fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c2b0f1ae13427ff0ae9ad22417d98eefea202f7

62,178

py

Python

bigtable/google/cloud/bigtable_v2/proto/bigtable_pb2.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

2

2021-11-26T07:08:43.000Z

2022-03-07T20:20:04.000Z

bigtable/google/cloud/bigtable_v2/proto/bigtable_pb2.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

6

2019-05-27T22:05:58.000Z

2019-08-05T16:46:16.000Z

bigtable/google/cloud/bigtable_v2/proto/bigtable_pb2.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

1

2021-07-21T17:59:33.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: google/cloud/bigtable_v2/proto/bigtable.proto import sys _b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 from google.cloud.bigtable_v2.proto import ( data_pb2 as google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2, ) from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2 from google.rpc import status_pb2 as google_dot_rpc_dot_status__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name="google/cloud/bigtable_v2/proto/bigtable.proto", package="google.bigtable.v2", syntax="proto3", serialized_options=_b( 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), dependencies=[ google_dot_api_dot_annotations__pb2.DESCRIPTOR, google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2.DESCRIPTOR, google_dot_protobuf_dot_wrappers__pb2.DESCRIPTOR, google_dot_rpc_dot_status__pb2.DESCRIPTOR, ], ) _READROWSREQUEST = _descriptor.Descriptor( name="ReadRowsRequest", full_name="google.bigtable.v2.ReadRowsRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.ReadRowsRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.ReadRowsRequest.app_profile_id", index=1, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rows", full_name="google.bigtable.v2.ReadRowsRequest.rows", index=2, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="filter", full_name="google.bigtable.v2.ReadRowsRequest.filter", index=3, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rows_limit", full_name="google.bigtable.v2.ReadRowsRequest.rows_limit", index=4, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=200, serialized_end=370, ) _READROWSRESPONSE_CELLCHUNK = _descriptor.Descriptor( name="CellChunk", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="family_name", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.family_name", index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="qualifier", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.qualifier", index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="timestamp_micros", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.timestamp_micros", index=3, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="labels", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.labels", index=4, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="value", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.value", index=5, number=6, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="value_size", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.value_size", index=6, number=7, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="reset_row", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.reset_row", index=7, number=8, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="commit_row", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.commit_row", index=8, number=9, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name="row_status", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.row_status", index=0, containing_type=None, fields=[], ) ], serialized_start=488, serialized_end=749, ) _READROWSRESPONSE = _descriptor.Descriptor( name="ReadRowsResponse", full_name="google.bigtable.v2.ReadRowsResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="chunks", full_name="google.bigtable.v2.ReadRowsResponse.chunks", index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="last_scanned_row_key", full_name="google.bigtable.v2.ReadRowsResponse.last_scanned_row_key", index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[_READROWSRESPONSE_CELLCHUNK], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=373, serialized_end=749, ) _SAMPLEROWKEYSREQUEST = _descriptor.Descriptor( name="SampleRowKeysRequest", full_name="google.bigtable.v2.SampleRowKeysRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.SampleRowKeysRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.SampleRowKeysRequest.app_profile_id", index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=751, serialized_end=817, ) _SAMPLEROWKEYSRESPONSE = _descriptor.Descriptor( name="SampleRowKeysResponse", full_name="google.bigtable.v2.SampleRowKeysResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.SampleRowKeysResponse.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="offset_bytes", full_name="google.bigtable.v2.SampleRowKeysResponse.offset_bytes", index=1, number=2, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=819, serialized_end=881, ) _MUTATEROWREQUEST = _descriptor.Descriptor( name="MutateRowRequest", full_name="google.bigtable.v2.MutateRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.MutateRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.MutateRowRequest.app_profile_id", index=1, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.MutateRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="mutations", full_name="google.bigtable.v2.MutateRowRequest.mutations", index=3, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=884, serialized_end=1012, ) _MUTATEROWRESPONSE = _descriptor.Descriptor( name="MutateRowResponse", full_name="google.bigtable.v2.MutateRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1014, serialized_end=1033, ) _MUTATEROWSREQUEST_ENTRY = _descriptor.Descriptor( name="Entry", full_name="google.bigtable.v2.MutateRowsRequest.Entry", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.MutateRowsRequest.Entry.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="mutations", full_name="google.bigtable.v2.MutateRowsRequest.Entry.mutations", index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1163, serialized_end=1236, ) _MUTATEROWSREQUEST = _descriptor.Descriptor( name="MutateRowsRequest", full_name="google.bigtable.v2.MutateRowsRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.MutateRowsRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.MutateRowsRequest.app_profile_id", index=1, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="entries", full_name="google.bigtable.v2.MutateRowsRequest.entries", index=2, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[_MUTATEROWSREQUEST_ENTRY], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1036, serialized_end=1236, ) _MUTATEROWSRESPONSE_ENTRY = _descriptor.Descriptor( name="Entry", full_name="google.bigtable.v2.MutateRowsResponse.Entry", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="index", full_name="google.bigtable.v2.MutateRowsResponse.Entry.index", index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="status", full_name="google.bigtable.v2.MutateRowsResponse.Entry.status", index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1324, serialized_end=1382, ) _MUTATEROWSRESPONSE = _descriptor.Descriptor( name="MutateRowsResponse", full_name="google.bigtable.v2.MutateRowsResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="entries", full_name="google.bigtable.v2.MutateRowsResponse.entries", index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[_MUTATEROWSRESPONSE_ENTRY], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1239, serialized_end=1382, ) _CHECKANDMUTATEROWREQUEST = _descriptor.Descriptor( name="CheckAndMutateRowRequest", full_name="google.bigtable.v2.CheckAndMutateRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.CheckAndMutateRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.CheckAndMutateRowRequest.app_profile_id", index=1, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.CheckAndMutateRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="predicate_filter", full_name="google.bigtable.v2.CheckAndMutateRowRequest.predicate_filter", index=3, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="true_mutations", full_name="google.bigtable.v2.CheckAndMutateRowRequest.true_mutations", index=4, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="false_mutations", full_name="google.bigtable.v2.CheckAndMutateRowRequest.false_mutations", index=5, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1385, serialized_end=1638, ) _CHECKANDMUTATEROWRESPONSE = _descriptor.Descriptor( name="CheckAndMutateRowResponse", full_name="google.bigtable.v2.CheckAndMutateRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="predicate_matched", full_name="google.bigtable.v2.CheckAndMutateRowResponse.predicate_matched", index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1640, serialized_end=1694, ) _READMODIFYWRITEROWREQUEST = _descriptor.Descriptor( name="ReadModifyWriteRowRequest", full_name="google.bigtable.v2.ReadModifyWriteRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.app_profile_id", index=1, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rules", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.rules", index=3, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1697, serialized_end=1841, ) _READMODIFYWRITEROWRESPONSE = _descriptor.Descriptor( name="ReadModifyWriteRowResponse", full_name="google.bigtable.v2.ReadModifyWriteRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row", full_name="google.bigtable.v2.ReadModifyWriteRowResponse.row", index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1843, serialized_end=1909, ) _READROWSREQUEST.fields_by_name[ "rows" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWSET _READROWSREQUEST.fields_by_name[ "filter" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWFILTER _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "family_name" ].message_type = google_dot_protobuf_dot_wrappers__pb2._STRINGVALUE _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "qualifier" ].message_type = google_dot_protobuf_dot_wrappers__pb2._BYTESVALUE _READROWSRESPONSE_CELLCHUNK.containing_type = _READROWSRESPONSE _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"].fields.append( _READROWSRESPONSE_CELLCHUNK.fields_by_name["reset_row"] ) _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "reset_row" ].containing_oneof = _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"] _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"].fields.append( _READROWSRESPONSE_CELLCHUNK.fields_by_name["commit_row"] ) _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "commit_row" ].containing_oneof = _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"] _READROWSRESPONSE.fields_by_name["chunks"].message_type = _READROWSRESPONSE_CELLCHUNK _MUTATEROWREQUEST.fields_by_name[ "mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _MUTATEROWSREQUEST_ENTRY.fields_by_name[ "mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _MUTATEROWSREQUEST_ENTRY.containing_type = _MUTATEROWSREQUEST _MUTATEROWSREQUEST.fields_by_name["entries"].message_type = _MUTATEROWSREQUEST_ENTRY _MUTATEROWSRESPONSE_ENTRY.fields_by_name[ "status" ].message_type = google_dot_rpc_dot_status__pb2._STATUS _MUTATEROWSRESPONSE_ENTRY.containing_type = _MUTATEROWSRESPONSE _MUTATEROWSRESPONSE.fields_by_name["entries"].message_type = _MUTATEROWSRESPONSE_ENTRY _CHECKANDMUTATEROWREQUEST.fields_by_name[ "predicate_filter" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWFILTER _CHECKANDMUTATEROWREQUEST.fields_by_name[ "true_mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _CHECKANDMUTATEROWREQUEST.fields_by_name[ "false_mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _READMODIFYWRITEROWREQUEST.fields_by_name[ "rules" ].message_type = ( google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._READMODIFYWRITERULE ) _READMODIFYWRITEROWRESPONSE.fields_by_name[ "row" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROW DESCRIPTOR.message_types_by_name["ReadRowsRequest"] = _READROWSREQUEST DESCRIPTOR.message_types_by_name["ReadRowsResponse"] = _READROWSRESPONSE DESCRIPTOR.message_types_by_name["SampleRowKeysRequest"] = _SAMPLEROWKEYSREQUEST DESCRIPTOR.message_types_by_name["SampleRowKeysResponse"] = _SAMPLEROWKEYSRESPONSE DESCRIPTOR.message_types_by_name["MutateRowRequest"] = _MUTATEROWREQUEST DESCRIPTOR.message_types_by_name["MutateRowResponse"] = _MUTATEROWRESPONSE DESCRIPTOR.message_types_by_name["MutateRowsRequest"] = _MUTATEROWSREQUEST DESCRIPTOR.message_types_by_name["MutateRowsResponse"] = _MUTATEROWSRESPONSE DESCRIPTOR.message_types_by_name["CheckAndMutateRowRequest"] = _CHECKANDMUTATEROWREQUEST DESCRIPTOR.message_types_by_name[ "CheckAndMutateRowResponse" ] = _CHECKANDMUTATEROWRESPONSE DESCRIPTOR.message_types_by_name[ "ReadModifyWriteRowRequest" ] = _READMODIFYWRITEROWREQUEST DESCRIPTOR.message_types_by_name[ "ReadModifyWriteRowResponse" ] = _READMODIFYWRITEROWRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ReadRowsRequest = _reflection.GeneratedProtocolMessageType( "ReadRowsRequest", (_message.Message,), dict( DESCRIPTOR=_READROWSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.ReadRows. Attributes: table_name: The unique name of the table from which to read. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. rows: The row keys and/or ranges to read. If not specified, reads from all rows. filter: The filter to apply to the contents of the specified row(s). If unset, reads the entirety of each row. rows_limit: The read will terminate after committing to N rows' worth of results. The default (zero) is to return all results. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadRowsRequest) ), ) _sym_db.RegisterMessage(ReadRowsRequest) ReadRowsResponse = _reflection.GeneratedProtocolMessageType( "ReadRowsResponse", (_message.Message,), dict( CellChunk=_reflection.GeneratedProtocolMessageType( "CellChunk", (_message.Message,), dict( DESCRIPTOR=_READROWSRESPONSE_CELLCHUNK, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Specifies a piece of a row's contents returned as part of the read response stream. Attributes: row_key: The row key for this chunk of data. If the row key is empty, this CellChunk is a continuation of the same row as the previous CellChunk in the response stream, even if that CellChunk was in a previous ReadRowsResponse message. family_name: The column family name for this chunk of data. If this message is not present this CellChunk is a continuation of the same column family as the previous CellChunk. The empty string can occur as a column family name in a response so clients must check explicitly for the presence of this message, not just for ``family_name.value`` being non-empty. qualifier: The column qualifier for this chunk of data. If this message is not present, this CellChunk is a continuation of the same column as the previous CellChunk. Column qualifiers may be empty so clients must check for the presence of this message, not just for ``qualifier.value`` being non-empty. timestamp_micros: The cell's stored timestamp, which also uniquely identifies it within its column. Values are always expressed in microseconds, but individual tables may set a coarser granularity to further restrict the allowed values. For example, a table which specifies millisecond granularity will only allow values of ``timestamp_micros`` which are multiples of 1000. Timestamps are only set in the first CellChunk per cell (for cells split into multiple chunks). labels: Labels applied to the cell by a [RowFilter][google.bigtable.v2.RowFilter]. Labels are only set on the first CellChunk per cell. value: The value stored in the cell. Cell values can be split across multiple CellChunks. In that case only the value field will be set in CellChunks after the first: the timestamp and labels will only be present in the first CellChunk, even if the first CellChunk came in a previous ReadRowsResponse. value_size: If this CellChunk is part of a chunked cell value and this is not the final chunk of that cell, value\_size will be set to the total length of the cell value. The client can use this size to pre-allocate memory to hold the full cell value. reset_row: Indicates that the client should drop all previous chunks for ``row_key``, as it will be re-read from the beginning. commit_row: Indicates that the client can safely process all previous chunks for ``row_key``, as its data has been fully read. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadRowsResponse.CellChunk) ), ), DESCRIPTOR=_READROWSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.ReadRows. Attributes: last_scanned_row_key: Optionally the server might return the row key of the last row it has scanned. The client can use this to construct a more efficient retry request if needed: any row keys or portions of ranges less than this row key can be dropped from the request. This is primarily useful for cases where the server has read a lot of data that was filtered out since the last committed row key, allowing the client to skip that work on a retry. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadRowsResponse) ), ) _sym_db.RegisterMessage(ReadRowsResponse) _sym_db.RegisterMessage(ReadRowsResponse.CellChunk) SampleRowKeysRequest = _reflection.GeneratedProtocolMessageType( "SampleRowKeysRequest", (_message.Message,), dict( DESCRIPTOR=_SAMPLEROWKEYSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.SampleRowKeys. Attributes: table_name: The unique name of the table from which to sample row keys. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.SampleRowKeysRequest) ), ) _sym_db.RegisterMessage(SampleRowKeysRequest) SampleRowKeysResponse = _reflection.GeneratedProtocolMessageType( "SampleRowKeysResponse", (_message.Message,), dict( DESCRIPTOR=_SAMPLEROWKEYSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.SampleRowKeys. Attributes: row_key: Sorted streamed sequence of sample row keys in the table. The table might have contents before the first row key in the list and after the last one, but a key containing the empty string indicates "end of table" and will be the last response given, if present. Note that row keys in this list may not have ever been written to or read from, and users should therefore not make any assumptions about the row key structure that are specific to their use case. offset_bytes: Approximate total storage space used by all rows in the table which precede ``row_key``. Buffering the contents of all rows between two subsequent samples would require space roughly equal to the difference in their ``offset_bytes`` fields. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.SampleRowKeysResponse) ), ) _sym_db.RegisterMessage(SampleRowKeysResponse) MutateRowRequest = _reflection.GeneratedProtocolMessageType( "MutateRowRequest", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.MutateRow. Attributes: table_name: The unique name of the table to which the mutation should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the mutation should be applied. mutations: Changes to be atomically applied to the specified row. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry and at most 100000. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowRequest) ), ) _sym_db.RegisterMessage(MutateRowRequest) MutateRowResponse = _reflection.GeneratedProtocolMessageType( "MutateRowResponse", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.MutateRow. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowResponse) ), ) _sym_db.RegisterMessage(MutateRowResponse) MutateRowsRequest = _reflection.GeneratedProtocolMessageType( "MutateRowsRequest", (_message.Message,), dict( Entry=_reflection.GeneratedProtocolMessageType( "Entry", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWSREQUEST_ENTRY, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Attributes: row_key: The key of the row to which the ``mutations`` should be applied. mutations: Changes to be atomically applied to the specified row. Mutations are applied in order, meaning that earlier mutations can be masked by later ones. You must specify at least one mutation. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsRequest.Entry) ), ), DESCRIPTOR=_MUTATEROWSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for BigtableService.MutateRows. Attributes: table_name: The unique name of the table to which the mutations should be applied. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. entries: The row keys and corresponding mutations to be applied in bulk. Each entry is applied as an atomic mutation, but the entries may be applied in arbitrary order (even between entries for the same row). At least one entry must be specified, and in total the entries can contain at most 100000 mutations. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsRequest) ), ) _sym_db.RegisterMessage(MutateRowsRequest) _sym_db.RegisterMessage(MutateRowsRequest.Entry) MutateRowsResponse = _reflection.GeneratedProtocolMessageType( "MutateRowsResponse", (_message.Message,), dict( Entry=_reflection.GeneratedProtocolMessageType( "Entry", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWSRESPONSE_ENTRY, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Attributes: index: The index into the original request's ``entries`` list of the Entry for which a result is being reported. status: The result of the request Entry identified by ``index``. Depending on how requests are batched during execution, it is possible for one Entry to fail due to an error with another Entry. In the event that this occurs, the same error will be reported for both entries. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsResponse.Entry) ), ), DESCRIPTOR=_MUTATEROWSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for BigtableService.MutateRows. Attributes: entries: One or more results for Entries from the batch request. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsResponse) ), ) _sym_db.RegisterMessage(MutateRowsResponse) _sym_db.RegisterMessage(MutateRowsResponse.Entry) CheckAndMutateRowRequest = _reflection.GeneratedProtocolMessageType( "CheckAndMutateRowRequest", (_message.Message,), dict( DESCRIPTOR=_CHECKANDMUTATEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.CheckAndMutateRow. Attributes: table_name: The unique name of the table to which the conditional mutation should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the conditional mutation should be applied. predicate_filter: The filter to be applied to the contents of the specified row. Depending on whether or not any results are yielded, either ``true_mutations`` or ``false_mutations`` will be executed. If unset, checks that the row contains any values at all. true_mutations: Changes to be atomically applied to the specified row if ``predicate_filter`` yields at least one cell when applied to ``row_key``. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry if ``false_mutations`` is empty, and at most 100000. false_mutations: Changes to be atomically applied to the specified row if ``predicate_filter`` does not yield any cells when applied to ``row_key``. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry if ``true_mutations`` is empty, and at most 100000. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.CheckAndMutateRowRequest) ), ) _sym_db.RegisterMessage(CheckAndMutateRowRequest) CheckAndMutateRowResponse = _reflection.GeneratedProtocolMessageType( "CheckAndMutateRowResponse", (_message.Message,), dict( DESCRIPTOR=_CHECKANDMUTATEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.CheckAndMutateRow. Attributes: predicate_matched: Whether or not the request's ``predicate_filter`` yielded any results for the specified row. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.CheckAndMutateRowResponse) ), ) _sym_db.RegisterMessage(CheckAndMutateRowResponse) ReadModifyWriteRowRequest = _reflection.GeneratedProtocolMessageType( "ReadModifyWriteRowRequest", (_message.Message,), dict( DESCRIPTOR=_READMODIFYWRITEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.ReadModifyWriteRow. Attributes: table_name: The unique name of the table to which the read/modify/write rules should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the read/modify/write rules should be applied. rules: Rules specifying how the specified row's contents are to be transformed into writes. Entries are applied in order, meaning that earlier rules will affect the results of later ones. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadModifyWriteRowRequest) ), ) _sym_db.RegisterMessage(ReadModifyWriteRowRequest) ReadModifyWriteRowResponse = _reflection.GeneratedProtocolMessageType( "ReadModifyWriteRowResponse", (_message.Message,), dict( DESCRIPTOR=_READMODIFYWRITEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.ReadModifyWriteRow. Attributes: row: A Row containing the new contents of all cells modified by the request. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadModifyWriteRowResponse) ), ) _sym_db.RegisterMessage(ReadModifyWriteRowResponse) DESCRIPTOR._options = None _BIGTABLE = _descriptor.ServiceDescriptor( name="Bigtable", full_name="google.bigtable.v2.Bigtable", file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=1912, serialized_end=2981, methods=[ _descriptor.MethodDescriptor( name="ReadRows", full_name="google.bigtable.v2.Bigtable.ReadRows", index=0, containing_service=None, input_type=_READROWSREQUEST, output_type=_READROWSRESPONSE, serialized_options=_b( '\202\323\344\223\002>"9/v2/{table_name=projects/*/instances/*/tables/*}:readRows:\001*' ), ), _descriptor.MethodDescriptor( name="SampleRowKeys", full_name="google.bigtable.v2.Bigtable.SampleRowKeys", index=1, containing_service=None, input_type=_SAMPLEROWKEYSREQUEST, output_type=_SAMPLEROWKEYSRESPONSE, serialized_options=_b( "\202\323\344\223\002@\022>/v2/{table_name=projects/*/instances/*/tables/*}:sampleRowKeys" ), ), _descriptor.MethodDescriptor( name="MutateRow", full_name="google.bigtable.v2.Bigtable.MutateRow", index=2, containing_service=None, input_type=_MUTATEROWREQUEST, output_type=_MUTATEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002?":/v2/{table_name=projects/*/instances/*/tables/*}:mutateRow:\001*' ), ), _descriptor.MethodDescriptor( name="MutateRows", full_name="google.bigtable.v2.Bigtable.MutateRows", index=3, containing_service=None, input_type=_MUTATEROWSREQUEST, output_type=_MUTATEROWSRESPONSE, serialized_options=_b( '\202\323\344\223\002@";/v2/{table_name=projects/*/instances/*/tables/*}:mutateRows:\001*' ), ), _descriptor.MethodDescriptor( name="CheckAndMutateRow", full_name="google.bigtable.v2.Bigtable.CheckAndMutateRow", index=4, containing_service=None, input_type=_CHECKANDMUTATEROWREQUEST, output_type=_CHECKANDMUTATEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002G"B/v2/{table_name=projects/*/instances/*/tables/*}:checkAndMutateRow:\001*' ), ), _descriptor.MethodDescriptor( name="ReadModifyWriteRow", full_name="google.bigtable.v2.Bigtable.ReadModifyWriteRow", index=5, containing_service=None, input_type=_READMODIFYWRITEROWREQUEST, output_type=_READMODIFYWRITEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002H"C/v2/{table_name=projects/*/instances/*/tables/*}:readModifyWriteRow:\001*' ), ), ], ) _sym_db.RegisterServiceDescriptor(_BIGTABLE) DESCRIPTOR.services_by_name["Bigtable"] = _BIGTABLE # @@protoc_insertion_point(module_scope)

35.878823

4,612

0.629515

import sys _b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1")) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 from google.cloud.bigtable_v2.proto import ( data_pb2 as google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2, ) from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2 from google.rpc import status_pb2 as google_dot_rpc_dot_status__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name="google/cloud/bigtable_v2/proto/bigtable.proto", package="google.bigtable.v2", syntax="proto3", serialized_options=_b( "\n\026com.google.bigtable.v2B\rBigtableProtoP\001Z:google.golang.org/genproto/googleapis/bigtable/v2;bigtable\252\002\030Google.Cloud.Bigtable.V2\312\002\030Google\\Cloud\\Bigtable\\V2" ), serialized_pb=_b( '\n-google/cloud/bigtable_v2/proto/bigtable.proto\x12\x12google.bigtable.v2\x1a\x1cgoogle/api/annotations.proto\x1a)google/cloud/bigtable_v2/proto/data.proto\x1a\x1egoogle/protobuf/wrappers.proto\x1a\x17google/rpc/status.proto"\xaa\x01\n\x0fReadRowsRequest\x12\x12\n\ntable_name\x18\x01 \x01(\t\x12\x16\n\x0e\x61pp_profile_id\x18\x05 \x01(\t\x12(\n\x04rows\x18\x02 \x01(\x0b\x32\x1a.google.bigtable.v2.RowSet\x12-\n\x06\x66ilter\x18\x03 \x01(\x0b\x32\x1d.google.bigtable.v2.RowFilter\x12\x12\n\nrows_limit\x18\x04 \x01(\x03"\xf8\x02\n\x10ReadRowsResponse\x12>\n\x06\x63hunks\x18\x01 \x03(\x0b\x32..google.bigtable.v2.ReadRowsResponse.CellChunk\x12\x1c\n\x14last_scanned_row_key\x18\x02 \x01(\x0c\x1a\x85\x02\n\tCellChunk\x12\x0f\n\x07row_key\x18\x01 \x01(\x0c\x12\x31\n\x0b\x66\x61mily_name\x18\x02 \x01(\x0b\x32\x1c.google.protobuf.StringValue\x12.\n\tqualifier\x18\x03 \x01(\x0b\x32\x1b.google.protobuf.BytesValue\x12\x18\n\x10timestamp_micros\x18\x04 \x01(\x03\x12\x0e\n\x06labels\x18\x05 \x03(\t\x12\r\n\x05value\x18\x06 \x01(\x0c\x12\x12\n\nvalue_size\x18\x07 \x01(\x05\x12\x13\n\treset_row\x18\x08 \x01(\x08H\x00\x12\x14\n\ncommit_row\x18\t \x01(\x08H\x00\x42\x0c\n\nrow_status"B\n\x14SampleRowKeysRequest\x12\x12\n\ntable_name\x18\x01 \x01(\t\x12\x16\n\x0e\x61pp_profile_id\x18\x02 \x01(\t">\n\x15SampleRowKeysResponse\x12\x0f\n\x07row_key\x18\x01 \x01(\x0c\x12\x14\n\x0coffset_bytes\x18\x02 \x01(\x03"\x80\x01\n\x10MutateRowRequest\x12\x12\n\ntable_name\x18\x01 \x01(\t\x12\x16\n\x0e\x61pp_profile_id\x18\x04 \x01(\t\x12\x0f\n\x07row_key\x18\x02 \x01(\x0c\x12/\n\tmutations\x18\x03 \x03(\x0b\x32\x1c.google.bigtable.v2.Mutation"\x13\n\x11MutateRowResponse"\xc8\x01\n\x11MutateRowsRequest\x12\x12\n\ntable_name\x18\x01 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\x03(\x0b\x32\x1c.google.bigtable.v2.Mutation"6\n\x19\x43heckAndMutateRowResponse\x12\x19\n\x11predicate_matched\x18\x01 \x01(\x08"\x90\x01\n\x19ReadModifyWriteRowRequest\x12\x12\n\ntable_name\x18\x01 \x01(\t\x12\x16\n\x0e\x61pp_profile_id\x18\x04 \x01(\t\x12\x0f\n\x07row_key\x18\x02 \x01(\x0c\x12\x36\n\x05rules\x18\x03 \x03(\x0b\x32\'.google.bigtable.v2.ReadModifyWriteRule"B\n\x1aReadModifyWriteRowResponse\x12$\n\x03row\x18\x01 \x01(\x0b\x32\x17.google.bigtable.v2.Row2\xad\x08\n\x08\x42igtable\x12\x9d\x01\n\x08ReadRows\x12#.google.bigtable.v2.ReadRowsRequest\x1a$.google.bigtable.v2.ReadRowsResponse"D\x82\xd3\xe4\x93\x02>"9/v2/{table_name=projects/*/instances/*/tables/*}:readRows:\x01*0\x01\x12\xae\x01\n\rSampleRowKeys\x12(.google.bigtable.v2.SampleRowKeysRequest\x1a).google.bigtable.v2.SampleRowKeysResponse"F\x82\xd3\xe4\x93\x02@\x12>/v2/{table_name=projects/*/instances/*/tables/*}:sampleRowKeys0\x01\x12\x9f\x01\n\tMutateRow\x12$.google.bigtable.v2.MutateRowRequest\x1a%.google.bigtable.v2.MutateRowResponse"E\x82\xd3\xe4\x93\x02?":/v2/{table_name=projects/*/instances/*/tables/*}:mutateRow:\x01*\x12\xa5\x01\n\nMutateRows\x12%.google.bigtable.v2.MutateRowsRequest\x1a&.google.bigtable.v2.MutateRowsResponse"F\x82\xd3\xe4\x93\x02@";/v2/{table_name=projects/*/instances/*/tables/*}:mutateRows:\x01*0\x01\x12\xbf\x01\n\x11\x43heckAndMutateRow\x12,.google.bigtable.v2.CheckAndMutateRowRequest\x1a-.google.bigtable.v2.CheckAndMutateRowResponse"M\x82\xd3\xe4\x93\x02G"B/v2/{table_name=projects/*/instances/*/tables/*}:checkAndMutateRow:\x01*\x12\xc3\x01\n\x12ReadModifyWriteRow\x12-.google.bigtable.v2.ReadModifyWriteRowRequest\x1a..google.bigtable.v2.ReadModifyWriteRowResponse"N\x82\xd3\xe4\x93\x02H"C/v2/{table_name=projects/*/instances/*/tables/*}:readModifyWriteRow:\x01*B\x9b\x01\n\x16\x63om.google.bigtable.v2B\rBigtableProtoP\x01Z:google.golang.org/genproto/googleapis/bigtable/v2;bigtable\xaa\x02\x18Google.Cloud.Bigtable.V2\xca\x02\x18Google\\Cloud\\Bigtable\\V2b\x06proto3' ), dependencies=[ google_dot_api_dot_annotations__pb2.DESCRIPTOR, google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2.DESCRIPTOR, google_dot_protobuf_dot_wrappers__pb2.DESCRIPTOR, google_dot_rpc_dot_status__pb2.DESCRIPTOR, ], ) _READROWSREQUEST = _descriptor.Descriptor( name="ReadRowsRequest", full_name="google.bigtable.v2.ReadRowsRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.ReadRowsRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.ReadRowsRequest.app_profile_id", index=1, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rows", full_name="google.bigtable.v2.ReadRowsRequest.rows", index=2, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="filter", full_name="google.bigtable.v2.ReadRowsRequest.filter", index=3, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rows_limit", full_name="google.bigtable.v2.ReadRowsRequest.rows_limit", index=4, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=200, serialized_end=370, ) _READROWSRESPONSE_CELLCHUNK = _descriptor.Descriptor( name="CellChunk", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="family_name", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.family_name", index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="qualifier", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.qualifier", index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="timestamp_micros", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.timestamp_micros", index=3, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="labels", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.labels", index=4, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="value", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.value", index=5, number=6, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="value_size", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.value_size", index=6, number=7, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="reset_row", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.reset_row", index=7, number=8, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="commit_row", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.commit_row", index=8, number=9, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name="row_status", full_name="google.bigtable.v2.ReadRowsResponse.CellChunk.row_status", index=0, containing_type=None, fields=[], ) ], serialized_start=488, serialized_end=749, ) _READROWSRESPONSE = _descriptor.Descriptor( name="ReadRowsResponse", full_name="google.bigtable.v2.ReadRowsResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="chunks", full_name="google.bigtable.v2.ReadRowsResponse.chunks", index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="last_scanned_row_key", full_name="google.bigtable.v2.ReadRowsResponse.last_scanned_row_key", index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[_READROWSRESPONSE_CELLCHUNK], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=373, serialized_end=749, ) _SAMPLEROWKEYSREQUEST = _descriptor.Descriptor( name="SampleRowKeysRequest", full_name="google.bigtable.v2.SampleRowKeysRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.SampleRowKeysRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.SampleRowKeysRequest.app_profile_id", index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=751, serialized_end=817, ) _SAMPLEROWKEYSRESPONSE = _descriptor.Descriptor( name="SampleRowKeysResponse", full_name="google.bigtable.v2.SampleRowKeysResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.SampleRowKeysResponse.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="offset_bytes", full_name="google.bigtable.v2.SampleRowKeysResponse.offset_bytes", index=1, number=2, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=819, serialized_end=881, ) _MUTATEROWREQUEST = _descriptor.Descriptor( name="MutateRowRequest", full_name="google.bigtable.v2.MutateRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.MutateRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.MutateRowRequest.app_profile_id", index=1, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.MutateRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="mutations", full_name="google.bigtable.v2.MutateRowRequest.mutations", index=3, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=884, serialized_end=1012, ) _MUTATEROWRESPONSE = _descriptor.Descriptor( name="MutateRowResponse", full_name="google.bigtable.v2.MutateRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1014, serialized_end=1033, ) _MUTATEROWSREQUEST_ENTRY = _descriptor.Descriptor( name="Entry", full_name="google.bigtable.v2.MutateRowsRequest.Entry", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.MutateRowsRequest.Entry.row_key", index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="mutations", full_name="google.bigtable.v2.MutateRowsRequest.Entry.mutations", index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1163, serialized_end=1236, ) _MUTATEROWSREQUEST = _descriptor.Descriptor( name="MutateRowsRequest", full_name="google.bigtable.v2.MutateRowsRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.MutateRowsRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.MutateRowsRequest.app_profile_id", index=1, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="entries", full_name="google.bigtable.v2.MutateRowsRequest.entries", index=2, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[_MUTATEROWSREQUEST_ENTRY], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1036, serialized_end=1236, ) _MUTATEROWSRESPONSE_ENTRY = _descriptor.Descriptor( name="Entry", full_name="google.bigtable.v2.MutateRowsResponse.Entry", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="index", full_name="google.bigtable.v2.MutateRowsResponse.Entry.index", index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="status", full_name="google.bigtable.v2.MutateRowsResponse.Entry.status", index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1324, serialized_end=1382, ) _MUTATEROWSRESPONSE = _descriptor.Descriptor( name="MutateRowsResponse", full_name="google.bigtable.v2.MutateRowsResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="entries", full_name="google.bigtable.v2.MutateRowsResponse.entries", index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[_MUTATEROWSRESPONSE_ENTRY], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1239, serialized_end=1382, ) _CHECKANDMUTATEROWREQUEST = _descriptor.Descriptor( name="CheckAndMutateRowRequest", full_name="google.bigtable.v2.CheckAndMutateRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.CheckAndMutateRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.CheckAndMutateRowRequest.app_profile_id", index=1, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.CheckAndMutateRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="predicate_filter", full_name="google.bigtable.v2.CheckAndMutateRowRequest.predicate_filter", index=3, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="true_mutations", full_name="google.bigtable.v2.CheckAndMutateRowRequest.true_mutations", index=4, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="false_mutations", full_name="google.bigtable.v2.CheckAndMutateRowRequest.false_mutations", index=5, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1385, serialized_end=1638, ) _CHECKANDMUTATEROWRESPONSE = _descriptor.Descriptor( name="CheckAndMutateRowResponse", full_name="google.bigtable.v2.CheckAndMutateRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="predicate_matched", full_name="google.bigtable.v2.CheckAndMutateRowResponse.predicate_matched", index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1640, serialized_end=1694, ) _READMODIFYWRITEROWREQUEST = _descriptor.Descriptor( name="ReadModifyWriteRowRequest", full_name="google.bigtable.v2.ReadModifyWriteRowRequest", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="table_name", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.table_name", index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="app_profile_id", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.app_profile_id", index=1, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode("utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="row_key", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.row_key", index=2, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), _descriptor.FieldDescriptor( name="rules", full_name="google.bigtable.v2.ReadModifyWriteRowRequest.rules", index=3, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ), ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1697, serialized_end=1841, ) _READMODIFYWRITEROWRESPONSE = _descriptor.Descriptor( name="ReadModifyWriteRowResponse", full_name="google.bigtable.v2.ReadModifyWriteRowResponse", filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name="row", full_name="google.bigtable.v2.ReadModifyWriteRowResponse.row", index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, ) ], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], serialized_start=1843, serialized_end=1909, ) _READROWSREQUEST.fields_by_name[ "rows" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWSET _READROWSREQUEST.fields_by_name[ "filter" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWFILTER _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "family_name" ].message_type = google_dot_protobuf_dot_wrappers__pb2._STRINGVALUE _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "qualifier" ].message_type = google_dot_protobuf_dot_wrappers__pb2._BYTESVALUE _READROWSRESPONSE_CELLCHUNK.containing_type = _READROWSRESPONSE _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"].fields.append( _READROWSRESPONSE_CELLCHUNK.fields_by_name["reset_row"] ) _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "reset_row" ].containing_oneof = _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"] _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"].fields.append( _READROWSRESPONSE_CELLCHUNK.fields_by_name["commit_row"] ) _READROWSRESPONSE_CELLCHUNK.fields_by_name[ "commit_row" ].containing_oneof = _READROWSRESPONSE_CELLCHUNK.oneofs_by_name["row_status"] _READROWSRESPONSE.fields_by_name["chunks"].message_type = _READROWSRESPONSE_CELLCHUNK _MUTATEROWREQUEST.fields_by_name[ "mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _MUTATEROWSREQUEST_ENTRY.fields_by_name[ "mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _MUTATEROWSREQUEST_ENTRY.containing_type = _MUTATEROWSREQUEST _MUTATEROWSREQUEST.fields_by_name["entries"].message_type = _MUTATEROWSREQUEST_ENTRY _MUTATEROWSRESPONSE_ENTRY.fields_by_name[ "status" ].message_type = google_dot_rpc_dot_status__pb2._STATUS _MUTATEROWSRESPONSE_ENTRY.containing_type = _MUTATEROWSRESPONSE _MUTATEROWSRESPONSE.fields_by_name["entries"].message_type = _MUTATEROWSRESPONSE_ENTRY _CHECKANDMUTATEROWREQUEST.fields_by_name[ "predicate_filter" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROWFILTER _CHECKANDMUTATEROWREQUEST.fields_by_name[ "true_mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _CHECKANDMUTATEROWREQUEST.fields_by_name[ "false_mutations" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._MUTATION _READMODIFYWRITEROWREQUEST.fields_by_name[ "rules" ].message_type = ( google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._READMODIFYWRITERULE ) _READMODIFYWRITEROWRESPONSE.fields_by_name[ "row" ].message_type = google_dot_cloud_dot_bigtable__v2_dot_proto_dot_data__pb2._ROW DESCRIPTOR.message_types_by_name["ReadRowsRequest"] = _READROWSREQUEST DESCRIPTOR.message_types_by_name["ReadRowsResponse"] = _READROWSRESPONSE DESCRIPTOR.message_types_by_name["SampleRowKeysRequest"] = _SAMPLEROWKEYSREQUEST DESCRIPTOR.message_types_by_name["SampleRowKeysResponse"] = _SAMPLEROWKEYSRESPONSE DESCRIPTOR.message_types_by_name["MutateRowRequest"] = _MUTATEROWREQUEST DESCRIPTOR.message_types_by_name["MutateRowResponse"] = _MUTATEROWRESPONSE DESCRIPTOR.message_types_by_name["MutateRowsRequest"] = _MUTATEROWSREQUEST DESCRIPTOR.message_types_by_name["MutateRowsResponse"] = _MUTATEROWSRESPONSE DESCRIPTOR.message_types_by_name["CheckAndMutateRowRequest"] = _CHECKANDMUTATEROWREQUEST DESCRIPTOR.message_types_by_name[ "CheckAndMutateRowResponse" ] = _CHECKANDMUTATEROWRESPONSE DESCRIPTOR.message_types_by_name[ "ReadModifyWriteRowRequest" ] = _READMODIFYWRITEROWREQUEST DESCRIPTOR.message_types_by_name[ "ReadModifyWriteRowResponse" ] = _READMODIFYWRITEROWRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ReadRowsRequest = _reflection.GeneratedProtocolMessageType( "ReadRowsRequest", (_message.Message,), dict( DESCRIPTOR=_READROWSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.ReadRows. Attributes: table_name: The unique name of the table from which to read. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. rows: The row keys and/or ranges to read. If not specified, reads from all rows. filter: The filter to apply to the contents of the specified row(s). If unset, reads the entirety of each row. rows_limit: The read will terminate after committing to N rows' worth of results. The default (zero) is to return all results. """, ), ) _sym_db.RegisterMessage(ReadRowsRequest) ReadRowsResponse = _reflection.GeneratedProtocolMessageType( "ReadRowsResponse", (_message.Message,), dict( CellChunk=_reflection.GeneratedProtocolMessageType( "CellChunk", (_message.Message,), dict( DESCRIPTOR=_READROWSRESPONSE_CELLCHUNK, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Specifies a piece of a row's contents returned as part of the read response stream. Attributes: row_key: The row key for this chunk of data. If the row key is empty, this CellChunk is a continuation of the same row as the previous CellChunk in the response stream, even if that CellChunk was in a previous ReadRowsResponse message. family_name: The column family name for this chunk of data. If this message is not present this CellChunk is a continuation of the same column family as the previous CellChunk. The empty string can occur as a column family name in a response so clients must check explicitly for the presence of this message, not just for ``family_name.value`` being non-empty. qualifier: The column qualifier for this chunk of data. If this message is not present, this CellChunk is a continuation of the same column as the previous CellChunk. Column qualifiers may be empty so clients must check for the presence of this message, not just for ``qualifier.value`` being non-empty. timestamp_micros: The cell's stored timestamp, which also uniquely identifies it within its column. Values are always expressed in microseconds, but individual tables may set a coarser granularity to further restrict the allowed values. For example, a table which specifies millisecond granularity will only allow values of ``timestamp_micros`` which are multiples of 1000. Timestamps are only set in the first CellChunk per cell (for cells split into multiple chunks). labels: Labels applied to the cell by a [RowFilter][google.bigtable.v2.RowFilter]. Labels are only set on the first CellChunk per cell. value: The value stored in the cell. Cell values can be split across multiple CellChunks. In that case only the value field will be set in CellChunks after the first: the timestamp and labels will only be present in the first CellChunk, even if the first CellChunk came in a previous ReadRowsResponse. value_size: If this CellChunk is part of a chunked cell value and this is not the final chunk of that cell, value\_size will be set to the total length of the cell value. The client can use this size to pre-allocate memory to hold the full cell value. reset_row: Indicates that the client should drop all previous chunks for ``row_key``, as it will be re-read from the beginning. commit_row: Indicates that the client can safely process all previous chunks for ``row_key``, as its data has been fully read. """, ), ), DESCRIPTOR=_READROWSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.ReadRows. Attributes: last_scanned_row_key: Optionally the server might return the row key of the last row it has scanned. The client can use this to construct a more efficient retry request if needed: any row keys or portions of ranges less than this row key can be dropped from the request. This is primarily useful for cases where the server has read a lot of data that was filtered out since the last committed row key, allowing the client to skip that work on a retry. """, ), ) _sym_db.RegisterMessage(ReadRowsResponse) _sym_db.RegisterMessage(ReadRowsResponse.CellChunk) SampleRowKeysRequest = _reflection.GeneratedProtocolMessageType( "SampleRowKeysRequest", (_message.Message,), dict( DESCRIPTOR=_SAMPLEROWKEYSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.SampleRowKeys. Attributes: table_name: The unique name of the table from which to sample row keys. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. """, ), ) _sym_db.RegisterMessage(SampleRowKeysRequest) SampleRowKeysResponse = _reflection.GeneratedProtocolMessageType( "SampleRowKeysResponse", (_message.Message,), dict( DESCRIPTOR=_SAMPLEROWKEYSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.SampleRowKeys. Attributes: row_key: Sorted streamed sequence of sample row keys in the table. The table might have contents before the first row key in the list and after the last one, but a key containing the empty string indicates "end of table" and will be the last response given, if present. Note that row keys in this list may not have ever been written to or read from, and users should therefore not make any assumptions about the row key structure that are specific to their use case. offset_bytes: Approximate total storage space used by all rows in the table which precede ``row_key``. Buffering the contents of all rows between two subsequent samples would require space roughly equal to the difference in their ``offset_bytes`` fields. """, ), ) _sym_db.RegisterMessage(SampleRowKeysResponse) MutateRowRequest = _reflection.GeneratedProtocolMessageType( "MutateRowRequest", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.MutateRow. Attributes: table_name: The unique name of the table to which the mutation should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the mutation should be applied. mutations: Changes to be atomically applied to the specified row. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry and at most 100000. """, ), ) _sym_db.RegisterMessage(MutateRowRequest) MutateRowResponse = _reflection.GeneratedProtocolMessageType( "MutateRowResponse", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.MutateRow. """, ), ) _sym_db.RegisterMessage(MutateRowResponse) MutateRowsRequest = _reflection.GeneratedProtocolMessageType( "MutateRowsRequest", (_message.Message,), dict( Entry=_reflection.GeneratedProtocolMessageType( "Entry", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWSREQUEST_ENTRY, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Attributes: row_key: The key of the row to which the ``mutations`` should be applied. mutations: Changes to be atomically applied to the specified row. Mutations are applied in order, meaning that earlier mutations can be masked by later ones. You must specify at least one mutation. """, ), ), DESCRIPTOR=_MUTATEROWSREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for BigtableService.MutateRows. Attributes: table_name: The unique name of the table to which the mutations should be applied. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. entries: The row keys and corresponding mutations to be applied in bulk. Each entry is applied as an atomic mutation, but the entries may be applied in arbitrary order (even between entries for the same row). At least one entry must be specified, and in total the entries can contain at most 100000 mutations. """, ), ) _sym_db.RegisterMessage(MutateRowsRequest) _sym_db.RegisterMessage(MutateRowsRequest.Entry) MutateRowsResponse = _reflection.GeneratedProtocolMessageType( "MutateRowsResponse", (_message.Message,), dict( Entry=_reflection.GeneratedProtocolMessageType( "Entry", (_message.Message,), dict( DESCRIPTOR=_MUTATEROWSRESPONSE_ENTRY, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Attributes: index: The index into the original request's ``entries`` list of the Entry for which a result is being reported. status: The result of the request Entry identified by ``index``. Depending on how requests are batched during execution, it is possible for one Entry to fail due to an error with another Entry. In the event that this occurs, the same error will be reported for both entries. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsResponse.Entry) ), ), DESCRIPTOR=_MUTATEROWSRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for BigtableService.MutateRows. Attributes: entries: One or more results for Entries from the batch request. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.MutateRowsResponse) ), ) _sym_db.RegisterMessage(MutateRowsResponse) _sym_db.RegisterMessage(MutateRowsResponse.Entry) CheckAndMutateRowRequest = _reflection.GeneratedProtocolMessageType( "CheckAndMutateRowRequest", (_message.Message,), dict( DESCRIPTOR=_CHECKANDMUTATEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.CheckAndMutateRow. Attributes: table_name: The unique name of the table to which the conditional mutation should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the conditional mutation should be applied. predicate_filter: The filter to be applied to the contents of the specified row. Depending on whether or not any results are yielded, either ``true_mutations`` or ``false_mutations`` will be executed. If unset, checks that the row contains any values at all. true_mutations: Changes to be atomically applied to the specified row if ``predicate_filter`` yields at least one cell when applied to ``row_key``. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry if ``false_mutations`` is empty, and at most 100000. false_mutations: Changes to be atomically applied to the specified row if ``predicate_filter`` does not yield any cells when applied to ``row_key``. Entries are applied in order, meaning that earlier mutations can be masked by later ones. Must contain at least one entry if ``true_mutations`` is empty, and at most 100000. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.CheckAndMutateRowRequest) ), ) _sym_db.RegisterMessage(CheckAndMutateRowRequest) CheckAndMutateRowResponse = _reflection.GeneratedProtocolMessageType( "CheckAndMutateRowResponse", (_message.Message,), dict( DESCRIPTOR=_CHECKANDMUTATEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.CheckAndMutateRow. Attributes: predicate_matched: Whether or not the request's ``predicate_filter`` yielded any results for the specified row. """, ), ) _sym_db.RegisterMessage(CheckAndMutateRowResponse) ReadModifyWriteRowRequest = _reflection.GeneratedProtocolMessageType( "ReadModifyWriteRowRequest", (_message.Message,), dict( DESCRIPTOR=_READMODIFYWRITEROWREQUEST, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Request message for Bigtable.ReadModifyWriteRow. Attributes: table_name: The unique name of the table to which the read/modify/write rules should be applied. Values are of the form ``projects/<project>/instances/<instance>/tables/<table>``. app_profile_id: This value specifies routing for replication. If not specified, the "default" application profile will be used. row_key: The key of the row to which the read/modify/write rules should be applied. rules: Rules specifying how the specified row's contents are to be transformed into writes. Entries are applied in order, meaning that earlier rules will affect the results of later ones. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadModifyWriteRowRequest) ), ) _sym_db.RegisterMessage(ReadModifyWriteRowRequest) ReadModifyWriteRowResponse = _reflection.GeneratedProtocolMessageType( "ReadModifyWriteRowResponse", (_message.Message,), dict( DESCRIPTOR=_READMODIFYWRITEROWRESPONSE, __module__="google.cloud.bigtable_v2.proto.bigtable_pb2", __doc__="""Response message for Bigtable.ReadModifyWriteRow. Attributes: row: A Row containing the new contents of all cells modified by the request. """, # @@protoc_insertion_point(class_scope:google.bigtable.v2.ReadModifyWriteRowResponse) ), ) _sym_db.RegisterMessage(ReadModifyWriteRowResponse) DESCRIPTOR._options = None _BIGTABLE = _descriptor.ServiceDescriptor( name="Bigtable", full_name="google.bigtable.v2.Bigtable", file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=1912, serialized_end=2981, methods=[ _descriptor.MethodDescriptor( name="ReadRows", full_name="google.bigtable.v2.Bigtable.ReadRows", index=0, containing_service=None, input_type=_READROWSREQUEST, output_type=_READROWSRESPONSE, serialized_options=_b( '\202\323\344\223\002>"9/v2/{table_name=projects/*/instances/*/tables/*}:readRows:\001*' ), ), _descriptor.MethodDescriptor( name="SampleRowKeys", full_name="google.bigtable.v2.Bigtable.SampleRowKeys", index=1, containing_service=None, input_type=_SAMPLEROWKEYSREQUEST, output_type=_SAMPLEROWKEYSRESPONSE, serialized_options=_b( "\202\323\344\223\002@\022>/v2/{table_name=projects/*/instances/*/tables/*}:sampleRowKeys" ), ), _descriptor.MethodDescriptor( name="MutateRow", full_name="google.bigtable.v2.Bigtable.MutateRow", index=2, containing_service=None, input_type=_MUTATEROWREQUEST, output_type=_MUTATEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002?":/v2/{table_name=projects/*/instances/*/tables/*}:mutateRow:\001*' ), ), _descriptor.MethodDescriptor( name="MutateRows", full_name="google.bigtable.v2.Bigtable.MutateRows", index=3, containing_service=None, input_type=_MUTATEROWSREQUEST, output_type=_MUTATEROWSRESPONSE, serialized_options=_b( '\202\323\344\223\002@";/v2/{table_name=projects/*/instances/*/tables/*}:mutateRows:\001*' ), ), _descriptor.MethodDescriptor( name="CheckAndMutateRow", full_name="google.bigtable.v2.Bigtable.CheckAndMutateRow", index=4, containing_service=None, input_type=_CHECKANDMUTATEROWREQUEST, output_type=_CHECKANDMUTATEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002G"B/v2/{table_name=projects/*/instances/*/tables/*}:checkAndMutateRow:\001*' ), ), _descriptor.MethodDescriptor( name="ReadModifyWriteRow", full_name="google.bigtable.v2.Bigtable.ReadModifyWriteRow", index=5, containing_service=None, input_type=_READMODIFYWRITEROWREQUEST, output_type=_READMODIFYWRITEROWRESPONSE, serialized_options=_b( '\202\323\344\223\002H"C/v2/{table_name=projects/*/instances/*/tables/*}:readModifyWriteRow:\001*' ), ), ], ) _sym_db.RegisterServiceDescriptor(_BIGTABLE) DESCRIPTOR.services_by_name["Bigtable"] = _BIGTABLE # @@protoc_insertion_point(module_scope)

true

1c2b0f985ef8e35ab5fe8006ac361bf9a33ddac1

18,262

py

Python

openpeerpower/components/xiaomi_miio/vacuum.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

1

2021-07-08T20:09:55.000Z

openpeerpower/components/xiaomi_miio/vacuum.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

47

2021-02-21T23:43:07.000Z

2022-03-31T06:07:10.000Z

openpeerpower/components/xiaomi_miio/vacuum.py

OpenPeerPower/core

f673dfac9f2d0c48fa30af37b0a99df9dd6640ee

[ "Apache-2.0" ]

null

"""Support for the Xiaomi vacuum cleaner robot.""" from functools import partial import logging from miio import DeviceException, Vacuum import voluptuous as vol from openpeerpower.components.vacuum import ( ATTR_CLEANED_AREA, PLATFORM_SCHEMA, STATE_CLEANING, STATE_DOCKED, STATE_ERROR, STATE_IDLE, STATE_PAUSED, STATE_RETURNING, SUPPORT_BATTERY, SUPPORT_CLEAN_SPOT, SUPPORT_FAN_SPEED, SUPPORT_LOCATE, SUPPORT_PAUSE, SUPPORT_RETURN_HOME, SUPPORT_SEND_COMMAND, SUPPORT_START, SUPPORT_STATE, SUPPORT_STOP, StateVacuumEntity, ) from openpeerpower.config_entries import SOURCE_IMPORT from openpeerpower.const import CONF_HOST, CONF_NAME, CONF_TOKEN, STATE_OFF, STATE_ON from openpeerpower.helpers import config_validation as cv, entity_platform from openpeerpower.util.dt import as_utc from .const import ( CONF_DEVICE, CONF_FLOW_TYPE, DOMAIN, SERVICE_CLEAN_SEGMENT, SERVICE_CLEAN_ZONE, SERVICE_GOTO, SERVICE_MOVE_REMOTE_CONTROL, SERVICE_MOVE_REMOTE_CONTROL_STEP, SERVICE_START_REMOTE_CONTROL, SERVICE_STOP_REMOTE_CONTROL, ) from .device import XiaomiMiioEntity _LOGGER = logging.getLogger(__name__) DEFAULT_NAME = "Xiaomi Vacuum cleaner" PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Required(CONF_TOKEN): vol.All(str, vol.Length(min=32, max=32)), vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, }, extra=vol.ALLOW_EXTRA, ) ATTR_CLEAN_START = "clean_start" ATTR_CLEAN_STOP = "clean_stop" ATTR_CLEANING_TIME = "cleaning_time" ATTR_DO_NOT_DISTURB = "do_not_disturb" ATTR_DO_NOT_DISTURB_START = "do_not_disturb_start" ATTR_DO_NOT_DISTURB_END = "do_not_disturb_end" ATTR_MAIN_BRUSH_LEFT = "main_brush_left" ATTR_SIDE_BRUSH_LEFT = "side_brush_left" ATTR_FILTER_LEFT = "filter_left" ATTR_SENSOR_DIRTY_LEFT = "sensor_dirty_left" ATTR_CLEANING_COUNT = "cleaning_count" ATTR_CLEANED_TOTAL_AREA = "total_cleaned_area" ATTR_CLEANING_TOTAL_TIME = "total_cleaning_time" ATTR_ERROR = "error" ATTR_RC_DURATION = "duration" ATTR_RC_ROTATION = "rotation" ATTR_RC_VELOCITY = "velocity" ATTR_STATUS = "status" ATTR_ZONE_ARRAY = "zone" ATTR_ZONE_REPEATER = "repeats" ATTR_TIMERS = "timers" ATTR_MOP_ATTACHED = "mop_attached" SUPPORT_XIAOMI = ( SUPPORT_STATE | SUPPORT_PAUSE | SUPPORT_STOP | SUPPORT_RETURN_HOME | SUPPORT_FAN_SPEED | SUPPORT_SEND_COMMAND | SUPPORT_LOCATE | SUPPORT_BATTERY | SUPPORT_CLEAN_SPOT | SUPPORT_START ) STATE_CODE_TO_STATE = { 1: STATE_IDLE, # "Starting" 2: STATE_IDLE, # "Charger disconnected" 3: STATE_IDLE, # "Idle" 4: STATE_CLEANING, # "Remote control active" 5: STATE_CLEANING, # "Cleaning" 6: STATE_RETURNING, # "Returning home" 7: STATE_CLEANING, # "Manual mode" 8: STATE_DOCKED, # "Charging" 9: STATE_ERROR, # "Charging problem" 10: STATE_PAUSED, # "Paused" 11: STATE_CLEANING, # "Spot cleaning" 12: STATE_ERROR, # "Error" 13: STATE_IDLE, # "Shutting down" 14: STATE_DOCKED, # "Updating" 15: STATE_RETURNING, # "Docking" 16: STATE_CLEANING, # "Going to target" 17: STATE_CLEANING, # "Zoned cleaning" 18: STATE_CLEANING, # "Segment cleaning" 100: STATE_DOCKED, # "Charging complete" 101: STATE_ERROR, # "Device offline" } async def async_setup_platform(opp, config, async_add_entities, discovery_info=None): """Import Miio configuration from YAML.""" _LOGGER.warning( "Loading Xiaomi Miio Vacuum via platform setup is deprecated; Please remove it from your configuration" ) opp.async_create_task( opp.config_entries.flow.async_init( DOMAIN, context={"source": SOURCE_IMPORT}, data=config, ) ) async def async_setup_entry(opp, config_entry, async_add_entities): """Set up the Xiaomi vacuum cleaner robot from a config entry.""" entities = [] if config_entry.data[CONF_FLOW_TYPE] == CONF_DEVICE: host = config_entry.data[CONF_HOST] token = config_entry.data[CONF_TOKEN] name = config_entry.title unique_id = config_entry.unique_id # Create handler _LOGGER.debug("Initializing with host %s (token %s...)", host, token[:5]) vacuum = Vacuum(host, token) mirobo = MiroboVacuum(name, vacuum, config_entry, unique_id) entities.append(mirobo) platform = entity_platform.async_get_current_platform() platform.async_register_entity_service( SERVICE_START_REMOTE_CONTROL, {}, MiroboVacuum.async_remote_control_start.__name__, ) platform.async_register_entity_service( SERVICE_STOP_REMOTE_CONTROL, {}, MiroboVacuum.async_remote_control_stop.__name__, ) platform.async_register_entity_service( SERVICE_MOVE_REMOTE_CONTROL, { vol.Optional(ATTR_RC_VELOCITY): vol.All( vol.Coerce(float), vol.Clamp(min=-0.29, max=0.29) ), vol.Optional(ATTR_RC_ROTATION): vol.All( vol.Coerce(int), vol.Clamp(min=-179, max=179) ), vol.Optional(ATTR_RC_DURATION): cv.positive_int, }, MiroboVacuum.async_remote_control_move.__name__, ) platform.async_register_entity_service( SERVICE_MOVE_REMOTE_CONTROL_STEP, { vol.Optional(ATTR_RC_VELOCITY): vol.All( vol.Coerce(float), vol.Clamp(min=-0.29, max=0.29) ), vol.Optional(ATTR_RC_ROTATION): vol.All( vol.Coerce(int), vol.Clamp(min=-179, max=179) ), vol.Optional(ATTR_RC_DURATION): cv.positive_int, }, MiroboVacuum.async_remote_control_move_step.__name__, ) platform.async_register_entity_service( SERVICE_CLEAN_ZONE, { vol.Required(ATTR_ZONE_ARRAY): vol.All( list, [ vol.ExactSequence( [ vol.Coerce(int), vol.Coerce(int), vol.Coerce(int), vol.Coerce(int), ] ) ], ), vol.Required(ATTR_ZONE_REPEATER): vol.All( vol.Coerce(int), vol.Clamp(min=1, max=3) ), }, MiroboVacuum.async_clean_zone.__name__, ) platform.async_register_entity_service( SERVICE_GOTO, { vol.Required("x_coord"): vol.Coerce(int), vol.Required("y_coord"): vol.Coerce(int), }, MiroboVacuum.async_goto.__name__, ) platform.async_register_entity_service( SERVICE_CLEAN_SEGMENT, {vol.Required("segments"): vol.Any(vol.Coerce(int), [vol.Coerce(int)])}, MiroboVacuum.async_clean_segment.__name__, ) async_add_entities(entities, update_before_add=True) class MiroboVacuum(XiaomiMiioEntity, StateVacuumEntity): """Representation of a Xiaomi Vacuum cleaner robot.""" def __init__(self, name, device, entry, unique_id): """Initialize the Xiaomi vacuum cleaner robot handler.""" super().__init__(name, device, entry, unique_id) self.vacuum_state = None self._available = False self.consumable_state = None self.clean_history = None self.dnd_state = None self.last_clean = None self._fan_speeds = None self._fan_speeds_reverse = None self._timers = None @property def state(self): """Return the status of the vacuum cleaner.""" if self.vacuum_state is not None: # The vacuum reverts back to an idle state after erroring out. # We want to keep returning an error until it has been cleared. if self.vacuum_state.got_error: return STATE_ERROR try: return STATE_CODE_TO_STATE[int(self.vacuum_state.state_code)] except KeyError: _LOGGER.error( "STATE not supported: %s, state_code: %s", self.vacuum_state.state, self.vacuum_state.state_code, ) return None @property def battery_level(self): """Return the battery level of the vacuum cleaner.""" if self.vacuum_state is not None: return self.vacuum_state.battery @property def fan_speed(self): """Return the fan speed of the vacuum cleaner.""" if self.vacuum_state is not None: speed = self.vacuum_state.fanspeed if speed in self._fan_speeds_reverse: return self._fan_speeds_reverse[speed] _LOGGER.debug("Unable to find reverse for %s", speed) return speed @property def fan_speed_list(self): """Get the list of available fan speed steps of the vacuum cleaner.""" return list(self._fan_speeds) if self._fan_speeds else [] @property def timers(self): """Get the list of added timers of the vacuum cleaner.""" return [ { "enabled": timer.enabled, "cron": timer.cron, "next_schedule": as_utc(timer.next_schedule), } for timer in self._timers ] @property def extra_state_attributes(self): """Return the specific state attributes of this vacuum cleaner.""" attrs = {} if self.vacuum_state is not None: attrs.update( { ATTR_DO_NOT_DISTURB: STATE_ON if self.dnd_state.enabled else STATE_OFF, ATTR_DO_NOT_DISTURB_START: str(self.dnd_state.start), ATTR_DO_NOT_DISTURB_END: str(self.dnd_state.end), # Not working --> 'Cleaning mode': # STATE_ON if self.vacuum_state.in_cleaning else STATE_OFF, ATTR_CLEANING_TIME: int( self.vacuum_state.clean_time.total_seconds() / 60 ), ATTR_CLEANED_AREA: int(self.vacuum_state.clean_area), ATTR_CLEANING_COUNT: int(self.clean_history.count), ATTR_CLEANED_TOTAL_AREA: int(self.clean_history.total_area), ATTR_CLEANING_TOTAL_TIME: int( self.clean_history.total_duration.total_seconds() / 60 ), ATTR_MAIN_BRUSH_LEFT: int( self.consumable_state.main_brush_left.total_seconds() / 3600 ), ATTR_SIDE_BRUSH_LEFT: int( self.consumable_state.side_brush_left.total_seconds() / 3600 ), ATTR_FILTER_LEFT: int( self.consumable_state.filter_left.total_seconds() / 3600 ), ATTR_SENSOR_DIRTY_LEFT: int( self.consumable_state.sensor_dirty_left.total_seconds() / 3600 ), ATTR_STATUS: str(self.vacuum_state.state), ATTR_MOP_ATTACHED: self.vacuum_state.is_water_box_attached, } ) if self.last_clean: attrs[ATTR_CLEAN_START] = self.last_clean.start attrs[ATTR_CLEAN_STOP] = self.last_clean.end if self.vacuum_state.got_error: attrs[ATTR_ERROR] = self.vacuum_state.error if self.timers: attrs[ATTR_TIMERS] = self.timers return attrs @property def available(self) -> bool: """Return True if entity is available.""" return self._available @property def supported_features(self): """Flag vacuum cleaner robot features that are supported.""" return SUPPORT_XIAOMI async def _try_command(self, mask_error, func, *args, **kwargs): """Call a vacuum command handling error messages.""" try: await self.opp.async_add_executor_job(partial(func, *args, **kwargs)) return True except DeviceException as exc: _LOGGER.error(mask_error, exc) return False async def async_start(self): """Start or resume the cleaning task.""" await self._try_command( "Unable to start the vacuum: %s", self._device.resume_or_start ) async def async_pause(self): """Pause the cleaning task.""" await self._try_command("Unable to set start/pause: %s", self._device.pause) async def async_stop(self, **kwargs): """Stop the vacuum cleaner.""" await self._try_command("Unable to stop: %s", self._device.stop) async def async_set_fan_speed(self, fan_speed, **kwargs): """Set fan speed.""" if fan_speed in self._fan_speeds: fan_speed = self._fan_speeds[fan_speed] else: try: fan_speed = int(fan_speed) except ValueError as exc: _LOGGER.error( "Fan speed step not recognized (%s). Valid speeds are: %s", exc, self.fan_speed_list, ) return await self._try_command( "Unable to set fan speed: %s", self._device.set_fan_speed, fan_speed ) async def async_return_to_base(self, **kwargs): """Set the vacuum cleaner to return to the dock.""" await self._try_command("Unable to return home: %s", self._device.home) async def async_clean_spot(self, **kwargs): """Perform a spot clean-up.""" await self._try_command( "Unable to start the vacuum for a spot clean-up: %s", self._device.spot ) async def async_locate(self, **kwargs): """Locate the vacuum cleaner.""" await self._try_command("Unable to locate the botvac: %s", self._device.find) async def async_send_command(self, command, params=None, **kwargs): """Send raw command.""" await self._try_command( "Unable to send command to the vacuum: %s", self._device.raw_command, command, params, ) async def async_remote_control_start(self): """Start remote control mode.""" await self._try_command( "Unable to start remote control the vacuum: %s", self._device.manual_start ) async def async_remote_control_stop(self): """Stop remote control mode.""" await self._try_command( "Unable to stop remote control the vacuum: %s", self._device.manual_stop ) async def async_remote_control_move( self, rotation: int = 0, velocity: float = 0.3, duration: int = 1500 ): """Move vacuum with remote control mode.""" await self._try_command( "Unable to move with remote control the vacuum: %s", self._device.manual_control, velocity=velocity, rotation=rotation, duration=duration, ) async def async_remote_control_move_step( self, rotation: int = 0, velocity: float = 0.2, duration: int = 1500 ): """Move vacuum one step with remote control mode.""" await self._try_command( "Unable to remote control the vacuum: %s", self._device.manual_control_once, velocity=velocity, rotation=rotation, duration=duration, ) async def async_goto(self, x_coord: int, y_coord: int): """Goto the specified coordinates.""" await self._try_command( "Unable to send the vacuum cleaner to the specified coordinates: %s", self._device.goto, x_coord=x_coord, y_coord=y_coord, ) async def async_clean_segment(self, segments): """Clean the specified segments(s).""" if isinstance(segments, int): segments = [segments] await self._try_command( "Unable to start cleaning of the specified segments: %s", self._device.segment_clean, segments=segments, ) def update(self): """Fetch state from the device.""" try: state = self._device.status() self.vacuum_state = state self._fan_speeds = self._device.fan_speed_presets() self._fan_speeds_reverse = {v: k for k, v in self._fan_speeds.items()} self.consumable_state = self._device.consumable_status() self.clean_history = self._device.clean_history() self.last_clean = self._device.last_clean_details() self.dnd_state = self._device.dnd_status() self._available = True except (OSError, DeviceException) as exc: if self._available: self._available = False _LOGGER.warning("Got exception while fetching the state: %s", exc) # Fetch timers separately, see #38285 try: self._timers = self._device.timer() except DeviceException as exc: _LOGGER.debug( "Unable to fetch timers, this may happen on some devices: %s", exc ) self._timers = [] async def async_clean_zone(self, zone, repeats=1): """Clean selected area for the number of repeats indicated.""" for _zone in zone: _zone.append(repeats) _LOGGER.debug("Zone with repeats: %s", zone) try: await self.opp.async_add_executor_job(self._device.zoned_clean, zone) except (OSError, DeviceException) as exc: _LOGGER.error("Unable to send zoned_clean command to the vacuum: %s", exc)

34.718631

111

0.59966

from functools import partial import logging from miio import DeviceException, Vacuum import voluptuous as vol from openpeerpower.components.vacuum import ( ATTR_CLEANED_AREA, PLATFORM_SCHEMA, STATE_CLEANING, STATE_DOCKED, STATE_ERROR, STATE_IDLE, STATE_PAUSED, STATE_RETURNING, SUPPORT_BATTERY, SUPPORT_CLEAN_SPOT, SUPPORT_FAN_SPEED, SUPPORT_LOCATE, SUPPORT_PAUSE, SUPPORT_RETURN_HOME, SUPPORT_SEND_COMMAND, SUPPORT_START, SUPPORT_STATE, SUPPORT_STOP, StateVacuumEntity, ) from openpeerpower.config_entries import SOURCE_IMPORT from openpeerpower.const import CONF_HOST, CONF_NAME, CONF_TOKEN, STATE_OFF, STATE_ON from openpeerpower.helpers import config_validation as cv, entity_platform from openpeerpower.util.dt import as_utc from .const import ( CONF_DEVICE, CONF_FLOW_TYPE, DOMAIN, SERVICE_CLEAN_SEGMENT, SERVICE_CLEAN_ZONE, SERVICE_GOTO, SERVICE_MOVE_REMOTE_CONTROL, SERVICE_MOVE_REMOTE_CONTROL_STEP, SERVICE_START_REMOTE_CONTROL, SERVICE_STOP_REMOTE_CONTROL, ) from .device import XiaomiMiioEntity _LOGGER = logging.getLogger(__name__) DEFAULT_NAME = "Xiaomi Vacuum cleaner" PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Required(CONF_TOKEN): vol.All(str, vol.Length(min=32, max=32)), vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, }, extra=vol.ALLOW_EXTRA, ) ATTR_CLEAN_START = "clean_start" ATTR_CLEAN_STOP = "clean_stop" ATTR_CLEANING_TIME = "cleaning_time" ATTR_DO_NOT_DISTURB = "do_not_disturb" ATTR_DO_NOT_DISTURB_START = "do_not_disturb_start" ATTR_DO_NOT_DISTURB_END = "do_not_disturb_end" ATTR_MAIN_BRUSH_LEFT = "main_brush_left" ATTR_SIDE_BRUSH_LEFT = "side_brush_left" ATTR_FILTER_LEFT = "filter_left" ATTR_SENSOR_DIRTY_LEFT = "sensor_dirty_left" ATTR_CLEANING_COUNT = "cleaning_count" ATTR_CLEANED_TOTAL_AREA = "total_cleaned_area" ATTR_CLEANING_TOTAL_TIME = "total_cleaning_time" ATTR_ERROR = "error" ATTR_RC_DURATION = "duration" ATTR_RC_ROTATION = "rotation" ATTR_RC_VELOCITY = "velocity" ATTR_STATUS = "status" ATTR_ZONE_ARRAY = "zone" ATTR_ZONE_REPEATER = "repeats" ATTR_TIMERS = "timers" ATTR_MOP_ATTACHED = "mop_attached" SUPPORT_XIAOMI = ( SUPPORT_STATE | SUPPORT_PAUSE | SUPPORT_STOP | SUPPORT_RETURN_HOME | SUPPORT_FAN_SPEED | SUPPORT_SEND_COMMAND | SUPPORT_LOCATE | SUPPORT_BATTERY | SUPPORT_CLEAN_SPOT | SUPPORT_START ) STATE_CODE_TO_STATE = { 1: STATE_IDLE, 2: STATE_IDLE, 3: STATE_IDLE, 4: STATE_CLEANING, 5: STATE_CLEANING, 6: STATE_RETURNING, 7: STATE_CLEANING, 8: STATE_DOCKED, 9: STATE_ERROR, 10: STATE_PAUSED, 11: STATE_CLEANING, 12: STATE_ERROR, 13: STATE_IDLE, 14: STATE_DOCKED, 15: STATE_RETURNING, 16: STATE_CLEANING, 17: STATE_CLEANING, 18: STATE_CLEANING, 100: STATE_DOCKED, 101: STATE_ERROR, } async def async_setup_platform(opp, config, async_add_entities, discovery_info=None): _LOGGER.warning( "Loading Xiaomi Miio Vacuum via platform setup is deprecated; Please remove it from your configuration" ) opp.async_create_task( opp.config_entries.flow.async_init( DOMAIN, context={"source": SOURCE_IMPORT}, data=config, ) ) async def async_setup_entry(opp, config_entry, async_add_entities): entities = [] if config_entry.data[CONF_FLOW_TYPE] == CONF_DEVICE: host = config_entry.data[CONF_HOST] token = config_entry.data[CONF_TOKEN] name = config_entry.title unique_id = config_entry.unique_id _LOGGER.debug("Initializing with host %s (token %s...)", host, token[:5]) vacuum = Vacuum(host, token) mirobo = MiroboVacuum(name, vacuum, config_entry, unique_id) entities.append(mirobo) platform = entity_platform.async_get_current_platform() platform.async_register_entity_service( SERVICE_START_REMOTE_CONTROL, {}, MiroboVacuum.async_remote_control_start.__name__, ) platform.async_register_entity_service( SERVICE_STOP_REMOTE_CONTROL, {}, MiroboVacuum.async_remote_control_stop.__name__, ) platform.async_register_entity_service( SERVICE_MOVE_REMOTE_CONTROL, { vol.Optional(ATTR_RC_VELOCITY): vol.All( vol.Coerce(float), vol.Clamp(min=-0.29, max=0.29) ), vol.Optional(ATTR_RC_ROTATION): vol.All( vol.Coerce(int), vol.Clamp(min=-179, max=179) ), vol.Optional(ATTR_RC_DURATION): cv.positive_int, }, MiroboVacuum.async_remote_control_move.__name__, ) platform.async_register_entity_service( SERVICE_MOVE_REMOTE_CONTROL_STEP, { vol.Optional(ATTR_RC_VELOCITY): vol.All( vol.Coerce(float), vol.Clamp(min=-0.29, max=0.29) ), vol.Optional(ATTR_RC_ROTATION): vol.All( vol.Coerce(int), vol.Clamp(min=-179, max=179) ), vol.Optional(ATTR_RC_DURATION): cv.positive_int, }, MiroboVacuum.async_remote_control_move_step.__name__, ) platform.async_register_entity_service( SERVICE_CLEAN_ZONE, { vol.Required(ATTR_ZONE_ARRAY): vol.All( list, [ vol.ExactSequence( [ vol.Coerce(int), vol.Coerce(int), vol.Coerce(int), vol.Coerce(int), ] ) ], ), vol.Required(ATTR_ZONE_REPEATER): vol.All( vol.Coerce(int), vol.Clamp(min=1, max=3) ), }, MiroboVacuum.async_clean_zone.__name__, ) platform.async_register_entity_service( SERVICE_GOTO, { vol.Required("x_coord"): vol.Coerce(int), vol.Required("y_coord"): vol.Coerce(int), }, MiroboVacuum.async_goto.__name__, ) platform.async_register_entity_service( SERVICE_CLEAN_SEGMENT, {vol.Required("segments"): vol.Any(vol.Coerce(int), [vol.Coerce(int)])}, MiroboVacuum.async_clean_segment.__name__, ) async_add_entities(entities, update_before_add=True) class MiroboVacuum(XiaomiMiioEntity, StateVacuumEntity): def __init__(self, name, device, entry, unique_id): super().__init__(name, device, entry, unique_id) self.vacuum_state = None self._available = False self.consumable_state = None self.clean_history = None self.dnd_state = None self.last_clean = None self._fan_speeds = None self._fan_speeds_reverse = None self._timers = None @property def state(self): if self.vacuum_state is not None: if self.vacuum_state.got_error: return STATE_ERROR try: return STATE_CODE_TO_STATE[int(self.vacuum_state.state_code)] except KeyError: _LOGGER.error( "STATE not supported: %s, state_code: %s", self.vacuum_state.state, self.vacuum_state.state_code, ) return None @property def battery_level(self): if self.vacuum_state is not None: return self.vacuum_state.battery @property def fan_speed(self): if self.vacuum_state is not None: speed = self.vacuum_state.fanspeed if speed in self._fan_speeds_reverse: return self._fan_speeds_reverse[speed] _LOGGER.debug("Unable to find reverse for %s", speed) return speed @property def fan_speed_list(self): return list(self._fan_speeds) if self._fan_speeds else [] @property def timers(self): return [ { "enabled": timer.enabled, "cron": timer.cron, "next_schedule": as_utc(timer.next_schedule), } for timer in self._timers ] @property def extra_state_attributes(self): attrs = {} if self.vacuum_state is not None: attrs.update( { ATTR_DO_NOT_DISTURB: STATE_ON if self.dnd_state.enabled else STATE_OFF, ATTR_DO_NOT_DISTURB_START: str(self.dnd_state.start), ATTR_DO_NOT_DISTURB_END: str(self.dnd_state.end), ATTR_CLEANING_TIME: int( self.vacuum_state.clean_time.total_seconds() / 60 ), ATTR_CLEANED_AREA: int(self.vacuum_state.clean_area), ATTR_CLEANING_COUNT: int(self.clean_history.count), ATTR_CLEANED_TOTAL_AREA: int(self.clean_history.total_area), ATTR_CLEANING_TOTAL_TIME: int( self.clean_history.total_duration.total_seconds() / 60 ), ATTR_MAIN_BRUSH_LEFT: int( self.consumable_state.main_brush_left.total_seconds() / 3600 ), ATTR_SIDE_BRUSH_LEFT: int( self.consumable_state.side_brush_left.total_seconds() / 3600 ), ATTR_FILTER_LEFT: int( self.consumable_state.filter_left.total_seconds() / 3600 ), ATTR_SENSOR_DIRTY_LEFT: int( self.consumable_state.sensor_dirty_left.total_seconds() / 3600 ), ATTR_STATUS: str(self.vacuum_state.state), ATTR_MOP_ATTACHED: self.vacuum_state.is_water_box_attached, } ) if self.last_clean: attrs[ATTR_CLEAN_START] = self.last_clean.start attrs[ATTR_CLEAN_STOP] = self.last_clean.end if self.vacuum_state.got_error: attrs[ATTR_ERROR] = self.vacuum_state.error if self.timers: attrs[ATTR_TIMERS] = self.timers return attrs @property def available(self) -> bool: return self._available @property def supported_features(self): return SUPPORT_XIAOMI async def _try_command(self, mask_error, func, *args, **kwargs): try: await self.opp.async_add_executor_job(partial(func, *args, **kwargs)) return True except DeviceException as exc: _LOGGER.error(mask_error, exc) return False async def async_start(self): await self._try_command( "Unable to start the vacuum: %s", self._device.resume_or_start ) async def async_pause(self): await self._try_command("Unable to set start/pause: %s", self._device.pause) async def async_stop(self, **kwargs): await self._try_command("Unable to stop: %s", self._device.stop) async def async_set_fan_speed(self, fan_speed, **kwargs): if fan_speed in self._fan_speeds: fan_speed = self._fan_speeds[fan_speed] else: try: fan_speed = int(fan_speed) except ValueError as exc: _LOGGER.error( "Fan speed step not recognized (%s). Valid speeds are: %s", exc, self.fan_speed_list, ) return await self._try_command( "Unable to set fan speed: %s", self._device.set_fan_speed, fan_speed ) async def async_return_to_base(self, **kwargs): await self._try_command("Unable to return home: %s", self._device.home) async def async_clean_spot(self, **kwargs): await self._try_command( "Unable to start the vacuum for a spot clean-up: %s", self._device.spot ) async def async_locate(self, **kwargs): await self._try_command("Unable to locate the botvac: %s", self._device.find) async def async_send_command(self, command, params=None, **kwargs): await self._try_command( "Unable to send command to the vacuum: %s", self._device.raw_command, command, params, ) async def async_remote_control_start(self): await self._try_command( "Unable to start remote control the vacuum: %s", self._device.manual_start ) async def async_remote_control_stop(self): await self._try_command( "Unable to stop remote control the vacuum: %s", self._device.manual_stop ) async def async_remote_control_move( self, rotation: int = 0, velocity: float = 0.3, duration: int = 1500 ): await self._try_command( "Unable to move with remote control the vacuum: %s", self._device.manual_control, velocity=velocity, rotation=rotation, duration=duration, ) async def async_remote_control_move_step( self, rotation: int = 0, velocity: float = 0.2, duration: int = 1500 ): await self._try_command( "Unable to remote control the vacuum: %s", self._device.manual_control_once, velocity=velocity, rotation=rotation, duration=duration, ) async def async_goto(self, x_coord: int, y_coord: int): await self._try_command( "Unable to send the vacuum cleaner to the specified coordinates: %s", self._device.goto, x_coord=x_coord, y_coord=y_coord, ) async def async_clean_segment(self, segments): if isinstance(segments, int): segments = [segments] await self._try_command( "Unable to start cleaning of the specified segments: %s", self._device.segment_clean, segments=segments, ) def update(self): try: state = self._device.status() self.vacuum_state = state self._fan_speeds = self._device.fan_speed_presets() self._fan_speeds_reverse = {v: k for k, v in self._fan_speeds.items()} self.consumable_state = self._device.consumable_status() self.clean_history = self._device.clean_history() self.last_clean = self._device.last_clean_details() self.dnd_state = self._device.dnd_status() self._available = True except (OSError, DeviceException) as exc: if self._available: self._available = False _LOGGER.warning("Got exception while fetching the state: %s", exc) try: self._timers = self._device.timer() except DeviceException as exc: _LOGGER.debug( "Unable to fetch timers, this may happen on some devices: %s", exc ) self._timers = [] async def async_clean_zone(self, zone, repeats=1): for _zone in zone: _zone.append(repeats) _LOGGER.debug("Zone with repeats: %s", zone) try: await self.opp.async_add_executor_job(self._device.zoned_clean, zone) except (OSError, DeviceException) as exc: _LOGGER.error("Unable to send zoned_clean command to the vacuum: %s", exc)

true

1c2b101cc49648ec3fb251fd993a7676d6e524ba

110

py

Python

gui/theme.py

YannThorimbert/FantasyStrategia

e9e26cbd95faba6f1223aaa34bc0b2c6e60cf5f5

[ "MIT" ]

null

gui/theme.py

YannThorimbert/FantasyStrategia

e9e26cbd95faba6f1223aaa34bc0b2c6e60cf5f5

[ "MIT" ]

null

gui/theme.py

YannThorimbert/FantasyStrategia

e9e26cbd95faba6f1223aaa34bc0b2c6e60cf5f5

[ "MIT" ]

null

import thorpy def set_theme(theme): thorpy.set_theme(theme) thorpy.style.DEF_COLOR = (150,150,150)

13.75

42

0.709091

import thorpy def set_theme(theme): thorpy.set_theme(theme) thorpy.style.DEF_COLOR = (150,150,150)

true

1c2b10e1d2d7618f869f5dc83857e9caf1174723

3,877

py

Python

nipype/interfaces/fsl/possum.py

mfalkiewicz/nipype

775e21b78fb1ffa2ff9cb12e6f052868bd44d052

[ "Apache-2.0" ]

1

2015-01-19T13:12:27.000Z

nipype/interfaces/fsl/possum.py

bpinsard/nipype

373bdddba9f675ef153951afa368729e2d8950d2

[ "Apache-2.0" ]

null

nipype/interfaces/fsl/possum.py

bpinsard/nipype

373bdddba9f675ef153951afa368729e2d8950d2

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """ The possum module provides classes for interfacing with `POSSUM <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/POSSUM>`_ command line tools. Please, check out the link for pertinent citations using POSSUM. .. Note:: This was written to work with FSL version 5.0.6. .. testsetup:: # Change directory to provide relative paths for doctests import os filepath = os.path.dirname( os.path.realpath( __file__ ) ) datadir = os.path.realpath(os.path.join(filepath, '../../testing/data')) os.chdir(datadir) """ from .base import FSLCommand, FSLCommandInputSpec from ..base import TraitedSpec, File, traits class B0CalcInputSpec(FSLCommandInputSpec): in_file = File(exists=True, mandatory=True, argstr='-i %s', position=0, desc='filename of input image (usually a tissue/air segmentation)') out_file = File(argstr='-o %s', position=1, name_source=['in_file'], name_template='%s_b0field', output_name='out_file', desc='filename of B0 output volume') x_grad = traits.Float(0.0, argstr='--gx=%0.4f', desc='Value for zeroth-order x-gradient field (per mm)') y_grad = traits.Float(0.0, argstr='--gy=%0.4f', desc='Value for zeroth-order y-gradient field (per mm)') z_grad = traits.Float(0.0, argstr='--gz=%0.4f', desc='Value for zeroth-order z-gradient field (per mm)') x_b0 = traits.Float(0.0, argstr='--b0x=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (x-component), in Tesla') y_b0 = traits.Float(0.0, argstr='--b0y=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (y-component), in Tesla') z_b0 = traits.Float(1.0, argstr='--b0=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (z-component), in Tesla') xyz_b0 = traits.Tuple( traits.Float, traits.Float, traits.Float, argstr='--b0x=%0.2f --b0y=%0.2f --b0=%0.2f', xor=['x_b0', 'y_b0', 'z_b0'], desc='Zeroth-order B0 field in Tesla') delta = traits.Float(-9.45e-6, argstr='-d %e', desc='Delta value (chi_tissue - chi_air)') chi_air = traits.Float( 4.0e-7, argstr='--chi0=%e', desc='susceptibility of air') compute_xyz = traits.Bool(False, argstr='--xyz', desc='calculate and save all 3 field components (i.e. x,y,z)') extendboundary = traits.Float(1.0, argstr='--extendboundary=%0.2f', desc='Relative proportion to extend voxels at boundary') directconv = traits.Bool(False, argstr='--directconv', desc='use direct (image space) convolution, not FFT') class B0CalcOutputSpec(TraitedSpec): out_file = File(exists=True, desc='filename of B0 output volume') class B0Calc(FSLCommand): """ B0 inhomogeneities occur at interfaces of materials with different magnetic susceptibilities, such as tissue-air interfaces. These differences lead to distortion in the local magnetic field, as Maxwell’s equations need to be satisfied. An example of B0 inhomogneity is the first volume of the 4D volume ```$FSLDIR/data/possum/b0_ppm.nii.gz```. Examples -------- >>> from nipype.interfaces.fsl import B0Calc >>> b0calc = B0Calc() >>> b0calc.inputs.in_file = 'tissue+air_map.nii' >>> b0calc.inputs.z_b0 = 3.0 >>> b0calc.inputs.output_type = "NIFTI_GZ" >>> b0calc.cmdline 'b0calc -i tissue+air_map.nii -o tissue+air_map_b0field.nii.gz --b0=3.00' """ _cmd = 'b0calc' input_spec = B0CalcInputSpec output_spec = B0CalcOutputSpec

42.604396

100

0.622904

from .base import FSLCommand, FSLCommandInputSpec from ..base import TraitedSpec, File, traits class B0CalcInputSpec(FSLCommandInputSpec): in_file = File(exists=True, mandatory=True, argstr='-i %s', position=0, desc='filename of input image (usually a tissue/air segmentation)') out_file = File(argstr='-o %s', position=1, name_source=['in_file'], name_template='%s_b0field', output_name='out_file', desc='filename of B0 output volume') x_grad = traits.Float(0.0, argstr='--gx=%0.4f', desc='Value for zeroth-order x-gradient field (per mm)') y_grad = traits.Float(0.0, argstr='--gy=%0.4f', desc='Value for zeroth-order y-gradient field (per mm)') z_grad = traits.Float(0.0, argstr='--gz=%0.4f', desc='Value for zeroth-order z-gradient field (per mm)') x_b0 = traits.Float(0.0, argstr='--b0x=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (x-component), in Tesla') y_b0 = traits.Float(0.0, argstr='--b0y=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (y-component), in Tesla') z_b0 = traits.Float(1.0, argstr='--b0=%0.2f', xor=['xyz_b0'], desc='Value for zeroth-order b0 field (z-component), in Tesla') xyz_b0 = traits.Tuple( traits.Float, traits.Float, traits.Float, argstr='--b0x=%0.2f --b0y=%0.2f --b0=%0.2f', xor=['x_b0', 'y_b0', 'z_b0'], desc='Zeroth-order B0 field in Tesla') delta = traits.Float(-9.45e-6, argstr='-d %e', desc='Delta value (chi_tissue - chi_air)') chi_air = traits.Float( 4.0e-7, argstr='--chi0=%e', desc='susceptibility of air') compute_xyz = traits.Bool(False, argstr='--xyz', desc='calculate and save all 3 field components (i.e. x,y,z)') extendboundary = traits.Float(1.0, argstr='--extendboundary=%0.2f', desc='Relative proportion to extend voxels at boundary') directconv = traits.Bool(False, argstr='--directconv', desc='use direct (image space) convolution, not FFT') class B0CalcOutputSpec(TraitedSpec): out_file = File(exists=True, desc='filename of B0 output volume') class B0Calc(FSLCommand): _cmd = 'b0calc' input_spec = B0CalcInputSpec output_spec = B0CalcOutputSpec

true

1c2b112f87c0b3e81cdf00f95790f71463ad4e52

269

py

Python

tester_web/tables/__init__.py

Ma-Jun-a/TMW_backend

32e15d18ee826c8b4167041690b33c417076b0d7

[ "MIT" ]

null

tester_web/tables/__init__.py

Ma-Jun-a/TMW_backend

32e15d18ee826c8b4167041690b33c417076b0d7

[ "MIT" ]

null

tester_web/tables/__init__.py

Ma-Jun-a/TMW_backend

32e15d18ee826c8b4167041690b33c417076b0d7

[ "MIT" ]

null

#执行此方法依据模型建表 from sqlalchemy.orm import sessionmaker from tester_web.tables.user import Model, engine def init_db(): Model.metadata.create_all(bind=engine) # 先迁移模型完成后再初始化session # init_db() Session = sessionmaker() db_session = Session(bind=engine)

20.692308

49

0.750929

from sqlalchemy.orm import sessionmaker from tester_web.tables.user import Model, engine def init_db(): Model.metadata.create_all(bind=engine) Session = sessionmaker() db_session = Session(bind=engine)

true

1c2b1196866d2ab7c2c40f6972b59e183b485d0e

417

py

Python

tests/functional/config_files/config.py

elementechemlyn/pdssandpit

0c3eff557b00ca721919135d804878a6ab583016

[ "MIT" ]

null

tests/functional/config_files/config.py

elementechemlyn/pdssandpit

0c3eff557b00ca721919135d804878a6ab583016

[ "MIT" ]

null

tests/functional/config_files/config.py

elementechemlyn/pdssandpit

0c3eff557b00ca721919135d804878a6ab583016

[ "MIT" ]

null

from .environment import ENV # Apigee Details ENVIRONMENT = ENV["environment"] BASE_URL = f"https://{ENVIRONMENT}.api.service.nhs.uk" # Unattended access details APPLICATION_RESTRICTED_API_KEY = ENV["application_restricted_api_key"] SIGNING_KEY = ENV["signing_key"] KEY_ID = ENV["key_id"] # PDS PDS_BASE_PATH = ENV["pds_base_path"] # App details CLIENT_ID = ENV['client_id'] CLIENT_SECRET = ENV['client_secret']

21.947368

70

0.767386

from .environment import ENV ENVIRONMENT = ENV["environment"] BASE_URL = f"https://{ENVIRONMENT}.api.service.nhs.uk" APPLICATION_RESTRICTED_API_KEY = ENV["application_restricted_api_key"] SIGNING_KEY = ENV["signing_key"] KEY_ID = ENV["key_id"] PDS_BASE_PATH = ENV["pds_base_path"] CLIENT_ID = ENV['client_id'] CLIENT_SECRET = ENV['client_secret']

true

1c2b11f5ebeb7193ca31b4b1407ed0dbc832c795

502

py

Python

js_events/migrations/0026_eventsconfig_allow_post.py

evgeny-dmi3ev/js-events

766fea79591ad0c4d2c0fcd5580f9699aa232c29

[ "BSD-3-Clause" ]

null

js_events/migrations/0026_eventsconfig_allow_post.py

evgeny-dmi3ev/js-events

766fea79591ad0c4d2c0fcd5580f9699aa232c29

[ "BSD-3-Clause" ]

null

js_events/migrations/0026_eventsconfig_allow_post.py

evgeny-dmi3ev/js-events

766fea79591ad0c4d2c0fcd5580f9699aa232c29

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.23 on 2019-08-28 15:46 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('js_events', '0025_create_placeholders'), ] operations = [ migrations.AddField( model_name='eventsconfig', name='allow_post', field=models.BooleanField(default=False, verbose_name='Allow POST requests'), ), ]

23.904762

89

0.641434

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('js_events', '0025_create_placeholders'), ] operations = [ migrations.AddField( model_name='eventsconfig', name='allow_post', field=models.BooleanField(default=False, verbose_name='Allow POST requests'), ), ]

true

1c2b12c18e1552b07dc7dbe9e5f32552a7e7e8b6

3,073

py

Python

sdk/datafactory/azure-mgmt-datafactory/azure/mgmt/datafactory/models/jira_object_dataset.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2021-09-07T18:36:04.000Z

sdk/datafactory/azure-mgmt-datafactory/azure/mgmt/datafactory/models/jira_object_dataset.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

2

2019-10-02T23:37:38.000Z

2020-10-02T01:17:31.000Z

sdk/datafactory/azure-mgmt-datafactory/azure/mgmt/datafactory/models/jira_object_dataset.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2019-06-17T22:18:23.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .dataset import Dataset class JiraObjectDataset(Dataset): """Jira Service dataset. All required parameters must be populated in order to send to Azure. :param additional_properties: Unmatched properties from the message are deserialized this collection :type additional_properties: dict[str, object] :param description: Dataset description. :type description: str :param structure: Columns that define the structure of the dataset. Type: array (or Expression with resultType array), itemType: DatasetDataElement. :type structure: object :param schema: Columns that define the physical type schema of the dataset. Type: array (or Expression with resultType array), itemType: DatasetSchemaDataElement. :type schema: object :param linked_service_name: Required. Linked service reference. :type linked_service_name: ~azure.mgmt.datafactory.models.LinkedServiceReference :param parameters: Parameters for dataset. :type parameters: dict[str, ~azure.mgmt.datafactory.models.ParameterSpecification] :param annotations: List of tags that can be used for describing the Dataset. :type annotations: list[object] :param folder: The folder that this Dataset is in. If not specified, Dataset will appear at the root level. :type folder: ~azure.mgmt.datafactory.models.DatasetFolder :param type: Required. Constant filled by server. :type type: str :param table_name: The table name. Type: string (or Expression with resultType string). :type table_name: object """ _validation = { 'linked_service_name': {'required': True}, 'type': {'required': True}, } _attribute_map = { 'additional_properties': {'key': '', 'type': '{object}'}, 'description': {'key': 'description', 'type': 'str'}, 'structure': {'key': 'structure', 'type': 'object'}, 'schema': {'key': 'schema', 'type': 'object'}, 'linked_service_name': {'key': 'linkedServiceName', 'type': 'LinkedServiceReference'}, 'parameters': {'key': 'parameters', 'type': '{ParameterSpecification}'}, 'annotations': {'key': 'annotations', 'type': '[object]'}, 'folder': {'key': 'folder', 'type': 'DatasetFolder'}, 'type': {'key': 'type', 'type': 'str'}, 'table_name': {'key': 'typeProperties.tableName', 'type': 'object'}, } def __init__(self, **kwargs): super(JiraObjectDataset, self).__init__(**kwargs) self.table_name = kwargs.get('table_name', None) self.type = 'JiraObject'

42.09589

94

0.645298

from .dataset import Dataset class JiraObjectDataset(Dataset): _validation = { 'linked_service_name': {'required': True}, 'type': {'required': True}, } _attribute_map = { 'additional_properties': {'key': '', 'type': '{object}'}, 'description': {'key': 'description', 'type': 'str'}, 'structure': {'key': 'structure', 'type': 'object'}, 'schema': {'key': 'schema', 'type': 'object'}, 'linked_service_name': {'key': 'linkedServiceName', 'type': 'LinkedServiceReference'}, 'parameters': {'key': 'parameters', 'type': '{ParameterSpecification}'}, 'annotations': {'key': 'annotations', 'type': '[object]'}, 'folder': {'key': 'folder', 'type': 'DatasetFolder'}, 'type': {'key': 'type', 'type': 'str'}, 'table_name': {'key': 'typeProperties.tableName', 'type': 'object'}, } def __init__(self, **kwargs): super(JiraObjectDataset, self).__init__(**kwargs) self.table_name = kwargs.get('table_name', None) self.type = 'JiraObject'

true

1c2b12ca1a7d5f6be3a49a9439835fd39b13bb6f

39,095

py

Python

tests/test_optimize.py

rmoyard/pennylane

14fdee89d8c3673840708b002c304aee4b31c507

[ "Apache-2.0" ]

3

2021-02-22T18:30:55.000Z

2021-02-23T10:54:58.000Z

tests/test_optimize.py

rmoyard/pennylane

14fdee89d8c3673840708b002c304aee4b31c507

[ "Apache-2.0" ]

null

tests/test_optimize.py

rmoyard/pennylane

14fdee89d8c3673840708b002c304aee4b31c507

[ "Apache-2.0" ]

1

2021-03-27T09:03:15.000Z

# Copyright 2018-2020 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Unit tests for the :mod:`pennylane` optimizers. """ # pylint: disable=redefined-outer-name import itertools as it import numpy as onp import pytest import pennylane as qml from pennylane import numpy as np from pennylane.optimize import ( GradientDescentOptimizer, MomentumOptimizer, NesterovMomentumOptimizer, AdagradOptimizer, RMSPropOptimizer, AdamOptimizer, RotoselectOptimizer, RotosolveOptimizer, ) x_vals = np.linspace(-10, 10, 16, endpoint=False) # Hyperparameters for optimizers stepsize = 0.1 gamma = 0.5 delta = 0.8 # function arguments in various formats mixed_list = [(0.2, 0.3), np.array([0.4, 0.2, 0.4]), 0.1] mixed_tuple = (np.array([0.2, 0.3]), [0.4, 0.2, 0.4], 0.1) flat_list = [0.2, 0.3, 0.1, 0.4, -0.1] multid_array = np.array([[0.1, 0.2], [-0.1, -0.4]]) multid_list = [[0.1, 0.2], [-0.1, -0.4]] # functions and their gradients fnames = ["test_function_1", "test_function_2", "test_function_3"] univariate_funcs = [np.sin, lambda x: np.exp(x / 10.0), lambda x: x ** 2] grad_uni_fns = [lambda x: (np.cos(x),), lambda x: (np.exp(x / 10.0) / 10.0,), lambda x: (2 * x,)] multivariate_funcs = [ lambda x: np.sin(x[0]) + np.cos(x[1]), lambda x: np.exp(x[0] / 3) * np.tanh(x[1]), lambda x: np.sum([x_ ** 2 for x_ in x]), ] grad_multi_funcs = [ lambda x: (np.array([np.cos(x[0]), -np.sin(x[1])]),), lambda x: ( np.array( [np.exp(x[0] / 3) / 3 * np.tanh(x[1]), np.exp(x[0] / 3) * (1 - np.tanh(x[1]) ** 2)] ), ), lambda x: (np.array([2 * x_ for x_ in x]),), ] mvar_mdim_funcs = [ lambda x: np.sin(x[0, 0]) + np.cos(x[1, 0]) - np.sin(x[0, 1]) + x[1, 1], lambda x: np.exp(x[0, 0] / 3) * np.tanh(x[0, 1]), lambda x: np.sum([x_[0] ** 2 for x_ in x]), ] grad_mvar_mdim_funcs = [ lambda x: (np.array([[np.cos(x[0, 0]), -np.cos(x[0, 1])], [-np.sin(x[1, 0]), 1.0]]),), lambda x: ( np.array( [ [ np.exp(x[0, 0] / 3) / 3 * np.tanh(x[0, 1]), np.exp(x[0, 0] / 3) * (1 - np.tanh(x[0, 1]) ** 2), ], [0.0, 0.0], ] ), ), lambda x: (np.array([[2 * x_[0], 0.0] for x_ in x]),), ] @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun(*variables): qml.RX(variables[0][1], wires=[0]) qml.RY(variables[1][2], wires=[0]) qml.RY(variables[2], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_flat(var): qml.RX(var[0], wires=[0]) qml.RY(var[1], wires=[0]) qml.RY(var[2], wires=[0]) qml.RX(var[3], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_mdarr(var): qml.RX(var[0, 1], wires=[0]) qml.RY(var[1, 0], wires=[0]) qml.RY(var[1, 1], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_mdlist(var): qml.RX(var[0][1], wires=[0]) qml.RY(var[1][0], wires=[0]) qml.RY(var[1][1], wires=[0]) return qml.expval(qml.PauliZ(0)) @pytest.fixture(scope="function") def bunch(): class A: sgd_opt = GradientDescentOptimizer(stepsize) mom_opt = MomentumOptimizer(stepsize, momentum=gamma) nesmom_opt = NesterovMomentumOptimizer(stepsize, momentum=gamma) adag_opt = AdagradOptimizer(stepsize) rms_opt = RMSPropOptimizer(stepsize, decay=gamma) adam_opt = AdamOptimizer(stepsize, beta1=gamma, beta2=delta) rotosolve_opt = RotosolveOptimizer() rotoselect_opt = RotoselectOptimizer() return A() class TestOptimizer: """Basic optimizer tests.""" def test_mixed_inputs_for_hybrid_optimization(self, bunch, tol): """Tests that gradient descent optimizer treats parameters of mixed types the same for hybrid optimization tasks.""" def hybrid_fun(variables): return quant_fun(*variables) + variables[0][1] hybrid_list = bunch.sgd_opt.step(hybrid_fun, mixed_list) hybrid_tuple = bunch.sgd_opt.step(hybrid_fun, mixed_tuple) assert hybrid_list[0] == pytest.approx(hybrid_tuple[0], abs=tol) assert hybrid_list[1] == pytest.approx(hybrid_tuple[1], abs=tol) assert hybrid_list[2] == pytest.approx(hybrid_tuple[2], abs=tol) def test_mixed_inputs_for_classical_optimization(self, bunch, tol): """Tests that gradient descent optimizer treats parameters of mixed types the same for purely classical optimization tasks.""" def class_fun(var): return var[0][1] * 2.0 + var[1][2] + var[2] class_list = bunch.sgd_opt.step(class_fun, mixed_list) class_tuple = bunch.sgd_opt.step(class_fun, mixed_tuple) assert class_list[0] == pytest.approx(class_tuple[0], abs=tol) assert class_list[1] == pytest.approx(class_tuple[1], abs=tol) assert class_list[2] == pytest.approx(class_tuple[2], abs=tol) def test_mixed_inputs_for_quantum_optimization(self, bunch, tol): """Tests that gradient descent optimizer treats parameters of mixed types the same for purely quantum optimization tasks.""" quant_list = bunch.sgd_opt.step(quant_fun, *mixed_list) quant_tuple = bunch.sgd_opt.step(quant_fun, *mixed_tuple) assert quant_list[0] == pytest.approx(quant_tuple[0], abs=tol) assert quant_list[1] == pytest.approx(quant_tuple[1], abs=tol) assert quant_list[2] == pytest.approx(quant_tuple[2], abs=tol) def test_array_and_list_return_same_update(self, bunch, tol): """Tests that gradient descent optimizer has the same output for lists and arrays.""" def hybrid_fun_mdarr(var): return quant_fun_mdarr(var) + var[0, 0] def hybrid_fun_mdlist(var): return quant_fun_mdlist(var) + var[0][0] array = bunch.sgd_opt.step(hybrid_fun_mdarr, multid_array) ls = bunch.sgd_opt.step(hybrid_fun_mdlist, multid_list) assert array == pytest.approx(np.asarray(ls), abs=tol) def test_step_and_cost_autograd_sgd_mixed_list(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the gradient-descent optimizer""" _, res = bunch.sgd_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_sgd_multid_array(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the gradient-descent optimizer""" _, res = bunch.sgd_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) def test_step_and_cost_autograd_nesterov_mixed_list(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the Nesterov momentum optimizer""" _, res = bunch.nesmom_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_nesterov_multid_array(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the Nesterov momentum optimizer""" _, res = bunch.nesmom_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) def test_step_and_cost_autograd_rotosolve_mixed_list(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the Rotosolve optimizer""" _, res = bunch.rotosolve_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_rotosolve_multid_array(self, bunch): """Test that the correct cost is returned via the step_and_cost method for the Rotosolve optimizer""" _, res = bunch.rotosolve_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) @pytest.mark.parametrize("params", [[1.7, 2.2], [-1.42, 0.1], [0.05, -0.8]]) def test_step_and_cost_autograd_rotoselect(self, bunch, params): """Test that the correct cost is returned via the step_and_cost method for the Rotoselect momentum optimizer""" generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1 _, _, res = bunch.rotoselect_opt.step_and_cost(cost_fn, params, generators) expected = cost_fn(params, generators) assert np.all(res == expected) @pytest.mark.parametrize("func, f_grad", list(zip(univariate_funcs, grad_uni_fns))) @pytest.mark.parametrize("var", [0, -3, 42]) def test_step_and_cost_supplied_grad(self, bunch, func, var, f_grad): """Test that returned cost is correct if gradient function is supplied""" _, res = bunch.sgd_opt.step_and_cost(func, var, grad_fn=f_grad) expected = func(var) assert np.all(res == expected) @pytest.mark.parametrize("x_start", x_vals) def test_gradient_descent_optimizer_univar(self, x_start, bunch, tol): """Tests that basic stochastic gradient descent takes gradient-descent steps correctly for uni-variate functions.""" # TODO parametrize this for also for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): x_new = bunch.sgd_opt.step(f, x_start) x_correct = x_start - gradf(x_start)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) def test_gradient_descent_optimizer_multivar(self, bunch, tol): """Tests that basic stochastic gradient descent takes gradient-descent steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): x_vec = x_vals[jdx : jdx + 2] x_new = bunch.sgd_opt.step(f, x_vec) x_correct = x_vec - gradf(x_vec)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) def test_gradient_descent_optimizer_multivar_multidim(self, bunch, tol): """Tests that basic stochastic gradient descent takes gradient-descent steps correctly for multi-variate functions and with higher dimensional inputs.""" for gradf, f, name in zip(grad_mvar_mdim_funcs, mvar_mdim_funcs, fnames): for jdx in range(len(x_vals[:-3])): x_vec = x_vals[jdx : jdx + 4] x_vec_multidim = np.reshape(x_vec, (2, 2)) x_new = bunch.sgd_opt.step(f, x_vec_multidim) x_correct = x_vec_multidim - gradf(x_vec_multidim)[0] * stepsize x_new_flat = x_new.flatten() x_correct_flat = x_correct.flatten() assert x_new_flat == pytest.approx(x_correct_flat, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_gradient_descent_optimizer_usergrad(self, x_start, bunch, tol): """Tests that basic stochastic gradient descent takes gradient-descent steps correctly using user-provided gradients.""" for gradf, f, name in zip(grad_uni_fns[::-1], univariate_funcs, fnames): x_new = bunch.sgd_opt.step(f, x_start, grad_fn=gradf) x_correct = x_start - gradf(x_start)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_momentum_optimizer_univar(self, x_start, bunch, tol): """Tests that momentum optimizer takes one and two steps correctly for uni-variate functions.""" for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.mom_opt.reset() x_onestep = bunch.mom_opt.step(f, x_start) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.mom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_start)[0] x_twosteps_target = x_onestep - (gradf(x_onestep)[0] + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_momentum_optimizer_multivar(self, bunch, tol): """Tests that momentum optimizer takes one and two steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.mom_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.mom_opt.step(f, x_vec) x_onestep_target = x_vec - gradf(x_vec)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.mom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_vec)[0] x_twosteps_target = x_onestep - (gradf(x_onestep)[0] + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_nesterovmomentum_optimizer_univar(self, x_start, bunch, tol): """Tests that nesterov momentum optimizer takes one and two steps correctly for uni-variate functions.""" for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.nesmom_opt.reset() x_onestep = bunch.nesmom_opt.step(f, x_start) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_start)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_nesterovmomentum_optimizer_multivar(self, bunch, tol): """Tests that nesterov momentum optimizer takes one and two steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.nesmom_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.nesmom_opt.step(f, x_vec) x_onestep_target = x_vec - gradf(x_vec)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_vec)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_nesterovmomentum_optimizer_usergrad(self, x_start, bunch, tol): """Tests that nesterov momentum optimizer takes gradient-descent steps correctly using user-provided gradients.""" for gradf, f, name in zip(grad_uni_fns[::-1], univariate_funcs, fnames): bunch.nesmom_opt.reset() x_onestep = bunch.nesmom_opt.step(f, x_start, grad_fn=gradf) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep, grad_fn=gradf) momentum_term = gamma * gradf(x_start)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_adagrad_optimizer_univar(self, x_start, bunch, tol): """Tests that adagrad optimizer takes one and two steps correctly for uni-variate functions.""" for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.adag_opt.reset() x_onestep = bunch.adag_opt.step(f, x_start) past_grads = gradf(x_start)[0] * gradf(x_start)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_start - gradf(x_start)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adag_opt.step(f, x_onestep) past_grads = ( gradf(x_start)[0] * gradf(x_start)[0] + gradf(x_onestep)[0] * gradf(x_onestep)[0] ) adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_adagrad_optimizer_multivar(self, bunch, tol): """Tests that adagrad optimizer takes one and two steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.adag_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.adag_opt.step(f, x_vec) past_grads = gradf(x_vec)[0] * gradf(x_vec)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_vec - gradf(x_vec)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adag_opt.step(f, x_onestep) past_grads = ( gradf(x_vec)[0] * gradf(x_vec)[0] + gradf(x_onestep)[0] * gradf(x_onestep)[0] ) adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_rmsprop_optimizer_univar(self, x_start, bunch, tol): """Tests that rmsprop optimizer takes one and two steps correctly for uni-variate functions.""" for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.rms_opt.reset() x_onestep = bunch.rms_opt.step(f, x_start) past_grads = (1 - gamma) * gradf(x_start)[0] * gradf(x_start)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_start - gradf(x_start)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rms_opt.step(f, x_onestep) past_grads = (1 - gamma) * gamma * gradf(x_start)[0] * gradf(x_start)[0] + ( 1 - gamma ) * gradf(x_onestep)[0] * gradf(x_onestep)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_rmsprop_optimizer_multivar(self, bunch, tol): """Tests that rmsprop optimizer takes one and two steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.rms_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.rms_opt.step(f, x_vec) past_grads = (1 - gamma) * gradf(x_vec)[0] * gradf(x_vec)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_vec - gradf(x_vec)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rms_opt.step(f, x_onestep) past_grads = (1 - gamma) * gamma * gradf(x_vec)[0] * gradf(x_vec)[0] + ( 1 - gamma ) * gradf(x_onestep)[0] * gradf(x_onestep)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_adam_optimizer_univar(self, x_start, bunch, tol): """Tests that adam optimizer takes one and two steps correctly for uni-variate functions.""" for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.adam_opt.reset() x_onestep = bunch.adam_opt.step(f, x_start) adapted_stepsize = stepsize * np.sqrt(1 - delta) / (1 - gamma) firstmoment = gradf(x_start)[0] secondmoment = gradf(x_start)[0] * gradf(x_start)[0] x_onestep_target = x_start - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adam_opt.step(f, x_onestep) adapted_stepsize = stepsize * np.sqrt(1 - delta ** 2) / (1 - gamma ** 2) firstmoment = gamma * gradf(x_start)[0] + (1 - gamma) * gradf(x_onestep)[0] secondmoment = ( delta * gradf(x_start)[0] * gradf(x_start)[0] + (1 - delta) * gradf(x_onestep)[0] * gradf(x_onestep)[0] ) x_twosteps_target = x_onestep - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_adam_optimizer_multivar(self, bunch, tol): """Tests that adam optimizer takes one and two steps correctly for multi-variate functions.""" for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.adam_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.adam_opt.step(f, x_vec) adapted_stepsize = stepsize * np.sqrt(1 - delta) / (1 - gamma) firstmoment = gradf(x_vec)[0] secondmoment = gradf(x_vec)[0] * gradf(x_vec)[0] x_onestep_target = x_vec - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adam_opt.step(f, x_onestep) adapted_stepsize = stepsize * np.sqrt(1 - delta ** 2) / (1 - gamma ** 2) firstmoment = gamma * gradf(x_vec)[0] + (1 - gamma) * gradf(x_onestep)[0] secondmoment = ( delta * gradf(x_vec)[0] * gradf(x_vec)[0] + (1 - delta) * gradf(x_onestep)[0] * gradf(x_onestep)[0] ) x_twosteps_target = x_onestep - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @staticmethod def rotosolve_step(f, x): """Helper function to test the Rotosolve and Rotoselect optimizers""" # make sure that x is an array if np.ndim(x) == 0: x = np.array([x]) # helper function for x[d] = theta def insert(xf, d, theta): xf[d] = theta return xf for d, _ in enumerate(x): H_0 = float(f(insert(x, d, 0))) H_p = float(f(insert(x, d, np.pi / 2))) H_m = float(f(insert(x, d, -np.pi / 2))) a = onp.arctan2(2 * H_0 - H_p - H_m, H_p - H_m) x[d] = -np.pi / 2 - a if x[d] <= -np.pi: x[d] += 2 * np.pi return x @pytest.mark.parametrize("x_start", x_vals) def test_rotosolve_optimizer_univar(self, x_start, bunch, tol): """Tests that rotosolve optimizer takes one and two steps correctly for uni-variate functions.""" for f in univariate_funcs: x_onestep = bunch.rotosolve_opt.step(f, x_start) x_onestep_target = self.rotosolve_step(f, x_start) assert np.allclose(x_onestep, x_onestep_target, atol=tol, rtol=0) x_twosteps = bunch.rotosolve_opt.step(f, x_onestep) x_twosteps_target = self.rotosolve_step(f, x_onestep_target) assert np.allclose(x_twosteps, x_twosteps_target, atol=tol, rtol=0) @pytest.mark.parametrize( "x_start", [ [1.2, 0.2], [-0.62, -2.1], [0.05, 0.8], [[0.3], [0.25]], [[-0.6], [0.45]], [[1.3], [-0.9]], ], ) def test_rotosolve_optimizer_multivar(self, x_start, bunch, tol): """Tests that rotosolve optimizer takes one and two steps correctly for multi-variate functions.""" for func in multivariate_funcs: # alter multivariate_func to accept nested lists of parameters f = lambda x: func(np.ravel(x)) x_onestep = bunch.rotosolve_opt.step(f, x_start) x_onestep_target = self.rotosolve_step(f, x_start) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rotosolve_opt.step(f, x_onestep) x_twosteps_target = self.rotosolve_step(f, x_onestep_target) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", [[1.2, 0.2], [-0.62, -2.1], [0.05, 0.8]]) @pytest.mark.parametrize( "generators", [list(tup) for tup in it.product([qml.RX, qml.RY, qml.RZ], repeat=2)] ) def test_rotoselect_optimizer(self, x_start, generators, bunch, tol): """Tests that rotoselect optimizer finds the optimal generators and parameters for the VQE circuit defined in `this rotoselect tutorial <https://pennylane.ai/qml/demos/tutorial_rotoselect.html>`_.""" # the optimal generators for the 2-qubit VQE circuit # H = 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1 optimal_generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1 f_best_gen = lambda x: cost_fn(x, optimal_generators) optimal_x_start = x_start.copy() # after four steps the optimzer should find the optimal generators/x_start values for _ in range(4): x_start, generators = bunch.rotoselect_opt.step(cost_fn, x_start, generators) optimal_x_start = self.rotosolve_step(f_best_gen, optimal_x_start) assert x_start == pytest.approx(optimal_x_start, abs=tol) assert generators == optimal_generators @pytest.mark.parametrize("x_start", [[1.2, 0.2], [-0.62, -2.1], [0.05, 0.8]]) def test_keywords_rotoselect(self, bunch, x_start, tol): """test rotoselect accepts keywords""" generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): # generators will be passed as a keyword arg ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators, shift=0.0): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * (Y_2 - shift) ** 2 + 0.8 * (Z_1 - shift) ** 2 - 0.2 * (X_1 - shift) ** 2 params_new, _, res_new = bunch.rotoselect_opt.step_and_cost( cost_fn, x_start, generators, shift=0.0 ) params_new2, _, res_new2 = bunch.rotoselect_opt.step_and_cost( cost_fn, x_start, generators, shift=1.0 ) assert params_new != pytest.approx(params_new2, abs=tol) assert res_new2 == pytest.approx(cost_fn(x_start, generators, shift=1.0), abs=tol) def test_update_stepsize(self): """Tests that the stepsize correctly updates""" eta = 0.5 opt = AdamOptimizer(eta) assert opt._stepsize == eta eta2 = 0.1 opt.update_stepsize(eta2) assert opt._stepsize == eta2 def reset(opt): if getattr(opt, "reset", None): opt.reset() @pytest.fixture def opt(opt_name): if opt_name == "gd": return GradientDescentOptimizer(stepsize) if opt_name == "nest": return NesterovMomentumOptimizer(stepsize, momentum=gamma) if opt_name == "moment": return MomentumOptimizer(stepsize, momentum=gamma) if opt_name == "ada": return AdagradOptimizer(stepsize) if opt_name == "rms": return RMSPropOptimizer(stepsize, decay=gamma) if opt_name == "adam": return AdamOptimizer(stepsize, beta1=gamma, beta2=delta) if opt_name == "roto": return RotosolveOptimizer() @pytest.mark.parametrize( "opt_name", [ "gd", "moment", "nest", "ada", "rms", "adam", "roto", ], ) class TestOverOpts: """Tests keywords, multiple arguements, and non-training arguments in relevent optimizers""" def test_kwargs(self, mocker, opt, opt_name, tol): """Test that the keywords get passed and alter the function""" class func_wrapper: @staticmethod def func(x, c=1.0): return (x - c) ** 2 x = 1.0 wrapper = func_wrapper() spy = mocker.spy(wrapper, "func") x_new_two = opt.step(wrapper.func, x, c=2.0) reset(opt) args2, kwargs2 = spy.call_args_list[-1] x_new_three_wc, cost_three = opt.step_and_cost(wrapper.func, x, c=3.0) reset(opt) args3, kwargs3 = spy.call_args_list[-1] if opt_name != "roto": assert args2 == (x,) assert args3 == (x,) else: assert x_new_two != pytest.approx(x, abs=tol) assert x_new_three_wc != pytest.approx(x, abs=tol) assert kwargs2 == {"c": 2.0} assert kwargs3 == {"c": 3.0} assert cost_three == pytest.approx(wrapper.func(x, c=3.0), abs=tol) def test_multi_args(self, mocker, opt, opt_name, tol): """Test passing multiple arguments to function""" class func_wrapper: @staticmethod def func(x, y, z): return x[0] * y[0] + z[0] wrapper = func_wrapper() spy = mocker.spy(wrapper, "func") x = np.array([1.0]) y = np.array([2.0]) z = np.array([3.0]) (x_new, y_new, z_new), cost = opt.step_and_cost(wrapper.func, x, y, z) reset(opt) args_called1, kwargs1 = spy.call_args_list[-1] # just take last call x_new2, y_new2, z_new2 = opt.step(wrapper.func, x_new, y_new, z_new) reset(opt) args_called2, kwargs2 = spy.call_args_list[-1] # just take last call if opt_name != "roto": assert args_called1 == (x, y, z) assert args_called2 == (x_new, y_new, z_new) else: assert x_new != pytest.approx(x, abs=tol) assert y_new != pytest.approx(y, abs=tol) assert z_new != pytest.approx(z, abs=tol) assert kwargs1 == {} assert kwargs2 == {} assert cost == pytest.approx(wrapper.func(x, y, z), abs=tol) def test_nontrainable_data(self, opt, opt_name, tol): """Check non-trainable argument does not get updated""" def func(x, data): return x[0] * data[0] x = np.array([1.0]) data = np.array([1.0], requires_grad=False) args_new = opt.step(func, x, data) reset(opt) args_new_wc, cost = opt.step_and_cost(func, *args_new) reset(opt) assert len(args_new) == pytest.approx(2, abs=tol) assert args_new[0] != pytest.approx(x, abs=tol) assert args_new[1] == pytest.approx(data, abs=tol) assert cost == pytest.approx(func(*args_new), abs=tol) def test_steps_the_same(self, opt, opt_name, tol): """Tests whether separating the args into different inputs affects their optimization step. Assumes single argument optimization is correct, as tested elsewhere.""" def func1(x, y, z): return x[0] * y[0] * z[0] def func2(args): return args[0][0] * args[1][0] * args[2][0] x = np.array([1.0]) y = np.array([2.0]) z = np.array([3.0]) args = (x, y, z) x_seperate, y_seperate, z_seperate = opt.step(func1, x, y, z) reset(opt) args_new = opt.step(func2, args) reset(opt) assert x_seperate == pytest.approx(args_new[0], abs=tol) assert y_seperate == pytest.approx(args_new[1], abs=tol) assert z_seperate == pytest.approx(args_new[2], abs=tol) def test_one_trainable_one_non_trainable(self, opt, opt_name, tol): """Tests that a cost function that takes one trainable and one non-trainable parameter executes well.""" dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(x, target): return (circuit(x) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) original_ev = ev (x, ev), cost = opt.step_and_cost(cost, x, ev) # check that the argument to RX doesn't change, as the X rotation doesn't influence <Z> assert x == 0 assert ev == original_ev def test_one_non_trainable_one_trainable(self, opt, opt_name, tol): """Tests that a cost function that takes one non-trainable and one trainable parameter executes well.""" dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(target, x): # Note: the order of the arguments has been swapped return (circuit(x) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) original_ev = ev (ev, x), cost = opt.step_and_cost(cost, ev, x) # check that the argument to RX doesn't change, as the X rotation doesn't influence <Z> assert x == 0 assert ev == original_ev def test_two_trainable_args(self, opt, opt_name, tol): """Tests that a cost function that takes at least two trainable arguments executes well.""" dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x, y): qml.RX(x, wires=0) qml.RX(y, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(x, y, target): return (circuit(x, y) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) y = np.tensor(0.0, requires_grad=True) original_ev = ev (x, y, ev), cost = opt.step_and_cost(cost, x, y, ev) # check that the argument to RX doesn't change, as the X rotation doesn't influence <Z> assert x == 0 assert ev == original_ev

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import itertools as it import numpy as onp import pytest import pennylane as qml from pennylane import numpy as np from pennylane.optimize import ( GradientDescentOptimizer, MomentumOptimizer, NesterovMomentumOptimizer, AdagradOptimizer, RMSPropOptimizer, AdamOptimizer, RotoselectOptimizer, RotosolveOptimizer, ) x_vals = np.linspace(-10, 10, 16, endpoint=False) stepsize = 0.1 gamma = 0.5 delta = 0.8 mixed_list = [(0.2, 0.3), np.array([0.4, 0.2, 0.4]), 0.1] mixed_tuple = (np.array([0.2, 0.3]), [0.4, 0.2, 0.4], 0.1) flat_list = [0.2, 0.3, 0.1, 0.4, -0.1] multid_array = np.array([[0.1, 0.2], [-0.1, -0.4]]) multid_list = [[0.1, 0.2], [-0.1, -0.4]] fnames = ["test_function_1", "test_function_2", "test_function_3"] univariate_funcs = [np.sin, lambda x: np.exp(x / 10.0), lambda x: x ** 2] grad_uni_fns = [lambda x: (np.cos(x),), lambda x: (np.exp(x / 10.0) / 10.0,), lambda x: (2 * x,)] multivariate_funcs = [ lambda x: np.sin(x[0]) + np.cos(x[1]), lambda x: np.exp(x[0] / 3) * np.tanh(x[1]), lambda x: np.sum([x_ ** 2 for x_ in x]), ] grad_multi_funcs = [ lambda x: (np.array([np.cos(x[0]), -np.sin(x[1])]),), lambda x: ( np.array( [np.exp(x[0] / 3) / 3 * np.tanh(x[1]), np.exp(x[0] / 3) * (1 - np.tanh(x[1]) ** 2)] ), ), lambda x: (np.array([2 * x_ for x_ in x]),), ] mvar_mdim_funcs = [ lambda x: np.sin(x[0, 0]) + np.cos(x[1, 0]) - np.sin(x[0, 1]) + x[1, 1], lambda x: np.exp(x[0, 0] / 3) * np.tanh(x[0, 1]), lambda x: np.sum([x_[0] ** 2 for x_ in x]), ] grad_mvar_mdim_funcs = [ lambda x: (np.array([[np.cos(x[0, 0]), -np.cos(x[0, 1])], [-np.sin(x[1, 0]), 1.0]]),), lambda x: ( np.array( [ [ np.exp(x[0, 0] / 3) / 3 * np.tanh(x[0, 1]), np.exp(x[0, 0] / 3) * (1 - np.tanh(x[0, 1]) ** 2), ], [0.0, 0.0], ] ), ), lambda x: (np.array([[2 * x_[0], 0.0] for x_ in x]),), ] @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun(*variables): qml.RX(variables[0][1], wires=[0]) qml.RY(variables[1][2], wires=[0]) qml.RY(variables[2], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_flat(var): qml.RX(var[0], wires=[0]) qml.RY(var[1], wires=[0]) qml.RY(var[2], wires=[0]) qml.RX(var[3], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_mdarr(var): qml.RX(var[0, 1], wires=[0]) qml.RY(var[1, 0], wires=[0]) qml.RY(var[1, 1], wires=[0]) return qml.expval(qml.PauliZ(0)) @qml.qnode(qml.device("default.qubit", wires=1)) def quant_fun_mdlist(var): qml.RX(var[0][1], wires=[0]) qml.RY(var[1][0], wires=[0]) qml.RY(var[1][1], wires=[0]) return qml.expval(qml.PauliZ(0)) @pytest.fixture(scope="function") def bunch(): class A: sgd_opt = GradientDescentOptimizer(stepsize) mom_opt = MomentumOptimizer(stepsize, momentum=gamma) nesmom_opt = NesterovMomentumOptimizer(stepsize, momentum=gamma) adag_opt = AdagradOptimizer(stepsize) rms_opt = RMSPropOptimizer(stepsize, decay=gamma) adam_opt = AdamOptimizer(stepsize, beta1=gamma, beta2=delta) rotosolve_opt = RotosolveOptimizer() rotoselect_opt = RotoselectOptimizer() return A() class TestOptimizer: def test_mixed_inputs_for_hybrid_optimization(self, bunch, tol): def hybrid_fun(variables): return quant_fun(*variables) + variables[0][1] hybrid_list = bunch.sgd_opt.step(hybrid_fun, mixed_list) hybrid_tuple = bunch.sgd_opt.step(hybrid_fun, mixed_tuple) assert hybrid_list[0] == pytest.approx(hybrid_tuple[0], abs=tol) assert hybrid_list[1] == pytest.approx(hybrid_tuple[1], abs=tol) assert hybrid_list[2] == pytest.approx(hybrid_tuple[2], abs=tol) def test_mixed_inputs_for_classical_optimization(self, bunch, tol): def class_fun(var): return var[0][1] * 2.0 + var[1][2] + var[2] class_list = bunch.sgd_opt.step(class_fun, mixed_list) class_tuple = bunch.sgd_opt.step(class_fun, mixed_tuple) assert class_list[0] == pytest.approx(class_tuple[0], abs=tol) assert class_list[1] == pytest.approx(class_tuple[1], abs=tol) assert class_list[2] == pytest.approx(class_tuple[2], abs=tol) def test_mixed_inputs_for_quantum_optimization(self, bunch, tol): quant_list = bunch.sgd_opt.step(quant_fun, *mixed_list) quant_tuple = bunch.sgd_opt.step(quant_fun, *mixed_tuple) assert quant_list[0] == pytest.approx(quant_tuple[0], abs=tol) assert quant_list[1] == pytest.approx(quant_tuple[1], abs=tol) assert quant_list[2] == pytest.approx(quant_tuple[2], abs=tol) def test_array_and_list_return_same_update(self, bunch, tol): def hybrid_fun_mdarr(var): return quant_fun_mdarr(var) + var[0, 0] def hybrid_fun_mdlist(var): return quant_fun_mdlist(var) + var[0][0] array = bunch.sgd_opt.step(hybrid_fun_mdarr, multid_array) ls = bunch.sgd_opt.step(hybrid_fun_mdlist, multid_list) assert array == pytest.approx(np.asarray(ls), abs=tol) def test_step_and_cost_autograd_sgd_mixed_list(self, bunch): _, res = bunch.sgd_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_sgd_multid_array(self, bunch): _, res = bunch.sgd_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) def test_step_and_cost_autograd_nesterov_mixed_list(self, bunch): _, res = bunch.nesmom_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_nesterov_multid_array(self, bunch): _, res = bunch.nesmom_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) def test_step_and_cost_autograd_rotosolve_mixed_list(self, bunch): _, res = bunch.rotosolve_opt.step_and_cost(quant_fun, *mixed_list) expected = quant_fun(*mixed_list) assert np.all(res == expected) def test_step_and_cost_autograd_rotosolve_multid_array(self, bunch): _, res = bunch.rotosolve_opt.step_and_cost(quant_fun_mdarr, multid_array) expected = quant_fun_mdarr(multid_array) assert np.all(res == expected) @pytest.mark.parametrize("params", [[1.7, 2.2], [-1.42, 0.1], [0.05, -0.8]]) def test_step_and_cost_autograd_rotoselect(self, bunch, params): generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1 _, _, res = bunch.rotoselect_opt.step_and_cost(cost_fn, params, generators) expected = cost_fn(params, generators) assert np.all(res == expected) @pytest.mark.parametrize("func, f_grad", list(zip(univariate_funcs, grad_uni_fns))) @pytest.mark.parametrize("var", [0, -3, 42]) def test_step_and_cost_supplied_grad(self, bunch, func, var, f_grad): _, res = bunch.sgd_opt.step_and_cost(func, var, grad_fn=f_grad) expected = func(var) assert np.all(res == expected) @pytest.mark.parametrize("x_start", x_vals) def test_gradient_descent_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): x_new = bunch.sgd_opt.step(f, x_start) x_correct = x_start - gradf(x_start)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) def test_gradient_descent_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): x_vec = x_vals[jdx : jdx + 2] x_new = bunch.sgd_opt.step(f, x_vec) x_correct = x_vec - gradf(x_vec)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) def test_gradient_descent_optimizer_multivar_multidim(self, bunch, tol): for gradf, f, name in zip(grad_mvar_mdim_funcs, mvar_mdim_funcs, fnames): for jdx in range(len(x_vals[:-3])): x_vec = x_vals[jdx : jdx + 4] x_vec_multidim = np.reshape(x_vec, (2, 2)) x_new = bunch.sgd_opt.step(f, x_vec_multidim) x_correct = x_vec_multidim - gradf(x_vec_multidim)[0] * stepsize x_new_flat = x_new.flatten() x_correct_flat = x_correct.flatten() assert x_new_flat == pytest.approx(x_correct_flat, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_gradient_descent_optimizer_usergrad(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns[::-1], univariate_funcs, fnames): x_new = bunch.sgd_opt.step(f, x_start, grad_fn=gradf) x_correct = x_start - gradf(x_start)[0] * stepsize assert x_new == pytest.approx(x_correct, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_momentum_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.mom_opt.reset() x_onestep = bunch.mom_opt.step(f, x_start) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.mom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_start)[0] x_twosteps_target = x_onestep - (gradf(x_onestep)[0] + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_momentum_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.mom_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.mom_opt.step(f, x_vec) x_onestep_target = x_vec - gradf(x_vec)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.mom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_vec)[0] x_twosteps_target = x_onestep - (gradf(x_onestep)[0] + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_nesterovmomentum_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.nesmom_opt.reset() x_onestep = bunch.nesmom_opt.step(f, x_start) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_start)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_nesterovmomentum_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.nesmom_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.nesmom_opt.step(f, x_vec) x_onestep_target = x_vec - gradf(x_vec)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep) momentum_term = gamma * gradf(x_vec)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_nesterovmomentum_optimizer_usergrad(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns[::-1], univariate_funcs, fnames): bunch.nesmom_opt.reset() x_onestep = bunch.nesmom_opt.step(f, x_start, grad_fn=gradf) x_onestep_target = x_start - gradf(x_start)[0] * stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.nesmom_opt.step(f, x_onestep, grad_fn=gradf) momentum_term = gamma * gradf(x_start)[0] shifted_grad_term = gradf(x_onestep - stepsize * momentum_term)[0] x_twosteps_target = x_onestep - (shifted_grad_term + momentum_term) * stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_adagrad_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.adag_opt.reset() x_onestep = bunch.adag_opt.step(f, x_start) past_grads = gradf(x_start)[0] * gradf(x_start)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_start - gradf(x_start)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adag_opt.step(f, x_onestep) past_grads = ( gradf(x_start)[0] * gradf(x_start)[0] + gradf(x_onestep)[0] * gradf(x_onestep)[0] ) adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_adagrad_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.adag_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.adag_opt.step(f, x_vec) past_grads = gradf(x_vec)[0] * gradf(x_vec)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_vec - gradf(x_vec)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adag_opt.step(f, x_onestep) past_grads = ( gradf(x_vec)[0] * gradf(x_vec)[0] + gradf(x_onestep)[0] * gradf(x_onestep)[0] ) adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_rmsprop_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.rms_opt.reset() x_onestep = bunch.rms_opt.step(f, x_start) past_grads = (1 - gamma) * gradf(x_start)[0] * gradf(x_start)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_start - gradf(x_start)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rms_opt.step(f, x_onestep) past_grads = (1 - gamma) * gamma * gradf(x_start)[0] * gradf(x_start)[0] + ( 1 - gamma ) * gradf(x_onestep)[0] * gradf(x_onestep)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_rmsprop_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.rms_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.rms_opt.step(f, x_vec) past_grads = (1 - gamma) * gradf(x_vec)[0] * gradf(x_vec)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_onestep_target = x_vec - gradf(x_vec)[0] * adapt_stepsize assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rms_opt.step(f, x_onestep) past_grads = (1 - gamma) * gamma * gradf(x_vec)[0] * gradf(x_vec)[0] + ( 1 - gamma ) * gradf(x_onestep)[0] * gradf(x_onestep)[0] adapt_stepsize = stepsize / np.sqrt(past_grads + 1e-8) x_twosteps_target = x_onestep - gradf(x_onestep)[0] * adapt_stepsize assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", x_vals) def test_adam_optimizer_univar(self, x_start, bunch, tol): for gradf, f, name in zip(grad_uni_fns, univariate_funcs, fnames): bunch.adam_opt.reset() x_onestep = bunch.adam_opt.step(f, x_start) adapted_stepsize = stepsize * np.sqrt(1 - delta) / (1 - gamma) firstmoment = gradf(x_start)[0] secondmoment = gradf(x_start)[0] * gradf(x_start)[0] x_onestep_target = x_start - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adam_opt.step(f, x_onestep) adapted_stepsize = stepsize * np.sqrt(1 - delta ** 2) / (1 - gamma ** 2) firstmoment = gamma * gradf(x_start)[0] + (1 - gamma) * gradf(x_onestep)[0] secondmoment = ( delta * gradf(x_start)[0] * gradf(x_start)[0] + (1 - delta) * gradf(x_onestep)[0] * gradf(x_onestep)[0] ) x_twosteps_target = x_onestep - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) def test_adam_optimizer_multivar(self, bunch, tol): for gradf, f, name in zip(grad_multi_funcs, multivariate_funcs, fnames): for jdx in range(len(x_vals[:-1])): bunch.adam_opt.reset() x_vec = x_vals[jdx : jdx + 2] x_onestep = bunch.adam_opt.step(f, x_vec) adapted_stepsize = stepsize * np.sqrt(1 - delta) / (1 - gamma) firstmoment = gradf(x_vec)[0] secondmoment = gradf(x_vec)[0] * gradf(x_vec)[0] x_onestep_target = x_vec - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.adam_opt.step(f, x_onestep) adapted_stepsize = stepsize * np.sqrt(1 - delta ** 2) / (1 - gamma ** 2) firstmoment = gamma * gradf(x_vec)[0] + (1 - gamma) * gradf(x_onestep)[0] secondmoment = ( delta * gradf(x_vec)[0] * gradf(x_vec)[0] + (1 - delta) * gradf(x_onestep)[0] * gradf(x_onestep)[0] ) x_twosteps_target = x_onestep - adapted_stepsize * firstmoment / ( np.sqrt(secondmoment) + 1e-8 ) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @staticmethod def rotosolve_step(f, x): if np.ndim(x) == 0: x = np.array([x]) def insert(xf, d, theta): xf[d] = theta return xf for d, _ in enumerate(x): H_0 = float(f(insert(x, d, 0))) H_p = float(f(insert(x, d, np.pi / 2))) H_m = float(f(insert(x, d, -np.pi / 2))) a = onp.arctan2(2 * H_0 - H_p - H_m, H_p - H_m) x[d] = -np.pi / 2 - a if x[d] <= -np.pi: x[d] += 2 * np.pi return x @pytest.mark.parametrize("x_start", x_vals) def test_rotosolve_optimizer_univar(self, x_start, bunch, tol): for f in univariate_funcs: x_onestep = bunch.rotosolve_opt.step(f, x_start) x_onestep_target = self.rotosolve_step(f, x_start) assert np.allclose(x_onestep, x_onestep_target, atol=tol, rtol=0) x_twosteps = bunch.rotosolve_opt.step(f, x_onestep) x_twosteps_target = self.rotosolve_step(f, x_onestep_target) assert np.allclose(x_twosteps, x_twosteps_target, atol=tol, rtol=0) @pytest.mark.parametrize( "x_start", [ [1.2, 0.2], [-0.62, -2.1], [0.05, 0.8], [[0.3], [0.25]], [[-0.6], [0.45]], [[1.3], [-0.9]], ], ) def test_rotosolve_optimizer_multivar(self, x_start, bunch, tol): for func in multivariate_funcs: f = lambda x: func(np.ravel(x)) x_onestep = bunch.rotosolve_opt.step(f, x_start) x_onestep_target = self.rotosolve_step(f, x_start) assert x_onestep == pytest.approx(x_onestep_target, abs=tol) x_twosteps = bunch.rotosolve_opt.step(f, x_onestep) x_twosteps_target = self.rotosolve_step(f, x_onestep_target) assert x_twosteps == pytest.approx(x_twosteps_target, abs=tol) @pytest.mark.parametrize("x_start", [[1.2, 0.2], [-0.62, -2.1], [0.05, 0.8]]) @pytest.mark.parametrize( "generators", [list(tup) for tup in it.product([qml.RX, qml.RY, qml.RZ], repeat=2)] ) def test_rotoselect_optimizer(self, x_start, generators, bunch, tol): optimal_generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * Y_2 + 0.8 * Z_1 - 0.2 * X_1 f_best_gen = lambda x: cost_fn(x, optimal_generators) optimal_x_start = x_start.copy() for _ in range(4): x_start, generators = bunch.rotoselect_opt.step(cost_fn, x_start, generators) optimal_x_start = self.rotosolve_step(f_best_gen, optimal_x_start) assert x_start == pytest.approx(optimal_x_start, abs=tol) assert generators == optimal_generators @pytest.mark.parametrize("x_start", [[1.2, 0.2], [-0.62, -2.1], [0.05, 0.8]]) def test_keywords_rotoselect(self, bunch, x_start, tol): generators = [qml.RY, qml.RX] possible_generators = [qml.RX, qml.RY, qml.RZ] bunch.rotoselect_opt.possible_generators = possible_generators dev = qml.device("default.qubit", shots=None, wires=2) def ansatz(params, generators): generators[0](params[0], wires=0) generators[1](params[1], wires=1) qml.CNOT(wires=[0, 1]) @qml.qnode(dev) def circuit_1(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1)) @qml.qnode(dev) def circuit_2(params, generators=None): ansatz(params, generators) return qml.expval(qml.PauliX(0)) def cost_fn(params, generators, shift=0.0): Z_1, Y_2 = circuit_1(params, generators=generators) X_1 = circuit_2(params, generators=generators) return 0.5 * (Y_2 - shift) ** 2 + 0.8 * (Z_1 - shift) ** 2 - 0.2 * (X_1 - shift) ** 2 params_new, _, res_new = bunch.rotoselect_opt.step_and_cost( cost_fn, x_start, generators, shift=0.0 ) params_new2, _, res_new2 = bunch.rotoselect_opt.step_and_cost( cost_fn, x_start, generators, shift=1.0 ) assert params_new != pytest.approx(params_new2, abs=tol) assert res_new2 == pytest.approx(cost_fn(x_start, generators, shift=1.0), abs=tol) def test_update_stepsize(self): eta = 0.5 opt = AdamOptimizer(eta) assert opt._stepsize == eta eta2 = 0.1 opt.update_stepsize(eta2) assert opt._stepsize == eta2 def reset(opt): if getattr(opt, "reset", None): opt.reset() @pytest.fixture def opt(opt_name): if opt_name == "gd": return GradientDescentOptimizer(stepsize) if opt_name == "nest": return NesterovMomentumOptimizer(stepsize, momentum=gamma) if opt_name == "moment": return MomentumOptimizer(stepsize, momentum=gamma) if opt_name == "ada": return AdagradOptimizer(stepsize) if opt_name == "rms": return RMSPropOptimizer(stepsize, decay=gamma) if opt_name == "adam": return AdamOptimizer(stepsize, beta1=gamma, beta2=delta) if opt_name == "roto": return RotosolveOptimizer() @pytest.mark.parametrize( "opt_name", [ "gd", "moment", "nest", "ada", "rms", "adam", "roto", ], ) class TestOverOpts: def test_kwargs(self, mocker, opt, opt_name, tol): class func_wrapper: @staticmethod def func(x, c=1.0): return (x - c) ** 2 x = 1.0 wrapper = func_wrapper() spy = mocker.spy(wrapper, "func") x_new_two = opt.step(wrapper.func, x, c=2.0) reset(opt) args2, kwargs2 = spy.call_args_list[-1] x_new_three_wc, cost_three = opt.step_and_cost(wrapper.func, x, c=3.0) reset(opt) args3, kwargs3 = spy.call_args_list[-1] if opt_name != "roto": assert args2 == (x,) assert args3 == (x,) else: assert x_new_two != pytest.approx(x, abs=tol) assert x_new_three_wc != pytest.approx(x, abs=tol) assert kwargs2 == {"c": 2.0} assert kwargs3 == {"c": 3.0} assert cost_three == pytest.approx(wrapper.func(x, c=3.0), abs=tol) def test_multi_args(self, mocker, opt, opt_name, tol): class func_wrapper: @staticmethod def func(x, y, z): return x[0] * y[0] + z[0] wrapper = func_wrapper() spy = mocker.spy(wrapper, "func") x = np.array([1.0]) y = np.array([2.0]) z = np.array([3.0]) (x_new, y_new, z_new), cost = opt.step_and_cost(wrapper.func, x, y, z) reset(opt) args_called1, kwargs1 = spy.call_args_list[-1] x_new2, y_new2, z_new2 = opt.step(wrapper.func, x_new, y_new, z_new) reset(opt) args_called2, kwargs2 = spy.call_args_list[-1] if opt_name != "roto": assert args_called1 == (x, y, z) assert args_called2 == (x_new, y_new, z_new) else: assert x_new != pytest.approx(x, abs=tol) assert y_new != pytest.approx(y, abs=tol) assert z_new != pytest.approx(z, abs=tol) assert kwargs1 == {} assert kwargs2 == {} assert cost == pytest.approx(wrapper.func(x, y, z), abs=tol) def test_nontrainable_data(self, opt, opt_name, tol): def func(x, data): return x[0] * data[0] x = np.array([1.0]) data = np.array([1.0], requires_grad=False) args_new = opt.step(func, x, data) reset(opt) args_new_wc, cost = opt.step_and_cost(func, *args_new) reset(opt) assert len(args_new) == pytest.approx(2, abs=tol) assert args_new[0] != pytest.approx(x, abs=tol) assert args_new[1] == pytest.approx(data, abs=tol) assert cost == pytest.approx(func(*args_new), abs=tol) def test_steps_the_same(self, opt, opt_name, tol): def func1(x, y, z): return x[0] * y[0] * z[0] def func2(args): return args[0][0] * args[1][0] * args[2][0] x = np.array([1.0]) y = np.array([2.0]) z = np.array([3.0]) args = (x, y, z) x_seperate, y_seperate, z_seperate = opt.step(func1, x, y, z) reset(opt) args_new = opt.step(func2, args) reset(opt) assert x_seperate == pytest.approx(args_new[0], abs=tol) assert y_seperate == pytest.approx(args_new[1], abs=tol) assert z_seperate == pytest.approx(args_new[2], abs=tol) def test_one_trainable_one_non_trainable(self, opt, opt_name, tol): dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(x, target): return (circuit(x) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) original_ev = ev (x, ev), cost = opt.step_and_cost(cost, x, ev) assert x == 0 assert ev == original_ev def test_one_non_trainable_one_trainable(self, opt, opt_name, tol): dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(target, x): return (circuit(x) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) original_ev = ev (ev, x), cost = opt.step_and_cost(cost, ev, x) assert x == 0 assert ev == original_ev def test_two_trainable_args(self, opt, opt_name, tol): dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(x, y): qml.RX(x, wires=0) qml.RX(y, wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) def cost(x, y, target): return (circuit(x, y) - target) ** 2 ev = np.tensor(0.7781, requires_grad=False) x = np.tensor(0.0, requires_grad=True) y = np.tensor(0.0, requires_grad=True) original_ev = ev (x, y, ev), cost = opt.step_and_cost(cost, x, y, ev) assert x == 0 assert ev == original_ev

true

1c2b137919800c11b9af9def7b77d30093b844cf

3,324

py

Python

lib/carbon/tests/benchmark_cache.py

readevalprint/carbon

5264d53a3ed6f97721ae76ae3821ca8ce4950a66

[ "Apache-2.0" ]

961

2015-01-01T14:20:35.000Z

2022-03-29T22:15:35.000Z

lib/carbon/tests/benchmark_cache.py

readevalprint/carbon

5264d53a3ed6f97721ae76ae3821ca8ce4950a66

[ "Apache-2.0" ]

611

2015-01-03T20:31:23.000Z

2022-03-31T21:30:23.000Z

lib/carbon/tests/benchmark_cache.py

readevalprint/carbon

5264d53a3ed6f97721ae76ae3821ca8ce4950a66

[ "Apache-2.0" ]

326

2015-01-03T14:55:33.000Z

2022-03-31T01:43:49.000Z

import timeit from carbon.cache import _MetricCache, DrainStrategy, \ NaiveStrategy, MaxStrategy, RandomStrategy, SortedStrategy, \ TimeSortedStrategy, BucketMaxStrategy metric_cache = _MetricCache(DrainStrategy) count = 0 strategies = { 'naive': NaiveStrategy, 'max': MaxStrategy, 'random': RandomStrategy, 'sorted': SortedStrategy, 'timesorted': TimeSortedStrategy, 'bucketmax': BucketMaxStrategy, } def command_store_foo(): global count count = count + 1 return metric_cache.store('foo', (count, 1.0)) def command_store_foo_n(): global count count = count + 1 return metric_cache.store("foo.%d" % count, (count, 1.0)) def command_drain(): while metric_cache: metric_cache.drain_metric() return metric_cache.size def print_stats(n, t): usec = t * 1e6 if usec < 1000: print(" datapoints: %-10d usecs: %d" % (n, int(usec))) else: msec = usec / 1000 if msec < 1000: print(" datapoints: %-10d msecs: %d" % (n, int(msec))) else: sec = msec / 1000 print(" datapoints: %-10d secs: %3g" % (n, sec)) if __name__ == '__main__': print("Benchmarking single metric MetricCache store...") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(DrainStrategy) t = timeit.timeit(command_store_foo, number=n) print_stats(n, t) print("Benchmarking unique metric MetricCache store...") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(DrainStrategy) t = timeit.timeit(command_store_foo_n, number=n) print_stats(n, t) print("Benchmarking single metric MetricCache store..., BucketMax") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(BucketMaxStrategy) t = timeit.timeit(command_store_foo, number=n) print_stats(n, t) print("Benchmarking unique metric MetricCache store..., BucketMax") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(BucketMaxStrategy) t = timeit.timeit(command_store_foo_n, number=n) print_stats(n, t) print("Benchmarking single metric MetricCache drain...") for name, strategy in sorted(strategies.items()): print("CACHE_WRITE_STRATEGY: %s" % name) for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(strategy) timeit.timeit(command_store_foo, number=n) t = timeit.timeit(command_drain, number=1) print_stats(n, t) print("Benchmarking unique metric MetricCache drain...") for name, strategy in sorted(strategies.items()): print("CACHE_WRITE_STRATEGY: %s" % name) for n in [1000, 10000, 100000, 1000000]: # remove me when strategy is fast if (name == 'max' and n > 10000) or (name == 'random' and n > 10000): print(" datapoints: %-10d [skipped]" % n) continue count = 0 metric_cache = _MetricCache(strategy) timeit.timeit(command_store_foo_n, number=n) t = timeit.timeit(command_drain, number=1) print_stats(n, t)

32.271845

81

0.620638

import timeit from carbon.cache import _MetricCache, DrainStrategy, \ NaiveStrategy, MaxStrategy, RandomStrategy, SortedStrategy, \ TimeSortedStrategy, BucketMaxStrategy metric_cache = _MetricCache(DrainStrategy) count = 0 strategies = { 'naive': NaiveStrategy, 'max': MaxStrategy, 'random': RandomStrategy, 'sorted': SortedStrategy, 'timesorted': TimeSortedStrategy, 'bucketmax': BucketMaxStrategy, } def command_store_foo(): global count count = count + 1 return metric_cache.store('foo', (count, 1.0)) def command_store_foo_n(): global count count = count + 1 return metric_cache.store("foo.%d" % count, (count, 1.0)) def command_drain(): while metric_cache: metric_cache.drain_metric() return metric_cache.size def print_stats(n, t): usec = t * 1e6 if usec < 1000: print(" datapoints: %-10d usecs: %d" % (n, int(usec))) else: msec = usec / 1000 if msec < 1000: print(" datapoints: %-10d msecs: %d" % (n, int(msec))) else: sec = msec / 1000 print(" datapoints: %-10d secs: %3g" % (n, sec)) if __name__ == '__main__': print("Benchmarking single metric MetricCache store...") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(DrainStrategy) t = timeit.timeit(command_store_foo, number=n) print_stats(n, t) print("Benchmarking unique metric MetricCache store...") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(DrainStrategy) t = timeit.timeit(command_store_foo_n, number=n) print_stats(n, t) print("Benchmarking single metric MetricCache store..., BucketMax") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(BucketMaxStrategy) t = timeit.timeit(command_store_foo, number=n) print_stats(n, t) print("Benchmarking unique metric MetricCache store..., BucketMax") for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(BucketMaxStrategy) t = timeit.timeit(command_store_foo_n, number=n) print_stats(n, t) print("Benchmarking single metric MetricCache drain...") for name, strategy in sorted(strategies.items()): print("CACHE_WRITE_STRATEGY: %s" % name) for n in [1000, 10000, 100000, 1000000]: count = 0 metric_cache = _MetricCache(strategy) timeit.timeit(command_store_foo, number=n) t = timeit.timeit(command_drain, number=1) print_stats(n, t) print("Benchmarking unique metric MetricCache drain...") for name, strategy in sorted(strategies.items()): print("CACHE_WRITE_STRATEGY: %s" % name) for n in [1000, 10000, 100000, 1000000]: if (name == 'max' and n > 10000) or (name == 'random' and n > 10000): print(" datapoints: %-10d [skipped]" % n) continue count = 0 metric_cache = _MetricCache(strategy) timeit.timeit(command_store_foo_n, number=n) t = timeit.timeit(command_drain, number=1) print_stats(n, t)

true

1c2b14c77c1356483a7ac7cfe0ef500bc57ab76f

10,555

py

Python

rllib/env/atari_wrappers.py

fbudrowski/ray

4853aa96cbbea76e69c3e48802ce7408f08669ee

[ "Apache-2.0" ]

null

rllib/env/atari_wrappers.py

fbudrowski/ray

4853aa96cbbea76e69c3e48802ce7408f08669ee

[ "Apache-2.0" ]

5

2021-08-25T16:17:15.000Z

2022-03-12T01:00:29.000Z

rllib/env/atari_wrappers.py

fbudrowski/ray

4853aa96cbbea76e69c3e48802ce7408f08669ee

[ "Apache-2.0" ]

2

2020-05-22T15:36:27.000Z

2020-05-22T15:52:03.000Z

import numpy as np from collections import deque import gym from gym import spaces import cv2 cv2.ocl.setUseOpenCL(False) def is_atari(env): if (hasattr(env.observation_space, "shape") and env.observation_space.shape is not None and len(env.observation_space.shape) <= 2): return False return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale") def get_wrapper_by_cls(env, cls): """Returns the gym env wrapper of the given class, or None.""" currentenv = env while True: if isinstance(currentenv, cls): return currentenv elif isinstance(currentenv, gym.Wrapper): currentenv = currentenv.env else: return None class MonitorEnv(gym.Wrapper): def __init__(self, env=None): """Record episodes stats prior to EpisodicLifeEnv, etc.""" gym.Wrapper.__init__(self, env) self._current_reward = None self._num_steps = None self._total_steps = None self._episode_rewards = [] self._episode_lengths = [] self._num_episodes = 0 self._num_returned = 0 def reset(self, **kwargs): obs = self.env.reset(**kwargs) if self._total_steps is None: self._total_steps = sum(self._episode_lengths) if self._current_reward is not None: self._episode_rewards.append(self._current_reward) self._episode_lengths.append(self._num_steps) self._num_episodes += 1 self._current_reward = 0 self._num_steps = 0 return obs def step(self, action): obs, rew, done, info = self.env.step(action) self._current_reward += rew self._num_steps += 1 self._total_steps += 1 return (obs, rew, done, info) def get_episode_rewards(self): return self._episode_rewards def get_episode_lengths(self): return self._episode_lengths def get_total_steps(self): return self._total_steps def next_episode_results(self): for i in range(self._num_returned, len(self._episode_rewards)): yield (self._episode_rewards[i], self._episode_lengths[i]) self._num_returned = len(self._episode_rewards) class NoopResetEnv(gym.Wrapper): def __init__(self, env, noop_max=30): """Sample initial states by taking random number of no-ops on reset. No-op is assumed to be action 0. """ gym.Wrapper.__init__(self, env) self.noop_max = noop_max self.override_num_noops = None self.noop_action = 0 assert env.unwrapped.get_action_meanings()[0] == "NOOP" def reset(self, **kwargs): """ Do no-op action for a number of steps in [1, noop_max].""" self.env.reset(**kwargs) if self.override_num_noops is not None: noops = self.override_num_noops else: noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) assert noops > 0 obs = None for _ in range(noops): obs, _, done, _ = self.env.step(self.noop_action) if done: obs = self.env.reset(**kwargs) return obs def step(self, ac): return self.env.step(ac) class ClipRewardEnv(gym.RewardWrapper): def __init__(self, env): gym.RewardWrapper.__init__(self, env) def reward(self, reward): """Bin reward to {+1, 0, -1} by its sign.""" return np.sign(reward) class FireResetEnv(gym.Wrapper): def __init__(self, env): """Take action on reset. For environments that are fixed until firing.""" gym.Wrapper.__init__(self, env) assert env.unwrapped.get_action_meanings()[1] == "FIRE" assert len(env.unwrapped.get_action_meanings()) >= 3 def reset(self, **kwargs): self.env.reset(**kwargs) obs, _, done, _ = self.env.step(1) if done: self.env.reset(**kwargs) obs, _, done, _ = self.env.step(2) if done: self.env.reset(**kwargs) return obs def step(self, ac): return self.env.step(ac) class EpisodicLifeEnv(gym.Wrapper): def __init__(self, env): """Make end-of-life == end-of-episode, but only reset on true game over. Done by DeepMind for the DQN and co. since it helps value estimation. """ gym.Wrapper.__init__(self, env) self.lives = 0 self.was_real_done = True def step(self, action): obs, reward, done, info = self.env.step(action) self.was_real_done = done # check current lives, make loss of life terminal, # then update lives to handle bonus lives lives = self.env.unwrapped.ale.lives() if lives < self.lives and lives > 0: # for Qbert sometimes we stay in lives == 0 condtion for a few fr # so its important to keep lives > 0, so that we only reset once # the environment advertises done. done = True self.lives = lives return obs, reward, done, info def reset(self, **kwargs): """Reset only when lives are exhausted. This way all states are still reachable even though lives are episodic, and the learner need not know about any of this behind-the-scenes. """ if self.was_real_done: obs = self.env.reset(**kwargs) else: # no-op step to advance from terminal/lost life state obs, _, _, _ = self.env.step(0) self.lives = self.env.unwrapped.ale.lives() return obs class MaxAndSkipEnv(gym.Wrapper): def __init__(self, env, skip=4): """Return only every `skip`-th frame""" gym.Wrapper.__init__(self, env) # most recent raw observations (for max pooling across time steps) self._obs_buffer = np.zeros( (2, ) + env.observation_space.shape, dtype=np.uint8) self._skip = skip def step(self, action): """Repeat action, sum reward, and max over last observations.""" total_reward = 0.0 done = None for i in range(self._skip): obs, reward, done, info = self.env.step(action) if i == self._skip - 2: self._obs_buffer[0] = obs if i == self._skip - 1: self._obs_buffer[1] = obs total_reward += reward if done: break # Note that the observation on the done=True frame # doesn't matter max_frame = self._obs_buffer.max(axis=0) return max_frame, total_reward, done, info def reset(self, **kwargs): return self.env.reset(**kwargs) class WarpFrame(gym.ObservationWrapper): def __init__(self, env, dim): """Warp frames to the specified size (dim x dim).""" gym.ObservationWrapper.__init__(self, env) self.width = dim self.height = dim self.observation_space = spaces.Box( low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8) def observation(self, frame): frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) frame = cv2.resize( frame, (self.width, self.height), interpolation=cv2.INTER_AREA) return frame[:, :, None] # TODO: (sven) Deprecated class. Remove once traj. view is the norm. class FrameStack(gym.Wrapper): def __init__(self, env, k): """Stack k last frames.""" gym.Wrapper.__init__(self, env) self.k = k self.frames = deque([], maxlen=k) shp = env.observation_space.shape self.observation_space = spaces.Box( low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=env.observation_space.dtype) def reset(self): ob = self.env.reset() for _ in range(self.k): self.frames.append(ob) return self._get_ob() def step(self, action): ob, reward, done, info = self.env.step(action) self.frames.append(ob) return self._get_ob(), reward, done, info def _get_ob(self): assert len(self.frames) == self.k return np.concatenate(self.frames, axis=2) class FrameStackTrajectoryView(gym.ObservationWrapper): def __init__(self, env): """No stacking. Trajectory View API takes care of this.""" gym.Wrapper.__init__(self, env) shp = env.observation_space.shape assert shp[2] == 1 self.observation_space = spaces.Box( low=0, high=255, shape=(shp[0], shp[1]), dtype=env.observation_space.dtype) def observation(self, observation): return np.squeeze(observation, axis=-1) class ScaledFloatFrame(gym.ObservationWrapper): def __init__(self, env): gym.ObservationWrapper.__init__(self, env) self.observation_space = gym.spaces.Box( low=0, high=1, shape=env.observation_space.shape, dtype=np.float32) def observation(self, observation): # careful! This undoes the memory optimization, use # with smaller replay buffers only. return np.array(observation).astype(np.float32) / 255.0 def wrap_deepmind( env, dim=84, # TODO: (sven) Remove once traj. view is norm. framestack=True, framestack_via_traj_view_api=False): """Configure environment for DeepMind-style Atari. Note that we assume reward clipping is done outside the wrapper. Args: dim (int): Dimension to resize observations to (dim x dim). framestack (bool): Whether to framestack observations. """ env = MonitorEnv(env) env = NoopResetEnv(env, noop_max=30) if env.spec is not None and "NoFrameskip" in env.spec.id: env = MaxAndSkipEnv(env, skip=4) env = EpisodicLifeEnv(env) if "FIRE" in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = WarpFrame(env, dim) # env = ScaledFloatFrame(env) # TODO: use for dqn? # env = ClipRewardEnv(env) # reward clipping is handled by policy eval # New way of frame stacking via the trajectory view API (model config key: # `num_framestacks=[int]`. if framestack_via_traj_view_api: env = FrameStackTrajectoryView(env) # Old way (w/o traj. view API) via model config key: `framestack=True`. # TODO: (sven) Remove once traj. view is norm. elif framestack is True: env = FrameStack(env, 4) return env

32.984375

80

0.61099

import numpy as np from collections import deque import gym from gym import spaces import cv2 cv2.ocl.setUseOpenCL(False) def is_atari(env): if (hasattr(env.observation_space, "shape") and env.observation_space.shape is not None and len(env.observation_space.shape) <= 2): return False return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale") def get_wrapper_by_cls(env, cls): currentenv = env while True: if isinstance(currentenv, cls): return currentenv elif isinstance(currentenv, gym.Wrapper): currentenv = currentenv.env else: return None class MonitorEnv(gym.Wrapper): def __init__(self, env=None): gym.Wrapper.__init__(self, env) self._current_reward = None self._num_steps = None self._total_steps = None self._episode_rewards = [] self._episode_lengths = [] self._num_episodes = 0 self._num_returned = 0 def reset(self, **kwargs): obs = self.env.reset(**kwargs) if self._total_steps is None: self._total_steps = sum(self._episode_lengths) if self._current_reward is not None: self._episode_rewards.append(self._current_reward) self._episode_lengths.append(self._num_steps) self._num_episodes += 1 self._current_reward = 0 self._num_steps = 0 return obs def step(self, action): obs, rew, done, info = self.env.step(action) self._current_reward += rew self._num_steps += 1 self._total_steps += 1 return (obs, rew, done, info) def get_episode_rewards(self): return self._episode_rewards def get_episode_lengths(self): return self._episode_lengths def get_total_steps(self): return self._total_steps def next_episode_results(self): for i in range(self._num_returned, len(self._episode_rewards)): yield (self._episode_rewards[i], self._episode_lengths[i]) self._num_returned = len(self._episode_rewards) class NoopResetEnv(gym.Wrapper): def __init__(self, env, noop_max=30): gym.Wrapper.__init__(self, env) self.noop_max = noop_max self.override_num_noops = None self.noop_action = 0 assert env.unwrapped.get_action_meanings()[0] == "NOOP" def reset(self, **kwargs): self.env.reset(**kwargs) if self.override_num_noops is not None: noops = self.override_num_noops else: noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) assert noops > 0 obs = None for _ in range(noops): obs, _, done, _ = self.env.step(self.noop_action) if done: obs = self.env.reset(**kwargs) return obs def step(self, ac): return self.env.step(ac) class ClipRewardEnv(gym.RewardWrapper): def __init__(self, env): gym.RewardWrapper.__init__(self, env) def reward(self, reward): return np.sign(reward) class FireResetEnv(gym.Wrapper): def __init__(self, env): gym.Wrapper.__init__(self, env) assert env.unwrapped.get_action_meanings()[1] == "FIRE" assert len(env.unwrapped.get_action_meanings()) >= 3 def reset(self, **kwargs): self.env.reset(**kwargs) obs, _, done, _ = self.env.step(1) if done: self.env.reset(**kwargs) obs, _, done, _ = self.env.step(2) if done: self.env.reset(**kwargs) return obs def step(self, ac): return self.env.step(ac) class EpisodicLifeEnv(gym.Wrapper): def __init__(self, env): gym.Wrapper.__init__(self, env) self.lives = 0 self.was_real_done = True def step(self, action): obs, reward, done, info = self.env.step(action) self.was_real_done = done lives = self.env.unwrapped.ale.lives() if lives < self.lives and lives > 0: done = True self.lives = lives return obs, reward, done, info def reset(self, **kwargs): if self.was_real_done: obs = self.env.reset(**kwargs) else: obs, _, _, _ = self.env.step(0) self.lives = self.env.unwrapped.ale.lives() return obs class MaxAndSkipEnv(gym.Wrapper): def __init__(self, env, skip=4): gym.Wrapper.__init__(self, env) self._obs_buffer = np.zeros( (2, ) + env.observation_space.shape, dtype=np.uint8) self._skip = skip def step(self, action): total_reward = 0.0 done = None for i in range(self._skip): obs, reward, done, info = self.env.step(action) if i == self._skip - 2: self._obs_buffer[0] = obs if i == self._skip - 1: self._obs_buffer[1] = obs total_reward += reward if done: break max_frame = self._obs_buffer.max(axis=0) return max_frame, total_reward, done, info def reset(self, **kwargs): return self.env.reset(**kwargs) class WarpFrame(gym.ObservationWrapper): def __init__(self, env, dim): gym.ObservationWrapper.__init__(self, env) self.width = dim self.height = dim self.observation_space = spaces.Box( low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8) def observation(self, frame): frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) frame = cv2.resize( frame, (self.width, self.height), interpolation=cv2.INTER_AREA) return frame[:, :, None] # TODO: (sven) Deprecated class. Remove once traj. view is the norm. class FrameStack(gym.Wrapper): def __init__(self, env, k): gym.Wrapper.__init__(self, env) self.k = k self.frames = deque([], maxlen=k) shp = env.observation_space.shape self.observation_space = spaces.Box( low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=env.observation_space.dtype) def reset(self): ob = self.env.reset() for _ in range(self.k): self.frames.append(ob) return self._get_ob() def step(self, action): ob, reward, done, info = self.env.step(action) self.frames.append(ob) return self._get_ob(), reward, done, info def _get_ob(self): assert len(self.frames) == self.k return np.concatenate(self.frames, axis=2) class FrameStackTrajectoryView(gym.ObservationWrapper): def __init__(self, env): gym.Wrapper.__init__(self, env) shp = env.observation_space.shape assert shp[2] == 1 self.observation_space = spaces.Box( low=0, high=255, shape=(shp[0], shp[1]), dtype=env.observation_space.dtype) def observation(self, observation): return np.squeeze(observation, axis=-1) class ScaledFloatFrame(gym.ObservationWrapper): def __init__(self, env): gym.ObservationWrapper.__init__(self, env) self.observation_space = gym.spaces.Box( low=0, high=1, shape=env.observation_space.shape, dtype=np.float32) def observation(self, observation): # careful! This undoes the memory optimization, use # with smaller replay buffers only. return np.array(observation).astype(np.float32) / 255.0 def wrap_deepmind( env, dim=84, # TODO: (sven) Remove once traj. view is norm. framestack=True, framestack_via_traj_view_api=False): env = MonitorEnv(env) env = NoopResetEnv(env, noop_max=30) if env.spec is not None and "NoFrameskip" in env.spec.id: env = MaxAndSkipEnv(env, skip=4) env = EpisodicLifeEnv(env) if "FIRE" in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = WarpFrame(env, dim) # env = ScaledFloatFrame(env) # TODO: use for dqn? # env = ClipRewardEnv(env) # reward clipping is handled by policy eval # New way of frame stacking via the trajectory view API (model config key: # `num_framestacks=[int]`. if framestack_via_traj_view_api: env = FrameStackTrajectoryView(env) # Old way (w/o traj. view API) via model config key: `framestack=True`. # TODO: (sven) Remove once traj. view is norm. elif framestack is True: env = FrameStack(env, 4) return env

true

1c2b16be4e7a1c043617782a7bd0de87fc9ab569

2,934

py

Python

examples/reproducing/hunziker2015.py

prisae/empymod

c01eae0ac51b37864c0b68bf0c207c1bd7c7e585

[ "Apache-2.0" ]

31

2017-06-07T00:47:10.000Z

2020-11-02T13:45:29.000Z

examples/reproducing/hunziker2015.py

prisae/empymod

c01eae0ac51b37864c0b68bf0c207c1bd7c7e585

[ "Apache-2.0" ]

97

2017-06-05T08:19:27.000Z

2020-11-30T15:25:07.000Z

examples/reproducing/hunziker2015.py

prisae/empymod

c01eae0ac51b37864c0b68bf0c207c1bd7c7e585

[ "Apache-2.0" ]

14

2017-11-05T13:24:29.000Z

2020-09-25T19:25:18.000Z

""" Hunziker et al., 2015, Geophysics ================================= Reproducing Figure 3 of the manual from `EMmod`. This example does, as such, not actually reproduce a figure of Hunziker et al., 2015, but of the manual that comes with the software accompanying the paper. With the software comes an example input file named `simplemod.scr`, and the corresponding result is shown in the manual of the code in Figure 3. If you are interested in reproducing the figures of the actual paper have a look at the notebooks in the repo `article-geo2017 <https://github.com/emsig/article-geo2017>`_. **Reference** - **Hunziker, J., J. Thorbecke, and E. Slob, 2015**, The electromagnetic response in a layered vertical transverse isotropic medium: A new look at an old problem: Geophysics, 80(1), F1–F18; DOI: `10.1190/geo2013-0411.1 <https://doi.org/10.1190/geo2013-0411.1>`_; Software: `software.seg.org/2015/0001 <https://software.seg.org/2015/0001>`_. """ import empymod import numpy as np import matplotlib.pyplot as plt ############################################################################### # Compute the data # ---------------- # # Compute the electric field with the parameters defined in `simplemod.scr`. # x- and y-offsets x = np.arange(4000)*7-1999.5*7 y = np.arange(1500)*10-749.5*10 # Create 2D arrays of them rx = np.repeat([x, ], np.size(y), axis=0) ry = np.repeat([y, ], np.size(x), axis=0) ry = ry.transpose() # Compute the electric field efield = empymod.dipole( src=[0, 0, 150], rec=[rx.ravel(), ry.ravel(), 200], depth=[0, 200, 1000, 1200], res=[2e14, 1/3, 1, 50, 1], aniso=[1, 1, np.sqrt(10), 1, 1], freqtime=0.5, epermH=[1, 80, 17, 2.1, 17], epermV=[1, 80, 17, 2.1, 17], mpermH=[1, 1, 1, 1, 1], mpermV=[1, 1, 1, 1, 1], ab=11, htarg={'pts_per_dec': -1}, ).reshape(np.shape(rx)) ############################################################################### # Plot # ---- # Create a similar colormap as Hunziker et al., 2015. cmap = plt.cm.get_cmap("jet", 61) plt.figure(figsize=(9, 8)) # 1. Amplitude plt.subplot(211) plt.title('Amplitude (V/m)') plt.xlabel('Offset (km)') plt.ylabel('Offset (km)') plt.pcolormesh(x/1e3, y/1e3, np.log10(efield.amp()), cmap=cmap, vmin=-16, vmax=-7, shading='nearest') plt.colorbar() # 2. Phase plt.subplot(212) plt.title('Phase (°)') plt.xlabel('Offset (km)') plt.ylabel('Offset (km)') plt.pcolormesh(x/1e3, y/1e3, efield.pha(deg=False, unwrap=False, lag=True), cmap=cmap, vmin=-np.pi, vmax=np.pi, shading='nearest') plt.colorbar() plt.tight_layout() plt.show() ############################################################################### # Original Figure # --------------- # # Figure 3 of the manual of `EMmod`. # # .. image:: ../../_static/figures/Hunziker2015.png ############################################################################### empymod.Report()

28.764706

79

0.578391

import empymod import numpy as np import matplotlib.pyplot as plt

true

1c2b187d41fee478aa7d74702fd8de6b79deee2c

49,508

py

Python

lib/coins.py

lancehall123/electrumx

b1cf21900a4fc45a821d920f1ce0a36950577d0c

[ "MIT" ]

null

lib/coins.py

lancehall123/electrumx

b1cf21900a4fc45a821d920f1ce0a36950577d0c

[ "MIT" ]

null

lib/coins.py

lancehall123/electrumx

b1cf21900a4fc45a821d920f1ce0a36950577d0c

[ "MIT" ]

null

# Copyright (c) 2016-2017, Neil Booth # Copyright (c) 2017, the ElectrumX authors # # All rights reserved. # # The MIT License (MIT) # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. '''Module providing coin abstraction. Anything coin-specific should go in this file and be subclassed where necessary for appropriate handling. ''' from collections import namedtuple import re import struct from decimal import Decimal from hashlib import sha256 from functools import partial import base64 import lib.util as util from lib.hash import Base58, hash160, double_sha256, hash_to_str from lib.script import ScriptPubKey, OpCodes import lib.tx as lib_tx from server.block_processor import BlockProcessor import server.daemon as daemon from server.session import ElectrumX, DashElectrumX Block = namedtuple("Block", "raw header transactions") OP_RETURN = OpCodes.OP_RETURN class CoinError(Exception): '''Exception raised for coin-related errors.''' class Coin(object): '''Base class of coin hierarchy.''' REORG_LIMIT = 200 # Not sure if these are coin-specific RPC_URL_REGEX = re.compile('.+@(\[[0-9a-fA-F:]+\]|[^:]+)(:[0-9]+)?') VALUE_PER_COIN = 100000000 CHUNK_SIZE = 2016 HASHX_LEN = 11 BASIC_HEADER_SIZE = 80 STATIC_BLOCK_HEADERS = True SESSIONCLS = ElectrumX DESERIALIZER = lib_tx.Deserializer DAEMON = daemon.Daemon BLOCK_PROCESSOR = BlockProcessor XPUB_VERBYTES = bytes('????', 'utf-8') XPRV_VERBYTES = bytes('????', 'utf-8') ENCODE_CHECK = Base58.encode_check DECODE_CHECK = Base58.decode_check # Peer discovery PEER_DEFAULT_PORTS = {'t': '50001', 's': '50002'} PEERS = [] @classmethod def lookup_coin_class(cls, name, net): '''Return a coin class given name and network. Raise an exception if unrecognised.''' req_attrs = ['TX_COUNT', 'TX_COUNT_HEIGHT', 'TX_PER_BLOCK'] for coin in util.subclasses(Coin): if (coin.NAME.lower() == name.lower() and coin.NET.lower() == net.lower()): coin_req_attrs = req_attrs.copy() missing = [attr for attr in coin_req_attrs if not hasattr(coin, attr)] if missing: raise CoinError('coin {} missing {} attributes' .format(name, missing)) return coin raise CoinError('unknown coin {} and network {} combination' .format(name, net)) @classmethod def sanitize_url(cls, url): # Remove surrounding ws and trailing /s url = url.strip().rstrip('/') match = cls.RPC_URL_REGEX.match(url) if not match: raise CoinError('invalid daemon URL: "{}"'.format(url)) if match.groups()[1] is None: url += ':{:d}'.format(cls.RPC_PORT) if not url.startswith('http://') and not url.startswith('https://'): url = 'http://' + url return url + '/' @classmethod def daemon_urls(cls, urls): return [cls.sanitize_url(url) for url in urls.split(',')] @classmethod def genesis_block(cls, block): '''Check the Genesis block is the right one for this coin. Return the block less its unspendable coinbase. ''' header = cls.block_header(block, 0) header_hex_hash = hash_to_str(cls.header_hash(header)) if header_hex_hash != cls.GENESIS_HASH: raise CoinError('genesis block has hash {} expected {}' .format(header_hex_hash, cls.GENESIS_HASH)) return header + bytes(1) @classmethod def hashX_from_script(cls, script): '''Returns a hashX from a script, or None if the script is provably unspendable so the output can be dropped. ''' if script and script[0] == OP_RETURN: return None return sha256(script).digest()[:cls.HASHX_LEN] @util.cachedproperty def address_handlers(cls): return ScriptPubKey.PayToHandlers( address=cls.P2PKH_address_from_hash160, script_hash=cls.P2SH_address_from_hash160, pubkey=cls.P2PKH_address_from_pubkey, unspendable=lambda: None, strange=lambda script: None, ) @classmethod def address_from_script(cls, script): '''Given a pk_script, return the adddress it pays to, or None.''' return ScriptPubKey.pay_to(cls.address_handlers, script) @staticmethod def lookup_xverbytes(verbytes): '''Return a (is_xpub, coin_class) pair given xpub/xprv verbytes.''' # Order means BTC testnet will override NMC testnet for coin in util.subclasses(Coin): if verbytes == coin.XPUB_VERBYTES: return True, coin if verbytes == coin.XPRV_VERBYTES: return False, coin raise CoinError('version bytes unrecognised') @classmethod def address_to_hashX(cls, address): '''Return a hashX given a coin address.''' return cls.hashX_from_script(cls.pay_to_address_script(address)) @classmethod def P2PKH_address_from_hash160(cls, hash160): '''Return a P2PKH address given a public key.''' assert len(hash160) == 20 return cls.ENCODE_CHECK(cls.P2PKH_VERBYTE + hash160) @classmethod def P2PKH_address_from_pubkey(cls, pubkey): '''Return a coin address given a public key.''' return cls.P2PKH_address_from_hash160(hash160(pubkey)) @classmethod def P2SH_address_from_hash160(cls, hash160): '''Return a coin address given a hash160.''' assert len(hash160) == 20 return cls.ENCODE_CHECK(cls.P2SH_VERBYTES[0] + hash160) @classmethod def multisig_address(cls, m, pubkeys): '''Return the P2SH address for an M of N multisig transaction. Pass the N pubkeys of which M are needed to sign it. If generating an address for a wallet, it is the caller's responsibility to sort them to ensure order does not matter for, e.g., wallet recovery. ''' script = cls.pay_to_multisig_script(m, pubkeys) return cls.P2SH_address_from_hash160(hash160(script)) @classmethod def pay_to_multisig_script(cls, m, pubkeys): '''Return a P2SH script for an M of N multisig transaction.''' return ScriptPubKey.multisig_script(m, pubkeys) @classmethod def pay_to_pubkey_script(cls, pubkey): '''Return a pubkey script that pays to a pubkey. Pass the raw pubkey bytes (length 33 or 65). ''' return ScriptPubKey.P2PK_script(pubkey) @classmethod def pay_to_address_script(cls, address): '''Return a pubkey script that pays to a pubkey hash. Pass the address (either P2PKH or P2SH) in base58 form. ''' raw = cls.DECODE_CHECK(address) # Require version byte(s) plus hash160. verbyte = -1 verlen = len(raw) - 20 if verlen > 0: verbyte, hash_bytes = raw[:verlen], raw[verlen:] if verbyte == cls.P2PKH_VERBYTE: return ScriptPubKey.P2PKH_script(hash_bytes) if verbyte in cls.P2SH_VERBYTES: return ScriptPubKey.P2SH_script(hash_bytes) raise CoinError('invalid address: {}'.format(address)) @classmethod def privkey_WIF(cls, privkey_bytes, compressed): '''Return the private key encoded in Wallet Import Format.''' payload = bytearray(cls.WIF_BYTE) + privkey_bytes if compressed: payload.append(0x01) return cls.ENCODE_CHECK(payload) @classmethod def header_hash(cls, header): '''Given a header return hash''' return double_sha256(header) @classmethod def header_prevhash(cls, header): '''Given a header return previous hash''' return header[4:36] @classmethod def static_header_offset(cls, height): '''Given a header height return its offset in the headers file. If header sizes change at some point, this is the only code that needs updating.''' assert cls.STATIC_BLOCK_HEADERS return height * cls.BASIC_HEADER_SIZE @classmethod def static_header_len(cls, height): '''Given a header height return its length.''' return cls.static_header_offset(height + 1) \ - cls.static_header_offset(height) @classmethod def block_header(cls, block, height): '''Returns the block header given a block and its height.''' return block[:cls.static_header_len(height)] @classmethod def block(cls, raw_block, height): '''Return a Block namedtuple given a raw block and its height.''' header = cls.block_header(raw_block, height) txs = cls.DESERIALIZER(raw_block, start=len(header)).read_tx_block() return Block(raw_block, header, txs) @classmethod def decimal_value(cls, value): '''Return the number of standard coin units as a Decimal given a quantity of smallest units. For example 1 BTC is returned for 100 million satoshis. ''' return Decimal(value) / cls.VALUE_PER_COIN @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits, nonce = struct.unpack('<III', header[68:80]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'timestamp': timestamp, 'bits': bits, 'nonce': nonce, } class AuxPowMixin(object): STATIC_BLOCK_HEADERS = False DESERIALIZER = lib_tx.DeserializerAuxPow @classmethod def header_hash(cls, header): '''Given a header return hash''' return double_sha256(header[:cls.BASIC_HEADER_SIZE]) @classmethod def block_header(cls, block, height): '''Return the AuxPow block header bytes''' deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class EquihashMixin(object): STATIC_BLOCK_HEADERS = False BASIC_HEADER_SIZE = 140 # Excluding Equihash solution DESERIALIZER = lib_tx.DeserializerEquihash @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'timestamp': timestamp, 'bits': bits, 'nonce': hash_to_str(header[108:140]), } @classmethod def block_header(cls, block, height): '''Return the block header bytes''' deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class ScryptMixin(object): DESERIALIZER = lib_tx.DeserializerTxTime HEADER_HASH = None @classmethod def header_hash(cls, header): '''Given a header return the hash.''' if cls.HEADER_HASH is None: import scrypt cls.HEADER_HASH = lambda x: scrypt.hash(x, x, 1024, 1, 1, 32) version, = struct.unpack('<I', header[:4]) if version > 6: return super().header_hash(header) else: return cls.HEADER_HASH(header) class KomodoMixin(object): P2PKH_VERBYTE = bytes.fromhex("3C") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("BC") GENESIS_HASH = ('027e3758c3a65b12aa1046462b486d0a' '63bfa1beae327897f56c5cfb7daaae71') DESERIALIZER = lib_tx.DeserializerZcash class BitcoinMixin(object): SHORTNAME = "BTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("00") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000000000019d6689c085ae165831e93' '4ff763ae46a2a6c172b3f1b60a8ce26f') RPC_PORT = 8332 class HOdlcoin(Coin): NAME = "HOdlcoin" SHORTNAME = "HODLC" NET = "mainnet" BASIC_HEADER_SIZE = 88 P2PKH_VERBYTE = bytes.fromhex("28") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("a8") GENESIS_HASH = ('008872e5582924544e5c707ee4b839bb' '82c28a9e94e917c94b40538d5658c04b') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 258858 TX_COUNT_HEIGHT = 382138 TX_PER_BLOCK = 5 class BitcoinCash(BitcoinMixin, Coin): NAME = "BitcoinCash" SHORTNAME = "BCC" TX_COUNT = 243631085 TX_COUNT_HEIGHT = 479636 TX_PER_BLOCK = 50 PEERS = [ 'electrum-abc.criptolayer.net s50012', 'electroncash.cascharia.com s50002', 'bch.arihanc.com t52001 s52002', 'bccarihace4jdcnt.onion t52001 s52002', 'jelectrum-cash.1209k.com s t', 'abc.vom-stausee.de t52001 s52002', 'abc1.hsmiths.com t60001 s60002', 'electroncash.checksum0.com s t', ] class BitcoinSegwit(BitcoinMixin, Coin): NAME = "BitcoinSegwit" DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 217380620 TX_COUNT_HEIGHT = 464000 TX_PER_BLOCK = 1800 PEERS = [ 'btc.smsys.me s995', 'E-X.not.fyi s t', 'elec.luggs.co s443', 'electrum.vom-stausee.de s t', 'electrum3.hachre.de p10000 s t', 'electrum.hsmiths.com s t', 'erbium1.sytes.net s t', 'helicarrier.bauerj.eu s t', 'hsmiths4fyqlw5xw.onion s t', 'luggscoqbymhvnkp.onion t80', 'ozahtqwp25chjdjd.onion s t', 'us11.einfachmalnettsein.de s t', 'ELEX01.blackpole.online s t', 'node.arihanc.com s t', 'arihancckjge66iv.onion s t', ] class BitcoinGold(EquihashMixin, BitcoinMixin, Coin): CHUNK_SIZE = 252 NAME = "BitcoinGold" SHORTNAME = "BTG" FORK_HEIGHT = 491407 P2PKH_VERBYTE = bytes.fromhex("26") P2SH_VERBYTES = [bytes.fromhex("17")] DESERIALIZER = lib_tx.DeserializerEquihashSegWit TX_COUNT = 265026255 TX_COUNT_HEIGHT = 499923 TX_PER_BLOCK = 50 REORG_LIMIT = 1000 RPC_PORT = 8338 @classmethod def header_hash(cls, header): '''Given a header return hash''' height, = struct.unpack('<I', header[68:72]) if height >= cls.FORK_HEIGHT: return double_sha256(header) else: return double_sha256(header[:68] + header[100:112]) @classmethod def electrum_header(cls, header, height): h = dict( block_height=height, version=struct.unpack('<I', header[:4])[0], prev_block_hash=hash_to_str(header[4:36]), merkle_root=hash_to_str(header[36:68]), timestamp=struct.unpack('<I', header[100:104])[0], reserved=hash_to_str(header[72:100]), bits=struct.unpack('<I', header[104:108])[0], nonce=hash_to_str(header[108:140]), solution=hash_to_str(header[140:]) ) return h class BitcoinGoldTestnet(BitcoinGold): FORK_HEIGHT = 1 SHORTNAME = "TBTG" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") TX_COUNT = 0 TX_COUNT_HEIGHT = 1 NET = 'testnet' RPC_PORT = 18338 GENESIS_HASH = ('00000000e0781ebe24b91eedc293adfe' 'a2f557b53ec379e78959de3853e6f9f6') class BitcoinGoldRegtest(BitcoinGold): FORK_HEIGHT = 2000 SHORTNAME = "TBTG" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") TX_COUNT = 0 TX_COUNT_HEIGHT = 1 NET = 'regtest' RPC_PORT = 18444 GENESIS_HASH = ('0f9188f13cb7b2c71f2a335e3a4fc328' 'bf5beb436012afca590b1a11466e2206') class Emercoin(Coin): NAME = "Emercoin" SHORTNAME = "EMC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("21") P2SH_VERBYTES = [bytes.fromhex("5c")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00000000bcccd459d036a588d1008fce' '8da3754b205736f32ddfd35350e84c2d') TX_COUNT = 217380620 TX_COUNT_HEIGHT = 464000 TX_PER_BLOCK = 1700 VALUE_PER_COIN = 1000000 RPC_PORT = 6662 DESERIALIZER = lib_tx.DeserializerTxTimeAuxPow PEERS = [] @classmethod def block_header(cls, block, height): '''Returns the block header given a block and its height.''' deserializer = cls.DESERIALIZER(block) if deserializer.is_merged_block(): return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) return block[:cls.static_header_len(height)] @classmethod def header_hash(cls, header): '''Given a header return hash''' return double_sha256(header[:cls.BASIC_HEADER_SIZE]) class BitcoinTestnetMixin(object): SHORTNAME = "XTN" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('000000000933ea01ad0ee984209779ba' 'aec3ced90fa3f408719526f8d77f4943') REORG_LIMIT = 8000 TX_COUNT = 12242438 TX_COUNT_HEIGHT = 1035428 TX_PER_BLOCK = 21 RPC_PORT = 18332 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} class BitcoinCashTestnet(BitcoinTestnetMixin, Coin): '''Bitcoin Testnet for Bitcoin Cash daemons.''' NAME = "BitcoinCash" PEERS = [ 'electrum-testnet-abc.criptolayer.net s50112', 'bchtestnet.arihanc.com t53001 s53002', 'ciiattqkgzebpp6jofjbrkhvhwmgnsfoayljdcrve2p3qmkbv3duaoyd.onion t53001 s53002', ] class BitcoinSegwitTestnet(BitcoinTestnetMixin, Coin): '''Bitcoin Testnet for Core bitcoind >= 0.13.1.''' NAME = "BitcoinSegwit" DESERIALIZER = lib_tx.DeserializerSegWit PEERS = [ 'electrum.akinbo.org s t', 'he36kyperp3kbuxu.onion s t', 'testnet.hsmiths.com t53011 s53012', 'hsmithsxurybd7uh.onion t53011 s53012', 'testnetnode.arihanc.com s t', 'w3e2orjpiiv2qwem3dw66d7c4krink4nhttngkylglpqe5r22n6n5wid.onion s t', ] class BitcoinSegwitRegtest(BitcoinSegwitTestnet): NAME = "BitcoinSegwit" NET = "regtest" GENESIS_HASH = ('0f9188f13cb7b2c71f2a335e3a4fc328' 'bf5beb436012afca590b1a11466e2206') PEERS= [] TX_COUNT = 1 TX_COUNT_HEIGHT = 1 class BitcoinNolnet(BitcoinCash): '''Bitcoin Unlimited nolimit testnet.''' NET = "nolnet" GENESIS_HASH = ('0000000057e31bd2066c939a63b7b862' '3bd0f10d8c001304bdfc1a7902ae6d35') PEERS = [] REORG_LIMIT = 8000 TX_COUNT = 583589 TX_COUNT_HEIGHT = 8617 TX_PER_BLOCK = 50 RPC_PORT = 28332 PEER_DEFAULT_PORTS = {'t': '52001', 's': '52002'} class Litecoin(Coin): NAME = "Litecoin" SHORTNAME = "LTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("30") P2SH_VERBYTES = [bytes.fromhex("32"), bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("b0") GENESIS_HASH = ('12a765e31ffd4059bada1e25190f6e98' 'c99d9714d334efa41a195a7e7e04bfe2') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 8908766 TX_COUNT_HEIGHT = 1105256 TX_PER_BLOCK = 10 RPC_PORT = 9332 REORG_LIMIT = 800 PEERS = [ 'elec.luggs.co s444', 'electrum-ltc.bysh.me s t', 'electrum-ltc.ddns.net s t', 'electrum-ltc.wilv.in s t', 'electrum.cryptomachine.com p1000 s t', 'electrum.ltc.xurious.com s t', 'eywr5eubdbbe2laq.onion s50008 t50007', ] class LitecoinTestnet(Litecoin): SHORTNAME = "XLT" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("3a"), bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('4966625a4b2851d9fdee139e56211a0d' '88575f59ed816ff5e6a63deb4e3e29a0') TX_COUNT = 21772 TX_COUNT_HEIGHT = 20800 TX_PER_BLOCK = 2 RPC_PORT = 19332 REORG_LIMIT = 4000 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrum-ltc.bysh.me s t', 'electrum.ltc.xurious.com s t', ] class Viacoin(AuxPowMixin, Coin): NAME="Viacoin" SHORTNAME = "VIA" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("47") P2SH_VERBYTES = [bytes.fromhex("21")] WIF_BYTE = bytes.fromhex("c7") GENESIS_HASH = ('4e9b54001f9976049830128ec0331515' 'eaabe35a70970d79971da1539a400ba1') TX_COUNT = 113638 TX_COUNT_HEIGHT = 3473674 TX_PER_BLOCK = 30 RPC_PORT = 5222 REORG_LIMIT = 5000 DESERIALIZER = lib_tx.DeserializerAuxPowSegWit PEERS = [ 'vialectrum.bitops.me s t', 'server.vialectrum.org s t', 'vialectrum.viacoin.net s t', 'viax1.bitops.me s t', ] class ViacoinTestnet(Viacoin): SHORTNAME = "TVI" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("7f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ff") GENESIS_HASH = ('00000007199508e34a9ff81e6ec0c477' 'a4cccff2a4767a8eee39c11db367b008') RPC_PORT = 25222 REORG_LIMIT = 2500 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'vialectrum.bysh.me s t', ] class ViacoinTestnetSegWit(ViacoinTestnet): NET = "testnet-segwit" DESERIALIZER = lib_tx.DeserializerSegWit # Source: namecoin.org class Namecoin(AuxPowMixin, Coin): NAME = "Namecoin" SHORTNAME = "NMC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("d7dd6370") XPRV_VERBYTES = bytes.fromhex("d7dc6e31") P2PKH_VERBYTE = bytes.fromhex("34") P2SH_VERBYTES = [bytes.fromhex("0d")] WIF_BYTE = bytes.fromhex("e4") GENESIS_HASH = ('000000000062b72c5e2ceb45fbc8587e' '807c155b0da735e6483dfba2f0a9c770') TX_COUNT = 4415768 TX_COUNT_HEIGHT = 329065 TX_PER_BLOCK = 10 PEERS = [ 'elec.luggs.co s446', ] class NamecoinTestnet(Namecoin): NAME = "Namecoin" SHORTNAME = "XNM" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('00000007199508e34a9ff81e6ec0c477' 'a4cccff2a4767a8eee39c11db367b008') class Dogecoin(AuxPowMixin, Coin): NAME = "Dogecoin" SHORTNAME = "DOGE" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("02facafd") XPRV_VERBYTES = bytes.fromhex("02fac398") P2PKH_VERBYTE = bytes.fromhex("1e") P2SH_VERBYTES = [bytes.fromhex("16")] WIF_BYTE = bytes.fromhex("9e") GENESIS_HASH = ('1a91e3dace36e2be3bf030a65679fe82' '1aa1d6ef92e7c9902eb318182c355691') TX_COUNT = 27583427 TX_COUNT_HEIGHT = 1604979 TX_PER_BLOCK = 20 REORG_LIMIT = 2000 class DogecoinTestnet(Dogecoin): NAME = "Dogecoin" SHORTNAME = "XDT" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("71") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("f1") GENESIS_HASH = ('bb0a78264637406b6360aad926284d54' '4d7049f45189db5664f3c4d07350559e') # Source: https://github.com/dashpay/dash class Dash(Coin): NAME = "Dash" SHORTNAME = "DASH" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("02fe52cc") XPRV_VERBYTES = bytes.fromhex("02fe52f8") GENESIS_HASH = ('00000ffd590b1485b3caadc19b22e637' '9c733355108f107a430458cdf3407ab6') P2PKH_VERBYTE = bytes.fromhex("4c") P2SH_VERBYTES = [bytes.fromhex("10")] WIF_BYTE = bytes.fromhex("cc") TX_COUNT_HEIGHT = 569399 TX_COUNT = 2157510 TX_PER_BLOCK = 4 RPC_PORT = 9998 PEERS = [ 'electrum.dash.org s t', 'electrum.masternode.io s t', 'electrum-drk.club s t', 'dashcrypto.space s t', 'electrum.dash.siampm.com s t', 'wl4sfwq2hwxnodof.onion s t', ] SESSIONCLS = DashElectrumX DAEMON = daemon.DashDaemon @classmethod def header_hash(cls, header): '''Given a header return the hash.''' import x11_hash return x11_hash.getPoWHash(header) class DashTestnet(Dash): SHORTNAME = "tDASH" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("3a805837") XPRV_VERBYTES = bytes.fromhex("3a8061a0") GENESIS_HASH = ('00000bafbc94add76cb75e2ec9289483' '7288a481e5c005f6563d91623bf8bc2c') P2PKH_VERBYTE = bytes.fromhex("8c") P2SH_VERBYTES = [bytes.fromhex("13")] WIF_BYTE = bytes.fromhex("ef") TX_COUNT_HEIGHT = 101619 TX_COUNT = 132681 TX_PER_BLOCK = 1 RPC_PORT = 19998 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrum.dash.siampm.com s t', ] class Argentum(AuxPowMixin, Coin): NAME = "Argentum" SHORTNAME = "ARG" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("17") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("97") GENESIS_HASH = ('88c667bc63167685e4e4da058fffdfe8' 'e007e5abffd6855de52ad59df7bb0bb2') TX_COUNT = 2263089 TX_COUNT_HEIGHT = 2050260 TX_PER_BLOCK = 2000 RPC_PORT = 13581 class ArgentumTestnet(Argentum): SHORTNAME = "XRG" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") REORG_LIMIT = 2000 class DigiByte(Coin): NAME = "DigiByte" SHORTNAME = "DGB" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1E") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('7497ea1b465eb39f1c8f507bc877078f' 'e016d6fcb6dfad3a64c98dcc6e1e8496') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 1046018 TX_COUNT_HEIGHT = 1435000 TX_PER_BLOCK = 1000 RPC_PORT = 12022 class DigiByteTestnet(DigiByte): NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('b5dca8039e300198e5fe7cd23bdd1728' 'e2a444af34c447dbd0916fa3430a68c2') RPC_PORT = 15022 REORG_LIMIT = 2000 class FairCoin(Coin): NAME = "FairCoin" SHORTNAME = "FAIR" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("5f") P2SH_VERBYTES = [bytes.fromhex("24")] WIF_BYTE = bytes.fromhex("df") GENESIS_HASH = ('beed44fa5e96150d95d56ebd5d262578' '1825a9407a5215dd7eda723373a0a1d7') BASIC_HEADER_SIZE = 108 TX_COUNT = 505 TX_COUNT_HEIGHT = 470 TX_PER_BLOCK = 1 RPC_PORT = 40405 PEER_DEFAULT_PORTS = {'t': '51811', 's': '51812'} PEERS = [ 'electrum.faircoin.world s', 'electrumfair.punto0.org s', ] @classmethod def block(cls, raw_block, height): '''Return a Block namedtuple given a raw block and its height.''' if height > 0: return super().block(raw_block, height) else: return Block(raw_block, cls.block_header(raw_block, height), []) @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, creatorId = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'payload_hash': hash_to_str(header[68:100]), 'timestamp': timestamp, 'creatorId': creatorId, } class Zcash(EquihashMixin, Coin): NAME = "Zcash" SHORTNAME = "ZEC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00040fe8ec8471911baa1db1266ea15d' 'd06b4a8a5c453883c000b031973dce08') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8232 REORG_LIMIT = 800 class SnowGem(EquihashMixin, Coin): NAME = "SnowGem" SHORTNAME = "SNG" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1C28") P2SH_VERBYTES = [bytes.fromhex("1C2D")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00068b35729d9d2b0c294ff1fe9af009' '4740524311a131de40e7f705e4c29a5b') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 140698 TX_COUNT_HEIGHT = 102802 TX_PER_BLOCK = 2 RPC_PORT = 16112 REORG_LIMIT = 800 CHUNK_SIZE = 200 @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'hash_reserved': hash_to_str(header[68:100]), 'timestamp': timestamp, 'bits': bits, 'nonce': hash_to_str(header[108:140]), 'n_solution': base64.b64encode(lib_tx.Deserializer(header, start=140)._read_varbytes()).decode('utf8') } class BitcoinZ(EquihashMixin, Coin): NAME = "BitcoinZ" SHORTNAME = "BTCZ" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('f499ee3d498b4298ac6a64205b8addb7' 'c43197e2a660229be65db8a4534d75c1') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 171976 TX_COUNT_HEIGHT = 81323 TX_PER_BLOCK = 3 RPC_PORT = 1979 REORG_LIMIT = 800 class Hush(EquihashMixin, Coin): NAME = "Hush" SHORTNAME = "HUSH" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ( '0003a67bc26fe564b75daf11186d3606' '52eb435a35ba3d9d3e7e5d5f8e62dc17') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8822 REORG_LIMIT = 800 class Zclassic(EquihashMixin, Coin): NAME = "Zclassic" SHORTNAME = "ZCL" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ( '0007104ccda289427919efc39dc9e4d4' '99804b7bebc22df55f8b834301260602') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8023 REORG_LIMIT = 800 class Koto(Coin): NAME = "Koto" SHORTNAME = "KOTO" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1836") P2SH_VERBYTES = [bytes.fromhex("183B")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('6d424c350729ae633275d51dc3496e16' 'cd1b1d195c164da00f39c499a2e9959e') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 158914 TX_COUNT_HEIGHT = 67574 TX_PER_BLOCK = 3 RPC_PORT = 8432 REORG_LIMIT = 800 PEERS = [ 'fr.kotocoin.info s t', 'electrum.kotocoin.info s t', ] class Komodo(KomodoMixin, EquihashMixin, Coin): NAME = "Komodo" SHORTNAME = "KMD" NET = "mainnet" TX_COUNT = 693629 TX_COUNT_HEIGHT = 491777 TX_PER_BLOCK = 2 RPC_PORT = 7771 REORG_LIMIT = 800 PEERS = [] class Monaize(KomodoMixin, EquihashMixin, Coin): NAME = "Monaize" SHORTNAME = "MNZ" NET = "mainnet" TX_COUNT = 256 TX_COUNT_HEIGHT = 128 TX_PER_BLOCK = 2 RPC_PORT = 14337 REORG_LIMIT = 800 PEERS = [] class Einsteinium(Coin): NAME = "Einsteinium" SHORTNAME = "EMC2" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("21") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("b0") GENESIS_HASH = ('4e56204bb7b8ac06f860ff1c845f03f9' '84303b5b97eb7b42868f714611aed94b') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2087559 TX_COUNT_HEIGHT = 1358517 TX_PER_BLOCK = 2 RPC_PORT = 41879 REORG_LIMIT = 2000 class Blackcoin(ScryptMixin, Coin): NAME = "Blackcoin" SHORTNAME = "BLK" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("19") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("99") GENESIS_HASH = ('000001faef25dec4fbcf906e6242621d' 'f2c183bf232f263d0ba5b101911e4563') DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 4594999 TX_COUNT_HEIGHT = 1667070 TX_PER_BLOCK = 3 RPC_PORT = 15715 REORG_LIMIT = 5000 class Bitbay(ScryptMixin, Coin): NAME = "Bitbay" SHORTNAME = "BAY" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("19") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("99") GENESIS_HASH = ('0000075685d3be1f253ce777174b1594' '354e79954d2a32a6f77fe9cba00e6467') TX_COUNT = 4594999 TX_COUNT_HEIGHT = 1667070 TX_PER_BLOCK = 3 RPC_PORT = 19914 REORG_LIMIT = 5000 class Peercoin(Coin): NAME = "Peercoin" SHORTNAME = "PPC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("37") P2SH_VERBYTES = [bytes.fromhex("75")] WIF_BYTE = bytes.fromhex("b7") GENESIS_HASH = ('0000000032fe677166d54963b62a4677' 'd8957e87c508eaa4fd7eb1c880cd27e3') DESERIALIZER = lib_tx.DeserializerTxTime DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 1207356 TX_COUNT_HEIGHT = 306425 TX_PER_BLOCK = 4 RPC_PORT = 9902 REORG_LIMIT = 5000 class Reddcoin(Coin): NAME = "Reddcoin" SHORTNAME = "RDD" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3d") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("bd") GENESIS_HASH = ('b868e0d95a3c3c0e0dadc67ee587aaf9' 'dc8acbf99e3b4b3110fad4eb74c1decc') DESERIALIZER = lib_tx.DeserializerReddcoin TX_COUNT = 5413508 TX_COUNT_HEIGHT = 1717382 TX_PER_BLOCK = 3 RPC_PORT = 45443 class Vertcoin(Coin): NAME = "Vertcoin" SHORTNAME = "VTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488B21E") XPRV_VERBYTES = bytes.fromhex("0488ADE4") P2PKH_VERBYTE = bytes.fromhex("47") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('4d96a915f49d40b1e5c2844d1ee2dccb' '90013a990ccea12c492d22110489f0c4') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2383423 TX_COUNT_HEIGHT = 759076 TX_PER_BLOCK = 3 RPC_PORT = 5888 REORG_LIMIT = 1000 class Monacoin(Coin): NAME = "Monacoin" SHORTNAME = "MONA" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488B21E") XPRV_VERBYTES = bytes.fromhex("0488ADE4") P2PKH_VERBYTE = bytes.fromhex("32") P2SH_VERBYTES = [bytes.fromhex("37"), bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("B0") GENESIS_HASH = ('ff9f1c0116d19de7c9963845e129f9ed' '1bfc0b376eb54fd7afa42e0d418c8bb6') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2568580 TX_COUNT_HEIGHT = 1029766 TX_PER_BLOCK = 2 RPC_PORT = 9402 REORG_LIMIT = 1000 PEERS = [ 'electrumx.tamami-foundation.org s t', 'electrumx2.tamami-foundation.org s t', 'electrumx3.tamami-foundation.org s t', 'electrumx1.monacoin.nl s t', 'electrumx2.monacoin.nl s t', 'electrumx1.monacoin.ninja s t', 'electrumx2.monacoin.ninja s t', 'electrumx1.movsign.info t', 'electrumx2.movsign.info s t', 'electrum-mona.bitbank.cc s t', ] class MonacoinTestnet(Monacoin): SHORTNAME = "XMN" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("75"), bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") GENESIS_HASH = ('a2b106ceba3be0c6d097b2a6a6aacf9d' '638ba8258ae478158f449c321061e0b2') TX_COUNT = 83602 TX_COUNT_HEIGHT = 83252 TX_PER_BLOCK = 1 RPC_PORT = 19402 REORG_LIMIT = 1000 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrumx1.testnet.monacoin.ninja s t', 'electrumx1.testnet.monacoin.nl s t', ] class Crown(AuxPowMixin, Coin): NAME = "Crown" SHORTNAME = "CRW" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("00") P2SH_VERBYTES = [bytes.fromhex("1c")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('0000000085370d5e122f64f4ab19c686' '14ff3df78c8d13cb814fd7e69a1dc6da') TX_COUNT = 13336629 TX_COUNT_HEIGHT = 1268206 TX_PER_BLOCK = 10 RPC_PORT = 9341 REORG_LIMIT = 1000 PEERS = [ 'sgp-crwseed.crowndns.info s t', 'blr-crwseed.crowndns.info s t', 'sfo-crwseed.crowndns.info s t', 'nyc-crwseed.crowndns.info s t', 'ams-crwseed.crowndns.info s t', 'tor-crwseed.crowndns.info s t', 'lon-crwseed.crowndns.info s t', 'fra-crwseed.crowndns.info s t', ] class Fujicoin(Coin): NAME = "Fujicoin" SHORTNAME = "FJC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("24") P2SH_VERBYTES = [bytes.fromhex("10")] WIF_BYTE = bytes.fromhex("a4") GENESIS_HASH = ('adb6d9cfd74075e7f91608add4bd2a2e' 'a636f70856183086842667a1597714a0') ESTIMATE_FEE = 0.001 RELAY_FEE = 0.001 TX_COUNT = 170478 TX_COUNT_HEIGHT = 1521676 TX_PER_BLOCK = 1 RPC_PORT = 3776 REORG_LIMIT = 1000 class Neblio(ScryptMixin, Coin): NAME = "Neblio" SHORTNAME = "NEBL" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("35") P2SH_VERBYTES = [bytes.fromhex("70")] WIF_BYTE = bytes.fromhex("b5") GENESIS_HASH = ('7286972be4dbc1463d256049b7471c25' '2e6557e222cab9be73181d359cd28bcc') TX_COUNT = 23675 TX_COUNT_HEIGHT = 22785 TX_PER_BLOCK = 1 RPC_PORT = 6326 REORG_LIMIT = 1000 class Bitzeny(Coin): NAME = "Bitzeny" SHORTNAME = "ZNY" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("51") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000009f7e55e9e3b4781e22bd87a7cfa' '4acada9e4340d43ca738bf4e9fb8f5ce') ESTIMATE_FEE = 0.001 RELAY_FEE = 0.001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT = 1000 TX_COUNT_HEIGHT = 10000 TX_PER_BLOCK = 1 RPC_PORT = 9252 REORG_LIMIT = 1000 class CanadaeCoin(AuxPowMixin, Coin): NAME = "CanadaeCoin" SHORTNAME = "CDN" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("1C") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("9c") GENESIS_HASH = ('863626dadaef221e2e2f30ff3dacae44' 'cabdae9e0028058072181b3fb675d94a') ESTIMATE_FEE = 0.0001 RELAY_FEE = 0.0001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT = 3455905 TX_COUNT_HEIGHT = 3645419 TX_PER_BLOCK = 1 RPC_PORT = 34330 REORG_LIMIT = 1000 class Denarius(Coin): NAME = "Denarius" SHORTNAME = "DNR" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("1E") #Address starts with a D P2SH_VERBYTES = [bytes.fromhex("5A")] WIF_BYTE = bytes.fromhex("9E") #WIF starts with a 6 GENESIS_HASH = ('00000d5dbbda01621cfc16bbc1f9bf32' '64d641a5dbf0de89fd0182c2c4828fcd') DESERIALIZER = lib_tx.DeserializerTxTime TX_COUNT = 4230 RPC_PORT = 32339 ESTIMATE_FEE = 0.00001 RELAY_FEE = 0.00001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT_HEIGHT = 306187 TX_PER_BLOCK = 4000 @classmethod def header_hash(cls, header): '''Given a header return the hash.''' import tribus_hash return tribus_hash.getPoWHash(header) class DenariusTestnet(Denarius): NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("12") P2SH_VERBYTES = [bytes.fromhex("74")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('000086bfe8264d241f7f8e5393f74778' '4b8ca2aa98bdd066278d590462a4fdb4') RPC_PORT = 32338 REORG_LIMIT = 2000 class Sibcoin(Dash): NAME = "Sibcoin" SHORTNAME = "SIB" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("3F") P2SH_VERBYTES = [bytes.fromhex("28")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00000c492bf73490420868bc577680bf' 'c4c60116e7e85343bc624787c21efa4c') DAEMON = daemon.DashDaemon TX_COUNT = 1000 TX_COUNT_HEIGHT = 10000 TX_PER_BLOCK = 1 RPC_PORT = 1944 REORG_LIMIT = 1000 PEERS = [] @classmethod def header_hash(cls, header): ''' Given a header return the hash for sibcoin. Need to download `x11_gost_hash` module Source code: https://github.com/ivansib/x11_gost_hash ''' import x11_gost_hash return x11_gost_hash.getPoWHash(header) class Chips(Coin): NAME = "Chips" SHORTNAME = "CHIPS" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3c") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("bc") GENESIS_HASH = ('0000006e75f6aa0efdbf7db03132aa4e' '4d0c84951537a6f5a7c39a0a9d30e1e7') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 145290 TX_COUNT_HEIGHT = 318637 TX_PER_BLOCK = 2 RPC_PORT = 57776 REORG_LIMIT = 800 class Feathercoin(Coin): NAME = "Feathercoin" SHORTNAME = "FTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488BC26") XPRV_VERBYTES = bytes.fromhex("0488DAEE") P2PKH_VERBYTE = bytes.fromhex("0E") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("8E") GENESIS_HASH = ('12a765e31ffd4059bada1e25190f6e98' 'c99d9714d334efa41a195a7e7e04bfe2') TX_COUNT = 3170843 TX_COUNT_HEIGHT = 1981777 TX_PER_BLOCK = 2 RPC_PORT = 9337 REORG_LIMIT = 2000 PEERS = [ 'electrumx-ch-1.feathercoin.ch s t', ] class Newyorkcoin(AuxPowMixin, Coin): NAME = "Newyorkcoin" SHORTNAME = "NYC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3c") P2SH_VERBYTES = [bytes.fromhex("16")] WIF_BYTE = bytes.fromhex("bc") GENESIS_HASH = ('5597f25c062a3038c7fd815fe46c67de' 'dfcb3c839fbc8e01ed4044540d08fe48') DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 5161944 TX_COUNT_HEIGHT = 3948743 TX_PER_BLOCK = 2 REORG_LIMIT = 2000 class Bitcore(BitcoinMixin, Coin): NAME = "Bitcore" SHORTNAME = "BTX" DESERIALIZER = lib_tx.DeserializerSegWit GENESIS_HASH = ('604148281e5c4b7f2487e5d03cd60d8e' '6f69411d613f6448034508cea52e9574') TX_COUNT = 126979 TX_COUNT_HEIGHT = 126946 TX_PER_BLOCK = 2 RPC_PORT = 8556 # source: https://github.com/obsidianplatform class Obsidian(Coin): NAME = "Obsidian" SHORTNAME = "ODN" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488c21e") XPRV_VERBYTES = bytes.fromhex("0488b2dd") P2PKH_VERBYTE = bytes.fromhex("4b") P2SH_VERBYTES = [bytes.fromhex("7d")] WIF_BYTE = bytes.fromhex("cb") GENESIS_HASH = ('0000006dd8a92f58e952fa61c9402b74' 'a381a69d1930fb5cc12c73273fab5f0a') RPC_PORT = 56661 TX_COUNT = 1067887 TX_PER_BLOCK = 2 TX_COUNT_HEIGHT = 500000 DAEMON = daemon.LegacyRPCDaemon @classmethod def header_hash(cls, header): '''Given a header return the hash.''' from hashlib import sha512 return sha512(header).digest()[:32] class BitcoinAtom(Coin): NAME = "BitcoinAtom" SHORTNAME = "BCA" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("17") P2SH_VERBYTES = [bytes.fromhex("0a")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000000000019d6689c085ae165831e93' '4ff763ae46a2a6c172b3f1b60a8ce26f') STATIC_BLOCK_HEADERS = False DESERIALIZER = lib_tx.DeserializerBitcoinAtom HEADER_SIZE_POST_FORK = 84 BLOCK_PROOF_OF_STAKE = 0x01 BLOCK_PROOF_OF_STAKE_FLAGS = b'\x01\x00\x00\x00' TX_COUNT = 295158744 TX_COUNT_HEIGHT = 589197 TX_PER_BLOCK = 10 RPC_PORT = 9136 REORG_LIMIT = 5000 @classmethod def header_hash(cls, header): '''Given a header return hash''' header_to_be_hashed = header[:cls.BASIC_HEADER_SIZE] # New block header format has some extra flags in the end if len(header) == cls.HEADER_SIZE_POST_FORK: flags, = struct.unpack('<I', header[-4:]) # Proof of work blocks have special serialization if flags & cls.BLOCK_PROOF_OF_STAKE != 0: header_to_be_hashed += cls.BLOCK_PROOF_OF_STAKE_FLAGS return double_sha256(header_to_be_hashed) @classmethod def block_header(cls, block, height): '''Return the block header bytes''' deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class Decred(Coin): NAME = "Decred" SHORTNAME = "DCR" NET = "mainnet" XPUB_VERBYTES = bytes('dpub', 'utf-8') XPRV_VERBYTES = bytes('dprv', 'utf-8') P2PKH_VERBYTE = bytes('Ds', 'utf-8') P2SH_VERBYTES = [bytes('Dc', 'utf-8')] WIF_BYTE = bytes('Pm', 'utf-8') GENESIS_HASH = ('298e5cc3d985bfe7f81dc135f360abe089edd4396b86d2de66b0cef42b21d980') DESERIALIZER = lib_tx.DeserializerDecred ENCODE_CHECK = partial(Base58.encode_check, hash_fn=lib_tx.DeserializerDecred.blake256) DECODE_CHECK = partial(Base58.decode_check, hash_fn=lib_tx.DeserializerDecred.blake256) HEADER_HASH = lib_tx.DeserializerDecred.blake256 BASIC_HEADER_SIZE = 180 ALLOW_ADVANCING_ERRORS = True TX_COUNT = 217380620 TX_COUNT_HEIGHT = 218875 TX_PER_BLOCK = 1000 RPC_PORT = 9109 @classmethod def header_hash(cls, header): '''Given a header return the hash.''' return cls.HEADER_HASH(header) @classmethod def block(cls, raw_block, height): '''Return a Block namedtuple given a raw block and its height.''' if height > 0: return super().block(raw_block, height) else: return Block(raw_block, cls.block_header(raw_block, height), []) class DecredTestnet(Decred): NAME = "Decred" NET = "testnet" XPUB_VERBYTES = bytes('tpub', 'utf-8') XPRV_VERBYTES = bytes('tprv', 'utf-8') P2PKH_VERBYTE = bytes('Ts', 'utf-8') P2SH_VERBYTES = [bytes('Tc', 'utf-8')] WIF_BYTE = bytes('Pt', 'utf-8') GENESIS_HASH = ('4261602a9d07d80ad47621a64ba6a07754902e496777edc4ff581946bd7bc29c') TX_COUNT = 3176305 TX_COUNT_HEIGHT = 254198 TX_PER_BLOCK = 1000 RPC_PORT = 19109

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from collections import namedtuple import re import struct from decimal import Decimal from hashlib import sha256 from functools import partial import base64 import lib.util as util from lib.hash import Base58, hash160, double_sha256, hash_to_str from lib.script import ScriptPubKey, OpCodes import lib.tx as lib_tx from server.block_processor import BlockProcessor import server.daemon as daemon from server.session import ElectrumX, DashElectrumX Block = namedtuple("Block", "raw header transactions") OP_RETURN = OpCodes.OP_RETURN class CoinError(Exception): class Coin(object): REORG_LIMIT = 200 RPC_URL_REGEX = re.compile('.+@(\[[0-9a-fA-F:]+\]|[^:]+)(:[0-9]+)?') VALUE_PER_COIN = 100000000 CHUNK_SIZE = 2016 HASHX_LEN = 11 BASIC_HEADER_SIZE = 80 STATIC_BLOCK_HEADERS = True SESSIONCLS = ElectrumX DESERIALIZER = lib_tx.Deserializer DAEMON = daemon.Daemon BLOCK_PROCESSOR = BlockProcessor XPUB_VERBYTES = bytes('????', 'utf-8') XPRV_VERBYTES = bytes('????', 'utf-8') ENCODE_CHECK = Base58.encode_check DECODE_CHECK = Base58.decode_check PEER_DEFAULT_PORTS = {'t': '50001', 's': '50002'} PEERS = [] @classmethod def lookup_coin_class(cls, name, net): req_attrs = ['TX_COUNT', 'TX_COUNT_HEIGHT', 'TX_PER_BLOCK'] for coin in util.subclasses(Coin): if (coin.NAME.lower() == name.lower() and coin.NET.lower() == net.lower()): coin_req_attrs = req_attrs.copy() missing = [attr for attr in coin_req_attrs if not hasattr(coin, attr)] if missing: raise CoinError('coin {} missing {} attributes' .format(name, missing)) return coin raise CoinError('unknown coin {} and network {} combination' .format(name, net)) @classmethod def sanitize_url(cls, url): url = url.strip().rstrip('/') match = cls.RPC_URL_REGEX.match(url) if not match: raise CoinError('invalid daemon URL: "{}"'.format(url)) if match.groups()[1] is None: url += ':{:d}'.format(cls.RPC_PORT) if not url.startswith('http://') and not url.startswith('https://'): url = 'http://' + url return url + '/' @classmethod def daemon_urls(cls, urls): return [cls.sanitize_url(url) for url in urls.split(',')] @classmethod def genesis_block(cls, block): header = cls.block_header(block, 0) header_hex_hash = hash_to_str(cls.header_hash(header)) if header_hex_hash != cls.GENESIS_HASH: raise CoinError('genesis block has hash {} expected {}' .format(header_hex_hash, cls.GENESIS_HASH)) return header + bytes(1) @classmethod def hashX_from_script(cls, script): if script and script[0] == OP_RETURN: return None return sha256(script).digest()[:cls.HASHX_LEN] @util.cachedproperty def address_handlers(cls): return ScriptPubKey.PayToHandlers( address=cls.P2PKH_address_from_hash160, script_hash=cls.P2SH_address_from_hash160, pubkey=cls.P2PKH_address_from_pubkey, unspendable=lambda: None, strange=lambda script: None, ) @classmethod def address_from_script(cls, script): return ScriptPubKey.pay_to(cls.address_handlers, script) @staticmethod def lookup_xverbytes(verbytes): for coin in util.subclasses(Coin): if verbytes == coin.XPUB_VERBYTES: return True, coin if verbytes == coin.XPRV_VERBYTES: return False, coin raise CoinError('version bytes unrecognised') @classmethod def address_to_hashX(cls, address): return cls.hashX_from_script(cls.pay_to_address_script(address)) @classmethod def P2PKH_address_from_hash160(cls, hash160): assert len(hash160) == 20 return cls.ENCODE_CHECK(cls.P2PKH_VERBYTE + hash160) @classmethod def P2PKH_address_from_pubkey(cls, pubkey): return cls.P2PKH_address_from_hash160(hash160(pubkey)) @classmethod def P2SH_address_from_hash160(cls, hash160): assert len(hash160) == 20 return cls.ENCODE_CHECK(cls.P2SH_VERBYTES[0] + hash160) @classmethod def multisig_address(cls, m, pubkeys): script = cls.pay_to_multisig_script(m, pubkeys) return cls.P2SH_address_from_hash160(hash160(script)) @classmethod def pay_to_multisig_script(cls, m, pubkeys): return ScriptPubKey.multisig_script(m, pubkeys) @classmethod def pay_to_pubkey_script(cls, pubkey): return ScriptPubKey.P2PK_script(pubkey) @classmethod def pay_to_address_script(cls, address): raw = cls.DECODE_CHECK(address) verbyte = -1 verlen = len(raw) - 20 if verlen > 0: verbyte, hash_bytes = raw[:verlen], raw[verlen:] if verbyte == cls.P2PKH_VERBYTE: return ScriptPubKey.P2PKH_script(hash_bytes) if verbyte in cls.P2SH_VERBYTES: return ScriptPubKey.P2SH_script(hash_bytes) raise CoinError('invalid address: {}'.format(address)) @classmethod def privkey_WIF(cls, privkey_bytes, compressed): payload = bytearray(cls.WIF_BYTE) + privkey_bytes if compressed: payload.append(0x01) return cls.ENCODE_CHECK(payload) @classmethod def header_hash(cls, header): return double_sha256(header) @classmethod def header_prevhash(cls, header): return header[4:36] @classmethod def static_header_offset(cls, height): assert cls.STATIC_BLOCK_HEADERS return height * cls.BASIC_HEADER_SIZE @classmethod def static_header_len(cls, height): return cls.static_header_offset(height + 1) \ - cls.static_header_offset(height) @classmethod def block_header(cls, block, height): return block[:cls.static_header_len(height)] @classmethod def block(cls, raw_block, height): header = cls.block_header(raw_block, height) txs = cls.DESERIALIZER(raw_block, start=len(header)).read_tx_block() return Block(raw_block, header, txs) @classmethod def decimal_value(cls, value): return Decimal(value) / cls.VALUE_PER_COIN @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits, nonce = struct.unpack('<III', header[68:80]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'timestamp': timestamp, 'bits': bits, 'nonce': nonce, } class AuxPowMixin(object): STATIC_BLOCK_HEADERS = False DESERIALIZER = lib_tx.DeserializerAuxPow @classmethod def header_hash(cls, header): return double_sha256(header[:cls.BASIC_HEADER_SIZE]) @classmethod def block_header(cls, block, height): deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class EquihashMixin(object): STATIC_BLOCK_HEADERS = False BASIC_HEADER_SIZE = 140 DESERIALIZER = lib_tx.DeserializerEquihash @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'timestamp': timestamp, 'bits': bits, 'nonce': hash_to_str(header[108:140]), } @classmethod def block_header(cls, block, height): deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class ScryptMixin(object): DESERIALIZER = lib_tx.DeserializerTxTime HEADER_HASH = None @classmethod def header_hash(cls, header): if cls.HEADER_HASH is None: import scrypt cls.HEADER_HASH = lambda x: scrypt.hash(x, x, 1024, 1, 1, 32) version, = struct.unpack('<I', header[:4]) if version > 6: return super().header_hash(header) else: return cls.HEADER_HASH(header) class KomodoMixin(object): P2PKH_VERBYTE = bytes.fromhex("3C") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("BC") GENESIS_HASH = ('027e3758c3a65b12aa1046462b486d0a' '63bfa1beae327897f56c5cfb7daaae71') DESERIALIZER = lib_tx.DeserializerZcash class BitcoinMixin(object): SHORTNAME = "BTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("00") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000000000019d6689c085ae165831e93' '4ff763ae46a2a6c172b3f1b60a8ce26f') RPC_PORT = 8332 class HOdlcoin(Coin): NAME = "HOdlcoin" SHORTNAME = "HODLC" NET = "mainnet" BASIC_HEADER_SIZE = 88 P2PKH_VERBYTE = bytes.fromhex("28") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("a8") GENESIS_HASH = ('008872e5582924544e5c707ee4b839bb' '82c28a9e94e917c94b40538d5658c04b') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 258858 TX_COUNT_HEIGHT = 382138 TX_PER_BLOCK = 5 class BitcoinCash(BitcoinMixin, Coin): NAME = "BitcoinCash" SHORTNAME = "BCC" TX_COUNT = 243631085 TX_COUNT_HEIGHT = 479636 TX_PER_BLOCK = 50 PEERS = [ 'electrum-abc.criptolayer.net s50012', 'electroncash.cascharia.com s50002', 'bch.arihanc.com t52001 s52002', 'bccarihace4jdcnt.onion t52001 s52002', 'jelectrum-cash.1209k.com s t', 'abc.vom-stausee.de t52001 s52002', 'abc1.hsmiths.com t60001 s60002', 'electroncash.checksum0.com s t', ] class BitcoinSegwit(BitcoinMixin, Coin): NAME = "BitcoinSegwit" DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 217380620 TX_COUNT_HEIGHT = 464000 TX_PER_BLOCK = 1800 PEERS = [ 'btc.smsys.me s995', 'E-X.not.fyi s t', 'elec.luggs.co s443', 'electrum.vom-stausee.de s t', 'electrum3.hachre.de p10000 s t', 'electrum.hsmiths.com s t', 'erbium1.sytes.net s t', 'helicarrier.bauerj.eu s t', 'hsmiths4fyqlw5xw.onion s t', 'luggscoqbymhvnkp.onion t80', 'ozahtqwp25chjdjd.onion s t', 'us11.einfachmalnettsein.de s t', 'ELEX01.blackpole.online s t', 'node.arihanc.com s t', 'arihancckjge66iv.onion s t', ] class BitcoinGold(EquihashMixin, BitcoinMixin, Coin): CHUNK_SIZE = 252 NAME = "BitcoinGold" SHORTNAME = "BTG" FORK_HEIGHT = 491407 P2PKH_VERBYTE = bytes.fromhex("26") P2SH_VERBYTES = [bytes.fromhex("17")] DESERIALIZER = lib_tx.DeserializerEquihashSegWit TX_COUNT = 265026255 TX_COUNT_HEIGHT = 499923 TX_PER_BLOCK = 50 REORG_LIMIT = 1000 RPC_PORT = 8338 @classmethod def header_hash(cls, header): height, = struct.unpack('<I', header[68:72]) if height >= cls.FORK_HEIGHT: return double_sha256(header) else: return double_sha256(header[:68] + header[100:112]) @classmethod def electrum_header(cls, header, height): h = dict( block_height=height, version=struct.unpack('<I', header[:4])[0], prev_block_hash=hash_to_str(header[4:36]), merkle_root=hash_to_str(header[36:68]), timestamp=struct.unpack('<I', header[100:104])[0], reserved=hash_to_str(header[72:100]), bits=struct.unpack('<I', header[104:108])[0], nonce=hash_to_str(header[108:140]), solution=hash_to_str(header[140:]) ) return h class BitcoinGoldTestnet(BitcoinGold): FORK_HEIGHT = 1 SHORTNAME = "TBTG" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") TX_COUNT = 0 TX_COUNT_HEIGHT = 1 NET = 'testnet' RPC_PORT = 18338 GENESIS_HASH = ('00000000e0781ebe24b91eedc293adfe' 'a2f557b53ec379e78959de3853e6f9f6') class BitcoinGoldRegtest(BitcoinGold): FORK_HEIGHT = 2000 SHORTNAME = "TBTG" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") TX_COUNT = 0 TX_COUNT_HEIGHT = 1 NET = 'regtest' RPC_PORT = 18444 GENESIS_HASH = ('0f9188f13cb7b2c71f2a335e3a4fc328' 'bf5beb436012afca590b1a11466e2206') class Emercoin(Coin): NAME = "Emercoin" SHORTNAME = "EMC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("21") P2SH_VERBYTES = [bytes.fromhex("5c")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00000000bcccd459d036a588d1008fce' '8da3754b205736f32ddfd35350e84c2d') TX_COUNT = 217380620 TX_COUNT_HEIGHT = 464000 TX_PER_BLOCK = 1700 VALUE_PER_COIN = 1000000 RPC_PORT = 6662 DESERIALIZER = lib_tx.DeserializerTxTimeAuxPow PEERS = [] @classmethod def block_header(cls, block, height): deserializer = cls.DESERIALIZER(block) if deserializer.is_merged_block(): return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) return block[:cls.static_header_len(height)] @classmethod def header_hash(cls, header): return double_sha256(header[:cls.BASIC_HEADER_SIZE]) class BitcoinTestnetMixin(object): SHORTNAME = "XTN" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('000000000933ea01ad0ee984209779ba' 'aec3ced90fa3f408719526f8d77f4943') REORG_LIMIT = 8000 TX_COUNT = 12242438 TX_COUNT_HEIGHT = 1035428 TX_PER_BLOCK = 21 RPC_PORT = 18332 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} class BitcoinCashTestnet(BitcoinTestnetMixin, Coin): NAME = "BitcoinCash" PEERS = [ 'electrum-testnet-abc.criptolayer.net s50112', 'bchtestnet.arihanc.com t53001 s53002', 'ciiattqkgzebpp6jofjbrkhvhwmgnsfoayljdcrve2p3qmkbv3duaoyd.onion t53001 s53002', ] class BitcoinSegwitTestnet(BitcoinTestnetMixin, Coin): NAME = "BitcoinSegwit" DESERIALIZER = lib_tx.DeserializerSegWit PEERS = [ 'electrum.akinbo.org s t', 'he36kyperp3kbuxu.onion s t', 'testnet.hsmiths.com t53011 s53012', 'hsmithsxurybd7uh.onion t53011 s53012', 'testnetnode.arihanc.com s t', 'w3e2orjpiiv2qwem3dw66d7c4krink4nhttngkylglpqe5r22n6n5wid.onion s t', ] class BitcoinSegwitRegtest(BitcoinSegwitTestnet): NAME = "BitcoinSegwit" NET = "regtest" GENESIS_HASH = ('0f9188f13cb7b2c71f2a335e3a4fc328' 'bf5beb436012afca590b1a11466e2206') PEERS= [] TX_COUNT = 1 TX_COUNT_HEIGHT = 1 class BitcoinNolnet(BitcoinCash): NET = "nolnet" GENESIS_HASH = ('0000000057e31bd2066c939a63b7b862' '3bd0f10d8c001304bdfc1a7902ae6d35') PEERS = [] REORG_LIMIT = 8000 TX_COUNT = 583589 TX_COUNT_HEIGHT = 8617 TX_PER_BLOCK = 50 RPC_PORT = 28332 PEER_DEFAULT_PORTS = {'t': '52001', 's': '52002'} class Litecoin(Coin): NAME = "Litecoin" SHORTNAME = "LTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("30") P2SH_VERBYTES = [bytes.fromhex("32"), bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("b0") GENESIS_HASH = ('12a765e31ffd4059bada1e25190f6e98' 'c99d9714d334efa41a195a7e7e04bfe2') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 8908766 TX_COUNT_HEIGHT = 1105256 TX_PER_BLOCK = 10 RPC_PORT = 9332 REORG_LIMIT = 800 PEERS = [ 'elec.luggs.co s444', 'electrum-ltc.bysh.me s t', 'electrum-ltc.ddns.net s t', 'electrum-ltc.wilv.in s t', 'electrum.cryptomachine.com p1000 s t', 'electrum.ltc.xurious.com s t', 'eywr5eubdbbe2laq.onion s50008 t50007', ] class LitecoinTestnet(Litecoin): SHORTNAME = "XLT" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("3a"), bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('4966625a4b2851d9fdee139e56211a0d' '88575f59ed816ff5e6a63deb4e3e29a0') TX_COUNT = 21772 TX_COUNT_HEIGHT = 20800 TX_PER_BLOCK = 2 RPC_PORT = 19332 REORG_LIMIT = 4000 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrum-ltc.bysh.me s t', 'electrum.ltc.xurious.com s t', ] class Viacoin(AuxPowMixin, Coin): NAME="Viacoin" SHORTNAME = "VIA" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("47") P2SH_VERBYTES = [bytes.fromhex("21")] WIF_BYTE = bytes.fromhex("c7") GENESIS_HASH = ('4e9b54001f9976049830128ec0331515' 'eaabe35a70970d79971da1539a400ba1') TX_COUNT = 113638 TX_COUNT_HEIGHT = 3473674 TX_PER_BLOCK = 30 RPC_PORT = 5222 REORG_LIMIT = 5000 DESERIALIZER = lib_tx.DeserializerAuxPowSegWit PEERS = [ 'vialectrum.bitops.me s t', 'server.vialectrum.org s t', 'vialectrum.viacoin.net s t', 'viax1.bitops.me s t', ] class ViacoinTestnet(Viacoin): SHORTNAME = "TVI" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("7f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ff") GENESIS_HASH = ('00000007199508e34a9ff81e6ec0c477' 'a4cccff2a4767a8eee39c11db367b008') RPC_PORT = 25222 REORG_LIMIT = 2500 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'vialectrum.bysh.me s t', ] class ViacoinTestnetSegWit(ViacoinTestnet): NET = "testnet-segwit" DESERIALIZER = lib_tx.DeserializerSegWit class Namecoin(AuxPowMixin, Coin): NAME = "Namecoin" SHORTNAME = "NMC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("d7dd6370") XPRV_VERBYTES = bytes.fromhex("d7dc6e31") P2PKH_VERBYTE = bytes.fromhex("34") P2SH_VERBYTES = [bytes.fromhex("0d")] WIF_BYTE = bytes.fromhex("e4") GENESIS_HASH = ('000000000062b72c5e2ceb45fbc8587e' '807c155b0da735e6483dfba2f0a9c770') TX_COUNT = 4415768 TX_COUNT_HEIGHT = 329065 TX_PER_BLOCK = 10 PEERS = [ 'elec.luggs.co s446', ] class NamecoinTestnet(Namecoin): NAME = "Namecoin" SHORTNAME = "XNM" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('00000007199508e34a9ff81e6ec0c477' 'a4cccff2a4767a8eee39c11db367b008') class Dogecoin(AuxPowMixin, Coin): NAME = "Dogecoin" SHORTNAME = "DOGE" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("02facafd") XPRV_VERBYTES = bytes.fromhex("02fac398") P2PKH_VERBYTE = bytes.fromhex("1e") P2SH_VERBYTES = [bytes.fromhex("16")] WIF_BYTE = bytes.fromhex("9e") GENESIS_HASH = ('1a91e3dace36e2be3bf030a65679fe82' '1aa1d6ef92e7c9902eb318182c355691') TX_COUNT = 27583427 TX_COUNT_HEIGHT = 1604979 TX_PER_BLOCK = 20 REORG_LIMIT = 2000 class DogecoinTestnet(Dogecoin): NAME = "Dogecoin" SHORTNAME = "XDT" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("71") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("f1") GENESIS_HASH = ('bb0a78264637406b6360aad926284d54' '4d7049f45189db5664f3c4d07350559e') class Dash(Coin): NAME = "Dash" SHORTNAME = "DASH" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("02fe52cc") XPRV_VERBYTES = bytes.fromhex("02fe52f8") GENESIS_HASH = ('00000ffd590b1485b3caadc19b22e637' '9c733355108f107a430458cdf3407ab6') P2PKH_VERBYTE = bytes.fromhex("4c") P2SH_VERBYTES = [bytes.fromhex("10")] WIF_BYTE = bytes.fromhex("cc") TX_COUNT_HEIGHT = 569399 TX_COUNT = 2157510 TX_PER_BLOCK = 4 RPC_PORT = 9998 PEERS = [ 'electrum.dash.org s t', 'electrum.masternode.io s t', 'electrum-drk.club s t', 'dashcrypto.space s t', 'electrum.dash.siampm.com s t', 'wl4sfwq2hwxnodof.onion s t', ] SESSIONCLS = DashElectrumX DAEMON = daemon.DashDaemon @classmethod def header_hash(cls, header): import x11_hash return x11_hash.getPoWHash(header) class DashTestnet(Dash): SHORTNAME = "tDASH" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("3a805837") XPRV_VERBYTES = bytes.fromhex("3a8061a0") GENESIS_HASH = ('00000bafbc94add76cb75e2ec9289483' '7288a481e5c005f6563d91623bf8bc2c') P2PKH_VERBYTE = bytes.fromhex("8c") P2SH_VERBYTES = [bytes.fromhex("13")] WIF_BYTE = bytes.fromhex("ef") TX_COUNT_HEIGHT = 101619 TX_COUNT = 132681 TX_PER_BLOCK = 1 RPC_PORT = 19998 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrum.dash.siampm.com s t', ] class Argentum(AuxPowMixin, Coin): NAME = "Argentum" SHORTNAME = "ARG" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("17") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("97") GENESIS_HASH = ('88c667bc63167685e4e4da058fffdfe8' 'e007e5abffd6855de52ad59df7bb0bb2') TX_COUNT = 2263089 TX_COUNT_HEIGHT = 2050260 TX_PER_BLOCK = 2000 RPC_PORT = 13581 class ArgentumTestnet(Argentum): SHORTNAME = "XRG" NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") REORG_LIMIT = 2000 class DigiByte(Coin): NAME = "DigiByte" SHORTNAME = "DGB" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1E") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('7497ea1b465eb39f1c8f507bc877078f' 'e016d6fcb6dfad3a64c98dcc6e1e8496') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 1046018 TX_COUNT_HEIGHT = 1435000 TX_PER_BLOCK = 1000 RPC_PORT = 12022 class DigiByteTestnet(DigiByte): NET = "testnet" P2PKH_VERBYTE = bytes.fromhex("6f") P2SH_VERBYTES = [bytes.fromhex("c4")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('b5dca8039e300198e5fe7cd23bdd1728' 'e2a444af34c447dbd0916fa3430a68c2') RPC_PORT = 15022 REORG_LIMIT = 2000 class FairCoin(Coin): NAME = "FairCoin" SHORTNAME = "FAIR" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("5f") P2SH_VERBYTES = [bytes.fromhex("24")] WIF_BYTE = bytes.fromhex("df") GENESIS_HASH = ('beed44fa5e96150d95d56ebd5d262578' '1825a9407a5215dd7eda723373a0a1d7') BASIC_HEADER_SIZE = 108 TX_COUNT = 505 TX_COUNT_HEIGHT = 470 TX_PER_BLOCK = 1 RPC_PORT = 40405 PEER_DEFAULT_PORTS = {'t': '51811', 's': '51812'} PEERS = [ 'electrum.faircoin.world s', 'electrumfair.punto0.org s', ] @classmethod def block(cls, raw_block, height): if height > 0: return super().block(raw_block, height) else: return Block(raw_block, cls.block_header(raw_block, height), []) @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, creatorId = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'payload_hash': hash_to_str(header[68:100]), 'timestamp': timestamp, 'creatorId': creatorId, } class Zcash(EquihashMixin, Coin): NAME = "Zcash" SHORTNAME = "ZEC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00040fe8ec8471911baa1db1266ea15d' 'd06b4a8a5c453883c000b031973dce08') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8232 REORG_LIMIT = 800 class SnowGem(EquihashMixin, Coin): NAME = "SnowGem" SHORTNAME = "SNG" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1C28") P2SH_VERBYTES = [bytes.fromhex("1C2D")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00068b35729d9d2b0c294ff1fe9af009' '4740524311a131de40e7f705e4c29a5b') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 140698 TX_COUNT_HEIGHT = 102802 TX_PER_BLOCK = 2 RPC_PORT = 16112 REORG_LIMIT = 800 CHUNK_SIZE = 200 @classmethod def electrum_header(cls, header, height): version, = struct.unpack('<I', header[:4]) timestamp, bits = struct.unpack('<II', header[100:108]) return { 'block_height': height, 'version': version, 'prev_block_hash': hash_to_str(header[4:36]), 'merkle_root': hash_to_str(header[36:68]), 'hash_reserved': hash_to_str(header[68:100]), 'timestamp': timestamp, 'bits': bits, 'nonce': hash_to_str(header[108:140]), 'n_solution': base64.b64encode(lib_tx.Deserializer(header, start=140)._read_varbytes()).decode('utf8') } class BitcoinZ(EquihashMixin, Coin): NAME = "BitcoinZ" SHORTNAME = "BTCZ" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('f499ee3d498b4298ac6a64205b8addb7' 'c43197e2a660229be65db8a4534d75c1') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 171976 TX_COUNT_HEIGHT = 81323 TX_PER_BLOCK = 3 RPC_PORT = 1979 REORG_LIMIT = 800 class Hush(EquihashMixin, Coin): NAME = "Hush" SHORTNAME = "HUSH" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ( '0003a67bc26fe564b75daf11186d3606' '52eb435a35ba3d9d3e7e5d5f8e62dc17') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8822 REORG_LIMIT = 800 class Zclassic(EquihashMixin, Coin): NAME = "Zclassic" SHORTNAME = "ZCL" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1CB8") P2SH_VERBYTES = [bytes.fromhex("1CBD")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ( '0007104ccda289427919efc39dc9e4d4' '99804b7bebc22df55f8b834301260602') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 329196 TX_COUNT_HEIGHT = 68379 TX_PER_BLOCK = 5 RPC_PORT = 8023 REORG_LIMIT = 800 class Koto(Coin): NAME = "Koto" SHORTNAME = "KOTO" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("1836") P2SH_VERBYTES = [bytes.fromhex("183B")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('6d424c350729ae633275d51dc3496e16' 'cd1b1d195c164da00f39c499a2e9959e') DESERIALIZER = lib_tx.DeserializerZcash TX_COUNT = 158914 TX_COUNT_HEIGHT = 67574 TX_PER_BLOCK = 3 RPC_PORT = 8432 REORG_LIMIT = 800 PEERS = [ 'fr.kotocoin.info s t', 'electrum.kotocoin.info s t', ] class Komodo(KomodoMixin, EquihashMixin, Coin): NAME = "Komodo" SHORTNAME = "KMD" NET = "mainnet" TX_COUNT = 693629 TX_COUNT_HEIGHT = 491777 TX_PER_BLOCK = 2 RPC_PORT = 7771 REORG_LIMIT = 800 PEERS = [] class Monaize(KomodoMixin, EquihashMixin, Coin): NAME = "Monaize" SHORTNAME = "MNZ" NET = "mainnet" TX_COUNT = 256 TX_COUNT_HEIGHT = 128 TX_PER_BLOCK = 2 RPC_PORT = 14337 REORG_LIMIT = 800 PEERS = [] class Einsteinium(Coin): NAME = "Einsteinium" SHORTNAME = "EMC2" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("21") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("b0") GENESIS_HASH = ('4e56204bb7b8ac06f860ff1c845f03f9' '84303b5b97eb7b42868f714611aed94b') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2087559 TX_COUNT_HEIGHT = 1358517 TX_PER_BLOCK = 2 RPC_PORT = 41879 REORG_LIMIT = 2000 class Blackcoin(ScryptMixin, Coin): NAME = "Blackcoin" SHORTNAME = "BLK" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("19") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("99") GENESIS_HASH = ('000001faef25dec4fbcf906e6242621d' 'f2c183bf232f263d0ba5b101911e4563') DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 4594999 TX_COUNT_HEIGHT = 1667070 TX_PER_BLOCK = 3 RPC_PORT = 15715 REORG_LIMIT = 5000 class Bitbay(ScryptMixin, Coin): NAME = "Bitbay" SHORTNAME = "BAY" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("19") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("99") GENESIS_HASH = ('0000075685d3be1f253ce777174b1594' '354e79954d2a32a6f77fe9cba00e6467') TX_COUNT = 4594999 TX_COUNT_HEIGHT = 1667070 TX_PER_BLOCK = 3 RPC_PORT = 19914 REORG_LIMIT = 5000 class Peercoin(Coin): NAME = "Peercoin" SHORTNAME = "PPC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("37") P2SH_VERBYTES = [bytes.fromhex("75")] WIF_BYTE = bytes.fromhex("b7") GENESIS_HASH = ('0000000032fe677166d54963b62a4677' 'd8957e87c508eaa4fd7eb1c880cd27e3') DESERIALIZER = lib_tx.DeserializerTxTime DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 1207356 TX_COUNT_HEIGHT = 306425 TX_PER_BLOCK = 4 RPC_PORT = 9902 REORG_LIMIT = 5000 class Reddcoin(Coin): NAME = "Reddcoin" SHORTNAME = "RDD" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3d") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("bd") GENESIS_HASH = ('b868e0d95a3c3c0e0dadc67ee587aaf9' 'dc8acbf99e3b4b3110fad4eb74c1decc') DESERIALIZER = lib_tx.DeserializerReddcoin TX_COUNT = 5413508 TX_COUNT_HEIGHT = 1717382 TX_PER_BLOCK = 3 RPC_PORT = 45443 class Vertcoin(Coin): NAME = "Vertcoin" SHORTNAME = "VTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488B21E") XPRV_VERBYTES = bytes.fromhex("0488ADE4") P2PKH_VERBYTE = bytes.fromhex("47") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('4d96a915f49d40b1e5c2844d1ee2dccb' '90013a990ccea12c492d22110489f0c4') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2383423 TX_COUNT_HEIGHT = 759076 TX_PER_BLOCK = 3 RPC_PORT = 5888 REORG_LIMIT = 1000 class Monacoin(Coin): NAME = "Monacoin" SHORTNAME = "MONA" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488B21E") XPRV_VERBYTES = bytes.fromhex("0488ADE4") P2PKH_VERBYTE = bytes.fromhex("32") P2SH_VERBYTES = [bytes.fromhex("37"), bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("B0") GENESIS_HASH = ('ff9f1c0116d19de7c9963845e129f9ed' '1bfc0b376eb54fd7afa42e0d418c8bb6') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 2568580 TX_COUNT_HEIGHT = 1029766 TX_PER_BLOCK = 2 RPC_PORT = 9402 REORG_LIMIT = 1000 PEERS = [ 'electrumx.tamami-foundation.org s t', 'electrumx2.tamami-foundation.org s t', 'electrumx3.tamami-foundation.org s t', 'electrumx1.monacoin.nl s t', 'electrumx2.monacoin.nl s t', 'electrumx1.monacoin.ninja s t', 'electrumx2.monacoin.ninja s t', 'electrumx1.movsign.info t', 'electrumx2.movsign.info s t', 'electrum-mona.bitbank.cc s t', ] class MonacoinTestnet(Monacoin): SHORTNAME = "XMN" NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587CF") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("6F") P2SH_VERBYTES = [bytes.fromhex("75"), bytes.fromhex("C4")] WIF_BYTE = bytes.fromhex("EF") GENESIS_HASH = ('a2b106ceba3be0c6d097b2a6a6aacf9d' '638ba8258ae478158f449c321061e0b2') TX_COUNT = 83602 TX_COUNT_HEIGHT = 83252 TX_PER_BLOCK = 1 RPC_PORT = 19402 REORG_LIMIT = 1000 PEER_DEFAULT_PORTS = {'t': '51001', 's': '51002'} PEERS = [ 'electrumx1.testnet.monacoin.ninja s t', 'electrumx1.testnet.monacoin.nl s t', ] class Crown(AuxPowMixin, Coin): NAME = "Crown" SHORTNAME = "CRW" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("00") P2SH_VERBYTES = [bytes.fromhex("1c")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('0000000085370d5e122f64f4ab19c686' '14ff3df78c8d13cb814fd7e69a1dc6da') TX_COUNT = 13336629 TX_COUNT_HEIGHT = 1268206 TX_PER_BLOCK = 10 RPC_PORT = 9341 REORG_LIMIT = 1000 PEERS = [ 'sgp-crwseed.crowndns.info s t', 'blr-crwseed.crowndns.info s t', 'sfo-crwseed.crowndns.info s t', 'nyc-crwseed.crowndns.info s t', 'ams-crwseed.crowndns.info s t', 'tor-crwseed.crowndns.info s t', 'lon-crwseed.crowndns.info s t', 'fra-crwseed.crowndns.info s t', ] class Fujicoin(Coin): NAME = "Fujicoin" SHORTNAME = "FJC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("24") P2SH_VERBYTES = [bytes.fromhex("10")] WIF_BYTE = bytes.fromhex("a4") GENESIS_HASH = ('adb6d9cfd74075e7f91608add4bd2a2e' 'a636f70856183086842667a1597714a0') ESTIMATE_FEE = 0.001 RELAY_FEE = 0.001 TX_COUNT = 170478 TX_COUNT_HEIGHT = 1521676 TX_PER_BLOCK = 1 RPC_PORT = 3776 REORG_LIMIT = 1000 class Neblio(ScryptMixin, Coin): NAME = "Neblio" SHORTNAME = "NEBL" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("35") P2SH_VERBYTES = [bytes.fromhex("70")] WIF_BYTE = bytes.fromhex("b5") GENESIS_HASH = ('7286972be4dbc1463d256049b7471c25' '2e6557e222cab9be73181d359cd28bcc') TX_COUNT = 23675 TX_COUNT_HEIGHT = 22785 TX_PER_BLOCK = 1 RPC_PORT = 6326 REORG_LIMIT = 1000 class Bitzeny(Coin): NAME = "Bitzeny" SHORTNAME = "ZNY" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("51") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000009f7e55e9e3b4781e22bd87a7cfa' '4acada9e4340d43ca738bf4e9fb8f5ce') ESTIMATE_FEE = 0.001 RELAY_FEE = 0.001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT = 1000 TX_COUNT_HEIGHT = 10000 TX_PER_BLOCK = 1 RPC_PORT = 9252 REORG_LIMIT = 1000 class CanadaeCoin(AuxPowMixin, Coin): NAME = "CanadaeCoin" SHORTNAME = "CDN" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("1C") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("9c") GENESIS_HASH = ('863626dadaef221e2e2f30ff3dacae44' 'cabdae9e0028058072181b3fb675d94a') ESTIMATE_FEE = 0.0001 RELAY_FEE = 0.0001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT = 3455905 TX_COUNT_HEIGHT = 3645419 TX_PER_BLOCK = 1 RPC_PORT = 34330 REORG_LIMIT = 1000 class Denarius(Coin): NAME = "Denarius" SHORTNAME = "DNR" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("1E") P2SH_VERBYTES = [bytes.fromhex("5A")] WIF_BYTE = bytes.fromhex("9E") GENESIS_HASH = ('00000d5dbbda01621cfc16bbc1f9bf32' '64d641a5dbf0de89fd0182c2c4828fcd') DESERIALIZER = lib_tx.DeserializerTxTime TX_COUNT = 4230 RPC_PORT = 32339 ESTIMATE_FEE = 0.00001 RELAY_FEE = 0.00001 DAEMON = daemon.FakeEstimateFeeDaemon TX_COUNT_HEIGHT = 306187 TX_PER_BLOCK = 4000 @classmethod def header_hash(cls, header): import tribus_hash return tribus_hash.getPoWHash(header) class DenariusTestnet(Denarius): NET = "testnet" XPUB_VERBYTES = bytes.fromhex("043587cf") XPRV_VERBYTES = bytes.fromhex("04358394") P2PKH_VERBYTE = bytes.fromhex("12") P2SH_VERBYTES = [bytes.fromhex("74")] WIF_BYTE = bytes.fromhex("ef") GENESIS_HASH = ('000086bfe8264d241f7f8e5393f74778' '4b8ca2aa98bdd066278d590462a4fdb4') RPC_PORT = 32338 REORG_LIMIT = 2000 class Sibcoin(Dash): NAME = "Sibcoin" SHORTNAME = "SIB" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488b21e") XPRV_VERBYTES = bytes.fromhex("0488ade4") P2PKH_VERBYTE = bytes.fromhex("3F") P2SH_VERBYTES = [bytes.fromhex("28")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('00000c492bf73490420868bc577680bf' 'c4c60116e7e85343bc624787c21efa4c') DAEMON = daemon.DashDaemon TX_COUNT = 1000 TX_COUNT_HEIGHT = 10000 TX_PER_BLOCK = 1 RPC_PORT = 1944 REORG_LIMIT = 1000 PEERS = [] @classmethod def header_hash(cls, header): import x11_gost_hash return x11_gost_hash.getPoWHash(header) class Chips(Coin): NAME = "Chips" SHORTNAME = "CHIPS" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3c") P2SH_VERBYTES = [bytes.fromhex("55")] WIF_BYTE = bytes.fromhex("bc") GENESIS_HASH = ('0000006e75f6aa0efdbf7db03132aa4e' '4d0c84951537a6f5a7c39a0a9d30e1e7') DESERIALIZER = lib_tx.DeserializerSegWit TX_COUNT = 145290 TX_COUNT_HEIGHT = 318637 TX_PER_BLOCK = 2 RPC_PORT = 57776 REORG_LIMIT = 800 class Feathercoin(Coin): NAME = "Feathercoin" SHORTNAME = "FTC" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488BC26") XPRV_VERBYTES = bytes.fromhex("0488DAEE") P2PKH_VERBYTE = bytes.fromhex("0E") P2SH_VERBYTES = [bytes.fromhex("05")] WIF_BYTE = bytes.fromhex("8E") GENESIS_HASH = ('12a765e31ffd4059bada1e25190f6e98' 'c99d9714d334efa41a195a7e7e04bfe2') TX_COUNT = 3170843 TX_COUNT_HEIGHT = 1981777 TX_PER_BLOCK = 2 RPC_PORT = 9337 REORG_LIMIT = 2000 PEERS = [ 'electrumx-ch-1.feathercoin.ch s t', ] class Newyorkcoin(AuxPowMixin, Coin): NAME = "Newyorkcoin" SHORTNAME = "NYC" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("3c") P2SH_VERBYTES = [bytes.fromhex("16")] WIF_BYTE = bytes.fromhex("bc") GENESIS_HASH = ('5597f25c062a3038c7fd815fe46c67de' 'dfcb3c839fbc8e01ed4044540d08fe48') DAEMON = daemon.LegacyRPCDaemon TX_COUNT = 5161944 TX_COUNT_HEIGHT = 3948743 TX_PER_BLOCK = 2 REORG_LIMIT = 2000 class Bitcore(BitcoinMixin, Coin): NAME = "Bitcore" SHORTNAME = "BTX" DESERIALIZER = lib_tx.DeserializerSegWit GENESIS_HASH = ('604148281e5c4b7f2487e5d03cd60d8e' '6f69411d613f6448034508cea52e9574') TX_COUNT = 126979 TX_COUNT_HEIGHT = 126946 TX_PER_BLOCK = 2 RPC_PORT = 8556 class Obsidian(Coin): NAME = "Obsidian" SHORTNAME = "ODN" NET = "mainnet" XPUB_VERBYTES = bytes.fromhex("0488c21e") XPRV_VERBYTES = bytes.fromhex("0488b2dd") P2PKH_VERBYTE = bytes.fromhex("4b") P2SH_VERBYTES = [bytes.fromhex("7d")] WIF_BYTE = bytes.fromhex("cb") GENESIS_HASH = ('0000006dd8a92f58e952fa61c9402b74' 'a381a69d1930fb5cc12c73273fab5f0a') RPC_PORT = 56661 TX_COUNT = 1067887 TX_PER_BLOCK = 2 TX_COUNT_HEIGHT = 500000 DAEMON = daemon.LegacyRPCDaemon @classmethod def header_hash(cls, header): from hashlib import sha512 return sha512(header).digest()[:32] class BitcoinAtom(Coin): NAME = "BitcoinAtom" SHORTNAME = "BCA" NET = "mainnet" P2PKH_VERBYTE = bytes.fromhex("17") P2SH_VERBYTES = [bytes.fromhex("0a")] WIF_BYTE = bytes.fromhex("80") GENESIS_HASH = ('000000000019d6689c085ae165831e93' '4ff763ae46a2a6c172b3f1b60a8ce26f') STATIC_BLOCK_HEADERS = False DESERIALIZER = lib_tx.DeserializerBitcoinAtom HEADER_SIZE_POST_FORK = 84 BLOCK_PROOF_OF_STAKE = 0x01 BLOCK_PROOF_OF_STAKE_FLAGS = b'\x01\x00\x00\x00' TX_COUNT = 295158744 TX_COUNT_HEIGHT = 589197 TX_PER_BLOCK = 10 RPC_PORT = 9136 REORG_LIMIT = 5000 @classmethod def header_hash(cls, header): header_to_be_hashed = header[:cls.BASIC_HEADER_SIZE] if len(header) == cls.HEADER_SIZE_POST_FORK: flags, = struct.unpack('<I', header[-4:]) if flags & cls.BLOCK_PROOF_OF_STAKE != 0: header_to_be_hashed += cls.BLOCK_PROOF_OF_STAKE_FLAGS return double_sha256(header_to_be_hashed) @classmethod def block_header(cls, block, height): deserializer = cls.DESERIALIZER(block) return deserializer.read_header(height, cls.BASIC_HEADER_SIZE) class Decred(Coin): NAME = "Decred" SHORTNAME = "DCR" NET = "mainnet" XPUB_VERBYTES = bytes('dpub', 'utf-8') XPRV_VERBYTES = bytes('dprv', 'utf-8') P2PKH_VERBYTE = bytes('Ds', 'utf-8') P2SH_VERBYTES = [bytes('Dc', 'utf-8')] WIF_BYTE = bytes('Pm', 'utf-8') GENESIS_HASH = ('298e5cc3d985bfe7f81dc135f360abe089edd4396b86d2de66b0cef42b21d980') DESERIALIZER = lib_tx.DeserializerDecred ENCODE_CHECK = partial(Base58.encode_check, hash_fn=lib_tx.DeserializerDecred.blake256) DECODE_CHECK = partial(Base58.decode_check, hash_fn=lib_tx.DeserializerDecred.blake256) HEADER_HASH = lib_tx.DeserializerDecred.blake256 BASIC_HEADER_SIZE = 180 ALLOW_ADVANCING_ERRORS = True TX_COUNT = 217380620 TX_COUNT_HEIGHT = 218875 TX_PER_BLOCK = 1000 RPC_PORT = 9109 @classmethod def header_hash(cls, header): return cls.HEADER_HASH(header) @classmethod def block(cls, raw_block, height): if height > 0: return super().block(raw_block, height) else: return Block(raw_block, cls.block_header(raw_block, height), []) class DecredTestnet(Decred): NAME = "Decred" NET = "testnet" XPUB_VERBYTES = bytes('tpub', 'utf-8') XPRV_VERBYTES = bytes('tprv', 'utf-8') P2PKH_VERBYTE = bytes('Ts', 'utf-8') P2SH_VERBYTES = [bytes('Tc', 'utf-8')] WIF_BYTE = bytes('Pt', 'utf-8') GENESIS_HASH = ('4261602a9d07d80ad47621a64ba6a07754902e496777edc4ff581946bd7bc29c') TX_COUNT = 3176305 TX_COUNT_HEIGHT = 254198 TX_PER_BLOCK = 1000 RPC_PORT = 19109

true

1c2b194566a9e96dba834338ec915a2289eb1837

682

py

Python

functions/markdown-to-html/markdown2html.py

truls/faas-profiler

d54ca0d9926f38c693f616ba4d08414aea823f51

[ "MIT" ]

null

functions/markdown-to-html/markdown2html.py

truls/faas-profiler

d54ca0d9926f38c693f616ba4d08414aea823f51

[ "MIT" ]

null

functions/markdown-to-html/markdown2html.py

truls/faas-profiler

d54ca0d9926f38c693f616ba4d08414aea823f51

[ "MIT" ]

null

# Copyright (c) 2019 Princeton University # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from markdown import markdown import base64 import json import base64 def main(params): try: md = json.loads(base64.decodebytes(params["__ow_body"].encode("utf-8")))["markdown"].encode("utf-8") md_text = base64.decodebytes(md).decode("utf-8") except KeyError: return {'Error' : 'Possibly lacking markdown parameter in request.'} test_id = params["__ow_query"].split("&")[0] html = markdown(md_text) return {"result": "ok", "html_response": html, "testid": test_id}

29.652174

108

0.690616

from markdown import markdown import base64 import json import base64 def main(params): try: md = json.loads(base64.decodebytes(params["__ow_body"].encode("utf-8")))["markdown"].encode("utf-8") md_text = base64.decodebytes(md).decode("utf-8") except KeyError: return {'Error' : 'Possibly lacking markdown parameter in request.'} test_id = params["__ow_query"].split("&")[0] html = markdown(md_text) return {"result": "ok", "html_response": html, "testid": test_id}

true

1c2b1a1f7735b02e97f5b6e0193d9fd6cf1a373c

6,205

py

Python

09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py

dolaameng/tensorflow_cookbook

ca9bcb892239e9276e9348689e06cd6d1edd19ef

[ "MIT" ]

null

09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py

dolaameng/tensorflow_cookbook

ca9bcb892239e9276e9348689e06cd6d1edd19ef

[ "MIT" ]

null

09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py

dolaameng/tensorflow_cookbook

ca9bcb892239e9276e9348689e06cd6d1edd19ef

[ "MIT" ]

1

2018-04-25T17:10:22.000Z

# Implementing an RNN in TensorFlow #---------------------------------- # # We implement an RNN in TensorFlow to predict spam/ham from texts # import os import re import io import requests import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from zipfile import ZipFile from tensorflow.python.framework import ops ops.reset_default_graph() # Start a graph sess = tf.Session() # Set RNN parameters epochs = 20 batch_size = 250 max_sequence_length = 25 rnn_size = 10 embedding_size = 50 min_word_frequency = 10 learning_rate = 0.0005 dropout_keep_prob = tf.placeholder(tf.float32) # Download or open data data_dir = 'temp' data_file = 'text_data.txt' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.isfile(os.path.join(data_dir, data_file)): zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip' r = requests.get(zip_url) z = ZipFile(io.BytesIO(r.content)) file = z.read('SMSSpamCollection') # Format Data text_data = file.decode() text_data = text_data.encode('ascii',errors='ignore') text_data = text_data.decode().split('\n') # Save data to text file with open(os.path.join(data_dir, data_file), 'w') as file_conn: for text in text_data: file_conn.write("{}\n".format(text)) else: # Open data from text file text_data = [] with open(os.path.join(data_dir, data_file), 'r') as file_conn: for row in file_conn: text_data.append(row) text_data = text_data[:-1] text_data = [x.split('\t') for x in text_data if len(x)>=1] [text_data_target, text_data_train] = [list(x) for x in zip(*text_data)] # Create a text cleaning function def clean_text(text_string): text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string) text_string = " ".join(text_string.split()) text_string = text_string.lower() return(text_string) # Clean texts text_data_train = [clean_text(x) for x in text_data_train] # Change texts into numeric vectors vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(max_sequence_length, min_frequency=min_word_frequency) text_processed = np.array(list(vocab_processor.fit_transform(text_data_train))) # Shuffle and split data text_processed = np.array(text_processed) text_data_target = np.array([1 if x=='ham' else 0 for x in text_data_target]) shuffled_ix = np.random.permutation(np.arange(len(text_data_target))) x_shuffled = text_processed[shuffled_ix] y_shuffled = text_data_target[shuffled_ix] # Split train/test set ix_cutoff = int(len(y_shuffled)*0.80) x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:] y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:] vocab_size = len(vocab_processor.vocabulary_) print("Vocabulary Size: {:d}".format(vocab_size)) print("80-20 Train Test split: {:d} -- {:d}".format(len(y_train), len(y_test))) # Create placeholders x_data = tf.placeholder(tf.int32, [None, max_sequence_length]) y_output = tf.placeholder(tf.int32, [None]) # Create embedding embedding_mat = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0)) embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data) #embedding_output_expanded = tf.expand_dims(embedding_output, -1) # Define the RNN cell cell = tf.nn.rnn_cell.BasicRNNCell(num_units = rnn_size) output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32) output = tf.nn.dropout(output, dropout_keep_prob) # Get output of RNN sequence output = tf.transpose(output, [1, 0, 2]) last = tf.gather(output, int(output.get_shape()[0]) - 1) weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1)) bias = tf.Variable(tf.constant(0.1, shape=[2])) logits_out = tf.nn.softmax(tf.matmul(last, weight) + bias) # Loss function losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits_out, y_output) # logits=float32, labels=int32 loss = tf.reduce_mean(losses) accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32)) optimizer = tf.train.RMSPropOptimizer(learning_rate) train_step = optimizer.minimize(loss) init = tf.initialize_all_variables() sess.run(init) train_loss = [] test_loss = [] train_accuracy = [] test_accuracy = [] # Start training for epoch in range(epochs): # Shuffle training data shuffled_ix = np.random.permutation(np.arange(len(x_train))) x_train = x_train[shuffled_ix] y_train = y_train[shuffled_ix] num_batches = int(len(x_train)/batch_size) + 1 # TO DO CALCULATE GENERATIONS ExACTLY for i in range(num_batches): # Select train data min_ix = i * batch_size max_ix = np.min([len(x_train), ((i+1) * batch_size)]) x_train_batch = x_train[min_ix:max_ix] y_train_batch = y_train[min_ix:max_ix] # Run train step train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob:0.5} sess.run(train_step, feed_dict=train_dict) # Run loss and accuracy for training temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict) train_loss.append(temp_train_loss) train_accuracy.append(temp_train_acc) # Run Eval Step test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob:1.0} temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict) test_loss.append(temp_test_loss) test_accuracy.append(temp_test_acc) print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch+1, temp_test_loss, temp_test_acc)) # Plot loss over time epoch_seq = np.arange(1, epochs+1) plt.plot(epoch_seq, train_loss, 'k--', label='Train Set') plt.plot(epoch_seq, test_loss, 'r-', label='Test Set') plt.title('Softmax Loss') plt.xlabel('Epochs') plt.ylabel('Softmax Loss') plt.legend(loc='upper left') plt.show() # Plot accuracy over time plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set') plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set') plt.title('Test Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend(loc='upper left') plt.show()

33.907104

111

0.714424

import os import re import io import requests import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from zipfile import ZipFile from tensorflow.python.framework import ops ops.reset_default_graph() sess = tf.Session() epochs = 20 batch_size = 250 max_sequence_length = 25 rnn_size = 10 embedding_size = 50 min_word_frequency = 10 learning_rate = 0.0005 dropout_keep_prob = tf.placeholder(tf.float32) data_dir = 'temp' data_file = 'text_data.txt' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.isfile(os.path.join(data_dir, data_file)): zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip' r = requests.get(zip_url) z = ZipFile(io.BytesIO(r.content)) file = z.read('SMSSpamCollection') text_data = file.decode() text_data = text_data.encode('ascii',errors='ignore') text_data = text_data.decode().split('\n') with open(os.path.join(data_dir, data_file), 'w') as file_conn: for text in text_data: file_conn.write("{}\n".format(text)) else: text_data = [] with open(os.path.join(data_dir, data_file), 'r') as file_conn: for row in file_conn: text_data.append(row) text_data = text_data[:-1] text_data = [x.split('\t') for x in text_data if len(x)>=1] [text_data_target, text_data_train] = [list(x) for x in zip(*text_data)] def clean_text(text_string): text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string) text_string = " ".join(text_string.split()) text_string = text_string.lower() return(text_string) text_data_train = [clean_text(x) for x in text_data_train] vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(max_sequence_length, min_frequency=min_word_frequency) text_processed = np.array(list(vocab_processor.fit_transform(text_data_train))) text_processed = np.array(text_processed) text_data_target = np.array([1 if x=='ham' else 0 for x in text_data_target]) shuffled_ix = np.random.permutation(np.arange(len(text_data_target))) x_shuffled = text_processed[shuffled_ix] y_shuffled = text_data_target[shuffled_ix] ix_cutoff = int(len(y_shuffled)*0.80) x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:] y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:] vocab_size = len(vocab_processor.vocabulary_) print("Vocabulary Size: {:d}".format(vocab_size)) print("80-20 Train Test split: {:d} -- {:d}".format(len(y_train), len(y_test))) x_data = tf.placeholder(tf.int32, [None, max_sequence_length]) y_output = tf.placeholder(tf.int32, [None]) embedding_mat = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0)) embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data) cell = tf.nn.rnn_cell.BasicRNNCell(num_units = rnn_size) output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32) output = tf.nn.dropout(output, dropout_keep_prob) output = tf.transpose(output, [1, 0, 2]) last = tf.gather(output, int(output.get_shape()[0]) - 1) weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1)) bias = tf.Variable(tf.constant(0.1, shape=[2])) logits_out = tf.nn.softmax(tf.matmul(last, weight) + bias) losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits_out, y_output) loss = tf.reduce_mean(losses) accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32)) optimizer = tf.train.RMSPropOptimizer(learning_rate) train_step = optimizer.minimize(loss) init = tf.initialize_all_variables() sess.run(init) train_loss = [] test_loss = [] train_accuracy = [] test_accuracy = [] for epoch in range(epochs): shuffled_ix = np.random.permutation(np.arange(len(x_train))) x_train = x_train[shuffled_ix] y_train = y_train[shuffled_ix] num_batches = int(len(x_train)/batch_size) + 1 for i in range(num_batches): min_ix = i * batch_size max_ix = np.min([len(x_train), ((i+1) * batch_size)]) x_train_batch = x_train[min_ix:max_ix] y_train_batch = y_train[min_ix:max_ix] train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob:0.5} sess.run(train_step, feed_dict=train_dict) temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict) train_loss.append(temp_train_loss) train_accuracy.append(temp_train_acc) test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob:1.0} temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict) test_loss.append(temp_test_loss) test_accuracy.append(temp_test_acc) print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch+1, temp_test_loss, temp_test_acc)) epoch_seq = np.arange(1, epochs+1) plt.plot(epoch_seq, train_loss, 'k--', label='Train Set') plt.plot(epoch_seq, test_loss, 'r-', label='Test Set') plt.title('Softmax Loss') plt.xlabel('Epochs') plt.ylabel('Softmax Loss') plt.legend(loc='upper left') plt.show() plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set') plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set') plt.title('Test Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend(loc='upper left') plt.show()

true

1c2b1a59f09f774fe08b5b66ca33edcbb2313f3a

925

py

Python

carbon/client/metrics/timer.py

mosquito/carbonate

5eca69602b9fc03dc0b982f9104c7ebb04159059

[ "MIT" ]

2

2017-12-21T15:40:12.000Z

2018-02-07T10:00:14.000Z

carbon/client/metrics/timer.py

mosquito/carbonate

5eca69602b9fc03dc0b982f9104c7ebb04159059

[ "MIT" ]

2

2016-12-02T08:53:48.000Z

2016-12-05T21:46:04.000Z

carbon/client/metrics/timer.py

mosquito/carbonate

5eca69602b9fc03dc0b982f9104c7ebb04159059

[ "MIT" ]

5

2015-07-22T14:31:28.000Z

2020-09-30T08:20:29.000Z

# encoding: utf-8 from time import time from threading import RLock from carbon.client.metrics.base import MeasurerBase, Metric class StopWatch(object): __slots__ = '_lock', '_current' def __init__(self): self._lock = RLock() self._current = None def start(self): with self._lock: self._current = time() def stop(self): assert self._current, "StopWatch not running" with self._lock: return time() - self._current class Timer(MeasurerBase): __slots__ = '_current', def __init__(self, cleanup=None): MeasurerBase.__init__(self, cleanup) self._current = None @classmethod def start(cls): watch = StopWatch() watch.start() return watch def stop(self, stop_watch): assert isinstance(stop_watch, StopWatch) self.add(Metric(name=self.name, value=stop_watch.stop()))

23.125

65

0.632432

from time import time from threading import RLock from carbon.client.metrics.base import MeasurerBase, Metric class StopWatch(object): __slots__ = '_lock', '_current' def __init__(self): self._lock = RLock() self._current = None def start(self): with self._lock: self._current = time() def stop(self): assert self._current, "StopWatch not running" with self._lock: return time() - self._current class Timer(MeasurerBase): __slots__ = '_current', def __init__(self, cleanup=None): MeasurerBase.__init__(self, cleanup) self._current = None @classmethod def start(cls): watch = StopWatch() watch.start() return watch def stop(self, stop_watch): assert isinstance(stop_watch, StopWatch) self.add(Metric(name=self.name, value=stop_watch.stop()))

true

1c2b1a6f4bbfe3d44da0ea4bdb00ceaaf3fb1cd7

181

py

Python

test_data/parse/unexpected/class_definitions/unexpected_double_description_for_a_property/meta_model.py

gillistephan/aas-core-codegen

5b89ea2ee35aecaca9a1bed7ac81d420cc560f29

[ "MIT" ]

5

2021-12-29T12:55:34.000Z

2022-03-01T17:57:21.000Z

test_data/parse/unexpected/class_definitions/unexpected_double_description_for_a_property/meta_model.py

gillistephan/aas-core-codegen

5b89ea2ee35aecaca9a1bed7ac81d420cc560f29

[ "MIT" ]

10

2021-12-29T02:15:55.000Z

2022-03-09T11:04:22.000Z

test_data/parse/unexpected/class_definitions/unexpected_double_description_for_a_property/meta_model.py

gillistephan/aas-core-codegen

5b89ea2ee35aecaca9a1bed7ac81d420cc560f29

[ "MIT" ]

2

2021-12-29T01:42:12.000Z

2022-02-15T13:46:33.000Z

class Something: """Represent something.""" """unexpected description""" some_property: int """some property""" __book_url__ = "dummy" __book_version__ = "dummy"

16.454545

32

0.657459

class Something: some_property: int __book_url__ = "dummy" __book_version__ = "dummy"

true

1c2b1df7891e280c81f4a7da992ea6b335bd7a32

3,501

py

Python

scripts/automation/trex_control_plane/interactive/trex/console/plugins/plugin_bird.py

MassimoGirondi/trex-core

404f2ce95db249bbf11c959a530f33bb5d10f94c

[ "Apache-2.0" ]

null

scripts/automation/trex_control_plane/interactive/trex/console/plugins/plugin_bird.py

MassimoGirondi/trex-core

404f2ce95db249bbf11c959a530f33bb5d10f94c

[ "Apache-2.0" ]

null

scripts/automation/trex_control_plane/interactive/trex/console/plugins/plugin_bird.py

MassimoGirondi/trex-core

404f2ce95db249bbf11c959a530f33bb5d10f94c

[ "Apache-2.0" ]

null

#!/usr/bin/python from trex.console.plugins import * from trex.stl.api import * from trex.pybird.bird_cfg_creator import * from trex.pybird.bird_zmq_client import * ''' Bird plugin ''' class Bird_Plugin(ConsolePlugin): def plugin_description(self): return 'Bird plugin for simple communication with PyBirdserver' def plugin_load(self): self.add_argument("-p", "--port", type = int, dest = 'port', required = True, help = 'port to use') self.add_argument("-m", "--mac", type = str, dest = 'mac', required = True, help = 'mac address to use') self.add_argument("--ipv4", type = str, dest = 'ipv4', help = 'src ip to use') self.add_argument("--ipv4-subnet", type = int, dest = 'ipv4_subnet', help = 'ipv4 subnet to use') self.add_argument("--ipv6-enable", action = "store_true", dest = 'ipv6_enabled', default = False, help = 'ipv6 enable, default False') self.add_argument("--ipv6-subnet", type = int, dest = 'ipv6_subnet', default = 127, help = 'ipv6 subnet ip to use, default 127') self.add_argument("--vlans", type = list, dest = 'vlans', help = 'vlans for bird node') self.add_argument("--tpids", type = list, dest = 'tpids', help = 'tpids for bird node') self.c = STLClient() self.pybird = PyBirdClient() self.pybird.connect() self.pybird.acquire() def plugin_unload(self): try: self.pybird.release()() self.pybird.disconnect() except Exception as e: print('Error while unloading bird plugin: \n' + str(e)) def do_add_bird_node(self, port, mac, ipv4, ipv4_subnet, ipv6_enabled, ipv6_subnet, vlans, tpids): ''' Simple adding bird node with arguments. ''' self.c.connect() self.c.acquire(force = True) self.c.set_bird_node(node_port = port, mac = mac, ipv4 = ipv4, ipv4_subnet = ipv4_subnet, ipv6_enabled = ipv6_enabled, ipv6_subnet = ipv6_subnet, vlans = vlans, tpids = tpids) def do_add_rip(self): ''' Adding rip protocol to bird configuration file. ''' curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_rip() self.pybird.set_config(cfg_creator.build_config()) def do_add_bgp(self): ''' Adding bgp protocol to bird configuration file. ''' curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_bgp() self.pybird.set_config(cfg_creator.build_config()) def do_add_ospf(self): ''' Adding ospf protocol to bird configuration file. ''' curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_ospf() self.pybird.set_config(cfg_creator.build_config())

37.645161

102

0.52585

from trex.console.plugins import * from trex.stl.api import * from trex.pybird.bird_cfg_creator import * from trex.pybird.bird_zmq_client import * class Bird_Plugin(ConsolePlugin): def plugin_description(self): return 'Bird plugin for simple communication with PyBirdserver' def plugin_load(self): self.add_argument("-p", "--port", type = int, dest = 'port', required = True, help = 'port to use') self.add_argument("-m", "--mac", type = str, dest = 'mac', required = True, help = 'mac address to use') self.add_argument("--ipv4", type = str, dest = 'ipv4', help = 'src ip to use') self.add_argument("--ipv4-subnet", type = int, dest = 'ipv4_subnet', help = 'ipv4 subnet to use') self.add_argument("--ipv6-enable", action = "store_true", dest = 'ipv6_enabled', default = False, help = 'ipv6 enable, default False') self.add_argument("--ipv6-subnet", type = int, dest = 'ipv6_subnet', default = 127, help = 'ipv6 subnet ip to use, default 127') self.add_argument("--vlans", type = list, dest = 'vlans', help = 'vlans for bird node') self.add_argument("--tpids", type = list, dest = 'tpids', help = 'tpids for bird node') self.c = STLClient() self.pybird = PyBirdClient() self.pybird.connect() self.pybird.acquire() def plugin_unload(self): try: self.pybird.release()() self.pybird.disconnect() except Exception as e: print('Error while unloading bird plugin: \n' + str(e)) def do_add_bird_node(self, port, mac, ipv4, ipv4_subnet, ipv6_enabled, ipv6_subnet, vlans, tpids): self.c.connect() self.c.acquire(force = True) self.c.set_bird_node(node_port = port, mac = mac, ipv4 = ipv4, ipv4_subnet = ipv4_subnet, ipv6_enabled = ipv6_enabled, ipv6_subnet = ipv6_subnet, vlans = vlans, tpids = tpids) def do_add_rip(self): curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_rip() self.pybird.set_config(cfg_creator.build_config()) def do_add_bgp(self): curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_bgp() self.pybird.set_config(cfg_creator.build_config()) def do_add_ospf(self): curr_conf = self.pybird.get_config() cfg_creator = BirdCFGCreator(curr_conf) cfg_creator.add_simple_ospf() self.pybird.set_config(cfg_creator.build_config())

true

1c2b1e7e48eede273a5b9885d4ebbc36fdb68f2b

1,004

py

Python

launch/link-intersection-brute-force.py

balazs-bamer/link-intersection-brute-force

1098d5555ebaa9c23c326f75c493b855199ff6bf

[ "MIT" ]

1

2021-04-27T10:40:50.000Z

launch/link-intersection-brute-force.py

balazs-bamer/link-intersection-brute-force

1098d5555ebaa9c23c326f75c493b855199ff6bf

[ "MIT" ]

1

2021-04-27T16:05:38.000Z

2021-04-28T11:52:39.000Z

launch/link-intersection-brute-force.py

balazs-bamer/link-intersection-brute-force

1098d5555ebaa9c23c326f75c493b855199ff6bf

[ "MIT" ]

null

import os from launch import LaunchDescription from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import DeclareLaunchArgument from launch.substitutions import LaunchConfiguration from launch_ros.actions import Node def generate_launch_description(): packageName = 'link-intersection-brute-force' nodeName = 'linkIntersectionBruteForce' urdfFilename = 'px150_coll.urdf' urdf = os.path.join( get_package_share_directory(packageName), urdfFilename) forbiddenLinksFilename = 'forbidden-links.txt' forbiddenLinks = os.path.join( get_package_share_directory(packageName), forbiddenLinksFilename) return LaunchDescription([ Node( package = packageName, namespace = 'cudaTrajectoryPlanner', executable = nodeName, name = nodeName, arguments = [urdf, forbiddenLinks, '/home/balazs/munka/cuda-trajectory-planner/ros-workspace/src/link-intersection-brute-force/'] ) ])

34.62069

135

0.77988

import os from launch import LaunchDescription from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import DeclareLaunchArgument from launch.substitutions import LaunchConfiguration from launch_ros.actions import Node def generate_launch_description(): packageName = 'link-intersection-brute-force' nodeName = 'linkIntersectionBruteForce' urdfFilename = 'px150_coll.urdf' urdf = os.path.join( get_package_share_directory(packageName), urdfFilename) forbiddenLinksFilename = 'forbidden-links.txt' forbiddenLinks = os.path.join( get_package_share_directory(packageName), forbiddenLinksFilename) return LaunchDescription([ Node( package = packageName, namespace = 'cudaTrajectoryPlanner', executable = nodeName, name = nodeName, arguments = [urdf, forbiddenLinks, '/home/balazs/munka/cuda-trajectory-planner/ros-workspace/src/link-intersection-brute-force/'] ) ])

true

1c2b1ea4f2fc78cf05b77d8b3ecf42626a31e9a5

2,161

py

Python

monarch/s3.py

LaurEars/monarch

0554df50edab6ccb67480038b8db72197d36783a

[ "MIT" ]

null

monarch/s3.py

LaurEars/monarch

0554df50edab6ccb67480038b8db72197d36783a

[ "MIT" ]

null

monarch/s3.py

LaurEars/monarch

0554df50edab6ccb67480038b8db72197d36783a

[ "MIT" ]

null

import os from datetime import datetime # 3rd Party Imports import boto from boto.s3.key import Key from click import echo from .utils import temp_directory, zipdir from .local import local_restore from .mongo import dump_db def get_s3_bucket(s3_settings): conn = boto.connect_s3(s3_settings['aws_access_key_id'], s3_settings['aws_secret_access_key']) bucket = conn.get_bucket(s3_settings['bucket_name']) return bucket def generate_uniqueish_key(s3_settings, environment, name_prefix): bucket = get_s3_bucket(s3_settings) if name_prefix and name_prefix != '': name_base = name_prefix else: name_base = environment['db_name'] name_attempt = "{}__{}.dmp.zip".format(name_base, datetime.utcnow().strftime("%Y_%m_%d")) key = bucket.get_key(name_attempt) if not key: key = Key(bucket) key.key = name_attempt return key else: counter = 1 while True: counter += 1 name_attempt = "{}__{}_{}.dmp.zip".format(name_base, datetime.utcnow().strftime("%Y_%m_%d"), counter) if bucket.get_key(name_attempt): continue else: key = Key(bucket) key.key = name_attempt return key def backup_to_s3(environment, s3_settings, name, query_set_class): dump_path = dump_db(environment, QuerySet=query_set_class) zipf = zipdir(dump_path) key = generate_uniqueish_key(s3_settings, environment, name) bytes_written = key.set_contents_from_filename(zipf.filename) # 4) print out the name of the bucket echo("Wrote {} btyes to s3".format(bytes_written)) def s3_restore(key, to_enviornment): with temp_directory() as temp_dir: zip_path = os.path.join(temp_dir, 'MongoDump.zip') key.get_contents_to_filename(zip_path) local_restore(zip_path, to_enviornment) def s3_backups(s3_config): """ a dict of key.name: key """ bucket = get_s3_bucket(s3_config) buckets = {} for key in bucket.get_all_keys(): buckets[key.name] = key return buckets

26.353659

98

0.654327

import os from datetime import datetime import boto from boto.s3.key import Key from click import echo from .utils import temp_directory, zipdir from .local import local_restore from .mongo import dump_db def get_s3_bucket(s3_settings): conn = boto.connect_s3(s3_settings['aws_access_key_id'], s3_settings['aws_secret_access_key']) bucket = conn.get_bucket(s3_settings['bucket_name']) return bucket def generate_uniqueish_key(s3_settings, environment, name_prefix): bucket = get_s3_bucket(s3_settings) if name_prefix and name_prefix != '': name_base = name_prefix else: name_base = environment['db_name'] name_attempt = "{}__{}.dmp.zip".format(name_base, datetime.utcnow().strftime("%Y_%m_%d")) key = bucket.get_key(name_attempt) if not key: key = Key(bucket) key.key = name_attempt return key else: counter = 1 while True: counter += 1 name_attempt = "{}__{}_{}.dmp.zip".format(name_base, datetime.utcnow().strftime("%Y_%m_%d"), counter) if bucket.get_key(name_attempt): continue else: key = Key(bucket) key.key = name_attempt return key def backup_to_s3(environment, s3_settings, name, query_set_class): dump_path = dump_db(environment, QuerySet=query_set_class) zipf = zipdir(dump_path) key = generate_uniqueish_key(s3_settings, environment, name) bytes_written = key.set_contents_from_filename(zipf.filename) echo("Wrote {} btyes to s3".format(bytes_written)) def s3_restore(key, to_enviornment): with temp_directory() as temp_dir: zip_path = os.path.join(temp_dir, 'MongoDump.zip') key.get_contents_to_filename(zip_path) local_restore(zip_path, to_enviornment) def s3_backups(s3_config): bucket = get_s3_bucket(s3_config) buckets = {} for key in bucket.get_all_keys(): buckets[key.name] = key return buckets

true

1c2b1f78a9f0d57e00fbd23e8fd849fef5b60c1f

3,062

py

Python

gqp_mc/fm.py

changhoonhahn/GQP_mock_challenge

831d5423edd9955ee1bda8d41e44d30cd3c6bd4b

[ "MIT" ]

3

2019-12-18T20:51:45.000Z

2021-12-11T05:59:24.000Z

gqp_mc/fm.py

changhoonhahn/GQP_mock_challenge

831d5423edd9955ee1bda8d41e44d30cd3c6bd4b

[ "MIT" ]

44

2020-02-20T06:02:00.000Z

2021-04-13T20:00:50.000Z

gqp_mc/fm.py

changhoonhahn/GQP_mock_challenge

831d5423edd9955ee1bda8d41e44d30cd3c6bd4b

[ "MIT" ]

7

2019-10-04T22:25:44.000Z

2020-07-20T02:05:03.000Z

''' submodule for forward modeling spectrophotometry ''' import os import numpy as np from speclite import filters as specFilter def Photo_DESI(wave, spectra, bands=['g', 'r', 'z']): ''' generate photometry by convolving the input spectrum with DECAM and WISE bandpasses: g, r, z, W1, W2, W3, W4 filters. :param wave: wavelength of input spectra in Angstroms. 2D array Nspec x Nwave. :param fluxes: fluxes of input spectra. This should be noiseless source spectra. 2D array Nspec x Nwave. In units of 10e-17 erg/s/cm2/A ''' wave = np.atleast_2d(wave) assert wave.shape[1] == spectra.shape[1] n_spec = spectra.shape[0] # number of spectra if wave.shape[0] == 1: wave = np.tile(wave, (n_spec, 1)) from astropy import units as U filter_dict = {'g': 'decam2014-g', 'r': 'decam2014-r', 'z': 'decam2014-z', 'w1': 'wise2010-W1', 'w2': 'wise2010-W2', 'w3': 'wise2010-W3', 'w4': 'wise2010-W4'} # load DECAM g, r, z and WISE W1-4 filter_response = specFilter.load_filters( *tuple([filter_dict[b] for b in bands])) # apply filters fluxes = np.zeros((n_spec, len(bands))) # photometric flux in nanomaggies for i in range(n_spec): spectrum = spectra[i] # apply filters flux = np.array(filter_response.get_ab_maggies( np.atleast_2d(spectrum) * 1e-17 * U.erg/U.s/U.cm**2/U.Angstrom, wave[i,:]*U.Angstrom)) # convert to nanomaggies fluxes[i,:] = 1e9 * np.array([flux[0][i] for i in range(len(bands))]) # calculate magnitudes (not advised due to NaNs) mags = 22.5 - 2.5 * np.log10(fluxes) return fluxes, mags def Spec_BGS(wave, flux, exptime, airmass, Isky, filename=None): ''' Given noiseless spectra, simulate noisy BGS spectra with Isky sky brightness, exptime sec exposure time, and airmass. Wrapper for FM.fakeDESIspec().simExposure :param wave: wavelength of spectra. Nwave :param flux: noiseless spectra in units of 1e-17 erg/s/cm2/A. Nspec x Nwave :param exptime: exposure time :param airmass: airmass :param Isky: [wave_sky, sky_brightness]. sky brightness is in units of 1e-17 erg / Ang / arcsec^2 / cm^2 / sec :param filename: If specified, the output fits file. (default: None) :return bgs_spec: data structure with all BGS data from the DESI spectrographs: bgs.wave['b'], bgs.wave['r'], bgs.wave['z'] bgs.flux['b'], bgs.flux['r'], bgs.flux['z'] bgs.ivar['b'], bgs.ivar['r'], bgs.ivar['z'] ''' # requires desiutil, desimodel, desisim, desispec, desitarget, # also requires numba, fitsio, healpy, pandas, astroplan... shoot me in the face! from feasibgs import forwardmodel as FM fdesi = FM.fakeDESIspec() bgs_spec = fdesi.simExposure(wave, flux, exptime=exptime, airmass=airmass, Isky=Isky, filename=filename) return bgs_spec

34.795455

109

0.621489

import os import numpy as np from speclite import filters as specFilter def Photo_DESI(wave, spectra, bands=['g', 'r', 'z']): wave = np.atleast_2d(wave) assert wave.shape[1] == spectra.shape[1] n_spec = spectra.shape[0] if wave.shape[0] == 1: wave = np.tile(wave, (n_spec, 1)) from astropy import units as U filter_dict = {'g': 'decam2014-g', 'r': 'decam2014-r', 'z': 'decam2014-z', 'w1': 'wise2010-W1', 'w2': 'wise2010-W2', 'w3': 'wise2010-W3', 'w4': 'wise2010-W4'} filter_response = specFilter.load_filters( *tuple([filter_dict[b] for b in bands])) fluxes = np.zeros((n_spec, len(bands))) for i in range(n_spec): spectrum = spectra[i] flux = np.array(filter_response.get_ab_maggies( np.atleast_2d(spectrum) * 1e-17 * U.erg/U.s/U.cm**2/U.Angstrom, wave[i,:]*U.Angstrom)) fluxes[i,:] = 1e9 * np.array([flux[0][i] for i in range(len(bands))]) mags = 22.5 - 2.5 * np.log10(fluxes) return fluxes, mags def Spec_BGS(wave, flux, exptime, airmass, Isky, filename=None): from feasibgs import forwardmodel as FM fdesi = FM.fakeDESIspec() bgs_spec = fdesi.simExposure(wave, flux, exptime=exptime, airmass=airmass, Isky=Isky, filename=filename) return bgs_spec

true

1c2b1fbfb14ddd021cc54211238b66fa242fea79

14,369

py

Python

cfgov/v1/migrations/0219_move_directors_notebook.py

flacoman91/consumerfinance.gov

64e3d68d1c023ae944baf66a99e54236e5976097

[ "CC0-1.0" ]

37

2020-08-18T19:52:39.000Z

2022-03-23T08:08:41.000Z

cfgov/v1/migrations/0219_move_directors_notebook.py

flacoman91/consumerfinance.gov

64e3d68d1c023ae944baf66a99e54236e5976097

[ "CC0-1.0" ]

338

2020-08-14T20:46:36.000Z

2022-03-31T20:49:32.000Z

cfgov/v1/migrations/0219_move_directors_notebook.py

raft-tech/cfgov-refresh

7c63c31fd6bb95ed4f7d368f1e1252175f0c71ca

[ "CC0-1.0" ]

14

2020-10-21T15:27:03.000Z

2022-03-17T03:16:36.000Z

# Generated by Django 2.2.12 on 2020-06-02 16:41 from django.db import migrations, models import v1.atomic_elements.molecules import v1.blocks import v1.models.snippets import wagtail.core.blocks import wagtail.core.fields import wagtail.images.blocks import wagtail.snippets.blocks class Migration(migrations.Migration): dependencies = [ ('v1', '0218_add_force_breadcrumbs'), ] operations = [ migrations.AlterField( model_name='cfgovpage', name='sidefoot', field=wagtail.core.fields.StreamField([('call_to_action', wagtail.core.blocks.StructBlock([('slug_text', wagtail.core.blocks.CharBlock(required=False)), ('paragraph_text', wagtail.core.blocks.RichTextBlock(required=False)), ('button', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False)), ('size', wagtail.core.blocks.ChoiceBlock(choices=[('regular', 'Regular'), ('large', 'Large Primary')]))]))])), ('related_links', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(required=False)), ('paragraph', wagtail.core.blocks.RichTextBlock(required=False)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])))])), ('related_posts', wagtail.core.blocks.StructBlock([('limit', wagtail.core.blocks.CharBlock(default='3', help_text='This limit applies to EACH TYPE of post this module retrieves, not the total number of retrieved posts.')), ('show_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='This toggles the heading and icon for the related types.', label='Show Heading and Icon?', required=False)), ('header_title', wagtail.core.blocks.CharBlock(default='Further reading', label='Slug Title')), ('relate_posts', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Blog Posts', required=False)), ('relate_newsroom', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Newsroom', required=False)), ('relate_events', wagtail.core.blocks.BooleanBlock(default=True, label='Events', required=False)), ('specific_categories', wagtail.core.blocks.ListBlock(wagtail.core.blocks.ChoiceBlock(choices=[('Blog', (('At the CFPB', 'At the CFPB'), ("Director's notebook", "Director's notebook"), ('Policy & Compliance', 'Policy and compliance'), ('Data, Research & Reports', 'Data, research, and reports'), ('Info for Consumers', 'Info for consumers'))), ('Newsroom', (('Op-Ed', 'Op-ed'), ('Press Release', 'Press release'), ('Speech', 'Speech'), ('Testimony', 'Testimony')))], required=False), required=False)), ('and_filtering', wagtail.core.blocks.BooleanBlock(default=False, help_text='If checked, related posts will only be pulled in if they match ALL topic tags set on this page. Otherwise, related posts can match any one topic tag.', label='Match all topic tags', required=False)), ('alternate_view_more_url', wagtail.core.blocks.CharBlock(help_text='By default, the "View more" link will go to the Activity Log, filtered based on the above parameters. Enter a URL in this field to override that link destination.', label='Alternate "View more" URL', required=False))])), ('related_metadata', wagtail.core.blocks.StructBlock([('slug', wagtail.core.blocks.CharBlock(max_length=100)), ('content', wagtail.core.blocks.StreamBlock([('text', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('blob', wagtail.core.blocks.RichTextBlock())], icon='pilcrow')), ('list', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])))], icon='list-ul')), ('date', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('date', wagtail.core.blocks.DateBlock())], icon='date')), ('topics', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Topics', max_length=100)), ('show_topics', wagtail.core.blocks.BooleanBlock(default=True, required=False))], icon='tag')), ('categories', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Categories', max_length=100)), ('show_categories', wagtail.core.blocks.BooleanBlock(default=True, required=False))], icon='list-ul'))])), ('is_half_width', wagtail.core.blocks.BooleanBlock(default=False, required=False))])), ('email_signup', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Stay informed', required=False)), ('default_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='If selected, heading will be styled as an H5 with green top rule. Deselect to style header as H3.', label='Default heading style', required=False)), ('text', wagtail.core.blocks.CharBlock(help_text='Write a sentence or two about what kinds of emails the user is signing up for, how frequently they will be sent, etc.', required=False)), ('gd_code', wagtail.core.blocks.CharBlock(help_text='Code for the topic (i.e., mailing list) you want people who submit this form to subscribe to. Format: USCFPB_###', label='GovDelivery code', required=False)), ('disclaimer_page', wagtail.core.blocks.PageChooserBlock(help_text='Choose the page that the "See Privacy Act statement" link should go to. If in doubt, use "Generic Email Sign-Up Privacy Act Statement".', label='Privacy Act statement', required=False))])), ('sidebar_contact', wagtail.core.blocks.StructBlock([('contact', wagtail.snippets.blocks.SnippetChooserBlock('v1.Contact')), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Add a horizontal rule line to top of contact block.', required=False))])), ('rss_feed', v1.atomic_elements.molecules.RSSFeed()), ('social_media', wagtail.core.blocks.StructBlock([('is_share_view', wagtail.core.blocks.BooleanBlock(default=True, help_text='If unchecked, social media icons will link users to official CFPB accounts. Do not fill in any further fields.', label='Desired action: share this page', required=False)), ('blurb', wagtail.core.blocks.CharBlock(default="Look what I found on the CFPB's site!", help_text='Sets the tweet text, email subject line, and LinkedIn post text.', required=False)), ('twitter_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for Twitter shares. If blank, will default to value of blurb field above.', max_length=100, required=False)), ('twitter_related', wagtail.core.blocks.CharBlock(help_text='(Optional) A comma-separated list of accounts related to the content of the shared URL. Do not enter the @ symbol. If blank, it will default to just "cfpb".', required=False)), ('twitter_hashtags', wagtail.core.blocks.CharBlock(help_text='(Optional) A comma-separated list of hashtags to be appended to default tweet text.', required=False)), ('twitter_lang', wagtail.core.blocks.CharBlock(help_text='(Optional) Loads text components in the specified language, if other than English. E.g., use "es" for Spanish. See https://dev.twitter.com/web/overview/languages for a list of supported language codes.', required=False)), ('email_title', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom subject for email shares. If blank, will default to value of blurb field above.', required=False)), ('email_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for email shares. If blank, will default to "Check out this page from the CFPB".', required=False)), ('email_signature', wagtail.core.blocks.CharBlock(help_text='(Optional) Adds a custom signature line to email shares. ', required=False)), ('linkedin_title', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom title for LinkedIn shares. If blank, will default to value of blurb field above.', required=False)), ('linkedin_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for LinkedIn shares.', required=False))])), ('reusable_text', v1.blocks.ReusableTextChooserBlock(v1.models.snippets.ReusableText))], blank=True), ), migrations.AlterField( model_name='cfgovpagecategory', name='name', field=models.CharField(choices=[('Administrative adjudication docket', (('administrative-adjudication', 'Administrative adjudication'), ('stipulation-and-constent-order', 'Stipulation and consent order'))), ('Amicus Brief', (('us-supreme-court', 'U.S. Supreme Court'), ('fed-circuit-court', 'Federal Circuit Court'), ('fed-district-court', 'Federal District Court'), ('state-court', 'State Court'))), ('Blog', (('at-the-cfpb', 'At the CFPB'), ('directors-notebook', "Director's notebook"), ('policy_compliance', 'Policy and compliance'), ('data-research-reports', 'Data, research, and reports'), ('info-for-consumers', 'Info for consumers'))), ('Consumer Reporting Companies', (('nationwide', 'Nationwide'), ('employment-screening', 'Employment screening'), ('tenant-screening', 'Tenant screening'), ('check-bank-screening', 'Check and bank screening'), ('personal-property-insurance', 'Personal property insurance'), ('medical', 'Medical'), ('low-income-and-subprime', 'Low-income and subprime'), ('supplementary-reports', 'Supplementary reports'), ('utilities', 'Utilities'), ('retail', 'Retail'), ('gaming', 'Gaming'))), ('Enforcement Action', (('civil-action', 'Civil Action'), ('administrative-proceeding', 'Administrative Proceeding'))), ('Final rule', (('interim-final-rule', 'Interim final rule'), ('final-rule', 'Final rule'))), ('FOIA Frequently Requested Record', (('report', 'Report'), ('log', 'Log'), ('record', 'Record'))), ('Implementation Resource', (('compliance-aid', 'Compliance aid'), ('official-guidance', 'Official guidance'))), ('Newsroom', (('op-ed', 'Op-ed'), ('press-release', 'Press release'), ('speech', 'Speech'), ('testimony', 'Testimony'))), ('Notice and Opportunity for Comment', (('notice-proposed-rule', 'Advance notice of proposed rulemaking'), ('proposed-rule', 'Proposed rule'), ('interim-final-rule-2', 'Interim final rule'), ('request-comment-info', 'Request for comment or information'), ('proposed-policy', 'Proposed policy'), ('intent-preempt-determ', 'Intent to make preemption determination'), ('info-collect-activity', 'Information collection activities'), ('notice-privacy-act', 'Notice related to Privacy Act'))), ('Research Report', (('consumer-complaint', 'Consumer complaint'), ('super-highlight', 'Supervisory Highlights'), ('data-point', 'Data point'), ('industry-markets', 'Industry and markets'), ('consumer-edu-empower', 'Consumer education and empowerment'), ('to-congress', 'To Congress'))), ('Rule Under Development', (('notice-proposed-rule-2', 'Advance notice of proposed rulemaking'), ('proposed-rule-2', 'Proposed rule'))), ('Story', (('auto-loans', 'Auto loans'), ('bank-accts-services', 'Bank accounts and services'), ('credit-cards', 'Credit cards'), ('credit-reports-scores', 'Credit reports and scores'), ('debt-collection', 'Debt collection'), ('money-transfers', 'Money transfers'), ('mortgages', 'Mortgages'), ('payday-loans', 'Payday loans'), ('prepaid-cards', 'Prepaid cards'), ('student-loans', 'Student loans')))], max_length=255), ), migrations.AlterField( model_name='sublandingpage', name='sidebar_breakout', field=wagtail.core.fields.StreamField([('slug', wagtail.core.blocks.CharBlock(icon='title')), ('heading', wagtail.core.blocks.CharBlock(icon='title')), ('paragraph', wagtail.core.blocks.RichTextBlock(icon='edit')), ('breakout_image', wagtail.core.blocks.StructBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('is_round', wagtail.core.blocks.BooleanBlock(default=True, label='Round?', required=False)), ('icon', wagtail.core.blocks.CharBlock(help_text='Enter icon class name.')), ('heading', wagtail.core.blocks.CharBlock(label='Introduction Heading', required=False)), ('body', wagtail.core.blocks.TextBlock(label='Introduction Body', required=False))], heading='Breakout Image', icon='image')), ('related_posts', wagtail.core.blocks.StructBlock([('limit', wagtail.core.blocks.CharBlock(default='3', help_text='This limit applies to EACH TYPE of post this module retrieves, not the total number of retrieved posts.')), ('show_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='This toggles the heading and icon for the related types.', label='Show Heading and Icon?', required=False)), ('header_title', wagtail.core.blocks.CharBlock(default='Further reading', label='Slug Title')), ('relate_posts', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Blog Posts', required=False)), ('relate_newsroom', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Newsroom', required=False)), ('relate_events', wagtail.core.blocks.BooleanBlock(default=True, label='Events', required=False)), ('specific_categories', wagtail.core.blocks.ListBlock(wagtail.core.blocks.ChoiceBlock(choices=[('Blog', (('At the CFPB', 'At the CFPB'), ("Director's notebook", "Director's notebook"), ('Policy & Compliance', 'Policy and compliance'), ('Data, Research & Reports', 'Data, research, and reports'), ('Info for Consumers', 'Info for consumers'))), ('Newsroom', (('Op-Ed', 'Op-ed'), ('Press Release', 'Press release'), ('Speech', 'Speech'), ('Testimony', 'Testimony')))], required=False), required=False)), ('and_filtering', wagtail.core.blocks.BooleanBlock(default=False, help_text='If checked, related posts will only be pulled in if they match ALL topic tags set on this page. Otherwise, related posts can match any one topic tag.', label='Match all topic tags', required=False)), ('alternate_view_more_url', wagtail.core.blocks.CharBlock(help_text='By default, the "View more" link will go to the Activity Log, filtered based on the above parameters. Enter a URL in this field to override that link destination.', label='Alternate "View more" URL', required=False))])), ('job_listing_list', wagtail.core.blocks.StructBlock([('more_jobs_page', wagtail.core.blocks.PageChooserBlock(help_text='Link to full list of jobs'))]))], blank=True), ), ]

399.138889

7,782

0.737351

from django.db import migrations, models import v1.atomic_elements.molecules import v1.blocks import v1.models.snippets import wagtail.core.blocks import wagtail.core.fields import wagtail.images.blocks import wagtail.snippets.blocks class Migration(migrations.Migration): dependencies = [ ('v1', '0218_add_force_breadcrumbs'), ] operations = [ migrations.AlterField( model_name='cfgovpage', name='sidefoot', field=wagtail.core.fields.StreamField([('call_to_action', wagtail.core.blocks.StructBlock([('slug_text', wagtail.core.blocks.CharBlock(required=False)), ('paragraph_text', wagtail.core.blocks.RichTextBlock(required=False)), ('button', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False)), ('size', wagtail.core.blocks.ChoiceBlock(choices=[('regular', 'Regular'), ('large', 'Large Primary')]))]))])), ('related_links', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(required=False)), ('paragraph', wagtail.core.blocks.RichTextBlock(required=False)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])))])), ('related_posts', wagtail.core.blocks.StructBlock([('limit', wagtail.core.blocks.CharBlock(default='3', help_text='This limit applies to EACH TYPE of post this module retrieves, not the total number of retrieved posts.')), ('show_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='This toggles the heading and icon for the related types.', label='Show Heading and Icon?', required=False)), ('header_title', wagtail.core.blocks.CharBlock(default='Further reading', label='Slug Title')), ('relate_posts', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Blog Posts', required=False)), ('relate_newsroom', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Newsroom', required=False)), ('relate_events', wagtail.core.blocks.BooleanBlock(default=True, label='Events', required=False)), ('specific_categories', wagtail.core.blocks.ListBlock(wagtail.core.blocks.ChoiceBlock(choices=[('Blog', (('At the CFPB', 'At the CFPB'), ("Director's notebook", "Director's notebook"), ('Policy & Compliance', 'Policy and compliance'), ('Data, Research & Reports', 'Data, research, and reports'), ('Info for Consumers', 'Info for consumers'))), ('Newsroom', (('Op-Ed', 'Op-ed'), ('Press Release', 'Press release'), ('Speech', 'Speech'), ('Testimony', 'Testimony')))], required=False), required=False)), ('and_filtering', wagtail.core.blocks.BooleanBlock(default=False, help_text='If checked, related posts will only be pulled in if they match ALL topic tags set on this page. Otherwise, related posts can match any one topic tag.', label='Match all topic tags', required=False)), ('alternate_view_more_url', wagtail.core.blocks.CharBlock(help_text='By default, the "View more" link will go to the Activity Log, filtered based on the above parameters. Enter a URL in this field to override that link destination.', label='Alternate "View more" URL', required=False))])), ('related_metadata', wagtail.core.blocks.StructBlock([('slug', wagtail.core.blocks.CharBlock(max_length=100)), ('content', wagtail.core.blocks.StreamBlock([('text', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('blob', wagtail.core.blocks.RichTextBlock())], icon='pilcrow')), ('list', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('links', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(required=False)), ('url', wagtail.core.blocks.CharBlock(default='/', required=False))])))], icon='list-ul')), ('date', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(max_length=100)), ('date', wagtail.core.blocks.DateBlock())], icon='date')), ('topics', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Topics', max_length=100)), ('show_topics', wagtail.core.blocks.BooleanBlock(default=True, required=False))], icon='tag')), ('categories', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Categories', max_length=100)), ('show_categories', wagtail.core.blocks.BooleanBlock(default=True, required=False))], icon='list-ul'))])), ('is_half_width', wagtail.core.blocks.BooleanBlock(default=False, required=False))])), ('email_signup', wagtail.core.blocks.StructBlock([('heading', wagtail.core.blocks.CharBlock(default='Stay informed', required=False)), ('default_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='If selected, heading will be styled as an H5 with green top rule. Deselect to style header as H3.', label='Default heading style', required=False)), ('text', wagtail.core.blocks.CharBlock(help_text='Write a sentence or two about what kinds of emails the user is signing up for, how frequently they will be sent, etc.', required=False)), ('gd_code', wagtail.core.blocks.CharBlock(help_text='Code for the topic (i.e., mailing list) you want people who submit this form to subscribe to. Format: USCFPB_###', label='GovDelivery code', required=False)), ('disclaimer_page', wagtail.core.blocks.PageChooserBlock(help_text='Choose the page that the "See Privacy Act statement" link should go to. If in doubt, use "Generic Email Sign-Up Privacy Act Statement".', label='Privacy Act statement', required=False))])), ('sidebar_contact', wagtail.core.blocks.StructBlock([('contact', wagtail.snippets.blocks.SnippetChooserBlock('v1.Contact')), ('has_top_rule_line', wagtail.core.blocks.BooleanBlock(default=False, help_text='Add a horizontal rule line to top of contact block.', required=False))])), ('rss_feed', v1.atomic_elements.molecules.RSSFeed()), ('social_media', wagtail.core.blocks.StructBlock([('is_share_view', wagtail.core.blocks.BooleanBlock(default=True, help_text='If unchecked, social media icons will link users to official CFPB accounts. Do not fill in any further fields.', label='Desired action: share this page', required=False)), ('blurb', wagtail.core.blocks.CharBlock(default="Look what I found on the CFPB's site!", help_text='Sets the tweet text, email subject line, and LinkedIn post text.', required=False)), ('twitter_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for Twitter shares. If blank, will default to value of blurb field above.', max_length=100, required=False)), ('twitter_related', wagtail.core.blocks.CharBlock(help_text='(Optional) A comma-separated list of accounts related to the content of the shared URL. Do not enter the @ symbol. If blank, it will default to just "cfpb".', required=False)), ('twitter_hashtags', wagtail.core.blocks.CharBlock(help_text='(Optional) A comma-separated list of hashtags to be appended to default tweet text.', required=False)), ('twitter_lang', wagtail.core.blocks.CharBlock(help_text='(Optional) Loads text components in the specified language, if other than English. E.g., use "es" for Spanish. See https://dev.twitter.com/web/overview/languages for a list of supported language codes.', required=False)), ('email_title', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom subject for email shares. If blank, will default to value of blurb field above.', required=False)), ('email_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for email shares. If blank, will default to "Check out this page from the CFPB".', required=False)), ('email_signature', wagtail.core.blocks.CharBlock(help_text='(Optional) Adds a custom signature line to email shares. ', required=False)), ('linkedin_title', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom title for LinkedIn shares. If blank, will default to value of blurb field above.', required=False)), ('linkedin_text', wagtail.core.blocks.CharBlock(help_text='(Optional) Custom text for LinkedIn shares.', required=False))])), ('reusable_text', v1.blocks.ReusableTextChooserBlock(v1.models.snippets.ReusableText))], blank=True), ), migrations.AlterField( model_name='cfgovpagecategory', name='name', field=models.CharField(choices=[('Administrative adjudication docket', (('administrative-adjudication', 'Administrative adjudication'), ('stipulation-and-constent-order', 'Stipulation and consent order'))), ('Amicus Brief', (('us-supreme-court', 'U.S. Supreme Court'), ('fed-circuit-court', 'Federal Circuit Court'), ('fed-district-court', 'Federal District Court'), ('state-court', 'State Court'))), ('Blog', (('at-the-cfpb', 'At the CFPB'), ('directors-notebook', "Director's notebook"), ('policy_compliance', 'Policy and compliance'), ('data-research-reports', 'Data, research, and reports'), ('info-for-consumers', 'Info for consumers'))), ('Consumer Reporting Companies', (('nationwide', 'Nationwide'), ('employment-screening', 'Employment screening'), ('tenant-screening', 'Tenant screening'), ('check-bank-screening', 'Check and bank screening'), ('personal-property-insurance', 'Personal property insurance'), ('medical', 'Medical'), ('low-income-and-subprime', 'Low-income and subprime'), ('supplementary-reports', 'Supplementary reports'), ('utilities', 'Utilities'), ('retail', 'Retail'), ('gaming', 'Gaming'))), ('Enforcement Action', (('civil-action', 'Civil Action'), ('administrative-proceeding', 'Administrative Proceeding'))), ('Final rule', (('interim-final-rule', 'Interim final rule'), ('final-rule', 'Final rule'))), ('FOIA Frequently Requested Record', (('report', 'Report'), ('log', 'Log'), ('record', 'Record'))), ('Implementation Resource', (('compliance-aid', 'Compliance aid'), ('official-guidance', 'Official guidance'))), ('Newsroom', (('op-ed', 'Op-ed'), ('press-release', 'Press release'), ('speech', 'Speech'), ('testimony', 'Testimony'))), ('Notice and Opportunity for Comment', (('notice-proposed-rule', 'Advance notice of proposed rulemaking'), ('proposed-rule', 'Proposed rule'), ('interim-final-rule-2', 'Interim final rule'), ('request-comment-info', 'Request for comment or information'), ('proposed-policy', 'Proposed policy'), ('intent-preempt-determ', 'Intent to make preemption determination'), ('info-collect-activity', 'Information collection activities'), ('notice-privacy-act', 'Notice related to Privacy Act'))), ('Research Report', (('consumer-complaint', 'Consumer complaint'), ('super-highlight', 'Supervisory Highlights'), ('data-point', 'Data point'), ('industry-markets', 'Industry and markets'), ('consumer-edu-empower', 'Consumer education and empowerment'), ('to-congress', 'To Congress'))), ('Rule Under Development', (('notice-proposed-rule-2', 'Advance notice of proposed rulemaking'), ('proposed-rule-2', 'Proposed rule'))), ('Story', (('auto-loans', 'Auto loans'), ('bank-accts-services', 'Bank accounts and services'), ('credit-cards', 'Credit cards'), ('credit-reports-scores', 'Credit reports and scores'), ('debt-collection', 'Debt collection'), ('money-transfers', 'Money transfers'), ('mortgages', 'Mortgages'), ('payday-loans', 'Payday loans'), ('prepaid-cards', 'Prepaid cards'), ('student-loans', 'Student loans')))], max_length=255), ), migrations.AlterField( model_name='sublandingpage', name='sidebar_breakout', field=wagtail.core.fields.StreamField([('slug', wagtail.core.blocks.CharBlock(icon='title')), ('heading', wagtail.core.blocks.CharBlock(icon='title')), ('paragraph', wagtail.core.blocks.RichTextBlock(icon='edit')), ('breakout_image', wagtail.core.blocks.StructBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('is_round', wagtail.core.blocks.BooleanBlock(default=True, label='Round?', required=False)), ('icon', wagtail.core.blocks.CharBlock(help_text='Enter icon class name.')), ('heading', wagtail.core.blocks.CharBlock(label='Introduction Heading', required=False)), ('body', wagtail.core.blocks.TextBlock(label='Introduction Body', required=False))], heading='Breakout Image', icon='image')), ('related_posts', wagtail.core.blocks.StructBlock([('limit', wagtail.core.blocks.CharBlock(default='3', help_text='This limit applies to EACH TYPE of post this module retrieves, not the total number of retrieved posts.')), ('show_heading', wagtail.core.blocks.BooleanBlock(default=True, help_text='This toggles the heading and icon for the related types.', label='Show Heading and Icon?', required=False)), ('header_title', wagtail.core.blocks.CharBlock(default='Further reading', label='Slug Title')), ('relate_posts', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Blog Posts', required=False)), ('relate_newsroom', wagtail.core.blocks.BooleanBlock(default=True, editable=False, label='Newsroom', required=False)), ('relate_events', wagtail.core.blocks.BooleanBlock(default=True, label='Events', required=False)), ('specific_categories', wagtail.core.blocks.ListBlock(wagtail.core.blocks.ChoiceBlock(choices=[('Blog', (('At the CFPB', 'At the CFPB'), ("Director's notebook", "Director's notebook"), ('Policy & Compliance', 'Policy and compliance'), ('Data, Research & Reports', 'Data, research, and reports'), ('Info for Consumers', 'Info for consumers'))), ('Newsroom', (('Op-Ed', 'Op-ed'), ('Press Release', 'Press release'), ('Speech', 'Speech'), ('Testimony', 'Testimony')))], required=False), required=False)), ('and_filtering', wagtail.core.blocks.BooleanBlock(default=False, help_text='If checked, related posts will only be pulled in if they match ALL topic tags set on this page. Otherwise, related posts can match any one topic tag.', label='Match all topic tags', required=False)), ('alternate_view_more_url', wagtail.core.blocks.CharBlock(help_text='By default, the "View more" link will go to the Activity Log, filtered based on the above parameters. Enter a URL in this field to override that link destination.', label='Alternate "View more" URL', required=False))])), ('job_listing_list', wagtail.core.blocks.StructBlock([('more_jobs_page', wagtail.core.blocks.PageChooserBlock(help_text='Link to full list of jobs'))]))], blank=True), ), ]

true

1c2b20126cfa1deb4d78e265e26c98ff9951782a

1,369

py

Python

ooobuild/dyn/frame/x_layout_manager2.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/dyn/frame/x_layout_manager2.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/dyn/frame/x_layout_manager2.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Interface Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.frame from typing import TYPE_CHECKING from ooo.oenv.env_const import UNO_ENVIRONMENT, UNO_RUNTIME _DYNAMIC = False if (not TYPE_CHECKING) and UNO_RUNTIME and UNO_ENVIRONMENT: _DYNAMIC = True if not TYPE_CHECKING and _DYNAMIC: from com.sun.star.frame import XLayoutManager2 as XLayoutManager2 setattr(XLayoutManager2, '__ooo_ns__', 'com.sun.star.frame') setattr(XLayoutManager2, '__ooo_full_ns__', 'com.sun.star.frame.XLayoutManager2') setattr(XLayoutManager2, '__ooo_type_name__', 'interface') else: from ...lo.frame.x_layout_manager2 import XLayoutManager2 as XLayoutManager2 __all__ = ['XLayoutManager2']

37

85

0.769905

from typing import TYPE_CHECKING from ooo.oenv.env_const import UNO_ENVIRONMENT, UNO_RUNTIME _DYNAMIC = False if (not TYPE_CHECKING) and UNO_RUNTIME and UNO_ENVIRONMENT: _DYNAMIC = True if not TYPE_CHECKING and _DYNAMIC: from com.sun.star.frame import XLayoutManager2 as XLayoutManager2 setattr(XLayoutManager2, '__ooo_ns__', 'com.sun.star.frame') setattr(XLayoutManager2, '__ooo_full_ns__', 'com.sun.star.frame.XLayoutManager2') setattr(XLayoutManager2, '__ooo_type_name__', 'interface') else: from ...lo.frame.x_layout_manager2 import XLayoutManager2 as XLayoutManager2 __all__ = ['XLayoutManager2']

true

1c2b22346e3ef10d78afbd239821aac46d72fd58

711

py

Python

main/service/type_service.py

emhayusa/4d_api

cbf380c150d6f7a01954346492fe9a7751a6603b

[ "MIT" ]

null

main/service/type_service.py

emhayusa/4d_api

cbf380c150d6f7a01954346492fe9a7751a6603b

[ "MIT" ]

null

main/service/type_service.py

emhayusa/4d_api

cbf380c150d6f7a01954346492fe9a7751a6603b

[ "MIT" ]

null

import uuid import datetime from app.main import db from app.main.model.type import Type def save_new(data): row = Type.query.filter_by(type_name=data['type_name']).first() if not row: new = Type( type_name=data['type_name'], ) save_changes(new) response_object = { 'status': 'success', 'message': 'Successfully inserted.' } return response_object, 201 else: response_object = { 'status': 'fail', 'message': 'Type already exists.', } return response_object, 409 def get_all(): return Type.query.order_by('id').all() def get_by_id(id): return Type.query.filter_by(id=id).first() def save_changes(data): db.session.add(data) db.session.commit()

20.911765

64

0.673699

import uuid import datetime from app.main import db from app.main.model.type import Type def save_new(data): row = Type.query.filter_by(type_name=data['type_name']).first() if not row: new = Type( type_name=data['type_name'], ) save_changes(new) response_object = { 'status': 'success', 'message': 'Successfully inserted.' } return response_object, 201 else: response_object = { 'status': 'fail', 'message': 'Type already exists.', } return response_object, 409 def get_all(): return Type.query.order_by('id').all() def get_by_id(id): return Type.query.filter_by(id=id).first() def save_changes(data): db.session.add(data) db.session.commit()

true

1c2b22572104c711e6f9f2ddfd62d842121e2ebe

261

py

Python

config.py

Mainakkundu/titanic-gcp-kubernet

d3d0cc9969f237aada4572ef8e627eb3aafb5fc9

[ "MIT" ]

6

2020-11-26T23:14:36.000Z

2021-04-16T03:21:34.000Z

config.py

Mainakkundu/titanic-gcp-kubernet

d3d0cc9969f237aada4572ef8e627eb3aafb5fc9

[ "MIT" ]

5

2020-04-22T01:58:38.000Z

2022-03-12T00:23:40.000Z

config.py

Mainakkundu/titanic-gcp-kubernet

d3d0cc9969f237aada4572ef8e627eb3aafb5fc9

[ "MIT" ]

null

from os import environ as env import multiprocessing PORT = int(env.get("PORT", 8080)) DEBUG_MODE = int(env.get("DEBUG_MODE", 1)) # Gunicorn config bind = ":" + str(PORT) workers = multiprocessing.cpu_count() * 2 + 1 threads = 2 * multiprocessing.cpu_count()

23.727273

45

0.712644

from os import environ as env import multiprocessing PORT = int(env.get("PORT", 8080)) DEBUG_MODE = int(env.get("DEBUG_MODE", 1)) bind = ":" + str(PORT) workers = multiprocessing.cpu_count() * 2 + 1 threads = 2 * multiprocessing.cpu_count()

true

1c2b22c206bc71ab4eeddd556a1edcdc75ff2aa4

10,120

py

Python

application/games/crosswordcreator/data/board.py

Tyler-Yates/game-box

dc838270c3777372c3eeaf1e09fb1962c36fc2a8

[ "MIT" ]

1

2020-12-13T02:41:19.000Z

application/games/crosswordcreator/data/board.py

Tyler-Yates/game-box

dc838270c3777372c3eeaf1e09fb1962c36fc2a8

[ "MIT" ]

null

application/games/crosswordcreator/data/board.py

Tyler-Yates/game-box

dc838270c3777372c3eeaf1e09fb1962c36fc2a8

[ "MIT" ]

null

from typing import List, Optional, Tuple, Set, Dict from ...common.word_manager import WordManager class Board: """ Represents a board for a single player. Boards are assumed to be square (equal number of rows and columns). The board is represented as a two dimensional matrix. Points on the board are represented as tuples of integers: (row, column). The upper-left corner of the board is point (0,0). The lower-right corner of the board is point (board_size-1, board_size-1). """ def __init__(self, player_id: str, board_size: int, word_manager: WordManager): self.player_id = player_id self.board_size = board_size self.board: List[List[Optional[str]]] = [[None for _ in range(board_size)] for _ in range(board_size)] self.word_manager = word_manager def _set_board(self, board: List[List[Optional[str]]]): # Helper method for tests to set the board how they like. self.board_size = len(board) self.board = board def add_tile(self, tile: str, row: int, col: int) -> Optional[str]: """ Adds a tile to the given position on the board. Args: tile: The tile row: The row col: The column Returns: The tile that was previously at that location. Could be None. """ previous_tile = self.board[row][col] self.board[row][col] = tile return previous_tile def remove_tile(self, row: int, col: int) -> Optional[str]: """ Removes the tile from the given position on the board. Args: row: The row col: The column Returns: The tile that was removed. Could be None. """ removed_tile = self.board[row][col] self.board[row][col] = None return removed_tile def _find_connected_tiles(self, row, col, non_empty_tiles_not_visited: set) -> None: """ Recursive function used to find all connected tiles from a given point. NOTE: non_empty_tiles_not_visited will be modified by this function. Args: row: The starting row col: The starting column non_empty_tiles_not_visited: The complete set of non-empty tiles for this function to work with """ non_empty_tiles_not_visited.remove((row, col)) if (row > 0) and (self.board[row - 1][col] is not None) and ((row - 1, col) in non_empty_tiles_not_visited): self._find_connected_tiles(row - 1, col, non_empty_tiles_not_visited) if ( (row < self.board_size - 1) and (self.board[row + 1][col] is not None) and ((row + 1, col) in non_empty_tiles_not_visited) ): self._find_connected_tiles(row + 1, col, non_empty_tiles_not_visited) if (col > 0) and (self.board[row][col - 1] is not None) and ((row, col - 1) in non_empty_tiles_not_visited): self._find_connected_tiles(row, col - 1, non_empty_tiles_not_visited) if ( (col < self.board_size - 1) and (self.board[row][col + 1] is not None) and ((row, col + 1) in non_empty_tiles_not_visited) ): self._find_connected_tiles(row, col + 1, non_empty_tiles_not_visited) def _check_connected(self) -> Set[Tuple[int, int]]: """ Function used to check if all tiles on the board are connected. Returns: A set of points on the board that are not connected. May be empty which indicates all tiles are connected. """ first_tile = None non_empty_tiles = set() # Find the first tile and all tiles that are non-empty for row in range(self.board_size): for col in range(self.board_size): tile = self.board[row][col] if tile is not None: non_empty_tiles.add((row, col)) if first_tile is None: first_tile = (row, col) # Traverse through all tiles reachable from the first tile. # Whatever tiles are left in non_empty_tiles are not connected. self._find_connected_tiles(first_tile[0], first_tile[1], non_empty_tiles) return non_empty_tiles def _check_valid_words(self) -> Set[Tuple[int, int]]: """ Helper method to check that all words in the crossword of the board are valid. Returns: A set of points that are part of invalid words. This may be empty, indicating the board is a valid crossword. """ invalid_points = set() # Check across each row for row in range(self.board_size): current_word = "" for col in range(self.board_size): tile = self.board[row][col] # If the position is blank, it's time to check if tile is None: # If we have a current word of length more than 1, check its validity if len(current_word) > 1: # If the word is not valid, add the points to the list of invalid points if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row, col - 1 - i)) # Now that we are done with our checks, we clear the current word to continue our search current_word = "" else: current_word += tile # The current word could go to the end of the board so we need to do an additional check if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row, self.board_size - 1 - i)) # Check down each column for col in range(self.board_size): current_word = "" for row in range(self.board_size): tile = self.board[row][col] # If the position is blank, it's time to check if tile is None: # If we have a current word of length more than 1, check its validity if len(current_word) > 1: # If the word is not valid, add the points to the list of invalid points if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row - 1 - i, col)) # Now that we are done with our checks, we clear the current word to continue our search current_word = "" else: current_word += tile # The current word could go to the end of the board so we need to do an additional check if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((self.board_size - 1 - i, col)) return invalid_points def board_is_valid_crossword(self) -> Set[Tuple[int, int]]: """ Returns whether the board represents a valid crossword. Returns: A set of invalid points on the board. If empty, the board is a valid crossword. """ # First check is to ensure that all tiles on the board are connected. unconnected_points = self._check_connected() if unconnected_points: return unconnected_points # Now, ensure all tiles make a valid crossword of recognized words. return self._check_valid_words() def shift_board_down(self) -> bool: """ Shifts the entire board down one row if possible. Returns: True if the shift occurred, False otherwise """ for c in range(self.board_size): if self.board[self.board_size - 1][c] is not None: return False for r in range(self.board_size - 1, 0, -1): for c in range(self.board_size): self.board[r][c] = self.board[r - 1][c] for c in range(self.board_size): self.board[0][c] = None return True def shift_board_up(self) -> bool: """ Shifts the entire board up one row if possible. Returns: True if the shift occurred, False otherwise """ for c in range(self.board_size): if self.board[0][c] is not None: return False for r in range(0, self.board_size - 1): for c in range(self.board_size): self.board[r][c] = self.board[r + 1][c] for c in range(self.board_size): self.board[self.board_size - 1][c] = None return True def shift_board_right(self) -> bool: """ Shifts the entire board right one row if possible. Returns: True if the shift occurred, False otherwise """ for r in range(self.board_size): if self.board[r][self.board_size - 1] is not None: return False for c in range(self.board_size - 1, 0, -1): for r in range(self.board_size): self.board[r][c] = self.board[r][c - 1] for r in range(self.board_size): self.board[r][0] = None return True def shift_board_left(self) -> bool: """ Shifts the entire board left one row if possible. Returns: True if the shift occurred, False otherwise """ for r in range(self.board_size): if self.board[r][0] is not None: return False for c in range(0, self.board_size - 1): for r in range(self.board_size): self.board[r][c] = self.board[r][c + 1] for r in range(self.board_size): self.board[r][self.board_size - 1] = None return True def get_json(self) -> Dict[str, object]: return {"board": self.board}

37.902622

118

0.573221

from typing import List, Optional, Tuple, Set, Dict from ...common.word_manager import WordManager class Board: def __init__(self, player_id: str, board_size: int, word_manager: WordManager): self.player_id = player_id self.board_size = board_size self.board: List[List[Optional[str]]] = [[None for _ in range(board_size)] for _ in range(board_size)] self.word_manager = word_manager def _set_board(self, board: List[List[Optional[str]]]): self.board_size = len(board) self.board = board def add_tile(self, tile: str, row: int, col: int) -> Optional[str]: previous_tile = self.board[row][col] self.board[row][col] = tile return previous_tile def remove_tile(self, row: int, col: int) -> Optional[str]: removed_tile = self.board[row][col] self.board[row][col] = None return removed_tile def _find_connected_tiles(self, row, col, non_empty_tiles_not_visited: set) -> None: non_empty_tiles_not_visited.remove((row, col)) if (row > 0) and (self.board[row - 1][col] is not None) and ((row - 1, col) in non_empty_tiles_not_visited): self._find_connected_tiles(row - 1, col, non_empty_tiles_not_visited) if ( (row < self.board_size - 1) and (self.board[row + 1][col] is not None) and ((row + 1, col) in non_empty_tiles_not_visited) ): self._find_connected_tiles(row + 1, col, non_empty_tiles_not_visited) if (col > 0) and (self.board[row][col - 1] is not None) and ((row, col - 1) in non_empty_tiles_not_visited): self._find_connected_tiles(row, col - 1, non_empty_tiles_not_visited) if ( (col < self.board_size - 1) and (self.board[row][col + 1] is not None) and ((row, col + 1) in non_empty_tiles_not_visited) ): self._find_connected_tiles(row, col + 1, non_empty_tiles_not_visited) def _check_connected(self) -> Set[Tuple[int, int]]: first_tile = None non_empty_tiles = set() for row in range(self.board_size): for col in range(self.board_size): tile = self.board[row][col] if tile is not None: non_empty_tiles.add((row, col)) if first_tile is None: first_tile = (row, col) self._find_connected_tiles(first_tile[0], first_tile[1], non_empty_tiles) return non_empty_tiles def _check_valid_words(self) -> Set[Tuple[int, int]]: invalid_points = set() for row in range(self.board_size): current_word = "" for col in range(self.board_size): tile = self.board[row][col] if tile is None: # If we have a current word of length more than 1, check its validity if len(current_word) > 1: # If the word is not valid, add the points to the list of invalid points if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row, col - 1 - i)) # Now that we are done with our checks, we clear the current word to continue our search current_word = "" else: current_word += tile # The current word could go to the end of the board so we need to do an additional check if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row, self.board_size - 1 - i)) # Check down each column for col in range(self.board_size): current_word = "" for row in range(self.board_size): tile = self.board[row][col] # If the position is blank, it's time to check if tile is None: if len(current_word) > 1: if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((row - 1 - i, col)) current_word = "" else: current_word += tile if not self.word_manager.is_word(current_word): for i in range(len(current_word)): invalid_points.add((self.board_size - 1 - i, col)) return invalid_points def board_is_valid_crossword(self) -> Set[Tuple[int, int]]: unconnected_points = self._check_connected() if unconnected_points: return unconnected_points return self._check_valid_words() def shift_board_down(self) -> bool: for c in range(self.board_size): if self.board[self.board_size - 1][c] is not None: return False for r in range(self.board_size - 1, 0, -1): for c in range(self.board_size): self.board[r][c] = self.board[r - 1][c] for c in range(self.board_size): self.board[0][c] = None return True def shift_board_up(self) -> bool: for c in range(self.board_size): if self.board[0][c] is not None: return False for r in range(0, self.board_size - 1): for c in range(self.board_size): self.board[r][c] = self.board[r + 1][c] for c in range(self.board_size): self.board[self.board_size - 1][c] = None return True def shift_board_right(self) -> bool: for r in range(self.board_size): if self.board[r][self.board_size - 1] is not None: return False for c in range(self.board_size - 1, 0, -1): for r in range(self.board_size): self.board[r][c] = self.board[r][c - 1] for r in range(self.board_size): self.board[r][0] = None return True def shift_board_left(self) -> bool: for r in range(self.board_size): if self.board[r][0] is not None: return False for c in range(0, self.board_size - 1): for r in range(self.board_size): self.board[r][c] = self.board[r][c + 1] for r in range(self.board_size): self.board[r][self.board_size - 1] = None return True def get_json(self) -> Dict[str, object]: return {"board": self.board}

true

1c2b236d2d55b6a39af3ed7fa660f24bc4e4454c

18,818

py

Python

log_complete/model_390.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

log_complete/model_390.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

log_complete/model_390.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

# exported from PySB model 'model' from pysb import Model, Monomer, Parameter, Expression, Compartment, Rule, Observable, Initial, MatchOnce, Annotation, ANY, WILD Model() Monomer('Ligand', ['Receptor']) Monomer('ParpU', ['C3A']) Monomer('C8A', ['BidU', 'C3pro']) Monomer('SmacM', ['BaxA']) Monomer('BaxM', ['BidM', 'BaxA']) Monomer('Apop', ['C3pro', 'Xiap']) Monomer('Fadd', ['Receptor', 'C8pro']) Monomer('SmacC', ['Xiap']) Monomer('ParpC') Monomer('Xiap', ['SmacC', 'Apop', 'C3A']) Monomer('C9') Monomer('C3ub') Monomer('C8pro', ['Fadd', 'C6A']) Monomer('C6A', ['C8pro']) Monomer('C3pro', ['Apop', 'C8A']) Monomer('CytoCM', ['BaxA']) Monomer('CytoCC') Monomer('BaxA', ['BaxM', 'BaxA_1', 'BaxA_2', 'SmacM', 'CytoCM']) Monomer('ApafI') Monomer('BidU', ['C8A']) Monomer('BidT') Monomer('C3A', ['Xiap', 'ParpU', 'C6pro']) Monomer('ApafA') Monomer('BidM', ['BaxM']) Monomer('Receptor', ['Ligand', 'Fadd']) Monomer('C6pro', ['C3A']) Parameter('bind_0_Ligand_binder_Receptor_binder_target_2kf', 1.0) Parameter('bind_0_Ligand_binder_Receptor_binder_target_1kr', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_2kf', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_1kr', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr', 1.0) Parameter('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr', 1.0) Parameter('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kf', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kr', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr', 1.0) Parameter('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr', 1.0) Parameter('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc', 1.0) Parameter('pore_formation_0_BaxA_pore_2kf', 1.0) Parameter('pore_formation_0_BaxA_pore_1kr', 1.0) Parameter('pore_formation_1_BaxA_pore_2kf', 1.0) Parameter('pore_formation_1_BaxA_pore_1kr', 1.0) Parameter('pore_formation_2_BaxA_pore_2kf', 1.0) Parameter('pore_formation_2_BaxA_pore_1kr', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr', 1.0) Parameter('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('Ligand_0', 1000.0) Parameter('ParpU_0', 1000000.0) Parameter('C8A_0', 0.0) Parameter('SmacM_0', 100000.0) Parameter('BaxM_0', 40000.0) Parameter('Apop_0', 0.0) Parameter('Fadd_0', 130000.0) Parameter('SmacC_0', 0.0) Parameter('ParpC_0', 0.0) Parameter('Xiap_0', 97500.0) Parameter('C9_0', 100000.0) Parameter('C3ub_0', 0.0) Parameter('C8pro_0', 130000.0) Parameter('C6A_0', 0.0) Parameter('C3pro_0', 21000.0) Parameter('CytoCM_0', 500000.0) Parameter('CytoCC_0', 0.0) Parameter('BaxA_0', 0.0) Parameter('ApafI_0', 100000.0) Parameter('BidU_0', 171000.0) Parameter('BidT_0', 0.0) Parameter('C3A_0', 0.0) Parameter('ApafA_0', 0.0) Parameter('BidM_0', 0.0) Parameter('Receptor_0', 100.0) Parameter('C6pro_0', 100.0) Observable('Ligand_obs', Ligand()) Observable('ParpU_obs', ParpU()) Observable('C8A_obs', C8A()) Observable('SmacM_obs', SmacM()) Observable('BaxM_obs', BaxM()) Observable('Apop_obs', Apop()) Observable('Fadd_obs', Fadd()) Observable('SmacC_obs', SmacC()) Observable('ParpC_obs', ParpC()) Observable('Xiap_obs', Xiap()) Observable('C9_obs', C9()) Observable('C3ub_obs', C3ub()) Observable('C8pro_obs', C8pro()) Observable('C6A_obs', C6A()) Observable('C3pro_obs', C3pro()) Observable('CytoCM_obs', CytoCM()) Observable('CytoCC_obs', CytoCC()) Observable('BaxA_obs', BaxA()) Observable('ApafI_obs', ApafI()) Observable('BidU_obs', BidU()) Observable('BidT_obs', BidT()) Observable('C3A_obs', C3A()) Observable('ApafA_obs', ApafA()) Observable('BidM_obs', BidM()) Observable('Receptor_obs', Receptor()) Observable('C6pro_obs', C6pro()) Rule('bind_0_Ligand_binder_Receptor_binder_target', Ligand(Receptor=None) + Receptor(Ligand=None, Fadd=None) | Ligand(Receptor=1) % Receptor(Ligand=1, Fadd=None), bind_0_Ligand_binder_Receptor_binder_target_2kf, bind_0_Ligand_binder_Receptor_binder_target_1kr) Rule('bind_0_Receptor_binder_Fadd_binder_target', Receptor(Ligand=ANY, Fadd=None) + Fadd(Receptor=None, C8pro=None) | Receptor(Ligand=ANY, Fadd=1) % Fadd(Receptor=1, C8pro=None), bind_0_Receptor_binder_Fadd_binder_target_2kf, bind_0_Receptor_binder_Fadd_binder_target_1kr) Rule('substrate_binding_0_Fadd_catalyzer_C8pro_substrate', Fadd(Receptor=ANY, C8pro=None) + C8pro(Fadd=None, C6A=None) | Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None), substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf, substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr) Rule('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product', Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None) >> Fadd(Receptor=ANY, C8pro=None) + C8A(BidU=None, C3pro=None), catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc) Rule('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=None, C3pro=None) + BidU(C8A=None) | C8A(BidU=1, C3pro=None) % BidU(C8A=1), catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf, catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr) Rule('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=1, C3pro=None) % BidU(C8A=1) >> C8A(BidU=None, C3pro=None) + BidT(), catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc) Rule('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex', ApafI() + CytoCC() | ApafA(), conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf, conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr) Rule('inhibition_0_SmacC_inhibitor_Xiap_inh_target', SmacC(Xiap=None) + Xiap(SmacC=None, Apop=None, C3A=None) | SmacC(Xiap=1) % Xiap(SmacC=1, Apop=None, C3A=None), inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf, inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr) Rule('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex', ApafA() + C9() | Apop(C3pro=None, Xiap=None), conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf, conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr) Rule('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=None, Xiap=None) + C3pro(Apop=None, C8A=None) | Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None), catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None) >> Apop(C3pro=None, Xiap=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('inhibition_0_Xiap_inhibitor_Apop_inh_target', Xiap(SmacC=None, Apop=None, C3A=None) + Apop(C3pro=None, Xiap=None) | Xiap(SmacC=None, Apop=1, C3A=None) % Apop(C3pro=None, Xiap=1), inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf, inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr) Rule('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=None) + C3A(Xiap=None, ParpU=None, C6pro=None) | Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None), catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf, catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr) Rule('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None) >> Xiap(SmacC=None, Apop=None, C3A=None) + C3ub(), catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc) Rule('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=None, C6pro=None) + ParpU(C3A=None) | C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1), catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf, catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr) Rule('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + ParpC(), catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc) Rule('equilibration_0_BidT_equil_a_BidM_equil_b', BidT() | BidM(BaxM=None), equilibration_0_BidT_equil_a_BidM_equil_b_1kf, equilibration_0_BidT_equil_a_BidM_equil_b_1kr) Rule('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=None) + BaxM(BidM=None, BaxA=None) | BidM(BaxM=1) % BaxM(BidM=1, BaxA=None), catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf, catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr) Rule('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=1) % BaxM(BidM=1, BaxA=None) >> BidM(BaxM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc) Rule('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxM(BidM=None, BaxA=None) | BaxA(BaxM=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1), self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf, self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr) Rule('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1) >> BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc) Rule('pore_formation_0_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None), pore_formation_0_BaxA_pore_2kf, pore_formation_0_BaxA_pore_1kr) Rule('pore_formation_1_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None), pore_formation_1_BaxA_pore_2kf, pore_formation_1_BaxA_pore_1kr) Rule('pore_formation_2_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None), pore_formation_2_BaxA_pore_2kf, pore_formation_2_BaxA_pore_1kr) Rule('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacM(BaxA=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5), transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf, transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr) Rule('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5) >> BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacC(Xiap=None), transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc) Rule('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCM(BaxA=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5), transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf, transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr) Rule('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5) >> BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCC(), transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc) Rule('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=None) + C3pro(Apop=None, C8A=None) | C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1), catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1) >> C8A(BidU=None, C3pro=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=None) + C6pro(C3A=None) | C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1), catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf, catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr) Rule('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + C6A(C8pro=None), catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc) Rule('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=None) + C8pro(Fadd=None, C6A=None) | C6A(C8pro=1) % C8pro(Fadd=None, C6A=1), catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf, catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr) Rule('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=1) % C8pro(Fadd=None, C6A=1) >> C6A(C8pro=None) + C8A(BidU=None, C3pro=None), catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc) Initial(Ligand(Receptor=None), Ligand_0) Initial(ParpU(C3A=None), ParpU_0) Initial(C8A(BidU=None, C3pro=None), C8A_0) Initial(SmacM(BaxA=None), SmacM_0) Initial(BaxM(BidM=None, BaxA=None), BaxM_0) Initial(Apop(C3pro=None, Xiap=None), Apop_0) Initial(Fadd(Receptor=None, C8pro=None), Fadd_0) Initial(SmacC(Xiap=None), SmacC_0) Initial(ParpC(), ParpC_0) Initial(Xiap(SmacC=None, Apop=None, C3A=None), Xiap_0) Initial(C9(), C9_0) Initial(C3ub(), C3ub_0) Initial(C8pro(Fadd=None, C6A=None), C8pro_0) Initial(C6A(C8pro=None), C6A_0) Initial(C3pro(Apop=None, C8A=None), C3pro_0) Initial(CytoCM(BaxA=None), CytoCM_0) Initial(CytoCC(), CytoCC_0) Initial(BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), BaxA_0) Initial(ApafI(), ApafI_0) Initial(BidU(C8A=None), BidU_0) Initial(BidT(), BidT_0) Initial(C3A(Xiap=None, ParpU=None, C6pro=None), C3A_0) Initial(ApafA(), ApafA_0) Initial(BidM(BaxM=None), BidM_0) Initial(Receptor(Ligand=None, Fadd=None), Receptor_0) Initial(C6pro(C3A=None), C6pro_0)

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from pysb import Model, Monomer, Parameter, Expression, Compartment, Rule, Observable, Initial, MatchOnce, Annotation, ANY, WILD Model() Monomer('Ligand', ['Receptor']) Monomer('ParpU', ['C3A']) Monomer('C8A', ['BidU', 'C3pro']) Monomer('SmacM', ['BaxA']) Monomer('BaxM', ['BidM', 'BaxA']) Monomer('Apop', ['C3pro', 'Xiap']) Monomer('Fadd', ['Receptor', 'C8pro']) Monomer('SmacC', ['Xiap']) Monomer('ParpC') Monomer('Xiap', ['SmacC', 'Apop', 'C3A']) Monomer('C9') Monomer('C3ub') Monomer('C8pro', ['Fadd', 'C6A']) Monomer('C6A', ['C8pro']) Monomer('C3pro', ['Apop', 'C8A']) Monomer('CytoCM', ['BaxA']) Monomer('CytoCC') Monomer('BaxA', ['BaxM', 'BaxA_1', 'BaxA_2', 'SmacM', 'CytoCM']) Monomer('ApafI') Monomer('BidU', ['C8A']) Monomer('BidT') Monomer('C3A', ['Xiap', 'ParpU', 'C6pro']) Monomer('ApafA') Monomer('BidM', ['BaxM']) Monomer('Receptor', ['Ligand', 'Fadd']) Monomer('C6pro', ['C3A']) Parameter('bind_0_Ligand_binder_Receptor_binder_target_2kf', 1.0) Parameter('bind_0_Ligand_binder_Receptor_binder_target_1kr', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_2kf', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_1kr', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr', 1.0) Parameter('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr', 1.0) Parameter('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kf', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kr', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr', 1.0) Parameter('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr', 1.0) Parameter('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc', 1.0) Parameter('pore_formation_0_BaxA_pore_2kf', 1.0) Parameter('pore_formation_0_BaxA_pore_1kr', 1.0) Parameter('pore_formation_1_BaxA_pore_2kf', 1.0) Parameter('pore_formation_1_BaxA_pore_1kr', 1.0) Parameter('pore_formation_2_BaxA_pore_2kf', 1.0) Parameter('pore_formation_2_BaxA_pore_1kr', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr', 1.0) Parameter('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('Ligand_0', 1000.0) Parameter('ParpU_0', 1000000.0) Parameter('C8A_0', 0.0) Parameter('SmacM_0', 100000.0) Parameter('BaxM_0', 40000.0) Parameter('Apop_0', 0.0) Parameter('Fadd_0', 130000.0) Parameter('SmacC_0', 0.0) Parameter('ParpC_0', 0.0) Parameter('Xiap_0', 97500.0) Parameter('C9_0', 100000.0) Parameter('C3ub_0', 0.0) Parameter('C8pro_0', 130000.0) Parameter('C6A_0', 0.0) Parameter('C3pro_0', 21000.0) Parameter('CytoCM_0', 500000.0) Parameter('CytoCC_0', 0.0) Parameter('BaxA_0', 0.0) Parameter('ApafI_0', 100000.0) Parameter('BidU_0', 171000.0) Parameter('BidT_0', 0.0) Parameter('C3A_0', 0.0) Parameter('ApafA_0', 0.0) Parameter('BidM_0', 0.0) Parameter('Receptor_0', 100.0) Parameter('C6pro_0', 100.0) Observable('Ligand_obs', Ligand()) Observable('ParpU_obs', ParpU()) Observable('C8A_obs', C8A()) Observable('SmacM_obs', SmacM()) Observable('BaxM_obs', BaxM()) Observable('Apop_obs', Apop()) Observable('Fadd_obs', Fadd()) Observable('SmacC_obs', SmacC()) Observable('ParpC_obs', ParpC()) Observable('Xiap_obs', Xiap()) Observable('C9_obs', C9()) Observable('C3ub_obs', C3ub()) Observable('C8pro_obs', C8pro()) Observable('C6A_obs', C6A()) Observable('C3pro_obs', C3pro()) Observable('CytoCM_obs', CytoCM()) Observable('CytoCC_obs', CytoCC()) Observable('BaxA_obs', BaxA()) Observable('ApafI_obs', ApafI()) Observable('BidU_obs', BidU()) Observable('BidT_obs', BidT()) Observable('C3A_obs', C3A()) Observable('ApafA_obs', ApafA()) Observable('BidM_obs', BidM()) Observable('Receptor_obs', Receptor()) Observable('C6pro_obs', C6pro()) Rule('bind_0_Ligand_binder_Receptor_binder_target', Ligand(Receptor=None) + Receptor(Ligand=None, Fadd=None) | Ligand(Receptor=1) % Receptor(Ligand=1, Fadd=None), bind_0_Ligand_binder_Receptor_binder_target_2kf, bind_0_Ligand_binder_Receptor_binder_target_1kr) Rule('bind_0_Receptor_binder_Fadd_binder_target', Receptor(Ligand=ANY, Fadd=None) + Fadd(Receptor=None, C8pro=None) | Receptor(Ligand=ANY, Fadd=1) % Fadd(Receptor=1, C8pro=None), bind_0_Receptor_binder_Fadd_binder_target_2kf, bind_0_Receptor_binder_Fadd_binder_target_1kr) Rule('substrate_binding_0_Fadd_catalyzer_C8pro_substrate', Fadd(Receptor=ANY, C8pro=None) + C8pro(Fadd=None, C6A=None) | Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None), substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf, substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr) Rule('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product', Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None) >> Fadd(Receptor=ANY, C8pro=None) + C8A(BidU=None, C3pro=None), catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc) Rule('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=None, C3pro=None) + BidU(C8A=None) | C8A(BidU=1, C3pro=None) % BidU(C8A=1), catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf, catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr) Rule('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=1, C3pro=None) % BidU(C8A=1) >> C8A(BidU=None, C3pro=None) + BidT(), catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc) Rule('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex', ApafI() + CytoCC() | ApafA(), conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf, conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr) Rule('inhibition_0_SmacC_inhibitor_Xiap_inh_target', SmacC(Xiap=None) + Xiap(SmacC=None, Apop=None, C3A=None) | SmacC(Xiap=1) % Xiap(SmacC=1, Apop=None, C3A=None), inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf, inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr) Rule('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex', ApafA() + C9() | Apop(C3pro=None, Xiap=None), conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf, conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr) Rule('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=None, Xiap=None) + C3pro(Apop=None, C8A=None) | Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None), catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None) >> Apop(C3pro=None, Xiap=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('inhibition_0_Xiap_inhibitor_Apop_inh_target', Xiap(SmacC=None, Apop=None, C3A=None) + Apop(C3pro=None, Xiap=None) | Xiap(SmacC=None, Apop=1, C3A=None) % Apop(C3pro=None, Xiap=1), inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf, inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr) Rule('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=None) + C3A(Xiap=None, ParpU=None, C6pro=None) | Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None), catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf, catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr) Rule('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None) >> Xiap(SmacC=None, Apop=None, C3A=None) + C3ub(), catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc) Rule('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=None, C6pro=None) + ParpU(C3A=None) | C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1), catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf, catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr) Rule('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + ParpC(), catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc) Rule('equilibration_0_BidT_equil_a_BidM_equil_b', BidT() | BidM(BaxM=None), equilibration_0_BidT_equil_a_BidM_equil_b_1kf, equilibration_0_BidT_equil_a_BidM_equil_b_1kr) Rule('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=None) + BaxM(BidM=None, BaxA=None) | BidM(BaxM=1) % BaxM(BidM=1, BaxA=None), catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf, catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr) Rule('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=1) % BaxM(BidM=1, BaxA=None) >> BidM(BaxM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc) Rule('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxM(BidM=None, BaxA=None) | BaxA(BaxM=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1), self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf, self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr) Rule('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1) >> BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc) Rule('pore_formation_0_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None), pore_formation_0_BaxA_pore_2kf, pore_formation_0_BaxA_pore_1kr) Rule('pore_formation_1_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None), pore_formation_1_BaxA_pore_2kf, pore_formation_1_BaxA_pore_1kr) Rule('pore_formation_2_BaxA_pore', BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None), pore_formation_2_BaxA_pore_2kf, pore_formation_2_BaxA_pore_1kr) Rule('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacM(BaxA=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5), transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf, transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr) Rule('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5) >> BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacC(Xiap=None), transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc) Rule('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCM(BaxA=None) | BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5), transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf, transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr) Rule('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5) >> BaxA(BaxM=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCC(), transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc) Rule('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=None) + C3pro(Apop=None, C8A=None) | C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1), catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1) >> C8A(BidU=None, C3pro=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=None) + C6pro(C3A=None) | C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1), catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf, catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr) Rule('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + C6A(C8pro=None), catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc) Rule('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=None) + C8pro(Fadd=None, C6A=None) | C6A(C8pro=1) % C8pro(Fadd=None, C6A=1), catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf, catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr) Rule('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=1) % C8pro(Fadd=None, C6A=1) >> C6A(C8pro=None) + C8A(BidU=None, C3pro=None), catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc) Initial(Ligand(Receptor=None), Ligand_0) Initial(ParpU(C3A=None), ParpU_0) Initial(C8A(BidU=None, C3pro=None), C8A_0) Initial(SmacM(BaxA=None), SmacM_0) Initial(BaxM(BidM=None, BaxA=None), BaxM_0) Initial(Apop(C3pro=None, Xiap=None), Apop_0) Initial(Fadd(Receptor=None, C8pro=None), Fadd_0) Initial(SmacC(Xiap=None), SmacC_0) Initial(ParpC(), ParpC_0) Initial(Xiap(SmacC=None, Apop=None, C3A=None), Xiap_0) Initial(C9(), C9_0) Initial(C3ub(), C3ub_0) Initial(C8pro(Fadd=None, C6A=None), C8pro_0) Initial(C6A(C8pro=None), C6A_0) Initial(C3pro(Apop=None, C8A=None), C3pro_0) Initial(CytoCM(BaxA=None), CytoCM_0) Initial(CytoCC(), CytoCC_0) Initial(BaxA(BaxM=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), BaxA_0) Initial(ApafI(), ApafI_0) Initial(BidU(C8A=None), BidU_0) Initial(BidT(), BidT_0) Initial(C3A(Xiap=None, ParpU=None, C6pro=None), C3A_0) Initial(ApafA(), ApafA_0) Initial(BidM(BaxM=None), BidM_0) Initial(Receptor(Ligand=None, Fadd=None), Receptor_0) Initial(C6pro(C3A=None), C6pro_0)

true

1c2b26105539ec751d049b502c83a687b11e6631

1,062

py

Python

gmm_code/plot_var_vs_clip.py

DPBayes/DP-MCMC-NeurIPS2019

bf5349835b2044135749ea6dbedea2e310d7d2f2

[ "MIT" ]

1

2021-06-29T00:35:10.000Z

gmm_code/plot_var_vs_clip.py

DPBayes/DP-MCMC-NeurIPS2019

bf5349835b2044135749ea6dbedea2e310d7d2f2

[ "MIT" ]

null

gmm_code/plot_var_vs_clip.py

DPBayes/DP-MCMC-NeurIPS2019

bf5349835b2044135749ea6dbedea2e310d7d2f2

[ "MIT" ]

1

2021-06-29T00:35:14.000Z

import numpy as np import pickle import matplotlib.pyplot as plt from plot_path import path def plot_fig3(fname): fontsize = 25 figsize = (8.5,6) temped_results = pickle.load(open(fname, 'rb')) temped_dp_mcmc_params = temped_results[0] temped_chain = temped_results[1] temped_privacy_params = temped_results[2] # Plot clipped proportion vs. proposal variance prop_vars = temped_dp_mcmc_params['prop_vars'] clip_counts = temped_dp_mcmc_params['clip_counts'] T = temped_dp_mcmc_params['T'] batch_size = temped_dp_mcmc_params['B'] plt.figure(figsize=figsize) plt.plot(prop_vars, clip_counts.sum(1)/T/batch_size) plt.title('Average proportion of \n clipped llr vs. proposal variance', fontsize=fontsize) plt.xlabel(r'$\sigma^2$', fontsize=fontsize) plt.ylabel(r'$\frac{\#(clipped)}{Tb}$', fontsize=fontsize) plt.setp(plt.gca().get_xticklabels(), fontsize=fontsize-1, rotation=45) plt.setp(plt.gca().get_yticklabels(), fontsize=fontsize-1) plt.tight_layout() plt.savefig(path+'prop_vs_clip.pdf',format='pdf', bbox_inches = 'tight') plt.close()

36.62069

91

0.76177

import numpy as np import pickle import matplotlib.pyplot as plt from plot_path import path def plot_fig3(fname): fontsize = 25 figsize = (8.5,6) temped_results = pickle.load(open(fname, 'rb')) temped_dp_mcmc_params = temped_results[0] temped_chain = temped_results[1] temped_privacy_params = temped_results[2] prop_vars = temped_dp_mcmc_params['prop_vars'] clip_counts = temped_dp_mcmc_params['clip_counts'] T = temped_dp_mcmc_params['T'] batch_size = temped_dp_mcmc_params['B'] plt.figure(figsize=figsize) plt.plot(prop_vars, clip_counts.sum(1)/T/batch_size) plt.title('Average proportion of \n clipped llr vs. proposal variance', fontsize=fontsize) plt.xlabel(r'$\sigma^2$', fontsize=fontsize) plt.ylabel(r'$\frac{\#(clipped)}{Tb}$', fontsize=fontsize) plt.setp(plt.gca().get_xticklabels(), fontsize=fontsize-1, rotation=45) plt.setp(plt.gca().get_yticklabels(), fontsize=fontsize-1) plt.tight_layout() plt.savefig(path+'prop_vs_clip.pdf',format='pdf', bbox_inches = 'tight') plt.close()

true

1c2b2692ef1f831acee54c7ccdf84e973f1db901

2,579

py

Python

iot_control/app.py

DDizzzy79/ScienceFair

b41c96f74ab7e1db752e9985a740130ee1abeb1f

[ "MIT" ]

1

2021-12-01T15:12:59.000Z

iot_control/app.py

DDizzzy79/ScienceFair

b41c96f74ab7e1db752e9985a740130ee1abeb1f

[ "MIT" ]

null

iot_control/app.py

DDizzzy79/ScienceFair

b41c96f74ab7e1db752e9985a740130ee1abeb1f

[ "MIT" ]

null

from flask import Flask,render_template import RPi.GPIO as GPIO import time import lcd1602 as lcd import time from luma.led_matrix.device import * from luma.core.interface.serial import spi, noop from luma.core.render import canvas from luma.core.legacy import text serial = spi(port=0, device=0, gpio=noop()) device = max7219(serial, cascaded=1 or 1, block_orientation=0,rotate=0 or 0, blocks_arranged_in_reverse_order=False) id = 1 GPIO.setmode(GPIO.BCM) GPIO.setup(20,GPIO.OUT) GPIO.setwarnings(False) lcd.init_lcd() time.sleep(1) lcd.turn_light(1) app = Flask(__name__) @app.route("/") def main(): return render_template("main.html") @app.route("/time") def on(): global id id = 1 '''time''' lcd.clear_lcd() while id!=0: nowtime = time.strftime('%m-%d %H:%M:%S',time.localtime(time.time())) hourtime = time.strftime('%H',time.localtime(time.time())) mintime = time.strftime('%M',time.localtime(time.time())) sectime = time.strftime('%S',time.localtime(time.time())) lcd.print_lcd(1,1,nowtime) if mintime == '59': if sectime == '00': lcd.turn_light(1) elif sectime == '59': lcd.turn_light(0) time.sleep(0.2) return render_template("main.html") @app.route("/print") def print(): global id id = 1 '''printHelloWorld''' lcd.clear_lcd() #GPIO.output(20,GPIO.LOW) lcd.print_lcd(0,0,"Hello World") return render_template("main.html") @app.route("/clear") def clear(): global id id = 0 lcd.clear_lcd() return render_template("main.html") @app.route("/left") def left(): for x in range(10): #print("drawing") for x in range(5): with canvas(device) as draw: text(draw, (0, 0), chr(27), fill="white") time.sleep(0.01) return render_template("main.html") @app.route("/right") def right(): for x in range(10): #print("drawing") for x in range(4): with canvas(device) as draw: text(draw, (0, 0), chr(26), fill="white") time.sleep(0.01) return render_template("main.html") @app.route("/line") def line(): for x in range(10): #print("drawing") for x in range(4): with canvas(device) as draw: text(draw, (0, 0), chr(24), fill="white") time.sleep(0.01) return render_template("main.html") if __name__=="__main__": app.run(host='0.0.0.0', port=8080, debug=True, threaded=True)

27.43617

116

0.597131

from flask import Flask,render_template import RPi.GPIO as GPIO import time import lcd1602 as lcd import time from luma.led_matrix.device import * from luma.core.interface.serial import spi, noop from luma.core.render import canvas from luma.core.legacy import text serial = spi(port=0, device=0, gpio=noop()) device = max7219(serial, cascaded=1 or 1, block_orientation=0,rotate=0 or 0, blocks_arranged_in_reverse_order=False) id = 1 GPIO.setmode(GPIO.BCM) GPIO.setup(20,GPIO.OUT) GPIO.setwarnings(False) lcd.init_lcd() time.sleep(1) lcd.turn_light(1) app = Flask(__name__) @app.route("/") def main(): return render_template("main.html") @app.route("/time") def on(): global id id = 1 lcd.clear_lcd() while id!=0: nowtime = time.strftime('%m-%d %H:%M:%S',time.localtime(time.time())) hourtime = time.strftime('%H',time.localtime(time.time())) mintime = time.strftime('%M',time.localtime(time.time())) sectime = time.strftime('%S',time.localtime(time.time())) lcd.print_lcd(1,1,nowtime) if mintime == '59': if sectime == '00': lcd.turn_light(1) elif sectime == '59': lcd.turn_light(0) time.sleep(0.2) return render_template("main.html") @app.route("/print") def print(): global id id = 1 lcd.clear_lcd() lcd.print_lcd(0,0,"Hello World") return render_template("main.html") @app.route("/clear") def clear(): global id id = 0 lcd.clear_lcd() return render_template("main.html") @app.route("/left") def left(): for x in range(10): for x in range(5): with canvas(device) as draw: text(draw, (0, 0), chr(27), fill="white") time.sleep(0.01) return render_template("main.html") @app.route("/right") def right(): for x in range(10): for x in range(4): with canvas(device) as draw: text(draw, (0, 0), chr(26), fill="white") time.sleep(0.01) return render_template("main.html") @app.route("/line") def line(): for x in range(10): for x in range(4): with canvas(device) as draw: text(draw, (0, 0), chr(24), fill="white") time.sleep(0.01) return render_template("main.html") if __name__=="__main__": app.run(host='0.0.0.0', port=8080, debug=True, threaded=True)

true

1c2b26e248bcb6fc179ce066242c7221429578e8

726

py

Python

touchdown/aws/kms/__init__.py

yaybu/touchdown

70ecda5191ce2d095bc074dcb23bfa1584464814

[ "Apache-2.0" ]

14

2015-01-05T18:18:04.000Z

2022-02-07T19:35:12.000Z

touchdown/aws/kms/__init__.py

yaybu/touchdown

70ecda5191ce2d095bc074dcb23bfa1584464814

[ "Apache-2.0" ]

106

2015-01-06T00:17:13.000Z

2019-09-07T00:35:32.000Z

touchdown/aws/kms/__init__.py

yaybu/touchdown

70ecda5191ce2d095bc074dcb23bfa1584464814

[ "Apache-2.0" ]

5

2015-01-30T10:18:24.000Z

2022-02-07T19:35:13.000Z

# Copyright 2015 Isotoma Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .alias import Alias from .grant import Grant from .key import Key from .wrapper import Wrapper __all__ = ["Alias", "Grant", "Key", "Wrapper"]

34.571429

74

0.757576

from .alias import Alias from .grant import Grant from .key import Key from .wrapper import Wrapper __all__ = ["Alias", "Grant", "Key", "Wrapper"]

true

1c2b28b316b72deb738bf4f5837b485b63c49ba7

490

py

Python

fairseq/__init__.py

beichao1314/fairseq

b1521f962e4ca670311c0cd0c8b1dadf310cb242

[ "BSD-3-Clause" ]

140

2019-06-10T04:02:07.000Z

2022-03-22T11:08:27.000Z

fairseq/__init__.py

beichao1314/fairseq

b1521f962e4ca670311c0cd0c8b1dadf310cb242

[ "BSD-3-Clause" ]

7

2019-04-24T09:07:06.000Z

2022-03-28T21:58:04.000Z

fairseq/__init__.py

beichao1314/fairseq

b1521f962e4ca670311c0cd0c8b1dadf310cb242

[ "BSD-3-Clause" ]

11

2019-06-21T05:31:17.000Z

2022-01-04T02:20:46.000Z

# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. from .multiprocessing_pdb import pdb __all__ = ['pdb'] import fairseq.criterions import fairseq.models import fairseq.modules import fairseq.optim import fairseq.optim.lr_scheduler import fairseq.tasks

27.222222

78

0.791837

from .multiprocessing_pdb import pdb __all__ = ['pdb'] import fairseq.criterions import fairseq.models import fairseq.modules import fairseq.optim import fairseq.optim.lr_scheduler import fairseq.tasks

true

1c2b29ee506620ebaf32b44c7796a7ef747c0674

31

py

Python

learn.py

ishmandoo/FlapPyBird-Neural

2cd733db090dd972e698a5d951b90f76f091babe

[ "MIT" ]

2

2019-11-13T22:14:30.000Z

2019-11-13T22:15:24.000Z

learn.py

ishmandoo/FlapPyBird

2cd733db090dd972e698a5d951b90f76f091babe

[ "MIT" ]

null

learn.py

ishmandoo/FlapPyBird

2cd733db090dd972e698a5d951b90f76f091babe

[ "MIT" ]

null

from flappy import main main()

10.333333

23

0.774194

from flappy import main main()

true

1c2b2a1bbaf27d3f3d13070304854ad20431ba7a

2,517

py

Python

convert.py

vkhurana/calibre-convert

7ae6cf4ca5c40386131a5a32a40e0bd68ecf77ce

[ "MIT" ]

null

convert.py

vkhurana/calibre-convert

7ae6cf4ca5c40386131a5a32a40e0bd68ecf77ce

[ "MIT" ]

null

convert.py

vkhurana/calibre-convert

7ae6cf4ca5c40386131a5a32a40e0bd68ecf77ce

[ "MIT" ]

null

import time import subprocess import os import sys import pyinotify from os.path import exists from pyinotify import WatchManager, Notifier, ProcessEvent, EventsCodes def Monitor(path): class PClose(ProcessEvent): temp_folder = "temp" def process_IN_CLOSE(self, event): src_folder = event.path dest_folder = os.path.join(src_folder, self.temp_folder) src_file = event.name and os.path.join(event.path, event.name) or event.path dest_file_temp = os.path.join(dest_folder, event.name + ".mobi") print ("IN_CLOSE_WRITE event: " + src_file) print ("src_folder: " + src_folder) print ("dest_folder: " + dest_folder) print ("src_file: " + src_file) print ("dest_file_temp: " + dest_file_temp) if not exists(dest_folder): print("creating temp folder: " + dest_folder) os.mkdir(dest_folder) # we only really care about created events. 'modified' is another file_type = ".epub"; if src_file.endswith(file_type): # pathinfo = os.path.split(src_file) dest_file = src_file + ".mobi" dest_exists = exists(dest_file) if not dest_exists: print("Converting %s to %s" % (src_file, dest_file_temp)) cmd = "ebook-convert" + " \"" + src_file + "\" \"" + dest_file_temp + "\"" print("cmd: %s" % cmd) ret = subprocess.call(cmd, shell=True) print("ret: %d" % ret) if ret == 0: print("success converting. moving.") os.rename(dest_file_temp, dest_file) else: print("error converting " + src_file) else: print("Skipping. File %s exists" % dest_file) wm = WatchManager() notifier = Notifier(wm, PClose()) wm.add_watch(path, pyinotify.IN_CLOSE_WRITE) try: while 1: notifier.process_events() if notifier.check_events(): notifier.read_events() except KeyboardInterrupt: notifier.stop() return if __name__ == '__main__': try: path = "/target" except IndexError: print ("error") else: print("Watching: %s" % path) Monitor(path)

37.014706

95

0.528804

import time import subprocess import os import sys import pyinotify from os.path import exists from pyinotify import WatchManager, Notifier, ProcessEvent, EventsCodes def Monitor(path): class PClose(ProcessEvent): temp_folder = "temp" def process_IN_CLOSE(self, event): src_folder = event.path dest_folder = os.path.join(src_folder, self.temp_folder) src_file = event.name and os.path.join(event.path, event.name) or event.path dest_file_temp = os.path.join(dest_folder, event.name + ".mobi") print ("IN_CLOSE_WRITE event: " + src_file) print ("src_folder: " + src_folder) print ("dest_folder: " + dest_folder) print ("src_file: " + src_file) print ("dest_file_temp: " + dest_file_temp) if not exists(dest_folder): print("creating temp folder: " + dest_folder) os.mkdir(dest_folder) file_type = ".epub"; if src_file.endswith(file_type): dest_file = src_file + ".mobi" dest_exists = exists(dest_file) if not dest_exists: print("Converting %s to %s" % (src_file, dest_file_temp)) cmd = "ebook-convert" + " \"" + src_file + "\" \"" + dest_file_temp + "\"" print("cmd: %s" % cmd) ret = subprocess.call(cmd, shell=True) print("ret: %d" % ret) if ret == 0: print("success converting. moving.") os.rename(dest_file_temp, dest_file) else: print("error converting " + src_file) else: print("Skipping. File %s exists" % dest_file) wm = WatchManager() notifier = Notifier(wm, PClose()) wm.add_watch(path, pyinotify.IN_CLOSE_WRITE) try: while 1: notifier.process_events() if notifier.check_events(): notifier.read_events() except KeyboardInterrupt: notifier.stop() return if __name__ == '__main__': try: path = "/target" except IndexError: print ("error") else: print("Watching: %s" % path) Monitor(path)

true

1c2b2a7cd5acfc7949b666466fb33ee224cd6528

2,889

py

Python

genlog/helper.py

ilpan/GenerateLog

1cc9779870473e21e6b42112b17489c59792d5c1

[ "MIT" ]

null

genlog/helper.py

ilpan/GenerateLog

1cc9779870473e21e6b42112b17489c59792d5c1

[ "MIT" ]

null

genlog/helper.py

ilpan/GenerateLog

1cc9779870473e21e6b42112b17489c59792d5c1

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ @author : ilpan @contact : pna.dev@outlook.com @file : helper.py @desc : 1）指定造数据的 client 的个数 2）指定 client 造数据的时间间隔（秒） 3）指定需要收集的用户最大数目 4）指定 flume 的 host:port @time : 18-3-18 下午12:10 """ import argparse import sys from genlog import __description__, __version__ from genlog.exception import * class Helper: def __init__(self): self._client_num = 100 self._interval = 0 self._user_num = 10000 self._remote_host_list = [] self._show = False def help(self): parser = argparse.ArgumentParser(description=__description__) parser.add_argument('-v', '--version', action='store_true', help='output version and exit') parser.add_argument('-n', '--client_num', type=int, default=888, help='the num of client that generate logs') parser.add_argument('-i', '--interval', type=int, default=60000, help='the time(ms) that a client show wait before next generating logs') parser.add_argument('-u', '--user_num', type=int, default=10000, help='the num of user that will be collected') parser.add_argument('-l', '--remote_host_list', default="0.0.0.0:2018,0.0.0.0:2019,0.0.0.0:2020,0.0.0.0:2021", help='remote host list that we send logs to (format: ip:port,ip:port...)') parser.add_argument('-s', '--show', action='store_true', help='show send logs info') # get arguments args = parser.parse_args() if args.version: print('genlog: ', __version__) sys.exit(0) def get_ip_port(host): host = host.strip() try: ip = host.split(':')[0] port = int(host.split(':')[1]) return (ip, port) except IndexError: raise WrongFormatError("与标准格式ip:port不一致") if args.remote_host_list is not None: host_list = args.remote_host_list.split(',') try: self._remote_host_list = [get_ip_port(host) for host in host_list] except WrongFormatError as e: print(e) sys.exit(1) if args.show: self._show = True if args.interval <= 1000: print('interval must greater than 1000(ms)') sys.exit(2) self._client_num, self._interval, self._user_num = args.client_num, args.interval, args.user_num @property def client_num(self): return self._client_num @property def interval(self): return self._interval @property def user_num(self): return self._user_num @property def remote_host_list(self): return self._remote_host_list @property def show(self): return self._show

30.09375

119

0.581862

import argparse import sys from genlog import __description__, __version__ from genlog.exception import * class Helper: def __init__(self): self._client_num = 100 self._interval = 0 self._user_num = 10000 self._remote_host_list = [] self._show = False def help(self): parser = argparse.ArgumentParser(description=__description__) parser.add_argument('-v', '--version', action='store_true', help='output version and exit') parser.add_argument('-n', '--client_num', type=int, default=888, help='the num of client that generate logs') parser.add_argument('-i', '--interval', type=int, default=60000, help='the time(ms) that a client show wait before next generating logs') parser.add_argument('-u', '--user_num', type=int, default=10000, help='the num of user that will be collected') parser.add_argument('-l', '--remote_host_list', default="0.0.0.0:2018,0.0.0.0:2019,0.0.0.0:2020,0.0.0.0:2021", help='remote host list that we send logs to (format: ip:port,ip:port...)') parser.add_argument('-s', '--show', action='store_true', help='show send logs info') args = parser.parse_args() if args.version: print('genlog: ', __version__) sys.exit(0) def get_ip_port(host): host = host.strip() try: ip = host.split(':')[0] port = int(host.split(':')[1]) return (ip, port) except IndexError: raise WrongFormatError("与标准格式ip:port不一致") if args.remote_host_list is not None: host_list = args.remote_host_list.split(',') try: self._remote_host_list = [get_ip_port(host) for host in host_list] except WrongFormatError as e: print(e) sys.exit(1) if args.show: self._show = True if args.interval <= 1000: print('interval must greater than 1000(ms)') sys.exit(2) self._client_num, self._interval, self._user_num = args.client_num, args.interval, args.user_num @property def client_num(self): return self._client_num @property def interval(self): return self._interval @property def user_num(self): return self._user_num @property def remote_host_list(self): return self._remote_host_list @property def show(self): return self._show

true

1c2b2ac70dc685491a57826d0eb2dc253498f493

3,852

py

Python

GRNetDetector/utils/metrics.py

565353780/gr-net

7dedb326bd5f8e12e0f8aa39e1c728fe68f26f4f

[ "MIT" ]

null

GRNetDetector/utils/metrics.py

565353780/gr-net

7dedb326bd5f8e12e0f8aa39e1c728fe68f26f4f

[ "MIT" ]

null

GRNetDetector/utils/metrics.py

565353780/gr-net

7dedb326bd5f8e12e0f8aa39e1c728fe68f26f4f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Author: Haozhe Xie # @Date: 2019-08-08 14:31:30 # @Last Modified by: Haozhe Xie # @Last Modified time: 2020-05-25 09:13:32 # @Email: cshzxie@gmail.com import logging import open3d from GRNetDetector.extensions.chamfer_dist import ChamferDistance class Metrics(object): ITEMS = [{ 'name': 'F-Score', 'enabled': True, 'eval_func': 'cls._get_f_score', 'is_greater_better': True, 'init_value': 0 }, { 'name': 'ChamferDistance', 'enabled': True, 'eval_func': 'cls._get_chamfer_distance', 'eval_object': ChamferDistance(ignore_zeros=True), 'is_greater_better': False, 'init_value': 32767 }] @classmethod def get(cls, pred, gt): _items = cls.items() _values = [0] * len(_items) for i, item in enumerate(_items): eval_func = eval(item['eval_func']) _values[i] = eval_func(pred, gt) return _values @classmethod def items(cls): return [i for i in cls.ITEMS if i['enabled']] @classmethod def names(cls): _items = cls.items() return [i['name'] for i in _items] @classmethod def _get_f_score(cls, pred, gt, th=0.01): """References: https://github.com/lmb-freiburg/what3d/blob/master/util.py""" pred = cls._get_open3d_ptcloud(pred) gt = cls._get_open3d_ptcloud(gt) dist1 = pred.compute_point_cloud_distance(gt) dist2 = gt.compute_point_cloud_distance(pred) recall = float(sum(d < th for d in dist2)) / float(len(dist2)) precision = float(sum(d < th for d in dist1)) / float(len(dist1)) return 2 * recall * precision / (recall + precision) if recall + precision else 0 @classmethod def _get_open3d_ptcloud(cls, tensor): tensor = tensor.squeeze().cpu().numpy() ptcloud = open3d.geometry.PointCloud() ptcloud.points = open3d.utility.Vector3dVector(tensor) return ptcloud @classmethod def _get_chamfer_distance(cls, pred, gt): chamfer_distance = cls.ITEMS[1]['eval_object'] return chamfer_distance(pred, gt).item() * 1000 def __init__(self, metric_name, values): self._items = Metrics.items() self._values = [item['init_value'] for item in self._items] self.metric_name = metric_name if type(values).__name__ == 'list': self._values = values elif type(values).__name__ == 'dict': metric_indexes = {} for idx, item in enumerate(self._items): item_name = item['name'] metric_indexes[item_name] = idx for k, v in values.items(): if k not in metric_indexes: logging.warn('Ignore Metric[Name=%s] due to disability.' % k) continue self._values[metric_indexes[k]] = v else: raise Exception('Unsupported value type: %s' % type(values)) def state_dict(self): _dict = dict() for i in range(len(self._items)): item = self._items[i]['name'] value = self._values[i] _dict[item] = value return _dict def __repr__(self): return str(self.state_dict()) def better_than(self, other): if other is None: return True _index = -1 for i, _item in enumerate(self._items): if _item['name'] == self.metric_name: _index = i break if _index == -1: raise Exception('Invalid metric name to compare.') _metric = self._items[i] _value = self._values[_index] other_value = other._values[_index] return _value > other_value if _metric['is_greater_better'] else _value < other_value

31.317073

93

0.58541

import logging import open3d from GRNetDetector.extensions.chamfer_dist import ChamferDistance class Metrics(object): ITEMS = [{ 'name': 'F-Score', 'enabled': True, 'eval_func': 'cls._get_f_score', 'is_greater_better': True, 'init_value': 0 }, { 'name': 'ChamferDistance', 'enabled': True, 'eval_func': 'cls._get_chamfer_distance', 'eval_object': ChamferDistance(ignore_zeros=True), 'is_greater_better': False, 'init_value': 32767 }] @classmethod def get(cls, pred, gt): _items = cls.items() _values = [0] * len(_items) for i, item in enumerate(_items): eval_func = eval(item['eval_func']) _values[i] = eval_func(pred, gt) return _values @classmethod def items(cls): return [i for i in cls.ITEMS if i['enabled']] @classmethod def names(cls): _items = cls.items() return [i['name'] for i in _items] @classmethod def _get_f_score(cls, pred, gt, th=0.01): pred = cls._get_open3d_ptcloud(pred) gt = cls._get_open3d_ptcloud(gt) dist1 = pred.compute_point_cloud_distance(gt) dist2 = gt.compute_point_cloud_distance(pred) recall = float(sum(d < th for d in dist2)) / float(len(dist2)) precision = float(sum(d < th for d in dist1)) / float(len(dist1)) return 2 * recall * precision / (recall + precision) if recall + precision else 0 @classmethod def _get_open3d_ptcloud(cls, tensor): tensor = tensor.squeeze().cpu().numpy() ptcloud = open3d.geometry.PointCloud() ptcloud.points = open3d.utility.Vector3dVector(tensor) return ptcloud @classmethod def _get_chamfer_distance(cls, pred, gt): chamfer_distance = cls.ITEMS[1]['eval_object'] return chamfer_distance(pred, gt).item() * 1000 def __init__(self, metric_name, values): self._items = Metrics.items() self._values = [item['init_value'] for item in self._items] self.metric_name = metric_name if type(values).__name__ == 'list': self._values = values elif type(values).__name__ == 'dict': metric_indexes = {} for idx, item in enumerate(self._items): item_name = item['name'] metric_indexes[item_name] = idx for k, v in values.items(): if k not in metric_indexes: logging.warn('Ignore Metric[Name=%s] due to disability.' % k) continue self._values[metric_indexes[k]] = v else: raise Exception('Unsupported value type: %s' % type(values)) def state_dict(self): _dict = dict() for i in range(len(self._items)): item = self._items[i]['name'] value = self._values[i] _dict[item] = value return _dict def __repr__(self): return str(self.state_dict()) def better_than(self, other): if other is None: return True _index = -1 for i, _item in enumerate(self._items): if _item['name'] == self.metric_name: _index = i break if _index == -1: raise Exception('Invalid metric name to compare.') _metric = self._items[i] _value = self._values[_index] other_value = other._values[_index] return _value > other_value if _metric['is_greater_better'] else _value < other_value

true

1c2b2b4f373264cdeee202bfa427475f8fd2cdf8

12,601

py

Python

espresso/speech_train.py

beat-buesser/espresso

bd6ba1f7745c90a2c3c8ff0a0d7332efeebcc808

[ "MIT" ]

1

2021-01-08T02:51:16.000Z

espresso/speech_train.py

beat-buesser/espresso

bd6ba1f7745c90a2c3c8ff0a0d7332efeebcc808

[ "MIT" ]

null

espresso/speech_train.py

beat-buesser/espresso

bd6ba1f7745c90a2c3c8ff0a0d7332efeebcc808

[ "MIT" ]

1

2021-09-10T15:35:58.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) Yiming Wang # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Train a new model on one or across multiple GPUs. """ import logging import math import os import sys import numpy as np import torch from fairseq import ( checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils, ) from fairseq.data import iterators from fairseq.logging import meters, metrics, progress_bar from fairseq.model_parallel.megatron_trainer import MegatronTrainer from fairseq.trainer import Trainer logging.basicConfig( format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) logger = logging.getLogger("espresso.speech_train") def main(args): utils.import_user_module(args) assert ( args.max_tokens is not None or args.batch_size is not None ), "Must specify batch size either with --max-tokens or --batch-size" metrics.reset() np.random.seed(args.seed) utils.set_torch_seed(args.seed) if distributed_utils.is_master(args): checkpoint_utils.verify_checkpoint_directory(args.save_dir) # Print args logger.info(args) # Setup task, e.g., translation, language modeling, etc. task = tasks.setup_task(args) # Load valid dataset (we load training data below, based on the latest checkpoint) for valid_sub_split in args.valid_subset.split(","): task.load_dataset(valid_sub_split, combine=False, epoch=1) # Build model and criterion model = task.build_model(args) criterion = task.build_criterion(args) logger.info(model) logger.info("task: {} ({})".format(args.task, task.__class__.__name__)) logger.info("model: {} ({})".format(args.arch, model.__class__.__name__)) logger.info( "criterion: {} ({})".format(args.criterion, criterion.__class__.__name__) ) logger.info( "num. model params: {} (num. trained: {})".format( sum(p.numel() for p in model.parameters()), sum(p.numel() for p in model.parameters() if p.requires_grad), ) ) # (optionally) Configure quantization if args.quantization_config_path is not None: quantizer = quantization_utils.Quantizer( config_path=args.quantization_config_path, max_epoch=args.max_epoch, max_update=args.max_update, ) else: quantizer = None # Build trainer if args.model_parallel_size == 1: trainer = Trainer(args, task, model, criterion, quantizer) else: trainer = MegatronTrainer(args, task, model, criterion) logger.info( "training on {} devices (GPUs/TPUs)".format(args.distributed_world_size) ) logger.info( "max input frames per GPU = {} and max sentences per GPU = {}".format( args.max_tokens, args.batch_size ) ) # Load the latest checkpoint if one is available and restore the # corresponding train iterator extra_state, epoch_itr = checkpoint_utils.load_checkpoint( args, trainer, # don't cache epoch iterators for sharded datasets disable_iterator_cache=task.has_sharded_data("train"), ) # Train until the learning rate gets too small max_epoch = args.max_epoch or math.inf lr = trainer.get_lr() train_meter = meters.StopwatchMeter() train_meter.start() while lr > args.min_lr and epoch_itr.next_epoch_idx <= max_epoch: # train for one epoch valid_losses, should_stop = train(args, trainer, task, epoch_itr) if should_stop: break # only use first validation loss to update the learning rate lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) epoch_itr = trainer.get_train_iterator( epoch_itr.next_epoch_idx, # sharded data: get train iterator for next epoch load_dataset=task.has_sharded_data("train"), # don't cache epoch iterators for sharded datasets disable_iterator_cache=task.has_sharded_data("train"), ) train_meter.stop() logger.info("done training in {:.1f} seconds".format(train_meter.sum)) def should_stop_early(args, valid_loss): # skip check if no validation was done in the current epoch if valid_loss is None: return False if args.patience <= 0: return False def is_better(a, b): return a > b if args.maximize_best_checkpoint_metric else a < b prev_best = getattr(should_stop_early, "best", None) if prev_best is None or is_better(valid_loss, prev_best): should_stop_early.best = valid_loss should_stop_early.num_runs = 0 return False else: should_stop_early.num_runs += 1 if should_stop_early.num_runs >= args.patience: logger.info( "early stop since valid performance hasn't improved for last {} runs".format( args.patience ) ) return True else: return False @metrics.aggregate("train") def train(args, trainer, task, epoch_itr): """Train the model for one epoch and return validation losses.""" # Initialize data iterator itr = epoch_itr.next_epoch_itr( fix_batches_to_gpus=args.fix_batches_to_gpus, shuffle=(epoch_itr.next_epoch_idx > args.curriculum), ) update_freq = ( args.update_freq[epoch_itr.epoch - 1] if epoch_itr.epoch <= len(args.update_freq) else args.update_freq[-1] ) itr = iterators.GroupedIterator(itr, update_freq) if getattr(args, "tpu", False): itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, log_format=args.log_format, log_interval=args.log_interval, epoch=epoch_itr.epoch, tensorboard_logdir=( args.tensorboard_logdir if distributed_utils.is_master(args) else None ), default_log_format=("tqdm" if not args.no_progress_bar else "simple"), ) trainer.begin_epoch(epoch_itr.epoch) if hasattr(trainer.criterion, "set_epoch"): trainer.criterion.set_epoch(epoch_itr.epoch) valid_losses = [None] valid_subsets = args.valid_subset.split(",") should_stop = False num_updates = trainer.get_num_updates() for i, samples in enumerate(progress): with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( "train_step-%d" % i ): log_output = trainer.train_step(samples) if log_output is not None: # not OOM, overflow, ... # log mid-epoch stats num_updates = trainer.get_num_updates() if num_updates % args.log_interval == 0: stats = get_training_stats(metrics.get_smoothed_values("train_inner")) progress.log(stats, tag="train_inner", step=num_updates) # reset mid-epoch stats after each log interval # the end-of-epoch stats will still be preserved metrics.reset_meters("train_inner") # update the state prior stored in the model for cross-entropy training if hasattr(task, "update_state_prior"): task.update_state_prior(trainer.get_model()) end_of_epoch = not itr.has_next() valid_losses, should_stop = validate_and_save( args, trainer, task, epoch_itr, valid_subsets, end_of_epoch ) if should_stop: break # log end-of-epoch stats logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) stats = get_training_stats(metrics.get_smoothed_values("train")) progress.print(stats, tag="train", step=num_updates) # reset epoch-level meters metrics.reset_meters("train") return valid_losses, should_stop def validate_and_save(args, trainer, task, epoch_itr, valid_subsets, end_of_epoch): num_updates = trainer.get_num_updates() max_update = args.max_update or math.inf do_save = ( (end_of_epoch and epoch_itr.epoch % args.save_interval == 0) or num_updates >= max_update or ( args.save_interval_updates > 0 and num_updates > 0 and num_updates % args.save_interval_updates == 0 and num_updates >= args.validate_after_updates ) ) do_validate = ( (not end_of_epoch and do_save) # validate during mid-epoch saves or (end_of_epoch and epoch_itr.epoch % args.validate_interval == 0) or num_updates >= max_update or ( args.validate_interval_updates > 0 and num_updates > 0 and num_updates % args.validate_interval_updates == 0 ) ) and not args.disable_validation # Validate valid_losses = [None] if do_validate: valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets) # Stopping conditions should_stop = ( should_stop_early(args, valid_losses[0]) or num_updates >= max_update or ( args.stop_time_hours > 0 and trainer.cumulative_training_time() / (60 * 60) > args.stop_time_hours ) ) # Save checkpoint if do_save or should_stop: logger.info("begin save checkpoint") checkpoint_utils.save_checkpoint(args, trainer, epoch_itr, valid_losses[0]) return valid_losses, should_stop def get_training_stats(stats): stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) return stats def validate(args, trainer, task, epoch_itr, subsets): """Evaluate the model on the validation set(s) and return the losses.""" if args.fixed_validation_seed is not None: # set fixed seed for every validation utils.set_torch_seed(args.fixed_validation_seed) trainer.begin_valid_epoch(epoch_itr.epoch) valid_losses = [] for subset in subsets: logger.info('begin validation on "{}" subset'.format(subset)) # Initialize data iterator itr = trainer.get_valid_iterator(subset).next_epoch_itr(shuffle=False) if getattr(args, "tpu", False): itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, log_format=args.log_format, log_interval=args.log_interval, epoch=epoch_itr.epoch, prefix=f"valid on '{subset}' subset", tensorboard_logdir=( args.tensorboard_logdir if distributed_utils.is_master(args) else None ), default_log_format=("tqdm" if not args.no_progress_bar else "simple"), ) # create a new root metrics aggregator so validation metrics # don't pollute other aggregators (e.g., train meters) with metrics.aggregate(new_root=True) as agg: for sample in progress: trainer.valid_step(sample) # log validation stats stats = get_valid_stats(args, trainer, agg.get_smoothed_values()) progress.print(stats, tag=subset, step=trainer.get_num_updates()) valid_losses.append(stats[args.best_checkpoint_metric]) return valid_losses def get_valid_stats(args, trainer, stats): stats["num_updates"] = trainer.get_num_updates() if hasattr(checkpoint_utils.save_checkpoint, "best"): key = "best_{0}".format(args.best_checkpoint_metric) best_function = max if args.maximize_best_checkpoint_metric else min stats[key] = best_function( checkpoint_utils.save_checkpoint.best, stats[args.best_checkpoint_metric] ) return stats def print_options_meaning_changes(args): """Options that have different meanings than those in the translation task are explained here. """ logger.info("--max-tokens is the maximum number of input frames in a batch") def cli_main(modify_parser=None): parser = options.get_training_parser() args = options.parse_args_and_arch(parser, modify_parser=modify_parser) print_options_meaning_changes(args) if args.profile: with torch.cuda.profiler.profile(): with torch.autograd.profiler.emit_nvtx(): distributed_utils.call_main(args, main) else: distributed_utils.call_main(args, main) if __name__ == "__main__": cli_main()

33.96496

93

0.660821

import logging import math import os import sys import numpy as np import torch from fairseq import ( checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils, ) from fairseq.data import iterators from fairseq.logging import meters, metrics, progress_bar from fairseq.model_parallel.megatron_trainer import MegatronTrainer from fairseq.trainer import Trainer logging.basicConfig( format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) logger = logging.getLogger("espresso.speech_train") def main(args): utils.import_user_module(args) assert ( args.max_tokens is not None or args.batch_size is not None ), "Must specify batch size either with --max-tokens or --batch-size" metrics.reset() np.random.seed(args.seed) utils.set_torch_seed(args.seed) if distributed_utils.is_master(args): checkpoint_utils.verify_checkpoint_directory(args.save_dir) logger.info(args) task = tasks.setup_task(args) for valid_sub_split in args.valid_subset.split(","): task.load_dataset(valid_sub_split, combine=False, epoch=1) model = task.build_model(args) criterion = task.build_criterion(args) logger.info(model) logger.info("task: {} ({})".format(args.task, task.__class__.__name__)) logger.info("model: {} ({})".format(args.arch, model.__class__.__name__)) logger.info( "criterion: {} ({})".format(args.criterion, criterion.__class__.__name__) ) logger.info( "num. model params: {} (num. trained: {})".format( sum(p.numel() for p in model.parameters()), sum(p.numel() for p in model.parameters() if p.requires_grad), ) ) if args.quantization_config_path is not None: quantizer = quantization_utils.Quantizer( config_path=args.quantization_config_path, max_epoch=args.max_epoch, max_update=args.max_update, ) else: quantizer = None if args.model_parallel_size == 1: trainer = Trainer(args, task, model, criterion, quantizer) else: trainer = MegatronTrainer(args, task, model, criterion) logger.info( "training on {} devices (GPUs/TPUs)".format(args.distributed_world_size) ) logger.info( "max input frames per GPU = {} and max sentences per GPU = {}".format( args.max_tokens, args.batch_size ) ) extra_state, epoch_itr = checkpoint_utils.load_checkpoint( args, trainer, disable_iterator_cache=task.has_sharded_data("train"), ) # Train until the learning rate gets too small max_epoch = args.max_epoch or math.inf lr = trainer.get_lr() train_meter = meters.StopwatchMeter() train_meter.start() while lr > args.min_lr and epoch_itr.next_epoch_idx <= max_epoch: # train for one epoch valid_losses, should_stop = train(args, trainer, task, epoch_itr) if should_stop: break # only use first validation loss to update the learning rate lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) epoch_itr = trainer.get_train_iterator( epoch_itr.next_epoch_idx, # sharded data: get train iterator for next epoch load_dataset=task.has_sharded_data("train"), # don't cache epoch iterators for sharded datasets disable_iterator_cache=task.has_sharded_data("train"), ) train_meter.stop() logger.info("done training in {:.1f} seconds".format(train_meter.sum)) def should_stop_early(args, valid_loss): if valid_loss is None: return False if args.patience <= 0: return False def is_better(a, b): return a > b if args.maximize_best_checkpoint_metric else a < b prev_best = getattr(should_stop_early, "best", None) if prev_best is None or is_better(valid_loss, prev_best): should_stop_early.best = valid_loss should_stop_early.num_runs = 0 return False else: should_stop_early.num_runs += 1 if should_stop_early.num_runs >= args.patience: logger.info( "early stop since valid performance hasn't improved for last {} runs".format( args.patience ) ) return True else: return False @metrics.aggregate("train") def train(args, trainer, task, epoch_itr): # Initialize data iterator itr = epoch_itr.next_epoch_itr( fix_batches_to_gpus=args.fix_batches_to_gpus, shuffle=(epoch_itr.next_epoch_idx > args.curriculum), ) update_freq = ( args.update_freq[epoch_itr.epoch - 1] if epoch_itr.epoch <= len(args.update_freq) else args.update_freq[-1] ) itr = iterators.GroupedIterator(itr, update_freq) if getattr(args, "tpu", False): itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, log_format=args.log_format, log_interval=args.log_interval, epoch=epoch_itr.epoch, tensorboard_logdir=( args.tensorboard_logdir if distributed_utils.is_master(args) else None ), default_log_format=("tqdm" if not args.no_progress_bar else "simple"), ) trainer.begin_epoch(epoch_itr.epoch) if hasattr(trainer.criterion, "set_epoch"): trainer.criterion.set_epoch(epoch_itr.epoch) valid_losses = [None] valid_subsets = args.valid_subset.split(",") should_stop = False num_updates = trainer.get_num_updates() for i, samples in enumerate(progress): with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( "train_step-%d" % i ): log_output = trainer.train_step(samples) if log_output is not None: # not OOM, overflow, ... # log mid-epoch stats num_updates = trainer.get_num_updates() if num_updates % args.log_interval == 0: stats = get_training_stats(metrics.get_smoothed_values("train_inner")) progress.log(stats, tag="train_inner", step=num_updates) # reset mid-epoch stats after each log interval # the end-of-epoch stats will still be preserved metrics.reset_meters("train_inner") # update the state prior stored in the model for cross-entropy training if hasattr(task, "update_state_prior"): task.update_state_prior(trainer.get_model()) end_of_epoch = not itr.has_next() valid_losses, should_stop = validate_and_save( args, trainer, task, epoch_itr, valid_subsets, end_of_epoch ) if should_stop: break # log end-of-epoch stats logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) stats = get_training_stats(metrics.get_smoothed_values("train")) progress.print(stats, tag="train", step=num_updates) # reset epoch-level meters metrics.reset_meters("train") return valid_losses, should_stop def validate_and_save(args, trainer, task, epoch_itr, valid_subsets, end_of_epoch): num_updates = trainer.get_num_updates() max_update = args.max_update or math.inf do_save = ( (end_of_epoch and epoch_itr.epoch % args.save_interval == 0) or num_updates >= max_update or ( args.save_interval_updates > 0 and num_updates > 0 and num_updates % args.save_interval_updates == 0 and num_updates >= args.validate_after_updates ) ) do_validate = ( (not end_of_epoch and do_save) # validate during mid-epoch saves or (end_of_epoch and epoch_itr.epoch % args.validate_interval == 0) or num_updates >= max_update or ( args.validate_interval_updates > 0 and num_updates > 0 and num_updates % args.validate_interval_updates == 0 ) ) and not args.disable_validation # Validate valid_losses = [None] if do_validate: valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets) # Stopping conditions should_stop = ( should_stop_early(args, valid_losses[0]) or num_updates >= max_update or ( args.stop_time_hours > 0 and trainer.cumulative_training_time() / (60 * 60) > args.stop_time_hours ) ) # Save checkpoint if do_save or should_stop: logger.info("begin save checkpoint") checkpoint_utils.save_checkpoint(args, trainer, epoch_itr, valid_losses[0]) return valid_losses, should_stop def get_training_stats(stats): stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) return stats def validate(args, trainer, task, epoch_itr, subsets): if args.fixed_validation_seed is not None: # set fixed seed for every validation utils.set_torch_seed(args.fixed_validation_seed) trainer.begin_valid_epoch(epoch_itr.epoch) valid_losses = [] for subset in subsets: logger.info('begin validation on "{}" subset'.format(subset)) # Initialize data iterator itr = trainer.get_valid_iterator(subset).next_epoch_itr(shuffle=False) if getattr(args, "tpu", False): itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, log_format=args.log_format, log_interval=args.log_interval, epoch=epoch_itr.epoch, prefix=f"valid on '{subset}' subset", tensorboard_logdir=( args.tensorboard_logdir if distributed_utils.is_master(args) else None ), default_log_format=("tqdm" if not args.no_progress_bar else "simple"), ) # create a new root metrics aggregator so validation metrics # don't pollute other aggregators (e.g., train meters) with metrics.aggregate(new_root=True) as agg: for sample in progress: trainer.valid_step(sample) stats = get_valid_stats(args, trainer, agg.get_smoothed_values()) progress.print(stats, tag=subset, step=trainer.get_num_updates()) valid_losses.append(stats[args.best_checkpoint_metric]) return valid_losses def get_valid_stats(args, trainer, stats): stats["num_updates"] = trainer.get_num_updates() if hasattr(checkpoint_utils.save_checkpoint, "best"): key = "best_{0}".format(args.best_checkpoint_metric) best_function = max if args.maximize_best_checkpoint_metric else min stats[key] = best_function( checkpoint_utils.save_checkpoint.best, stats[args.best_checkpoint_metric] ) return stats def print_options_meaning_changes(args): logger.info("--max-tokens is the maximum number of input frames in a batch") def cli_main(modify_parser=None): parser = options.get_training_parser() args = options.parse_args_and_arch(parser, modify_parser=modify_parser) print_options_meaning_changes(args) if args.profile: with torch.cuda.profiler.profile(): with torch.autograd.profiler.emit_nvtx(): distributed_utils.call_main(args, main) else: distributed_utils.call_main(args, main) if __name__ == "__main__": cli_main()

true

1c2b2b6e112085f3589e4822c5885ce013b1bc21

2,518

py

Python

object_tracking.py

UAVs-at-Berkeley/flywave

483012ab34af5b465ecb8750ade9b9e7a2ca5c4e

[ "MIT" ]

6

2018-05-02T15:34:23.000Z

2021-04-13T19:28:13.000Z

object_tracking.py

UAVs-at-Berkeley/flywave

483012ab34af5b465ecb8750ade9b9e7a2ca5c4e

[ "MIT" ]

1

2018-04-19T16:11:33.000Z

2018-05-02T22:53:07.000Z

object_tracking.py

UAVs-at-Berkeley/flywave

483012ab34af5b465ecb8750ade9b9e7a2ca5c4e

[ "MIT" ]

7

2018-04-19T01:59:03.000Z

2022-01-02T13:18:26.000Z

""" Demo of the Bebop vision code (basically flies around and saves out photos as it flies) """ from Bebop import Bebop from DroneVision import DroneVision import threading import cv2 import time isAlive = False class UserVision: def __init__(self, vision): self.index = 0 self.vision = vision def save_pictures(self, args): #print("saving picture") img = self.vision.get_latest_valid_picture() # cv2.imshow("Video", img) filename = "/rightout/test_image_%06d.png" % self.index # cv2.imwrite(filename, img) self.index +=1 def detect(self, args): img = self.vision.get_latest_valid_picture() # make my bebop object bebop = Bebop() # connect to the bebop success = bebop.connect(5) if (success): # start up the video bebopVision = DroneVision(bebop, is_bebop=True) userVision = UserVision(bebopVision) bebopVision.set_user_callback_function(userVision.save_pictures, user_callback_args=None) success = bebopVision.open_video() if (success): print("Vision successfully started!") #removed the user call to this function (it now happens in open_video()) #bebopVision.start_video_buffering() # skipping actually flying for safety purposes indoors - if you want # different pictures, move the bebop around by hand print("Fly me around by hand!") bebop.smart_sleep(5) print("Moving the camera using velocity") # bebop.pan_tilt_camera_velocity(pan_velocity=0, tilt_velocity=-2, duration=4) # # bebop.safe_takeoff(10) # bebop.fly_direct(roll=0, pitch=0, yaw=0, vertical_movement=20, duration=1) count = 0 # while True and count < 30: # bebop.smart_sleep(1) # if cv2.waitKey(1) & 0xFF == ord('q'): # break # count += 1 # bebop.fly_direct(roll=15, pitch=0, yaw=0, vertical_movement=0, duration=3) # bebop.fly_direct(roll=0, pitch=15, yaw=0, vertical_movement=0, duration=3) # bebop.fly_direct(roll=0, pitch=0, yaw=20, vertical_movement=0, duration=4) # # bebop.fly_direct(roll=0, pitch=0, yaw=0, vertical_movement=-10, duration=1) # bebop.smart_sleep(50) bebop.safe_land(10) print("Finishing demo and stopping vision") bebopVision.close_video() # disconnect nicely so we don't need a reboot bebop.disconnect() else: print("Error connecting to bebop. Retry")

31.873418

93

0.651708

from Bebop import Bebop from DroneVision import DroneVision import threading import cv2 import time isAlive = False class UserVision: def __init__(self, vision): self.index = 0 self.vision = vision def save_pictures(self, args): img = self.vision.get_latest_valid_picture() filename = "/rightout/test_image_%06d.png" % self.index self.index +=1 def detect(self, args): img = self.vision.get_latest_valid_picture() bebop = Bebop() success = bebop.connect(5) if (success): bebopVision = DroneVision(bebop, is_bebop=True) userVision = UserVision(bebopVision) bebopVision.set_user_callback_function(userVision.save_pictures, user_callback_args=None) success = bebopVision.open_video() if (success): print("Vision successfully started!") print("Fly me around by hand!") bebop.smart_sleep(5) print("Moving the camera using velocity") count = 0 bebop.safe_land(10) print("Finishing demo and stopping vision") bebopVision.close_video() bebop.disconnect() else: print("Error connecting to bebop. Retry")

true

1c2b2c18b70a298c667528f485ac2535fef0d885

853

py

Python

src/config/configs_parser.py

changleibox/flutter_build_script

a93a7d9ce276b68c3a2d34b5830a4fc9683e574b

[ "Apache-2.0" ]

null

src/config/configs_parser.py

changleibox/flutter_build_script

a93a7d9ce276b68c3a2d34b5830a4fc9683e574b

[ "Apache-2.0" ]

null

src/config/configs_parser.py

changleibox/flutter_build_script

a93a7d9ce276b68c3a2d34b5830a4fc9683e574b

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2020 CHANGLEI. All rights reserved. # Created by changlei on 2020/6/30. import json import os import yaml from src.system import Paths def __resolve_configs_file(): with open(Paths.config_path, 'r', encoding='utf-8') as f: return yaml.safe_load(f) def __create_config_file(): with open(Paths.config_path, 'w', encoding='utf-8') as f, \ open(Paths.config_template_path, 'r', encoding='utf-8') as template: yaml.safe_dump( data=json.loads(template.read()), stream=f, default_style=None, sort_keys=False, allow_unicode=True, indent=2, ) def get_config(): if not os.path.exists(Paths.config_path): __create_config_file() return __resolve_configs_file()

23.694444

80

0.62837

import json import os import yaml from src.system import Paths def __resolve_configs_file(): with open(Paths.config_path, 'r', encoding='utf-8') as f: return yaml.safe_load(f) def __create_config_file(): with open(Paths.config_path, 'w', encoding='utf-8') as f, \ open(Paths.config_template_path, 'r', encoding='utf-8') as template: yaml.safe_dump( data=json.loads(template.read()), stream=f, default_style=None, sort_keys=False, allow_unicode=True, indent=2, ) def get_config(): if not os.path.exists(Paths.config_path): __create_config_file() return __resolve_configs_file()

true

1c2b2cac081ab4554009e172551ff7f519612cde

1,119

py

Python

Code/skipthoughts/skipthoughts_dir/training/vocab.py

mattaq31/recognition-forge

c5a6e36d2e81a66ad8c7eb2f108b6821610a7ba9

[ "BSD-2-Clause" ]

null

Code/skipthoughts/skipthoughts_dir/training/vocab.py

mattaq31/recognition-forge

c5a6e36d2e81a66ad8c7eb2f108b6821610a7ba9

[ "BSD-2-Clause" ]

null

Code/skipthoughts/skipthoughts_dir/training/vocab.py

mattaq31/recognition-forge

c5a6e36d2e81a66ad8c7eb2f108b6821610a7ba9

[ "BSD-2-Clause" ]

1

2019-06-25T11:40:57.000Z

""" Constructing and loading dictionaries """ import _pickle as pkl import numpy from collections import OrderedDict def build_dictionary(text): """ Build a dictionary text: list of sentences (pre-tokenized) """ wordcount = OrderedDict() for cc in text: words = cc.split() for w in words: if w not in wordcount: wordcount[w] = 0 wordcount[w] += 1 words = list(wordcount.keys()) freqs = list(wordcount.values()) sorted_idx = numpy.argsort(freqs)[::-1] worddict = OrderedDict() for idx, sidx in enumerate(sorted_idx): worddict[words[sidx]] = idx+2 # 0: <eos>, 1: <unk> return worddict, wordcount def load_dictionary(loc='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl'): """ Load a dictionary """ with open(loc, 'rb') as f: worddict = pkl.load(f) return worddict def save_dictionary(worddict, wordcount, loc): """ Save a dictionary to the specified location """ with open(loc, 'wb') as f: pkl.dump(worddict, f) pkl.dump(wordcount, f)

23.808511

81

0.613047

import _pickle as pkl import numpy from collections import OrderedDict def build_dictionary(text): wordcount = OrderedDict() for cc in text: words = cc.split() for w in words: if w not in wordcount: wordcount[w] = 0 wordcount[w] += 1 words = list(wordcount.keys()) freqs = list(wordcount.values()) sorted_idx = numpy.argsort(freqs)[::-1] worddict = OrderedDict() for idx, sidx in enumerate(sorted_idx): worddict[words[sidx]] = idx+2 return worddict, wordcount def load_dictionary(loc='/ais/gobi3/u/rkiros/bookgen/book_dictionary_large.pkl'): with open(loc, 'rb') as f: worddict = pkl.load(f) return worddict def save_dictionary(worddict, wordcount, loc): with open(loc, 'wb') as f: pkl.dump(worddict, f) pkl.dump(wordcount, f)

true

1c2b2cc3484323921bb899b5611005bf15da3919

729

py

Python

tree/bottom_view_of_a_binary_tree/bottom_view.py

chrisjdavie/compsci_basics

dab0d377c6cf040ddda2c9c9f8373e996b1a594c

[ "MIT" ]

null

tree/bottom_view_of_a_binary_tree/bottom_view.py

chrisjdavie/compsci_basics

dab0d377c6cf040ddda2c9c9f8373e996b1a594c

[ "MIT" ]

null

tree/bottom_view_of_a_binary_tree/bottom_view.py

chrisjdavie/compsci_basics

dab0d377c6cf040ddda2c9c9f8373e996b1a594c

[ "MIT" ]

null

class BottomViewData: """List functionality using a negative indexed list. An over-complicated way of avoiding sorting, to keep the algo to O(N)""" def __init__(self, len_data): self._len_data = len_data self._data = [None]*(2*len_data-1) def _key_zeroed(self, key): return self._len_data-1+key def __setitem__(self, key, depth_val): key_z = self._key_zeroed(key) if self._data[key_z] is None or self._data[key_z][0] <= depth_val[0]: self._data[self._len_data-1+key] = depth_val def __getitem__(self, key): return self._data[self._key_zeroed(key)] def view(self): return [ item[1] for item in self._data if item is not None ]

31.695652

77

0.647462

class BottomViewData: def __init__(self, len_data): self._len_data = len_data self._data = [None]*(2*len_data-1) def _key_zeroed(self, key): return self._len_data-1+key def __setitem__(self, key, depth_val): key_z = self._key_zeroed(key) if self._data[key_z] is None or self._data[key_z][0] <= depth_val[0]: self._data[self._len_data-1+key] = depth_val def __getitem__(self, key): return self._data[self._key_zeroed(key)] def view(self): return [ item[1] for item in self._data if item is not None ]

true

1c2b2d87b02fa9fbc37371aba75d8666759ee1b5

786

py

Python

discussion/urls.py

Bruskoo/PublicDiscussion

f3e9cba88fc48078ac3570a6f562dcad7612ef0a

[ "MIT" ]

null

discussion/urls.py

Bruskoo/PublicDiscussion

f3e9cba88fc48078ac3570a6f562dcad7612ef0a

[ "MIT" ]

null

discussion/urls.py

Bruskoo/PublicDiscussion

f3e9cba88fc48078ac3570a6f562dcad7612ef0a

[ "MIT" ]

null

from django.urls import path from .views import ( ArticleCreateView, ArticleDeleteView, ArticleListView, # CommentCreateView, ArticleUpdateView, SearchView, ArticleDetailView ) urlpatterns = [ path("article/add/", ArticleCreateView.as_view(), name="article-create"), path("article/<int:pk>/", ArticleDetailView.as_view(), name="article-detail"), path("article/<int:pk>/delete/", ArticleDeleteView.as_view(), name="article-delete"), path("article/<int:pk>/update/", ArticleUpdateView.as_view(), name="article-update"), # path("article/<int:pk>/comment/", CommentCreateView.as_view(), name="article-comment"), path('search/', SearchView.as_view(), name='search-results'), path("", ArticleListView.as_view(), name="article-list"), ]

39.3

93

0.697201

from django.urls import path from .views import ( ArticleCreateView, ArticleDeleteView, ArticleListView, ArticleUpdateView, SearchView, ArticleDetailView ) urlpatterns = [ path("article/add/", ArticleCreateView.as_view(), name="article-create"), path("article/<int:pk>/", ArticleDetailView.as_view(), name="article-detail"), path("article/<int:pk>/delete/", ArticleDeleteView.as_view(), name="article-delete"), path("article/<int:pk>/update/", ArticleUpdateView.as_view(), name="article-update"), path('search/', SearchView.as_view(), name='search-results'), path("", ArticleListView.as_view(), name="article-list"), ]

true

1c2b2da8382be12f3d6521d7c9eb17eee7eb103f

27,208

py

Python

pydantic/types.py

bluetech/pydantic

b7a8ef25c667b5dd4c4cd0b109c6625d1a57139a

[ "MIT" ]

null

pydantic/types.py

bluetech/pydantic

b7a8ef25c667b5dd4c4cd0b109c6625d1a57139a

[ "MIT" ]

null

pydantic/types.py

bluetech/pydantic

b7a8ef25c667b5dd4c4cd0b109c6625d1a57139a

[ "MIT" ]

null

import math import re import warnings from decimal import Decimal from enum import Enum from pathlib import Path from types import new_class from typing import ( TYPE_CHECKING, Any, Callable, ClassVar, Dict, List, Optional, Pattern, Set, Tuple, Type, TypeVar, Union, cast, overload, ) from uuid import UUID from weakref import WeakSet from . import errors from .utils import import_string, update_not_none from .validators import ( bytes_validator, constr_length_validator, constr_lower, constr_strip_whitespace, decimal_validator, float_validator, int_validator, list_validator, number_multiple_validator, number_size_validator, path_exists_validator, path_validator, set_validator, str_validator, strict_bytes_validator, strict_float_validator, strict_int_validator, strict_str_validator, ) __all__ = [ 'NoneStr', 'NoneBytes', 'StrBytes', 'NoneStrBytes', 'StrictStr', 'ConstrainedBytes', 'conbytes', 'ConstrainedList', 'conlist', 'ConstrainedSet', 'conset', 'ConstrainedStr', 'constr', 'PyObject', 'ConstrainedInt', 'conint', 'PositiveInt', 'NegativeInt', 'NonNegativeInt', 'NonPositiveInt', 'ConstrainedFloat', 'confloat', 'PositiveFloat', 'NegativeFloat', 'NonNegativeFloat', 'NonPositiveFloat', 'ConstrainedDecimal', 'condecimal', 'UUID1', 'UUID3', 'UUID4', 'UUID5', 'FilePath', 'DirectoryPath', 'Json', 'JsonWrapper', 'SecretStr', 'SecretBytes', 'StrictBool', 'StrictBytes', 'StrictInt', 'StrictFloat', 'PaymentCardNumber', 'ByteSize', ] NoneStr = Optional[str] NoneBytes = Optional[bytes] StrBytes = Union[str, bytes] NoneStrBytes = Optional[StrBytes] OptionalInt = Optional[int] OptionalIntFloat = Union[OptionalInt, float] OptionalIntFloatDecimal = Union[OptionalIntFloat, Decimal] StrIntFloat = Union[str, int, float] if TYPE_CHECKING: from .dataclasses import Dataclass # noqa: F401 from .main import BaseConfig, BaseModel # noqa: F401 from .typing import CallableGenerator ModelOrDc = Type[Union['BaseModel', 'Dataclass']] T = TypeVar('T') _DEFINED_TYPES: 'WeakSet[type]' = WeakSet() @overload def _registered(typ: Type[T]) -> Type[T]: pass @overload def _registered(typ: 'ConstrainedNumberMeta') -> 'ConstrainedNumberMeta': pass def _registered(typ: Union[Type[T], 'ConstrainedNumberMeta']) -> Union[Type[T], 'ConstrainedNumberMeta']: # In order to generate valid examples of constrained types, Hypothesis needs # to inspect the type object - so we keep a weakref to each contype object # until it can be registered. When (or if) our Hypothesis plugin is loaded, # it monkeypatches this function. # If Hypothesis is never used, the total effect is to keep a weak reference # which has minimal memory usage and doesn't even affect garbage collection. _DEFINED_TYPES.add(typ) return typ class ConstrainedBytes(bytes): strip_whitespace = False to_lower = False min_length: OptionalInt = None max_length: OptionalInt = None strict: bool = False @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_bytes_validator if cls.strict else bytes_validator yield constr_strip_whitespace yield constr_lower yield constr_length_validator class StrictBytes(ConstrainedBytes): strict = True def conbytes( *, strip_whitespace: bool = False, to_lower: bool = False, min_length: int = None, max_length: int = None ) -> Type[bytes]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strip_whitespace=strip_whitespace, to_lower=to_lower, min_length=min_length, max_length=max_length) return _registered(type('ConstrainedBytesValue', (ConstrainedBytes,), namespace)) # This types superclass should be List[T], but cython chokes on that... class ConstrainedList(list): # type: ignore # Needed for pydantic to detect that this is a list __origin__ = list __args__: Tuple[Type[T], ...] # type: ignore min_items: Optional[int] = None max_items: Optional[int] = None item_type: Type[T] # type: ignore @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.list_length_validator @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) @classmethod def list_length_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]': if v is None: return None v = list_validator(v) v_len = len(v) if cls.min_items is not None and v_len < cls.min_items: raise errors.ListMinLengthError(limit_value=cls.min_items) if cls.max_items is not None and v_len > cls.max_items: raise errors.ListMaxLengthError(limit_value=cls.max_items) return v def conlist(item_type: Type[T], *, min_items: int = None, max_items: int = None) -> Type[List[T]]: # __args__ is needed to conform to typing generics api namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': (item_type,)} # We use new_class to be able to deal with Generic types return new_class('ConstrainedListValue', (ConstrainedList,), {}, lambda ns: ns.update(namespace)) # This types superclass should be Set[T], but cython chokes on that... class ConstrainedSet(set): # type: ignore # Needed for pydantic to detect that this is a set __origin__ = set __args__: Set[Type[T]] # type: ignore min_items: Optional[int] = None max_items: Optional[int] = None item_type: Type[T] # type: ignore @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.set_length_validator @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) @classmethod def set_length_validator(cls, v: 'Optional[Set[T]]') -> 'Optional[Set[T]]': if v is None: return None v = set_validator(v) v_len = len(v) if cls.min_items is not None and v_len < cls.min_items: raise errors.SetMinLengthError(limit_value=cls.min_items) if cls.max_items is not None and v_len > cls.max_items: raise errors.SetMaxLengthError(limit_value=cls.max_items) return v def conset(item_type: Type[T], *, min_items: int = None, max_items: int = None) -> Type[Set[T]]: # __args__ is needed to conform to typing generics api namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]} # We use new_class to be able to deal with Generic types return new_class('ConstrainedSetValue', (ConstrainedSet,), {}, lambda ns: ns.update(namespace)) class ConstrainedStr(str): strip_whitespace = False to_lower = False min_length: OptionalInt = None max_length: OptionalInt = None curtail_length: OptionalInt = None regex: Optional[Pattern[str]] = None strict = False @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, minLength=cls.min_length, maxLength=cls.max_length, pattern=cls.regex and cls.regex.pattern ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_str_validator if cls.strict else str_validator yield constr_strip_whitespace yield constr_lower yield constr_length_validator yield cls.validate @classmethod def validate(cls, value: Union[str]) -> Union[str]: if cls.curtail_length and len(value) > cls.curtail_length: value = value[: cls.curtail_length] if cls.regex: if not cls.regex.match(value): raise errors.StrRegexError(pattern=cls.regex.pattern) return value def constr( *, strip_whitespace: bool = False, to_lower: bool = False, strict: bool = False, min_length: int = None, max_length: int = None, curtail_length: int = None, regex: str = None, ) -> Type[str]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict( strip_whitespace=strip_whitespace, to_lower=to_lower, strict=strict, min_length=min_length, max_length=max_length, curtail_length=curtail_length, regex=regex and re.compile(regex), ) return _registered(type('ConstrainedStrValue', (ConstrainedStr,), namespace)) class StrictStr(ConstrainedStr): strict = True if TYPE_CHECKING: StrictBool = bool else: class StrictBool(int): """ StrictBool to allow for bools which are not type-coerced. """ @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='boolean') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, value: Any) -> bool: """ Ensure that we only allow bools. """ if isinstance(value, bool): return value raise errors.StrictBoolError() class PyObject: validate_always = True @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, value: Any) -> Any: if isinstance(value, Callable): # type: ignore return value try: value = str_validator(value) except errors.StrError: raise errors.PyObjectError(error_message='value is neither a valid import path not a valid callable') try: return import_string(value) except ImportError as e: raise errors.PyObjectError(error_message=str(e)) if TYPE_CHECKING: def __call__(self, *args: Any, **kwargs: Any) -> Any: ... class ConstrainedNumberMeta(type): def __new__(cls, name: str, bases: Any, dct: Dict[str, Any]) -> 'ConstrainedInt': # type: ignore new_cls = cast('ConstrainedInt', type.__new__(cls, name, bases, dct)) if new_cls.gt is not None and new_cls.ge is not None: raise errors.ConfigError('bounds gt and ge cannot be specified at the same time') if new_cls.lt is not None and new_cls.le is not None: raise errors.ConfigError('bounds lt and le cannot be specified at the same time') return _registered(new_cls) # type: ignore class ConstrainedInt(int, metaclass=ConstrainedNumberMeta): strict: bool = False gt: OptionalInt = None ge: OptionalInt = None lt: OptionalInt = None le: OptionalInt = None multiple_of: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_int_validator if cls.strict else int_validator yield number_size_validator yield number_multiple_validator def conint( *, strict: bool = False, gt: int = None, ge: int = None, lt: int = None, le: int = None, multiple_of: int = None ) -> Type[int]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of) return type('ConstrainedIntValue', (ConstrainedInt,), namespace) class PositiveInt(ConstrainedInt): gt = 0 class NegativeInt(ConstrainedInt): lt = 0 class NonPositiveInt(ConstrainedInt): le = 0 class NonNegativeInt(ConstrainedInt): ge = 0 class StrictInt(ConstrainedInt): strict = True class ConstrainedFloat(float, metaclass=ConstrainedNumberMeta): strict: bool = False gt: OptionalIntFloat = None ge: OptionalIntFloat = None lt: OptionalIntFloat = None le: OptionalIntFloat = None multiple_of: OptionalIntFloat = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) # Modify constraints to account for differences between IEEE floats and JSON if field_schema.get('exclusiveMinimum') == -math.inf: del field_schema['exclusiveMinimum'] if field_schema.get('minimum') == -math.inf: del field_schema['minimum'] if field_schema.get('exclusiveMaximum') == math.inf: del field_schema['exclusiveMaximum'] if field_schema.get('maximum') == math.inf: del field_schema['maximum'] @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_float_validator if cls.strict else float_validator yield number_size_validator yield number_multiple_validator def confloat( *, strict: bool = False, gt: float = None, ge: float = None, lt: float = None, le: float = None, multiple_of: float = None, ) -> Type[float]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of) return type('ConstrainedFloatValue', (ConstrainedFloat,), namespace) class PositiveFloat(ConstrainedFloat): gt = 0 class NegativeFloat(ConstrainedFloat): lt = 0 class NonPositiveFloat(ConstrainedFloat): le = 0 class NonNegativeFloat(ConstrainedFloat): ge = 0 class StrictFloat(ConstrainedFloat): strict = True class ConstrainedDecimal(Decimal, metaclass=ConstrainedNumberMeta): gt: OptionalIntFloatDecimal = None ge: OptionalIntFloatDecimal = None lt: OptionalIntFloatDecimal = None le: OptionalIntFloatDecimal = None max_digits: OptionalInt = None decimal_places: OptionalInt = None multiple_of: OptionalIntFloatDecimal = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield decimal_validator yield number_size_validator yield number_multiple_validator yield cls.validate @classmethod def validate(cls, value: Decimal) -> Decimal: digit_tuple, exponent = value.as_tuple()[1:] if exponent in {'F', 'n', 'N'}: raise errors.DecimalIsNotFiniteError() if exponent >= 0: # A positive exponent adds that many trailing zeros. digits = len(digit_tuple) + exponent decimals = 0 else: # If the absolute value of the negative exponent is larger than the # number of digits, then it's the same as the number of digits, # because it'll consume all of the digits in digit_tuple and then # add abs(exponent) - len(digit_tuple) leading zeros after the # decimal point. if abs(exponent) > len(digit_tuple): digits = decimals = abs(exponent) else: digits = len(digit_tuple) decimals = abs(exponent) whole_digits = digits - decimals if cls.max_digits is not None and digits > cls.max_digits: raise errors.DecimalMaxDigitsError(max_digits=cls.max_digits) if cls.decimal_places is not None and decimals > cls.decimal_places: raise errors.DecimalMaxPlacesError(decimal_places=cls.decimal_places) if cls.max_digits is not None and cls.decimal_places is not None: expected = cls.max_digits - cls.decimal_places if whole_digits > expected: raise errors.DecimalWholeDigitsError(whole_digits=expected) return value def condecimal( *, gt: Decimal = None, ge: Decimal = None, lt: Decimal = None, le: Decimal = None, max_digits: int = None, decimal_places: int = None, multiple_of: Decimal = None, ) -> Type[Decimal]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict( gt=gt, ge=ge, lt=lt, le=le, max_digits=max_digits, decimal_places=decimal_places, multiple_of=multiple_of ) return type('ConstrainedDecimalValue', (ConstrainedDecimal,), namespace) class UUID1(UUID): _required_version = 1 @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='string', format=f'uuid{cls._required_version}') class UUID3(UUID1): _required_version = 3 class UUID4(UUID1): _required_version = 4 class UUID5(UUID1): _required_version = 5 class FilePath(Path): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(format='file-path') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield path_validator yield path_exists_validator yield cls.validate @classmethod def validate(cls, value: Path) -> Path: if not value.is_file(): raise errors.PathNotAFileError(path=value) return value class DirectoryPath(Path): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(format='directory-path') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield path_validator yield path_exists_validator yield cls.validate @classmethod def validate(cls, value: Path) -> Path: if not value.is_dir(): raise errors.PathNotADirectoryError(path=value) return value class JsonWrapper: pass class JsonMeta(type): def __getitem__(self, t: Type[Any]) -> Type[JsonWrapper]: return _registered(type('JsonWrapperValue', (JsonWrapper,), {'inner_type': t})) class Json(metaclass=JsonMeta): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='string', format='json-string') class SecretStr: min_length: OptionalInt = None max_length: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, type='string', writeOnly=True, format='password', minLength=cls.min_length, maxLength=cls.max_length, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate yield constr_length_validator @classmethod def validate(cls, value: Any) -> 'SecretStr': if isinstance(value, cls): return value value = str_validator(value) return cls(value) def __init__(self, value: str): self._secret_value = value def __repr__(self) -> str: return f"SecretStr('{self}')" def __str__(self) -> str: return '**********' if self._secret_value else '' def __eq__(self, other: Any) -> bool: return isinstance(other, SecretStr) and self.get_secret_value() == other.get_secret_value() def __len__(self) -> int: return len(self._secret_value) def display(self) -> str: warnings.warn('`secret_str.display()` is deprecated, use `str(secret_str)` instead', DeprecationWarning) return str(self) def get_secret_value(self) -> str: return self._secret_value class SecretBytes: min_length: OptionalInt = None max_length: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, type='string', writeOnly=True, format='password', minLength=cls.min_length, maxLength=cls.max_length, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate yield constr_length_validator @classmethod def validate(cls, value: Any) -> 'SecretBytes': if isinstance(value, cls): return value value = bytes_validator(value) return cls(value) def __init__(self, value: bytes): self._secret_value = value def __repr__(self) -> str: return f"SecretBytes(b'{self}')" def __str__(self) -> str: return '**********' if self._secret_value else '' def __eq__(self, other: Any) -> bool: return isinstance(other, SecretBytes) and self.get_secret_value() == other.get_secret_value() def __len__(self) -> int: return len(self._secret_value) def display(self) -> str: warnings.warn('`secret_bytes.display()` is deprecated, use `str(secret_bytes)` instead', DeprecationWarning) return str(self) def get_secret_value(self) -> bytes: return self._secret_value class PaymentCardBrand(str, Enum): # If you add another card type, please also add it to the # Hypothesis strategy in `pydantic._hypothesis_plugin`. amex = 'American Express' mastercard = 'Mastercard' visa = 'Visa' other = 'other' def __str__(self) -> str: return self.value class PaymentCardNumber(str): """ Based on: https://en.wikipedia.org/wiki/Payment_card_number """ strip_whitespace: ClassVar[bool] = True min_length: ClassVar[int] = 12 max_length: ClassVar[int] = 19 bin: str last4: str brand: PaymentCardBrand def __init__(self, card_number: str): self.bin = card_number[:6] self.last4 = card_number[-4:] self.brand = self._get_brand(card_number) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield str_validator yield constr_strip_whitespace yield constr_length_validator yield cls.validate_digits yield cls.validate_luhn_check_digit yield cls yield cls.validate_length_for_brand @property def masked(self) -> str: num_masked = len(self) - 10 # len(bin) + len(last4) == 10 return f'{self.bin}{"*" * num_masked}{self.last4}' @classmethod def validate_digits(cls, card_number: str) -> str: if not card_number.isdigit(): raise errors.NotDigitError return card_number @classmethod def validate_luhn_check_digit(cls, card_number: str) -> str: """ Based on: https://en.wikipedia.org/wiki/Luhn_algorithm """ sum_ = int(card_number[-1]) length = len(card_number) parity = length % 2 for i in range(length - 1): digit = int(card_number[i]) if i % 2 == parity: digit *= 2 if digit > 9: digit -= 9 sum_ += digit valid = sum_ % 10 == 0 if not valid: raise errors.LuhnValidationError return card_number @classmethod def validate_length_for_brand(cls, card_number: 'PaymentCardNumber') -> 'PaymentCardNumber': """ Validate length based on BIN for major brands: https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN) """ required_length: Optional[int] = None if card_number.brand in {PaymentCardBrand.visa, PaymentCardBrand.mastercard}: required_length = 16 valid = len(card_number) == required_length elif card_number.brand == PaymentCardBrand.amex: required_length = 15 valid = len(card_number) == required_length else: valid = True if not valid: raise errors.InvalidLengthForBrand(brand=card_number.brand, required_length=required_length) return card_number @staticmethod def _get_brand(card_number: str) -> PaymentCardBrand: if card_number[0] == '4': brand = PaymentCardBrand.visa elif 51 <= int(card_number[:2]) <= 55: brand = PaymentCardBrand.mastercard elif card_number[:2] in {'34', '37'}: brand = PaymentCardBrand.amex else: brand = PaymentCardBrand.other return brand BYTE_SIZES = { 'b': 1, 'kb': 10 ** 3, 'mb': 10 ** 6, 'gb': 10 ** 9, 'tb': 10 ** 12, 'pb': 10 ** 15, 'eb': 10 ** 18, 'kib': 2 ** 10, 'mib': 2 ** 20, 'gib': 2 ** 30, 'tib': 2 ** 40, 'pib': 2 ** 50, 'eib': 2 ** 60, } BYTE_SIZES.update({k.lower()[0]: v for k, v in BYTE_SIZES.items() if 'i' not in k}) byte_string_re = re.compile(r'^\s*(\d*\.?\d+)\s*(\w+)?', re.IGNORECASE) class ByteSize(int): @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, v: StrIntFloat) -> 'ByteSize': try: return cls(int(v)) except ValueError: pass str_match = byte_string_re.match(str(v)) if str_match is None: raise errors.InvalidByteSize() scalar, unit = str_match.groups() if unit is None: unit = 'b' try: unit_mult = BYTE_SIZES[unit.lower()] except KeyError: raise errors.InvalidByteSizeUnit(unit=unit) return cls(int(float(scalar) * unit_mult)) def human_readable(self, decimal: bool = False) -> str: if decimal: divisor = 1000 units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB'] final_unit = 'EB' else: divisor = 1024 units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'] final_unit = 'EiB' num = float(self) for unit in units: if abs(num) < divisor: return f'{num:0.1f}{unit}' num /= divisor return f'{num:0.1f}{final_unit}' def to(self, unit: str) -> float: try: unit_div = BYTE_SIZES[unit.lower()] except KeyError: raise errors.InvalidByteSizeUnit(unit=unit) return self / unit_div

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0.641392

import math import re import warnings from decimal import Decimal from enum import Enum from pathlib import Path from types import new_class from typing import ( TYPE_CHECKING, Any, Callable, ClassVar, Dict, List, Optional, Pattern, Set, Tuple, Type, TypeVar, Union, cast, overload, ) from uuid import UUID from weakref import WeakSet from . import errors from .utils import import_string, update_not_none from .validators import ( bytes_validator, constr_length_validator, constr_lower, constr_strip_whitespace, decimal_validator, float_validator, int_validator, list_validator, number_multiple_validator, number_size_validator, path_exists_validator, path_validator, set_validator, str_validator, strict_bytes_validator, strict_float_validator, strict_int_validator, strict_str_validator, ) __all__ = [ 'NoneStr', 'NoneBytes', 'StrBytes', 'NoneStrBytes', 'StrictStr', 'ConstrainedBytes', 'conbytes', 'ConstrainedList', 'conlist', 'ConstrainedSet', 'conset', 'ConstrainedStr', 'constr', 'PyObject', 'ConstrainedInt', 'conint', 'PositiveInt', 'NegativeInt', 'NonNegativeInt', 'NonPositiveInt', 'ConstrainedFloat', 'confloat', 'PositiveFloat', 'NegativeFloat', 'NonNegativeFloat', 'NonPositiveFloat', 'ConstrainedDecimal', 'condecimal', 'UUID1', 'UUID3', 'UUID4', 'UUID5', 'FilePath', 'DirectoryPath', 'Json', 'JsonWrapper', 'SecretStr', 'SecretBytes', 'StrictBool', 'StrictBytes', 'StrictInt', 'StrictFloat', 'PaymentCardNumber', 'ByteSize', ] NoneStr = Optional[str] NoneBytes = Optional[bytes] StrBytes = Union[str, bytes] NoneStrBytes = Optional[StrBytes] OptionalInt = Optional[int] OptionalIntFloat = Union[OptionalInt, float] OptionalIntFloatDecimal = Union[OptionalIntFloat, Decimal] StrIntFloat = Union[str, int, float] if TYPE_CHECKING: from .dataclasses import Dataclass from .main import BaseConfig, BaseModel from .typing import CallableGenerator ModelOrDc = Type[Union['BaseModel', 'Dataclass']] T = TypeVar('T') _DEFINED_TYPES: 'WeakSet[type]' = WeakSet() @overload def _registered(typ: Type[T]) -> Type[T]: pass @overload def _registered(typ: 'ConstrainedNumberMeta') -> 'ConstrainedNumberMeta': pass def _registered(typ: Union[Type[T], 'ConstrainedNumberMeta']) -> Union[Type[T], 'ConstrainedNumberMeta']: _DEFINED_TYPES.add(typ) return typ class ConstrainedBytes(bytes): strip_whitespace = False to_lower = False min_length: OptionalInt = None max_length: OptionalInt = None strict: bool = False @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_bytes_validator if cls.strict else bytes_validator yield constr_strip_whitespace yield constr_lower yield constr_length_validator class StrictBytes(ConstrainedBytes): strict = True def conbytes( *, strip_whitespace: bool = False, to_lower: bool = False, min_length: int = None, max_length: int = None ) -> Type[bytes]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strip_whitespace=strip_whitespace, to_lower=to_lower, min_length=min_length, max_length=max_length) return _registered(type('ConstrainedBytesValue', (ConstrainedBytes,), namespace)) # This types superclass should be List[T], but cython chokes on that... class ConstrainedList(list): # type: ignore # Needed for pydantic to detect that this is a list __origin__ = list __args__: Tuple[Type[T], ...] # type: ignore min_items: Optional[int] = None max_items: Optional[int] = None item_type: Type[T] # type: ignore @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.list_length_validator @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) @classmethod def list_length_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]': if v is None: return None v = list_validator(v) v_len = len(v) if cls.min_items is not None and v_len < cls.min_items: raise errors.ListMinLengthError(limit_value=cls.min_items) if cls.max_items is not None and v_len > cls.max_items: raise errors.ListMaxLengthError(limit_value=cls.max_items) return v def conlist(item_type: Type[T], *, min_items: int = None, max_items: int = None) -> Type[List[T]]: # __args__ is needed to conform to typing generics api namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': (item_type,)} # We use new_class to be able to deal with Generic types return new_class('ConstrainedListValue', (ConstrainedList,), {}, lambda ns: ns.update(namespace)) # This types superclass should be Set[T], but cython chokes on that... class ConstrainedSet(set): # type: ignore # Needed for pydantic to detect that this is a set __origin__ = set __args__: Set[Type[T]] # type: ignore min_items: Optional[int] = None max_items: Optional[int] = None item_type: Type[T] # type: ignore @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.set_length_validator @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) @classmethod def set_length_validator(cls, v: 'Optional[Set[T]]') -> 'Optional[Set[T]]': if v is None: return None v = set_validator(v) v_len = len(v) if cls.min_items is not None and v_len < cls.min_items: raise errors.SetMinLengthError(limit_value=cls.min_items) if cls.max_items is not None and v_len > cls.max_items: raise errors.SetMaxLengthError(limit_value=cls.max_items) return v def conset(item_type: Type[T], *, min_items: int = None, max_items: int = None) -> Type[Set[T]]: # __args__ is needed to conform to typing generics api namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]} # We use new_class to be able to deal with Generic types return new_class('ConstrainedSetValue', (ConstrainedSet,), {}, lambda ns: ns.update(namespace)) class ConstrainedStr(str): strip_whitespace = False to_lower = False min_length: OptionalInt = None max_length: OptionalInt = None curtail_length: OptionalInt = None regex: Optional[Pattern[str]] = None strict = False @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, minLength=cls.min_length, maxLength=cls.max_length, pattern=cls.regex and cls.regex.pattern ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_str_validator if cls.strict else str_validator yield constr_strip_whitespace yield constr_lower yield constr_length_validator yield cls.validate @classmethod def validate(cls, value: Union[str]) -> Union[str]: if cls.curtail_length and len(value) > cls.curtail_length: value = value[: cls.curtail_length] if cls.regex: if not cls.regex.match(value): raise errors.StrRegexError(pattern=cls.regex.pattern) return value def constr( *, strip_whitespace: bool = False, to_lower: bool = False, strict: bool = False, min_length: int = None, max_length: int = None, curtail_length: int = None, regex: str = None, ) -> Type[str]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict( strip_whitespace=strip_whitespace, to_lower=to_lower, strict=strict, min_length=min_length, max_length=max_length, curtail_length=curtail_length, regex=regex and re.compile(regex), ) return _registered(type('ConstrainedStrValue', (ConstrainedStr,), namespace)) class StrictStr(ConstrainedStr): strict = True if TYPE_CHECKING: StrictBool = bool else: class StrictBool(int): """ StrictBool to allow for bools which are not type-coerced. """ @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='boolean') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, value: Any) -> bool: """ Ensure that we only allow bools. """ if isinstance(value, bool): return value raise errors.StrictBoolError() class PyObject: validate_always = True @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, value: Any) -> Any: if isinstance(value, Callable): # type: ignore return value try: value = str_validator(value) except errors.StrError: raise errors.PyObjectError(error_message='value is neither a valid import path not a valid callable') try: return import_string(value) except ImportError as e: raise errors.PyObjectError(error_message=str(e)) if TYPE_CHECKING: def __call__(self, *args: Any, **kwargs: Any) -> Any: ... class ConstrainedNumberMeta(type): def __new__(cls, name: str, bases: Any, dct: Dict[str, Any]) -> 'ConstrainedInt': # type: ignore new_cls = cast('ConstrainedInt', type.__new__(cls, name, bases, dct)) if new_cls.gt is not None and new_cls.ge is not None: raise errors.ConfigError('bounds gt and ge cannot be specified at the same time') if new_cls.lt is not None and new_cls.le is not None: raise errors.ConfigError('bounds lt and le cannot be specified at the same time') return _registered(new_cls) # type: ignore class ConstrainedInt(int, metaclass=ConstrainedNumberMeta): strict: bool = False gt: OptionalInt = None ge: OptionalInt = None lt: OptionalInt = None le: OptionalInt = None multiple_of: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_int_validator if cls.strict else int_validator yield number_size_validator yield number_multiple_validator def conint( *, strict: bool = False, gt: int = None, ge: int = None, lt: int = None, le: int = None, multiple_of: int = None ) -> Type[int]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of) return type('ConstrainedIntValue', (ConstrainedInt,), namespace) class PositiveInt(ConstrainedInt): gt = 0 class NegativeInt(ConstrainedInt): lt = 0 class NonPositiveInt(ConstrainedInt): le = 0 class NonNegativeInt(ConstrainedInt): ge = 0 class StrictInt(ConstrainedInt): strict = True class ConstrainedFloat(float, metaclass=ConstrainedNumberMeta): strict: bool = False gt: OptionalIntFloat = None ge: OptionalIntFloat = None lt: OptionalIntFloat = None le: OptionalIntFloat = None multiple_of: OptionalIntFloat = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) # Modify constraints to account for differences between IEEE floats and JSON if field_schema.get('exclusiveMinimum') == -math.inf: del field_schema['exclusiveMinimum'] if field_schema.get('minimum') == -math.inf: del field_schema['minimum'] if field_schema.get('exclusiveMaximum') == math.inf: del field_schema['exclusiveMaximum'] if field_schema.get('maximum') == math.inf: del field_schema['maximum'] @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield strict_float_validator if cls.strict else float_validator yield number_size_validator yield number_multiple_validator def confloat( *, strict: bool = False, gt: float = None, ge: float = None, lt: float = None, le: float = None, multiple_of: float = None, ) -> Type[float]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of) return type('ConstrainedFloatValue', (ConstrainedFloat,), namespace) class PositiveFloat(ConstrainedFloat): gt = 0 class NegativeFloat(ConstrainedFloat): lt = 0 class NonPositiveFloat(ConstrainedFloat): le = 0 class NonNegativeFloat(ConstrainedFloat): ge = 0 class StrictFloat(ConstrainedFloat): strict = True class ConstrainedDecimal(Decimal, metaclass=ConstrainedNumberMeta): gt: OptionalIntFloatDecimal = None ge: OptionalIntFloatDecimal = None lt: OptionalIntFloatDecimal = None le: OptionalIntFloatDecimal = None max_digits: OptionalInt = None decimal_places: OptionalInt = None multiple_of: OptionalIntFloatDecimal = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le, multipleOf=cls.multiple_of, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield decimal_validator yield number_size_validator yield number_multiple_validator yield cls.validate @classmethod def validate(cls, value: Decimal) -> Decimal: digit_tuple, exponent = value.as_tuple()[1:] if exponent in {'F', 'n', 'N'}: raise errors.DecimalIsNotFiniteError() if exponent >= 0: # A positive exponent adds that many trailing zeros. digits = len(digit_tuple) + exponent decimals = 0 else: # If the absolute value of the negative exponent is larger than the # number of digits, then it's the same as the number of digits, # add abs(exponent) - len(digit_tuple) leading zeros after the # decimal point. if abs(exponent) > len(digit_tuple): digits = decimals = abs(exponent) else: digits = len(digit_tuple) decimals = abs(exponent) whole_digits = digits - decimals if cls.max_digits is not None and digits > cls.max_digits: raise errors.DecimalMaxDigitsError(max_digits=cls.max_digits) if cls.decimal_places is not None and decimals > cls.decimal_places: raise errors.DecimalMaxPlacesError(decimal_places=cls.decimal_places) if cls.max_digits is not None and cls.decimal_places is not None: expected = cls.max_digits - cls.decimal_places if whole_digits > expected: raise errors.DecimalWholeDigitsError(whole_digits=expected) return value def condecimal( *, gt: Decimal = None, ge: Decimal = None, lt: Decimal = None, le: Decimal = None, max_digits: int = None, decimal_places: int = None, multiple_of: Decimal = None, ) -> Type[Decimal]: # use kwargs then define conf in a dict to aid with IDE type hinting namespace = dict( gt=gt, ge=ge, lt=lt, le=le, max_digits=max_digits, decimal_places=decimal_places, multiple_of=multiple_of ) return type('ConstrainedDecimalValue', (ConstrainedDecimal,), namespace) class UUID1(UUID): _required_version = 1 @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='string', format=f'uuid{cls._required_version}') class UUID3(UUID1): _required_version = 3 class UUID4(UUID1): _required_version = 4 class UUID5(UUID1): _required_version = 5 class FilePath(Path): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(format='file-path') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield path_validator yield path_exists_validator yield cls.validate @classmethod def validate(cls, value: Path) -> Path: if not value.is_file(): raise errors.PathNotAFileError(path=value) return value class DirectoryPath(Path): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(format='directory-path') @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield path_validator yield path_exists_validator yield cls.validate @classmethod def validate(cls, value: Path) -> Path: if not value.is_dir(): raise errors.PathNotADirectoryError(path=value) return value class JsonWrapper: pass class JsonMeta(type): def __getitem__(self, t: Type[Any]) -> Type[JsonWrapper]: return _registered(type('JsonWrapperValue', (JsonWrapper,), {'inner_type': t})) class Json(metaclass=JsonMeta): @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: field_schema.update(type='string', format='json-string') class SecretStr: min_length: OptionalInt = None max_length: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, type='string', writeOnly=True, format='password', minLength=cls.min_length, maxLength=cls.max_length, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate yield constr_length_validator @classmethod def validate(cls, value: Any) -> 'SecretStr': if isinstance(value, cls): return value value = str_validator(value) return cls(value) def __init__(self, value: str): self._secret_value = value def __repr__(self) -> str: return f"SecretStr('{self}')" def __str__(self) -> str: return '**********' if self._secret_value else '' def __eq__(self, other: Any) -> bool: return isinstance(other, SecretStr) and self.get_secret_value() == other.get_secret_value() def __len__(self) -> int: return len(self._secret_value) def display(self) -> str: warnings.warn('`secret_str.display()` is deprecated, use `str(secret_str)` instead', DeprecationWarning) return str(self) def get_secret_value(self) -> str: return self._secret_value class SecretBytes: min_length: OptionalInt = None max_length: OptionalInt = None @classmethod def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: update_not_none( field_schema, type='string', writeOnly=True, format='password', minLength=cls.min_length, maxLength=cls.max_length, ) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate yield constr_length_validator @classmethod def validate(cls, value: Any) -> 'SecretBytes': if isinstance(value, cls): return value value = bytes_validator(value) return cls(value) def __init__(self, value: bytes): self._secret_value = value def __repr__(self) -> str: return f"SecretBytes(b'{self}')" def __str__(self) -> str: return '**********' if self._secret_value else '' def __eq__(self, other: Any) -> bool: return isinstance(other, SecretBytes) and self.get_secret_value() == other.get_secret_value() def __len__(self) -> int: return len(self._secret_value) def display(self) -> str: warnings.warn('`secret_bytes.display()` is deprecated, use `str(secret_bytes)` instead', DeprecationWarning) return str(self) def get_secret_value(self) -> bytes: return self._secret_value class PaymentCardBrand(str, Enum): # If you add another card type, please also add it to the # Hypothesis strategy in `pydantic._hypothesis_plugin`. amex = 'American Express' mastercard = 'Mastercard' visa = 'Visa' other = 'other' def __str__(self) -> str: return self.value class PaymentCardNumber(str): strip_whitespace: ClassVar[bool] = True min_length: ClassVar[int] = 12 max_length: ClassVar[int] = 19 bin: str last4: str brand: PaymentCardBrand def __init__(self, card_number: str): self.bin = card_number[:6] self.last4 = card_number[-4:] self.brand = self._get_brand(card_number) @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield str_validator yield constr_strip_whitespace yield constr_length_validator yield cls.validate_digits yield cls.validate_luhn_check_digit yield cls yield cls.validate_length_for_brand @property def masked(self) -> str: num_masked = len(self) - 10 # len(bin) + len(last4) == 10 return f'{self.bin}{"*" * num_masked}{self.last4}' @classmethod def validate_digits(cls, card_number: str) -> str: if not card_number.isdigit(): raise errors.NotDigitError return card_number @classmethod def validate_luhn_check_digit(cls, card_number: str) -> str: sum_ = int(card_number[-1]) length = len(card_number) parity = length % 2 for i in range(length - 1): digit = int(card_number[i]) if i % 2 == parity: digit *= 2 if digit > 9: digit -= 9 sum_ += digit valid = sum_ % 10 == 0 if not valid: raise errors.LuhnValidationError return card_number @classmethod def validate_length_for_brand(cls, card_number: 'PaymentCardNumber') -> 'PaymentCardNumber': required_length: Optional[int] = None if card_number.brand in {PaymentCardBrand.visa, PaymentCardBrand.mastercard}: required_length = 16 valid = len(card_number) == required_length elif card_number.brand == PaymentCardBrand.amex: required_length = 15 valid = len(card_number) == required_length else: valid = True if not valid: raise errors.InvalidLengthForBrand(brand=card_number.brand, required_length=required_length) return card_number @staticmethod def _get_brand(card_number: str) -> PaymentCardBrand: if card_number[0] == '4': brand = PaymentCardBrand.visa elif 51 <= int(card_number[:2]) <= 55: brand = PaymentCardBrand.mastercard elif card_number[:2] in {'34', '37'}: brand = PaymentCardBrand.amex else: brand = PaymentCardBrand.other return brand BYTE_SIZES = { 'b': 1, 'kb': 10 ** 3, 'mb': 10 ** 6, 'gb': 10 ** 9, 'tb': 10 ** 12, 'pb': 10 ** 15, 'eb': 10 ** 18, 'kib': 2 ** 10, 'mib': 2 ** 20, 'gib': 2 ** 30, 'tib': 2 ** 40, 'pib': 2 ** 50, 'eib': 2 ** 60, } BYTE_SIZES.update({k.lower()[0]: v for k, v in BYTE_SIZES.items() if 'i' not in k}) byte_string_re = re.compile(r'^\s*(\d*\.?\d+)\s*(\w+)?', re.IGNORECASE) class ByteSize(int): @classmethod def __get_validators__(cls) -> 'CallableGenerator': yield cls.validate @classmethod def validate(cls, v: StrIntFloat) -> 'ByteSize': try: return cls(int(v)) except ValueError: pass str_match = byte_string_re.match(str(v)) if str_match is None: raise errors.InvalidByteSize() scalar, unit = str_match.groups() if unit is None: unit = 'b' try: unit_mult = BYTE_SIZES[unit.lower()] except KeyError: raise errors.InvalidByteSizeUnit(unit=unit) return cls(int(float(scalar) * unit_mult)) def human_readable(self, decimal: bool = False) -> str: if decimal: divisor = 1000 units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB'] final_unit = 'EB' else: divisor = 1024 units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'] final_unit = 'EiB' num = float(self) for unit in units: if abs(num) < divisor: return f'{num:0.1f}{unit}' num /= divisor return f'{num:0.1f}{final_unit}' def to(self, unit: str) -> float: try: unit_div = BYTE_SIZES[unit.lower()] except KeyError: raise errors.InvalidByteSizeUnit(unit=unit) return self / unit_div

true

1c2b2dcba360df8497d5ba43d575b2ba81dcabef

2,790

py

Python

networks/unet.py

songpeng326/pytorch-semantic-segmentation

366259cbc3220744c3a633766075f1d06b1c0b3f

[ "MIT" ]

88

2018-04-04T11:02:55.000Z

2022-01-04T16:32:54.000Z

networks/unet.py

ZhenhLi/pytorch-semantic-segmentation

7469de95cdb0fbfe9b00b93a8b068c35d398c6cf

[ "MIT" ]

8

2018-04-09T07:52:35.000Z

2019-04-12T07:35:23.000Z

networks/unet.py

ZhenhLi/pytorch-semantic-segmentation

7469de95cdb0fbfe9b00b93a8b068c35d398c6cf

[ "MIT" ]

32

2018-05-30T04:05:05.000Z

2021-04-22T15:45:56.000Z

import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from torch.utils import model_zoo from torchvision import models class UNetEnc(nn.Module): def __init__(self, in_channels, features, out_channels): super().__init__() self.up = nn.Sequential( nn.Conv2d(in_channels, features, 3), nn.ReLU(inplace=True), nn.Conv2d(features, features, 3), nn.ReLU(inplace=True), nn.ConvTranspose2d(features, out_channels, 2, stride=2), nn.ReLU(inplace=True), ) def forward(self, x): return self.up(x) class UNetDec(nn.Module): def __init__(self, in_channels, out_channels, dropout=False): super().__init__() layers = [ nn.Conv2d(in_channels, out_channels, 3), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, 3), nn.ReLU(inplace=True), ] if dropout: layers += [nn.Dropout(.5)] layers += [nn.MaxPool2d(2, stride=2, ceil_mode=True)] self.down = nn.Sequential(*layers) def forward(self, x): return self.down(x) class UNet(nn.Module): def __init__(self, num_classes): super().__init__() self.dec1 = UNetDec(3, 64) self.dec2 = UNetDec(64, 128) self.dec3 = UNetDec(128, 256) self.dec4 = UNetDec(256, 512, dropout=True) self.center = nn.Sequential( nn.Conv2d(512, 1024, 3), nn.ReLU(inplace=True), nn.Conv2d(1024, 1024, 3), nn.ReLU(inplace=True), nn.Dropout(), nn.ConvTranspose2d(1024, 512, 2, stride=2), nn.ReLU(inplace=True), ) self.enc4 = UNetEnc(1024, 512, 256) self.enc3 = UNetEnc(512, 256, 128) self.enc2 = UNetEnc(256, 128, 64) self.enc1 = nn.Sequential( nn.Conv2d(128, 64, 3), nn.ReLU(inplace=True), nn.Conv2d(64, 64, 3), nn.ReLU(inplace=True), ) self.final = nn.Conv2d(64, num_classes, 1) def forward(self, x): dec1 = self.dec1(x) dec2 = self.dec2(dec1) dec3 = self.dec3(dec2) dec4 = self.dec4(dec3) center = self.center(dec4) enc4 = self.enc4(torch.cat([ center, F.upsample_bilinear(dec4, center.size()[2:])], 1)) enc3 = self.enc3(torch.cat([ enc4, F.upsample_bilinear(dec3, enc4.size()[2:])], 1)) enc2 = self.enc2(torch.cat([ enc3, F.upsample_bilinear(dec2, enc3.size()[2:])], 1)) enc1 = self.enc1(torch.cat([ enc2, F.upsample_bilinear(dec1, enc2.size()[2:])], 1)) return F.upsample_bilinear(self.final(enc1), x.size()[2:])

30

70

0.558781

import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from torch.utils import model_zoo from torchvision import models class UNetEnc(nn.Module): def __init__(self, in_channels, features, out_channels): super().__init__() self.up = nn.Sequential( nn.Conv2d(in_channels, features, 3), nn.ReLU(inplace=True), nn.Conv2d(features, features, 3), nn.ReLU(inplace=True), nn.ConvTranspose2d(features, out_channels, 2, stride=2), nn.ReLU(inplace=True), ) def forward(self, x): return self.up(x) class UNetDec(nn.Module): def __init__(self, in_channels, out_channels, dropout=False): super().__init__() layers = [ nn.Conv2d(in_channels, out_channels, 3), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, 3), nn.ReLU(inplace=True), ] if dropout: layers += [nn.Dropout(.5)] layers += [nn.MaxPool2d(2, stride=2, ceil_mode=True)] self.down = nn.Sequential(*layers) def forward(self, x): return self.down(x) class UNet(nn.Module): def __init__(self, num_classes): super().__init__() self.dec1 = UNetDec(3, 64) self.dec2 = UNetDec(64, 128) self.dec3 = UNetDec(128, 256) self.dec4 = UNetDec(256, 512, dropout=True) self.center = nn.Sequential( nn.Conv2d(512, 1024, 3), nn.ReLU(inplace=True), nn.Conv2d(1024, 1024, 3), nn.ReLU(inplace=True), nn.Dropout(), nn.ConvTranspose2d(1024, 512, 2, stride=2), nn.ReLU(inplace=True), ) self.enc4 = UNetEnc(1024, 512, 256) self.enc3 = UNetEnc(512, 256, 128) self.enc2 = UNetEnc(256, 128, 64) self.enc1 = nn.Sequential( nn.Conv2d(128, 64, 3), nn.ReLU(inplace=True), nn.Conv2d(64, 64, 3), nn.ReLU(inplace=True), ) self.final = nn.Conv2d(64, num_classes, 1) def forward(self, x): dec1 = self.dec1(x) dec2 = self.dec2(dec1) dec3 = self.dec3(dec2) dec4 = self.dec4(dec3) center = self.center(dec4) enc4 = self.enc4(torch.cat([ center, F.upsample_bilinear(dec4, center.size()[2:])], 1)) enc3 = self.enc3(torch.cat([ enc4, F.upsample_bilinear(dec3, enc4.size()[2:])], 1)) enc2 = self.enc2(torch.cat([ enc3, F.upsample_bilinear(dec2, enc3.size()[2:])], 1)) enc1 = self.enc1(torch.cat([ enc2, F.upsample_bilinear(dec1, enc2.size()[2:])], 1)) return F.upsample_bilinear(self.final(enc1), x.size()[2:])

true

1c2b2e18eb5e04cc32cc54e6818b65ee64684e89

909

py

Python

regulation/debug.py

pierrehebert/photovoltaic_optimizer

5c20d2fccabc2e3e8a7c471a2e83a6061a8fd235

[ "Apache-2.0" ]

2

2020-04-15T12:02:16.000Z

2020-05-18T02:13:46.000Z

regulation/debug.py

pierrehebert/photovoltaic_optimizer

5c20d2fccabc2e3e8a7c471a2e83a6061a8fd235

[ "Apache-2.0" ]

null

regulation/debug.py

pierrehebert/photovoltaic_optimizer

5c20d2fccabc2e3e8a7c471a2e83a6061a8fd235

[ "Apache-2.0" ]

null

# Copyright (C) 2018-2019 Pierre Hébert # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging logger = logging.getLogger('power_regulation') logger.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) def debug(indent, msg): logger.info((' '*indent)+str(msg))

32.464286

74

0.754675

import logging logger = logging.getLogger('power_regulation') logger.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) def debug(indent, msg): logger.info((' '*indent)+str(msg))

true

1c2b2e96e15eac01204fe0230ea6ab7b6887d64f

8,028

py

Python

main.py

comp5331-Xtimeseries/mWDN

3805f90230b93d04f86201079358ec1f6dd6bb2d

[ "MIT" ]

null

main.py

comp5331-Xtimeseries/mWDN

3805f90230b93d04f86201079358ec1f6dd6bb2d

[ "MIT" ]

null

main.py

comp5331-Xtimeseries/mWDN

3805f90230b93d04f86201079358ec1f6dd6bb2d

[ "MIT" ]

null

import argparse import math import time import torch import torch.nn as nn from models import LSTNet from models.mWDN import mWDN # from tsai.models import mWDN import numpy as np; import importlib import Datasets from utils import *; import Optim def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size): model.eval(); total_loss = 0; total_loss_l1 = 0; n_samples = 0; predict = None; test = None; for X, Y in data.get_batches(X, Y, batch_size, False): if args.cuda: X = X.cuda(); Y = Y.cuda(); output = model(X); if predict is None: predict = output; test = Y; else: predict = torch.cat((predict,output)); test = torch.cat((test, Y)); scale = data.scale.expand(output.size(0), data.m) total_loss += evaluateL2(output * scale, Y * scale).item() total_loss_l1 += evaluateL1(output * scale, Y * scale).item() n_samples += (output.size(0) * data.m); rse = math.sqrt(total_loss / n_samples)/data.rse rae = (total_loss_l1/n_samples)/data.rae predict = predict.data.cpu().numpy(); Ytest = test.data.cpu().numpy(); sigma_p = (predict).std(axis = 0); sigma_g = (Ytest).std(axis = 0); mean_p = predict.mean(axis = 0) mean_g = Ytest.mean(axis = 0) index = (sigma_g!=0); correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g); correlation = (correlation[index]).mean(); return rse, rae, correlation; def train(data, X, Y, model, criterion, optim, batch_size): model.train(); total_loss = 0; n_samples = 0; for X, Y in data.get_batches(X, Y, batch_size, True): if args.cuda: X = X.cuda(); Y = Y.cuda(); model.zero_grad(); output = model(X); scale = data.scale.expand(output.size(0), data.m) loss = criterion(output * scale, Y * scale); loss.backward(); grad_norm = optim.step(); total_loss += loss.item(); n_samples += (output.size(0) * data.m); return total_loss / n_samples parser = argparse.ArgumentParser(description='PyTorch Time series forecasting') parser.add_argument('--data', type=str, required=True, help='location of the data file') parser.add_argument('--model', type=str, default='LSTNet', help='') parser.add_argument('--hidCNN', type=int, default=100, help='number of CNN hidden units') parser.add_argument('--hidRNN', type=int, default=100, help='number of RNN hidden units') parser.add_argument('--window', type=int, default=24 * 7, help='window size') parser.add_argument('--CNN_kernel', type=int, default=6, help='the kernel size of the CNN layers') parser.add_argument('--highway_window', type=int, default=24, help='The window size of the highway component') parser.add_argument('--clip', type=float, default=10., help='gradient clipping') parser.add_argument('--epochs', type=int, default=100, help='upper epoch limit') parser.add_argument('--batch_size', type=int, default=128, metavar='N', help='batch size') parser.add_argument('--dropout', type=float, default=0.2, help='dropout applied to layers (0 = no dropout)') parser.add_argument('--seed', type=int, default=54321, help='random seed') parser.add_argument('--gpu', type=int, default=None) parser.add_argument('--log_interval', type=int, default=2000, metavar='N', help='report interval') parser.add_argument('--save', type=str, default='model/model.pt', help='path to save the final model') parser.add_argument('--cuda', type=str, default=True) parser.add_argument('--optim', type=str, default='adam') parser.add_argument('--lr', type=float, default=0.001) parser.add_argument('--horizon', type=int, default=12) parser.add_argument('--skip', type=float, default=24) parser.add_argument('--hidSkip', type=int, default=5) parser.add_argument('--L1Loss', type=bool, default=True) parser.add_argument('--normalize', type=int, default=2) parser.add_argument('--output_fun', type=str, default='sigmoid') parser.add_argument('--c_in', type=int, default=3) parser.add_argument('--seq_len', type=int, default=12) parser.add_argument('--c_out', type=int, default=2) parser.add_argument('--levels', type=int, default=3) args = parser.parse_args() args.cuda = args.gpu is not None if args.cuda: torch.cuda.set_device(args.gpu) # Set the random seed manually for reproducibility. torch.manual_seed(args.seed) if torch.cuda.is_available(): if not args.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") else: torch.cuda.manual_seed(args.seed) if args.data=="solar": dSet=Datasets.Solar().data elif args.data=="exchange_rate": dSet=Datasets.ExchangeRate().data elif args.data=="electricity": dSet=Datasets.Electricity().data elif args.data=="traffic": dSet=Datasets.Traffic().data Data = Data_utility(dSet, 0.6, 0.2, args.cuda, args.horizon, args.window, args.normalize); print(Data.rse); Data.train[0]=Data.train[0].permute(0,2,1) Data.valid[0]=Data.valid[0].permute(0,2,1) Data.test[0]=Data.test[0].permute(0,2,1) if args.model == "LSTNet": model = eval(args.model).Model(args, Data); elif args.model == "mWDN": model = mWDN(args) model.float() if args.cuda: model.cuda() else: model.cpu() nParams = sum([p.nelement() for p in model.parameters()]) print('* number of parameters: %d' % nParams) # for name, param in model.named_parameters(): # print(name) # param_in_param = [p for p in model.parameters()] # param_in_named_param = [p for name, p in model.named_parameters()] # for param1, param2 in zip(param_in_param, param_in_named_param): # assert param1.shape == param2.shape # if args.L1Loss: criterion = nn.L1Loss(size_average=False); else: criterion = nn.MSELoss(size_average=False); evaluateL2 = nn.MSELoss(size_average=False); evaluateL1 = nn.L1Loss(size_average=False) if args.cuda: criterion = criterion.cuda() evaluateL1 = evaluateL1.cuda(); evaluateL2 = evaluateL2.cuda(); best_val = 10000000; optim = Optim.Optim( model.parameters(), args.optim, args.lr, args.clip, ) # At any point you can hit Ctrl + C to break out of training early. try: print('begin training'); for epoch in range(1, args.epochs+1): epoch_start_time = time.time() train_loss = train(Data, Data.train[0], Data.train[1], model, criterion, optim, args.batch_size) val_loss, val_rae, val_corr = evaluate(Data, Data.valid[0], Data.valid[1], model, evaluateL2, evaluateL1, args.batch_size); print('| end of epoch {:3d} | time: {:5.2f}s | train_loss {:5.4f} | valid rse {:5.4f} | valid rae {:5.4f} | valid corr {:5.4f}'.format(epoch, (time.time() - epoch_start_time), train_loss, val_loss, val_rae, val_corr)) # Save the model if the validation loss is the best we've seen so far. if val_loss < best_val: with open(args.save, 'wb') as f: torch.save(model, f) best_val = val_loss if epoch % 5 == 0: test_acc, test_rae, test_corr = evaluate(Data, Data.test[0], Data.test[1], model, evaluateL2, evaluateL1, args.batch_size); print ("test rse {:5.4f} | test rae {:5.4f} | test corr {:5.4f}".format(test_acc, test_rae, test_corr)) except KeyboardInterrupt: print('-' * 89) print('Exiting from training early') # Load the best saved model. with open(args.save, 'rb') as f: model = torch.load(f) test_acc, test_rae, test_corr = evaluate(Data, Data.test[0], Data.test[1], model, evaluateL2, evaluateL1, args.batch_size); print ("test rse {:5.4f} | test rae {:5.4f} | test corr {:5.4f}".format(test_acc, test_rae, test_corr))

38.228571

226

0.643

import argparse import math import time import torch import torch.nn as nn from models import LSTNet from models.mWDN import mWDN import numpy as np; import importlib import Datasets from utils import *; import Optim def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size): model.eval(); total_loss = 0; total_loss_l1 = 0; n_samples = 0; predict = None; test = None; for X, Y in data.get_batches(X, Y, batch_size, False): if args.cuda: X = X.cuda(); Y = Y.cuda(); output = model(X); if predict is None: predict = output; test = Y; else: predict = torch.cat((predict,output)); test = torch.cat((test, Y)); scale = data.scale.expand(output.size(0), data.m) total_loss += evaluateL2(output * scale, Y * scale).item() total_loss_l1 += evaluateL1(output * scale, Y * scale).item() n_samples += (output.size(0) * data.m); rse = math.sqrt(total_loss / n_samples)/data.rse rae = (total_loss_l1/n_samples)/data.rae predict = predict.data.cpu().numpy(); Ytest = test.data.cpu().numpy(); sigma_p = (predict).std(axis = 0); sigma_g = (Ytest).std(axis = 0); mean_p = predict.mean(axis = 0) mean_g = Ytest.mean(axis = 0) index = (sigma_g!=0); correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g); correlation = (correlation[index]).mean(); return rse, rae, correlation; def train(data, X, Y, model, criterion, optim, batch_size): model.train(); total_loss = 0; n_samples = 0; for X, Y in data.get_batches(X, Y, batch_size, True): if args.cuda: X = X.cuda(); Y = Y.cuda(); model.zero_grad(); output = model(X); scale = data.scale.expand(output.size(0), data.m) loss = criterion(output * scale, Y * scale); loss.backward(); grad_norm = optim.step(); total_loss += loss.item(); n_samples += (output.size(0) * data.m); return total_loss / n_samples parser = argparse.ArgumentParser(description='PyTorch Time series forecasting') parser.add_argument('--data', type=str, required=True, help='location of the data file') parser.add_argument('--model', type=str, default='LSTNet', help='') parser.add_argument('--hidCNN', type=int, default=100, help='number of CNN hidden units') parser.add_argument('--hidRNN', type=int, default=100, help='number of RNN hidden units') parser.add_argument('--window', type=int, default=24 * 7, help='window size') parser.add_argument('--CNN_kernel', type=int, default=6, help='the kernel size of the CNN layers') parser.add_argument('--highway_window', type=int, default=24, help='The window size of the highway component') parser.add_argument('--clip', type=float, default=10., help='gradient clipping') parser.add_argument('--epochs', type=int, default=100, help='upper epoch limit') parser.add_argument('--batch_size', type=int, default=128, metavar='N', help='batch size') parser.add_argument('--dropout', type=float, default=0.2, help='dropout applied to layers (0 = no dropout)') parser.add_argument('--seed', type=int, default=54321, help='random seed') parser.add_argument('--gpu', type=int, default=None) parser.add_argument('--log_interval', type=int, default=2000, metavar='N', help='report interval') parser.add_argument('--save', type=str, default='model/model.pt', help='path to save the final model') parser.add_argument('--cuda', type=str, default=True) parser.add_argument('--optim', type=str, default='adam') parser.add_argument('--lr', type=float, default=0.001) parser.add_argument('--horizon', type=int, default=12) parser.add_argument('--skip', type=float, default=24) parser.add_argument('--hidSkip', type=int, default=5) parser.add_argument('--L1Loss', type=bool, default=True) parser.add_argument('--normalize', type=int, default=2) parser.add_argument('--output_fun', type=str, default='sigmoid') parser.add_argument('--c_in', type=int, default=3) parser.add_argument('--seq_len', type=int, default=12) parser.add_argument('--c_out', type=int, default=2) parser.add_argument('--levels', type=int, default=3) args = parser.parse_args() args.cuda = args.gpu is not None if args.cuda: torch.cuda.set_device(args.gpu) torch.manual_seed(args.seed) if torch.cuda.is_available(): if not args.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") else: torch.cuda.manual_seed(args.seed) if args.data=="solar": dSet=Datasets.Solar().data elif args.data=="exchange_rate": dSet=Datasets.ExchangeRate().data elif args.data=="electricity": dSet=Datasets.Electricity().data elif args.data=="traffic": dSet=Datasets.Traffic().data Data = Data_utility(dSet, 0.6, 0.2, args.cuda, args.horizon, args.window, args.normalize); print(Data.rse); Data.train[0]=Data.train[0].permute(0,2,1) Data.valid[0]=Data.valid[0].permute(0,2,1) Data.test[0]=Data.test[0].permute(0,2,1) if args.model == "LSTNet": model = eval(args.model).Model(args, Data); elif args.model == "mWDN": model = mWDN(args) model.float() if args.cuda: model.cuda() else: model.cpu() nParams = sum([p.nelement() for p in model.parameters()]) print('* number of parameters: %d' % nParams) if args.L1Loss: criterion = nn.L1Loss(size_average=False); else: criterion = nn.MSELoss(size_average=False); evaluateL2 = nn.MSELoss(size_average=False); evaluateL1 = nn.L1Loss(size_average=False) if args.cuda: criterion = criterion.cuda() evaluateL1 = evaluateL1.cuda(); evaluateL2 = evaluateL2.cuda(); best_val = 10000000; optim = Optim.Optim( model.parameters(), args.optim, args.lr, args.clip, ) try: print('begin training'); for epoch in range(1, args.epochs+1): epoch_start_time = time.time() train_loss = train(Data, Data.train[0], Data.train[1], model, criterion, optim, args.batch_size) val_loss, val_rae, val_corr = evaluate(Data, Data.valid[0], Data.valid[1], model, evaluateL2, evaluateL1, args.batch_size); print('| end of epoch {:3d} | time: {:5.2f}s | train_loss {:5.4f} | valid rse {:5.4f} | valid rae {:5.4f} | valid corr {:5.4f}'.format(epoch, (time.time() - epoch_start_time), train_loss, val_loss, val_rae, val_corr)) if val_loss < best_val: with open(args.save, 'wb') as f: torch.save(model, f) best_val = val_loss if epoch % 5 == 0: test_acc, test_rae, test_corr = evaluate(Data, Data.test[0], Data.test[1], model, evaluateL2, evaluateL1, args.batch_size); print ("test rse {:5.4f} | test rae {:5.4f} | test corr {:5.4f}".format(test_acc, test_rae, test_corr)) except KeyboardInterrupt: print('-' * 89) print('Exiting from training early') # Load the best saved model. with open(args.save, 'rb') as f: model = torch.load(f) test_acc, test_rae, test_corr = evaluate(Data, Data.test[0], Data.test[1], model, evaluateL2, evaluateL1, args.batch_size); print ("test rse {:5.4f} | test rae {:5.4f} | test corr {:5.4f}".format(test_acc, test_rae, test_corr))

true

1c2b2eacd034df49e2f4737731a48a56bfa4e8af

1,571

py

Python

tools/Polygraphy/polygraphy/config.py

hwkyai/TensorRT

d04182cd0086c70db4a8ad30e0d7675c4eb33782

[ "Apache-2.0" ]

null

tools/Polygraphy/polygraphy/config.py

hwkyai/TensorRT

d04182cd0086c70db4a8ad30e0d7675c4eb33782

[ "Apache-2.0" ]

null

tools/Polygraphy/polygraphy/config.py

hwkyai/TensorRT

d04182cd0086c70db4a8ad30e0d7675c4eb33782

[ "Apache-2.0" ]

null

# # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os INTERNAL_CORRECTNESS_CHECKS = bool(os.environ.get("POLYGRAPHY_INTERNAL_CORRECTNESS_CHECKS", "0") != "0") """ Whether internal correctness checks are enabled. This can be configured by setting the 'POLYGRAPHY_INTERNAL_CORRECTNESS_CHECKS' environment variable. """ AUTOINSTALL_DEPS = bool(os.environ.get("POLYGRAPHY_AUTOINSTALL_DEPS", "0") != "0") """ Whether Polygraphy will automatically install required Python packages at runtime. This can be configured by setting the 'POLYGRAPHY_AUTOINSTALL_DEPS' environment variable. """ ARRAY_SWAP_THRESHOLD_MB = int(os.environ.get("POLYGRAPHY_ARRAY_SWAP_THRESHOLD_MB", "-1")) """ The threshold, in megabytes, above which Polygraphy will evict a NumPy array from memory and swap it to disk. A negative value disables swapping and a value of 0 causes all arrays to be saved to disk. Disabled by default. This can be configured by setting the 'POLYGRAPHY_ARRAY_SWAP_THRESHOLD_MB' environment variable. """

41.342105

109

0.782304

import os INTERNAL_CORRECTNESS_CHECKS = bool(os.environ.get("POLYGRAPHY_INTERNAL_CORRECTNESS_CHECKS", "0") != "0") AUTOINSTALL_DEPS = bool(os.environ.get("POLYGRAPHY_AUTOINSTALL_DEPS", "0") != "0") ARRAY_SWAP_THRESHOLD_MB = int(os.environ.get("POLYGRAPHY_ARRAY_SWAP_THRESHOLD_MB", "-1"))

true

1c2b2f0ce5b70b75b13a6cbbbbe690ee0279af9c

708

py

Python

defining_classes/6_flower.py

Minkov/python-oop-2020-06

63b830a42b7abfac5bee576a81ee7626c47a80bc

[ "MIT" ]

3

2020-07-04T11:32:42.000Z

2020-08-14T08:43:25.000Z

defining_classes/6_flower.py

Minkov/python-oop-2020-06

63b830a42b7abfac5bee576a81ee7626c47a80bc

[ "MIT" ]

null

defining_classes/6_flower.py

Minkov/python-oop-2020-06

63b830a42b7abfac5bee576a81ee7626c47a80bc

[ "MIT" ]

2

2020-07-09T07:17:37.000Z

2021-02-22T22:55:52.000Z

class Flower: def __init__(self, name, water_requirements): self.name = name self.water_requirements = water_requirements self.current_water = 0 self.is_happy = False def water(self, quantity): self.current_water += quantity self.is_happy = self.get_happy_status() def get_happy_status(self): return self.water_requirements <= self.current_water def status(self): if self.is_happy: return f'{self.name} is happy' else: return f'{self.name} is not happy' flower = Flower("Lilly", 100) flower.water(50) print(flower.status()) flower.water(100) print(flower.status())

26.222222

61

0.620056

class Flower: def __init__(self, name, water_requirements): self.name = name self.water_requirements = water_requirements self.current_water = 0 self.is_happy = False def water(self, quantity): self.current_water += quantity self.is_happy = self.get_happy_status() def get_happy_status(self): return self.water_requirements <= self.current_water def status(self): if self.is_happy: return f'{self.name} is happy' else: return f'{self.name} is not happy' flower = Flower("Lilly", 100) flower.water(50) print(flower.status()) flower.water(100) print(flower.status())

true

1c2b2f67485747ddf6766f04c0cc97347b89b0ef

10,985

py

Python

hyperlib/manifold/poincare.py

sourface94/hyperlib

2353475a843070588a9faf62f075cb6c75082e48

[ "MIT" ]

null

hyperlib/manifold/poincare.py

sourface94/hyperlib

2353475a843070588a9faf62f075cb6c75082e48

[ "MIT" ]

null

hyperlib/manifold/poincare.py

sourface94/hyperlib

2353475a843070588a9faf62f075cb6c75082e48

[ "MIT" ]

null

import tensorflow as tf from .base import Manifold from ..utils.math import tanh, atanh_ class Poincare(Manifold): """ Implementation of the poincare manifold,. This class can be used for mathematical functions on the poincare manifold. """ def __init__(self,): super(Poincare, self).__init__() self.name = "Poincare" self.min_norm = 1e-15 self.eps = {tf.float32: 4e-3, tf.float64: 1e-5} self.k = 1.0 # scale of the hyperbolic space, k > 0. def mobius_matvec(self, m, x, c): """ Generalization for matrix-vector multiplication to hyperbolic space defined as math:: M \otimes_c x = (1/\sqrt{c}) \tanh\left( \frac{\|Mx\|_2}{\|x\|_2}\tanh^{-1}(\sqrt{c}\|x\|_2) \right)\frac{Mx}{\|Mx\|_2} Args: m : Tensor for multiplication x : Tensor point on poincare ball c : Tensor of size 1 representing the hyperbolic curvature. Returns Mobius matvec result """ sqrt_c = c ** 0.5 x_norm = tf.norm(x, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(x_norm) x_norm = tf.clip_by_value( x_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) mx = x @ m mx_norm = tf.norm(mx, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(mx_norm) mx_norm = tf.clip_by_value( mx_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) res_c = ( tanh(mx_norm / x_norm * atanh_(sqrt_c * x_norm)) * mx / (mx_norm * sqrt_c) ) cond = tf.reduce_prod( tf.cast((mx == 0), tf.uint8, name=None), axis=-1, keepdims=True ) res_0 = tf.zeros(1, dtype=res_c.dtype) res = tf.where(tf.cast(cond, tf.bool), res_0, res_c) return res def expmap(self, u, p, c): sqrt_c = c ** 0.5 #u_norm = u.norm(dim=-1, p=2, keepdim=True).clamp_min(self.min_norm) u_norm = tf.norm(u, axis=-1, ord=2, keepdims=True) u_norm = tf.clip_by_value( u_norm, clip_value_min=self.min_norm, clip_value_max=tf.math.reduce_max(u_norm) ) second_term = ( tanh(sqrt_c / 2 * self._lambda(p, c) * u_norm) * u / (sqrt_c * u_norm) ) gamma_1 = self.mobius_add(p, second_term, c) return gamma_1 def hyp_act(self, act, x, c_in, c_out): """Apply an activation function to a tensor in the hyperbolic space""" xt = act(self.logmap0(x, c=c_in)) return self.proj(self.expmap0(xt, c=c_out), c=c_out) # meijke implementation def expmap_m(self, u, x, c=1.0): """ Exponential map of u at p in the Poincare ball """ #u += 1e-15 #avoid u=0 u = tf.cast(u, tf.float64) x = tf.cast(x, tf.float64) c = tf.cast(c, tf.float64) sqrt_c = tf.math.sqrt(c) u_norm = self.clipped_norm(u) second_term = ( tanh(sqrt_c / 2 * self.lambda_x(x, c) * u_norm) * u / (sqrt_c * u_norm) ) return self.mobius_add(x, second_term, c) def expmap0(self, u, c): """ Hyperbolic exponential map at zero in the Poincare ball model. Args: u: tensor of size B x dimension representing tangent vectors. c: tensor of size 1 representing the hyperbolic curvature. Returns: Tensor of shape B x dimension. """ sqrt_c = c ** 0.5 max_num = tf.math.reduce_max(u) u_norm = tf.clip_by_value( tf.norm(u, axis=-1, ord=2, keepdims=True), clip_value_min=self.min_norm, clip_value_max=max_num, ) gamma_1 = tf.math.tanh(sqrt_c * u_norm) * u / (sqrt_c * u_norm) return gamma_1 def logmap0(self, p, c): """ Hyperbolic logarithmic map at zero in the Poincare ball model. Args: p: tensor of size B x dimension representing hyperbolic points. c: tensor of size 1 representing the hyperbolic curvature. Returns: Tensor of shape B x dimension. """ sqrt_c = c ** 0.5 p_norm = tf.norm(p, axis=-1, ord=2, keepdims=True) max_num = tf.math.reduce_max(p_norm) p_norm = tf.clip_by_value( p_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) scale = 1.0 / sqrt_c * atanh_(sqrt_c * p_norm) / p_norm return scale * p def proj(self, x, c): """ Safe projection on the manifold for numerical stability. This was mentioned in [1] Args: x : Tensor point on the Poincare ball c : Tensor of size 1 representing the hyperbolic curvature. Returns: Projected vector on the manifold References: [1] Hyperbolic Neural Networks, NIPS2018 https://arxiv.org/abs/1805.09112 """ x_for_norm = tf.norm(x, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(x_for_norm) norm = tf.clip_by_value( x_for_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) maxnorm = (1 - self.eps[x.dtype]) / (c ** 0.5) # tf.math.reduce_max(x) cond = norm > maxnorm projected = x / norm * maxnorm return tf.where(cond, projected, x) def mobius_add(self, x, y, c): """Element-wise Mobius addition. Args: x: Tensor of size B x dimension representing hyperbolic points. y: Tensor of size B x dimension representing hyperbolic points. c: Tensor of size 1 representing the absolute hyperbolic curvature. Returns: Tensor of shape B x dimension representing the element-wise Mobius addition of x and y. """ cx2 = c * tf.reduce_sum(x * x, axis=-1, keepdims=True) cy2 = c * tf.reduce_sum(y * y, axis=-1, keepdims=True) cxy = c * tf.reduce_sum(x * y, axis=-1, keepdims=True) num = (1 + 2 * cxy + cy2) * x + (1 - cx2) * y denom = 1 + 2 * cxy + cx2 * cy2 return self.proj(num / tf.maximum(denom, self.min_norm), c) # additions def _lambda(self, x, c=1.0, keepdims=False): """Compute the conformal factor :math:`lambda_x^k`""" #k = tf.cast(self.k, x.dtype) norm_x_2 = tf.reduce_sum(x * x, axis=-1, keepdims=keepdims) res = 2.0 / (1.0 - c * norm_x_2) max_num = tf.math.reduce_max(res) return tf.clip_by_value( res, clip_value_min=self.min_norm, clip_value_max=max_num ) def inner(self, x, u, v, keepdims=False): lambda_x = self._lambda(x, keepdims=keepdims) return tf.reduce_sum(u * v, axis=-1, keepdims=keepdims) * lambda_x ** 2 def proju(self, x, u): lambda_x = self._lambda(x, keepdims=True) return u / lambda_x ** 2 def projx(self, x): sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype)) norm = tf.linalg.norm(x, axis=-1, keepdims=True) def get_eps(val): return np.finfo(val.dtype.name).eps return tf.where( sqrt_k * norm < tf.ones_like(norm), x, x / (sqrt_k * norm + 10 * get_eps(x)), ) def egrad2rgrad(self, x, u): lambda_x = self._lambda(x, keepdims=True) return u / lambda_x ** 2 def _mobius_add(self, x, y): """Compute the Möbius addition of :math:`x` and :math:`y` in :math:`\mathcal{D}^{n}_{k}` :math:`x \oplus y = \frac{(1 + 2k\langle x, y\rangle + k||y||^2)x + (1 - k||x||^2)y}{1 + 2k\langle x,y\rangle + k^2||x||^2||y||^2}` """ x_2 = tf.reduce_sum(tf.math.square(x), axis=-1, keepdims=True) y_2 = tf.reduce_sum(tf.math.square(y), axis=-1, keepdims=True) x_y = tf.reduce_sum(x * y, axis=-1, keepdims=True) k = tf.cast(self.k, x.dtype) return ((1 + 2 * k * x_y + k * y_2) * x + (1 - k * x_2) * y) / ( 1 + 2 * k * x_y + k ** 2 * x_2 * y_2 ) def _gyration(self, u, v, w): """Compute the gyration of :math:`u`, :math:`v`, :math:`w`: :math:`\operatorname{gyr}[u, v]w = \ominus (u \oplus_\kappa v) \oplus (u \oplus_\kappa (v \oplus_\kappa w))` """ min_u_v = -self._mobius_add(u, v) v_w = self._mobius_add(v, w) u_v_w = self._mobius_add(u, v_w) return self._mobius_add(min_u_v, u_v_w) def ptransp(self, x, y, v): lambda_x = self._lambda(x, keepdims=True) lambda_y = self._lambda(y, keepdims=True) return self._gyration(y, -x, v) * lambda_x / lambda_y transp = ptransp def exp(self, x, u): sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype)) norm_u = tf.linalg.norm(u, axis=-1, keepdims=True) lambda_x = self._lambda(x, keepdims=True) y = ( tf.math.tanh(sqrt_k * norm_u * lambda_x / 2.0) * u / (sqrt_k * norm_u) ) return self._mobius_add(x, y) retr = exp # hmath meijke def clipped_norm(self, x, max_norm = None): """ Clipped Euclidean norm of x """ x_norm = tf.norm(x, axis=-1, ord=2, keepdims=True) if max_norm is None: max_norm= tf.math.reduce_max(x_norm) return tf.clip_by_value( x_norm, clip_value_min=self.min_norm, clip_value_max=max_norm, ) def gyr(self, x, y, z, c=1.0): """ Ungar's gryation operation defined in [1]. math:: gyr[x,y]z = \ominus (x \oplus y)\oplus(x \oplus (y \oplus z)) where \oplus is Mobius addition and \ominus is the left inverse. Args: x, y, z: Tensors of size B x dim in the Poincare ball of curvature c Returns: Tensor of size B x dim Reference: [1] A. Ungar, A Gryovector Space Approach to Hyperbolic Geometry """ xy = tf.reduce_sum( x*y, axis=-1, keepdims=True) yz = tf.reduce_sum( y*z, axis=-1, keepdims=True) xz = tf.reduce_sum( x*z, axis=-1, keepdims=True) x2 = tf.reduce_sum( x*x, axis=-1, keepdims=True) y2 = tf.reduce_sum( y*y, axis=-1, keepdims=True) z2 = tf.reduce_sum( z*z, axis=-1, keepdims=True) A = c*yz - c**2 * xz * y2 + 2 * c**2 * xy * yz B = c**2 * yz * x2 + c * xz C = 1 + 2 * c* xy + c**2 * x2 * y2 return tf.add(2*tf.divide(A * x - B * y, C), z) def lambda_x(self, x, c=1.0): """ Poincare conformal factor at point x """ cx2 = c * tf.reduce_sum(x * x, axis=-1, keepdims=True) return 2.0 / (1.0 - cx2) def parallel_transport(self, x, y, v, c=1.0): """ The parallel transport of the tangent vector v from the tangent space at x to the tangent space at y """ return tf.divide(self.lambda_x(x,c), self.lambda_x(y,c)) * self.gyr(y,-x,v)

36.374172

121

0.558398

import tensorflow as tf from .base import Manifold from ..utils.math import tanh, atanh_ class Poincare(Manifold): def __init__(self,): super(Poincare, self).__init__() self.name = "Poincare" self.min_norm = 1e-15 self.eps = {tf.float32: 4e-3, tf.float64: 1e-5} self.k = 1.0 def mobius_matvec(self, m, x, c): sqrt_c = c ** 0.5 x_norm = tf.norm(x, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(x_norm) x_norm = tf.clip_by_value( x_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) mx = x @ m mx_norm = tf.norm(mx, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(mx_norm) mx_norm = tf.clip_by_value( mx_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) res_c = ( tanh(mx_norm / x_norm * atanh_(sqrt_c * x_norm)) * mx / (mx_norm * sqrt_c) ) cond = tf.reduce_prod( tf.cast((mx == 0), tf.uint8, name=None), axis=-1, keepdims=True ) res_0 = tf.zeros(1, dtype=res_c.dtype) res = tf.where(tf.cast(cond, tf.bool), res_0, res_c) return res def expmap(self, u, p, c): sqrt_c = c ** 0.5 u_norm = tf.norm(u, axis=-1, ord=2, keepdims=True) u_norm = tf.clip_by_value( u_norm, clip_value_min=self.min_norm, clip_value_max=tf.math.reduce_max(u_norm) ) second_term = ( tanh(sqrt_c / 2 * self._lambda(p, c) * u_norm) * u / (sqrt_c * u_norm) ) gamma_1 = self.mobius_add(p, second_term, c) return gamma_1 def hyp_act(self, act, x, c_in, c_out): xt = act(self.logmap0(x, c=c_in)) return self.proj(self.expmap0(xt, c=c_out), c=c_out) def expmap_m(self, u, x, c=1.0): = tf.cast(u, tf.float64) x = tf.cast(x, tf.float64) c = tf.cast(c, tf.float64) sqrt_c = tf.math.sqrt(c) u_norm = self.clipped_norm(u) second_term = ( tanh(sqrt_c / 2 * self.lambda_x(x, c) * u_norm) * u / (sqrt_c * u_norm) ) return self.mobius_add(x, second_term, c) def expmap0(self, u, c): sqrt_c = c ** 0.5 max_num = tf.math.reduce_max(u) u_norm = tf.clip_by_value( tf.norm(u, axis=-1, ord=2, keepdims=True), clip_value_min=self.min_norm, clip_value_max=max_num, ) gamma_1 = tf.math.tanh(sqrt_c * u_norm) * u / (sqrt_c * u_norm) return gamma_1 def logmap0(self, p, c): sqrt_c = c ** 0.5 p_norm = tf.norm(p, axis=-1, ord=2, keepdims=True) max_num = tf.math.reduce_max(p_norm) p_norm = tf.clip_by_value( p_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) scale = 1.0 / sqrt_c * atanh_(sqrt_c * p_norm) / p_norm return scale * p def proj(self, x, c): x_for_norm = tf.norm(x, axis=-1, keepdims=True, ord=2) max_num = tf.math.reduce_max(x_for_norm) norm = tf.clip_by_value( x_for_norm, clip_value_min=self.min_norm, clip_value_max=max_num ) maxnorm = (1 - self.eps[x.dtype]) / (c ** 0.5) cond = norm > maxnorm projected = x / norm * maxnorm return tf.where(cond, projected, x) def mobius_add(self, x, y, c): cx2 = c * tf.reduce_sum(x * x, axis=-1, keepdims=True) cy2 = c * tf.reduce_sum(y * y, axis=-1, keepdims=True) cxy = c * tf.reduce_sum(x * y, axis=-1, keepdims=True) num = (1 + 2 * cxy + cy2) * x + (1 - cx2) * y denom = 1 + 2 * cxy + cx2 * cy2 return self.proj(num / tf.maximum(denom, self.min_norm), c) def _lambda(self, x, c=1.0, keepdims=False): norm_x_2 = tf.reduce_sum(x * x, axis=-1, keepdims=keepdims) res = 2.0 / (1.0 - c * norm_x_2) max_num = tf.math.reduce_max(res) return tf.clip_by_value( res, clip_value_min=self.min_norm, clip_value_max=max_num ) def inner(self, x, u, v, keepdims=False): lambda_x = self._lambda(x, keepdims=keepdims) return tf.reduce_sum(u * v, axis=-1, keepdims=keepdims) * lambda_x ** 2 def proju(self, x, u): lambda_x = self._lambda(x, keepdims=True) return u / lambda_x ** 2 def projx(self, x): sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype)) norm = tf.linalg.norm(x, axis=-1, keepdims=True) def get_eps(val): return np.finfo(val.dtype.name).eps return tf.where( sqrt_k * norm < tf.ones_like(norm), x, x / (sqrt_k * norm + 10 * get_eps(x)), ) def egrad2rgrad(self, x, u): lambda_x = self._lambda(x, keepdims=True) return u / lambda_x ** 2 def _mobius_add(self, x, y): x_2 = tf.reduce_sum(tf.math.square(x), axis=-1, keepdims=True) y_2 = tf.reduce_sum(tf.math.square(y), axis=-1, keepdims=True) x_y = tf.reduce_sum(x * y, axis=-1, keepdims=True) k = tf.cast(self.k, x.dtype) return ((1 + 2 * k * x_y + k * y_2) * x + (1 - k * x_2) * y) / ( 1 + 2 * k * x_y + k ** 2 * x_2 * y_2 ) def _gyration(self, u, v, w): min_u_v = -self._mobius_add(u, v) v_w = self._mobius_add(v, w) u_v_w = self._mobius_add(u, v_w) return self._mobius_add(min_u_v, u_v_w) def ptransp(self, x, y, v): lambda_x = self._lambda(x, keepdims=True) lambda_y = self._lambda(y, keepdims=True) return self._gyration(y, -x, v) * lambda_x / lambda_y transp = ptransp def exp(self, x, u): sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype)) norm_u = tf.linalg.norm(u, axis=-1, keepdims=True) lambda_x = self._lambda(x, keepdims=True) y = ( tf.math.tanh(sqrt_k * norm_u * lambda_x / 2.0) * u / (sqrt_k * norm_u) ) return self._mobius_add(x, y) retr = exp def clipped_norm(self, x, max_norm = None): x_norm = tf.norm(x, axis=-1, ord=2, keepdims=True) if max_norm is None: max_norm= tf.math.reduce_max(x_norm) return tf.clip_by_value( x_norm, clip_value_min=self.min_norm, clip_value_max=max_norm, ) def gyr(self, x, y, z, c=1.0): xy = tf.reduce_sum( x*y, axis=-1, keepdims=True) yz = tf.reduce_sum( y*z, axis=-1, keepdims=True) xz = tf.reduce_sum( x*z, axis=-1, keepdims=True) x2 = tf.reduce_sum( x*x, axis=-1, keepdims=True) y2 = tf.reduce_sum( y*y, axis=-1, keepdims=True) z2 = tf.reduce_sum( z*z, axis=-1, keepdims=True) A = c*yz - c**2 * xz * y2 + 2 * c**2 * xy * yz B = c**2 * yz * x2 + c * xz C = 1 + 2 * c* xy + c**2 * x2 * y2 return tf.add(2*tf.divide(A * x - B * y, C), z) def lambda_x(self, x, c=1.0): cx2 = c * tf.reduce_sum(x * x, axis=-1, keepdims=True) return 2.0 / (1.0 - cx2) def parallel_transport(self, x, y, v, c=1.0): return tf.divide(self.lambda_x(x,c), self.lambda_x(y,c)) * self.gyr(y,-x,v)

true

1c2b2f742468695c7088ead48076b290d5274a1a

8,268

py

Python

conoha/api/network.py

ttk1/conoha-cli

d1c68ee63e9c61a0a727a24206a1fd8aa4abcf13

[ "MIT" ]

null

conoha/api/network.py

ttk1/conoha-cli

d1c68ee63e9c61a0a727a24206a1fd8aa4abcf13

[ "MIT" ]

null

conoha/api/network.py

ttk1/conoha-cli

d1c68ee63e9c61a0a727a24206a1fd8aa4abcf13

[ "MIT" ]

null

''' Network API の呼び出し部分 ''' from conoha import config from conoha.util import http endpoint = config.get_config()['endpoint']['network'] def list_networks(): ''' https://www.conoha.jp/docs/neutron-get_networks_list.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/networks', headers) def create_network(): ''' https://www.conoha.jp/docs/neutron-add_network.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.post(f'{endpoint}/networks', None, headers) def delete_network(network_id): ''' https://www.conoha.jp/docs/neutron-remove_network.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/networks/{network_id}', headers) def describe_network(network_id): ''' https://www.conoha.jp/docs/neutron-get_networks_detail_specified.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/networks/{network_id}', headers) ########################################################################### def create_port(network_id, ip_address, subnet_id, security_group_ids=None): ''' https://www.conoha.jp/docs/neutron-add_port.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } # 必須項目 data = { 'port': { 'network_id': network_id, 'fixed_ips': [{ 'ip_address': ip_address, 'subnet_id': subnet_id }] } } # Optional 項目 if security_group_ids is not None: data['port']['security_groups'] = security_group_ids return http.post(f'{endpoint}/ports', data, headers) def update_port(port_id, security_group_ids): ''' https://www.conoha.jp/docs/neutron-update_port.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } data = { 'port': { 'security_groups': security_group_ids } } return http.put(f'{endpoint}/ports/{port_id}', data, headers) def delete_port(port_id): ''' https://www.conoha.jp/docs/neutron-remove_port.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/ports/{port_id}', headers) def list_ports(): ''' https://www.conoha.jp/docs/neutron-get_ports_list.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/ports', headers) def describe_port(port_id): ''' https://www.conoha.jp/docs/neutron-get_ports_detail_specified.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/ports/{port_id}', headers) ########################################################################### def create_subnet(network_id, cidr): ''' https://www.conoha.jp/docs/neutron-add_subnet.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } data = { 'subnet': { 'network_id': network_id, 'cidr': cidr } } return http.post(f'{endpoint}/subnets', data, headers) def delete_subnet(subnet_id): ''' https://www.conoha.jp/docs/neutron-remove_subnet.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/subnets/{subnet_id}', headers) def list_subnets(): ''' https://www.conoha.jp/docs/neutron-get_subnets_list.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/subnets', headers) def describe_subnet(subnet_id): ''' https://www.conoha.jp/docs/neutron-get_subnets_detail_specified.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/subnets/{subnet_id}', headers) ########################################################################### def create_security_group(name, description=None): ''' https://www.conoha.jp/docs/neutron-create_secgroup.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } # 必須項目 data = { 'security_group': { 'name': name } } # Optional 項目 if description is not None: data['security_group']['description'] = description return http.post(f'{endpoint}/security-groups', data, headers) def delete_security_group(security_group_id): ''' https://www.conoha.jp/docs/neutron-delete_secgroup.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/security-groups/{security_group_id}', headers) def list_security_groups(): ''' https://www.conoha.jp/docs/neutron-get_secgroups_list.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/security-groups', headers) def describe_security_group(security_group_id): ''' https://www.conoha.jp/docs/neutron-get_secgroups_detail_specified.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/security-groups/{security_group_id}', headers) ########################################################################### def create_security_group_rule(direction, ether_type, security_group_id, port_range_min=None, port_range_max=None, protocol=None, remote_group_id=None, remote_ip_prefix=None): ''' https://www.conoha.jp/docs/neutron-create_rule_on_secgroup.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } # 必須項目 data = { 'security_group_rule': { 'direction': direction, 'ethertype': ether_type, 'security_group_id': security_group_id } } # Optional 項目 if port_range_min is not None: data['security_group_rule']['port_range_min'] = port_range_min if port_range_max is not None: data['security_group_rule']['port_range_max'] = port_range_max if protocol is not None and protocol != 'null': data['security_group_rule']['protocol'] = protocol if remote_group_id is not None: data['security_group_rule']['remote_group_id'] = remote_group_id if remote_ip_prefix is not None: data['security_group_rule']['remote_ip_prefix'] = remote_ip_prefix return http.post(f'{endpoint}/security-group-rules', data, headers) def delete_security_group_rule(rule_id): ''' https://www.conoha.jp/docs/neutron-delete_rule_on_secgroup.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/security-group-rules/{rule_id}', headers) def list_security_group_rules(): ''' https://www.conoha.jp/docs/neutron-get_rules_on_secgroup.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/security-group-rules', headers) def describe_security_group_rule(rule_id): ''' https://www.conoha.jp/docs/neutron-get_rules_detail_specified.php ''' headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/security-group-rules/{rule_id}', headers)

26.5

87

0.583454

from conoha import config from conoha.util import http endpoint = config.get_config()['endpoint']['network'] def list_networks(): headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/networks', headers) def create_network(): headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.post(f'{endpoint}/networks', None, headers) def delete_network(network_id): headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.delete(f'{endpoint}/networks/{network_id}', headers) def describe_network(network_id): headers = { 'Accept': 'application/json', 'X-Auth-Token': config.get_token()['id'] } return http.get(f'{endpoint}/networks/{network_id}', headers)

true

1c2b3275f73874280dbbf256213b60596356c59d

183

py

Python

gym_multigrid/envs/__init__.py

n0whereRuoxi/gym-multigrid

98809bd40b3d4a0bfa1ab909b1a748fe82d71b60

[ "Apache-2.0" ]

null

gym_multigrid/envs/__init__.py

n0whereRuoxi/gym-multigrid

98809bd40b3d4a0bfa1ab909b1a748fe82d71b60

[ "Apache-2.0" ]

null

gym_multigrid/envs/__init__.py

n0whereRuoxi/gym-multigrid

98809bd40b3d4a0bfa1ab909b1a748fe82d71b60

[ "Apache-2.0" ]

null

from gym_multigrid.envs.collect_game import CollectGame4HEnv10x10N2 from gym_multigrid.envs.soccer_game import SoccerGame4HEnv10x15N2 from gym_multigrid.envs.doorkey import DoorKeyEnv

61

67

0.907104

from gym_multigrid.envs.collect_game import CollectGame4HEnv10x10N2 from gym_multigrid.envs.soccer_game import SoccerGame4HEnv10x15N2 from gym_multigrid.envs.doorkey import DoorKeyEnv

true

1c2b32fec97d43e768fb54eff2e7f5f1493499f4

71

py

Python

src/fdk_organization_bff/config/__init__.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

null

src/fdk_organization_bff/config/__init__.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

47

2020-05-14T07:54:48.000Z

2022-03-29T22:17:08.000Z

src/fdk_organization_bff/config/__init__.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

null

"""Config package. Modules: config """ from .config import Config

10.142857

26

0.676056

from .config import Config

true

1c2b3475de2a1e1297fba8b659a529d7453411fa

42,050

py

Python

.mywaflib/waflib/Build.py

tobiasraabe/crypto

5b40049169cfbf02f4979a55e8abdb77b834b820

[ "BSD-3-Clause" ]

null

.mywaflib/waflib/Build.py

tobiasraabe/crypto

5b40049169cfbf02f4979a55e8abdb77b834b820

[ "BSD-3-Clause" ]

1

2017-08-31T15:55:24.000Z

.mywaflib/waflib/Build.py

tobiasraabe/crypto

5b40049169cfbf02f4979a55e8abdb77b834b820

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # encoding: utf-8 # Thomas Nagy, 2005-2016 (ita) """ Classes related to the build phase (build, clean, install, step, etc) The inheritance tree is the following: """ import os, sys, errno, re, shutil, stat try: import cPickle except ImportError: import pickle as cPickle from waflib import Node, Runner, TaskGen, Utils, ConfigSet, Task, Logs, Options, Context, Errors CACHE_DIR = 'c4che' """Name of the cache directory""" CACHE_SUFFIX = '_cache.py' """ConfigSet cache files for variants are written under :py:attr:´waflib.Build.CACHE_DIR´ in the form ´variant_name´_cache.py""" INSTALL = 1337 """Positive value '->' install, see :py:attr:`waflib.Build.BuildContext.is_install`""" UNINSTALL = -1337 """Negative value '<-' uninstall, see :py:attr:`waflib.Build.BuildContext.is_install`""" SAVED_ATTRS = 'root node_sigs task_sigs imp_sigs raw_deps node_deps'.split() """Build class members to save between the runs; these should be all dicts except for `root` which represents a :py:class:`waflib.Node.Node` instance """ CFG_FILES = 'cfg_files' """Files from the build directory to hash before starting the build (``config.h`` written during the configuration)""" POST_AT_ONCE = 0 """Post mode: all task generators are posted before any task executed""" POST_LAZY = 1 """Post mode: post the task generators group after group, the tasks in the next group are created when the tasks in the previous groups are done""" PROTOCOL = -1 if sys.platform == 'cli': PROTOCOL = 0 class BuildContext(Context.Context): '''executes the build''' cmd = 'build' variant = '' def __init__(self, **kw): super(BuildContext, self).__init__(**kw) self.is_install = 0 """Non-zero value when installing or uninstalling file""" self.top_dir = kw.get('top_dir', Context.top_dir) """See :py:attr:`waflib.Context.top_dir`; prefer :py:attr:`waflib.Build.BuildContext.srcnode`""" self.out_dir = kw.get('out_dir', Context.out_dir) """See :py:attr:`waflib.Context.out_dir`; prefer :py:attr:`waflib.Build.BuildContext.bldnode`""" self.run_dir = kw.get('run_dir', Context.run_dir) """See :py:attr:`waflib.Context.run_dir`""" self.launch_dir = Context.launch_dir """See :py:attr:`waflib.Context.out_dir`; prefer :py:meth:`waflib.Build.BuildContext.launch_node`""" self.post_mode = POST_LAZY """Whether to post the task generators at once or group-by-group (default is group-by-group)""" self.cache_dir = kw.get('cache_dir') if not self.cache_dir: self.cache_dir = os.path.join(self.out_dir, CACHE_DIR) self.all_envs = {} """Map names to :py:class:`waflib.ConfigSet.ConfigSet`, the empty string must map to the default environment""" # ======================================= # # cache variables self.node_sigs = {} """Dict mapping build nodes to task identifier (uid), it indicates whether a task created a particular file (persists across builds)""" self.task_sigs = {} """Dict mapping task identifiers (uid) to task signatures (persists across builds)""" self.imp_sigs = {} """Dict mapping task identifiers (uid) to implicit task dependencies used for scanning targets (persists across builds)""" self.node_deps = {} """Dict mapping task identifiers (uid) to node dependencies found by :py:meth:`waflib.Task.Task.scan` (persists across builds)""" self.raw_deps = {} """Dict mapping task identifiers (uid) to custom data returned by :py:meth:`waflib.Task.Task.scan` (persists across builds)""" self.task_gen_cache_names = {} self.jobs = Options.options.jobs """Amount of jobs to run in parallel""" self.targets = Options.options.targets """List of targets to build (default: \*)""" self.keep = Options.options.keep """Whether the build should continue past errors""" self.progress_bar = Options.options.progress_bar """ Level of progress status: 0. normal output 1. progress bar 2. IDE output 3. No output at all """ # Manual dependencies. self.deps_man = Utils.defaultdict(list) """Manual dependencies set by :py:meth:`waflib.Build.BuildContext.add_manual_dependency`""" # just the structure here self.current_group = 0 """ Current build group """ self.groups = [] """ List containing lists of task generators """ self.group_names = {} """ Map group names to the group lists. See :py:meth:`waflib.Build.BuildContext.add_group` """ for v in SAVED_ATTRS: if not hasattr(self, v): setattr(self, v, {}) def get_variant_dir(self): """Getter for the variant_dir attribute""" if not self.variant: return self.out_dir return os.path.join(self.out_dir, self.variant) variant_dir = property(get_variant_dir, None) def __call__(self, *k, **kw): """ Create a task generator and add it to the current build group. The following forms are equivalent:: def build(bld): tg = bld(a=1, b=2) def build(bld): tg = bld() tg.a = 1 tg.b = 2 def build(bld): tg = TaskGen.task_gen(a=1, b=2) bld.add_to_group(tg, None) :param group: group name to add the task generator to :type group: string """ kw['bld'] = self ret = TaskGen.task_gen(*k, **kw) self.task_gen_cache_names = {} # reset the cache, each time self.add_to_group(ret, group=kw.get('group')) return ret def rule(self, *k, **kw): """ Wrapper for creating a task generator using the decorator notation. The following code:: @bld.rule(target="foo") def _(tsk): print("bar") is equivalent to:: def bar(tsk): print("bar") bld( target = "foo", rule = bar, ) """ def f(rule): ret = self(*k, **kw) ret.rule = rule return ret return f def __copy__(self): """ Build contexts cannot be copied :raises: :py:class:`waflib.Errors.WafError` """ raise Errors.WafError('build contexts cannot be copied') def load_envs(self): """ The configuration command creates files of the form ``build/c4che/NAMEcache.py``. This method creates a :py:class:`waflib.ConfigSet.ConfigSet` instance for each ``NAME`` by reading those files and stores them in :py:attr:`waflib.Build.BuildContext.allenvs`. """ node = self.root.find_node(self.cache_dir) if not node: raise Errors.WafError('The project was not configured: run "waf configure" first!') lst = node.ant_glob('**/*%s' % CACHE_SUFFIX, quiet=True) if not lst: raise Errors.WafError('The cache directory is empty: reconfigure the project') for x in lst: name = x.path_from(node).replace(CACHE_SUFFIX, '').replace('\\', '/') env = ConfigSet.ConfigSet(x.abspath()) self.all_envs[name] = env for f in env[CFG_FILES]: newnode = self.root.find_resource(f) if not newnode or not newnode.exists(): raise Errors.WafError('Missing configuration file %r, reconfigure the project!' % f) def init_dirs(self): """ Initialize the project directory and the build directory by creating the nodes :py:attr:`waflib.Build.BuildContext.srcnode` and :py:attr:`waflib.Build.BuildContext.bldnode` corresponding to ``top_dir`` and ``variant_dir`` respectively. The ``bldnode`` directory is created if necessary. """ if not (os.path.isabs(self.top_dir) and os.path.isabs(self.out_dir)): raise Errors.WafError('The project was not configured: run "waf configure" first!') self.path = self.srcnode = self.root.find_dir(self.top_dir) self.bldnode = self.root.make_node(self.variant_dir) self.bldnode.mkdir() def execute(self): """ Restore data from previous builds and call :py:meth:`waflib.Build.BuildContext.execute_build`. Overrides from :py:func:`waflib.Context.Context.execute` """ self.restore() if not self.all_envs: self.load_envs() self.execute_build() def execute_build(self): """ Execute the build by: * reading the scripts (see :py:meth:`waflib.Context.Context.recurse`) * calling :py:meth:`waflib.Build.BuildContext.pre_build` to call user build functions * calling :py:meth:`waflib.Build.BuildContext.compile` to process the tasks * calling :py:meth:`waflib.Build.BuildContext.post_build` to call user build functions """ Logs.info("Waf: Entering directory `%s'", self.variant_dir) self.recurse([self.run_dir]) self.pre_build() # display the time elapsed in the progress bar self.timer = Utils.Timer() try: self.compile() finally: if self.progress_bar == 1 and sys.stderr.isatty(): c = self.producer.processed or 1 m = self.progress_line(c, c, Logs.colors.BLUE, Logs.colors.NORMAL) Logs.info(m, extra={'stream': sys.stderr, 'c1': Logs.colors.cursor_off, 'c2' : Logs.colors.cursor_on}) Logs.info("Waf: Leaving directory `%s'", self.variant_dir) try: self.producer.bld = None del self.producer except AttributeError: pass self.post_build() def restore(self): """ Load data from a previous run, sets the attributes listed in :py:const:`waflib.Build.SAVED_ATTRS` """ try: env = ConfigSet.ConfigSet(os.path.join(self.cache_dir, 'build.config.py')) except EnvironmentError: pass else: if env.version < Context.HEXVERSION: raise Errors.WafError('Version mismatch! reconfigure the project') for t in env.tools: self.setup(**t) dbfn = os.path.join(self.variant_dir, Context.DBFILE) try: data = Utils.readf(dbfn, 'rb') except (EnvironmentError, EOFError): # handle missing file/empty file Logs.debug('build: Could not load the build cache %s (missing)', dbfn) else: try: Node.pickle_lock.acquire() Node.Nod3 = self.node_class try: data = cPickle.loads(data) except Exception as e: Logs.debug('build: Could not pickle the build cache %s: %r', dbfn, e) else: for x in SAVED_ATTRS: setattr(self, x, data.get(x, {})) finally: Node.pickle_lock.release() self.init_dirs() def store(self): """ Store data for next runs, set the attributes listed in :py:const:`waflib.Build.SAVED_ATTRS`. Uses a temporary file to avoid problems on ctrl+c. """ data = {} for x in SAVED_ATTRS: data[x] = getattr(self, x) db = os.path.join(self.variant_dir, Context.DBFILE) try: Node.pickle_lock.acquire() Node.Nod3 = self.node_class x = cPickle.dumps(data, PROTOCOL) finally: Node.pickle_lock.release() Utils.writef(db + '.tmp', x, m='wb') try: st = os.stat(db) os.remove(db) if not Utils.is_win32: # win32 has no chown but we're paranoid os.chown(db + '.tmp', st.st_uid, st.st_gid) except (AttributeError, OSError): pass # do not use shutil.move (copy is not thread-safe) os.rename(db + '.tmp', db) def compile(self): """ Run the build by creating an instance of :py:class:`waflib.Runner.Parallel` The cache file is written when at least a task was executed. :raises: :py:class:`waflib.Errors.BuildError` in case the build fails """ Logs.debug('build: compile()') # delegate the producer-consumer logic to another object to reduce the complexity self.producer = Runner.Parallel(self, self.jobs) self.producer.biter = self.get_build_iterator() try: self.producer.start() except KeyboardInterrupt: self.store() raise else: if self.producer.dirty: self.store() if self.producer.error: raise Errors.BuildError(self.producer.error) def setup(self, tool, tooldir=None, funs=None): """ Import waf tools defined during the configuration:: def configure(conf): conf.load('glib2') def build(bld): pass # glib2 is imported implicitly :param tool: tool list :type tool: list :param tooldir: optional tool directory (sys.path) :type tooldir: list of string :param funs: unused variable """ if isinstance(tool, list): for i in tool: self.setup(i, tooldir) return module = Context.load_tool(tool, tooldir) if hasattr(module, "setup"): module.setup(self) def get_env(self): """Getter for the env property""" try: return self.all_envs[self.variant] except KeyError: return self.all_envs[''] def set_env(self, val): """Setter for the env property""" self.all_envs[self.variant] = val env = property(get_env, set_env) def add_manual_dependency(self, path, value): """ Adds a dependency from a node object to a value:: def build(bld): bld.add_manual_dependency( bld.path.find_resource('wscript'), bld.root.find_resource('/etc/fstab')) :param path: file path :type path: string or :py:class:`waflib.Node.Node` :param value: value to depend :type value: :py:class:`waflib.Node.Node`, byte object, or function returning a byte object """ if not path: raise ValueError('Invalid input path %r' % path) if isinstance(path, Node.Node): node = path elif os.path.isabs(path): node = self.root.find_resource(path) else: node = self.path.find_resource(path) if not node: raise ValueError('Could not find the path %r' % path) if isinstance(value, list): self.deps_man[node].extend(value) else: self.deps_man[node].append(value) def launch_node(self): """Returns the launch directory as a :py:class:`waflib.Node.Node` object (cached)""" try: # private cache return self.p_ln except AttributeError: self.p_ln = self.root.find_dir(self.launch_dir) return self.p_ln def hash_env_vars(self, env, vars_lst): """ Hashes configuration set variables:: def build(bld): bld.hash_env_vars(bld.env, ['CXX', 'CC']) This method uses an internal cache. :param env: Configuration Set :type env: :py:class:`waflib.ConfigSet.ConfigSet` :param vars_lst: list of variables :type vars_list: list of string """ if not env.table: env = env.parent if not env: return Utils.SIG_NIL idx = str(id(env)) + str(vars_lst) try: cache = self.cache_env except AttributeError: cache = self.cache_env = {} else: try: return self.cache_env[idx] except KeyError: pass lst = [env[a] for a in vars_lst] cache[idx] = ret = Utils.h_list(lst) Logs.debug('envhash: %s %r', Utils.to_hex(ret), lst) return ret def get_tgen_by_name(self, name): """ Fetches a task generator by its name or its target attribute; the name must be unique in a build:: def build(bld): tg = bld(name='foo') tg == bld.get_tgen_by_name('foo') This method use a private internal cache. :param name: Task generator name :raises: :py:class:`waflib.Errors.WafError` in case there is no task genenerator by that name """ cache = self.task_gen_cache_names if not cache: # create the index lazily for g in self.groups: for tg in g: try: cache[tg.name] = tg except AttributeError: # raised if not a task generator, which should be uncommon pass try: return cache[name] except KeyError: raise Errors.WafError('Could not find a task generator for the name %r' % name) def progress_line(self, idx, total, col1, col2): """ Computes a progress bar line displayed when running ``waf -p`` :returns: progress bar line :rtype: string """ if not sys.stderr.isatty(): return '' n = len(str(total)) Utils.rot_idx += 1 ind = Utils.rot_chr[Utils.rot_idx % 4] pc = (100. * idx)/total fs = "[%%%dd/%%d][%%s%%2d%%%%%%s][%s][" % (n, ind) left = fs % (idx, total, col1, pc, col2) right = '][%s%s%s]' % (col1, self.timer, col2) cols = Logs.get_term_cols() - len(left) - len(right) + 2*len(col1) + 2*len(col2) if cols < 7: cols = 7 ratio = ((cols * idx)//total) - 1 bar = ('='*ratio+'>').ljust(cols) msg = Logs.indicator % (left, bar, right) return msg def declare_chain(self, *k, **kw): """ Wraps :py:func:`waflib.TaskGen.declare_chain` for convenience """ return TaskGen.declare_chain(*k, **kw) def pre_build(self): """Executes user-defined methods before the build starts, see :py:meth:`waflib.Build.BuildContext.add_pre_fun`""" for m in getattr(self, 'pre_funs', []): m(self) def post_build(self): """Executes user-defined methods after the build is successful, see :py:meth:`waflib.Build.BuildContext.add_post_fun`""" for m in getattr(self, 'post_funs', []): m(self) def add_pre_fun(self, meth): """ Binds a callback method to execute after the scripts are read and before the build starts:: def mycallback(bld): print("Hello, world!") def build(bld): bld.add_pre_fun(mycallback) """ try: self.pre_funs.append(meth) except AttributeError: self.pre_funs = [meth] def add_post_fun(self, meth): """ Binds a callback method to execute immediately after the build is successful:: def call_ldconfig(bld): bld.exec_command('/sbin/ldconfig') def build(bld): if bld.cmd == 'install': bld.add_pre_fun(call_ldconfig) """ try: self.post_funs.append(meth) except AttributeError: self.post_funs = [meth] def get_group(self, x): """ Returns the build group named `x`, or the current group if `x` is None :param x: name or number or None :type x: string, int or None """ if not self.groups: self.add_group() if x is None: return self.groups[self.current_group] if x in self.group_names: return self.group_names[x] return self.groups[x] def add_to_group(self, tgen, group=None): """Adds a task or a task generator to the build; there is no attempt to remove it if it was already added.""" assert(isinstance(tgen, TaskGen.task_gen) or isinstance(tgen, Task.TaskBase)) tgen.bld = self self.get_group(group).append(tgen) def get_group_name(self, g): """ Returns the name of the input build group :param g: build group object or build group index :type g: integer or list :return: name :rtype: string """ if not isinstance(g, list): g = self.groups[g] for x in self.group_names: if id(self.group_names[x]) == id(g): return x return '' def get_group_idx(self, tg): """ Returns the index of the group containing the task generator given as argument:: def build(bld): tg = bld(name='nada') 0 == bld.get_group_idx(tg) :param tg: Task generator object :type tg: :py:class:`waflib.TaskGen.task_gen` :rtype: int """ se = id(tg) for i, tmp in enumerate(self.groups): for t in tmp: if id(t) == se: return i return None def add_group(self, name=None, move=True): """ Adds a new group of tasks/task generators. By default the new group becomes the default group for new task generators (make sure to create build groups in order). :param name: name for this group :type name: string :param move: set this new group as default group (True by default) :type move: bool :raises: :py:class:`waflib.Errors.WafError` if a group by the name given already exists """ if name and name in self.group_names: raise Errors.WafError('add_group: name %s already present', name) g = [] self.group_names[name] = g self.groups.append(g) if move: self.current_group = len(self.groups) - 1 def set_group(self, idx): """ Sets the build group at position idx as current so that newly added task generators are added to this one by default:: def build(bld): bld(rule='touch ${TGT}', target='foo.txt') bld.add_group() # now the current group is 1 bld(rule='touch ${TGT}', target='bar.txt') bld.set_group(0) # now the current group is 0 bld(rule='touch ${TGT}', target='truc.txt') # build truc.txt before bar.txt :param idx: group name or group index :type idx: string or int """ if isinstance(idx, str): g = self.group_names[idx] for i, tmp in enumerate(self.groups): if id(g) == id(tmp): self.current_group = i break else: self.current_group = idx def total(self): """ Approximate task count: this value may be inaccurate if task generators are posted lazily (see :py:attr:`waflib.Build.BuildContext.post_mode`). The value :py:attr:`waflib.Runner.Parallel.total` is updated during the task execution. :rtype: int """ total = 0 for group in self.groups: for tg in group: try: total += len(tg.tasks) except AttributeError: total += 1 return total def get_targets(self): """ Returns the task generator corresponding to the 'targets' list; used internally by :py:meth:`waflib.Build.BuildContext.get_build_iterator` to perform partial builds:: $ waf --targets=myprogram,myshlib """ to_post = [] min_grp = 0 for name in self.targets.split(','): tg = self.get_tgen_by_name(name) m = self.get_group_idx(tg) if m > min_grp: min_grp = m to_post = [tg] elif m == min_grp: to_post.append(tg) return (min_grp, to_post) def get_all_task_gen(self): """ Returns a list of all task generators for troubleshooting purposes. """ lst = [] for g in self.groups: lst.extend(g) return lst def post_group(self): """ Post task generators from the group indexed by self.cur; used internally by :py:meth:`waflib.Build.BuildContext.get_build_iterator` """ if self.targets == '*': for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: f() elif self.targets: if self.cur < self._min_grp: for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: f() else: for tg in self._exact_tg: tg.post() else: ln = self.launch_node() if ln.is_child_of(self.bldnode): Logs.warn('Building from the build directory, forcing --targets=*') ln = self.srcnode elif not ln.is_child_of(self.srcnode): Logs.warn('CWD %s is not under %s, forcing --targets=* (run distclean?)', ln.abspath(), self.srcnode.abspath()) ln = self.srcnode for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: if tg.path.is_child_of(ln): f() def get_tasks_group(self, idx): """ Returns all task instances for the build group at position idx, used internally by :py:meth:`waflib.Build.BuildContext.get_build_iterator` :rtype: list of :py:class:`waflib.Task.TaskBase` """ tasks = [] for tg in self.groups[idx]: try: tasks.extend(tg.tasks) except AttributeError: # not a task generator tasks.append(tg) return tasks def get_build_iterator(self): """ Creates a Python generator object that returns lists of tasks that may be processed in parallel. :return: tasks which can be executed immediatly :rtype: generator returning lists of :py:class:`waflib.Task.TaskBase` """ self.cur = 0 if self.targets and self.targets != '*': (self._min_grp, self._exact_tg) = self.get_targets() global lazy_post if self.post_mode != POST_LAZY: while self.cur < len(self.groups): self.post_group() self.cur += 1 self.cur = 0 while self.cur < len(self.groups): # first post the task generators for the group if self.post_mode != POST_AT_ONCE: self.post_group() # then extract the tasks tasks = self.get_tasks_group(self.cur) # if the constraints are set properly (ext_in/ext_out, before/after) # the call to set_file_constraints may be removed (can be a 15% penalty on no-op rebuilds) # (but leave set_file_constraints for the installation step) # # if the tasks have only files, set_file_constraints is required but set_precedence_constraints is not necessary # Task.set_file_constraints(tasks) Task.set_precedence_constraints(tasks) self.cur_tasks = tasks self.cur += 1 if not tasks: # return something else the build will stop continue yield tasks while 1: yield [] def install_files(self, dest, files, **kw): """ Creates a task generator to install files on the system:: def build(bld): bld.install_files('${DATADIR}', self.path.find_resource('wscript')) :param dest: path representing the destination directory :type dest: :py:class:`waflib.Node.Node` or string (absolute path) :param files: input files :type files: list of strings or list of :py:class:`waflib.Node.Node` :param env: configuration set to expand *dest* :type env: :py:class:`waflib.ConfigSet.ConfigSet` :param relative_trick: preserve the folder hierarchy when installing whole folders :type relative_trick: bool :param cwd: parent node for searching srcfile, when srcfile is not an instance of :py:class:`waflib.Node.Node` :type cwd: :py:class:`waflib.Node.Node` :param postpone: execute the task immediately to perform the installation (False by default) :type postpone: bool """ assert(dest) tg = self(features='install_task', install_to=dest, install_from=files, **kw) tg.dest = tg.install_to tg.type = 'install_files' if not kw.get('postpone', True): tg.post() return tg def install_as(self, dest, srcfile, **kw): """ Creates a task generator to install a file on the system with a different name:: def build(bld): bld.install_as('${PREFIX}/bin', 'myapp', chmod=Utils.O755) :param dest: destination file :type dest: :py:class:`waflib.Node.Node` or string (absolute path) :param srcfile: input file :type srcfile: string or :py:class:`waflib.Node.Node` :param cwd: parent node for searching srcfile, when srcfile is not an instance of :py:class:`waflib.Node.Node` :type cwd: :py:class:`waflib.Node.Node` :param env: configuration set for performing substitutions in dest :type env: :py:class:`waflib.ConfigSet.ConfigSet` :param postpone: execute the task immediately to perform the installation (False by default) :type postpone: bool """ assert(dest) tg = self(features='install_task', install_to=dest, install_from=srcfile, **kw) tg.dest = tg.install_to tg.type = 'install_as' if not kw.get('postpone', True): tg.post() return tg def symlink_as(self, dest, src, **kw): """ Creates a task generator to install a symlink:: def build(bld): bld.symlink_as('${PREFIX}/lib/libfoo.so', 'libfoo.so.1.2.3') :param dest: absolute path of the symlink :type dest: :py:class:`waflib.Node.Node` or string (absolute path) :param src: link contents, which is a relative or abolute path which may exist or not :type src: string :param env: configuration set for performing substitutions in dest :type env: :py:class:`waflib.ConfigSet.ConfigSet` :param add: add the task created to a build group - set ``False`` only if the installation task is created after the build has started :type add: bool :param postpone: execute the task immediately to perform the installation :type postpone: bool :param relative_trick: make the symlink relative (default: ``False``) :type relative_trick: bool """ assert(dest) tg = self(features='install_task', install_to=dest, install_from=src, **kw) tg.dest = tg.install_to tg.type = 'symlink_as' tg.link = src # TODO if add: self.add_to_group(tsk) if not kw.get('postpone', True): tg.post() return tg @TaskGen.feature('install_task') @TaskGen.before_method('process_rule', 'process_source') def process_install_task(self): """Creates the installation task for the current task generator; uses :py:func:`waflib.Build.add_install_task` internally.""" self.add_install_task(**self.__dict__) @TaskGen.taskgen_method def add_install_task(self, **kw): """ Creates the installation task for the current task generator, and executes it immediately if necessary :returns: An installation task :rtype: :py:class:`waflib.Build.inst` """ if not self.bld.is_install: return if not kw['install_to']: return if kw['type'] == 'symlink_as' and Utils.is_win32: if kw.get('win32_install'): kw['type'] = 'install_as' else: # just exit return tsk = self.install_task = self.create_task('inst') tsk.chmod = kw.get('chmod', Utils.O644) tsk.link = kw.get('link', '') or kw.get('install_from', '') tsk.relative_trick = kw.get('relative_trick', False) tsk.type = kw['type'] tsk.install_to = tsk.dest = kw['install_to'] tsk.install_from = kw['install_from'] tsk.relative_base = kw.get('cwd') or kw.get('relative_base', self.path) tsk.install_user = kw.get('install_user') tsk.install_group = kw.get('install_group') tsk.init_files() if not kw.get('postpone', True): tsk.run_now() return tsk @TaskGen.taskgen_method def add_install_files(self, **kw): """ Creates an installation task for files :returns: An installation task :rtype: :py:class:`waflib.Build.inst` """ kw['type'] = 'install_files' return self.add_install_task(**kw) @TaskGen.taskgen_method def add_install_as(self, **kw): """ Creates an installation task for a single file :returns: An installation task :rtype: :py:class:`waflib.Build.inst` """ kw['type'] = 'install_as' return self.add_install_task(**kw) @TaskGen.taskgen_method def add_symlink_as(self, **kw): """ Creates an installation task for a symbolic link :returns: An installation task :rtype: :py:class:`waflib.Build.inst` """ kw['type'] = 'symlink_as' return self.add_install_task(**kw) class inst(Task.Task): """Task that installs files or symlinks; it is typically executed by :py:class:`waflib.Build.InstallContext` and :py:class:`waflib.Build.UnInstallContext`""" def __str__(self): """Returns an empty string to disable the standard task display""" return '' def uid(self): """Returns a unique identifier for the task""" lst = self.inputs + self.outputs + [self.link, self.generator.path.abspath()] return Utils.h_list(lst) def init_files(self): """ Initializes the task input and output nodes """ if self.type == 'symlink_as': inputs = [] else: inputs = self.generator.to_nodes(self.install_from) if self.type == 'install_as': assert len(inputs) == 1 self.set_inputs(inputs) dest = self.get_install_path() outputs = [] if self.type == 'symlink_as': if self.relative_trick: self.link = os.path.relpath(self.link, os.path.dirname(dest)) outputs.append(self.generator.bld.root.make_node(dest)) elif self.type == 'install_as': outputs.append(self.generator.bld.root.make_node(dest)) else: for y in inputs: if self.relative_trick: destfile = os.path.join(dest, y.path_from(self.relative_base)) else: destfile = os.path.join(dest, y.name) outputs.append(self.generator.bld.root.make_node(destfile)) self.set_outputs(outputs) def runnable_status(self): """ Installation tasks are always executed, so this method returns either :py:const:`waflib.Task.ASK_LATER` or :py:const:`waflib.Task.RUN_ME`. """ ret = super(inst, self).runnable_status() if ret == Task.SKIP_ME and self.generator.bld.is_install: return Task.RUN_ME return ret def post_run(self): """ Disables any post-run operations """ pass def get_install_path(self, destdir=True): """ Returns the destination path where files will be installed, pre-pending `destdir`. :rtype: string """ if isinstance(self.install_to, Node.Node): dest = self.install_to.abspath() else: dest = Utils.subst_vars(self.install_to, self.env) if destdir and Options.options.destdir: dest = os.path.join(Options.options.destdir, os.path.splitdrive(dest)[1].lstrip(os.sep)) return dest def copy_fun(self, src, tgt): """ Copies a file from src to tgt, preserving permissions and trying to work around path limitations on Windows platforms. On Unix-like platforms, the owner/group of the target file may be set through install_user/install_group :param src: absolute path :type src: string :param tgt: absolute path :type tgt: string """ # override this if you want to strip executables # kw['tsk'].source is the task that created the files in the build if Utils.is_win32 and len(tgt) > 259 and not tgt.startswith('\\\\?\\'): tgt = '\\\\?\\' + tgt shutil.copy2(src, tgt) self.fix_perms(tgt) def rm_empty_dirs(self, tgt): """ Removes empty folders recursively when uninstalling. :param tgt: absolute path :type tgt: string """ while tgt: tgt = os.path.dirname(tgt) try: os.rmdir(tgt) except OSError: break def run(self): """ Performs file or symlink installation """ is_install = self.generator.bld.is_install if not is_install: # unnecessary? return for x in self.outputs: if is_install == INSTALL: x.parent.mkdir() if self.type == 'symlink_as': fun = is_install == INSTALL and self.do_link or self.do_unlink fun(self.link, self.outputs[0].abspath()) else: fun = is_install == INSTALL and self.do_install or self.do_uninstall launch_node = self.generator.bld.launch_node() for x, y in zip(self.inputs, self.outputs): fun(x.abspath(), y.abspath(), x.path_from(launch_node)) def run_now(self): """ Try executing the installation task right now :raises: :py:class:`waflib.Errors.TaskNotReady` """ status = self.runnable_status() if status not in (Task.RUN_ME, Task.SKIP_ME): raise Errors.TaskNotReady('Could not process %r: status %r' % (self, status)) self.run() self.hasrun = Task.SUCCESS def do_install(self, src, tgt, lbl, **kw): """ Copies a file from src to tgt with given file permissions. The actual copy is only performed if the source and target file sizes or timestamps differ. When the copy occurs, the file is always first removed and then copied so as to prevent stale inodes. :param src: file name as absolute path :type src: string :param tgt: file destination, as absolute path :type tgt: string :param lbl: file source description :type lbl: string :param chmod: installation mode :type chmod: int :raises: :py:class:`waflib.Errors.WafError` if the file cannot be written """ if not Options.options.force: # check if the file is already there to avoid a copy try: st1 = os.stat(tgt) st2 = os.stat(src) except OSError: pass else: # same size and identical timestamps -> make no copy if st1.st_mtime + 2 >= st2.st_mtime and st1.st_size == st2.st_size: if not self.generator.bld.progress_bar: Logs.info('- install %s (from %s)', tgt, lbl) return False if not self.generator.bld.progress_bar: Logs.info('+ install %s (from %s)', tgt, lbl) # Give best attempt at making destination overwritable, # like the 'install' utility used by 'make install' does. try: os.chmod(tgt, Utils.O644 | stat.S_IMODE(os.stat(tgt).st_mode)) except EnvironmentError: pass # following is for shared libs and stale inodes (-_-) try: os.remove(tgt) except OSError: pass try: self.copy_fun(src, tgt) except EnvironmentError as e: if not os.path.exists(src): Logs.error('File %r does not exist', src) elif not os.path.isfile(src): Logs.error('Input %r is not a file', src) raise Errors.WafError('Could not install the file %r' % tgt, e) def fix_perms(self, tgt): """ Change the ownership of the file/folder/link pointed by the given path This looks up for `install_user` or `install_group` attributes on the task or on the task generator:: def build(bld): bld.install_as('${PREFIX}/wscript', 'wscript', install_user='nobody', install_group='nogroup') bld.symlink_as('${PREFIX}/wscript_link', Utils.subst_vars('${PREFIX}/wscript', bld.env), install_user='nobody', install_group='nogroup') """ if not Utils.is_win32: user = getattr(self, 'install_user', None) or getattr(self.generator, 'install_user', None) group = getattr(self, 'install_group', None) or getattr(self.generator, 'install_group', None) if user or group: Utils.lchown(tgt, user or -1, group or -1) if not os.path.islink(tgt): os.chmod(tgt, self.chmod) def do_link(self, src, tgt, **kw): """ Creates a symlink from tgt to src. :param src: file name as absolute path :type src: string :param tgt: file destination, as absolute path :type tgt: string """ if os.path.islink(tgt) and os.readlink(tgt) == src: if not self.generator.bld.progress_bar: Logs.info('- symlink %s (to %s)', tgt, src) else: try: os.remove(tgt) except OSError: pass if not self.generator.bld.progress_bar: Logs.info('+ symlink %s (to %s)', tgt, src) os.symlink(src, tgt) self.fix_perms(tgt) def do_uninstall(self, src, tgt, lbl, **kw): """ See :py:meth:`waflib.Build.inst.do_install` """ if not self.generator.bld.progress_bar: Logs.info('- remove %s', tgt) #self.uninstall.append(tgt) try: os.remove(tgt) except OSError as e: if e.errno != errno.ENOENT: if not getattr(self, 'uninstall_error', None): self.uninstall_error = True Logs.warn('build: some files could not be uninstalled (retry with -vv to list them)') if Logs.verbose > 1: Logs.warn('Could not remove %s (error code %r)', e.filename, e.errno) self.rm_empty_dirs(tgt) def do_unlink(self, src, tgt, **kw): """ See :py:meth:`waflib.Build.inst.do_link` """ try: if not self.generator.bld.progress_bar: Logs.info('- remove %s', tgt) os.remove(tgt) except OSError: pass self.rm_empty_dirs(tgt) class InstallContext(BuildContext): '''installs the targets on the system''' cmd = 'install' def __init__(self, **kw): super(InstallContext, self).__init__(**kw) self.is_install = INSTALL class UninstallContext(InstallContext): '''removes the targets installed''' cmd = 'uninstall' def __init__(self, **kw): super(UninstallContext, self).__init__(**kw) self.is_install = UNINSTALL def execute(self): """ See :py:func:`waflib.Build.BuildContext.execute`. """ # TODO just mark the tasks are already run with hasrun=Task.SKIPPED? try: # do not execute any tasks def runnable_status(self): return Task.SKIP_ME setattr(Task.Task, 'runnable_status_back', Task.Task.runnable_status) setattr(Task.Task, 'runnable_status', runnable_status) super(UninstallContext, self).execute() finally: setattr(Task.Task, 'runnable_status', Task.Task.runnable_status_back) class CleanContext(BuildContext): '''cleans the project''' cmd = 'clean' def execute(self): """ See :py:func:`waflib.Build.BuildContext.execute`. """ self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir]) try: self.clean() finally: self.store() def clean(self): """Remove files from the build directory if possible, and reset the caches""" Logs.debug('build: clean called') if self.bldnode != self.srcnode: # would lead to a disaster if top == out lst = [] for env in self.all_envs.values(): lst.extend(self.root.find_or_declare(f) for f in env[CFG_FILES]) for n in self.bldnode.ant_glob('**/*', excl='.lock* *conf_check_*/** config.log c4che/*', quiet=True): if n in lst: continue n.delete() self.root.children = {} for v in SAVED_ATTRS: if v == 'root': continue setattr(self, v, {}) class ListContext(BuildContext): '''lists the targets to execute''' cmd = 'list' def execute(self): """ See :py:func:`waflib.Build.BuildContext.execute`. """ self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir]) self.pre_build() # display the time elapsed in the progress bar self.timer = Utils.Timer() for g in self.groups: for tg in g: try: f = tg.post except AttributeError: pass else: f() try: # force the cache initialization self.get_tgen_by_name('') except Errors.WafError: pass for k in sorted(self.task_gen_cache_names.keys()): Logs.pprint('GREEN', k) class StepContext(BuildContext): '''executes tasks in a step-by-step fashion, for debugging''' cmd = 'step' def __init__(self, **kw): super(StepContext, self).__init__(**kw) self.files = Options.options.files def compile(self): """ Overrides :py:meth:`waflib.Build.BuildContext.compile` to perform a partial build on tasks matching the input/output pattern given (regular expression matching):: $ waf step --files=foo.c,bar.c,in:truc.c,out:bar.o $ waf step --files=in:foo.cpp.1.o # link task only """ if not self.files: Logs.warn('Add a pattern for the debug build, for example "waf step --files=main.c,app"') BuildContext.compile(self) return targets = [] if self.targets and self.targets != '*': targets = self.targets.split(',') for g in self.groups: for tg in g: if targets and tg.name not in targets: continue try: f = tg.post except AttributeError: pass else: f() for pat in self.files.split(','): matcher = self.get_matcher(pat) for tg in g: if isinstance(tg, Task.TaskBase): lst = [tg] else: lst = tg.tasks for tsk in lst: do_exec = False for node in getattr(tsk, 'inputs', []): if matcher(node, output=False): do_exec = True break for node in getattr(tsk, 'outputs', []): if matcher(node, output=True): do_exec = True break if do_exec: ret = tsk.run() Logs.info('%s -> exit %r', tsk, ret) def get_matcher(self, pat): """ Converts a step pattern into a function :param: pat: pattern of the form in:truc.c,out:bar.o :returns: Python function that uses Node objects as inputs and returns matches :rtype: function """ # this returns a function inn = True out = True if pat.startswith('in:'): out = False pat = pat.replace('in:', '') elif pat.startswith('out:'): inn = False pat = pat.replace('out:', '') anode = self.root.find_node(pat) pattern = None if not anode: if not pat.startswith('^'): pat = '^.+?%s' % pat if not pat.endswith('$'): pat = '%s$' % pat pattern = re.compile(pat) def match(node, output): if output == True and not out: return False if output == False and not inn: return False if anode: return anode == node else: return pattern.match(node.abspath()) return match class EnvContext(BuildContext): """Subclass EnvContext to create commands that require configuration data in 'env'""" fun = cmd = None def execute(self): """ See :py:func:`waflib.Build.BuildContext.execute`. """ self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir])

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import os, sys, errno, re, shutil, stat try: import cPickle except ImportError: import pickle as cPickle from waflib import Node, Runner, TaskGen, Utils, ConfigSet, Task, Logs, Options, Context, Errors CACHE_DIR = 'c4che' CACHE_SUFFIX = '_cache.py' INSTALL = 1337 UNINSTALL = -1337 SAVED_ATTRS = 'root node_sigs task_sigs imp_sigs raw_deps node_deps'.split() CFG_FILES = 'cfg_files' POST_AT_ONCE = 0 POST_LAZY = 1 PROTOCOL = -1 if sys.platform == 'cli': PROTOCOL = 0 class BuildContext(Context.Context): cmd = 'build' variant = '' def __init__(self, **kw): super(BuildContext, self).__init__(**kw) self.is_install = 0 self.top_dir = kw.get('top_dir', Context.top_dir) self.out_dir = kw.get('out_dir', Context.out_dir) self.run_dir = kw.get('run_dir', Context.run_dir) self.launch_dir = Context.launch_dir self.post_mode = POST_LAZY self.cache_dir = kw.get('cache_dir') if not self.cache_dir: self.cache_dir = os.path.join(self.out_dir, CACHE_DIR) self.all_envs = {} self.node_sigs = {} self.task_sigs = {} self.imp_sigs = {} self.node_deps = {} self.raw_deps = {} self.task_gen_cache_names = {} self.jobs = Options.options.jobs self.targets = Options.options.targets self.keep = Options.options.keep self.progress_bar = Options.options.progress_bar self.deps_man = Utils.defaultdict(list) self.current_group = 0 self.groups = [] self.group_names = {} for v in SAVED_ATTRS: if not hasattr(self, v): setattr(self, v, {}) def get_variant_dir(self): if not self.variant: return self.out_dir return os.path.join(self.out_dir, self.variant) variant_dir = property(get_variant_dir, None) def __call__(self, *k, **kw): kw['bld'] = self ret = TaskGen.task_gen(*k, **kw) self.task_gen_cache_names = {} self.add_to_group(ret, group=kw.get('group')) return ret def rule(self, *k, **kw): def f(rule): ret = self(*k, **kw) ret.rule = rule return ret return f def __copy__(self): raise Errors.WafError('build contexts cannot be copied') def load_envs(self): node = self.root.find_node(self.cache_dir) if not node: raise Errors.WafError('The project was not configured: run "waf configure" first!') lst = node.ant_glob('**/*%s' % CACHE_SUFFIX, quiet=True) if not lst: raise Errors.WafError('The cache directory is empty: reconfigure the project') for x in lst: name = x.path_from(node).replace(CACHE_SUFFIX, '').replace('\\', '/') env = ConfigSet.ConfigSet(x.abspath()) self.all_envs[name] = env for f in env[CFG_FILES]: newnode = self.root.find_resource(f) if not newnode or not newnode.exists(): raise Errors.WafError('Missing configuration file %r, reconfigure the project!' % f) def init_dirs(self): if not (os.path.isabs(self.top_dir) and os.path.isabs(self.out_dir)): raise Errors.WafError('The project was not configured: run "waf configure" first!') self.path = self.srcnode = self.root.find_dir(self.top_dir) self.bldnode = self.root.make_node(self.variant_dir) self.bldnode.mkdir() def execute(self): self.restore() if not self.all_envs: self.load_envs() self.execute_build() def execute_build(self): Logs.info("Waf: Entering directory `%s'", self.variant_dir) self.recurse([self.run_dir]) self.pre_build() # display the time elapsed in the progress bar self.timer = Utils.Timer() try: self.compile() finally: if self.progress_bar == 1 and sys.stderr.isatty(): c = self.producer.processed or 1 m = self.progress_line(c, c, Logs.colors.BLUE, Logs.colors.NORMAL) Logs.info(m, extra={'stream': sys.stderr, 'c1': Logs.colors.cursor_off, 'c2' : Logs.colors.cursor_on}) Logs.info("Waf: Leaving directory `%s'", self.variant_dir) try: self.producer.bld = None del self.producer except AttributeError: pass self.post_build() def restore(self): try: env = ConfigSet.ConfigSet(os.path.join(self.cache_dir, 'build.config.py')) except EnvironmentError: pass else: if env.version < Context.HEXVERSION: raise Errors.WafError('Version mismatch! reconfigure the project') for t in env.tools: self.setup(**t) dbfn = os.path.join(self.variant_dir, Context.DBFILE) try: data = Utils.readf(dbfn, 'rb') except (EnvironmentError, EOFError): Logs.debug('build: Could not load the build cache %s (missing)', dbfn) else: try: Node.pickle_lock.acquire() Node.Nod3 = self.node_class try: data = cPickle.loads(data) except Exception as e: Logs.debug('build: Could not pickle the build cache %s: %r', dbfn, e) else: for x in SAVED_ATTRS: setattr(self, x, data.get(x, {})) finally: Node.pickle_lock.release() self.init_dirs() def store(self): data = {} for x in SAVED_ATTRS: data[x] = getattr(self, x) db = os.path.join(self.variant_dir, Context.DBFILE) try: Node.pickle_lock.acquire() Node.Nod3 = self.node_class x = cPickle.dumps(data, PROTOCOL) finally: Node.pickle_lock.release() Utils.writef(db + '.tmp', x, m='wb') try: st = os.stat(db) os.remove(db) if not Utils.is_win32: os.chown(db + '.tmp', st.st_uid, st.st_gid) except (AttributeError, OSError): pass # do not use shutil.move (copy is not thread-safe) os.rename(db + '.tmp', db) def compile(self): Logs.debug('build: compile()') # delegate the producer-consumer logic to another object to reduce the complexity self.producer = Runner.Parallel(self, self.jobs) self.producer.biter = self.get_build_iterator() try: self.producer.start() except KeyboardInterrupt: self.store() raise else: if self.producer.dirty: self.store() if self.producer.error: raise Errors.BuildError(self.producer.error) def setup(self, tool, tooldir=None, funs=None): if isinstance(tool, list): for i in tool: self.setup(i, tooldir) return module = Context.load_tool(tool, tooldir) if hasattr(module, "setup"): module.setup(self) def get_env(self): try: return self.all_envs[self.variant] except KeyError: return self.all_envs[''] def set_env(self, val): self.all_envs[self.variant] = val env = property(get_env, set_env) def add_manual_dependency(self, path, value): if not path: raise ValueError('Invalid input path %r' % path) if isinstance(path, Node.Node): node = path elif os.path.isabs(path): node = self.root.find_resource(path) else: node = self.path.find_resource(path) if not node: raise ValueError('Could not find the path %r' % path) if isinstance(value, list): self.deps_man[node].extend(value) else: self.deps_man[node].append(value) def launch_node(self): try: # private cache return self.p_ln except AttributeError: self.p_ln = self.root.find_dir(self.launch_dir) return self.p_ln def hash_env_vars(self, env, vars_lst): if not env.table: env = env.parent if not env: return Utils.SIG_NIL idx = str(id(env)) + str(vars_lst) try: cache = self.cache_env except AttributeError: cache = self.cache_env = {} else: try: return self.cache_env[idx] except KeyError: pass lst = [env[a] for a in vars_lst] cache[idx] = ret = Utils.h_list(lst) Logs.debug('envhash: %s %r', Utils.to_hex(ret), lst) return ret def get_tgen_by_name(self, name): cache = self.task_gen_cache_names if not cache: # create the index lazily for g in self.groups: for tg in g: try: cache[tg.name] = tg except AttributeError: # raised if not a task generator, which should be uncommon pass try: return cache[name] except KeyError: raise Errors.WafError('Could not find a task generator for the name %r' % name) def progress_line(self, idx, total, col1, col2): if not sys.stderr.isatty(): return '' n = len(str(total)) Utils.rot_idx += 1 ind = Utils.rot_chr[Utils.rot_idx % 4] pc = (100. * idx)/total fs = "[%%%dd/%%d][%%s%%2d%%%%%%s][%s][" % (n, ind) left = fs % (idx, total, col1, pc, col2) right = '][%s%s%s]' % (col1, self.timer, col2) cols = Logs.get_term_cols() - len(left) - len(right) + 2*len(col1) + 2*len(col2) if cols < 7: cols = 7 ratio = ((cols * idx)//total) - 1 bar = ('='*ratio+'>').ljust(cols) msg = Logs.indicator % (left, bar, right) return msg def declare_chain(self, *k, **kw): return TaskGen.declare_chain(*k, **kw) def pre_build(self): for m in getattr(self, 'pre_funs', []): m(self) def post_build(self): for m in getattr(self, 'post_funs', []): m(self) def add_pre_fun(self, meth): try: self.pre_funs.append(meth) except AttributeError: self.pre_funs = [meth] def add_post_fun(self, meth): try: self.post_funs.append(meth) except AttributeError: self.post_funs = [meth] def get_group(self, x): if not self.groups: self.add_group() if x is None: return self.groups[self.current_group] if x in self.group_names: return self.group_names[x] return self.groups[x] def add_to_group(self, tgen, group=None): assert(isinstance(tgen, TaskGen.task_gen) or isinstance(tgen, Task.TaskBase)) tgen.bld = self self.get_group(group).append(tgen) def get_group_name(self, g): if not isinstance(g, list): g = self.groups[g] for x in self.group_names: if id(self.group_names[x]) == id(g): return x return '' def get_group_idx(self, tg): se = id(tg) for i, tmp in enumerate(self.groups): for t in tmp: if id(t) == se: return i return None def add_group(self, name=None, move=True): if name and name in self.group_names: raise Errors.WafError('add_group: name %s already present', name) g = [] self.group_names[name] = g self.groups.append(g) if move: self.current_group = len(self.groups) - 1 def set_group(self, idx): if isinstance(idx, str): g = self.group_names[idx] for i, tmp in enumerate(self.groups): if id(g) == id(tmp): self.current_group = i break else: self.current_group = idx def total(self): total = 0 for group in self.groups: for tg in group: try: total += len(tg.tasks) except AttributeError: total += 1 return total def get_targets(self): to_post = [] min_grp = 0 for name in self.targets.split(','): tg = self.get_tgen_by_name(name) m = self.get_group_idx(tg) if m > min_grp: min_grp = m to_post = [tg] elif m == min_grp: to_post.append(tg) return (min_grp, to_post) def get_all_task_gen(self): lst = [] for g in self.groups: lst.extend(g) return lst def post_group(self): if self.targets == '*': for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: f() elif self.targets: if self.cur < self._min_grp: for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: f() else: for tg in self._exact_tg: tg.post() else: ln = self.launch_node() if ln.is_child_of(self.bldnode): Logs.warn('Building from the build directory, forcing --targets=*') ln = self.srcnode elif not ln.is_child_of(self.srcnode): Logs.warn('CWD %s is not under %s, forcing --targets=* (run distclean?)', ln.abspath(), self.srcnode.abspath()) ln = self.srcnode for tg in self.groups[self.cur]: try: f = tg.post except AttributeError: pass else: if tg.path.is_child_of(ln): f() def get_tasks_group(self, idx): tasks = [] for tg in self.groups[idx]: try: tasks.extend(tg.tasks) except AttributeError: # not a task generator tasks.append(tg) return tasks def get_build_iterator(self): self.cur = 0 if self.targets and self.targets != '*': (self._min_grp, self._exact_tg) = self.get_targets() global lazy_post if self.post_mode != POST_LAZY: while self.cur < len(self.groups): self.post_group() self.cur += 1 self.cur = 0 while self.cur < len(self.groups): # first post the task generators for the group if self.post_mode != POST_AT_ONCE: self.post_group() # then extract the tasks tasks = self.get_tasks_group(self.cur) # if the constraints are set properly (ext_in/ext_out, before/after) # the call to set_file_constraints may be removed (can be a 15% penalty on no-op rebuilds) # (but leave set_file_constraints for the installation step) # # if the tasks have only files, set_file_constraints is required but set_precedence_constraints is not necessary # Task.set_file_constraints(tasks) Task.set_precedence_constraints(tasks) self.cur_tasks = tasks self.cur += 1 if not tasks: # return something else the build will stop continue yield tasks while 1: yield [] def install_files(self, dest, files, **kw): assert(dest) tg = self(features='install_task', install_to=dest, install_from=files, **kw) tg.dest = tg.install_to tg.type = 'install_files' if not kw.get('postpone', True): tg.post() return tg def install_as(self, dest, srcfile, **kw): assert(dest) tg = self(features='install_task', install_to=dest, install_from=srcfile, **kw) tg.dest = tg.install_to tg.type = 'install_as' if not kw.get('postpone', True): tg.post() return tg def symlink_as(self, dest, src, **kw): assert(dest) tg = self(features='install_task', install_to=dest, install_from=src, **kw) tg.dest = tg.install_to tg.type = 'symlink_as' tg.link = src # TODO if add: self.add_to_group(tsk) if not kw.get('postpone', True): tg.post() return tg @TaskGen.feature('install_task') @TaskGen.before_method('process_rule', 'process_source') def process_install_task(self): self.add_install_task(**self.__dict__) @TaskGen.taskgen_method def add_install_task(self, **kw): if not self.bld.is_install: return if not kw['install_to']: return if kw['type'] == 'symlink_as' and Utils.is_win32: if kw.get('win32_install'): kw['type'] = 'install_as' else: # just exit return tsk = self.install_task = self.create_task('inst') tsk.chmod = kw.get('chmod', Utils.O644) tsk.link = kw.get('link', '') or kw.get('install_from', '') tsk.relative_trick = kw.get('relative_trick', False) tsk.type = kw['type'] tsk.install_to = tsk.dest = kw['install_to'] tsk.install_from = kw['install_from'] tsk.relative_base = kw.get('cwd') or kw.get('relative_base', self.path) tsk.install_user = kw.get('install_user') tsk.install_group = kw.get('install_group') tsk.init_files() if not kw.get('postpone', True): tsk.run_now() return tsk @TaskGen.taskgen_method def add_install_files(self, **kw): kw['type'] = 'install_files' return self.add_install_task(**kw) @TaskGen.taskgen_method def add_install_as(self, **kw): kw['type'] = 'install_as' return self.add_install_task(**kw) @TaskGen.taskgen_method def add_symlink_as(self, **kw): kw['type'] = 'symlink_as' return self.add_install_task(**kw) class inst(Task.Task): def __str__(self): return '' def uid(self): lst = self.inputs + self.outputs + [self.link, self.generator.path.abspath()] return Utils.h_list(lst) def init_files(self): if self.type == 'symlink_as': inputs = [] else: inputs = self.generator.to_nodes(self.install_from) if self.type == 'install_as': assert len(inputs) == 1 self.set_inputs(inputs) dest = self.get_install_path() outputs = [] if self.type == 'symlink_as': if self.relative_trick: self.link = os.path.relpath(self.link, os.path.dirname(dest)) outputs.append(self.generator.bld.root.make_node(dest)) elif self.type == 'install_as': outputs.append(self.generator.bld.root.make_node(dest)) else: for y in inputs: if self.relative_trick: destfile = os.path.join(dest, y.path_from(self.relative_base)) else: destfile = os.path.join(dest, y.name) outputs.append(self.generator.bld.root.make_node(destfile)) self.set_outputs(outputs) def runnable_status(self): ret = super(inst, self).runnable_status() if ret == Task.SKIP_ME and self.generator.bld.is_install: return Task.RUN_ME return ret def post_run(self): pass def get_install_path(self, destdir=True): if isinstance(self.install_to, Node.Node): dest = self.install_to.abspath() else: dest = Utils.subst_vars(self.install_to, self.env) if destdir and Options.options.destdir: dest = os.path.join(Options.options.destdir, os.path.splitdrive(dest)[1].lstrip(os.sep)) return dest def copy_fun(self, src, tgt): # override this if you want to strip executables # kw['tsk'].source is the task that created the files in the build if Utils.is_win32 and len(tgt) > 259 and not tgt.startswith('\\\\?\\'): tgt = '\\\\?\\' + tgt shutil.copy2(src, tgt) self.fix_perms(tgt) def rm_empty_dirs(self, tgt): while tgt: tgt = os.path.dirname(tgt) try: os.rmdir(tgt) except OSError: break def run(self): is_install = self.generator.bld.is_install if not is_install: # unnecessary? return for x in self.outputs: if is_install == INSTALL: x.parent.mkdir() if self.type == 'symlink_as': fun = is_install == INSTALL and self.do_link or self.do_unlink fun(self.link, self.outputs[0].abspath()) else: fun = is_install == INSTALL and self.do_install or self.do_uninstall launch_node = self.generator.bld.launch_node() for x, y in zip(self.inputs, self.outputs): fun(x.abspath(), y.abspath(), x.path_from(launch_node)) def run_now(self): status = self.runnable_status() if status not in (Task.RUN_ME, Task.SKIP_ME): raise Errors.TaskNotReady('Could not process %r: status %r' % (self, status)) self.run() self.hasrun = Task.SUCCESS def do_install(self, src, tgt, lbl, **kw): if not Options.options.force: # check if the file is already there to avoid a copy try: st1 = os.stat(tgt) st2 = os.stat(src) except OSError: pass else: # same size and identical timestamps -> make no copy if st1.st_mtime + 2 >= st2.st_mtime and st1.st_size == st2.st_size: if not self.generator.bld.progress_bar: Logs.info('- install %s (from %s)', tgt, lbl) return False if not self.generator.bld.progress_bar: Logs.info('+ install %s (from %s)', tgt, lbl) # Give best attempt at making destination overwritable, # like the 'install' utility used by 'make install' does. try: os.chmod(tgt, Utils.O644 | stat.S_IMODE(os.stat(tgt).st_mode)) except EnvironmentError: pass # following is for shared libs and stale inodes (-_-) try: os.remove(tgt) except OSError: pass try: self.copy_fun(src, tgt) except EnvironmentError as e: if not os.path.exists(src): Logs.error('File %r does not exist', src) elif not os.path.isfile(src): Logs.error('Input %r is not a file', src) raise Errors.WafError('Could not install the file %r' % tgt, e) def fix_perms(self, tgt): if not Utils.is_win32: user = getattr(self, 'install_user', None) or getattr(self.generator, 'install_user', None) group = getattr(self, 'install_group', None) or getattr(self.generator, 'install_group', None) if user or group: Utils.lchown(tgt, user or -1, group or -1) if not os.path.islink(tgt): os.chmod(tgt, self.chmod) def do_link(self, src, tgt, **kw): if os.path.islink(tgt) and os.readlink(tgt) == src: if not self.generator.bld.progress_bar: Logs.info('- symlink %s (to %s)', tgt, src) else: try: os.remove(tgt) except OSError: pass if not self.generator.bld.progress_bar: Logs.info('+ symlink %s (to %s)', tgt, src) os.symlink(src, tgt) self.fix_perms(tgt) def do_uninstall(self, src, tgt, lbl, **kw): if not self.generator.bld.progress_bar: Logs.info('- remove %s', tgt) #self.uninstall.append(tgt) try: os.remove(tgt) except OSError as e: if e.errno != errno.ENOENT: if not getattr(self, 'uninstall_error', None): self.uninstall_error = True Logs.warn('build: some files could not be uninstalled (retry with -vv to list them)') if Logs.verbose > 1: Logs.warn('Could not remove %s (error code %r)', e.filename, e.errno) self.rm_empty_dirs(tgt) def do_unlink(self, src, tgt, **kw): try: if not self.generator.bld.progress_bar: Logs.info('- remove %s', tgt) os.remove(tgt) except OSError: pass self.rm_empty_dirs(tgt) class InstallContext(BuildContext): cmd = 'install' def __init__(self, **kw): super(InstallContext, self).__init__(**kw) self.is_install = INSTALL class UninstallContext(InstallContext): cmd = 'uninstall' def __init__(self, **kw): super(UninstallContext, self).__init__(**kw) self.is_install = UNINSTALL def execute(self): # TODO just mark the tasks are already run with hasrun=Task.SKIPPED? try: # do not execute any tasks def runnable_status(self): return Task.SKIP_ME setattr(Task.Task, 'runnable_status_back', Task.Task.runnable_status) setattr(Task.Task, 'runnable_status', runnable_status) super(UninstallContext, self).execute() finally: setattr(Task.Task, 'runnable_status', Task.Task.runnable_status_back) class CleanContext(BuildContext): cmd = 'clean' def execute(self): self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir]) try: self.clean() finally: self.store() def clean(self): Logs.debug('build: clean called') if self.bldnode != self.srcnode: # would lead to a disaster if top == out lst = [] for env in self.all_envs.values(): lst.extend(self.root.find_or_declare(f) for f in env[CFG_FILES]) for n in self.bldnode.ant_glob('**/*', excl='.lock* *conf_check_*/** config.log c4che/*', quiet=True): if n in lst: continue n.delete() self.root.children = {} for v in SAVED_ATTRS: if v == 'root': continue setattr(self, v, {}) class ListContext(BuildContext): cmd = 'list' def execute(self): self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir]) self.pre_build() # display the time elapsed in the progress bar self.timer = Utils.Timer() for g in self.groups: for tg in g: try: f = tg.post except AttributeError: pass else: f() try: # force the cache initialization self.get_tgen_by_name('') except Errors.WafError: pass for k in sorted(self.task_gen_cache_names.keys()): Logs.pprint('GREEN', k) class StepContext(BuildContext): cmd = 'step' def __init__(self, **kw): super(StepContext, self).__init__(**kw) self.files = Options.options.files def compile(self): if not self.files: Logs.warn('Add a pattern for the debug build, for example "waf step --files=main.c,app"') BuildContext.compile(self) return targets = [] if self.targets and self.targets != '*': targets = self.targets.split(',') for g in self.groups: for tg in g: if targets and tg.name not in targets: continue try: f = tg.post except AttributeError: pass else: f() for pat in self.files.split(','): matcher = self.get_matcher(pat) for tg in g: if isinstance(tg, Task.TaskBase): lst = [tg] else: lst = tg.tasks for tsk in lst: do_exec = False for node in getattr(tsk, 'inputs', []): if matcher(node, output=False): do_exec = True break for node in getattr(tsk, 'outputs', []): if matcher(node, output=True): do_exec = True break if do_exec: ret = tsk.run() Logs.info('%s -> exit %r', tsk, ret) def get_matcher(self, pat): # this returns a function inn = True out = True if pat.startswith('in:'): out = False pat = pat.replace('in:', '') elif pat.startswith('out:'): inn = False pat = pat.replace('out:', '') anode = self.root.find_node(pat) pattern = None if not anode: if not pat.startswith('^'): pat = '^.+?%s' % pat if not pat.endswith('$'): pat = '%s$' % pat pattern = re.compile(pat) def match(node, output): if output == True and not out: return False if output == False and not inn: return False if anode: return anode == node else: return pattern.match(node.abspath()) return match class EnvContext(BuildContext): fun = cmd = None def execute(self): self.restore() if not self.all_envs: self.load_envs() self.recurse([self.run_dir])

true

1c2b3491c1eb3dd28653acaba916c1f7d1bdac0a

1,841

py

Python

var/spack/repos/builtin/packages/py-cmake/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/py-cmake/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

8

2021-11-09T20:28:40.000Z

2022-03-15T03:26:33.000Z

var/spack/repos/builtin/packages/py-cmake/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-08T20:37:20.000Z

2019-03-31T15:19:26.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class PyCmake(PythonPackage): """CMake is an open-source, cross-platform family of tools designed to build, test and package software """ homepage = "https://cmake.org" git = "https://github.com/scikit-build/cmake-python-distributions.git" pypi = "cmake/cmake-3.22.2.tar.gz" version('3.22.2', sha256='b5bd5eeb488b13cf64ec963800f3d979eaeb90b4382861b86909df503379e219') version('3.21.4', sha256='30fa5ed8a5ad66dcd263adb87f3ce3dc2d0ec0ac3958f5becff577e4b62cd065') version('3.18.0', sha256='52b98c5ee70b5fa30a8623e96482227e065292f78794eb085fdf0fecb204b79b') depends_on('ninja', type='build') depends_on('py-scikit-build@0.12:', type='build') depends_on('py-setuptools@42:', type='build') depends_on('git', type='build') depends_on('cmake@3.22.2', type=('build', 'link', 'run'), when='@3.22.2') depends_on('cmake@3.21.4', type=('build', 'link', 'run'), when='@3.21.4') depends_on('cmake@3.18.0', type=('build', 'link', 'run'), when='@3.18.0') # see: # https://github.com/scikit-build/cmake-python-distributions/issues/227 # https://github.com/spack/spack/pull/28760#issuecomment-1029362288 for v in ['3.22.2', '3.21.4', '3.18.0']: resource(name='cmake-src', git='https://gitlab.kitware.com/cmake/cmake.git', commit='v{0}'.format(v), when='@{0}'.format(v), destination='cmake-src', placement='cmake-src') def install_options(self, spec, prefix): return [ '-DBUILD_CMAKE_FROM_SOURCE=ON', '-DCMakeProject_SOURCE_DIR=cmake-src' ]

41.840909

96

0.661597

from spack.package import * class PyCmake(PythonPackage): homepage = "https://cmake.org" git = "https://github.com/scikit-build/cmake-python-distributions.git" pypi = "cmake/cmake-3.22.2.tar.gz" version('3.22.2', sha256='b5bd5eeb488b13cf64ec963800f3d979eaeb90b4382861b86909df503379e219') version('3.21.4', sha256='30fa5ed8a5ad66dcd263adb87f3ce3dc2d0ec0ac3958f5becff577e4b62cd065') version('3.18.0', sha256='52b98c5ee70b5fa30a8623e96482227e065292f78794eb085fdf0fecb204b79b') depends_on('ninja', type='build') depends_on('py-scikit-build@0.12:', type='build') depends_on('py-setuptools@42:', type='build') depends_on('git', type='build') depends_on('cmake@3.22.2', type=('build', 'link', 'run'), when='@3.22.2') depends_on('cmake@3.21.4', type=('build', 'link', 'run'), when='@3.21.4') depends_on('cmake@3.18.0', type=('build', 'link', 'run'), when='@3.18.0') '3.21.4', '3.18.0']: resource(name='cmake-src', git='https://gitlab.kitware.com/cmake/cmake.git', commit='v{0}'.format(v), when='@{0}'.format(v), destination='cmake-src', placement='cmake-src') def install_options(self, spec, prefix): return [ '-DBUILD_CMAKE_FROM_SOURCE=ON', '-DCMakeProject_SOURCE_DIR=cmake-src' ]

true

1c2b34a62d85606c90a9cd041550a4133f0739cd

1,849

py

Python

setup.py

thomwiggers/httpserver

88a3a35619ce5185347c6764f211878e898e6aad

[ "BSD-3-Clause" ]

3

2017-03-04T12:47:39.000Z

2018-05-04T13:44:47.000Z

setup.py

thomwiggers/httpserver

88a3a35619ce5185347c6764f211878e898e6aad

[ "BSD-3-Clause" ]

null

setup.py

thomwiggers/httpserver

88a3a35619ce5185347c6764f211878e898e6aad

[ "BSD-3-Clause" ]

7

2015-03-22T15:05:54.000Z

2022-02-07T07:02:20.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys from setuptools import setup from setuptools.command.test import test as TestCommand with open('README.rst') as readme_file: readme = readme_file.read() with open('HISTORY.rst') as history_file: history = history_file.read().replace('.. :changelog:', '') class PyTest(TestCommand): def finalize_options(self): TestCommand.finalize_options(self) self.test_args = [] self.test_suite = True def run_tests(self): import pytest errcode = pytest.main(self.test_args) sys.exit(errcode) requirements = [ 'docopt' ] test_requirements = [ 'pytest', 'selenium>=3.8', 'freezegun', ] setup( name='httpserver', version='1.1.0', description="Asyncio implementation of an HTTP server", long_description=readme + '\n\n' + history, author="Thom Wiggers and Luuk Scholten", author_email='thom@thomwiggers.nl, info@luukscholten.com', maintainer="Thom Wiggers", maintainer_email='thom@thomwiggers.nl', url='https://github.com/thomwiggers/httpserver', packages=[ 'httpserver', ], package_dir={'httpserver': 'httpserver'}, entry_points={ 'console_scripts': [ 'httpserver = httpserver:run' ] }, include_package_data=True, install_requires=requirements, license="BSD", zip_safe=False, keywords='httpserver', classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', ], test_suite='tests', tests_require=test_requirements, cmdclass={'test': PyTest} )

24.653333

63

0.629529

import sys from setuptools import setup from setuptools.command.test import test as TestCommand with open('README.rst') as readme_file: readme = readme_file.read() with open('HISTORY.rst') as history_file: history = history_file.read().replace('.. :changelog:', '') class PyTest(TestCommand): def finalize_options(self): TestCommand.finalize_options(self) self.test_args = [] self.test_suite = True def run_tests(self): import pytest errcode = pytest.main(self.test_args) sys.exit(errcode) requirements = [ 'docopt' ] test_requirements = [ 'pytest', 'selenium>=3.8', 'freezegun', ] setup( name='httpserver', version='1.1.0', description="Asyncio implementation of an HTTP server", long_description=readme + '\n\n' + history, author="Thom Wiggers and Luuk Scholten", author_email='thom@thomwiggers.nl, info@luukscholten.com', maintainer="Thom Wiggers", maintainer_email='thom@thomwiggers.nl', url='https://github.com/thomwiggers/httpserver', packages=[ 'httpserver', ], package_dir={'httpserver': 'httpserver'}, entry_points={ 'console_scripts': [ 'httpserver = httpserver:run' ] }, include_package_data=True, install_requires=requirements, license="BSD", zip_safe=False, keywords='httpserver', classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', ], test_suite='tests', tests_require=test_requirements, cmdclass={'test': PyTest} )

true

1c2b34d3bca81235599234099d80fbe425c8eaa7

1,319

py

Python

modular_program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

modular_program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

modular_program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

from tkinter import * root = Tk() root.geometry("1000x600") root.resizable(True,True) root.title("DASHBOARD") #CREATE A START PAGE start = Frame(root,bg='#539bf9', height=600, width=1000) start.pack() start_heading = Label(start,text="GLOBAL WATCHTOWER \n SV 21 INTERFACE",font = "Verdana 30 bold",bg="#539bf9") start_heading.place(rely=0.1,relx=0.1, relwidth= 0.8) team_name_label = Label(start,text="ENTER YOUR TEAM NAME",font = "Verdana 30 bold",bg="#539bf9") team_name_label.place(rely=0.4, relx = 0.1, relwidth = 0.8) team_name_entry = Entry(start, text = 'ENTER TEAM NAME',bg="#f0f0f0").place(relx=0.1,rely=0.5, relwidth=0.8, relheight=0.15) button = Button(start,text="GO TO FINISH" ,bg="gray",font = "Verdana 15 bold", command = lambda: switch_frame(start,finish)) button.place(rely=0.65,relx=0.1, relwidth=0.4, relheight=0.15) #CREATE A FINISH PAGE finish = Frame(root,bg='#FF0000', height=600, width=1000) fr = Label(finish,text="VIRUS HAS BEEN STOPPED",font = "Verdana 30 bold",bg="#4472C4") fr.place(rely=0.1,relx=0.1, relwidth= 0.8) button2 = Button(finish,text="GO TO START" ,bg="gray",font = "Verdana 15 bold", command = lambda: switch_frame(finish,start)) button2.place(rely=0.65,relx=0.5, relwidth=0.4, relheight=0.15) def switch_frame (old,new): old.pack_forget() new.pack() mainloop()

39.969697

125

0.714936

from tkinter import * root = Tk() root.geometry("1000x600") root.resizable(True,True) root.title("DASHBOARD") start = Frame(root,bg='#539bf9', height=600, width=1000) start.pack() start_heading = Label(start,text="GLOBAL WATCHTOWER \n SV 21 INTERFACE",font = "Verdana 30 bold",bg="#539bf9") start_heading.place(rely=0.1,relx=0.1, relwidth= 0.8) team_name_label = Label(start,text="ENTER YOUR TEAM NAME",font = "Verdana 30 bold",bg="#539bf9") team_name_label.place(rely=0.4, relx = 0.1, relwidth = 0.8) team_name_entry = Entry(start, text = 'ENTER TEAM NAME',bg="#f0f0f0").place(relx=0.1,rely=0.5, relwidth=0.8, relheight=0.15) button = Button(start,text="GO TO FINISH" ,bg="gray",font = "Verdana 15 bold", command = lambda: switch_frame(start,finish)) button.place(rely=0.65,relx=0.1, relwidth=0.4, relheight=0.15) finish = Frame(root,bg='#FF0000', height=600, width=1000) fr = Label(finish,text="VIRUS HAS BEEN STOPPED",font = "Verdana 30 bold",bg="#4472C4") fr.place(rely=0.1,relx=0.1, relwidth= 0.8) button2 = Button(finish,text="GO TO START" ,bg="gray",font = "Verdana 15 bold", command = lambda: switch_frame(finish,start)) button2.place(rely=0.65,relx=0.5, relwidth=0.4, relheight=0.15) def switch_frame (old,new): old.pack_forget() new.pack() mainloop()

true

1c2b35533c323ec4950a6b63a97c10d9551b6a66

501

py

Python

orders/serializers/orderserializer.py

mrearsbig/store

f311c48f8e79f6d6fb7bf2c8c9a0b65d1b271ff0

[ "MIT" ]

1

2021-11-26T21:39:52.000Z

orders/serializers/orderserializer.py

mrearsbig/backend

f311c48f8e79f6d6fb7bf2c8c9a0b65d1b271ff0

[ "MIT" ]

null

orders/serializers/orderserializer.py

mrearsbig/backend

f311c48f8e79f6d6fb7bf2c8c9a0b65d1b271ff0

[ "MIT" ]

null

from rest_framework.serializers import ModelSerializer from orders.models import Order class OrderSerializer(ModelSerializer): class Meta: model = Order fields = '__all__' def to_representation(self, instance): return { 'id': instance.id, 'date': instance.date, 'shipping': instance.shipping, 'total': instance.total, 'client': { 'username': instance.client.username } }

26.368421

54

0.57485

from rest_framework.serializers import ModelSerializer from orders.models import Order class OrderSerializer(ModelSerializer): class Meta: model = Order fields = '__all__' def to_representation(self, instance): return { 'id': instance.id, 'date': instance.date, 'shipping': instance.shipping, 'total': instance.total, 'client': { 'username': instance.client.username } }

true

1c2b3604c195de56b7956ab80df51689484ff57b

815

py

Python

lib/mplcairo/tk.py

TomJohnZ/mplcairo

b5d119cacd39eeeb6f5e166e11f3ede52b5a28fd

[ "MIT" ]

55

2019-08-03T00:01:19.000Z

2022-03-02T21:46:51.000Z

lib/mplcairo/tk.py

TomJohnZ/mplcairo

b5d119cacd39eeeb6f5e166e11f3ede52b5a28fd

[ "MIT" ]

23

2019-09-07T14:52:43.000Z

2022-03-05T19:46:52.000Z

lib/mplcairo/tk.py

TomJohnZ/mplcairo

b5d119cacd39eeeb6f5e166e11f3ede52b5a28fd

[ "MIT" ]

16

2018-03-15T11:57:47.000Z

2019-03-23T06:03:06.000Z

from functools import partial from matplotlib.backends._backend_tk import _BackendTk, FigureCanvasTk from . import _util from .base import FigureCanvasCairo try: from matplotlib.backends._backend_tk import blit as _mpl3_blit _tk_blit = partial(_mpl3_blit, offsets=(0, 1, 2, 3)) except ImportError: from matplotlib.backends.tkagg import blit as _mpl2_blit _tk_blit = partial(_mpl2_blit, colormode=2) class FigureCanvasTkCairo(FigureCanvasCairo, FigureCanvasTk): def draw(self): super().draw() self.blit() def blit(self, bbox=None): buf = _util.cairo_to_straight_rgba8888( self.get_renderer()._get_buffer()) _tk_blit(self._tkphoto, buf, bbox=bbox) @_BackendTk.export class _BackendTkCairo(_BackendTk): FigureCanvas = FigureCanvasTkCairo

27.166667

70

0.737423

from functools import partial from matplotlib.backends._backend_tk import _BackendTk, FigureCanvasTk from . import _util from .base import FigureCanvasCairo try: from matplotlib.backends._backend_tk import blit as _mpl3_blit _tk_blit = partial(_mpl3_blit, offsets=(0, 1, 2, 3)) except ImportError: from matplotlib.backends.tkagg import blit as _mpl2_blit _tk_blit = partial(_mpl2_blit, colormode=2) class FigureCanvasTkCairo(FigureCanvasCairo, FigureCanvasTk): def draw(self): super().draw() self.blit() def blit(self, bbox=None): buf = _util.cairo_to_straight_rgba8888( self.get_renderer()._get_buffer()) _tk_blit(self._tkphoto, buf, bbox=bbox) @_BackendTk.export class _BackendTkCairo(_BackendTk): FigureCanvas = FigureCanvasTkCairo

true

1c2b36405b1140476ad624fc00313de58fe5b45e

9,495

py

Python

tests/unit/test_dynamodb.py

ongzhixian/dana_trading_bot

746d080a42f6c43ab9a96df7b272062a88f47f56

[ "MIT" ]

null

tests/unit/test_dynamodb.py

ongzhixian/dana_trading_bot

746d080a42f6c43ab9a96df7b272062a88f47f56

[ "MIT" ]

null

tests/unit/test_dynamodb.py

ongzhixian/dana_trading_bot

746d080a42f6c43ab9a96df7b272062a88f47f56

[ "MIT" ]

null

import unittest import boto3 from boto3.dynamodb.types import Binary, Decimal import requests import warnings from unittest.mock import patch, Mock from mods.ddb import store, retrieve, remove class TestDdb(unittest.TestCase): # Dictionary of mock test data # 'RequestId': '75N4RS5BDFE4NGOK864RHBFBH7VV4KQNSO5AEMVJF66Q9ASUAAJG', mock_data = { 'RESPONSE_METADATA' : { 'ResponseMetadata': { 'RequestId': 'AAA4RS5BDFE4NGOK864RHBFBH7VV4KQNSO5AEMVJF66Q9ASUAAJG', 'HTTPStatusCode': 200, 'HTTPHeaders': { 'server': 'Server', 'date': 'Sat, 21 Aug 2021 13:07:47 GMT', 'content-type': 'application/x-amz-json-1.0', 'content-length': '2', 'connection': 'keep-alive', 'x-amzn-requestid': '75N4RS5BDFE4NGOK864RHBFBH7VV4KQNSO5AEMVJF66Q9ASUAAJG', 'x-amz-crc32': '2745614147' }, 'RetryAttempts': 0 } }, 'PLAIN_TEXT_ITEM' : { 'id' : 'sample7', "Author": "William Shakespeare", "Title": "Romeo", "Category": "Drama" }, 'RETRIEVE_ITEM' : { 'Item': { 'info': { 'rating': Decimal('3'), 'plot': 'awful' }, 'app': { 'name': 'some generic app', 'version': Decimal('10') }, 'and some binary': Binary(b'\x00\x01\x02'), 'year': Decimal('2021'), 'comment': 'alone', 'some numbers': Decimal('99'), 'id': 'enc2', 'title': 'my horrible movie', 'example': 'data' }, 'ResponseMetadata': { 'RequestId': 'OBKNV1BON1MCPUDFVS0A1LC513VV4KQNSO5AEMVJF66Q9ASUAAJG', 'HTTPStatusCode': 200, 'HTTPHeaders': { 'server': 'Server', 'date': 'Sat, 21 Aug 2021 14:19:31 GMT', 'content-type': 'application/x-amz-json-1.0', 'content-length': '308', 'connection': 'keep-alive', 'x-amzn-requestid': 'OBKNV1BON1MCPUDFVS0A1LC513VV4KQNSO5AEMVJF66Q9ASUAAJG', 'x-amz-crc32': '1593809059' }, 'RetryAttempts': 0 } } } def test_store_data(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act mock_table = Mock() mock_table.put_item.return_value = self.mock_data['RESPONSE_METADATA'] response = store(mock_table, plaintext_item) response_metadata = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) def test_store_encrypted_data(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act # Create an instance of a mock(EncryptedTable) mock_table = Mock() mock_table.put_item.return_value = self.mock_data['RESPONSE_METADATA'] #with patch('dynamodb_encryption_sdk.encrypted.table.EncryptedTable') as mock_encrypted_table: with patch('mods.ddb.EncryptedTable') as mock_encrypted_table: mock_encrypted_table.return_value = mock_table response = store(Mock(), plaintext_item, encrypt=True) # with patch(f'{__name__}.store') as mock_module_method: # mock_module_method.return_value = self.mock_data['RESPONSE_METADATA'] # response = store(dana_table, plaintext_item, encrypt=True) # mock_table = Mock() # mock_table.put_item.return_value = self.mock_data['RESPONSE_METADATA'] # response = store(dana_table, plaintext_item, encrypt=True) # print(response) response_metadata = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) #@unittest.skip("Reduce noise while checking other tests") def test_retrieve_data(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] #plaintext_item['id'] = 'SAMPLE1 dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act mock_table = Mock() mock_table.get_item.return_value = self.mock_data['RETRIEVE_ITEM'] response = retrieve(mock_table, {'id': 'SAMPLE1'}) # with patch(f'{__name__}.retrieve') as mock_module_method: # mock_module_method.return_value = self.mock_data['RETRIEVE_ITEM'] # response = retrieve(dana_table, {'id': 'SAMPLE1'}) response_metadata = None response_item = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'Item' in response: response_item = response['Item'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertIsNotNone(response_item) self.assertEqual(200, http_status_code) #@unittest.skip # no reason needed def test_retrieve_encrypted_data(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] #plaintext_item['id'] = 'SAMPLE1 dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act mock_table = Mock() mock_table.get_item.return_value = self.mock_data['RETRIEVE_ITEM'] #with patch('dynamodb_encryption_sdk.encrypted.table.EncryptedTable') as mock_encrypted_table: with patch('mods.ddb.EncryptedTable') as mock_encrypted_table: mock_encrypted_table.return_value = mock_table response = retrieve(dana_table, {'id': 'SAMPLE1'}, encrypt=True) # with patch(f'{__name__}.retrieve') as mock_module_method: # mock_module_method.return_value = self.mock_data['RETRIEVE_ITEM'] # response = retrieve(dana_table, {'id': 'SAMPLE1'}, encrypt=True) response_metadata = None response_item = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'Item' in response: response_item = response['Item'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertIsNotNone(response_item) self.assertEqual(200, http_status_code) def test_remove_item(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] #plaintext_item['id'] = 'SAMPLE1 dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act # The following line works when we run test directly (aka: python -m unittest tests\unit\test_dynamodb.py) # with patch('tests.unit.test_dynamodb.remove') as mock_module_method: # But patching will fail when we "discover" tests (aka: python -m unittest discover -s tests\unit -v) # Because of the way patching works in Python, name patch method f"{__name__}.remove" #with patch(f"{__name__}.remove") as mock_module_method: # with patch("mods.ddb.remove") as mock_module_method: # mock_module_method.return_value = self.mock_data['RESPONSE_METADATA'] mock_table = Mock() mock_table.delete_item.return_value = self.mock_data['RESPONSE_METADATA'] response = remove(mock_table, {'id': 'sample8'}) #print(response) response_metadata = None http_status_code = 0 if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) # if __name__ == '__main__': # unittest.main()

34.154676

114

0.596419

import unittest import boto3 from boto3.dynamodb.types import Binary, Decimal import requests import warnings from unittest.mock import patch, Mock from mods.ddb import store, retrieve, remove class TestDdb(unittest.TestCase): mock_data = { 'RESPONSE_METADATA' : { 'ResponseMetadata': { 'RequestId': 'AAA4RS5BDFE4NGOK864RHBFBH7VV4KQNSO5AEMVJF66Q9ASUAAJG', 'HTTPStatusCode': 200, 'HTTPHeaders': { 'server': 'Server', 'date': 'Sat, 21 Aug 2021 13:07:47 GMT', 'content-type': 'application/x-amz-json-1.0', 'content-length': '2', 'connection': 'keep-alive', 'x-amzn-requestid': '75N4RS5BDFE4NGOK864RHBFBH7VV4KQNSO5AEMVJF66Q9ASUAAJG', 'x-amz-crc32': '2745614147' }, 'RetryAttempts': 0 } }, 'PLAIN_TEXT_ITEM' : { 'id' : 'sample7', "Author": "William Shakespeare", "Title": "Romeo", "Category": "Drama" }, 'RETRIEVE_ITEM' : { 'Item': { 'info': { 'rating': Decimal('3'), 'plot': 'awful' }, 'app': { 'name': 'some generic app', 'version': Decimal('10') }, 'and some binary': Binary(b'\x00\x01\x02'), 'year': Decimal('2021'), 'comment': 'alone', 'some numbers': Decimal('99'), 'id': 'enc2', 'title': 'my horrible movie', 'example': 'data' }, 'ResponseMetadata': { 'RequestId': 'OBKNV1BON1MCPUDFVS0A1LC513VV4KQNSO5AEMVJF66Q9ASUAAJG', 'HTTPStatusCode': 200, 'HTTPHeaders': { 'server': 'Server', 'date': 'Sat, 21 Aug 2021 14:19:31 GMT', 'content-type': 'application/x-amz-json-1.0', 'content-length': '308', 'connection': 'keep-alive', 'x-amzn-requestid': 'OBKNV1BON1MCPUDFVS0A1LC513VV4KQNSO5AEMVJF66Q9ASUAAJG', 'x-amz-crc32': '1593809059' }, 'RetryAttempts': 0 } } } def test_store_data(self): warnings.simplefilter("ignore", ResourceWarning) plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') mock_table = Mock() mock_table.put_item.return_value = self.mock_data['RESPONSE_METADATA'] response = store(mock_table, plaintext_item) response_metadata = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) def test_store_encrypted_data(self): warnings.simplefilter("ignore", ResourceWarning) plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') mock_table = Mock() mock_table.put_item.return_value = self.mock_data['RESPONSE_METADATA'] with patch('mods.ddb.EncryptedTable') as mock_encrypted_table: mock_encrypted_table.return_value = mock_table response = store(Mock(), plaintext_item, encrypt=True) response_metadata = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) def test_retrieve_data(self): warnings.simplefilter("ignore", ResourceWarning) plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act mock_table = Mock() mock_table.get_item.return_value = self.mock_data['RETRIEVE_ITEM'] response = retrieve(mock_table, {'id': 'SAMPLE1'}) # with patch(f'{__name__}.retrieve') as mock_module_method: # mock_module_method.return_value = self.mock_data['RETRIEVE_ITEM'] # response = retrieve(dana_table, {'id': 'SAMPLE1'}) response_metadata = None response_item = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'Item' in response: response_item = response['Item'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertIsNotNone(response_item) self.assertEqual(200, http_status_code) #@unittest.skip # no reason needed def test_retrieve_encrypted_data(self): warnings.simplefilter("ignore", ResourceWarning) # Arrange plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] #plaintext_item['id'] = 'SAMPLE1 dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') mock_table = Mock() mock_table.get_item.return_value = self.mock_data['RETRIEVE_ITEM'] with patch('mods.ddb.EncryptedTable') as mock_encrypted_table: mock_encrypted_table.return_value = mock_table response = retrieve(dana_table, {'id': 'SAMPLE1'}, encrypt=True) response_metadata = None response_item = None if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'Item' in response: response_item = response['Item'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] self.assertIsNotNone(response_metadata) self.assertIsNotNone(response_item) self.assertEqual(200, http_status_code) def test_remove_item(self): warnings.simplefilter("ignore", ResourceWarning) plaintext_item = self.mock_data['PLAIN_TEXT_ITEM'] dynamodb = boto3.resource('dynamodb') dana_table = dynamodb.Table('dana_table') # Act # The following line works when we run test directly (aka: python -m unittest tests\unit\test_dynamodb.py) # with patch('tests.unit.test_dynamodb.remove') as mock_module_method: # But patching will fail when we "discover" tests (aka: python -m unittest discover -s tests\unit -v) # Because of the way patching works in Python, name patch method f"{__name__}.remove" #with patch(f"{__name__}.remove") as mock_module_method: # with patch("mods.ddb.remove") as mock_module_method: # mock_module_method.return_value = self.mock_data['RESPONSE_METADATA'] mock_table = Mock() mock_table.delete_item.return_value = self.mock_data['RESPONSE_METADATA'] response = remove(mock_table, {'id': 'sample8'}) #print(response) response_metadata = None http_status_code = 0 if 'ResponseMetadata' in response: response_metadata = response['ResponseMetadata'] if 'HTTPStatusCode' in response_metadata: http_status_code = response_metadata['HTTPStatusCode'] # Assert(s) self.assertIsNotNone(response_metadata) self.assertEqual(200, http_status_code) # if __name__ == '__main__': # unittest.main()

true

1c2b3794e77dd8e127e577dd7e5b38673d933e02

16,440

py

Python

src/poetry/mixology/incompatibility.py

mmacchia/poetry

7c53db9680d021bac99cc366a3bbc88ebbffdf0f

[ "MIT" ]

null

src/poetry/mixology/incompatibility.py

mmacchia/poetry

7c53db9680d021bac99cc366a3bbc88ebbffdf0f

[ "MIT" ]

null

src/poetry/mixology/incompatibility.py

mmacchia/poetry

7c53db9680d021bac99cc366a3bbc88ebbffdf0f

[ "MIT" ]

null

from typing import Callable from typing import Dict from typing import Iterator from typing import List from typing import Optional from typing import Union from poetry.mixology.incompatibility_cause import ConflictCause from poetry.mixology.incompatibility_cause import DependencyCause from poetry.mixology.incompatibility_cause import IncompatibilityCause from poetry.mixology.incompatibility_cause import NoVersionsCause from poetry.mixology.incompatibility_cause import PackageNotFoundCause from poetry.mixology.incompatibility_cause import PlatformCause from poetry.mixology.incompatibility_cause import PythonCause from poetry.mixology.incompatibility_cause import RootCause from poetry.mixology.term import Term class Incompatibility: def __init__(self, terms: List[Term], cause: IncompatibilityCause) -> None: # Remove the root package from generated incompatibilities, since it will # always be satisfied. This makes error reporting clearer, and may also # make solving more efficient. if ( len(terms) != 1 and isinstance(cause, ConflictCause) and any(term.is_positive() and term.dependency.is_root for term in terms) ): terms = [ term for term in terms if not term.is_positive() or not term.dependency.is_root ] if ( len(terms) == 1 # Short-circuit in the common case of a two-term incompatibility with # two different packages (for example, a dependency). or len(terms) == 2 and terms[0].dependency.complete_name != terms[-1].dependency.complete_name ): pass else: # Coalesce multiple terms about the same package if possible. by_name: Dict[str, Dict[str, Term]] = {} for term in terms: if term.dependency.complete_name not in by_name: by_name[term.dependency.complete_name] = {} by_ref = by_name[term.dependency.complete_name] ref = term.dependency.complete_name if ref in by_ref: by_ref[ref] = by_ref[ref].intersect(term) # If we have two terms that refer to the same package but have a null # intersection, they're mutually exclusive, making this incompatibility # irrelevant, since we already know that mutually exclusive version # ranges are incompatible. We should never derive an irrelevant # incompatibility. assert by_ref[ref] is not None else: by_ref[ref] = term new_terms = [] for by_ref in by_name.values(): positive_terms = [ term for term in by_ref.values() if term.is_positive() ] if positive_terms: new_terms += positive_terms continue new_terms += list(by_ref.values()) terms = new_terms self._terms = terms self._cause = cause @property def terms(self) -> List[Term]: return self._terms @property def cause( self, ) -> Union[ RootCause, NoVersionsCause, DependencyCause, ConflictCause, PythonCause, PlatformCause, PackageNotFoundCause, ]: return self._cause @property def external_incompatibilities( self, ) -> Iterator[Union[ConflictCause, "Incompatibility"]]: """ Returns all external incompatibilities in this incompatibility's derivation graph. """ if isinstance(self._cause, ConflictCause): cause: ConflictCause = self._cause yield from cause.conflict.external_incompatibilities yield from cause.other.external_incompatibilities else: yield self def is_failure(self) -> bool: return len(self._terms) == 0 or ( len(self._terms) == 1 and self._terms[0].dependency.is_root ) def __str__(self) -> str: if isinstance(self._cause, DependencyCause): assert len(self._terms) == 2 depender = self._terms[0] dependee = self._terms[1] assert depender.is_positive() assert not dependee.is_positive() return "{} depends on {}".format( self._terse(depender, allow_every=True), self._terse(dependee) ) elif isinstance(self._cause, PythonCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() cause: PythonCause = self._cause text = "{} requires ".format(self._terse(self._terms[0], allow_every=True)) text += f"Python {cause.python_version}" return text elif isinstance(self._cause, PlatformCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() cause: PlatformCause = self._cause text = "{} requires ".format(self._terse(self._terms[0], allow_every=True)) text += f"platform {cause.platform}" return text elif isinstance(self._cause, NoVersionsCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() return "no versions of {} match {}".format( self._terms[0].dependency.name, self._terms[0].constraint ) elif isinstance(self._cause, PackageNotFoundCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() return "{} doesn't exist".format(self._terms[0].dependency.name) elif isinstance(self._cause, RootCause): assert len(self._terms) == 1 assert not self._terms[0].is_positive() assert self._terms[0].dependency.is_root return "{} is {}".format( self._terms[0].dependency.name, self._terms[0].dependency.constraint ) elif self.is_failure(): return "version solving failed" if len(self._terms) == 1: term = self._terms[0] if term.constraint.is_any(): return "{} is {}".format( term.dependency.name, "forbidden" if term.is_positive() else "required", ) else: return "{} is {}".format( term.dependency.name, "forbidden" if term.is_positive() else "required", ) if len(self._terms) == 2: term1 = self._terms[0] term2 = self._terms[1] if term1.is_positive() == term2.is_positive(): if term1.is_positive(): package1 = ( term1.dependency.name if term1.constraint.is_any() else self._terse(term1) ) package2 = ( term2.dependency.name if term2.constraint.is_any() else self._terse(term2) ) return f"{package1} is incompatible with {package2}" else: return "either {} or {}".format( self._terse(term1), self._terse(term2) ) positive = [] negative = [] for term in self._terms: if term.is_positive(): positive.append(self._terse(term)) else: negative.append(self._terse(term)) if positive and negative: if len(positive) == 1: positive_term = [term for term in self._terms if term.is_positive()][0] return "{} requires {}".format( self._terse(positive_term, allow_every=True), " or ".join(negative) ) else: return "if {} then {}".format( " and ".join(positive), " or ".join(negative) ) elif positive: return "one of {} must be false".format(" or ".join(positive)) else: return "one of {} must be true".format(" or ".join(negative)) def and_to_string( self, other: "Incompatibility", details: dict, this_line: Optional[int], other_line: Optional[int], ) -> str: requires_both = self._try_requires_both(other, details, this_line, other_line) if requires_both is not None: return requires_both requires_through = self._try_requires_through( other, details, this_line, other_line ) if requires_through is not None: return requires_through requires_forbidden = self._try_requires_forbidden( other, details, this_line, other_line ) if requires_forbidden is not None: return requires_forbidden buffer = [str(self)] if this_line is not None: buffer.append(" " + str(this_line)) buffer.append(" and {}".format(str(other))) if other_line is not None: buffer.append(" " + str(other_line)) return "\n".join(buffer) def _try_requires_both( self, other: "Incompatibility", details: dict, this_line: Optional[int], other_line: Optional[int], ) -> Optional[str]: if len(self._terms) == 1 or len(other.terms) == 1: return None this_positive = self._single_term_where(lambda term: term.is_positive()) if this_positive is None: return None other_positive = other._single_term_where(lambda term: term.is_positive()) if other_positive is None: return None if this_positive.dependency != other_positive.dependency: return None this_negatives = " or ".join( [self._terse(term) for term in self._terms if not term.is_positive()] ) other_negatives = " or ".join( [self._terse(term) for term in other.terms if not term.is_positive()] ) buffer = [self._terse(this_positive, allow_every=True) + " "] is_dependency = isinstance(self.cause, DependencyCause) and isinstance( other.cause, DependencyCause ) if is_dependency: buffer.append("depends on") else: buffer.append("requires") buffer.append(f" both {this_negatives}") if this_line is not None: buffer.append(f" ({this_line})") buffer.append(f" and {other_negatives}") if other_line is not None: buffer.append(f" ({other_line})") return "".join(buffer) def _try_requires_through( self, other: "Incompatibility", details: dict, this_line: int, other_line: int ) -> Optional[str]: if len(self._terms) == 1 or len(other.terms) == 1: return None this_negative = self._single_term_where(lambda term: not term.is_positive()) other_negative = other._single_term_where(lambda term: not term.is_positive()) if this_negative is None and other_negative is None: return None this_positive = self._single_term_where(lambda term: term.is_positive()) other_positive = self._single_term_where(lambda term: term.is_positive()) if ( this_negative is not None and other_positive is not None and this_negative.dependency.name == other_positive.dependency.name and this_negative.inverse.satisfies(other_positive) ): prior = self prior_negative = this_negative prior_line = this_line latter = other latter_line = other_line elif ( other_negative is not None and this_positive is not None and other_negative.dependency.name == this_positive.dependency.name and other_negative.inverse.satisfies(this_positive) ): prior = other prior_negative = other_negative prior_line = other_line latter = self latter_line = this_line else: return None prior_positives = [term for term in prior.terms if term.is_positive()] buffer = [] if len(prior_positives) > 1: prior_string = " or ".join([self._terse(term) for term in prior_positives]) buffer.append(f"if {prior_string} then ") else: if isinstance(prior.cause, DependencyCause): verb = "depends on" else: verb = "requires" buffer.append( "{} {} ".format(self._terse(prior_positives[0], allow_every=True), verb) ) buffer.append(self._terse(prior_negative)) if prior_line is not None: buffer.append(f" ({prior_line})") buffer.append(" which ") if isinstance(latter.cause, DependencyCause): buffer.append("depends on ") else: buffer.append("requires ") buffer.append( " or ".join( [self._terse(term) for term in latter.terms if not term.is_positive()] ) ) if latter_line is not None: buffer.append(f" ({latter_line})") return "".join(buffer) def _try_requires_forbidden( self, other: "Incompatibility", details: dict, this_line: int, other_line: int ) -> Optional[str]: if len(self._terms) != 1 and len(other.terms) != 1: return None if len(self.terms) == 1: prior = other latter = self prior_line = other_line latter_line = this_line else: prior = self latter = other prior_line = this_line latter_line = other_line negative = prior._single_term_where(lambda term: not term.is_positive()) if negative is None: return None if not negative.inverse.satisfies(latter.terms[0]): return None positives = [t for t in prior.terms if t.is_positive()] buffer = [] if len(positives) > 1: prior_string = " or ".join([self._terse(term) for term in positives]) buffer.append(f"if {prior_string} then ") else: buffer.append(self._terse(positives[0], allow_every=True)) if isinstance(prior.cause, DependencyCause): buffer.append(" depends on ") else: buffer.append(" requires ") buffer.append(self._terse(latter.terms[0]) + " ") if prior_line is not None: buffer.append(f"({prior_line}) ") if isinstance(latter.cause, PythonCause): cause: PythonCause = latter.cause buffer.append(f"which requires Python {cause.python_version}") elif isinstance(latter.cause, NoVersionsCause): buffer.append("which doesn't match any versions") elif isinstance(latter.cause, PackageNotFoundCause): buffer.append("which doesn't exist") else: buffer.append("which is forbidden") if latter_line is not None: buffer.append(f" ({latter_line})") return "".join(buffer) def _terse(self, term: Term, allow_every: bool = False) -> str: if allow_every and term.constraint.is_any(): return f"every version of {term.dependency.complete_name}" if term.dependency.is_root: return term.dependency.pretty_name return "{} ({})".format( term.dependency.pretty_name, term.dependency.pretty_constraint ) def _single_term_where(self, callable: Callable[[Term], bool]) -> Optional[Term]: found = None for term in self._terms: if not callable(term): continue if found is not None: return None found = term return found def __repr__(self) -> str: return "<Incompatibility {}>".format(str(self))

34.393305

91

0.568127

from typing import Callable from typing import Dict from typing import Iterator from typing import List from typing import Optional from typing import Union from poetry.mixology.incompatibility_cause import ConflictCause from poetry.mixology.incompatibility_cause import DependencyCause from poetry.mixology.incompatibility_cause import IncompatibilityCause from poetry.mixology.incompatibility_cause import NoVersionsCause from poetry.mixology.incompatibility_cause import PackageNotFoundCause from poetry.mixology.incompatibility_cause import PlatformCause from poetry.mixology.incompatibility_cause import PythonCause from poetry.mixology.incompatibility_cause import RootCause from poetry.mixology.term import Term class Incompatibility: def __init__(self, terms: List[Term], cause: IncompatibilityCause) -> None: if ( len(terms) != 1 and isinstance(cause, ConflictCause) and any(term.is_positive() and term.dependency.is_root for term in terms) ): terms = [ term for term in terms if not term.is_positive() or not term.dependency.is_root ] if ( len(terms) == 1 or len(terms) == 2 and terms[0].dependency.complete_name != terms[-1].dependency.complete_name ): pass else: by_name: Dict[str, Dict[str, Term]] = {} for term in terms: if term.dependency.complete_name not in by_name: by_name[term.dependency.complete_name] = {} by_ref = by_name[term.dependency.complete_name] ref = term.dependency.complete_name if ref in by_ref: by_ref[ref] = by_ref[ref].intersect(term) # irrelevant, since we already know that mutually exclusive version # ranges are incompatible. We should never derive an irrelevant # incompatibility. assert by_ref[ref] is not None else: by_ref[ref] = term new_terms = [] for by_ref in by_name.values(): positive_terms = [ term for term in by_ref.values() if term.is_positive() ] if positive_terms: new_terms += positive_terms continue new_terms += list(by_ref.values()) terms = new_terms self._terms = terms self._cause = cause @property def terms(self) -> List[Term]: return self._terms @property def cause( self, ) -> Union[ RootCause, NoVersionsCause, DependencyCause, ConflictCause, PythonCause, PlatformCause, PackageNotFoundCause, ]: return self._cause @property def external_incompatibilities( self, ) -> Iterator[Union[ConflictCause, "Incompatibility"]]: if isinstance(self._cause, ConflictCause): cause: ConflictCause = self._cause yield from cause.conflict.external_incompatibilities yield from cause.other.external_incompatibilities else: yield self def is_failure(self) -> bool: return len(self._terms) == 0 or ( len(self._terms) == 1 and self._terms[0].dependency.is_root ) def __str__(self) -> str: if isinstance(self._cause, DependencyCause): assert len(self._terms) == 2 depender = self._terms[0] dependee = self._terms[1] assert depender.is_positive() assert not dependee.is_positive() return "{} depends on {}".format( self._terse(depender, allow_every=True), self._terse(dependee) ) elif isinstance(self._cause, PythonCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() cause: PythonCause = self._cause text = "{} requires ".format(self._terse(self._terms[0], allow_every=True)) text += f"Python {cause.python_version}" return text elif isinstance(self._cause, PlatformCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() cause: PlatformCause = self._cause text = "{} requires ".format(self._terse(self._terms[0], allow_every=True)) text += f"platform {cause.platform}" return text elif isinstance(self._cause, NoVersionsCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() return "no versions of {} match {}".format( self._terms[0].dependency.name, self._terms[0].constraint ) elif isinstance(self._cause, PackageNotFoundCause): assert len(self._terms) == 1 assert self._terms[0].is_positive() return "{} doesn't exist".format(self._terms[0].dependency.name) elif isinstance(self._cause, RootCause): assert len(self._terms) == 1 assert not self._terms[0].is_positive() assert self._terms[0].dependency.is_root return "{} is {}".format( self._terms[0].dependency.name, self._terms[0].dependency.constraint ) elif self.is_failure(): return "version solving failed" if len(self._terms) == 1: term = self._terms[0] if term.constraint.is_any(): return "{} is {}".format( term.dependency.name, "forbidden" if term.is_positive() else "required", ) else: return "{} is {}".format( term.dependency.name, "forbidden" if term.is_positive() else "required", ) if len(self._terms) == 2: term1 = self._terms[0] term2 = self._terms[1] if term1.is_positive() == term2.is_positive(): if term1.is_positive(): package1 = ( term1.dependency.name if term1.constraint.is_any() else self._terse(term1) ) package2 = ( term2.dependency.name if term2.constraint.is_any() else self._terse(term2) ) return f"{package1} is incompatible with {package2}" else: return "either {} or {}".format( self._terse(term1), self._terse(term2) ) positive = [] negative = [] for term in self._terms: if term.is_positive(): positive.append(self._terse(term)) else: negative.append(self._terse(term)) if positive and negative: if len(positive) == 1: positive_term = [term for term in self._terms if term.is_positive()][0] return "{} requires {}".format( self._terse(positive_term, allow_every=True), " or ".join(negative) ) else: return "if {} then {}".format( " and ".join(positive), " or ".join(negative) ) elif positive: return "one of {} must be false".format(" or ".join(positive)) else: return "one of {} must be true".format(" or ".join(negative)) def and_to_string( self, other: "Incompatibility", details: dict, this_line: Optional[int], other_line: Optional[int], ) -> str: requires_both = self._try_requires_both(other, details, this_line, other_line) if requires_both is not None: return requires_both requires_through = self._try_requires_through( other, details, this_line, other_line ) if requires_through is not None: return requires_through requires_forbidden = self._try_requires_forbidden( other, details, this_line, other_line ) if requires_forbidden is not None: return requires_forbidden buffer = [str(self)] if this_line is not None: buffer.append(" " + str(this_line)) buffer.append(" and {}".format(str(other))) if other_line is not None: buffer.append(" " + str(other_line)) return "\n".join(buffer) def _try_requires_both( self, other: "Incompatibility", details: dict, this_line: Optional[int], other_line: Optional[int], ) -> Optional[str]: if len(self._terms) == 1 or len(other.terms) == 1: return None this_positive = self._single_term_where(lambda term: term.is_positive()) if this_positive is None: return None other_positive = other._single_term_where(lambda term: term.is_positive()) if other_positive is None: return None if this_positive.dependency != other_positive.dependency: return None this_negatives = " or ".join( [self._terse(term) for term in self._terms if not term.is_positive()] ) other_negatives = " or ".join( [self._terse(term) for term in other.terms if not term.is_positive()] ) buffer = [self._terse(this_positive, allow_every=True) + " "] is_dependency = isinstance(self.cause, DependencyCause) and isinstance( other.cause, DependencyCause ) if is_dependency: buffer.append("depends on") else: buffer.append("requires") buffer.append(f" both {this_negatives}") if this_line is not None: buffer.append(f" ({this_line})") buffer.append(f" and {other_negatives}") if other_line is not None: buffer.append(f" ({other_line})") return "".join(buffer) def _try_requires_through( self, other: "Incompatibility", details: dict, this_line: int, other_line: int ) -> Optional[str]: if len(self._terms) == 1 or len(other.terms) == 1: return None this_negative = self._single_term_where(lambda term: not term.is_positive()) other_negative = other._single_term_where(lambda term: not term.is_positive()) if this_negative is None and other_negative is None: return None this_positive = self._single_term_where(lambda term: term.is_positive()) other_positive = self._single_term_where(lambda term: term.is_positive()) if ( this_negative is not None and other_positive is not None and this_negative.dependency.name == other_positive.dependency.name and this_negative.inverse.satisfies(other_positive) ): prior = self prior_negative = this_negative prior_line = this_line latter = other latter_line = other_line elif ( other_negative is not None and this_positive is not None and other_negative.dependency.name == this_positive.dependency.name and other_negative.inverse.satisfies(this_positive) ): prior = other prior_negative = other_negative prior_line = other_line latter = self latter_line = this_line else: return None prior_positives = [term for term in prior.terms if term.is_positive()] buffer = [] if len(prior_positives) > 1: prior_string = " or ".join([self._terse(term) for term in prior_positives]) buffer.append(f"if {prior_string} then ") else: if isinstance(prior.cause, DependencyCause): verb = "depends on" else: verb = "requires" buffer.append( "{} {} ".format(self._terse(prior_positives[0], allow_every=True), verb) ) buffer.append(self._terse(prior_negative)) if prior_line is not None: buffer.append(f" ({prior_line})") buffer.append(" which ") if isinstance(latter.cause, DependencyCause): buffer.append("depends on ") else: buffer.append("requires ") buffer.append( " or ".join( [self._terse(term) for term in latter.terms if not term.is_positive()] ) ) if latter_line is not None: buffer.append(f" ({latter_line})") return "".join(buffer) def _try_requires_forbidden( self, other: "Incompatibility", details: dict, this_line: int, other_line: int ) -> Optional[str]: if len(self._terms) != 1 and len(other.terms) != 1: return None if len(self.terms) == 1: prior = other latter = self prior_line = other_line latter_line = this_line else: prior = self latter = other prior_line = this_line latter_line = other_line negative = prior._single_term_where(lambda term: not term.is_positive()) if negative is None: return None if not negative.inverse.satisfies(latter.terms[0]): return None positives = [t for t in prior.terms if t.is_positive()] buffer = [] if len(positives) > 1: prior_string = " or ".join([self._terse(term) for term in positives]) buffer.append(f"if {prior_string} then ") else: buffer.append(self._terse(positives[0], allow_every=True)) if isinstance(prior.cause, DependencyCause): buffer.append(" depends on ") else: buffer.append(" requires ") buffer.append(self._terse(latter.terms[0]) + " ") if prior_line is not None: buffer.append(f"({prior_line}) ") if isinstance(latter.cause, PythonCause): cause: PythonCause = latter.cause buffer.append(f"which requires Python {cause.python_version}") elif isinstance(latter.cause, NoVersionsCause): buffer.append("which doesn't match any versions") elif isinstance(latter.cause, PackageNotFoundCause): buffer.append("which doesn't exist") else: buffer.append("which is forbidden") if latter_line is not None: buffer.append(f" ({latter_line})") return "".join(buffer) def _terse(self, term: Term, allow_every: bool = False) -> str: if allow_every and term.constraint.is_any(): return f"every version of {term.dependency.complete_name}" if term.dependency.is_root: return term.dependency.pretty_name return "{} ({})".format( term.dependency.pretty_name, term.dependency.pretty_constraint ) def _single_term_where(self, callable: Callable[[Term], bool]) -> Optional[Term]: found = None for term in self._terms: if not callable(term): continue if found is not None: return None found = term return found def __repr__(self) -> str: return "<Incompatibility {}>".format(str(self))

true

1c2b37b0c65c4c2670dc787d819a3d20aeae9092

93

py

Python

skeleton/pixelwars/apps.py

GenchoBG/HackTues3

1457d44d6f6aeef158e49f91ce4a40246afe9c62

[ "MIT" ]

null

skeleton/pixelwars/apps.py

GenchoBG/HackTues3

1457d44d6f6aeef158e49f91ce4a40246afe9c62

[ "MIT" ]

null

skeleton/pixelwars/apps.py

GenchoBG/HackTues3

1457d44d6f6aeef158e49f91ce4a40246afe9c62

[ "MIT" ]

null

from django.apps import AppConfig class PixelwarsConfig(AppConfig): name = 'pixelwars'

15.5

33

0.763441

from django.apps import AppConfig class PixelwarsConfig(AppConfig): name = 'pixelwars'

true

1c2b380a48509c5afcad0bf4a34adea41e89cc5e

530

py

Python

scripts/data_pop.py

ifryed/LinearNet

f4fbdcdc98c275a6c21c9efbbc357aa9e88aed6c

[ "MIT" ]

3

2021-10-05T20:43:13.000Z

2021-10-09T20:59:47.000Z

scripts/data_pop.py

ifryed/LinearNet

f4fbdcdc98c275a6c21c9efbbc357aa9e88aed6c

[ "MIT" ]

null

scripts/data_pop.py

ifryed/LinearNet

f4fbdcdc98c275a6c21c9efbbc357aa9e88aed6c

[ "MIT" ]

null

import os import sys import numpy as np from skimage import io def main(): images = os.listdir(sys.argv[1]) pop_n = int(sys.argv[2]) if len(sys.argv) > 1 else 200 img = io.imread(os.path.join(sys.argv[1], images[0])) h, w = img.shape[:2] crop_size = 256 for i in range(pop_n): x, y = np.random.randint(0, w - crop_size), np.random.randint(0, h - crop_size) io.imsave(sys.argv[1] + '/img_{}.png'.format(i), img[y:y + crop_size, x: x + crop_size]) if __name__ == '__main__': main()

24.090909

96

0.609434

import os import sys import numpy as np from skimage import io def main(): images = os.listdir(sys.argv[1]) pop_n = int(sys.argv[2]) if len(sys.argv) > 1 else 200 img = io.imread(os.path.join(sys.argv[1], images[0])) h, w = img.shape[:2] crop_size = 256 for i in range(pop_n): x, y = np.random.randint(0, w - crop_size), np.random.randint(0, h - crop_size) io.imsave(sys.argv[1] + '/img_{}.png'.format(i), img[y:y + crop_size, x: x + crop_size]) if __name__ == '__main__': main()

true

1c2b393f8b5be892bd281a729a6d6f4b16059d4d

31,890

py

Python

tensorflow_federated/python/core/api/intrinsics_test.py

abhinavsp0730/federated

7c5821f85cb2d0379f33bf2b5e02f97d51a16427

[ "Apache-2.0" ]

null

tensorflow_federated/python/core/api/intrinsics_test.py

abhinavsp0730/federated

7c5821f85cb2d0379f33bf2b5e02f97d51a16427

[ "Apache-2.0" ]

null

tensorflow_federated/python/core/api/intrinsics_test.py

abhinavsp0730/federated

7c5821f85cb2d0379f33bf2b5e02f97d51a16427

[ "Apache-2.0" ]

null

# Lint as: python3 # Copyright 2018, The TensorFlow Federated Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import itertools import warnings from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_federated.python.common_libs import anonymous_tuple from tensorflow_federated.python.common_libs import test as common_test from tensorflow_federated.python.core.api import computation_types from tensorflow_federated.python.core.api import computations from tensorflow_federated.python.core.api import intrinsics from tensorflow_federated.python.core.api import placements from tensorflow_federated.python.core.api import value_base from tensorflow_federated.python.core.impl.executors import default_executor from tensorflow_federated.python.core.impl.executors import executor_stacks from tensorflow_federated.python.core.impl.executors import executor_test_utils tf.compat.v1.enable_v2_behavior() class IntrinsicsTest(parameterized.TestCase): def assert_type(self, value, type_string): self.assertEqual(value.type_signature.compact_representation(), type_string) def test_federated_broadcast_with_server_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_broadcast(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@SERVER -> int32@CLIENTS)') def test_federated_broadcast_with_server_non_all_equal_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType( tf.int32, placements.SERVER, all_equal=False)) def _(x): return intrinsics.federated_broadcast(x) def test_federated_broadcast_with_client_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS, True)) def _(x): return intrinsics.federated_broadcast(x) def test_federated_broadcast_with_non_federated_val(self): with self.assertRaises(TypeError): @computations.federated_computation(tf.int32) def _(x): return intrinsics.federated_broadcast(x) def test_federated_eval_rand_on_clients(self): @computations.federated_computation def rand_on_clients(): @computations.tf_computation def rand(): return tf.random.normal([]) val = intrinsics.federated_eval(rand, placements.CLIENTS) self.assertIsInstance(val, value_base.Value) return val self.assert_type(rand_on_clients, '( -> {float32}@CLIENTS)') def test_federated_eval_rand_on_server(self): @computations.federated_computation def rand_on_server(): @computations.tf_computation def rand(): return tf.random.normal([]) val = intrinsics.federated_eval(rand, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val self.assert_type(rand_on_server, '( -> float32@SERVER)') def test_federated_map_with_client_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS, True)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@CLIENTS -> {bool}@CLIENTS)') def test_federated_map_with_client_non_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> {bool}@CLIENTS)') def test_federated_map_with_server_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@SERVER -> bool@SERVER)') def test_federated_map_injected_zip_with_server_int(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.int32, placements.SERVER) ]) def foo(x, y): val = intrinsics.federated_map( computations.tf_computation(lambda x, y: x > 10, [tf.int32, tf.int32]), [x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER,int32@SERVER> -> bool@SERVER)') def test_federated_map_injected_zip_fails_different_placements(self): def foo(x, y): val = intrinsics.federated_map( computations.tf_computation(lambda x, y: x > 10, [tf.int32, tf.int32]), [x, y]) self.assertIsInstance(val, value_base.Value) return val with self.assertRaisesRegex( TypeError, 'The value to be mapped must be a FederatedType or implicitly ' 'convertible to a FederatedType.'): computations.federated_computation(foo, [ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.int32, placements.CLIENTS) ]) def test_federated_map_with_non_federated_val(self): with self.assertRaises(TypeError): @computations.federated_computation(tf.int32) def _(x): return intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) def test_federated_sum_with_client_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32@SERVER)') def test_federated_sum_with_client_string(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.string, placements.CLIENTS)) def _(x): return intrinsics.federated_sum(x) def test_federated_sum_with_server_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def _(x): return intrinsics.federated_sum(x) def test_federated_zip_with_client_non_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{int32}@CLIENTS,bool@CLIENTS> -> {<int32,bool>}@CLIENTS)') def test_federated_zip_with_single_unnamed_int_client(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<{int32}@CLIENTS> -> {<int32>}@CLIENTS)') def test_federated_zip_with_single_unnamed_int_server(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER> -> <int32>@SERVER)') def test_federated_zip_with_single_named_bool_clients(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.bool, placements.CLIENTS)), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<a={bool}@CLIENTS> -> {<a=bool>}@CLIENTS)') def test_federated_zip_with_single_named_bool_server(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.bool, placements.SERVER)), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<a=bool@SERVER> -> <a=bool>@SERVER)') def test_federated_zip_with_names_client_non_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): a = {'x': x, 'y': y} val = intrinsics.federated_zip(a) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{int32}@CLIENTS,bool@CLIENTS> -> {<x=int32,y=bool>}@CLIENTS)') def test_federated_zip_with_client_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS, True), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<int32@CLIENTS,bool@CLIENTS> -> {<int32,bool>}@CLIENTS)') def test_federated_zip_with_names_client_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS, True), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(arg): a = {'x': arg[0], 'y': arg[1]} val = intrinsics.federated_zip(a) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<int32@CLIENTS,bool@CLIENTS> -> {<x=int32,y=bool>}@CLIENTS)') def test_federated_zip_with_server_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.bool, placements.SERVER) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER,bool@SERVER> -> <int32,bool>@SERVER)') def test_federated_zip_with_names_server_int_and_bool(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.int32, placements.SERVER)), ('b', computation_types.FederatedType(tf.bool, placements.SERVER)), ]) def foo(arg): val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<a=int32@SERVER,b=bool@SERVER> -> <a=int32,b=bool>@SERVER)') def test_federated_zip_error_different_placements(self): with self.assertRaises(TypeError): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.int32, placements.SERVER)), ('b', computation_types.FederatedType(tf.bool, placements.CLIENTS)), ]) def _(arg): return intrinsics.federated_zip(arg) def test_federated_collect_with_client_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_collect(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32*@SERVER)') def test_federated_collect_with_server_int_fails(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def _(x): return intrinsics.federated_collect(x) def test_federated_mean_with_client_float32_without_weight(self): @computations.federated_computation( computation_types.FederatedType(tf.float32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_mean(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({float32}@CLIENTS -> float32@SERVER)') def test_federated_mean_with_all_equal_client_float32_without_weight(self): federated_all_equal_float = computation_types.FederatedType( tf.float32, placements.CLIENTS, all_equal=True) @computations.federated_computation(federated_all_equal_float) def foo(x): val = intrinsics.federated_mean(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(float32@CLIENTS -> float32@SERVER)') def test_federated_mean_with_all_equal_client_float32_with_weight(self): federated_all_equal_float = computation_types.FederatedType( tf.float32, placements.CLIENTS, all_equal=True) @computations.federated_computation(federated_all_equal_float) def foo(x): val = intrinsics.federated_mean(x, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(float32@CLIENTS -> float32@SERVER)') def test_federated_mean_with_client_tuple_with_int32_weight(self): @computations.federated_computation([ computation_types.FederatedType([('x', tf.float64), ('y', tf.float64)], placements.CLIENTS), computation_types.FederatedType(tf.int32, placements.CLIENTS) ]) def foo(x, y): val = intrinsics.federated_mean(x, y) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{<x=float64,y=float64>}@CLIENTS,{int32}@CLIENTS> ' '-> <x=float64,y=float64>@SERVER)') def test_federated_mean_with_client_int32_fails(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def _(x): return intrinsics.federated_mean(x) def test_federated_mean_with_string_weight_fails(self): with self.assertRaises(TypeError): @computations.federated_computation([ computation_types.FederatedType(tf.float32, placements.CLIENTS), computation_types.FederatedType(tf.string, placements.CLIENTS) ]) def _(x, y): return intrinsics.federated_mean(x, y) def test_federated_aggregate_with_client_int(self): # The representation used during the aggregation process will be a named # tuple with 2 elements - the integer 'total' that represents the sum of # elements encountered, and the integer element 'count'. # pylint: disable=invalid-name Accumulator = collections.namedtuple('Accumulator', 'total count') # pylint: enable=invalid-name accumulator_type = computation_types.NamedTupleType( Accumulator(tf.int32, tf.int32)) # The operator to use during the first stage simply adds an element to the # total and updates the count. @computations.tf_computation([accumulator_type, tf.int32]) def accumulate(accu, elem): return Accumulator(accu.total + elem, accu.count + 1) # The operator to use during the second stage simply adds total and count. @computations.tf_computation([accumulator_type, accumulator_type]) def merge(x, y): return Accumulator(x.total + y.total, x.count + y.count) # The operator to use during the final stage simply computes the ratio. @computations.tf_computation(accumulator_type) def report(accu): return tf.cast(accu.total, tf.float32) / tf.cast(accu.count, tf.float32) @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_aggregate(x, Accumulator(0, 0), accumulate, merge, report) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> float32@SERVER)') def test_federated_aggregate_with_federated_zero_fails(self): @computations.federated_computation() def build_federated_zero(): val = intrinsics.federated_value(0, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val @computations.tf_computation([tf.int32, tf.int32]) def accumulate(accu, elem): return accu + elem # The operator to use during the second stage simply adds total and count. @computations.tf_computation([tf.int32, tf.int32]) def merge(x, y): return x + y # The operator to use during the final stage simply computes the ratio. @computations.tf_computation(tf.int32) def report(accu): return accu def foo(x): return intrinsics.federated_aggregate(x, build_federated_zero(), accumulate, merge, report) with self.assertRaisesRegex( TypeError, 'Expected `zero` to be assignable to type int32, ' 'but was of incompatible type int32@SERVER'): computations.federated_computation( foo, computation_types.FederatedType(tf.int32, placements.CLIENTS)) def test_federated_aggregate_with_unknown_dimension(self): Accumulator = collections.namedtuple('Accumulator', ['samples']) # pylint: disable=invalid-name accumulator_type = computation_types.NamedTupleType( Accumulator( samples=computation_types.TensorType(dtype=tf.int32, shape=[None]))) @computations.tf_computation() def build_empty_accumulator(): return Accumulator(samples=tf.zeros(shape=[0], dtype=tf.int32)) # The operator to use during the first stage simply adds an element to the # tensor, increasing its size. @computations.tf_computation([accumulator_type, tf.int32]) def accumulate(accu, elem): return Accumulator( samples=tf.concat( [accu.samples, tf.expand_dims(elem, axis=0)], axis=0)) # The operator to use during the second stage simply adds total and count. @computations.tf_computation([accumulator_type, accumulator_type]) def merge(x, y): return Accumulator(samples=tf.concat([x.samples, y.samples], axis=0)) # The operator to use during the final stage simply computes the ratio. @computations.tf_computation(accumulator_type) def report(accu): return accu @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_aggregate(x, build_empty_accumulator(), accumulate, merge, report) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> <samples=int32[?]>@SERVER)') def test_federated_reduce_with_tf_add_raw_constant(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): plus = computations.tf_computation(tf.add, [tf.int32, tf.int32]) val = intrinsics.federated_reduce(x, 0, plus) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32@SERVER)') def test_num_over_temperature_threshold_example(self): @computations.federated_computation([ computation_types.FederatedType(tf.float32, placements.CLIENTS), computation_types.FederatedType(tf.float32, placements.SERVER) ]) def foo(temperatures, threshold): val = intrinsics.federated_sum( intrinsics.federated_map( computations.tf_computation( lambda x, y: tf.cast(tf.greater(x, y), tf.int32), [tf.float32, tf.float32]), [temperatures, intrinsics.federated_broadcast(threshold)])) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<{float32}@CLIENTS,float32@SERVER> -> int32@SERVER)') @parameterized.named_parameters(('test_n_2', 2), ('test_n_3', 3), ('test_n_5', 5)) def test_n_tuple_federated_zip_tensor_args(self, n): fed_type = computation_types.FederatedType(tf.int32, placements.CLIENTS) initial_tuple_type = computation_types.NamedTupleType([fed_type] * n) final_fed_type = computation_types.FederatedType([tf.int32] * n, placements.CLIENTS) function_type = computation_types.FunctionType(initial_tuple_type, final_fed_type) @computations.federated_computation( [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * n) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, function_type.compact_representation()) @parameterized.named_parameters( ('test_n_2_int', 2, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_3_int', 3, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_5_int', 5, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_2_tuple', 2, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS)), ('test_n_3_tuple', 3, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS)), ('test_n_5_tuple', 5, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS))) def test_named_n_tuple_federated_zip(self, n, fed_type): initial_tuple_type = computation_types.NamedTupleType([fed_type] * n) named_fed_type = computation_types.FederatedType( [(str(k), fed_type.member) for k in range(n)], placements.CLIENTS) mixed_fed_type = computation_types.FederatedType( [(str(k), fed_type.member) if k % 2 == 0 else fed_type.member for k in range(n)], placements.CLIENTS) named_function_type = computation_types.FunctionType( initial_tuple_type, named_fed_type) mixed_function_type = computation_types.FunctionType( initial_tuple_type, mixed_fed_type) @computations.federated_computation([fed_type] * n) def foo(x): arg = {str(k): x[k] for k in range(n)} val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, named_function_type.compact_representation()) def _make_test_tuple(x, k): """Make a test tuple with a name if k is even, otherwise unnamed.""" if k % 2 == 0: return str(k), x[k] else: return None, x[k] @computations.federated_computation([fed_type] * n) def bar(x): arg = anonymous_tuple.AnonymousTuple( _make_test_tuple(x, k) for k in range(n)) val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type(bar, mixed_function_type.compact_representation()) @parameterized.named_parameters([ ('test_n_' + str(n) + '_m_' + str(m), n, m) for n, m in itertools.product([1, 2, 3], [1, 2, 3]) ]) def test_n_tuple_federated_zip_mixed_args(self, n, m): tuple_fed_type = computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS) single_fed_type = computation_types.FederatedType(tf.int32, placements.CLIENTS) initial_tuple_type = computation_types.NamedTupleType([tuple_fed_type] * n + [single_fed_type] * m) final_fed_type = computation_types.FederatedType( [[tf.int32, tf.int32]] * n + [tf.int32] * m, placements.CLIENTS) function_type = computation_types.FunctionType(initial_tuple_type, final_fed_type) @computations.federated_computation([ computation_types.FederatedType( computation_types.NamedTupleType([tf.int32, tf.int32]), placements.CLIENTS) ] * n + [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * m) def baz(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(baz, function_type.compact_representation()) def test_federated_apply_raises_warning(self): with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_apply( computations.tf_computation(lambda x: x * x, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assertLen(w, 1) self.assertIsInstance(w[0].category(), DeprecationWarning) self.assertIn('tff.federated_apply() is deprecated', str(w[0].message)) self.assert_type(foo, '(int32@SERVER -> int32@SERVER)') def test_federated_value_with_bool_on_clients(self): @computations.federated_computation(tf.bool) def foo(x): val = intrinsics.federated_value(x, placements.CLIENTS) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(bool -> bool@CLIENTS)') def test_federated_value_raw_np_scalar(self): @computations.federated_computation def test_np_values(): floatv = np.float64(0) tff_float = intrinsics.federated_value(floatv, placements.SERVER) self.assertIsInstance(tff_float, value_base.Value) self.assert_type(tff_float, 'float64@SERVER') intv = np.int64(0) tff_int = intrinsics.federated_value(intv, placements.SERVER) self.assertIsInstance(tff_int, value_base.Value) self.assert_type(tff_int, 'int64@SERVER') return (tff_float, tff_int) floatv, intv = test_np_values() self.assertEqual(floatv, 0.0) self.assertEqual(intv, 0) def test_federated_value_raw_tf_scalar_variable(self): v = tf.Variable(initial_value=0., name='test_var') with self.assertRaisesRegex( TypeError, 'TensorFlow construct (.*) has been ' 'encountered in a federated context.'): _ = intrinsics.federated_value(v, placements.SERVER) def test_federated_value_with_bool_on_server(self): @computations.federated_computation(tf.bool) def foo(x): val = intrinsics.federated_value(x, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(bool -> bool@SERVER)') def test_sequence_sum(self): @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> int32)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> int32@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {int32}@CLIENTS)') def test_sequence_map(self): @computations.tf_computation(tf.int32) def over_threshold(x): return x > 10 @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> bool*)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> bool*@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {bool*}@CLIENTS)') def test_sequence_reduce(self): add_numbers = computations.tf_computation(tf.add, [tf.int32, tf.int32]) @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> int32)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> int32@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {int32}@CLIENTS)') @executor_test_utils.executors( ('local', executor_stacks.local_executor_factory()),) def test_federated_zip_with_twenty_elements_local_executor(self): n = 20 n_clients = 2 @computations.federated_computation( [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * n) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val data = [list(range(n_clients)) for _ in range(n)] # This would not have ever returned when local executor was scaling # factorially with number of elements zipped foo(data) if __name__ == '__main__': default_executor.initialize_default_executor() common_test.main()

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import collections import itertools import warnings from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_federated.python.common_libs import anonymous_tuple from tensorflow_federated.python.common_libs import test as common_test from tensorflow_federated.python.core.api import computation_types from tensorflow_federated.python.core.api import computations from tensorflow_federated.python.core.api import intrinsics from tensorflow_federated.python.core.api import placements from tensorflow_federated.python.core.api import value_base from tensorflow_federated.python.core.impl.executors import default_executor from tensorflow_federated.python.core.impl.executors import executor_stacks from tensorflow_federated.python.core.impl.executors import executor_test_utils tf.compat.v1.enable_v2_behavior() class IntrinsicsTest(parameterized.TestCase): def assert_type(self, value, type_string): self.assertEqual(value.type_signature.compact_representation(), type_string) def test_federated_broadcast_with_server_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_broadcast(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@SERVER -> int32@CLIENTS)') def test_federated_broadcast_with_server_non_all_equal_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType( tf.int32, placements.SERVER, all_equal=False)) def _(x): return intrinsics.federated_broadcast(x) def test_federated_broadcast_with_client_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS, True)) def _(x): return intrinsics.federated_broadcast(x) def test_federated_broadcast_with_non_federated_val(self): with self.assertRaises(TypeError): @computations.federated_computation(tf.int32) def _(x): return intrinsics.federated_broadcast(x) def test_federated_eval_rand_on_clients(self): @computations.federated_computation def rand_on_clients(): @computations.tf_computation def rand(): return tf.random.normal([]) val = intrinsics.federated_eval(rand, placements.CLIENTS) self.assertIsInstance(val, value_base.Value) return val self.assert_type(rand_on_clients, '( -> {float32}@CLIENTS)') def test_federated_eval_rand_on_server(self): @computations.federated_computation def rand_on_server(): @computations.tf_computation def rand(): return tf.random.normal([]) val = intrinsics.federated_eval(rand, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val self.assert_type(rand_on_server, '( -> float32@SERVER)') def test_federated_map_with_client_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS, True)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@CLIENTS -> {bool}@CLIENTS)') def test_federated_map_with_client_non_all_equal_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> {bool}@CLIENTS)') def test_federated_map_with_server_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(int32@SERVER -> bool@SERVER)') def test_federated_map_injected_zip_with_server_int(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.int32, placements.SERVER) ]) def foo(x, y): val = intrinsics.federated_map( computations.tf_computation(lambda x, y: x > 10, [tf.int32, tf.int32]), [x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER,int32@SERVER> -> bool@SERVER)') def test_federated_map_injected_zip_fails_different_placements(self): def foo(x, y): val = intrinsics.federated_map( computations.tf_computation(lambda x, y: x > 10, [tf.int32, tf.int32]), [x, y]) self.assertIsInstance(val, value_base.Value) return val with self.assertRaisesRegex( TypeError, 'The value to be mapped must be a FederatedType or implicitly ' 'convertible to a FederatedType.'): computations.federated_computation(foo, [ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.int32, placements.CLIENTS) ]) def test_federated_map_with_non_federated_val(self): with self.assertRaises(TypeError): @computations.federated_computation(tf.int32) def _(x): return intrinsics.federated_map( computations.tf_computation(lambda x: x > 10, tf.int32), x) def test_federated_sum_with_client_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32@SERVER)') def test_federated_sum_with_client_string(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.string, placements.CLIENTS)) def _(x): return intrinsics.federated_sum(x) def test_federated_sum_with_server_int(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def _(x): return intrinsics.federated_sum(x) def test_federated_zip_with_client_non_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{int32}@CLIENTS,bool@CLIENTS> -> {<int32,bool>}@CLIENTS)') def test_federated_zip_with_single_unnamed_int_client(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<{int32}@CLIENTS> -> {<int32>}@CLIENTS)') def test_federated_zip_with_single_unnamed_int_server(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER> -> <int32>@SERVER)') def test_federated_zip_with_single_named_bool_clients(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.bool, placements.CLIENTS)), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<a={bool}@CLIENTS> -> {<a=bool>}@CLIENTS)') def test_federated_zip_with_single_named_bool_server(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.bool, placements.SERVER)), ]) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<a=bool@SERVER> -> <a=bool>@SERVER)') def test_federated_zip_with_names_client_non_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): a = {'x': x, 'y': y} val = intrinsics.federated_zip(a) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{int32}@CLIENTS,bool@CLIENTS> -> {<x=int32,y=bool>}@CLIENTS)') def test_federated_zip_with_client_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS, True), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<int32@CLIENTS,bool@CLIENTS> -> {<int32,bool>}@CLIENTS)') def test_federated_zip_with_names_client_all_equal_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.CLIENTS, True), computation_types.FederatedType(tf.bool, placements.CLIENTS, True) ]) def foo(arg): a = {'x': arg[0], 'y': arg[1]} val = intrinsics.federated_zip(a) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<int32@CLIENTS,bool@CLIENTS> -> {<x=int32,y=bool>}@CLIENTS)') def test_federated_zip_with_server_int_and_bool(self): @computations.federated_computation([ computation_types.FederatedType(tf.int32, placements.SERVER), computation_types.FederatedType(tf.bool, placements.SERVER) ]) def foo(x, y): val = intrinsics.federated_zip([x, y]) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<int32@SERVER,bool@SERVER> -> <int32,bool>@SERVER)') def test_federated_zip_with_names_server_int_and_bool(self): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.int32, placements.SERVER)), ('b', computation_types.FederatedType(tf.bool, placements.SERVER)), ]) def foo(arg): val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<a=int32@SERVER,b=bool@SERVER> -> <a=int32,b=bool>@SERVER)') def test_federated_zip_error_different_placements(self): with self.assertRaises(TypeError): @computations.federated_computation([ ('a', computation_types.FederatedType(tf.int32, placements.SERVER)), ('b', computation_types.FederatedType(tf.bool, placements.CLIENTS)), ]) def _(arg): return intrinsics.federated_zip(arg) def test_federated_collect_with_client_int(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_collect(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32*@SERVER)') def test_federated_collect_with_server_int_fails(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def _(x): return intrinsics.federated_collect(x) def test_federated_mean_with_client_float32_without_weight(self): @computations.federated_computation( computation_types.FederatedType(tf.float32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_mean(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({float32}@CLIENTS -> float32@SERVER)') def test_federated_mean_with_all_equal_client_float32_without_weight(self): federated_all_equal_float = computation_types.FederatedType( tf.float32, placements.CLIENTS, all_equal=True) @computations.federated_computation(federated_all_equal_float) def foo(x): val = intrinsics.federated_mean(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(float32@CLIENTS -> float32@SERVER)') def test_federated_mean_with_all_equal_client_float32_with_weight(self): federated_all_equal_float = computation_types.FederatedType( tf.float32, placements.CLIENTS, all_equal=True) @computations.federated_computation(federated_all_equal_float) def foo(x): val = intrinsics.federated_mean(x, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(float32@CLIENTS -> float32@SERVER)') def test_federated_mean_with_client_tuple_with_int32_weight(self): @computations.federated_computation([ computation_types.FederatedType([('x', tf.float64), ('y', tf.float64)], placements.CLIENTS), computation_types.FederatedType(tf.int32, placements.CLIENTS) ]) def foo(x, y): val = intrinsics.federated_mean(x, y) self.assertIsInstance(val, value_base.Value) return val self.assert_type( foo, '(<{<x=float64,y=float64>}@CLIENTS,{int32}@CLIENTS> ' '-> <x=float64,y=float64>@SERVER)') def test_federated_mean_with_client_int32_fails(self): with self.assertRaises(TypeError): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def _(x): return intrinsics.federated_mean(x) def test_federated_mean_with_string_weight_fails(self): with self.assertRaises(TypeError): @computations.federated_computation([ computation_types.FederatedType(tf.float32, placements.CLIENTS), computation_types.FederatedType(tf.string, placements.CLIENTS) ]) def _(x, y): return intrinsics.federated_mean(x, y) def test_federated_aggregate_with_client_int(self): Accumulator = collections.namedtuple('Accumulator', 'total count') accumulator_type = computation_types.NamedTupleType( Accumulator(tf.int32, tf.int32)) @computations.tf_computation([accumulator_type, tf.int32]) def accumulate(accu, elem): return Accumulator(accu.total + elem, accu.count + 1) @computations.tf_computation([accumulator_type, accumulator_type]) def merge(x, y): return Accumulator(x.total + y.total, x.count + y.count) @computations.tf_computation(accumulator_type) def report(accu): return tf.cast(accu.total, tf.float32) / tf.cast(accu.count, tf.float32) @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_aggregate(x, Accumulator(0, 0), accumulate, merge, report) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> float32@SERVER)') def test_federated_aggregate_with_federated_zero_fails(self): @computations.federated_computation() def build_federated_zero(): val = intrinsics.federated_value(0, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val @computations.tf_computation([tf.int32, tf.int32]) def accumulate(accu, elem): return accu + elem @computations.tf_computation([tf.int32, tf.int32]) def merge(x, y): return x + y @computations.tf_computation(tf.int32) def report(accu): return accu def foo(x): return intrinsics.federated_aggregate(x, build_federated_zero(), accumulate, merge, report) with self.assertRaisesRegex( TypeError, 'Expected `zero` to be assignable to type int32, ' 'but was of incompatible type int32@SERVER'): computations.federated_computation( foo, computation_types.FederatedType(tf.int32, placements.CLIENTS)) def test_federated_aggregate_with_unknown_dimension(self): Accumulator = collections.namedtuple('Accumulator', ['samples']) accumulator_type = computation_types.NamedTupleType( Accumulator( samples=computation_types.TensorType(dtype=tf.int32, shape=[None]))) @computations.tf_computation() def build_empty_accumulator(): return Accumulator(samples=tf.zeros(shape=[0], dtype=tf.int32)) @computations.tf_computation([accumulator_type, tf.int32]) def accumulate(accu, elem): return Accumulator( samples=tf.concat( [accu.samples, tf.expand_dims(elem, axis=0)], axis=0)) @computations.tf_computation([accumulator_type, accumulator_type]) def merge(x, y): return Accumulator(samples=tf.concat([x.samples, y.samples], axis=0)) @computations.tf_computation(accumulator_type) def report(accu): return accu @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): val = intrinsics.federated_aggregate(x, build_empty_accumulator(), accumulate, merge, report) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> <samples=int32[?]>@SERVER)') def test_federated_reduce_with_tf_add_raw_constant(self): @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.CLIENTS)) def foo(x): plus = computations.tf_computation(tf.add, [tf.int32, tf.int32]) val = intrinsics.federated_reduce(x, 0, plus) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '({int32}@CLIENTS -> int32@SERVER)') def test_num_over_temperature_threshold_example(self): @computations.federated_computation([ computation_types.FederatedType(tf.float32, placements.CLIENTS), computation_types.FederatedType(tf.float32, placements.SERVER) ]) def foo(temperatures, threshold): val = intrinsics.federated_sum( intrinsics.federated_map( computations.tf_computation( lambda x, y: tf.cast(tf.greater(x, y), tf.int32), [tf.float32, tf.float32]), [temperatures, intrinsics.federated_broadcast(threshold)])) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(<{float32}@CLIENTS,float32@SERVER> -> int32@SERVER)') @parameterized.named_parameters(('test_n_2', 2), ('test_n_3', 3), ('test_n_5', 5)) def test_n_tuple_federated_zip_tensor_args(self, n): fed_type = computation_types.FederatedType(tf.int32, placements.CLIENTS) initial_tuple_type = computation_types.NamedTupleType([fed_type] * n) final_fed_type = computation_types.FederatedType([tf.int32] * n, placements.CLIENTS) function_type = computation_types.FunctionType(initial_tuple_type, final_fed_type) @computations.federated_computation( [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * n) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, function_type.compact_representation()) @parameterized.named_parameters( ('test_n_2_int', 2, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_3_int', 3, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_5_int', 5, computation_types.FederatedType(tf.int32, placements.CLIENTS)), ('test_n_2_tuple', 2, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS)), ('test_n_3_tuple', 3, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS)), ('test_n_5_tuple', 5, computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS))) def test_named_n_tuple_federated_zip(self, n, fed_type): initial_tuple_type = computation_types.NamedTupleType([fed_type] * n) named_fed_type = computation_types.FederatedType( [(str(k), fed_type.member) for k in range(n)], placements.CLIENTS) mixed_fed_type = computation_types.FederatedType( [(str(k), fed_type.member) if k % 2 == 0 else fed_type.member for k in range(n)], placements.CLIENTS) named_function_type = computation_types.FunctionType( initial_tuple_type, named_fed_type) mixed_function_type = computation_types.FunctionType( initial_tuple_type, mixed_fed_type) @computations.federated_computation([fed_type] * n) def foo(x): arg = {str(k): x[k] for k in range(n)} val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, named_function_type.compact_representation()) def _make_test_tuple(x, k): if k % 2 == 0: return str(k), x[k] else: return None, x[k] @computations.federated_computation([fed_type] * n) def bar(x): arg = anonymous_tuple.AnonymousTuple( _make_test_tuple(x, k) for k in range(n)) val = intrinsics.federated_zip(arg) self.assertIsInstance(val, value_base.Value) return val self.assert_type(bar, mixed_function_type.compact_representation()) @parameterized.named_parameters([ ('test_n_' + str(n) + '_m_' + str(m), n, m) for n, m in itertools.product([1, 2, 3], [1, 2, 3]) ]) def test_n_tuple_federated_zip_mixed_args(self, n, m): tuple_fed_type = computation_types.FederatedType([tf.int32, tf.int32], placements.CLIENTS) single_fed_type = computation_types.FederatedType(tf.int32, placements.CLIENTS) initial_tuple_type = computation_types.NamedTupleType([tuple_fed_type] * n + [single_fed_type] * m) final_fed_type = computation_types.FederatedType( [[tf.int32, tf.int32]] * n + [tf.int32] * m, placements.CLIENTS) function_type = computation_types.FunctionType(initial_tuple_type, final_fed_type) @computations.federated_computation([ computation_types.FederatedType( computation_types.NamedTupleType([tf.int32, tf.int32]), placements.CLIENTS) ] * n + [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * m) def baz(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(baz, function_type.compact_representation()) def test_federated_apply_raises_warning(self): with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') @computations.federated_computation( computation_types.FederatedType(tf.int32, placements.SERVER)) def foo(x): val = intrinsics.federated_apply( computations.tf_computation(lambda x: x * x, tf.int32), x) self.assertIsInstance(val, value_base.Value) return val self.assertLen(w, 1) self.assertIsInstance(w[0].category(), DeprecationWarning) self.assertIn('tff.federated_apply() is deprecated', str(w[0].message)) self.assert_type(foo, '(int32@SERVER -> int32@SERVER)') def test_federated_value_with_bool_on_clients(self): @computations.federated_computation(tf.bool) def foo(x): val = intrinsics.federated_value(x, placements.CLIENTS) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(bool -> bool@CLIENTS)') def test_federated_value_raw_np_scalar(self): @computations.federated_computation def test_np_values(): floatv = np.float64(0) tff_float = intrinsics.federated_value(floatv, placements.SERVER) self.assertIsInstance(tff_float, value_base.Value) self.assert_type(tff_float, 'float64@SERVER') intv = np.int64(0) tff_int = intrinsics.federated_value(intv, placements.SERVER) self.assertIsInstance(tff_int, value_base.Value) self.assert_type(tff_int, 'int64@SERVER') return (tff_float, tff_int) floatv, intv = test_np_values() self.assertEqual(floatv, 0.0) self.assertEqual(intv, 0) def test_federated_value_raw_tf_scalar_variable(self): v = tf.Variable(initial_value=0., name='test_var') with self.assertRaisesRegex( TypeError, 'TensorFlow construct (.*) has been ' 'encountered in a federated context.'): _ = intrinsics.federated_value(v, placements.SERVER) def test_federated_value_with_bool_on_server(self): @computations.federated_computation(tf.bool) def foo(x): val = intrinsics.federated_value(x, placements.SERVER) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo, '(bool -> bool@SERVER)') def test_sequence_sum(self): @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> int32)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> int32@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_sum(x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {int32}@CLIENTS)') def test_sequence_map(self): @computations.tf_computation(tf.int32) def over_threshold(x): return x > 10 @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> bool*)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> bool*@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_map(over_threshold, x) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {bool*}@CLIENTS)') def test_sequence_reduce(self): add_numbers = computations.tf_computation(tf.add, [tf.int32, tf.int32]) @computations.federated_computation( computation_types.SequenceType(tf.int32)) def foo1(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo1, '(int32* -> int32)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.SERVER)) def foo2(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo2, '(int32*@SERVER -> int32@SERVER)') @computations.federated_computation( computation_types.FederatedType( computation_types.SequenceType(tf.int32), placements.CLIENTS)) def foo3(x): val = intrinsics.sequence_reduce(x, 0, add_numbers) self.assertIsInstance(val, value_base.Value) return val self.assert_type(foo3, '({int32*}@CLIENTS -> {int32}@CLIENTS)') @executor_test_utils.executors( ('local', executor_stacks.local_executor_factory()),) def test_federated_zip_with_twenty_elements_local_executor(self): n = 20 n_clients = 2 @computations.federated_computation( [computation_types.FederatedType(tf.int32, placements.CLIENTS)] * n) def foo(x): val = intrinsics.federated_zip(x) self.assertIsInstance(val, value_base.Value) return val data = [list(range(n_clients)) for _ in range(n)] foo(data) if __name__ == '__main__': default_executor.initialize_default_executor() common_test.main()

true

1c2b394e3a4820d61b1052b437d5ca661ba3b5b2

4,911

py

Python

src/CurlCallee.py

8ldesign/DataFeedsTester

187af657cde369baef9f1e00222db5c42320307b

[ "MIT" ]

null

src/CurlCallee.py

8ldesign/DataFeedsTester

187af657cde369baef9f1e00222db5c42320307b

[ "MIT" ]

null

src/CurlCallee.py

8ldesign/DataFeedsTester

187af657cde369baef9f1e00222db5c42320307b

[ "MIT" ]

null

import logging, re, os, fnmatch, requests from requests import exceptions from FileConstants import FileConstants from HTTPConstants import HTTPConstants def main(): try: calleeObj = CurlCallee() calleeObj.execute() except KeyboardInterrupt: logging.info("Program exited by user") class CurlCallee: fileConstants = FileConstants() httpConstants = HTTPConstants() logging.basicConfig(format='%(asctime)s %(message)s') logging.getLogger().setLevel(logging.INFO) logging.getLogger("requests").setLevel(logging.WARNING) urls = dict() def findFile(self, pattern, path): result = [] for root, dirs, files in os.walk(path): for name in files: if fnmatch.fnmatch(name, pattern): result.append(os.path.join(root, name)) return result def gatherUrls(self, urls): try: for key in self.fileConstants.feedDictionary: urls[key] = [] for file in self.fileConstants.feedDictionary.get(key): result = self.findFile(file, os.path.pardir) if result: file = result[0] else: logging.error("Didn't find feed file for " + key) print("Gathering URLs from " + file + " of type " + key) with open(file,'r') as fileName: lines = fileName.read().splitlines() for line in lines: lineUrls = re.findall(r'(?:http|https|www)(?:[^\\\]"<>]*)', line) #lineUrls = re.findall(r'(https?://\S+)', line) urls[key].append(lineUrls) except IOError: logging.error("Check the file names in FileConstants file") except UnicodeDecodeError: logging.error("Make sure that the file is in UTF-8 format " + file) def printUrls(self, urls): counter = 0 for key in urls: for value in urls.get(key): if isinstance(value, list): for innerValue in value: counter += 1 logging.info (str(counter) + ". " + innerValue + " \n") else: counter += 1 logging.info (str(counter) + ". " + value + " \n") def testUrls(self, urls): counter = 0 for key in urls: for value in urls.get(key): if isinstance(value, list): for innerValue in value: counter += 1 curlResponse = self.sendCurlRequest(innerValue) logging.info(str(counter) + ". " + innerValue + " : " + str(curlResponse) ) else: counter += 1 responseCode = self.sendCurlRequest(value) if(responseCode == 'OK'): logging.info(str(counter) + ". " + value + " : " + responseCode ) else: logging.warning(str(counter) + ". " + value + " : " + responseCode) def sendCurlRequest(self,site_url): try: curlResponse = requests.get(site_url, timeout=10) return self.processedCurlResponse(curlResponse) except (requests.exceptions.InvalidSchema): return 'INVALID URL' except (requests.exceptions.ConnectionError, ConnectionError): return 'CONNECTION ERROR' except (requests.exceptions.ReadTimeout): return 'READTIMEOUT ERROR' except (AttributeError): return 'ATTRIBUTE ERROR' except (requests.exceptions.TooManyRedirects): return 'TOO MANY REDIRECTS' def processedCurlResponse(self, curlResponse): status_code = str(curlResponse.status_code) curl_history = curlResponse.history respCodeDict = self.httpConstants.responseCodeDictionary if not curl_history: # If the curl response doesn't have a history, return the status code if (str(status_code) in respCodeDict.keys()): return respCodeDict.get(str(status_code)) else: return 'UNKNOWN' else: # curl response has a history - return the first of the response codes for curlHistResponse in curl_history: if(str(curlHistResponse.status_code) in respCodeDict.keys()): return respCodeDict.get(str(curlHistResponse.status_code)) else: # if none of the response codes are matching, return the original status_code itself return respCodeDict.get(status_code) def execute(self): self.gatherUrls(self.urls) self.testUrls(self.urls)

40.925

104

0.547343

import logging, re, os, fnmatch, requests from requests import exceptions from FileConstants import FileConstants from HTTPConstants import HTTPConstants def main(): try: calleeObj = CurlCallee() calleeObj.execute() except KeyboardInterrupt: logging.info("Program exited by user") class CurlCallee: fileConstants = FileConstants() httpConstants = HTTPConstants() logging.basicConfig(format='%(asctime)s %(message)s') logging.getLogger().setLevel(logging.INFO) logging.getLogger("requests").setLevel(logging.WARNING) urls = dict() def findFile(self, pattern, path): result = [] for root, dirs, files in os.walk(path): for name in files: if fnmatch.fnmatch(name, pattern): result.append(os.path.join(root, name)) return result def gatherUrls(self, urls): try: for key in self.fileConstants.feedDictionary: urls[key] = [] for file in self.fileConstants.feedDictionary.get(key): result = self.findFile(file, os.path.pardir) if result: file = result[0] else: logging.error("Didn't find feed file for " + key) print("Gathering URLs from " + file + " of type " + key) with open(file,'r') as fileName: lines = fileName.read().splitlines() for line in lines: lineUrls = re.findall(r'(?:http|https|www)(?:[^\\\]"<>]*)', line) #lineUrls = re.findall(r'(https?://\S+)', line) urls[key].append(lineUrls) except IOError: logging.error("Check the file names in FileConstants file") except UnicodeDecodeError: logging.error("Make sure that the file is in UTF-8 format " + file) def printUrls(self, urls): counter = 0 for key in urls: for value in urls.get(key): if isinstance(value, list): for innerValue in value: counter += 1 logging.info (str(counter) + ". " + innerValue + " \n") else: counter += 1 logging.info (str(counter) + ". " + value + " \n") def testUrls(self, urls): counter = 0 for key in urls: for value in urls.get(key): if isinstance(value, list): for innerValue in value: counter += 1 curlResponse = self.sendCurlRequest(innerValue) logging.info(str(counter) + ". " + innerValue + " : " + str(curlResponse) ) else: counter += 1 responseCode = self.sendCurlRequest(value) if(responseCode == 'OK'): logging.info(str(counter) + ". " + value + " : " + responseCode ) else: logging.warning(str(counter) + ". " + value + " : " + responseCode) def sendCurlRequest(self,site_url): try: curlResponse = requests.get(site_url, timeout=10) return self.processedCurlResponse(curlResponse) except (requests.exceptions.InvalidSchema): return 'INVALID URL' except (requests.exceptions.ConnectionError, ConnectionError): return 'CONNECTION ERROR' except (requests.exceptions.ReadTimeout): return 'READTIMEOUT ERROR' except (AttributeError): return 'ATTRIBUTE ERROR' except (requests.exceptions.TooManyRedirects): return 'TOO MANY REDIRECTS' def processedCurlResponse(self, curlResponse): status_code = str(curlResponse.status_code) curl_history = curlResponse.history respCodeDict = self.httpConstants.responseCodeDictionary if not curl_history: # If the curl response doesn't have a history, return the status code if (str(status_code) in respCodeDict.keys()): return respCodeDict.get(str(status_code)) else: return 'UNKNOWN' else: # curl response has a history - return the first of the response codes for curlHistResponse in curl_history: if(str(curlHistResponse.status_code) in respCodeDict.keys()): return respCodeDict.get(str(curlHistResponse.status_code)) else: # if none of the response codes are matching, return the original status_code itself return respCodeDict.get(status_code) def execute(self): self.gatherUrls(self.urls) self.testUrls(self.urls)

true

1c2b3ae4f9373b4a2647979ff3fbb85f9a020e94

601

py

Python

test/programytest/parser/template/graph_tests/test_think.py

cdoebler1/AIML2

ee692ec5ea3794cd1bc4cc8ec2a6b5e5c20a0d6a

[ "MIT" ]

345

2016-11-23T22:37:04.000Z

2022-03-30T20:44:44.000Z

test/programytest/parser/template/graph_tests/test_think.py

MikeyBeez/program-y

00d7a0c7d50062f18f0ab6f4a041068e119ef7f0

[ "MIT" ]

275

2016-12-07T10:30:28.000Z

2022-02-08T21:28:33.000Z

test/programytest/parser/template/graph_tests/test_think.py

VProgramMist/modified-program-y

f32efcafafd773683b3fe30054d5485fe9002b7d

[ "MIT" ]

159

2016-11-28T18:59:30.000Z

2022-03-20T18:02:44.000Z

import xml.etree.ElementTree as ET from programy.parser.template.nodes.think import TemplateThinkNode from programytest.parser.template.graph_tests.graph_test_client import TemplateGraphTestClient class TemplateGraphThinkTests(TemplateGraphTestClient): def test_think(self): template = ET.fromstring(""" <template> <think>XYZ</think> </template> """) root = self._graph.parse_template_expression(template) self.assertIsNotNone(root) node = root.children[0] self.assertIsNotNone(node) self.assertIsInstance(node, TemplateThinkNode)

28.619048

94

0.737105

import xml.etree.ElementTree as ET from programy.parser.template.nodes.think import TemplateThinkNode from programytest.parser.template.graph_tests.graph_test_client import TemplateGraphTestClient class TemplateGraphThinkTests(TemplateGraphTestClient): def test_think(self): template = ET.fromstring(""" <template> <think>XYZ</think> </template> """) root = self._graph.parse_template_expression(template) self.assertIsNotNone(root) node = root.children[0] self.assertIsNotNone(node) self.assertIsInstance(node, TemplateThinkNode)

true

1c2b3b3dbb6a36d103d655777cd6f8d51f19e477

4,145

py

Python

tests/utils/test_vault.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

tests/utils/test_vault.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

tests/utils/test_vault.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

import pytest import mlrun from mlrun import code_to_function, get_run_db, mlconf, new_project, new_task from mlrun.utils.vault import VaultStore from tests.conftest import examples_path, out_path, verify_state # Set a proper token value for Vault test user_token = "" # Set test secrets and configurations - you may need to modify these. def _set_vault_mlrun_configuration(api_server_port=None): if api_server_port: mlconf.dbpath = f"http://localhost:{api_server_port}" mlconf.secret_stores.vault.url = "http://localhost:8200" mlconf.secret_stores.vault.user_token = user_token # Verify that local activation of Vault functionality is successful. This does not # test the API-server implementation, which is verified in other tests @pytest.mark.skipif(user_token == "", reason="no vault configuration") def test_direct_vault_usage(): _set_vault_mlrun_configuration() project_name = "the-blair-witch-project" vault = VaultStore() vault.delete_vault_secrets(project=project_name) secrets = vault.get_secrets(None, project=project_name) assert len(secrets) == 0, "Secrets were not deleted" expected_secrets = {"secret1": "123456", "secret2": "654321"} vault.add_vault_secrets(expected_secrets, project=project_name) secrets = vault.get_secrets(None, project=project_name) assert ( secrets == expected_secrets ), "Vault contains different set of secrets than expected" secrets = vault.get_secrets(["secret1"], project=project_name) assert len(secrets) == 1 and secrets["secret1"] == expected_secrets["secret1"] # Test the same thing for user user_name = "pikachu" vault.delete_vault_secrets(user=user_name) secrets = vault.get_secrets(None, user=user_name) assert len(secrets) == 0, "Secrets were not deleted" vault.add_vault_secrets(expected_secrets, user=user_name) secrets = vault.get_secrets(None, user=user_name) assert ( secrets == expected_secrets ), "Vault contains different set of secrets than expected" # Cleanup vault.delete_vault_secrets(project=project_name) vault.delete_vault_secrets(user=user_name) @pytest.mark.skipif(user_token == "", reason="no vault configuration") def test_vault_end_to_end(): # This requires an MLRun API server to run and work with Vault. This port should # be configured to allow access to the server. api_server_port = 57764 _set_vault_mlrun_configuration(api_server_port) project_name = "abc" func_name = "vault-function" aws_key_value = "1234567890" github_key_value = "proj1Key!!!" project = new_project(project_name) # This call will initialize Vault infrastructure and add the given secrets # It executes on the API server project.set_secrets( {"aws_key": aws_key_value, "github_key": github_key_value}, provider=mlrun.api.schemas.SecretProviderName.vault, ) # This API executes on the client side project_secrets = project.get_vault_secret_keys() assert project_secrets == ["aws_key", "github_key"], "secrets not created" # Create function and set container configuration function = code_to_function( name=func_name, filename=f"{examples_path}/vault_function.py", handler="vault_func", project=project_name, kind="job", ) function.spec.image = "saarcoiguazio/mlrun:unstable" # Create context for the execution spec = new_task( project=project_name, name="vault_test_run", handler="vault_func", out_path=out_path, params={"secrets": ["password", "path", "github_key", "aws_key"]}, ) spec.with_secrets("vault", []) result = function.run(spec) verify_state(result) db = get_run_db().connect() state, log = db.get_log(result.metadata.uid, project=project_name) log = str(log) print(state) assert ( log.find(f"value: {aws_key_value}") != -1 ), "secret value not detected in function output" assert ( log.find(f"value: {github_key_value}") != -1 ), "secret value not detected in function output"

34.541667

84

0.710977

import pytest import mlrun from mlrun import code_to_function, get_run_db, mlconf, new_project, new_task from mlrun.utils.vault import VaultStore from tests.conftest import examples_path, out_path, verify_state user_token = "" def _set_vault_mlrun_configuration(api_server_port=None): if api_server_port: mlconf.dbpath = f"http://localhost:{api_server_port}" mlconf.secret_stores.vault.url = "http://localhost:8200" mlconf.secret_stores.vault.user_token = user_token @pytest.mark.skipif(user_token == "", reason="no vault configuration") def test_direct_vault_usage(): _set_vault_mlrun_configuration() project_name = "the-blair-witch-project" vault = VaultStore() vault.delete_vault_secrets(project=project_name) secrets = vault.get_secrets(None, project=project_name) assert len(secrets) == 0, "Secrets were not deleted" expected_secrets = {"secret1": "123456", "secret2": "654321"} vault.add_vault_secrets(expected_secrets, project=project_name) secrets = vault.get_secrets(None, project=project_name) assert ( secrets == expected_secrets ), "Vault contains different set of secrets than expected" secrets = vault.get_secrets(["secret1"], project=project_name) assert len(secrets) == 1 and secrets["secret1"] == expected_secrets["secret1"] user_name = "pikachu" vault.delete_vault_secrets(user=user_name) secrets = vault.get_secrets(None, user=user_name) assert len(secrets) == 0, "Secrets were not deleted" vault.add_vault_secrets(expected_secrets, user=user_name) secrets = vault.get_secrets(None, user=user_name) assert ( secrets == expected_secrets ), "Vault contains different set of secrets than expected" vault.delete_vault_secrets(project=project_name) vault.delete_vault_secrets(user=user_name) @pytest.mark.skipif(user_token == "", reason="no vault configuration") def test_vault_end_to_end(): api_server_port = 57764 _set_vault_mlrun_configuration(api_server_port) project_name = "abc" func_name = "vault-function" aws_key_value = "1234567890" github_key_value = "proj1Key!!!" project = new_project(project_name) project.set_secrets( {"aws_key": aws_key_value, "github_key": github_key_value}, provider=mlrun.api.schemas.SecretProviderName.vault, ) project_secrets = project.get_vault_secret_keys() assert project_secrets == ["aws_key", "github_key"], "secrets not created" function = code_to_function( name=func_name, filename=f"{examples_path}/vault_function.py", handler="vault_func", project=project_name, kind="job", ) function.spec.image = "saarcoiguazio/mlrun:unstable" spec = new_task( project=project_name, name="vault_test_run", handler="vault_func", out_path=out_path, params={"secrets": ["password", "path", "github_key", "aws_key"]}, ) spec.with_secrets("vault", []) result = function.run(spec) verify_state(result) db = get_run_db().connect() state, log = db.get_log(result.metadata.uid, project=project_name) log = str(log) print(state) assert ( log.find(f"value: {aws_key_value}") != -1 ), "secret value not detected in function output" assert ( log.find(f"value: {github_key_value}") != -1 ), "secret value not detected in function output"

true

1c2b3b9071125c065a9e9fb7879789b2fcfd2848

5,455

py

Python

keras/utils/test_utils.py

entraned/keras

9400be98783135a1d42dd238f4e6c3aa048eceea

[ "MIT" ]

1

2019-03-31T00:51:26.000Z

keras/utils/test_utils.py

entraned/keras

9400be98783135a1d42dd238f4e6c3aa048eceea

[ "MIT" ]

null

keras/utils/test_utils.py

entraned/keras

9400be98783135a1d42dd238f4e6c3aa048eceea

[ "MIT" ]

1

2021-03-08T02:28:07.000Z

"""Utilities related to Keras unit tests.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from numpy.testing import assert_allclose import six from .generic_utils import has_arg from ..engine import Model, Input from ..models import Sequential from ..models import model_from_json from .. import backend as K def get_test_data(num_train=1000, num_test=500, input_shape=(10,), output_shape=(2,), classification=True, num_classes=2): """Generates test data to train a model on. classification=True overrides output_shape (i.e. output_shape is set to (1,)) and the output consists in integers in [0, num_classes-1]. Otherwise: float output with shape output_shape. """ samples = num_train + num_test if classification: y = np.random.randint(0, num_classes, size=(samples,)) X = np.zeros((samples,) + input_shape, dtype=np.float32) for i in range(samples): X[i] = np.random.normal(loc=y[i], scale=0.7, size=input_shape) else: y_loc = np.random.random((samples,)) X = np.zeros((samples,) + input_shape, dtype=np.float32) y = np.zeros((samples,) + output_shape, dtype=np.float32) for i in range(samples): X[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=input_shape) y[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=output_shape) return (X[:num_train], y[:num_train]), (X[num_train:], y[num_train:]) def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None, input_data=None, expected_output=None, expected_output_dtype=None, fixed_batch_size=False): """Test routine for a layer with a single input tensor and single output tensor. """ # generate input data if input_data is None: assert input_shape if not input_dtype: input_dtype = K.floatx() input_data_shape = list(input_shape) for i, e in enumerate(input_data_shape): if e is None: input_data_shape[i] = np.random.randint(1, 4) input_data = (10 * np.random.random(input_data_shape)) input_data = input_data.astype(input_dtype) else: if input_shape is None: input_shape = input_data.shape if input_dtype is None: input_dtype = input_data.dtype if expected_output_dtype is None: expected_output_dtype = input_dtype # instantiation layer = layer_cls(**kwargs) # test get_weights , set_weights at layer level weights = layer.get_weights() layer.set_weights(weights) # test and instantiation from weights # Checking for empty weights array to avoid a problem where some # legacy layers return bad values from get_weights() if has_arg(layer_cls.__init__, 'weights') and len(weights): kwargs['weights'] = weights layer = layer_cls(**kwargs) expected_output_shape = layer.compute_output_shape(input_shape) def _layer_in_model_test(model): actual_output = model.predict(input_data) actual_output_shape = actual_output.shape for expected_dim, actual_dim in zip(expected_output_shape, actual_output_shape): if expected_dim is not None: assert expected_dim == actual_dim if expected_output is not None: assert_allclose(actual_output, expected_output, rtol=1e-3) # test serialization, weight setting at model level model_config = model.get_config() recovered_model = model.__class__.from_config(model_config) if model.weights: weights = model.get_weights() recovered_model.set_weights(weights) _output = recovered_model.predict(input_data) assert_allclose(_output, actual_output, rtol=1e-3) # test training mode (e.g. useful when the layer has a # different behavior at training and testing time). if has_arg(layer.call, 'training'): model.compile('rmsprop', 'mse') model.train_on_batch(input_data, actual_output) return actual_output # test in functional API if fixed_batch_size: x = Input(batch_shape=input_shape, dtype=input_dtype) else: x = Input(shape=input_shape[1:], dtype=input_dtype) y = layer(x) assert K.dtype(y) == expected_output_dtype # check with the functional API model = Model(x, y) _layer_in_model_test(model) # test as first layer in Sequential API layer_config = layer.get_config() layer_config['batch_input_shape'] = input_shape layer = layer.__class__.from_config(layer_config) # check with the sequential API model = Sequential() model.add(layer) actual_output = _layer_in_model_test(model) # for further checks in the caller function return actual_output def keras_test(func): """Function wrapper to clean up after TensorFlow tests. # Arguments func: test function to clean up after. # Returns A function wrapping the input function. """ @six.wraps(func) def wrapper(*args, **kwargs): output = func(*args, **kwargs) if K.backend() == 'tensorflow' or K.backend() == 'cntk': K.clear_session() return output return wrapper

35.422078

79

0.659028

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from numpy.testing import assert_allclose import six from .generic_utils import has_arg from ..engine import Model, Input from ..models import Sequential from ..models import model_from_json from .. import backend as K def get_test_data(num_train=1000, num_test=500, input_shape=(10,), output_shape=(2,), classification=True, num_classes=2): samples = num_train + num_test if classification: y = np.random.randint(0, num_classes, size=(samples,)) X = np.zeros((samples,) + input_shape, dtype=np.float32) for i in range(samples): X[i] = np.random.normal(loc=y[i], scale=0.7, size=input_shape) else: y_loc = np.random.random((samples,)) X = np.zeros((samples,) + input_shape, dtype=np.float32) y = np.zeros((samples,) + output_shape, dtype=np.float32) for i in range(samples): X[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=input_shape) y[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=output_shape) return (X[:num_train], y[:num_train]), (X[num_train:], y[num_train:]) def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None, input_data=None, expected_output=None, expected_output_dtype=None, fixed_batch_size=False): if input_data is None: assert input_shape if not input_dtype: input_dtype = K.floatx() input_data_shape = list(input_shape) for i, e in enumerate(input_data_shape): if e is None: input_data_shape[i] = np.random.randint(1, 4) input_data = (10 * np.random.random(input_data_shape)) input_data = input_data.astype(input_dtype) else: if input_shape is None: input_shape = input_data.shape if input_dtype is None: input_dtype = input_data.dtype if expected_output_dtype is None: expected_output_dtype = input_dtype layer = layer_cls(**kwargs) weights = layer.get_weights() layer.set_weights(weights) if has_arg(layer_cls.__init__, 'weights') and len(weights): kwargs['weights'] = weights layer = layer_cls(**kwargs) expected_output_shape = layer.compute_output_shape(input_shape) def _layer_in_model_test(model): actual_output = model.predict(input_data) actual_output_shape = actual_output.shape for expected_dim, actual_dim in zip(expected_output_shape, actual_output_shape): if expected_dim is not None: assert expected_dim == actual_dim if expected_output is not None: assert_allclose(actual_output, expected_output, rtol=1e-3) model_config = model.get_config() recovered_model = model.__class__.from_config(model_config) if model.weights: weights = model.get_weights() recovered_model.set_weights(weights) _output = recovered_model.predict(input_data) assert_allclose(_output, actual_output, rtol=1e-3) if has_arg(layer.call, 'training'): model.compile('rmsprop', 'mse') model.train_on_batch(input_data, actual_output) return actual_output if fixed_batch_size: x = Input(batch_shape=input_shape, dtype=input_dtype) else: x = Input(shape=input_shape[1:], dtype=input_dtype) y = layer(x) assert K.dtype(y) == expected_output_dtype model = Model(x, y) _layer_in_model_test(model) layer_config = layer.get_config() layer_config['batch_input_shape'] = input_shape layer = layer.__class__.from_config(layer_config) model = Sequential() model.add(layer) actual_output = _layer_in_model_test(model) return actual_output def keras_test(func): @six.wraps(func) def wrapper(*args, **kwargs): output = func(*args, **kwargs) if K.backend() == 'tensorflow' or K.backend() == 'cntk': K.clear_session() return output return wrapper

true

1c2b3c1e5a3c81bd0768611b83ddf8ff8f4054ec

21,136

py

Python

statsmodels/base/elastic_net.py

xiaowei1234/statsmodels

a8faaf72b7881620552acace6ca352b8bc628dcd

[ "BSD-3-Clause" ]

null

statsmodels/base/elastic_net.py

xiaowei1234/statsmodels

a8faaf72b7881620552acace6ca352b8bc628dcd

[ "BSD-3-Clause" ]

null

statsmodels/base/elastic_net.py

xiaowei1234/statsmodels

a8faaf72b7881620552acace6ca352b8bc628dcd

[ "BSD-3-Clause" ]

4

2020-04-07T00:06:17.000Z

2021-06-17T15:11:36.000Z

import numpy as np from statsmodels.base.model import Results import statsmodels.base.wrapper as wrap from statsmodels.tools.decorators import cache_readonly from statsmodels.base.constraint import ConstraintProjector """ Elastic net regularization. Routines for fitting regression models using elastic net regularization. The elastic net minimizes the objective function -llf / nobs + alpha((1 - L1_wt) * sum(params**2) / 2 + L1_wt * sum(abs(params))) The algorithm implemented here closely follows the implementation in the R glmnet package, documented here: http://cran.r-project.org/web/packages/glmnet/index.html and here: http://www.jstatsoft.org/v33/i01/paper This routine should work for any regression model that implements loglike, score, and hess. """ def _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds): """ Negative penalized log-likelihood functions. Returns the negative penalized log-likelihood, its derivative, and its Hessian. The penalty only includes the smooth (L2) term. All three functions have argument signature (x, model), where ``x`` is a point in the parameter space and ``model`` is an arbitrary statsmodels regression model. """ def nploglike(params, model): nobs = model.nobs pen_llf = alpha[k] * (1 - L1_wt) * np.sum(params**2) / 2 llf = model.loglike(np.r_[params], **loglike_kwds) return - llf / nobs + pen_llf def npscore(params, model): nobs = model.nobs pen_grad = alpha[k] * (1 - L1_wt) * params gr = -model.score(np.r_[params], **score_kwds)[0] / nobs return gr + pen_grad def nphess(params, model): nobs = model.nobs pen_hess = alpha[k] * (1 - L1_wt) h = -model.hessian(np.r_[params], **hess_kwds)[0, 0] / nobs + pen_hess return h return nploglike, npscore, nphess def fit_elasticnet(model, method="coord_descent", maxiter=100, alpha=0., L1_wt=1., start_params=None, cnvrg_tol=1e-7, zero_tol=1e-8, refit=False, check_step=True, loglike_kwds=None, score_kwds=None, hess_kwds=None): """ Return an elastic net regularized fit to a regression model. Parameters ---------- model : model object A statsmodels object implementing ``loglike``, ``score``, and ``hessian``. method : {'coord_descent'} Only the coordinate descent algorithm is implemented. maxiter : int The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. L1_wt : scalar The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit. start_params : array_like Starting values for `params`. cnvrg_tol : scalar If `params` changes by less than this amount (in sup-norm) in one iteration cycle, the algorithm terminates with convergence. zero_tol : scalar Any estimated coefficient smaller than this value is replaced with zero. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. check_step : bool If True, confirm that the first step is an improvement and search further if it is not. loglike_kwds : dict-like or None Keyword arguments for the log-likelihood function. score_kwds : dict-like or None Keyword arguments for the score function. hess_kwds : dict-like or None Keyword arguments for the Hessian function. Returns ------- Results A results object. Notes ----- The ``elastic net`` penalty is a combination of L1 and L2 penalties. The function that is minimized is: -loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1) where |*|_1 and |*|_2 are the L1 and L2 norms. The computational approach used here is to obtain a quadratic approximation to the smooth part of the target function: -loglike/n + alpha*(1-L1_wt)*|params|_2^2/2 then repeatedly optimize the L1 penalized version of this function along coordinate axes. """ k_exog = model.exog.shape[1] loglike_kwds = {} if loglike_kwds is None else loglike_kwds score_kwds = {} if score_kwds is None else score_kwds hess_kwds = {} if hess_kwds is None else hess_kwds if np.isscalar(alpha): alpha = alpha * np.ones(k_exog) # Define starting params if start_params is None: params = np.zeros(k_exog) else: params = start_params.copy() btol = 1e-4 params_zero = np.zeros(len(params), dtype=bool) init_args = model._get_init_kwds() # we do not need a copy of init_args b/c get_init_kwds provides new dict init_args['hasconst'] = False model_offset = init_args.pop('offset', None) if 'exposure' in init_args and init_args['exposure'] is not None: if model_offset is None: model_offset = np.log(init_args.pop('exposure')) else: model_offset += np.log(init_args.pop('exposure')) fgh_list = [ _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds) for k in range(k_exog)] for itr in range(maxiter): # Sweep through the parameters params_save = params.copy() for k in range(k_exog): # Under the active set method, if a parameter becomes # zero we do not try to change it again. # TODO : give the user the option to switch this off if params_zero[k]: continue # Set the offset to account for the variables that are # being held fixed in the current coordinate # optimization. params0 = params.copy() params0[k] = 0 offset = np.dot(model.exog, params0) if model_offset is not None: offset += model_offset # Create a one-variable model for optimization. model_1var = model.__class__( model.endog, model.exog[:, k], offset=offset, **init_args) # Do the one-dimensional optimization. func, grad, hess = fgh_list[k] params[k] = _opt_1d( func, grad, hess, model_1var, params[k], alpha[k]*L1_wt, tol=btol, check_step=check_step) # Update the active set if itr > 0 and np.abs(params[k]) < zero_tol: params_zero[k] = True params[k] = 0. # Check for convergence pchange = np.max(np.abs(params - params_save)) if pchange < cnvrg_tol: break # Set approximate zero coefficients to be exactly zero params[np.abs(params) < zero_tol] = 0 if not refit: results = RegularizedResults(model, params) return RegularizedResultsWrapper(results) # Fit the reduced model to get standard errors and other # post-estimation results. ii = np.flatnonzero(params) cov = np.zeros((k_exog, k_exog)) init_args = dict([(k, getattr(model, k, None)) for k in model._init_keys]) if len(ii) > 0: model1 = model.__class__( model.endog, model.exog[:, ii], **init_args) rslt = model1.fit() params[ii] = rslt.params cov[np.ix_(ii, ii)] = rslt.normalized_cov_params else: # Hack: no variables were selected but we need to run fit in # order to get the correct results class. So just fit a model # with one variable. model1 = model.__class__(model.endog, model.exog[:, 0], **init_args) rslt = model1.fit(maxiter=0) # fit may return a results or a results wrapper if issubclass(rslt.__class__, wrap.ResultsWrapper): klass = rslt._results.__class__ else: klass = rslt.__class__ # Not all models have a scale if hasattr(rslt, 'scale'): scale = rslt.scale else: scale = 1. # The degrees of freedom should reflect the number of parameters # in the refit model, not including the zeros that are displayed # to indicate which variables were dropped. See issue #1723 for # discussion about setting df parameters in model and results # classes. p, q = model.df_model, model.df_resid model.df_model = len(ii) model.df_resid = model.nobs - model.df_model # Assuming a standard signature for creating results classes. refit = klass(model, params, cov, scale=scale) refit.regularized = True refit.method = method refit.fit_history = {'iteration': itr + 1} # Restore df in model class, see issue #1723 for discussion. model.df_model, model.df_resid = p, q return refit def fit_elasticnet_constrained(model, method="coord_descent", maxiter=100, alpha=0., L1_wt=1., start_params=None, cnvrg_tol=1e-7, zero_tol=1e-8, refit=False, check_step=True, loglike_kwds=None, score_kwds=None, hess_kwds=None, param_limits = None, A_constr=None, b_constr=None, verbose=False): """ Return an elastic net regularized fit to a regression model. Parameters ---------- model : model object A statsmodels object implementing ``loglike``, ``score``, and ``hessian``. method : {'coord_descent'} Only the coordinate descent algorithm is implemented. maxiter : int The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. L1_wt : scalar The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit. start_params : array_like Starting values for `params`. cnvrg_tol : scalar If `params` changes by less than this amount (in sup-norm) in one iteration cycle, the algorithm terminates with convergence. zero_tol : scalar Any estimated coefficient smaller than this value is replaced with zero. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. check_step : bool If True, confirm that the first step is an improvement and search further if it is not. loglike_kwds : dict-like or None Keyword arguments for the log-likelihood function. score_kwds : dict-like or None Keyword arguments for the score function. hess_kwds : dict-like or None Keyword arguments for the Hessian function. A_constr: array-like The matrix for linear constraint `A @ params <= b` b_constr: array-like The right-hand-side vector for linear constraint `A @ params <= b`. Returns ------- Results A results object. Notes ----- The ``elastic net`` penalty is a combination of L1 and L2 penalties. The function that is minimized is: -loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1) where |*|_1 and |*|_2 are the L1 and L2 norms. The computational approach used here is to obtain a quadratic approximation to the smooth part of the target function: -loglike/n + alpha*(1-L1_wt)*|params|_2^2/2 then repeatedly optimize the L1 penalized version of this function along coordinate axes. """ k_exog = model.exog.shape[1] loglike_kwds = {} if loglike_kwds is None else loglike_kwds score_kwds = {} if score_kwds is None else score_kwds hess_kwds = {} if hess_kwds is None else hess_kwds if np.isscalar(alpha): alpha = alpha * np.ones(k_exog) # Define starting params if start_params is None: params = np.zeros(k_exog) else: params = start_params.copy() btol = 1e-4 params_zero = np.zeros(len(params), dtype=bool) init_args = model._get_init_kwds() # we do not need a copy of init_args b/c get_init_kwds provides new dict init_args['hasconst'] = False model_offset = init_args.pop('offset', None) if 'exposure' in init_args and init_args['exposure'] is not None: if model_offset is None: model_offset = np.log(init_args.pop('exposure')) else: model_offset += np.log(init_args.pop('exposure')) fgh_list = [ _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds) for k in range(k_exog)] # set up constraint enforcement if constraint is provided if A_constr is not None: x_min = [l[0] for l in param_limits] x_max = [l[1] for l in param_limits] proj = ConstraintProjector(x_min, x_max, A_constr, b_constr) for itr in range(maxiter): # Sweep through the parameters params_save = params.copy() for k in range(k_exog): # Under the active set method, if a parameter becomes # zero we do not try to change it again. # TODO : give the user the option to switch this off if params_zero[k]: continue # Set the offset to account for the variables that are # being held fixed in the current coordinate # optimization. params0 = params.copy() params0[k] = 0 offset = np.dot(model.exog, params0) if model_offset is not None: offset += model_offset # Create a one-variable model for optimization. model_1var = model.__class__( model.endog, model.exog[:, k], offset=offset, **init_args) # Do the one-dimensional optimization. func, grad, hess = fgh_list[k] params[k] = _opt_1d( func, grad, hess, model_1var, params[k], alpha[k]*L1_wt, tol=btol, check_step=check_step) # set the parameter to be within the limits if not param_limits is None: params[k] = max(param_limits[k][0], min(param_limits[k][1], params[k])) # Update the active set if itr > 0 and np.abs(params[k]) < zero_tol: params_zero[k] = True params[k] = 0. if A_constr is not None: # enforce the constraint # TODO: can this interfere with the way active set is defined? params = proj.project(params) # Check for convergence pchange = np.max(np.abs(params - params_save)) if pchange < cnvrg_tol: break if verbose: print(f'Elastic Net done after {itr}/{maxiter} iterations. pchange={pchange:0.2e} (cnvrg_tol={cnvrg_tol:0.2e})') # Set approximate zero coefficients to be exactly zero params[np.abs(params) < zero_tol] = 0 if not refit: results = RegularizedResults(model, params) return RegularizedResultsWrapper(results) # Fit the reduced model to get standard errors and other # post-estimation results. ii = np.flatnonzero(params) cov = np.zeros((k_exog, k_exog)) init_args = dict([(k, getattr(model, k, None)) for k in model._init_keys]) if len(ii) > 0: model1 = model.__class__( model.endog, model.exog[:, ii], **init_args) rslt = model1.fit() params[ii] = rslt.params cov[np.ix_(ii, ii)] = rslt.normalized_cov_params else: # Hack: no variables were selected but we need to run fit in # order to get the correct results class. So just fit a model # with one variable. model1 = model.__class__(model.endog, model.exog[:, 0], **init_args) rslt = model1.fit(maxiter=0) # fit may return a results or a results wrapper if issubclass(rslt.__class__, wrap.ResultsWrapper): klass = rslt._results.__class__ else: klass = rslt.__class__ # Not all models have a scale if hasattr(rslt, 'scale'): scale = rslt.scale else: scale = 1. # The degrees of freedom should reflect the number of parameters # in the refit model, not including the zeros that are displayed # to indicate which variables were dropped. See issue #1723 for # discussion about setting df parameters in model and results # classes. p, q = model.df_model, model.df_resid model.df_model = len(ii) model.df_resid = model.nobs - model.df_model # Assuming a standard signature for creating results classes. refit = klass(model, params, cov, scale=scale) refit.regularized = True refit.method = method refit.fit_history = {'iteration': itr + 1} # Restore df in model class, see issue #1723 for discussion. model.df_model, model.df_resid = p, q return refit def _opt_1d(func, grad, hess, model, start, L1_wt, tol, check_step=True): """ One-dimensional helper for elastic net. Parameters ---------- func : function A smooth function of a single variable to be optimized with L1 penaty. grad : function The gradient of `func`. hess : function The Hessian of `func`. model : statsmodels model The model being fit. start : real A starting value for the function argument L1_wt : non-negative real The weight for the L1 penalty function. tol : non-negative real A convergence threshold. check_step : bool If True, check that the first step is an improvement and use bisection if it is not. If False, return after the first step regardless. Notes ----- ``func``, ``grad``, and ``hess`` have argument signature (x, model), where ``x`` is a point in the parameter space and ``model`` is the model being fit. If the log-likelihood for the model is exactly quadratic, the global minimum is returned in one step. Otherwise numerical bisection is used. Returns ------- The argmin of the objective function. """ # Overview: # We want to minimize L(x) + L1_wt*abs(x), where L() is a smooth # loss function that includes the log-likelihood and L2 penalty. # This is a 1-dimensional optimization. If L(x) is exactly # quadratic we can solve for the argmin exactly. Otherwise we # approximate L(x) with a quadratic function Q(x) and try to use # the minimizer of Q(x) + L1_wt*abs(x). But if this yields an # uphill step for the actual target function L(x) + L1_wt*abs(x), # then we fall back to a expensive line search. The line search # is never needed for OLS. x = start f = func(x, model) b = grad(x, model) c = hess(x, model) d = b - c*x # The optimum is achieved by hard thresholding to zero if L1_wt > np.abs(d): return 0. # x + h is the minimizer of the Q(x) + L1_wt*abs(x) if d >= 0: h = (L1_wt - b) / c elif d < 0: h = -(L1_wt + b) / c else: return np.nan # If the new point is not uphill for the target function, take it # and return. This check is a bit expensive and un-necessary for # OLS if not check_step: return x + h f1 = func(x + h, model) + L1_wt*np.abs(x + h) if f1 <= f + L1_wt*np.abs(x) + 1e-10: return x + h # Fallback for models where the loss is not quadratic from scipy.optimize import brent x_opt = brent(func, args=(model,), brack=(x-1, x+1), tol=tol) return x_opt class RegularizedResults(Results): """ Results for models estimated using regularization Parameters ---------- model : Model The model instance used to estimate the parameters. params : ndarray The estimated (regularized) parameters. """ def __init__(self, model, params): super(RegularizedResults, self).__init__(model, params) @cache_readonly def fittedvalues(self): """ The predicted values from the model at the estimated parameters. """ return self.model.predict(self.params) class RegularizedResultsWrapper(wrap.ResultsWrapper): _attrs = { 'params': 'columns', 'resid': 'rows', 'fittedvalues': 'rows', } _wrap_attrs = _attrs wrap.populate_wrapper(RegularizedResultsWrapper, # noqa:E305 RegularizedResults)

34.535948

120

0.633232

import numpy as np from statsmodels.base.model import Results import statsmodels.base.wrapper as wrap from statsmodels.tools.decorators import cache_readonly from statsmodels.base.constraint import ConstraintProjector def _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds): def nploglike(params, model): nobs = model.nobs pen_llf = alpha[k] * (1 - L1_wt) * np.sum(params**2) / 2 llf = model.loglike(np.r_[params], **loglike_kwds) return - llf / nobs + pen_llf def npscore(params, model): nobs = model.nobs pen_grad = alpha[k] * (1 - L1_wt) * params gr = -model.score(np.r_[params], **score_kwds)[0] / nobs return gr + pen_grad def nphess(params, model): nobs = model.nobs pen_hess = alpha[k] * (1 - L1_wt) h = -model.hessian(np.r_[params], **hess_kwds)[0, 0] / nobs + pen_hess return h return nploglike, npscore, nphess def fit_elasticnet(model, method="coord_descent", maxiter=100, alpha=0., L1_wt=1., start_params=None, cnvrg_tol=1e-7, zero_tol=1e-8, refit=False, check_step=True, loglike_kwds=None, score_kwds=None, hess_kwds=None): k_exog = model.exog.shape[1] loglike_kwds = {} if loglike_kwds is None else loglike_kwds score_kwds = {} if score_kwds is None else score_kwds hess_kwds = {} if hess_kwds is None else hess_kwds if np.isscalar(alpha): alpha = alpha * np.ones(k_exog) if start_params is None: params = np.zeros(k_exog) else: params = start_params.copy() btol = 1e-4 params_zero = np.zeros(len(params), dtype=bool) init_args = model._get_init_kwds() init_args['hasconst'] = False model_offset = init_args.pop('offset', None) if 'exposure' in init_args and init_args['exposure'] is not None: if model_offset is None: model_offset = np.log(init_args.pop('exposure')) else: model_offset += np.log(init_args.pop('exposure')) fgh_list = [ _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds) for k in range(k_exog)] for itr in range(maxiter): params_save = params.copy() for k in range(k_exog): if params_zero[k]: continue params0 = params.copy() params0[k] = 0 offset = np.dot(model.exog, params0) if model_offset is not None: offset += model_offset model_1var = model.__class__( model.endog, model.exog[:, k], offset=offset, **init_args) func, grad, hess = fgh_list[k] params[k] = _opt_1d( func, grad, hess, model_1var, params[k], alpha[k]*L1_wt, tol=btol, check_step=check_step) if itr > 0 and np.abs(params[k]) < zero_tol: params_zero[k] = True params[k] = 0. pchange = np.max(np.abs(params - params_save)) if pchange < cnvrg_tol: break params[np.abs(params) < zero_tol] = 0 if not refit: results = RegularizedResults(model, params) return RegularizedResultsWrapper(results) ii = np.flatnonzero(params) cov = np.zeros((k_exog, k_exog)) init_args = dict([(k, getattr(model, k, None)) for k in model._init_keys]) if len(ii) > 0: model1 = model.__class__( model.endog, model.exog[:, ii], **init_args) rslt = model1.fit() params[ii] = rslt.params cov[np.ix_(ii, ii)] = rslt.normalized_cov_params else: model1 = model.__class__(model.endog, model.exog[:, 0], **init_args) rslt = model1.fit(maxiter=0) if issubclass(rslt.__class__, wrap.ResultsWrapper): klass = rslt._results.__class__ else: klass = rslt.__class__ if hasattr(rslt, 'scale'): scale = rslt.scale else: scale = 1. p, q = model.df_model, model.df_resid model.df_model = len(ii) model.df_resid = model.nobs - model.df_model refit = klass(model, params, cov, scale=scale) refit.regularized = True refit.method = method refit.fit_history = {'iteration': itr + 1} model.df_resid = p, q return refit def fit_elasticnet_constrained(model, method="coord_descent", maxiter=100, alpha=0., L1_wt=1., start_params=None, cnvrg_tol=1e-7, zero_tol=1e-8, refit=False, check_step=True, loglike_kwds=None, score_kwds=None, hess_kwds=None, param_limits = None, A_constr=None, b_constr=None, verbose=False): k_exog = model.exog.shape[1] loglike_kwds = {} if loglike_kwds is None else loglike_kwds score_kwds = {} if score_kwds is None else score_kwds hess_kwds = {} if hess_kwds is None else hess_kwds if np.isscalar(alpha): alpha = alpha * np.ones(k_exog) if start_params is None: params = np.zeros(k_exog) else: params = start_params.copy() btol = 1e-4 params_zero = np.zeros(len(params), dtype=bool) init_args = model._get_init_kwds() init_args['hasconst'] = False model_offset = init_args.pop('offset', None) if 'exposure' in init_args and init_args['exposure'] is not None: if model_offset is None: model_offset = np.log(init_args.pop('exposure')) else: model_offset += np.log(init_args.pop('exposure')) fgh_list = [ _gen_npfuncs(k, L1_wt, alpha, loglike_kwds, score_kwds, hess_kwds) for k in range(k_exog)] if A_constr is not None: x_min = [l[0] for l in param_limits] x_max = [l[1] for l in param_limits] proj = ConstraintProjector(x_min, x_max, A_constr, b_constr) for itr in range(maxiter): params_save = params.copy() for k in range(k_exog): if params_zero[k]: continue params0 = params.copy() params0[k] = 0 offset = np.dot(model.exog, params0) if model_offset is not None: offset += model_offset model_1var = model.__class__( model.endog, model.exog[:, k], offset=offset, **init_args) func, grad, hess = fgh_list[k] params[k] = _opt_1d( func, grad, hess, model_1var, params[k], alpha[k]*L1_wt, tol=btol, check_step=check_step) if not param_limits is None: params[k] = max(param_limits[k][0], min(param_limits[k][1], params[k])) if itr > 0 and np.abs(params[k]) < zero_tol: params_zero[k] = True params[k] = 0. if A_constr is not None: params = proj.project(params) pchange = np.max(np.abs(params - params_save)) if pchange < cnvrg_tol: break if verbose: print(f'Elastic Net done after {itr}/{maxiter} iterations. pchange={pchange:0.2e} (cnvrg_tol={cnvrg_tol:0.2e})') params[np.abs(params) < zero_tol] = 0 if not refit: results = RegularizedResults(model, params) return RegularizedResultsWrapper(results) ii = np.flatnonzero(params) cov = np.zeros((k_exog, k_exog)) init_args = dict([(k, getattr(model, k, None)) for k in model._init_keys]) if len(ii) > 0: model1 = model.__class__( model.endog, model.exog[:, ii], **init_args) rslt = model1.fit() params[ii] = rslt.params cov[np.ix_(ii, ii)] = rslt.normalized_cov_params else: model1 = model.__class__(model.endog, model.exog[:, 0], **init_args) rslt = model1.fit(maxiter=0) if issubclass(rslt.__class__, wrap.ResultsWrapper): klass = rslt._results.__class__ else: klass = rslt.__class__ if hasattr(rslt, 'scale'): scale = rslt.scale else: scale = 1. p, q = model.df_model, model.df_resid model.df_model = len(ii) model.df_resid = model.nobs - model.df_model refit = klass(model, params, cov, scale=scale) refit.regularized = True refit.method = method refit.fit_history = {'iteration': itr + 1} model.df_resid = p, q return refit def _opt_1d(func, grad, hess, model, start, L1_wt, tol, check_step=True): x = start f = func(x, model) b = grad(x, model) c = hess(x, model) d = b - c*x if L1_wt > np.abs(d): return 0. if d >= 0: h = (L1_wt - b) / c elif d < 0: h = -(L1_wt + b) / c else: return np.nan if not check_step: return x + h f1 = func(x + h, model) + L1_wt*np.abs(x + h) if f1 <= f + L1_wt*np.abs(x) + 1e-10: return x + h from scipy.optimize import brent x_opt = brent(func, args=(model,), brack=(x-1, x+1), tol=tol) return x_opt class RegularizedResults(Results): def __init__(self, model, params): super(RegularizedResults, self).__init__(model, params) @cache_readonly def fittedvalues(self): return self.model.predict(self.params) class RegularizedResultsWrapper(wrap.ResultsWrapper): _attrs = { 'params': 'columns', 'resid': 'rows', 'fittedvalues': 'rows', } _wrap_attrs = _attrs wrap.populate_wrapper(RegularizedResultsWrapper, RegularizedResults)

true

1c2b3cdb92e9d64974b419e6e1547380d9683c17

2,948

py

Python

platforms/osx/build_framework.py

thisisgopalmandal/opencv

4e2ef8c8f57644ccb8e762a37f70a61007c6be1c

[ "BSD-3-Clause" ]

56

2020-03-24T15:17:56.000Z

2022-03-21T13:44:08.000Z

platforms/osx/build_framework.py

thisisgopalmandal/opencv

4e2ef8c8f57644ccb8e762a37f70a61007c6be1c

[ "BSD-3-Clause" ]

6

2021-03-08T13:41:24.000Z

2022-02-19T08:10:24.000Z

platforms/osx/build_framework.py

thisisgopalmandal/opencv

4e2ef8c8f57644ccb8e762a37f70a61007c6be1c

[ "BSD-3-Clause" ]

15

2020-05-06T13:41:20.000Z

2022-03-31T19:15:47.000Z

#!/usr/bin/env python """ The script builds OpenCV.framework for OSX. """ from __future__ import print_function import os, os.path, sys, argparse, traceback, multiprocessing # import common code sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios')) from build_framework import Builder MACOSX_DEPLOYMENT_TARGET='10.12' # default, can be changed via command line options or environment variable class OSXBuilder(Builder): def getToolchain(self, arch, target): return None def getBuildCommand(self, archs, target): buildcmd = [ "xcodebuild", "MACOSX_DEPLOYMENT_TARGET=" + os.environ['MACOSX_DEPLOYMENT_TARGET'], "ARCHS=%s" % archs[0], "-sdk", target.lower(), "-configuration", "Debug" if self.debug else "Release", "-parallelizeTargets", "-jobs", str(multiprocessing.cpu_count()) ] return buildcmd def getInfoPlist(self, builddirs): return os.path.join(builddirs[0], "osx", "Info.plist") if __name__ == "__main__": folder = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../..")) parser = argparse.ArgumentParser(description='The script builds OpenCV.framework for OSX.') parser.add_argument('out', metavar='OUTDIR', help='folder to put built framework') parser.add_argument('--opencv', metavar='DIR', default=folder, help='folder with opencv repository (default is "../.." relative to script location)') parser.add_argument('--contrib', metavar='DIR', default=None, help='folder with opencv_contrib repository (default is "None" - build only main framework)') parser.add_argument('--without', metavar='MODULE', default=[], action='append', help='OpenCV modules to exclude from the framework') parser.add_argument('--disable', metavar='FEATURE', default=[], action='append', help='OpenCV features to disable (add WITH_*=OFF)') parser.add_argument('--enable_nonfree', default=False, dest='enablenonfree', action='store_true', help='enable non-free modules (disabled by default)') parser.add_argument('--macosx_deployment_target', default=os.environ.get('MACOSX_DEPLOYMENT_TARGET', MACOSX_DEPLOYMENT_TARGET), help='specify MACOSX_DEPLOYMENT_TARGET') parser.add_argument('--debug', action='store_true', help='Build "Debug" binaries (CMAKE_BUILD_TYPE=Debug)') parser.add_argument('--debug_info', action='store_true', help='Build with debug information (useful for Release mode: BUILD_WITH_DEBUG_INFO=ON)') args = parser.parse_args() os.environ['MACOSX_DEPLOYMENT_TARGET'] = args.macosx_deployment_target print('Using MACOSX_DEPLOYMENT_TARGET=' + os.environ['MACOSX_DEPLOYMENT_TARGET']) b = OSXBuilder(args.opencv, args.contrib, False, False, args.without, args.disable, args.enablenonfree, [ (["x86_64"], "MacOSX") ], args.debug, args.debug_info) b.build(args.out)

49.966102

172

0.700814

from __future__ import print_function import os, os.path, sys, argparse, traceback, multiprocessing sys.path.insert(0, os.path.abspath(os.path.abspath(os.path.dirname(__file__))+'/../ios')) from build_framework import Builder MACOSX_DEPLOYMENT_TARGET='10.12' class OSXBuilder(Builder): def getToolchain(self, arch, target): return None def getBuildCommand(self, archs, target): buildcmd = [ "xcodebuild", "MACOSX_DEPLOYMENT_TARGET=" + os.environ['MACOSX_DEPLOYMENT_TARGET'], "ARCHS=%s" % archs[0], "-sdk", target.lower(), "-configuration", "Debug" if self.debug else "Release", "-parallelizeTargets", "-jobs", str(multiprocessing.cpu_count()) ] return buildcmd def getInfoPlist(self, builddirs): return os.path.join(builddirs[0], "osx", "Info.plist") if __name__ == "__main__": folder = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../..")) parser = argparse.ArgumentParser(description='The script builds OpenCV.framework for OSX.') parser.add_argument('out', metavar='OUTDIR', help='folder to put built framework') parser.add_argument('--opencv', metavar='DIR', default=folder, help='folder with opencv repository (default is "../.." relative to script location)') parser.add_argument('--contrib', metavar='DIR', default=None, help='folder with opencv_contrib repository (default is "None" - build only main framework)') parser.add_argument('--without', metavar='MODULE', default=[], action='append', help='OpenCV modules to exclude from the framework') parser.add_argument('--disable', metavar='FEATURE', default=[], action='append', help='OpenCV features to disable (add WITH_*=OFF)') parser.add_argument('--enable_nonfree', default=False, dest='enablenonfree', action='store_true', help='enable non-free modules (disabled by default)') parser.add_argument('--macosx_deployment_target', default=os.environ.get('MACOSX_DEPLOYMENT_TARGET', MACOSX_DEPLOYMENT_TARGET), help='specify MACOSX_DEPLOYMENT_TARGET') parser.add_argument('--debug', action='store_true', help='Build "Debug" binaries (CMAKE_BUILD_TYPE=Debug)') parser.add_argument('--debug_info', action='store_true', help='Build with debug information (useful for Release mode: BUILD_WITH_DEBUG_INFO=ON)') args = parser.parse_args() os.environ['MACOSX_DEPLOYMENT_TARGET'] = args.macosx_deployment_target print('Using MACOSX_DEPLOYMENT_TARGET=' + os.environ['MACOSX_DEPLOYMENT_TARGET']) b = OSXBuilder(args.opencv, args.contrib, False, False, args.without, args.disable, args.enablenonfree, [ (["x86_64"], "MacOSX") ], args.debug, args.debug_info) b.build(args.out)

true

1c2b3cff1088f0e7ddef17027fc2cacfb3cb8c7c

275

py

Python

apps/public/schemas.py

aeasringnar/tornado-RESTfulAPI

911b8d52fdcc8f5a5b96343e74d0ac987f661bd4

[ "MIT" ]

5

2020-07-31T10:14:09.000Z

2022-03-03T06:04:21.000Z

apps/public/schemas.py

aeasringnar/tornado-RESTfulAPI

911b8d52fdcc8f5a5b96343e74d0ac987f661bd4

[ "MIT" ]

2

2021-06-08T22:12:15.000Z

2022-01-13T03:09:14.000Z

apps/public/schemas.py

aeasringnar/tornado-RESTfulAPI

911b8d52fdcc8f5a5b96343e74d0ac987f661bd4

[ "MIT" ]

4

2020-08-20T15:35:20.000Z

2022-03-29T11:10:06.000Z

from marshmallow import Schema, fields, ValidationError, validate, validates, pre_load, validates_schema from base.schema import BaseSchema class GetMobielCoseSchema(BaseSchema): mobile = fields.String(label='手机号', required=True, error_messages={"required": "请输入手机号。"})

45.833333

104

0.796364

from marshmallow import Schema, fields, ValidationError, validate, validates, pre_load, validates_schema from base.schema import BaseSchema class GetMobielCoseSchema(BaseSchema): mobile = fields.String(label='手机号', required=True, error_messages={"required": "请输入手机号。"})

true

1c2b3ee2cd92e219a0e9956a2ecceaeadd5559a8

543

py

Python

misc/code reference.py

flyingpizza/kaggel-workouts

744a27736fa7878b24f2fc4dc43e956c49b21fef

[ "MIT" ]

null

misc/code reference.py

flyingpizza/kaggel-workouts

744a27736fa7878b24f2fc4dc43e956c49b21fef

[ "MIT" ]

null

misc/code reference.py

flyingpizza/kaggel-workouts

744a27736fa7878b24f2fc4dc43e956c49b21fef

[ "MIT" ]

null

# code to create subplot import seaborn as sns import matplotlib.pyplot as plt fig = plt.figure(figsize = (18, 20)) for index in range(len(cat_features)): plt.subplot(8, 5, index + 1) sns.countplot(data = train.dropna(), x = train.loc[:, cat_features[index]]) plt.xticks(rotation = 90) plt.tight_layout() # code to create heatmap import seaborn as sns import matplotlib.pyplot as plt plt.figure(figsize=(10,8)) sns.heatmap(train_data.corr(), center = 0) plt.title("Correlations Between Columns") plt.show()

21.72

79

0.694291

import seaborn as sns import matplotlib.pyplot as plt fig = plt.figure(figsize = (18, 20)) for index in range(len(cat_features)): plt.subplot(8, 5, index + 1) sns.countplot(data = train.dropna(), x = train.loc[:, cat_features[index]]) plt.xticks(rotation = 90) plt.tight_layout() import seaborn as sns import matplotlib.pyplot as plt plt.figure(figsize=(10,8)) sns.heatmap(train_data.corr(), center = 0) plt.title("Correlations Between Columns") plt.show()

true

1c2b3f19b287c8900a950d123b13e171d11d9b47

16,788

py

Python

manimlib/mobject/svg/svg_mobject.py

pu17/manim_project

dfea9f6b40c6f78f918970ca5e4574b92839bf0d

[ "MIT" ]

1

2021-02-01T00:40:34.000Z

manimlib/mobject/svg/svg_mobject.py

mohamedballa/manim

fe85d4e02f6935c49fb0b88eebbd492dfff2d324

[ "MIT" ]

1

2021-02-02T03:43:05.000Z

manimlib/mobject/svg/svg_mobject.py

mohamedballa/manim

fe85d4e02f6935c49fb0b88eebbd492dfff2d324

[ "MIT" ]

null

import itertools as it import re import string import warnings import os import hashlib from xml.dom import minidom from manimlib.constants import DEFAULT_STROKE_WIDTH from manimlib.constants import ORIGIN, UP, DOWN, LEFT, RIGHT from manimlib.constants import BLACK from manimlib.constants import WHITE from manimlib.mobject.geometry import Circle from manimlib.mobject.geometry import Rectangle from manimlib.mobject.geometry import RoundedRectangle from manimlib.mobject.types.vectorized_mobject import VGroup from manimlib.mobject.types.vectorized_mobject import VMobject from manimlib.utils.color import * from manimlib.utils.config_ops import digest_config from manimlib.utils.directories import get_mobject_data_dir from manimlib.utils.images import get_full_vector_image_path def check_and_fix_percent_bug(sym): # This is an ugly patch addressing something which should be # addressed at a deeper level. # The svg path for percent symbols have a known bug, so this # checks if the symbol is (probably) a percentage sign, and # splits it so that it's displayed properly. if len(sym.get_points()) not in [315, 324, 372, 468, 483] or len(sym.get_subpaths()) != 4: return sym = sym.family_members_with_points()[0] new_sym = VMobject() path_lengths = [len(path) for path in sym.get_subpaths()] sym_points = sym.get_points() if len(sym_points) in [315, 324, 372]: n = sum(path_lengths[:2]) p1 = sym_points[:n] p2 = sym_points[n:] elif len(sym_points) in [468, 483]: p1 = np.vstack([ sym_points[:path_lengths[0]], sym_points[-path_lengths[3]:] ]) p2 = sym_points[path_lengths[0]:sum(path_lengths[:3])] sym.set_points(p1) new_sym.set_points(p2) sym.add(new_sym) sym.refresh_triangulation() def string_to_numbers(num_string): num_string = num_string.replace("-", ",-") num_string = num_string.replace("e,-", "e-") return [ float(s) for s in re.split("[ ,]", num_string) if s != "" ] class SVGMobject(VMobject): CONFIG = { "should_center": True, "height": 2, "width": None, # Must be filled in in a subclass, or when called "file_name": None, "unpack_groups": True, # if False, creates a hierarchy of VGroups # TODO, style components should be read in, not defaulted "stroke_width": DEFAULT_STROKE_WIDTH, "fill_opacity": 1.0, "path_string_config": {} } def __init__(self, file_name=None, **kwargs): digest_config(self, kwargs) self.file_name = file_name or self.file_name if file_name is None: raise Exception("Must specify file for SVGMobject") self.file_path = get_full_vector_image_path(file_name) super().__init__(**kwargs) self.move_into_position() def move_into_position(self): if self.should_center: self.center() if self.height is not None: self.set_height(self.height) if self.width is not None: self.set_width(self.width) def init_points(self): doc = minidom.parse(self.file_path) self.ref_to_element = {} for svg in doc.getElementsByTagName("svg"): mobjects = self.get_mobjects_from(svg) if self.unpack_groups: self.add(*mobjects) else: self.add(*mobjects[0].submobjects) doc.unlink() def get_mobjects_from(self, element): result = [] if not isinstance(element, minidom.Element): return result if element.tagName == 'defs': self.update_ref_to_element(element) elif element.tagName == 'style': pass # TODO, handle style elif element.tagName in ['g', 'svg', 'symbol']: result += it.chain(*[ self.get_mobjects_from(child) for child in element.childNodes ]) elif element.tagName == 'path': result.append(self.path_string_to_mobject( element.getAttribute('d') )) elif element.tagName == 'use': result += self.use_to_mobjects(element) elif element.tagName == 'rect': result.append(self.rect_to_mobject(element)) elif element.tagName == 'circle': result.append(self.circle_to_mobject(element)) elif element.tagName == 'ellipse': result.append(self.ellipse_to_mobject(element)) elif element.tagName in ['polygon', 'polyline']: result.append(self.polygon_to_mobject(element)) else: pass # TODO # warnings.warn("Unknown element type: " + element.tagName) result = [m for m in result if m is not None] self.handle_transforms(element, VGroup(*result)) if len(result) > 1 and not self.unpack_groups: result = [VGroup(*result)] return result def g_to_mobjects(self, g_element): mob = VGroup(*self.get_mobjects_from(g_element)) self.handle_transforms(g_element, mob) return mob.submobjects def path_string_to_mobject(self, path_string): return VMobjectFromSVGPathstring( path_string, **self.path_string_config, ) def use_to_mobjects(self, use_element): # Remove initial "#" character ref = use_element.getAttribute("xlink:href")[1:] if ref not in self.ref_to_element: warnings.warn(f"{ref} not recognized") return VGroup() return self.get_mobjects_from( self.ref_to_element[ref] ) def attribute_to_float(self, attr): stripped_attr = "".join([ char for char in attr if char in string.digits + "." + "-" ]) return float(stripped_attr) def polygon_to_mobject(self, polygon_element): path_string = polygon_element.getAttribute("points") for digit in string.digits: path_string = path_string.replace(f" {digit}", f"L {digit}") path_string = path_string.replace("L", "M", 1) return self.path_string_to_mobject(path_string) def circle_to_mobject(self, circle_element): x, y, r = [ self.attribute_to_float( circle_element.getAttribute(key) ) if circle_element.hasAttribute(key) else 0.0 for key in ("cx", "cy", "r") ] return Circle(radius=r).shift(x * RIGHT + y * DOWN) def ellipse_to_mobject(self, circle_element): x, y, rx, ry = [ self.attribute_to_float( circle_element.getAttribute(key) ) if circle_element.hasAttribute(key) else 0.0 for key in ("cx", "cy", "rx", "ry") ] return Circle().scale(rx * RIGHT + ry * UP).shift(x * RIGHT + y * DOWN) def rect_to_mobject(self, rect_element): fill_color = rect_element.getAttribute("fill") stroke_color = rect_element.getAttribute("stroke") stroke_width = rect_element.getAttribute("stroke-width") corner_radius = rect_element.getAttribute("rx") # input preprocessing if fill_color in ["", "none", "#FFF", "#FFFFFF"] or Color(fill_color) == Color(WHITE): opacity = 0 fill_color = BLACK # shdn't be necessary but avoids error msgs if fill_color in ["#000", "#000000"]: fill_color = WHITE if stroke_color in ["", "none", "#FFF", "#FFFFFF"] or Color(stroke_color) == Color(WHITE): stroke_width = 0 stroke_color = BLACK if stroke_color in ["#000", "#000000"]: stroke_color = WHITE if stroke_width in ["", "none", "0"]: stroke_width = 0 if corner_radius in ["", "0", "none"]: corner_radius = 0 corner_radius = float(corner_radius) if corner_radius == 0: mob = Rectangle( width=self.attribute_to_float( rect_element.getAttribute("width") ), height=self.attribute_to_float( rect_element.getAttribute("height") ), stroke_width=stroke_width, stroke_color=stroke_color, fill_color=fill_color, fill_opacity=opacity ) else: mob = RoundedRectangle( width=self.attribute_to_float( rect_element.getAttribute("width") ), height=self.attribute_to_float( rect_element.getAttribute("height") ), stroke_width=stroke_width, stroke_color=stroke_color, fill_color=fill_color, fill_opacity=opacity, corner_radius=corner_radius ) mob.shift(mob.get_center() - mob.get_corner(UP + LEFT)) return mob def handle_transforms(self, element, mobject): # TODO, this could use some cleaning... x, y = 0, 0 try: x = self.attribute_to_float(element.getAttribute('x')) # Flip y y = -self.attribute_to_float(element.getAttribute('y')) mobject.shift([x, y, 0]) except Exception: pass transform = element.getAttribute('transform') try: # transform matrix prefix = "matrix(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] transform = string_to_numbers(transform) transform = np.array(transform).reshape([3, 2]) x = transform[2][0] y = -transform[2][1] matrix = np.identity(self.dim) matrix[:2, :2] = transform[:2, :] matrix[1] *= -1 matrix[:, 1] *= -1 for mob in mobject.family_members_with_points(): mob.apply_matrix(matrix.T) mobject.shift(x * RIGHT + y * UP) except: pass try: # transform scale prefix = "scale(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] scale_values = string_to_numbers(transform) if len(scale_values) == 2: scale_x, scale_y = scale_values mobject.scale(np.array([scale_x, scale_y, 1]), about_point=ORIGIN) elif len(scale_values) == 1: scale = scale_values[0] mobject.scale(np.array([scale, scale, 1]), about_point=ORIGIN) except: pass try: # transform translate prefix = "translate(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] x, y = string_to_numbers(transform) mobject.shift(x * RIGHT + y * DOWN) except: pass # TODO, ... def flatten(self, input_list): output_list = [] for i in input_list: if isinstance(i, list): output_list.extend(self.flatten(i)) else: output_list.append(i) return output_list def get_all_childNodes_have_id(self, element): all_childNodes_have_id = [] if not isinstance(element, minidom.Element): return if element.hasAttribute('id'): return [element] for e in element.childNodes: all_childNodes_have_id.append(self.get_all_childNodes_have_id(e)) return self.flatten([e for e in all_childNodes_have_id if e]) def update_ref_to_element(self, defs): new_refs = dict([(e.getAttribute('id'), e) for e in self.get_all_childNodes_have_id(defs)]) self.ref_to_element.update(new_refs) class VMobjectFromSVGPathstring(VMobject): CONFIG = { "long_lines": True, "should_subdivide_sharp_curves": False, "should_remove_null_curves": False, } def __init__(self, path_string, **kwargs): self.path_string = path_string super().__init__(**kwargs) def init_points(self): # After a given svg_path has been converted into points, the result # will be saved to a file so that future calls for the same path # don't need to retrace the same computation. hasher = hashlib.sha256(self.path_string.encode()) path_hash = hasher.hexdigest()[:16] points_filepath = os.path.join(get_mobject_data_dir(), f"{path_hash}_points.npy") tris_filepath = os.path.join(get_mobject_data_dir(), f"{path_hash}_tris.npy") if os.path.exists(points_filepath) and os.path.exists(tris_filepath): self.set_points(np.load(points_filepath)) else: self.relative_point = np.array(ORIGIN) for command, coord_string in self.get_commands_and_coord_strings(): new_points = self.string_to_points(command, coord_string) self.handle_command(command, new_points) if self.should_subdivide_sharp_curves: # For a healthy triangulation later self.subdivide_sharp_curves() if self.should_remove_null_curves: # Get rid of any null curves self.set_points(self.get_points_without_null_curves()) # SVG treats y-coordinate differently self.stretch(-1, 1, about_point=ORIGIN) # Save to a file for future use np.save(points_filepath, self.get_points()) check_and_fix_percent_bug(self) def get_commands_and_coord_strings(self): all_commands = list(self.get_command_to_function_map().keys()) all_commands += [c.lower() for c in all_commands] pattern = "[{}]".format("".join(all_commands)) return zip( re.findall(pattern, self.path_string), re.split(pattern, self.path_string)[1:] ) def handle_command(self, command, new_points): if command.islower(): # Treat it as a relative command new_points += self.relative_point func, n_points = self.command_to_function(command) func(*new_points[:n_points]) leftover_points = new_points[n_points:] # Recursively handle the rest of the points if len(leftover_points) > 0: if command.upper() == "M": # Treat following points as relative line coordinates command = "l" if command.islower(): leftover_points -= self.relative_point self.relative_point = self.get_last_point() self.handle_command(command, leftover_points) else: # Command is over, reset for future relative commands self.relative_point = self.get_last_point() def string_to_points(self, command, coord_string): numbers = string_to_numbers(coord_string) if command.upper() in ["H", "V"]: i = {"H": 0, "V": 1}[command.upper()] xy = np.zeros((len(numbers), 2)) xy[:, i] = numbers if command.isupper(): xy[:, 1 - i] = self.relative_point[1 - i] elif command.upper() == "A": raise Exception("Not implemented") else: xy = np.array(numbers).reshape((len(numbers) // 2, 2)) result = np.zeros((xy.shape[0], self.dim)) result[:, :2] = xy return result def command_to_function(self, command): return self.get_command_to_function_map()[command.upper()] def get_command_to_function_map(self): """ Associates svg command to VMobject function, and the number of arguments it takes in """ return { "M": (self.start_new_path, 1), "L": (self.add_line_to, 1), "H": (self.add_line_to, 1), "V": (self.add_line_to, 1), "C": (self.add_cubic_bezier_curve_to, 3), "S": (self.add_smooth_cubic_curve_to, 2), "Q": (self.add_quadratic_bezier_curve_to, 2), "T": (self.add_smooth_curve_to, 1), "A": (self.add_quadratic_bezier_curve_to, 2), # TODO "Z": (self.close_path, 0), } def get_original_path_string(self): return self.path_string

36.977974

99

0.591077

import itertools as it import re import string import warnings import os import hashlib from xml.dom import minidom from manimlib.constants import DEFAULT_STROKE_WIDTH from manimlib.constants import ORIGIN, UP, DOWN, LEFT, RIGHT from manimlib.constants import BLACK from manimlib.constants import WHITE from manimlib.mobject.geometry import Circle from manimlib.mobject.geometry import Rectangle from manimlib.mobject.geometry import RoundedRectangle from manimlib.mobject.types.vectorized_mobject import VGroup from manimlib.mobject.types.vectorized_mobject import VMobject from manimlib.utils.color import * from manimlib.utils.config_ops import digest_config from manimlib.utils.directories import get_mobject_data_dir from manimlib.utils.images import get_full_vector_image_path def check_and_fix_percent_bug(sym): if len(sym.get_points()) not in [315, 324, 372, 468, 483] or len(sym.get_subpaths()) != 4: return sym = sym.family_members_with_points()[0] new_sym = VMobject() path_lengths = [len(path) for path in sym.get_subpaths()] sym_points = sym.get_points() if len(sym_points) in [315, 324, 372]: n = sum(path_lengths[:2]) p1 = sym_points[:n] p2 = sym_points[n:] elif len(sym_points) in [468, 483]: p1 = np.vstack([ sym_points[:path_lengths[0]], sym_points[-path_lengths[3]:] ]) p2 = sym_points[path_lengths[0]:sum(path_lengths[:3])] sym.set_points(p1) new_sym.set_points(p2) sym.add(new_sym) sym.refresh_triangulation() def string_to_numbers(num_string): num_string = num_string.replace("-", ",-") num_string = num_string.replace("e,-", "e-") return [ float(s) for s in re.split("[ ,]", num_string) if s != "" ] class SVGMobject(VMobject): CONFIG = { "should_center": True, "height": 2, "width": None, # Must be filled in in a subclass, or when called "file_name": None, "unpack_groups": True, # if False, creates a hierarchy of VGroups # TODO, style components should be read in, not defaulted "stroke_width": DEFAULT_STROKE_WIDTH, "fill_opacity": 1.0, "path_string_config": {} } def __init__(self, file_name=None, **kwargs): digest_config(self, kwargs) self.file_name = file_name or self.file_name if file_name is None: raise Exception("Must specify file for SVGMobject") self.file_path = get_full_vector_image_path(file_name) super().__init__(**kwargs) self.move_into_position() def move_into_position(self): if self.should_center: self.center() if self.height is not None: self.set_height(self.height) if self.width is not None: self.set_width(self.width) def init_points(self): doc = minidom.parse(self.file_path) self.ref_to_element = {} for svg in doc.getElementsByTagName("svg"): mobjects = self.get_mobjects_from(svg) if self.unpack_groups: self.add(*mobjects) else: self.add(*mobjects[0].submobjects) doc.unlink() def get_mobjects_from(self, element): result = [] if not isinstance(element, minidom.Element): return result if element.tagName == 'defs': self.update_ref_to_element(element) elif element.tagName == 'style': pass # TODO, handle style elif element.tagName in ['g', 'svg', 'symbol']: result += it.chain(*[ self.get_mobjects_from(child) for child in element.childNodes ]) elif element.tagName == 'path': result.append(self.path_string_to_mobject( element.getAttribute('d') )) elif element.tagName == 'use': result += self.use_to_mobjects(element) elif element.tagName == 'rect': result.append(self.rect_to_mobject(element)) elif element.tagName == 'circle': result.append(self.circle_to_mobject(element)) elif element.tagName == 'ellipse': result.append(self.ellipse_to_mobject(element)) elif element.tagName in ['polygon', 'polyline']: result.append(self.polygon_to_mobject(element)) else: pass # TODO # warnings.warn("Unknown element type: " + element.tagName) result = [m for m in result if m is not None] self.handle_transforms(element, VGroup(*result)) if len(result) > 1 and not self.unpack_groups: result = [VGroup(*result)] return result def g_to_mobjects(self, g_element): mob = VGroup(*self.get_mobjects_from(g_element)) self.handle_transforms(g_element, mob) return mob.submobjects def path_string_to_mobject(self, path_string): return VMobjectFromSVGPathstring( path_string, **self.path_string_config, ) def use_to_mobjects(self, use_element): # Remove initial "#" character ref = use_element.getAttribute("xlink:href")[1:] if ref not in self.ref_to_element: warnings.warn(f"{ref} not recognized") return VGroup() return self.get_mobjects_from( self.ref_to_element[ref] ) def attribute_to_float(self, attr): stripped_attr = "".join([ char for char in attr if char in string.digits + "." + "-" ]) return float(stripped_attr) def polygon_to_mobject(self, polygon_element): path_string = polygon_element.getAttribute("points") for digit in string.digits: path_string = path_string.replace(f" {digit}", f"L {digit}") path_string = path_string.replace("L", "M", 1) return self.path_string_to_mobject(path_string) def circle_to_mobject(self, circle_element): x, y, r = [ self.attribute_to_float( circle_element.getAttribute(key) ) if circle_element.hasAttribute(key) else 0.0 for key in ("cx", "cy", "r") ] return Circle(radius=r).shift(x * RIGHT + y * DOWN) def ellipse_to_mobject(self, circle_element): x, y, rx, ry = [ self.attribute_to_float( circle_element.getAttribute(key) ) if circle_element.hasAttribute(key) else 0.0 for key in ("cx", "cy", "rx", "ry") ] return Circle().scale(rx * RIGHT + ry * UP).shift(x * RIGHT + y * DOWN) def rect_to_mobject(self, rect_element): fill_color = rect_element.getAttribute("fill") stroke_color = rect_element.getAttribute("stroke") stroke_width = rect_element.getAttribute("stroke-width") corner_radius = rect_element.getAttribute("rx") # input preprocessing if fill_color in ["", "none", "#FFF", "#FFFFFF"] or Color(fill_color) == Color(WHITE): opacity = 0 fill_color = BLACK # shdn't be necessary but avoids error msgs if fill_color in ["#000", "#000000"]: fill_color = WHITE if stroke_color in ["", "none", "#FFF", "#FFFFFF"] or Color(stroke_color) == Color(WHITE): stroke_width = 0 stroke_color = BLACK if stroke_color in ["#000", "#000000"]: stroke_color = WHITE if stroke_width in ["", "none", "0"]: stroke_width = 0 if corner_radius in ["", "0", "none"]: corner_radius = 0 corner_radius = float(corner_radius) if corner_radius == 0: mob = Rectangle( width=self.attribute_to_float( rect_element.getAttribute("width") ), height=self.attribute_to_float( rect_element.getAttribute("height") ), stroke_width=stroke_width, stroke_color=stroke_color, fill_color=fill_color, fill_opacity=opacity ) else: mob = RoundedRectangle( width=self.attribute_to_float( rect_element.getAttribute("width") ), height=self.attribute_to_float( rect_element.getAttribute("height") ), stroke_width=stroke_width, stroke_color=stroke_color, fill_color=fill_color, fill_opacity=opacity, corner_radius=corner_radius ) mob.shift(mob.get_center() - mob.get_corner(UP + LEFT)) return mob def handle_transforms(self, element, mobject): x, y = 0, 0 try: x = self.attribute_to_float(element.getAttribute('x')) y = -self.attribute_to_float(element.getAttribute('y')) mobject.shift([x, y, 0]) except Exception: pass transform = element.getAttribute('transform') try: prefix = "matrix(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] transform = string_to_numbers(transform) transform = np.array(transform).reshape([3, 2]) x = transform[2][0] y = -transform[2][1] matrix = np.identity(self.dim) matrix[:2, :2] = transform[:2, :] matrix[1] *= -1 matrix[:, 1] *= -1 for mob in mobject.family_members_with_points(): mob.apply_matrix(matrix.T) mobject.shift(x * RIGHT + y * UP) except: pass try: prefix = "scale(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] scale_values = string_to_numbers(transform) if len(scale_values) == 2: scale_x, scale_y = scale_values mobject.scale(np.array([scale_x, scale_y, 1]), about_point=ORIGIN) elif len(scale_values) == 1: scale = scale_values[0] mobject.scale(np.array([scale, scale, 1]), about_point=ORIGIN) except: pass try: prefix = "translate(" suffix = ")" if not transform.startswith(prefix) or not transform.endswith(suffix): raise Exception() transform = transform[len(prefix):-len(suffix)] x, y = string_to_numbers(transform) mobject.shift(x * RIGHT + y * DOWN) except: pass def flatten(self, input_list): output_list = [] for i in input_list: if isinstance(i, list): output_list.extend(self.flatten(i)) else: output_list.append(i) return output_list def get_all_childNodes_have_id(self, element): all_childNodes_have_id = [] if not isinstance(element, minidom.Element): return if element.hasAttribute('id'): return [element] for e in element.childNodes: all_childNodes_have_id.append(self.get_all_childNodes_have_id(e)) return self.flatten([e for e in all_childNodes_have_id if e]) def update_ref_to_element(self, defs): new_refs = dict([(e.getAttribute('id'), e) for e in self.get_all_childNodes_have_id(defs)]) self.ref_to_element.update(new_refs) class VMobjectFromSVGPathstring(VMobject): CONFIG = { "long_lines": True, "should_subdivide_sharp_curves": False, "should_remove_null_curves": False, } def __init__(self, path_string, **kwargs): self.path_string = path_string super().__init__(**kwargs) def init_points(self): hasher = hashlib.sha256(self.path_string.encode()) path_hash = hasher.hexdigest()[:16] points_filepath = os.path.join(get_mobject_data_dir(), f"{path_hash}_points.npy") tris_filepath = os.path.join(get_mobject_data_dir(), f"{path_hash}_tris.npy") if os.path.exists(points_filepath) and os.path.exists(tris_filepath): self.set_points(np.load(points_filepath)) else: self.relative_point = np.array(ORIGIN) for command, coord_string in self.get_commands_and_coord_strings(): new_points = self.string_to_points(command, coord_string) self.handle_command(command, new_points) if self.should_subdivide_sharp_curves: # For a healthy triangulation later self.subdivide_sharp_curves() if self.should_remove_null_curves: # Get rid of any null curves self.set_points(self.get_points_without_null_curves()) # SVG treats y-coordinate differently self.stretch(-1, 1, about_point=ORIGIN) # Save to a file for future use np.save(points_filepath, self.get_points()) check_and_fix_percent_bug(self) def get_commands_and_coord_strings(self): all_commands = list(self.get_command_to_function_map().keys()) all_commands += [c.lower() for c in all_commands] pattern = "[{}]".format("".join(all_commands)) return zip( re.findall(pattern, self.path_string), re.split(pattern, self.path_string)[1:] ) def handle_command(self, command, new_points): if command.islower(): # Treat it as a relative command new_points += self.relative_point func, n_points = self.command_to_function(command) func(*new_points[:n_points]) leftover_points = new_points[n_points:] # Recursively handle the rest of the points if len(leftover_points) > 0: if command.upper() == "M": # Treat following points as relative line coordinates command = "l" if command.islower(): leftover_points -= self.relative_point self.relative_point = self.get_last_point() self.handle_command(command, leftover_points) else: # Command is over, reset for future relative commands self.relative_point = self.get_last_point() def string_to_points(self, command, coord_string): numbers = string_to_numbers(coord_string) if command.upper() in ["H", "V"]: i = {"H": 0, "V": 1}[command.upper()] xy = np.zeros((len(numbers), 2)) xy[:, i] = numbers if command.isupper(): xy[:, 1 - i] = self.relative_point[1 - i] elif command.upper() == "A": raise Exception("Not implemented") else: xy = np.array(numbers).reshape((len(numbers) // 2, 2)) result = np.zeros((xy.shape[0], self.dim)) result[:, :2] = xy return result def command_to_function(self, command): return self.get_command_to_function_map()[command.upper()] def get_command_to_function_map(self): return { "M": (self.start_new_path, 1), "L": (self.add_line_to, 1), "H": (self.add_line_to, 1), "V": (self.add_line_to, 1), "C": (self.add_cubic_bezier_curve_to, 3), "S": (self.add_smooth_cubic_curve_to, 2), "Q": (self.add_quadratic_bezier_curve_to, 2), "T": (self.add_smooth_curve_to, 1), "A": (self.add_quadratic_bezier_curve_to, 2), # TODO "Z": (self.close_path, 0), } def get_original_path_string(self): return self.path_string

true

1c2b3fef6027163a9008d96f75b22e02d4bff261

704

py

Python

3_complex_deps/setup.py

fracpete/python-console-scripts

d453e492fc19ebc25dee75a2921b27772f9247b3

[ "MIT" ]

null

3_complex_deps/setup.py

fracpete/python-console-scripts

d453e492fc19ebc25dee75a2921b27772f9247b3

[ "MIT" ]

null

3_complex_deps/setup.py

fracpete/python-console-scripts

d453e492fc19ebc25dee75a2921b27772f9247b3

[ "MIT" ]

null

from setuptools import setup setup( name="mysuperduperproject", description="My super duper Project.", classifiers=[ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Topic :: Scientific/Engineering', 'Programming Language :: Python :: 3', ], license='MIT License', package_dir={ '': 'src' }, packages=[ 'msdp', ], version="0.0.1", author='Peter Reutemann', author_email='fracpete@gmail.com', install_requires=[ "numpy", "docker-banner-gen", ], entry_points={ "console_scripts": [ "msdp-hello=msdp.hello:sys_main", ] } )

21.333333

49

0.548295

from setuptools import setup setup( name="mysuperduperproject", description="My super duper Project.", classifiers=[ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Topic :: Scientific/Engineering', 'Programming Language :: Python :: 3', ], license='MIT License', package_dir={ '': 'src' }, packages=[ 'msdp', ], version="0.0.1", author='Peter Reutemann', author_email='fracpete@gmail.com', install_requires=[ "numpy", "docker-banner-gen", ], entry_points={ "console_scripts": [ "msdp-hello=msdp.hello:sys_main", ] } )

true

1c2b4033a165cb65c328c3308770545073b6325e

3,458

py

Python

sphinx/util/images.py

pvcraven/sphinx

b103b3c24ac8d983498f1170d8e104f8cd72c3df

[ "BSD-2-Clause" ]

null

sphinx/util/images.py

pvcraven/sphinx

b103b3c24ac8d983498f1170d8e104f8cd72c3df

[ "BSD-2-Clause" ]

null

sphinx/util/images.py

pvcraven/sphinx

b103b3c24ac8d983498f1170d8e104f8cd72c3df

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ sphinx.util.images ~~~~~~~~~~~~~~~~~~ Image utility functions for Sphinx. :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ from __future__ import absolute_import import base64 import imghdr import imagesize from os import path from collections import OrderedDict from six import PY3, BytesIO, iteritems from typing import NamedTuple try: from PIL import Image # check for the Python Imaging Library except ImportError: try: import Image except ImportError: Image = None if False: # For type annotation from typing import Dict, IO, List, Tuple # NOQA if PY3: unicode = str # special alias for static typing... mime_suffixes = OrderedDict([ ('.gif', 'image/gif'), ('.jpg', 'image/jpeg'), ('.png', 'image/png'), ('.pdf', 'application/pdf'), ('.svg', 'image/svg+xml'), ('.svgz', 'image/svg+xml'), ]) # type: Dict[unicode, unicode] DataURI = NamedTuple('DataURI', [('mimetype', unicode), ('charset', unicode), ('data', bytes)]) def get_image_size(filename): # type: (unicode) -> Tuple[int, int] try: size = imagesize.get(filename) if size[0] == -1: size = None if size is None and Image: # fallback to PIL im = Image.open(filename) size = im.size try: im.fp.close() except Exception: pass return size except Exception: return None def guess_mimetype_for_stream(stream, default=None): # type: (IO, unicode) -> unicode imgtype = imghdr.what(stream) if imgtype: return 'image/' + imgtype else: return default def guess_mimetype(filename='', content=None, default=None): # type: (unicode, unicode, unicode) -> unicode _, ext = path.splitext(filename.lower()) if ext in mime_suffixes: return mime_suffixes[ext] elif content: return guess_mimetype_for_stream(BytesIO(content), default=default) elif path.exists(filename): with open(filename, 'rb') as f: return guess_mimetype_for_stream(f, default=default) return default def get_image_extension(mimetype): # type: (unicode) -> unicode for ext, _mimetype in iteritems(mime_suffixes): if mimetype == _mimetype: return ext return None def parse_data_uri(uri): # type: (unicode) -> DataURI if not uri.startswith('data:'): return None # data:[<MIME-type>][;charset=<encoding>][;base64],<data> mimetype = u'text/plain' charset = u'US-ASCII' properties, data = uri[5:].split(',', 1) for prop in properties.split(';'): if prop == 'base64': pass # skip elif prop.startswith('charset='): charset = prop[8:] elif prop: mimetype = prop image_data = base64.b64decode(data) return DataURI(mimetype, charset, image_data) def test_svg(h, f): """An additional imghdr library helper; test the header is SVG's or not.""" try: if '<svg' in h.decode('utf-8').lower(): return 'svg+xml' except UnicodeDecodeError: pass # install test_svg() to imghdr # refs: https://docs.python.org/3.6/library/imghdr.html#imghdr.tests imghdr.tests.append(test_svg)

25.240876

79

0.600925

from __future__ import absolute_import import base64 import imghdr import imagesize from os import path from collections import OrderedDict from six import PY3, BytesIO, iteritems from typing import NamedTuple try: from PIL import Image except ImportError: try: import Image except ImportError: Image = None if False: from typing import Dict, IO, List, Tuple if PY3: unicode = str mime_suffixes = OrderedDict([ ('.gif', 'image/gif'), ('.jpg', 'image/jpeg'), ('.png', 'image/png'), ('.pdf', 'application/pdf'), ('.svg', 'image/svg+xml'), ('.svgz', 'image/svg+xml'), ]) DataURI = NamedTuple('DataURI', [('mimetype', unicode), ('charset', unicode), ('data', bytes)]) def get_image_size(filename): try: size = imagesize.get(filename) if size[0] == -1: size = None if size is None and Image: im = Image.open(filename) size = im.size try: im.fp.close() except Exception: pass return size except Exception: return None def guess_mimetype_for_stream(stream, default=None): imgtype = imghdr.what(stream) if imgtype: return 'image/' + imgtype else: return default def guess_mimetype(filename='', content=None, default=None): _, ext = path.splitext(filename.lower()) if ext in mime_suffixes: return mime_suffixes[ext] elif content: return guess_mimetype_for_stream(BytesIO(content), default=default) elif path.exists(filename): with open(filename, 'rb') as f: return guess_mimetype_for_stream(f, default=default) return default def get_image_extension(mimetype): for ext, _mimetype in iteritems(mime_suffixes): if mimetype == _mimetype: return ext return None def parse_data_uri(uri): if not uri.startswith('data:'): return None mimetype = u'text/plain' charset = u'US-ASCII' properties, data = uri[5:].split(',', 1) for prop in properties.split(';'): if prop == 'base64': pass elif prop.startswith('charset='): charset = prop[8:] elif prop: mimetype = prop image_data = base64.b64decode(data) return DataURI(mimetype, charset, image_data) def test_svg(h, f): try: if '<svg' in h.decode('utf-8').lower(): return 'svg+xml' except UnicodeDecodeError: pass .append(test_svg)

true

1c2b403d8f76a046dcd02a038b6389cdf69f814c

446

py

Python

sacrerouge/data/dataset_readers/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

81

2020-07-10T15:45:08.000Z

2022-03-30T12:19:11.000Z

sacrerouge/data/dataset_readers/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

29

2020-08-03T21:50:45.000Z

2022-02-23T14:34:16.000Z

sacrerouge/data/dataset_readers/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

7

2020-08-14T09:54:08.000Z

2022-03-30T12:19:25.000Z

from sacrerouge.data.dataset_readers.dataset_reader import DatasetReader from sacrerouge.data.dataset_readers.document_based import DocumentBasedDatasetReader, SplitDocumentBasedDatasetReader from sacrerouge.data.dataset_readers.pyramid_based import PyramidBasedDatasetReader from sacrerouge.data.dataset_readers.reference_based import ReferenceBasedDatasetReader from sacrerouge.data.dataset_readers.summary_only import SummaryOnlyDatasetReader

74.333333

118

0.91704

from sacrerouge.data.dataset_readers.dataset_reader import DatasetReader from sacrerouge.data.dataset_readers.document_based import DocumentBasedDatasetReader, SplitDocumentBasedDatasetReader from sacrerouge.data.dataset_readers.pyramid_based import PyramidBasedDatasetReader from sacrerouge.data.dataset_readers.reference_based import ReferenceBasedDatasetReader from sacrerouge.data.dataset_readers.summary_only import SummaryOnlyDatasetReader

true

1c2b4074fba3021590b0a2b809fcd8d7de83cb64

887

py

Python

webdriver.py

aLily11/xmu-daily-report

ee99d9669d7c318de20d88f8d6723693f9b48e7b

[ "MIT" ]

null

webdriver.py

aLily11/xmu-daily-report

ee99d9669d7c318de20d88f8d6723693f9b48e7b

[ "MIT" ]

null

webdriver.py

aLily11/xmu-daily-report

ee99d9669d7c318de20d88f8d6723693f9b48e7b

[ "MIT" ]

null

from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.chrome.webdriver import WebDriver from utils import debug chrome_options = Options() # 谷歌文档提到需要加上这个属性来规避bug chrome_options.add_argument('--disable-gpu') # 隐藏滚动条, 应对一些特殊页面 chrome_options.add_argument('--hide-scrollbars') # 不加载图片, 提升速度 chrome_options.add_argument('blink-settings=imagesEnabled=false') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument("--disable-dev-shm-usage") chrome_options.add_argument('--headless') driver = None def refresh(): close() global driver if debug: driver = webdriver.Edge() else: driver = webdriver.Chrome(options=chrome_options) driver.maximize_window() def get() -> WebDriver: global driver return driver def close(): if driver is not None: driver.close()

22.175

65

0.742954

from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.chrome.webdriver import WebDriver from utils import debug chrome_options = Options() chrome_options.add_argument('--disable-gpu') chrome_options.add_argument('--hide-scrollbars') chrome_options.add_argument('blink-settings=imagesEnabled=false') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument("--disable-dev-shm-usage") chrome_options.add_argument('--headless') driver = None def refresh(): close() global driver if debug: driver = webdriver.Edge() else: driver = webdriver.Chrome(options=chrome_options) driver.maximize_window() def get() -> WebDriver: global driver return driver def close(): if driver is not None: driver.close()

true

1c2b4110f88f48555cdc775f285c52646d4c6b49

1,081

py

Python

bvs/background_verification/doctype/verify_address_check4/verify_address_check4.py

vhrspvl/vhrs-bvs

56667039d9cc09ad0b092e5e6c5dd6598ff41e7b

[ "MIT" ]

1

2021-08-19T11:16:47.000Z

bvs/background_verification/doctype/verify_address_check4/verify_address_check4.py

vhrspvl/vhrs-bvs

56667039d9cc09ad0b092e5e6c5dd6598ff41e7b

[ "MIT" ]

null

bvs/background_verification/doctype/verify_address_check4/verify_address_check4.py

vhrspvl/vhrs-bvs

56667039d9cc09ad0b092e5e6c5dd6598ff41e7b

[ "MIT" ]

4

2018-03-21T05:57:54.000Z

2020-11-26T00:37:29.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2018, VHRS and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document class VerifyAddressCheck4(Document): pass @frappe.whitelist() def get_check(applicant_id): address_check4_id = frappe.get_list("Address Check4", filters={"applicant_id":applicant_id}, fields=("name")) # frappe.errprint(employment_check1_id) return address_check4_id @frappe.whitelist() def get_tat(): aadhar = frappe.db.sql(""" select name from `tabVerify Address Check4` where status = 'Pending'""", as_dict = 1) for a in aadhar: aadhar_id = frappe.get_doc("Verify Address Check4",a["name"]) tat = aadhar_id.tat in_date = aadhar_id.in_date if in_date: today = date.today() day = (today - in_date).days tat = tat - day aadhar_id.update({ "tat": tat }) aadhar_id.save(ignore_permissions=True) frappe.db.commit()

30.885714

116

0.653099

from __future__ import unicode_literals import frappe from frappe.model.document import Document class VerifyAddressCheck4(Document): pass @frappe.whitelist() def get_check(applicant_id): address_check4_id = frappe.get_list("Address Check4", filters={"applicant_id":applicant_id}, fields=("name")) return address_check4_id @frappe.whitelist() def get_tat(): aadhar = frappe.db.sql(""" select name from `tabVerify Address Check4` where status = 'Pending'""", as_dict = 1) for a in aadhar: aadhar_id = frappe.get_doc("Verify Address Check4",a["name"]) tat = aadhar_id.tat in_date = aadhar_id.in_date if in_date: today = date.today() day = (today - in_date).days tat = tat - day aadhar_id.update({ "tat": tat }) aadhar_id.save(ignore_permissions=True) frappe.db.commit()

true

1c2b42828634a45d43436a3c4ea189c43a454a0f

1,510

py

Python

backend/KeywordMatch.py

jonathanjameswatson/web-app

af4a0f54a06fcd4dfabd19c05b83369533116c7b

[ "MIT" ]

null

backend/KeywordMatch.py

jonathanjameswatson/web-app

af4a0f54a06fcd4dfabd19c05b83369533116c7b

[ "MIT" ]

1

2022-01-22T15:49:41.000Z

backend/KeywordMatch.py

jonathanjameswatson/web-app

af4a0f54a06fcd4dfabd19c05b83369533116c7b

[ "MIT" ]

3

2022-01-22T14:23:15.000Z

2022-01-22T18:01:39.000Z

import yake from nltk.stem import PorterStemmer class KeywordMatch: def __init__(self): language = "en" max_ngram_size = 3 deduplication_threshold = 0.9 numOfKeywords = 5 self.custom_kw_extractor = yake.KeywordExtractor( lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, top=numOfKeywords, features=None, ) def stem_phrases(self, words): stemmed = set() stemmer = PorterStemmer() for word in words: stemmed.add(" ".join([stemmer.stem(x) for x in word.split(" ")])) return stemmed def find_keyword_match(self, text1, text2): keywords1 = [ x[0] for x in sorted( self.custom_kw_extractor.extract_keywords(text1), key=lambda x: x[1], reverse=True, ) ] keywords2 = [ x[0] for x in sorted( self.custom_kw_extractor.extract_keywords(text2), key=lambda x: x[1], reverse=True, ) ] keyword_set_1 = self.stem_phrases(keywords1) keyword_set_2 = self.stem_phrases(keywords2) if len(keyword_set_1) + len(keyword_set_2) <= 6: threshold = 1 else: threshold = 2 score = len(set.intersection(keyword_set_1, keyword_set_2)) return score if score >= threshold else None

27.454545

77

0.543709

import yake from nltk.stem import PorterStemmer class KeywordMatch: def __init__(self): language = "en" max_ngram_size = 3 deduplication_threshold = 0.9 numOfKeywords = 5 self.custom_kw_extractor = yake.KeywordExtractor( lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, top=numOfKeywords, features=None, ) def stem_phrases(self, words): stemmed = set() stemmer = PorterStemmer() for word in words: stemmed.add(" ".join([stemmer.stem(x) for x in word.split(" ")])) return stemmed def find_keyword_match(self, text1, text2): keywords1 = [ x[0] for x in sorted( self.custom_kw_extractor.extract_keywords(text1), key=lambda x: x[1], reverse=True, ) ] keywords2 = [ x[0] for x in sorted( self.custom_kw_extractor.extract_keywords(text2), key=lambda x: x[1], reverse=True, ) ] keyword_set_1 = self.stem_phrases(keywords1) keyword_set_2 = self.stem_phrases(keywords2) if len(keyword_set_1) + len(keyword_set_2) <= 6: threshold = 1 else: threshold = 2 score = len(set.intersection(keyword_set_1, keyword_set_2)) return score if score >= threshold else None

true

1c2b428b594614f8cb16beb47d54c5472904cb6b

1,819

py

Python

vsts/vsts/test/v4_1/models/test_result_model_base.py

kenkuo/azure-devops-python-api

9e920bd25e938fa89ff7f60153e5b9e113ca839d

[ "MIT" ]

null

vsts/vsts/test/v4_1/models/test_result_model_base.py

kenkuo/azure-devops-python-api

9e920bd25e938fa89ff7f60153e5b9e113ca839d

[ "MIT" ]

null

vsts/vsts/test/v4_1/models/test_result_model_base.py

kenkuo/azure-devops-python-api

9e920bd25e938fa89ff7f60153e5b9e113ca839d

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest.serialization import Model class TestResultModelBase(Model): """TestResultModelBase. :param comment: :type comment: str :param completed_date: :type completed_date: datetime :param duration_in_ms: :type duration_in_ms: float :param error_message: :type error_message: str :param outcome: :type outcome: str :param started_date: :type started_date: datetime """ _attribute_map = { 'comment': {'key': 'comment', 'type': 'str'}, 'completed_date': {'key': 'completedDate', 'type': 'iso-8601'}, 'duration_in_ms': {'key': 'durationInMs', 'type': 'float'}, 'error_message': {'key': 'errorMessage', 'type': 'str'}, 'outcome': {'key': 'outcome', 'type': 'str'}, 'started_date': {'key': 'startedDate', 'type': 'iso-8601'} } def __init__(self, comment=None, completed_date=None, duration_in_ms=None, error_message=None, outcome=None, started_date=None): super(TestResultModelBase, self).__init__() self.comment = comment self.completed_date = completed_date self.duration_in_ms = duration_in_ms self.error_message = error_message self.outcome = outcome self.started_date = started_date

39.543478

132

0.563496

from msrest.serialization import Model class TestResultModelBase(Model): _attribute_map = { 'comment': {'key': 'comment', 'type': 'str'}, 'completed_date': {'key': 'completedDate', 'type': 'iso-8601'}, 'duration_in_ms': {'key': 'durationInMs', 'type': 'float'}, 'error_message': {'key': 'errorMessage', 'type': 'str'}, 'outcome': {'key': 'outcome', 'type': 'str'}, 'started_date': {'key': 'startedDate', 'type': 'iso-8601'} } def __init__(self, comment=None, completed_date=None, duration_in_ms=None, error_message=None, outcome=None, started_date=None): super(TestResultModelBase, self).__init__() self.comment = comment self.completed_date = completed_date self.duration_in_ms = duration_in_ms self.error_message = error_message self.outcome = outcome self.started_date = started_date

true

1c2b43d5f5cacb1854bbd69115dbf2fdc09465c1

1,109

py

Python

utils/samplers.py

nvvaulin/medical_imaging

ff00fc43ac0edcfb2151478f89e6c82be40af433

[ "Apache-2.0" ]

null

utils/samplers.py

nvvaulin/medical_imaging

ff00fc43ac0edcfb2151478f89e6c82be40af433

[ "Apache-2.0" ]

null

utils/samplers.py

nvvaulin/medical_imaging

ff00fc43ac0edcfb2151478f89e6c82be40af433

[ "Apache-2.0" ]

null

import numpy as np import torch class WeightedClassRandomSampler(torch.utils.data.WeightedRandomSampler): def __init__(self, labels, class_weights=None, label_names=None, names_weights=None): if class_weights is None: class_weights = [names_weights.get(i, None) for i in label_names] mask = np.array([not (i is None) for i in class_weights]) if mask.sum() < len(mask): labels = labels[:, mask] labels = np.concatenate((labels, (labels.max(1) == 0)[:, None]), 1) assert (labels.sum(1).max() != 1).sum() == 0, 'for weighted classes labels should be one hot encoded' class_ratios = labels.mean(0) class_weights = np.array(class_weights, dtype=np.float32) if mask.sum() < len(mask): class_weights = class_weights[mask] class_weights = np.concatenate((class_weights, np.array([1. - class_weights.sum()]))) else: class_weights /=class_weights.sum() weights = ((class_weights / class_ratios)[None, :] * labels).max(1) super().__init__(weights, len(labels))

46.208333

109

0.633904

import numpy as np import torch class WeightedClassRandomSampler(torch.utils.data.WeightedRandomSampler): def __init__(self, labels, class_weights=None, label_names=None, names_weights=None): if class_weights is None: class_weights = [names_weights.get(i, None) for i in label_names] mask = np.array([not (i is None) for i in class_weights]) if mask.sum() < len(mask): labels = labels[:, mask] labels = np.concatenate((labels, (labels.max(1) == 0)[:, None]), 1) assert (labels.sum(1).max() != 1).sum() == 0, 'for weighted classes labels should be one hot encoded' class_ratios = labels.mean(0) class_weights = np.array(class_weights, dtype=np.float32) if mask.sum() < len(mask): class_weights = class_weights[mask] class_weights = np.concatenate((class_weights, np.array([1. - class_weights.sum()]))) else: class_weights /=class_weights.sum() weights = ((class_weights / class_ratios)[None, :] * labels).max(1) super().__init__(weights, len(labels))

true

1c2b445c7e3b23d11298841c4f31e3e72c0b3203

90,293

py

Python

tests/unit/gapic/dialogflow_v2beta1/test_versions.py

LaudateCorpus1/python-dialogflow

0d6bebd2c28d46bfd06d42da30778d3b55a1878e

[ "Apache-2.0" ]

null

tests/unit/gapic/dialogflow_v2beta1/test_versions.py

LaudateCorpus1/python-dialogflow

0d6bebd2c28d46bfd06d42da30778d3b55a1878e

[ "Apache-2.0" ]

null

tests/unit/gapic/dialogflow_v2beta1/test_versions.py

LaudateCorpus1/python-dialogflow

0d6bebd2c28d46bfd06d42da30778d3b55a1878e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import mock import grpc from grpc.experimental import aio import math import pytest from proto.marshal.rules.dates import DurationRule, TimestampRule from google.api_core import client_options from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import grpc_helpers from google.api_core import grpc_helpers_async from google.api_core import path_template from google.auth import credentials as ga_credentials from google.auth.exceptions import MutualTLSChannelError from google.cloud.dialogflow_v2beta1.services.versions import VersionsAsyncClient from google.cloud.dialogflow_v2beta1.services.versions import VersionsClient from google.cloud.dialogflow_v2beta1.services.versions import pagers from google.cloud.dialogflow_v2beta1.services.versions import transports from google.cloud.dialogflow_v2beta1.types import version from google.cloud.dialogflow_v2beta1.types import version as gcd_version from google.oauth2 import service_account from google.protobuf import field_mask_pb2 # type: ignore from google.protobuf import timestamp_pb2 # type: ignore import google.auth def client_cert_source_callback(): return b"cert bytes", b"key bytes" # If default endpoint is localhost, then default mtls endpoint will be the same. # This method modifies the default endpoint so the client can produce a different # mtls endpoint for endpoint testing purposes. def modify_default_endpoint(client): return ( "foo.googleapis.com" if ("localhost" in client.DEFAULT_ENDPOINT) else client.DEFAULT_ENDPOINT ) def test__get_default_mtls_endpoint(): api_endpoint = "example.googleapis.com" api_mtls_endpoint = "example.mtls.googleapis.com" sandbox_endpoint = "example.sandbox.googleapis.com" sandbox_mtls_endpoint = "example.mtls.sandbox.googleapis.com" non_googleapi = "api.example.com" assert VersionsClient._get_default_mtls_endpoint(None) is None assert VersionsClient._get_default_mtls_endpoint(api_endpoint) == api_mtls_endpoint assert ( VersionsClient._get_default_mtls_endpoint(api_mtls_endpoint) == api_mtls_endpoint ) assert ( VersionsClient._get_default_mtls_endpoint(sandbox_endpoint) == sandbox_mtls_endpoint ) assert ( VersionsClient._get_default_mtls_endpoint(sandbox_mtls_endpoint) == sandbox_mtls_endpoint ) assert VersionsClient._get_default_mtls_endpoint(non_googleapi) == non_googleapi @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient,]) def test_versions_client_from_service_account_info(client_class): creds = ga_credentials.AnonymousCredentials() with mock.patch.object( service_account.Credentials, "from_service_account_info" ) as factory: factory.return_value = creds info = {"valid": True} client = client_class.from_service_account_info(info) assert client.transport._credentials == creds assert isinstance(client, client_class) assert client.transport._host == "dialogflow.googleapis.com:443" @pytest.mark.parametrize( "transport_class,transport_name", [ (transports.VersionsGrpcTransport, "grpc"), (transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_service_account_always_use_jwt( transport_class, transport_name ): with mock.patch.object( service_account.Credentials, "with_always_use_jwt_access", create=True ) as use_jwt: creds = service_account.Credentials(None, None, None) transport = transport_class(credentials=creds, always_use_jwt_access=True) use_jwt.assert_called_once_with(True) with mock.patch.object( service_account.Credentials, "with_always_use_jwt_access", create=True ) as use_jwt: creds = service_account.Credentials(None, None, None) transport = transport_class(credentials=creds, always_use_jwt_access=False) use_jwt.assert_not_called() @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient,]) def test_versions_client_from_service_account_file(client_class): creds = ga_credentials.AnonymousCredentials() with mock.patch.object( service_account.Credentials, "from_service_account_file" ) as factory: factory.return_value = creds client = client_class.from_service_account_file("dummy/file/path.json") assert client.transport._credentials == creds assert isinstance(client, client_class) client = client_class.from_service_account_json("dummy/file/path.json") assert client.transport._credentials == creds assert isinstance(client, client_class) assert client.transport._host == "dialogflow.googleapis.com:443" def test_versions_client_get_transport_class(): transport = VersionsClient.get_transport_class() available_transports = [ transports.VersionsGrpcTransport, ] assert transport in available_transports transport = VersionsClient.get_transport_class("grpc") assert transport == transports.VersionsGrpcTransport @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) def test_versions_client_client_options(client_class, transport_class, transport_name): # Check that if channel is provided we won't create a new one. with mock.patch.object(VersionsClient, "get_transport_class") as gtc: transport = transport_class(credentials=ga_credentials.AnonymousCredentials()) client = client_class(transport=transport) gtc.assert_not_called() # Check that if channel is provided via str we will create a new one. with mock.patch.object(VersionsClient, "get_transport_class") as gtc: client = client_class(transport=transport_name) gtc.assert_called() # Check the case api_endpoint is provided. options = client_options.ClientOptions(api_endpoint="squid.clam.whelk") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name, client_options=options) patched.assert_called_once_with( credentials=None, credentials_file=None, host="squid.clam.whelk", scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT is # "never". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "never"}): with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT is # "always". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "always"}): with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_MTLS_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT has # unsupported value. with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "Unsupported"}): with pytest.raises(MutualTLSChannelError): client = client_class(transport=transport_name) # Check the case GOOGLE_API_USE_CLIENT_CERTIFICATE has unsupported value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "Unsupported"} ): with pytest.raises(ValueError): client = client_class(transport=transport_name) # Check the case quota_project_id is provided options = client_options.ClientOptions(quota_project_id="octopus") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id="octopus", client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize( "client_class,transport_class,transport_name,use_client_cert_env", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc", "true"), ( VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio", "true", ), (VersionsClient, transports.VersionsGrpcTransport, "grpc", "false"), ( VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio", "false", ), ], ) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) @mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "auto"}) def test_versions_client_mtls_env_auto( client_class, transport_class, transport_name, use_client_cert_env ): # This tests the endpoint autoswitch behavior. Endpoint is autoswitched to the default # mtls endpoint, if GOOGLE_API_USE_CLIENT_CERTIFICATE is "true" and client cert exists. # Check the case client_cert_source is provided. Whether client cert is used depends on # GOOGLE_API_USE_CLIENT_CERTIFICATE value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): options = client_options.ClientOptions( client_cert_source=client_cert_source_callback ) with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) if use_client_cert_env == "false": expected_client_cert_source = None expected_host = client.DEFAULT_ENDPOINT else: expected_client_cert_source = client_cert_source_callback expected_host = client.DEFAULT_MTLS_ENDPOINT patched.assert_called_once_with( credentials=None, credentials_file=None, host=expected_host, scopes=None, client_cert_source_for_mtls=expected_client_cert_source, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case ADC client cert is provided. Whether client cert is used depends on # GOOGLE_API_USE_CLIENT_CERTIFICATE value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=True, ): with mock.patch( "google.auth.transport.mtls.default_client_cert_source", return_value=client_cert_source_callback, ): if use_client_cert_env == "false": expected_host = client.DEFAULT_ENDPOINT expected_client_cert_source = None else: expected_host = client.DEFAULT_MTLS_ENDPOINT expected_client_cert_source = client_cert_source_callback patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=expected_host, scopes=None, client_cert_source_for_mtls=expected_client_cert_source, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case client_cert_source and ADC client cert are not provided. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=False, ): patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient]) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) def test_versions_client_get_mtls_endpoint_and_cert_source(client_class): mock_client_cert_source = mock.Mock() # Test the case GOOGLE_API_USE_CLIENT_CERTIFICATE is "true". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): mock_api_endpoint = "foo" options = client_options.ClientOptions( client_cert_source=mock_client_cert_source, api_endpoint=mock_api_endpoint ) api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source( options ) assert api_endpoint == mock_api_endpoint assert cert_source == mock_client_cert_source # Test the case GOOGLE_API_USE_CLIENT_CERTIFICATE is "false". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "false"}): mock_client_cert_source = mock.Mock() mock_api_endpoint = "foo" options = client_options.ClientOptions( client_cert_source=mock_client_cert_source, api_endpoint=mock_api_endpoint ) api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source( options ) assert api_endpoint == mock_api_endpoint assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "never". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "never"}): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_ENDPOINT assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "always". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "always"}): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_MTLS_ENDPOINT assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "auto" and default cert doesn't exist. with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=False, ): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_ENDPOINT assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "auto" and default cert exists. with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=True, ): with mock.patch( "google.auth.transport.mtls.default_client_cert_source", return_value=mock_client_cert_source, ): ( api_endpoint, cert_source, ) = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_MTLS_ENDPOINT assert cert_source == mock_client_cert_source @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_client_options_scopes( client_class, transport_class, transport_name ): # Check the case scopes are provided. options = client_options.ClientOptions(scopes=["1", "2"],) with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=["1", "2"], client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_client_options_credentials_file( client_class, transport_class, transport_name ): # Check the case credentials file is provided. options = client_options.ClientOptions(credentials_file="credentials.json") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file="credentials.json", host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) def test_versions_client_client_options_from_dict(): with mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsGrpcTransport.__init__" ) as grpc_transport: grpc_transport.return_value = None client = VersionsClient(client_options={"api_endpoint": "squid.clam.whelk"}) grpc_transport.assert_called_once_with( credentials=None, credentials_file=None, host="squid.clam.whelk", scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize("request_type", [version.ListVersionsRequest, dict,]) def test_list_versions(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.ListVersionsResponse( next_page_token="next_page_token_value", ) response = client.list_versions(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() # Establish that the response is the type that we expect. assert isinstance(response, pagers.ListVersionsPager) assert response.next_page_token == "next_page_token_value" def test_list_versions_empty_call(): # This test is a coverage failsafe to make sure that totally empty calls, # i.e. request == None and no flattened fields passed, work. client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: client.list_versions() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() @pytest.mark.asyncio async def test_list_versions_async( transport: str = "grpc_asyncio", request_type=version.ListVersionsRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse(next_page_token="next_page_token_value",) ) response = await client.list_versions(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() # Establish that the response is the type that we expect. assert isinstance(response, pagers.ListVersionsAsyncPager) assert response.next_page_token == "next_page_token_value" @pytest.mark.asyncio async def test_list_versions_async_from_dict(): await test_list_versions_async(request_type=dict) def test_list_versions_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.ListVersionsRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = version.ListVersionsResponse() client.list_versions(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] @pytest.mark.asyncio async def test_list_versions_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.ListVersionsRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse() ) await client.list_versions(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] def test_list_versions_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.ListVersionsResponse() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.list_versions(parent="parent_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val def test_list_versions_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.list_versions( version.ListVersionsRequest(), parent="parent_value", ) @pytest.mark.asyncio async def test_list_versions_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.ListVersionsResponse() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse() ) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.list_versions(parent="parent_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val @pytest.mark.asyncio async def test_list_versions_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.list_versions( version.ListVersionsRequest(), parent="parent_value", ) def test_list_versions_pager(transport_name: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials, transport=transport_name, ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Set the response to a series of pages. call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) metadata = () metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", ""),)), ) pager = client.list_versions(request={}) assert pager._metadata == metadata results = [i for i in pager] assert len(results) == 6 assert all(isinstance(i, version.Version) for i in results) def test_list_versions_pages(transport_name: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials, transport=transport_name, ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.list_versions), "__call__") as call: # Set the response to a series of pages. call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) pages = list(client.list_versions(request={}).pages) for page_, token in zip(pages, ["abc", "def", "ghi", ""]): assert page_.raw_page.next_page_token == token @pytest.mark.asyncio async def test_list_versions_async_pager(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials,) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client.transport.list_versions), "__call__", new_callable=mock.AsyncMock ) as call: # Set the response to a series of pages. call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) async_pager = await client.list_versions(request={},) assert async_pager.next_page_token == "abc" responses = [] async for response in async_pager: responses.append(response) assert len(responses) == 6 assert all(isinstance(i, version.Version) for i in responses) @pytest.mark.asyncio async def test_list_versions_async_pages(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials,) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client.transport.list_versions), "__call__", new_callable=mock.AsyncMock ) as call: # Set the response to a series of pages. call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) pages = [] async for page_ in (await client.list_versions(request={})).pages: pages.append(page_) for page_, token in zip(pages, ["abc", "def", "ghi", ""]): assert page_.raw_page.next_page_token == token @pytest.mark.parametrize("request_type", [version.GetVersionRequest, dict,]) def test_get_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.Version( name="name_value", description="description_value", version_number=1518, status=version.Version.VersionStatus.IN_PROGRESS, ) response = client.get_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == version.Version.VersionStatus.IN_PROGRESS def test_get_version_empty_call(): # This test is a coverage failsafe to make sure that totally empty calls, # i.e. request == None and no flattened fields passed, work. client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: client.get_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() @pytest.mark.asyncio async def test_get_version_async( transport: str = "grpc_asyncio", request_type=version.GetVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.Version( name="name_value", description="description_value", version_number=1518, status=version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.get_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_get_version_async_from_dict(): await test_get_version_async(request_type=dict) def test_get_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.GetVersionRequest() request.name = "name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = version.Version() client.get_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] @pytest.mark.asyncio async def test_get_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.GetVersionRequest() request.name = "name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(version.Version()) await client.get_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] def test_get_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.Version() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.get_version(name="name_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val def test_get_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.get_version( version.GetVersionRequest(), name="name_value", ) @pytest.mark.asyncio async def test_get_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.get_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(version.Version()) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.get_version(name="name_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val @pytest.mark.asyncio async def test_get_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.get_version( version.GetVersionRequest(), name="name_value", ) @pytest.mark.parametrize("request_type", [gcd_version.CreateVersionRequest, dict,]) def test_create_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) response = client.create_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS def test_create_version_empty_call(): # This test is a coverage failsafe to make sure that totally empty calls, # i.e. request == None and no flattened fields passed, work. client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: client.create_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() @pytest.mark.asyncio async def test_create_version_async( transport: str = "grpc_asyncio", request_type=gcd_version.CreateVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.create_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_create_version_async_from_dict(): await test_create_version_async(request_type=dict) def test_create_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = gcd_version.CreateVersionRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = gcd_version.Version() client.create_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] @pytest.mark.asyncio async def test_create_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = gcd_version.CreateVersionRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) await client.create_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] def test_create_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.create_version( parent="parent_value", version=gcd_version.Version(name="name_value"), ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val def test_create_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.create_version( gcd_version.CreateVersionRequest(), parent="parent_value", version=gcd_version.Version(name="name_value"), ) @pytest.mark.asyncio async def test_create_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.create_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.create_version( parent="parent_value", version=gcd_version.Version(name="name_value"), ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val @pytest.mark.asyncio async def test_create_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.create_version( gcd_version.CreateVersionRequest(), parent="parent_value", version=gcd_version.Version(name="name_value"), ) @pytest.mark.parametrize("request_type", [gcd_version.UpdateVersionRequest, dict,]) def test_update_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) response = client.update_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS def test_update_version_empty_call(): # This test is a coverage failsafe to make sure that totally empty calls, # i.e. request == None and no flattened fields passed, work. client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: client.update_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() @pytest.mark.asyncio async def test_update_version_async( transport: str = "grpc_asyncio", request_type=gcd_version.UpdateVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.update_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() # Establish that the response is the type that we expect. assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_update_version_async_from_dict(): await test_update_version_async(request_type=dict) def test_update_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = gcd_version.UpdateVersionRequest() request.version.name = "version.name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = gcd_version.Version() client.update_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "version.name=version.name/value",) in kw[ "metadata" ] @pytest.mark.asyncio async def test_update_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = gcd_version.UpdateVersionRequest() request.version.name = "version.name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) await client.update_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "version.name=version.name/value",) in kw[ "metadata" ] def test_update_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version() # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.update_version( version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val arg = args[0].update_mask mock_val = field_mask_pb2.FieldMask(paths=["paths_value"]) assert arg == mock_val def test_update_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.update_version( gcd_version.UpdateVersionRequest(), version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) @pytest.mark.asyncio async def test_update_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.update_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = gcd_version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.update_version( version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val arg = args[0].update_mask mock_val = field_mask_pb2.FieldMask(paths=["paths_value"]) assert arg == mock_val @pytest.mark.asyncio async def test_update_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.update_version( gcd_version.UpdateVersionRequest(), version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) @pytest.mark.parametrize("request_type", [version.DeleteVersionRequest, dict,]) def test_delete_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = None response = client.delete_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() # Establish that the response is the type that we expect. assert response is None def test_delete_version_empty_call(): # This test is a coverage failsafe to make sure that totally empty calls, # i.e. request == None and no flattened fields passed, work. client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: client.delete_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() @pytest.mark.asyncio async def test_delete_version_async( transport: str = "grpc_asyncio", request_type=version.DeleteVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = request_type() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) response = await client.delete_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() # Establish that the response is the type that we expect. assert response is None @pytest.mark.asyncio async def test_delete_version_async_from_dict(): await test_delete_version_async(request_type=dict) def test_delete_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.DeleteVersionRequest() request.name = "name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = None client.delete_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] @pytest.mark.asyncio async def test_delete_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = version.DeleteVersionRequest() request.name = "name/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) await client.delete_version(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] def test_delete_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = None # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.delete_version(name="name_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val def test_delete_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.delete_version( version.DeleteVersionRequest(), name="name_value", ) @pytest.mark.asyncio async def test_delete_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object(type(client.transport.delete_version), "__call__") as call: # Designate an appropriate return value for the call. call.return_value = None call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.delete_version(name="name_value",) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val @pytest.mark.asyncio async def test_delete_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.delete_version( version.DeleteVersionRequest(), name="name_value", ) def test_credentials_transport_error(): # It is an error to provide credentials and a transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) # It is an error to provide a credentials file and a transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( client_options={"credentials_file": "credentials.json"}, transport=transport, ) # It is an error to provide an api_key and a transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) options = client_options.ClientOptions() options.api_key = "api_key" with pytest.raises(ValueError): client = VersionsClient(client_options=options, transport=transport,) # It is an error to provide an api_key and a credential. options = mock.Mock() options.api_key = "api_key" with pytest.raises(ValueError): client = VersionsClient( client_options=options, credentials=ga_credentials.AnonymousCredentials() ) # It is an error to provide scopes and a transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( client_options={"scopes": ["1", "2"]}, transport=transport, ) def test_transport_instance(): # A client may be instantiated with a custom transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) client = VersionsClient(transport=transport) assert client.transport is transport def test_transport_get_channel(): # A client may be instantiated with a custom transport instance. transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) channel = transport.grpc_channel assert channel transport = transports.VersionsGrpcAsyncIOTransport( credentials=ga_credentials.AnonymousCredentials(), ) channel = transport.grpc_channel assert channel @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport,], ) def test_transport_adc(transport_class): # Test default credentials are used if not provided. with mock.patch.object(google.auth, "default") as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport_class() adc.assert_called_once() def test_transport_grpc_default(): # A client should use the gRPC transport by default. client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) assert isinstance(client.transport, transports.VersionsGrpcTransport,) def test_versions_base_transport_error(): # Passing both a credentials object and credentials_file should raise an error with pytest.raises(core_exceptions.DuplicateCredentialArgs): transport = transports.VersionsTransport( credentials=ga_credentials.AnonymousCredentials(), credentials_file="credentials.json", ) def test_versions_base_transport(): # Instantiate the base transport. with mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport.__init__" ) as Transport: Transport.return_value = None transport = transports.VersionsTransport( credentials=ga_credentials.AnonymousCredentials(), ) # Every method on the transport should just blindly # raise NotImplementedError. methods = ( "list_versions", "get_version", "create_version", "update_version", "delete_version", ) for method in methods: with pytest.raises(NotImplementedError): getattr(transport, method)(request=object()) with pytest.raises(NotImplementedError): transport.close() def test_versions_base_transport_with_credentials_file(): # Instantiate the base transport with a credentials file with mock.patch.object( google.auth, "load_credentials_from_file", autospec=True ) as load_creds, mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport._prep_wrapped_messages" ) as Transport: Transport.return_value = None load_creds.return_value = (ga_credentials.AnonymousCredentials(), None) transport = transports.VersionsTransport( credentials_file="credentials.json", quota_project_id="octopus", ) load_creds.assert_called_once_with( "credentials.json", scopes=None, default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id="octopus", ) def test_versions_base_transport_with_adc(): # Test the default credentials are used if credentials and credentials_file are None. with mock.patch.object(google.auth, "default", autospec=True) as adc, mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport._prep_wrapped_messages" ) as Transport: Transport.return_value = None adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport = transports.VersionsTransport() adc.assert_called_once() def test_versions_auth_adc(): # If no credentials are provided, we should use ADC credentials. with mock.patch.object(google.auth, "default", autospec=True) as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) VersionsClient() adc.assert_called_once_with( scopes=None, default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id=None, ) @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport,], ) def test_versions_transport_auth_adc(transport_class): # If credentials and host are not provided, the transport class should use # ADC credentials. with mock.patch.object(google.auth, "default", autospec=True) as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport_class(quota_project_id="octopus", scopes=["1", "2"]) adc.assert_called_once_with( scopes=["1", "2"], default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id="octopus", ) @pytest.mark.parametrize( "transport_class,grpc_helpers", [ (transports.VersionsGrpcTransport, grpc_helpers), (transports.VersionsGrpcAsyncIOTransport, grpc_helpers_async), ], ) def test_versions_transport_create_channel(transport_class, grpc_helpers): # If credentials and host are not provided, the transport class should use # ADC credentials. with mock.patch.object( google.auth, "default", autospec=True ) as adc, mock.patch.object( grpc_helpers, "create_channel", autospec=True ) as create_channel: creds = ga_credentials.AnonymousCredentials() adc.return_value = (creds, None) transport_class(quota_project_id="octopus", scopes=["1", "2"]) create_channel.assert_called_with( "dialogflow.googleapis.com:443", credentials=creds, credentials_file=None, quota_project_id="octopus", default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), scopes=["1", "2"], default_host="dialogflow.googleapis.com", ssl_credentials=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_grpc_transport_client_cert_source_for_mtls(transport_class): cred = ga_credentials.AnonymousCredentials() # Check ssl_channel_credentials is used if provided. with mock.patch.object(transport_class, "create_channel") as mock_create_channel: mock_ssl_channel_creds = mock.Mock() transport_class( host="squid.clam.whelk", credentials=cred, ssl_channel_credentials=mock_ssl_channel_creds, ) mock_create_channel.assert_called_once_with( "squid.clam.whelk:443", credentials=cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_channel_creds, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) # Check if ssl_channel_credentials is not provided, then client_cert_source_for_mtls # is used. with mock.patch.object(transport_class, "create_channel", return_value=mock.Mock()): with mock.patch("grpc.ssl_channel_credentials") as mock_ssl_cred: transport_class( credentials=cred, client_cert_source_for_mtls=client_cert_source_callback, ) expected_cert, expected_key = client_cert_source_callback() mock_ssl_cred.assert_called_once_with( certificate_chain=expected_cert, private_key=expected_key ) def test_versions_host_no_port(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="dialogflow.googleapis.com" ), ) assert client.transport._host == "dialogflow.googleapis.com:443" def test_versions_host_with_port(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="dialogflow.googleapis.com:8000" ), ) assert client.transport._host == "dialogflow.googleapis.com:8000" def test_versions_grpc_transport_channel(): channel = grpc.secure_channel("http://localhost/", grpc.local_channel_credentials()) # Check that channel is used if provided. transport = transports.VersionsGrpcTransport( host="squid.clam.whelk", channel=channel, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert transport._ssl_channel_credentials == None def test_versions_grpc_asyncio_transport_channel(): channel = aio.secure_channel("http://localhost/", grpc.local_channel_credentials()) # Check that channel is used if provided. transport = transports.VersionsGrpcAsyncIOTransport( host="squid.clam.whelk", channel=channel, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert transport._ssl_channel_credentials == None # Remove this test when deprecated arguments (api_mtls_endpoint, client_cert_source) are # removed from grpc/grpc_asyncio transport constructor. @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_transport_channel_mtls_with_client_cert_source(transport_class): with mock.patch( "grpc.ssl_channel_credentials", autospec=True ) as grpc_ssl_channel_cred: with mock.patch.object( transport_class, "create_channel" ) as grpc_create_channel: mock_ssl_cred = mock.Mock() grpc_ssl_channel_cred.return_value = mock_ssl_cred mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel cred = ga_credentials.AnonymousCredentials() with pytest.warns(DeprecationWarning): with mock.patch.object(google.auth, "default") as adc: adc.return_value = (cred, None) transport = transport_class( host="squid.clam.whelk", api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=client_cert_source_callback, ) adc.assert_called_once() grpc_ssl_channel_cred.assert_called_once_with( certificate_chain=b"cert bytes", private_key=b"key bytes" ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_cred, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) assert transport.grpc_channel == mock_grpc_channel assert transport._ssl_channel_credentials == mock_ssl_cred # Remove this test when deprecated arguments (api_mtls_endpoint, client_cert_source) are # removed from grpc/grpc_asyncio transport constructor. @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_transport_channel_mtls_with_adc(transport_class): mock_ssl_cred = mock.Mock() with mock.patch.multiple( "google.auth.transport.grpc.SslCredentials", __init__=mock.Mock(return_value=None), ssl_credentials=mock.PropertyMock(return_value=mock_ssl_cred), ): with mock.patch.object( transport_class, "create_channel" ) as grpc_create_channel: mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel mock_cred = mock.Mock() with pytest.warns(DeprecationWarning): transport = transport_class( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=None, ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_cred, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) assert transport.grpc_channel == mock_grpc_channel def test_version_path(): project = "squid" version = "clam" expected = "projects/{project}/agent/versions/{version}".format( project=project, version=version, ) actual = VersionsClient.version_path(project, version) assert expected == actual def test_parse_version_path(): expected = { "project": "whelk", "version": "octopus", } path = VersionsClient.version_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_version_path(path) assert expected == actual def test_common_billing_account_path(): billing_account = "oyster" expected = "billingAccounts/{billing_account}".format( billing_account=billing_account, ) actual = VersionsClient.common_billing_account_path(billing_account) assert expected == actual def test_parse_common_billing_account_path(): expected = { "billing_account": "nudibranch", } path = VersionsClient.common_billing_account_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_common_billing_account_path(path) assert expected == actual def test_common_folder_path(): folder = "cuttlefish" expected = "folders/{folder}".format(folder=folder,) actual = VersionsClient.common_folder_path(folder) assert expected == actual def test_parse_common_folder_path(): expected = { "folder": "mussel", } path = VersionsClient.common_folder_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_common_folder_path(path) assert expected == actual def test_common_organization_path(): organization = "winkle" expected = "organizations/{organization}".format(organization=organization,) actual = VersionsClient.common_organization_path(organization) assert expected == actual def test_parse_common_organization_path(): expected = { "organization": "nautilus", } path = VersionsClient.common_organization_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_common_organization_path(path) assert expected == actual def test_common_project_path(): project = "scallop" expected = "projects/{project}".format(project=project,) actual = VersionsClient.common_project_path(project) assert expected == actual def test_parse_common_project_path(): expected = { "project": "abalone", } path = VersionsClient.common_project_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_common_project_path(path) assert expected == actual def test_common_location_path(): project = "squid" location = "clam" expected = "projects/{project}/locations/{location}".format( project=project, location=location, ) actual = VersionsClient.common_location_path(project, location) assert expected == actual def test_parse_common_location_path(): expected = { "project": "whelk", "location": "octopus", } path = VersionsClient.common_location_path(**expected) # Check that the path construction is reversible. actual = VersionsClient.parse_common_location_path(path) assert expected == actual def test_client_with_default_client_info(): client_info = gapic_v1.client_info.ClientInfo() with mock.patch.object( transports.VersionsTransport, "_prep_wrapped_messages" ) as prep: client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info) with mock.patch.object( transports.VersionsTransport, "_prep_wrapped_messages" ) as prep: transport_class = VersionsClient.get_transport_class() transport = transport_class( credentials=ga_credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info) @pytest.mark.asyncio async def test_transport_close_async(): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc_asyncio", ) with mock.patch.object( type(getattr(client.transport, "grpc_channel")), "close" ) as close: async with client: close.assert_not_called() close.assert_called_once() def test_transport_close(): transports = { "grpc": "_grpc_channel", } for transport, close_name in transports.items(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport ) with mock.patch.object( type(getattr(client.transport, close_name)), "close" ) as close: with client: close.assert_not_called() close.assert_called_once() def test_client_ctx(): transports = [ "grpc", ] for transport in transports: client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport ) # Test client calls underlying transport. with mock.patch.object(type(client.transport), "close") as close: close.assert_not_called() with client: pass close.assert_called() @pytest.mark.parametrize( "client_class,transport_class", [ (VersionsClient, transports.VersionsGrpcTransport), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport), ], ) def test_api_key_credentials(client_class, transport_class): with mock.patch.object( google.auth._default, "get_api_key_credentials", create=True ) as get_api_key_credentials: mock_cred = mock.Mock() get_api_key_credentials.return_value = mock_cred options = client_options.ClientOptions() options.api_key = "api_key" with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options) patched.assert_called_once_with( credentials=mock_cred, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, )

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import os import mock import grpc from grpc.experimental import aio import math import pytest from proto.marshal.rules.dates import DurationRule, TimestampRule from google.api_core import client_options from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import grpc_helpers from google.api_core import grpc_helpers_async from google.api_core import path_template from google.auth import credentials as ga_credentials from google.auth.exceptions import MutualTLSChannelError from google.cloud.dialogflow_v2beta1.services.versions import VersionsAsyncClient from google.cloud.dialogflow_v2beta1.services.versions import VersionsClient from google.cloud.dialogflow_v2beta1.services.versions import pagers from google.cloud.dialogflow_v2beta1.services.versions import transports from google.cloud.dialogflow_v2beta1.types import version from google.cloud.dialogflow_v2beta1.types import version as gcd_version from google.oauth2 import service_account from google.protobuf import field_mask_pb2 from google.protobuf import timestamp_pb2 import google.auth def client_cert_source_callback(): return b"cert bytes", b"key bytes" def modify_default_endpoint(client): return ( "foo.googleapis.com" if ("localhost" in client.DEFAULT_ENDPOINT) else client.DEFAULT_ENDPOINT ) def test__get_default_mtls_endpoint(): api_endpoint = "example.googleapis.com" api_mtls_endpoint = "example.mtls.googleapis.com" sandbox_endpoint = "example.sandbox.googleapis.com" sandbox_mtls_endpoint = "example.mtls.sandbox.googleapis.com" non_googleapi = "api.example.com" assert VersionsClient._get_default_mtls_endpoint(None) is None assert VersionsClient._get_default_mtls_endpoint(api_endpoint) == api_mtls_endpoint assert ( VersionsClient._get_default_mtls_endpoint(api_mtls_endpoint) == api_mtls_endpoint ) assert ( VersionsClient._get_default_mtls_endpoint(sandbox_endpoint) == sandbox_mtls_endpoint ) assert ( VersionsClient._get_default_mtls_endpoint(sandbox_mtls_endpoint) == sandbox_mtls_endpoint ) assert VersionsClient._get_default_mtls_endpoint(non_googleapi) == non_googleapi @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient,]) def test_versions_client_from_service_account_info(client_class): creds = ga_credentials.AnonymousCredentials() with mock.patch.object( service_account.Credentials, "from_service_account_info" ) as factory: factory.return_value = creds info = {"valid": True} client = client_class.from_service_account_info(info) assert client.transport._credentials == creds assert isinstance(client, client_class) assert client.transport._host == "dialogflow.googleapis.com:443" @pytest.mark.parametrize( "transport_class,transport_name", [ (transports.VersionsGrpcTransport, "grpc"), (transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_service_account_always_use_jwt( transport_class, transport_name ): with mock.patch.object( service_account.Credentials, "with_always_use_jwt_access", create=True ) as use_jwt: creds = service_account.Credentials(None, None, None) transport = transport_class(credentials=creds, always_use_jwt_access=True) use_jwt.assert_called_once_with(True) with mock.patch.object( service_account.Credentials, "with_always_use_jwt_access", create=True ) as use_jwt: creds = service_account.Credentials(None, None, None) transport = transport_class(credentials=creds, always_use_jwt_access=False) use_jwt.assert_not_called() @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient,]) def test_versions_client_from_service_account_file(client_class): creds = ga_credentials.AnonymousCredentials() with mock.patch.object( service_account.Credentials, "from_service_account_file" ) as factory: factory.return_value = creds client = client_class.from_service_account_file("dummy/file/path.json") assert client.transport._credentials == creds assert isinstance(client, client_class) client = client_class.from_service_account_json("dummy/file/path.json") assert client.transport._credentials == creds assert isinstance(client, client_class) assert client.transport._host == "dialogflow.googleapis.com:443" def test_versions_client_get_transport_class(): transport = VersionsClient.get_transport_class() available_transports = [ transports.VersionsGrpcTransport, ] assert transport in available_transports transport = VersionsClient.get_transport_class("grpc") assert transport == transports.VersionsGrpcTransport @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) def test_versions_client_client_options(client_class, transport_class, transport_name): with mock.patch.object(VersionsClient, "get_transport_class") as gtc: transport = transport_class(credentials=ga_credentials.AnonymousCredentials()) client = client_class(transport=transport) gtc.assert_not_called() # Check that if channel is provided via str we will create a new one. with mock.patch.object(VersionsClient, "get_transport_class") as gtc: client = client_class(transport=transport_name) gtc.assert_called() # Check the case api_endpoint is provided. options = client_options.ClientOptions(api_endpoint="squid.clam.whelk") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name, client_options=options) patched.assert_called_once_with( credentials=None, credentials_file=None, host="squid.clam.whelk", scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT is # "never". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "never"}): with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT is # "always". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "always"}): with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_MTLS_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS_ENDPOINT has # unsupported value. with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "Unsupported"}): with pytest.raises(MutualTLSChannelError): client = client_class(transport=transport_name) # Check the case GOOGLE_API_USE_CLIENT_CERTIFICATE has unsupported value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "Unsupported"} ): with pytest.raises(ValueError): client = client_class(transport=transport_name) # Check the case quota_project_id is provided options = client_options.ClientOptions(quota_project_id="octopus") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id="octopus", client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize( "client_class,transport_class,transport_name,use_client_cert_env", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc", "true"), ( VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio", "true", ), (VersionsClient, transports.VersionsGrpcTransport, "grpc", "false"), ( VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio", "false", ), ], ) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) @mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "auto"}) def test_versions_client_mtls_env_auto( client_class, transport_class, transport_name, use_client_cert_env ): # This tests the endpoint autoswitch behavior. Endpoint is autoswitched to the default # mtls endpoint, if GOOGLE_API_USE_CLIENT_CERTIFICATE is "true" and client cert exists. # Check the case client_cert_source is provided. Whether client cert is used depends on # GOOGLE_API_USE_CLIENT_CERTIFICATE value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): options = client_options.ClientOptions( client_cert_source=client_cert_source_callback ) with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) if use_client_cert_env == "false": expected_client_cert_source = None expected_host = client.DEFAULT_ENDPOINT else: expected_client_cert_source = client_cert_source_callback expected_host = client.DEFAULT_MTLS_ENDPOINT patched.assert_called_once_with( credentials=None, credentials_file=None, host=expected_host, scopes=None, client_cert_source_for_mtls=expected_client_cert_source, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case ADC client cert is provided. Whether client cert is used depends on # GOOGLE_API_USE_CLIENT_CERTIFICATE value. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=True, ): with mock.patch( "google.auth.transport.mtls.default_client_cert_source", return_value=client_cert_source_callback, ): if use_client_cert_env == "false": expected_host = client.DEFAULT_ENDPOINT expected_client_cert_source = None else: expected_host = client.DEFAULT_MTLS_ENDPOINT expected_client_cert_source = client_cert_source_callback patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=expected_host, scopes=None, client_cert_source_for_mtls=expected_client_cert_source, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) # Check the case client_cert_source and ADC client cert are not provided. with mock.patch.dict( os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": use_client_cert_env} ): with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=False, ): patched.return_value = None client = client_class(transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize("client_class", [VersionsClient, VersionsAsyncClient]) @mock.patch.object( VersionsClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsClient) ) @mock.patch.object( VersionsAsyncClient, "DEFAULT_ENDPOINT", modify_default_endpoint(VersionsAsyncClient), ) def test_versions_client_get_mtls_endpoint_and_cert_source(client_class): mock_client_cert_source = mock.Mock() # Test the case GOOGLE_API_USE_CLIENT_CERTIFICATE is "true". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): mock_api_endpoint = "foo" options = client_options.ClientOptions( client_cert_source=mock_client_cert_source, api_endpoint=mock_api_endpoint ) api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source( options ) assert api_endpoint == mock_api_endpoint assert cert_source == mock_client_cert_source # Test the case GOOGLE_API_USE_CLIENT_CERTIFICATE is "false". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "false"}): mock_client_cert_source = mock.Mock() mock_api_endpoint = "foo" options = client_options.ClientOptions( client_cert_source=mock_client_cert_source, api_endpoint=mock_api_endpoint ) api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source( options ) assert api_endpoint == mock_api_endpoint assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "never". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "never"}): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_ENDPOINT assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "always". with mock.patch.dict(os.environ, {"GOOGLE_API_USE_MTLS_ENDPOINT": "always"}): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_MTLS_ENDPOINT assert cert_source is None # Test the case GOOGLE_API_USE_MTLS_ENDPOINT is "auto" and default cert doesn't exist. with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=False, ): api_endpoint, cert_source = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_ENDPOINT assert cert_source is None with mock.patch.dict(os.environ, {"GOOGLE_API_USE_CLIENT_CERTIFICATE": "true"}): with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=True, ): with mock.patch( "google.auth.transport.mtls.default_client_cert_source", return_value=mock_client_cert_source, ): ( api_endpoint, cert_source, ) = client_class.get_mtls_endpoint_and_cert_source() assert api_endpoint == client_class.DEFAULT_MTLS_ENDPOINT assert cert_source == mock_client_cert_source @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_client_options_scopes( client_class, transport_class, transport_name ): options = client_options.ClientOptions(scopes=["1", "2"],) with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=["1", "2"], client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ (VersionsClient, transports.VersionsGrpcTransport, "grpc"), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport, "grpc_asyncio"), ], ) def test_versions_client_client_options_credentials_file( client_class, transport_class, transport_name ): options = client_options.ClientOptions(credentials_file="credentials.json") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options, transport=transport_name) patched.assert_called_once_with( credentials=None, credentials_file="credentials.json", host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) def test_versions_client_client_options_from_dict(): with mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsGrpcTransport.__init__" ) as grpc_transport: grpc_transport.return_value = None client = VersionsClient(client_options={"api_endpoint": "squid.clam.whelk"}) grpc_transport.assert_called_once_with( credentials=None, credentials_file=None, host="squid.clam.whelk", scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, ) @pytest.mark.parametrize("request_type", [version.ListVersionsRequest, dict,]) def test_list_versions(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = version.ListVersionsResponse( next_page_token="next_page_token_value", ) response = client.list_versions(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() assert isinstance(response, pagers.ListVersionsPager) assert response.next_page_token == "next_page_token_value" def test_list_versions_empty_call(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) with mock.patch.object(type(client.transport.list_versions), "__call__") as call: client.list_versions() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() @pytest.mark.asyncio async def test_list_versions_async( transport: str = "grpc_asyncio", request_type=version.ListVersionsRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse(next_page_token="next_page_token_value",) ) response = await client.list_versions(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.ListVersionsRequest() assert isinstance(response, pagers.ListVersionsAsyncPager) assert response.next_page_token == "next_page_token_value" @pytest.mark.asyncio async def test_list_versions_async_from_dict(): await test_list_versions_async(request_type=dict) def test_list_versions_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.ListVersionsRequest() request.parent = "parent/value" with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = version.ListVersionsResponse() client.list_versions(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] @pytest.mark.asyncio async def test_list_versions_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.ListVersionsRequest() request.parent = "parent/value" with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse() ) await client.list_versions(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] def test_list_versions_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = version.ListVersionsResponse() client.list_versions(parent="parent_value",) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val def test_list_versions_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): client.list_versions( version.ListVersionsRequest(), parent="parent_value", ) @pytest.mark.asyncio async def test_list_versions_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.return_value = version.ListVersionsResponse() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.ListVersionsResponse() ) response = await client.list_versions(parent="parent_value",) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val @pytest.mark.asyncio async def test_list_versions_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): await client.list_versions( version.ListVersionsRequest(), parent="parent_value", ) def test_list_versions_pager(transport_name: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials, transport=transport_name, ) with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) metadata = () metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", ""),)), ) pager = client.list_versions(request={}) assert pager._metadata == metadata results = [i for i in pager] assert len(results) == 6 assert all(isinstance(i, version.Version) for i in results) def test_list_versions_pages(transport_name: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials, transport=transport_name, ) with mock.patch.object(type(client.transport.list_versions), "__call__") as call: call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) pages = list(client.list_versions(request={}).pages) for page_, token in zip(pages, ["abc", "def", "ghi", ""]): assert page_.raw_page.next_page_token == token @pytest.mark.asyncio async def test_list_versions_async_pager(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials,) with mock.patch.object( type(client.transport.list_versions), "__call__", new_callable=mock.AsyncMock ) as call: call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) async_pager = await client.list_versions(request={},) assert async_pager.next_page_token == "abc" responses = [] async for response in async_pager: responses.append(response) assert len(responses) == 6 assert all(isinstance(i, version.Version) for i in responses) @pytest.mark.asyncio async def test_list_versions_async_pages(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials,) with mock.patch.object( type(client.transport.list_versions), "__call__", new_callable=mock.AsyncMock ) as call: call.side_effect = ( version.ListVersionsResponse( versions=[version.Version(), version.Version(), version.Version(),], next_page_token="abc", ), version.ListVersionsResponse(versions=[], next_page_token="def",), version.ListVersionsResponse( versions=[version.Version(),], next_page_token="ghi", ), version.ListVersionsResponse( versions=[version.Version(), version.Version(),], ), RuntimeError, ) pages = [] async for page_ in (await client.list_versions(request={})).pages: pages.append(page_) for page_, token in zip(pages, ["abc", "def", "ghi", ""]): assert page_.raw_page.next_page_token == token @pytest.mark.parametrize("request_type", [version.GetVersionRequest, dict,]) def test_get_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = version.Version( name="name_value", description="description_value", version_number=1518, status=version.Version.VersionStatus.IN_PROGRESS, ) response = client.get_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() assert isinstance(response, version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == version.Version.VersionStatus.IN_PROGRESS def test_get_version_empty_call(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) with mock.patch.object(type(client.transport.get_version), "__call__") as call: client.get_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() @pytest.mark.asyncio async def test_get_version_async( transport: str = "grpc_asyncio", request_type=version.GetVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( version.Version( name="name_value", description="description_value", version_number=1518, status=version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.get_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.GetVersionRequest() assert isinstance(response, version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_get_version_async_from_dict(): await test_get_version_async(request_type=dict) def test_get_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.GetVersionRequest() request.name = "name/value" with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = version.Version() client.get_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] @pytest.mark.asyncio async def test_get_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.GetVersionRequest() request.name = "name/value" with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(version.Version()) await client.get_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] def test_get_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = version.Version() client.get_version(name="name_value",) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val def test_get_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): client.get_version( version.GetVersionRequest(), name="name_value", ) @pytest.mark.asyncio async def test_get_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.get_version), "__call__") as call: call.return_value = version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(version.Version()) response = await client.get_version(name="name_value",) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val @pytest.mark.asyncio async def test_get_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): await client.get_version( version.GetVersionRequest(), name="name_value", ) @pytest.mark.parametrize("request_type", [gcd_version.CreateVersionRequest, dict,]) def test_create_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) response = client.create_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS def test_create_version_empty_call(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) with mock.patch.object(type(client.transport.create_version), "__call__") as call: client.create_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() @pytest.mark.asyncio async def test_create_version_async( transport: str = "grpc_asyncio", request_type=gcd_version.CreateVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.create_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.CreateVersionRequest() assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_create_version_async_from_dict(): await test_create_version_async(request_type=dict) def test_create_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) request = gcd_version.CreateVersionRequest() request.parent = "parent/value" with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = gcd_version.Version() client.create_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] @pytest.mark.asyncio async def test_create_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) request = gcd_version.CreateVersionRequest() request.parent = "parent/value" with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) await client.create_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value",) in kw["metadata"] def test_create_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = gcd_version.Version() client.create_version( parent="parent_value", version=gcd_version.Version(name="name_value"), ) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val def test_create_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): client.create_version( gcd_version.CreateVersionRequest(), parent="parent_value", version=gcd_version.Version(name="name_value"), ) @pytest.mark.asyncio async def test_create_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.create_version), "__call__") as call: call.return_value = gcd_version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) response = await client.create_version( parent="parent_value", version=gcd_version.Version(name="name_value"), ) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].parent mock_val = "parent_value" assert arg == mock_val arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val @pytest.mark.asyncio async def test_create_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): await client.create_version( gcd_version.CreateVersionRequest(), parent="parent_value", version=gcd_version.Version(name="name_value"), ) @pytest.mark.parametrize("request_type", [gcd_version.UpdateVersionRequest, dict,]) def test_update_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) response = client.update_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS def test_update_version_empty_call(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) with mock.patch.object(type(client.transport.update_version), "__call__") as call: client.update_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() @pytest.mark.asyncio async def test_update_version_async( transport: str = "grpc_asyncio", request_type=gcd_version.UpdateVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( gcd_version.Version( name="name_value", description="description_value", version_number=1518, status=gcd_version.Version.VersionStatus.IN_PROGRESS, ) ) response = await client.update_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == gcd_version.UpdateVersionRequest() assert isinstance(response, gcd_version.Version) assert response.name == "name_value" assert response.description == "description_value" assert response.version_number == 1518 assert response.status == gcd_version.Version.VersionStatus.IN_PROGRESS @pytest.mark.asyncio async def test_update_version_async_from_dict(): await test_update_version_async(request_type=dict) def test_update_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) request = gcd_version.UpdateVersionRequest() request.version.name = "version.name/value" with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = gcd_version.Version() client.update_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "version.name=version.name/value",) in kw[ "metadata" ] @pytest.mark.asyncio async def test_update_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) request = gcd_version.UpdateVersionRequest() request.version.name = "version.name/value" with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) await client.update_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "version.name=version.name/value",) in kw[ "metadata" ] def test_update_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = gcd_version.Version() client.update_version( version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val arg = args[0].update_mask mock_val = field_mask_pb2.FieldMask(paths=["paths_value"]) assert arg == mock_val def test_update_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): client.update_version( gcd_version.UpdateVersionRequest(), version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) @pytest.mark.asyncio async def test_update_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.update_version), "__call__") as call: call.return_value = gcd_version.Version() call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(gcd_version.Version()) response = await client.update_version( version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].version mock_val = gcd_version.Version(name="name_value") assert arg == mock_val arg = args[0].update_mask mock_val = field_mask_pb2.FieldMask(paths=["paths_value"]) assert arg == mock_val @pytest.mark.asyncio async def test_update_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): await client.update_version( gcd_version.UpdateVersionRequest(), version=gcd_version.Version(name="name_value"), update_mask=field_mask_pb2.FieldMask(paths=["paths_value"]), ) @pytest.mark.parametrize("request_type", [version.DeleteVersionRequest, dict,]) def test_delete_version(request_type, transport: str = "grpc"): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = None response = client.delete_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() assert response is None def test_delete_version_empty_call(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc", ) with mock.patch.object(type(client.transport.delete_version), "__call__") as call: client.delete_version() call.assert_called() _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() @pytest.mark.asyncio async def test_delete_version_async( transport: str = "grpc_asyncio", request_type=version.DeleteVersionRequest ): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) request = request_type() with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) response = await client.delete_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == version.DeleteVersionRequest() assert response is None @pytest.mark.asyncio async def test_delete_version_async_from_dict(): await test_delete_version_async(request_type=dict) def test_delete_version_field_headers(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.DeleteVersionRequest() request.name = "name/value" with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = None client.delete_version(request) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] @pytest.mark.asyncio async def test_delete_version_field_headers_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) request = version.DeleteVersionRequest() request.name = "name/value" with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) await client.delete_version(request) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "name=name/value",) in kw["metadata"] def test_delete_version_flattened(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = None client.delete_version(name="name_value",) assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val def test_delete_version_flattened_error(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): client.delete_version( version.DeleteVersionRequest(), name="name_value", ) @pytest.mark.asyncio async def test_delete_version_flattened_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with mock.patch.object(type(client.transport.delete_version), "__call__") as call: call.return_value = None call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(None) response = await client.delete_version(name="name_value",) assert len(call.mock_calls) _, args, _ = call.mock_calls[0] arg = args[0].name mock_val = "name_value" assert arg == mock_val @pytest.mark.asyncio async def test_delete_version_flattened_error_async(): client = VersionsAsyncClient(credentials=ga_credentials.AnonymousCredentials(),) with pytest.raises(ValueError): await client.delete_version( version.DeleteVersionRequest(), name="name_value", ) def test_credentials_transport_error(): transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport, ) transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( client_options={"credentials_file": "credentials.json"}, transport=transport, ) transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) options = client_options.ClientOptions() options.api_key = "api_key" with pytest.raises(ValueError): client = VersionsClient(client_options=options, transport=transport,) options = mock.Mock() options.api_key = "api_key" with pytest.raises(ValueError): client = VersionsClient( client_options=options, credentials=ga_credentials.AnonymousCredentials() ) transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) with pytest.raises(ValueError): client = VersionsClient( client_options={"scopes": ["1", "2"]}, transport=transport, ) def test_transport_instance(): transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) client = VersionsClient(transport=transport) assert client.transport is transport def test_transport_get_channel(): transport = transports.VersionsGrpcTransport( credentials=ga_credentials.AnonymousCredentials(), ) channel = transport.grpc_channel assert channel transport = transports.VersionsGrpcAsyncIOTransport( credentials=ga_credentials.AnonymousCredentials(), ) channel = transport.grpc_channel assert channel @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport,], ) def test_transport_adc(transport_class): with mock.patch.object(google.auth, "default") as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport_class() adc.assert_called_once() def test_transport_grpc_default(): client = VersionsClient(credentials=ga_credentials.AnonymousCredentials(),) assert isinstance(client.transport, transports.VersionsGrpcTransport,) def test_versions_base_transport_error(): with pytest.raises(core_exceptions.DuplicateCredentialArgs): transport = transports.VersionsTransport( credentials=ga_credentials.AnonymousCredentials(), credentials_file="credentials.json", ) def test_versions_base_transport(): with mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport.__init__" ) as Transport: Transport.return_value = None transport = transports.VersionsTransport( credentials=ga_credentials.AnonymousCredentials(), ) methods = ( "list_versions", "get_version", "create_version", "update_version", "delete_version", ) for method in methods: with pytest.raises(NotImplementedError): getattr(transport, method)(request=object()) with pytest.raises(NotImplementedError): transport.close() def test_versions_base_transport_with_credentials_file(): with mock.patch.object( google.auth, "load_credentials_from_file", autospec=True ) as load_creds, mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport._prep_wrapped_messages" ) as Transport: Transport.return_value = None load_creds.return_value = (ga_credentials.AnonymousCredentials(), None) transport = transports.VersionsTransport( credentials_file="credentials.json", quota_project_id="octopus", ) load_creds.assert_called_once_with( "credentials.json", scopes=None, default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id="octopus", ) def test_versions_base_transport_with_adc(): with mock.patch.object(google.auth, "default", autospec=True) as adc, mock.patch( "google.cloud.dialogflow_v2beta1.services.versions.transports.VersionsTransport._prep_wrapped_messages" ) as Transport: Transport.return_value = None adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport = transports.VersionsTransport() adc.assert_called_once() def test_versions_auth_adc(): with mock.patch.object(google.auth, "default", autospec=True) as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) VersionsClient() adc.assert_called_once_with( scopes=None, default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id=None, ) @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport,], ) def test_versions_transport_auth_adc(transport_class): with mock.patch.object(google.auth, "default", autospec=True) as adc: adc.return_value = (ga_credentials.AnonymousCredentials(), None) transport_class(quota_project_id="octopus", scopes=["1", "2"]) adc.assert_called_once_with( scopes=["1", "2"], default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), quota_project_id="octopus", ) @pytest.mark.parametrize( "transport_class,grpc_helpers", [ (transports.VersionsGrpcTransport, grpc_helpers), (transports.VersionsGrpcAsyncIOTransport, grpc_helpers_async), ], ) def test_versions_transport_create_channel(transport_class, grpc_helpers): with mock.patch.object( google.auth, "default", autospec=True ) as adc, mock.patch.object( grpc_helpers, "create_channel", autospec=True ) as create_channel: creds = ga_credentials.AnonymousCredentials() adc.return_value = (creds, None) transport_class(quota_project_id="octopus", scopes=["1", "2"]) create_channel.assert_called_with( "dialogflow.googleapis.com:443", credentials=creds, credentials_file=None, quota_project_id="octopus", default_scopes=( "https://www.googleapis.com/auth/cloud-platform", "https://www.googleapis.com/auth/dialogflow", ), scopes=["1", "2"], default_host="dialogflow.googleapis.com", ssl_credentials=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_grpc_transport_client_cert_source_for_mtls(transport_class): cred = ga_credentials.AnonymousCredentials() with mock.patch.object(transport_class, "create_channel") as mock_create_channel: mock_ssl_channel_creds = mock.Mock() transport_class( host="squid.clam.whelk", credentials=cred, ssl_channel_credentials=mock_ssl_channel_creds, ) mock_create_channel.assert_called_once_with( "squid.clam.whelk:443", credentials=cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_channel_creds, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) with mock.patch.object(transport_class, "create_channel", return_value=mock.Mock()): with mock.patch("grpc.ssl_channel_credentials") as mock_ssl_cred: transport_class( credentials=cred, client_cert_source_for_mtls=client_cert_source_callback, ) expected_cert, expected_key = client_cert_source_callback() mock_ssl_cred.assert_called_once_with( certificate_chain=expected_cert, private_key=expected_key ) def test_versions_host_no_port(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="dialogflow.googleapis.com" ), ) assert client.transport._host == "dialogflow.googleapis.com:443" def test_versions_host_with_port(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="dialogflow.googleapis.com:8000" ), ) assert client.transport._host == "dialogflow.googleapis.com:8000" def test_versions_grpc_transport_channel(): channel = grpc.secure_channel("http://localhost/", grpc.local_channel_credentials()) transport = transports.VersionsGrpcTransport( host="squid.clam.whelk", channel=channel, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert transport._ssl_channel_credentials == None def test_versions_grpc_asyncio_transport_channel(): channel = aio.secure_channel("http://localhost/", grpc.local_channel_credentials()) transport = transports.VersionsGrpcAsyncIOTransport( host="squid.clam.whelk", channel=channel, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert transport._ssl_channel_credentials == None @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_transport_channel_mtls_with_client_cert_source(transport_class): with mock.patch( "grpc.ssl_channel_credentials", autospec=True ) as grpc_ssl_channel_cred: with mock.patch.object( transport_class, "create_channel" ) as grpc_create_channel: mock_ssl_cred = mock.Mock() grpc_ssl_channel_cred.return_value = mock_ssl_cred mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel cred = ga_credentials.AnonymousCredentials() with pytest.warns(DeprecationWarning): with mock.patch.object(google.auth, "default") as adc: adc.return_value = (cred, None) transport = transport_class( host="squid.clam.whelk", api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=client_cert_source_callback, ) adc.assert_called_once() grpc_ssl_channel_cred.assert_called_once_with( certificate_chain=b"cert bytes", private_key=b"key bytes" ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_cred, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) assert transport.grpc_channel == mock_grpc_channel assert transport._ssl_channel_credentials == mock_ssl_cred @pytest.mark.parametrize( "transport_class", [transports.VersionsGrpcTransport, transports.VersionsGrpcAsyncIOTransport], ) def test_versions_transport_channel_mtls_with_adc(transport_class): mock_ssl_cred = mock.Mock() with mock.patch.multiple( "google.auth.transport.grpc.SslCredentials", __init__=mock.Mock(return_value=None), ssl_credentials=mock.PropertyMock(return_value=mock_ssl_cred), ): with mock.patch.object( transport_class, "create_channel" ) as grpc_create_channel: mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel mock_cred = mock.Mock() with pytest.warns(DeprecationWarning): transport = transport_class( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=None, ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, credentials_file=None, scopes=None, ssl_credentials=mock_ssl_cred, quota_project_id=None, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) assert transport.grpc_channel == mock_grpc_channel def test_version_path(): project = "squid" version = "clam" expected = "projects/{project}/agent/versions/{version}".format( project=project, version=version, ) actual = VersionsClient.version_path(project, version) assert expected == actual def test_parse_version_path(): expected = { "project": "whelk", "version": "octopus", } path = VersionsClient.version_path(**expected) actual = VersionsClient.parse_version_path(path) assert expected == actual def test_common_billing_account_path(): billing_account = "oyster" expected = "billingAccounts/{billing_account}".format( billing_account=billing_account, ) actual = VersionsClient.common_billing_account_path(billing_account) assert expected == actual def test_parse_common_billing_account_path(): expected = { "billing_account": "nudibranch", } path = VersionsClient.common_billing_account_path(**expected) actual = VersionsClient.parse_common_billing_account_path(path) assert expected == actual def test_common_folder_path(): folder = "cuttlefish" expected = "folders/{folder}".format(folder=folder,) actual = VersionsClient.common_folder_path(folder) assert expected == actual def test_parse_common_folder_path(): expected = { "folder": "mussel", } path = VersionsClient.common_folder_path(**expected) actual = VersionsClient.parse_common_folder_path(path) assert expected == actual def test_common_organization_path(): organization = "winkle" expected = "organizations/{organization}".format(organization=organization,) actual = VersionsClient.common_organization_path(organization) assert expected == actual def test_parse_common_organization_path(): expected = { "organization": "nautilus", } path = VersionsClient.common_organization_path(**expected) actual = VersionsClient.parse_common_organization_path(path) assert expected == actual def test_common_project_path(): project = "scallop" expected = "projects/{project}".format(project=project,) actual = VersionsClient.common_project_path(project) assert expected == actual def test_parse_common_project_path(): expected = { "project": "abalone", } path = VersionsClient.common_project_path(**expected) actual = VersionsClient.parse_common_project_path(path) assert expected == actual def test_common_location_path(): project = "squid" location = "clam" expected = "projects/{project}/locations/{location}".format( project=project, location=location, ) actual = VersionsClient.common_location_path(project, location) assert expected == actual def test_parse_common_location_path(): expected = { "project": "whelk", "location": "octopus", } path = VersionsClient.common_location_path(**expected) actual = VersionsClient.parse_common_location_path(path) assert expected == actual def test_client_with_default_client_info(): client_info = gapic_v1.client_info.ClientInfo() with mock.patch.object( transports.VersionsTransport, "_prep_wrapped_messages" ) as prep: client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info) with mock.patch.object( transports.VersionsTransport, "_prep_wrapped_messages" ) as prep: transport_class = VersionsClient.get_transport_class() transport = transport_class( credentials=ga_credentials.AnonymousCredentials(), client_info=client_info, ) prep.assert_called_once_with(client_info) @pytest.mark.asyncio async def test_transport_close_async(): client = VersionsAsyncClient( credentials=ga_credentials.AnonymousCredentials(), transport="grpc_asyncio", ) with mock.patch.object( type(getattr(client.transport, "grpc_channel")), "close" ) as close: async with client: close.assert_not_called() close.assert_called_once() def test_transport_close(): transports = { "grpc": "_grpc_channel", } for transport, close_name in transports.items(): client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport ) with mock.patch.object( type(getattr(client.transport, close_name)), "close" ) as close: with client: close.assert_not_called() close.assert_called_once() def test_client_ctx(): transports = [ "grpc", ] for transport in transports: client = VersionsClient( credentials=ga_credentials.AnonymousCredentials(), transport=transport ) with mock.patch.object(type(client.transport), "close") as close: close.assert_not_called() with client: pass close.assert_called() @pytest.mark.parametrize( "client_class,transport_class", [ (VersionsClient, transports.VersionsGrpcTransport), (VersionsAsyncClient, transports.VersionsGrpcAsyncIOTransport), ], ) def test_api_key_credentials(client_class, transport_class): with mock.patch.object( google.auth._default, "get_api_key_credentials", create=True ) as get_api_key_credentials: mock_cred = mock.Mock() get_api_key_credentials.return_value = mock_cred options = client_options.ClientOptions() options.api_key = "api_key" with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options) patched.assert_called_once_with( credentials=mock_cred, credentials_file=None, host=client.DEFAULT_ENDPOINT, scopes=None, client_cert_source_for_mtls=None, quota_project_id=None, client_info=transports.base.DEFAULT_CLIENT_INFO, always_use_jwt_access=True, )

true

1c2b446ecc23d924dba0c07b4632ad36b37b677c

522

py

Python

python/blender/render_minimal_demo.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

23

2015-06-08T13:01:00.000Z

2021-12-30T08:20:04.000Z

python/blender/render_minimal_demo.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

1

2020-10-22T02:36:10.000Z

python/blender/render_minimal_demo.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

7

2017-10-31T09:48:14.000Z

2022-01-04T15:59:45.000Z

# A minimal example of render of the default scene. # The blender default scene contain a cube, a lamp and a camera. # USAGE: blender --background --python render_minimal_demo.py # See http://wiki.blender.org/index.php/Doc:2.6/Manual/Extensions/Python import bpy # Alias render = bpy.context.scene.render # Set render resolution render.resolution_x = 800 render.resolution_y = 600 # Set Scenes output filename render.filepath = 'out.png' # Render Scene and store the scene bpy.ops.render.render(write_still=True)

23.727273

72

0.764368

import bpy render = bpy.context.scene.render render.resolution_x = 800 render.resolution_y = 600 render.filepath = 'out.png' bpy.ops.render.render(write_still=True)

true

1c2b44b17c7a42098dd9c09f3dbf8d2d50c5c78a

8,629

py

Python

tests/test_split.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

2,382

2015-01-04T03:16:59.000Z

2021-12-10T15:48:56.000Z

tests/test_split.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

1,009

2015-01-03T23:44:02.000Z

2021-12-10T16:02:42.000Z

tests/test_split.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

467

2015-01-19T23:18:33.000Z

2021-12-09T18:31:28.000Z

from shapely.ops import split from . import unittest from shapely.errors import GeometryTypeError from shapely.geometry import Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon, GeometryCollection from shapely.ops import linemerge, unary_union class TestSplitGeometry(unittest.TestCase): # helper class for testing below def helper(self, geom, splitter, expected_chunks): s = split(geom, splitter) self.assertEqual(s.type, "GeometryCollection") self.assertEqual(len(s.geoms), expected_chunks) if expected_chunks > 1: # split --> expected collection that when merged is again equal to original geometry if s.geoms[0].type == 'LineString': self.assertTrue(linemerge(s).simplify(0.000001).equals(geom)) elif s.geoms[0].type == 'Polygon': union = unary_union(s).simplify(0.000001) self.assertTrue(union.equals(geom)) self.assertEqual(union.area, geom.area) else: raise ValueError elif expected_chunks == 1: # not split --> expected equal to line self.assertTrue(s.geoms[0].equals(geom)) def test_split_closed_line_with_point(self): # point at start/end of closed ring -> return equal # see GH #524 ls = LineString([(0,0), (0, 1), (1, 1), (1, 0), (0, 0)]) splitter = Point(0, 0) self.helper(ls, splitter, 1) class TestSplitPolygon(TestSplitGeometry): poly_simple = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)]) poly_hole = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)], [[(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]]) def test_split_poly_with_line(self): # crossing at 2 points --> return 2 segments splitter = LineString([(1, 3), (1, -3)]) self.helper(self.poly_simple, splitter, 2) self.helper(self.poly_hole, splitter, 2) # touching the boundary--> return equal splitter = LineString([(0, 2), (5, 2)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) # inside the polygon --> return equal splitter = LineString([(0.2, 0.2), (1.7, 1.7), (3, 2)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) # outside the polygon --> return equal splitter = LineString([(0, 3), (3, 3) , (3, 0)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) def test_split_poly_with_other(self): with self.assertRaises(GeometryTypeError): split(self.poly_simple, Point(1, 1)) with self.assertRaises(GeometryTypeError): split(self.poly_simple, MultiPoint([(1, 1), (3, 4)])) with self.assertRaises(GeometryTypeError): split(self.poly_simple, self.poly_hole) class TestSplitLine(TestSplitGeometry): ls = LineString([(0, 0), (1.5, 1.5), (3.0, 4.0)]) def test_split_line_with_point(self): # point on line interior --> return 2 segments splitter = Point(1, 1) self.helper(self.ls, splitter, 2) # point on line point --> return 2 segments splitter = Point(1.5, 1.5) self.helper(self.ls, splitter, 2) # point on boundary --> return equal splitter = Point(3, 4) self.helper(self.ls, splitter, 1) # point on exterior of line --> return equal splitter = Point(2, 2) self.helper(self.ls, splitter, 1) def test_split_line_with_multipoint(self): # points on line interior --> return 4 segments splitter = MultiPoint([(1,1), (1.5, 1.5), (0.5, 0.5)]) self.helper(self.ls, splitter, 4) # points on line interior and boundary -> return 2 segments splitter = MultiPoint([(1, 1), (3, 4)]) self.helper(self.ls, splitter, 2) # point on linear interior but twice --> return 2 segments splitter = MultiPoint([(1, 1), (1.5, 1.5), (1, 1)]) self.helper(self.ls, splitter, 3) def test_split_line_with_line(self): # crosses at one point --> return 2 segments splitter = LineString([(0, 1), (1, 0)]) self.helper(self.ls, splitter, 2) # crosses at two points --> return 3 segments splitter = LineString([(0, 1), (1, 0), (1, 2)]) self.helper(self.ls, splitter, 3) # overlaps --> raise splitter = LineString([(0, 0), (15, 15)]) with self.assertRaises(ValueError): self.helper(self.ls, splitter, 1) # does not cross --> return equal splitter = LineString([(0, 1), (0, 2)]) self.helper(self.ls, splitter, 1) # is touching the boundary --> return equal splitter = LineString([(-1, 1), (1, -1)]) self.assertTrue(splitter.touches(self.ls)) self.helper(self.ls, splitter, 1) # splitter boundary touches interior of line --> return 2 segments splitter = LineString([(0, 1), (1, 1)]) # touches at (1, 1) self.assertTrue(splitter.touches(self.ls)) self.helper(self.ls, splitter, 2) def test_split_line_with_multiline(self): # crosses at one point --> return 2 segments splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 0), (2, -2)]]) self.helper(self.ls, splitter, 2) # crosses at two points --> return 3 segments splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 2), (2, 0)]]) self.helper(self.ls, splitter, 3) # crosses at three points --> return 4 segments splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 2), (2, 0), (2.2, 3.2)]]) self.helper(self.ls, splitter, 4) # overlaps --> raise splitter = MultiLineString([[(0, 0), (1.5, 1.5)], [(1.5, 1.5), (3, 4)]]) with self.assertRaises(ValueError): self.helper(self.ls, splitter, 1) # does not cross --> return equal splitter = MultiLineString([[(0, 1), (0, 2)], [(1, 0), (2, 0)]]) self.helper(self.ls, splitter, 1) def test_split_line_with_polygon(self): # crosses at two points --> return 3 segments splitter = Polygon([(1, 0), (1, 2), (2, 2), (2, 0), (1, 0)]) self.helper(self.ls, splitter, 3) # crosses at one point and touches boundary --> return 2 segments splitter = Polygon([(0, 0), (1, 2), (2, 2), (1, 0), (0, 0)]) self.helper(self.ls, splitter, 2) # exterior crosses at one point and touches at (0, 0) # interior crosses at two points splitter = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)], [[(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]]) self.helper(self.ls, splitter, 4) def test_split_line_with_multipolygon(self): poly1 = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)]) # crosses at one point and touches at (0, 0) poly2 = Polygon([(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]) # crosses at two points poly3 = Polygon([(0, 0), (0, -2), (-2, -2), (-2, 0), (0, 0)]) # not crossing splitter = MultiPolygon([poly1, poly2, poly3]) self.helper(self.ls, splitter, 4) class TestSplitClosedRing(TestSplitGeometry): ls = LineString([[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]) def test_split_closed_ring_with_point(self): splitter = Point([0.0, 0.0]) self.helper(self.ls, splitter, 1) splitter = Point([0.0, 0.5]) self.helper(self.ls, splitter, 2) result = split(self.ls, splitter) assert result.geoms[0].coords[:] == [(0, 0), (0.0, 0.5)] assert result.geoms[1].coords[:] == [(0.0, 0.5), (0, 1), (1, 1), (1, 0), (0, 0)] # previously failed, see GH#585 splitter = Point([0.5, 0.0]) self.helper(self.ls, splitter, 2) result = split(self.ls, splitter) assert result.geoms[0].coords[:] == [(0, 0), (0, 1), (1, 1), (1, 0), (0.5, 0)] assert result.geoms[1].coords[:] == [(0.5, 0), (0, 0)] splitter = Point([2.0, 2.0]) self.helper(self.ls, splitter, 1) class TestSplitMulti(TestSplitGeometry): def test_split_multiline_with_point(self): # a cross-like multilinestring with a point in the middle --> return 4 line segments l1 = LineString([(0, 1), (2, 1)]) l2 = LineString([(1, 0), (1, 2)]) ml = MultiLineString([l1, l2]) splitter = Point((1, 1)) self.helper(ml, splitter, 4) def test_split_multiline_with_multipoint(self): # a cross-like multilinestring with a point in middle, a point on one of the lines and a point in the exterior # --> return 4+1 line segments l1 = LineString([(0, 1), (3, 1)]) l2 = LineString([(1, 0), (1, 2)]) ml = MultiLineString([l1, l2]) splitter = MultiPoint([(1, 1), (2, 1), (4, 2)]) self.helper(ml, splitter, 5) def test_split_multipolygon_with_line(self): # two polygons with a crossing line --> return 4 triangles poly1 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]) poly2 = Polygon([(1, 1), (1, 2), (2, 2), (2, 1), (1, 1)]) mpoly = MultiPolygon([poly1, poly2]) ls = LineString([(-1, -1), (3, 3)]) self.helper(mpoly, ls, 4) # two polygons away from the crossing line --> return identity poly1 = Polygon([(10, 10), (10, 11), (11, 11), (11, 10), (10, 10)]) poly2 = Polygon([(-10, -10), (-10, -11), (-11, -11), (-11, -10), (-10, -10)]) mpoly = MultiPolygon([poly1, poly2]) ls = LineString([(-1, -1), (3, 3)]) self.helper(mpoly, ls, 2)

37.354978

126

0.636343

from shapely.ops import split from . import unittest from shapely.errors import GeometryTypeError from shapely.geometry import Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon, GeometryCollection from shapely.ops import linemerge, unary_union class TestSplitGeometry(unittest.TestCase): def helper(self, geom, splitter, expected_chunks): s = split(geom, splitter) self.assertEqual(s.type, "GeometryCollection") self.assertEqual(len(s.geoms), expected_chunks) if expected_chunks > 1: if s.geoms[0].type == 'LineString': self.assertTrue(linemerge(s).simplify(0.000001).equals(geom)) elif s.geoms[0].type == 'Polygon': union = unary_union(s).simplify(0.000001) self.assertTrue(union.equals(geom)) self.assertEqual(union.area, geom.area) else: raise ValueError elif expected_chunks == 1: self.assertTrue(s.geoms[0].equals(geom)) def test_split_closed_line_with_point(self): s = LineString([(0,0), (0, 1), (1, 1), (1, 0), (0, 0)]) splitter = Point(0, 0) self.helper(ls, splitter, 1) class TestSplitPolygon(TestSplitGeometry): poly_simple = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)]) poly_hole = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)], [[(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]]) def test_split_poly_with_line(self): splitter = LineString([(1, 3), (1, -3)]) self.helper(self.poly_simple, splitter, 2) self.helper(self.poly_hole, splitter, 2) splitter = LineString([(0, 2), (5, 2)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) splitter = LineString([(0.2, 0.2), (1.7, 1.7), (3, 2)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) splitter = LineString([(0, 3), (3, 3) , (3, 0)]) self.helper(self.poly_simple, splitter, 1) self.helper(self.poly_hole, splitter, 1) def test_split_poly_with_other(self): with self.assertRaises(GeometryTypeError): split(self.poly_simple, Point(1, 1)) with self.assertRaises(GeometryTypeError): split(self.poly_simple, MultiPoint([(1, 1), (3, 4)])) with self.assertRaises(GeometryTypeError): split(self.poly_simple, self.poly_hole) class TestSplitLine(TestSplitGeometry): ls = LineString([(0, 0), (1.5, 1.5), (3.0, 4.0)]) def test_split_line_with_point(self): splitter = Point(1, 1) self.helper(self.ls, splitter, 2) splitter = Point(1.5, 1.5) self.helper(self.ls, splitter, 2) splitter = Point(3, 4) self.helper(self.ls, splitter, 1) splitter = Point(2, 2) self.helper(self.ls, splitter, 1) def test_split_line_with_multipoint(self): splitter = MultiPoint([(1,1), (1.5, 1.5), (0.5, 0.5)]) self.helper(self.ls, splitter, 4) splitter = MultiPoint([(1, 1), (3, 4)]) self.helper(self.ls, splitter, 2) splitter = MultiPoint([(1, 1), (1.5, 1.5), (1, 1)]) self.helper(self.ls, splitter, 3) def test_split_line_with_line(self): splitter = LineString([(0, 1), (1, 0)]) self.helper(self.ls, splitter, 2) splitter = LineString([(0, 1), (1, 0), (1, 2)]) self.helper(self.ls, splitter, 3) splitter = LineString([(0, 0), (15, 15)]) with self.assertRaises(ValueError): self.helper(self.ls, splitter, 1) splitter = LineString([(0, 1), (0, 2)]) self.helper(self.ls, splitter, 1) splitter = LineString([(-1, 1), (1, -1)]) self.assertTrue(splitter.touches(self.ls)) self.helper(self.ls, splitter, 1) splitter = LineString([(0, 1), (1, 1)]) self.assertTrue(splitter.touches(self.ls)) self.helper(self.ls, splitter, 2) def test_split_line_with_multiline(self): splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 0), (2, -2)]]) self.helper(self.ls, splitter, 2) splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 2), (2, 0)]]) self.helper(self.ls, splitter, 3) splitter = MultiLineString([[(0, 1), (1, 0)], [(0, 2), (2, 0), (2.2, 3.2)]]) self.helper(self.ls, splitter, 4) splitter = MultiLineString([[(0, 0), (1.5, 1.5)], [(1.5, 1.5), (3, 4)]]) with self.assertRaises(ValueError): self.helper(self.ls, splitter, 1) splitter = MultiLineString([[(0, 1), (0, 2)], [(1, 0), (2, 0)]]) self.helper(self.ls, splitter, 1) def test_split_line_with_polygon(self): splitter = Polygon([(1, 0), (1, 2), (2, 2), (2, 0), (1, 0)]) self.helper(self.ls, splitter, 3) splitter = Polygon([(0, 0), (1, 2), (2, 2), (1, 0), (0, 0)]) self.helper(self.ls, splitter, 2) splitter = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)], [[(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]]) self.helper(self.ls, splitter, 4) def test_split_line_with_multipolygon(self): poly1 = Polygon([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)]) poly2 = Polygon([(0.5, 0.5), (0.5, 1.5), (1.5, 1.5), (1.5, 0.5), (0.5, 0.5)]) poly3 = Polygon([(0, 0), (0, -2), (-2, -2), (-2, 0), (0, 0)]) splitter = MultiPolygon([poly1, poly2, poly3]) self.helper(self.ls, splitter, 4) class TestSplitClosedRing(TestSplitGeometry): ls = LineString([[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]) def test_split_closed_ring_with_point(self): splitter = Point([0.0, 0.0]) self.helper(self.ls, splitter, 1) splitter = Point([0.0, 0.5]) self.helper(self.ls, splitter, 2) result = split(self.ls, splitter) assert result.geoms[0].coords[:] == [(0, 0), (0.0, 0.5)] assert result.geoms[1].coords[:] == [(0.0, 0.5), (0, 1), (1, 1), (1, 0), (0, 0)] plitter = Point([0.5, 0.0]) self.helper(self.ls, splitter, 2) result = split(self.ls, splitter) assert result.geoms[0].coords[:] == [(0, 0), (0, 1), (1, 1), (1, 0), (0.5, 0)] assert result.geoms[1].coords[:] == [(0.5, 0), (0, 0)] splitter = Point([2.0, 2.0]) self.helper(self.ls, splitter, 1) class TestSplitMulti(TestSplitGeometry): def test_split_multiline_with_point(self): l1 = LineString([(0, 1), (2, 1)]) l2 = LineString([(1, 0), (1, 2)]) ml = MultiLineString([l1, l2]) splitter = Point((1, 1)) self.helper(ml, splitter, 4) def test_split_multiline_with_multipoint(self): l1 = LineString([(0, 1), (3, 1)]) l2 = LineString([(1, 0), (1, 2)]) ml = MultiLineString([l1, l2]) splitter = MultiPoint([(1, 1), (2, 1), (4, 2)]) self.helper(ml, splitter, 5) def test_split_multipolygon_with_line(self): poly1 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]) poly2 = Polygon([(1, 1), (1, 2), (2, 2), (2, 1), (1, 1)]) mpoly = MultiPolygon([poly1, poly2]) ls = LineString([(-1, -1), (3, 3)]) self.helper(mpoly, ls, 4) poly1 = Polygon([(10, 10), (10, 11), (11, 11), (11, 10), (10, 10)]) poly2 = Polygon([(-10, -10), (-10, -11), (-11, -11), (-11, -10), (-10, -10)]) mpoly = MultiPolygon([poly1, poly2]) ls = LineString([(-1, -1), (3, 3)]) self.helper(mpoly, ls, 2)

true

1c2b44ecfb13bdce6c05318e53447706b0408ecb

124,747

py

Python

nikola/nikola.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

null

nikola/nikola.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

null

nikola/nikola.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright © 2012-2020 Roberto Alsina and others. # Permission is hereby granted, free of charge, to any # person obtaining a copy of this software and associated # documentation files (the "Software"), to deal in the # Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the # Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice # shall be included in all copies or substantial portions of # the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR # PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS # OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR # OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """The main Nikola site object.""" import datetime import io import json import functools import logging import operator import os import sys import mimetypes from collections import defaultdict from copy import copy from urllib.parse import urlparse, urlsplit, urlunsplit, urljoin, unquote, parse_qs import dateutil.tz import lxml.etree import lxml.html import natsort import PyRSS2Gen as rss from pkg_resources import resource_filename from blinker import signal from yapsy.PluginManager import PluginManager from . import DEBUG, SHOW_TRACEBACKS, filters, utils, hierarchy_utils, shortcodes from . import metadata_extractors from .metadata_extractors import default_metadata_extractors_by from .post import Post # NOQA from .plugin_categories import ( Command, LateTask, PageCompiler, CompilerExtension, MarkdownExtension, RestExtension, MetadataExtractor, ShortcodePlugin, Task, TaskMultiplier, TemplateSystem, SignalHandler, ConfigPlugin, PostScanner, Taxonomy, ) from .state import Persistor try: import pyphen except ImportError: pyphen = None if DEBUG: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.ERROR) # Default "Read more..." link DEFAULT_INDEX_READ_MORE_LINK = '<p class="more"><a href="{link}">{read_more}…</a></p>' DEFAULT_FEED_READ_MORE_LINK = '<p><a href="{link}">{read_more}…</a> ({min_remaining_read})</p>' config_changed = utils.config_changed __all__ = ('Nikola',) # We store legal values for some settings here. For internal use. LEGAL_VALUES = { 'DEFAULT_THEME': 'bootblog4', 'COMMENT_SYSTEM': [ 'disqus', 'facebook', 'intensedebate', 'isso', 'muut', 'commento', ], 'TRANSLATIONS': { 'af': 'Afrikaans', 'ar': 'Arabic', 'az': 'Azerbaijani', 'bg': 'Bulgarian', 'bs': 'Bosnian', 'ca': 'Catalan', ('cs', 'cz'): 'Czech', 'da': 'Danish', 'de': 'German', ('el', '!gr'): 'Greek', 'en': 'English', 'eo': 'Esperanto', 'es': 'Spanish', 'et': 'Estonian', 'eu': 'Basque', 'fa': 'Persian', 'fi': 'Finnish', 'fr': 'French', 'fur': 'Friulian', 'gl': 'Galician', 'he': 'Hebrew', 'hi': 'Hindi', 'hr': 'Croatian', 'hu': 'Hungarian', 'ia': 'Interlingua', 'id': 'Indonesian', 'it': 'Italian', ('ja', '!jp'): 'Japanese', 'ko': 'Korean', 'lt': 'Lithuanian', 'ml': 'Malayalam', 'mr': 'Marathi', 'nb': 'Norwegian (Bokmål)', 'nl': 'Dutch', 'pa': 'Punjabi', 'pl': 'Polish', 'pt': 'Portuguese', 'pt_br': 'Portuguese (Brazil)', 'ru': 'Russian', 'sk': 'Slovak', 'sl': 'Slovene', 'sq': 'Albanian', 'sr': 'Serbian (Cyrillic)', 'sr_latin': 'Serbian (Latin)', 'sv': 'Swedish', 'te': 'Telugu', 'th': 'Thai', ('tr', '!tr_TR'): 'Turkish', 'uk': 'Ukrainian', 'ur': 'Urdu', 'vi': 'Vietnamese', 'zh_cn': 'Chinese (Simplified)', 'zh_tw': 'Chinese (Traditional)' }, '_TRANSLATIONS_WITH_COUNTRY_SPECIFIERS': { # This dict is used in `init` in case of locales that exist with a # country specifier. If there is no other locale that has the same # language with a different country, ``nikola init`` (but nobody else!) # will accept it, warning the user about it. # This dict is currently empty. }, 'LOCALES_BASE': { # A list of locale mappings to apply for every site. Can be overridden in the config. 'sr_latin': 'sr_Latn', }, 'RTL_LANGUAGES': ('ar', 'fa', 'he', 'ur'), 'LUXON_LOCALES': defaultdict(lambda: 'en', **{ 'af': 'af', 'ar': 'ar', 'az': 'az', 'bg': 'bg', 'bn': 'bn', 'bs': 'bs', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en', 'eo': 'eo', 'es': 'es', 'et': 'et', 'eu': 'eu', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'fur': 'fur', 'gl': 'gl', 'hi': 'hi', 'he': 'he', 'hr': 'hr', 'hu': 'hu', 'ia': 'ia', 'id': 'id', 'it': 'it', 'ja': 'ja', 'ko': 'ko', 'lt': 'lt', 'ml': 'ml', 'mr': 'mr', 'nb': 'nb', 'nl': 'nl', 'pa': 'pa', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt-BR', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sq': 'sq', 'sr': 'sr-Cyrl', 'sr_latin': 'sr-Latn', 'sv': 'sv', 'te': 'te', 'tr': 'tr', 'th': 'th', 'uk': 'uk', 'ur': 'ur', 'vi': 'vi', 'zh_cn': 'zh-CN', 'zh_tw': 'zh-TW' }), # TODO: remove in v9 'MOMENTJS_LOCALES': defaultdict(lambda: 'en', **{ 'af': 'af', 'ar': 'ar', 'az': 'az', 'bg': 'bg', 'bn': 'bn', 'bs': 'bs', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en', 'eo': 'eo', 'es': 'es', 'et': 'et', 'eu': 'eu', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'gl': 'gl', 'hi': 'hi', 'he': 'he', 'hr': 'hr', 'hu': 'hu', 'id': 'id', 'it': 'it', 'ja': 'ja', 'ko': 'ko', 'lt': 'lt', 'ml': 'ml', 'mr': 'mr', 'nb': 'nb', 'nl': 'nl', 'pa': 'pa-in', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt-br', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sq': 'sq', 'sr': 'sr-cyrl', 'sr_latin': 'sr', 'sv': 'sv', 'te': 'te', 'tr': 'tr', 'th': 'th', 'uk': 'uk', 'ur': 'ur', 'vi': 'vi', 'zh_cn': 'zh-cn', 'zh_tw': 'zh-tw' }), 'PYPHEN_LOCALES': { 'af': 'af', 'bg': 'bg', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en_US', 'es': 'es', 'et': 'et', 'fr': 'fr', 'hr': 'hr', 'hu': 'hu', 'it': 'it', 'lt': 'lt', 'nb': 'nb', 'nl': 'nl', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt_BR', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sr': 'sr', 'sv': 'sv', 'te': 'te', 'uk': 'uk', }, 'DOCUTILS_LOCALES': { 'af': 'af', 'ca': 'ca', 'da': 'da', 'de': 'de', 'en': 'en', 'eo': 'eo', 'es': 'es', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'gl': 'gl', 'he': 'he', 'it': 'it', 'ja': 'ja', 'lt': 'lt', 'nl': 'nl', 'pl': 'pl', 'pt': 'pt_br', # hope nobody will mind 'pt_br': 'pt_br', 'ru': 'ru', 'sk': 'sk', 'sv': 'sv', 'zh_cn': 'zh_cn', 'zh_tw': 'zh_tw' }, "METADATA_MAPPING": ["yaml", "toml", "rest_docinfo", "markdown_metadata"], } # Mapping old pre-taxonomy plugin names to new post-taxonomy plugin names TAXONOMY_COMPATIBILITY_PLUGIN_NAME_MAP = { "render_archive": ["classify_archive"], "render_authors": ["classify_authors"], "render_indexes": ["classify_page_index", "classify_sections"], # "classify_indexes" removed from list (see #2591 and special-case logic below) "render_tags": ["classify_categories", "classify_tags"], } # Default value for the pattern used to name translated files DEFAULT_TRANSLATIONS_PATTERN = '{path}.{lang}.{ext}' def _enclosure(post, lang): """Add an enclosure to RSS.""" enclosure = post.meta('enclosure', lang) if enclosure: try: length = int(post.meta('enclosure_length', lang) or 0) except KeyError: length = 0 except ValueError: utils.LOGGER.warning("Invalid enclosure length for post {0}".format(post.source_path)) length = 0 url = enclosure mime = mimetypes.guess_type(url)[0] return url, length, mime class Nikola(object): """Class that handles site generation. Takes a site config as argument on creation. """ def __init__(self, **config): """Initialize proper environment for running tasks.""" # Register our own path handlers self.path_handlers = { 'slug': self.slug_path, 'post_path': self.post_path, 'root': self.root_path, 'filename': self.filename_path, } self.strict = False self.posts = [] self.all_posts = [] self.posts_per_year = defaultdict(list) self.posts_per_month = defaultdict(list) self.posts_per_tag = defaultdict(list) self.posts_per_category = defaultdict(list) self.tags_per_language = defaultdict(list) self.post_per_file = {} self.timeline = [] self.pages = [] self._scanned = False self._template_system = None self._THEMES = None self._MESSAGES = None self.filters = {} self.debug = DEBUG self.show_tracebacks = SHOW_TRACEBACKS self.colorful = config.pop('__colorful__', False) self.invariant = config.pop('__invariant__', False) self.quiet = config.pop('__quiet__', False) self._doit_config = config.pop('DOIT_CONFIG', {}) self.original_cwd = config.pop('__cwd__', False) self.configuration_filename = config.pop('__configuration_filename__', False) self.configured = bool(config) self.injected_deps = defaultdict(list) self.shortcode_registry = {} self.metadata_extractors_by = default_metadata_extractors_by() self.rst_transforms = [] self.template_hooks = { 'extra_head': utils.TemplateHookRegistry('extra_head', self), 'body_end': utils.TemplateHookRegistry('body_end', self), 'page_header': utils.TemplateHookRegistry('page_header', self), 'menu': utils.TemplateHookRegistry('menu', self), 'menu_alt': utils.TemplateHookRegistry('menu_alt', self), 'page_footer': utils.TemplateHookRegistry('page_footer', self), } # Maintain API utils.generic_rss_renderer = self.generic_rss_renderer # This is the default config self.config = { 'ARCHIVE_PATH': "", 'ARCHIVE_FILENAME': "archive.html", 'ARCHIVES_ARE_INDEXES': False, 'AUTHOR_PATH': 'authors', 'AUTHOR_PAGES_ARE_INDEXES': False, 'AUTHOR_PAGES_DESCRIPTIONS': {}, 'AUTHORLIST_MINIMUM_POSTS': 1, 'BLOG_AUTHOR': 'Default Author', 'BLOG_TITLE': 'Default Title', 'BLOG_EMAIL': '', 'BLOG_DESCRIPTION': 'Default Description', 'BODY_END': "", 'CACHE_FOLDER': 'cache', 'CATEGORIES_INDEX_PATH': '', 'CATEGORY_PATH': None, # None means: same as TAG_PATH 'CATEGORY_PAGES_ARE_INDEXES': None, # None means: same as TAG_PAGES_ARE_INDEXES 'CATEGORY_DESCRIPTIONS': {}, 'CATEGORY_TITLES': {}, 'CATEGORY_PREFIX': 'cat_', 'CATEGORY_ALLOW_HIERARCHIES': False, 'CATEGORY_OUTPUT_FLAT_HIERARCHY': False, 'CATEGORY_DESTPATH_AS_DEFAULT': False, 'CATEGORY_DESTPATH_TRIM_PREFIX': False, 'CATEGORY_DESTPATH_FIRST_DIRECTORY_ONLY': True, 'CATEGORY_DESTPATH_NAMES': {}, 'CATEGORY_PAGES_FOLLOW_DESTPATH': False, 'CATEGORY_TRANSLATIONS': [], 'CATEGORY_TRANSLATIONS_ADD_DEFAULTS': False, 'CODE_COLOR_SCHEME': 'default', 'COMMENT_SYSTEM': 'disqus', 'COMMENTS_IN_GALLERIES': False, 'COMMENTS_IN_PAGES': False, 'COMPILERS': { "rest": ('.txt', '.rst'), "markdown": ('.md', '.mdown', '.markdown'), "textile": ('.textile',), "txt2tags": ('.t2t',), "bbcode": ('.bb',), "wiki": ('.wiki',), "ipynb": ('.ipynb',), "html": ('.html', '.htm') }, 'CONTENT_FOOTER': '', 'CONTENT_FOOTER_FORMATS': {}, 'RSS_COPYRIGHT': '', 'RSS_COPYRIGHT_PLAIN': '', 'RSS_COPYRIGHT_FORMATS': {}, 'COPY_SOURCES': True, 'CREATE_ARCHIVE_NAVIGATION': False, 'CREATE_MONTHLY_ARCHIVE': False, 'CREATE_SINGLE_ARCHIVE': False, 'CREATE_FULL_ARCHIVES': False, 'CREATE_DAILY_ARCHIVE': False, 'DATE_FORMAT': 'yyyy-MM-dd HH:mm', 'DISABLE_INDEXES': False, 'DISABLE_MAIN_ATOM_FEED': False, 'DISABLE_MAIN_RSS_FEED': False, 'MOMENTJS_DATE_FORMAT': 'YYYY-MM-DD HH:mm', 'LUXON_DATE_FORMAT': {}, 'DATE_FANCINESS': 0, 'DEFAULT_LANG': "en", 'DEPLOY_COMMANDS': {'default': []}, 'DISABLED_PLUGINS': [], 'EXTRA_PLUGINS_DIRS': [], 'EXTRA_THEMES_DIRS': [], 'COMMENT_SYSTEM_ID': 'nikolademo', 'ENABLE_AUTHOR_PAGES': True, 'EXIF_WHITELIST': {}, 'EXTRA_HEAD_DATA': '', 'FAVICONS': (), 'FEED_LENGTH': 10, 'FILE_METADATA_REGEXP': None, 'FILE_METADATA_UNSLUGIFY_TITLES': True, 'ADDITIONAL_METADATA': {}, 'FILES_FOLDERS': {'files': ''}, 'FILTERS': {}, 'FORCE_ISO8601': False, 'FRONT_INDEX_HEADER': '', 'GALLERY_FOLDERS': {'galleries': 'galleries'}, 'GALLERY_SORT_BY_DATE': True, 'GALLERIES_USE_THUMBNAIL': False, 'GALLERIES_DEFAULT_THUMBNAIL': None, 'GLOBAL_CONTEXT_FILLER': [], 'GZIP_COMMAND': None, 'GZIP_FILES': False, 'GZIP_EXTENSIONS': ('.txt', '.htm', '.html', '.css', '.js', '.json', '.xml'), 'HIDDEN_AUTHORS': [], 'HIDDEN_TAGS': [], 'HIDE_REST_DOCINFO': False, 'HIDDEN_CATEGORIES': [], 'HYPHENATE': False, 'IMAGE_FOLDERS': {'images': ''}, 'INDEX_DISPLAY_POST_COUNT': 10, 'INDEX_FILE': 'index.html', 'INDEX_TEASERS': False, 'IMAGE_THUMBNAIL_SIZE': 400, 'IMAGE_THUMBNAIL_FORMAT': '{name}.thumbnail{ext}', 'INDEXES_TITLE': "", 'INDEXES_PAGES': "", 'INDEXES_PAGES_MAIN': False, 'INDEXES_PRETTY_PAGE_URL': False, 'INDEXES_STATIC': True, 'INDEX_PATH': '', 'IPYNB_CONFIG': {}, 'KATEX_AUTO_RENDER': '', 'LICENSE': '', 'LINK_CHECK_WHITELIST': [], 'LISTINGS_FOLDERS': {'listings': 'listings'}, 'LOGO_URL': '', 'DEFAULT_PREVIEW_IMAGE': None, 'NAVIGATION_LINKS': {}, 'NAVIGATION_ALT_LINKS': {}, 'MARKDOWN_EXTENSIONS': ['fenced_code', 'codehilite', 'extra'], 'MARKDOWN_EXTENSION_CONFIGS': {}, 'MAX_IMAGE_SIZE': 1280, 'MATHJAX_CONFIG': '', 'METADATA_FORMAT': 'nikola', 'METADATA_MAPPING': {}, 'NEW_POST_DATE_PATH': False, 'NEW_POST_DATE_PATH_FORMAT': '%Y/%m/%d', 'OLD_THEME_SUPPORT': True, 'OUTPUT_FOLDER': 'output', 'POSTS': (("posts/*.txt", "posts", "post.tmpl"),), 'PRESERVE_EXIF_DATA': False, 'PRESERVE_ICC_PROFILES': False, 'PAGES': (("pages/*.txt", "pages", "page.tmpl"),), 'PANDOC_OPTIONS': [], 'PRETTY_URLS': True, 'FUTURE_IS_NOW': False, 'INDEX_READ_MORE_LINK': DEFAULT_INDEX_READ_MORE_LINK, 'REDIRECTIONS': [], 'ROBOTS_EXCLUSIONS': [], 'GENERATE_ATOM': False, 'ATOM_EXTENSION': '.atom', 'ATOM_PATH': '', 'ATOM_FILENAME_BASE': 'index', 'FEED_TEASERS': True, 'FEED_PLAIN': False, 'FEED_READ_MORE_LINK': DEFAULT_FEED_READ_MORE_LINK, 'FEED_LINKS_APPEND_QUERY': False, 'GENERATE_RSS': True, 'RSS_EXTENSION': '.xml', 'RSS_LINK': None, 'RSS_PATH': '', 'RSS_FILENAME_BASE': 'rss', 'SEARCH_FORM': '', 'SHOW_BLOG_TITLE': True, 'SHOW_INDEX_PAGE_NAVIGATION': False, 'SHOW_SOURCELINK': True, 'SHOW_UNTRANSLATED_POSTS': True, 'SLUG_AUTHOR_PATH': True, 'SLUG_TAG_PATH': True, 'SOCIAL_BUTTONS_CODE': '', 'SITE_URL': 'https://example.com/', 'PAGE_INDEX': False, 'SECTION_PATH': '', 'STRIP_INDEXES': True, 'TAG_PATH': 'categories', 'TAG_PAGES_ARE_INDEXES': False, 'TAG_DESCRIPTIONS': {}, 'TAG_TITLES': {}, 'TAG_TRANSLATIONS': [], 'TAG_TRANSLATIONS_ADD_DEFAULTS': False, 'TAGS_INDEX_PATH': '', 'TAGLIST_MINIMUM_POSTS': 1, 'TEMPLATE_FILTERS': {}, 'THEME': LEGAL_VALUES['DEFAULT_THEME'], 'THEME_COLOR': '#5670d4', # light "corporate blue" 'THEME_CONFIG': {}, 'THUMBNAIL_SIZE': 180, 'TRANSLATIONS_PATTERN': DEFAULT_TRANSLATIONS_PATTERN, 'URL_TYPE': 'rel_path', 'USE_BUNDLES': True, 'USE_CDN': False, 'USE_CDN_WARNING': True, 'USE_REST_DOCINFO_METADATA': False, 'USE_FILENAME_AS_TITLE': True, 'USE_KATEX': False, 'USE_SLUGIFY': True, 'USE_TAG_METADATA': True, 'TIMEZONE': 'UTC', 'WARN_ABOUT_TAG_METADATA': True, 'DEPLOY_DRAFTS': True, 'DEPLOY_FUTURE': False, 'SCHEDULE_ALL': False, 'SCHEDULE_RULE': '', 'DEMOTE_HEADERS': 1, 'GITHUB_SOURCE_BRANCH': 'master', 'GITHUB_DEPLOY_BRANCH': 'gh-pages', 'GITHUB_REMOTE_NAME': 'origin', 'GITHUB_COMMIT_SOURCE': False, # WARNING: conf.py.in overrides this with True for backwards compatibility 'META_GENERATOR_TAG': True, 'REST_FILE_INSERTION_ENABLED': True, 'TYPES_TO_HIDE_TITLE': [], } # set global_context for template rendering self._GLOBAL_CONTEXT = {} # dependencies for all pages, not included in global context self.ALL_PAGE_DEPS = {} self.config.update(config) # __builtins__ contains useless cruft if '__builtins__' in self.config: try: del self.config['__builtins__'] except KeyError: del self.config[b'__builtins__'] self.config['__colorful__'] = self.colorful self.config['__invariant__'] = self.invariant self.config['__quiet__'] = self.quiet # Use ATOM_PATH when set self.config['ATOM_PATH'] = self.config['ATOM_PATH'] or self.config['INDEX_PATH'] # Make sure we have sane NAVIGATION_LINKS and NAVIGATION_ALT_LINKS. if not self.config['NAVIGATION_LINKS']: self.config['NAVIGATION_LINKS'] = {self.config['DEFAULT_LANG']: ()} if not self.config['NAVIGATION_ALT_LINKS']: self.config['NAVIGATION_ALT_LINKS'] = {self.config['DEFAULT_LANG']: ()} # Translatability configuration. self.config['TRANSLATIONS'] = self.config.get('TRANSLATIONS', {self.config['DEFAULT_LANG']: ''}) for k, v in self.config['TRANSLATIONS'].items(): if os.path.isabs(v): self.config['TRANSLATIONS'][k] = os.path.relpath(v, '/') utils.TranslatableSetting.default_lang = self.config['DEFAULT_LANG'] self.TRANSLATABLE_SETTINGS = ('BLOG_AUTHOR', 'BLOG_TITLE', 'BLOG_DESCRIPTION', 'LICENSE', 'CONTENT_FOOTER', 'SOCIAL_BUTTONS_CODE', 'SEARCH_FORM', 'BODY_END', 'EXTRA_HEAD_DATA', 'NAVIGATION_LINKS', 'NAVIGATION_ALT_LINKS', 'FRONT_INDEX_HEADER', 'INDEX_READ_MORE_LINK', 'FEED_READ_MORE_LINK', 'INDEXES_TITLE', 'CATEGORY_DESTPATH_NAMES', 'INDEXES_PAGES', 'INDEXES_PRETTY_PAGE_URL', 'THEME_CONFIG', # PATH options (Issue #1914) 'ARCHIVE_PATH', 'ARCHIVE_FILENAME', 'TAG_PATH', 'TAGS_INDEX_PATH', 'CATEGORY_PATH', 'CATEGORIES_INDEX_PATH', 'SECTION_PATH', 'INDEX_PATH', 'ATOM_PATH', 'RSS_PATH', 'RSS_FILENAME_BASE', 'ATOM_FILENAME_BASE', 'AUTHOR_PATH', 'DATE_FORMAT', 'LUXON_DATE_FORMAT', 'MOMENTJS_DATE_FORMAT', # TODO: remove in v9 'RSS_COPYRIGHT', 'RSS_COPYRIGHT_PLAIN', # Issue #2970 'MARKDOWN_EXTENSION_CONFIGS', ) self._GLOBAL_CONTEXT_TRANSLATABLE = ('blog_author', 'blog_title', 'blog_description', 'license', 'content_footer', 'social_buttons_code', 'search_form', 'body_end', 'extra_head_data', 'date_format', 'js_date_format', 'luxon_date_format', 'front_index_header', 'theme_config', ) self._ALL_PAGE_DEPS_TRANSLATABLE = ('atom_path', 'rss_path', 'rss_filename_base', 'atom_filename_base', ) # WARNING: navigation_(alt_)links SHOULD NOT be added to the list above. # Themes ask for [lang] there and we should provide it. # Luxon setup is a dict of dicts, so we need to set up the default here. if not self.config['LUXON_DATE_FORMAT']: self.config['LUXON_DATE_FORMAT'] = {self.config['DEFAULT_LANG']: {'preset': False, 'format': 'yyyy-MM-dd HH:mm'}} # TODO: remove Moment.js stuff in v9 if 'JS_DATE_FORMAT' in self.config: utils.LOGGER.warning("Moment.js was replaced by Luxon in the default themes, which uses different date formats.") utils.LOGGER.warning("If you’re using a built-in theme, set LUXON_DATE_FORMAT. If your theme uses Moment.js, you can silence this warning by renaming JS_DATE_FORMAT to MOMENTJS_DATE_FORMAT.") utils.LOGGER.warning("Sample Luxon config: LUXON_DATE_FORMAT = " + str(self.config['LUXON_DATE_FORMAT'])) self.config['MOMENTJS_DATE_FORMAT'] = self.config['LUXON_DATE_FORMAT'] # We first have to massage MOMENTJS_DATE_FORMAT and LUXON_DATE_FORMAT, otherwise we run into trouble if 'MOMENTJS_DATE_FORMAT' in self.config: if isinstance(self.config['MOMENTJS_DATE_FORMAT'], dict): for k in self.config['MOMENTJS_DATE_FORMAT']: self.config['MOMENTJS_DATE_FORMAT'][k] = json.dumps(self.config['MOMENTJS_DATE_FORMAT'][k]) else: self.config['MOMENTJS_DATE_FORMAT'] = json.dumps(self.config['MOMENTJS_DATE_FORMAT']) if 'LUXON_DATE_FORMAT' in self.config: for k in self.config['LUXON_DATE_FORMAT']: self.config['LUXON_DATE_FORMAT'][k] = json.dumps(self.config['LUXON_DATE_FORMAT'][k]) for i in self.TRANSLATABLE_SETTINGS: try: self.config[i] = utils.TranslatableSetting(i, self.config[i], self.config['TRANSLATIONS']) except KeyError: pass # A EXIF_WHITELIST implies you want to keep EXIF data if self.config['EXIF_WHITELIST'] and not self.config['PRESERVE_EXIF_DATA']: utils.LOGGER.warning('Setting EXIF_WHITELIST implies PRESERVE_EXIF_DATA is set to True') self.config['PRESERVE_EXIF_DATA'] = True # Setting PRESERVE_EXIF_DATA with an empty EXIF_WHITELIST implies 'keep everything' if self.config['PRESERVE_EXIF_DATA'] and not self.config['EXIF_WHITELIST']: utils.LOGGER.warning('You are setting PRESERVE_EXIF_DATA and not EXIF_WHITELIST so EXIF data is not really kept.') if 'UNSLUGIFY_TITLES' in self.config: utils.LOGGER.warning('The UNSLUGIFY_TITLES setting was renamed to FILE_METADATA_UNSLUGIFY_TITLES.') self.config['FILE_METADATA_UNSLUGIFY_TITLES'] = self.config['UNSLUGIFY_TITLES'] if 'TAG_PAGES_TITLES' in self.config: utils.LOGGER.warning('The TAG_PAGES_TITLES setting was renamed to TAG_TITLES.') self.config['TAG_TITLES'] = self.config['TAG_PAGES_TITLES'] if 'TAG_PAGES_DESCRIPTIONS' in self.config: utils.LOGGER.warning('The TAG_PAGES_DESCRIPTIONS setting was renamed to TAG_DESCRIPTIONS.') self.config['TAG_DESCRIPTIONS'] = self.config['TAG_PAGES_DESCRIPTIONS'] if 'CATEGORY_PAGES_TITLES' in self.config: utils.LOGGER.warning('The CATEGORY_PAGES_TITLES setting was renamed to CATEGORY_TITLES.') self.config['CATEGORY_TITLES'] = self.config['CATEGORY_PAGES_TITLES'] if 'CATEGORY_PAGES_DESCRIPTIONS' in self.config: utils.LOGGER.warning('The CATEGORY_PAGES_DESCRIPTIONS setting was renamed to CATEGORY_DESCRIPTIONS.') self.config['CATEGORY_DESCRIPTIONS'] = self.config['CATEGORY_PAGES_DESCRIPTIONS'] if 'DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED' in self.config: utils.LOGGER.warning('The DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED setting was renamed and split to DISABLE_INDEXES and DISABLE_MAIN_ATOM_FEED.') self.config['DISABLE_INDEXES'] = self.config['DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED'] self.config['DISABLE_MAIN_ATOM_FEED'] = self.config['DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED'] if 'DISABLE_INDEXES_PLUGIN_RSS_FEED' in self.config: utils.LOGGER.warning('The DISABLE_INDEXES_PLUGIN_RSS_FEED setting was renamed to DISABLE_MAIN_RSS_FEED.') self.config['DISABLE_MAIN_RSS_FEED'] = self.config['DISABLE_INDEXES_PLUGIN_RSS_FEED'] for val in self.config['DATE_FORMAT'].values.values(): if '%' in val: utils.LOGGER.error('The DATE_FORMAT setting needs to be upgraded.') utils.LOGGER.warning("Nikola now uses CLDR-style date strings. http://cldr.unicode.org/translation/date-time") utils.LOGGER.warning("Example: %Y-%m-%d %H:%M ==> yyyy-MM-dd HH:mm") utils.LOGGER.warning("(note it’s different to what moment.js uses!)") sys.exit(1) # Silently upgrade LOCALES (remove encoding) locales = LEGAL_VALUES['LOCALES_BASE'] if 'LOCALES' in self.config: for k, v in self.config['LOCALES'].items(): self.config['LOCALES'][k] = v.split('.')[0] locales.update(self.config['LOCALES']) self.config['LOCALES'] = locales if self.config.get('POSTS_SECTIONS'): utils.LOGGER.warning("The sections feature has been removed and its functionality has been merged into categories.") utils.LOGGER.warning("For more information on how to migrate, please read: https://getnikola.com/blog/upgrading-to-nikola-v8.html#sections-were-replaced-by-categories") for section_config_suffix, cat_config_suffix in ( ('DESCRIPTIONS', 'DESCRIPTIONS'), ('TITLE', 'TITLES'), ('TRANSLATIONS', 'TRANSLATIONS') ): section_config = 'POSTS_SECTION_' + section_config_suffix cat_config = 'CATEGORY_' + cat_config_suffix if section_config in self.config: self.config[section_config].update(self.config[cat_config]) self.config[cat_config] = self.config[section_config] self.config['CATEGORY_DESTPATH_NAMES'] = self.config.get('POSTS_SECTION_NAME', {}) # Need to mark this translatable manually. self.config['CATEGORY_DESTPATH_NAMES'] = utils.TranslatableSetting('CATEGORY_DESTPATH_NAMES', self.config['CATEGORY_DESTPATH_NAMES'], self.config['TRANSLATIONS']) self.config['CATEGORY_DESTPATH_AS_DEFAULT'] = not self.config.get('POSTS_SECTION_FROM_META') utils.LOGGER.info("Setting CATEGORY_DESTPATH_AS_DEFAULT = " + str(self.config['CATEGORY_DESTPATH_AS_DEFAULT'])) if self.config.get('CATEGORY_PAGES_FOLLOW_DESTPATH') and (not self.config.get('CATEGORY_ALLOW_HIERARCHIES') or self.config.get('CATEGORY_OUTPUT_FLAT_HIERARCHY')): utils.LOGGER.error('CATEGORY_PAGES_FOLLOW_DESTPATH requires CATEGORY_ALLOW_HIERARCHIES = True, CATEGORY_OUTPUT_FLAT_HIERARCHY = False.') sys.exit(1) # Handle CONTENT_FOOTER and RSS_COPYRIGHT* properly. # We provide the arguments to format in CONTENT_FOOTER_FORMATS and RSS_COPYRIGHT_FORMATS. self.config['CONTENT_FOOTER'].langformat(self.config['CONTENT_FOOTER_FORMATS']) self.config['RSS_COPYRIGHT'].langformat(self.config['RSS_COPYRIGHT_FORMATS']) self.config['RSS_COPYRIGHT_PLAIN'].langformat(self.config['RSS_COPYRIGHT_FORMATS']) # propagate USE_SLUGIFY utils.USE_SLUGIFY = self.config['USE_SLUGIFY'] # Make sure we have pyphen installed if we are using it if self.config.get('HYPHENATE') and pyphen is None: utils.LOGGER.warning('To use the hyphenation, you have to install ' 'the "pyphen" package.') utils.LOGGER.warning('Setting HYPHENATE to False.') self.config['HYPHENATE'] = False # FIXME: Internally, we still use post_pages because it's a pain to change it self.config['post_pages'] = [] for i1, i2, i3 in self.config['POSTS']: self.config['post_pages'].append([i1, i2, i3, True]) for i1, i2, i3 in self.config['PAGES']: self.config['post_pages'].append([i1, i2, i3, False]) # Handle old plugin names (from before merging the taxonomy PR #2535) for old_plugin_name, new_plugin_names in TAXONOMY_COMPATIBILITY_PLUGIN_NAME_MAP.items(): if old_plugin_name in self.config['DISABLED_PLUGINS']: missing_plugins = [] for plugin_name in new_plugin_names: if plugin_name not in self.config['DISABLED_PLUGINS']: missing_plugins.append(plugin_name) if missing_plugins: utils.LOGGER.warning('The "{}" plugin was replaced by several taxonomy plugins (see PR #2535): {}'.format(old_plugin_name, ', '.join(new_plugin_names))) utils.LOGGER.warning('You are currently disabling "{}", but not the following new taxonomy plugins: {}'.format(old_plugin_name, ', '.join(missing_plugins))) utils.LOGGER.warning('Please also disable these new plugins or remove "{}" from the DISABLED_PLUGINS list.'.format(old_plugin_name)) self.config['DISABLED_PLUGINS'].extend(missing_plugins) # Special-case logic for "render_indexes" to fix #2591 if 'render_indexes' in self.config['DISABLED_PLUGINS']: if 'generate_rss' in self.config['DISABLED_PLUGINS'] or self.config['GENERATE_RSS'] is False: if 'classify_indexes' not in self.config['DISABLED_PLUGINS']: utils.LOGGER.warning('You are disabling the "render_indexes" plugin, as well as disabling the "generate_rss" plugin or setting GENERATE_RSS to False. To achieve the same effect, please disable the "classify_indexes" plugin in the future.') self.config['DISABLED_PLUGINS'].append('classify_indexes') else: if not self.config['DISABLE_INDEXES']: utils.LOGGER.warning('You are disabling the "render_indexes" plugin, but not the generation of RSS feeds. Please put "DISABLE_INDEXES = True" into your configuration instead.') self.config['DISABLE_INDEXES'] = True # Disable RSS. For a successful disable, we must have both the option # false and the plugin disabled through the official means. if 'generate_rss' in self.config['DISABLED_PLUGINS'] and self.config['GENERATE_RSS'] is True: utils.LOGGER.warning('Please use GENERATE_RSS to disable RSS feed generation, instead of mentioning generate_rss in DISABLED_PLUGINS.') self.config['GENERATE_RSS'] = False self.config['DISABLE_MAIN_RSS_FEED'] = True # PRETTY_URLS defaults to enabling STRIP_INDEXES unless explicitly disabled if self.config.get('PRETTY_URLS') and 'STRIP_INDEXES' not in config: self.config['STRIP_INDEXES'] = True if not self.config.get('COPY_SOURCES'): self.config['SHOW_SOURCELINK'] = False if self.config['CATEGORY_PATH']._inp is None: self.config['CATEGORY_PATH'] = self.config['TAG_PATH'] if self.config['CATEGORY_PAGES_ARE_INDEXES'] is None: self.config['CATEGORY_PAGES_ARE_INDEXES'] = self.config['TAG_PAGES_ARE_INDEXES'] self.default_lang = self.config['DEFAULT_LANG'] self.translations = self.config['TRANSLATIONS'] utils.LocaleBorg.initialize(self.config.get('LOCALES', {}), self.default_lang) # BASE_URL defaults to SITE_URL if 'BASE_URL' not in self.config: self.config['BASE_URL'] = self.config.get('SITE_URL') # BASE_URL should *always* end in / if self.config['BASE_URL'] and self.config['BASE_URL'][-1] != '/': utils.LOGGER.warning("Your BASE_URL doesn't end in / -- adding it, but please fix it in your config file!") self.config['BASE_URL'] += '/' try: _bnl = urlsplit(self.config['BASE_URL']).netloc _bnl.encode('ascii') urlsplit(self.config['SITE_URL']).netloc.encode('ascii') except (UnicodeEncodeError, UnicodeDecodeError): utils.LOGGER.error("Your BASE_URL or SITE_URL contains an IDN expressed in Unicode. Please convert it to Punycode.") utils.LOGGER.error("Punycode of {}: {}".format(_bnl, _bnl.encode('idna'))) sys.exit(1) # Load built-in metadata extractors metadata_extractors.load_defaults(self, self.metadata_extractors_by) if metadata_extractors.DEFAULT_EXTRACTOR is None: utils.LOGGER.error("Could not find default meta extractor ({})".format( metadata_extractors.DEFAULT_EXTRACTOR_NAME)) sys.exit(1) # The Pelican metadata format requires a markdown extension if config.get('METADATA_FORMAT', 'nikola').lower() == 'pelican': if 'markdown.extensions.meta' not in config.get('MARKDOWN_EXTENSIONS', []) \ and 'markdown' in self.config['COMPILERS']: utils.LOGGER.warning( 'To use the Pelican metadata format, you need to add ' '"markdown.extensions.meta" to your MARKDOWN_EXTENSIONS setting.') # We use one global tzinfo object all over Nikola. try: self.tzinfo = dateutil.tz.gettz(self.config['TIMEZONE']) except Exception as exc: utils.LOGGER.warning("Error getting TZ: {}", exc) self.tzinfo = dateutil.tz.gettz() self.config['__tzinfo__'] = self.tzinfo # Store raw compilers for internal use (need a copy for that) self.config['_COMPILERS_RAW'] = {} for k, v in self.config['COMPILERS'].items(): self.config['_COMPILERS_RAW'][k] = list(v) # Get search path for themes self.themes_dirs = ['themes'] + self.config['EXTRA_THEMES_DIRS'] # Register default filters filter_name_format = 'filters.{0}' for filter_name, filter_definition in filters.__dict__.items(): # Ignore objects whose name starts with an underscore, or which are not callable if filter_name.startswith('_') or not callable(filter_definition): continue # Register all other objects as filters self.register_filter(filter_name_format.format(filter_name), filter_definition) self._set_global_context_from_config() self._set_all_page_deps_from_config() # Read data files only if a site exists (Issue #2708) if self.configured: self._set_global_context_from_data() # Set persistent state facility self.state = Persistor('state_data.json') # Set cache facility self.cache = Persistor(os.path.join(self.config['CACHE_FOLDER'], 'cache_data.json')) # Create directories for persistors only if a site exists (Issue #2334) if self.configured: self.state._set_site(self) self.cache._set_site(self) def _filter_duplicate_plugins(self, plugin_list): """Find repeated plugins and discard the less local copy.""" def plugin_position_in_places(plugin): # plugin here is a tuple: # (path to the .plugin file, path to plugin module w/o .py, plugin metadata) for i, place in enumerate(self._plugin_places): if plugin[0].startswith(place): return i utils.LOGGER.warn("Duplicate plugin found in unexpected location: {}".format(plugin[0])) return len(self._plugin_places) plugin_dict = defaultdict(list) for data in plugin_list: plugin_dict[data[2].name].append(data) result = [] for _, plugins in plugin_dict.items(): if len(plugins) > 1: # Sort by locality plugins.sort(key=plugin_position_in_places) utils.LOGGER.debug("Plugin {} exists in multiple places, using {}".format( plugins[-1][2].name, plugins[-1][0])) result.append(plugins[-1]) return result def init_plugins(self, commands_only=False, load_all=False): """Load plugins as needed.""" self.plugin_manager = PluginManager(categories_filter={ "Command": Command, "Task": Task, "LateTask": LateTask, "TemplateSystem": TemplateSystem, "PageCompiler": PageCompiler, "TaskMultiplier": TaskMultiplier, "CompilerExtension": CompilerExtension, "MarkdownExtension": MarkdownExtension, "RestExtension": RestExtension, "MetadataExtractor": MetadataExtractor, "ShortcodePlugin": ShortcodePlugin, "SignalHandler": SignalHandler, "ConfigPlugin": ConfigPlugin, "PostScanner": PostScanner, "Taxonomy": Taxonomy, }) self.plugin_manager.getPluginLocator().setPluginInfoExtension('plugin') extra_plugins_dirs = self.config['EXTRA_PLUGINS_DIRS'] self._plugin_places = [ resource_filename('nikola', 'plugins'), os.path.expanduser(os.path.join('~', '.nikola', 'plugins')), os.path.join(os.getcwd(), 'plugins'), ] + [path for path in extra_plugins_dirs if path] compilers = defaultdict(set) # Also add aliases for combinations with TRANSLATIONS_PATTERN for compiler, exts in self.config['COMPILERS'].items(): for ext in exts: compilers[compiler].add(ext) for lang in self.config['TRANSLATIONS'].keys(): candidate = utils.get_translation_candidate(self.config, "f" + ext, lang) compilers[compiler].add(candidate) # Avoid redundant compilers (if load_all is False): # Remove compilers (and corresponding compiler extensions) that are not marked as # needed by any PostScanner plugin and put them into self.disabled_compilers # (respectively self.disabled_compiler_extensions). self.config['COMPILERS'] = {} self.disabled_compilers = {} self.disabled_compiler_extensions = defaultdict(list) self.plugin_manager.getPluginLocator().setPluginPlaces(self._plugin_places) self.plugin_manager.locatePlugins() bad_candidates = set([]) if not load_all: for p in self.plugin_manager._candidates: if commands_only: if p[-1].details.has_option('Nikola', 'PluginCategory'): # FIXME TemplateSystem should not be needed if p[-1].details.get('Nikola', 'PluginCategory') not in {'Command', 'Template'}: bad_candidates.add(p) else: bad_candidates.add(p) elif self.configured: # Not commands-only, and configured # Remove blacklisted plugins if p[-1].name in self.config['DISABLED_PLUGINS']: bad_candidates.add(p) utils.LOGGER.debug('Not loading disabled plugin {}', p[-1].name) # Remove compilers we don't use if p[-1].details.has_option('Nikola', 'PluginCategory') and p[-1].details.get('Nikola', 'PluginCategory') in ('Compiler', 'PageCompiler'): bad_candidates.add(p) self.disabled_compilers[p[-1].name] = p # Remove compiler extensions we don't need if p[-1].details.has_option('Nikola', 'compiler') and p[-1].details.get('Nikola', 'compiler') in self.disabled_compilers: bad_candidates.add(p) self.disabled_compiler_extensions[p[-1].details.get('Nikola', 'compiler')].append(p) self.plugin_manager._candidates = list(set(self.plugin_manager._candidates) - bad_candidates) self.plugin_manager._candidates = self._filter_duplicate_plugins(self.plugin_manager._candidates) self.plugin_manager.loadPlugins() # Search for compiler plugins which we disabled but shouldn't have self._activate_plugins_of_category("PostScanner") if not load_all: file_extensions = set() for post_scanner in [p.plugin_object for p in self.plugin_manager.getPluginsOfCategory('PostScanner')]: exts = post_scanner.supported_extensions() if exts is not None: file_extensions.update(exts) else: # Stop scanning for more: once we get None, we have to load all compilers anyway utils.LOGGER.debug("Post scanner {0!r} does not implement `supported_extensions`, loading all compilers".format(post_scanner)) file_extensions = None break to_add = [] for k, v in compilers.items(): if file_extensions is None or file_extensions.intersection(v): self.config['COMPILERS'][k] = sorted(list(v)) p = self.disabled_compilers.pop(k, None) if p: to_add.append(p) for p in self.disabled_compiler_extensions.pop(k, []): to_add.append(p) for _, p in self.disabled_compilers.items(): utils.LOGGER.debug('Not loading unneeded compiler {}', p[-1].name) for _, plugins in self.disabled_compiler_extensions.items(): for p in plugins: utils.LOGGER.debug('Not loading compiler extension {}', p[-1].name) if to_add: self.plugin_manager._candidates = self._filter_duplicate_plugins(to_add) self.plugin_manager.loadPlugins() # Jupyter theme configuration. If a website has ipynb enabled in post_pages # we should enable the Jupyter CSS (leaving that up to the theme itself). if 'needs_ipython_css' not in self._GLOBAL_CONTEXT: self._GLOBAL_CONTEXT['needs_ipython_css'] = 'ipynb' in self.config['COMPILERS'] # Activate metadata extractors and prepare them for use for p in self._activate_plugins_of_category("MetadataExtractor"): metadata_extractors.classify_extractor(p.plugin_object, self.metadata_extractors_by) self._activate_plugins_of_category("Taxonomy") self.taxonomy_plugins = {} for taxonomy in [p.plugin_object for p in self.plugin_manager.getPluginsOfCategory('Taxonomy')]: if not taxonomy.is_enabled(): continue if taxonomy.classification_name in self.taxonomy_plugins: utils.LOGGER.error("Found more than one taxonomy with classification name '{}'!".format(taxonomy.classification_name)) sys.exit(1) self.taxonomy_plugins[taxonomy.classification_name] = taxonomy self._activate_plugins_of_category("SignalHandler") # Emit signal for SignalHandlers which need to start running immediately. signal('sighandlers_loaded').send(self) self._commands = {} command_plugins = self._activate_plugins_of_category("Command") for plugin_info in command_plugins: plugin_info.plugin_object.short_help = plugin_info.description self._commands[plugin_info.name] = plugin_info.plugin_object self._activate_plugins_of_category("Task") self._activate_plugins_of_category("LateTask") self._activate_plugins_of_category("TaskMultiplier") # Activate all required compiler plugins self.compiler_extensions = self._activate_plugins_of_category("CompilerExtension") for plugin_info in self.plugin_manager.getPluginsOfCategory("PageCompiler"): if plugin_info.name in self.config["COMPILERS"].keys(): self.plugin_manager.activatePluginByName(plugin_info.name) plugin_info.plugin_object.set_site(self) # Activate shortcode plugins self._activate_plugins_of_category("ShortcodePlugin") # Load compiler plugins self.compilers = {} self.inverse_compilers = {} for plugin_info in self.plugin_manager.getPluginsOfCategory( "PageCompiler"): self.compilers[plugin_info.name] = \ plugin_info.plugin_object # Load config plugins and register templated shortcodes self._activate_plugins_of_category("ConfigPlugin") self._register_templated_shortcodes() # Check with registered filters and configure filters for actions in self.config['FILTERS'].values(): for i, f in enumerate(actions): if isinstance(f, str): # Check whether this denotes a registered filter _f = self.filters.get(f) if _f is not None: f = _f actions[i] = f if hasattr(f, 'configuration_variables'): args = {} for arg, config in f.configuration_variables.items(): if config in self.config: args[arg] = self.config[config] if args: actions[i] = functools.partial(f, **args) # Signal that we are configured signal('configured').send(self) def _set_global_context_from_config(self): """Create global context from configuration. These are options that are used by templates, so they always need to be available. """ self._GLOBAL_CONTEXT['url_type'] = self.config['URL_TYPE'] self._GLOBAL_CONTEXT['timezone'] = self.tzinfo self._GLOBAL_CONTEXT['_link'] = self.link try: self._GLOBAL_CONTEXT['set_locale'] = utils.LocaleBorg().set_locale except utils.LocaleBorgUninitializedException: self._GLOBAL_CONTEXT['set_locale'] = None self._GLOBAL_CONTEXT['rel_link'] = self.rel_link self._GLOBAL_CONTEXT['abs_link'] = self.abs_link self._GLOBAL_CONTEXT['exists'] = self.file_exists self._GLOBAL_CONTEXT['index_display_post_count'] = self.config[ 'INDEX_DISPLAY_POST_COUNT'] self._GLOBAL_CONTEXT['index_file'] = self.config['INDEX_FILE'] self._GLOBAL_CONTEXT['use_bundles'] = self.config['USE_BUNDLES'] self._GLOBAL_CONTEXT['use_cdn'] = self.config.get("USE_CDN") self._GLOBAL_CONTEXT['theme_color'] = self.config.get("THEME_COLOR") self._GLOBAL_CONTEXT['theme_config'] = self.config.get("THEME_CONFIG") self._GLOBAL_CONTEXT['favicons'] = self.config['FAVICONS'] self._GLOBAL_CONTEXT['date_format'] = self.config.get('DATE_FORMAT') self._GLOBAL_CONTEXT['blog_author'] = self.config.get('BLOG_AUTHOR') self._GLOBAL_CONTEXT['blog_title'] = self.config.get('BLOG_TITLE') self._GLOBAL_CONTEXT['blog_email'] = self.config.get('BLOG_EMAIL') self._GLOBAL_CONTEXT['show_blog_title'] = self.config.get('SHOW_BLOG_TITLE') self._GLOBAL_CONTEXT['logo_url'] = self.config.get('LOGO_URL') self._GLOBAL_CONTEXT['blog_description'] = self.config.get('BLOG_DESCRIPTION') self._GLOBAL_CONTEXT['front_index_header'] = self.config.get('FRONT_INDEX_HEADER') self._GLOBAL_CONTEXT['color_hsl_adjust_hex'] = utils.color_hsl_adjust_hex self._GLOBAL_CONTEXT['colorize_str_from_base_color'] = utils.colorize_str_from_base_color self._GLOBAL_CONTEXT['blog_url'] = self.config.get('SITE_URL') self._GLOBAL_CONTEXT['template_hooks'] = self.template_hooks self._GLOBAL_CONTEXT['body_end'] = self.config.get('BODY_END') self._GLOBAL_CONTEXT['social_buttons_code'] = self.config.get('SOCIAL_BUTTONS_CODE') self._GLOBAL_CONTEXT['translations'] = self.config.get('TRANSLATIONS') self._GLOBAL_CONTEXT['license'] = self.config.get('LICENSE') self._GLOBAL_CONTEXT['search_form'] = self.config.get('SEARCH_FORM') self._GLOBAL_CONTEXT['comment_system'] = self.config.get('COMMENT_SYSTEM') self._GLOBAL_CONTEXT['comment_system_id'] = self.config.get('COMMENT_SYSTEM_ID') self._GLOBAL_CONTEXT['site_has_comments'] = bool(self.config.get('COMMENT_SYSTEM')) self._GLOBAL_CONTEXT['mathjax_config'] = self.config.get( 'MATHJAX_CONFIG') self._GLOBAL_CONTEXT['use_katex'] = self.config.get('USE_KATEX') self._GLOBAL_CONTEXT['katex_auto_render'] = self.config.get('KATEX_AUTO_RENDER') self._GLOBAL_CONTEXT['content_footer'] = self.config.get( 'CONTENT_FOOTER') self._GLOBAL_CONTEXT['generate_atom'] = self.config.get('GENERATE_ATOM') self._GLOBAL_CONTEXT['generate_rss'] = self.config.get('GENERATE_RSS') self._GLOBAL_CONTEXT['rss_link'] = self.config.get('RSS_LINK') self._GLOBAL_CONTEXT['navigation_links'] = self.config.get('NAVIGATION_LINKS') self._GLOBAL_CONTEXT['navigation_alt_links'] = self.config.get('NAVIGATION_ALT_LINKS') self._GLOBAL_CONTEXT['twitter_card'] = self.config.get( 'TWITTER_CARD', {}) self._GLOBAL_CONTEXT['hide_sourcelink'] = not self.config.get( 'SHOW_SOURCELINK') self._GLOBAL_CONTEXT['show_sourcelink'] = self.config.get( 'SHOW_SOURCELINK') self._GLOBAL_CONTEXT['extra_head_data'] = self.config.get('EXTRA_HEAD_DATA') self._GLOBAL_CONTEXT['date_fanciness'] = self.config.get('DATE_FANCINESS') self._GLOBAL_CONTEXT['luxon_locales'] = LEGAL_VALUES['LUXON_LOCALES'] self._GLOBAL_CONTEXT['luxon_date_format'] = self.config.get('LUXON_DATE_FORMAT') # TODO: remove in v9 self._GLOBAL_CONTEXT['js_date_format'] = self.config.get('MOMENTJS_DATE_FORMAT') self._GLOBAL_CONTEXT['momentjs_locales'] = LEGAL_VALUES['MOMENTJS_LOCALES'] # Patch missing locales into momentjs defaulting to English (Issue #3216) for l in self._GLOBAL_CONTEXT['translations']: if l not in self._GLOBAL_CONTEXT['momentjs_locales']: self._GLOBAL_CONTEXT['momentjs_locales'][l] = "" self._GLOBAL_CONTEXT['hidden_tags'] = self.config.get('HIDDEN_TAGS') self._GLOBAL_CONTEXT['hidden_categories'] = self.config.get('HIDDEN_CATEGORIES') self._GLOBAL_CONTEXT['hidden_authors'] = self.config.get('HIDDEN_AUTHORS') self._GLOBAL_CONTEXT['url_replacer'] = self.url_replacer self._GLOBAL_CONTEXT['sort_posts'] = utils.sort_posts self._GLOBAL_CONTEXT['smartjoin'] = utils.smartjoin self._GLOBAL_CONTEXT['colorize_str'] = utils.colorize_str self._GLOBAL_CONTEXT['meta_generator_tag'] = self.config.get('META_GENERATOR_TAG') self._GLOBAL_CONTEXT.update(self.config.get('GLOBAL_CONTEXT', {})) def _set_global_context_from_data(self): """Load files from data/ and put them in the global context.""" self._GLOBAL_CONTEXT['data'] = {} for root, dirs, files in os.walk('data', followlinks=True): for fname in files: fname = os.path.join(root, fname) data = utils.load_data(fname) key = os.path.splitext(fname.split(os.sep, 1)[1])[0] self._GLOBAL_CONTEXT['data'][key] = data # Offer global_data as an alias for data (Issue #2488) self._GLOBAL_CONTEXT['global_data'] = self._GLOBAL_CONTEXT['data'] def _set_all_page_deps_from_config(self): """Save dependencies for all pages from configuration. Changes of values in this dict will force a rebuild of all pages. Unlike global context, contents are NOT available to templates. """ self.ALL_PAGE_DEPS['atom_extension'] = self.config.get('ATOM_EXTENSION') self.ALL_PAGE_DEPS['atom_path'] = self.config.get('ATOM_PATH') self.ALL_PAGE_DEPS['rss_extension'] = self.config.get('RSS_EXTENSION') self.ALL_PAGE_DEPS['rss_path'] = self.config.get('RSS_PATH') self.ALL_PAGE_DEPS['rss_filename_base'] = self.config.get('RSS_FILENAME_BASE') self.ALL_PAGE_DEPS['atom_filename_base'] = self.config.get('ATOM_FILENAME_BASE') self.ALL_PAGE_DEPS['slug_author_path'] = self.config.get('SLUG_AUTHOR_PATH') self.ALL_PAGE_DEPS['slug_tag_path'] = self.config.get('SLUG_TAG_PATH') self.ALL_PAGE_DEPS['locale'] = self.config.get('LOCALE') def _activate_plugins_of_category(self, category): """Activate all the plugins of a given category and return them.""" # this code duplicated in tests/base.py plugins = [] for plugin_info in self.plugin_manager.getPluginsOfCategory(category): self.plugin_manager.activatePluginByName(plugin_info.name) plugin_info.plugin_object.set_site(self) plugins.append(plugin_info) return plugins def _get_themes(self): if self._THEMES is None: try: self._THEMES = utils.get_theme_chain(self.config['THEME'], self.themes_dirs) except Exception: if self.config['THEME'] != LEGAL_VALUES['DEFAULT_THEME']: utils.LOGGER.warning('''Cannot load theme "{0}", using '{1}' instead.'''.format( self.config['THEME'], LEGAL_VALUES['DEFAULT_THEME'])) self.config['THEME'] = LEGAL_VALUES['DEFAULT_THEME'] return self._get_themes() raise # Check consistency of USE_CDN and the current THEME (Issue #386) if self.config['USE_CDN'] and self.config['USE_CDN_WARNING']: bootstrap_path = utils.get_asset_path(os.path.join( 'assets', 'css', 'bootstrap.min.css'), self._THEMES) if bootstrap_path and bootstrap_path.split(os.sep)[-4] not in ['bootstrap', 'bootstrap3', 'bootstrap4']: utils.LOGGER.warning('The USE_CDN option may be incompatible with your theme, because it uses a hosted version of bootstrap.') return self._THEMES THEMES = property(_get_themes) def _get_messages(self): try: if self._MESSAGES is None: self._MESSAGES = utils.load_messages(self.THEMES, self.translations, self.default_lang, themes_dirs=self.themes_dirs) return self._MESSAGES except utils.LanguageNotFoundError as e: utils.LOGGER.error('''Cannot load language "{0}". Please make sure it is supported by Nikola itself, or that you have the appropriate messages files in your themes.'''.format(e.lang)) sys.exit(1) MESSAGES = property(_get_messages) def _get_global_context(self): """Initialize some parts of GLOBAL_CONTEXT only when it's queried.""" if 'messages' not in self._GLOBAL_CONTEXT: self._GLOBAL_CONTEXT['messages'] = self.MESSAGES if 'has_custom_css' not in self._GLOBAL_CONTEXT: # check if custom css exist and is not empty custom_css_path = utils.get_asset_path( 'assets/css/custom.css', self.THEMES, self.config['FILES_FOLDERS'] ) if custom_css_path and self.file_exists(custom_css_path, not_empty=True): self._GLOBAL_CONTEXT['has_custom_css'] = True else: self._GLOBAL_CONTEXT['has_custom_css'] = False return self._GLOBAL_CONTEXT GLOBAL_CONTEXT = property(_get_global_context) def _get_template_system(self): if self._template_system is None: # Load template plugin template_sys_name = utils.get_template_engine(self.THEMES) pi = self.plugin_manager.getPluginByName( template_sys_name, "TemplateSystem") if pi is None: sys.stderr.write("Error loading {0} template system " "plugin\n".format(template_sys_name)) sys.exit(1) self._template_system = pi.plugin_object lookup_dirs = ['templates'] + [os.path.join(utils.get_theme_path(name), "templates") for name in self.THEMES] self._template_system.set_directories(lookup_dirs, self.config['CACHE_FOLDER']) self._template_system.set_site(self) return self._template_system template_system = property(_get_template_system) def get_compiler(self, source_name): """Get the correct compiler for a post from `conf.COMPILERS`. To make things easier for users, the mapping in conf.py is compiler->[extensions], although this is less convenient for us. The majority of this function is reversing that dictionary and error checking. """ ext = os.path.splitext(source_name)[1] try: compiler = self.inverse_compilers[ext] except KeyError: # Find the correct compiler for this files extension lang_exts_tab = list(self.config['COMPILERS'].items()) langs = [lang for lang, exts in lang_exts_tab if ext in exts or len([ext_ for ext_ in exts if source_name.endswith(ext_)]) > 0] if len(langs) != 1: if len(set(langs)) > 1: sys.exit("Your file extension->compiler definition is " "ambiguous.\nPlease remove one of the file " "extensions from 'COMPILERS' in conf.py\n(The " "error is in one of {0})".format(', '.join(langs))) elif len(langs) > 1: langs = langs[:1] else: sys.exit("COMPILERS in conf.py does not tell me how to " "handle '{0}' extensions.".format(ext)) lang = langs[0] try: compiler = self.compilers[lang] except KeyError: sys.exit("Cannot find '{0}' compiler; " "it might require an extra plugin -- " "do you have it installed?".format(lang)) self.inverse_compilers[ext] = compiler return compiler def render_template(self, template_name, output_name, context, url_type=None, is_fragment=False): """Render a template with the global context. If ``output_name`` is None, will return a string and all URL normalization will be ignored (including the link:// scheme). If ``output_name`` is a string, URLs will be normalized and the resultant HTML will be saved to the named file (path must start with OUTPUT_FOLDER). The argument ``url_type`` allows to override the ``URL_TYPE`` configuration. If ``is_fragment`` is set to ``True``, a HTML fragment will be rendered and not a whole HTML document. """ local_context = {} local_context["template_name"] = template_name local_context.update(self.GLOBAL_CONTEXT) local_context.update(context) for k in self._GLOBAL_CONTEXT_TRANSLATABLE: local_context[k] = local_context[k](local_context['lang']) local_context['is_rtl'] = local_context['lang'] in LEGAL_VALUES['RTL_LANGUAGES'] local_context['url_type'] = self.config['URL_TYPE'] if url_type is None else url_type local_context["translations_feedorder"] = sorted( local_context["translations"], key=lambda x: (int(x != local_context['lang']), x) ) # string, arguments local_context["formatmsg"] = lambda s, *a: s % a for h in local_context['template_hooks'].values(): h.context = context for func in self.config['GLOBAL_CONTEXT_FILLER']: func(local_context, template_name) data = self.template_system.render_template( template_name, None, local_context) if output_name is None: return data if not output_name.startswith(self.config["OUTPUT_FOLDER"]): raise ValueError("Output path for templates must start with OUTPUT_FOLDER") url_part = output_name[len(self.config["OUTPUT_FOLDER"]) + 1:] # Treat our site as if output/ is "/" and then make all URLs relative, # making the site "relocatable" src = os.sep + url_part src = os.path.normpath(src) # The os.sep is because normpath will change "/" to "\" on windows src = "/".join(src.split(os.sep)) utils.makedirs(os.path.dirname(output_name)) parser = lxml.html.HTMLParser(remove_blank_text=True) if is_fragment: doc = lxml.html.fragment_fromstring(data.strip(), parser) else: doc = lxml.html.document_fromstring(data.strip(), parser) self.rewrite_links(doc, src, context['lang'], url_type) if is_fragment: # doc.text contains text before the first HTML, or None if there was no text # The text after HTML elements is added by tostring() (because its implicit # argument with_tail has default value True). data = (doc.text or '').encode('utf-8') + b''.join([lxml.html.tostring(child, encoding='utf-8', method='html') for child in doc.iterchildren()]) else: data = lxml.html.tostring(doc, encoding='utf8', method='html', pretty_print=True, doctype='<!DOCTYPE html>') with open(output_name, "wb+") as post_file: post_file.write(data) def rewrite_links(self, doc, src, lang, url_type=None): """Replace links in document to point to the right places.""" # First let lxml replace most of them doc.rewrite_links(lambda dst: self.url_replacer(src, dst, lang, url_type), resolve_base_href=False) # lxml ignores srcset in img and source elements, so do that by hand objs = list(doc.xpath('(//img|//source)')) for obj in objs: if 'srcset' in obj.attrib: urls = [u.strip() for u in obj.attrib['srcset'].split(',')] urls = [self.url_replacer(src, dst, lang, url_type) for dst in urls] obj.set('srcset', ', '.join(urls)) def url_replacer(self, src, dst, lang=None, url_type=None): """Mangle URLs. * Replaces link:// URLs with real links * Makes dst relative to src * Leaves fragments unchanged * Leaves full URLs unchanged * Avoids empty links src is the URL where this link is used dst is the link to be mangled lang is used for language-sensitive URLs in link:// url_type is used to determine final link appearance, defaulting to URL_TYPE from config """ # Avoid mangling links within the page if dst.startswith('#'): return dst parsed_src = urlsplit(src) src_elems = parsed_src.path.split('/')[1:] dst_url = urlparse(dst) if lang is None: lang = self.default_lang if url_type is None: url_type = self.config.get('URL_TYPE') if dst_url.scheme and dst_url.scheme not in ['http', 'https', 'link']: return dst # Refuse to replace links that are full URLs. if dst_url.netloc: if dst_url.scheme == 'link': # Magic link if dst_url.query: # If query strings are used in magic link, they will be # passed to the path handler as keyword arguments (strings) link_kwargs = {unquote(k): unquote(v[-1]) for k, v in parse_qs(dst_url.query).items()} else: link_kwargs = {} # unquote from issue #2934 dst = self.link(dst_url.netloc, unquote(dst_url.path.lstrip('/')), lang, **link_kwargs) if dst_url.fragment: dst += '#' + dst_url.fragment # Assuming the site is served over one of these, and # since those are the only URLs we want to rewrite... else: if '%' in dst_url.netloc: # convert lxml percent-encoded garbage to punycode nl = unquote(dst_url.netloc) try: nl = nl.decode('utf-8') except AttributeError: # python 3: already unicode pass nl = nl.encode('idna') if isinstance(nl, bytes): nl = nl.decode('latin-1') # so idna stays unchanged dst = urlunsplit((dst_url.scheme, nl, dst_url.path, dst_url.query, dst_url.fragment)) return dst elif dst_url.scheme == 'link': # Magic absolute path link: dst = dst_url.path return dst # Refuse to replace links that consist of a fragment only if ((not dst_url.scheme) and (not dst_url.netloc) and (not dst_url.path) and (not dst_url.params) and (not dst_url.query) and dst_url.fragment): return dst # Normalize dst = urljoin(src, dst) # Avoid empty links. if src == dst: if url_type == 'absolute': dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) return dst elif url_type == 'full_path': dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) return utils.full_path_from_urlparse(urlparse(dst)) else: return "#" # Check that link can be made relative, otherwise return dest parsed_dst = urlsplit(dst) if parsed_src[:2] != parsed_dst[:2]: if url_type == 'absolute': dst = urljoin(self.config['BASE_URL'], dst) return dst if url_type in ('full_path', 'absolute'): dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) if url_type == 'full_path': parsed = urlparse(urljoin(self.config['BASE_URL'], dst.lstrip('/'))) dst = utils.full_path_from_urlparse(parsed) return dst # Now both paths are on the same site and absolute dst_elems = parsed_dst.path.split('/')[1:] i = 0 for (i, s), d in zip(enumerate(src_elems), dst_elems): if s != d: break # Now i is the longest common prefix result = '/'.join(['..'] * (len(src_elems) - i - 1) + dst_elems[i:]) if not result and not parsed_dst.fragment: result = "." # Don't forget the query part of the link if parsed_dst.query: result += "?" + parsed_dst.query # Don't forget the fragment (anchor) part of the link if parsed_dst.fragment: result += "#" + parsed_dst.fragment if not result: raise ValueError("Failed to parse link: {0}".format((src, dst, i, src_elems, dst_elems))) return result def _make_renderfunc(self, t_data, fname=None): """Return a function that can be registered as a template shortcode. The returned function has access to the passed template data and accepts any number of positional and keyword arguments. Positional arguments values are added as a tuple under the key ``_args`` to the keyword argument dict and then the latter provides the template context. Global context keys are made available as part of the context, respecting locale. As a special quirk, the "data" key from global_context is available only as "global_data" because of name clobbering. """ def render_shortcode(*args, **kw): context = self.GLOBAL_CONTEXT.copy() context.update(kw) context['_args'] = args context['lang'] = utils.LocaleBorg().current_lang for k in self._GLOBAL_CONTEXT_TRANSLATABLE: context[k] = context[k](context['lang']) output = self.template_system.render_template_to_string(t_data, context) if fname is not None: dependencies = [fname] + self.template_system.get_deps(fname) else: dependencies = [] return output, dependencies return render_shortcode def _register_templated_shortcodes(self): """Register shortcodes based on templates. This will register a shortcode for any template found in shortcodes/ folders and a generic "template" shortcode which will consider the content in the shortcode as a template in itself. """ self.register_shortcode('template', self._template_shortcode_handler) builtin_sc_dir = resource_filename( 'nikola', os.path.join('data', 'shortcodes', utils.get_template_engine(self.THEMES))) for sc_dir in [builtin_sc_dir, 'shortcodes']: if not os.path.isdir(sc_dir): continue for fname in os.listdir(sc_dir): name, ext = os.path.splitext(fname) if ext != '.tmpl': continue with open(os.path.join(sc_dir, fname)) as fd: self.register_shortcode(name, self._make_renderfunc( fd.read(), os.path.join(sc_dir, fname))) def _template_shortcode_handler(self, *args, **kw): t_data = kw.pop('data', '') context = self.GLOBAL_CONTEXT.copy() context.update(kw) context['_args'] = args context['lang'] = utils.LocaleBorg().current_lang for k in self._GLOBAL_CONTEXT_TRANSLATABLE: context[k] = context[k](context['lang']) output = self.template_system.render_template_to_string(t_data, context) dependencies = self.template_system.get_string_deps(t_data) return output, dependencies def register_shortcode(self, name, f): """Register function f to handle shortcode "name".""" if name in self.shortcode_registry: utils.LOGGER.warning('Shortcode name conflict: {}', name) return self.shortcode_registry[name] = f def apply_shortcodes(self, data, filename=None, lang=None, extra_context=None): """Apply shortcodes from the registry on data.""" if extra_context is None: extra_context = {} if lang is None: lang = utils.LocaleBorg().current_lang return shortcodes.apply_shortcodes(data, self.shortcode_registry, self, filename, lang=lang, extra_context=extra_context) def apply_shortcodes_uuid(self, data, _shortcodes, filename=None, lang=None, extra_context=None): """Apply shortcodes from the registry on data.""" if lang is None: lang = utils.LocaleBorg().current_lang if extra_context is None: extra_context = {} deps = [] for k, v in _shortcodes.items(): replacement, _deps = shortcodes.apply_shortcodes(v, self.shortcode_registry, self, filename, lang=lang, extra_context=extra_context) data = data.replace(k, replacement) deps.extend(_deps) return data, deps def _get_rss_copyright(self, lang, rss_plain): if rss_plain: return ( self.config['RSS_COPYRIGHT_PLAIN'](lang) or lxml.html.fromstring(self.config['RSS_COPYRIGHT'](lang)).text_content().strip()) else: return self.config['RSS_COPYRIGHT'](lang) def generic_rss_feed(self, lang, title, link, description, timeline, rss_teasers, rss_plain, feed_length=10, feed_url=None, enclosure=_enclosure, rss_links_append_query=None, copyright_=None): """Generate an ExtendedRSS2 feed object for later use.""" rss_obj = utils.ExtendedRSS2( title=title, link=utils.encodelink(link), description=description, lastBuildDate=datetime.datetime.utcnow(), generator='Nikola (getnikola.com)', language=lang ) if copyright_ is None: copyright_ = self._get_rss_copyright(lang, rss_plain) # Use the configured or specified copyright string if present. if copyright_: rss_obj.copyright = copyright_ if feed_url: absurl = '/' + feed_url[len(self.config['BASE_URL']):] rss_obj.xsl_stylesheet_href = self.url_replacer(absurl, "/assets/xml/rss.xsl") items = [] feed_append_query = None if rss_links_append_query: if rss_links_append_query is True: raise ValueError("RSS_LINKS_APPEND_QUERY (or FEED_LINKS_APPEND_QUERY) cannot be True. Valid values are False or a formattable string.") feed_append_query = rss_links_append_query.format( feedRelUri='/' + feed_url[len(self.config['BASE_URL']):], feedFormat="rss") for post in timeline[:feed_length]: data = post.text(lang, teaser_only=rss_teasers, strip_html=rss_plain, feed_read_more_link=True, feed_links_append_query=feed_append_query) if feed_url is not None and data: # Massage the post's HTML (unless plain) if not rss_plain: if 'previewimage' in post.meta[lang] and post.meta[lang]['previewimage'] not in data: data = "<figure><img src=\"{}\"></figure> {}".format(post.meta[lang]['previewimage'], data) # FIXME: this is duplicated with code in Post.text() try: doc = lxml.html.document_fromstring(data) doc.rewrite_links(lambda dst: self.url_replacer(post.permalink(), dst, lang, 'absolute')) try: body = doc.body data = (body.text or '') + ''.join( [lxml.html.tostring(child, encoding='unicode') for child in body.iterchildren()]) except IndexError: # No body there, it happens sometimes data = '' except lxml.etree.ParserError as e: if str(e) == "Document is empty": data = "" else: # let other errors raise raise args = { 'title': post.title(lang) if post.should_show_title() else None, 'link': post.permalink(lang, absolute=True, query=feed_append_query), 'description': data, # PyRSS2Gen's pubDate is GMT time. 'pubDate': (post.date if post.date.tzinfo is None else post.date.astimezone(dateutil.tz.tzutc())), 'categories': post._tags.get(lang, []), 'creator': post.author(lang), 'guid': post.guid(lang), } if post.author(lang): rss_obj.rss_attrs["xmlns:dc"] = "http://purl.org/dc/elements/1.1/" if enclosure: # enclosure callback returns None if post has no enclosure, or a # 3-tuple of (url, length (0 is valid), mimetype) enclosure_details = enclosure(post=post, lang=lang) if enclosure_details is not None: args['enclosure'] = rss.Enclosure(*enclosure_details) items.append(utils.ExtendedItem(**args)) rss_obj.items = items rss_obj.self_url = feed_url rss_obj.rss_attrs["xmlns:atom"] = "http://www.w3.org/2005/Atom" return rss_obj def generic_rss_renderer(self, lang, title, link, description, timeline, output_path, rss_teasers, rss_plain, feed_length=10, feed_url=None, enclosure=_enclosure, rss_links_append_query=None, copyright_=None): """Take all necessary data, and render a RSS feed in output_path.""" rss_obj = self.generic_rss_feed(lang, title, link, description, timeline, rss_teasers, rss_plain, feed_length=feed_length, feed_url=feed_url, enclosure=enclosure, rss_links_append_query=rss_links_append_query, copyright_=copyright_) utils.rss_writer(rss_obj, output_path) def path(self, kind, name, lang=None, is_link=False, **kwargs): r"""Build the path to a certain kind of page. These are mostly defined by plugins by registering via the register_path_handler method, except for slug, post_path, root and filename which are defined in this class' init method. Here's some of the others, for historical reasons: * root (name is ignored) * tag_index (name is ignored) * tag (and name is the tag name) * tag_rss (name is the tag name) * category (and name is the category name) * category_rss (and name is the category name) * archive (and name is the year, or None for the main archive index) * index (name is the number in index-number) * rss (name is ignored) * gallery (name is the gallery name) * listing (name is the source code file name) * post_path (name is 1st element in a POSTS/PAGES tuple) * slug (name is the slug of a post or page) * filename (name is the source filename of a post/page, in DEFAULT_LANG, relative to conf.py) The returned value is either a path relative to output, like "categories/whatever.html", or an absolute URL ("https://getnikola.com/"), if path handler returns a string. If is_link is True, the path is absolute and uses "/" as separator (ex: "/archive/index.html"). If is_link is False, the path is relative to output and uses the platform's separator. (ex: "archive\index.html") If the registered path handler returns a string instead of path component list - it's considered to be an absolute URL and returned as is. """ if lang is None: lang = utils.LocaleBorg().current_lang try: path = self.path_handlers[kind](name, lang, **kwargs) except KeyError: utils.LOGGER.warning("Unknown path request of kind: {0}".format(kind)) return "" # If path handler returns a string we consider it to be an absolute URL not requiring any # further processing, i.e 'https://getnikola.com/'. See Issue #2876. if isinstance(path, str): return path if path is None: path = "#" else: path = [os.path.normpath(p) for p in path if p != '.'] # Fix Issue #1028 if is_link: link = '/' + ('/'.join(path)) index_len = len(self.config['INDEX_FILE']) if self.config['STRIP_INDEXES'] and \ link[-(1 + index_len):] == '/' + self.config['INDEX_FILE']: return link[:-index_len] else: return link else: return os.path.join(*path) def post_path(self, name, lang): """Link to the destination of an element in the POSTS/PAGES settings. Example: link://post_path/posts => /blog """ return [_f for _f in [self.config['TRANSLATIONS'][lang], os.path.dirname(name), self.config['INDEX_FILE']] if _f] def root_path(self, name, lang): """Link to the current language's root. Example: link://root_path => / link://root_path => /translations/spanish/ """ d = self.config['TRANSLATIONS'][lang] if d: return [d, ''] else: return [] def slug_path(self, name, lang): """Return a link to a post with given slug, if not ambiguous. Example: link://slug/yellow-camaro => /posts/cars/awful/yellow-camaro/index.html """ results = [p for p in self.timeline if p.meta('slug') == name] if not results: utils.LOGGER.warning("Cannot resolve path request for slug: {0}".format(name)) else: if len(results) > 1: utils.LOGGER.warning('Ambiguous path request for slug: {0}'.format(name)) return [_f for _f in results[0].permalink(lang).split('/')] def filename_path(self, name, lang): """Link to post or page by source filename. Example: link://filename/manual.txt => /docs/handbook.html """ results = [p for p in self.timeline if p.source_path == name] if not results: utils.LOGGER.warning("Cannot resolve path request for filename: {0}".format(name)) else: if len(results) > 1: utils.LOGGER.error("Ambiguous path request for filename: {0}".format(name)) return [_f for _f in results[0].permalink(lang).split('/') if _f] def register_path_handler(self, kind, f): """Register a path handler.""" if kind in self.path_handlers: utils.LOGGER.warning('Conflicting path handlers for kind: {0}'.format(kind)) else: self.path_handlers[kind] = f def link(self, *args, **kwargs): """Create a link.""" url = self.path(*args, is_link=True, **kwargs) url = utils.encodelink(url) return url def abs_link(self, dst, protocol_relative=False): """Get an absolute link.""" # Normalize if dst: # Mako templates and empty strings evaluate to False dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) else: dst = self.config['BASE_URL'] url = urlparse(dst).geturl() if protocol_relative: url = url.split(":", 1)[1] url = utils.encodelink(url) return url def rel_link(self, src, dst): """Get a relative link.""" # Normalize src = urljoin(self.config['BASE_URL'], src) dst = urljoin(src, dst) # Avoid empty links. if src == dst: return "#" # Check that link can be made relative, otherwise return dest parsed_src = urlsplit(src) parsed_dst = urlsplit(dst) if parsed_src[:2] != parsed_dst[:2]: return utils.encodelink(dst) # Now both paths are on the same site and absolute src_elems = parsed_src.path.split('/')[1:] dst_elems = parsed_dst.path.split('/')[1:] i = 0 for (i, s), d in zip(enumerate(src_elems), dst_elems): if s != d: break else: i += 1 # Now i is the longest common prefix url = '/'.join(['..'] * (len(src_elems) - i - 1) + dst_elems[i:]) url = utils.encodelink(url) return url def register_filter(self, filter_name, filter_definition): """Register a filter. filter_name should be a name not confusable with an actual executable. filter_definition should be a callable accepting one argument (the filename). """ if filter_name in self.filters: utils.LOGGER.warning('''The filter "{0}" is defined more than once.'''.format(filter_name)) self.filters[filter_name] = filter_definition def file_exists(self, path, not_empty=False): """Check if the file exists. If not_empty is True, it also must not be empty.""" exists = os.path.exists(path) if exists and not_empty: exists = os.stat(path).st_size > 0 return exists def clean_task_paths(self, task): """Normalize target paths in the task.""" targets = task.get('targets', None) if targets is not None: task['targets'] = [os.path.normpath(t) for t in targets] return task def gen_tasks(self, name, plugin_category, doc=''): """Generate tasks.""" def flatten(task): """Flatten lists of tasks.""" if isinstance(task, dict): yield task else: for t in task: for ft in flatten(t): yield ft task_dep = [] for pluginInfo in self.plugin_manager.getPluginsOfCategory(plugin_category): for task in flatten(pluginInfo.plugin_object.gen_tasks()): if 'basename' not in task: raise ValueError("Task {0} does not have a basename".format(task)) task = self.clean_task_paths(task) if 'task_dep' not in task: task['task_dep'] = [] task['task_dep'].extend(self.injected_deps[task['basename']]) yield task for multi in self.plugin_manager.getPluginsOfCategory("TaskMultiplier"): flag = False for task in multi.plugin_object.process(task, name): flag = True yield self.clean_task_paths(task) if flag: task_dep.append('{0}_{1}'.format(name, multi.plugin_object.name)) if pluginInfo.plugin_object.is_default: task_dep.append(pluginInfo.plugin_object.name) yield { 'basename': name, 'doc': doc, 'actions': None, 'clean': True, 'task_dep': task_dep } def parse_category_name(self, category_name): """Parse a category name into a hierarchy.""" if self.config['CATEGORY_ALLOW_HIERARCHIES']: try: return hierarchy_utils.parse_escaped_hierarchical_category_name(category_name) except Exception as e: utils.LOGGER.error(str(e)) sys.exit(1) else: return [category_name] if len(category_name) > 0 else [] def category_path_to_category_name(self, category_path): """Translate a category path to a category name.""" if self.config['CATEGORY_ALLOW_HIERARCHIES']: return hierarchy_utils.join_hierarchical_category_path(category_path) else: return ''.join(category_path) def _add_post_to_category(self, post, category_name): """Add a post to a category.""" category_path = self.parse_category_name(category_name) current_path = [] current_subtree = self.category_hierarchy for current in category_path: current_path.append(current) if current not in current_subtree: current_subtree[current] = {} current_subtree = current_subtree[current] self.posts_per_category[self.category_path_to_category_name(current_path)].append(post) def _sort_category_hierarchy(self): """Sort category hierarchy.""" # First create a hierarchy of TreeNodes self.category_hierarchy_lookup = {} def create_hierarchy(cat_hierarchy, parent=None): """Create category hierarchy.""" result = [] for name, children in cat_hierarchy.items(): node = hierarchy_utils.TreeNode(name, parent) node.children = create_hierarchy(children, node) node.category_path = [pn.name for pn in node.get_path()] node.category_name = self.category_path_to_category_name(node.category_path) self.category_hierarchy_lookup[node.category_name] = node if node.category_name not in self.config.get('HIDDEN_CATEGORIES'): result.append(node) return natsort.natsorted(result, key=lambda e: e.name, alg=natsort.ns.F | natsort.ns.IC) root_list = create_hierarchy(self.category_hierarchy) # Next, flatten the hierarchy self.category_hierarchy = hierarchy_utils.flatten_tree_structure(root_list) @staticmethod def sort_posts_chronologically(posts, lang=None): """Sort a list of posts chronologically. This function also takes priority, title and source path into account. """ # Last tie breaker: sort by source path (A-Z) posts = sorted(posts, key=lambda p: p.source_path) # Next tie breaker: sort by title if language is given (A-Z) if lang is not None: posts = natsort.natsorted(posts, key=lambda p: p.title(lang), alg=natsort.ns.F | natsort.ns.IC) # Next tie breaker: sort by date (reverse chronological order) posts = sorted(posts, key=lambda p: p.date, reverse=True) # Finally, sort by priority meta value (descending) posts = sorted(posts, key=lambda p: int(p.meta('priority')) if p.meta('priority') else 0, reverse=True) # Return result return posts def scan_posts(self, really=False, ignore_quit=False, quiet=False): """Scan all the posts. The `quiet` option is ignored. """ if self._scanned and not really: return # Reset things self.posts = [] self.all_posts = [] self.posts_per_year = defaultdict(list) self.posts_per_month = defaultdict(list) self.posts_per_tag = defaultdict(list) self.posts_per_category = defaultdict(list) self.tags_per_language = defaultdict(list) self.category_hierarchy = {} self.post_per_file = {} self.post_per_input_file = {} self.timeline = [] self.pages = [] for p in sorted(self.plugin_manager.getPluginsOfCategory('PostScanner'), key=operator.attrgetter('name')): try: timeline = p.plugin_object.scan() except Exception: utils.LOGGER.error('Error reading timeline') raise # FIXME: can there be conflicts here? self.timeline.extend(timeline) quit = False # Classify posts per year/tag/month/whatever slugged_tags = defaultdict(set) for post in self.timeline: if post.use_in_feeds: self.posts.append(post) self.posts_per_year[str(post.date.year)].append(post) self.posts_per_month[ '{0}/{1:02d}'.format(post.date.year, post.date.month)].append(post) for lang in self.config['TRANSLATIONS'].keys(): for tag in post.tags_for_language(lang): _tag_slugified = utils.slugify(tag, lang) slugged_tags[lang].add(_tag_slugified) if post not in self.posts_per_tag[tag]: self.posts_per_tag[tag].append(post) self.tags_per_language[lang].extend(post.tags_for_language(lang)) self._add_post_to_category(post, post.meta('category')) if post.is_post: # unpublished posts self.all_posts.append(post) else: self.pages.append(post) for lang in self.config['TRANSLATIONS'].keys(): dest = post.destination_path(lang=lang) src_dest = post.destination_path(lang=lang, extension=post.source_ext()) src_file = post.translated_source_path(lang=lang) if dest in self.post_per_file: utils.LOGGER.error('Two posts are trying to generate {0}: {1} and {2}'.format( dest, self.post_per_file[dest].source_path, post.source_path)) quit = True if (src_dest in self.post_per_file) and self.config['COPY_SOURCES']: utils.LOGGER.error('Two posts are trying to generate {0}: {1} and {2}'.format( src_dest, self.post_per_file[dest].source_path, post.source_path)) quit = True self.post_per_file[dest] = post self.post_per_file[src_dest] = post if src_file is not None: self.post_per_input_file[src_file] = post # deduplicate tags_per_language self.tags_per_language[lang] = list(set(self.tags_per_language[lang])) # Sort everything. self.timeline = self.sort_posts_chronologically(self.timeline) self.posts = self.sort_posts_chronologically(self.posts) self.all_posts = self.sort_posts_chronologically(self.all_posts) self.pages = self.sort_posts_chronologically(self.pages) self._sort_category_hierarchy() for i, p in enumerate(self.posts[1:]): p.next_post = self.posts[i] for i, p in enumerate(self.posts[:-1]): p.prev_post = self.posts[i + 1] self._scanned = True if not self.quiet: print("done!", file=sys.stderr) if quit and not ignore_quit: sys.exit(1) signal('scanned').send(self) def generic_renderer(self, lang, output_name, template_name, filters, file_deps=None, uptodate_deps=None, context=None, context_deps_remove=None, post_deps_dict=None, url_type=None, is_fragment=False): """Create tasks for rendering pages and post lists and other related pages. lang is the current language. output_name is the destination file name. template_name is the template to be used. filters is the list of filters (usually site.config['FILTERS']) which will be used to post-process the result. file_deps (optional) is a list of additional file dependencies (next to template and its dependencies). uptodate_deps (optional) is a list of additional entries added to the task's uptodate list. context (optional) a dict used as a basis for the template context. The lang parameter will always be added. context_deps_remove (optional) is a list of keys to remove from the context after using it as an uptodate dependency. This should name all keys containing non-trivial Python objects; they can be replaced by adding JSON-style dicts in post_deps_dict. post_deps_dict (optional) is a dict merged into the copy of context which is used as an uptodate dependency. url_type (optional) allows to override the ``URL_TYPE`` configuration. is_fragment (optional) allows to write a HTML fragment instead of a HTML document. """ utils.LocaleBorg().set_locale(lang) file_deps = copy(file_deps) if file_deps else [] file_deps += self.template_system.template_deps(template_name) file_deps = sorted(list(filter(None, file_deps))) context = copy(context) if context else {} context["lang"] = lang deps_dict = copy(context) if context_deps_remove: for key in context_deps_remove: deps_dict.pop(key) deps_dict['OUTPUT_FOLDER'] = self.config['OUTPUT_FOLDER'] deps_dict['TRANSLATIONS'] = self.config['TRANSLATIONS'] deps_dict['global'] = self.GLOBAL_CONTEXT deps_dict['all_page_deps'] = self.ALL_PAGE_DEPS if post_deps_dict: deps_dict.update(post_deps_dict) for k, v in self.GLOBAL_CONTEXT['template_hooks'].items(): deps_dict['||template_hooks|{0}||'.format(k)] = v.calculate_deps() for k in self._GLOBAL_CONTEXT_TRANSLATABLE: deps_dict[k] = deps_dict['global'][k](lang) for k in self._ALL_PAGE_DEPS_TRANSLATABLE: deps_dict[k] = deps_dict['all_page_deps'][k](lang) deps_dict['navigation_links'] = deps_dict['global']['navigation_links'](lang) deps_dict['navigation_alt_links'] = deps_dict['global']['navigation_alt_links'](lang) task = { 'name': os.path.normpath(output_name), 'targets': [output_name], 'file_dep': file_deps, 'actions': [(self.render_template, [template_name, output_name, context, url_type, is_fragment])], 'clean': True, 'uptodate': [config_changed(deps_dict, 'nikola.nikola.Nikola.generic_renderer')] + ([] if uptodate_deps is None else uptodate_deps) } return utils.apply_filters(task, filters) def generic_page_renderer(self, lang, post, filters, context=None): """Render post fragments to final HTML pages.""" extension = post.compiler.extension() output_name = os.path.join(self.config['OUTPUT_FOLDER'], post.destination_path(lang, extension)) deps = post.deps(lang) uptodate_deps = post.deps_uptodate(lang) deps.extend(utils.get_asset_path(x, self.THEMES) for x in ('bundles', 'parent', 'engine')) _theme_ini = utils.get_asset_path(self.config['THEME'] + '.theme', self.THEMES) if _theme_ini: deps.append(_theme_ini) context = copy(context) if context else {} context['post'] = post context['title'] = post.title(lang) context['description'] = post.description(lang) context['permalink'] = post.permalink(lang) if 'crumbs' not in context: crumb_path = post.permalink(lang).lstrip('/') if crumb_path.endswith(self.config['INDEX_FILE']): crumb_path = crumb_path[:-len(self.config['INDEX_FILE'])] if crumb_path.endswith('/'): context['crumbs'] = utils.get_crumbs(crumb_path.rstrip('/'), is_file=False) else: context['crumbs'] = utils.get_crumbs(crumb_path, is_file=True) if 'pagekind' not in context: context['pagekind'] = ['generic_page'] if post.use_in_feeds: context['enable_comments'] = True else: context['enable_comments'] = self.config['COMMENTS_IN_PAGES'] deps_dict = {} if post.prev_post: deps_dict['PREV_LINK'] = [post.prev_post.permalink(lang)] if post.next_post: deps_dict['NEXT_LINK'] = [post.next_post.permalink(lang)] deps_dict['comments'] = context['enable_comments'] if post: deps_dict['post_translations'] = post.translated_to signal('render_post').send({ 'site': self, 'post': post, 'lang': lang, 'context': context, 'deps_dict': deps_dict, }) yield self.generic_renderer(lang, output_name, post.template_name, filters, file_deps=deps, uptodate_deps=uptodate_deps, context=context, context_deps_remove=['post'], post_deps_dict=deps_dict, url_type=post.url_type) def generic_post_list_renderer(self, lang, posts, output_name, template_name, filters, extra_context): """Render pages with lists of posts.""" deps = [] uptodate_deps = [] for post in posts: deps += post.deps(lang) uptodate_deps += post.deps_uptodate(lang) context = {} context["posts"] = posts context["title"] = self.config['BLOG_TITLE'](lang) context["description"] = self.config['BLOG_DESCRIPTION'](lang) context["prevlink"] = None context["nextlink"] = None if extra_context: context.update(extra_context) if 'has_other_languages' not in context: context['has_other_languages'] = False post_deps_dict = {} post_deps_dict["posts"] = [(p.meta[lang]['title'], p.permalink(lang)) for p in posts] return self.generic_renderer(lang, output_name, template_name, filters, file_deps=deps, uptodate_deps=uptodate_deps, context=context, post_deps_dict=post_deps_dict) def atom_feed_renderer(self, lang, posts, output_path, filters, extra_context): """Render Atom feeds and archives with lists of posts. Feeds are considered archives when no future updates to them are expected. """ def atom_link(link_rel, link_type, link_href): link = lxml.etree.Element("link") link.set("rel", link_rel) link.set("type", link_type) link.set("href", utils.encodelink(link_href)) return link utils.LocaleBorg().set_locale(lang) deps = [] uptodate_deps = [] for post in posts: deps += post.deps(lang) uptodate_deps += post.deps_uptodate(lang) context = {} blog_title = self.config['BLOG_TITLE'](lang) context["posts"] = posts context["title"] = blog_title context["description"] = self.config['BLOG_DESCRIPTION'](lang) context["lang"] = lang context.update(extra_context) context["title"] = "{0} ({1})".format(blog_title, context["title"]) if blog_title != context["title"] else blog_title deps_context = copy(context) deps_context["posts"] = [(p.meta[lang]['title'], p.permalink(lang)) for p in posts] deps_context["global"] = self.GLOBAL_CONTEXT deps_context["all_page_deps"] = self.ALL_PAGE_DEPS for k in self._GLOBAL_CONTEXT_TRANSLATABLE: deps_context[k] = deps_context['global'][k](lang) for k in self._ALL_PAGE_DEPS_TRANSLATABLE: deps_context[k] = deps_context['all_page_deps'][k](lang) feed_xsl_link = self.abs_link("/assets/xml/atom.xsl") feed_root = lxml.etree.Element("feed") feed_root.addprevious(lxml.etree.ProcessingInstruction( "xml-stylesheet", 'href="' + utils.encodelink(feed_xsl_link) + '" type="text/xsl media="all"')) feed_root.set("{http://www.w3.org/XML/1998/namespace}lang", lang) feed_root.set("xmlns", "http://www.w3.org/2005/Atom") feed_title = lxml.etree.SubElement(feed_root, "title") feed_title.text = context["title"] feed_id = lxml.etree.SubElement(feed_root, "id") feed_id.text = self.abs_link(context["feedlink"]) feed_updated = lxml.etree.SubElement(feed_root, "updated") feed_updated.text = utils.LocaleBorg().formatted_date('webiso', datetime.datetime.now(tz=dateutil.tz.tzutc())) feed_author = lxml.etree.SubElement(feed_root, "author") feed_author_name = lxml.etree.SubElement(feed_author, "name") feed_author_name.text = self.config["BLOG_AUTHOR"](lang) feed_root.append(atom_link("self", "application/atom+xml", self.abs_link(context["feedlink"]))) feed_root.append(atom_link("alternate", "text/html", self.abs_link(context["permalink"]))) feed_generator = lxml.etree.SubElement(feed_root, "generator") feed_generator.set("uri", "https://getnikola.com/") feed_generator.text = "Nikola" feed_append_query = None if self.config["FEED_LINKS_APPEND_QUERY"]: feed_append_query = self.config["FEED_LINKS_APPEND_QUERY"].format( feedRelUri=context["feedlink"], feedFormat="atom") def atom_post_text(post, text): if not self.config["FEED_PLAIN"]: if 'previewimage' in post.meta[lang] and post.meta[lang]['previewimage'] not in text: text = "<figure><img src=\"{}\"></figure> {}".format(post.meta[lang]['previewimage'], text) # FIXME: this is duplicated with code in Post.text() and generic_rss_renderer try: doc = lxml.html.document_fromstring(text) doc.rewrite_links(lambda dst: self.url_replacer(post.permalink(lang), dst, lang, 'absolute')) try: body = doc.body text = (body.text or '') + ''.join( [lxml.html.tostring(child, encoding='unicode') for child in body.iterchildren()]) except IndexError: # No body there, it happens sometimes text = '' except lxml.etree.ParserError as e: if str(e) == "Document is empty": text = "" else: # let other errors raise raise return text.strip() for post in posts: summary = atom_post_text(post, post.text(lang, teaser_only=True, strip_html=self.config["FEED_PLAIN"], feed_read_more_link=True, feed_links_append_query=feed_append_query)) content = None if not self.config["FEED_TEASERS"]: content = atom_post_text(post, post.text(lang, teaser_only=self.config["FEED_TEASERS"], strip_html=self.config["FEED_PLAIN"], feed_read_more_link=True, feed_links_append_query=feed_append_query)) entry_root = lxml.etree.SubElement(feed_root, "entry") entry_title = lxml.etree.SubElement(entry_root, "title") entry_title.text = post.title(lang) entry_id = lxml.etree.SubElement(entry_root, "id") entry_id.text = post.permalink(lang, absolute=True) entry_updated = lxml.etree.SubElement(entry_root, "updated") entry_updated.text = post.formatted_updated('webiso') entry_published = lxml.etree.SubElement(entry_root, "published") entry_published.text = post.formatted_date('webiso') entry_author = lxml.etree.SubElement(entry_root, "author") entry_author_name = lxml.etree.SubElement(entry_author, "name") entry_author_name.text = post.author(lang) entry_root.append(atom_link("alternate", "text/html", post.permalink(lang, absolute=True, query=feed_append_query))) entry_summary = lxml.etree.SubElement(entry_root, "summary") if not self.config["FEED_PLAIN"]: entry_summary.set("type", "html") else: entry_summary.set("type", "text") entry_summary.text = summary if content: entry_content = lxml.etree.SubElement(entry_root, "content") if not self.config["FEED_PLAIN"]: entry_content.set("type", "html") else: entry_content.set("type", "text") entry_content.text = content for category in post.tags_for_language(lang): entry_category = lxml.etree.SubElement(entry_root, "category") entry_category.set("term", utils.slugify(category, lang)) entry_category.set("label", category) dst_dir = os.path.dirname(output_path) utils.makedirs(dst_dir) with io.open(output_path, "w+", encoding="utf-8") as atom_file: data = lxml.etree.tostring(feed_root.getroottree(), encoding="UTF-8", pretty_print=True, xml_declaration=True) if isinstance(data, bytes): data = data.decode('utf-8') atom_file.write(data) def generic_index_renderer(self, lang, posts, indexes_title, template_name, context_source, kw, basename, page_link, page_path, additional_dependencies=None): """Create an index page. lang: The language posts: A list of posts indexes_title: Title template_name: Name of template file context_source: This will be copied and extended and used as every page's context kw: An extended version will be used for uptodate dependencies basename: Basename for task page_link: A function accepting an index i, the displayed page number, the number of pages, and a boolean force_addition which creates a link to the i-th page (where i ranges between 0 and num_pages-1). The displayed page (between 1 and num_pages) is the number (optionally) displayed as 'page %d' on the rendered page. If force_addition is True, the appendum (inserting '-%d' etc.) should be done also for i == 0. page_path: A function accepting an index i, the displayed page number, the number of pages, and a boolean force_addition, which creates a path to the i-th page. All arguments are as the ones for page_link. additional_dependencies: a list of dependencies which will be added to task['uptodate'] Note: if context['featured'] is present, it must be a list of posts, whose dependencies will be taken added to task['uptodate']. """ # Update kw kw = kw.copy() kw["tag_pages_are_indexes"] = self.config['TAG_PAGES_ARE_INDEXES'] kw["index_display_post_count"] = self.config['INDEX_DISPLAY_POST_COUNT'] kw["index_teasers"] = self.config['INDEX_TEASERS'] kw["indexes_pages"] = self.config['INDEXES_PAGES'](lang) kw["indexes_pages_main"] = self.config['INDEXES_PAGES_MAIN'] kw["indexes_static"] = self.config['INDEXES_STATIC'] kw['indexes_pretty_page_url'] = self.config["INDEXES_PRETTY_PAGE_URL"] kw['show_index_page_navigation'] = self.config['SHOW_INDEX_PAGE_NAVIGATION'] if additional_dependencies is None: additional_dependencies = [] # Split in smaller lists lists = [] if kw["indexes_static"]: lists.append(posts[:kw["index_display_post_count"]]) posts = posts[kw["index_display_post_count"]:] while posts: lists.append(posts[-kw["index_display_post_count"]:]) posts = posts[:-kw["index_display_post_count"]] else: while posts: lists.append(posts[:kw["index_display_post_count"]]) posts = posts[kw["index_display_post_count"]:] if not lists: lists.append([]) num_pages = len(lists) displayed_page_numbers = [utils.get_displayed_page_number(i, num_pages, self) for i in range(num_pages)] page_links = [page_link(i, page_number, num_pages, False) for i, page_number in enumerate(displayed_page_numbers)] if kw['show_index_page_navigation']: # Since the list displayed_page_numbers is not necessarily # sorted -- in case INDEXES_STATIC is True, it is of the # form [num_pages, 1, 2, ..., num_pages - 1] -- we order it # via a map. This allows to not replicate the logic of # utils.get_displayed_page_number() here. if not kw["indexes_pages_main"] and not kw["indexes_static"]: temp_map = {page_number: link for page_number, link in zip(displayed_page_numbers, page_links)} else: temp_map = {page_number - 1: link for page_number, link in zip(displayed_page_numbers, page_links)} page_links_context = [temp_map[i] for i in range(num_pages)] for i, post_list in enumerate(lists): context = context_source.copy() if 'pagekind' not in context: context['pagekind'] = ['index'] if 'has_other_languages' not in context: context['has_other_languages'] = False ipages_i = displayed_page_numbers[i] if kw["indexes_pages"]: indexes_pages = kw["indexes_pages"] % ipages_i else: if kw["indexes_pages_main"]: ipages_msg = "page %d" else: ipages_msg = "old posts, page %d" indexes_pages = " (" + \ kw["messages"][lang][ipages_msg] % ipages_i + ")" if i > 0 or kw["indexes_pages_main"]: context["title"] = indexes_title + indexes_pages else: context["title"] = indexes_title context["prevlink"] = None context["nextlink"] = None context['index_teasers'] = kw['index_teasers'] prevlink = None nextlink = None if kw["indexes_static"]: if i > 0: if i < num_pages - 1: prevlink = i + 1 elif i == num_pages - 1: prevlink = 0 if num_pages > 1: if i > 1: nextlink = i - 1 elif i == 0: nextlink = num_pages - 1 else: if i >= 1: prevlink = i - 1 if i < num_pages - 1: nextlink = i + 1 if prevlink is not None: context["prevlink"] = page_links[prevlink] context["prevfeedlink"] = page_link(prevlink, displayed_page_numbers[prevlink], num_pages, False, extension=".atom") if nextlink is not None: context["nextlink"] = page_links[nextlink] context["nextfeedlink"] = page_link(nextlink, displayed_page_numbers[nextlink], num_pages, False, extension=".atom") context['show_index_page_navigation'] = kw['show_index_page_navigation'] if kw['show_index_page_navigation']: context['page_links'] = page_links_context if not kw["indexes_pages_main"] and not kw["indexes_static"]: context['current_page'] = ipages_i else: context['current_page'] = ipages_i - 1 context['prev_next_links_reversed'] = kw['indexes_static'] context["permalink"] = page_links[i] context["is_frontmost_index"] = i == 0 # Add dependencies to featured posts if 'featured' in context: for post in context['featured']: additional_dependencies += post.deps_uptodate(lang) output_name = os.path.join(kw['output_folder'], page_path(i, ipages_i, num_pages, False)) task = self.generic_post_list_renderer( lang, post_list, output_name, template_name, kw['filters'], context, ) task['uptodate'] = task['uptodate'] + [utils.config_changed(kw, 'nikola.nikola.Nikola.generic_index_renderer')] + additional_dependencies task['basename'] = basename yield task if kw["indexes_pages_main"] and kw['indexes_pretty_page_url'](lang): # create redirection output_name = os.path.join(kw['output_folder'], page_path(0, displayed_page_numbers[0], num_pages, True)) link = page_links[0] yield utils.apply_filters({ 'basename': basename, 'name': output_name, 'targets': [output_name], 'actions': [(utils.create_redirect, (output_name, link))], 'clean': True, 'uptodate': [utils.config_changed(kw, 'nikola.nikola.Nikola.generic_index_renderer')], }, kw["filters"]) def generic_atom_renderer(self, lang, posts, context_source, kw, basename, classification, kind, additional_dependencies=None): """Create an Atom feed. lang: The language posts: A list of posts context_source: This will be copied and extended and used as every page's context kw: An extended version will be used for uptodate dependencies basename: Basename for task classification: name of current classification (used to generate links) kind: classification kind (used to generate links) additional_dependencies: a list of dependencies which will be added to task['uptodate'] """ # Update kw kw = kw.copy() kw["feed_length"] = self.config['FEED_LENGTH'] kw['generate_atom'] = self.config["GENERATE_ATOM"] kw['feed_links_append_query'] = self.config["FEED_LINKS_APPEND_QUERY"] kw['feed_teasers'] = self.config['FEED_TEASERS'] kw['feed_plain'] = self.config['FEED_PLAIN'] if additional_dependencies is None: additional_dependencies = [] post_list = posts[:kw["feed_length"]] feedlink = self.link(kind + "_atom", classification, lang) feedpath = self.path(kind + "_atom", classification, lang) context = context_source.copy() if 'has_other_languages' not in context: context['has_other_languages'] = False output_name = os.path.join(kw['output_folder'], feedpath) context["feedlink"] = feedlink task = { "basename": basename, "name": output_name, "file_dep": sorted([_.base_path for _ in post_list]), "task_dep": ['render_posts'], "targets": [output_name], "actions": [(self.atom_feed_renderer, (lang, post_list, output_name, kw['filters'], context,))], "clean": True, "uptodate": [utils.config_changed(kw, 'nikola.nikola.Nikola.atom_feed_renderer')] + additional_dependencies } yield utils.apply_filters(task, kw['filters']) def __repr__(self): """Representation of a Nikola site.""" return '<Nikola Site: {0!r}>'.format(self.config['BLOG_TITLE'](self.config['DEFAULT_LANG']))

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import datetime import io import json import functools import logging import operator import os import sys import mimetypes from collections import defaultdict from copy import copy from urllib.parse import urlparse, urlsplit, urlunsplit, urljoin, unquote, parse_qs import dateutil.tz import lxml.etree import lxml.html import natsort import PyRSS2Gen as rss from pkg_resources import resource_filename from blinker import signal from yapsy.PluginManager import PluginManager from . import DEBUG, SHOW_TRACEBACKS, filters, utils, hierarchy_utils, shortcodes from . import metadata_extractors from .metadata_extractors import default_metadata_extractors_by from .post import Post from .plugin_categories import ( Command, LateTask, PageCompiler, CompilerExtension, MarkdownExtension, RestExtension, MetadataExtractor, ShortcodePlugin, Task, TaskMultiplier, TemplateSystem, SignalHandler, ConfigPlugin, PostScanner, Taxonomy, ) from .state import Persistor try: import pyphen except ImportError: pyphen = None if DEBUG: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.ERROR) DEFAULT_INDEX_READ_MORE_LINK = '<p class="more"><a href="{link}">{read_more}…</a></p>' DEFAULT_FEED_READ_MORE_LINK = '<p><a href="{link}">{read_more}…</a> ({min_remaining_read})</p>' config_changed = utils.config_changed __all__ = ('Nikola',) LEGAL_VALUES = { 'DEFAULT_THEME': 'bootblog4', 'COMMENT_SYSTEM': [ 'disqus', 'facebook', 'intensedebate', 'isso', 'muut', 'commento', ], 'TRANSLATIONS': { 'af': 'Afrikaans', 'ar': 'Arabic', 'az': 'Azerbaijani', 'bg': 'Bulgarian', 'bs': 'Bosnian', 'ca': 'Catalan', ('cs', 'cz'): 'Czech', 'da': 'Danish', 'de': 'German', ('el', '!gr'): 'Greek', 'en': 'English', 'eo': 'Esperanto', 'es': 'Spanish', 'et': 'Estonian', 'eu': 'Basque', 'fa': 'Persian', 'fi': 'Finnish', 'fr': 'French', 'fur': 'Friulian', 'gl': 'Galician', 'he': 'Hebrew', 'hi': 'Hindi', 'hr': 'Croatian', 'hu': 'Hungarian', 'ia': 'Interlingua', 'id': 'Indonesian', 'it': 'Italian', ('ja', '!jp'): 'Japanese', 'ko': 'Korean', 'lt': 'Lithuanian', 'ml': 'Malayalam', 'mr': 'Marathi', 'nb': 'Norwegian (Bokmål)', 'nl': 'Dutch', 'pa': 'Punjabi', 'pl': 'Polish', 'pt': 'Portuguese', 'pt_br': 'Portuguese (Brazil)', 'ru': 'Russian', 'sk': 'Slovak', 'sl': 'Slovene', 'sq': 'Albanian', 'sr': 'Serbian (Cyrillic)', 'sr_latin': 'Serbian (Latin)', 'sv': 'Swedish', 'te': 'Telugu', 'th': 'Thai', ('tr', '!tr_TR'): 'Turkish', 'uk': 'Ukrainian', 'ur': 'Urdu', 'vi': 'Vietnamese', 'zh_cn': 'Chinese (Simplified)', 'zh_tw': 'Chinese (Traditional)' }, '_TRANSLATIONS_WITH_COUNTRY_SPECIFIERS': { }, 'LOCALES_BASE': { 'sr_latin': 'sr_Latn', }, 'RTL_LANGUAGES': ('ar', 'fa', 'he', 'ur'), 'LUXON_LOCALES': defaultdict(lambda: 'en', **{ 'af': 'af', 'ar': 'ar', 'az': 'az', 'bg': 'bg', 'bn': 'bn', 'bs': 'bs', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en', 'eo': 'eo', 'es': 'es', 'et': 'et', 'eu': 'eu', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'fur': 'fur', 'gl': 'gl', 'hi': 'hi', 'he': 'he', 'hr': 'hr', 'hu': 'hu', 'ia': 'ia', 'id': 'id', 'it': 'it', 'ja': 'ja', 'ko': 'ko', 'lt': 'lt', 'ml': 'ml', 'mr': 'mr', 'nb': 'nb', 'nl': 'nl', 'pa': 'pa', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt-BR', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sq': 'sq', 'sr': 'sr-Cyrl', 'sr_latin': 'sr-Latn', 'sv': 'sv', 'te': 'te', 'tr': 'tr', 'th': 'th', 'uk': 'uk', 'ur': 'ur', 'vi': 'vi', 'zh_cn': 'zh-CN', 'zh_tw': 'zh-TW' }), 'MOMENTJS_LOCALES': defaultdict(lambda: 'en', **{ 'af': 'af', 'ar': 'ar', 'az': 'az', 'bg': 'bg', 'bn': 'bn', 'bs': 'bs', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en', 'eo': 'eo', 'es': 'es', 'et': 'et', 'eu': 'eu', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'gl': 'gl', 'hi': 'hi', 'he': 'he', 'hr': 'hr', 'hu': 'hu', 'id': 'id', 'it': 'it', 'ja': 'ja', 'ko': 'ko', 'lt': 'lt', 'ml': 'ml', 'mr': 'mr', 'nb': 'nb', 'nl': 'nl', 'pa': 'pa-in', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt-br', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sq': 'sq', 'sr': 'sr-cyrl', 'sr_latin': 'sr', 'sv': 'sv', 'te': 'te', 'tr': 'tr', 'th': 'th', 'uk': 'uk', 'ur': 'ur', 'vi': 'vi', 'zh_cn': 'zh-cn', 'zh_tw': 'zh-tw' }), 'PYPHEN_LOCALES': { 'af': 'af', 'bg': 'bg', 'ca': 'ca', 'cs': 'cs', 'cz': 'cs', 'da': 'da', 'de': 'de', 'el': 'el', 'en': 'en_US', 'es': 'es', 'et': 'et', 'fr': 'fr', 'hr': 'hr', 'hu': 'hu', 'it': 'it', 'lt': 'lt', 'nb': 'nb', 'nl': 'nl', 'pl': 'pl', 'pt': 'pt', 'pt_br': 'pt_BR', 'ru': 'ru', 'sk': 'sk', 'sl': 'sl', 'sr': 'sr', 'sv': 'sv', 'te': 'te', 'uk': 'uk', }, 'DOCUTILS_LOCALES': { 'af': 'af', 'ca': 'ca', 'da': 'da', 'de': 'de', 'en': 'en', 'eo': 'eo', 'es': 'es', 'fa': 'fa', 'fi': 'fi', 'fr': 'fr', 'gl': 'gl', 'he': 'he', 'it': 'it', 'ja': 'ja', 'lt': 'lt', 'nl': 'nl', 'pl': 'pl', 'pt': 'pt_br', 'pt_br': 'pt_br', 'ru': 'ru', 'sk': 'sk', 'sv': 'sv', 'zh_cn': 'zh_cn', 'zh_tw': 'zh_tw' }, "METADATA_MAPPING": ["yaml", "toml", "rest_docinfo", "markdown_metadata"], } TAXONOMY_COMPATIBILITY_PLUGIN_NAME_MAP = { "render_archive": ["classify_archive"], "render_authors": ["classify_authors"], "render_indexes": ["classify_page_index", "classify_sections"], gories", "classify_tags"], } DEFAULT_TRANSLATIONS_PATTERN = '{path}.{lang}.{ext}' def _enclosure(post, lang): enclosure = post.meta('enclosure', lang) if enclosure: try: length = int(post.meta('enclosure_length', lang) or 0) except KeyError: length = 0 except ValueError: utils.LOGGER.warning("Invalid enclosure length for post {0}".format(post.source_path)) length = 0 url = enclosure mime = mimetypes.guess_type(url)[0] return url, length, mime class Nikola(object): def __init__(self, **config): self.path_handlers = { 'slug': self.slug_path, 'post_path': self.post_path, 'root': self.root_path, 'filename': self.filename_path, } self.strict = False self.posts = [] self.all_posts = [] self.posts_per_year = defaultdict(list) self.posts_per_month = defaultdict(list) self.posts_per_tag = defaultdict(list) self.posts_per_category = defaultdict(list) self.tags_per_language = defaultdict(list) self.post_per_file = {} self.timeline = [] self.pages = [] self._scanned = False self._template_system = None self._THEMES = None self._MESSAGES = None self.filters = {} self.debug = DEBUG self.show_tracebacks = SHOW_TRACEBACKS self.colorful = config.pop('__colorful__', False) self.invariant = config.pop('__invariant__', False) self.quiet = config.pop('__quiet__', False) self._doit_config = config.pop('DOIT_CONFIG', {}) self.original_cwd = config.pop('__cwd__', False) self.configuration_filename = config.pop('__configuration_filename__', False) self.configured = bool(config) self.injected_deps = defaultdict(list) self.shortcode_registry = {} self.metadata_extractors_by = default_metadata_extractors_by() self.rst_transforms = [] self.template_hooks = { 'extra_head': utils.TemplateHookRegistry('extra_head', self), 'body_end': utils.TemplateHookRegistry('body_end', self), 'page_header': utils.TemplateHookRegistry('page_header', self), 'menu': utils.TemplateHookRegistry('menu', self), 'menu_alt': utils.TemplateHookRegistry('menu_alt', self), 'page_footer': utils.TemplateHookRegistry('page_footer', self), } utils.generic_rss_renderer = self.generic_rss_renderer self.config = { 'ARCHIVE_PATH': "", 'ARCHIVE_FILENAME': "archive.html", 'ARCHIVES_ARE_INDEXES': False, 'AUTHOR_PATH': 'authors', 'AUTHOR_PAGES_ARE_INDEXES': False, 'AUTHOR_PAGES_DESCRIPTIONS': {}, 'AUTHORLIST_MINIMUM_POSTS': 1, 'BLOG_AUTHOR': 'Default Author', 'BLOG_TITLE': 'Default Title', 'BLOG_EMAIL': '', 'BLOG_DESCRIPTION': 'Default Description', 'BODY_END': "", 'CACHE_FOLDER': 'cache', 'CATEGORIES_INDEX_PATH': '', 'CATEGORY_PATH': None, 'CATEGORY_PAGES_ARE_INDEXES': None, 'CATEGORY_DESCRIPTIONS': {}, 'CATEGORY_TITLES': {}, 'CATEGORY_PREFIX': 'cat_', 'CATEGORY_ALLOW_HIERARCHIES': False, 'CATEGORY_OUTPUT_FLAT_HIERARCHY': False, 'CATEGORY_DESTPATH_AS_DEFAULT': False, 'CATEGORY_DESTPATH_TRIM_PREFIX': False, 'CATEGORY_DESTPATH_FIRST_DIRECTORY_ONLY': True, 'CATEGORY_DESTPATH_NAMES': {}, 'CATEGORY_PAGES_FOLLOW_DESTPATH': False, 'CATEGORY_TRANSLATIONS': [], 'CATEGORY_TRANSLATIONS_ADD_DEFAULTS': False, 'CODE_COLOR_SCHEME': 'default', 'COMMENT_SYSTEM': 'disqus', 'COMMENTS_IN_GALLERIES': False, 'COMMENTS_IN_PAGES': False, 'COMPILERS': { "rest": ('.txt', '.rst'), "markdown": ('.md', '.mdown', '.markdown'), "textile": ('.textile',), "txt2tags": ('.t2t',), "bbcode": ('.bb',), "wiki": ('.wiki',), "ipynb": ('.ipynb',), "html": ('.html', '.htm') }, 'CONTENT_FOOTER': '', 'CONTENT_FOOTER_FORMATS': {}, 'RSS_COPYRIGHT': '', 'RSS_COPYRIGHT_PLAIN': '', 'RSS_COPYRIGHT_FORMATS': {}, 'COPY_SOURCES': True, 'CREATE_ARCHIVE_NAVIGATION': False, 'CREATE_MONTHLY_ARCHIVE': False, 'CREATE_SINGLE_ARCHIVE': False, 'CREATE_FULL_ARCHIVES': False, 'CREATE_DAILY_ARCHIVE': False, 'DATE_FORMAT': 'yyyy-MM-dd HH:mm', 'DISABLE_INDEXES': False, 'DISABLE_MAIN_ATOM_FEED': False, 'DISABLE_MAIN_RSS_FEED': False, 'MOMENTJS_DATE_FORMAT': 'YYYY-MM-DD HH:mm', 'LUXON_DATE_FORMAT': {}, 'DATE_FANCINESS': 0, 'DEFAULT_LANG': "en", 'DEPLOY_COMMANDS': {'default': []}, 'DISABLED_PLUGINS': [], 'EXTRA_PLUGINS_DIRS': [], 'EXTRA_THEMES_DIRS': [], 'COMMENT_SYSTEM_ID': 'nikolademo', 'ENABLE_AUTHOR_PAGES': True, 'EXIF_WHITELIST': {}, 'EXTRA_HEAD_DATA': '', 'FAVICONS': (), 'FEED_LENGTH': 10, 'FILE_METADATA_REGEXP': None, 'FILE_METADATA_UNSLUGIFY_TITLES': True, 'ADDITIONAL_METADATA': {}, 'FILES_FOLDERS': {'files': ''}, 'FILTERS': {}, 'FORCE_ISO8601': False, 'FRONT_INDEX_HEADER': '', 'GALLERY_FOLDERS': {'galleries': 'galleries'}, 'GALLERY_SORT_BY_DATE': True, 'GALLERIES_USE_THUMBNAIL': False, 'GALLERIES_DEFAULT_THUMBNAIL': None, 'GLOBAL_CONTEXT_FILLER': [], 'GZIP_COMMAND': None, 'GZIP_FILES': False, 'GZIP_EXTENSIONS': ('.txt', '.htm', '.html', '.css', '.js', '.json', '.xml'), 'HIDDEN_AUTHORS': [], 'HIDDEN_TAGS': [], 'HIDE_REST_DOCINFO': False, 'HIDDEN_CATEGORIES': [], 'HYPHENATE': False, 'IMAGE_FOLDERS': {'images': ''}, 'INDEX_DISPLAY_POST_COUNT': 10, 'INDEX_FILE': 'index.html', 'INDEX_TEASERS': False, 'IMAGE_THUMBNAIL_SIZE': 400, 'IMAGE_THUMBNAIL_FORMAT': '{name}.thumbnail{ext}', 'INDEXES_TITLE': "", 'INDEXES_PAGES': "", 'INDEXES_PAGES_MAIN': False, 'INDEXES_PRETTY_PAGE_URL': False, 'INDEXES_STATIC': True, 'INDEX_PATH': '', 'IPYNB_CONFIG': {}, 'KATEX_AUTO_RENDER': '', 'LICENSE': '', 'LINK_CHECK_WHITELIST': [], 'LISTINGS_FOLDERS': {'listings': 'listings'}, 'LOGO_URL': '', 'DEFAULT_PREVIEW_IMAGE': None, 'NAVIGATION_LINKS': {}, 'NAVIGATION_ALT_LINKS': {}, 'MARKDOWN_EXTENSIONS': ['fenced_code', 'codehilite', 'extra'], 'MARKDOWN_EXTENSION_CONFIGS': {}, 'MAX_IMAGE_SIZE': 1280, 'MATHJAX_CONFIG': '', 'METADATA_FORMAT': 'nikola', 'METADATA_MAPPING': {}, 'NEW_POST_DATE_PATH': False, 'NEW_POST_DATE_PATH_FORMAT': '%Y/%m/%d', 'OLD_THEME_SUPPORT': True, 'OUTPUT_FOLDER': 'output', 'POSTS': (("posts/*.txt", "posts", "post.tmpl"),), 'PRESERVE_EXIF_DATA': False, 'PRESERVE_ICC_PROFILES': False, 'PAGES': (("pages/*.txt", "pages", "page.tmpl"),), 'PANDOC_OPTIONS': [], 'PRETTY_URLS': True, 'FUTURE_IS_NOW': False, 'INDEX_READ_MORE_LINK': DEFAULT_INDEX_READ_MORE_LINK, 'REDIRECTIONS': [], 'ROBOTS_EXCLUSIONS': [], 'GENERATE_ATOM': False, 'ATOM_EXTENSION': '.atom', 'ATOM_PATH': '', 'ATOM_FILENAME_BASE': 'index', 'FEED_TEASERS': True, 'FEED_PLAIN': False, 'FEED_READ_MORE_LINK': DEFAULT_FEED_READ_MORE_LINK, 'FEED_LINKS_APPEND_QUERY': False, 'GENERATE_RSS': True, 'RSS_EXTENSION': '.xml', 'RSS_LINK': None, 'RSS_PATH': '', 'RSS_FILENAME_BASE': 'rss', 'SEARCH_FORM': '', 'SHOW_BLOG_TITLE': True, 'SHOW_INDEX_PAGE_NAVIGATION': False, 'SHOW_SOURCELINK': True, 'SHOW_UNTRANSLATED_POSTS': True, 'SLUG_AUTHOR_PATH': True, 'SLUG_TAG_PATH': True, 'SOCIAL_BUTTONS_CODE': '', 'SITE_URL': 'https://example.com/', 'PAGE_INDEX': False, 'SECTION_PATH': '', 'STRIP_INDEXES': True, 'TAG_PATH': 'categories', 'TAG_PAGES_ARE_INDEXES': False, 'TAG_DESCRIPTIONS': {}, 'TAG_TITLES': {}, 'TAG_TRANSLATIONS': [], 'TAG_TRANSLATIONS_ADD_DEFAULTS': False, 'TAGS_INDEX_PATH': '', 'TAGLIST_MINIMUM_POSTS': 1, 'TEMPLATE_FILTERS': {}, 'THEME': LEGAL_VALUES['DEFAULT_THEME'], 'THEME_COLOR': '#5670d4', 'THEME_CONFIG': {}, 'THUMBNAIL_SIZE': 180, 'TRANSLATIONS_PATTERN': DEFAULT_TRANSLATIONS_PATTERN, 'URL_TYPE': 'rel_path', 'USE_BUNDLES': True, 'USE_CDN': False, 'USE_CDN_WARNING': True, 'USE_REST_DOCINFO_METADATA': False, 'USE_FILENAME_AS_TITLE': True, 'USE_KATEX': False, 'USE_SLUGIFY': True, 'USE_TAG_METADATA': True, 'TIMEZONE': 'UTC', 'WARN_ABOUT_TAG_METADATA': True, 'DEPLOY_DRAFTS': True, 'DEPLOY_FUTURE': False, 'SCHEDULE_ALL': False, 'SCHEDULE_RULE': '', 'DEMOTE_HEADERS': 1, 'GITHUB_SOURCE_BRANCH': 'master', 'GITHUB_DEPLOY_BRANCH': 'gh-pages', 'GITHUB_REMOTE_NAME': 'origin', 'GITHUB_COMMIT_SOURCE': False, 'META_GENERATOR_TAG': True, 'REST_FILE_INSERTION_ENABLED': True, 'TYPES_TO_HIDE_TITLE': [], } self._GLOBAL_CONTEXT = {} self.ALL_PAGE_DEPS = {} self.config.update(config) if '__builtins__' in self.config: try: del self.config['__builtins__'] except KeyError: del self.config[b'__builtins__'] self.config['__colorful__'] = self.colorful self.config['__invariant__'] = self.invariant self.config['__quiet__'] = self.quiet self.config['ATOM_PATH'] = self.config['ATOM_PATH'] or self.config['INDEX_PATH'] if not self.config['NAVIGATION_LINKS']: self.config['NAVIGATION_LINKS'] = {self.config['DEFAULT_LANG']: ()} if not self.config['NAVIGATION_ALT_LINKS']: self.config['NAVIGATION_ALT_LINKS'] = {self.config['DEFAULT_LANG']: ()} self.config['TRANSLATIONS'] = self.config.get('TRANSLATIONS', {self.config['DEFAULT_LANG']: ''}) for k, v in self.config['TRANSLATIONS'].items(): if os.path.isabs(v): self.config['TRANSLATIONS'][k] = os.path.relpath(v, '/') utils.TranslatableSetting.default_lang = self.config['DEFAULT_LANG'] self.TRANSLATABLE_SETTINGS = ('BLOG_AUTHOR', 'BLOG_TITLE', 'BLOG_DESCRIPTION', 'LICENSE', 'CONTENT_FOOTER', 'SOCIAL_BUTTONS_CODE', 'SEARCH_FORM', 'BODY_END', 'EXTRA_HEAD_DATA', 'NAVIGATION_LINKS', 'NAVIGATION_ALT_LINKS', 'FRONT_INDEX_HEADER', 'INDEX_READ_MORE_LINK', 'FEED_READ_MORE_LINK', 'INDEXES_TITLE', 'CATEGORY_DESTPATH_NAMES', 'INDEXES_PAGES', 'INDEXES_PRETTY_PAGE_URL', 'THEME_CONFIG', 'ARCHIVE_PATH', 'ARCHIVE_FILENAME', 'TAG_PATH', 'TAGS_INDEX_PATH', 'CATEGORY_PATH', 'CATEGORIES_INDEX_PATH', 'SECTION_PATH', 'INDEX_PATH', 'ATOM_PATH', 'RSS_PATH', 'RSS_FILENAME_BASE', 'ATOM_FILENAME_BASE', 'AUTHOR_PATH', 'DATE_FORMAT', 'LUXON_DATE_FORMAT', 'MOMENTJS_DATE_FORMAT', 'RSS_COPYRIGHT', 'RSS_COPYRIGHT_PLAIN', 'MARKDOWN_EXTENSION_CONFIGS', ) self._GLOBAL_CONTEXT_TRANSLATABLE = ('blog_author', 'blog_title', 'blog_description', 'license', 'content_footer', 'social_buttons_code', 'search_form', 'body_end', 'extra_head_data', 'date_format', 'js_date_format', 'luxon_date_format', 'front_index_header', 'theme_config', ) self._ALL_PAGE_DEPS_TRANSLATABLE = ('atom_path', 'rss_path', 'rss_filename_base', 'atom_filename_base', ) if not self.config['LUXON_DATE_FORMAT']: self.config['LUXON_DATE_FORMAT'] = {self.config['DEFAULT_LANG']: {'preset': False, 'format': 'yyyy-MM-dd HH:mm'}} if 'JS_DATE_FORMAT' in self.config: utils.LOGGER.warning("Moment.js was replaced by Luxon in the default themes, which uses different date formats.") utils.LOGGER.warning("If you’re using a built-in theme, set LUXON_DATE_FORMAT. If your theme uses Moment.js, you can silence this warning by renaming JS_DATE_FORMAT to MOMENTJS_DATE_FORMAT.") utils.LOGGER.warning("Sample Luxon config: LUXON_DATE_FORMAT = " + str(self.config['LUXON_DATE_FORMAT'])) self.config['MOMENTJS_DATE_FORMAT'] = self.config['LUXON_DATE_FORMAT'] if 'MOMENTJS_DATE_FORMAT' in self.config: if isinstance(self.config['MOMENTJS_DATE_FORMAT'], dict): for k in self.config['MOMENTJS_DATE_FORMAT']: self.config['MOMENTJS_DATE_FORMAT'][k] = json.dumps(self.config['MOMENTJS_DATE_FORMAT'][k]) else: self.config['MOMENTJS_DATE_FORMAT'] = json.dumps(self.config['MOMENTJS_DATE_FORMAT']) if 'LUXON_DATE_FORMAT' in self.config: for k in self.config['LUXON_DATE_FORMAT']: self.config['LUXON_DATE_FORMAT'][k] = json.dumps(self.config['LUXON_DATE_FORMAT'][k]) for i in self.TRANSLATABLE_SETTINGS: try: self.config[i] = utils.TranslatableSetting(i, self.config[i], self.config['TRANSLATIONS']) except KeyError: pass if self.config['EXIF_WHITELIST'] and not self.config['PRESERVE_EXIF_DATA']: utils.LOGGER.warning('Setting EXIF_WHITELIST implies PRESERVE_EXIF_DATA is set to True') self.config['PRESERVE_EXIF_DATA'] = True if self.config['PRESERVE_EXIF_DATA'] and not self.config['EXIF_WHITELIST']: utils.LOGGER.warning('You are setting PRESERVE_EXIF_DATA and not EXIF_WHITELIST so EXIF data is not really kept.') if 'UNSLUGIFY_TITLES' in self.config: utils.LOGGER.warning('The UNSLUGIFY_TITLES setting was renamed to FILE_METADATA_UNSLUGIFY_TITLES.') self.config['FILE_METADATA_UNSLUGIFY_TITLES'] = self.config['UNSLUGIFY_TITLES'] if 'TAG_PAGES_TITLES' in self.config: utils.LOGGER.warning('The TAG_PAGES_TITLES setting was renamed to TAG_TITLES.') self.config['TAG_TITLES'] = self.config['TAG_PAGES_TITLES'] if 'TAG_PAGES_DESCRIPTIONS' in self.config: utils.LOGGER.warning('The TAG_PAGES_DESCRIPTIONS setting was renamed to TAG_DESCRIPTIONS.') self.config['TAG_DESCRIPTIONS'] = self.config['TAG_PAGES_DESCRIPTIONS'] if 'CATEGORY_PAGES_TITLES' in self.config: utils.LOGGER.warning('The CATEGORY_PAGES_TITLES setting was renamed to CATEGORY_TITLES.') self.config['CATEGORY_TITLES'] = self.config['CATEGORY_PAGES_TITLES'] if 'CATEGORY_PAGES_DESCRIPTIONS' in self.config: utils.LOGGER.warning('The CATEGORY_PAGES_DESCRIPTIONS setting was renamed to CATEGORY_DESCRIPTIONS.') self.config['CATEGORY_DESCRIPTIONS'] = self.config['CATEGORY_PAGES_DESCRIPTIONS'] if 'DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED' in self.config: utils.LOGGER.warning('The DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED setting was renamed and split to DISABLE_INDEXES and DISABLE_MAIN_ATOM_FEED.') self.config['DISABLE_INDEXES'] = self.config['DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED'] self.config['DISABLE_MAIN_ATOM_FEED'] = self.config['DISABLE_INDEXES_PLUGIN_INDEX_AND_ATOM_FEED'] if 'DISABLE_INDEXES_PLUGIN_RSS_FEED' in self.config: utils.LOGGER.warning('The DISABLE_INDEXES_PLUGIN_RSS_FEED setting was renamed to DISABLE_MAIN_RSS_FEED.') self.config['DISABLE_MAIN_RSS_FEED'] = self.config['DISABLE_INDEXES_PLUGIN_RSS_FEED'] for val in self.config['DATE_FORMAT'].values.values(): if '%' in val: utils.LOGGER.error('The DATE_FORMAT setting needs to be upgraded.') utils.LOGGER.warning("Nikola now uses CLDR-style date strings. http://cldr.unicode.org/translation/date-time") utils.LOGGER.warning("Example: %Y-%m-%d %H:%M ==> yyyy-MM-dd HH:mm") utils.LOGGER.warning("(note it’s different to what moment.js uses!)") sys.exit(1) locales = LEGAL_VALUES['LOCALES_BASE'] if 'LOCALES' in self.config: for k, v in self.config['LOCALES'].items(): self.config['LOCALES'][k] = v.split('.')[0] locales.update(self.config['LOCALES']) self.config['LOCALES'] = locales if self.config.get('POSTS_SECTIONS'): utils.LOGGER.warning("The sections feature has been removed and its functionality has been merged into categories.") utils.LOGGER.warning("For more information on how to migrate, please read: https://getnikola.com/blog/upgrading-to-nikola-v8.html#sections-were-replaced-by-categories") for section_config_suffix, cat_config_suffix in ( ('DESCRIPTIONS', 'DESCRIPTIONS'), ('TITLE', 'TITLES'), ('TRANSLATIONS', 'TRANSLATIONS') ): section_config = 'POSTS_SECTION_' + section_config_suffix cat_config = 'CATEGORY_' + cat_config_suffix if section_config in self.config: self.config[section_config].update(self.config[cat_config]) self.config[cat_config] = self.config[section_config] self.config['CATEGORY_DESTPATH_NAMES'] = self.config.get('POSTS_SECTION_NAME', {}) self.config['CATEGORY_DESTPATH_NAMES'] = utils.TranslatableSetting('CATEGORY_DESTPATH_NAMES', self.config['CATEGORY_DESTPATH_NAMES'], self.config['TRANSLATIONS']) self.config['CATEGORY_DESTPATH_AS_DEFAULT'] = not self.config.get('POSTS_SECTION_FROM_META') utils.LOGGER.info("Setting CATEGORY_DESTPATH_AS_DEFAULT = " + str(self.config['CATEGORY_DESTPATH_AS_DEFAULT'])) if self.config.get('CATEGORY_PAGES_FOLLOW_DESTPATH') and (not self.config.get('CATEGORY_ALLOW_HIERARCHIES') or self.config.get('CATEGORY_OUTPUT_FLAT_HIERARCHY')): utils.LOGGER.error('CATEGORY_PAGES_FOLLOW_DESTPATH requires CATEGORY_ALLOW_HIERARCHIES = True, CATEGORY_OUTPUT_FLAT_HIERARCHY = False.') sys.exit(1) self.config['CONTENT_FOOTER'].langformat(self.config['CONTENT_FOOTER_FORMATS']) self.config['RSS_COPYRIGHT'].langformat(self.config['RSS_COPYRIGHT_FORMATS']) self.config['RSS_COPYRIGHT_PLAIN'].langformat(self.config['RSS_COPYRIGHT_FORMATS']) utils.USE_SLUGIFY = self.config['USE_SLUGIFY'] if self.config.get('HYPHENATE') and pyphen is None: utils.LOGGER.warning('To use the hyphenation, you have to install ' 'the "pyphen" package.') utils.LOGGER.warning('Setting HYPHENATE to False.') self.config['HYPHENATE'] = False self.config['post_pages'] = [] for i1, i2, i3 in self.config['POSTS']: self.config['post_pages'].append([i1, i2, i3, True]) for i1, i2, i3 in self.config['PAGES']: self.config['post_pages'].append([i1, i2, i3, False]) # Handle old plugin names (from before merging the taxonomy PR #2535) for old_plugin_name, new_plugin_names in TAXONOMY_COMPATIBILITY_PLUGIN_NAME_MAP.items(): if old_plugin_name in self.config['DISABLED_PLUGINS']: missing_plugins = [] for plugin_name in new_plugin_names: if plugin_name not in self.config['DISABLED_PLUGINS']: missing_plugins.append(plugin_name) if missing_plugins: utils.LOGGER.warning('The "{}" plugin was replaced by several taxonomy plugins (see PR utils.LOGGER.warning('You are currently disabling "{}", but not the following new taxonomy plugins: {}'.format(old_plugin_name, ', '.join(missing_plugins))) utils.LOGGER.warning('Please also disable these new plugins or remove "{}" from the DISABLED_PLUGINS list.'.format(old_plugin_name)) self.config['DISABLED_PLUGINS'].extend(missing_plugins) # Special-case logic for "render_indexes" to fix #2591 if 'render_indexes' in self.config['DISABLED_PLUGINS']: if 'generate_rss' in self.config['DISABLED_PLUGINS'] or self.config['GENERATE_RSS'] is False: if 'classify_indexes' not in self.config['DISABLED_PLUGINS']: utils.LOGGER.warning('You are disabling the "render_indexes" plugin, as well as disabling the "generate_rss" plugin or setting GENERATE_RSS to False. To achieve the same effect, please disable the "classify_indexes" plugin in the future.') self.config['DISABLED_PLUGINS'].append('classify_indexes') else: if not self.config['DISABLE_INDEXES']: utils.LOGGER.warning('You are disabling the "render_indexes" plugin, but not the generation of RSS feeds. Please put "DISABLE_INDEXES = True" into your configuration instead.') self.config['DISABLE_INDEXES'] = True # Disable RSS. For a successful disable, we must have both the option # false and the plugin disabled through the official means. if 'generate_rss' in self.config['DISABLED_PLUGINS'] and self.config['GENERATE_RSS'] is True: utils.LOGGER.warning('Please use GENERATE_RSS to disable RSS feed generation, instead of mentioning generate_rss in DISABLED_PLUGINS.') self.config['GENERATE_RSS'] = False self.config['DISABLE_MAIN_RSS_FEED'] = True # PRETTY_URLS defaults to enabling STRIP_INDEXES unless explicitly disabled if self.config.get('PRETTY_URLS') and 'STRIP_INDEXES' not in config: self.config['STRIP_INDEXES'] = True if not self.config.get('COPY_SOURCES'): self.config['SHOW_SOURCELINK'] = False if self.config['CATEGORY_PATH']._inp is None: self.config['CATEGORY_PATH'] = self.config['TAG_PATH'] if self.config['CATEGORY_PAGES_ARE_INDEXES'] is None: self.config['CATEGORY_PAGES_ARE_INDEXES'] = self.config['TAG_PAGES_ARE_INDEXES'] self.default_lang = self.config['DEFAULT_LANG'] self.translations = self.config['TRANSLATIONS'] utils.LocaleBorg.initialize(self.config.get('LOCALES', {}), self.default_lang) # BASE_URL defaults to SITE_URL if 'BASE_URL' not in self.config: self.config['BASE_URL'] = self.config.get('SITE_URL') # BASE_URL should *always* end in / if self.config['BASE_URL'] and self.config['BASE_URL'][-1] != '/': utils.LOGGER.warning("Your BASE_URL doesn't end in / -- adding it, but please fix it in your config file!") self.config['BASE_URL'] += '/' try: _bnl = urlsplit(self.config['BASE_URL']).netloc _bnl.encode('ascii') urlsplit(self.config['SITE_URL']).netloc.encode('ascii') except (UnicodeEncodeError, UnicodeDecodeError): utils.LOGGER.error("Your BASE_URL or SITE_URL contains an IDN expressed in Unicode. Please convert it to Punycode.") utils.LOGGER.error("Punycode of {}: {}".format(_bnl, _bnl.encode('idna'))) sys.exit(1) metadata_extractors.load_defaults(self, self.metadata_extractors_by) if metadata_extractors.DEFAULT_EXTRACTOR is None: utils.LOGGER.error("Could not find default meta extractor ({})".format( metadata_extractors.DEFAULT_EXTRACTOR_NAME)) sys.exit(1) if config.get('METADATA_FORMAT', 'nikola').lower() == 'pelican': if 'markdown.extensions.meta' not in config.get('MARKDOWN_EXTENSIONS', []) \ and 'markdown' in self.config['COMPILERS']: utils.LOGGER.warning( 'To use the Pelican metadata format, you need to add ' '"markdown.extensions.meta" to your MARKDOWN_EXTENSIONS setting.') try: self.tzinfo = dateutil.tz.gettz(self.config['TIMEZONE']) except Exception as exc: utils.LOGGER.warning("Error getting TZ: {}", exc) self.tzinfo = dateutil.tz.gettz() self.config['__tzinfo__'] = self.tzinfo self.config['_COMPILERS_RAW'] = {} for k, v in self.config['COMPILERS'].items(): self.config['_COMPILERS_RAW'][k] = list(v) self.themes_dirs = ['themes'] + self.config['EXTRA_THEMES_DIRS'] filter_name_format = 'filters.{0}' for filter_name, filter_definition in filters.__dict__.items(): if filter_name.startswith('_') or not callable(filter_definition): continue self.register_filter(filter_name_format.format(filter_name), filter_definition) self._set_global_context_from_config() self._set_all_page_deps_from_config() if self.configured: self._set_global_context_from_data() self.state = Persistor('state_data.json') self.cache = Persistor(os.path.join(self.config['CACHE_FOLDER'], 'cache_data.json')) if self.configured: self.state._set_site(self) self.cache._set_site(self) def _filter_duplicate_plugins(self, plugin_list): def plugin_position_in_places(plugin): for i, place in enumerate(self._plugin_places): if plugin[0].startswith(place): return i utils.LOGGER.warn("Duplicate plugin found in unexpected location: {}".format(plugin[0])) return len(self._plugin_places) plugin_dict = defaultdict(list) for data in plugin_list: plugin_dict[data[2].name].append(data) result = [] for _, plugins in plugin_dict.items(): if len(plugins) > 1: plugins.sort(key=plugin_position_in_places) utils.LOGGER.debug("Plugin {} exists in multiple places, using {}".format( plugins[-1][2].name, plugins[-1][0])) result.append(plugins[-1]) return result def init_plugins(self, commands_only=False, load_all=False): self.plugin_manager = PluginManager(categories_filter={ "Command": Command, "Task": Task, "LateTask": LateTask, "TemplateSystem": TemplateSystem, "PageCompiler": PageCompiler, "TaskMultiplier": TaskMultiplier, "CompilerExtension": CompilerExtension, "MarkdownExtension": MarkdownExtension, "RestExtension": RestExtension, "MetadataExtractor": MetadataExtractor, "ShortcodePlugin": ShortcodePlugin, "SignalHandler": SignalHandler, "ConfigPlugin": ConfigPlugin, "PostScanner": PostScanner, "Taxonomy": Taxonomy, }) self.plugin_manager.getPluginLocator().setPluginInfoExtension('plugin') extra_plugins_dirs = self.config['EXTRA_PLUGINS_DIRS'] self._plugin_places = [ resource_filename('nikola', 'plugins'), os.path.expanduser(os.path.join('~', '.nikola', 'plugins')), os.path.join(os.getcwd(), 'plugins'), ] + [path for path in extra_plugins_dirs if path] compilers = defaultdict(set) for compiler, exts in self.config['COMPILERS'].items(): for ext in exts: compilers[compiler].add(ext) for lang in self.config['TRANSLATIONS'].keys(): candidate = utils.get_translation_candidate(self.config, "f" + ext, lang) compilers[compiler].add(candidate) self.config['COMPILERS'] = {} self.disabled_compilers = {} self.disabled_compiler_extensions = defaultdict(list) self.plugin_manager.getPluginLocator().setPluginPlaces(self._plugin_places) self.plugin_manager.locatePlugins() bad_candidates = set([]) if not load_all: for p in self.plugin_manager._candidates: if commands_only: if p[-1].details.has_option('Nikola', 'PluginCategory'): if p[-1].details.get('Nikola', 'PluginCategory') not in {'Command', 'Template'}: bad_candidates.add(p) else: bad_candidates.add(p) elif self.configured: if p[-1].name in self.config['DISABLED_PLUGINS']: bad_candidates.add(p) utils.LOGGER.debug('Not loading disabled plugin {}', p[-1].name) if p[-1].details.has_option('Nikola', 'PluginCategory') and p[-1].details.get('Nikola', 'PluginCategory') in ('Compiler', 'PageCompiler'): bad_candidates.add(p) self.disabled_compilers[p[-1].name] = p # Remove compiler extensions we don't need if p[-1].details.has_option('Nikola', 'compiler') and p[-1].details.get('Nikola', 'compiler') in self.disabled_compilers: bad_candidates.add(p) self.disabled_compiler_extensions[p[-1].details.get('Nikola', 'compiler')].append(p) self.plugin_manager._candidates = list(set(self.plugin_manager._candidates) - bad_candidates) self.plugin_manager._candidates = self._filter_duplicate_plugins(self.plugin_manager._candidates) self.plugin_manager.loadPlugins() self._activate_plugins_of_category("PostScanner") if not load_all: file_extensions = set() for post_scanner in [p.plugin_object for p in self.plugin_manager.getPluginsOfCategory('PostScanner')]: exts = post_scanner.supported_extensions() if exts is not None: file_extensions.update(exts) else: # Stop scanning for more: once we get None, we have to load all compilers anyway utils.LOGGER.debug("Post scanner {0!r} does not implement `supported_extensions`, loading all compilers".format(post_scanner)) file_extensions = None break to_add = [] for k, v in compilers.items(): if file_extensions is None or file_extensions.intersection(v): self.config['COMPILERS'][k] = sorted(list(v)) p = self.disabled_compilers.pop(k, None) if p: to_add.append(p) for p in self.disabled_compiler_extensions.pop(k, []): to_add.append(p) for _, p in self.disabled_compilers.items(): utils.LOGGER.debug('Not loading unneeded compiler {}', p[-1].name) for _, plugins in self.disabled_compiler_extensions.items(): for p in plugins: utils.LOGGER.debug('Not loading compiler extension {}', p[-1].name) if to_add: self.plugin_manager._candidates = self._filter_duplicate_plugins(to_add) self.plugin_manager.loadPlugins() # Jupyter theme configuration. If a website has ipynb enabled in post_pages # we should enable the Jupyter CSS (leaving that up to the theme itself). if 'needs_ipython_css' not in self._GLOBAL_CONTEXT: self._GLOBAL_CONTEXT['needs_ipython_css'] = 'ipynb' in self.config['COMPILERS'] # Activate metadata extractors and prepare them for use for p in self._activate_plugins_of_category("MetadataExtractor"): metadata_extractors.classify_extractor(p.plugin_object, self.metadata_extractors_by) self._activate_plugins_of_category("Taxonomy") self.taxonomy_plugins = {} for taxonomy in [p.plugin_object for p in self.plugin_manager.getPluginsOfCategory('Taxonomy')]: if not taxonomy.is_enabled(): continue if taxonomy.classification_name in self.taxonomy_plugins: utils.LOGGER.error("Found more than one taxonomy with classification name '{}'!".format(taxonomy.classification_name)) sys.exit(1) self.taxonomy_plugins[taxonomy.classification_name] = taxonomy self._activate_plugins_of_category("SignalHandler") # Emit signal for SignalHandlers which need to start running immediately. signal('sighandlers_loaded').send(self) self._commands = {} command_plugins = self._activate_plugins_of_category("Command") for plugin_info in command_plugins: plugin_info.plugin_object.short_help = plugin_info.description self._commands[plugin_info.name] = plugin_info.plugin_object self._activate_plugins_of_category("Task") self._activate_plugins_of_category("LateTask") self._activate_plugins_of_category("TaskMultiplier") # Activate all required compiler plugins self.compiler_extensions = self._activate_plugins_of_category("CompilerExtension") for plugin_info in self.plugin_manager.getPluginsOfCategory("PageCompiler"): if plugin_info.name in self.config["COMPILERS"].keys(): self.plugin_manager.activatePluginByName(plugin_info.name) plugin_info.plugin_object.set_site(self) # Activate shortcode plugins self._activate_plugins_of_category("ShortcodePlugin") # Load compiler plugins self.compilers = {} self.inverse_compilers = {} for plugin_info in self.plugin_manager.getPluginsOfCategory( "PageCompiler"): self.compilers[plugin_info.name] = \ plugin_info.plugin_object # Load config plugins and register templated shortcodes self._activate_plugins_of_category("ConfigPlugin") self._register_templated_shortcodes() # Check with registered filters and configure filters for actions in self.config['FILTERS'].values(): for i, f in enumerate(actions): if isinstance(f, str): # Check whether this denotes a registered filter _f = self.filters.get(f) if _f is not None: f = _f actions[i] = f if hasattr(f, 'configuration_variables'): args = {} for arg, config in f.configuration_variables.items(): if config in self.config: args[arg] = self.config[config] if args: actions[i] = functools.partial(f, **args) # Signal that we are configured signal('configured').send(self) def _set_global_context_from_config(self): self._GLOBAL_CONTEXT['url_type'] = self.config['URL_TYPE'] self._GLOBAL_CONTEXT['timezone'] = self.tzinfo self._GLOBAL_CONTEXT['_link'] = self.link try: self._GLOBAL_CONTEXT['set_locale'] = utils.LocaleBorg().set_locale except utils.LocaleBorgUninitializedException: self._GLOBAL_CONTEXT['set_locale'] = None self._GLOBAL_CONTEXT['rel_link'] = self.rel_link self._GLOBAL_CONTEXT['abs_link'] = self.abs_link self._GLOBAL_CONTEXT['exists'] = self.file_exists self._GLOBAL_CONTEXT['index_display_post_count'] = self.config[ 'INDEX_DISPLAY_POST_COUNT'] self._GLOBAL_CONTEXT['index_file'] = self.config['INDEX_FILE'] self._GLOBAL_CONTEXT['use_bundles'] = self.config['USE_BUNDLES'] self._GLOBAL_CONTEXT['use_cdn'] = self.config.get("USE_CDN") self._GLOBAL_CONTEXT['theme_color'] = self.config.get("THEME_COLOR") self._GLOBAL_CONTEXT['theme_config'] = self.config.get("THEME_CONFIG") self._GLOBAL_CONTEXT['favicons'] = self.config['FAVICONS'] self._GLOBAL_CONTEXT['date_format'] = self.config.get('DATE_FORMAT') self._GLOBAL_CONTEXT['blog_author'] = self.config.get('BLOG_AUTHOR') self._GLOBAL_CONTEXT['blog_title'] = self.config.get('BLOG_TITLE') self._GLOBAL_CONTEXT['blog_email'] = self.config.get('BLOG_EMAIL') self._GLOBAL_CONTEXT['show_blog_title'] = self.config.get('SHOW_BLOG_TITLE') self._GLOBAL_CONTEXT['logo_url'] = self.config.get('LOGO_URL') self._GLOBAL_CONTEXT['blog_description'] = self.config.get('BLOG_DESCRIPTION') self._GLOBAL_CONTEXT['front_index_header'] = self.config.get('FRONT_INDEX_HEADER') self._GLOBAL_CONTEXT['color_hsl_adjust_hex'] = utils.color_hsl_adjust_hex self._GLOBAL_CONTEXT['colorize_str_from_base_color'] = utils.colorize_str_from_base_color self._GLOBAL_CONTEXT['blog_url'] = self.config.get('SITE_URL') self._GLOBAL_CONTEXT['template_hooks'] = self.template_hooks self._GLOBAL_CONTEXT['body_end'] = self.config.get('BODY_END') self._GLOBAL_CONTEXT['social_buttons_code'] = self.config.get('SOCIAL_BUTTONS_CODE') self._GLOBAL_CONTEXT['translations'] = self.config.get('TRANSLATIONS') self._GLOBAL_CONTEXT['license'] = self.config.get('LICENSE') self._GLOBAL_CONTEXT['search_form'] = self.config.get('SEARCH_FORM') self._GLOBAL_CONTEXT['comment_system'] = self.config.get('COMMENT_SYSTEM') self._GLOBAL_CONTEXT['comment_system_id'] = self.config.get('COMMENT_SYSTEM_ID') self._GLOBAL_CONTEXT['site_has_comments'] = bool(self.config.get('COMMENT_SYSTEM')) self._GLOBAL_CONTEXT['mathjax_config'] = self.config.get( 'MATHJAX_CONFIG') self._GLOBAL_CONTEXT['use_katex'] = self.config.get('USE_KATEX') self._GLOBAL_CONTEXT['katex_auto_render'] = self.config.get('KATEX_AUTO_RENDER') self._GLOBAL_CONTEXT['content_footer'] = self.config.get( 'CONTENT_FOOTER') self._GLOBAL_CONTEXT['generate_atom'] = self.config.get('GENERATE_ATOM') self._GLOBAL_CONTEXT['generate_rss'] = self.config.get('GENERATE_RSS') self._GLOBAL_CONTEXT['rss_link'] = self.config.get('RSS_LINK') self._GLOBAL_CONTEXT['navigation_links'] = self.config.get('NAVIGATION_LINKS') self._GLOBAL_CONTEXT['navigation_alt_links'] = self.config.get('NAVIGATION_ALT_LINKS') self._GLOBAL_CONTEXT['twitter_card'] = self.config.get( 'TWITTER_CARD', {}) self._GLOBAL_CONTEXT['hide_sourcelink'] = not self.config.get( 'SHOW_SOURCELINK') self._GLOBAL_CONTEXT['show_sourcelink'] = self.config.get( 'SHOW_SOURCELINK') self._GLOBAL_CONTEXT['extra_head_data'] = self.config.get('EXTRA_HEAD_DATA') self._GLOBAL_CONTEXT['date_fanciness'] = self.config.get('DATE_FANCINESS') self._GLOBAL_CONTEXT['luxon_locales'] = LEGAL_VALUES['LUXON_LOCALES'] self._GLOBAL_CONTEXT['luxon_date_format'] = self.config.get('LUXON_DATE_FORMAT') # TODO: remove in v9 self._GLOBAL_CONTEXT['js_date_format'] = self.config.get('MOMENTJS_DATE_FORMAT') self._GLOBAL_CONTEXT['momentjs_locales'] = LEGAL_VALUES['MOMENTJS_LOCALES'] # Patch missing locales into momentjs defaulting to English (Issue #3216) for l in self._GLOBAL_CONTEXT['translations']: if l not in self._GLOBAL_CONTEXT['momentjs_locales']: self._GLOBAL_CONTEXT['momentjs_locales'][l] = "" self._GLOBAL_CONTEXT['hidden_tags'] = self.config.get('HIDDEN_TAGS') self._GLOBAL_CONTEXT['hidden_categories'] = self.config.get('HIDDEN_CATEGORIES') self._GLOBAL_CONTEXT['hidden_authors'] = self.config.get('HIDDEN_AUTHORS') self._GLOBAL_CONTEXT['url_replacer'] = self.url_replacer self._GLOBAL_CONTEXT['sort_posts'] = utils.sort_posts self._GLOBAL_CONTEXT['smartjoin'] = utils.smartjoin self._GLOBAL_CONTEXT['colorize_str'] = utils.colorize_str self._GLOBAL_CONTEXT['meta_generator_tag'] = self.config.get('META_GENERATOR_TAG') self._GLOBAL_CONTEXT.update(self.config.get('GLOBAL_CONTEXT', {})) def _set_global_context_from_data(self): self._GLOBAL_CONTEXT['data'] = {} for root, dirs, files in os.walk('data', followlinks=True): for fname in files: fname = os.path.join(root, fname) data = utils.load_data(fname) key = os.path.splitext(fname.split(os.sep, 1)[1])[0] self._GLOBAL_CONTEXT['data'][key] = data # Offer global_data as an alias for data (Issue #2488) self._GLOBAL_CONTEXT['global_data'] = self._GLOBAL_CONTEXT['data'] def _set_all_page_deps_from_config(self): self.ALL_PAGE_DEPS['atom_extension'] = self.config.get('ATOM_EXTENSION') self.ALL_PAGE_DEPS['atom_path'] = self.config.get('ATOM_PATH') self.ALL_PAGE_DEPS['rss_extension'] = self.config.get('RSS_EXTENSION') self.ALL_PAGE_DEPS['rss_path'] = self.config.get('RSS_PATH') self.ALL_PAGE_DEPS['rss_filename_base'] = self.config.get('RSS_FILENAME_BASE') self.ALL_PAGE_DEPS['atom_filename_base'] = self.config.get('ATOM_FILENAME_BASE') self.ALL_PAGE_DEPS['slug_author_path'] = self.config.get('SLUG_AUTHOR_PATH') self.ALL_PAGE_DEPS['slug_tag_path'] = self.config.get('SLUG_TAG_PATH') self.ALL_PAGE_DEPS['locale'] = self.config.get('LOCALE') def _activate_plugins_of_category(self, category): # this code duplicated in tests/base.py plugins = [] for plugin_info in self.plugin_manager.getPluginsOfCategory(category): self.plugin_manager.activatePluginByName(plugin_info.name) plugin_info.plugin_object.set_site(self) plugins.append(plugin_info) return plugins def _get_themes(self): if self._THEMES is None: try: self._THEMES = utils.get_theme_chain(self.config['THEME'], self.themes_dirs) except Exception: if self.config['THEME'] != LEGAL_VALUES['DEFAULT_THEME']: utils.LOGGER.warning('''Cannot load theme "{0}", using '{1}' instead.'''.format( self.config['THEME'], LEGAL_VALUES['DEFAULT_THEME'])) self.config['THEME'] = LEGAL_VALUES['DEFAULT_THEME'] return self._get_themes() raise # Check consistency of USE_CDN and the current THEME (Issue #386) if self.config['USE_CDN'] and self.config['USE_CDN_WARNING']: bootstrap_path = utils.get_asset_path(os.path.join( 'assets', 'css', 'bootstrap.min.css'), self._THEMES) if bootstrap_path and bootstrap_path.split(os.sep)[-4] not in ['bootstrap', 'bootstrap3', 'bootstrap4']: utils.LOGGER.warning('The USE_CDN option may be incompatible with your theme, because it uses a hosted version of bootstrap.') return self._THEMES THEMES = property(_get_themes) def _get_messages(self): try: if self._MESSAGES is None: self._MESSAGES = utils.load_messages(self.THEMES, self.translations, self.default_lang, themes_dirs=self.themes_dirs) return self._MESSAGES except utils.LanguageNotFoundError as e: utils.LOGGER.error('''Cannot load language "{0}". Please make sure it is supported by Nikola itself, or that you have the appropriate messages files in your themes.'''.format(e.lang)) sys.exit(1) MESSAGES = property(_get_messages) def _get_global_context(self): if 'messages' not in self._GLOBAL_CONTEXT: self._GLOBAL_CONTEXT['messages'] = self.MESSAGES if 'has_custom_css' not in self._GLOBAL_CONTEXT: # check if custom css exist and is not empty custom_css_path = utils.get_asset_path( 'assets/css/custom.css', self.THEMES, self.config['FILES_FOLDERS'] ) if custom_css_path and self.file_exists(custom_css_path, not_empty=True): self._GLOBAL_CONTEXT['has_custom_css'] = True else: self._GLOBAL_CONTEXT['has_custom_css'] = False return self._GLOBAL_CONTEXT GLOBAL_CONTEXT = property(_get_global_context) def _get_template_system(self): if self._template_system is None: # Load template plugin template_sys_name = utils.get_template_engine(self.THEMES) pi = self.plugin_manager.getPluginByName( template_sys_name, "TemplateSystem") if pi is None: sys.stderr.write("Error loading {0} template system " "plugin\n".format(template_sys_name)) sys.exit(1) self._template_system = pi.plugin_object lookup_dirs = ['templates'] + [os.path.join(utils.get_theme_path(name), "templates") for name in self.THEMES] self._template_system.set_directories(lookup_dirs, self.config['CACHE_FOLDER']) self._template_system.set_site(self) return self._template_system template_system = property(_get_template_system) def get_compiler(self, source_name): ext = os.path.splitext(source_name)[1] try: compiler = self.inverse_compilers[ext] except KeyError: # Find the correct compiler for this files extension lang_exts_tab = list(self.config['COMPILERS'].items()) langs = [lang for lang, exts in lang_exts_tab if ext in exts or len([ext_ for ext_ in exts if source_name.endswith(ext_)]) > 0] if len(langs) != 1: if len(set(langs)) > 1: sys.exit("Your file extension->compiler definition is " "ambiguous.\nPlease remove one of the file " "extensions from 'COMPILERS' in conf.py\n(The " "error is in one of {0})".format(', '.join(langs))) elif len(langs) > 1: langs = langs[:1] else: sys.exit("COMPILERS in conf.py does not tell me how to " "handle '{0}' extensions.".format(ext)) lang = langs[0] try: compiler = self.compilers[lang] except KeyError: sys.exit("Cannot find '{0}' compiler; " "it might require an extra plugin -- " "do you have it installed?".format(lang)) self.inverse_compilers[ext] = compiler return compiler def render_template(self, template_name, output_name, context, url_type=None, is_fragment=False): local_context = {} local_context["template_name"] = template_name local_context.update(self.GLOBAL_CONTEXT) local_context.update(context) for k in self._GLOBAL_CONTEXT_TRANSLATABLE: local_context[k] = local_context[k](local_context['lang']) local_context['is_rtl'] = local_context['lang'] in LEGAL_VALUES['RTL_LANGUAGES'] local_context['url_type'] = self.config['URL_TYPE'] if url_type is None else url_type local_context["translations_feedorder"] = sorted( local_context["translations"], key=lambda x: (int(x != local_context['lang']), x) ) # string, arguments local_context["formatmsg"] = lambda s, *a: s % a for h in local_context['template_hooks'].values(): h.context = context for func in self.config['GLOBAL_CONTEXT_FILLER']: func(local_context, template_name) data = self.template_system.render_template( template_name, None, local_context) if output_name is None: return data if not output_name.startswith(self.config["OUTPUT_FOLDER"]): raise ValueError("Output path for templates must start with OUTPUT_FOLDER") url_part = output_name[len(self.config["OUTPUT_FOLDER"]) + 1:] # Treat our site as if output/ is "/" and then make all URLs relative, # making the site "relocatable" src = os.sep + url_part src = os.path.normpath(src) # The os.sep is because normpath will change "/" to "\" on windows src = "/".join(src.split(os.sep)) utils.makedirs(os.path.dirname(output_name)) parser = lxml.html.HTMLParser(remove_blank_text=True) if is_fragment: doc = lxml.html.fragment_fromstring(data.strip(), parser) else: doc = lxml.html.document_fromstring(data.strip(), parser) self.rewrite_links(doc, src, context['lang'], url_type) if is_fragment: # doc.text contains text before the first HTML, or None if there was no text # The text after HTML elements is added by tostring() (because its implicit # argument with_tail has default value True). data = (doc.text or '').encode('utf-8') + b''.join([lxml.html.tostring(child, encoding='utf-8', method='html') for child in doc.iterchildren()]) else: data = lxml.html.tostring(doc, encoding='utf8', method='html', pretty_print=True, doctype='<!DOCTYPE html>') with open(output_name, "wb+") as post_file: post_file.write(data) def rewrite_links(self, doc, src, lang, url_type=None): # First let lxml replace most of them doc.rewrite_links(lambda dst: self.url_replacer(src, dst, lang, url_type), resolve_base_href=False) # lxml ignores srcset in img and source elements, so do that by hand objs = list(doc.xpath('(//img|//source)')) for obj in objs: if 'srcset' in obj.attrib: urls = [u.strip() for u in obj.attrib['srcset'].split(',')] urls = [self.url_replacer(src, dst, lang, url_type) for dst in urls] obj.set('srcset', ', '.join(urls)) def url_replacer(self, src, dst, lang=None, url_type=None): # Avoid mangling links within the page if dst.startswith(' return dst parsed_src = urlsplit(src) src_elems = parsed_src.path.split('/')[1:] dst_url = urlparse(dst) if lang is None: lang = self.default_lang if url_type is None: url_type = self.config.get('URL_TYPE') if dst_url.scheme and dst_url.scheme not in ['http', 'https', 'link']: return dst # Refuse to replace links that are full URLs. if dst_url.netloc: if dst_url.scheme == 'link': # Magic link if dst_url.query: # If query strings are used in magic link, they will be # passed to the path handler as keyword arguments (strings) link_kwargs = {unquote(k): unquote(v[-1]) for k, v in parse_qs(dst_url.query).items()} else: link_kwargs = {} # unquote from issue #2934 dst = self.link(dst_url.netloc, unquote(dst_url.path.lstrip('/')), lang, **link_kwargs) if dst_url.fragment: dst += ' # Assuming the site is served over one of these, and # since those are the only URLs we want to rewrite... else: if '%' in dst_url.netloc: # convert lxml percent-encoded garbage to punycode nl = unquote(dst_url.netloc) try: nl = nl.decode('utf-8') except AttributeError: # python 3: already unicode pass nl = nl.encode('idna') if isinstance(nl, bytes): nl = nl.decode('latin-1') # so idna stays unchanged dst = urlunsplit((dst_url.scheme, nl, dst_url.path, dst_url.query, dst_url.fragment)) return dst elif dst_url.scheme == 'link': # Magic absolute path link: dst = dst_url.path return dst # Refuse to replace links that consist of a fragment only if ((not dst_url.scheme) and (not dst_url.netloc) and (not dst_url.path) and (not dst_url.params) and (not dst_url.query) and dst_url.fragment): return dst # Normalize dst = urljoin(src, dst) # Avoid empty links. if src == dst: if url_type == 'absolute': dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) return dst elif url_type == 'full_path': dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) return utils.full_path_from_urlparse(urlparse(dst)) else: return "#" # Check that link can be made relative, otherwise return dest parsed_dst = urlsplit(dst) if parsed_src[:2] != parsed_dst[:2]: if url_type == 'absolute': dst = urljoin(self.config['BASE_URL'], dst) return dst if url_type in ('full_path', 'absolute'): dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) if url_type == 'full_path': parsed = urlparse(urljoin(self.config['BASE_URL'], dst.lstrip('/'))) dst = utils.full_path_from_urlparse(parsed) return dst # Now both paths are on the same site and absolute dst_elems = parsed_dst.path.split('/')[1:] i = 0 for (i, s), d in zip(enumerate(src_elems), dst_elems): if s != d: break # Now i is the longest common prefix result = '/'.join(['..'] * (len(src_elems) - i - 1) + dst_elems[i:]) if not result and not parsed_dst.fragment: result = "." # Don't forget the query part of the link if parsed_dst.query: result += "?" + parsed_dst.query if parsed_dst.fragment: result += "#" + parsed_dst.fragment if not result: raise ValueError("Failed to parse link: {0}".format((src, dst, i, src_elems, dst_elems))) return result def _make_renderfunc(self, t_data, fname=None): def render_shortcode(*args, **kw): context = self.GLOBAL_CONTEXT.copy() context.update(kw) context['_args'] = args context['lang'] = utils.LocaleBorg().current_lang for k in self._GLOBAL_CONTEXT_TRANSLATABLE: context[k] = context[k](context['lang']) output = self.template_system.render_template_to_string(t_data, context) if fname is not None: dependencies = [fname] + self.template_system.get_deps(fname) else: dependencies = [] return output, dependencies return render_shortcode def _register_templated_shortcodes(self): self.register_shortcode('template', self._template_shortcode_handler) builtin_sc_dir = resource_filename( 'nikola', os.path.join('data', 'shortcodes', utils.get_template_engine(self.THEMES))) for sc_dir in [builtin_sc_dir, 'shortcodes']: if not os.path.isdir(sc_dir): continue for fname in os.listdir(sc_dir): name, ext = os.path.splitext(fname) if ext != '.tmpl': continue with open(os.path.join(sc_dir, fname)) as fd: self.register_shortcode(name, self._make_renderfunc( fd.read(), os.path.join(sc_dir, fname))) def _template_shortcode_handler(self, *args, **kw): t_data = kw.pop('data', '') context = self.GLOBAL_CONTEXT.copy() context.update(kw) context['_args'] = args context['lang'] = utils.LocaleBorg().current_lang for k in self._GLOBAL_CONTEXT_TRANSLATABLE: context[k] = context[k](context['lang']) output = self.template_system.render_template_to_string(t_data, context) dependencies = self.template_system.get_string_deps(t_data) return output, dependencies def register_shortcode(self, name, f): if name in self.shortcode_registry: utils.LOGGER.warning('Shortcode name conflict: {}', name) return self.shortcode_registry[name] = f def apply_shortcodes(self, data, filename=None, lang=None, extra_context=None): if extra_context is None: extra_context = {} if lang is None: lang = utils.LocaleBorg().current_lang return shortcodes.apply_shortcodes(data, self.shortcode_registry, self, filename, lang=lang, extra_context=extra_context) def apply_shortcodes_uuid(self, data, _shortcodes, filename=None, lang=None, extra_context=None): if lang is None: lang = utils.LocaleBorg().current_lang if extra_context is None: extra_context = {} deps = [] for k, v in _shortcodes.items(): replacement, _deps = shortcodes.apply_shortcodes(v, self.shortcode_registry, self, filename, lang=lang, extra_context=extra_context) data = data.replace(k, replacement) deps.extend(_deps) return data, deps def _get_rss_copyright(self, lang, rss_plain): if rss_plain: return ( self.config['RSS_COPYRIGHT_PLAIN'](lang) or lxml.html.fromstring(self.config['RSS_COPYRIGHT'](lang)).text_content().strip()) else: return self.config['RSS_COPYRIGHT'](lang) def generic_rss_feed(self, lang, title, link, description, timeline, rss_teasers, rss_plain, feed_length=10, feed_url=None, enclosure=_enclosure, rss_links_append_query=None, copyright_=None): rss_obj = utils.ExtendedRSS2( title=title, link=utils.encodelink(link), description=description, lastBuildDate=datetime.datetime.utcnow(), generator='Nikola (getnikola.com)', language=lang ) if copyright_ is None: copyright_ = self._get_rss_copyright(lang, rss_plain) # Use the configured or specified copyright string if present. if copyright_: rss_obj.copyright = copyright_ if feed_url: absurl = '/' + feed_url[len(self.config['BASE_URL']):] rss_obj.xsl_stylesheet_href = self.url_replacer(absurl, "/assets/xml/rss.xsl") items = [] feed_append_query = None if rss_links_append_query: if rss_links_append_query is True: raise ValueError("RSS_LINKS_APPEND_QUERY (or FEED_LINKS_APPEND_QUERY) cannot be True. Valid values are False or a formattable string.") feed_append_query = rss_links_append_query.format( feedRelUri='/' + feed_url[len(self.config['BASE_URL']):], feedFormat="rss") for post in timeline[:feed_length]: data = post.text(lang, teaser_only=rss_teasers, strip_html=rss_plain, feed_read_more_link=True, feed_links_append_query=feed_append_query) if feed_url is not None and data: # Massage the post's HTML (unless plain) if not rss_plain: if 'previewimage' in post.meta[lang] and post.meta[lang]['previewimage'] not in data: data = "<figure><img src=\"{}\"></figure> {}".format(post.meta[lang]['previewimage'], data) try: doc = lxml.html.document_fromstring(data) doc.rewrite_links(lambda dst: self.url_replacer(post.permalink(), dst, lang, 'absolute')) try: body = doc.body data = (body.text or '') + ''.join( [lxml.html.tostring(child, encoding='unicode') for child in body.iterchildren()]) except IndexError: data = '' except lxml.etree.ParserError as e: if str(e) == "Document is empty": data = "" else: raise args = { 'title': post.title(lang) if post.should_show_title() else None, 'link': post.permalink(lang, absolute=True, query=feed_append_query), 'description': data, 'pubDate': (post.date if post.date.tzinfo is None else post.date.astimezone(dateutil.tz.tzutc())), 'categories': post._tags.get(lang, []), 'creator': post.author(lang), 'guid': post.guid(lang), } if post.author(lang): rss_obj.rss_attrs["xmlns:dc"] = "http://purl.org/dc/elements/1.1/" if enclosure: # enclosure callback returns None if post has no enclosure, or a # 3-tuple of (url, length (0 is valid), mimetype) enclosure_details = enclosure(post=post, lang=lang) if enclosure_details is not None: args['enclosure'] = rss.Enclosure(*enclosure_details) items.append(utils.ExtendedItem(**args)) rss_obj.items = items rss_obj.self_url = feed_url rss_obj.rss_attrs["xmlns:atom"] = "http://www.w3.org/2005/Atom" return rss_obj def generic_rss_renderer(self, lang, title, link, description, timeline, output_path, rss_teasers, rss_plain, feed_length=10, feed_url=None, enclosure=_enclosure, rss_links_append_query=None, copyright_=None): rss_obj = self.generic_rss_feed(lang, title, link, description, timeline, rss_teasers, rss_plain, feed_length=feed_length, feed_url=feed_url, enclosure=enclosure, rss_links_append_query=rss_links_append_query, copyright_=copyright_) utils.rss_writer(rss_obj, output_path) def path(self, kind, name, lang=None, is_link=False, **kwargs): if lang is None: lang = utils.LocaleBorg().current_lang try: path = self.path_handlers[kind](name, lang, **kwargs) except KeyError: utils.LOGGER.warning("Unknown path request of kind: {0}".format(kind)) return "" # If path handler returns a string we consider it to be an absolute URL not requiring any # further processing, i.e 'https://getnikola.com/'. See Issue #2876. if isinstance(path, str): return path if path is None: path = "#" else: path = [os.path.normpath(p) for p in path if p != '.'] # Fix Issue #1028 if is_link: link = '/' + ('/'.join(path)) index_len = len(self.config['INDEX_FILE']) if self.config['STRIP_INDEXES'] and \ link[-(1 + index_len):] == '/' + self.config['INDEX_FILE']: return link[:-index_len] else: return link else: return os.path.join(*path) def post_path(self, name, lang): return [_f for _f in [self.config['TRANSLATIONS'][lang], os.path.dirname(name), self.config['INDEX_FILE']] if _f] def root_path(self, name, lang): d = self.config['TRANSLATIONS'][lang] if d: return [d, ''] else: return [] def slug_path(self, name, lang): results = [p for p in self.timeline if p.meta('slug') == name] if not results: utils.LOGGER.warning("Cannot resolve path request for slug: {0}".format(name)) else: if len(results) > 1: utils.LOGGER.warning('Ambiguous path request for slug: {0}'.format(name)) return [_f for _f in results[0].permalink(lang).split('/')] def filename_path(self, name, lang): results = [p for p in self.timeline if p.source_path == name] if not results: utils.LOGGER.warning("Cannot resolve path request for filename: {0}".format(name)) else: if len(results) > 1: utils.LOGGER.error("Ambiguous path request for filename: {0}".format(name)) return [_f for _f in results[0].permalink(lang).split('/') if _f] def register_path_handler(self, kind, f): if kind in self.path_handlers: utils.LOGGER.warning('Conflicting path handlers for kind: {0}'.format(kind)) else: self.path_handlers[kind] = f def link(self, *args, **kwargs): url = self.path(*args, is_link=True, **kwargs) url = utils.encodelink(url) return url def abs_link(self, dst, protocol_relative=False): # Normalize if dst: # Mako templates and empty strings evaluate to False dst = urljoin(self.config['BASE_URL'], dst.lstrip('/')) else: dst = self.config['BASE_URL'] url = urlparse(dst).geturl() if protocol_relative: url = url.split(":", 1)[1] url = utils.encodelink(url) return url def rel_link(self, src, dst): # Normalize src = urljoin(self.config['BASE_URL'], src) dst = urljoin(src, dst) # Avoid empty links. if src == dst: return "#" # Check that link can be made relative, otherwise return dest parsed_src = urlsplit(src) parsed_dst = urlsplit(dst) if parsed_src[:2] != parsed_dst[:2]: return utils.encodelink(dst) # Now both paths are on the same site and absolute src_elems = parsed_src.path.split('/')[1:] dst_elems = parsed_dst.path.split('/')[1:] i = 0 for (i, s), d in zip(enumerate(src_elems), dst_elems): if s != d: break else: i += 1 # Now i is the longest common prefix url = '/'.join(['..'] * (len(src_elems) - i - 1) + dst_elems[i:]) url = utils.encodelink(url) return url def register_filter(self, filter_name, filter_definition): if filter_name in self.filters: utils.LOGGER.warning('''The filter "{0}" is defined more than once.'''.format(filter_name)) self.filters[filter_name] = filter_definition def file_exists(self, path, not_empty=False): exists = os.path.exists(path) if exists and not_empty: exists = os.stat(path).st_size > 0 return exists def clean_task_paths(self, task): targets = task.get('targets', None) if targets is not None: task['targets'] = [os.path.normpath(t) for t in targets] return task def gen_tasks(self, name, plugin_category, doc=''): def flatten(task): if isinstance(task, dict): yield task else: for t in task: for ft in flatten(t): yield ft task_dep = [] for pluginInfo in self.plugin_manager.getPluginsOfCategory(plugin_category): for task in flatten(pluginInfo.plugin_object.gen_tasks()): if 'basename' not in task: raise ValueError("Task {0} does not have a basename".format(task)) task = self.clean_task_paths(task) if 'task_dep' not in task: task['task_dep'] = [] task['task_dep'].extend(self.injected_deps[task['basename']]) yield task for multi in self.plugin_manager.getPluginsOfCategory("TaskMultiplier"): flag = False for task in multi.plugin_object.process(task, name): flag = True yield self.clean_task_paths(task) if flag: task_dep.append('{0}_{1}'.format(name, multi.plugin_object.name)) if pluginInfo.plugin_object.is_default: task_dep.append(pluginInfo.plugin_object.name) yield { 'basename': name, 'doc': doc, 'actions': None, 'clean': True, 'task_dep': task_dep } def parse_category_name(self, category_name): if self.config['CATEGORY_ALLOW_HIERARCHIES']: try: return hierarchy_utils.parse_escaped_hierarchical_category_name(category_name) except Exception as e: utils.LOGGER.error(str(e)) sys.exit(1) else: return [category_name] if len(category_name) > 0 else [] def category_path_to_category_name(self, category_path): if self.config['CATEGORY_ALLOW_HIERARCHIES']: return hierarchy_utils.join_hierarchical_category_path(category_path) else: return ''.join(category_path) def _add_post_to_category(self, post, category_name): category_path = self.parse_category_name(category_name) current_path = [] current_subtree = self.category_hierarchy for current in category_path: current_path.append(current) if current not in current_subtree: current_subtree[current] = {} current_subtree = current_subtree[current] self.posts_per_category[self.category_path_to_category_name(current_path)].append(post) def _sort_category_hierarchy(self): # First create a hierarchy of TreeNodes self.category_hierarchy_lookup = {} def create_hierarchy(cat_hierarchy, parent=None): result = [] for name, children in cat_hierarchy.items(): node = hierarchy_utils.TreeNode(name, parent) node.children = create_hierarchy(children, node) node.category_path = [pn.name for pn in node.get_path()] node.category_name = self.category_path_to_category_name(node.category_path) self.category_hierarchy_lookup[node.category_name] = node if node.category_name not in self.config.get('HIDDEN_CATEGORIES'): result.append(node) return natsort.natsorted(result, key=lambda e: e.name, alg=natsort.ns.F | natsort.ns.IC) root_list = create_hierarchy(self.category_hierarchy) # Next, flatten the hierarchy self.category_hierarchy = hierarchy_utils.flatten_tree_structure(root_list) @staticmethod def sort_posts_chronologically(posts, lang=None): # Last tie breaker: sort by source path (A-Z) posts = sorted(posts, key=lambda p: p.source_path) # Next tie breaker: sort by title if language is given (A-Z) if lang is not None: posts = natsort.natsorted(posts, key=lambda p: p.title(lang), alg=natsort.ns.F | natsort.ns.IC) # Next tie breaker: sort by date (reverse chronological order) posts = sorted(posts, key=lambda p: p.date, reverse=True) # Finally, sort by priority meta value (descending) posts = sorted(posts, key=lambda p: int(p.meta('priority')) if p.meta('priority') else 0, reverse=True) # Return result return posts def scan_posts(self, really=False, ignore_quit=False, quiet=False): if self._scanned and not really: return # Reset things self.posts = [] self.all_posts = [] self.posts_per_year = defaultdict(list) self.posts_per_month = defaultdict(list) self.posts_per_tag = defaultdict(list) self.posts_per_category = defaultdict(list) self.tags_per_language = defaultdict(list) self.category_hierarchy = {} self.post_per_file = {} self.post_per_input_file = {} self.timeline = [] self.pages = [] for p in sorted(self.plugin_manager.getPluginsOfCategory('PostScanner'), key=operator.attrgetter('name')): try: timeline = p.plugin_object.scan() except Exception: utils.LOGGER.error('Error reading timeline') raise # FIXME: can there be conflicts here? self.timeline.extend(timeline) quit = False # Classify posts per year/tag/month/whatever slugged_tags = defaultdict(set) for post in self.timeline: if post.use_in_feeds: self.posts.append(post) self.posts_per_year[str(post.date.year)].append(post) self.posts_per_month[ '{0}/{1:02d}'.format(post.date.year, post.date.month)].append(post) for lang in self.config['TRANSLATIONS'].keys(): for tag in post.tags_for_language(lang): _tag_slugified = utils.slugify(tag, lang) slugged_tags[lang].add(_tag_slugified) if post not in self.posts_per_tag[tag]: self.posts_per_tag[tag].append(post) self.tags_per_language[lang].extend(post.tags_for_language(lang)) self._add_post_to_category(post, post.meta('category')) if post.is_post: # unpublished posts self.all_posts.append(post) else: self.pages.append(post) for lang in self.config['TRANSLATIONS'].keys(): dest = post.destination_path(lang=lang) src_dest = post.destination_path(lang=lang, extension=post.source_ext()) src_file = post.translated_source_path(lang=lang) if dest in self.post_per_file: utils.LOGGER.error('Two posts are trying to generate {0}: {1} and {2}'.format( dest, self.post_per_file[dest].source_path, post.source_path)) quit = True if (src_dest in self.post_per_file) and self.config['COPY_SOURCES']: utils.LOGGER.error('Two posts are trying to generate {0}: {1} and {2}'.format( src_dest, self.post_per_file[dest].source_path, post.source_path)) quit = True self.post_per_file[dest] = post self.post_per_file[src_dest] = post if src_file is not None: self.post_per_input_file[src_file] = post # deduplicate tags_per_language self.tags_per_language[lang] = list(set(self.tags_per_language[lang])) # Sort everything. self.timeline = self.sort_posts_chronologically(self.timeline) self.posts = self.sort_posts_chronologically(self.posts) self.all_posts = self.sort_posts_chronologically(self.all_posts) self.pages = self.sort_posts_chronologically(self.pages) self._sort_category_hierarchy() for i, p in enumerate(self.posts[1:]): p.next_post = self.posts[i] for i, p in enumerate(self.posts[:-1]): p.prev_post = self.posts[i + 1] self._scanned = True if not self.quiet: print("done!", file=sys.stderr) if quit and not ignore_quit: sys.exit(1) signal('scanned').send(self) def generic_renderer(self, lang, output_name, template_name, filters, file_deps=None, uptodate_deps=None, context=None, context_deps_remove=None, post_deps_dict=None, url_type=None, is_fragment=False): utils.LocaleBorg().set_locale(lang) file_deps = copy(file_deps) if file_deps else [] file_deps += self.template_system.template_deps(template_name) file_deps = sorted(list(filter(None, file_deps))) context = copy(context) if context else {} context["lang"] = lang deps_dict = copy(context) if context_deps_remove: for key in context_deps_remove: deps_dict.pop(key) deps_dict['OUTPUT_FOLDER'] = self.config['OUTPUT_FOLDER'] deps_dict['TRANSLATIONS'] = self.config['TRANSLATIONS'] deps_dict['global'] = self.GLOBAL_CONTEXT deps_dict['all_page_deps'] = self.ALL_PAGE_DEPS if post_deps_dict: deps_dict.update(post_deps_dict) for k, v in self.GLOBAL_CONTEXT['template_hooks'].items(): deps_dict['||template_hooks|{0}||'.format(k)] = v.calculate_deps() for k in self._GLOBAL_CONTEXT_TRANSLATABLE: deps_dict[k] = deps_dict['global'][k](lang) for k in self._ALL_PAGE_DEPS_TRANSLATABLE: deps_dict[k] = deps_dict['all_page_deps'][k](lang) deps_dict['navigation_links'] = deps_dict['global']['navigation_links'](lang) deps_dict['navigation_alt_links'] = deps_dict['global']['navigation_alt_links'](lang) task = { 'name': os.path.normpath(output_name), 'targets': [output_name], 'file_dep': file_deps, 'actions': [(self.render_template, [template_name, output_name, context, url_type, is_fragment])], 'clean': True, 'uptodate': [config_changed(deps_dict, 'nikola.nikola.Nikola.generic_renderer')] + ([] if uptodate_deps is None else uptodate_deps) } return utils.apply_filters(task, filters) def generic_page_renderer(self, lang, post, filters, context=None): extension = post.compiler.extension() output_name = os.path.join(self.config['OUTPUT_FOLDER'], post.destination_path(lang, extension)) deps = post.deps(lang) uptodate_deps = post.deps_uptodate(lang) deps.extend(utils.get_asset_path(x, self.THEMES) for x in ('bundles', 'parent', 'engine')) _theme_ini = utils.get_asset_path(self.config['THEME'] + '.theme', self.THEMES) if _theme_ini: deps.append(_theme_ini) context = copy(context) if context else {} context['post'] = post context['title'] = post.title(lang) context['description'] = post.description(lang) context['permalink'] = post.permalink(lang) if 'crumbs' not in context: crumb_path = post.permalink(lang).lstrip('/') if crumb_path.endswith(self.config['INDEX_FILE']): crumb_path = crumb_path[:-len(self.config['INDEX_FILE'])] if crumb_path.endswith('/'): context['crumbs'] = utils.get_crumbs(crumb_path.rstrip('/'), is_file=False) else: context['crumbs'] = utils.get_crumbs(crumb_path, is_file=True) if 'pagekind' not in context: context['pagekind'] = ['generic_page'] if post.use_in_feeds: context['enable_comments'] = True else: context['enable_comments'] = self.config['COMMENTS_IN_PAGES'] deps_dict = {} if post.prev_post: deps_dict['PREV_LINK'] = [post.prev_post.permalink(lang)] if post.next_post: deps_dict['NEXT_LINK'] = [post.next_post.permalink(lang)] deps_dict['comments'] = context['enable_comments'] if post: deps_dict['post_translations'] = post.translated_to signal('render_post').send({ 'site': self, 'post': post, 'lang': lang, 'context': context, 'deps_dict': deps_dict, }) yield self.generic_renderer(lang, output_name, post.template_name, filters, file_deps=deps, uptodate_deps=uptodate_deps, context=context, context_deps_remove=['post'], post_deps_dict=deps_dict, url_type=post.url_type) def generic_post_list_renderer(self, lang, posts, output_name, template_name, filters, extra_context): deps = [] uptodate_deps = [] for post in posts: deps += post.deps(lang) uptodate_deps += post.deps_uptodate(lang) context = {} context["posts"] = posts context["title"] = self.config['BLOG_TITLE'](lang) context["description"] = self.config['BLOG_DESCRIPTION'](lang) context["prevlink"] = None context["nextlink"] = None if extra_context: context.update(extra_context) if 'has_other_languages' not in context: context['has_other_languages'] = False post_deps_dict = {} post_deps_dict["posts"] = [(p.meta[lang]['title'], p.permalink(lang)) for p in posts] return self.generic_renderer(lang, output_name, template_name, filters, file_deps=deps, uptodate_deps=uptodate_deps, context=context, post_deps_dict=post_deps_dict) def atom_feed_renderer(self, lang, posts, output_path, filters, extra_context): def atom_link(link_rel, link_type, link_href): link = lxml.etree.Element("link") link.set("rel", link_rel) link.set("type", link_type) link.set("href", utils.encodelink(link_href)) return link utils.LocaleBorg().set_locale(lang) deps = [] uptodate_deps = [] for post in posts: deps += post.deps(lang) uptodate_deps += post.deps_uptodate(lang) context = {} blog_title = self.config['BLOG_TITLE'](lang) context["posts"] = posts context["title"] = blog_title context["description"] = self.config['BLOG_DESCRIPTION'](lang) context["lang"] = lang context.update(extra_context) context["title"] = "{0} ({1})".format(blog_title, context["title"]) if blog_title != context["title"] else blog_title deps_context = copy(context) deps_context["posts"] = [(p.meta[lang]['title'], p.permalink(lang)) for p in posts] deps_context["global"] = self.GLOBAL_CONTEXT deps_context["all_page_deps"] = self.ALL_PAGE_DEPS for k in self._GLOBAL_CONTEXT_TRANSLATABLE: deps_context[k] = deps_context['global'][k](lang) for k in self._ALL_PAGE_DEPS_TRANSLATABLE: deps_context[k] = deps_context['all_page_deps'][k](lang) feed_xsl_link = self.abs_link("/assets/xml/atom.xsl") feed_root = lxml.etree.Element("feed") feed_root.addprevious(lxml.etree.ProcessingInstruction( "xml-stylesheet", 'href="' + utils.encodelink(feed_xsl_link) + '" type="text/xsl media="all"')) feed_root.set("{http://www.w3.org/XML/1998/namespace}lang", lang) feed_root.set("xmlns", "http://www.w3.org/2005/Atom") feed_title = lxml.etree.SubElement(feed_root, "title") feed_title.text = context["title"] feed_id = lxml.etree.SubElement(feed_root, "id") feed_id.text = self.abs_link(context["feedlink"]) feed_updated = lxml.etree.SubElement(feed_root, "updated") feed_updated.text = utils.LocaleBorg().formatted_date('webiso', datetime.datetime.now(tz=dateutil.tz.tzutc())) feed_author = lxml.etree.SubElement(feed_root, "author") feed_author_name = lxml.etree.SubElement(feed_author, "name") feed_author_name.text = self.config["BLOG_AUTHOR"](lang) feed_root.append(atom_link("self", "application/atom+xml", self.abs_link(context["feedlink"]))) feed_root.append(atom_link("alternate", "text/html", self.abs_link(context["permalink"]))) feed_generator = lxml.etree.SubElement(feed_root, "generator") feed_generator.set("uri", "https://getnikola.com/") feed_generator.text = "Nikola" feed_append_query = None if self.config["FEED_LINKS_APPEND_QUERY"]: feed_append_query = self.config["FEED_LINKS_APPEND_QUERY"].format( feedRelUri=context["feedlink"], feedFormat="atom") def atom_post_text(post, text): if not self.config["FEED_PLAIN"]: if 'previewimage' in post.meta[lang] and post.meta[lang]['previewimage'] not in text: text = "<figure><img src=\"{}\"></figure> {}".format(post.meta[lang]['previewimage'], text) # FIXME: this is duplicated with code in Post.text() and generic_rss_renderer try: doc = lxml.html.document_fromstring(text) doc.rewrite_links(lambda dst: self.url_replacer(post.permalink(lang), dst, lang, 'absolute')) try: body = doc.body text = (body.text or '') + ''.join( [lxml.html.tostring(child, encoding='unicode') for child in body.iterchildren()]) except IndexError: # No body there, it happens sometimes text = '' except lxml.etree.ParserError as e: if str(e) == "Document is empty": text = "" else: # let other errors raise raise return text.strip() for post in posts: summary = atom_post_text(post, post.text(lang, teaser_only=True, strip_html=self.config["FEED_PLAIN"], feed_read_more_link=True, feed_links_append_query=feed_append_query)) content = None if not self.config["FEED_TEASERS"]: content = atom_post_text(post, post.text(lang, teaser_only=self.config["FEED_TEASERS"], strip_html=self.config["FEED_PLAIN"], feed_read_more_link=True, feed_links_append_query=feed_append_query)) entry_root = lxml.etree.SubElement(feed_root, "entry") entry_title = lxml.etree.SubElement(entry_root, "title") entry_title.text = post.title(lang) entry_id = lxml.etree.SubElement(entry_root, "id") entry_id.text = post.permalink(lang, absolute=True) entry_updated = lxml.etree.SubElement(entry_root, "updated") entry_updated.text = post.formatted_updated('webiso') entry_published = lxml.etree.SubElement(entry_root, "published") entry_published.text = post.formatted_date('webiso') entry_author = lxml.etree.SubElement(entry_root, "author") entry_author_name = lxml.etree.SubElement(entry_author, "name") entry_author_name.text = post.author(lang) entry_root.append(atom_link("alternate", "text/html", post.permalink(lang, absolute=True, query=feed_append_query))) entry_summary = lxml.etree.SubElement(entry_root, "summary") if not self.config["FEED_PLAIN"]: entry_summary.set("type", "html") else: entry_summary.set("type", "text") entry_summary.text = summary if content: entry_content = lxml.etree.SubElement(entry_root, "content") if not self.config["FEED_PLAIN"]: entry_content.set("type", "html") else: entry_content.set("type", "text") entry_content.text = content for category in post.tags_for_language(lang): entry_category = lxml.etree.SubElement(entry_root, "category") entry_category.set("term", utils.slugify(category, lang)) entry_category.set("label", category) dst_dir = os.path.dirname(output_path) utils.makedirs(dst_dir) with io.open(output_path, "w+", encoding="utf-8") as atom_file: data = lxml.etree.tostring(feed_root.getroottree(), encoding="UTF-8", pretty_print=True, xml_declaration=True) if isinstance(data, bytes): data = data.decode('utf-8') atom_file.write(data) def generic_index_renderer(self, lang, posts, indexes_title, template_name, context_source, kw, basename, page_link, page_path, additional_dependencies=None): # Update kw kw = kw.copy() kw["tag_pages_are_indexes"] = self.config['TAG_PAGES_ARE_INDEXES'] kw["index_display_post_count"] = self.config['INDEX_DISPLAY_POST_COUNT'] kw["index_teasers"] = self.config['INDEX_TEASERS'] kw["indexes_pages"] = self.config['INDEXES_PAGES'](lang) kw["indexes_pages_main"] = self.config['INDEXES_PAGES_MAIN'] kw["indexes_static"] = self.config['INDEXES_STATIC'] kw['indexes_pretty_page_url'] = self.config["INDEXES_PRETTY_PAGE_URL"] kw['show_index_page_navigation'] = self.config['SHOW_INDEX_PAGE_NAVIGATION'] if additional_dependencies is None: additional_dependencies = [] # Split in smaller lists lists = [] if kw["indexes_static"]: lists.append(posts[:kw["index_display_post_count"]]) posts = posts[kw["index_display_post_count"]:] while posts: lists.append(posts[-kw["index_display_post_count"]:]) posts = posts[:-kw["index_display_post_count"]] else: while posts: lists.append(posts[:kw["index_display_post_count"]]) posts = posts[kw["index_display_post_count"]:] if not lists: lists.append([]) num_pages = len(lists) displayed_page_numbers = [utils.get_displayed_page_number(i, num_pages, self) for i in range(num_pages)] page_links = [page_link(i, page_number, num_pages, False) for i, page_number in enumerate(displayed_page_numbers)] if kw['show_index_page_navigation']: # Since the list displayed_page_numbers is not necessarily # sorted -- in case INDEXES_STATIC is True, it is of the # form [num_pages, 1, 2, ..., num_pages - 1] -- we order it # via a map. This allows to not replicate the logic of # utils.get_displayed_page_number() here. if not kw["indexes_pages_main"] and not kw["indexes_static"]: temp_map = {page_number: link for page_number, link in zip(displayed_page_numbers, page_links)} else: temp_map = {page_number - 1: link for page_number, link in zip(displayed_page_numbers, page_links)} page_links_context = [temp_map[i] for i in range(num_pages)] for i, post_list in enumerate(lists): context = context_source.copy() if 'pagekind' not in context: context['pagekind'] = ['index'] if 'has_other_languages' not in context: context['has_other_languages'] = False ipages_i = displayed_page_numbers[i] if kw["indexes_pages"]: indexes_pages = kw["indexes_pages"] % ipages_i else: if kw["indexes_pages_main"]: ipages_msg = "page %d" else: ipages_msg = "old posts, page %d" indexes_pages = " (" + \ kw["messages"][lang][ipages_msg] % ipages_i + ")" if i > 0 or kw["indexes_pages_main"]: context["title"] = indexes_title + indexes_pages else: context["title"] = indexes_title context["prevlink"] = None context["nextlink"] = None context['index_teasers'] = kw['index_teasers'] prevlink = None nextlink = None if kw["indexes_static"]: if i > 0: if i < num_pages - 1: prevlink = i + 1 elif i == num_pages - 1: prevlink = 0 if num_pages > 1: if i > 1: nextlink = i - 1 elif i == 0: nextlink = num_pages - 1 else: if i >= 1: prevlink = i - 1 if i < num_pages - 1: nextlink = i + 1 if prevlink is not None: context["prevlink"] = page_links[prevlink] context["prevfeedlink"] = page_link(prevlink, displayed_page_numbers[prevlink], num_pages, False, extension=".atom") if nextlink is not None: context["nextlink"] = page_links[nextlink] context["nextfeedlink"] = page_link(nextlink, displayed_page_numbers[nextlink], num_pages, False, extension=".atom") context['show_index_page_navigation'] = kw['show_index_page_navigation'] if kw['show_index_page_navigation']: context['page_links'] = page_links_context if not kw["indexes_pages_main"] and not kw["indexes_static"]: context['current_page'] = ipages_i else: context['current_page'] = ipages_i - 1 context['prev_next_links_reversed'] = kw['indexes_static'] context["permalink"] = page_links[i] context["is_frontmost_index"] = i == 0 # Add dependencies to featured posts if 'featured' in context: for post in context['featured']: additional_dependencies += post.deps_uptodate(lang) output_name = os.path.join(kw['output_folder'], page_path(i, ipages_i, num_pages, False)) task = self.generic_post_list_renderer( lang, post_list, output_name, template_name, kw['filters'], context, ) task['uptodate'] = task['uptodate'] + [utils.config_changed(kw, 'nikola.nikola.Nikola.generic_index_renderer')] + additional_dependencies task['basename'] = basename yield task if kw["indexes_pages_main"] and kw['indexes_pretty_page_url'](lang): # create redirection output_name = os.path.join(kw['output_folder'], page_path(0, displayed_page_numbers[0], num_pages, True)) link = page_links[0] yield utils.apply_filters({ 'basename': basename, 'name': output_name, 'targets': [output_name], 'actions': [(utils.create_redirect, (output_name, link))], 'clean': True, 'uptodate': [utils.config_changed(kw, 'nikola.nikola.Nikola.generic_index_renderer')], }, kw["filters"]) def generic_atom_renderer(self, lang, posts, context_source, kw, basename, classification, kind, additional_dependencies=None): # Update kw kw = kw.copy() kw["feed_length"] = self.config['FEED_LENGTH'] kw['generate_atom'] = self.config["GENERATE_ATOM"] kw['feed_links_append_query'] = self.config["FEED_LINKS_APPEND_QUERY"] kw['feed_teasers'] = self.config['FEED_TEASERS'] kw['feed_plain'] = self.config['FEED_PLAIN'] if additional_dependencies is None: additional_dependencies = [] post_list = posts[:kw["feed_length"]] feedlink = self.link(kind + "_atom", classification, lang) feedpath = self.path(kind + "_atom", classification, lang) context = context_source.copy() if 'has_other_languages' not in context: context['has_other_languages'] = False output_name = os.path.join(kw['output_folder'], feedpath) context["feedlink"] = feedlink task = { "basename": basename, "name": output_name, "file_dep": sorted([_.base_path for _ in post_list]), "task_dep": ['render_posts'], "targets": [output_name], "actions": [(self.atom_feed_renderer, (lang, post_list, output_name, kw['filters'], context,))], "clean": True, "uptodate": [utils.config_changed(kw, 'nikola.nikola.Nikola.atom_feed_renderer')] + additional_dependencies } yield utils.apply_filters(task, kw['filters']) def __repr__(self): return '<Nikola Site: {0!r}>'.format(self.config['BLOG_TITLE'](self.config['DEFAULT_LANG']))

true

1c2b460701bd3142ad4a0f4b29e7973ff50a5b70

1,584

py

Python

setup.py

anton44eg/fixturegen

dde56578911efaf802a11fe7341becda4febb15d

[ "MIT" ]

null

setup.py

anton44eg/fixturegen

dde56578911efaf802a11fe7341becda4febb15d

[ "MIT" ]

null

setup.py

anton44eg/fixturegen

dde56578911efaf802a11fe7341becda4febb15d

[ "MIT" ]

null

from __future__ import absolute_import import os from setuptools import setup, find_packages VERSION = '0.8' BASEDIR = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(BASEDIR, 'README.rst')).read() setup( name='fixturegen', version=VERSION, packages=find_packages(), include_package_data=True, install_requires=[ "mako >= 1.0", "click >= 3.0", "sqlalchemy >= 0.6" ], entry_points={ 'console_scripts': ['fixturegen-sqlalchemy = fixturegen.cli:sqlalchemy'], }, url='https://github.com/anton44eg/fixturegen', download_url='https://github.com/anton44eg/fixturegen/archive/{0}.tar.gz' .format(VERSION), license='MIT', author='Anton Simernia', author_email='anton.simernya@gmail.com', keywords=['fixture', 'sqlalchemy', 'testing'], description='Fixture generator for fixture module', long_description=README, package_data={ 'fixturegen': ['templates/*.mako'], }, zip_safe=False, classifiers=[ 'Environment :: Console', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Topic :: Software Development :: Testing', 'Topic :: Database', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', ], test_suite='test_fixturegen', setup_requires=[ "flake8", "nose>=1.0", "coverage" ] )

28.8

77

0.611111

from __future__ import absolute_import import os from setuptools import setup, find_packages VERSION = '0.8' BASEDIR = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(BASEDIR, 'README.rst')).read() setup( name='fixturegen', version=VERSION, packages=find_packages(), include_package_data=True, install_requires=[ "mako >= 1.0", "click >= 3.0", "sqlalchemy >= 0.6" ], entry_points={ 'console_scripts': ['fixturegen-sqlalchemy = fixturegen.cli:sqlalchemy'], }, url='https://github.com/anton44eg/fixturegen', download_url='https://github.com/anton44eg/fixturegen/archive/{0}.tar.gz' .format(VERSION), license='MIT', author='Anton Simernia', author_email='anton.simernya@gmail.com', keywords=['fixture', 'sqlalchemy', 'testing'], description='Fixture generator for fixture module', long_description=README, package_data={ 'fixturegen': ['templates/*.mako'], }, zip_safe=False, classifiers=[ 'Environment :: Console', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Topic :: Software Development :: Testing', 'Topic :: Database', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', ], test_suite='test_fixturegen', setup_requires=[ "flake8", "nose>=1.0", "coverage" ] )

true

1c2b462f393bafc01b1dadb21b016768535775d3

580

py

Python

tuples.py

vikhyatprabhu/python-intermediate

c318c515eb13376f5dfd0f4c3b3d74c8c403ab44

[ "MIT" ]

null

tuples.py

vikhyatprabhu/python-intermediate

c318c515eb13376f5dfd0f4c3b3d74c8c403ab44

[ "MIT" ]

null

tuples.py

vikhyatprabhu/python-intermediate

c318c515eb13376f5dfd0f4c3b3d74c8c403ab44

[ "MIT" ]

null

#Tuple : ordered , immutable , allows duplicate mytuple = ("Vikhyat" , 28 , "Shirali") print(mytuple) singletuple = ("Vikhyat",) print(singletuple) #Tuple from iterable mytuple = tuple(["Vikhyat" , 28 , "Shirali"]) print(mytuple) item = mytuple[0] print(item) # NOT SUPPORTED assignment # mytuple[1] = 29 #Loop for x in mytuple: print(x) letters = ('a' , 'b' , 'b' , 'c' , 'a', 'e', 'e') print(letters.count('b')) print(letters.index('e')) import timeit print(timeit.timeit(stmt="[0,1,2,3,4,5]", number=10000)) print(timeit.timeit(stmt="(0,1,2,3,4,5)", number=10000))

20

56

0.648276

mytuple = ("Vikhyat" , 28 , "Shirali") print(mytuple) singletuple = ("Vikhyat",) print(singletuple) mytuple = tuple(["Vikhyat" , 28 , "Shirali"]) print(mytuple) item = mytuple[0] print(item) for x in mytuple: print(x) letters = ('a' , 'b' , 'b' , 'c' , 'a', 'e', 'e') print(letters.count('b')) print(letters.index('e')) import timeit print(timeit.timeit(stmt="[0,1,2,3,4,5]", number=10000)) print(timeit.timeit(stmt="(0,1,2,3,4,5)", number=10000))

true

1c2b47d3c39b4194cfb90ff049a80f39236b1d76

1,403

py

Python

trackash/users/tests/test_views.py

black-redoc/trackash

99ded8445eaaa1bdf616d43c36ba402356e2f9d3

[ "MIT" ]

null

trackash/users/tests/test_views.py

black-redoc/trackash

99ded8445eaaa1bdf616d43c36ba402356e2f9d3

[ "MIT" ]

null

trackash/users/tests/test_views.py

black-redoc/trackash

99ded8445eaaa1bdf616d43c36ba402356e2f9d3

[ "MIT" ]

null

import pytest from django.test import RequestFactory from trackash.users.models import User from trackash.users.views import UserRedirectView, UserUpdateView pytestmark = pytest.mark.django_db class TestUserUpdateView: """ TODO: extracting view initialization code as class-scoped fixture would be great if only pytest-django supported non-function-scoped fixture db access -- this is a work-in-progress for now: https://github.com/pytest-dev/pytest-django/pull/258 """ def test_get_success_url(self, user: User, request_factory: RequestFactory): view = UserUpdateView() request = request_factory.get("/fake-url/") request.user = user view.request = request assert view.get_success_url() == f"/users/{user.username}/" def test_get_object(self, user: User, request_factory: RequestFactory): view = UserUpdateView() request = request_factory.get("/fake-url/") request.user = user view.request = request assert view.get_object() == user class TestUserRedirectView: def test_get_redirect_url(self, user: User, request_factory: RequestFactory): view = UserRedirectView() request = request_factory.get("/fake-url") request.user = user view.request = request assert view.get_redirect_url() == f"/users/{user.username}/"

29.851064

81

0.684248

import pytest from django.test import RequestFactory from trackash.users.models import User from trackash.users.views import UserRedirectView, UserUpdateView pytestmark = pytest.mark.django_db class TestUserUpdateView: def test_get_success_url(self, user: User, request_factory: RequestFactory): view = UserUpdateView() request = request_factory.get("/fake-url/") request.user = user view.request = request assert view.get_success_url() == f"/users/{user.username}/" def test_get_object(self, user: User, request_factory: RequestFactory): view = UserUpdateView() request = request_factory.get("/fake-url/") request.user = user view.request = request assert view.get_object() == user class TestUserRedirectView: def test_get_redirect_url(self, user: User, request_factory: RequestFactory): view = UserRedirectView() request = request_factory.get("/fake-url") request.user = user view.request = request assert view.get_redirect_url() == f"/users/{user.username}/"

true

1c2b486f75bc1d0322615792bab4ef66e4af1fc3

1,843

py

Python

project_euler/python/058_spiral_primes.py

Sabihxh/ProjectEuler

8ab1387f41cbce0d5216ed98fa06d754cbc324c1

[ "MIT" ]

1

2018-03-20T12:04:06.000Z

project_euler/python/058_spiral_primes.py

Sabihxh/ProjectEuler

8ab1387f41cbce0d5216ed98fa06d754cbc324c1

[ "MIT" ]

null

project_euler/python/058_spiral_primes.py

Sabihxh/ProjectEuler

8ab1387f41cbce0d5216ed98fa06d754cbc324c1

[ "MIT" ]

null

from utils import is_prime problem = """ Starting with 1 and spiralling anticlockwise in the following way, a square spiral with side length 7 is formed. 37 36 35 34 33 32 31 38 17 16 15 14 13 30 39 18 5 4 3 12 29 40 19 6 1 2 11 28 41 20 7 8 9 10 27 42 21 22 23 24 25 26 43 44 45 46 47 48 49 It is interesting to note that the odd squares lie along the bottom right diagonal, but what is more interesting is that 8 out of the 13 numbers lying along both diagonals are prime; that is, a ratio of 8/13 ≈ 62%. If one complete new layer is wrapped around the spiral above, a square spiral with side length 9 will be formed. If this process is continued, what is the side length of the square spiral for which the ratio of primes along both diagonals first falls below 10%? """ def is_prime(n): if n == 2 or n == 3: return True if n < 2 or n % 2 == 0: return False if n < 9: return True if n % 3 == 0: return False r = int(n ** 0.5) f = 5 while f <= r: if n % f == 0: return False if n % (f + 2) == 0: return False f += 6 return True def solution(): target_ratio = 0.1 # coefficients of the 3 quadratic equations for non-squared diagonal numbers coefficients = [(4, -10, 7), (4, -8, 5), (4, -6, 3)] primes_count = 0 for n in range(1, 100000): side_length = (2*n) - 1 diagonal_count = (4*n) - 3 print(f'n: {n}, side_length: {side_length}, diagonal_count: {diagonal_count}') for coeff in coefficients: a, b, c = coeff s = a*(n**2) + (b*n) + c print(f'coeff: {coeff}', s) if is_prime(s): primes_count += 1 ratio = primes_count/diagonal_count print(f'primes_count: {primes_count}, diagonal_count: {diagonal_count} ratio: {ratio}') if n > 2 and ratio < target_ratio: print(f'side_length: {side_length}') break print('*'*100) if __name__ == "__main__": solution()

26.328571

89

0.66522

from utils import is_prime problem = """ Starting with 1 and spiralling anticlockwise in the following way, a square spiral with side length 7 is formed. 37 36 35 34 33 32 31 38 17 16 15 14 13 30 39 18 5 4 3 12 29 40 19 6 1 2 11 28 41 20 7 8 9 10 27 42 21 22 23 24 25 26 43 44 45 46 47 48 49 It is interesting to note that the odd squares lie along the bottom right diagonal, but what is more interesting is that 8 out of the 13 numbers lying along both diagonals are prime; that is, a ratio of 8/13 ≈ 62%. If one complete new layer is wrapped around the spiral above, a square spiral with side length 9 will be formed. If this process is continued, what is the side length of the square spiral for which the ratio of primes along both diagonals first falls below 10%? """ def is_prime(n): if n == 2 or n == 3: return True if n < 2 or n % 2 == 0: return False if n < 9: return True if n % 3 == 0: return False r = int(n ** 0.5) f = 5 while f <= r: if n % f == 0: return False if n % (f + 2) == 0: return False f += 6 return True def solution(): target_ratio = 0.1 coefficients = [(4, -10, 7), (4, -8, 5), (4, -6, 3)] primes_count = 0 for n in range(1, 100000): side_length = (2*n) - 1 diagonal_count = (4*n) - 3 print(f'n: {n}, side_length: {side_length}, diagonal_count: {diagonal_count}') for coeff in coefficients: a, b, c = coeff s = a*(n**2) + (b*n) + c print(f'coeff: {coeff}', s) if is_prime(s): primes_count += 1 ratio = primes_count/diagonal_count print(f'primes_count: {primes_count}, diagonal_count: {diagonal_count} ratio: {ratio}') if n > 2 and ratio < target_ratio: print(f'side_length: {side_length}') break print('*'*100) if __name__ == "__main__": solution()

true

1c2b49b6074b2fa09d9c940499b2b83155e28ee7

2,394

py

Python

chaospy/distributions/operators/arccos.py

krystophny/chaospy

e09f8e3f6dfc26145f15774edd5b03665140712f

[ "MIT" ]

1

2019-12-20T00:32:44.000Z

chaospy/distributions/operators/arccos.py

QianWanghhu/chaospy

18ff6c4fc56c632825e53fb24e17de51a7febd7d

[ "MIT" ]

null

chaospy/distributions/operators/arccos.py

QianWanghhu/chaospy

18ff6c4fc56c632825e53fb24e17de51a7febd7d

[ "MIT" ]

null

"""Arc-Cosine.""" import numpy from ..baseclass import Dist from .. import evaluation, approximation class Arccos(Dist): """ Arc-Cosine. Args: dist (Dist): Distribution to perform transformation on. Example: >>> distribution = chaospy.Arccos(chaospy.Uniform(0, 1)) >>> print(distribution) Arccos(Uniform(lower=0, upper=1)) >>> q = numpy.linspace(0, 1, 6)[1:-1] >>> print(numpy.around(distribution.inv(q), 4)) [0.6435 0.9273 1.1593 1.3694] >>> print(numpy.around(distribution.fwd(distribution.inv(q)), 4)) [0.2 0.4 0.6 0.8] >>> print(numpy.around(distribution.pdf(distribution.inv(q)), 4)) [0.6 0.8 0.9165 0.9798] >>> print(numpy.around(distribution.sample(4), 4)) [1.2171 0.4843 1.5211 1.0265] >>> print(numpy.around(distribution.mom(1), 4)) 1.0 >>> print(numpy.around(distribution.ttr([0, 1, 2]), 4)) [[1. 0.8406 0.8083] [1. 0.1416 0.1492]] """ def __init__(self, dist): assert isinstance(dist, Dist) assert numpy.all(dist.range() >= -1) assert numpy.all(dist.range() <= 1) Dist.__init__(self, dist=dist) def _pdf(self, x, dist, cache): return evaluation.evaluate_density( dist, numpy.cos(x), cache=cache)*numpy.sin(x) def _cdf(self, x, dist, cache): return 1-evaluation.evaluate_forward(dist, numpy.cos(x), cache=cache) def _ppf(self, q, dist, cache): return numpy.arccos(evaluation.evaluate_inverse(dist, 1-q, cache=cache)) def _bnd(self, x, dist, cache): return numpy.arccos(evaluation.evaluate_bound( dist, numpy.cos(x), cache=cache))[::-1] def _mom(self, x, dist, cache): return approximation.approximate_moment(self, x) def __len__(self): return len(self.prm["dist"]) def __str__(self): return self.__class__.__name__ + "(" + str(self.prm["dist"]) + ")" def _fwd_cache(self, cache): dist = evaluation.get_forward_cache(self.prm["dist"], cache) if not isinstance(dist, Dist): return numpy.arccos(dist) return self def _inv_cache(self, cache): dist = evaluation.get_forward_cache(self.prm["dist"], cache) if not isinstance(dist, Dist): return numpy.cos(dist) return self

31.92

80

0.591061

import numpy from ..baseclass import Dist from .. import evaluation, approximation class Arccos(Dist): def __init__(self, dist): assert isinstance(dist, Dist) assert numpy.all(dist.range() >= -1) assert numpy.all(dist.range() <= 1) Dist.__init__(self, dist=dist) def _pdf(self, x, dist, cache): return evaluation.evaluate_density( dist, numpy.cos(x), cache=cache)*numpy.sin(x) def _cdf(self, x, dist, cache): return 1-evaluation.evaluate_forward(dist, numpy.cos(x), cache=cache) def _ppf(self, q, dist, cache): return numpy.arccos(evaluation.evaluate_inverse(dist, 1-q, cache=cache)) def _bnd(self, x, dist, cache): return numpy.arccos(evaluation.evaluate_bound( dist, numpy.cos(x), cache=cache))[::-1] def _mom(self, x, dist, cache): return approximation.approximate_moment(self, x) def __len__(self): return len(self.prm["dist"]) def __str__(self): return self.__class__.__name__ + "(" + str(self.prm["dist"]) + ")" def _fwd_cache(self, cache): dist = evaluation.get_forward_cache(self.prm["dist"], cache) if not isinstance(dist, Dist): return numpy.arccos(dist) return self def _inv_cache(self, cache): dist = evaluation.get_forward_cache(self.prm["dist"], cache) if not isinstance(dist, Dist): return numpy.cos(dist) return self

true

1c2b4a02dbba8f5c98568033d3c3db8e69c4b68a

393

py

Python

sdnantwr/wsgi.py

eewinkk/sdnantwr

3fc395a2ae268efd7db4d4d4e3424c0c4252ed5b

[ "MIT" ]

null

sdnantwr/wsgi.py

eewinkk/sdnantwr

3fc395a2ae268efd7db4d4d4e3424c0c4252ed5b

[ "MIT" ]

null

sdnantwr/wsgi.py

eewinkk/sdnantwr

3fc395a2ae268efd7db4d4d4e3424c0c4252ed5b

[ "MIT" ]

null

""" WSGI config for sdnantwr project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sdnantwr.settings') application = get_wsgi_application()

23.117647

78

0.78626

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sdnantwr.settings') application = get_wsgi_application()

true

1c2b4a1c07a03c84645790de2fd147b0a49af942

779

py

Python

Python Files/Dataset_Formating/Audio_splicing.py

brennanMosher/Music-Genre-Recognition-using-a-Machine-Learning-Appraoch

7834fe5d709e894322ad76ef118067febaa78bce

[ "MIT" ]

1

2021-04-13T16:22:27.000Z

Python Files/Dataset_Formating/Audio_splicing.py

brennanMosher/Music-Genre-Recognition-using-a-Machine-Learning-Appraoch

7834fe5d709e894322ad76ef118067febaa78bce

[ "MIT" ]

null

Python Files/Dataset_Formating/Audio_splicing.py

brennanMosher/Music-Genre-Recognition-using-a-Machine-Learning-Appraoch

7834fe5d709e894322ad76ef118067febaa78bce

[ "MIT" ]

null

from pydub import AudioSegment import os import math from pathlib import Path ''' Splice wav files into multiple segments. ''' LENGTH = 3 # Set splice length in seconds def splice(audioPath, outputPath): # try: # os.mkdir('Spliced Spectrogram training') # Need to figure out where to put this # except OSError: # print("Creation of the directory failed") audio = AudioSegment.from_wav(audioPath) count = math.ceil(audio.duration_seconds/LENGTH) # Do we want the last part of audio? t1 = 0 t2 = LENGTH*1000 for i in range(count): newAudio = audio[t1:t2] newPath = outputPath+Path(audioPath).stem+'_splice'+str(i)+'.wav' newAudio.export(newPath, format="wav") t1 = t2 t2 = t2 + LENGTH*1000

25.129032

89

0.65982

from pydub import AudioSegment import os import math from pathlib import Path LENGTH = 3 def splice(audioPath, outputPath): rom_wav(audioPath) count = math.ceil(audio.duration_seconds/LENGTH) t1 = 0 t2 = LENGTH*1000 for i in range(count): newAudio = audio[t1:t2] newPath = outputPath+Path(audioPath).stem+'_splice'+str(i)+'.wav' newAudio.export(newPath, format="wav") t1 = t2 t2 = t2 + LENGTH*1000

true

1c2b4a412455052ce8ddb06dd979e5dc0bf88080

4,941

py

Python

mstools/molecule/molecule.py

Xiangyan93/mstools

7143dbfc2eb4e82e6631652a0c1b38a793dcc678

[ "MIT" ]

null

mstools/molecule/molecule.py

Xiangyan93/mstools

7143dbfc2eb4e82e6631652a0c1b38a793dcc678

[ "MIT" ]

null

mstools/molecule/molecule.py

Xiangyan93/mstools

7143dbfc2eb4e82e6631652a0c1b38a793dcc678

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from typing import Dict, Iterator, List, Optional, Union, Literal, Tuple import warnings import random from openbabel import openbabel import openbabel.pybel as pybel from .saved_mol2 import get_smiles_mol2_dict class Molecule: """This class is used to create molecular 3D structure from SMILES. Parameters ---------- smiles: str SMILES string of input molecule. algorithm: 'openbabel' The algorithm used to generate molecular 3D structure. read_saved: bool Set to True, if the molecule is saved in saved_mol2, then the structure in it will directly used. seed: int Random seed. """ def __init__(self, smiles: str, algorithm: Literal['openbabel'] = 'openbabel', read_saved: bool = True, seed: int = 0): self.smiles = smiles self.algorithm = algorithm self.read_saved = read_saved self.seed = seed if algorithm == 'openbabel': self.mol = self._mol_openbabel(minimize=True) elif algorithm == 'rdkit': # TODO pass else: raise RuntimeError(f'Unknown 3D coordinates generate algorithm {algorithm}') @property def charge(self) -> int: if self.algorithm == 'openbabel': return self.mol.charge @property def spin(self) -> int: if self.algorithm == 'openbabel': return self.mol.spin @property def n_atoms(self) -> int: if self.algorithm == 'openbabel': return len(self.mol.atoms) @property def molwt(self) -> float: if self.algorithm == 'openbabel': return self.mol.molwt @property def formula(self) -> str: if self.algorithm == 'openbabel': return self.mol.formula @property def smiles2mol2(self) -> Dict[str, str]: if not hasattr(self, '__smiles2mol2_dict'): self.__smiles2mol2_dict = get_smiles_mol2_dict() return self.__smiles2mol2_dict def write(self, file: str = None, filetype: Literal['pdb', 'mol2', 'xyz'] = 'mol2'): if self.algorithm == 'openbabel': mol = self.mol if file is not None: mol.write(filetype, file, overwrite=True) else: return mol.write(filetype) def _conformers_openbabel(self, n_select: int = 10, n_try: int = 10) -> List[pybel.Molecule]: """Generate a list of conformers using openbabel. Parameters ---------- n_select: int The number of conformers to be returned. n_try The number of conformers try to generated by random noise to coordinates. Returns ------- conformers: List[pybel.Molecule] A list consists of n_select pybel.Molecule objects. """ if n_select == 0: return [] random.seed(self.seed) ff = openbabel.OBForceField.FindForceField('mmff94') if n_try is None: n_try = n_select if n_try < n_select: warnings.warn( f'n_try={n_try} is set to be smaller than n_select={n_select}. ' f'n_try is set to {n_select}') n_try = n_select x_list = [] for atom in self.mol.atoms: for x in atom.coords: x_list.append(x) xmin, xmax = min(x_list), max(x_list) xspan = xmax - xmin conformers = [] for i in range(n_try): conformer = self._mol_openbabel(minimize=False) for atom in conformer.atoms: obatom = atom.OBAtom random_coord = [(random.random() * xspan + xmin) * k for k in [2, 1, 0.5]] obatom.SetVector(*random_coord) conformer.localopt() ff.Setup(conformer.OBMol) conformer.OBMol.SetEnergy(ff.Energy()) conformers.append(conformer) conformers.sort(key=lambda x: x.energy) return conformers[:n_select] def _mol_openbabel(self, minimize: bool = False) -> pybel.Molecule: """Generate a openbabel conformer. Parameters ---------- minimize: bool If True, the molecular coordinates will be optimized using classical force field. Returns ------- mol: pybel.Molecule A pybel.Molecule object. """ try: mol = pybel.readstring('smi', self.smiles) except: raise RuntimeError('Cannot create molecule from SMILES using openbabel.') if self.read_saved and self.smiles in self.smiles2mol2: mol = next(pybel.readfile('mol2', self.smiles2mol2[self.smiles])) else: mol.addh() mol.make3D() if minimize: mol.localopt() return mol

31.272152

105

0.571747

from typing import Dict, Iterator, List, Optional, Union, Literal, Tuple import warnings import random from openbabel import openbabel import openbabel.pybel as pybel from .saved_mol2 import get_smiles_mol2_dict class Molecule: def __init__(self, smiles: str, algorithm: Literal['openbabel'] = 'openbabel', read_saved: bool = True, seed: int = 0): self.smiles = smiles self.algorithm = algorithm self.read_saved = read_saved self.seed = seed if algorithm == 'openbabel': self.mol = self._mol_openbabel(minimize=True) elif algorithm == 'rdkit': pass else: raise RuntimeError(f'Unknown 3D coordinates generate algorithm {algorithm}') @property def charge(self) -> int: if self.algorithm == 'openbabel': return self.mol.charge @property def spin(self) -> int: if self.algorithm == 'openbabel': return self.mol.spin @property def n_atoms(self) -> int: if self.algorithm == 'openbabel': return len(self.mol.atoms) @property def molwt(self) -> float: if self.algorithm == 'openbabel': return self.mol.molwt @property def formula(self) -> str: if self.algorithm == 'openbabel': return self.mol.formula @property def smiles2mol2(self) -> Dict[str, str]: if not hasattr(self, '__smiles2mol2_dict'): self.__smiles2mol2_dict = get_smiles_mol2_dict() return self.__smiles2mol2_dict def write(self, file: str = None, filetype: Literal['pdb', 'mol2', 'xyz'] = 'mol2'): if self.algorithm == 'openbabel': mol = self.mol if file is not None: mol.write(filetype, file, overwrite=True) else: return mol.write(filetype) def _conformers_openbabel(self, n_select: int = 10, n_try: int = 10) -> List[pybel.Molecule]: if n_select == 0: return [] random.seed(self.seed) ff = openbabel.OBForceField.FindForceField('mmff94') if n_try is None: n_try = n_select if n_try < n_select: warnings.warn( f'n_try={n_try} is set to be smaller than n_select={n_select}. ' f'n_try is set to {n_select}') n_try = n_select x_list = [] for atom in self.mol.atoms: for x in atom.coords: x_list.append(x) xmin, xmax = min(x_list), max(x_list) xspan = xmax - xmin conformers = [] for i in range(n_try): conformer = self._mol_openbabel(minimize=False) for atom in conformer.atoms: obatom = atom.OBAtom random_coord = [(random.random() * xspan + xmin) * k for k in [2, 1, 0.5]] obatom.SetVector(*random_coord) conformer.localopt() ff.Setup(conformer.OBMol) conformer.OBMol.SetEnergy(ff.Energy()) conformers.append(conformer) conformers.sort(key=lambda x: x.energy) return conformers[:n_select] def _mol_openbabel(self, minimize: bool = False) -> pybel.Molecule: try: mol = pybel.readstring('smi', self.smiles) except: raise RuntimeError('Cannot create molecule from SMILES using openbabel.') if self.read_saved and self.smiles in self.smiles2mol2: mol = next(pybel.readfile('mol2', self.smiles2mol2[self.smiles])) else: mol.addh() mol.make3D() if minimize: mol.localopt() return mol

true

1c2b4a485b7b9fbeba082c6fd516e9b17e38a7db

4,142

py

Python

Week3-Web-Development-Using-Python/fastapi/service.py

gdgedmonton/Python-Bootcamp-2020

2d5e78608c5e94d4db97e084c2f71ac0eefb213f

[ "MIT" ]

3

2021-01-15T23:24:37.000Z

2021-08-13T04:01:11.000Z

Week3-Web-Development-Using-Python/fastapi/service.py

gdgedmonton/Python-Bootcamp-2020

2d5e78608c5e94d4db97e084c2f71ac0eefb213f

[ "MIT" ]

null

Week3-Web-Development-Using-Python/fastapi/service.py

gdgedmonton/Python-Bootcamp-2020

2d5e78608c5e94d4db97e084c2f71ac0eefb213f

[ "MIT" ]

1

2021-01-31T20:11:49.000Z

from datetime import datetime from typing import List from urllib.parse import urlunparse import uuid import aiohttp from fastapi import FastAPI, HTTPException from fuzzywuzzy import process from pydantic import AnyHttpUrl, BaseModel, Field from starlette.requests import Request async def fetch_team_wins() -> dict: """Use the NHL API to get teams and goals this season""" standings_url = "https://statsapi.web.nhl.com/api/v1/standings?season=20192020" session = aiohttp.ClientSession() resp = await session.get(standings_url) await session.close() standings = await resp.json() teams = {} try: for record in standings["records"]: for team_record in record["teamRecords"]: teams.update({team_record["team"]["name"]: team_record["goalsScored"]}) except KeyError: raise HTTPException(status_code=400, detail="Invalid standings response") return teams DB = {} # Yikes! Please use a real database, not just a dictionary... NAMESPACE_UUID = uuid.uuid4() def db_uid(name: str) -> str: return str(uuid.uuid3(NAMESPACE_UUID, name)) app = FastAPI(name="Franklin's hockey pool") class Links(BaseModel): self: AnyHttpUrl submissions: AnyHttpUrl rules: AnyHttpUrl @classmethod def from_url(cls, url): links = cls( self=str(url), submissions=urlunparse( (url.scheme, url.netloc, "/submissions", "", "", "") ), rules=urlunparse((url.scheme, url.netloc, "/rules", "", "", "")), ) return links class RulesResponse(BaseModel): rules: str links: Links @app.get("/rules", response_model=RulesResponse) async def get_the_pool_rules(request: Request): """Get the rules and links to submissions""" rules = ( "Pick three teams, guess how many combined goals they will have " "at the end of the season, closest guess takes all!" ) return {"rules": rules, "links": Links.from_url(request.url)} class SubmissionsResponse(BaseModel): submissions: List[AnyHttpUrl] links: Links @app.get("/submissions", response_model=SubmissionsResponse) async def get_submissions(request: Request): """Get links to all submissions""" submissions = [f"{str(request.url)}/{db_uid(sub.name)}" for sub in DB.values()] return {"submissions": submissions, "links": Links.from_url(request.url)} class Submission(BaseModel): name: str = Field( ..., description="user name", ) teams: List[str] = Field( ..., description="team choices", min_items=3, max_items=3, ) prediction: int = Field( ..., description="predicted total points at end of season", ge=0, ) class SubmissionDB(Submission): uid: str time: datetime class SubmissionPostResponse(Submission): links: Links @app.post("/submissions", response_model=SubmissionPostResponse) async def post_submission(submission: Submission, request: Request): """Add your submission to the pool""" uid = db_uid(submission.name) if uid in DB: raise HTTPException( status_code=422, detail=f"Entry already exists for {submission.name}" ) DB[uid] = SubmissionDB(uid=uid, time=datetime.utcnow(), **submission.dict()) links = Links.from_url(request.url) links.self += f"/{uid}" return dict(links=links, **submission.dict()) class SubmissionGetResponse(SubmissionPostResponse): current_score: int @app.get("/submissions/{uid}", response_model=SubmissionGetResponse) async def get_submission(uid: str, request: Request): """Get a submission with the current score""" if uid not in DB: raise HTTPException(status_code=404, detail="Not found") submission = DB[uid] standings = await fetch_team_wins() current_score = 0 for team in submission.teams: match = process.extractOne(team, standings.keys()) current_score += standings[match[0]] links = Links.from_url(request.url) return dict(links=links, current_score=current_score, **submission.dict())

28.965035

87

0.667069

from datetime import datetime from typing import List from urllib.parse import urlunparse import uuid import aiohttp from fastapi import FastAPI, HTTPException from fuzzywuzzy import process from pydantic import AnyHttpUrl, BaseModel, Field from starlette.requests import Request async def fetch_team_wins() -> dict: standings_url = "https://statsapi.web.nhl.com/api/v1/standings?season=20192020" session = aiohttp.ClientSession() resp = await session.get(standings_url) await session.close() standings = await resp.json() teams = {} try: for record in standings["records"]: for team_record in record["teamRecords"]: teams.update({team_record["team"]["name"]: team_record["goalsScored"]}) except KeyError: raise HTTPException(status_code=400, detail="Invalid standings response") return teams DB = {} NAMESPACE_UUID = uuid.uuid4() def db_uid(name: str) -> str: return str(uuid.uuid3(NAMESPACE_UUID, name)) app = FastAPI(name="Franklin's hockey pool") class Links(BaseModel): self: AnyHttpUrl submissions: AnyHttpUrl rules: AnyHttpUrl @classmethod def from_url(cls, url): links = cls( self=str(url), submissions=urlunparse( (url.scheme, url.netloc, "/submissions", "", "", "") ), rules=urlunparse((url.scheme, url.netloc, "/rules", "", "", "")), ) return links class RulesResponse(BaseModel): rules: str links: Links @app.get("/rules", response_model=RulesResponse) async def get_the_pool_rules(request: Request): rules = ( "Pick three teams, guess how many combined goals they will have " "at the end of the season, closest guess takes all!" ) return {"rules": rules, "links": Links.from_url(request.url)} class SubmissionsResponse(BaseModel): submissions: List[AnyHttpUrl] links: Links @app.get("/submissions", response_model=SubmissionsResponse) async def get_submissions(request: Request): submissions = [f"{str(request.url)}/{db_uid(sub.name)}" for sub in DB.values()] return {"submissions": submissions, "links": Links.from_url(request.url)} class Submission(BaseModel): name: str = Field( ..., description="user name", ) teams: List[str] = Field( ..., description="team choices", min_items=3, max_items=3, ) prediction: int = Field( ..., description="predicted total points at end of season", ge=0, ) class SubmissionDB(Submission): uid: str time: datetime class SubmissionPostResponse(Submission): links: Links @app.post("/submissions", response_model=SubmissionPostResponse) async def post_submission(submission: Submission, request: Request): uid = db_uid(submission.name) if uid in DB: raise HTTPException( status_code=422, detail=f"Entry already exists for {submission.name}" ) DB[uid] = SubmissionDB(uid=uid, time=datetime.utcnow(), **submission.dict()) links = Links.from_url(request.url) links.self += f"/{uid}" return dict(links=links, **submission.dict()) class SubmissionGetResponse(SubmissionPostResponse): current_score: int @app.get("/submissions/{uid}", response_model=SubmissionGetResponse) async def get_submission(uid: str, request: Request): if uid not in DB: raise HTTPException(status_code=404, detail="Not found") submission = DB[uid] standings = await fetch_team_wins() current_score = 0 for team in submission.teams: match = process.extractOne(team, standings.keys()) current_score += standings[match[0]] links = Links.from_url(request.url) return dict(links=links, current_score=current_score, **submission.dict())

true

1c2b4b0457b868b3077a5a8dbc6d0f7f70328b3f

12,842

py

Python

lib/net/fcn.py

Guo-Xiaoqing/ThresholdNet

460026bdacd9d5e577e9b4ae1370e8c9924fcfc3

[ "MIT" ]

7

2020-12-29T14:09:27.000Z

2021-07-08T07:12:24.000Z

lib/net/fcn.py

CityU-AIM-Group/ThresholdNet

e82da9f1266c07518c4037d0a0b3afd6290ca33d

[ "MIT" ]

null

lib/net/fcn.py

CityU-AIM-Group/ThresholdNet

e82da9f1266c07518c4037d0a0b3afd6290ca33d

[ "MIT" ]

2

2021-04-08T11:59:07.000Z

2021-08-09T15:16:41.000Z

# -*- coding: utf-8 -*- from __future__ import print_function import torch import torch.nn as nn import torch.optim as optim from torchvision import models from torchvision.models.vgg import VGG class FCN32s(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32) score = self.bn1(self.relu(self.deconv1(x5))) # size=(N, 512, x.H/16, x.W/16) score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8) score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4) score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2) score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W) score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1) return score # size=(N, n_class, x.H/1, x.W/1) class FCN16s(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32) x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16) score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16) score = self.bn1(score + x4) # element-wise add, size=(N, 512, x.H/16, x.W/16) score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8) score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4) score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2) score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W) score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1) return score # size=(N, n_class, x.H/1, x.W/1) class FCN8s(nn.Module): def __init__(self, cfg): super().__init__() self.n_class = cfg.MODEL_NUM_CLASSES self.pretrained_net = VGGNet(requires_grad=True, remove_fc=True) self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, cfg.MODEL_NUM_CLASSES, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32) x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16) x3 = output['x3'] # size=(N, 256, x.H/8, x.W/8) score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16) score = self.bn1(score + x4) # element-wise add, size=(N, 512, x.H/16, x.W/16) score = self.relu(self.deconv2(score)) # size=(N, 256, x.H/8, x.W/8) score = self.bn2(score + x3) # element-wise add, size=(N, 256, x.H/8, x.W/8) score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4) score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2) score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W) score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1) return score # size=(N, n_class, x.H/1, x.W/1) class FCNs(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32) x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16) x3 = output['x3'] # size=(N, 256, x.H/8, x.W/8) x2 = output['x2'] # size=(N, 128, x.H/4, x.W/4) x1 = output['x1'] # size=(N, 64, x.H/2, x.W/2) score = self.bn1(self.relu(self.deconv1(x5))) # size=(N, 512, x.H/16, x.W/16) score = score + x4 # element-wise add, size=(N, 512, x.H/16, x.W/16) score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8) score = score + x3 # element-wise add, size=(N, 256, x.H/8, x.W/8) score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4) score = score + x2 # element-wise add, size=(N, 128, x.H/4, x.W/4) score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2) score = score + x1 # element-wise add, size=(N, 64, x.H/2, x.W/2) score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W) score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1) return score # size=(N, n_class, x.H/1, x.W/1) class VGGNet(VGG): def __init__(self, pretrained=False, model='vgg16', requires_grad=True, remove_fc=True, show_params=False): super().__init__(make_layers(cfg[model])) self.ranges = ranges[model] if pretrained: exec("self.load_state_dict(models.%s(pretrained=True).state_dict())" % model) if not requires_grad: for param in super().parameters(): param.requires_grad = False if remove_fc: # delete redundant fully-connected layer params, can save memory del self.classifier if show_params: for name, param in self.named_parameters(): print(name, param.size()) def forward(self, x): output = {} # get the output of each maxpooling layer (5 maxpool in VGG net) for idx in range(len(self.ranges)): for layer in range(self.ranges[idx][0], self.ranges[idx][1]): x = self.features[layer](x) output["x%d"%(idx+1)] = x return output ranges = { 'vgg11': ((0, 3), (3, 6), (6, 11), (11, 16), (16, 21)), 'vgg13': ((0, 5), (5, 10), (10, 15), (15, 20), (20, 25)), 'vgg16': ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31)), 'vgg19': ((0, 5), (5, 10), (10, 19), (19, 28), (28, 37)) } # cropped version from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py cfg = { 'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } def make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) if __name__ == "__main__": batch_size, n_class, h, w = 10, 20, 160, 160 # test output size vgg_model = VGGNet(requires_grad=True) input = torch.autograd.Variable(torch.randn(batch_size, 3, 224, 224)) output = vgg_model(input) assert output['x5'].size() == torch.Size([batch_size, 512, 7, 7]) fcn_model = FCN32s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCN16s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCN8s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCNs(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) print("Pass size check") # test a random batch, loss should decrease fcn_model = FCNs(pretrained_net=vgg_model, n_class=n_class) criterion = nn.BCELoss() optimizer = optim.SGD(fcn_model.parameters(), lr=1e-3, momentum=0.9) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) y = torch.autograd.Variable(torch.randn(batch_size, n_class, h, w), requires_grad=False) for iter in range(10): optimizer.zero_grad() output = fcn_model(input) output = nn.functional.sigmoid(output) loss = criterion(output, y) loss.backward() print("iter{}, loss {}".format(iter, loss.data[0])) optimizer.step()

48.460377

117

0.592275

from __future__ import print_function import torch import torch.nn as nn import torch.optim as optim from torchvision import models from torchvision.models.vgg import VGG class FCN32s(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] score = self.bn1(self.relu(self.deconv1(x5))) score = self.bn2(self.relu(self.deconv2(score))) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCN16s(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] x4 = output['x4'] score = self.relu(self.deconv1(x5)) score = self.bn1(score + x4) score = self.bn2(self.relu(self.deconv2(score))) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCN8s(nn.Module): def __init__(self, cfg): super().__init__() self.n_class = cfg.MODEL_NUM_CLASSES self.pretrained_net = VGGNet(requires_grad=True, remove_fc=True) self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, cfg.MODEL_NUM_CLASSES, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] x4 = output['x4'] x3 = output['x3'] score = self.relu(self.deconv1(x5)) score = self.bn1(score + x4) score = self.relu(self.deconv2(score)) score = self.bn2(score + x3) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCNs(nn.Module): def __init__(self, pretrained_net, n_class): super().__init__() self.n_class = n_class self.pretrained_net = pretrained_net self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn1 = nn.BatchNorm2d(512) self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn2 = nn.BatchNorm2d(256) self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn3 = nn.BatchNorm2d(128) self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn4 = nn.BatchNorm2d(64) self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1) self.bn5 = nn.BatchNorm2d(32) self.classifier = nn.Conv2d(32, n_class, kernel_size=1) def forward(self, x): output = self.pretrained_net(x) x5 = output['x5'] x4 = output['x4'] x3 = output['x3'] x2 = output['x2'] x1 = output['x1'] score = self.bn1(self.relu(self.deconv1(x5))) score = score + x4 score = self.bn2(self.relu(self.deconv2(score))) score = score + x3 score = self.bn3(self.relu(self.deconv3(score))) score = score + x2 score = self.bn4(self.relu(self.deconv4(score))) score = score + x1 score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class VGGNet(VGG): def __init__(self, pretrained=False, model='vgg16', requires_grad=True, remove_fc=True, show_params=False): super().__init__(make_layers(cfg[model])) self.ranges = ranges[model] if pretrained: exec("self.load_state_dict(models.%s(pretrained=True).state_dict())" % model) if not requires_grad: for param in super().parameters(): param.requires_grad = False if remove_fc: del self.classifier if show_params: for name, param in self.named_parameters(): print(name, param.size()) def forward(self, x): output = {} for idx in range(len(self.ranges)): for layer in range(self.ranges[idx][0], self.ranges[idx][1]): x = self.features[layer](x) output["x%d"%(idx+1)] = x return output ranges = { 'vgg11': ((0, 3), (3, 6), (6, 11), (11, 16), (16, 21)), 'vgg13': ((0, 5), (5, 10), (10, 15), (15, 20), (20, 25)), 'vgg16': ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31)), 'vgg19': ((0, 5), (5, 10), (10, 19), (19, 28), (28, 37)) } cfg = { 'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } def make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) if __name__ == "__main__": batch_size, n_class, h, w = 10, 20, 160, 160 vgg_model = VGGNet(requires_grad=True) input = torch.autograd.Variable(torch.randn(batch_size, 3, 224, 224)) output = vgg_model(input) assert output['x5'].size() == torch.Size([batch_size, 512, 7, 7]) fcn_model = FCN32s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCN16s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCN8s(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) fcn_model = FCNs(pretrained_net=vgg_model, n_class=n_class) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) output = fcn_model(input) assert output.size() == torch.Size([batch_size, n_class, h, w]) print("Pass size check") fcn_model = FCNs(pretrained_net=vgg_model, n_class=n_class) criterion = nn.BCELoss() optimizer = optim.SGD(fcn_model.parameters(), lr=1e-3, momentum=0.9) input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w)) y = torch.autograd.Variable(torch.randn(batch_size, n_class, h, w), requires_grad=False) for iter in range(10): optimizer.zero_grad() output = fcn_model(input) output = nn.functional.sigmoid(output) loss = criterion(output, y) loss.backward() print("iter{}, loss {}".format(iter, loss.data[0])) optimizer.step()

true

1c2b4bf497975578da315d9f93461e4bcfe65e56

2,198

py

Python

tests/python-opcua/examples/simple-client-server-xml/server.py

iit-danieli-joint-lab/opcua-modeling-tool

f8c3d940a61334b0e6deda9099844a6b429d7c08

[ "MIT" ]

32

2018-03-27T12:25:24.000Z

2022-01-11T21:20:06.000Z

tests/python-opcua/examples/simple-client-server-xml/server.py

iit-danieli-joint-lab/opcua-modeling-tool

f8c3d940a61334b0e6deda9099844a6b429d7c08

[ "MIT" ]

42

2020-08-20T04:01:12.000Z

2021-01-09T18:50:21.000Z

python-opcua/examples/simple-client-server-xml/server.py

ssriblo/ionic-smarthome-test-1

060bc247e0b8295d6cd869d90b364756515cfc19

[ "MIT" ]

12

2018-06-04T20:06:06.000Z

2021-07-02T22:09:53.000Z

import os.path try: from IPython import embed except ImportError: import code def embed(): vars = globals() vars.update(locals()) shell = code.InteractiveConsole(vars) shell.interact() from opcua import ua, uamethod, Server @uamethod def say_hello_xml(parent, happy): print("Calling say_hello_xml") if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return result @uamethod def say_hello(parent, happy): if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return result @uamethod def say_hello_array(parent, happy): if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return [result, "Actually I am"] class HelloServer: def __init__(self, endpoint, name, model_filepath): self.server = Server() # This need to be imported at the start or else it will overwrite the data self.server.import_xml(model_filepath) self.server.set_endpoint(endpoint) self.server.set_server_name(name) objects = self.server.get_objects_node() freeopcua_namespace = self.server.get_namespace_index("urn:freeopcua:python:server") hellower = objects.get_child("0:Hellower") hellower_say_hello = hellower.get_child("0:SayHello") self.server.link_method(hellower_say_hello, say_hello_xml) hellower.add_method( freeopcua_namespace, "SayHello2", say_hello, [ua.VariantType.Boolean], [ua.VariantType.String]) hellower.add_method( freeopcua_namespace, "SayHelloArray", say_hello_array, [ua.VariantType.Boolean], [ua.VariantType.String]) def __enter__(self): self.server.start() return self.server def __exit__(self, exc_type, exc_val, exc_tb): self.server.stop() if __name__ == '__main__': script_dir = os.path.dirname(__file__) with HelloServer( "opc.tcp://0.0.0.0:40840/freeopcua/server/", "FreeOpcUa Example Server", os.path.join(script_dir, "test_saying.xml")) as server: embed()

25.55814

117

0.645132

import os.path try: from IPython import embed except ImportError: import code def embed(): vars = globals() vars.update(locals()) shell = code.InteractiveConsole(vars) shell.interact() from opcua import ua, uamethod, Server @uamethod def say_hello_xml(parent, happy): print("Calling say_hello_xml") if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return result @uamethod def say_hello(parent, happy): if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return result @uamethod def say_hello_array(parent, happy): if happy: result = "I'm happy" else: result = "I'm not happy" print(result) return [result, "Actually I am"] class HelloServer: def __init__(self, endpoint, name, model_filepath): self.server = Server() self.server.import_xml(model_filepath) self.server.set_endpoint(endpoint) self.server.set_server_name(name) objects = self.server.get_objects_node() freeopcua_namespace = self.server.get_namespace_index("urn:freeopcua:python:server") hellower = objects.get_child("0:Hellower") hellower_say_hello = hellower.get_child("0:SayHello") self.server.link_method(hellower_say_hello, say_hello_xml) hellower.add_method( freeopcua_namespace, "SayHello2", say_hello, [ua.VariantType.Boolean], [ua.VariantType.String]) hellower.add_method( freeopcua_namespace, "SayHelloArray", say_hello_array, [ua.VariantType.Boolean], [ua.VariantType.String]) def __enter__(self): self.server.start() return self.server def __exit__(self, exc_type, exc_val, exc_tb): self.server.stop() if __name__ == '__main__': script_dir = os.path.dirname(__file__) with HelloServer( "opc.tcp://0.0.0.0:40840/freeopcua/server/", "FreeOpcUa Example Server", os.path.join(script_dir, "test_saying.xml")) as server: embed()

true

1c2b4c8a02572640013e7d4e503ee3614d3ab2b7

598

py

Python

codes/views.py

DanielArturoAlejoAlvarez/Cersei

365cb4e554146143fb3521a09ebf9fadb127a564

[ "MIT" ]

5

2020-04-07T14:31:45.000Z

2021-04-30T05:11:43.000Z

codes/views.py

DanielArturoAlejoAlvarez/Cersei

365cb4e554146143fb3521a09ebf9fadb127a564

[ "MIT" ]

null

codes/views.py

DanielArturoAlejoAlvarez/Cersei

365cb4e554146143fb3521a09ebf9fadb127a564

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. from rest_framework import viewsets from .models import Language,Paradigm,Programmer from .serializers import LanguageSerializer,ParadigmSerializer,ProgrammerSerializer class LanguageView(viewsets.ModelViewSet): queryset=Language.objects.all() serializer_class=LanguageSerializer class ParadigmView(viewsets.ModelViewSet): queryset=Paradigm.objects.all() serializer_class=ParadigmSerializer class ProgrammerView(viewsets.ModelViewSet): queryset=Programmer.objects.all() serializer_class=ProgrammerSerializer

31.473684

83

0.832776

from django.shortcuts import render from rest_framework import viewsets from .models import Language,Paradigm,Programmer from .serializers import LanguageSerializer,ParadigmSerializer,ProgrammerSerializer class LanguageView(viewsets.ModelViewSet): queryset=Language.objects.all() serializer_class=LanguageSerializer class ParadigmView(viewsets.ModelViewSet): queryset=Paradigm.objects.all() serializer_class=ParadigmSerializer class ProgrammerView(viewsets.ModelViewSet): queryset=Programmer.objects.all() serializer_class=ProgrammerSerializer

true

1c2b4ca4ff9cd52813ee18f751ac9262164b8b7c

825

py

Python

test/post.py

6923403/Python_Demo

69ebc7fe5589b46a470c7d88507ce2c73d4c6678

[ "MIT" ]

null

test/post.py

6923403/Python_Demo

69ebc7fe5589b46a470c7d88507ce2c73d4c6678

[ "MIT" ]

null

test/post.py

6923403/Python_Demo

69ebc7fe5589b46a470c7d88507ce2c73d4c6678

[ "MIT" ]

null

import requests import json def main(): host='http://fanyi.youdao.com/translate_o?smartresult=dict&smartresult=rule' word = host endpoint = "post" url=''.join([host, endpoint]) m_data={ "i": "晚安", "from": "AUTO", "to": "AUTO", "smartresult": "dict", "client": "fanyideskweb", "salt": "16027840699404", "sign": "3049c9ec63fc27774b93f384a0497330", "lts": "1602784069940", "bv": "0c00cda0db2530a31944351caf80d8b0", "doctype": "json", "version": "2.1", "keyfrom": "fanyi.web", "action": "FY_BY_CLICKBUTTION" } response=requests.post(url,m_data) # 将Json格式字符串转字典 content=json.loads(response.text) # print(content['translateResult'][0][0]['tgt']) print(content) if __name__ == '__main__': main()

24.264706

81

0.591515

import requests import json def main(): host='http://fanyi.youdao.com/translate_o?smartresult=dict&smartresult=rule' word = host endpoint = "post" url=''.join([host, endpoint]) m_data={ "i": "晚安", "from": "AUTO", "to": "AUTO", "smartresult": "dict", "client": "fanyideskweb", "salt": "16027840699404", "sign": "3049c9ec63fc27774b93f384a0497330", "lts": "1602784069940", "bv": "0c00cda0db2530a31944351caf80d8b0", "doctype": "json", "version": "2.1", "keyfrom": "fanyi.web", "action": "FY_BY_CLICKBUTTION" } response=requests.post(url,m_data) content=json.loads(response.text) print(content) if __name__ == '__main__': main()

true

1c2b4caaa1a2a9e62eca83baf2fcc132b0a26879

8,564

py

Python

great_expectations/expectations/core/expect_column_values_to_be_unique.py

andyjessen/great_expectations

74f7f2aa7b51144f34156ed49490dae4edaa5cb7

[ "Apache-2.0" ]

null

great_expectations/expectations/core/expect_column_values_to_be_unique.py

andyjessen/great_expectations

74f7f2aa7b51144f34156ed49490dae4edaa5cb7

[ "Apache-2.0" ]

null

great_expectations/expectations/core/expect_column_values_to_be_unique.py

andyjessen/great_expectations

74f7f2aa7b51144f34156ed49490dae4edaa5cb7

[ "Apache-2.0" ]

null

from typing import Optional from great_expectations.core.expectation_configuration import ExpectationConfiguration from great_expectations.expectations.expectation import ( ColumnMapExpectation, InvalidExpectationConfigurationError, ) from great_expectations.expectations.util import render_evaluation_parameter_string from great_expectations.render.renderer.renderer import renderer from great_expectations.render.types import RenderedStringTemplateContent from great_expectations.render.util import ( num_to_str, parse_row_condition_string_pandas_engine, substitute_none_for_missing, ) try: import sqlalchemy as sa # noqa: F401 except ImportError: pass class ExpectColumnValuesToBeUnique(ColumnMapExpectation): """Expect each column value to be unique. This expectation detects duplicates. All duplicated values are counted as exceptions. For example, `[1, 2, 3, 3, 3]` will return `[3, 3, 3]` in `result.exceptions_list`, with \ `unexpected_percent = 60.0`. expect_column_values_to_be_unique is a \ :func:`column_map_expectation <great_expectations.execution_engine.execution_engine.MetaExecutionEngine .column_map_expectation>`. Args: column (str): \ The column name. Keyword Args: mostly (None or a float between 0 and 1): \ Return `"success": True` if at least mostly fraction of values match the expectation. \ For more detail, see :ref:`mostly`. Other Parameters: result_format (str or None): \ Which output mode to use: `BOOLEAN_ONLY`, `BASIC`, `COMPLETE`, or `SUMMARY`. For more detail, see :ref:`result_format <result_format>`. include_config (boolean): \ If True, then include the expectation config as part of the result object. \ For more detail, see :ref:`include_config`. catch_exceptions (boolean or None): \ If True, then catch exceptions and include them as part of the result object. \ For more detail, see :ref:`catch_exceptions`. meta (dict or None): \ A JSON-serializable dictionary (nesting allowed) that will be included in the output without \ modification. For more detail, see :ref:`meta`. Returns: An ExpectationSuiteValidationResult Exact fields vary depending on the values passed to :ref:`result_format <result_format>` and :ref:`include_config`, :ref:`catch_exceptions`, and :ref:`meta`. """ # This dictionary contains metadata for display in the public gallery library_metadata = { "maturity": "production", "tags": ["core expectation", "column map expectation"], "contributors": ["@great_expectations"], "requirements": [], "has_full_test_suite": True, "manually_reviewed_code": True, } map_metric = "column_values.unique" success_keys = ("mostly",) default_kwarg_values = { "row_condition": None, "condition_parser": None, # we expect this to be explicitly set whenever a row_condition is passed "mostly": 1, "parse_strings_as_datetimes": False, "result_format": "BASIC", "include_config": True, "catch_exceptions": True, } args_keys = ("column",) def validate_configuration( self, configuration: Optional[ExpectationConfiguration] ) -> None: super().validate_configuration(configuration) try: assert ( "column" in configuration.kwargs ), "'column' parameter is required for column map expectations" if "mostly" in configuration.kwargs: mostly = configuration.kwargs["mostly"] assert isinstance( mostly, (int, float) ), "'mostly' parameter must be an integer or float" assert 0 <= mostly <= 1, "'mostly' parameter must be between 0 and 1" except AssertionError as e: raise InvalidExpectationConfigurationError(str(e)) @classmethod def _atomic_prescriptive_template( cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs, ): runtime_configuration = runtime_configuration or {} include_column_name = runtime_configuration.get("include_column_name", True) include_column_name = ( include_column_name if include_column_name is not None else True ) styling = runtime_configuration.get("styling") params = substitute_none_for_missing( configuration.kwargs, ["column", "mostly", "row_condition", "condition_parser"], ) params_with_json_schema = { "column": {"schema": {"type": "string"}, "value": params.get("column")}, "mostly": {"schema": {"type": "number"}, "value": params.get("mostly")}, "mostly_pct": { "schema": {"type": "string"}, "value": params.get("mostly_pct"), }, "row_condition": { "schema": {"type": "string"}, "value": params.get("row_condition"), }, "condition_parser": { "schema": {"type": "string"}, "value": params.get("condition_parser"), }, } if include_column_name: template_str = "$column values must be unique" else: template_str = "values must be unique" if params["mostly"] is not None and params["mostly"] < 1.0: params_with_json_schema["mostly_pct"]["value"] = num_to_str( params["mostly"] * 100, precision=15, no_scientific=True ) # params["mostly_pct"] = "{:.14f}".format(params["mostly"]*100).rstrip("0").rstrip(".") template_str += ", at least $mostly_pct % of the time." else: template_str += "." if params["row_condition"] is not None: ( conditional_template_str, conditional_params, ) = parse_row_condition_string_pandas_engine( params["row_condition"], with_schema=True ) template_str = f"{conditional_template_str}, then {template_str}" params_with_json_schema.update(conditional_params) return (template_str, params_with_json_schema, styling) @classmethod @renderer(renderer_type="renderer.prescriptive") @render_evaluation_parameter_string def _prescriptive_renderer( cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs, ): runtime_configuration = runtime_configuration or {} include_column_name = runtime_configuration.get("include_column_name", True) include_column_name = ( include_column_name if include_column_name is not None else True ) styling = runtime_configuration.get("styling") params = substitute_none_for_missing( configuration.kwargs, ["column", "mostly", "row_condition", "condition_parser"], ) if include_column_name: template_str = "$column values must be unique" else: template_str = "values must be unique" if params["mostly"] is not None and params["mostly"] < 1.0: params["mostly_pct"] = num_to_str( params["mostly"] * 100, precision=15, no_scientific=True ) # params["mostly_pct"] = "{:.14f}".format(params["mostly"]*100).rstrip("0").rstrip(".") template_str += ", at least $mostly_pct % of the time." else: template_str += "." if params["row_condition"] is not None: ( conditional_template_str, conditional_params, ) = parse_row_condition_string_pandas_engine(params["row_condition"]) template_str = f"{conditional_template_str}, then {template_str}" params.update(conditional_params) return [ RenderedStringTemplateContent( **{ "content_block_type": "string_template", "string_template": { "template": template_str, "params": params, "styling": styling, }, } ) ]

38.232143

107

0.608711

from typing import Optional from great_expectations.core.expectation_configuration import ExpectationConfiguration from great_expectations.expectations.expectation import ( ColumnMapExpectation, InvalidExpectationConfigurationError, ) from great_expectations.expectations.util import render_evaluation_parameter_string from great_expectations.render.renderer.renderer import renderer from great_expectations.render.types import RenderedStringTemplateContent from great_expectations.render.util import ( num_to_str, parse_row_condition_string_pandas_engine, substitute_none_for_missing, ) try: import sqlalchemy as sa except ImportError: pass class ExpectColumnValuesToBeUnique(ColumnMapExpectation): library_metadata = { "maturity": "production", "tags": ["core expectation", "column map expectation"], "contributors": ["@great_expectations"], "requirements": [], "has_full_test_suite": True, "manually_reviewed_code": True, } map_metric = "column_values.unique" success_keys = ("mostly",) default_kwarg_values = { "row_condition": None, "condition_parser": None, "mostly": 1, "parse_strings_as_datetimes": False, "result_format": "BASIC", "include_config": True, "catch_exceptions": True, } args_keys = ("column",) def validate_configuration( self, configuration: Optional[ExpectationConfiguration] ) -> None: super().validate_configuration(configuration) try: assert ( "column" in configuration.kwargs ), "'column' parameter is required for column map expectations" if "mostly" in configuration.kwargs: mostly = configuration.kwargs["mostly"] assert isinstance( mostly, (int, float) ), "'mostly' parameter must be an integer or float" assert 0 <= mostly <= 1, "'mostly' parameter must be between 0 and 1" except AssertionError as e: raise InvalidExpectationConfigurationError(str(e)) @classmethod def _atomic_prescriptive_template( cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs, ): runtime_configuration = runtime_configuration or {} include_column_name = runtime_configuration.get("include_column_name", True) include_column_name = ( include_column_name if include_column_name is not None else True ) styling = runtime_configuration.get("styling") params = substitute_none_for_missing( configuration.kwargs, ["column", "mostly", "row_condition", "condition_parser"], ) params_with_json_schema = { "column": {"schema": {"type": "string"}, "value": params.get("column")}, "mostly": {"schema": {"type": "number"}, "value": params.get("mostly")}, "mostly_pct": { "schema": {"type": "string"}, "value": params.get("mostly_pct"), }, "row_condition": { "schema": {"type": "string"}, "value": params.get("row_condition"), }, "condition_parser": { "schema": {"type": "string"}, "value": params.get("condition_parser"), }, } if include_column_name: template_str = "$column values must be unique" else: template_str = "values must be unique" if params["mostly"] is not None and params["mostly"] < 1.0: params_with_json_schema["mostly_pct"]["value"] = num_to_str( params["mostly"] * 100, precision=15, no_scientific=True ) template_str += ", at least $mostly_pct % of the time." else: template_str += "." if params["row_condition"] is not None: ( conditional_template_str, conditional_params, ) = parse_row_condition_string_pandas_engine( params["row_condition"], with_schema=True ) template_str = f"{conditional_template_str}, then {template_str}" params_with_json_schema.update(conditional_params) return (template_str, params_with_json_schema, styling) @classmethod @renderer(renderer_type="renderer.prescriptive") @render_evaluation_parameter_string def _prescriptive_renderer( cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs, ): runtime_configuration = runtime_configuration or {} include_column_name = runtime_configuration.get("include_column_name", True) include_column_name = ( include_column_name if include_column_name is not None else True ) styling = runtime_configuration.get("styling") params = substitute_none_for_missing( configuration.kwargs, ["column", "mostly", "row_condition", "condition_parser"], ) if include_column_name: template_str = "$column values must be unique" else: template_str = "values must be unique" if params["mostly"] is not None and params["mostly"] < 1.0: params["mostly_pct"] = num_to_str( params["mostly"] * 100, precision=15, no_scientific=True ) template_str += ", at least $mostly_pct % of the time." else: template_str += "." if params["row_condition"] is not None: ( conditional_template_str, conditional_params, ) = parse_row_condition_string_pandas_engine(params["row_condition"]) template_str = f"{conditional_template_str}, then {template_str}" params.update(conditional_params) return [ RenderedStringTemplateContent( **{ "content_block_type": "string_template", "string_template": { "template": template_str, "params": params, "styling": styling, }, } ) ]

true

1c2b4d2407ea1b399ac8adc0f3b5894a3aaab7e3

542

py

Python

multiples_of_x_and_y/calculator.py

corker/multiples_of_x_and_y

38a9da13594a3de0a6b8f018193fde20ba38eb7d

[ "MIT" ]

null

multiples_of_x_and_y/calculator.py

corker/multiples_of_x_and_y

38a9da13594a3de0a6b8f018193fde20ba38eb7d

[ "MIT" ]

null

multiples_of_x_and_y/calculator.py

corker/multiples_of_x_and_y

38a9da13594a3de0a6b8f018193fde20ba38eb7d

[ "MIT" ]

null

MIN_GOAL = 1 def calculate(x, y, goal): assert x > 0 assert y > 0 assert goal >= MIN_GOAL range_numbers = range(MIN_GOAL, goal) condition = as_condition(x, y) filtered_numbers = filter(condition, range_numbers) return tuple(filtered_numbers) def as_condition(x, y): assert x > 0 assert y > 0 selector = lambda value: can_divide(x, value) | can_divide(y, value) return selector def can_divide(divider, dividend): assert divider > 0 assert dividend > 0 return dividend % divider == 0

23.565217

72

0.667897

MIN_GOAL = 1 def calculate(x, y, goal): assert x > 0 assert y > 0 assert goal >= MIN_GOAL range_numbers = range(MIN_GOAL, goal) condition = as_condition(x, y) filtered_numbers = filter(condition, range_numbers) return tuple(filtered_numbers) def as_condition(x, y): assert x > 0 assert y > 0 selector = lambda value: can_divide(x, value) | can_divide(y, value) return selector def can_divide(divider, dividend): assert divider > 0 assert dividend > 0 return dividend % divider == 0

true

1c2b4debf7e4d8dde139c8cb3e4ed6b1436b41ad

6,817

py

Python

ch_05/src/classifier.py

real-slim-chadi/Python-Object-Oriented-Programming---4th-edition

7c486866171786b620795fa33a79ec9ac9a8ba1b

[ "MIT" ]

43

2021-06-03T18:39:09.000Z

2022-03-29T20:32:13.000Z

ch_05/src/classifier.py

real-slim-chadi/Python-Object-Oriented-Programming---4th-edition

7c486866171786b620795fa33a79ec9ac9a8ba1b

[ "MIT" ]

9

2022-03-12T01:04:07.000Z

2022-03-12T01:05:01.000Z

ch_05/src/classifier.py

real-slim-chadi/Python-Object-Oriented-Programming---4th-edition

7c486866171786b620795fa33a79ec9ac9a8ba1b

[ "MIT" ]

36

2021-06-19T07:14:09.000Z

2022-03-12T22:17:09.000Z

""" Python 3 Object-Oriented Programming Case Study Chapter 5, When to Use Object-Oriented Programming """ from __future__ import annotations import base64 import csv from enum import Enum, auto from functools import wraps from pathlib import Path from typing import ( cast, Optional, Callable, Any, Type, Set, Mapping, overload, Iterable, Union, Iterator, ) import werkzeug.security from flask import Flask, current_app, jsonify, request, abort, g, Response class Role(str, Enum): UNDEFINED = "" BOTANIST = "botanist" RESEARCHER = "researcher" class User: """ A user. Has a Role: Botanist or Researcher. The password must be of the form: ``method$salt$hexdigest``. For example: ``"md5$ZD8agylg$90c2494aa8a4965b20410e4cdb9e823d"`` """ headers = ["username", "email", "real_name", "role", "password"] def __init__( self, username: str, email: str, real_name: str, role: Role, password: Optional[str] = None, ) -> None: self.username = username self.email = email self.real_name = real_name self.role = role self.password = password @staticmethod def from_dict(csv_row: dict[str, str]) -> "User": return User( username=csv_row["username"], email=csv_row["email"], real_name=csv_row["real_name"], role=Role(csv_row["role"]), password=csv_row["password"], ) def __eq__(self, other: Any) -> bool: other = cast(User, other) return all( [ self.username == other.username, self.email == other.email, self.real_name == other.real_name, self.role == other.role, ] ) def set_password(self, plain_text: str) -> None: self.password = werkzeug.security.generate_password_hash(plain_text) def is_valid_password(self, plain_text: str) -> bool: return werkzeug.security.check_password_hash( self.password or "md5$$", plain_text ) def __repr__(self) -> str: return ( f"{self.__class__.__name__}(" f"username={self.username!r}, " f"email={self.email!r}, " f"real_name={self.real_name!r}, " f"role={self.role.value!r}, " f"password={self.password!r})" ) def asdict(self) -> dict[str, Optional[str]]: return { "username": self.username, "email": self.email, "real_name": self.real_name, "role": self.role.value, "password": self.password, } class Users: def __init__(self, init: Optional[dict[str, User]] = None) -> None: self.users = init or {} self.anonymous = User("", "", "", Role.UNDEFINED) self.app: Optional[Flask] = None def init_app(self, app: Flask) -> None: self.app = app self.app.config.setdefault("USER_FILE", Path("users.csv")) def get_user(self, name: str, default: Optional[User] = None) -> User: if not self.app: raise RuntimeError("Users not bound to an app") if not self.users: # Load file when needed. with self.app.config["USER_FILE"].open() as user_file: row_iter = csv.DictReader(user_file) user_iter = (User.from_dict(row) for row in row_iter if row) self.users = {user.username: user for user in user_iter} return self.users.get(name, default or self.anonymous) def add_user(self, user: User) -> None: if user.username in self.users: raise ValueError("Duplicate Username") self.users[user.username] = user def save(self) -> None: if not self.app: raise RuntimeError("Users not bound to an app") with self.app.config["USER_FILE"].open("w", newline="") as user_file: writer = csv.DictWriter(user_file, User.headers) writer.writeheader() writer.writerows(u.asdict() for u in self.users.values()) def __len__(self) -> int: return len(self.users) def values(self) -> Iterator[User]: return iter(self.users.values()) class NotAuthorized(Exception): status_code = 401 def __init__( self, message: str, status_code: Optional[int] = None, payload: Optional[dict[str, str]] = None, ) -> None: super().__init__(message) self.message = message if status_code is not None: self.status_code = status_code self.payload = payload def asdict(self) -> dict[str, Any]: rv: dict[str, Any] = dict(self.payload or ()) rv["message"] = self.message return rv def authenticate(view_function: Callable[..., Response]) -> Callable[..., Response]: @wraps(view_function) def decorated_function(*args: str) -> Response: auth_body = request.headers.get("Authorization", "").split(" ") auth_type, credentials = auth_body if len(auth_body) == 2 else ("", ":") username, _, password = ( base64.b64decode(credentials).decode("utf-8").partition(":") ) g.user = users.get_user(username) # type: ignore[attr-defined] conditions = [ auth_type.upper() == "BASIC", g.user.is_valid_password(password), # type: ignore[attr-defined] ] if not all(conditions): raise NotAuthorized("Unknown User") return view_function(*args) return decorated_function class Config: USER_FILE = Path("data/users.csv") class Demo(Config): ENV = "development" DEBUG = True TESTING = True app = Flask(__name__) app.config.from_object(Demo) # os.environ["CLASSIFIER_CONFIG"] users = Users() users.init_app(app) @app.errorhandler(NotAuthorized) # type: ignore[misc] def handle_unauthorized(error: NotAuthorized) -> Response: response = jsonify(error.asdict()) response.status_code = error.status_code return response @app.route("/health") def user_list() -> Response: # Be sure the users database gets loaded. users.get_user("") response = {"status": "OK", "user_count": len(users)} if app.config["TESTING"]: response["users"] = [u.asdict() for u in users.values()] return jsonify(response) @app.route("/whoami") @authenticate def who_am_i() -> Response: app.logger.info(f"whoami with {request.headers}: User {g.user}") # type: ignore[attr-defined] return jsonify( { "status": "OK", "user": g.user.asdict(), # type: ignore[attr-defined] } ) if __name__ == "__main__": app.run(ssl_context="adhoc")

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from __future__ import annotations import base64 import csv from enum import Enum, auto from functools import wraps from pathlib import Path from typing import ( cast, Optional, Callable, Any, Type, Set, Mapping, overload, Iterable, Union, Iterator, ) import werkzeug.security from flask import Flask, current_app, jsonify, request, abort, g, Response class Role(str, Enum): UNDEFINED = "" BOTANIST = "botanist" RESEARCHER = "researcher" class User: headers = ["username", "email", "real_name", "role", "password"] def __init__( self, username: str, email: str, real_name: str, role: Role, password: Optional[str] = None, ) -> None: self.username = username self.email = email self.real_name = real_name self.role = role self.password = password @staticmethod def from_dict(csv_row: dict[str, str]) -> "User": return User( username=csv_row["username"], email=csv_row["email"], real_name=csv_row["real_name"], role=Role(csv_row["role"]), password=csv_row["password"], ) def __eq__(self, other: Any) -> bool: other = cast(User, other) return all( [ self.username == other.username, self.email == other.email, self.real_name == other.real_name, self.role == other.role, ] ) def set_password(self, plain_text: str) -> None: self.password = werkzeug.security.generate_password_hash(plain_text) def is_valid_password(self, plain_text: str) -> bool: return werkzeug.security.check_password_hash( self.password or "md5$$", plain_text ) def __repr__(self) -> str: return ( f"{self.__class__.__name__}(" f"username={self.username!r}, " f"email={self.email!r}, " f"real_name={self.real_name!r}, " f"role={self.role.value!r}, " f"password={self.password!r})" ) def asdict(self) -> dict[str, Optional[str]]: return { "username": self.username, "email": self.email, "real_name": self.real_name, "role": self.role.value, "password": self.password, } class Users: def __init__(self, init: Optional[dict[str, User]] = None) -> None: self.users = init or {} self.anonymous = User("", "", "", Role.UNDEFINED) self.app: Optional[Flask] = None def init_app(self, app: Flask) -> None: self.app = app self.app.config.setdefault("USER_FILE", Path("users.csv")) def get_user(self, name: str, default: Optional[User] = None) -> User: if not self.app: raise RuntimeError("Users not bound to an app") if not self.users: with self.app.config["USER_FILE"].open() as user_file: row_iter = csv.DictReader(user_file) user_iter = (User.from_dict(row) for row in row_iter if row) self.users = {user.username: user for user in user_iter} return self.users.get(name, default or self.anonymous) def add_user(self, user: User) -> None: if user.username in self.users: raise ValueError("Duplicate Username") self.users[user.username] = user def save(self) -> None: if not self.app: raise RuntimeError("Users not bound to an app") with self.app.config["USER_FILE"].open("w", newline="") as user_file: writer = csv.DictWriter(user_file, User.headers) writer.writeheader() writer.writerows(u.asdict() for u in self.users.values()) def __len__(self) -> int: return len(self.users) def values(self) -> Iterator[User]: return iter(self.users.values()) class NotAuthorized(Exception): status_code = 401 def __init__( self, message: str, status_code: Optional[int] = None, payload: Optional[dict[str, str]] = None, ) -> None: super().__init__(message) self.message = message if status_code is not None: self.status_code = status_code self.payload = payload def asdict(self) -> dict[str, Any]: rv: dict[str, Any] = dict(self.payload or ()) rv["message"] = self.message return rv def authenticate(view_function: Callable[..., Response]) -> Callable[..., Response]: @wraps(view_function) def decorated_function(*args: str) -> Response: auth_body = request.headers.get("Authorization", "").split(" ") auth_type, credentials = auth_body if len(auth_body) == 2 else ("", ":") username, _, password = ( base64.b64decode(credentials).decode("utf-8").partition(":") ) g.user = users.get_user(username) conditions = [ auth_type.upper() == "BASIC", g.user.is_valid_password(password), ] if not all(conditions): raise NotAuthorized("Unknown User") return view_function(*args) return decorated_function class Config: USER_FILE = Path("data/users.csv") class Demo(Config): ENV = "development" DEBUG = True TESTING = True app = Flask(__name__) app.config.from_object(Demo) users = Users() users.init_app(app) @app.errorhandler(NotAuthorized) def handle_unauthorized(error: NotAuthorized) -> Response: response = jsonify(error.asdict()) response.status_code = error.status_code return response @app.route("/health") def user_list() -> Response: users.get_user("") response = {"status": "OK", "user_count": len(users)} if app.config["TESTING"]: response["users"] = [u.asdict() for u in users.values()] return jsonify(response) @app.route("/whoami") @authenticate def who_am_i() -> Response: app.logger.info(f"whoami with {request.headers}: User {g.user}") return jsonify( { "status": "OK", "user": g.user.asdict(), } ) if __name__ == "__main__": app.run(ssl_context="adhoc")

true

1c2b4eecc8e1717a45520c3b8840de0d0f2b3a1f

41

py

Python

pyml_ensemble/model/__init__.py

anthonymorast/pyml-ensemble

a52e454f4c8d92412b3ee66140f78d19da32b53c

[ "MIT" ]

null

pyml_ensemble/model/__init__.py

anthonymorast/pyml-ensemble

a52e454f4c8d92412b3ee66140f78d19da32b53c

[ "MIT" ]

5

2020-02-13T03:55:53.000Z

2021-02-12T17:53:15.000Z

pyml_ensemble/model/__init__.py

anthonymorast/pyml-ensemble

a52e454f4c8d92412b3ee66140f78d19da32b53c

[ "MIT" ]

null

from .model import * from .tree import *

13.666667

20

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from .model import * from .tree import *

true