FromString,
)
class DataJoinServiceServicer(object):
# missing associated documentation comment in .proto file
pass
def StartPartition(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def SyncPartition(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def FinishPartition(self, request, context):
# missing associated documentation comment in .proto file
pass
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_DataJoinServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'StartPartition': grpc.unary_unary_rpc_method_handler(
servicer.StartPartition,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.StartPartitionRequest.FromString,
response_serializer=DataJoin_dot_common_dot_data__join__service__pb2.StartPartitionResponse.SerializeToString,
),
'SyncPartition': grpc.unary_unary_rpc_method_handler(
servicer.SyncPartition,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.SyncPartitionRequest.FromString,
response_serializer=DataJoin_dot_common_dot_common__pb2.Status.SerializeToString,
),
'FinishPartition': grpc.unary_unary_rpc_method_handler(
servicer.FinishPartition,
request_deserializer=DataJoin_dot_common_dot_data__join__service__pb2.FinishPartitionRequest.FromString,
response_serializer=DataJoin_dot_common_dot_data__join__service__pb2.FinishPartitionResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'DataJoin.common.DataJoinService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
| 48.151639 | 121 | 0.79011 | 1,208 | 11,749 | 7.212748 | 0.078642 | 0.058878 | 0.085849 | 0.100999 | 0.836107 | 0.836107 | 0.764834 | 0.698037 | 0.693676 | 0.64421 | 0 | 0.004979 | 0.145204 | 11,749 | 243 | 122 | 48.349794 | 0.862591 | 0.085284 | 0 | 0.47644 | 1 | 0 | 0.124077 | 0.060776 | 0 | 0 | 0 | 0 | 0 | 1 | 0.078534 | false | 0.078534 | 0.020942 | 0 | 0.120419 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
f1453df39630ccbac737fb26c58220cb233970e4 | 32,045 | py | Python | code/inception_v1.py | statisticszhang/Image-classification-caffe-model | 33084ca0841e768dae84db582e15bb29ffeeaaec | [
"MIT"
] | 1 | 2020-06-03T12:53:43.000Z | 2020-06-03T12:53:43.000Z | code/inception_v1.py | statisticszhang/Image-classification-caffe-model | 33084ca0841e768dae84db582e15bb29ffeeaaec | [
"MIT"
] | null | null | null | code/inception_v1.py | statisticszhang/Image-classification-caffe-model | 33084ca0841e768dae84db582e15bb29ffeeaaec | [
"MIT"
] | null | null | null | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, fc_param, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=fc_param['num_output'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type=fc_param['weight_type'], std=fc_param['weight_std']),
bias_filler=dict(type='constant', value=fc_param['bias_value']))
relu = L.ReLU(fc, in_place=True)
drop = L.Dropout(fc, in_place=True,
dropout_param=dict(dropout_ratio=dropout_ratio))
return fc, relu, drop
def factorization_conv_bn_scale_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', std=1),
bias_filler=dict(type='constant', value=0.2))
conv_bn = L.BatchNorm(conv, use_global_stats=False, in_place=True)
conv_scale = L.Scale(conv, scale_param=dict(bias_term=True), in_place=True)
conv_relu = L.ReLU(conv, in_place=True)
return conv, conv_bn, conv_scale, conv_relu
def inception(bottom, conv_output):
conv_1x1 = L.Convolution(bottom, kernel_size=1, num_output=conv_output['conv_1x1'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
conv_1x1_relu = L.ReLU(conv_1x1, in_place=True)
conv_3x3_reduce = L.Convolution(bottom, kernel_size=1, num_output=conv_output['conv_3x3_reduce'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
conv_3x3_reduce_relu = L.ReLU(conv_3x3_reduce, in_place=True)
conv_3x3 = L.Convolution(conv_3x3_reduce, kernel_size=3, num_output=conv_output['conv_3x3'], pad=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
conv_3x3_relu = L.ReLU(conv_3x3, in_place=True)
conv_5x5_reduce = L.Convolution(bottom, kernel_size=1, num_output=conv_output['conv_5x5_reduce'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
conv_5x5_reduce_relu = L.ReLU(conv_5x5_reduce, in_place=True)
conv_5x5 = L.Convolution(conv_5x5_reduce, kernel_size=5, num_output=conv_output['conv_5x5'], pad=2,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
conv_5x5_relu = L.ReLU(conv_5x5, in_place=True)
pool = L.Pooling(bottom, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
pool_proj = L.Convolution(pool, kernel_size=1, num_output=conv_output['pool_proj'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0.2))
pool_proj_relu = L.ReLU(pool_proj, in_place=True)
concat = L.Concat(conv_1x1, conv_3x3, conv_5x5, pool_proj)
return conv_1x1, conv_1x1_relu, conv_3x3_reduce, conv_3x3_reduce_relu, conv_3x3, conv_3x3_relu, conv_5x5_reduce, \
conv_5x5_reduce_relu, conv_5x5, conv_5x5_relu, pool, pool_proj, pool_proj_relu, concat
def inception_bn(bottom, conv_output):
conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu = \
factorization_conv_bn_scale_relu(bottom, num_output=conv_output['conv_1x1'], kernel_size=1)
conv_3x3_reduce, conv_3x3_reduce_bn, conv_3x3_reduce_scale, conv_3x3_reduce_relu = \
factorization_conv_bn_scale_relu(bottom, num_output=conv_output['conv_3x3_reduce'], kernel_size=1)
conv_3x3, conv_3x3_bn, conv_3x3_scale, conv_3x3_relu = \
factorization_conv_bn_scale_relu(conv_3x3_reduce, num_output=conv_output['conv_3x3'], kernel_size=3, pad=1)
conv_5x5_reduce, conv_5x5_reduce_bn, conv_5x5_reduce_scale, conv_5x5_reduce_relu = \
factorization_conv_bn_scale_relu(bottom, num_output=conv_output['conv_5x5_reduce'], kernel_size=1)
conv_5x5, conv_5x5_bn, conv_5x5_scale, conv_5x5_relu = \
factorization_conv_bn_scale_relu(conv_5x5_reduce, num_output=conv_output['conv_5x5'], kernel_size=5, pad=2)
pool = L.Pooling(bottom, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
pool_proj, pool_proj_bn, pool_proj_scale, pool_proj_relu = \
factorization_conv_bn_scale_relu(pool, num_output=conv_output['pool_proj'], kernel_size=1)
concat = L.Concat(conv_1x1, conv_3x3, conv_5x5, pool_proj)
return conv_1x1, conv_1x1_bn, conv_1x1_scale, conv_1x1_relu, conv_3x3_reduce, conv_3x3_reduce_bn, \
conv_3x3_reduce_scale, conv_3x3_reduce_relu, conv_3x3, conv_3x3_bn, conv_3x3_scale, conv_3x3_relu, \
conv_5x5_reduce, conv_5x5_reduce_bn, conv_5x5_reduce_scale, conv_5x5_reduce_relu, conv_5x5, conv_5x5_bn, \
conv_5x5_scale, conv_5x5_relu, pool, pool_proj, pool_proj_bn, pool_proj_scale, pool_proj_relu, concat
class InceptionV1(object):
def __init__(self, lmdb_train, lmdb_test, num_output):
self.train_data = lmdb_train
self.test_data = lmdb_test
self.classifier_num = num_output
def inception_v1_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=mirror))
n.conv1_7x7_s2 = L.Convolution(n.data, num_output=64, kernel_size=7, stride=2, pad=3,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv1_relu_7x7 = L.ReLU(n.conv1_7x7_s2, in_place=True)
n.pool1_3x3_s2 = L.Pooling(n.conv1_7x7_s2, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
n.pool1_norm1 = L.LRN(n.pool1_3x3_s2, local_size=5, alpha=1e-4, beta=0.75)
n.conv2_3x3_reduce = L.Convolution(n.pool1_norm1, kernel_size=1, num_output=64, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv2_relu_3x3_reduce = L.ReLU(n.conv2_3x3_reduce, in_place=True)
n.conv2_3x3 = L.Convolution(n.conv2_3x3_reduce, num_output=192, kernel_size=3, stride=1, pad=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv2_relu_3x3 = L.ReLU(n.conv2_3x3, in_place=True)
n.conv2_norm2 = L.LRN(n.conv2_3x3, local_size=5, alpha=1e-4, beta=0.75)
n.pool2_3x3_s2 = L.Pooling(n.conv2_norm2, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
n.inception_3a_1x1, n.inception_3a_relu_1x1, n.inception_3a_3x3_reduce, n.inception_3a_relu_3x3_reduce, \
n.inception_3a_3x3, n.inception_3a_relu_3x3, n.inception_3a_5x5_reduce, n.inception_3a_relu_5x5_reduce, \
n.inception_3a_5x5, n.inception_3a_relu_5x5, n.inception_3a_pool, n.inception_3a_pool_proj, \
n.inception_3a_relu_pool_proj, n.inception_3a_output = \
inception(n.pool2_3x3_s2, dict(conv_1x1=64, conv_3x3_reduce=96, conv_3x3=128, conv_5x5_reduce=16,
conv_5x5=32, pool_proj=32))
n.inception_3b_1x1, n.inception_3b_relu_1x1, n.inception_3b_3x3_reduce, n.inception_3b_relu_3x3_reduce, \
n.inception_3b_3x3, n.inception_3b_relu_3x3, n.inception_3b_5x5_reduce, n.inception_3b_relu_5x5_reduce, \
n.inception_3b_5x5, n.inception_3b_relu_5x5, n.inception_3b_pool, n.inception_3b_pool_proj, \
n.inception_3b_relu_pool_proj, n.inception_3b_output = \
inception(n.inception_3a_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=192, conv_5x5_reduce=32,
conv_5x5=96, pool_proj=64))
n.pool3_3x3_s2 = L.Pooling(n.inception_3b_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_4a_1x1, n.inception_4a_relu_1x1, n.inception_4a_3x3_reduce, n.inception_4a_relu_3x3_reduce, \
n.inception_4a_3x3, n.inception_4a_relu_3x3, n.inception_4a_5x5_reduce, n.inception_4a_relu_5x5_reduce, \
n.inception_4a_5x5, n.inception_4a_relu_5x5, n.inception_4a_pool, n.inception_4a_pool_proj, \
n.inception_4a_relu_pool_proj, n.inception_4a_output = \
inception(n.pool3_3x3_s2, dict(conv_1x1=192, conv_3x3_reduce=96, conv_3x3=208, conv_5x5_reduce=16,
conv_5x5=48, pool_proj=64))
# loss 1
n.loss1_ave_pool = L.Pooling(n.inception_4a_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss1_conv = L.Convolution(n.loss1_ave_pool, num_output=128, kernel_size=1, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.loss1_relu_conv = L.ReLU(n.loss1_conv, in_place=True)
n.loss1_fc, n.loss1_relu_fc, n.loss1_drop_fc = \
fc_relu_drop(n.loss1_conv, dict(num_output=1024, weight_type='xavier', weight_std=1, bias_type='constant',
bias_value=0.2), dropout_ratio=0.7)
n.loss1_classifier = L.InnerProduct(n.loss1_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss1_loss = L.SoftmaxWithLoss(n.loss1_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss1_accuracy_top1 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1))
n.loss1_accuracy_top5 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4b_1x1, n.inception_4b_relu_1x1, n.inception_4b_3x3_reduce, n.inception_4b_relu_3x3_reduce, \
n.inception_4b_3x3, n.inception_4b_relu_3x3, n.inception_4b_5x5_reduce, n.inception_4b_relu_5x5_reduce, \
n.inception_4b_5x5, n.inception_4b_relu_5x5, n.inception_4b_pool, n.inception_4b_pool_proj, \
n.inception_4b_relu_pool_proj, n.inception_4b_output = \
inception(n.inception_4a_output, dict(conv_1x1=160, conv_3x3_reduce=112, conv_3x3=224, conv_5x5_reduce=24,
conv_5x5=64, pool_proj=64))
n.inception_4c_1x1, n.inception_4c_relu_1x1, n.inception_4c_3x3_reduce, n.inception_4c_relu_3x3_reduce, \
n.inception_4c_3x3, n.inception_4c_relu_3x3, n.inception_4c_5x5_reduce, n.inception_4c_relu_5x5_reduce, \
n.inception_4c_5x5, n.inception_4c_relu_5x5, n.inception_4c_pool, n.inception_4c_pool_proj, \
n.inception_4c_relu_pool_proj, n.inception_4c_output = \
inception(n.inception_4b_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=256, conv_5x5_reduce=24,
conv_5x5=64, pool_proj=64))
n.inception_4d_1x1, n.inception_4d_relu_1x1, n.inception_4d_3x3_reduce, n.inception_4d_relu_3x3_reduce, \
n.inception_4d_3x3, n.inception_4d_relu_3x3, n.inception_4d_5x5_reduce, n.inception_4d_relu_5x5_reduce, \
n.inception_4d_5x5, n.inception_4d_relu_5x5, n.inception_4d_pool, n.inception_4d_pool_proj, \
n.inception_4d_relu_pool_proj, n.inception_4d_output = \
inception(n.inception_4c_output, dict(conv_1x1=112, conv_3x3_reduce=144, conv_3x3=288, conv_5x5_reduce=32,
conv_5x5=64, pool_proj=64))
# loss 2
n.loss2_ave_pool = L.Pooling(n.inception_4d_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss2_conv = L.Convolution(n.loss2_ave_pool, num_output=128, kernel_size=1, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.loss2_relu_conv = L.ReLU(n.loss2_conv, in_place=True)
n.loss2_fc, n.loss2_relu_fc, n.loss2_drop_fc = \
fc_relu_drop(n.loss2_conv, dict(num_output=1024, weight_type='xavier', weight_std=1, bias_type='constant',
bias_value=0.2), dropout_ratio=0.7)
n.loss2_classifier = L.InnerProduct(n.loss2_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss2_loss = L.SoftmaxWithLoss(n.loss2_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss2_accuracy_top1 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1))
n.loss2_accuracy_top5 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4e_1x1, n.inception_4e_relu_1x1, n.inception_4e_3x3_reduce, n.inception_4e_relu_3x3_reduce, \
n.inception_4e_3x3, n.inception_4e_relu_3x3, n.inception_4e_5x5_reduce, n.inception_4e_relu_5x5_reduce, \
n.inception_4e_5x5, n.inception_4e_relu_5x5, n.inception_4e_pool, n.inception_4e_pool_proj, \
n.inception_4e_relu_pool_proj, n.inception_4e_output = \
inception(n.inception_4d_output, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320, conv_5x5_reduce=32,
conv_5x5=128, pool_proj=128))
n.pool4_3x3_s2 = L.Pooling(n.inception_4e_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_5a_1x1, n.inception_5a_relu_1x1, n.inception_5a_3x3_reduce, n.inception_5a_relu_3x3_reduce, \
n.inception_5a_3x3, n.inception_5a_relu_3x3, n.inception_5a_5x5_reduce, n.inception_5a_relu_5x5_reduce, \
n.inception_5a_5x5, n.inception_5a_relu_5x5, n.inception_5a_pool, n.inception_5a_pool_proj, \
n.inception_5a_relu_pool_proj, n.inception_5a_output = \
inception(n.pool4_3x3_s2, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320, conv_5x5_reduce=32,
conv_5x5=128, pool_proj=128))
n.inception_5b_1x1, n.inception_5b_relu_1x1, n.inception_5b_3x3_reduce, n.inception_5b_relu_3x3_reduce, \
n.inception_5b_3x3, n.inception_5b_relu_3x3, n.inception_5b_5x5_reduce, n.inception_5b_relu_5x5_reduce, \
n.inception_5b_5x5, n.inception_5b_relu_5x5, n.inception_5b_pool, n.inception_5b_pool_proj, \
n.inception_5b_relu_pool_proj, n.inception_5b_output = \
inception(n.inception_5a_output, dict(conv_1x1=384, conv_3x3_reduce=192, conv_3x3=384, conv_5x5_reduce=48,
conv_5x5=128, pool_proj=128))
n.pool5_7x7_s1 = L.Pooling(n.inception_5b_output, kernel_size=7, stride=1, pool=P.Pooling.AVE)
n.pool5_drop_7x7_s1 = L.Dropout(n.pool5_7x7_s1, in_place=True,
dropout_param=dict(dropout_ratio=0.4))
n.loss3_classifier = L.InnerProduct(n.pool5_7x7_s1, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss3_loss = L.SoftmaxWithLoss(n.loss3_classifier, n.label, loss_weight=1)
if phase == 'TRAIN':
pass
else:
n.loss3_accuracy_top1 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1))
n.loss3_accuracy_top5 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
def inception_bn_proto(self, batch_size, phase='TRAIN'): # inception_bn
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=mirror))
n.conv1_7x7_s2, n.conv1_7x7_s2_bn, n.conv1_7x7_s2_scale, n.conv1_7x7_relu = \
factorization_conv_bn_scale_relu(n.data, num_output=64, kernel_size=7, stride=2, pad=3)
n.pool1_3x3_s2 = L.Pooling(n.conv1_7x7_s2, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.conv2_3x3_reduce, n.conv2_3x3_reduce_bn, n.conv2_3x3_reduce_scale, n.conv2_3x3_reduce_relu = \
factorization_conv_bn_scale_relu(n.pool1_3x3_s2, num_output=64, kernel_size=1)
n.conv2_3x3, n.conv2_3x3_bn, n.conv2_3x3_scale, n.conv2_3x3_relu = \
factorization_conv_bn_scale_relu(n.conv2_3x3_reduce, num_output=192, kernel_size=3, pad=1)
n.pool2_3x3_s2 = L.Pooling(n.conv2_3x3, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_3a_1x1, n.inception_3a_1x1_bn, n.inception_3a_1x1_scale, n.inception_3a_relu_1x1, \
n.inception_3a_3x3_reduce, n.inception_3a_3x3_reduce_bn, n.inception_3a_3x3_reduce_scale, \
n.inception_3a_relu_3x3_reduce, n.inception_3a_3x3, n.inception_3a_3x3_bn, n.inception_3a_3x3_scale, \
n.inception_3a_relu_3x3, n.inception_3a_5x5_reduce, n.inception_3a_5x5_reduce_bn, \
n.inception_3a_5x5_reduce_scale, n.inception_3a_relu_5x5_reduce, n.inception_3a_5x5, n.inception_3a_5x5_bn, \
n.inception_3a_5x5_scale, n.inception_3a_relu_5x5, n.inception_3a_pool, n.inception_3a_pool_proj, \
n.inception_3a_pool_proj_bn, n.inception_3a_pool_proj_scale, n.inception_3a_relu_pool_proj, \
n.inception_3a_output = \
inception_bn(n.pool2_3x3_s2, dict(conv_1x1=64, conv_3x3_reduce=96, conv_3x3=128, conv_5x5_reduce=16,
conv_5x5=32, pool_proj=32))
n.inception_3b_1x1, n.inception_3b_1x1_bn, n.inception_3b_1x1_scale, n.inception_3b_relu_1x1, \
n.inception_3b_3x3_reduce, n.inception_3b_3x3_reduce_bn, n.inception_3b_3x3_reduce_scale, \
n.inception_3b_relu_3x3_reduce, n.inception_3b_3x3, n.inception_3b_3x3_bn, n.inception_3b_3x3_scale, \
n.inception_3b_relu_3x3, n.inception_3b_5x5_reduce, n.inception_3b_5x5_reduce_bn, \
n.inception_3b_5x5_reduce_scale, n.inception_3b_relu_5x5_reduce, n.inception_3b_5x5, n.inception_3b_5x5_bn, \
n.inception_3b_5x5_scale, n.inception_3b_relu_5x5, n.inception_3b_pool, n.inception_3b_pool_proj, \
n.inception_3b_pool_proj_bn, n.inception_3b_pool_proj_scale, n.inception_3b_relu_pool_proj, \
n.inception_3b_output = \
inception_bn(n.inception_3a_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=192,
conv_5x5_reduce=32, conv_5x5=96, pool_proj=64))
n.pool3_3x3_s2 = L.Pooling(n.inception_3b_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_4a_1x1, n.inception_4a_1x1_bn, n.inception_4a_1x1_scale, n.inception_4a_relu_1x1, \
n.inception_4a_3x3_reduce, n.inception_4a_3x3_reduce_bn, n.inception_4a_3x3_reduce_scale, \
n.inception_4a_relu_3x3_reduce, n.inception_4a_3x3, n.inception_4a_3x3_bn, n.inception_4a_3x3_scale, \
n.inception_4a_relu_3x3, n.inception_4a_5x5_reduce, n.inception_4a_5x5_reduce_bn, \
n.inception_4a_5x5_reduce_scale, n.inception_4a_relu_5x5_reduce, n.inception_4a_5x5, n.inception_4a_5x5_bn, \
n.inception_4a_5x5_scale, n.inception_4a_relu_5x5, n.inception_4a_pool, n.inception_4a_pool_proj, \
n.inception_4a_pool_proj_bn, n.inception_4a_pool_proj_scale, n.inception_4a_relu_pool_proj, \
n.inception_4a_output = \
inception_bn(n.pool3_3x3_s2, dict(conv_1x1=192, conv_3x3_reduce=96, conv_3x3=208, conv_5x5_reduce=16,
conv_5x5=48, pool_proj=64))
# loss 1
n.loss1_ave_pool = L.Pooling(n.inception_4a_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss1_conv, n.loss1_conv_bn, n.loss1_conv_scale, n.loss1_relu_conv = \
factorization_conv_bn_scale_relu(n.loss1_ave_pool, num_output=128, kernel_size=1)
n.loss1_fc, n.loss1_relu_fc, n.loss1_drop_fc = \
fc_relu_drop(n.loss1_conv, dict(num_output=1024, weight_type='xavier', weight_std=1,
bias_type='constant', bias_value=0.2), dropout_ratio=0.7)
n.loss1_classifier = L.InnerProduct(n.loss1_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss1_loss = L.SoftmaxWithLoss(n.loss1_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss1_accuracy_top1 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1))
n.loss1_accuracy_top5 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4b_1x1, n.inception_4b_1x1_bn, n.inception_4b_1x1_scale, n.inception_4b_relu_1x1, \
n.inception_4b_3x3_reduce, n.inception_4b_3x3_reduce_bn, n.inception_4b_3x3_reduce_scale, \
n.inception_4b_relu_3x3_reduce, n.inception_4b_3x3, n.inception_4b_3x3_bn, n.inception_4b_3x3_scale, \
n.inception_4b_relu_3x3, n.inception_4b_5x5_reduce, n.inception_4b_5x5_reduce_bn, \
n.inception_4b_5x5_reduce_scale, n.inception_4b_relu_5x5_reduce, n.inception_4b_5x5, n.inception_4b_5x5_bn, \
n.inception_4b_5x5_scale, n.inception_4b_relu_5x5, n.inception_4b_pool, n.inception_4b_pool_proj, \
n.inception_4b_pool_proj_bn, n.inception_4b_pool_proj_scale, n.inception_4b_relu_pool_proj, \
n.inception_4b_output = \
inception_bn(n.inception_4a_output, dict(conv_1x1=160, conv_3x3_reduce=112, conv_3x3=224,
conv_5x5_reduce=24, conv_5x5=64, pool_proj=64))
n.inception_4c_1x1, n.inception_4c_1x1_bn, n.inception_4c_1x1_scale, n.inception_4c_relu_1x1, \
n.inception_4c_3x3_reduce, n.inception_4c_3x3_reduce_bn, n.inception_4c_3x3_reduce_scale, \
n.inception_4c_relu_3x3_reduce, n.inception_4c_3x3, n.inception_4c_3x3_bn, n.inception_4c_3x3_scale, \
n.inception_4c_relu_3x3, n.inception_4c_5x5_reduce, n.inception_4c_5x5_reduce_bn, \
n.inception_4c_5x5_reduce_scale, n.inception_4c_relu_5x5_reduce, n.inception_4c_5x5, n.inception_4c_5x5_bn, \
n.inception_4c_5x5_scale, n.inception_4c_relu_5x5, n.inception_4c_pool, n.inception_4c_pool_proj, \
n.inception_4c_pool_proj_bn, n.inception_4c_pool_proj_scale, n.inception_4c_relu_pool_proj, \
n.inception_4c_output = \
inception_bn(n.inception_4b_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=256,
conv_5x5_reduce=24, conv_5x5=64, pool_proj=64))
n.inception_4d_1x1, n.inception_4d_1x1_bn, n.inception_4d_1x1_scale, n.inception_4d_relu_1x1, \
n.inception_4d_3x3_reduce, n.inception_4d_3x3_reduce_bn, n.inception_4d_3x3_reduce_scale, \
n.inception_4d_relu_3x3_reduce, n.inception_4d_3x3, n.inception_4d_3x3_bn, n.inception_4d_3x3_scale, \
n.inception_4d_relu_3x3, n.inception_4d_5x5_reduce, n.inception_4d_5x5_reduce_bn, \
n.inception_4d_5x5_reduce_scale, n.inception_4d_relu_5x5_reduce, n.inception_4d_5x5, n.inception_4d_5x5_bn, \
n.inception_4d_5x5_scale, n.inception_4d_relu_5x5, n.inception_4d_pool, n.inception_4d_pool_proj, \
n.inception_4d_pool_proj_bn, n.inception_4d_pool_proj_scale, n.inception_4d_relu_pool_proj, \
n.inception_4d_output = \
inception_bn(n.inception_4c_output, dict(conv_1x1=112, conv_3x3_reduce=144, conv_3x3=288,
conv_5x5_reduce=32, conv_5x5=64, pool_proj=64))
# loss 2
n.loss2_ave_pool = L.Pooling(n.inception_4d_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss2_conv, n.loss2_conv_bn, n.loss2_conv_scale, n.loss2_relu_conv = \
factorization_conv_bn_scale_relu(n.loss2_ave_pool, num_output=128, kernel_size=1)
n.loss2_fc, n.loss2_relu_fc, n.loss2_drop_fc = \
fc_relu_drop(n.loss2_conv, dict(num_output=1024, weight_type='xavier', weight_std=1,
bias_type='constant', bias_value=0.2), dropout_ratio=0.7)
n.loss2_classifier = L.InnerProduct(n.loss2_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss2_loss = L.SoftmaxWithLoss(n.loss2_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss2_accuracy_top1 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1))
n.loss2_accuracy_top5 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4e_1x1, n.inception_4e_1x1_bn, n.inception_4e_1x1_scale, n.inception_4e_relu_1x1, \
n.inception_4e_3x3_reduce, n.inception_4e_3x3_reduce_bn, n.inception_4e_3x3_reduce_scale, \
n.inception_4e_relu_3x3_reduce, n.inception_4e_3x3, n.inception_4e_3x3_bn, n.inception_4e_3x3_scale, \
n.inception_4e_relu_3x3, n.inception_4e_5x5_reduce, n.inception_4e_5x5_reduce_bn, \
n.inception_4e_5x5_reduce_scale, n.inception_4e_relu_5x5_reduce, n.inception_4e_5x5, n.inception_4e_5x5_bn, \
n.inception_4e_5x5_scale, n.inception_4e_relu_5x5, n.inception_4e_pool, n.inception_4e_pool_proj, \
n.inception_4e_pool_proj_bn, n.inception_4e_pool_proj_scale, n.inception_4e_relu_pool_proj, \
n.inception_4e_output = \
inception_bn(n.inception_4d_output, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320,
conv_5x5_reduce=32, conv_5x5=128, pool_proj=128))
n.pool4_3x3_s2 = L.Pooling(n.inception_4e_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_5a_1x1, n.inception_5a_1x1_bn, n.inception_5a_1x1_scale, n.inception_5a_relu_1x1, \
n.inception_5a_3x3_reduce, n.inception_5a_3x3_reduce_bn, n.inception_5a_3x3_reduce_scale, \
n.inception_5a_relu_3x3_reduce, n.inception_5a_3x3, n.inception_5a_3x3_bn, n.inception_5a_3x3_scale, \
n.inception_5a_relu_3x3, n.inception_5a_5x5_reduce, n.inception_5a_5x5_reduce_bn, \
n.inception_5a_5x5_reduce_scale, n.inception_5a_relu_5x5_reduce, n.inception_5a_5x5, n.inception_5a_5x5_bn, \
n.inception_5a_5x5_scale, n.inception_5a_relu_5x5, n.inception_5a_pool, n.inception_5a_pool_proj, \
n.inception_5a_pool_proj_bn, n.inception_5a_pool_proj_scale, n.inception_5a_relu_pool_proj, \
n.inception_5a_output = \
inception_bn(n.pool4_3x3_s2, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320,
conv_5x5_reduce=32, conv_5x5=128, pool_proj=128))
n.inception_5b_1x1, n.inception_5b_1x1_bn, n.inception_5b_1x1_scale, n.inception_5b_relu_1x1, \
n.inception_5b_3x3_reduce, n.inception_5b_3x3_reduce_bn, n.inception_5b_3x3_reduce_scale, \
n.inception_5b_relu_3x3_reduce, n.inception_5b_3x3, n.inception_5b_3x3_bn, n.inception_5b_3x3_scale, \
n.inception_5b_relu_3x3, n.inception_5b_5x5_reduce, n.inception_5b_5x5_reduce_bn, \
n.inception_5b_5x5_reduce_scale, n.inception_5b_relu_5x5_reduce, n.inception_5b_5x5, n.inception_5b_5x5_bn, \
n.inception_5b_5x5_scale, n.inception_5b_relu_5x5, n.inception_5b_pool, n.inception_5b_pool_proj, \
n.inception_5b_pool_proj_bn, n.inception_5b_pool_proj_scale, n.inception_5b_relu_pool_proj, \
n.inception_5b_output = \
inception_bn(n.inception_5a_output, dict(conv_1x1=384, conv_3x3_reduce=192, conv_3x3=384,
conv_5x5_reduce=48, conv_5x5=128, pool_proj=128))
n.pool5_7x7_s1 = L.Pooling(n.inception_5b_output, kernel_size=7, stride=1, pool=P.Pooling.AVE)
n.pool5_drop_7x7_s1 = L.Dropout(n.pool5_7x7_s1, in_place=True,
dropout_param=dict(dropout_ratio=0.4))
n.loss3_classifier = L.InnerProduct(n.pool5_7x7_s1, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss3_loss = L.SoftmaxWithLoss(n.loss3_classifier, n.label, loss_weight=1)
if phase == 'TRAIN':
pass
else:
n.loss3_accuracy_top1 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1))
n.loss3_accuracy_top5 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
| 76.662679 | 118 | 0.665658 | 4,895 | 32,045 | 3.925638 | 0.031869 | 0.198793 | 0.05995 | 0.035595 | 0.920899 | 0.839821 | 0.806776 | 0.796732 | 0.781016 | 0.777529 | 0 | 0.088488 | 0.234732 | 32,045 | 417 | 119 | 76.846523 | 0.695103 | 0.001248 | 0 | 0.427441 | 0 | 0 | 0.016657 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.01847 | false | 0.015831 | 0.007916 | 0 | 0.044855 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
74d8cf6f33ccb8ad60019836c6ce0433d2f6674e | 989 | py | Python | python/tests/generated/api/multiline_field/test_string_value.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 17 | 2019-04-15T21:03:37.000Z | 2022-01-24T11:03:34.000Z | python/tests/generated/api/multiline_field/test_string_value.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 20 | 2019-03-13T23:23:40.000Z | 2022-03-29T13:40:57.000Z | python/tests/generated/api/multiline_field/test_string_value.py | eno-lang/enolib | 4175f7c1e8246493b6758c29bddc80d20eaf15f7 | [
"MIT"
] | 4 | 2019-04-15T21:18:03.000Z | 2019-09-21T16:18:10.000Z | import enolib
def test_querying_an_existing_required_string_value_from_a_multline_field_produces_the_expected_result():
input = ("-- multiline_field\n"
"value\n"
"-- multiline_field")
output = enolib.parse(input).field('multiline_field').required_string_value()
expected = ("value")
assert output == expected
def test_querying_an_existing_optional_string_value_from_a_multline_field_produces_the_expected_result():
input = ("-- multiline_field\n"
"value\n"
"-- multiline_field")
output = enolib.parse(input).field('multiline_field').optional_string_value()
expected = ("value")
assert output == expected
def test_querying_a_missing_optional_string_value_from_a_multline_field_produces_the_expected_result():
input = ("-- multiline_field\n"
"-- multiline_field")
output = enolib.parse(input).field('multiline_field').optional_string_value()
assert output == None | 31.903226 | 105 | 0.71183 | 115 | 989 | 5.626087 | 0.234783 | 0.194745 | 0.117465 | 0.074189 | 0.919629 | 0.865533 | 0.865533 | 0.865533 | 0.865533 | 0.865533 | 0 | 0 | 0.184024 | 989 | 31 | 106 | 31.903226 | 0.801735 | 0 | 0 | 0.7 | 0 | 0 | 0.184848 | 0 | 0 | 0 | 0 | 0 | 0.15 | 1 | 0.15 | false | 0 | 0.05 | 0 | 0.2 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
74e51ffb30c374d38c63450a487b5acf81ee3fa0 | 147 | py | Python | models/dmcp/__init__.py | haolibai/dmcp | 7d9e88230850d8094a040d8c4eaf8b0d0393a210 | [
"CC-BY-4.0"
] | 119 | 2020-05-08T01:05:50.000Z | 2022-03-02T07:36:24.000Z | models/dmcp/__init__.py | haolibai/dmcp | 7d9e88230850d8094a040d8c4eaf8b0d0393a210 | [
"CC-BY-4.0"
] | 13 | 2020-05-08T08:57:33.000Z | 2021-09-02T09:14:51.000Z | models/dmcp/__init__.py | haolibai/dmcp | 7d9e88230850d8094a040d8c4eaf8b0d0393a210 | [
"CC-BY-4.0"
] | 23 | 2020-05-08T03:18:24.000Z | 2021-08-28T16:04:31.000Z | # -*- coding:utf-8 -*-
from models.dmcp.dmcp_resnet import dmcp_resnet18, dmcp_resnet50
from models.dmcp.dmcp_mobilenet import dmcp_mobilenet_v2
| 29.4 | 64 | 0.802721 | 22 | 147 | 5.090909 | 0.545455 | 0.178571 | 0.25 | 0.321429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045455 | 0.102041 | 147 | 4 | 65 | 36.75 | 0.80303 | 0.142857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 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 | 6 |
2d16b7f01258524b940e0d0a33fdce79344e4604 | 46 | py | Python | exercises/house/house.py | RJTK/python | f9678d629735f75354bbd543eb7f10220a498dae | [
"MIT"
] | 1 | 2021-05-15T19:59:04.000Z | 2021-05-15T19:59:04.000Z | exercises/house/house.py | RJTK/python | f9678d629735f75354bbd543eb7f10220a498dae | [
"MIT"
] | null | null | null | exercises/house/house.py | RJTK/python | f9678d629735f75354bbd543eb7f10220a498dae | [
"MIT"
] | 2 | 2018-03-03T08:32:12.000Z | 2019-08-22T11:55:53.000Z | def verse():
pass
def rhyme():
pass
| 6.571429 | 12 | 0.521739 | 6 | 46 | 4 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.347826 | 46 | 6 | 13 | 7.666667 | 0.8 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
7455fd03bd5380bc746dccbc502ed94e92c5d1ab | 192 | py | Python | metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/glossary/__init__.py | john-bodley/datahub | 28c008f939f709eb8b401c26a954be529a52752f | [
"Apache-2.0"
] | null | null | null | metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/glossary/__init__.py | john-bodley/datahub | 28c008f939f709eb8b401c26a954be529a52752f | [
"Apache-2.0"
] | 3 | 2022-02-14T13:39:45.000Z | 2022-02-27T17:32:49.000Z | metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/glossary/__init__.py | john-bodley/datahub | 28c008f939f709eb8b401c26a954be529a52752f | [
"Apache-2.0"
] | null | null | null | from .....schema_classes import GlossaryNodeInfoClass
from .....schema_classes import GlossaryTermInfoClass
GlossaryNodeInfo = GlossaryNodeInfoClass
GlossaryTermInfo = GlossaryTermInfoClass
| 27.428571 | 53 | 0.854167 | 14 | 192 | 11.571429 | 0.571429 | 0.123457 | 0.209877 | 0.283951 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 192 | 6 | 54 | 32 | 0.920455 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
745f5b51b645a1da27effd5a9d7a2ee10e496be0 | 25 | py | Python | next/boot/me/__init__.py | 27Saumya/next | 807de4a8e007211bc943545b61d0fdbb8c78e375 | [
"Apache-2.0"
] | 2 | 2022-03-17T07:43:40.000Z | 2022-03-17T08:13:20.000Z | next/boot/me/__init__.py | 27Saumya/next | 807de4a8e007211bc943545b61d0fdbb8c78e375 | [
"Apache-2.0"
] | null | null | null | next/boot/me/__init__.py | 27Saumya/next | 807de4a8e007211bc943545b61d0fdbb8c78e375 | [
"Apache-2.0"
] | null | null | null | from .check_core import * | 25 | 25 | 0.8 | 4 | 25 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12 | 25 | 1 | 25 | 25 | 0.863636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
77666b9449d744dc8e4bf3a5e86a104534c4c9ae | 80 | py | Python | forecasting/tests/unittests/test_forecasting.py | zhiqiangdon/autogluon | 71ee7ef0f05d8f0aad112d8c1719174aa33194d9 | [
"Apache-2.0"
] | 4,462 | 2019-12-09T17:41:07.000Z | 2022-03-31T22:00:41.000Z | forecasting/tests/unittests/test_forecasting.py | zhiqiangdon/autogluon | 71ee7ef0f05d8f0aad112d8c1719174aa33194d9 | [
"Apache-2.0"
] | 1,408 | 2019-12-09T17:48:59.000Z | 2022-03-31T20:24:12.000Z | forecasting/tests/unittests/test_forecasting.py | zhiqiangdon/autogluon | 71ee7ef0f05d8f0aad112d8c1719174aa33194d9 | [
"Apache-2.0"
] | 623 | 2019-12-10T02:04:18.000Z | 2022-03-20T17:11:01.000Z | import autogluon.forecasting
def test_forecasting():
print("Not Implemented")
| 16 | 28 | 0.8 | 9 | 80 | 7 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 80 | 4 | 29 | 20 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0.1875 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 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 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
776cebc3cba47424d985c42c72b441bbcfd15151 | 41 | py | Python | src/anoapycore/statmodel/__init__.py | ah4d1/anoapycore | b530a7fd97e0f97659b3936733db8bc906efaa3e | [
"BSD-3-Clause"
] | null | null | null | src/anoapycore/statmodel/__init__.py | ah4d1/anoapycore | b530a7fd97e0f97659b3936733db8bc906efaa3e | [
"BSD-3-Clause"
] | null | null | null | src/anoapycore/statmodel/__init__.py | ah4d1/anoapycore | b530a7fd97e0f97659b3936733db8bc906efaa3e | [
"BSD-3-Clause"
] | null | null | null | from . import linreg # linear_regression
| 20.5 | 40 | 0.804878 | 5 | 41 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146341 | 41 | 1 | 41 | 41 | 0.914286 | 0.414634 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7795438c73df39f721c3145a3c9e92ce8e8eb8c2 | 46 | py | Python | wx_pay/utils/__init__.py | lianxiaopang/camel-store-api | b8021250bf3d8cf7adc566deebdba55225148316 | [
"Apache-2.0"
] | 12 | 2020-02-01T01:52:01.000Z | 2021-04-28T15:06:43.000Z | wx_pay/utils/__init__.py | lianxiaopang/camel-store-api | b8021250bf3d8cf7adc566deebdba55225148316 | [
"Apache-2.0"
] | 5 | 2020-02-06T08:07:58.000Z | 2020-06-02T13:03:45.000Z | wx_pay/utils/__init__.py | lianxiaopang/camel-store-api | b8021250bf3d8cf7adc566deebdba55225148316 | [
"Apache-2.0"
] | 11 | 2020-02-03T13:07:46.000Z | 2020-11-29T01:44:06.000Z | from wx_pay.utils.dict2xml import dict_to_xml
| 23 | 45 | 0.869565 | 9 | 46 | 4.111111 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02381 | 0.086957 | 46 | 1 | 46 | 46 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
77b34b7575279b996b0eb8c6f47198510d672574 | 48 | py | Python | src/data_loaders/__init__.py | aida-ugent/FIPR | 723b3330fd95542803bf72184411b3fcfa48c168 | [
"Apache-2.0"
] | null | null | null | src/data_loaders/__init__.py | aida-ugent/FIPR | 723b3330fd95542803bf72184411b3fcfa48c168 | [
"Apache-2.0"
] | null | null | null | src/data_loaders/__init__.py | aida-ugent/FIPR | 723b3330fd95542803bf72184411b3fcfa48c168 | [
"Apache-2.0"
] | null | null | null | from data_loaders.data_loader import DataLoader
| 24 | 47 | 0.895833 | 7 | 48 | 5.857143 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 48 | 1 | 48 | 48 | 0.931818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
77d1a2f27576cac5fc243e1c7b8b134936621643 | 47 | py | Python | dependencies/georeference maps/pythongis/app/__init__.py | karimbahgat/AutoMap | eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f | [
"MIT"
] | 4 | 2015-12-05T14:31:55.000Z | 2018-02-09T05:54:36.000Z | dependencies/georeference maps/pythongis/app/__init__.py | karimbahgat/AutoMap | eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f | [
"MIT"
] | 1 | 2022-01-13T02:52:09.000Z | 2022-01-13T02:52:09.000Z | dependencies/georeference maps/pythongis/app/__init__.py | karimbahgat/AutoMap | eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f | [
"MIT"
] | 1 | 2018-10-24T01:08:11.000Z | 2018-10-24T01:08:11.000Z |
from . import map, controls, builder, dialogs
| 15.666667 | 45 | 0.744681 | 6 | 47 | 5.833333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.170213 | 47 | 2 | 46 | 23.5 | 0.897436 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bb1f21009b5fce2157481da012250ad20d304d69 | 31 | py | Python | idem/exec/test.py | brejoc/idem | 12a901f60e77d6fc8a4c8a7496ac0b54b588cf7a | [
"Apache-2.0"
] | 48 | 2019-05-21T16:10:49.000Z | 2021-12-04T18:02:20.000Z | idem/exec/test.py | brejoc/idem | 12a901f60e77d6fc8a4c8a7496ac0b54b588cf7a | [
"Apache-2.0"
] | 43 | 2019-05-21T22:39:44.000Z | 2020-02-07T16:37:29.000Z | idem/exec/test.py | brejoc/idem | 12a901f60e77d6fc8a4c8a7496ac0b54b588cf7a | [
"Apache-2.0"
] | 18 | 2019-05-21T16:10:42.000Z | 2019-12-13T16:28:36.000Z | def ping(hub):
return True
| 10.333333 | 15 | 0.645161 | 5 | 31 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.258065 | 31 | 2 | 16 | 15.5 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
bb23ad3bea410c3c09625c4bb6e6926ba7972859 | 110 | py | Python | scibert/__init__.py | georgetown-cset/ai-relevant-papers | bab36b738385208165d7c4f269ccd2afa91fc2c3 | [
"Apache-2.0"
] | 7 | 2020-02-19T19:26:53.000Z | 2021-06-22T14:46:30.000Z | scibert/__init__.py | georgetown-cset/ai-relevant-papers | bab36b738385208165d7c4f269ccd2afa91fc2c3 | [
"Apache-2.0"
] | null | null | null | scibert/__init__.py | georgetown-cset/ai-relevant-papers | bab36b738385208165d7c4f269ccd2afa91fc2c3 | [
"Apache-2.0"
] | 2 | 2020-03-13T05:32:17.000Z | 2021-02-23T07:41:34.000Z | import scibert.dataset_readers.classification_dataset_reader
import scibert.models.multilabel_text_classifier
| 36.666667 | 60 | 0.927273 | 13 | 110 | 7.461538 | 0.769231 | 0.268041 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036364 | 110 | 2 | 61 | 55 | 0.915094 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
24b55c8ba4789760076a195a3f60b3adf5107d8b | 3,327 | py | Python | publications/ss_vfnas/dp_utils.py | UMDataScienceLab/research | 279ee21444817903cb9ef9dc9d9583a502865336 | [
"Apache-2.0"
] | 49 | 2020-11-04T03:15:59.000Z | 2022-03-23T12:21:15.000Z | publications/ss_vfnas/dp_utils.py | UMDataScienceLab/research | 279ee21444817903cb9ef9dc9d9583a502865336 | [
"Apache-2.0"
] | 2 | 2021-09-12T02:36:42.000Z | 2021-11-25T13:19:58.000Z | publications/ss_vfnas/dp_utils.py | UMDataScienceLab/research | 279ee21444817903cb9ef9dc9d9583a502865336 | [
"Apache-2.0"
] | 11 | 2020-11-11T12:14:49.000Z | 2022-03-08T16:17:05.000Z | """
differential privacy utility functions
author: hyq
"""
import torch
def add_dp_v1(input_tensor, clip_value=5.0, variance=1.0, device=torch.device('cpu')):
"""
cpu version
:param input_tensor:
:param clip_value:
:param variance:
:param device:
:return:
"""
input_copy = input_tensor.detach().clone()
with torch.no_grad():
up_norm_factor = max(torch.max(torch.norm(input_copy, dim=1)).item()/clip_value, 1.0)
input_noised = input_copy / up_norm_factor + torch.normal(0, variance, input_tensor.shape)
input_with_dp = torch.autograd.Variable(input_noised, requires_grad=True).to(device)
return input_with_dp
def add_dp_v2(input_tensor, clip_value=5.0, variance=1.0, device=torch.device('cuda')):
"""
gpu compatible version
:param input_tensor: variable
:param clip_value: clipping value of 2-norm
:param variance: variance of gaussian noise
:param device:
:return: variable with dp applied
"""
input_copy = input_tensor.detach().clone()
with torch.no_grad():
# clip 2-norm per sample
norm_factor = torch.div(torch.max(torch.norm(input_copy, dim=1)), clip_value+1e-6).clamp(min=1.0)
# add gaussian noise
input_noised = torch.div(input_copy, norm_factor) + torch.normal(0, variance, input_tensor.shape, device=device)
input_with_dp = torch.autograd.Variable(input_noised, requires_grad=True).to(device)
return input_with_dp
def add_dp_v3(input_tensor, clip_value=5.0, variance=1.0):
"""
gpu compatible version
:param input_tensor: variable
:param clip_value: clipping value of 2-norm
:param variance: variance of gaussian noise
:param device:
:return: variable with dp applied
"""
input_copy = input_tensor.detach().clone()
with torch.no_grad():
# clip 2-norm per sample
norm_factor = torch.div(torch.max(torch.norm(input_copy, dim=1)), clip_value+1e-6).clamp(min=1.0)
# add gaussian noise
input_noised = torch.div(input_copy, norm_factor) + torch.normal(0, variance, input_tensor.shape).cuda()
input_with_dp = torch.autograd.Variable(input_noised, requires_grad=True).cuda()
return input_with_dp
def add_dp_to_list(input_tensor_list, clip_value=5.0, variance=1.0):
"""
gpu compatible version
:param input_tensor: variable
:param clip_value: clipping value of 2-norm
:param variance: variance of gaussian noise
:param device:
:return: variable with dp applied
"""
output_list = []
for input_tensor in input_tensor_list:
if isinstance(input_tensor, tuple):
input_copy = input_tensor[0].detach().clone()
else:
input_copy = input_tensor.detach().clone()
with torch.no_grad():
# clip 2-norm per sample
norm_factor = torch.div(torch.max(torch.norm(input_copy, dim=1)), clip_value+1e-6).clamp(min=1.0)
# add gaussian noise
input_noised = torch.div(input_copy, norm_factor) + torch.normal(0, variance, input_copy.shape).cuda()
input_with_dp = torch.autograd.Variable(input_noised, requires_grad=True).cuda()
output_list.append(input_with_dp)
return output_list | 38.241379 | 121 | 0.667869 | 464 | 3,327 | 4.571121 | 0.155172 | 0.098538 | 0.04149 | 0.047148 | 0.819425 | 0.819425 | 0.819425 | 0.807638 | 0.793494 | 0.76662 | 0 | 0.018533 | 0.221521 | 3,327 | 87 | 122 | 38.241379 | 0.800386 | 0.25248 | 0 | 0.514286 | 0 | 0 | 0.003112 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.114286 | false | 0 | 0.028571 | 0 | 0.257143 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
24c6fe545f1591d33d4219bb7d8ab53f074818ba | 44 | py | Python | test_project/conftest.py | wishmaestro/drf-fat-models | 09b8c8a15140044e570db4e9af3354c42768ec5c | [
"MIT"
] | null | null | null | test_project/conftest.py | wishmaestro/drf-fat-models | 09b8c8a15140044e570db4e9af3354c42768ec5c | [
"MIT"
] | null | null | null | test_project/conftest.py | wishmaestro/drf-fat-models | 09b8c8a15140044e570db4e9af3354c42768ec5c | [
"MIT"
] | null | null | null | from tests.fixtures import * # noqa: F401
| 22 | 43 | 0.704545 | 6 | 44 | 5.166667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085714 | 0.204545 | 44 | 1 | 44 | 44 | 0.8 | 0.227273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
24db57894e4da9ed824f986896058d292b929972 | 68 | py | Python | nitorch/tools/qmri/physio/__init__.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 46 | 2020-07-31T10:14:05.000Z | 2022-03-24T12:51:46.000Z | nitorch/tools/qmri/physio/__init__.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 36 | 2020-10-06T19:01:38.000Z | 2022-02-03T18:07:35.000Z | nitorch/tools/qmri/physio/__init__.py | balbasty/nitorch | d30c3125a8a66ea1434f2b39ed03338afd9724b4 | [
"MIT"
] | 6 | 2021-01-05T14:59:05.000Z | 2021-11-18T18:26:45.000Z | from ._fit import *
from ._sample import *
from ._plot import *
| 8.5 | 22 | 0.676471 | 9 | 68 | 4.777778 | 0.555556 | 0.465116 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 68 | 7 | 23 | 9.714286 | 0.826923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
705d5af4386d9f0419bea80f6012a91aadc1e866 | 93 | py | Python | hermit/signer/__init__.py | rsbondi/hermit | 0007d8077547484efa173295090775b3cd5ce75b | [
"Apache-2.0"
] | 1 | 2021-07-23T16:43:06.000Z | 2021-07-23T16:43:06.000Z | hermit/signer/__init__.py | rsbondi/hermit | 0007d8077547484efa173295090775b3cd5ce75b | [
"Apache-2.0"
] | null | null | null | hermit/signer/__init__.py | rsbondi/hermit | 0007d8077547484efa173295090775b3cd5ce75b | [
"Apache-2.0"
] | 1 | 2020-07-09T22:29:08.000Z | 2020-07-09T22:29:08.000Z | from .base import *
from .bitcoin_signer import *
from .echo_signer import *
| 23.25 | 30 | 0.634409 | 11 | 93 | 5.181818 | 0.545455 | 0.350877 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.301075 | 93 | 3 | 31 | 31 | 0.876923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7094664482c31f03deaf8b425e3717e056ad45d5 | 5,636 | py | Python | src/camera/test_process_img.py | stay-whimsical/screamchess | 4950d480f8f33db2bc3f2d94eea5dc6706ae8087 | [
"MIT"
] | 2 | 2019-06-19T20:25:12.000Z | 2021-06-04T04:43:36.000Z | src/camera/test_process_img.py | pablo-meier/screamchess | 4950d480f8f33db2bc3f2d94eea5dc6706ae8087 | [
"MIT"
] | 8 | 2017-08-19T07:09:55.000Z | 2017-08-20T21:11:11.000Z | src/camera/test_process_img.py | pablo-meier/screamchess | 4950d480f8f33db2bc3f2d94eea5dc6706ae8087 | [
"MIT"
] | 1 | 2020-04-17T00:19:43.000Z | 2020-04-17T00:19:43.000Z | import unittest
import src.camera.process_img as pi
import time
class TestBoardState(unittest.TestCase):
def test_start_state(self):
state = pi.get_board_state('src/camera/qrcodes/start_state.png')
print('state = ' + str(state))
class TestQrScan(unittest.TestCase):
def test_scanner(self):
start = time.time()
img = pi.open_image('src/camera/qrcodes/WR1.png')
self.assertEquals(pi.scan_qr_code(img), ['WR1'])
img = pi.open_image('src/camera/qrcodes/WR2.png')
self.assertEquals(pi.scan_qr_code(img), ['WR2'])
img = pi.open_image('src/camera/qrcodes/WB1.png')
self.assertEquals(pi.scan_qr_code(img), ['WB1'])
img = pi.open_image('src/camera/qrcodes/WB2.png')
self.assertEquals(pi.scan_qr_code(img), ['WB2'])
img = pi.open_image('src/camera/qrcodes/WK1.png')
self.assertEquals(pi.scan_qr_code(img), ['WK1'])
img = pi.open_image('src/camera/qrcodes/WK2.png')
self.assertEquals(pi.scan_qr_code(img), ['WK2'])
img = pi.open_image('src/camera/qrcodes/WK.png')
self.assertEquals(pi.scan_qr_code(img), ['WK'])
img = pi.open_image('src/camera/qrcodes/WQ.png')
self.assertEquals(pi.scan_qr_code(img), ['WQ'])
img = pi.open_image('src/camera/qrcodes/WP1.png')
self.assertEquals(pi.scan_qr_code(img), ['WP1'])
img = pi.open_image('src/camera/qrcodes/WP2.png')
self.assertEquals(pi.scan_qr_code(img), ['WP2'])
img = pi.open_image('src/camera/qrcodes/WP3.png')
self.assertEquals(pi.scan_qr_code(img), ['WP3'])
img = pi.open_image('src/camera/qrcodes/WP4.png')
self.assertEquals(pi.scan_qr_code(img), ['WP4'])
img = pi.open_image('src/camera/qrcodes/WP5.png')
self.assertEquals(pi.scan_qr_code(img), ['WP5'])
img = pi.open_image('src/camera/qrcodes/WP6.png')
self.assertEquals(pi.scan_qr_code(img), ['WP6'])
img = pi.open_image('src/camera/qrcodes/WP7.png')
self.assertEquals(pi.scan_qr_code(img), ['WP7'])
img = pi.open_image('src/camera/qrcodes/WP8.png')
self.assertEquals(pi.scan_qr_code(img), ['WP8'])
end = time.time()
print('Scanned and asserted vals for 32 images in ' + str(end - start)
+ ' about ' + str((end-start)/32) + ' per image on average')
class TestWhiteCheck(unittest.TestCase):
def setUp(self):
self.pixels = [(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255),
(255, 255, 255, 255), (255, 255, 255, 255),
(255, 255, 255, 255)] # len 42, e.g. 6x7
def test_row_white_success(self):
self.assertTrue(pi.row_white(self.pixels, 0, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 1, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 2, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 3, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 4, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 5, 6, 10))
self.assertTrue(pi.row_white(self.pixels, 6, 6, 10))
def test_col_white_success(self):
self.assertTrue(pi.col_white(self.pixels, 1, 10))
self.assertTrue(pi.col_white(self.pixels, 2, 10))
self.assertTrue(pi.col_white(self.pixels, 3, 10))
self.assertTrue(pi.col_white(self.pixels, 4, 10))
self.assertTrue(pi.col_white(self.pixels, 5, 10))
self.assertTrue(pi.col_white(self.pixels, 6, 10))
def test_row_white_fail(self):
pixels = self.pixels[:]
# Add a solid black row to the bottom
pixels.extend([(0,0,0,255) for x in xrange(6)])
self.assertFalse(pi.row_white(pixels, 7, 6, 10))
def test_col_white_fail(self):
pixels = self.pixels[:]
# Add a solid black row to the bottom
pixels.extend([(0,0,0,255) for x in xrange(6)])
self.assertFalse(pi.col_white(pixels, 1, 10))
self.assertFalse(pi.col_white(pixels, 2, 10))
self.assertFalse(pi.col_white(pixels, 3, 10))
self.assertFalse(pi.col_white(pixels, 4, 10))
self.assertFalse(pi.col_white(pixels, 5, 10))
self.assertFalse(pi.col_white(pixels, 6, 10))
if __name__ == '__main__':
unittest.main() | 47.361345 | 78 | 0.55252 | 778 | 5,636 | 3.880463 | 0.125964 | 0.331898 | 0.494866 | 0.655846 | 0.809871 | 0.809871 | 0.786022 | 0.549851 | 0.310036 | 0.236502 | 0 | 0.15488 | 0.287438 | 5,636 | 119 | 79 | 47.361345 | 0.596863 | 0.015614 | 0 | 0.285714 | 0 | 0 | 0.104779 | 0.080794 | 0 | 0 | 0 | 0 | 0.352381 | 1 | 0.066667 | false | 0 | 0.028571 | 0 | 0.12381 | 0.019048 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
70a7c3bb1ef7e4791df77bff38a16e9ea90df0cf | 102 | py | Python | mcinfo/__init__.py | randomdude999/mcinfo | 11c6e9c85a86fe6507b7a5924769214cb3dbe92d | [
"MIT"
] | null | null | null | mcinfo/__init__.py | randomdude999/mcinfo | 11c6e9c85a86fe6507b7a5924769214cb3dbe92d | [
"MIT"
] | null | null | null | mcinfo/__init__.py | randomdude999/mcinfo | 11c6e9c85a86fe6507b7a5924769214cb3dbe92d | [
"MIT"
] | null | null | null | from mcinfo import cli, normal_info, nbt, recipes
__all__ = ["cli", "normal_info", "nbt", "recipes"]
| 25.5 | 50 | 0.696078 | 14 | 102 | 4.642857 | 0.642857 | 0.276923 | 0.4 | 0.492308 | 0.707692 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137255 | 102 | 3 | 51 | 34 | 0.738636 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
5617bba93fefecdc6d60602c09c10e496df6b560 | 78 | py | Python | kii/results/update.py | ta2xeo/python3-kii | 892da42601318bcc15e70378614be76d68681881 | [
"MIT"
] | 2 | 2018-02-04T21:16:02.000Z | 2021-12-01T16:51:43.000Z | kii/results/update.py | ta2xeo/python3-kii | 892da42601318bcc15e70378614be76d68681881 | [
"MIT"
] | null | null | null | kii/results/update.py | ta2xeo/python3-kii | 892da42601318bcc15e70378614be76d68681881 | [
"MIT"
] | null | null | null | from .object import ObjectResult
class UpdateResult(ObjectResult):
pass
| 13 | 33 | 0.782051 | 8 | 78 | 7.625 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 78 | 5 | 34 | 15.6 | 0.938462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
3b0aaa93b4b69a629b462cdfdde8b2dc6908a4b9 | 66 | py | Python | src/core/models/__init__.py | cyberstruggle/chalumeau | a912c0c1390f4b5b6cce061febeeb96303642b74 | [
"Apache-2.0"
] | 99 | 2020-07-26T16:25:24.000Z | 2022-02-01T17:49:26.000Z | src/core/models/__init__.py | cyberstruggle/chalumeau | a912c0c1390f4b5b6cce061febeeb96303642b74 | [
"Apache-2.0"
] | 1 | 2020-09-07T05:11:54.000Z | 2020-09-12T18:12:42.000Z | src/core/models/__init__.py | cyberstruggle/chalumeau | a912c0c1390f4b5b6cce061febeeb96303642b74 | [
"Apache-2.0"
] | 20 | 2020-07-26T17:34:59.000Z | 2021-08-20T06:12:19.000Z | from .guid import *
from .timestamped import *
from .base import * | 22 | 26 | 0.742424 | 9 | 66 | 5.444444 | 0.555556 | 0.408163 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 66 | 3 | 27 | 22 | 0.890909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3b38980697493d8d51f3c5beec13a72b25d4002c | 33 | py | Python | mutable/__init__.py | jburgy/mutable | df0fbb1be091bc06ef468569822de355815a6111 | [
"MIT"
] | null | null | null | mutable/__init__.py | jburgy/mutable | df0fbb1be091bc06ef468569822de355815a6111 | [
"MIT"
] | null | null | null | mutable/__init__.py | jburgy/mutable | df0fbb1be091bc06ef468569822de355815a6111 | [
"MIT"
] | null | null | null | from .core import mutates, scope
| 16.5 | 32 | 0.787879 | 5 | 33 | 5.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151515 | 33 | 1 | 33 | 33 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3b4991fe32f637b2e5a69df99285c2efa7467fd5 | 83 | py | Python | scale/util/test/__init__.py | kaydoh/scale | 1b6a3b879ffe83e10d3b9d9074835a4c3bf476ee | [
"Apache-2.0"
] | 121 | 2015-11-18T18:15:33.000Z | 2022-03-10T01:55:00.000Z | scale/util/test/__init__.py | kaydoh/scale | 1b6a3b879ffe83e10d3b9d9074835a4c3bf476ee | [
"Apache-2.0"
] | 1,415 | 2015-12-23T23:36:04.000Z | 2022-01-07T14:10:09.000Z | scale/util/test/__init__.py | kaydoh/scale | 1b6a3b879ffe83e10d3b9d9074835a4c3bf476ee | [
"Apache-2.0"
] | 66 | 2015-12-03T20:38:56.000Z | 2020-07-27T15:28:11.000Z | import logging
# Disable logging for unit tests
logging.disable(logging.CRITICAL)
| 16.6 | 33 | 0.819277 | 11 | 83 | 6.181818 | 0.636364 | 0.411765 | 0.617647 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.120482 | 83 | 4 | 34 | 20.75 | 0.931507 | 0.361446 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
d9247f880e5b5375e7d6bfa61a94fce18f64c5b9 | 21 | py | Python | subsurface/__init__.py | MonEstCha/subsurface | 13b46f5a5a8731ccbd4e22e18cc43f01b537947c | [
"Apache-2.0"
] | 1 | 2020-07-28T00:56:55.000Z | 2020-07-28T00:56:55.000Z | subsurface/__init__.py | roderickperez/subsurface | 13b46f5a5a8731ccbd4e22e18cc43f01b537947c | [
"Apache-2.0"
] | 1 | 2020-11-07T12:36:21.000Z | 2020-11-07T12:36:21.000Z | subsurface/__init__.py | MonEstCha/subsurface | 13b46f5a5a8731ccbd4e22e18cc43f01b537947c | [
"Apache-2.0"
] | 1 | 2020-08-07T16:46:55.000Z | 2020-08-07T16:46:55.000Z | from .curve import *
| 10.5 | 20 | 0.714286 | 3 | 21 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190476 | 21 | 1 | 21 | 21 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d94863a90a263bdb49a39bfea9e753a51cb2d19a | 172 | py | Python | tests/unit/data/migrations/2_add_name_to_bad_decks.py | trike-city/mox | 299c070433c8f4ee6d93c729567ef3e37631c078 | [
"MIT"
] | null | null | null | tests/unit/data/migrations/2_add_name_to_bad_decks.py | trike-city/mox | 299c070433c8f4ee6d93c729567ef3e37631c078 | [
"MIT"
] | 24 | 2019-07-14T17:16:45.000Z | 2019-08-08T17:24:36.000Z | tests/unit/data/migrations/2_add_name_to_bad_decks.py | trike-city/mox | 299c070433c8f4ee6d93c729567ef3e37631c078 | [
"MIT"
] | null | null | null | def up(database):
database.execute('ALTER TABLE bad_decks ADD COLUMN name TEXT;')
def down(database):
database.execute('ALTER TABLE bad_decks DROP COLUMN name;')
| 24.571429 | 67 | 0.738372 | 25 | 172 | 5 | 0.56 | 0.256 | 0.368 | 0.448 | 0.656 | 0.656 | 0.656 | 0 | 0 | 0 | 0 | 0 | 0.156977 | 172 | 6 | 68 | 28.666667 | 0.862069 | 0 | 0 | 0 | 0 | 0 | 0.476744 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0 | 0.5 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d96beebde6aa5ff5602bd67eb4e4207c116afc44 | 272,243 | py | Python | analysis/CypA/3k0n_pdbs/rmsd_per_res_view.py | chonglab-pitt/ff15ipq-19F | 6f472b1ef2bc5e28a9c84ea47be692968441af89 | [
"BSD-3-Clause"
] | null | null | null | analysis/CypA/3k0n_pdbs/rmsd_per_res_view.py | chonglab-pitt/ff15ipq-19F | 6f472b1ef2bc5e28a9c84ea47be692968441af89 | [
"BSD-3-Clause"
] | null | null | null | analysis/CypA/3k0n_pdbs/rmsd_per_res_view.py | chonglab-pitt/ff15ipq-19F | 6f472b1ef2bc5e28a9c84ea47be692968441af89 | [
"BSD-3-Clause"
] | 1 | 2022-03-27T18:23:37.000Z | 2022-03-27T18:23:37.000Z | import cPickle, base64
try:
from SimpleSession.versions.v65 import beginRestore,\
registerAfterModelsCB, reportRestoreError, checkVersion
except ImportError:
from chimera import UserError
raise UserError('Cannot open session that was saved in a'
' newer version of Chimera; update your version')
checkVersion([1, 15, 42258])
import chimera
from chimera import replyobj
replyobj.status('Restoring session...', \
blankAfter=0)
replyobj.status('Beginning session restore...', \
blankAfter=0, secondary=True)
beginRestore()
def restoreCoreModels():
from SimpleSession.versions.v65 import init, restoreViewer, \
restoreMolecules, restoreColors, restoreSurfaces, \
restoreVRML, restorePseudoBondGroups, restoreModelAssociations
molInfo = cPickle.loads(base64.b64decode('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'))
resInfo = cPickle.loads(base64.b64decode('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'))
atomInfo = cPickle.loads(base64.b64decode('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'))
bondInfo = cPickle.loads(base64.b64decode('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'))
crdInfo = cPickle.loads(base64.b64decode('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'))
surfInfo = {'category': (0, None, {}), 'probeRadius': (0, None, {}), 'pointSize': (0, None, {}), 'name': [], 'density': (0, None, {}), 'colorMode': (0, None, {}), 'useLighting': (0, None, {}), 'transparencyBlendMode': (0, None, {}), 'molecule': [], 'smoothLines': (0, None, {}), 'lineWidth': (0, None, {}), 'allComponents': (0, None, {}), 'twoSidedLighting': (0, None, {}), 'customVisibility': [], 'drawMode': (0, None, {}), 'display': (0, None, {}), 'customColors': []}
vrmlInfo = {'subid': (0, None, {}), 'display': (0, None, {}), 'id': (0, None, {}), 'vrmlString': [], 'name': (0, None, {})}
colors = {u'Ru': ((0.141176, 0.560784, 0.560784), 1, u'default'), u'Re': ((0.14902, 0.490196, 0.670588), 1, u'default'), u'Rf': ((0.8, 0, 0.34902), 1, u'default'), u'Ra': ((0, 0.490196, 0), 1, u'default'), u'Rb': ((0.439216, 0.180392, 0.690196), 1, u'default'), u'Rn': ((0.258824, 0.509804, 0.588235), 1, u'default'), u'Rh': ((0.0392157, 0.490196, 0.54902), 1, u'default'), u'Be': ((0.760784, 1, 0), 1, u'default'), u'Ba': ((0, 0.788235, 0), 1, u'default'), u'Bh': ((0.878431, 0, 0.219608), 1, u'default'), u'Bi': ((0.619608, 0.309804, 0.709804), 1, u'default'), u'Bk': ((0.541176, 0.309804, 0.890196), 1, u'default'), u'Br': ((0.65098, 0.160784, 0.160784), 1, u'default'), u'H': ((1, 1, 1), 1, u'default'), u'P': ((1, 0.501961, 0), 1, u'default'), u'Os': ((0.14902, 0.4, 0.588235), 1, u'default'), u'Es': ((0.701961, 0.121569, 0.831373), 1, u'default'), u'Hg': ((0.721569, 0.721569, 0.815686), 1, u'default'), u'Ge': ((0.4, 0.560784, 0.560784), 1, u'default'), u'Gd': ((0.270588, 1, 0.780392), 1, u'default'), u'Ga': ((0.760784, 0.560784, 0.560784), 1, u'default'), u'Pr': ((0.85098, 1, 0.780392), 1, u'default'),
u'slate gray': ((0.439216, 0.501961, 0.564706), 1, u'default'), u'Pt': ((0.815686, 0.815686, 0.878431), 1, u'default'), u'Pu': ((0, 0.419608, 1), 1, u'default'), u'C': ((0.564706, 0.564706, 0.564706), 1, u'default'), u'Pb': ((0.341176, 0.34902, 0.380392), 1, u'default'), u'Pa': ((0, 0.631373, 1), 1, u'default'), u'Pd': ((0, 0.411765, 0.521569), 1, u'default'), u'Cd': ((1, 0.85098, 0.560784), 1, u'default'), u'Po': ((0.670588, 0.360784, 0), 1, u'default'), u'Pm': ((0.639216, 1, 0.780392), 1, u'default'), u'Hs': ((0.901961, 0, 0.180392), 1, u'default'), u'Ho': ((0, 1, 0.611765), 1, u'default'), u'Hf': ((0.301961, 0.760784, 1), 1, u'default'), u'K': ((0.560784, 0.25098, 0.831373), 1, u'default'), u'He': ((0.85098, 1, 1), 1, u'default'), u'Md': ((0.701961, 0.0509804, 0.65098), 1, u'default'), u'Mg': ((0.541176, 1, 0), 1, u'default'), u'Mo': ((0.329412, 0.709804, 0.709804), 1, u'default'), u'Mn': ((0.611765, 0.478431, 0.780392), 1, u'default'), u'O': ((1, 0.0509804, 0.0509804), 1, u'default'), u'Mt': ((0.921569, 0, 0.14902), 1, u'default'), u'S': ((1, 1, 0.188235), 1, u'default'),
u'W': ((0.129412, 0.580392, 0.839216), 1, u'default'), u'Zn': ((0.490196, 0.501961, 0.690196), 1, u'default'), u'Eu': ((0.380392, 1, 0.780392), 1, u'default'), u'Zr': ((0.580392, 0.878431, 0.878431), 1, u'default'), u'Er': ((0, 0.901961, 0.458824), 1, u'default'), u'Ni': ((0.313725, 0.815686, 0.313725), 1, u'default'), u'No': ((0.741176, 0.0509804, 0.529412), 1, u'default'), u'Na': ((0.670588, 0.360784, 0.94902), 1, u'default'), u'Nb': ((0.45098, 0.760784, 0.788235), 1, u'default'), u'Nd': ((0.780392, 1, 0.780392), 1, u'default'), u'Ne': ((0.701961, 0.890196, 0.960784), 1, u'default'), u'Np': ((0, 0.501961, 1), 1, u'default'), u'Fr': ((0.258824, 0, 0.4), 1, u'default'), u'Fe': ((0.878431, 0.4, 0.2), 1, u'default'), u'Fm': ((0.701961, 0.121569, 0.729412), 1, u'default'), u'B': ((1, 0.709804, 0.709804), 1, u'default'), u'F': ((0.564706, 0.878431, 0.313725), 1, u'default'), u'Sr': ((0, 1, 0), 1, u'default'), u'N': ((0.188235, 0.313725, 0.972549), 1, u'default'), u'Kr': ((0.360784, 0.721569, 0.819608), 1, u'default'), u'Si': ((0.941176, 0.784314, 0.627451), 1, u'default'),
u'Sn': ((0.4, 0.501961, 0.501961), 1, u'default'), u'Sm': ((0.560784, 1, 0.780392), 1, u'default'), u'V': ((0.65098, 0.65098, 0.670588), 1, u'default'), u'Sc': ((0.901961, 0.901961, 0.901961), 1, u'default'), u'Sb': ((0.619608, 0.388235, 0.709804), 1, u'default'), u'Sg': ((0.85098, 0, 0.270588), 1, u'default'), u'Se': ((1, 0.631373, 0), 1, u'default'), u'Co': ((0.941176, 0.564706, 0.627451), 1, u'default'), u'Cm': ((0.470588, 0.360784, 0.890196), 1, u'default'), u'Cl': ((0.121569, 0.941176, 0.121569), 1, u'default'), u'Ca': ((0.239216, 1, 0), 1, u'default'), u'Cf': ((0.631373, 0.211765, 0.831373), 1, u'default'), u'Ce': ((1, 1, 0.780392), 1, u'default'), u'Xe': ((0.258824, 0.619608, 0.690196), 1, u'default'), u'Lu': ((0, 0.670588, 0.141176), 1, u'default'), u'Cs': ((0.341176, 0.0901961, 0.560784), 1, u'default'), u'Cr': ((0.541176, 0.6, 0.780392), 1, u'default'), u'Cu': ((0.784314, 0.501961, 0.2), 1, u'default'), u'La': ((0.439216, 0.831373, 1), 1, u'default'), u'Li': ((0.8, 0.501961, 1), 1, u'default'), u'Tl': ((0.65098, 0.329412, 0.301961), 1, u'default'),
u'Tm': ((0, 0.831373, 0.321569), 1, u'default'), u'Lr': ((0.780392, 0, 0.4), 1, u'default'), u'Th': ((0, 0.729412, 1), 1, u'default'), u'Ti': ((0.74902, 0.760784, 0.780392), 1, u'default'), u'tan': ((0.823529, 0.705882, 0.54902), 1, u'default'), u'Te': ((0.831373, 0.478431, 0), 1, u'default'), u'Tb': ((0.188235, 1, 0.780392), 1, u'default'), u'Tc': ((0.231373, 0.619608, 0.619608), 1, u'default'), u'Ta': ((0.301961, 0.65098, 1), 1, u'default'), u'pink': ((1, 0.752941, 0.796078), 1, u'default'), u'Yb': ((0, 0.74902, 0.219608), 1, u'default'), u'Db': ((0.819608, 0, 0.309804), 1, u'default'), u'Dy': ((0.121569, 1, 0.780392), 1, u'default'), u'I': ((0.580392, 0, 0.580392), 1, u'default'), u'medium purple': ((0.576471, 0.439216, 0.858824), 1, u'default'), u'U': ((0, 0.560784, 1), 1, u'default'), u'Y': ((0.580392, 1, 1), 1, u'default'), u'Ac': ((0.439216, 0.670588, 0.980392), 1, u'default'), u'Ag': ((0.752941, 0.752941, 0.752941), 1, u'default'), u'Ir': ((0.0901961, 0.329412, 0.529412), 1, u'default'), u'Am': ((0.329412, 0.360784, 0.94902), 1, u'default'), u'Al': ((0.74902, 0.65098, 0.65098), 1, u'default'),
u'As': ((0.741176, 0.501961, 0.890196), 1, u'default'), u'Ar': ((0.501961, 0.819608, 0.890196), 1, u'default'), u'Au': ((1, 0.819608, 0.137255), 1, u'default'), u'At': ((0.458824, 0.309804, 0.270588), 1, u'default'), u'In': ((0.65098, 0.458824, 0.45098), 1, u'default')}
materials = {u'default': ((0.85, 0.85, 0.85), 30)}
pbInfo = {'category': [u'distance monitor'], 'bondInfo': [{'color': (0, None, {}), 'atoms': [], 'label': (0, None, {}), 'halfbond': (0, None, {}), 'labelColor': (0, None, {}), 'labelOffset': (0, None, {}), 'drawMode': (0, None, {}), 'display': (0, None, {})}], 'lineType': (1, 2, {}), 'color': (1, 152, {}), 'optional': {'fixedLabels': (True, False, (1, False, {}))}, 'display': (1, True, {}), 'showStubBonds': (1, False, {}), 'lineWidth': (1, 1, {}), 'stickScale': (1, 1, {}), 'id': [-2]}
modelAssociations = {}
colorInfo = (154, (u'', (0.397317, 0.397317, 1, 1)), {(u'', (0.360904, 0.360904, 1, 1)): [14], (u'', (0.0848101, 0.0848101, 1, 1)): [29], (u'', (0.207416, 0.207416, 1, 1)): [15], (u'', (1, 0.89052, 0.89052, 1)): [100], (u'', (0.188057, 0.188057, 1, 1)): [26], (u'', (0.448019, 0.448019, 1, 1)): [80], (u'', (0.0603811, 0.0603811, 1, 1)): [40], (u'', (0.206955, 0.206955, 1, 1)): [16], (u'', (0.171464, 0.171464, 1, 1)): [4], (u'', (0.138738, 0.138738, 1, 1)): [129], (u'', (0.312046, 0.312046, 1, 1)): [69], (u'', (0.253048, 0.253048, 1, 1)): [48], (u'', (0.137816, 0.137816, 1, 1)): [127], (u'', (0.624553, 0.624553, 1, 1)): [81], (u'', (1, 0.89666, 0.89666, 1)): [99], (u'', (0.148879, 0.148879, 1, 1)): [24], (u'', (0.0640685, 0.0640685, 1, 1)): [124], (u'', (0.0691387, 0.0691387, 1, 1)): [28], (u'', (0.036874, 0.036874, 1, 1)): [21], (u'', (0.0686778, 0.0686778, 1, 1)): [19], (u'', (0.085732, 0.085732, 1, 1)): [94], (u'', (0.469682, 0.469682, 1, 1)): [70], (u'', (0.1498, 0.1498, 1, 1)): [86], (u'', (0.586296, 0.586296, 1, 1)): [67], (u'', (0.05485, 0.05485, 1, 1)): [27], (u'', (0.0225853, 0.0225853, 1, 1)): [36], (u'', (0.0322647, 0.0322647, 1, 1)): [121],
(u'', (0.0202807, 0.0202807, 1, 1)): [38], (u'', (0.0410223, 0.0410223, 1, 1)): [33], (u'', (0.236915, 0.236915, 1, 1)): [84], (u'', (0, 0, 1, 1)): [34], (u'green', (0, 1, 0, 1)): [153], (u'', (0.376114, 0.376114, 1, 1)): [76], (u'', (0.136434, 0.136434, 1, 1)): [114], (u'', (0.137356, 0.137356, 1, 1)): [11], (u'', (0.0165933, 0.0165933, 1, 1)): [122], (u'', (0.455854, 0.455854, 1, 1)): [78], (u'', (0.434191, 0.434191, 1, 1)): [77], (u'', (0.105552, 0.105552, 1, 1)): [113], (u'', (0.0622248, 0.0622248, 1, 1)): [30], (u'', (0.303288, 0.303288, 1, 1)): [64], (u'', (0.194971, 0.194971, 1, 1)): [58], (u'', (0.349381, 0.349381, 1, 1)): [95], (u'', (1, 0.861046, 0.861046, 1)): [98], (u'', (0.527759, 0.527759, 1, 1)): [150], (u'', (0.237837, 0.237837, 1, 1)): [72], (u'', (0.0861929, 0.0861929, 1, 1)): [109], (u'', (1, 0, 0, 1)): [138], (u'', (0.104169, 0.104169, 1, 1)): [107], (u'', (0.221705, 0.221705, 1, 1)): [60], (u'tan', (0.823529, 0.705882, 0.54902, 1)): [0], (u'', (0.012445, 0.012445, 1, 1)): [148], (u'', (0.0193588, 0.0193588, 1, 1)): [8], (u'', (0.728261, 0.728261, 1, 1)): [102],
(u'', (0.2194, 0.2194, 1, 1)): [134], (u'', (0.0493189, 0.0493189, 1, 1)): [32], (u'', (0.455393, 0.455393, 1, 1)): [83], (u'', (0.0986379, 0.0986379, 1, 1)): [25], (u'', (0.103708, 0.103708, 1, 1)): [18], (u'', (0.196815, 0.196815, 1, 1)): [85], (u'', (0.0617639, 0.0617639, 1, 1)): [91], (u'', (0.475674, 0.475674, 1, 1)): [140], (u'', (0.073287, 0.073287, 1, 1)): [46], (u'', (0.122606, 0.122606, 1, 1)): [106], (u'', (0.453089, 0.453089, 1, 1)): [13], (u'', (0.0995597, 0.0995597, 1, 1)): [146], (u'', (0.0677559, 0.0677559, 1, 1)): [120], (u'', (0.385794, 0.385794, 1, 1)): [51], (u'', (0.333709, 0.333709, 1, 1)): [47], (u'', (0.332788, 0.332788, 1, 1)): [103], (u'', (1, 0.665785, 0.665785, 1)): [1], (u'', (0.16962, 0.16962, 1, 1)): [105], (u'', (0.297296, 0.297296, 1, 1)): [52], (u'', (0.855476, 0.855476, 1, 1)): [101], (u'', (0.0543891, 0.0543891, 1, 1)): [108], (u'', (0.0341084, 0.0341084, 1, 1)): [92], (u'', (0.170081, 0.170081, 1, 1)): [143], (u'', (0.228619, 0.228619, 1, 1)): [57], (u'', (0.0866538, 0.0866538, 1, 1)): [87], (u'', (0.84764, 0.84764, 1, 1)): [139],
(u'', (0.328178, 0.328178, 1, 1)): [54], (u'', (0.297757, 0.297757, 1, 1)): [63], (u'', (0.240142, 0.240142, 1, 1)): [142], (u'', (0.91217, 0.91217, 1, 1)): [136], (u'', (0.763291, 0.763291, 1, 1)): [2], (u'', (0.115231, 0.115231, 1, 1)): [111], (u'', (0.477979, 0.477979, 1, 1)): [135], (u'', (0.42866, 0.42866, 1, 1)): [79], (u'', (0.0889584, 0.0889584, 1, 1)): [90], (u'', (0.0405614, 0.0405614, 1, 1)): [93], (u'', (0.280703, 0.280703, 1, 1)): [56], (u'', (0.232306, 0.232306, 1, 1)): [17], (u'', (0.0908021, 0.0908021, 1, 1)): [149], (u'', (1, 0.689118, 0.689118, 1)): [151], (u'', (0.794634, 0.794634, 1, 1)): [97], (u'', (0.0248899, 0.0248899, 1, 1)): [20], (u'', (0.389942, 0.389942, 1, 1)): [96], (u'', (0.0318038, 0.0318038, 1, 1)): [7], (u'', (0.101864, 0.101864, 1, 1)): [116], (u'', (0.123067, 0.123067, 1, 1)): [9], (u'', (0.244751, 0.244751, 1, 1)): [71], (u'', (0.106474, 0.106474, 1, 1)): [41], (u'', (0.146113, 0.146113, 1, 1)): [112], (u'', (0.260422, 0.260422, 1, 1)): [75], (u'', (0.179761, 0.179761, 1, 1)): [23], (u'', (0.0175151, 0.0175151, 1, 1)): [147],
(u'', (0.318499, 0.318499, 1, 1)): [133], (u'', (0.0354912, 0.0354912, 1, 1)): [31], (u'', (1, 0.303508, 0.303508, 1)): [137], (u'', (0.197737, 0.197737, 1, 1)): [130], (u'', (0.123528, 0.123528, 1, 1)): [128], (u'', (0.0483971, 0.0483971, 1, 1)): [44], (u'', (0.160863, 0.160863, 1, 1)): [62], (u'', (0.18944, 0.18944, 1, 1)): [42], (u'', (0.0092185, 0.0092185, 1, 1)): [35], (u'', (0.243368, 0.243368, 1, 1)): [132], (u'', (0.314351, 0.314351, 1, 1)): [50], (u'', (0.117536, 0.117536, 1, 1)): [88], (u'', (0.218478, 0.218478, 1, 1)): [73], (u'', (0.0262727, 0.0262727, 1, 1)): [45], (u'', (0.091724, 0.091724, 1, 1)): [5], (u'', (0.494572, 0.494572, 1, 1)): [141], (u'', (0.296375, 0.296375, 1, 1)): [104], (u'', (0.396395, 0.396395, 1, 1)): [55], (u'', (0.143808, 0.143808, 1, 1)): [61], (u'', (0.0447097, 0.0447097, 1, 1)): [123], (u'', (0.173769, 0.173769, 1, 1)): [117], (u'', (0.11984, 0.11984, 1, 1)): [126], (u'', (0.0419441, 0.0419441, 1, 1)): [6], (u'', (0.224009, 0.224009, 1, 1)): [131], (u'', (0.157636, 0.157636, 1, 1)): [110], (u'', (0.544352, 0.544352, 1, 1)): [66],
(u'', (0.132746, 0.132746, 1, 1)): [118], (u'', (0.0138277, 0.0138277, 1, 1)): [37], (u'', (0.0884975, 0.0884975, 1, 1)): [125], (u'', (0.363669, 0.363669, 1, 1)): [3], (u'', (0.188518, 0.188518, 1, 1)): [145], (u'', (0.230462, 0.230462, 1, 1)): [74], (u'', (0.0811227, 0.0811227, 1, 1)): [10], (u'', (0.140582, 0.140582, 1, 1)): [59], (u'', (0.0709824, 0.0709824, 1, 1)): [89], (u'', (0.0912631, 0.0912631, 1, 1)): [115], (u'', (0.44387, 0.44387, 1, 1)): [65], (u'', (0.152566, 0.152566, 1, 1)): [144], (u'yellow', (1, 1, 0, 1)): [152], (u'', (0.22954, 0.22954, 1, 1)): [49], (u'', (0.060842, 0.060842, 1, 1)): [39], (u'', (0.656817, 0.656817, 1, 1)): [82], (u'', (0.0520845, 0.0520845, 1, 1)): [43], (u'', (0.383028, 0.383028, 1, 1)): [53], (u'', (0.0451706, 0.0451706, 1, 1)): [22], (u'', (0.24936, 0.24936, 1, 1)): [12], (u'', (0.0649904, 0.0649904, 1, 1)): [119]})
viewerInfo = {'cameraAttrs': {'center': (3.9169999728203, 12.300499990463, 13.858499985695), 'fieldOfView': 28.68002421842, 'nearFar': (38.383372145352, -12.627372145352), 'ortho': False, 'eyeSeparation': 50.8, 'focal': 13.858499985695}, 'viewerAttrs': {'silhouetteColor': None, 'clipping': False, 'showSilhouette': False, 'showShadows': False, 'viewSize': 31.513154317216, 'labelsOnTop': True, 'depthCueRange': (0.5, 1), 'silhouetteWidth': 2, 'singleLayerTransparency': True, 'shadowTextureSize': 2048, 'backgroundImage': [None, 1, 2, 1, 0, 0], 'backgroundGradient': [('Chimera default', [(1, 1, 1, 1), (0, 0, 1, 1)], 1), 1, 0, 0], 'depthCue': True, 'highlight': 0, 'scaleFactor': 1.232690049046, 'angleDependentTransparency': True, 'backgroundMethod': 0}, 'viewerHL': 153, 'cameraMode': 'mono', 'detail': 1.5, 'viewerFog': None, 'viewerBG': None}
replyobj.status("Initializing session restore...", blankAfter=0,
secondary=True)
from SimpleSession.versions.v65 import expandSummary
init(dict(enumerate(expandSummary(colorInfo))))
replyobj.status("Restoring colors...", blankAfter=0,
secondary=True)
restoreColors(colors, materials)
replyobj.status("Restoring molecules...", blankAfter=0,
secondary=True)
restoreMolecules(molInfo, resInfo, atomInfo, bondInfo, crdInfo)
replyobj.status("Restoring surfaces...", blankAfter=0,
secondary=True)
restoreSurfaces(surfInfo)
replyobj.status("Restoring VRML models...", blankAfter=0,
secondary=True)
restoreVRML(vrmlInfo)
replyobj.status("Restoring pseudobond groups...", blankAfter=0,
secondary=True)
restorePseudoBondGroups(pbInfo)
replyobj.status("Restoring model associations...", blankAfter=0,
secondary=True)
restoreModelAssociations(modelAssociations)
replyobj.status("Restoring camera...", blankAfter=0,
secondary=True)
restoreViewer(viewerInfo)
try:
restoreCoreModels()
except:
reportRestoreError("Error restoring core models")
replyobj.status("Restoring extension info...", blankAfter=0,
secondary=True)
try:
import StructMeasure
from StructMeasure.DistMonitor import restoreDistances
registerAfterModelsCB(restoreDistances, 1)
except:
reportRestoreError("Error restoring distances in session")
def restoreMidasBase():
formattedPositions = {}
import Midas
Midas.restoreMidasBase(formattedPositions)
try:
restoreMidasBase()
except:
reportRestoreError('Error restoring Midas base state')
def restoreMidasText():
from Midas import midas_text
midas_text.aliases = {}
midas_text.userSurfCategories = {}
try:
restoreMidasText()
except:
reportRestoreError('Error restoring Midas text state')
def restore_cap_attributes():
cap_attributes = \
{
'cap_attributes': [ ],
'cap_color': None,
'cap_offset': 0.01,
'class': 'Caps_State',
'default_cap_offset': 0.01,
'mesh_style': False,
'shown': True,
'subdivision_factor': 1.0,
'version': 1,
}
import SurfaceCap.session
SurfaceCap.session.restore_cap_attributes(cap_attributes)
registerAfterModelsCB(restore_cap_attributes)
def restore_volume_data():
volume_data_state = \
{
'class': 'Volume_Manager_State',
'data_and_regions_state': [ ],
'version': 2,
}
from VolumeViewer import session
session.restore_volume_data_state(volume_data_state)
try:
restore_volume_data()
except:
reportRestoreError('Error restoring volume data')
geomData = {'AxisManager': {}, 'CentroidManager': {}, 'PlaneManager': {}}
try:
from StructMeasure.Geometry import geomManager
geomManager._restoreSession(geomData)
except:
reportRestoreError("Error restoring geometry objects in session")
def restoreSession_RibbonStyleEditor():
import SimpleSession
import RibbonStyleEditor
userScalings = []
userXSections = []
userResidueClasses = []
residueData = [(1, 'Chimera default', 'rounded', u'amino acid'), (2, 'Chimera default', 'rounded', u'amino acid'), (3, 'Chimera default', 'rounded', u'amino acid'), (4, 'Chimera default', 'rounded', u'amino acid'), (5, 'Chimera default', 'rounded', u'amino acid'), (6, 'Chimera default', 'rounded', u'amino acid'), (7, 'Chimera default', 'rounded', u'amino acid'), (8, 'Chimera default', 'rounded', u'amino acid'), (9, 'Chimera default', 'rounded', u'amino acid'), (10, 'Chimera default', 'rounded', u'amino acid'), (11, 'Chimera default', 'rounded', u'amino acid'), (12, 'Chimera default', 'rounded', u'amino acid'), (13, 'Chimera default', 'rounded', u'amino acid'), (14, 'Chimera default', 'rounded', u'amino acid'), (15, 'Chimera default', 'rounded', u'amino acid'), (16, 'Chimera default', 'rounded', u'amino acid'), (17, 'Chimera default', 'rounded', u'amino acid'), (18, 'Chimera default', 'rounded', u'amino acid'), (19, 'Chimera default', 'rounded', u'amino acid'), (20, 'Chimera default', 'rounded', u'amino acid'), (21, 'Chimera default', 'rounded', u'amino acid'), (22, 'Chimera default', 'rounded', u'amino acid'),
(23, 'Chimera default', 'rounded', u'amino acid'), (24, 'Chimera default', 'rounded', u'amino acid'), (25, 'Chimera default', 'rounded', u'amino acid'), (26, 'Chimera default', 'rounded', u'amino acid'), (27, 'Chimera default', 'rounded', u'amino acid'), (28, 'Chimera default', 'rounded', u'amino acid'), (29, 'Chimera default', 'rounded', u'amino acid'), (30, 'Chimera default', 'rounded', u'amino acid'), (31, 'Chimera default', 'rounded', u'amino acid'), (32, 'Chimera default', 'rounded', u'amino acid'), (33, 'Chimera default', 'rounded', u'amino acid'), (34, 'Chimera default', 'rounded', u'amino acid'), (35, 'Chimera default', 'rounded', u'amino acid'), (36, 'Chimera default', 'rounded', u'amino acid'), (37, 'Chimera default', 'rounded', u'amino acid'), (38, 'Chimera default', 'rounded', u'amino acid'), (39, 'Chimera default', 'rounded', u'amino acid'), (40, 'Chimera default', 'rounded', u'amino acid'), (41, 'Chimera default', 'rounded', u'amino acid'), (42, 'Chimera default', 'rounded', u'amino acid'), (43, 'Chimera default', 'rounded', u'amino acid'),
(44, 'Chimera default', 'rounded', u'amino acid'), (45, 'Chimera default', 'rounded', u'amino acid'), (46, 'Chimera default', 'rounded', u'amino acid'), (47, 'Chimera default', 'rounded', u'amino acid'), (48, 'Chimera default', 'rounded', u'amino acid'), (49, 'Chimera default', 'rounded', u'amino acid'), (50, 'Chimera default', 'rounded', u'amino acid'), (51, 'Chimera default', 'rounded', u'amino acid'), (52, 'Chimera default', 'rounded', u'amino acid'), (53, 'Chimera default', 'rounded', u'amino acid'), (54, 'Chimera default', 'rounded', u'amino acid'), (55, 'Chimera default', 'rounded', u'amino acid'), (56, 'Chimera default', 'rounded', u'amino acid'), (57, 'Chimera default', 'rounded', u'amino acid'), (58, 'Chimera default', 'rounded', u'amino acid'), (59, 'Chimera default', 'rounded', u'amino acid'), (60, 'Chimera default', 'rounded', u'amino acid'), (61, 'Chimera default', 'rounded', u'amino acid'), (62, 'Chimera default', 'rounded', u'amino acid'), (63, 'Chimera default', 'rounded', u'amino acid'), (64, 'Chimera default', 'rounded', u'amino acid'),
(65, 'Chimera default', 'rounded', u'amino acid'), (66, 'Chimera default', 'rounded', u'amino acid'), (67, 'Chimera default', 'rounded', u'amino acid'), (68, 'Chimera default', 'rounded', u'amino acid'), (69, 'Chimera default', 'rounded', u'amino acid'), (70, 'Chimera default', 'rounded', u'amino acid'), (71, 'Chimera default', 'rounded', u'amino acid'), (72, 'Chimera default', 'rounded', u'amino acid'), (73, 'Chimera default', 'rounded', u'amino acid'), (74, 'Chimera default', 'rounded', u'amino acid'), (75, 'Chimera default', 'rounded', u'amino acid'), (76, 'Chimera default', 'rounded', u'amino acid'), (77, 'Chimera default', 'rounded', u'amino acid'), (78, 'Chimera default', 'rounded', u'amino acid'), (79, 'Chimera default', 'rounded', u'amino acid'), (80, 'Chimera default', 'rounded', u'amino acid'), (81, 'Chimera default', 'rounded', u'amino acid'), (82, 'Chimera default', 'rounded', u'amino acid'), (83, 'Chimera default', 'rounded', u'amino acid'), (84, 'Chimera default', 'rounded', u'amino acid'), (85, 'Chimera default', 'rounded', u'amino acid'),
(86, 'Chimera default', 'rounded', u'amino acid'), (87, 'Chimera default', 'rounded', u'amino acid'), (88, 'Chimera default', 'rounded', u'amino acid'), (89, 'Chimera default', 'rounded', u'amino acid'), (90, 'Chimera default', 'rounded', u'amino acid'), (91, 'Chimera default', 'rounded', u'amino acid'), (92, 'Chimera default', 'rounded', u'amino acid'), (93, 'Chimera default', 'rounded', u'amino acid'), (94, 'Chimera default', 'rounded', u'amino acid'), (95, 'Chimera default', 'rounded', u'amino acid'), (96, 'Chimera default', 'rounded', u'amino acid'), (97, 'Chimera default', 'rounded', u'amino acid'), (98, 'Chimera default', 'rounded', u'amino acid'), (99, 'Chimera default', 'rounded', u'amino acid'), (100, 'Chimera default', 'rounded', u'amino acid'), (101, 'Chimera default', 'rounded', u'amino acid'), (102, 'Chimera default', 'rounded', u'amino acid'), (103, 'Chimera default', 'rounded', u'amino acid'), (104, 'Chimera default', 'rounded', u'amino acid'), (105, 'Chimera default', 'rounded', u'amino acid'), (106, 'Chimera default', 'rounded', u'amino acid'),
(107, 'Chimera default', 'rounded', u'amino acid'), (108, 'Chimera default', 'rounded', u'amino acid'), (109, 'Chimera default', 'rounded', u'amino acid'), (110, 'Chimera default', 'rounded', u'amino acid'), (111, 'Chimera default', 'rounded', u'amino acid'), (112, 'Chimera default', 'rounded', u'amino acid'), (113, 'Chimera default', 'rounded', u'amino acid'), (114, 'Chimera default', 'rounded', u'amino acid'), (115, 'Chimera default', 'rounded', u'amino acid'), (116, 'Chimera default', 'rounded', u'amino acid'), (117, 'Chimera default', 'rounded', u'amino acid'), (118, 'Chimera default', 'rounded', u'amino acid'), (119, 'Chimera default', 'rounded', u'amino acid'), (120, 'Chimera default', 'rounded', u'amino acid'), (121, 'Chimera default', 'rounded', u'amino acid'), (122, 'Chimera default', 'rounded', u'amino acid'), (123, 'Chimera default', 'rounded', u'amino acid'), (124, 'Chimera default', 'rounded', u'amino acid'), (125, 'Chimera default', 'rounded', u'amino acid'), (126, 'Chimera default', 'rounded', u'amino acid'), (127, 'Chimera default', 'rounded', u'amino acid'),
(128, 'Chimera default', 'rounded', u'amino acid'), (129, 'Chimera default', 'rounded', u'amino acid'), (130, 'Chimera default', 'rounded', u'amino acid'), (131, 'Chimera default', 'rounded', u'amino acid'), (132, 'Chimera default', 'rounded', u'amino acid'), (133, 'Chimera default', 'rounded', u'amino acid'), (134, 'Chimera default', 'rounded', u'amino acid'), (135, 'Chimera default', 'rounded', u'amino acid'), (136, 'Chimera default', 'rounded', u'amino acid'), (137, 'Chimera default', 'rounded', u'amino acid'), (138, 'Chimera default', 'rounded', u'amino acid'), (139, 'Chimera default', 'rounded', u'amino acid'), (140, 'Chimera default', 'rounded', u'amino acid'), (141, 'Chimera default', 'rounded', u'amino acid'), (142, 'Chimera default', 'rounded', u'amino acid'), (143, 'Chimera default', 'rounded', u'amino acid'), (144, 'Chimera default', 'rounded', u'amino acid'), (145, 'Chimera default', 'rounded', u'amino acid'), (146, 'Chimera default', 'rounded', u'amino acid'), (147, 'Chimera default', 'rounded', u'amino acid'), (148, 'Chimera default', 'rounded', u'amino acid'),
(149, 'Chimera default', 'rounded', u'amino acid'), (150, 'Chimera default', 'rounded', u'amino acid'), (151, 'Chimera default', 'rounded', u'amino acid'), (152, 'Chimera default', 'rounded', u'amino acid'), (153, 'Chimera default', 'rounded', u'amino acid'), (154, 'Chimera default', 'rounded', u'amino acid'), (155, 'Chimera default', 'rounded', u'amino acid'), (156, 'Chimera default', 'rounded', u'amino acid'), (157, 'Chimera default', 'rounded', u'amino acid'), (158, 'Chimera default', 'rounded', u'amino acid'), (159, 'Chimera default', 'rounded', u'amino acid'), (160, 'Chimera default', 'rounded', u'amino acid'), (161, 'Chimera default', 'rounded', u'amino acid'), (162, 'Chimera default', 'rounded', u'amino acid'), (163, 'Chimera default', 'rounded', u'amino acid'), (164, 'Chimera default', 'rounded', u'amino acid'), (165, 'Chimera default', 'rounded', u'amino acid')]
flags = RibbonStyleEditor.NucleicDefault1
SimpleSession.registerAfterModelsCB(RibbonStyleEditor.restoreState,
(userScalings, userXSections,
userResidueClasses, residueData, flags))
try:
restoreSession_RibbonStyleEditor()
except:
reportRestoreError("Error restoring RibbonStyleEditor state")
trPickle = 'gAJjQW5pbWF0ZS5UcmFuc2l0aW9ucwpUcmFuc2l0aW9ucwpxASmBcQJ9cQMoVQxjdXN0b21fc2NlbmVxBGNBbmltYXRlLlRyYW5zaXRpb24KVHJhbnNpdGlvbgpxBSmBcQZ9cQcoVQZmcmFtZXNxCEsBVQ1kaXNjcmV0ZUZyYW1lcQlLAVUKcHJvcGVydGllc3EKXXELVQNhbGxxDGFVBG5hbWVxDWgEVQRtb2RlcQ5VBmxpbmVhcnEPdWJVCGtleWZyYW1lcRBoBSmBcRF9cRIoaAhLFGgJSwFoCl1xE2gMYWgNaBBoDmgPdWJVBXNjZW5lcRRoBSmBcRV9cRYoaAhLAWgJSwFoCl1xF2gMYWgNaBRoDmgPdWJ1Yi4='
scPickle = 'gAJjQW5pbWF0ZS5TY2VuZXMKU2NlbmVzCnEBKYFxAn1xA1UHbWFwX2lkc3EEfXNiLg=='
kfPickle = 'gAJjQW5pbWF0ZS5LZXlmcmFtZXMKS2V5ZnJhbWVzCnEBKYFxAn1xA1UHZW50cmllc3EEXXEFc2Iu'
def restoreAnimation():
'A method to unpickle and restore animation objects'
# Scenes must be unpickled after restoring transitions, because each
# scene links to a 'scene' transition. Likewise, keyframes must be
# unpickled after restoring scenes, because each keyframe links to a scene.
# The unpickle process is left to the restore* functions, it's
# important that it doesn't happen prior to calling those functions.
import SimpleSession
from Animate.Session import restoreTransitions
from Animate.Session import restoreScenes
from Animate.Session import restoreKeyframes
SimpleSession.registerAfterModelsCB(restoreTransitions, trPickle)
SimpleSession.registerAfterModelsCB(restoreScenes, scPickle)
SimpleSession.registerAfterModelsCB(restoreKeyframes, kfPickle)
try:
restoreAnimation()
except:
reportRestoreError('Error in Animate.Session')
def restoreLightController():
import Lighting
Lighting._setFromParams({'ratio': 1.25, 'brightness': 1.16, 'material': [30.0, (0.85, 0.85, 0.85), 1.0], 'back': [(0.35740674433659325, 0.6604015517481454, -0.6604015517481455), (1.0, 1.0, 1.0), 0.0], 'mode': 'two-point', 'key': [(-0.35740674433659325, 0.6604015517481454, 0.6604015517481455), (1.0, 1.0, 1.0), 1.0], 'contrast': 0.83, 'fill': [(0.25056280708573153, 0.25056280708573153, 0.9351131265310293), (1.0, 1.0, 1.0), 0.0]})
try:
restoreLightController()
except:
reportRestoreError("Error restoring lighting parameters")
def restore_surface_color_mapping():
try:
surface_color_state = \
{
'class': 'Surface_Colorings_State',
'coloring_table': {},
'geometry': None,
'is_visible': False,
'version': 2,
}
import SurfaceColor.session
SurfaceColor.session.restore_surface_color_state(surface_color_state)
except:
reportRestoreError('Error restoring surface color mapping')
registerAfterModelsCB(restore_surface_color_mapping)
try:
import Ilabel
il = Ilabel.LabelsModel(create=False)
if il:
il.destroy()
il = Ilabel.LabelsModel()
il.restoreSession({'labelIDs': ['label2d_id_0'], 'curLabel': 0, 'labels': [{'opacity': 1.0, 'lines': [[{'args': (u'A',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'v',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'g',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u' ',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'R',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'e',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u's',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u' ',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'R',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'M',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'S',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'D',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u' ',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'(',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u'\xc5',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}, {'args': (u')',), 'kw': {'baselineOffset': 0, 'fontName': 'Sans Serif', 'style': 0, 'rgba': (1, 1, 1, 1.0), 'size': 24}}]], 'shown': True, 'args': ((0.7645914396887159, 0.8175074183976261),), 'kw': {'margin': 9.0, 'outline': 0.0, 'background': None}}], 'labelUID': 1})
del Ilabel, il
except:
reportRestoreError("Error restoring IlabelModel instance in session")
try:
from Ilabel.Arrows import ArrowsModel
ArrowsModel().restore({'arrows': []})
except:
reportRestoreError("Error restoring 2D arrows in session")
try:
from Ilabel.ColorKey import getKeyModel
getKeyModel()._restoreSession({'label spacing': 'proportional to value', 'label justification': 'decimal point', 'font size': 24, 'label positions': 'right/bottom', 'show ticks': False, 'border width': 2, 'label offset': 0, 'color depiction': 'blended', 'label color': (1, 1, 1), 'font name': 'Sans Serif', 'tick length': 10, 'border color': (1, 1, 1, 1.0), 'key position': [(0.8832684824902723, 0.7655786350148368), (0.8482490272373541, 0.19584569732937684)], 'font typeface': 0, 'tick thickness': 4, 'colors/labels': [((0, 0, 1, 1), '0'), ((1, 1, 1, 1), '2.5'), ((1, 0, 0, 1), '5')]})
except:
reportRestoreError("Error restoring color key")
def restore2DLabelDialog(info):
from chimera.dialogs import find
from Ilabel.gui import IlabelDialog
dlg = find(IlabelDialog.name)
if dlg is not None:
dlg.destroy()
dlg = find(IlabelDialog.name, create=True)
dlg._restoreSession(info)
import SimpleSession
SimpleSession.registerAfterModelsCB(restore2DLabelDialog, {'mouse func': 'normal', 'sel ranges': (), 'dialog shown': 0})
def restoreRemainder():
from SimpleSession.versions.v65 import restoreWindowSize, \
restoreOpenStates, restoreSelections, restoreFontInfo, \
restoreOpenModelsAttrs, restoreModelClip, restoreSilhouettes
curSelIds = [1099, 1096, 1856, 1100, 1840, 1837, 1101, 1838, 1839, 1638, 1841, 1842, 1843, 1846, 1844, 1102, 1845, 1640, 1847, 1848, 1849, 1103, 1850, 1851, 1641, 1852, 1853, 1854, 1855, 1654, 1104, 1655, 1656, 1657, 1658, 1659, 1105, 1660, 1643, 1094, 1662, 1663, 1664, 1665, 1666, 1661, 1667, 1644, 1107, 1645, 1652, 1108, 1646, 1649, 1642, 1647, 1639, 1093, 1648, 989, 990, 991, 992, 993, 1095, 994, 995, 1097, 1098, 998, 999, 1000, 1001, 1092, 1002, 1003, 1653, 1004, 1005, 1006, 996, 1007, 1008, 1009, 1650, 1010, 1651, 1011, 1012, 1106, 997, 3617, 4161, 4364, 3507, 4163, 3515, 3508, 4362, 4363, 3621, 4162, 4365, 4164, 4165, 4368, 3610, 4167, 4168, 3611, 4371, 4372, 3522, 4171, 4172, 4375, 4166, 4175, 4378, 4177, 4178, 3612, 4381, 4180, 4181, 4182, 4183, 4184, 4185, 4186, 4369, 4187, 4188, 4189, 4190, 3614, 4169, 4170, 3616, 3619, 4373, 3503, 4370, 3504, 3505, 3506, 3608, 3618, 3609, 3509, 4173, 3511, 3613, 3510, 3513, 3615, 4174, 4376, 4377, 3516, 3517, 3518, 4366, 3620, 3519, 3520, 3521, 4374, 4367, 3623, 3523, 3524, 3525, 4176, 3514, 3622, 3512, 4379, 4380]
savedSels = []
openModelsAttrs = { 'cofrMethod': 4 }
windowSize = (1028, 674)
xformMap = {0: (((-0.094220313915663, 0.99219194913096, 0.081717002669839), 155.51249197235), (1.3829440365597, -0.6672615798726, 24.984074796782), True)}
fontInfo = {'face': (u'Sans Serif', 'Bold', 18)}
clipPlaneInfo = {}
silhouettes = {0: True, 5200: True}
replyobj.status("Restoring window...", blankAfter=0,
secondary=True)
restoreWindowSize(windowSize)
replyobj.status("Restoring open states...", blankAfter=0,
secondary=True)
restoreOpenStates(xformMap)
replyobj.status("Restoring font info...", blankAfter=0,
secondary=True)
restoreFontInfo(fontInfo)
replyobj.status("Restoring selections...", blankAfter=0,
secondary=True)
restoreSelections(curSelIds, savedSels)
replyobj.status("Restoring openModel attributes...", blankAfter=0,
secondary=True)
restoreOpenModelsAttrs(openModelsAttrs)
replyobj.status("Restoring model clipping...", blankAfter=0,
secondary=True)
restoreModelClip(clipPlaneInfo)
replyobj.status("Restoring per-model silhouettes...", blankAfter=0,
secondary=True)
restoreSilhouettes(silhouettes)
replyobj.status("Restoring remaining extension info...", blankAfter=0,
secondary=True)
try:
restoreRemainder()
except:
reportRestoreError("Error restoring post-model state")
from SimpleSession.versions.v65 import makeAfterModelsCBs
makeAfterModelsCBs()
from SimpleSession.versions.v65 import endRestore
replyobj.status('Finishing restore...', blankAfter=0, secondary=True)
endRestore({})
replyobj.status('', secondary=True)
replyobj.status('Restore finished.')
| 858.810726 | 109,226 | 0.938672 | 8,069 | 272,243 | 31.662783 | 0.456066 | 0.001793 | 0.013562 | 0.014208 | 0.033622 | 0.030448 | 0.006697 | 0.005343 | 0.004403 | 0.004208 | 0 | 0.13116 | 0.016309 | 272,243 | 316 | 109,227 | 861.528481 | 0.822851 | 0.001422 | 0 | 0.191011 | 0 | 0.014981 | 0.90743 | 0.871872 | 0 | 1 | 0 | 0 | 0 | 1 | 0.041199 | false | 0 | 0.11985 | 0 | 0.161049 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d9793f9b40bf7aa9411ac568cd048ae29ebc9e05 | 126 | py | Python | tests/test_prism.py | CurtLH/prism-python | 82cf5ef75509550741c73ccf62c4541ff881e469 | [
"Apache-2.0"
] | 14 | 2020-04-02T00:41:46.000Z | 2022-01-06T03:58:53.000Z | tests/test_prism.py | CurtLH/prism-python | 82cf5ef75509550741c73ccf62c4541ff881e469 | [
"Apache-2.0"
] | 15 | 2019-12-05T22:51:04.000Z | 2021-06-25T20:56:42.000Z | tests/test_prism.py | CurtLH/prism-python | 82cf5ef75509550741c73ccf62c4541ff881e469 | [
"Apache-2.0"
] | 5 | 2019-12-05T22:55:49.000Z | 2020-11-18T18:31:59.000Z | import prism
def test_load_schema(schema_file):
schema = prism.load_schema(schema_file)
assert type(schema) is dict
| 18 | 43 | 0.761905 | 19 | 126 | 4.789474 | 0.578947 | 0.21978 | 0.351648 | 0.43956 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 126 | 6 | 44 | 21 | 0.866667 | 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 | 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 | 6 |
d979dfbf5daa3cf11354e84446fa3e360df6dfbe | 3,430 | py | Python | tests/analysis/test_analysis__plot_normalized_variance_comparison.py | kamilazdybal/PCAfold | 251ca0dc8c8f98976266b94147504247ddd09bd2 | [
"MIT"
] | 1 | 2022-02-01T08:57:18.000Z | 2022-02-01T08:57:18.000Z | tests/analysis/test_analysis__plot_normalized_variance_comparison.py | kamilazdybal/PCAfold | 251ca0dc8c8f98976266b94147504247ddd09bd2 | [
"MIT"
] | null | null | null | tests/analysis/test_analysis__plot_normalized_variance_comparison.py | kamilazdybal/PCAfold | 251ca0dc8c8f98976266b94147504247ddd09bd2 | [
"MIT"
] | 1 | 2022-03-13T13:19:56.000Z | 2022-03-13T13:19:56.000Z | import unittest
import numpy as np
from PCAfold import preprocess
from PCAfold import reduction
from PCAfold import analysis
class Analysis(unittest.TestCase):
def test_analysis__plot_normalized_variance_comparison__allowed_calls(self):
X = np.random.rand(100,5)
Y = np.random.rand(100,5)
pca_X = reduction.PCA(X, n_components=2)
pca_Y = reduction.PCA(Y, n_components=2)
principal_components_X = pca_X.transform(X)
principal_components_Y = pca_Y.transform(Y)
variance_data_X = analysis.compute_normalized_variance(principal_components_X, X, depvar_names=['A', 'B', 'C', 'D', 'E'], bandwidth_values=np.logspace(-3, 2, 20), scale_unit_box=True)
variance_data_Y = analysis.compute_normalized_variance(principal_components_Y, Y, depvar_names=['F', 'G', 'H', 'I', 'J'], bandwidth_values=np.logspace(-3, 2, 20), scale_unit_box=True)
# try:
# plt = analysis.plot_normalized_variance_comparison((variance_data_X, variance_data_Y),
# ([0,1,2], [0,1,2]),
# ('Blues', 'Reds'))
# plt.close()
# except:
# self.assertTrue(False)
#
# try:
# plt = analysis.plot_normalized_variance_comparison((variance_data_X, variance_data_Y),
# ([0,1,2], [0,1,2]),
# ('Blues', 'Reds'),
# figure_size=(10,5),
# title='Normalized variance comparison',
# save_filename=None)
# plt.close()
# except:
# self.assertTrue(False)
# ------------------------------------------------------------------------------
def test_analysis__plot_normalized_variance_comparison__not_allowed_calls(self):
X = np.random.rand(100,5)
Y = np.random.rand(100,5)
pca_X = reduction.PCA(X, n_components=2)
pca_Y = reduction.PCA(Y, n_components=2)
principal_components_X = pca_X.transform(X)
principal_components_Y = pca_Y.transform(Y)
variance_data_X = analysis.compute_normalized_variance(principal_components_X, X, depvar_names=['A', 'B', 'C', 'D', 'E'], bandwidth_values=np.logspace(-3, 2, 20), scale_unit_box=True)
variance_data_Y = analysis.compute_normalized_variance(principal_components_Y, Y, depvar_names=['F', 'G', 'H', 'I', 'J'], bandwidth_values=np.logspace(-3, 2, 20), scale_unit_box=True)
with self.assertRaises(ValueError):
plt = analysis.plot_normalized_variance_comparison((variance_data_X, variance_data_Y), ([0,1,2], [0,1,2]), ('Blues', 'Reds'), figure_size=[1])
plt.close()
with self.assertRaises(ValueError):
plt = analysis.plot_normalized_variance_comparison((variance_data_X, variance_data_Y), ([0,1,2], [0,1,2]), ('Blues', 'Reds'), title=[1])
plt.close()
with self.assertRaises(ValueError):
plt = analysis.plot_normalized_variance_comparison((variance_data_X, variance_data_Y), ([0,1,2], [0,1,2]), ('Blues', 'Reds'), save_filename=[1])
plt.close()
# ------------------------------------------------------------------------------
| 49.710145 | 191 | 0.557726 | 394 | 3,430 | 4.555838 | 0.195431 | 0.093593 | 0.016713 | 0.116992 | 0.879109 | 0.879109 | 0.84234 | 0.789972 | 0.789972 | 0.789972 | 0 | 0.028916 | 0.274052 | 3,430 | 68 | 192 | 50.441176 | 0.691968 | 0.271429 | 0 | 0.666667 | 0 | 0 | 0.018967 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 1 | 0.060606 | false | 0 | 0.151515 | 0 | 0.242424 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
793e92fc67806be9cae12afc54d655e82137fdd6 | 23 | py | Python | kde/__init__.py | dagobash/kde | bb00460a47cfe25563f401fad498f7cafaa51fcf | [
"MIT"
] | null | null | null | kde/__init__.py | dagobash/kde | bb00460a47cfe25563f401fad498f7cafaa51fcf | [
"MIT"
] | null | null | null | kde/__init__.py | dagobash/kde | bb00460a47cfe25563f401fad498f7cafaa51fcf | [
"MIT"
] | null | null | null | from kde.kde import KDE | 23 | 23 | 0.826087 | 5 | 23 | 3.8 | 0.6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 23 | 1 | 23 | 23 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
79537ffea4d5375276bfc704c798294f6a262693 | 113 | py | Python | functions.py | rickyu/zspam | f8fffff197c4ed877eafe59ab2f3b9a6d9d7a203 | [
"MIT"
] | null | null | null | functions.py | rickyu/zspam | f8fffff197c4ed877eafe59ab2f3b9a6d9d7a203 | [
"MIT"
] | null | null | null | functions.py | rickyu/zspam | f8fffff197c4ed877eafe59ab2f3b9a6d9d7a203 | [
"MIT"
] | null | null | null | #encoding:utf-8
from datetime import datetime
def user_register_time(mid):
return datetime.now().toordinal() | 22.6 | 37 | 0.778761 | 16 | 113 | 5.375 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.01 | 0.115044 | 113 | 5 | 37 | 22.6 | 0.85 | 0.123894 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
796d7c8fb7487eb9cc2ee378a5c50448e6bf7ed2 | 114 | py | Python | orb_simulator/orbsim_language/orbsim_ast/restart_sim_node.py | dmguezjaviersnet/IA-Sim-Comp-Project | 8165b9546efc45f98091a3774e2dae4f45942048 | [
"MIT"
] | 1 | 2022-01-19T22:49:09.000Z | 2022-01-19T22:49:09.000Z | orb_simulator/orbsim_language/orbsim_ast/restart_sim_node.py | dmguezjaviersnet/IA-Sim-Comp-Project | 8165b9546efc45f98091a3774e2dae4f45942048 | [
"MIT"
] | 15 | 2021-11-10T14:25:02.000Z | 2022-02-12T19:17:11.000Z | orb_simulator/orbsim_language/orbsim_ast/restart_sim_node.py | dmguezjaviersnet/IA-Sim-Comp-Project | 8165b9546efc45f98091a3774e2dae4f45942048 | [
"MIT"
] | null | null | null | from orbsim_language.orbsim_ast.statement_node import StatementNode
class RestartSimNode(StatementNode):
pass | 28.5 | 67 | 0.859649 | 13 | 114 | 7.307692 | 0.846154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096491 | 114 | 4 | 68 | 28.5 | 0.92233 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
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